ok

Co-authored-by: Andrei <andrei@Andreis-MacBook-Pro.local>

.github/actions/prepare-build/action.yml

@@ -1,42 +0,0 @@
-name: "Prepare Build (OpenAPI generation)"
-description: "Sets up Python with uv, installs deps, generates OpenAPI schema and Python client, uploads artifact"
-inputs:
-  docker-username:
-    required: true
-  docker-password:
-    required: true
-runs:
-  using: "composite"
-  steps:
-    - name: Setup Python and Install Dependencies
-      uses: ./.github/actions/setup-python-and-install-dependencies
-
-    - name: Generate OpenAPI schema
-      shell: bash
-      working-directory: backend
-      env:
-        PYTHONPATH: "."
-      run: |
-        python scripts/onyx_openapi_schema.py --filename generated/openapi.json
-
-    # needed for pulling openapitools/openapi-generator-cli
-    # otherwise, we hit the "Unauthenticated users" limit
-    # https://docs.docker.com/docker-hub/usage/
-    - name: Login to Docker Hub
-      uses: docker/login-action@5e57cd118135c172c3672efd75eb46360885c0ef # ratchet:docker/login-action@v3
-      with:
-        username: ${{ inputs['docker-username'] }}
-        password: ${{ inputs['docker-password'] }}
-
-    - name: Generate OpenAPI Python client
-      shell: bash
-      run: |
-        docker run --rm \
-          -v "${{ github.workspace }}/backend/generated:/local" \
-          openapitools/openapi-generator-cli generate \
-          -i /local/openapi.json \
-          -g python \
-          -o /local/onyx_openapi_client \
-          --package-name onyx_openapi_client \
-          --skip-validate-spec \
-          --openapi-normalizer "SIMPLIFY_ONEOF_ANYOF=true,SET_OAS3_NULLABLE=true"

.github/actions/setup-python-and-install-dependencies/action.yml

@@ -1,5 +1,9 @@
 name: "Setup Python and Install Dependencies"
 description: "Sets up Python with uv and installs deps"
+inputs:
+  requirements:
+    description: "Newline-separated list of requirement files to install (relative to repo root)"
+    required: true
 runs:
   using: "composite"
   steps:
@@ -9,11 +13,26 @@ runs:
       # with:
       #   enable-cache: true
 
+    - name: Compute requirements hash
+      id: req-hash
+      shell: bash
+      env:
+        REQUIREMENTS: ${{ inputs.requirements }}
+      run: |
+        # Hash the contents of the specified requirement files
+        hash=""
+        while IFS= read -r req; do
+          if [ -n "$req" ] && [ -f "$req" ]; then
+            hash="$hash$(sha256sum "$req")"
+          fi
+        done <<< "$REQUIREMENTS"
+        echo "hash=$(echo "$hash" | sha256sum | cut -d' ' -f1)" >> "$GITHUB_OUTPUT"
+
     - name: Cache uv cache directory
       uses: runs-on/cache@50350ad4242587b6c8c2baa2e740b1bc11285ff4 # ratchet:runs-on/cache@v4
       with:
         path: ~/.cache/uv
-        key: ${{ runner.os }}-uv-${{ hashFiles('backend/requirements/*.txt', 'backend/pyproject.toml') }}
+        key: ${{ runner.os }}-uv-${{ steps.req-hash.outputs.hash }}
         restore-keys: |
           ${{ runner.os }}-uv-
 
@@ -38,8 +57,16 @@ runs:
 
     - name: Install Python dependencies with uv
       shell: bash
+      env:
+        REQUIREMENTS: ${{ inputs.requirements }}
       run: |
-        uv pip install \
-          -r backend/requirements/default.txt \
-          -r backend/requirements/dev.txt \
-          -r backend/requirements/model_server.txt
+        # Build the uv pip install command with each requirement file as array elements
+        cmd=("uv" "pip" "install")
+        while IFS= read -r req; do
+          # Skip empty lines
+          if [ -n "$req" ]; then
+            cmd+=("-r" "$req")
+          fi
+        done <<< "$REQUIREMENTS"
+        echo "Running: ${cmd[*]}"
+        "${cmd[@]}"

.github/workflows/check-lazy-imports.yml

@@ -16,6 +16,7 @@ permissions:
 jobs:
   check-lazy-imports:
     runs-on: ubuntu-latest
+    timeout-minutes: 45
 
     steps:
     - name: Checkout code

.github/workflows/deployment.yml

@@ -18,6 +18,7 @@ jobs:
   determine-builds:
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
+    timeout-minutes: 90
     outputs:
       build-web: ${{ steps.check.outputs.build-web }}
       build-web-cloud: ${{ steps.check.outputs.build-web-cloud }}
@@ -90,6 +91,7 @@ jobs:
       - runner=4cpu-linux-x64
       - run-id=${{ github.run_id }}-web-amd64
       - extras=ecr-cache
+    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -147,6 +149,7 @@ jobs:
       - runner=4cpu-linux-arm64
       - run-id=${{ github.run_id }}-web-arm64
       - extras=ecr-cache
+    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -206,6 +209,7 @@ jobs:
       - runner=2cpu-linux-x64
       - run-id=${{ github.run_id }}-merge-web
       - extras=ecr-cache
+    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: onyxdotapp/onyx-web-server
     steps:
@@ -253,6 +257,7 @@ jobs:
       - runner=4cpu-linux-x64
       - run-id=${{ github.run_id }}-web-cloud-amd64
       - extras=ecr-cache
+    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -318,6 +323,7 @@ jobs:
       - runner=4cpu-linux-arm64
       - run-id=${{ github.run_id }}-web-cloud-arm64
       - extras=ecr-cache
+    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -385,6 +391,7 @@ jobs:
       - runner=2cpu-linux-x64
       - run-id=${{ github.run_id }}-merge-web-cloud
       - extras=ecr-cache
+    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: onyxdotapp/onyx-web-server-cloud
     steps:
@@ -429,6 +436,7 @@ jobs:
       - runner=2cpu-linux-x64
       - run-id=${{ github.run_id }}-backend-amd64
       - extras=ecr-cache
+    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -485,6 +493,7 @@ jobs:
       - runner=2cpu-linux-arm64
       - run-id=${{ github.run_id }}-backend-arm64
       - extras=ecr-cache
+    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -543,6 +552,7 @@ jobs:
       - runner=2cpu-linux-x64
       - run-id=${{ github.run_id }}-merge-backend
       - extras=ecr-cache
+    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'onyxdotapp/onyx-backend-cloud' || 'onyxdotapp/onyx-backend' }}
     steps:
@@ -591,6 +601,7 @@ jobs:
       - run-id=${{ github.run_id }}-model-server-amd64
       - volume=40gb
       - extras=ecr-cache
+    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -654,6 +665,7 @@ jobs:
       - run-id=${{ github.run_id }}-model-server-arm64
       - volume=40gb
       - extras=ecr-cache
+    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -718,6 +730,7 @@ jobs:
       - runner=2cpu-linux-x64
       - run-id=${{ github.run_id }}-merge-model-server
       - extras=ecr-cache
+    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'onyxdotapp/onyx-model-server-cloud' || 'onyxdotapp/onyx-model-server' }}
     steps:
@@ -767,6 +780,7 @@ jobs:
       - runner=2cpu-linux-arm64
       - run-id=${{ github.run_id }}-trivy-scan-web
       - extras=ecr-cache
+    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: onyxdotapp/onyx-web-server
     steps:
@@ -806,6 +820,7 @@ jobs:
       - runner=2cpu-linux-arm64
       - run-id=${{ github.run_id }}-trivy-scan-web-cloud
       - extras=ecr-cache
+    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: onyxdotapp/onyx-web-server-cloud
     steps:
@@ -845,6 +860,7 @@ jobs:
       - runner=2cpu-linux-arm64
       - run-id=${{ github.run_id }}-trivy-scan-backend
       - extras=ecr-cache
+    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'onyxdotapp/onyx-backend-cloud' || 'onyxdotapp/onyx-backend' }}
     steps:
@@ -891,6 +907,7 @@ jobs:
       - runner=2cpu-linux-arm64
       - run-id=${{ github.run_id }}-trivy-scan-model-server
       - extras=ecr-cache
+    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'onyxdotapp/onyx-model-server-cloud' || 'onyxdotapp/onyx-model-server' }}
     steps:
@@ -937,6 +954,7 @@ jobs:
     if: always() && (needs.build-web-amd64.result == 'failure' || needs.build-web-arm64.result == 'failure' || needs.merge-web.result == 'failure' || needs.build-web-cloud-amd64.result == 'failure' || needs.build-web-cloud-arm64.result == 'failure' || needs.merge-web-cloud.result == 'failure' || needs.build-backend-amd64.result == 'failure' || needs.build-backend-arm64.result == 'failure' || needs.merge-backend.result == 'failure' || needs.build-model-server-amd64.result == 'failure' || needs.build-model-server-arm64.result == 'failure' || needs.merge-model-server.result == 'failure') && github.event_name != 'workflow_dispatch'
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
+    timeout-minutes: 90
     steps:
       - name: Checkout
         uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6

.github/workflows/docker-tag-beta.yml

@@ -18,6 +18,7 @@ jobs:
     # See https://runs-on.com/runners/linux/
     # use a lower powered instance since this just does i/o to docker hub
     runs-on: [runs-on, runner=2cpu-linux-x64, "run-id=${{ github.run_id }}-tag"]
+    timeout-minutes: 45
     steps:
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@e468171a9de216ec08956ac3ada2f0791b6bd435 # ratchet:docker/setup-buildx-action@v3

.github/workflows/docker-tag-latest.yml

@@ -18,6 +18,7 @@ jobs:
     # See https://runs-on.com/runners/linux/
     # use a lower powered instance since this just does i/o to docker hub
     runs-on: [runs-on, runner=2cpu-linux-x64, "run-id=${{ github.run_id }}-tag"]
+    timeout-minutes: 45
     steps:
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@e468171a9de216ec08956ac3ada2f0791b6bd435 # ratchet:docker/setup-buildx-action@v3

.github/workflows/helm-chart-releases.yml

@@ -12,6 +12,7 @@ jobs:
     permissions:
       contents: write
     runs-on: ubuntu-latest
+    timeout-minutes: 45
     steps:
       - name: Checkout
         uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6

.github/workflows/nightly-close-stale-issues.yml

@@ -11,6 +11,7 @@ permissions:
 jobs:
   stale:
     runs-on: ubuntu-latest
+    timeout-minutes: 45
     steps:
       - uses: actions/stale@5f858e3efba33a5ca4407a664cc011ad407f2008 # ratchet:actions/stale@v10
         with:

.github/workflows/nightly-scan-licenses.yml

@@ -20,6 +20,7 @@ jobs:
   scan-licenses:
     # See https://runs-on.com/runners/linux/
     runs-on: [runs-on,runner=2cpu-linux-x64,"run-id=${{ github.run_id }}-scan-licenses"]
+    timeout-minutes: 45
     permissions:
       actions: read
       contents: read
@@ -89,6 +90,7 @@ jobs:
   scan-trivy:
     # See https://runs-on.com/runners/linux/
     runs-on: [runs-on,runner=2cpu-linux-x64,"run-id=${{ github.run_id }}-scan-trivy"]
+    timeout-minutes: 45
 
     steps:
     - name: Set up Docker Buildx

.github/workflows/pr-external-dependency-unit-tests.yml

@@ -12,12 +12,14 @@ permissions:
   contents: read
 
 env:
-  # AWS
-  S3_AWS_ACCESS_KEY_ID: ${{ secrets.S3_AWS_ACCESS_KEY_ID }}
-  S3_AWS_SECRET_ACCESS_KEY: ${{ secrets.S3_AWS_SECRET_ACCESS_KEY }}
+  # AWS credentials for S3-specific test
+  S3_AWS_ACCESS_KEY_ID_FOR_TEST: ${{ secrets.S3_AWS_ACCESS_KEY_ID }}
+  S3_AWS_SECRET_ACCESS_KEY_FOR_TEST: ${{ secrets.S3_AWS_SECRET_ACCESS_KEY }}
 
   # MinIO
   S3_ENDPOINT_URL: "http://localhost:9004"
+  S3_AWS_ACCESS_KEY_ID: "minioadmin"
+  S3_AWS_SECRET_ACCESS_KEY: "minioadmin"
 
   # Confluence
   CONFLUENCE_TEST_SPACE_URL: ${{ vars.CONFLUENCE_TEST_SPACE_URL }}
@@ -33,12 +35,13 @@ env:
 
   # Code Interpreter
   # TODO: debug why this is failing and enable
-  # CODE_INTERPRETER_BASE_URL: http://localhost:8000
+  CODE_INTERPRETER_BASE_URL: http://localhost:8000
 
 jobs:
   discover-test-dirs:
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
+    timeout-minutes: 45
     outputs:
       test-dirs: ${{ steps.set-matrix.outputs.test-dirs }}
     steps:
@@ -62,6 +65,7 @@ jobs:
       - runner=2cpu-linux-arm64
       - ${{ format('run-id={0}-external-dependency-unit-tests-job-{1}', github.run_id, strategy['job-index']) }}
       - extras=s3-cache
+    timeout-minutes: 45
     strategy:
       fail-fast: false
       matrix:
@@ -81,6 +85,11 @@ jobs:
 
       - name: Setup Python and Install Dependencies
         uses: ./.github/actions/setup-python-and-install-dependencies
+        with:
+          requirements: |
+            backend/requirements/default.txt
+            backend/requirements/dev.txt
+            backend/requirements/ee.txt
 
       - name: Setup Playwright
         uses: ./.github/actions/setup-playwright
@@ -94,6 +103,12 @@ jobs:
           username: ${{ secrets.DOCKER_USERNAME }}
           password: ${{ secrets.DOCKER_TOKEN }}
 
+      - name: Create .env file for Docker Compose
+        run: |
+          cat <<EOF > deployment/docker_compose/.env
+          CODE_INTERPRETER_BETA_ENABLED=true
+          EOF
+
       - name: Set up Standard Dependencies
         run: |
           cd deployment/docker_compose
@@ -125,3 +140,30 @@ jobs:
             -xv \
             --ff \
             backend/tests/external_dependency_unit/${TEST_DIR}
+
+      - name: Collect Docker logs on failure
+        if: failure()
+        run: |
+          mkdir -p docker-logs
+          cd deployment/docker_compose
+
+          # Get list of running containers
+          containers=$(docker compose -f docker-compose.yml -f docker-compose.dev.yml ps -q)
+
+          # Collect logs from each container
+          for container in $containers; do
+            container_name=$(docker inspect --format='{{.Name}}' $container | sed 's/^\///')
+            echo "Collecting logs from $container_name..."
+            docker logs $container > ../../docker-logs/${container_name}.log 2>&1
+          done
+
+          cd ../..
+          echo "Docker logs collected in docker-logs directory"
+
+      - name: Upload Docker logs
+        if: failure()
+        uses: actions/upload-artifact@v4
+        with:
+          name: docker-logs-${{ matrix.test-dir }}
+          path: docker-logs/
+          retention-days: 7

.github/workflows/pr-helm-chart-testing.yml

@@ -16,6 +16,7 @@ jobs:
   helm-chart-check:
     # See https://runs-on.com/runners/linux/
     runs-on: [runs-on,runner=8cpu-linux-x64,hdd=256,"run-id=${{ github.run_id }}-helm-chart-check"]
+    timeout-minutes: 45
 
     # fetch-depth 0 is required for helm/chart-testing-action
     steps:

.github/workflows/pr-integration-tests.yml

@@ -35,6 +35,7 @@ jobs:
   discover-test-dirs:
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
+    timeout-minutes: 45
     outputs:
       test-dirs: ${{ steps.set-matrix.outputs.test-dirs }}
     steps:
@@ -66,6 +67,7 @@ jobs:
 
   build-backend-image:
     runs-on: [runs-on, runner=1cpu-linux-arm64, "run-id=${{ github.run_id }}-build-backend-image", "extras=ecr-cache"]
+    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
@@ -102,6 +104,7 @@ jobs:
 
   build-model-server-image:
     runs-on: [runs-on, runner=1cpu-linux-arm64, "run-id=${{ github.run_id }}-build-model-server-image", "extras=ecr-cache"]
+    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
@@ -136,6 +139,7 @@ jobs:
 
   build-integration-image:
     runs-on: [runs-on, runner=2cpu-linux-arm64, "run-id=${{ github.run_id }}-build-integration-image", "extras=ecr-cache"]
+    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
@@ -181,6 +185,7 @@ jobs:
       - runner=4cpu-linux-arm64
       - ${{ format('run-id={0}-integration-tests-job-{1}', github.run_id, strategy['job-index']) }}
       - extras=ecr-cache
+    timeout-minutes: 45
 
     strategy:
       fail-fast: false
@@ -362,6 +367,7 @@ jobs:
         build-integration-image,
       ]
     runs-on: [runs-on, runner=8cpu-linux-arm64, "run-id=${{ github.run_id }}-multitenant-tests", "extras=ecr-cache"]
+    timeout-minutes: 45
 
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
@@ -491,6 +497,7 @@ jobs:
   required:
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
+    timeout-minutes: 45
     needs: [integration-tests, multitenant-tests]
     if: ${{ always() }}
     steps:

.github/workflows/pr-jest-tests.yml

@@ -12,6 +12,7 @@ jobs:
   jest-tests:
     name: Jest Tests
     runs-on: ubuntu-latest
+    timeout-minutes: 45
     steps:
       - name: Checkout code
         uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6

.github/workflows/pr-labeler.yml

@@ -16,6 +16,7 @@ permissions:
 jobs:
   validate_pr_title:
     runs-on: ubuntu-latest
+    timeout-minutes: 45
     steps:
       - name: Check PR title for Conventional Commits
         env:

.github/workflows/pr-linear-check.yml

@@ -13,6 +13,7 @@ permissions:
 jobs:
   linear-check:
     runs-on: ubuntu-latest
+    timeout-minutes: 45
     steps:
       - name: Check PR body for Linear link or override
         env:

.github/workflows/pr-mit-integration-tests.yml

@@ -32,6 +32,7 @@ jobs:
   discover-test-dirs:
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
+    timeout-minutes: 45
     outputs:
       test-dirs: ${{ steps.set-matrix.outputs.test-dirs }}
     steps:
@@ -62,6 +63,7 @@ jobs:
 
   build-backend-image:
     runs-on: [runs-on, runner=1cpu-linux-arm64, "run-id=${{ github.run_id }}-build-backend-image", "extras=ecr-cache"]
+    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
@@ -96,6 +98,7 @@ jobs:
 
   build-model-server-image:
     runs-on: [runs-on, runner=1cpu-linux-arm64, "run-id=${{ github.run_id }}-build-model-server-image", "extras=ecr-cache"]
+    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
@@ -129,6 +132,7 @@ jobs:
 
   build-integration-image:
     runs-on: [runs-on, runner=2cpu-linux-arm64, "run-id=${{ github.run_id }}-build-integration-image", "extras=ecr-cache"]
+    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
@@ -174,6 +178,7 @@ jobs:
       - runner=4cpu-linux-arm64
       - ${{ format('run-id={0}-integration-tests-mit-job-{1}', github.run_id, strategy['job-index']) }}
       - extras=ecr-cache
+    timeout-minutes: 45
 
     strategy:
       fail-fast: false
@@ -349,6 +354,7 @@ jobs:
   required:
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
+    timeout-minutes: 45
     needs: [integration-tests-mit]
     if: ${{ always() }}
     steps:

.github/workflows/pr-playwright-tests.yml

@@ -47,6 +47,7 @@ env:
 jobs:
   build-web-image:
     runs-on: [runs-on, runner=4cpu-linux-arm64, "run-id=${{ github.run_id }}-build-web-image", "extras=ecr-cache"]
+    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
@@ -82,6 +83,7 @@ jobs:
 
   build-backend-image:
     runs-on: [runs-on, runner=1cpu-linux-arm64, "run-id=${{ github.run_id }}-build-backend-image", "extras=ecr-cache"]
+    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
@@ -118,6 +120,7 @@ jobs:
 
   build-model-server-image:
     runs-on: [runs-on, runner=1cpu-linux-arm64, "run-id=${{ github.run_id }}-build-model-server-image", "extras=ecr-cache"]
+    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
@@ -160,6 +163,7 @@ jobs:
       - "run-id=${{ github.run_id }}-playwright-tests-${{ matrix.project }}"
       - "extras=ecr-cache"
       - volume=50gb
+    timeout-minutes: 45
     strategy:
       fail-fast: false
       matrix:
@@ -378,6 +382,7 @@ jobs:
   playwright-required:
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
+    timeout-minutes: 45
     needs: [playwright-tests]
     if: ${{ always() }}
     steps:

.github/workflows/pr-python-checks.yml

@@ -14,11 +14,30 @@ permissions:
   contents: read
 
 jobs:
+  validate-requirements:
+    runs-on: ubuntu-slim
+    timeout-minutes: 45
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
+        with:
+          persist-credentials: false
+
+      - name: Setup uv
+        uses: astral-sh/setup-uv@caf0cab7a618c569241d31dcd442f54681755d39 # ratchet:astral-sh/setup-uv@v3
+        # TODO: Enable caching once there is a uv.lock file checked in.
+        # with:
+        #   enable-cache: true
+
+      - name: Validate requirements lock files
+        run: ./backend/scripts/compile_requirements.py --check
+
   mypy-check:
     # See https://runs-on.com/runners/linux/
     # Note: Mypy seems quite optimized for x64 compared to arm64.
     # Similarly, mypy is single-threaded and incremental, so 2cpu is sufficient.
     runs-on: [runs-on, runner=2cpu-linux-x64, "run-id=${{ github.run_id }}-mypy-check", "extras=s3-cache"]
+    timeout-minutes: 45
 
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
@@ -27,6 +46,23 @@ jobs:
         with:
           persist-credentials: false
 
+      - name: Setup Python and Install Dependencies
+        uses: ./.github/actions/setup-python-and-install-dependencies
+        with:
+          requirements: |
+            backend/requirements/default.txt
+            backend/requirements/dev.txt
+            backend/requirements/model_server.txt
+            backend/requirements/ee.txt
+
+      - name: Generate OpenAPI schema
+        shell: bash
+        working-directory: backend
+        env:
+          PYTHONPATH: "."
+        run: |
+          python scripts/onyx_openapi_schema.py --filename generated/openapi.json
+
       # needed for pulling openapitools/openapi-generator-cli
       # otherwise, we hit the "Unauthenticated users" limit
       # https://docs.docker.com/docker-hub/usage/
@@ -36,11 +72,18 @@ jobs:
           username: ${{ secrets.DOCKER_USERNAME }}
           password: ${{ secrets.DOCKER_TOKEN }}
 
-      - name: Prepare build
-        uses: ./.github/actions/prepare-build
-        with:
-          docker-username: ${{ secrets.DOCKER_USERNAME }}
-          docker-password: ${{ secrets.DOCKER_TOKEN }}
+      - name: Generate OpenAPI Python client
+        shell: bash
+        run: |
+          docker run --rm \
+            -v "${{ github.workspace }}/backend/generated:/local" \
+            openapitools/openapi-generator-cli generate \
+            -i /local/openapi.json \
+            -g python \
+            -o /local/onyx_openapi_client \
+            --package-name onyx_openapi_client \
+            --skip-validate-spec \
+            --openapi-normalizer "SIMPLIFY_ONEOF_ANYOF=true,SET_OAS3_NULLABLE=true"
 
       - name: Cache mypy cache
         if: ${{ vars.DISABLE_MYPY_CACHE != 'true' }}

.github/workflows/pr-python-connector-tests.yml

@@ -126,6 +126,7 @@ jobs:
   connectors-check:
     # See https://runs-on.com/runners/linux/
     runs-on: [runs-on, runner=8cpu-linux-x64, "run-id=${{ github.run_id }}-connectors-check", "extras=s3-cache"]
+    timeout-minutes: 45
 
     env:
       PYTHONPATH: ./backend
@@ -140,6 +141,10 @@ jobs:
 
       - name: Setup Python and Install Dependencies
         uses: ./.github/actions/setup-python-and-install-dependencies
+        with:
+          requirements: |
+            backend/requirements/default.txt
+            backend/requirements/dev.txt
 
       - name: Setup Playwright
         uses: ./.github/actions/setup-playwright

.github/workflows/pr-python-model-tests.yml

@@ -32,6 +32,7 @@ jobs:
   model-check:
     # See https://runs-on.com/runners/linux/
     runs-on: [runs-on,runner=8cpu-linux-x64,"run-id=${{ github.run_id }}-model-check"]
+    timeout-minutes: 45
 
     env:
       PYTHONPATH: ./backend

.github/workflows/pr-python-tests.yml

@@ -17,6 +17,7 @@ jobs:
   backend-check:
     # See https://runs-on.com/runners/linux/
     runs-on: [runs-on, runner=2cpu-linux-arm64, "run-id=${{ github.run_id }}-backend-check"]
+    timeout-minutes: 45
 
 
     env:
@@ -36,6 +37,12 @@ jobs:
 
     - name: Setup Python and Install Dependencies
       uses: ./.github/actions/setup-python-and-install-dependencies
+      with:
+        requirements: |
+          backend/requirements/default.txt
+          backend/requirements/dev.txt
+          backend/requirements/model_server.txt
+          backend/requirements/ee.txt
 
     - name: Run Tests
       shell: script -q -e -c "bash --noprofile --norc -eo pipefail {0}"

.github/workflows/pr-quality-checks.yml

@@ -14,6 +14,7 @@ jobs:
   quality-checks:
     # See https://runs-on.com/runners/linux/
     runs-on: [runs-on, runner=1cpu-linux-arm64, "run-id=${{ github.run_id }}-quality-checks"]
+    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
@@ -26,5 +27,13 @@ jobs:
       - name: Setup Terraform
         uses: hashicorp/setup-terraform@b9cd54a3c349d3f38e8881555d616ced269862dd # ratchet:hashicorp/setup-terraform@v3
       - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # ratchet:pre-commit/action@v3.0.1
+        env:
+          # uv-run is mypy's id and mypy is covered by the Python Checks which caches dependencies better.
+          SKIP: uv-run
         with:
           extra_args: ${{ github.event_name == 'pull_request' && format('--from-ref {0} --to-ref {1}', github.event.pull_request.base.sha, github.event.pull_request.head.sha) || '' }}
+      - name: Check Actions
+        uses: giner/check-actions@28d366c7cbbe235f9624a88aa31a628167eee28c # ratchet:giner/check-actions@v1.0.1
+        with:
+          check_permissions: false
+          check_versions: false

.github/workflows/sync_foss.yml

@@ -9,6 +9,7 @@ on:
 jobs:
   sync-foss:
     runs-on: ubuntu-latest
+    timeout-minutes: 45
     permissions:
       contents: read
     steps:

.github/workflows/tag-nightly.yml

@@ -11,6 +11,7 @@ permissions:
 jobs:
   create-and-push-tag:
     runs-on: ubuntu-slim
+    timeout-minutes: 45
 
     steps:
       # actions using GITHUB_TOKEN cannot trigger another workflow, but we do want this to trigger docker pushes

.github/workflows/zizmor.yml

@@ -12,6 +12,7 @@ jobs:
   zizmor:
     name: zizmor
     runs-on: ubuntu-slim
+    timeout-minutes: 45
     permissions:
       security-events: write # needed for SARIF uploads
     steps:

.gitignore

@@ -49,5 +49,7 @@ CLAUDE.md
 # Local .terraform.lock.hcl file
 .terraform.lock.hcl
 
+node_modules
+
 # MCP configs
 .playwright-mcp

.pre-commit-config.yaml

@@ -1,4 +1,20 @@
+default_install_hook_types:
+  - pre-commit
+  - post-checkout
+  - post-merge
+  - post-rewrite
 repos:
+  - repo: https://github.com/astral-sh/uv-pre-commit
+    # This revision is from https://github.com/astral-sh/uv-pre-commit/pull/53
+    rev: d30b4298e4fb63ce8609e29acdbcf4c9018a483c
+    hooks:
+      - id: uv-sync
+      - id: uv-run
+        name: mypy
+        args: ["mypy"]
+        pass_filenames: true
+        files: ^backend/.*\.py$
+
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.6.0
     hooks:
@@ -71,31 +87,3 @@ repos:
         entry: python3 backend/scripts/check_lazy_imports.py
         language: system
         files: ^backend/(?!\.venv/).*\.py$
-
-  # We would like to have a mypy pre-commit hook, but due to the fact that
-  # pre-commit runs in it's own isolated environment, we would need to install
-  # and keep in sync all dependencies so mypy has access to the appropriate type
-  # stubs. This does not seem worth it at the moment, so for now we will stick to
-  # having mypy run via Github Actions / manually by contributors
-  # - repo: https://github.com/pre-commit/mirrors-mypy
-  #   rev: v1.1.1
-  #   hooks:
-  #     - id: mypy
-  #       exclude: ^tests/
-  #       # below are needed for type stubs since pre-commit runs in it's own
-  #       # isolated environment. Unfortunately, this needs to be kept in sync
-  #       # with requirements/dev.txt + requirements/default.txt
-  #       additional_dependencies: [
-  #         alembic==1.10.4,
-  #         types-beautifulsoup4==4.12.0.3,
-  #         types-html5lib==1.1.11.13,
-  #         types-oauthlib==3.2.0.9,
-  #         types-psycopg2==2.9.21.10,
-  #         types-python-dateutil==2.8.19.13,
-  #         types-regex==2023.3.23.1,
-  #         types-requests==2.28.11.17,
-  #         types-retry==0.9.9.3,
-  #         types-urllib3==1.26.25.11
-  #       ]
-  #       # TODO: add back once errors are addressed
-  #       # args: [--strict]

backend/alembic/versions/4f8a2b3c1d9e_add_open_url_tool.py

@@ -0,0 +1,104 @@
+"""add_open_url_tool
+
+Revision ID: 4f8a2b3c1d9e
+Revises: a852cbe15577
+Create Date: 2025-11-24 12:00:00.000000
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "4f8a2b3c1d9e"
+down_revision = "a852cbe15577"
+branch_labels = None
+depends_on = None
+
+
+OPEN_URL_TOOL = {
+    "name": "OpenURLTool",
+    "display_name": "Open URL",
+    "description": (
+        "The Open URL Action allows the agent to fetch and read contents of web pages."
+    ),
+    "in_code_tool_id": "OpenURLTool",
+    "enabled": True,
+}
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+
+    # Check if tool already exists
+    existing = conn.execute(
+        sa.text("SELECT id FROM tool WHERE in_code_tool_id = :in_code_tool_id"),
+        {"in_code_tool_id": OPEN_URL_TOOL["in_code_tool_id"]},
+    ).fetchone()
+
+    if existing:
+        tool_id = existing[0]
+        # Update existing tool
+        conn.execute(
+            sa.text(
+                """
+                UPDATE tool
+                SET name = :name,
+                    display_name = :display_name,
+                    description = :description
+                WHERE in_code_tool_id = :in_code_tool_id
+                """
+            ),
+            OPEN_URL_TOOL,
+        )
+    else:
+        # Insert new tool
+        conn.execute(
+            sa.text(
+                """
+                INSERT INTO tool (name, display_name, description, in_code_tool_id, enabled)
+                VALUES (:name, :display_name, :description, :in_code_tool_id, :enabled)
+                """
+            ),
+            OPEN_URL_TOOL,
+        )
+        # Get the newly inserted tool's id
+        result = conn.execute(
+            sa.text("SELECT id FROM tool WHERE in_code_tool_id = :in_code_tool_id"),
+            {"in_code_tool_id": OPEN_URL_TOOL["in_code_tool_id"]},
+        ).fetchone()
+        tool_id = result[0]  # type: ignore
+
+    # Associate the tool with all existing personas
+    # Get all persona IDs
+    persona_ids = conn.execute(sa.text("SELECT id FROM persona")).fetchall()
+
+    for (persona_id,) in persona_ids:
+        # Check if association already exists
+        exists = conn.execute(
+            sa.text(
+                """
+                SELECT 1 FROM persona__tool
+                WHERE persona_id = :persona_id AND tool_id = :tool_id
+                """
+            ),
+            {"persona_id": persona_id, "tool_id": tool_id},
+        ).fetchone()
+
+        if not exists:
+            conn.execute(
+                sa.text(
+                    """
+                    INSERT INTO persona__tool (persona_id, tool_id)
+                    VALUES (:persona_id, :tool_id)
+                    """
+                ),
+                {"persona_id": persona_id, "tool_id": tool_id},
+            )
+
+
+def downgrade() -> None:
+    # We don't remove the tool on downgrade since it's fine to have it around.
+    # If we upgrade again, it will be a no-op.
+    pass

backend/alembic/versions/6436661d5b65_add_created_at_in_project_userfile.py

@@ -0,0 +1,44 @@
+"""add_created_at_in_project_userfile
+
+Revision ID: 6436661d5b65
+Revises: c7e9f4a3b2d1
+Create Date: 2025-11-24 11:50:24.536052
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+# revision identifiers, used by Alembic.
+revision = "6436661d5b65"
+down_revision = "c7e9f4a3b2d1"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Add created_at column to project__user_file table
+    op.add_column(
+        "project__user_file",
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            server_default=sa.text("now()"),
+            nullable=False,
+        ),
+    )
+    # Add composite index on (project_id, created_at DESC)
+    op.create_index(
+        "ix_project__user_file_project_id_created_at",
+        "project__user_file",
+        ["project_id", sa.text("created_at DESC")],
+    )
+
+
+def downgrade() -> None:
+    # Remove composite index on (project_id, created_at)
+    op.drop_index(
+        "ix_project__user_file_project_id_created_at", table_name="project__user_file"
+    )
+    # Remove created_at column from project__user_file table
+    op.drop_column("project__user_file", "created_at")

backend/alembic/versions/a852cbe15577_new_chat_history.py

@@ -0,0 +1,572 @@
+"""New Chat History
+
+Revision ID: a852cbe15577
+Revises: 6436661d5b65
+Create Date: 2025-11-08 15:16:37.781308
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = "a852cbe15577"
+down_revision = "6436661d5b65"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Drop research agent tables (if they exist)
+    op.execute("DROP TABLE IF EXISTS research_agent_iteration_sub_step CASCADE")
+    op.execute("DROP TABLE IF EXISTS research_agent_iteration CASCADE")
+
+    # Drop agent sub query and sub question tables (if they exist)
+    op.execute("DROP TABLE IF EXISTS agent__sub_query__search_doc CASCADE")
+    op.execute("DROP TABLE IF EXISTS agent__sub_query CASCADE")
+    op.execute("DROP TABLE IF EXISTS agent__sub_question CASCADE")
+
+    # Update ChatMessage table
+    # Rename parent_message to parent_message_id and make it a foreign key (if not already done)
+    conn = op.get_bind()
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT column_name FROM information_schema.columns
+        WHERE table_name = 'chat_message' AND column_name = 'parent_message'
+    """
+        )
+    )
+    if result.fetchone():
+        op.alter_column(
+            "chat_message", "parent_message", new_column_name="parent_message_id"
+        )
+        op.create_foreign_key(
+            "fk_chat_message_parent_message_id",
+            "chat_message",
+            "chat_message",
+            ["parent_message_id"],
+            ["id"],
+        )
+
+    # Rename latest_child_message to latest_child_message_id and make it a foreign key (if not already done)
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT column_name FROM information_schema.columns
+        WHERE table_name = 'chat_message' AND column_name = 'latest_child_message'
+    """
+        )
+    )
+    if result.fetchone():
+        op.alter_column(
+            "chat_message",
+            "latest_child_message",
+            new_column_name="latest_child_message_id",
+        )
+        op.create_foreign_key(
+            "fk_chat_message_latest_child_message_id",
+            "chat_message",
+            "chat_message",
+            ["latest_child_message_id"],
+            ["id"],
+        )
+
+    # Add reasoning_tokens column (if not exists)
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT column_name FROM information_schema.columns
+        WHERE table_name = 'chat_message' AND column_name = 'reasoning_tokens'
+    """
+        )
+    )
+    if not result.fetchone():
+        op.add_column(
+            "chat_message", sa.Column("reasoning_tokens", sa.Text(), nullable=True)
+        )
+
+    # Drop columns no longer needed (if they exist)
+    for col in [
+        "rephrased_query",
+        "alternate_assistant_id",
+        "overridden_model",
+        "is_agentic",
+        "refined_answer_improvement",
+        "research_type",
+        "research_plan",
+        "research_answer_purpose",
+    ]:
+        result = conn.execute(
+            sa.text(
+                f"""
+            SELECT column_name FROM information_schema.columns
+            WHERE table_name = 'chat_message' AND column_name = '{col}'
+        """
+            )
+        )
+        if result.fetchone():
+            op.drop_column("chat_message", col)
+
+    # Update ToolCall table
+    # Add chat_session_id column (if not exists)
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT column_name FROM information_schema.columns
+        WHERE table_name = 'tool_call' AND column_name = 'chat_session_id'
+    """
+        )
+    )
+    if not result.fetchone():
+        op.add_column(
+            "tool_call",
+            sa.Column("chat_session_id", postgresql.UUID(as_uuid=True), nullable=False),
+        )
+        op.create_foreign_key(
+            "fk_tool_call_chat_session_id",
+            "tool_call",
+            "chat_session",
+            ["chat_session_id"],
+            ["id"],
+        )
+
+    # Rename message_id to parent_chat_message_id and make nullable (if not already done)
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT column_name FROM information_schema.columns
+        WHERE table_name = 'tool_call' AND column_name = 'message_id'
+    """
+        )
+    )
+    if result.fetchone():
+        op.alter_column(
+            "tool_call",
+            "message_id",
+            new_column_name="parent_chat_message_id",
+            nullable=True,
+        )
+
+    # Add parent_tool_call_id (if not exists)
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT column_name FROM information_schema.columns
+        WHERE table_name = 'tool_call' AND column_name = 'parent_tool_call_id'
+    """
+        )
+    )
+    if not result.fetchone():
+        op.add_column(
+            "tool_call", sa.Column("parent_tool_call_id", sa.Integer(), nullable=True)
+        )
+        op.create_foreign_key(
+            "fk_tool_call_parent_tool_call_id",
+            "tool_call",
+            "tool_call",
+            ["parent_tool_call_id"],
+            ["id"],
+        )
+    op.drop_constraint("uq_tool_call_message_id", "tool_call", type_="unique")
+
+    # Add turn_number, tool_id (if not exists)
+    for col_name in ["turn_number", "tool_id"]:
+        result = conn.execute(
+            sa.text(
+                f"""
+            SELECT column_name FROM information_schema.columns
+            WHERE table_name = 'tool_call' AND column_name = '{col_name}'
+        """
+            )
+        )
+        if not result.fetchone():
+            op.add_column(
+                "tool_call",
+                sa.Column(col_name, sa.Integer(), nullable=False, server_default="0"),
+            )
+
+    # Add tool_call_id as String (if not exists)
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT column_name FROM information_schema.columns
+        WHERE table_name = 'tool_call' AND column_name = 'tool_call_id'
+    """
+        )
+    )
+    if not result.fetchone():
+        op.add_column(
+            "tool_call",
+            sa.Column("tool_call_id", sa.String(), nullable=False, server_default=""),
+        )
+
+    # Add reasoning_tokens (if not exists)
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT column_name FROM information_schema.columns
+        WHERE table_name = 'tool_call' AND column_name = 'reasoning_tokens'
+    """
+        )
+    )
+    if not result.fetchone():
+        op.add_column(
+            "tool_call", sa.Column("reasoning_tokens", sa.Text(), nullable=True)
+        )
+
+    # Rename tool_arguments to tool_call_arguments (if not already done)
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT column_name FROM information_schema.columns
+        WHERE table_name = 'tool_call' AND column_name = 'tool_arguments'
+    """
+        )
+    )
+    if result.fetchone():
+        op.alter_column(
+            "tool_call", "tool_arguments", new_column_name="tool_call_arguments"
+        )
+
+    # Rename tool_result to tool_call_response and change type from JSONB to Text (if not already done)
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT column_name, data_type FROM information_schema.columns
+        WHERE table_name = 'tool_call' AND column_name = 'tool_result'
+    """
+        )
+    )
+    tool_result_row = result.fetchone()
+    if tool_result_row:
+        op.alter_column(
+            "tool_call", "tool_result", new_column_name="tool_call_response"
+        )
+        # Change type from JSONB to Text
+        op.execute(
+            sa.text(
+                """
+            ALTER TABLE tool_call
+            ALTER COLUMN tool_call_response TYPE TEXT
+            USING tool_call_response::text
+        """
+            )
+        )
+    else:
+        # Check if tool_call_response already exists and is JSONB, then convert to Text
+        result = conn.execute(
+            sa.text(
+                """
+            SELECT data_type FROM information_schema.columns
+            WHERE table_name = 'tool_call' AND column_name = 'tool_call_response'
+        """
+            )
+        )
+        tool_call_response_row = result.fetchone()
+        if tool_call_response_row and tool_call_response_row[0] == "jsonb":
+            op.execute(
+                sa.text(
+                    """
+                ALTER TABLE tool_call
+                ALTER COLUMN tool_call_response TYPE TEXT
+                USING tool_call_response::text
+            """
+                )
+            )
+
+    # Add tool_call_tokens (if not exists)
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT column_name FROM information_schema.columns
+        WHERE table_name = 'tool_call' AND column_name = 'tool_call_tokens'
+    """
+        )
+    )
+    if not result.fetchone():
+        op.add_column(
+            "tool_call",
+            sa.Column(
+                "tool_call_tokens", sa.Integer(), nullable=False, server_default="0"
+            ),
+        )
+
+    # Add generated_images column for image generation tool replay (if not exists)
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT column_name FROM information_schema.columns
+        WHERE table_name = 'tool_call' AND column_name = 'generated_images'
+    """
+        )
+    )
+    if not result.fetchone():
+        op.add_column(
+            "tool_call",
+            sa.Column("generated_images", postgresql.JSONB(), nullable=True),
+        )
+
+    # Drop tool_name column (if exists)
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT column_name FROM information_schema.columns
+        WHERE table_name = 'tool_call' AND column_name = 'tool_name'
+    """
+        )
+    )
+    if result.fetchone():
+        op.drop_column("tool_call", "tool_name")
+
+    # Create tool_call__search_doc association table (if not exists)
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT table_name FROM information_schema.tables
+        WHERE table_name = 'tool_call__search_doc'
+    """
+        )
+    )
+    if not result.fetchone():
+        op.create_table(
+            "tool_call__search_doc",
+            sa.Column("tool_call_id", sa.Integer(), nullable=False),
+            sa.Column("search_doc_id", sa.Integer(), nullable=False),
+            sa.ForeignKeyConstraint(
+                ["tool_call_id"], ["tool_call.id"], ondelete="CASCADE"
+            ),
+            sa.ForeignKeyConstraint(
+                ["search_doc_id"], ["search_doc.id"], ondelete="CASCADE"
+            ),
+            sa.PrimaryKeyConstraint("tool_call_id", "search_doc_id"),
+        )
+
+    # Add replace_base_system_prompt to persona table (if not exists)
+    result = conn.execute(
+        sa.text(
+            """
+        SELECT column_name FROM information_schema.columns
+        WHERE table_name = 'persona' AND column_name = 'replace_base_system_prompt'
+    """
+        )
+    )
+    if not result.fetchone():
+        op.add_column(
+            "persona",
+            sa.Column(
+                "replace_base_system_prompt",
+                sa.Boolean(),
+                nullable=False,
+                server_default="false",
+            ),
+        )
+
+
+def downgrade() -> None:
+    # Reverse persona changes
+    op.drop_column("persona", "replace_base_system_prompt")
+
+    # Drop tool_call__search_doc association table
+    op.execute("DROP TABLE IF EXISTS tool_call__search_doc CASCADE")
+
+    # Reverse ToolCall changes
+    op.add_column("tool_call", sa.Column("tool_name", sa.String(), nullable=False))
+    op.drop_column("tool_call", "tool_id")
+    op.drop_column("tool_call", "tool_call_tokens")
+    op.drop_column("tool_call", "generated_images")
+    # Change tool_call_response back to JSONB before renaming
+    op.execute(
+        sa.text(
+            """
+        ALTER TABLE tool_call
+        ALTER COLUMN tool_call_response TYPE JSONB
+        USING tool_call_response::jsonb
+    """
+        )
+    )
+    op.alter_column("tool_call", "tool_call_response", new_column_name="tool_result")
+    op.alter_column(
+        "tool_call", "tool_call_arguments", new_column_name="tool_arguments"
+    )
+    op.drop_column("tool_call", "reasoning_tokens")
+    op.drop_column("tool_call", "tool_call_id")
+    op.drop_column("tool_call", "turn_number")
+    op.drop_constraint(
+        "fk_tool_call_parent_tool_call_id", "tool_call", type_="foreignkey"
+    )
+    op.drop_column("tool_call", "parent_tool_call_id")
+    op.alter_column(
+        "tool_call",
+        "parent_chat_message_id",
+        new_column_name="message_id",
+        nullable=False,
+    )
+    op.drop_constraint("fk_tool_call_chat_session_id", "tool_call", type_="foreignkey")
+    op.drop_column("tool_call", "chat_session_id")
+
+    op.add_column(
+        "chat_message",
+        sa.Column(
+            "research_answer_purpose",
+            sa.Enum("INTRO", "DEEP_DIVE", name="researchanswerpurpose"),
+            nullable=True,
+        ),
+    )
+    op.add_column(
+        "chat_message", sa.Column("research_plan", postgresql.JSONB(), nullable=True)
+    )
+    op.add_column(
+        "chat_message",
+        sa.Column(
+            "research_type",
+            sa.Enum("SIMPLE", "DEEP", name="researchtype"),
+            nullable=True,
+        ),
+    )
+    op.add_column(
+        "chat_message",
+        sa.Column("refined_answer_improvement", sa.Boolean(), nullable=True),
+    )
+    op.add_column(
+        "chat_message",
+        sa.Column("is_agentic", sa.Boolean(), nullable=False, server_default="false"),
+    )
+    op.add_column(
+        "chat_message", sa.Column("overridden_model", sa.String(), nullable=True)
+    )
+    op.add_column(
+        "chat_message", sa.Column("alternate_assistant_id", sa.Integer(), nullable=True)
+    )
+    op.add_column(
+        "chat_message", sa.Column("rephrased_query", sa.Text(), nullable=True)
+    )
+    op.drop_column("chat_message", "reasoning_tokens")
+    op.drop_constraint(
+        "fk_chat_message_latest_child_message_id", "chat_message", type_="foreignkey"
+    )
+    op.alter_column(
+        "chat_message",
+        "latest_child_message_id",
+        new_column_name="latest_child_message",
+    )
+    op.drop_constraint(
+        "fk_chat_message_parent_message_id", "chat_message", type_="foreignkey"
+    )
+    op.alter_column(
+        "chat_message", "parent_message_id", new_column_name="parent_message"
+    )
+
+    # Recreate agent sub question and sub query tables
+    op.create_table(
+        "agent__sub_question",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column("primary_question_id", sa.Integer(), nullable=False),
+        sa.Column("chat_session_id", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column("sub_question", sa.Text(), nullable=False),
+        sa.Column("level", sa.Integer(), nullable=False),
+        sa.Column("level_question_num", sa.Integer(), nullable=False),
+        sa.Column(
+            "time_created",
+            sa.DateTime(timezone=True),
+            server_default=sa.text("now()"),
+            nullable=False,
+        ),
+        sa.Column("sub_answer", sa.Text(), nullable=False),
+        sa.Column("sub_question_doc_results", postgresql.JSONB(), nullable=False),
+        sa.ForeignKeyConstraint(
+            ["primary_question_id"], ["chat_message.id"], ondelete="CASCADE"
+        ),
+        sa.ForeignKeyConstraint(["chat_session_id"], ["chat_session.id"]),
+        sa.PrimaryKeyConstraint("id"),
+    )
+
+    op.create_table(
+        "agent__sub_query",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column("parent_question_id", sa.Integer(), nullable=False),
+        sa.Column("chat_session_id", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column("sub_query", sa.Text(), nullable=False),
+        sa.Column(
+            "time_created",
+            sa.DateTime(timezone=True),
+            server_default=sa.text("now()"),
+            nullable=False,
+        ),
+        sa.ForeignKeyConstraint(
+            ["parent_question_id"], ["agent__sub_question.id"], ondelete="CASCADE"
+        ),
+        sa.ForeignKeyConstraint(["chat_session_id"], ["chat_session.id"]),
+        sa.PrimaryKeyConstraint("id"),
+    )
+
+    op.create_table(
+        "agent__sub_query__search_doc",
+        sa.Column("sub_query_id", sa.Integer(), nullable=False),
+        sa.Column("search_doc_id", sa.Integer(), nullable=False),
+        sa.ForeignKeyConstraint(
+            ["sub_query_id"], ["agent__sub_query.id"], ondelete="CASCADE"
+        ),
+        sa.ForeignKeyConstraint(["search_doc_id"], ["search_doc.id"]),
+        sa.PrimaryKeyConstraint("sub_query_id", "search_doc_id"),
+    )
+
+    # Recreate research agent tables
+    op.create_table(
+        "research_agent_iteration",
+        sa.Column("id", sa.Integer(), autoincrement=True, nullable=False),
+        sa.Column("primary_question_id", sa.Integer(), nullable=False),
+        sa.Column("iteration_nr", sa.Integer(), nullable=False),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            server_default=sa.text("now()"),
+            nullable=False,
+        ),
+        sa.Column("purpose", sa.String(), nullable=True),
+        sa.Column("reasoning", sa.String(), nullable=True),
+        sa.ForeignKeyConstraint(
+            ["primary_question_id"], ["chat_message.id"], ondelete="CASCADE"
+        ),
+        sa.PrimaryKeyConstraint("id"),
+        sa.UniqueConstraint(
+            "primary_question_id",
+            "iteration_nr",
+            name="_research_agent_iteration_unique_constraint",
+        ),
+    )
+
+    op.create_table(
+        "research_agent_iteration_sub_step",
+        sa.Column("id", sa.Integer(), autoincrement=True, nullable=False),
+        sa.Column("primary_question_id", sa.Integer(), nullable=False),
+        sa.Column("iteration_nr", sa.Integer(), nullable=False),
+        sa.Column("iteration_sub_step_nr", sa.Integer(), nullable=False),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            server_default=sa.text("now()"),
+            nullable=False,
+        ),
+        sa.Column("sub_step_instructions", sa.String(), nullable=True),
+        sa.Column("sub_step_tool_id", sa.Integer(), nullable=True),
+        sa.Column("reasoning", sa.String(), nullable=True),
+        sa.Column("sub_answer", sa.String(), nullable=True),
+        sa.Column("cited_doc_results", postgresql.JSONB(), nullable=False),
+        sa.Column("claims", postgresql.JSONB(), nullable=True),
+        sa.Column("is_web_fetch", sa.Boolean(), nullable=True),
+        sa.Column("queries", postgresql.JSONB(), nullable=True),
+        sa.Column("generated_images", postgresql.JSONB(), nullable=True),
+        sa.Column("additional_data", postgresql.JSONB(), nullable=True),
+        sa.ForeignKeyConstraint(
+            ["primary_question_id", "iteration_nr"],
+            [
+                "research_agent_iteration.primary_question_id",
+                "research_agent_iteration.iteration_nr",
+            ],
+            ondelete="CASCADE",
+        ),
+        sa.ForeignKeyConstraint(["sub_step_tool_id"], ["tool.id"], ondelete="SET NULL"),
+        sa.PrimaryKeyConstraint("id"),
+    )

backend/ee/onyx/db/analytics.py

@@ -199,10 +199,7 @@ def fetch_persona_message_analytics(
             ChatMessage.chat_session_id == ChatSession.id,
         )
         .where(
-            or_(
-                ChatMessage.alternate_assistant_id == persona_id,
-                ChatSession.persona_id == persona_id,
-            ),
+            ChatSession.persona_id == persona_id,
             ChatMessage.time_sent >= start,
             ChatMessage.time_sent <= end,
             ChatMessage.message_type == MessageType.ASSISTANT,
@@ -231,10 +228,7 @@ def fetch_persona_unique_users(
             ChatMessage.chat_session_id == ChatSession.id,
         )
         .where(
-            or_(
-                ChatMessage.alternate_assistant_id == persona_id,
-                ChatSession.persona_id == persona_id,
-            ),
+            ChatSession.persona_id == persona_id,
             ChatMessage.time_sent >= start,
             ChatMessage.time_sent <= end,
             ChatMessage.message_type == MessageType.ASSISTANT,
@@ -265,10 +259,7 @@ def fetch_assistant_message_analytics(
             ChatMessage.chat_session_id == ChatSession.id,
         )
         .where(
-            or_(
-                ChatMessage.alternate_assistant_id == assistant_id,
-                ChatSession.persona_id == assistant_id,
-            ),
+            ChatSession.persona_id == assistant_id,
             ChatMessage.time_sent >= start,
             ChatMessage.time_sent <= end,
             ChatMessage.message_type == MessageType.ASSISTANT,
@@ -299,10 +290,7 @@ def fetch_assistant_unique_users(
             ChatMessage.chat_session_id == ChatSession.id,
         )
         .where(
-            or_(
-                ChatMessage.alternate_assistant_id == assistant_id,
-                ChatSession.persona_id == assistant_id,
-            ),
+            ChatSession.persona_id == assistant_id,
             ChatMessage.time_sent >= start,
             ChatMessage.time_sent <= end,
             ChatMessage.message_type == MessageType.ASSISTANT,
@@ -332,10 +320,7 @@ def fetch_assistant_unique_users_total(
             ChatMessage.chat_session_id == ChatSession.id,
         )
         .where(
-            or_(
-                ChatMessage.alternate_assistant_id == assistant_id,
-                ChatSession.persona_id == assistant_id,
-            ),
+            ChatSession.persona_id == assistant_id,
             ChatMessage.time_sent >= start,
             ChatMessage.time_sent <= end,
             ChatMessage.message_type == MessageType.ASSISTANT,

backend/ee/onyx/db/usage_export.py

@@ -55,18 +55,7 @@ def get_empty_chat_messages_entries__paginated(
 
             # Get assistant name (from session persona, or alternate if specified)
             assistant_name = None
-            if message.alternate_assistant_id:
-                # If there's an alternate assistant, we need to fetch it
-                from onyx.db.models import Persona
-
-                alternate_persona = (
-                    db_session.query(Persona)
-                    .filter(Persona.id == message.alternate_assistant_id)
-                    .first()
-                )
-                if alternate_persona:
-                    assistant_name = alternate_persona.name
-            elif chat_session.persona:
+            if chat_session.persona:
                 assistant_name = chat_session.persona.name
 
             message_skeletons.append(

backend/ee/onyx/db/user_group.py

@@ -581,6 +581,48 @@ def update_user_curator_relationship(
     db_session.commit()
 
 
+def add_users_to_user_group(
+    db_session: Session,
+    user: User | None,
+    user_group_id: int,
+    user_ids: list[UUID],
+) -> UserGroup:
+    db_user_group = fetch_user_group(db_session=db_session, user_group_id=user_group_id)
+    if db_user_group is None:
+        raise ValueError(f"UserGroup with id '{user_group_id}' not found")
+
+    missing_users = [
+        user_id for user_id in user_ids if fetch_user_by_id(db_session, user_id) is None
+    ]
+    if missing_users:
+        raise ValueError(
+            f"User(s) not found: {', '.join(str(user_id) for user_id in missing_users)}"
+        )
+
+    _check_user_group_is_modifiable(db_user_group)
+
+    current_user_ids = [user.id for user in db_user_group.users]
+    current_user_ids_set = set(current_user_ids)
+    new_user_ids = [
+        user_id for user_id in user_ids if user_id not in current_user_ids_set
+    ]
+
+    if not new_user_ids:
+        return db_user_group
+
+    user_group_update = UserGroupUpdate(
+        user_ids=current_user_ids + new_user_ids,
+        cc_pair_ids=[cc_pair.id for cc_pair in db_user_group.cc_pairs],
+    )
+
+    return update_user_group(
+        db_session=db_session,
+        user=user,
+        user_group_id=user_group_id,
+        user_group_update=user_group_update,
+    )
+
+
 def update_user_group(
     db_session: Session,
     user: User | None,
@@ -603,6 +645,17 @@ def update_user_group(
     added_user_ids = list(updated_user_ids - current_user_ids)
     removed_user_ids = list(current_user_ids - updated_user_ids)
 
+    if added_user_ids:
+        missing_users = [
+            user_id
+            for user_id in added_user_ids
+            if fetch_user_by_id(db_session, user_id) is None
+        ]
+        if missing_users:
+            raise ValueError(
+                f"User(s) not found: {', '.join(str(user_id) for user_id in missing_users)}"
+            )
+
     # LEAVING THIS HERE FOR NOW FOR GIVING DIFFERENT ROLES
     # ACCESS TO DIFFERENT PERMISSIONS
     # if (removed_user_ids or added_user_ids) and (

backend/ee/onyx/server/query_and_chat/chat_backend.py

@@ -9,7 +9,7 @@ from ee.onyx.server.query_and_chat.models import (
 )
 from onyx.auth.users import current_user
 from onyx.chat.chat_utils import combine_message_thread
-from onyx.chat.chat_utils import create_chat_chain
+from onyx.chat.chat_utils import create_chat_history_chain
 from onyx.chat.models import ChatBasicResponse
 from onyx.chat.process_message import gather_stream
 from onyx.chat.process_message import stream_chat_message_objects
@@ -69,7 +69,7 @@ def handle_simplified_chat_message(
         chat_session_id = chat_message_req.chat_session_id
 
     try:
-        parent_message, _ = create_chat_chain(
+        parent_message, _ = create_chat_history_chain(
             chat_session_id=chat_session_id, db_session=db_session
         )
     except Exception:

backend/ee/onyx/server/query_and_chat/models.py

@@ -8,10 +8,29 @@ from pydantic import model_validator
 
 from onyx.chat.models import ThreadMessage
 from onyx.configs.constants import DocumentSource
+from onyx.context.search.models import BaseFilters
+from onyx.context.search.models import BasicChunkRequest
 from onyx.context.search.models import ChunkContext
+from onyx.context.search.models import InferenceChunk
 from onyx.context.search.models import RetrievalDetails
 from onyx.server.manage.models import StandardAnswer
-from onyx.server.query_and_chat.streaming_models import SubQuestionIdentifier
+
+
+class StandardAnswerRequest(BaseModel):
+    message: str
+    slack_bot_categories: list[str]
+
+
+class StandardAnswerResponse(BaseModel):
+    standard_answers: list[StandardAnswer] = Field(default_factory=list)
+
+
+class DocumentSearchRequest(BasicChunkRequest):
+    user_selected_filters: BaseFilters | None = None
+
+
+class DocumentSearchResponse(BaseModel):
+    top_documents: list[InferenceChunk]
 
 
 class BasicCreateChatMessageRequest(ChunkContext):
@@ -71,17 +90,17 @@ class SimpleDoc(BaseModel):
     metadata: dict | None
 
 
-class AgentSubQuestion(SubQuestionIdentifier):
+class AgentSubQuestion(BaseModel):
     sub_question: str
     document_ids: list[str]
 
 
-class AgentAnswer(SubQuestionIdentifier):
+class AgentAnswer(BaseModel):
     answer: str
     answer_type: Literal["agent_sub_answer", "agent_level_answer"]
 
 
-class AgentSubQuery(SubQuestionIdentifier):
+class AgentSubQuery(BaseModel):
     sub_query: str
     query_id: int
 
@@ -127,12 +146,3 @@ class AgentSubQuery(SubQuestionIdentifier):
             sorted(level_question_dict.items(), key=lambda x: (x is None, x))
         )
         return sorted_dict
-
-
-class StandardAnswerRequest(BaseModel):
-    message: str
-    slack_bot_categories: list[str]
-
-
-class StandardAnswerResponse(BaseModel):
-    standard_answers: list[StandardAnswer] = Field(default_factory=list)

backend/ee/onyx/server/query_history/api.py

@@ -24,7 +24,7 @@ from onyx.auth.users import current_admin_user
 from onyx.auth.users import get_display_email
 from onyx.background.celery.versioned_apps.client import app as client_app
 from onyx.background.task_utils import construct_query_history_report_name
-from onyx.chat.chat_utils import create_chat_chain
+from onyx.chat.chat_utils import create_chat_history_chain
 from onyx.configs.app_configs import ONYX_QUERY_HISTORY_TYPE
 from onyx.configs.constants import FileOrigin
 from onyx.configs.constants import FileType
@@ -123,10 +123,9 @@ def snapshot_from_chat_session(
 ) -> ChatSessionSnapshot | None:
     try:
         # Older chats may not have the right structure
-        last_message, messages = create_chat_chain(
+        messages = create_chat_history_chain(
             chat_session_id=chat_session.id, db_session=db_session
         )
-        messages.append(last_message)
     except RuntimeError:
         return None
 

backend/ee/onyx/server/user_group/api.py

@@ -4,12 +4,14 @@ from fastapi import HTTPException
 from sqlalchemy.exc import IntegrityError
 from sqlalchemy.orm import Session
 
+from ee.onyx.db.user_group import add_users_to_user_group
 from ee.onyx.db.user_group import fetch_user_groups
 from ee.onyx.db.user_group import fetch_user_groups_for_user
 from ee.onyx.db.user_group import insert_user_group
 from ee.onyx.db.user_group import prepare_user_group_for_deletion
 from ee.onyx.db.user_group import update_user_curator_relationship
 from ee.onyx.db.user_group import update_user_group
+from ee.onyx.server.user_group.models import AddUsersToUserGroupRequest
 from ee.onyx.server.user_group.models import SetCuratorRequest
 from ee.onyx.server.user_group.models import UserGroup
 from ee.onyx.server.user_group.models import UserGroupCreate
@@ -79,6 +81,26 @@ def patch_user_group(
         raise HTTPException(status_code=404, detail=str(e))
 
 
+@router.post("/admin/user-group/{user_group_id}/add-users")
+def add_users(
+    user_group_id: int,
+    add_users_request: AddUsersToUserGroupRequest,
+    user: User | None = Depends(current_curator_or_admin_user),
+    db_session: Session = Depends(get_session),
+) -> UserGroup:
+    try:
+        return UserGroup.from_model(
+            add_users_to_user_group(
+                db_session=db_session,
+                user=user,
+                user_group_id=user_group_id,
+                user_ids=add_users_request.user_ids,
+            )
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=404, detail=str(e))
+
+
 @router.post("/admin/user-group/{user_group_id}/set-curator")
 def set_user_curator(
     user_group_id: int,

backend/ee/onyx/server/user_group/models.py

@@ -87,6 +87,10 @@ class UserGroupUpdate(BaseModel):
     cc_pair_ids: list[int]
 
 
+class AddUsersToUserGroupRequest(BaseModel):
+    user_ids: list[UUID]
+
+
 class SetCuratorRequest(BaseModel):
     user_id: UUID
     is_curator: bool

backend/onyx/agents/agent_framework/query.py

@@ -1,365 +1,309 @@
-import json
-from collections.abc import Callable
-from collections.abc import Iterator
-from collections.abc import Sequence
-from dataclasses import dataclass
-from typing import Any
+# import json
+# from collections.abc import Callable
+# from collections.abc import Iterator
+# from collections.abc import Sequence
+# from dataclasses import dataclass
+# from typing import Any
 
-import onyx.tracing.framework._error_tracing as _error_tracing
-from onyx.agents.agent_framework.models import RunItemStreamEvent
-from onyx.agents.agent_framework.models import StreamEvent
-from onyx.agents.agent_framework.models import ToolCallOutputStreamItem
-from onyx.agents.agent_framework.models import ToolCallStreamItem
-from onyx.llm.interfaces import LanguageModelInput
-from onyx.llm.interfaces import LLM
-from onyx.llm.interfaces import ToolChoiceOptions
-from onyx.llm.message_types import ChatCompletionMessage
-from onyx.llm.message_types import ToolCall
-from onyx.llm.model_response import ModelResponseStream
-from onyx.tools.tool import RunContextWrapper
-from onyx.tools.tool import Tool
-from onyx.tracing.framework.create import agent_span
-from onyx.tracing.framework.create import function_span
-from onyx.tracing.framework.create import generation_span
-from onyx.tracing.framework.spans import SpanError
+# from onyx.agents.agent_framework.models import RunItemStreamEvent
+# from onyx.agents.agent_framework.models import StreamEvent
+# from onyx.agents.agent_framework.models import ToolCallStreamItem
+# from onyx.llm.interfaces import LanguageModelInput
+# from onyx.llm.interfaces import LLM
+# from onyx.llm.interfaces import ToolChoiceOptions
+# from onyx.llm.message_types import ChatCompletionMessage
+# from onyx.llm.message_types import ToolCall
+# from onyx.llm.model_response import ModelResponseStream
+# from onyx.tools.tool import Tool
+# from onyx.tracing.framework.create import agent_span
+# from onyx.tracing.framework.create import generation_span
 
 
-@dataclass
-class QueryResult:
-    stream: Iterator[StreamEvent]
-    new_messages_stateful: list[ChatCompletionMessage]
+# @dataclass
+# class QueryResult:
+#     stream: Iterator[StreamEvent]
+#     new_messages_stateful: list[ChatCompletionMessage]
 
 
-def _serialize_tool_output(output: Any) -> str:
-    if isinstance(output, str):
-        return output
-    try:
-        return json.dumps(output)
-    except TypeError:
-        return str(output)
+# def _serialize_tool_output(output: Any) -> str:
+#     if isinstance(output, str):
+#         return output
+#     try:
+#         return json.dumps(output)
+#     except TypeError:
+#         return str(output)
 
 
-def _parse_tool_calls_from_message_content(
-    content: str,
-) -> list[dict[str, Any]]:
-    """Parse JSON content that represents tool call instructions."""
-    try:
-        parsed_content = json.loads(content)
-    except json.JSONDecodeError:
-        return []
+# def _parse_tool_calls_from_message_content(
+#     content: str,
+# ) -> list[dict[str, Any]]:
+#     """Parse JSON content that represents tool call instructions."""
+#     try:
+#         parsed_content = json.loads(content)
+#     except json.JSONDecodeError:
+#         return []
 
-    if isinstance(parsed_content, dict):
-        candidates = [parsed_content]
-    elif isinstance(parsed_content, list):
-        candidates = [item for item in parsed_content if isinstance(item, dict)]
-    else:
-        return []
+#     if isinstance(parsed_content, dict):
+#         candidates = [parsed_content]
+#     elif isinstance(parsed_content, list):
+#         candidates = [item for item in parsed_content if isinstance(item, dict)]
+#     else:
+#         return []
 
-    tool_calls: list[dict[str, Any]] = []
+#     tool_calls: list[dict[str, Any]] = []
 
-    for candidate in candidates:
-        name = candidate.get("name")
-        arguments = candidate.get("arguments")
+#     for candidate in candidates:
+#         name = candidate.get("name")
+#         arguments = candidate.get("arguments")
 
-        if not isinstance(name, str) or arguments is None:
-            continue
+#         if not isinstance(name, str) or arguments is None:
+#             continue
 
-        if not isinstance(arguments, dict):
-            continue
+#         if not isinstance(arguments, dict):
+#             continue
 
-        call_id = candidate.get("id")
-        arguments_str = json.dumps(arguments)
-        tool_calls.append(
-            {
-                "id": call_id,
-                "name": name,
-                "arguments": arguments_str,
-            }
-        )
+#         call_id = candidate.get("id")
+#         arguments_str = json.dumps(arguments)
+#         tool_calls.append(
+#             {
+#                 "id": call_id,
+#                 "name": name,
+#                 "arguments": arguments_str,
+#             }
+#         )
 
-    return tool_calls
+#     return tool_calls
 
 
-def _try_convert_content_to_tool_calls_for_non_tool_calling_llms(
-    tool_calls_in_progress: dict[int, dict[str, Any]],
-    content_parts: list[str],
-    structured_response_format: dict | None,
-    next_synthetic_tool_call_id: Callable[[], str],
-) -> None:
-    """Populate tool_calls_in_progress when a non-tool-calling LLM returns JSON content describing tool calls."""
-    if tool_calls_in_progress or not content_parts or structured_response_format:
-        return
+# def _try_convert_content_to_tool_calls_for_non_tool_calling_llms(
+#     tool_calls_in_progress: dict[int, dict[str, Any]],
+#     content_parts: list[str],
+#     structured_response_format: dict | None,
+#     next_synthetic_tool_call_id: Callable[[], str],
+# ) -> None:
+#     """Populate tool_calls_in_progress when a non-tool-calling LLM returns JSON content describing tool calls."""
+#     if tool_calls_in_progress or not content_parts or structured_response_format:
+#         return
 
-    tool_calls_from_content = _parse_tool_calls_from_message_content(
-        "".join(content_parts)
-    )
+#     tool_calls_from_content = _parse_tool_calls_from_message_content(
+#         "".join(content_parts)
+#     )
 
-    if not tool_calls_from_content:
-        return
+#     if not tool_calls_from_content:
+#         return
 
-    content_parts.clear()
+#     content_parts.clear()
 
-    for index, tool_call_data in enumerate(tool_calls_from_content):
-        call_id = tool_call_data["id"] or next_synthetic_tool_call_id()
-        tool_calls_in_progress[index] = {
-            "id": call_id,
-            "name": tool_call_data["name"],
-            "arguments": tool_call_data["arguments"],
-        }
+#     for index, tool_call_data in enumerate(tool_calls_from_content):
+#         call_id = tool_call_data["id"] or next_synthetic_tool_call_id()
+#         tool_calls_in_progress[index] = {
+#             "id": call_id,
+#             "name": tool_call_data["name"],
+#             "arguments": tool_call_data["arguments"],
+#         }
 
 
-def _update_tool_call_with_delta(
-    tool_calls_in_progress: dict[int, dict[str, Any]],
-    tool_call_delta: Any,
-) -> None:
-    index = tool_call_delta.index
+# def _update_tool_call_with_delta(
+#     tool_calls_in_progress: dict[int, dict[str, Any]],
+#     tool_call_delta: Any,
+# ) -> None:
+#     index = tool_call_delta.index
 
-    if index not in tool_calls_in_progress:
-        tool_calls_in_progress[index] = {
-            "id": None,
-            "name": None,
-            "arguments": "",
-        }
+#     if index not in tool_calls_in_progress:
+#         tool_calls_in_progress[index] = {
+#             "id": None,
+#             "name": None,
+#             "arguments": "",
+#         }
 
-    if tool_call_delta.id:
-        tool_calls_in_progress[index]["id"] = tool_call_delta.id
+#     if tool_call_delta.id:
+#         tool_calls_in_progress[index]["id"] = tool_call_delta.id
 
-    if tool_call_delta.function:
-        if tool_call_delta.function.name:
-            tool_calls_in_progress[index]["name"] = tool_call_delta.function.name
+#     if tool_call_delta.function:
+#         if tool_call_delta.function.name:
+#             tool_calls_in_progress[index]["name"] = tool_call_delta.function.name
 
-        if tool_call_delta.function.arguments:
-            tool_calls_in_progress[index][
-                "arguments"
-            ] += tool_call_delta.function.arguments
+#         if tool_call_delta.function.arguments:
+#             tool_calls_in_progress[index][
+#                 "arguments"
+#             ] += tool_call_delta.function.arguments
 
 
-def query(
-    llm_with_default_settings: LLM,
-    messages: LanguageModelInput,
-    tools: Sequence[Tool],
-    context: Any,
-    tool_choice: ToolChoiceOptions | None = None,
-    structured_response_format: dict | None = None,
-) -> QueryResult:
-    tool_definitions = [tool.tool_definition() for tool in tools]
-    tools_by_name = {tool.name: tool for tool in tools}
+# def query(
+#     llm_with_default_settings: LLM,
+#     messages: LanguageModelInput,
+#     tools: Sequence[Tool],
+#     context: Any,
+#     tool_choice: ToolChoiceOptions | None = None,
+#     structured_response_format: dict | None = None,
+# ) -> QueryResult:
+#     tool_definitions = [tool.tool_definition() for tool in tools]
+#     tools_by_name = {tool.name: tool for tool in tools}
 
-    new_messages_stateful: list[ChatCompletionMessage] = []
+#     new_messages_stateful: list[ChatCompletionMessage] = []
 
-    current_span = agent_span(
-        name="agent_framework_query",
-        output_type="dict" if structured_response_format else "str",
-    )
-    current_span.start(mark_as_current=True)
-    current_span.span_data.tools = [t.name for t in tools]
+#     current_span = agent_span(
+#         name="agent_framework_query",
+#         output_type="dict" if structured_response_format else "str",
+#     )
+#     current_span.start(mark_as_current=True)
+#     current_span.span_data.tools = [t.name for t in tools]
 
-    def stream_generator() -> Iterator[StreamEvent]:
-        message_started = False
-        reasoning_started = False
+#     def stream_generator() -> Iterator[StreamEvent]:
+#         message_started = False
+#         reasoning_started = False
 
-        tool_calls_in_progress: dict[int, dict[str, Any]] = {}
+#         tool_calls_in_progress: dict[int, dict[str, Any]] = {}
 
-        content_parts: list[str] = []
+#         content_parts: list[str] = []
 
-        synthetic_tool_call_counter = 0
+#         synthetic_tool_call_counter = 0
 
-        def _next_synthetic_tool_call_id() -> str:
-            nonlocal synthetic_tool_call_counter
-            call_id = f"synthetic_tool_call_{synthetic_tool_call_counter}"
-            synthetic_tool_call_counter += 1
-            return call_id
+#         def _next_synthetic_tool_call_id() -> str:
+#             nonlocal synthetic_tool_call_counter
+#             call_id = f"synthetic_tool_call_{synthetic_tool_call_counter}"
+#             synthetic_tool_call_counter += 1
+#             return call_id
 
-        with generation_span(  # type: ignore[misc]
-            model=llm_with_default_settings.config.model_name,
-            model_config={
-                "base_url": str(llm_with_default_settings.config.api_base or ""),
-                "model_impl": "litellm",
-            },
-        ) as span_generation:
-            # Only set input if messages is a sequence (not a string)
-            # ChatCompletionMessage TypedDicts are compatible with Mapping[str, Any] at runtime
-            if isinstance(messages, Sequence) and not isinstance(messages, str):
-                # Convert ChatCompletionMessage sequence to Sequence[Mapping[str, Any]]
-                span_generation.span_data.input = [dict(msg) for msg in messages]  # type: ignore[assignment]
-            for chunk in llm_with_default_settings.stream(
-                prompt=messages,
-                tools=tool_definitions,
-                tool_choice=tool_choice,
-                structured_response_format=structured_response_format,
-            ):
-                assert isinstance(chunk, ModelResponseStream)
-                usage = getattr(chunk, "usage", None)
-                if usage:
-                    span_generation.span_data.usage = {
-                        "input_tokens": usage.prompt_tokens,
-                        "output_tokens": usage.completion_tokens,
-                        "cache_read_input_tokens": usage.cache_read_input_tokens,
-                        "cache_creation_input_tokens": usage.cache_creation_input_tokens,
-                    }
+#         with generation_span(  # type: ignore[misc]
+#             model=llm_with_default_settings.config.model_name,
+#             model_config={
+#                 "base_url": str(llm_with_default_settings.config.api_base or ""),
+#                 "model_impl": "litellm",
+#             },
+#         ) as span_generation:
+#             # Only set input if messages is a sequence (not a string)
+#             # ChatCompletionMessage TypedDicts are compatible with Mapping[str, Any] at runtime
+#             if isinstance(messages, Sequence) and not isinstance(messages, str):
+#                 # Convert ChatCompletionMessage sequence to Sequence[Mapping[str, Any]]
+#                 span_generation.span_data.input = [dict(msg) for msg in messages]  # type: ignore[assignment]
+#             for chunk in llm_with_default_settings.stream(
+#                 prompt=messages,
+#                 tools=tool_definitions,
+#                 tool_choice=tool_choice,
+#                 structured_response_format=structured_response_format,
+#             ):
+#                 assert isinstance(chunk, ModelResponseStream)
+#                 usage = getattr(chunk, "usage", None)
+#                 if usage:
+#                     span_generation.span_data.usage = {
+#                         "input_tokens": usage.prompt_tokens,
+#                         "output_tokens": usage.completion_tokens,
+#                         "cache_read_input_tokens": usage.cache_read_input_tokens,
+#                         "cache_creation_input_tokens": usage.cache_creation_input_tokens,
+#                     }
 
-                delta = chunk.choice.delta
-                finish_reason = chunk.choice.finish_reason
+#                 delta = chunk.choice.delta
+#                 finish_reason = chunk.choice.finish_reason
 
-                if delta.reasoning_content:
-                    if not reasoning_started:
-                        yield RunItemStreamEvent(type="reasoning_start")
-                        reasoning_started = True
+#                 if delta.reasoning_content:
+#                     if not reasoning_started:
+#                         yield RunItemStreamEvent(type="reasoning_start")
+#                         reasoning_started = True
 
-                if delta.content:
-                    if reasoning_started:
-                        yield RunItemStreamEvent(type="reasoning_done")
-                        reasoning_started = False
-                    content_parts.append(delta.content)
-                    if not message_started:
-                        yield RunItemStreamEvent(type="message_start")
-                        message_started = True
+#                 if delta.content:
+#                     if reasoning_started:
+#                         yield RunItemStreamEvent(type="reasoning_done")
+#                         reasoning_started = False
+#                     content_parts.append(delta.content)
+#                     if not message_started:
+#                         yield RunItemStreamEvent(type="message_start")
+#                         message_started = True
 
-                if delta.tool_calls:
-                    if reasoning_started:
-                        yield RunItemStreamEvent(type="reasoning_done")
-                        reasoning_started = False
-                    if message_started:
-                        yield RunItemStreamEvent(type="message_done")
-                        message_started = False
+#                 if delta.tool_calls:
+#                     if reasoning_started:
+#                         yield RunItemStreamEvent(type="reasoning_done")
+#                         reasoning_started = False
+#                     if message_started:
+#                         yield RunItemStreamEvent(type="message_done")
+#                         message_started = False
 
-                    for tool_call_delta in delta.tool_calls:
-                        _update_tool_call_with_delta(
-                            tool_calls_in_progress, tool_call_delta
-                        )
+#                     for tool_call_delta in delta.tool_calls:
+#                         _update_tool_call_with_delta(
+#                             tool_calls_in_progress, tool_call_delta
+#                         )
 
-                yield chunk
+#                 yield chunk
 
-                if not finish_reason:
-                    continue
+#                 if not finish_reason:
+#                     continue
 
-                if reasoning_started:
-                    yield RunItemStreamEvent(type="reasoning_done")
-                    reasoning_started = False
-                if message_started:
-                    yield RunItemStreamEvent(type="message_done")
-                    message_started = False
+#                 if reasoning_started:
+#                     yield RunItemStreamEvent(type="reasoning_done")
+#                     reasoning_started = False
+#                 if message_started:
+#                     yield RunItemStreamEvent(type="message_done")
+#                     message_started = False
 
-                if tool_choice != "none":
-                    _try_convert_content_to_tool_calls_for_non_tool_calling_llms(
-                        tool_calls_in_progress,
-                        content_parts,
-                        structured_response_format,
-                        _next_synthetic_tool_call_id,
-                    )
+#                 if tool_choice != "none":
+#                     _try_convert_content_to_tool_calls_for_non_tool_calling_llms(
+#                         tool_calls_in_progress,
+#                         content_parts,
+#                         structured_response_format,
+#                         _next_synthetic_tool_call_id,
+#                     )
 
-                if content_parts:
-                    new_messages_stateful.append(
-                        {
-                            "role": "assistant",
-                            "content": "".join(content_parts),
-                        }
-                    )
-            span_generation.span_data.output = new_messages_stateful
+#                 if content_parts:
+#                     new_messages_stateful.append(
+#                         {
+#                             "role": "assistant",
+#                             "content": "".join(content_parts),
+#                         }
+#                     )
+#             span_generation.span_data.output = new_messages_stateful
 
-        # Execute tool calls outside of the stream loop and generation_span
-        if tool_calls_in_progress:
-            sorted_tool_calls = sorted(tool_calls_in_progress.items())
+#         # Execute tool calls outside of the stream loop and generation_span
+#         if tool_calls_in_progress:
+#             sorted_tool_calls = sorted(tool_calls_in_progress.items())
 
-            # Build tool calls for the message and execute tools
-            assistant_tool_calls: list[ToolCall] = []
-            tool_outputs: dict[str, str] = {}
+#             # Build tool calls for the message and execute tools
+#             assistant_tool_calls: list[ToolCall] = []
 
-            for _, tool_call_data in sorted_tool_calls:
-                call_id = tool_call_data["id"]
-                name = tool_call_data["name"]
-                arguments_str = tool_call_data["arguments"]
+#             for _, tool_call_data in sorted_tool_calls:
+#                 call_id = tool_call_data["id"]
+#                 name = tool_call_data["name"]
+#                 arguments_str = tool_call_data["arguments"]
 
-                if call_id is None or name is None:
-                    continue
+#                 if call_id is None or name is None:
+#                     continue
 
-                assistant_tool_calls.append(
-                    {
-                        "id": call_id,
-                        "type": "function",
-                        "function": {
-                            "name": name,
-                            "arguments": arguments_str,
-                        },
-                    }
-                )
+#                 assistant_tool_calls.append(
+#                     {
+#                         "id": call_id,
+#                         "type": "function",
+#                         "function": {
+#                             "name": name,
+#                             "arguments": arguments_str,
+#                         },
+#                     }
+#                 )
 
-                yield RunItemStreamEvent(
-                    type="tool_call",
-                    details=ToolCallStreamItem(
-                        call_id=call_id,
-                        name=name,
-                        arguments=arguments_str,
-                    ),
-                )
+#                 yield RunItemStreamEvent(
+#                     type="tool_call",
+#                     details=ToolCallStreamItem(
+#                         call_id=call_id,
+#                         name=name,
+#                         arguments=arguments_str,
+#                     ),
+#                 )
 
-                if name in tools_by_name:
-                    tool = tools_by_name[name]
-                    arguments = json.loads(arguments_str)
+#                 if name in tools_by_name:
+#                     tools_by_name[name]
+#                     json.loads(arguments_str)
 
-                    run_context = RunContextWrapper(context=context)
+# run_context = RunContextWrapper(context=context)
 
-                    # TODO: Instead of executing sequentially, execute in parallel
-                    # In practice, it's not a must right now since we don't use parallel
-                    # tool calls, so kicking the can down the road for now.
-                    with function_span(tool.name) as span_fn:
-                        span_fn.span_data.input = arguments
-                        try:
-                            output = tool.run_v2(run_context, **arguments)
-                            tool_outputs[call_id] = _serialize_tool_output(output)
-                            span_fn.span_data.output = output
-                        except Exception as e:
-                            _error_tracing.attach_error_to_current_span(
-                                SpanError(
-                                    message="Error running tool",
-                                    data={"tool_name": tool.name, "error": str(e)},
-                                )
-                            )
-                            # Treat the error as the tool output so the framework can continue
-                            error_output = f"Error: {str(e)}"
-                            tool_outputs[call_id] = error_output
-                            output = error_output
+# TODO: Instead of executing sequentially, execute in parallel
+# In practice, it's not a must right now since we don't use parallel
+# tool calls, so kicking the can down the road for now.
 
-                    yield RunItemStreamEvent(
-                        type="tool_call_output",
-                        details=ToolCallOutputStreamItem(
-                            call_id=call_id,
-                            output=output,
-                        ),
-                    )
-                else:
-                    not_found_output = f"Tool {name} not found"
-                    tool_outputs[call_id] = _serialize_tool_output(not_found_output)
-                    yield RunItemStreamEvent(
-                        type="tool_call_output",
-                        details=ToolCallOutputStreamItem(
-                            call_id=call_id,
-                            output=not_found_output,
-                        ),
-                    )
+# TODO broken for now, no need for a run_v2
+# output = tool.run_v2(run_context, **arguments)
 
-            new_messages_stateful.append(
-                {
-                    "role": "assistant",
-                    "content": None,
-                    "tool_calls": assistant_tool_calls,
-                }
-            )
-
-            for _, tool_call_data in sorted_tool_calls:
-                call_id = tool_call_data["id"]
-
-                if call_id in tool_outputs:
-                    new_messages_stateful.append(
-                        {
-                            "role": "tool",
-                            "content": tool_outputs[call_id],
-                            "tool_call_id": call_id,
-                        }
-                    )
-        current_span.finish(reset_current=True)
-
-    return QueryResult(
-        stream=stream_generator(),
-        new_messages_stateful=new_messages_stateful,
-    )
+# yield RunItemStreamEvent(
+#     type="tool_call_output",
+#     details=ToolCallOutputStreamItem(
+#         call_id=call_id,
+#         output=output,
+#     ),
+# )

backend/onyx/agents/agent_search/core_state.py

@@ -1,21 +1,21 @@
-from operator import add
-from typing import Annotated
+# from operator import add
+# from typing import Annotated
 
-from pydantic import BaseModel
+# from pydantic import BaseModel
 
 
-class CoreState(BaseModel):
-    """
-    This is the core state that is shared across all subgraphs.
-    """
+# class CoreState(BaseModel):
+#     """
+#     This is the core state that is shared across all subgraphs.
+#     """
 
-    log_messages: Annotated[list[str], add] = []
-    current_step_nr: int = 1
+#     log_messages: Annotated[list[str], add] = []
+#     current_step_nr: int = 1
 
 
-class SubgraphCoreState(BaseModel):
-    """
-    This is the core state that is shared across all subgraphs.
-    """
+# class SubgraphCoreState(BaseModel):
+#     """
+#     This is the core state that is shared across all subgraphs.
+#     """
 
-    log_messages: Annotated[list[str], add] = []
+#     log_messages: Annotated[list[str], add] = []

backend/onyx/agents/agent_search/dc_search_analysis/edges.py

@@ -1,62 +1,62 @@
-from collections.abc import Hashable
-from typing import cast
+# from collections.abc import Hashable
+# from typing import cast
 
-from langchain_core.runnables.config import RunnableConfig
-from langgraph.types import Send
+# from langchain_core.runnables.config import RunnableConfig
+# from langgraph.types import Send
 
-from onyx.agents.agent_search.dc_search_analysis.states import ObjectInformationInput
-from onyx.agents.agent_search.dc_search_analysis.states import (
-    ObjectResearchInformationUpdate,
-)
-from onyx.agents.agent_search.dc_search_analysis.states import ObjectSourceInput
-from onyx.agents.agent_search.dc_search_analysis.states import (
-    SearchSourcesObjectsUpdate,
-)
-from onyx.agents.agent_search.models import GraphConfig
+# from onyx.agents.agent_search.dc_search_analysis.states import ObjectInformationInput
+# from onyx.agents.agent_search.dc_search_analysis.states import (
+#     ObjectResearchInformationUpdate,
+# )
+# from onyx.agents.agent_search.dc_search_analysis.states import ObjectSourceInput
+# from onyx.agents.agent_search.dc_search_analysis.states import (
+#     SearchSourcesObjectsUpdate,
+# )
+# from onyx.agents.agent_search.models import GraphConfig
 
 
-def parallel_object_source_research_edge(
-    state: SearchSourcesObjectsUpdate, config: RunnableConfig
-) -> list[Send | Hashable]:
-    """
-    LangGraph edge to parallelize the research for an individual object and source
-    """
+# def parallel_object_source_research_edge(
+#     state: SearchSourcesObjectsUpdate, config: RunnableConfig
+# ) -> list[Send | Hashable]:
+#     """
+#     LangGraph edge to parallelize the research for an individual object and source
+#     """
 
-    search_objects = state.analysis_objects
-    search_sources = state.analysis_sources
+#     search_objects = state.analysis_objects
+#     search_sources = state.analysis_sources
 
-    object_source_combinations = [
-        (object, source) for object in search_objects for source in search_sources
-    ]
+#     object_source_combinations = [
+#         (object, source) for object in search_objects for source in search_sources
+#     ]
 
-    return [
-        Send(
-            "research_object_source",
-            ObjectSourceInput(
-                object_source_combination=object_source_combination,
-                log_messages=[],
-            ),
-        )
-        for object_source_combination in object_source_combinations
-    ]
+#     return [
+#         Send(
+#             "research_object_source",
+#             ObjectSourceInput(
+#                 object_source_combination=object_source_combination,
+#                 log_messages=[],
+#             ),
+#         )
+#         for object_source_combination in object_source_combinations
+#     ]
 
 
-def parallel_object_research_consolidation_edge(
-    state: ObjectResearchInformationUpdate, config: RunnableConfig
-) -> list[Send | Hashable]:
-    """
-    LangGraph edge to parallelize the research for an individual object and source
-    """
-    cast(GraphConfig, config["metadata"]["config"])
-    object_research_information_results = state.object_research_information_results
+# def parallel_object_research_consolidation_edge(
+#     state: ObjectResearchInformationUpdate, config: RunnableConfig
+# ) -> list[Send | Hashable]:
+#     """
+#     LangGraph edge to parallelize the research for an individual object and source
+#     """
+#     cast(GraphConfig, config["metadata"]["config"])
+#     object_research_information_results = state.object_research_information_results
 
-    return [
-        Send(
-            "consolidate_object_research",
-            ObjectInformationInput(
-                object_information=object_information,
-                log_messages=[],
-            ),
-        )
-        for object_information in object_research_information_results
-    ]
+#     return [
+#         Send(
+#             "consolidate_object_research",
+#             ObjectInformationInput(
+#                 object_information=object_information,
+#                 log_messages=[],
+#             ),
+#         )
+#         for object_information in object_research_information_results
+#     ]

backend/onyx/agents/agent_search/dc_search_analysis/graph_builder.py

@@ -1,103 +1,103 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
+# from langgraph.graph import END
+# from langgraph.graph import START
+# from langgraph.graph import StateGraph
 
-from onyx.agents.agent_search.dc_search_analysis.edges import (
-    parallel_object_research_consolidation_edge,
-)
-from onyx.agents.agent_search.dc_search_analysis.edges import (
-    parallel_object_source_research_edge,
-)
-from onyx.agents.agent_search.dc_search_analysis.nodes.a1_search_objects import (
-    search_objects,
-)
-from onyx.agents.agent_search.dc_search_analysis.nodes.a2_research_object_source import (
-    research_object_source,
-)
-from onyx.agents.agent_search.dc_search_analysis.nodes.a3_structure_research_by_object import (
-    structure_research_by_object,
-)
-from onyx.agents.agent_search.dc_search_analysis.nodes.a4_consolidate_object_research import (
-    consolidate_object_research,
-)
-from onyx.agents.agent_search.dc_search_analysis.nodes.a5_consolidate_research import (
-    consolidate_research,
-)
-from onyx.agents.agent_search.dc_search_analysis.states import MainInput
-from onyx.agents.agent_search.dc_search_analysis.states import MainState
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dc_search_analysis.edges import (
+#     parallel_object_research_consolidation_edge,
+# )
+# from onyx.agents.agent_search.dc_search_analysis.edges import (
+#     parallel_object_source_research_edge,
+# )
+# from onyx.agents.agent_search.dc_search_analysis.nodes.a1_search_objects import (
+#     search_objects,
+# )
+# from onyx.agents.agent_search.dc_search_analysis.nodes.a2_research_object_source import (
+#     research_object_source,
+# )
+# from onyx.agents.agent_search.dc_search_analysis.nodes.a3_structure_research_by_object import (
+#     structure_research_by_object,
+# )
+# from onyx.agents.agent_search.dc_search_analysis.nodes.a4_consolidate_object_research import (
+#     consolidate_object_research,
+# )
+# from onyx.agents.agent_search.dc_search_analysis.nodes.a5_consolidate_research import (
+#     consolidate_research,
+# )
+# from onyx.agents.agent_search.dc_search_analysis.states import MainInput
+# from onyx.agents.agent_search.dc_search_analysis.states import MainState
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
-test_mode = False
+# test_mode = False
 
 
-def divide_and_conquer_graph_builder(test_mode: bool = False) -> StateGraph:
-    """
-    LangGraph graph builder for the knowledge graph  search process.
-    """
+# def divide_and_conquer_graph_builder(test_mode: bool = False) -> StateGraph:
+#     """
+#     LangGraph graph builder for the knowledge graph  search process.
+#     """
 
-    graph = StateGraph(
-        state_schema=MainState,
-        input=MainInput,
-    )
+#     graph = StateGraph(
+#         state_schema=MainState,
+#         input=MainInput,
+#     )
 
-    ### Add nodes ###
+#     ### Add nodes ###
 
-    graph.add_node(
-        "search_objects",
-        search_objects,
-    )
+#     graph.add_node(
+#         "search_objects",
+#         search_objects,
+#     )
 
-    graph.add_node(
-        "structure_research_by_source",
-        structure_research_by_object,
-    )
+#     graph.add_node(
+#         "structure_research_by_source",
+#         structure_research_by_object,
+#     )
 
-    graph.add_node(
-        "research_object_source",
-        research_object_source,
-    )
+#     graph.add_node(
+#         "research_object_source",
+#         research_object_source,
+#     )
 
-    graph.add_node(
-        "consolidate_object_research",
-        consolidate_object_research,
-    )
+#     graph.add_node(
+#         "consolidate_object_research",
+#         consolidate_object_research,
+#     )
 
-    graph.add_node(
-        "consolidate_research",
-        consolidate_research,
-    )
+#     graph.add_node(
+#         "consolidate_research",
+#         consolidate_research,
+#     )
 
-    ### Add edges ###
+#     ### Add edges ###
 
-    graph.add_edge(start_key=START, end_key="search_objects")
+#     graph.add_edge(start_key=START, end_key="search_objects")
 
-    graph.add_conditional_edges(
-        source="search_objects",
-        path=parallel_object_source_research_edge,
-        path_map=["research_object_source"],
-    )
+#     graph.add_conditional_edges(
+#         source="search_objects",
+#         path=parallel_object_source_research_edge,
+#         path_map=["research_object_source"],
+#     )
 
-    graph.add_edge(
-        start_key="research_object_source",
-        end_key="structure_research_by_source",
-    )
+#     graph.add_edge(
+#         start_key="research_object_source",
+#         end_key="structure_research_by_source",
+#     )
 
-    graph.add_conditional_edges(
-        source="structure_research_by_source",
-        path=parallel_object_research_consolidation_edge,
-        path_map=["consolidate_object_research"],
-    )
+#     graph.add_conditional_edges(
+#         source="structure_research_by_source",
+#         path=parallel_object_research_consolidation_edge,
+#         path_map=["consolidate_object_research"],
+#     )
 
-    graph.add_edge(
-        start_key="consolidate_object_research",
-        end_key="consolidate_research",
-    )
+#     graph.add_edge(
+#         start_key="consolidate_object_research",
+#         end_key="consolidate_research",
+#     )
 
-    graph.add_edge(
-        start_key="consolidate_research",
-        end_key=END,
-    )
+#     graph.add_edge(
+#         start_key="consolidate_research",
+#         end_key=END,
+#     )
 
-    return graph
+#     return graph

backend/onyx/agents/agent_search/dc_search_analysis/nodes/a1_search_objects.py

@@ -1,146 +1,146 @@
-from typing import cast
+# from typing import cast
 
-from langchain_core.messages import HumanMessage
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.messages import HumanMessage
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dc_search_analysis.ops import extract_section
-from onyx.agents.agent_search.dc_search_analysis.ops import research
-from onyx.agents.agent_search.dc_search_analysis.states import MainState
-from onyx.agents.agent_search.dc_search_analysis.states import (
-    SearchSourcesObjectsUpdate,
-)
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    trim_prompt_piece,
-)
-from onyx.prompts.agents.dc_prompts import DC_OBJECT_NO_BASE_DATA_EXTRACTION_PROMPT
-from onyx.prompts.agents.dc_prompts import DC_OBJECT_SEPARATOR
-from onyx.prompts.agents.dc_prompts import DC_OBJECT_WITH_BASE_DATA_EXTRACTION_PROMPT
-from onyx.secondary_llm_flows.source_filter import strings_to_document_sources
-from onyx.utils.logger import setup_logger
-from onyx.utils.threadpool_concurrency import run_with_timeout
+# from onyx.agents.agent_search.dc_search_analysis.ops import extract_section
+# from onyx.agents.agent_search.dc_search_analysis.ops import research
+# from onyx.agents.agent_search.dc_search_analysis.states import MainState
+# from onyx.agents.agent_search.dc_search_analysis.states import (
+#     SearchSourcesObjectsUpdate,
+# )
+# from onyx.agents.agent_search.models import GraphConfig
+# from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
+#     trim_prompt_piece,
+# )
+# from onyx.prompts.agents.dc_prompts import DC_OBJECT_NO_BASE_DATA_EXTRACTION_PROMPT
+# from onyx.prompts.agents.dc_prompts import DC_OBJECT_SEPARATOR
+# from onyx.prompts.agents.dc_prompts import DC_OBJECT_WITH_BASE_DATA_EXTRACTION_PROMPT
+# from onyx.secondary_llm_flows.source_filter import strings_to_document_sources
+# from onyx.utils.logger import setup_logger
+# from onyx.utils.threadpool_concurrency import run_with_timeout
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def search_objects(
-    state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> SearchSourcesObjectsUpdate:
-    """
-    LangGraph node to start the agentic search process.
-    """
+# def search_objects(
+#     state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
+# ) -> SearchSourcesObjectsUpdate:
+#     """
+#     LangGraph node to start the agentic search process.
+#     """
 
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    question = graph_config.inputs.prompt_builder.raw_user_query
-    search_tool = graph_config.tooling.search_tool
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
+#     question = graph_config.inputs.prompt_builder.raw_user_query
+#     search_tool = graph_config.tooling.search_tool
 
-    if search_tool is None or graph_config.inputs.persona is None:
-        raise ValueError("Search tool and persona must be provided for DivCon search")
+#     if search_tool is None or graph_config.inputs.persona is None:
+#         raise ValueError("Search tool and persona must be provided for DivCon search")
 
-    try:
-        instructions = graph_config.inputs.persona.system_prompt or ""
+#     try:
+#         instructions = graph_config.inputs.persona.system_prompt or ""
 
-        agent_1_instructions = extract_section(
-            instructions, "Agent Step 1:", "Agent Step 2:"
-        )
-        if agent_1_instructions is None:
-            raise ValueError("Agent 1 instructions not found")
+#         agent_1_instructions = extract_section(
+#             instructions, "Agent Step 1:", "Agent Step 2:"
+#         )
+#         if agent_1_instructions is None:
+#             raise ValueError("Agent 1 instructions not found")
 
-        agent_1_base_data = extract_section(instructions, "|Start Data|", "|End Data|")
+#         agent_1_base_data = extract_section(instructions, "|Start Data|", "|End Data|")
 
-        agent_1_task = extract_section(
-            agent_1_instructions, "Task:", "Independent Research Sources:"
-        )
-        if agent_1_task is None:
-            raise ValueError("Agent 1 task not found")
+#         agent_1_task = extract_section(
+#             agent_1_instructions, "Task:", "Independent Research Sources:"
+#         )
+#         if agent_1_task is None:
+#             raise ValueError("Agent 1 task not found")
 
-        agent_1_independent_sources_str = extract_section(
-            agent_1_instructions, "Independent Research Sources:", "Output Objective:"
-        )
-        if agent_1_independent_sources_str is None:
-            raise ValueError("Agent 1 Independent Research Sources not found")
+#         agent_1_independent_sources_str = extract_section(
+#             agent_1_instructions, "Independent Research Sources:", "Output Objective:"
+#         )
+#         if agent_1_independent_sources_str is None:
+#             raise ValueError("Agent 1 Independent Research Sources not found")
 
-        document_sources = strings_to_document_sources(
-            [
-                x.strip().lower()
-                for x in agent_1_independent_sources_str.split(DC_OBJECT_SEPARATOR)
-            ]
-        )
+#         document_sources = strings_to_document_sources(
+#             [
+#                 x.strip().lower()
+#                 for x in agent_1_independent_sources_str.split(DC_OBJECT_SEPARATOR)
+#             ]
+#         )
 
-        agent_1_output_objective = extract_section(
-            agent_1_instructions, "Output Objective:"
-        )
-        if agent_1_output_objective is None:
-            raise ValueError("Agent 1 output objective not found")
+#         agent_1_output_objective = extract_section(
+#             agent_1_instructions, "Output Objective:"
+#         )
+#         if agent_1_output_objective is None:
+#             raise ValueError("Agent 1 output objective not found")
 
-    except Exception as e:
-        raise ValueError(
-            f"Agent 1 instructions not found or not formatted correctly: {e}"
-        )
+#     except Exception as e:
+#         raise ValueError(
+#             f"Agent 1 instructions not found or not formatted correctly: {e}"
+#         )
 
-    # Extract objects
+#     # Extract objects
 
-    if agent_1_base_data is None:
-        # Retrieve chunks for objects
+#     if agent_1_base_data is None:
+#         # Retrieve chunks for objects
 
-        retrieved_docs = research(question, search_tool)[:10]
+#         retrieved_docs = research(question, search_tool)[:10]
 
-        document_texts_list = []
-        for doc_num, doc in enumerate(retrieved_docs):
-            chunk_text = "Document " + str(doc_num) + ":\n" + doc.content
-            document_texts_list.append(chunk_text)
+#         document_texts_list = []
+#         for doc_num, doc in enumerate(retrieved_docs):
+#             chunk_text = "Document " + str(doc_num) + ":\n" + doc.content
+#             document_texts_list.append(chunk_text)
 
-        document_texts = "\n\n".join(document_texts_list)
+#         document_texts = "\n\n".join(document_texts_list)
 
-        dc_object_extraction_prompt = DC_OBJECT_NO_BASE_DATA_EXTRACTION_PROMPT.format(
-            question=question,
-            task=agent_1_task,
-            document_text=document_texts,
-            objects_of_interest=agent_1_output_objective,
-        )
-    else:
-        dc_object_extraction_prompt = DC_OBJECT_WITH_BASE_DATA_EXTRACTION_PROMPT.format(
-            question=question,
-            task=agent_1_task,
-            base_data=agent_1_base_data,
-            objects_of_interest=agent_1_output_objective,
-        )
+#         dc_object_extraction_prompt = DC_OBJECT_NO_BASE_DATA_EXTRACTION_PROMPT.format(
+#             question=question,
+#             task=agent_1_task,
+#             document_text=document_texts,
+#             objects_of_interest=agent_1_output_objective,
+#         )
+#     else:
+#         dc_object_extraction_prompt = DC_OBJECT_WITH_BASE_DATA_EXTRACTION_PROMPT.format(
+#             question=question,
+#             task=agent_1_task,
+#             base_data=agent_1_base_data,
+#             objects_of_interest=agent_1_output_objective,
+#         )
 
-    msg = [
-        HumanMessage(
-            content=trim_prompt_piece(
-                config=graph_config.tooling.primary_llm.config,
-                prompt_piece=dc_object_extraction_prompt,
-                reserved_str="",
-            ),
-        )
-    ]
-    primary_llm = graph_config.tooling.primary_llm
-    # Grader
-    try:
-        llm_response = run_with_timeout(
-            30,
-            primary_llm.invoke_langchain,
-            prompt=msg,
-            timeout_override=30,
-            max_tokens=300,
-        )
+# msg = [
+#     HumanMessage(
+#         content=trim_prompt_piece(
+#             config=graph_config.tooling.primary_llm.config,
+#             prompt_piece=dc_object_extraction_prompt,
+#             reserved_str="",
+#         ),
+#     )
+# ]
+# primary_llm = graph_config.tooling.primary_llm
+# # Grader
+# try:
+#     llm_response = run_with_timeout(
+#         30,
+#         primary_llm.invoke_langchain,
+#         prompt=msg,
+#         timeout_override=30,
+#         max_tokens=300,
+#     )
 
-        cleaned_response = (
-            str(llm_response.content)
-            .replace("```json\n", "")
-            .replace("\n```", "")
-            .replace("\n", "")
-        )
-        cleaned_response = cleaned_response.split("OBJECTS:")[1]
-        object_list = [x.strip() for x in cleaned_response.split(";")]
+#         cleaned_response = (
+#             str(llm_response.content)
+#             .replace("```json\n", "")
+#             .replace("\n```", "")
+#             .replace("\n", "")
+#         )
+#         cleaned_response = cleaned_response.split("OBJECTS:")[1]
+#         object_list = [x.strip() for x in cleaned_response.split(";")]
 
-    except Exception as e:
-        raise ValueError(f"Error in search_objects: {e}")
+#     except Exception as e:
+#         raise ValueError(f"Error in search_objects: {e}")
 
-    return SearchSourcesObjectsUpdate(
-        analysis_objects=object_list,
-        analysis_sources=document_sources,
-        log_messages=["Agent 1 Task done"],
-    )
+#     return SearchSourcesObjectsUpdate(
+#         analysis_objects=object_list,
+#         analysis_sources=document_sources,
+#         log_messages=["Agent 1 Task done"],
+#     )

backend/onyx/agents/agent_search/dc_search_analysis/nodes/a2_research_object_source.py

@@ -1,180 +1,180 @@
-from datetime import datetime
-from datetime import timedelta
-from datetime import timezone
-from typing import cast
+# from datetime import datetime
+# from datetime import timedelta
+# from datetime import timezone
+# from typing import cast
 
-from langchain_core.messages import HumanMessage
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.messages import HumanMessage
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dc_search_analysis.ops import extract_section
-from onyx.agents.agent_search.dc_search_analysis.ops import research
-from onyx.agents.agent_search.dc_search_analysis.states import ObjectSourceInput
-from onyx.agents.agent_search.dc_search_analysis.states import (
-    ObjectSourceResearchUpdate,
-)
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    trim_prompt_piece,
-)
-from onyx.prompts.agents.dc_prompts import DC_OBJECT_SOURCE_RESEARCH_PROMPT
-from onyx.utils.logger import setup_logger
-from onyx.utils.threadpool_concurrency import run_with_timeout
+# from onyx.agents.agent_search.dc_search_analysis.ops import extract_section
+# from onyx.agents.agent_search.dc_search_analysis.ops import research
+# from onyx.agents.agent_search.dc_search_analysis.states import ObjectSourceInput
+# from onyx.agents.agent_search.dc_search_analysis.states import (
+#     ObjectSourceResearchUpdate,
+# )
+# from onyx.agents.agent_search.models import GraphConfig
+# from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
+#     trim_prompt_piece,
+# )
+# from onyx.prompts.agents.dc_prompts import DC_OBJECT_SOURCE_RESEARCH_PROMPT
+# from onyx.utils.logger import setup_logger
+# from onyx.utils.threadpool_concurrency import run_with_timeout
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def research_object_source(
-    state: ObjectSourceInput,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> ObjectSourceResearchUpdate:
-    """
-    LangGraph node to start the agentic search process.
-    """
-    datetime.now()
+# def research_object_source(
+#     state: ObjectSourceInput,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> ObjectSourceResearchUpdate:
+#     """
+#     LangGraph node to start the agentic search process.
+#     """
+#     datetime.now()
 
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    search_tool = graph_config.tooling.search_tool
-    question = graph_config.inputs.prompt_builder.raw_user_query
-    object, document_source = state.object_source_combination
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
+#     search_tool = graph_config.tooling.search_tool
+#     question = graph_config.inputs.prompt_builder.raw_user_query
+#     object, document_source = state.object_source_combination
 
-    if search_tool is None or graph_config.inputs.persona is None:
-        raise ValueError("Search tool and persona must be provided for DivCon search")
+#     if search_tool is None or graph_config.inputs.persona is None:
+#         raise ValueError("Search tool and persona must be provided for DivCon search")
 
-    try:
-        instructions = graph_config.inputs.persona.system_prompt or ""
+#     try:
+#         instructions = graph_config.inputs.persona.system_prompt or ""
 
-        agent_2_instructions = extract_section(
-            instructions, "Agent Step 2:", "Agent Step 3:"
-        )
-        if agent_2_instructions is None:
-            raise ValueError("Agent 2 instructions not found")
+#         agent_2_instructions = extract_section(
+#             instructions, "Agent Step 2:", "Agent Step 3:"
+#         )
+#         if agent_2_instructions is None:
+#             raise ValueError("Agent 2 instructions not found")
 
-        agent_2_task = extract_section(
-            agent_2_instructions, "Task:", "Independent Research Sources:"
-        )
-        if agent_2_task is None:
-            raise ValueError("Agent 2 task not found")
+#         agent_2_task = extract_section(
+#             agent_2_instructions, "Task:", "Independent Research Sources:"
+#         )
+#         if agent_2_task is None:
+#             raise ValueError("Agent 2 task not found")
 
-        agent_2_time_cutoff = extract_section(
-            agent_2_instructions, "Time Cutoff:", "Research Topics:"
-        )
+#         agent_2_time_cutoff = extract_section(
+#             agent_2_instructions, "Time Cutoff:", "Research Topics:"
+#         )
 
-        agent_2_research_topics = extract_section(
-            agent_2_instructions, "Research Topics:", "Output Objective"
-        )
+#         agent_2_research_topics = extract_section(
+#             agent_2_instructions, "Research Topics:", "Output Objective"
+#         )
 
-        agent_2_output_objective = extract_section(
-            agent_2_instructions, "Output Objective:"
-        )
-        if agent_2_output_objective is None:
-            raise ValueError("Agent 2 output objective not found")
+#         agent_2_output_objective = extract_section(
+#             agent_2_instructions, "Output Objective:"
+#         )
+#         if agent_2_output_objective is None:
+#             raise ValueError("Agent 2 output objective not found")
 
-    except Exception:
-        raise ValueError(
-            "Agent 1 instructions not found or not formatted correctly: {e}"
-        )
+#     except Exception:
+#         raise ValueError(
+#             "Agent 1 instructions not found or not formatted correctly: {e}"
+#         )
 
-    # Populate prompt
+#     # Populate prompt
 
-    # Retrieve chunks for objects
+#     # Retrieve chunks for objects
 
-    if agent_2_time_cutoff is not None and agent_2_time_cutoff.strip() != "":
-        if agent_2_time_cutoff.strip().endswith("d"):
-            try:
-                days = int(agent_2_time_cutoff.strip()[:-1])
-                agent_2_source_start_time = datetime.now(timezone.utc) - timedelta(
-                    days=days
-                )
-            except ValueError:
-                raise ValueError(
-                    f"Invalid time cutoff format: {agent_2_time_cutoff}. Expected format: '<number>d'"
-                )
-        else:
-            raise ValueError(
-                f"Invalid time cutoff format: {agent_2_time_cutoff}. Expected format: '<number>d'"
-            )
-    else:
-        agent_2_source_start_time = None
+#     if agent_2_time_cutoff is not None and agent_2_time_cutoff.strip() != "":
+#         if agent_2_time_cutoff.strip().endswith("d"):
+#             try:
+#                 days = int(agent_2_time_cutoff.strip()[:-1])
+#                 agent_2_source_start_time = datetime.now(timezone.utc) - timedelta(
+#                     days=days
+#                 )
+#             except ValueError:
+#                 raise ValueError(
+#                     f"Invalid time cutoff format: {agent_2_time_cutoff}. Expected format: '<number>d'"
+#                 )
+#         else:
+#             raise ValueError(
+#                 f"Invalid time cutoff format: {agent_2_time_cutoff}. Expected format: '<number>d'"
+#             )
+#     else:
+#         agent_2_source_start_time = None
 
-    document_sources = [document_source] if document_source else None
+#     document_sources = [document_source] if document_source else None
 
-    if len(question.strip()) > 0:
-        research_area = f"{question} for {object}"
-    elif agent_2_research_topics and len(agent_2_research_topics.strip()) > 0:
-        research_area = f"{agent_2_research_topics} for {object}"
-    else:
-        research_area = object
+#     if len(question.strip()) > 0:
+#         research_area = f"{question} for {object}"
+#     elif agent_2_research_topics and len(agent_2_research_topics.strip()) > 0:
+#         research_area = f"{agent_2_research_topics} for {object}"
+#     else:
+#         research_area = object
 
-    retrieved_docs = research(
-        question=research_area,
-        search_tool=search_tool,
-        document_sources=document_sources,
-        time_cutoff=agent_2_source_start_time,
-    )
+#     retrieved_docs = research(
+#         question=research_area,
+#         search_tool=search_tool,
+#         document_sources=document_sources,
+#         time_cutoff=agent_2_source_start_time,
+#     )
 
-    # Generate document text
+#     # Generate document text
 
-    document_texts_list = []
-    for doc_num, doc in enumerate(retrieved_docs):
-        chunk_text = "Document " + str(doc_num) + ":\n" + doc.content
-        document_texts_list.append(chunk_text)
+#     document_texts_list = []
+#     for doc_num, doc in enumerate(retrieved_docs):
+#         chunk_text = "Document " + str(doc_num) + ":\n" + doc.content
+#         document_texts_list.append(chunk_text)
 
-    document_texts = "\n\n".join(document_texts_list)
+#     document_texts = "\n\n".join(document_texts_list)
 
-    # Built prompt
+#     # Built prompt
 
-    today = datetime.now().strftime("%A, %Y-%m-%d")
+#     today = datetime.now().strftime("%A, %Y-%m-%d")
 
-    dc_object_source_research_prompt = (
-        DC_OBJECT_SOURCE_RESEARCH_PROMPT.format(
-            today=today,
-            question=question,
-            task=agent_2_task,
-            document_text=document_texts,
-            format=agent_2_output_objective,
-        )
-        .replace("---object---", object)
-        .replace("---source---", document_source.value)
-    )
+#     dc_object_source_research_prompt = (
+#         DC_OBJECT_SOURCE_RESEARCH_PROMPT.format(
+#             today=today,
+#             question=question,
+#             task=agent_2_task,
+#             document_text=document_texts,
+#             format=agent_2_output_objective,
+#         )
+#         .replace("---object---", object)
+#         .replace("---source---", document_source.value)
+#     )
 
-    # Run LLM
+#     # Run LLM
 
-    msg = [
-        HumanMessage(
-            content=trim_prompt_piece(
-                config=graph_config.tooling.primary_llm.config,
-                prompt_piece=dc_object_source_research_prompt,
-                reserved_str="",
-            ),
-        )
-    ]
-    primary_llm = graph_config.tooling.primary_llm
-    # Grader
-    try:
-        llm_response = run_with_timeout(
-            30,
-            primary_llm.invoke_langchain,
-            prompt=msg,
-            timeout_override=30,
-            max_tokens=300,
-        )
+# msg = [
+#     HumanMessage(
+#         content=trim_prompt_piece(
+#             config=graph_config.tooling.primary_llm.config,
+#             prompt_piece=dc_object_source_research_prompt,
+#             reserved_str="",
+#         ),
+#     )
+# ]
+# primary_llm = graph_config.tooling.primary_llm
+# # Grader
+# try:
+#     llm_response = run_with_timeout(
+#         30,
+#         primary_llm.invoke_langchain,
+#         prompt=msg,
+#         timeout_override=30,
+#         max_tokens=300,
+#     )
 
-        cleaned_response = str(llm_response.content).replace("```json\n", "")
-        cleaned_response = cleaned_response.split("RESEARCH RESULTS:")[1]
-        object_research_results = {
-            "object": object,
-            "source": document_source.value,
-            "research_result": cleaned_response,
-        }
+#         cleaned_response = str(llm_response.content).replace("```json\n", "")
+#         cleaned_response = cleaned_response.split("RESEARCH RESULTS:")[1]
+#         object_research_results = {
+#             "object": object,
+#             "source": document_source.value,
+#             "research_result": cleaned_response,
+#         }
 
-    except Exception as e:
-        raise ValueError(f"Error in research_object_source: {e}")
+#     except Exception as e:
+#         raise ValueError(f"Error in research_object_source: {e}")
 
-    logger.debug("DivCon Step A2 - Object Source Research - completed for an object")
+#     logger.debug("DivCon Step A2 - Object Source Research - completed for an object")
 
-    return ObjectSourceResearchUpdate(
-        object_source_research_results=[object_research_results],
-        log_messages=["Agent Step 2 done for one object"],
-    )
+#     return ObjectSourceResearchUpdate(
+#         object_source_research_results=[object_research_results],
+#         log_messages=["Agent Step 2 done for one object"],
+#     )

backend/onyx/agents/agent_search/dc_search_analysis/nodes/a3_structure_research_by_object.py

@@ -1,48 +1,48 @@
-from collections import defaultdict
-from typing import Dict
-from typing import List
+# from collections import defaultdict
+# from typing import Dict
+# from typing import List
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dc_search_analysis.states import MainState
-from onyx.agents.agent_search.dc_search_analysis.states import (
-    ObjectResearchInformationUpdate,
-)
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dc_search_analysis.states import MainState
+# from onyx.agents.agent_search.dc_search_analysis.states import (
+#     ObjectResearchInformationUpdate,
+# )
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def structure_research_by_object(
-    state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> ObjectResearchInformationUpdate:
-    """
-    LangGraph node to start the agentic search process.
-    """
+# def structure_research_by_object(
+#     state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
+# ) -> ObjectResearchInformationUpdate:
+#     """
+#     LangGraph node to start the agentic search process.
+#     """
 
-    object_source_research_results = state.object_source_research_results
+#     object_source_research_results = state.object_source_research_results
 
-    object_research_information_results: List[Dict[str, str]] = []
-    object_research_information_results_list: Dict[str, List[str]] = defaultdict(list)
+#     object_research_information_results: List[Dict[str, str]] = []
+#     object_research_information_results_list: Dict[str, List[str]] = defaultdict(list)
 
-    for object_source_research in object_source_research_results:
-        object = object_source_research["object"]
-        source = object_source_research["source"]
-        research_result = object_source_research["research_result"]
+#     for object_source_research in object_source_research_results:
+#         object = object_source_research["object"]
+#         source = object_source_research["source"]
+#         research_result = object_source_research["research_result"]
 
-        object_research_information_results_list[object].append(
-            f"Source: {source}\n{research_result}"
-        )
+#         object_research_information_results_list[object].append(
+#             f"Source: {source}\n{research_result}"
+#         )
 
-    for object, information in object_research_information_results_list.items():
-        object_research_information_results.append(
-            {"object": object, "information": "\n".join(information)}
-        )
+#     for object, information in object_research_information_results_list.items():
+#         object_research_information_results.append(
+#             {"object": object, "information": "\n".join(information)}
+#         )
 
-    logger.debug("DivCon Step A3 - Object Research Information Structuring - completed")
+#     logger.debug("DivCon Step A3 - Object Research Information Structuring - completed")
 
-    return ObjectResearchInformationUpdate(
-        object_research_information_results=object_research_information_results,
-        log_messages=["A3 - Object Research Information structured"],
-    )
+#     return ObjectResearchInformationUpdate(
+#         object_research_information_results=object_research_information_results,
+#         log_messages=["A3 - Object Research Information structured"],
+#     )

backend/onyx/agents/agent_search/dc_search_analysis/nodes/a4_consolidate_object_research.py

@@ -1,103 +1,103 @@
-from typing import cast
+# from typing import cast
 
-from langchain_core.messages import HumanMessage
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.messages import HumanMessage
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dc_search_analysis.ops import extract_section
-from onyx.agents.agent_search.dc_search_analysis.states import ObjectInformationInput
-from onyx.agents.agent_search.dc_search_analysis.states import ObjectResearchUpdate
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    trim_prompt_piece,
-)
-from onyx.prompts.agents.dc_prompts import DC_OBJECT_CONSOLIDATION_PROMPT
-from onyx.utils.logger import setup_logger
-from onyx.utils.threadpool_concurrency import run_with_timeout
+# from onyx.agents.agent_search.dc_search_analysis.ops import extract_section
+# from onyx.agents.agent_search.dc_search_analysis.states import ObjectInformationInput
+# from onyx.agents.agent_search.dc_search_analysis.states import ObjectResearchUpdate
+# from onyx.agents.agent_search.models import GraphConfig
+# from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
+#     trim_prompt_piece,
+# )
+# from onyx.prompts.agents.dc_prompts import DC_OBJECT_CONSOLIDATION_PROMPT
+# from onyx.utils.logger import setup_logger
+# from onyx.utils.threadpool_concurrency import run_with_timeout
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def consolidate_object_research(
-    state: ObjectInformationInput,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> ObjectResearchUpdate:
-    """
-    LangGraph node to start the agentic search process.
-    """
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    search_tool = graph_config.tooling.search_tool
-    question = graph_config.inputs.prompt_builder.raw_user_query
+# def consolidate_object_research(
+#     state: ObjectInformationInput,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> ObjectResearchUpdate:
+#     """
+#     LangGraph node to start the agentic search process.
+#     """
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
+#     search_tool = graph_config.tooling.search_tool
+#     question = graph_config.inputs.prompt_builder.raw_user_query
 
-    if search_tool is None or graph_config.inputs.persona is None:
-        raise ValueError("Search tool and persona must be provided for DivCon search")
+#     if search_tool is None or graph_config.inputs.persona is None:
+#         raise ValueError("Search tool and persona must be provided for DivCon search")
 
-    instructions = graph_config.inputs.persona.system_prompt or ""
+#     instructions = graph_config.inputs.persona.system_prompt or ""
 
-    agent_4_instructions = extract_section(
-        instructions, "Agent Step 4:", "Agent Step 5:"
-    )
-    if agent_4_instructions is None:
-        raise ValueError("Agent 4 instructions not found")
-    agent_4_output_objective = extract_section(
-        agent_4_instructions, "Output Objective:"
-    )
-    if agent_4_output_objective is None:
-        raise ValueError("Agent 4 output objective not found")
+#     agent_4_instructions = extract_section(
+#         instructions, "Agent Step 4:", "Agent Step 5:"
+#     )
+#     if agent_4_instructions is None:
+#         raise ValueError("Agent 4 instructions not found")
+#     agent_4_output_objective = extract_section(
+#         agent_4_instructions, "Output Objective:"
+#     )
+#     if agent_4_output_objective is None:
+#         raise ValueError("Agent 4 output objective not found")
 
-    object_information = state.object_information
+#     object_information = state.object_information
 
-    object = object_information["object"]
-    information = object_information["information"]
+#     object = object_information["object"]
+#     information = object_information["information"]
 
-    # Create a prompt for the object consolidation
+#     # Create a prompt for the object consolidation
 
-    dc_object_consolidation_prompt = DC_OBJECT_CONSOLIDATION_PROMPT.format(
-        question=question,
-        object=object,
-        information=information,
-        format=agent_4_output_objective,
-    )
+#     dc_object_consolidation_prompt = DC_OBJECT_CONSOLIDATION_PROMPT.format(
+#         question=question,
+#         object=object,
+#         information=information,
+#         format=agent_4_output_objective,
+#     )
 
-    # Run LLM
+#     # Run LLM
 
-    msg = [
-        HumanMessage(
-            content=trim_prompt_piece(
-                config=graph_config.tooling.primary_llm.config,
-                prompt_piece=dc_object_consolidation_prompt,
-                reserved_str="",
-            ),
-        )
-    ]
-    primary_llm = graph_config.tooling.primary_llm
-    # Grader
-    try:
-        llm_response = run_with_timeout(
-            30,
-            primary_llm.invoke_langchain,
-            prompt=msg,
-            timeout_override=30,
-            max_tokens=300,
-        )
+# msg = [
+#     HumanMessage(
+#         content=trim_prompt_piece(
+#             config=graph_config.tooling.primary_llm.config,
+#             prompt_piece=dc_object_consolidation_prompt,
+#             reserved_str="",
+#         ),
+#     )
+# ]
+# primary_llm = graph_config.tooling.primary_llm
+# # Grader
+# try:
+#     llm_response = run_with_timeout(
+#         30,
+#         primary_llm.invoke_langchain,
+#         prompt=msg,
+#         timeout_override=30,
+#         max_tokens=300,
+#     )
 
-        cleaned_response = str(llm_response.content).replace("```json\n", "")
-        consolidated_information = cleaned_response.split("INFORMATION:")[1]
+#         cleaned_response = str(llm_response.content).replace("```json\n", "")
+#         consolidated_information = cleaned_response.split("INFORMATION:")[1]
 
-    except Exception as e:
-        raise ValueError(f"Error in consolidate_object_research: {e}")
+#     except Exception as e:
+#         raise ValueError(f"Error in consolidate_object_research: {e}")
 
-    object_research_results = {
-        "object": object,
-        "research_result": consolidated_information,
-    }
+#     object_research_results = {
+#         "object": object,
+#         "research_result": consolidated_information,
+#     }
 
-    logger.debug(
-        "DivCon Step A4 - Object Research Consolidation - completed for an object"
-    )
+#     logger.debug(
+#         "DivCon Step A4 - Object Research Consolidation - completed for an object"
+#     )
 
-    return ObjectResearchUpdate(
-        object_research_results=[object_research_results],
-        log_messages=["Agent Source Consilidation done"],
-    )
+#     return ObjectResearchUpdate(
+#         object_research_results=[object_research_results],
+#         log_messages=["Agent Source Consilidation done"],
+#     )

backend/onyx/agents/agent_search/dc_search_analysis/nodes/a5_consolidate_research.py

@@ -1,127 +1,127 @@
-from typing import cast
+# from typing import cast
 
-from langchain_core.messages import HumanMessage
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.messages import HumanMessage
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dc_search_analysis.ops import extract_section
-from onyx.agents.agent_search.dc_search_analysis.states import MainState
-from onyx.agents.agent_search.dc_search_analysis.states import ResearchUpdate
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    trim_prompt_piece,
-)
-from onyx.agents.agent_search.shared_graph_utils.llm import stream_llm_answer
-from onyx.prompts.agents.dc_prompts import DC_FORMATTING_NO_BASE_DATA_PROMPT
-from onyx.prompts.agents.dc_prompts import DC_FORMATTING_WITH_BASE_DATA_PROMPT
-from onyx.utils.logger import setup_logger
-from onyx.utils.threadpool_concurrency import run_with_timeout
+# from onyx.agents.agent_search.dc_search_analysis.ops import extract_section
+# from onyx.agents.agent_search.dc_search_analysis.states import MainState
+# from onyx.agents.agent_search.dc_search_analysis.states import ResearchUpdate
+# from onyx.agents.agent_search.models import GraphConfig
+# from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
+#     trim_prompt_piece,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.llm import stream_llm_answer
+# from onyx.prompts.agents.dc_prompts import DC_FORMATTING_NO_BASE_DATA_PROMPT
+# from onyx.prompts.agents.dc_prompts import DC_FORMATTING_WITH_BASE_DATA_PROMPT
+# from onyx.utils.logger import setup_logger
+# from onyx.utils.threadpool_concurrency import run_with_timeout
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def consolidate_research(
-    state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> ResearchUpdate:
-    """
-    LangGraph node to start the agentic search process.
-    """
+# def consolidate_research(
+#     state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
+# ) -> ResearchUpdate:
+#     """
+#     LangGraph node to start the agentic search process.
+#     """
 
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
 
-    search_tool = graph_config.tooling.search_tool
+#     search_tool = graph_config.tooling.search_tool
 
-    if search_tool is None or graph_config.inputs.persona is None:
-        raise ValueError("Search tool and persona must be provided for DivCon search")
+#     if search_tool is None or graph_config.inputs.persona is None:
+#         raise ValueError("Search tool and persona must be provided for DivCon search")
 
-    # Populate prompt
-    instructions = graph_config.inputs.persona.system_prompt or ""
+#     # Populate prompt
+#     instructions = graph_config.inputs.persona.system_prompt or ""
 
-    try:
-        agent_5_instructions = extract_section(
-            instructions, "Agent Step 5:", "Agent End"
-        )
-        if agent_5_instructions is None:
-            raise ValueError("Agent 5 instructions not found")
-        agent_5_base_data = extract_section(instructions, "|Start Data|", "|End Data|")
-        agent_5_task = extract_section(
-            agent_5_instructions, "Task:", "Independent Research Sources:"
-        )
-        if agent_5_task is None:
-            raise ValueError("Agent 5 task not found")
-        agent_5_output_objective = extract_section(
-            agent_5_instructions, "Output Objective:"
-        )
-        if agent_5_output_objective is None:
-            raise ValueError("Agent 5 output objective not found")
-    except ValueError as e:
-        raise ValueError(
-            f"Instructions for Agent Step 5 were not properly formatted: {e}"
-        )
+#     try:
+#         agent_5_instructions = extract_section(
+#             instructions, "Agent Step 5:", "Agent End"
+#         )
+#         if agent_5_instructions is None:
+#             raise ValueError("Agent 5 instructions not found")
+#         agent_5_base_data = extract_section(instructions, "|Start Data|", "|End Data|")
+#         agent_5_task = extract_section(
+#             agent_5_instructions, "Task:", "Independent Research Sources:"
+#         )
+#         if agent_5_task is None:
+#             raise ValueError("Agent 5 task not found")
+#         agent_5_output_objective = extract_section(
+#             agent_5_instructions, "Output Objective:"
+#         )
+#         if agent_5_output_objective is None:
+#             raise ValueError("Agent 5 output objective not found")
+#     except ValueError as e:
+#         raise ValueError(
+#             f"Instructions for Agent Step 5 were not properly formatted: {e}"
+#         )
 
-    research_result_list = []
+#     research_result_list = []
 
-    if agent_5_task.strip() == "*concatenate*":
-        object_research_results = state.object_research_results
+#     if agent_5_task.strip() == "*concatenate*":
+#         object_research_results = state.object_research_results
 
-        for object_research_result in object_research_results:
-            object = object_research_result["object"]
-            research_result = object_research_result["research_result"]
-            research_result_list.append(f"Object: {object}\n\n{research_result}")
+#         for object_research_result in object_research_results:
+#             object = object_research_result["object"]
+#             research_result = object_research_result["research_result"]
+#             research_result_list.append(f"Object: {object}\n\n{research_result}")
 
-        research_results = "\n\n".join(research_result_list)
+#         research_results = "\n\n".join(research_result_list)
 
-    else:
-        raise NotImplementedError("Only '*concatenate*' is currently supported")
+#     else:
+#         raise NotImplementedError("Only '*concatenate*' is currently supported")
 
-    # Create a prompt for the object consolidation
+#     # Create a prompt for the object consolidation
 
-    if agent_5_base_data is None:
-        dc_formatting_prompt = DC_FORMATTING_NO_BASE_DATA_PROMPT.format(
-            text=research_results,
-            format=agent_5_output_objective,
-        )
-    else:
-        dc_formatting_prompt = DC_FORMATTING_WITH_BASE_DATA_PROMPT.format(
-            base_data=agent_5_base_data,
-            text=research_results,
-            format=agent_5_output_objective,
-        )
+#     if agent_5_base_data is None:
+#         dc_formatting_prompt = DC_FORMATTING_NO_BASE_DATA_PROMPT.format(
+#             text=research_results,
+#             format=agent_5_output_objective,
+#         )
+#     else:
+#         dc_formatting_prompt = DC_FORMATTING_WITH_BASE_DATA_PROMPT.format(
+#             base_data=agent_5_base_data,
+#             text=research_results,
+#             format=agent_5_output_objective,
+#         )
 
-    # Run LLM
+#     # Run LLM
 
-    msg = [
-        HumanMessage(
-            content=trim_prompt_piece(
-                config=graph_config.tooling.primary_llm.config,
-                prompt_piece=dc_formatting_prompt,
-                reserved_str="",
-            ),
-        )
-    ]
+#     msg = [
+#         HumanMessage(
+#             content=trim_prompt_piece(
+#                 config=graph_config.tooling.primary_llm.config,
+#                 prompt_piece=dc_formatting_prompt,
+#                 reserved_str="",
+#             ),
+#         )
+#     ]
 
-    try:
-        _ = run_with_timeout(
-            60,
-            lambda: stream_llm_answer(
-                llm=graph_config.tooling.primary_llm,
-                prompt=msg,
-                event_name="initial_agent_answer",
-                writer=writer,
-                agent_answer_level=0,
-                agent_answer_question_num=0,
-                agent_answer_type="agent_level_answer",
-                timeout_override=30,
-                max_tokens=None,
-            ),
-        )
+#     try:
+#         _ = run_with_timeout(
+#             60,
+#             lambda: stream_llm_answer(
+#                 llm=graph_config.tooling.primary_llm,
+#                 prompt=msg,
+#                 event_name="initial_agent_answer",
+#                 writer=writer,
+#                 agent_answer_level=0,
+#                 agent_answer_question_num=0,
+#                 agent_answer_type="agent_level_answer",
+#                 timeout_override=30,
+#                 max_tokens=None,
+#             ),
+#         )
 
-    except Exception as e:
-        raise ValueError(f"Error in consolidate_research: {e}")
+#     except Exception as e:
+#         raise ValueError(f"Error in consolidate_research: {e}")
 
-    logger.debug("DivCon Step A5 - Final Generation - completed")
+#     logger.debug("DivCon Step A5 - Final Generation - completed")
 
-    return ResearchUpdate(
-        research_results=research_results,
-        log_messages=["Agent Source Consilidation done"],
-    )
+#     return ResearchUpdate(
+#         research_results=research_results,
+#         log_messages=["Agent Source Consilidation done"],
+#     )

backend/onyx/agents/agent_search/dc_search_analysis/ops.py

@@ -1,61 +1,50 @@
-from datetime import datetime
-from typing import cast
+# from datetime import datetime
+# from typing import cast
 
-from onyx.chat.models import LlmDoc
-from onyx.configs.constants import DocumentSource
-from onyx.context.search.models import InferenceSection
-from onyx.db.engine.sql_engine import get_session_with_current_tenant
-from onyx.tools.models import SearchToolOverrideKwargs
-from onyx.tools.tool_implementations.search.search_tool import (
-    FINAL_CONTEXT_DOCUMENTS_ID,
-)
-from onyx.tools.tool_implementations.search.search_tool import SearchTool
+# from onyx.chat.models import LlmDoc
+# from onyx.configs.constants import DocumentSource
+# from onyx.tools.models import SearchToolOverrideKwargs
+# from onyx.tools.tool_implementations.search.search_tool import (
+#     FINAL_CONTEXT_DOCUMENTS_ID,
+# )
+# from onyx.tools.tool_implementations.search.search_tool import SearchTool
 
 
-def research(
-    question: str,
-    search_tool: SearchTool,
-    document_sources: list[DocumentSource] | None = None,
-    time_cutoff: datetime | None = None,
-) -> list[LlmDoc]:
-    # new db session to avoid concurrency issues
+# def research(
+#     question: str,
+#     search_tool: SearchTool,
+#     document_sources: list[DocumentSource] | None = None,
+#     time_cutoff: datetime | None = None,
+# ) -> list[LlmDoc]:
+#     # new db session to avoid concurrency issues
 
-    callback_container: list[list[InferenceSection]] = []
-    retrieved_docs: list[LlmDoc] = []
+#     retrieved_docs: list[LlmDoc] = []
 
-    with get_session_with_current_tenant() as db_session:
-        for tool_response in search_tool.run(
-            query=question,
-            override_kwargs=SearchToolOverrideKwargs(
-                force_no_rerank=False,
-                alternate_db_session=db_session,
-                retrieved_sections_callback=callback_container.append,
-                skip_query_analysis=True,
-                document_sources=document_sources,
-                time_cutoff=time_cutoff,
-            ),
-        ):
-            # get retrieved docs to send to the rest of the graph
-            if tool_response.id == FINAL_CONTEXT_DOCUMENTS_ID:
-                retrieved_docs = cast(list[LlmDoc], tool_response.response)[:10]
-                break
-    return retrieved_docs
+#     for tool_response in search_tool.run(
+#         query=question,
+#         override_kwargs=SearchToolOverrideKwargs(original_query=question),
+#     ):
+#         # get retrieved docs to send to the rest of the graph
+#         if tool_response.id == FINAL_CONTEXT_DOCUMENTS_ID:
+#             retrieved_docs = cast(list[LlmDoc], tool_response.response)[:10]
+#             break
+#     return retrieved_docs
 
 
-def extract_section(
-    text: str, start_marker: str, end_marker: str | None = None
-) -> str | None:
-    """Extract text between markers, returning None if markers not found"""
-    parts = text.split(start_marker)
+# def extract_section(
+#     text: str, start_marker: str, end_marker: str | None = None
+# ) -> str | None:
+#     """Extract text between markers, returning None if markers not found"""
+#     parts = text.split(start_marker)
 
-    if len(parts) == 1:
-        return None
+#     if len(parts) == 1:
+#         return None
 
-    after_start = parts[1].strip()
+#     after_start = parts[1].strip()
 
-    if not end_marker:
-        return after_start
+#     if not end_marker:
+#         return after_start
 
-    extract = after_start.split(end_marker)[0]
+#     extract = after_start.split(end_marker)[0]
 
-    return extract.strip()
+#     return extract.strip()

backend/onyx/agents/agent_search/dc_search_analysis/states.py

@@ -1,72 +1,72 @@
-from operator import add
-from typing import Annotated
-from typing import Dict
-from typing import TypedDict
+# from operator import add
+# from typing import Annotated
+# from typing import Dict
+# from typing import TypedDict
 
-from pydantic import BaseModel
+# from pydantic import BaseModel
 
-from onyx.agents.agent_search.core_state import CoreState
-from onyx.agents.agent_search.orchestration.states import ToolCallUpdate
-from onyx.agents.agent_search.orchestration.states import ToolChoiceInput
-from onyx.agents.agent_search.orchestration.states import ToolChoiceUpdate
-from onyx.configs.constants import DocumentSource
+# from onyx.agents.agent_search.core_state import CoreState
+# from onyx.agents.agent_search.orchestration.states import ToolCallUpdate
+# from onyx.agents.agent_search.orchestration.states import ToolChoiceInput
+# from onyx.agents.agent_search.orchestration.states import ToolChoiceUpdate
+# from onyx.configs.constants import DocumentSource
 
 
-### States ###
-class LoggerUpdate(BaseModel):
-    log_messages: Annotated[list[str], add] = []
+# ### States ###
+# class LoggerUpdate(BaseModel):
+#     log_messages: Annotated[list[str], add] = []
 
 
-class SearchSourcesObjectsUpdate(LoggerUpdate):
-    analysis_objects: list[str] = []
-    analysis_sources: list[DocumentSource] = []
+# class SearchSourcesObjectsUpdate(LoggerUpdate):
+#     analysis_objects: list[str] = []
+#     analysis_sources: list[DocumentSource] = []
 
 
-class ObjectSourceInput(LoggerUpdate):
-    object_source_combination: tuple[str, DocumentSource]
+# class ObjectSourceInput(LoggerUpdate):
+#     object_source_combination: tuple[str, DocumentSource]
 
 
-class ObjectSourceResearchUpdate(LoggerUpdate):
-    object_source_research_results: Annotated[list[Dict[str, str]], add] = []
+# class ObjectSourceResearchUpdate(LoggerUpdate):
+#     object_source_research_results: Annotated[list[Dict[str, str]], add] = []
 
 
-class ObjectInformationInput(LoggerUpdate):
-    object_information: Dict[str, str]
+# class ObjectInformationInput(LoggerUpdate):
+#     object_information: Dict[str, str]
 
 
-class ObjectResearchInformationUpdate(LoggerUpdate):
-    object_research_information_results: Annotated[list[Dict[str, str]], add] = []
+# class ObjectResearchInformationUpdate(LoggerUpdate):
+#     object_research_information_results: Annotated[list[Dict[str, str]], add] = []
 
 
-class ObjectResearchUpdate(LoggerUpdate):
-    object_research_results: Annotated[list[Dict[str, str]], add] = []
+# class ObjectResearchUpdate(LoggerUpdate):
+#     object_research_results: Annotated[list[Dict[str, str]], add] = []
 
 
-class ResearchUpdate(LoggerUpdate):
-    research_results: str | None = None
+# class ResearchUpdate(LoggerUpdate):
+#     research_results: str | None = None
 
 
-## Graph Input State
-class MainInput(CoreState):
-    pass
+# ## Graph Input State
+# class MainInput(CoreState):
+#     pass
 
 
-## Graph State
-class MainState(
-    # This includes the core state
-    MainInput,
-    ToolChoiceInput,
-    ToolCallUpdate,
-    ToolChoiceUpdate,
-    SearchSourcesObjectsUpdate,
-    ObjectSourceResearchUpdate,
-    ObjectResearchInformationUpdate,
-    ObjectResearchUpdate,
-    ResearchUpdate,
-):
-    pass
+# ## Graph State
+# class MainState(
+#     # This includes the core state
+#     MainInput,
+#     ToolChoiceInput,
+#     ToolCallUpdate,
+#     ToolChoiceUpdate,
+#     SearchSourcesObjectsUpdate,
+#     ObjectSourceResearchUpdate,
+#     ObjectResearchInformationUpdate,
+#     ObjectResearchUpdate,
+#     ResearchUpdate,
+# ):
+#     pass
 
 
-## Graph Output State - presently not used
-class MainOutput(TypedDict):
-    log_messages: list[str]
+# ## Graph Output State - presently not used
+# class MainOutput(TypedDict):
+#     log_messages: list[str]

backend/onyx/agents/agent_search/deep_search/main/models.py

@@ -1,36 +1,36 @@
-from pydantic import BaseModel
+# from pydantic import BaseModel
 
 
-class RefinementSubQuestion(BaseModel):
-    sub_question: str
-    sub_question_id: str
-    verified: bool
-    answered: bool
-    answer: str
+# class RefinementSubQuestion(BaseModel):
+#     sub_question: str
+#     sub_question_id: str
+#     verified: bool
+#     answered: bool
+#     answer: str
 
 
-class AgentTimings(BaseModel):
-    base_duration_s: float | None
-    refined_duration_s: float | None
-    full_duration_s: float | None
+# class AgentTimings(BaseModel):
+#     base_duration_s: float | None
+#     refined_duration_s: float | None
+#     full_duration_s: float | None
 
 
-class AgentBaseMetrics(BaseModel):
-    num_verified_documents_total: int | None
-    num_verified_documents_core: int | None
-    verified_avg_score_core: float | None
-    num_verified_documents_base: int | float | None
-    verified_avg_score_base: float | None = None
-    base_doc_boost_factor: float | None = None
-    support_boost_factor: float | None = None
-    duration_s: float | None = None
+# class AgentBaseMetrics(BaseModel):
+#     num_verified_documents_total: int | None
+#     num_verified_documents_core: int | None
+#     verified_avg_score_core: float | None
+#     num_verified_documents_base: int | float | None
+#     verified_avg_score_base: float | None = None
+#     base_doc_boost_factor: float | None = None
+#     support_boost_factor: float | None = None
+#     duration_s: float | None = None
 
 
-class AgentRefinedMetrics(BaseModel):
-    refined_doc_boost_factor: float | None = None
-    refined_question_boost_factor: float | None = None
-    duration_s: float | None = None
+# class AgentRefinedMetrics(BaseModel):
+#     refined_doc_boost_factor: float | None = None
+#     refined_question_boost_factor: float | None = None
+#     duration_s: float | None = None
 
 
-class AgentAdditionalMetrics(BaseModel):
-    pass
+# class AgentAdditionalMetrics(BaseModel):
+#     pass

backend/onyx/agents/agent_search/dr/conditional_edges.py

@@ -1,61 +1,61 @@
-from collections.abc import Hashable
+# from collections.abc import Hashable
 
-from langgraph.graph import END
-from langgraph.types import Send
+# from langgraph.graph import END
+# from langgraph.types import Send
 
-from onyx.agents.agent_search.dr.enums import DRPath
-from onyx.agents.agent_search.dr.states import MainState
+# from onyx.agents.agent_search.dr.enums import DRPath
+# from onyx.agents.agent_search.dr.states import MainState
 
 
-def decision_router(state: MainState) -> list[Send | Hashable] | DRPath | str:
-    if not state.tools_used:
-        raise IndexError("state.tools_used cannot be empty")
+# def decision_router(state: MainState) -> list[Send | Hashable] | DRPath | str:
+#     if not state.tools_used:
+#         raise IndexError("state.tools_used cannot be empty")
 
-    # next_tool is either a generic tool name or a DRPath string
-    next_tool_name = state.tools_used[-1]
+#     # next_tool is either a generic tool name or a DRPath string
+#     next_tool_name = state.tools_used[-1]
 
-    available_tools = state.available_tools
-    if not available_tools:
-        raise ValueError("No tool is available. This should not happen.")
+#     available_tools = state.available_tools
+#     if not available_tools:
+#         raise ValueError("No tool is available. This should not happen.")
 
-    if next_tool_name in available_tools:
-        next_tool_path = available_tools[next_tool_name].path
-    elif next_tool_name == DRPath.END.value:
-        return END
-    elif next_tool_name == DRPath.LOGGER.value:
-        return DRPath.LOGGER
-    elif next_tool_name == DRPath.CLOSER.value:
-        return DRPath.CLOSER
-    else:
-        return DRPath.ORCHESTRATOR
+#     if next_tool_name in available_tools:
+#         next_tool_path = available_tools[next_tool_name].path
+#     elif next_tool_name == DRPath.END.value:
+#         return END
+#     elif next_tool_name == DRPath.LOGGER.value:
+#         return DRPath.LOGGER
+#     elif next_tool_name == DRPath.CLOSER.value:
+#         return DRPath.CLOSER
+#     else:
+#         return DRPath.ORCHESTRATOR
 
-    # handle invalid paths
-    if next_tool_path == DRPath.CLARIFIER:
-        raise ValueError("CLARIFIER is not a valid path during iteration")
+#     # handle invalid paths
+#     if next_tool_path == DRPath.CLARIFIER:
+#         raise ValueError("CLARIFIER is not a valid path during iteration")
 
-    # handle tool calls without a query
-    if (
-        next_tool_path
-        in (
-            DRPath.INTERNAL_SEARCH,
-            DRPath.WEB_SEARCH,
-            DRPath.KNOWLEDGE_GRAPH,
-            DRPath.IMAGE_GENERATION,
-        )
-        and len(state.query_list) == 0
-    ):
-        return DRPath.CLOSER
+#     # handle tool calls without a query
+#     if (
+#         next_tool_path
+#         in (
+#             DRPath.INTERNAL_SEARCH,
+#             DRPath.WEB_SEARCH,
+#             DRPath.KNOWLEDGE_GRAPH,
+#             DRPath.IMAGE_GENERATION,
+#         )
+#         and len(state.query_list) == 0
+#     ):
+#         return DRPath.CLOSER
 
-    return next_tool_path
+#     return next_tool_path
 
 
-def completeness_router(state: MainState) -> DRPath | str:
-    if not state.tools_used:
-        raise IndexError("tools_used cannot be empty")
+# def completeness_router(state: MainState) -> DRPath | str:
+#     if not state.tools_used:
+#         raise IndexError("tools_used cannot be empty")
 
-    # go to closer if path is CLOSER or no queries
-    next_path = state.tools_used[-1]
+#     # go to closer if path is CLOSER or no queries
+#     next_path = state.tools_used[-1]
 
-    if next_path == DRPath.ORCHESTRATOR.value:
-        return DRPath.ORCHESTRATOR
-    return DRPath.LOGGER
+#     if next_path == DRPath.ORCHESTRATOR.value:
+#         return DRPath.ORCHESTRATOR
+#     return DRPath.LOGGER

backend/onyx/agents/agent_search/dr/constants.py

@@ -1,31 +1,27 @@
-from onyx.agents.agent_search.dr.enums import DRPath
-from onyx.agents.agent_search.dr.enums import ResearchType
+# from onyx.agents.agent_search.dr.enums import DRPath
 
-MAX_CHAT_HISTORY_MESSAGES = (
-    3  # note: actual count is x2 to account for user and assistant messages
-)
+# MAX_CHAT_HISTORY_MESSAGES = (
+#     3  # note: actual count is x2 to account for user and assistant messages
+# )
 
-MAX_DR_PARALLEL_SEARCH = 4
+# MAX_DR_PARALLEL_SEARCH = 4
 
-# TODO: test more, generally not needed/adds unnecessary iterations
-MAX_NUM_CLOSER_SUGGESTIONS = (
-    0  # how many times the closer can send back to the orchestrator
-)
+# # TODO: test more, generally not needed/adds unnecessary iterations
+# MAX_NUM_CLOSER_SUGGESTIONS = (
+#     0  # how many times the closer can send back to the orchestrator
+# )
 
-CLARIFICATION_REQUEST_PREFIX = "PLEASE CLARIFY:"
-HIGH_LEVEL_PLAN_PREFIX = "The Plan:"
+# CLARIFICATION_REQUEST_PREFIX = "PLEASE CLARIFY:"
+# HIGH_LEVEL_PLAN_PREFIX = "The Plan:"
 
-AVERAGE_TOOL_COSTS: dict[DRPath, float] = {
-    DRPath.INTERNAL_SEARCH: 1.0,
-    DRPath.KNOWLEDGE_GRAPH: 2.0,
-    DRPath.WEB_SEARCH: 1.5,
-    DRPath.IMAGE_GENERATION: 3.0,
-    DRPath.GENERIC_TOOL: 1.5,  # TODO: see todo in OrchestratorTool
-    DRPath.CLOSER: 0.0,
-}
+# AVERAGE_TOOL_COSTS: dict[DRPath, float] = {
+#     DRPath.INTERNAL_SEARCH: 1.0,
+#     DRPath.KNOWLEDGE_GRAPH: 2.0,
+#     DRPath.WEB_SEARCH: 1.5,
+#     DRPath.IMAGE_GENERATION: 3.0,
+#     DRPath.GENERIC_TOOL: 1.5,  # TODO: see todo in OrchestratorTool
+#     DRPath.CLOSER: 0.0,
+# }
 
-DR_TIME_BUDGET_BY_TYPE = {
-    ResearchType.THOUGHTFUL: 3.0,
-    ResearchType.DEEP: 12.0,
-    ResearchType.FAST: 0.5,
-}
+# # Default time budget for agentic search (when use_agentic_search is True)
+# DR_TIME_BUDGET_DEFAULT = 12.0

backend/onyx/agents/agent_search/dr/dr_prompt_builder.py

@@ -1,112 +1,111 @@
-from datetime import datetime
+# from datetime import datetime
 
-from onyx.agents.agent_search.dr.enums import DRPath
-from onyx.agents.agent_search.dr.enums import ResearchType
-from onyx.agents.agent_search.dr.models import DRPromptPurpose
-from onyx.agents.agent_search.dr.models import OrchestratorTool
-from onyx.prompts.dr_prompts import GET_CLARIFICATION_PROMPT
-from onyx.prompts.dr_prompts import KG_TYPES_DESCRIPTIONS
-from onyx.prompts.dr_prompts import ORCHESTRATOR_DEEP_INITIAL_PLAN_PROMPT
-from onyx.prompts.dr_prompts import ORCHESTRATOR_DEEP_ITERATIVE_DECISION_PROMPT
-from onyx.prompts.dr_prompts import ORCHESTRATOR_FAST_ITERATIVE_DECISION_PROMPT
-from onyx.prompts.dr_prompts import ORCHESTRATOR_FAST_ITERATIVE_REASONING_PROMPT
-from onyx.prompts.dr_prompts import ORCHESTRATOR_NEXT_STEP_PURPOSE_PROMPT
-from onyx.prompts.dr_prompts import TOOL_DIFFERENTIATION_HINTS
-from onyx.prompts.dr_prompts import TOOL_QUESTION_HINTS
-from onyx.prompts.prompt_template import PromptTemplate
+# from onyx.agents.agent_search.dr.enums import DRPath
+# from onyx.agents.agent_search.dr.models import DRPromptPurpose
+# from onyx.agents.agent_search.dr.models import OrchestratorTool
+# from onyx.prompts.dr_prompts import GET_CLARIFICATION_PROMPT
+# from onyx.prompts.dr_prompts import KG_TYPES_DESCRIPTIONS
+# from onyx.prompts.dr_prompts import ORCHESTRATOR_DEEP_INITIAL_PLAN_PROMPT
+# from onyx.prompts.dr_prompts import ORCHESTRATOR_DEEP_ITERATIVE_DECISION_PROMPT
+# from onyx.prompts.dr_prompts import ORCHESTRATOR_FAST_ITERATIVE_DECISION_PROMPT
+# from onyx.prompts.dr_prompts import ORCHESTRATOR_FAST_ITERATIVE_REASONING_PROMPT
+# from onyx.prompts.dr_prompts import ORCHESTRATOR_NEXT_STEP_PURPOSE_PROMPT
+# from onyx.prompts.dr_prompts import TOOL_DIFFERENTIATION_HINTS
+# from onyx.prompts.dr_prompts import TOOL_QUESTION_HINTS
+# from onyx.prompts.prompt_template import PromptTemplate
 
 
-def get_dr_prompt_orchestration_templates(
-    purpose: DRPromptPurpose,
-    research_type: ResearchType,
-    available_tools: dict[str, OrchestratorTool],
-    entity_types_string: str | None = None,
-    relationship_types_string: str | None = None,
-    reasoning_result: str | None = None,
-    tool_calls_string: str | None = None,
-) -> PromptTemplate:
-    available_tools = available_tools or {}
-    tool_names = list(available_tools.keys())
-    tool_description_str = "\n\n".join(
-        f"- {tool_name}: {tool.description}"
-        for tool_name, tool in available_tools.items()
-    )
-    tool_cost_str = "\n".join(
-        f"{tool_name}: {tool.cost}" for tool_name, tool in available_tools.items()
-    )
+# def get_dr_prompt_orchestration_templates(
+#     purpose: DRPromptPurpose,
+#     use_agentic_search: bool,
+#     available_tools: dict[str, OrchestratorTool],
+#     entity_types_string: str | None = None,
+#     relationship_types_string: str | None = None,
+#     reasoning_result: str | None = None,
+#     tool_calls_string: str | None = None,
+# ) -> PromptTemplate:
+#     available_tools = available_tools or {}
+#     tool_names = list(available_tools.keys())
+#     tool_description_str = "\n\n".join(
+#         f"- {tool_name}: {tool.description}"
+#         for tool_name, tool in available_tools.items()
+#     )
+#     tool_cost_str = "\n".join(
+#         f"{tool_name}: {tool.cost}" for tool_name, tool in available_tools.items()
+#     )
 
-    tool_differentiations: list[str] = [
-        TOOL_DIFFERENTIATION_HINTS[(tool_1, tool_2)]
-        for tool_1 in available_tools
-        for tool_2 in available_tools
-        if (tool_1, tool_2) in TOOL_DIFFERENTIATION_HINTS
-    ]
-    tool_differentiation_hint_string = (
-        "\n".join(tool_differentiations) or "(No differentiating hints available)"
-    )
-    # TODO: add tool deliniation pairs for custom tools as well
+#     tool_differentiations: list[str] = [
+#         TOOL_DIFFERENTIATION_HINTS[(tool_1, tool_2)]
+#         for tool_1 in available_tools
+#         for tool_2 in available_tools
+#         if (tool_1, tool_2) in TOOL_DIFFERENTIATION_HINTS
+#     ]
+#     tool_differentiation_hint_string = (
+#         "\n".join(tool_differentiations) or "(No differentiating hints available)"
+#     )
+#     # TODO: add tool deliniation pairs for custom tools as well
 
-    tool_question_hint_string = (
-        "\n".join(
-            "- " + TOOL_QUESTION_HINTS[tool]
-            for tool in available_tools
-            if tool in TOOL_QUESTION_HINTS
-        )
-        or "(No examples available)"
-    )
+#     tool_question_hint_string = (
+#         "\n".join(
+#             "- " + TOOL_QUESTION_HINTS[tool]
+#             for tool in available_tools
+#             if tool in TOOL_QUESTION_HINTS
+#         )
+#         or "(No examples available)"
+#     )
 
-    if DRPath.KNOWLEDGE_GRAPH.value in available_tools and (
-        entity_types_string or relationship_types_string
-    ):
+#     if DRPath.KNOWLEDGE_GRAPH.value in available_tools and (
+#         entity_types_string or relationship_types_string
+#     ):
 
-        kg_types_descriptions = KG_TYPES_DESCRIPTIONS.build(
-            possible_entities=entity_types_string or "",
-            possible_relationships=relationship_types_string or "",
-        )
-    else:
-        kg_types_descriptions = "(The Knowledge Graph is not used.)"
+#         kg_types_descriptions = KG_TYPES_DESCRIPTIONS.build(
+#             possible_entities=entity_types_string or "",
+#             possible_relationships=relationship_types_string or "",
+#         )
+#     else:
+#         kg_types_descriptions = "(The Knowledge Graph is not used.)"
 
-    if purpose == DRPromptPurpose.PLAN:
-        if research_type == ResearchType.THOUGHTFUL:
-            raise ValueError("plan generation is not supported for FAST time budget")
-        base_template = ORCHESTRATOR_DEEP_INITIAL_PLAN_PROMPT
+#     if purpose == DRPromptPurpose.PLAN:
+#         if not use_agentic_search:
+#             raise ValueError("plan generation is only supported for agentic search")
+#         base_template = ORCHESTRATOR_DEEP_INITIAL_PLAN_PROMPT
 
-    elif purpose == DRPromptPurpose.NEXT_STEP_REASONING:
-        if research_type == ResearchType.THOUGHTFUL:
-            base_template = ORCHESTRATOR_FAST_ITERATIVE_REASONING_PROMPT
-        else:
-            raise ValueError(
-                "reasoning is not separately required for DEEP time budget"
-            )
+#     elif purpose == DRPromptPurpose.NEXT_STEP_REASONING:
+#         if not use_agentic_search:
+#             base_template = ORCHESTRATOR_FAST_ITERATIVE_REASONING_PROMPT
+#         else:
+#             raise ValueError(
+#                 "reasoning is not separately required for agentic search"
+#             )
 
-    elif purpose == DRPromptPurpose.NEXT_STEP_PURPOSE:
-        base_template = ORCHESTRATOR_NEXT_STEP_PURPOSE_PROMPT
+#     elif purpose == DRPromptPurpose.NEXT_STEP_PURPOSE:
+#         base_template = ORCHESTRATOR_NEXT_STEP_PURPOSE_PROMPT
 
-    elif purpose == DRPromptPurpose.NEXT_STEP:
-        if research_type == ResearchType.THOUGHTFUL:
-            base_template = ORCHESTRATOR_FAST_ITERATIVE_DECISION_PROMPT
-        else:
-            base_template = ORCHESTRATOR_DEEP_ITERATIVE_DECISION_PROMPT
+#     elif purpose == DRPromptPurpose.NEXT_STEP:
+#         if not use_agentic_search:
+#             base_template = ORCHESTRATOR_FAST_ITERATIVE_DECISION_PROMPT
+#         else:
+#             base_template = ORCHESTRATOR_DEEP_ITERATIVE_DECISION_PROMPT
 
-    elif purpose == DRPromptPurpose.CLARIFICATION:
-        if research_type == ResearchType.THOUGHTFUL:
-            raise ValueError("clarification is not supported for FAST time budget")
-        base_template = GET_CLARIFICATION_PROMPT
+#     elif purpose == DRPromptPurpose.CLARIFICATION:
+#         if not use_agentic_search:
+#             raise ValueError("clarification is only supported for agentic search")
+#         base_template = GET_CLARIFICATION_PROMPT
 
-    else:
-        # for mypy, clearly a mypy bug
-        raise ValueError(f"Invalid purpose: {purpose}")
+#     else:
+#         # for mypy, clearly a mypy bug
+#         raise ValueError(f"Invalid purpose: {purpose}")
 
-    return base_template.partial_build(
-        num_available_tools=str(len(tool_names)),
-        available_tools=", ".join(tool_names),
-        tool_choice_options=" or ".join(tool_names),
-        current_time=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-        kg_types_descriptions=kg_types_descriptions,
-        tool_descriptions=tool_description_str,
-        tool_differentiation_hints=tool_differentiation_hint_string,
-        tool_question_hints=tool_question_hint_string,
-        average_tool_costs=tool_cost_str,
-        reasoning_result=reasoning_result or "(No reasoning result provided.)",
-        tool_calls_string=tool_calls_string or "(No tool calls provided.)",
-    )
+#     return base_template.partial_build(
+#         num_available_tools=str(len(tool_names)),
+#         available_tools=", ".join(tool_names),
+#         tool_choice_options=" or ".join(tool_names),
+#         current_time=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+#         kg_types_descriptions=kg_types_descriptions,
+#         tool_descriptions=tool_description_str,
+#         tool_differentiation_hints=tool_differentiation_hint_string,
+#         tool_question_hints=tool_question_hint_string,
+#         average_tool_costs=tool_cost_str,
+#         reasoning_result=reasoning_result or "(No reasoning result provided.)",
+#         tool_calls_string=tool_calls_string or "(No tool calls provided.)",
+#     )

backend/onyx/agents/agent_search/dr/enums.py

@@ -1,32 +1,22 @@
-from enum import Enum
+# from enum import Enum
 
 
-class ResearchType(str, Enum):
-    """Research type options for agent search operations"""
+# class ResearchAnswerPurpose(str, Enum):
+#     """Research answer purpose options for agent search operations"""
 
-    # BASIC = "BASIC"
-    LEGACY_AGENTIC = "LEGACY_AGENTIC"  # only used for legacy agentic search migrations
-    THOUGHTFUL = "THOUGHTFUL"
-    DEEP = "DEEP"
-    FAST = "FAST"
+#     ANSWER = "ANSWER"
+#     CLARIFICATION_REQUEST = "CLARIFICATION_REQUEST"
 
 
-class ResearchAnswerPurpose(str, Enum):
-    """Research answer purpose options for agent search operations"""
-
-    ANSWER = "ANSWER"
-    CLARIFICATION_REQUEST = "CLARIFICATION_REQUEST"
-
-
-class DRPath(str, Enum):
-    CLARIFIER = "Clarifier"
-    ORCHESTRATOR = "Orchestrator"
-    INTERNAL_SEARCH = "Internal Search"
-    GENERIC_TOOL = "Generic Tool"
-    KNOWLEDGE_GRAPH = "Knowledge Graph Search"
-    WEB_SEARCH = "Web Search"
-    IMAGE_GENERATION = "Image Generation"
-    GENERIC_INTERNAL_TOOL = "Generic Internal Tool"
-    CLOSER = "Closer"
-    LOGGER = "Logger"
-    END = "End"
+# class DRPath(str, Enum):
+#     CLARIFIER = "Clarifier"
+#     ORCHESTRATOR = "Orchestrator"
+#     INTERNAL_SEARCH = "Internal Search"
+#     GENERIC_TOOL = "Generic Tool"
+#     KNOWLEDGE_GRAPH = "Knowledge Graph Search"
+#     WEB_SEARCH = "Web Search"
+#     IMAGE_GENERATION = "Image Generation"
+#     GENERIC_INTERNAL_TOOL = "Generic Internal Tool"
+#     CLOSER = "Closer"
+#     LOGGER = "Logger"
+#     END = "End"

backend/onyx/agents/agent_search/dr/graph_builder.py

@@ -1,88 +1,88 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
+# from langgraph.graph import END
+# from langgraph.graph import START
+# from langgraph.graph import StateGraph
 
-from onyx.agents.agent_search.dr.conditional_edges import completeness_router
-from onyx.agents.agent_search.dr.conditional_edges import decision_router
-from onyx.agents.agent_search.dr.enums import DRPath
-from onyx.agents.agent_search.dr.nodes.dr_a0_clarification import clarifier
-from onyx.agents.agent_search.dr.nodes.dr_a1_orchestrator import orchestrator
-from onyx.agents.agent_search.dr.nodes.dr_a2_closer import closer
-from onyx.agents.agent_search.dr.nodes.dr_a3_logger import logging
-from onyx.agents.agent_search.dr.states import MainInput
-from onyx.agents.agent_search.dr.states import MainState
-from onyx.agents.agent_search.dr.sub_agents.basic_search.dr_basic_search_graph_builder import (
-    dr_basic_search_graph_builder,
-)
-from onyx.agents.agent_search.dr.sub_agents.custom_tool.dr_custom_tool_graph_builder import (
-    dr_custom_tool_graph_builder,
-)
-from onyx.agents.agent_search.dr.sub_agents.generic_internal_tool.dr_generic_internal_tool_graph_builder import (
-    dr_generic_internal_tool_graph_builder,
-)
-from onyx.agents.agent_search.dr.sub_agents.image_generation.dr_image_generation_graph_builder import (
-    dr_image_generation_graph_builder,
-)
-from onyx.agents.agent_search.dr.sub_agents.kg_search.dr_kg_search_graph_builder import (
-    dr_kg_search_graph_builder,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_graph_builder import (
-    dr_ws_graph_builder,
-)
+# from onyx.agents.agent_search.dr.conditional_edges import completeness_router
+# from onyx.agents.agent_search.dr.conditional_edges import decision_router
+# from onyx.agents.agent_search.dr.enums import DRPath
+# from onyx.agents.agent_search.dr.nodes.dr_a0_clarification import clarifier
+# from onyx.agents.agent_search.dr.nodes.dr_a1_orchestrator import orchestrator
+# from onyx.agents.agent_search.dr.nodes.dr_a2_closer import closer
+# from onyx.agents.agent_search.dr.nodes.dr_a3_logger import logging
+# from onyx.agents.agent_search.dr.states import MainInput
+# from onyx.agents.agent_search.dr.states import MainState
+# from onyx.agents.agent_search.dr.sub_agents.basic_search.dr_basic_search_graph_builder import (
+#     dr_basic_search_graph_builder,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.custom_tool.dr_custom_tool_graph_builder import (
+#     dr_custom_tool_graph_builder,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.generic_internal_tool.dr_generic_internal_tool_graph_builder import (
+#     dr_generic_internal_tool_graph_builder,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.image_generation.dr_image_generation_graph_builder import (
+#     dr_image_generation_graph_builder,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.kg_search.dr_kg_search_graph_builder import (
+#     dr_kg_search_graph_builder,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_graph_builder import (
+#     dr_ws_graph_builder,
+# )
 
-# from onyx.agents.agent_search.dr.sub_agents.basic_search.dr_basic_search_2_act import search
+# # from onyx.agents.agent_search.dr.sub_agents.basic_search.dr_basic_search_2_act import search
 
 
-def dr_graph_builder() -> StateGraph:
-    """
-    LangGraph graph builder for the deep research agent.
-    """
+# def dr_graph_builder() -> StateGraph:
+#     """
+#     LangGraph graph builder for the deep research agent.
+#     """
 
-    graph = StateGraph(state_schema=MainState, input=MainInput)
+#     graph = StateGraph(state_schema=MainState, input=MainInput)
 
-    ### Add nodes ###
+#     ### Add nodes ###
 
-    graph.add_node(DRPath.CLARIFIER, clarifier)
+#     graph.add_node(DRPath.CLARIFIER, clarifier)
 
-    graph.add_node(DRPath.ORCHESTRATOR, orchestrator)
+#     graph.add_node(DRPath.ORCHESTRATOR, orchestrator)
 
-    basic_search_graph = dr_basic_search_graph_builder().compile()
-    graph.add_node(DRPath.INTERNAL_SEARCH, basic_search_graph)
+#     basic_search_graph = dr_basic_search_graph_builder().compile()
+#     graph.add_node(DRPath.INTERNAL_SEARCH, basic_search_graph)
 
-    kg_search_graph = dr_kg_search_graph_builder().compile()
-    graph.add_node(DRPath.KNOWLEDGE_GRAPH, kg_search_graph)
+#     kg_search_graph = dr_kg_search_graph_builder().compile()
+#     graph.add_node(DRPath.KNOWLEDGE_GRAPH, kg_search_graph)
 
-    internet_search_graph = dr_ws_graph_builder().compile()
-    graph.add_node(DRPath.WEB_SEARCH, internet_search_graph)
+#     internet_search_graph = dr_ws_graph_builder().compile()
+#     graph.add_node(DRPath.WEB_SEARCH, internet_search_graph)
 
-    image_generation_graph = dr_image_generation_graph_builder().compile()
-    graph.add_node(DRPath.IMAGE_GENERATION, image_generation_graph)
+#     image_generation_graph = dr_image_generation_graph_builder().compile()
+#     graph.add_node(DRPath.IMAGE_GENERATION, image_generation_graph)
 
-    custom_tool_graph = dr_custom_tool_graph_builder().compile()
-    graph.add_node(DRPath.GENERIC_TOOL, custom_tool_graph)
+#     custom_tool_graph = dr_custom_tool_graph_builder().compile()
+#     graph.add_node(DRPath.GENERIC_TOOL, custom_tool_graph)
 
-    generic_internal_tool_graph = dr_generic_internal_tool_graph_builder().compile()
-    graph.add_node(DRPath.GENERIC_INTERNAL_TOOL, generic_internal_tool_graph)
+#     generic_internal_tool_graph = dr_generic_internal_tool_graph_builder().compile()
+#     graph.add_node(DRPath.GENERIC_INTERNAL_TOOL, generic_internal_tool_graph)
 
-    graph.add_node(DRPath.CLOSER, closer)
-    graph.add_node(DRPath.LOGGER, logging)
+#     graph.add_node(DRPath.CLOSER, closer)
+#     graph.add_node(DRPath.LOGGER, logging)
 
-    ### Add edges ###
+#     ### Add edges ###
 
-    graph.add_edge(start_key=START, end_key=DRPath.CLARIFIER)
+#     graph.add_edge(start_key=START, end_key=DRPath.CLARIFIER)
 
-    graph.add_conditional_edges(DRPath.CLARIFIER, decision_router)
+#     graph.add_conditional_edges(DRPath.CLARIFIER, decision_router)
 
-    graph.add_conditional_edges(DRPath.ORCHESTRATOR, decision_router)
+#     graph.add_conditional_edges(DRPath.ORCHESTRATOR, decision_router)
 
-    graph.add_edge(start_key=DRPath.INTERNAL_SEARCH, end_key=DRPath.ORCHESTRATOR)
-    graph.add_edge(start_key=DRPath.KNOWLEDGE_GRAPH, end_key=DRPath.ORCHESTRATOR)
-    graph.add_edge(start_key=DRPath.WEB_SEARCH, end_key=DRPath.ORCHESTRATOR)
-    graph.add_edge(start_key=DRPath.IMAGE_GENERATION, end_key=DRPath.ORCHESTRATOR)
-    graph.add_edge(start_key=DRPath.GENERIC_TOOL, end_key=DRPath.ORCHESTRATOR)
-    graph.add_edge(start_key=DRPath.GENERIC_INTERNAL_TOOL, end_key=DRPath.ORCHESTRATOR)
+#     graph.add_edge(start_key=DRPath.INTERNAL_SEARCH, end_key=DRPath.ORCHESTRATOR)
+#     graph.add_edge(start_key=DRPath.KNOWLEDGE_GRAPH, end_key=DRPath.ORCHESTRATOR)
+#     graph.add_edge(start_key=DRPath.WEB_SEARCH, end_key=DRPath.ORCHESTRATOR)
+#     graph.add_edge(start_key=DRPath.IMAGE_GENERATION, end_key=DRPath.ORCHESTRATOR)
+#     graph.add_edge(start_key=DRPath.GENERIC_TOOL, end_key=DRPath.ORCHESTRATOR)
+#     graph.add_edge(start_key=DRPath.GENERIC_INTERNAL_TOOL, end_key=DRPath.ORCHESTRATOR)
 
-    graph.add_conditional_edges(DRPath.CLOSER, completeness_router)
-    graph.add_edge(start_key=DRPath.LOGGER, end_key=END)
+#     graph.add_conditional_edges(DRPath.CLOSER, completeness_router)
+#     graph.add_edge(start_key=DRPath.LOGGER, end_key=END)
 
-    return graph
+#     return graph

backend/onyx/agents/agent_search/dr/models.py

@@ -1,131 +1,131 @@
-from enum import Enum
+# from enum import Enum
 
-from pydantic import BaseModel
-from pydantic import ConfigDict
+# from pydantic import BaseModel
 
-from onyx.agents.agent_search.dr.enums import DRPath
-from onyx.agents.agent_search.dr.sub_agents.image_generation.models import (
-    GeneratedImage,
-)
-from onyx.context.search.models import InferenceSection
-from onyx.tools.tool import Tool
+# from onyx.agents.agent_search.dr.enums import DRPath
+# from onyx.agents.agent_search.dr.sub_agents.image_generation.models import (
+#     GeneratedImage,
+# )
+# from onyx.context.search.models import InferenceSection
+# from onyx.tools.tool import Tool
 
 
-class OrchestratorStep(BaseModel):
-    tool: str
-    questions: list[str]
+# class OrchestratorStep(BaseModel):
+#     tool: str
+#     questions: list[str]
 
 
-class OrchestratorDecisonsNoPlan(BaseModel):
-    reasoning: str
-    next_step: OrchestratorStep
+# class OrchestratorDecisonsNoPlan(BaseModel):
+#     reasoning: str
+#     next_step: OrchestratorStep
 
 
-class OrchestrationPlan(BaseModel):
-    reasoning: str
-    plan: str
+# class OrchestrationPlan(BaseModel):
+#     reasoning: str
+#     plan: str
 
 
-class ClarificationGenerationResponse(BaseModel):
-    clarification_needed: bool
-    clarification_question: str
+# class ClarificationGenerationResponse(BaseModel):
+#     clarification_needed: bool
+#     clarification_question: str
 
 
-class DecisionResponse(BaseModel):
-    reasoning: str
-    decision: str
+# class DecisionResponse(BaseModel):
+#     reasoning: str
+#     decision: str
 
 
-class QueryEvaluationResponse(BaseModel):
-    reasoning: str
-    query_permitted: bool
+# class QueryEvaluationResponse(BaseModel):
+#     reasoning: str
+#     query_permitted: bool
 
 
-class OrchestrationClarificationInfo(BaseModel):
-    clarification_question: str
-    clarification_response: str | None = None
+# class OrchestrationClarificationInfo(BaseModel):
+#     clarification_question: str
+#     clarification_response: str | None = None
 
 
-class WebSearchAnswer(BaseModel):
-    urls_to_open_indices: list[int]
+# class WebSearchAnswer(BaseModel):
+#     urls_to_open_indices: list[int]
 
 
-class SearchAnswer(BaseModel):
-    reasoning: str
-    answer: str
-    claims: list[str] | None = None
+# class SearchAnswer(BaseModel):
+#     reasoning: str
+#     answer: str
+#     claims: list[str] | None = None
 
 
-class TestInfoCompleteResponse(BaseModel):
-    reasoning: str
-    complete: bool
-    gaps: list[str]
+# class TestInfoCompleteResponse(BaseModel):
+#     reasoning: str
+#     complete: bool
+#     gaps: list[str]
 
 
-# TODO: revisit with custom tools implementation in v2
-# each tool should be a class with the attributes below, plus the actual tool implementation
-# this will also allow custom tools to have their own cost
-class OrchestratorTool(BaseModel):
-    tool_id: int
-    name: str
-    llm_path: str  # the path for the LLM to refer by
-    path: DRPath  # the actual path in the graph
-    description: str
-    metadata: dict[str, str]
-    cost: float
-    tool_object: Tool | None = None  # None for CLOSER
+# # TODO: revisit with custom tools implementation in v2
+# # each tool should be a class with the attributes below, plus the actual tool implementation
+# # this will also allow custom tools to have their own cost
+# class OrchestratorTool(BaseModel):
+#     tool_id: int
+#     name: str
+#     llm_path: str  # the path for the LLM to refer by
+#     path: DRPath  # the actual path in the graph
+#     description: str
+#     metadata: dict[str, str]
+#     cost: float
+#     tool_object: Tool | None = None  # None for CLOSER
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+#     class Config:
+#         arbitrary_types_allowed = True
 
 
-class IterationInstructions(BaseModel):
-    iteration_nr: int
-    plan: str | None
-    reasoning: str
-    purpose: str
+# class IterationInstructions(BaseModel):
+#     iteration_nr: int
+#     plan: str | None
+#     reasoning: str
+#     purpose: str
 
 
-class IterationAnswer(BaseModel):
-    tool: str
-    tool_id: int
-    iteration_nr: int
-    parallelization_nr: int
-    question: str
-    reasoning: str | None
-    answer: str
-    cited_documents: dict[int, InferenceSection]
-    background_info: str | None = None
-    claims: list[str] | None = None
-    additional_data: dict[str, str] | None = None
-    response_type: str | None = None
-    data: dict | list | str | int | float | bool | None = None
-    file_ids: list[str] | None = None
-    # TODO: This is not ideal, but we'll can rework the schema
-    # for deep research later
-    is_web_fetch: bool = False
-    # for image generation step-types
-    generated_images: list[GeneratedImage] | None = None
-    # for multi-query search tools (v2 web search and internal search)
-    # TODO: Clean this up to be more flexible to tools
-    queries: list[str] | None = None
+# class IterationAnswer(BaseModel):
+#     tool: str
+#     tool_id: int
+#     iteration_nr: int
+#     parallelization_nr: int
+#     question: str
+#     reasoning: str | None
+#     answer: str
+#     cited_documents: dict[int, InferenceSection]
+#     background_info: str | None = None
+#     claims: list[str] | None = None
+#     additional_data: dict[str, str] | None = None
+#     response_type: str | None = None
+#     data: dict | list | str | int | float | bool | None = None
+#     file_ids: list[str] | None = None
+#     # TODO: This is not ideal, but we'll can rework the schema
+#     # for deep research later
+#     is_web_fetch: bool = False
+#     # for image generation step-types
+#     generated_images: list[GeneratedImage] | None = None
+#     # for multi-query search tools (v2 web search and internal search)
+#     # TODO: Clean this up to be more flexible to tools
+#     queries: list[str] | None = None
 
 
-class AggregatedDRContext(BaseModel):
-    context: str
-    cited_documents: list[InferenceSection]
-    is_internet_marker_dict: dict[str, bool]
-    global_iteration_responses: list[IterationAnswer]
+# class AggregatedDRContext(BaseModel):
+#     context: str
+#     cited_documents: list[InferenceSection]
+#     is_internet_marker_dict: dict[str, bool]
+#     global_iteration_responses: list[IterationAnswer]
 
 
-class DRPromptPurpose(str, Enum):
-    PLAN = "PLAN"
-    NEXT_STEP = "NEXT_STEP"
-    NEXT_STEP_REASONING = "NEXT_STEP_REASONING"
-    NEXT_STEP_PURPOSE = "NEXT_STEP_PURPOSE"
-    CLARIFICATION = "CLARIFICATION"
+# class DRPromptPurpose(str, Enum):
+#     PLAN = "PLAN"
+#     NEXT_STEP = "NEXT_STEP"
+#     NEXT_STEP_REASONING = "NEXT_STEP_REASONING"
+#     NEXT_STEP_PURPOSE = "NEXT_STEP_PURPOSE"
+#     CLARIFICATION = "CLARIFICATION"
 
 
-class BaseSearchProcessingResponse(BaseModel):
-    specified_source_types: list[str]
-    rewritten_query: str
-    time_filter: str
+# class BaseSearchProcessingResponse(BaseModel):
+#     specified_source_types: list[str]
+#     rewritten_query: str
+#     time_filter: str

backend/onyx/agents/agent_search/dr/nodes/dr_a2_closer.py

@@ -1,423 +1,418 @@
-import re
-from datetime import datetime
-from typing import cast
+# import re
+# from datetime import datetime
+# from typing import cast
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
-from sqlalchemy.orm import Session
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
+# from sqlalchemy.orm import Session
 
-from onyx.agents.agent_search.dr.constants import MAX_CHAT_HISTORY_MESSAGES
-from onyx.agents.agent_search.dr.constants import MAX_NUM_CLOSER_SUGGESTIONS
-from onyx.agents.agent_search.dr.enums import DRPath
-from onyx.agents.agent_search.dr.enums import ResearchAnswerPurpose
-from onyx.agents.agent_search.dr.enums import ResearchType
-from onyx.agents.agent_search.dr.models import AggregatedDRContext
-from onyx.agents.agent_search.dr.models import TestInfoCompleteResponse
-from onyx.agents.agent_search.dr.states import FinalUpdate
-from onyx.agents.agent_search.dr.states import MainState
-from onyx.agents.agent_search.dr.states import OrchestrationUpdate
-from onyx.agents.agent_search.dr.sub_agents.image_generation.models import (
-    GeneratedImageFullResult,
-)
-from onyx.agents.agent_search.dr.utils import aggregate_context
-from onyx.agents.agent_search.dr.utils import convert_inference_sections_to_search_docs
-from onyx.agents.agent_search.dr.utils import get_chat_history_string
-from onyx.agents.agent_search.dr.utils import get_prompt_question
-from onyx.agents.agent_search.dr.utils import parse_plan_to_dict
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.llm import invoke_llm_json
-from onyx.agents.agent_search.shared_graph_utils.llm import stream_llm_answer
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.agents.agent_search.utils import create_question_prompt
-from onyx.chat.chat_utils import llm_doc_from_inference_section
-from onyx.configs.agent_configs import TF_DR_TIMEOUT_LONG
-from onyx.context.search.models import InferenceSection
-from onyx.db.chat import create_search_doc_from_inference_section
-from onyx.db.chat import update_db_session_with_messages
-from onyx.db.models import ChatMessage__SearchDoc
-from onyx.db.models import ResearchAgentIteration
-from onyx.db.models import ResearchAgentIterationSubStep
-from onyx.db.models import SearchDoc as DbSearchDoc
-from onyx.llm.utils import check_number_of_tokens
-from onyx.prompts.chat_prompts import PROJECT_INSTRUCTIONS_SEPARATOR
-from onyx.prompts.dr_prompts import FINAL_ANSWER_PROMPT_W_SUB_ANSWERS
-from onyx.prompts.dr_prompts import FINAL_ANSWER_PROMPT_WITHOUT_SUB_ANSWERS
-from onyx.prompts.dr_prompts import TEST_INFO_COMPLETE_PROMPT
-from onyx.server.query_and_chat.streaming_models import CitationDelta
-from onyx.server.query_and_chat.streaming_models import CitationStart
-from onyx.server.query_and_chat.streaming_models import MessageStart
-from onyx.server.query_and_chat.streaming_models import SectionEnd
-from onyx.server.query_and_chat.streaming_models import StreamingType
-from onyx.utils.logger import setup_logger
-from onyx.utils.threadpool_concurrency import run_with_timeout
+# from onyx.agents.agent_search.dr.constants import MAX_CHAT_HISTORY_MESSAGES
+# from onyx.agents.agent_search.dr.constants import MAX_NUM_CLOSER_SUGGESTIONS
+# from onyx.agents.agent_search.dr.enums import DRPath
+# from onyx.agents.agent_search.dr.enums import ResearchAnswerPurpose
+# from onyx.agents.agent_search.dr.models import AggregatedDRContext
+# from onyx.agents.agent_search.dr.models import TestInfoCompleteResponse
+# from onyx.agents.agent_search.dr.states import FinalUpdate
+# from onyx.agents.agent_search.dr.states import MainState
+# from onyx.agents.agent_search.dr.states import OrchestrationUpdate
+# from onyx.agents.agent_search.dr.sub_agents.image_generation.models import (
+#     GeneratedImageFullResult,
+# )
+# from onyx.agents.agent_search.dr.utils import aggregate_context
+# from onyx.agents.agent_search.dr.utils import convert_inference_sections_to_search_docs
+# from onyx.agents.agent_search.dr.utils import get_chat_history_string
+# from onyx.agents.agent_search.dr.utils import get_prompt_question
+# from onyx.agents.agent_search.dr.utils import parse_plan_to_dict
+# from onyx.agents.agent_search.models import GraphConfig
+# from onyx.agents.agent_search.shared_graph_utils.llm import invoke_llm_json
+# from onyx.agents.agent_search.shared_graph_utils.llm import stream_llm_answer
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.agents.agent_search.utils import create_question_prompt
+# from onyx.chat.chat_utils import llm_doc_from_inference_section
+# from onyx.configs.agent_configs import TF_DR_TIMEOUT_LONG
+# from onyx.context.search.models import InferenceSection
+# from onyx.db.chat import create_search_doc_from_inference_section
+# from onyx.db.chat import update_db_session_with_messages
+# from onyx.db.models import ChatMessage__SearchDoc
+# from onyx.db.models import ResearchAgentIteration
+# from onyx.db.models import ResearchAgentIterationSubStep
+# from onyx.db.models import SearchDoc as DbSearchDoc
+# from onyx.llm.utils import check_number_of_tokens
+# from onyx.prompts.chat_prompts import PROJECT_INSTRUCTIONS_SEPARATOR
+# from onyx.prompts.dr_prompts import FINAL_ANSWER_PROMPT_W_SUB_ANSWERS
+# from onyx.prompts.dr_prompts import FINAL_ANSWER_PROMPT_WITHOUT_SUB_ANSWERS
+# from onyx.prompts.dr_prompts import TEST_INFO_COMPLETE_PROMPT
+# from onyx.server.query_and_chat.streaming_models import CitationDelta
+# from onyx.server.query_and_chat.streaming_models import CitationStart
+# from onyx.server.query_and_chat.streaming_models import MessageStart
+# from onyx.server.query_and_chat.streaming_models import SectionEnd
+# from onyx.server.query_and_chat.streaming_models import StreamingType
+# from onyx.utils.logger import setup_logger
+# from onyx.utils.threadpool_concurrency import run_with_timeout
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def extract_citation_numbers(text: str) -> list[int]:
-    """
-    Extract all citation numbers from text in the format [[<number>]] or [[<number_1>, <number_2>, ...]].
-    Returns a list of all unique citation numbers found.
-    """
-    # Pattern to match [[number]] or [[number1, number2, ...]]
-    pattern = r"\[\[(\d+(?:,\s*\d+)*)\]\]"
-    matches = re.findall(pattern, text)
+# def extract_citation_numbers(text: str) -> list[int]:
+#     """
+#     Extract all citation numbers from text in the format [[<number>]] or [[<number_1>, <number_2>, ...]].
+#     Returns a list of all unique citation numbers found.
+#     """
+#     # Pattern to match [[number]] or [[number1, number2, ...]]
+#     pattern = r"\[\[(\d+(?:,\s*\d+)*)\]\]"
+#     matches = re.findall(pattern, text)
 
-    cited_numbers = []
-    for match in matches:
-        # Split by comma and extract all numbers
-        numbers = [int(num.strip()) for num in match.split(",")]
-        cited_numbers.extend(numbers)
+#     cited_numbers = []
+#     for match in matches:
+#         # Split by comma and extract all numbers
+#         numbers = [int(num.strip()) for num in match.split(",")]
+#         cited_numbers.extend(numbers)
 
-    return list(set(cited_numbers))  # Return unique numbers
+#     return list(set(cited_numbers))  # Return unique numbers
 
 
-def replace_citation_with_link(match: re.Match[str], docs: list[DbSearchDoc]) -> str:
-    citation_content = match.group(1)  # e.g., "3" or "3, 5, 7"
-    numbers = [int(num.strip()) for num in citation_content.split(",")]
+# def replace_citation_with_link(match: re.Match[str], docs: list[DbSearchDoc]) -> str:
+#     citation_content = match.group(1)  # e.g., "3" or "3, 5, 7"
+#     numbers = [int(num.strip()) for num in citation_content.split(",")]
 
-    # For multiple citations like [[3, 5, 7]], create separate linked citations
-    linked_citations = []
-    for num in numbers:
-        if num - 1 < len(docs):  # Check bounds
-            link = docs[num - 1].link or ""
-            linked_citations.append(f"[[{num}]]({link})")
-        else:
-            linked_citations.append(f"[[{num}]]")  # No link if out of bounds
+#     # For multiple citations like [[3, 5, 7]], create separate linked citations
+#     linked_citations = []
+#     for num in numbers:
+#         if num - 1 < len(docs):  # Check bounds
+#             link = docs[num - 1].link or ""
+#             linked_citations.append(f"[[{num}]]({link})")
+#         else:
+#             linked_citations.append(f"[[{num}]]")  # No link if out of bounds
 
-    return "".join(linked_citations)
+#     return "".join(linked_citations)
 
 
-def insert_chat_message_search_doc_pair(
-    message_id: int, search_doc_ids: list[int], db_session: Session
-) -> None:
-    """
-    Insert a pair of message_id and search_doc_id into the chat_message__search_doc table.
+# def insert_chat_message_search_doc_pair(
+#     message_id: int, search_doc_ids: list[int], db_session: Session
+# ) -> None:
+#     """
+#     Insert a pair of message_id and search_doc_id into the chat_message__search_doc table.
 
-    Args:
-        message_id: The ID of the chat message
-        search_doc_id: The ID of the search document
-        db_session: The database session
-    """
-    for search_doc_id in search_doc_ids:
-        chat_message_search_doc = ChatMessage__SearchDoc(
-            chat_message_id=message_id, search_doc_id=search_doc_id
-        )
-        db_session.add(chat_message_search_doc)
+#     Args:
+#         message_id: The ID of the chat message
+#         search_doc_id: The ID of the search document
+#         db_session: The database session
+#     """
+#     for search_doc_id in search_doc_ids:
+#         chat_message_search_doc = ChatMessage__SearchDoc(
+#             chat_message_id=message_id, search_doc_id=search_doc_id
+#         )
+#         db_session.add(chat_message_search_doc)
 
 
-def save_iteration(
-    state: MainState,
-    graph_config: GraphConfig,
-    aggregated_context: AggregatedDRContext,
-    final_answer: str,
-    all_cited_documents: list[InferenceSection],
-    is_internet_marker_dict: dict[str, bool],
-) -> None:
-    db_session = graph_config.persistence.db_session
-    message_id = graph_config.persistence.message_id
-    research_type = graph_config.behavior.research_type
-    db_session = graph_config.persistence.db_session
+# def save_iteration(
+#     state: MainState,
+#     graph_config: GraphConfig,
+#     aggregated_context: AggregatedDRContext,
+#     final_answer: str,
+#     all_cited_documents: list[InferenceSection],
+#     is_internet_marker_dict: dict[str, bool],
+# ) -> None:
+#     db_session = graph_config.persistence.db_session
+#     message_id = graph_config.persistence.message_id
 
-    # first, insert the search_docs
-    search_docs = [
-        create_search_doc_from_inference_section(
-            inference_section=inference_section,
-            is_internet=is_internet_marker_dict.get(
-                inference_section.center_chunk.document_id, False
-            ),  # TODO: revisit
-            db_session=db_session,
-            commit=False,
-        )
-        for inference_section in all_cited_documents
-    ]
+#     # first, insert the search_docs
+#     search_docs = [
+#         create_search_doc_from_inference_section(
+#             inference_section=inference_section,
+#             is_internet=is_internet_marker_dict.get(
+#                 inference_section.center_chunk.document_id, False
+#             ),  # TODO: revisit
+#             db_session=db_session,
+#             commit=False,
+#         )
+#         for inference_section in all_cited_documents
+#     ]
 
-    # then, map_search_docs to message
-    insert_chat_message_search_doc_pair(
-        message_id, [search_doc.id for search_doc in search_docs], db_session
-    )
+#     # then, map_search_docs to message
+#     insert_chat_message_search_doc_pair(
+#         message_id, [search_doc.id for search_doc in search_docs], db_session
+#     )
 
-    # lastly, insert the citations
-    citation_dict: dict[int, int] = {}
-    cited_doc_nrs = extract_citation_numbers(final_answer)
-    for cited_doc_nr in cited_doc_nrs:
-        citation_dict[cited_doc_nr] = search_docs[cited_doc_nr - 1].id
+#     # lastly, insert the citations
+#     citation_dict: dict[int, int] = {}
+#     cited_doc_nrs = extract_citation_numbers(final_answer)
+#     for cited_doc_nr in cited_doc_nrs:
+#         citation_dict[cited_doc_nr] = search_docs[cited_doc_nr - 1].id
 
-    # TODO: generate plan as dict in the first place
-    plan_of_record = state.plan_of_record.plan if state.plan_of_record else ""
-    plan_of_record_dict = parse_plan_to_dict(plan_of_record)
+#     # TODO: generate plan as dict in the first place
+#     plan_of_record = state.plan_of_record.plan if state.plan_of_record else ""
+#     plan_of_record_dict = parse_plan_to_dict(plan_of_record)
 
-    # Update the chat message and its parent message in database
-    update_db_session_with_messages(
-        db_session=db_session,
-        chat_message_id=message_id,
-        chat_session_id=graph_config.persistence.chat_session_id,
-        is_agentic=graph_config.behavior.use_agentic_search,
-        message=final_answer,
-        citations=citation_dict,
-        research_type=research_type,
-        research_plan=plan_of_record_dict,
-        final_documents=search_docs,
-        update_parent_message=True,
-        research_answer_purpose=ResearchAnswerPurpose.ANSWER,
-    )
+#     # Update the chat message and its parent message in database
+#     update_db_session_with_messages(
+#         db_session=db_session,
+#         chat_message_id=message_id,
+#         chat_session_id=graph_config.persistence.chat_session_id,
+#         is_agentic=graph_config.behavior.use_agentic_search,
+#         message=final_answer,
+#         citations=citation_dict,
+#         research_type=None,  # research_type is deprecated
+#         research_plan=plan_of_record_dict,
+#         final_documents=search_docs,
+#         update_parent_message=True,
+#         research_answer_purpose=ResearchAnswerPurpose.ANSWER,
+#     )
 
-    for iteration_preparation in state.iteration_instructions:
-        research_agent_iteration_step = ResearchAgentIteration(
-            primary_question_id=message_id,
-            reasoning=iteration_preparation.reasoning,
-            purpose=iteration_preparation.purpose,
-            iteration_nr=iteration_preparation.iteration_nr,
-        )
-        db_session.add(research_agent_iteration_step)
+#     for iteration_preparation in state.iteration_instructions:
+#         research_agent_iteration_step = ResearchAgentIteration(
+#             primary_question_id=message_id,
+#             reasoning=iteration_preparation.reasoning,
+#             purpose=iteration_preparation.purpose,
+#             iteration_nr=iteration_preparation.iteration_nr,
+#         )
+#         db_session.add(research_agent_iteration_step)
 
-    for iteration_answer in aggregated_context.global_iteration_responses:
+#     for iteration_answer in aggregated_context.global_iteration_responses:
 
-        retrieved_search_docs = convert_inference_sections_to_search_docs(
-            list(iteration_answer.cited_documents.values())
-        )
+#         retrieved_search_docs = convert_inference_sections_to_search_docs(
+#             list(iteration_answer.cited_documents.values())
+#         )
 
-        # Convert SavedSearchDoc objects to JSON-serializable format
-        serialized_search_docs = [doc.model_dump() for doc in retrieved_search_docs]
+#         # Convert SavedSearchDoc objects to JSON-serializable format
+#         serialized_search_docs = [doc.model_dump() for doc in retrieved_search_docs]
 
-        research_agent_iteration_sub_step = ResearchAgentIterationSubStep(
-            primary_question_id=message_id,
-            iteration_nr=iteration_answer.iteration_nr,
-            iteration_sub_step_nr=iteration_answer.parallelization_nr,
-            sub_step_instructions=iteration_answer.question,
-            sub_step_tool_id=iteration_answer.tool_id,
-            sub_answer=iteration_answer.answer,
-            reasoning=iteration_answer.reasoning,
-            claims=iteration_answer.claims,
-            cited_doc_results=serialized_search_docs,
-            generated_images=(
-                GeneratedImageFullResult(images=iteration_answer.generated_images)
-                if iteration_answer.generated_images
-                else None
-            ),
-            additional_data=iteration_answer.additional_data,
-            queries=iteration_answer.queries,
-        )
-        db_session.add(research_agent_iteration_sub_step)
+#         research_agent_iteration_sub_step = ResearchAgentIterationSubStep(
+#             primary_question_id=message_id,
+#             iteration_nr=iteration_answer.iteration_nr,
+#             iteration_sub_step_nr=iteration_answer.parallelization_nr,
+#             sub_step_instructions=iteration_answer.question,
+#             sub_step_tool_id=iteration_answer.tool_id,
+#             sub_answer=iteration_answer.answer,
+#             reasoning=iteration_answer.reasoning,
+#             claims=iteration_answer.claims,
+#             cited_doc_results=serialized_search_docs,
+#             generated_images=(
+#                 GeneratedImageFullResult(images=iteration_answer.generated_images)
+#                 if iteration_answer.generated_images
+#                 else None
+#             ),
+#             additional_data=iteration_answer.additional_data,
+#             queries=iteration_answer.queries,
+#         )
+#         db_session.add(research_agent_iteration_sub_step)
 
-    db_session.commit()
+#     db_session.commit()
 
 
-def closer(
-    state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> FinalUpdate | OrchestrationUpdate:
-    """
-    LangGraph node to close the DR process and finalize the answer.
-    """
+# def closer(
+#     state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
+# ) -> FinalUpdate | OrchestrationUpdate:
+#     """
+#     LangGraph node to close the DR process and finalize the answer.
+#     """
 
-    node_start_time = datetime.now()
-    # TODO: generate final answer using all the previous steps
-    # (right now, answers from each step are concatenated onto each other)
-    # Also, add missing fields once usage in UI is clear.
+#     node_start_time = datetime.now()
+#     # TODO: generate final answer using all the previous steps
+#     # (right now, answers from each step are concatenated onto each other)
+#     # Also, add missing fields once usage in UI is clear.
 
-    current_step_nr = state.current_step_nr
+#     current_step_nr = state.current_step_nr
 
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    base_question = state.original_question
-    if not base_question:
-        raise ValueError("Question is required for closer")
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
+#     base_question = state.original_question
+#     if not base_question:
+#         raise ValueError("Question is required for closer")
 
-    research_type = graph_config.behavior.research_type
+#     use_agentic_search = graph_config.behavior.use_agentic_search
 
-    assistant_system_prompt: str = state.assistant_system_prompt or ""
-    assistant_task_prompt = state.assistant_task_prompt
+#     assistant_system_prompt: str = state.assistant_system_prompt or ""
+#     assistant_task_prompt = state.assistant_task_prompt
 
-    uploaded_context = state.uploaded_test_context or ""
+#     uploaded_context = state.uploaded_test_context or ""
 
-    clarification = state.clarification
-    prompt_question = get_prompt_question(base_question, clarification)
+#     clarification = state.clarification
+#     prompt_question = get_prompt_question(base_question, clarification)
 
-    chat_history_string = (
-        get_chat_history_string(
-            graph_config.inputs.prompt_builder.message_history,
-            MAX_CHAT_HISTORY_MESSAGES,
-        )
-        or "(No chat history yet available)"
-    )
+#     chat_history_string = (
+#         get_chat_history_string(
+#             graph_config.inputs.prompt_builder.message_history,
+#             MAX_CHAT_HISTORY_MESSAGES,
+#         )
+#         or "(No chat history yet available)"
+#     )
 
-    aggregated_context_w_docs = aggregate_context(
-        state.iteration_responses, include_documents=True
-    )
+#     aggregated_context_w_docs = aggregate_context(
+#         state.iteration_responses, include_documents=True
+#     )
 
-    aggregated_context_wo_docs = aggregate_context(
-        state.iteration_responses, include_documents=False
-    )
+#     aggregated_context_wo_docs = aggregate_context(
+#         state.iteration_responses, include_documents=False
+#     )
 
-    iteration_responses_w_docs_string = aggregated_context_w_docs.context
-    iteration_responses_wo_docs_string = aggregated_context_wo_docs.context
-    all_cited_documents = aggregated_context_w_docs.cited_documents
+#     iteration_responses_w_docs_string = aggregated_context_w_docs.context
+#     iteration_responses_wo_docs_string = aggregated_context_wo_docs.context
+#     all_cited_documents = aggregated_context_w_docs.cited_documents
 
-    num_closer_suggestions = state.num_closer_suggestions
+#     num_closer_suggestions = state.num_closer_suggestions
 
-    if (
-        num_closer_suggestions < MAX_NUM_CLOSER_SUGGESTIONS
-        and research_type == ResearchType.DEEP
-    ):
-        test_info_complete_prompt = TEST_INFO_COMPLETE_PROMPT.build(
-            base_question=prompt_question,
-            questions_answers_claims=iteration_responses_wo_docs_string,
-            chat_history_string=chat_history_string,
-            high_level_plan=(
-                state.plan_of_record.plan
-                if state.plan_of_record
-                else "No plan available"
-            ),
-        )
+#     if (
+#         num_closer_suggestions < MAX_NUM_CLOSER_SUGGESTIONS
+#         and use_agentic_search
+#     ):
+#         test_info_complete_prompt = TEST_INFO_COMPLETE_PROMPT.build(
+#             base_question=prompt_question,
+#             questions_answers_claims=iteration_responses_wo_docs_string,
+#             chat_history_string=chat_history_string,
+#             high_level_plan=(
+#                 state.plan_of_record.plan
+#                 if state.plan_of_record
+#                 else "No plan available"
+#             ),
+#         )
 
-        test_info_complete_json = invoke_llm_json(
-            llm=graph_config.tooling.primary_llm,
-            prompt=create_question_prompt(
-                assistant_system_prompt,
-                test_info_complete_prompt + (assistant_task_prompt or ""),
-            ),
-            schema=TestInfoCompleteResponse,
-            timeout_override=TF_DR_TIMEOUT_LONG,
-            # max_tokens=1000,
-        )
+#         test_info_complete_json = invoke_llm_json(
+#             llm=graph_config.tooling.primary_llm,
+#             prompt=create_question_prompt(
+#                 assistant_system_prompt,
+#                 test_info_complete_prompt + (assistant_task_prompt or ""),
+#             ),
+#             schema=TestInfoCompleteResponse,
+#             timeout_override=TF_DR_TIMEOUT_LONG,
+#             # max_tokens=1000,
+#         )
 
-        if test_info_complete_json.complete:
-            pass
+#         if test_info_complete_json.complete:
+#             pass
 
-        else:
-            return OrchestrationUpdate(
-                tools_used=[DRPath.ORCHESTRATOR.value],
-                query_list=[],
-                log_messages=[
-                    get_langgraph_node_log_string(
-                        graph_component="main",
-                        node_name="closer",
-                        node_start_time=node_start_time,
-                    )
-                ],
-                gaps=test_info_complete_json.gaps,
-                num_closer_suggestions=num_closer_suggestions + 1,
-            )
+#         else:
+#             return OrchestrationUpdate(
+#                 tools_used=[DRPath.ORCHESTRATOR.value],
+#                 query_list=[],
+#                 log_messages=[
+#                     get_langgraph_node_log_string(
+#                         graph_component="main",
+#                         node_name="closer",
+#                         node_start_time=node_start_time,
+#                     )
+#                 ],
+#                 gaps=test_info_complete_json.gaps,
+#                 num_closer_suggestions=num_closer_suggestions + 1,
+#             )
 
-    retrieved_search_docs = convert_inference_sections_to_search_docs(
-        all_cited_documents
-    )
+#     retrieved_search_docs = convert_inference_sections_to_search_docs(
+#         all_cited_documents
+#     )
 
-    write_custom_event(
-        current_step_nr,
-        MessageStart(
-            content="",
-            final_documents=retrieved_search_docs,
-        ),
-        writer,
-    )
+#     write_custom_event(
+#         current_step_nr,
+#         MessageStart(
+#             content="",
+#             final_documents=retrieved_search_docs,
+#         ),
+#         writer,
+#     )
 
-    if research_type in [ResearchType.THOUGHTFUL, ResearchType.FAST]:
-        final_answer_base_prompt = FINAL_ANSWER_PROMPT_WITHOUT_SUB_ANSWERS
-    elif research_type == ResearchType.DEEP:
-        final_answer_base_prompt = FINAL_ANSWER_PROMPT_W_SUB_ANSWERS
-    else:
-        raise ValueError(f"Invalid research type: {research_type}")
+#     if not use_agentic_search:
+#         final_answer_base_prompt = FINAL_ANSWER_PROMPT_WITHOUT_SUB_ANSWERS
+#     else:
+#         final_answer_base_prompt = FINAL_ANSWER_PROMPT_W_SUB_ANSWERS
 
-    estimated_final_answer_prompt_tokens = check_number_of_tokens(
-        final_answer_base_prompt.build(
-            base_question=prompt_question,
-            iteration_responses_string=iteration_responses_w_docs_string,
-            chat_history_string=chat_history_string,
-            uploaded_context=uploaded_context,
-        )
-    )
+#     estimated_final_answer_prompt_tokens = check_number_of_tokens(
+#         final_answer_base_prompt.build(
+#             base_question=prompt_question,
+#             iteration_responses_string=iteration_responses_w_docs_string,
+#             chat_history_string=chat_history_string,
+#             uploaded_context=uploaded_context,
+#         )
+#     )
 
-    # for DR, rely only on sub-answers and claims to save tokens if context is too long
-    # TODO: consider compression step for Thoughtful mode if context is too long.
-    # Should generally not be the case though.
+#     # for DR, rely only on sub-answers and claims to save tokens if context is too long
+#     # TODO: consider compression step for Thoughtful mode if context is too long.
+#     # Should generally not be the case though.
 
-    max_allowed_input_tokens = graph_config.tooling.primary_llm.config.max_input_tokens
+#     max_allowed_input_tokens = graph_config.tooling.primary_llm.config.max_input_tokens
 
-    if (
-        estimated_final_answer_prompt_tokens > 0.8 * max_allowed_input_tokens
-        and research_type == ResearchType.DEEP
-    ):
-        iteration_responses_string = iteration_responses_wo_docs_string
-    else:
-        iteration_responses_string = iteration_responses_w_docs_string
+#     if (
+#         estimated_final_answer_prompt_tokens > 0.8 * max_allowed_input_tokens
+#         and use_agentic_search
+#     ):
+#         iteration_responses_string = iteration_responses_wo_docs_string
+#     else:
+#         iteration_responses_string = iteration_responses_w_docs_string
 
-    final_answer_prompt = final_answer_base_prompt.build(
-        base_question=prompt_question,
-        iteration_responses_string=iteration_responses_string,
-        chat_history_string=chat_history_string,
-        uploaded_context=uploaded_context,
-    )
+#     final_answer_prompt = final_answer_base_prompt.build(
+#         base_question=prompt_question,
+#         iteration_responses_string=iteration_responses_string,
+#         chat_history_string=chat_history_string,
+#         uploaded_context=uploaded_context,
+#     )
 
-    if graph_config.inputs.project_instructions:
-        assistant_system_prompt = (
-            assistant_system_prompt
-            + PROJECT_INSTRUCTIONS_SEPARATOR
-            + (graph_config.inputs.project_instructions or "")
-        )
+#     if graph_config.inputs.project_instructions:
+#         assistant_system_prompt = (
+#             assistant_system_prompt
+#             + PROJECT_INSTRUCTIONS_SEPARATOR
+#             + (graph_config.inputs.project_instructions or "")
+#         )
 
-    all_context_llmdocs = [
-        llm_doc_from_inference_section(inference_section)
-        for inference_section in all_cited_documents
-    ]
+#     all_context_llmdocs = [
+#         llm_doc_from_inference_section(inference_section)
+#         for inference_section in all_cited_documents
+#     ]
 
-    try:
-        streamed_output, _, citation_infos = run_with_timeout(
-            int(3 * TF_DR_TIMEOUT_LONG),
-            lambda: stream_llm_answer(
-                llm=graph_config.tooling.primary_llm,
-                prompt=create_question_prompt(
-                    assistant_system_prompt,
-                    final_answer_prompt + (assistant_task_prompt or ""),
-                ),
-                event_name="basic_response",
-                writer=writer,
-                agent_answer_level=0,
-                agent_answer_question_num=0,
-                agent_answer_type="agent_level_answer",
-                timeout_override=int(2 * TF_DR_TIMEOUT_LONG),
-                answer_piece=StreamingType.MESSAGE_DELTA.value,
-                ind=current_step_nr,
-                context_docs=all_context_llmdocs,
-                replace_citations=True,
-                # max_tokens=None,
-            ),
-        )
+#     try:
+#         streamed_output, _, citation_infos = run_with_timeout(
+#             int(3 * TF_DR_TIMEOUT_LONG),
+#             lambda: stream_llm_answer(
+#                 llm=graph_config.tooling.primary_llm,
+#                 prompt=create_question_prompt(
+#                     assistant_system_prompt,
+#                     final_answer_prompt + (assistant_task_prompt or ""),
+#                 ),
+#                 event_name="basic_response",
+#                 writer=writer,
+#                 agent_answer_level=0,
+#                 agent_answer_question_num=0,
+#                 agent_answer_type="agent_level_answer",
+#                 timeout_override=int(2 * TF_DR_TIMEOUT_LONG),
+#                 answer_piece=StreamingType.MESSAGE_DELTA.value,
+#                 ind=current_step_nr,
+#                 context_docs=all_context_llmdocs,
+#                 replace_citations=True,
+#                 # max_tokens=None,
+#             ),
+#         )
 
-        final_answer = "".join(streamed_output)
-    except Exception as e:
-        raise ValueError(f"Error in consolidate_research: {e}")
+#         final_answer = "".join(streamed_output)
+#     except Exception as e:
+#         raise ValueError(f"Error in consolidate_research: {e}")
 
-    write_custom_event(current_step_nr, SectionEnd(), writer)
+#     write_custom_event(current_step_nr, SectionEnd(), writer)
 
-    current_step_nr += 1
+#     current_step_nr += 1
 
-    write_custom_event(current_step_nr, CitationStart(), writer)
-    write_custom_event(current_step_nr, CitationDelta(citations=citation_infos), writer)
-    write_custom_event(current_step_nr, SectionEnd(), writer)
+#     write_custom_event(current_step_nr, CitationStart(), writer)
+#     write_custom_event(current_step_nr, CitationDelta(citations=citation_infos), writer)
+#     write_custom_event(current_step_nr, SectionEnd(), writer)
 
-    current_step_nr += 1
+#     current_step_nr += 1
 
-    # Log the research agent steps
-    # save_iteration(
-    #     state,
-    #     graph_config,
-    #     aggregated_context,
-    #     final_answer,
-    #     all_cited_documents,
-    #     is_internet_marker_dict,
-    # )
+#     # Log the research agent steps
+#     # save_iteration(
+#     #     state,
+#     #     graph_config,
+#     #     aggregated_context,
+#     #     final_answer,
+#     #     all_cited_documents,
+#     #     is_internet_marker_dict,
+#     # )
 
-    return FinalUpdate(
-        final_answer=final_answer,
-        all_cited_documents=all_cited_documents,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="main",
-                node_name="closer",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return FinalUpdate(
+#         final_answer=final_answer,
+#         all_cited_documents=all_cited_documents,
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="main",
+#                 node_name="closer",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/nodes/dr_a3_logger.py

@@ -1,248 +1,246 @@
-import re
-from datetime import datetime
-from typing import cast
+# import re
+# from datetime import datetime
+# from typing import cast
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
-from sqlalchemy.orm import Session
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
+# from sqlalchemy.orm import Session
 
-from onyx.agents.agent_search.dr.enums import ResearchAnswerPurpose
-from onyx.agents.agent_search.dr.models import AggregatedDRContext
-from onyx.agents.agent_search.dr.states import LoggerUpdate
-from onyx.agents.agent_search.dr.states import MainState
-from onyx.agents.agent_search.dr.sub_agents.image_generation.models import (
-    GeneratedImageFullResult,
-)
-from onyx.agents.agent_search.dr.utils import aggregate_context
-from onyx.agents.agent_search.dr.utils import convert_inference_sections_to_search_docs
-from onyx.agents.agent_search.dr.utils import parse_plan_to_dict
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.context.search.models import InferenceSection
-from onyx.db.chat import create_search_doc_from_inference_section
-from onyx.db.chat import update_db_session_with_messages
-from onyx.db.models import ChatMessage__SearchDoc
-from onyx.db.models import ResearchAgentIteration
-from onyx.db.models import ResearchAgentIterationSubStep
-from onyx.db.models import SearchDoc as DbSearchDoc
-from onyx.natural_language_processing.utils import get_tokenizer
-from onyx.server.query_and_chat.streaming_models import OverallStop
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.enums import ResearchAnswerPurpose
+# from onyx.agents.agent_search.dr.models import AggregatedDRContext
+# from onyx.agents.agent_search.dr.states import LoggerUpdate
+# from onyx.agents.agent_search.dr.states import MainState
+# from onyx.agents.agent_search.dr.sub_agents.image_generation.models import (
+#     GeneratedImageFullResult,
+# )
+# from onyx.agents.agent_search.dr.utils import aggregate_context
+# from onyx.agents.agent_search.dr.utils import convert_inference_sections_to_search_docs
+# from onyx.agents.agent_search.dr.utils import parse_plan_to_dict
+# from onyx.agents.agent_search.models import GraphConfig
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.context.search.models import InferenceSection
+# from onyx.db.chat import create_search_doc_from_inference_section
+# from onyx.db.chat import update_db_session_with_messages
+# from onyx.db.models import ChatMessage__SearchDoc
+# from onyx.db.models import ResearchAgentIteration
+# from onyx.db.models import ResearchAgentIterationSubStep
+# from onyx.db.models import SearchDoc as DbSearchDoc
+# from onyx.natural_language_processing.utils import get_tokenizer
+# from onyx.server.query_and_chat.streaming_models import OverallStop
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def _extract_citation_numbers(text: str) -> list[int]:
-    """
-    Extract all citation numbers from text in the format [[<number>]] or [[<number_1>, <number_2>, ...]].
-    Returns a list of all unique citation numbers found.
-    """
-    # Pattern to match [[number]] or [[number1, number2, ...]]
-    pattern = r"\[\[(\d+(?:,\s*\d+)*)\]\]"
-    matches = re.findall(pattern, text)
+# def _extract_citation_numbers(text: str) -> list[int]:
+#     """
+#     Extract all citation numbers from text in the format [[<number>]] or [[<number_1>, <number_2>, ...]].
+#     Returns a list of all unique citation numbers found.
+#     """
+#     # Pattern to match [[number]] or [[number1, number2, ...]]
+#     pattern = r"\[\[(\d+(?:,\s*\d+)*)\]\]"
+#     matches = re.findall(pattern, text)
 
-    cited_numbers = []
-    for match in matches:
-        # Split by comma and extract all numbers
-        numbers = [int(num.strip()) for num in match.split(",")]
-        cited_numbers.extend(numbers)
+#     cited_numbers = []
+#     for match in matches:
+#         # Split by comma and extract all numbers
+#         numbers = [int(num.strip()) for num in match.split(",")]
+#         cited_numbers.extend(numbers)
 
-    return list(set(cited_numbers))  # Return unique numbers
+#     return list(set(cited_numbers))  # Return unique numbers
 
 
-def replace_citation_with_link(match: re.Match[str], docs: list[DbSearchDoc]) -> str:
-    citation_content = match.group(1)  # e.g., "3" or "3, 5, 7"
-    numbers = [int(num.strip()) for num in citation_content.split(",")]
+# def replace_citation_with_link(match: re.Match[str], docs: list[DbSearchDoc]) -> str:
+#     citation_content = match.group(1)  # e.g., "3" or "3, 5, 7"
+#     numbers = [int(num.strip()) for num in citation_content.split(",")]
 
-    # For multiple citations like [[3, 5, 7]], create separate linked citations
-    linked_citations = []
-    for num in numbers:
-        if num - 1 < len(docs):  # Check bounds
-            link = docs[num - 1].link or ""
-            linked_citations.append(f"[[{num}]]({link})")
-        else:
-            linked_citations.append(f"[[{num}]]")  # No link if out of bounds
+#     # For multiple citations like [[3, 5, 7]], create separate linked citations
+#     linked_citations = []
+#     for num in numbers:
+#         if num - 1 < len(docs):  # Check bounds
+#             link = docs[num - 1].link or ""
+#             linked_citations.append(f"[[{num}]]({link})")
+#         else:
+#             linked_citations.append(f"[[{num}]]")  # No link if out of bounds
 
-    return "".join(linked_citations)
+#     return "".join(linked_citations)
 
 
-def _insert_chat_message_search_doc_pair(
-    message_id: int, search_doc_ids: list[int], db_session: Session
-) -> None:
-    """
-    Insert a pair of message_id and search_doc_id into the chat_message__search_doc table.
+# def _insert_chat_message_search_doc_pair(
+#     message_id: int, search_doc_ids: list[int], db_session: Session
+# ) -> None:
+#     """
+#     Insert a pair of message_id and search_doc_id into the chat_message__search_doc table.
 
-    Args:
-        message_id: The ID of the chat message
-        search_doc_id: The ID of the search document
-        db_session: The database session
-    """
-    for search_doc_id in search_doc_ids:
-        chat_message_search_doc = ChatMessage__SearchDoc(
-            chat_message_id=message_id, search_doc_id=search_doc_id
-        )
-        db_session.add(chat_message_search_doc)
+#     Args:
+#         message_id: The ID of the chat message
+#         search_doc_id: The ID of the search document
+#         db_session: The database session
+#     """
+#     for search_doc_id in search_doc_ids:
+#         chat_message_search_doc = ChatMessage__SearchDoc(
+#             chat_message_id=message_id, search_doc_id=search_doc_id
+#         )
+#         db_session.add(chat_message_search_doc)
 
 
-def save_iteration(
-    state: MainState,
-    graph_config: GraphConfig,
-    aggregated_context: AggregatedDRContext,
-    final_answer: str,
-    all_cited_documents: list[InferenceSection],
-    is_internet_marker_dict: dict[str, bool],
-    num_tokens: int,
-) -> None:
-    db_session = graph_config.persistence.db_session
-    message_id = graph_config.persistence.message_id
-    research_type = graph_config.behavior.research_type
-    db_session = graph_config.persistence.db_session
+# def save_iteration(
+#     state: MainState,
+#     graph_config: GraphConfig,
+#     aggregated_context: AggregatedDRContext,
+#     final_answer: str,
+#     all_cited_documents: list[InferenceSection],
+#     is_internet_marker_dict: dict[str, bool],
+#     num_tokens: int,
+# ) -> None:
+#     db_session = graph_config.persistence.db_session
+#     message_id = graph_config.persistence.message_id
 
-    # first, insert the search_docs
-    search_docs = [
-        create_search_doc_from_inference_section(
-            inference_section=inference_section,
-            is_internet=is_internet_marker_dict.get(
-                inference_section.center_chunk.document_id, False
-            ),  # TODO: revisit
-            db_session=db_session,
-            commit=False,
-        )
-        for inference_section in all_cited_documents
-    ]
+#     # first, insert the search_docs
+#     search_docs = [
+#         create_search_doc_from_inference_section(
+#             inference_section=inference_section,
+#             is_internet=is_internet_marker_dict.get(
+#                 inference_section.center_chunk.document_id, False
+#             ),  # TODO: revisit
+#             db_session=db_session,
+#             commit=False,
+#         )
+#         for inference_section in all_cited_documents
+#     ]
 
-    # then, map_search_docs to message
-    _insert_chat_message_search_doc_pair(
-        message_id, [search_doc.id for search_doc in search_docs], db_session
-    )
+#     # then, map_search_docs to message
+#     _insert_chat_message_search_doc_pair(
+#         message_id, [search_doc.id for search_doc in search_docs], db_session
+#     )
 
-    # lastly, insert the citations
-    citation_dict: dict[int, int] = {}
-    cited_doc_nrs = _extract_citation_numbers(final_answer)
-    if search_docs:
-        for cited_doc_nr in cited_doc_nrs:
-            citation_dict[cited_doc_nr] = search_docs[cited_doc_nr - 1].id
+#     # lastly, insert the citations
+#     citation_dict: dict[int, int] = {}
+#     cited_doc_nrs = _extract_citation_numbers(final_answer)
+#     if search_docs:
+#         for cited_doc_nr in cited_doc_nrs:
+#             citation_dict[cited_doc_nr] = search_docs[cited_doc_nr - 1].id
 
-    # TODO: generate plan as dict in the first place
-    plan_of_record = state.plan_of_record.plan if state.plan_of_record else ""
-    plan_of_record_dict = parse_plan_to_dict(plan_of_record)
+#     # TODO: generate plan as dict in the first place
+#     plan_of_record = state.plan_of_record.plan if state.plan_of_record else ""
+#     plan_of_record_dict = parse_plan_to_dict(plan_of_record)
 
-    # Update the chat message and its parent message in database
-    update_db_session_with_messages(
-        db_session=db_session,
-        chat_message_id=message_id,
-        chat_session_id=graph_config.persistence.chat_session_id,
-        is_agentic=graph_config.behavior.use_agentic_search,
-        message=final_answer,
-        citations=citation_dict,
-        research_type=research_type,
-        research_plan=plan_of_record_dict,
-        final_documents=search_docs,
-        update_parent_message=True,
-        research_answer_purpose=ResearchAnswerPurpose.ANSWER,
-        token_count=num_tokens,
-    )
+#     # Update the chat message and its parent message in database
+#     update_db_session_with_messages(
+#         db_session=db_session,
+#         chat_message_id=message_id,
+#         chat_session_id=graph_config.persistence.chat_session_id,
+#         is_agentic=graph_config.behavior.use_agentic_search,
+#         message=final_answer,
+#         citations=citation_dict,
+#         research_type=None,  # research_type is deprecated
+#         research_plan=plan_of_record_dict,
+#         final_documents=search_docs,
+#         update_parent_message=True,
+#         research_answer_purpose=ResearchAnswerPurpose.ANSWER,
+#         token_count=num_tokens,
+#     )
 
-    for iteration_preparation in state.iteration_instructions:
-        research_agent_iteration_step = ResearchAgentIteration(
-            primary_question_id=message_id,
-            reasoning=iteration_preparation.reasoning,
-            purpose=iteration_preparation.purpose,
-            iteration_nr=iteration_preparation.iteration_nr,
-        )
-        db_session.add(research_agent_iteration_step)
+#     for iteration_preparation in state.iteration_instructions:
+#         research_agent_iteration_step = ResearchAgentIteration(
+#             primary_question_id=message_id,
+#             reasoning=iteration_preparation.reasoning,
+#             purpose=iteration_preparation.purpose,
+#             iteration_nr=iteration_preparation.iteration_nr,
+#         )
+#         db_session.add(research_agent_iteration_step)
 
-    for iteration_answer in aggregated_context.global_iteration_responses:
+#     for iteration_answer in aggregated_context.global_iteration_responses:
 
-        retrieved_search_docs = convert_inference_sections_to_search_docs(
-            list(iteration_answer.cited_documents.values())
-        )
+#         retrieved_search_docs = convert_inference_sections_to_search_docs(
+#             list(iteration_answer.cited_documents.values())
+#         )
 
-        # Convert SavedSearchDoc objects to JSON-serializable format
-        serialized_search_docs = [doc.model_dump() for doc in retrieved_search_docs]
+#         # Convert SavedSearchDoc objects to JSON-serializable format
+#         serialized_search_docs = [doc.model_dump() for doc in retrieved_search_docs]
 
-        research_agent_iteration_sub_step = ResearchAgentIterationSubStep(
-            primary_question_id=message_id,
-            iteration_nr=iteration_answer.iteration_nr,
-            iteration_sub_step_nr=iteration_answer.parallelization_nr,
-            sub_step_instructions=iteration_answer.question,
-            sub_step_tool_id=iteration_answer.tool_id,
-            sub_answer=iteration_answer.answer,
-            reasoning=iteration_answer.reasoning,
-            claims=iteration_answer.claims,
-            cited_doc_results=serialized_search_docs,
-            generated_images=(
-                GeneratedImageFullResult(images=iteration_answer.generated_images)
-                if iteration_answer.generated_images
-                else None
-            ),
-            additional_data=iteration_answer.additional_data,
-            queries=iteration_answer.queries,
-        )
-        db_session.add(research_agent_iteration_sub_step)
+#         research_agent_iteration_sub_step = ResearchAgentIterationSubStep(
+#             primary_question_id=message_id,
+#             iteration_nr=iteration_answer.iteration_nr,
+#             iteration_sub_step_nr=iteration_answer.parallelization_nr,
+#             sub_step_instructions=iteration_answer.question,
+#             sub_step_tool_id=iteration_answer.tool_id,
+#             sub_answer=iteration_answer.answer,
+#             reasoning=iteration_answer.reasoning,
+#             claims=iteration_answer.claims,
+#             cited_doc_results=serialized_search_docs,
+#             generated_images=(
+#                 GeneratedImageFullResult(images=iteration_answer.generated_images)
+#                 if iteration_answer.generated_images
+#                 else None
+#             ),
+#             additional_data=iteration_answer.additional_data,
+#             queries=iteration_answer.queries,
+#         )
+#         db_session.add(research_agent_iteration_sub_step)
 
-    db_session.commit()
+#     db_session.commit()
 
 
-def logging(
-    state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> LoggerUpdate:
-    """
-    LangGraph node to close the DR process and finalize the answer.
-    """
+# def logging(
+#     state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
+# ) -> LoggerUpdate:
+#     """
+#     LangGraph node to close the DR process and finalize the answer.
+#     """
 
-    node_start_time = datetime.now()
-    # TODO: generate final answer using all the previous steps
-    # (right now, answers from each step are concatenated onto each other)
-    # Also, add missing fields once usage in UI is clear.
+#     node_start_time = datetime.now()
+#     # TODO: generate final answer using all the previous steps
+#     # (right now, answers from each step are concatenated onto each other)
+#     # Also, add missing fields once usage in UI is clear.
 
-    current_step_nr = state.current_step_nr
+#     current_step_nr = state.current_step_nr
 
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    base_question = state.original_question
-    if not base_question:
-        raise ValueError("Question is required for closer")
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
+#     base_question = state.original_question
+#     if not base_question:
+#         raise ValueError("Question is required for closer")
 
-    aggregated_context = aggregate_context(
-        state.iteration_responses, include_documents=True
-    )
+#     aggregated_context = aggregate_context(
+#         state.iteration_responses, include_documents=True
+#     )
 
-    all_cited_documents = aggregated_context.cited_documents
+#     all_cited_documents = aggregated_context.cited_documents
 
-    is_internet_marker_dict = aggregated_context.is_internet_marker_dict
+#     is_internet_marker_dict = aggregated_context.is_internet_marker_dict
 
-    final_answer = state.final_answer or ""
-    llm_provider = graph_config.tooling.primary_llm.config.model_provider
-    llm_model_name = graph_config.tooling.primary_llm.config.model_name
+#     final_answer = state.final_answer or ""
+#     llm_provider = graph_config.tooling.primary_llm.config.model_provider
+#     llm_model_name = graph_config.tooling.primary_llm.config.model_name
 
-    llm_tokenizer = get_tokenizer(
-        model_name=llm_model_name,
-        provider_type=llm_provider,
-    )
-    num_tokens = len(llm_tokenizer.encode(final_answer or ""))
+#     llm_tokenizer = get_tokenizer(
+#         model_name=llm_model_name,
+#         provider_type=llm_provider,
+#     )
+#     num_tokens = len(llm_tokenizer.encode(final_answer or ""))
 
-    write_custom_event(current_step_nr, OverallStop(), writer)
+#     write_custom_event(current_step_nr, OverallStop(), writer)
 
-    # Log the research agent steps
-    save_iteration(
-        state,
-        graph_config,
-        aggregated_context,
-        final_answer,
-        all_cited_documents,
-        is_internet_marker_dict,
-        num_tokens,
-    )
+#     # Log the research agent steps
+#     save_iteration(
+#         state,
+#         graph_config,
+#         aggregated_context,
+#         final_answer,
+#         all_cited_documents,
+#         is_internet_marker_dict,
+#         num_tokens,
+#     )
 
-    return LoggerUpdate(
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="main",
-                node_name="logger",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return LoggerUpdate(
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="main",
+#                 node_name="logger",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/process_llm_stream.py

@@ -1,132 +1,131 @@
-from collections.abc import Iterator
-from typing import cast
+# from collections.abc import Iterator
+# from typing import cast
 
-from langchain_core.messages import AIMessageChunk
-from langchain_core.messages import BaseMessage
-from langgraph.types import StreamWriter
-from pydantic import BaseModel
+# from langchain_core.messages import AIMessageChunk
+# from langchain_core.messages import BaseMessage
+# from langgraph.types import StreamWriter
+# from pydantic import BaseModel
 
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.chat.chat_utils import saved_search_docs_from_llm_docs
-from onyx.chat.models import AgentAnswerPiece
-from onyx.chat.models import CitationInfo
-from onyx.chat.models import LlmDoc
-from onyx.chat.models import OnyxAnswerPiece
-from onyx.chat.stream_processing.answer_response_handler import AnswerResponseHandler
-from onyx.chat.stream_processing.answer_response_handler import CitationResponseHandler
-from onyx.chat.stream_processing.answer_response_handler import (
-    PassThroughAnswerResponseHandler,
-)
-from onyx.chat.stream_processing.utils import map_document_id_order
-from onyx.context.search.models import InferenceSection
-from onyx.server.query_and_chat.streaming_models import CitationDelta
-from onyx.server.query_and_chat.streaming_models import CitationStart
-from onyx.server.query_and_chat.streaming_models import MessageDelta
-from onyx.server.query_and_chat.streaming_models import MessageStart
-from onyx.server.query_and_chat.streaming_models import SectionEnd
-from onyx.utils.logger import setup_logger
+# from onyx.chat.chat_utils import saved_search_docs_from_llm_docs
+# from onyx.chat.models import AgentAnswerPiece
+# from onyx.chat.models import CitationInfo
+# from onyx.chat.models import LlmDoc
+# from onyx.chat.models import OnyxAnswerPiece
+# from onyx.chat.stream_processing.answer_response_handler import AnswerResponseHandler
+# from onyx.chat.stream_processing.answer_response_handler import CitationResponseHandler
+# from onyx.chat.stream_processing.answer_response_handler import (
+#     PassThroughAnswerResponseHandler,
+# )
+# from onyx.chat.stream_processing.utils import map_document_id_order
+# from onyx.context.search.models import InferenceSection
+# from onyx.server.query_and_chat.streaming_models import CitationDelta
+# from onyx.server.query_and_chat.streaming_models import CitationStart
+# from onyx.server.query_and_chat.streaming_models import MessageDelta
+# from onyx.server.query_and_chat.streaming_models import MessageStart
+# from onyx.server.query_and_chat.streaming_models import SectionEnd
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-class BasicSearchProcessedStreamResults(BaseModel):
-    ai_message_chunk: AIMessageChunk = AIMessageChunk(content="")
-    full_answer: str | None = None
-    cited_references: list[InferenceSection] = []
-    retrieved_documents: list[LlmDoc] = []
+# class BasicSearchProcessedStreamResults(BaseModel):
+#     ai_message_chunk: AIMessageChunk = AIMessageChunk(content="")
+#     full_answer: str | None = None
+#     cited_references: list[InferenceSection] = []
+#     retrieved_documents: list[LlmDoc] = []
 
 
-def process_llm_stream(
-    messages: Iterator[BaseMessage],
-    should_stream_answer: bool,
-    writer: StreamWriter,
-    ind: int,
-    search_results: list[LlmDoc] | None = None,
-    generate_final_answer: bool = False,
-    chat_message_id: str | None = None,
-) -> BasicSearchProcessedStreamResults:
-    tool_call_chunk = AIMessageChunk(content="")
+# def process_llm_stream(
+#     messages: Iterator[BaseMessage],
+#     should_stream_answer: bool,
+#     writer: StreamWriter,
+#     ind: int,
+#     search_results: list[LlmDoc] | None = None,
+#     generate_final_answer: bool = False,
+#     chat_message_id: str | None = None,
+# ) -> BasicSearchProcessedStreamResults:
+#     tool_call_chunk = AIMessageChunk(content="")
 
-    if search_results:
-        answer_handler: AnswerResponseHandler = CitationResponseHandler(
-            context_docs=search_results,
-            doc_id_to_rank_map=map_document_id_order(search_results),
-        )
-    else:
-        answer_handler = PassThroughAnswerResponseHandler()
+#     if search_results:
+#         answer_handler: AnswerResponseHandler = CitationResponseHandler(
+#             context_docs=search_results,
+#             doc_id_to_rank_map=map_document_id_order(search_results),
+#         )
+#     else:
+#         answer_handler = PassThroughAnswerResponseHandler()
 
-    full_answer = ""
-    start_final_answer_streaming_set = False
-    # Accumulate citation infos if handler emits them
-    collected_citation_infos: list[CitationInfo] = []
+#     full_answer = ""
+#     start_final_answer_streaming_set = False
+#     # Accumulate citation infos if handler emits them
+#     collected_citation_infos: list[CitationInfo] = []
 
-    # This stream will be the llm answer if no tool is chosen. When a tool is chosen,
-    # the stream will contain AIMessageChunks with tool call information.
-    for message in messages:
+#     # This stream will be the llm answer if no tool is chosen. When a tool is chosen,
+#     # the stream will contain AIMessageChunks with tool call information.
+#     for message in messages:
 
-        answer_piece = message.content
-        if not isinstance(answer_piece, str):
-            # this is only used for logging, so fine to
-            # just add the string representation
-            answer_piece = str(answer_piece)
-        full_answer += answer_piece
+#         answer_piece = message.content
+#         if not isinstance(answer_piece, str):
+#             # this is only used for logging, so fine to
+#             # just add the string representation
+#             answer_piece = str(answer_piece)
+#         full_answer += answer_piece
 
-        if isinstance(message, AIMessageChunk) and (
-            message.tool_call_chunks or message.tool_calls
-        ):
-            tool_call_chunk += message  # type: ignore
-        elif should_stream_answer:
-            for response_part in answer_handler.handle_response_part(message):
+#         if isinstance(message, AIMessageChunk) and (
+#             message.tool_call_chunks or message.tool_calls
+#         ):
+#             tool_call_chunk += message  # type: ignore
+#         elif should_stream_answer:
+#             for response_part in answer_handler.handle_response_part(message):
 
-                # only stream out answer parts
-                if (
-                    isinstance(response_part, (OnyxAnswerPiece, AgentAnswerPiece))
-                    and generate_final_answer
-                    and response_part.answer_piece
-                ):
-                    if chat_message_id is None:
-                        raise ValueError(
-                            "chat_message_id is required when generating final answer"
-                        )
+#                 # only stream out answer parts
+#                 if (
+#                     isinstance(response_part, (OnyxAnswerPiece, AgentAnswerPiece))
+#                     and generate_final_answer
+#                     and response_part.answer_piece
+#                 ):
+#                     if chat_message_id is None:
+#                         raise ValueError(
+#                             "chat_message_id is required when generating final answer"
+#                         )
 
-                    if not start_final_answer_streaming_set:
-                        # Convert LlmDocs to SavedSearchDocs
-                        saved_search_docs = saved_search_docs_from_llm_docs(
-                            search_results
-                        )
-                        write_custom_event(
-                            ind,
-                            MessageStart(content="", final_documents=saved_search_docs),
-                            writer,
-                        )
-                        start_final_answer_streaming_set = True
+#                     if not start_final_answer_streaming_set:
+#                         # Convert LlmDocs to SavedSearchDocs
+#                         saved_search_docs = saved_search_docs_from_llm_docs(
+#                             search_results
+#                         )
+#                         write_custom_event(
+#                             ind,
+#                             MessageStart(content="", final_documents=saved_search_docs),
+#                             writer,
+#                         )
+#                         start_final_answer_streaming_set = True
 
-                    write_custom_event(
-                        ind,
-                        MessageDelta(content=response_part.answer_piece),
-                        writer,
-                    )
-                # collect citation info objects
-                elif isinstance(response_part, CitationInfo):
-                    collected_citation_infos.append(response_part)
+#                     write_custom_event(
+#                         ind,
+#                         MessageDelta(content=response_part.answer_piece),
+#                         writer,
+#                     )
+#                 # collect citation info objects
+#                 elif isinstance(response_part, CitationInfo):
+#                     collected_citation_infos.append(response_part)
 
-    if generate_final_answer and start_final_answer_streaming_set:
-        # start_final_answer_streaming_set is only set if the answer is verbal and not a tool call
-        write_custom_event(
-            ind,
-            SectionEnd(),
-            writer,
-        )
+#     if generate_final_answer and start_final_answer_streaming_set:
+#         # start_final_answer_streaming_set is only set if the answer is verbal and not a tool call
+#         write_custom_event(
+#             ind,
+#             SectionEnd(),
+#             writer,
+#         )
 
-        # Emit citations section if any were collected
-        if collected_citation_infos:
-            write_custom_event(ind, CitationStart(), writer)
-            write_custom_event(
-                ind, CitationDelta(citations=collected_citation_infos), writer
-            )
-            write_custom_event(ind, SectionEnd(), writer)
+#         # Emit citations section if any were collected
+#         if collected_citation_infos:
+#             write_custom_event(ind, CitationStart(), writer)
+#             write_custom_event(
+#                 ind, CitationDelta(citations=collected_citation_infos), writer
+#             )
+#             write_custom_event(ind, SectionEnd(), writer)
 
-    logger.debug(f"Full answer: {full_answer}")
-    return BasicSearchProcessedStreamResults(
-        ai_message_chunk=cast(AIMessageChunk, tool_call_chunk), full_answer=full_answer
-    )
+#     logger.debug(f"Full answer: {full_answer}")
+#     return BasicSearchProcessedStreamResults(
+#         ai_message_chunk=cast(AIMessageChunk, tool_call_chunk), full_answer=full_answer
+#     )

backend/onyx/agents/agent_search/dr/states.py

@@ -1,82 +1,82 @@
-from operator import add
-from typing import Annotated
-from typing import Any
-from typing import TypedDict
+# from operator import add
+# from typing import Annotated
+# from typing import Any
+# from typing import TypedDict
 
-from pydantic import BaseModel
+# from pydantic import BaseModel
 
-from onyx.agents.agent_search.core_state import CoreState
-from onyx.agents.agent_search.dr.models import IterationAnswer
-from onyx.agents.agent_search.dr.models import IterationInstructions
-from onyx.agents.agent_search.dr.models import OrchestrationClarificationInfo
-from onyx.agents.agent_search.dr.models import OrchestrationPlan
-from onyx.agents.agent_search.dr.models import OrchestratorTool
-from onyx.context.search.models import InferenceSection
-from onyx.db.connector import DocumentSource
+# from onyx.agents.agent_search.core_state import CoreState
+# from onyx.agents.agent_search.dr.models import IterationAnswer
+# from onyx.agents.agent_search.dr.models import IterationInstructions
+# from onyx.agents.agent_search.dr.models import OrchestrationClarificationInfo
+# from onyx.agents.agent_search.dr.models import OrchestrationPlan
+# from onyx.agents.agent_search.dr.models import OrchestratorTool
+# from onyx.context.search.models import InferenceSection
+# from onyx.db.connector import DocumentSource
 
-### States ###
+# ### States ###
 
 
-class LoggerUpdate(BaseModel):
-    log_messages: Annotated[list[str], add] = []
+# class LoggerUpdate(BaseModel):
+#     log_messages: Annotated[list[str], add] = []
 
 
-class OrchestrationUpdate(LoggerUpdate):
-    tools_used: Annotated[list[str], add] = []
-    query_list: list[str] = []
-    iteration_nr: int = 0
-    current_step_nr: int = 1
-    plan_of_record: OrchestrationPlan | None = None  # None for Thoughtful
-    remaining_time_budget: float = 2.0  # set by default to about 2 searches
-    num_closer_suggestions: int = 0  # how many times the closer was suggested
-    gaps: list[str] = (
-        []
-    )  # gaps that may be identified by the closer before being able to answer the question.
-    iteration_instructions: Annotated[list[IterationInstructions], add] = []
+# class OrchestrationUpdate(LoggerUpdate):
+#     tools_used: Annotated[list[str], add] = []
+#     query_list: list[str] = []
+#     iteration_nr: int = 0
+#     current_step_nr: int = 1
+#     plan_of_record: OrchestrationPlan | None = None  # None for Thoughtful
+#     remaining_time_budget: float = 2.0  # set by default to about 2 searches
+#     num_closer_suggestions: int = 0  # how many times the closer was suggested
+#     gaps: list[str] = (
+#         []
+#     )  # gaps that may be identified by the closer before being able to answer the question.
+#     iteration_instructions: Annotated[list[IterationInstructions], add] = []
 
 
-class OrchestrationSetup(OrchestrationUpdate):
-    original_question: str | None = None
-    chat_history_string: str | None = None
-    clarification: OrchestrationClarificationInfo | None = None
-    available_tools: dict[str, OrchestratorTool] | None = None
-    num_closer_suggestions: int = 0  # how many times the closer was suggested
+# class OrchestrationSetup(OrchestrationUpdate):
+#     original_question: str | None = None
+#     chat_history_string: str | None = None
+#     clarification: OrchestrationClarificationInfo | None = None
+#     available_tools: dict[str, OrchestratorTool] | None = None
+#     num_closer_suggestions: int = 0  # how many times the closer was suggested
 
-    active_source_types: list[DocumentSource] | None = None
-    active_source_types_descriptions: str | None = None
-    assistant_system_prompt: str | None = None
-    assistant_task_prompt: str | None = None
-    uploaded_test_context: str | None = None
-    uploaded_image_context: list[dict[str, Any]] | None = None
+#     active_source_types: list[DocumentSource] | None = None
+#     active_source_types_descriptions: str | None = None
+#     assistant_system_prompt: str | None = None
+#     assistant_task_prompt: str | None = None
+#     uploaded_test_context: str | None = None
+#     uploaded_image_context: list[dict[str, Any]] | None = None
 
 
-class AnswerUpdate(LoggerUpdate):
-    iteration_responses: Annotated[list[IterationAnswer], add] = []
+# class AnswerUpdate(LoggerUpdate):
+#     iteration_responses: Annotated[list[IterationAnswer], add] = []
 
 
-class FinalUpdate(LoggerUpdate):
-    final_answer: str | None = None
-    all_cited_documents: list[InferenceSection] = []
+# class FinalUpdate(LoggerUpdate):
+#     final_answer: str | None = None
+#     all_cited_documents: list[InferenceSection] = []
 
 
-## Graph Input State
-class MainInput(CoreState):
-    pass
+# ## Graph Input State
+# class MainInput(CoreState):
+#     pass
 
 
-## Graph State
-class MainState(
-    # This includes the core state
-    MainInput,
-    OrchestrationSetup,
-    AnswerUpdate,
-    FinalUpdate,
-):
-    pass
+# ## Graph State
+# class MainState(
+#     # This includes the core state
+#     MainInput,
+#     OrchestrationSetup,
+#     AnswerUpdate,
+#     FinalUpdate,
+# ):
+#     pass
 
 
-## Graph Output State
-class MainOutput(TypedDict):
-    log_messages: list[str]
-    final_answer: str | None
-    all_cited_documents: list[InferenceSection]
+# ## Graph Output State
+# class MainOutput(TypedDict):
+#     log_messages: list[str]
+#     final_answer: str | None
+#     all_cited_documents: list[InferenceSection]

backend/onyx/agents/agent_search/dr/sub_agents/basic_search/dr_basic_search_1_branch.py

@@ -1,47 +1,47 @@
-from datetime import datetime
+# from datetime import datetime
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.states import LoggerUpdate
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.server.query_and_chat.streaming_models import SearchToolStart
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.states import LoggerUpdate
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.server.query_and_chat.streaming_models import SearchToolStart
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def basic_search_branch(
-    state: SubAgentInput, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> LoggerUpdate:
-    """
-    LangGraph node to perform a standard search as part of the DR process.
-    """
+# def basic_search_branch(
+#     state: SubAgentInput, config: RunnableConfig, writer: StreamWriter = lambda _: None
+# ) -> LoggerUpdate:
+#     """
+#     LangGraph node to perform a standard search as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
-    iteration_nr = state.iteration_nr
-    current_step_nr = state.current_step_nr
+#     node_start_time = datetime.now()
+#     iteration_nr = state.iteration_nr
+#     current_step_nr = state.current_step_nr
 
-    logger.debug(f"Search start for Basic Search {iteration_nr} at {datetime.now()}")
+#     logger.debug(f"Search start for Basic Search {iteration_nr} at {datetime.now()}")
 
-    write_custom_event(
-        current_step_nr,
-        SearchToolStart(
-            is_internet_search=False,
-        ),
-        writer,
-    )
+#     write_custom_event(
+#         current_step_nr,
+#         SearchToolStart(
+#             is_internet_search=False,
+#         ),
+#         writer,
+#     )
 
-    return LoggerUpdate(
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="basic_search",
-                node_name="branching",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return LoggerUpdate(
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="basic_search",
+#                 node_name="branching",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/basic_search/dr_basic_search_2_act.py

@@ -1,286 +1,261 @@
-import re
-from datetime import datetime
-from typing import cast
-from uuid import UUID
+# import re
+# from datetime import datetime
+# from typing import cast
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.enums import ResearchType
-from onyx.agents.agent_search.dr.models import BaseSearchProcessingResponse
-from onyx.agents.agent_search.dr.models import IterationAnswer
-from onyx.agents.agent_search.dr.models import SearchAnswer
-from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
-from onyx.agents.agent_search.dr.sub_agents.states import BranchUpdate
-from onyx.agents.agent_search.dr.utils import convert_inference_sections_to_search_docs
-from onyx.agents.agent_search.dr.utils import extract_document_citations
-from onyx.agents.agent_search.kb_search.graph_utils import build_document_context
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.llm import invoke_llm_json
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.agents.agent_search.utils import create_question_prompt
-from onyx.chat.models import LlmDoc
-from onyx.configs.agent_configs import TF_DR_TIMEOUT_LONG
-from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
-from onyx.context.search.models import InferenceSection
-from onyx.db.connector import DocumentSource
-from onyx.db.engine.sql_engine import get_session_with_current_tenant
-from onyx.prompts.dr_prompts import BASE_SEARCH_PROCESSING_PROMPT
-from onyx.prompts.dr_prompts import INTERNAL_SEARCH_PROMPTS
-from onyx.secondary_llm_flows.source_filter import strings_to_document_sources
-from onyx.server.query_and_chat.streaming_models import SearchToolDelta
-from onyx.tools.models import SearchToolOverrideKwargs
-from onyx.tools.tool_implementations.search.search_tool import (
-    SEARCH_RESPONSE_SUMMARY_ID,
-)
-from onyx.tools.tool_implementations.search.search_tool import SearchResponseSummary
-from onyx.tools.tool_implementations.search.search_tool import SearchTool
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.models import BaseSearchProcessingResponse
+# from onyx.agents.agent_search.dr.models import IterationAnswer
+# from onyx.agents.agent_search.dr.models import SearchAnswer
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchUpdate
+# from onyx.agents.agent_search.dr.utils import convert_inference_sections_to_search_docs
+# from onyx.agents.agent_search.dr.utils import extract_document_citations
+# from onyx.agents.agent_search.kb_search.graph_utils import build_document_context
+# from onyx.agents.agent_search.models import GraphConfig
+# from onyx.agents.agent_search.shared_graph_utils.llm import invoke_llm_json
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.agents.agent_search.utils import create_question_prompt
+# from onyx.chat.models import LlmDoc
+# from onyx.configs.agent_configs import TF_DR_TIMEOUT_LONG
+# from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
+# from onyx.context.search.models import InferenceSection
+# from onyx.db.connector import DocumentSource
+# from onyx.prompts.dr_prompts import BASE_SEARCH_PROCESSING_PROMPT
+# from onyx.prompts.dr_prompts import INTERNAL_SEARCH_PROMPTS
+# from onyx.secondary_llm_flows.source_filter import strings_to_document_sources
+# from onyx.server.query_and_chat.streaming_models import SearchToolDelta
+# from onyx.tools.models import SearchToolOverrideKwargs
+# from onyx.tools.tool_implementations.search.search_tool import SearchTool
+# from onyx.tools.tool_implementations.search_like_tool_utils import (
+#     SEARCH_INFERENCE_SECTIONS_ID,
+# )
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def basic_search(
-    state: BranchInput,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> BranchUpdate:
-    """
-    LangGraph node to perform a standard search as part of the DR process.
-    """
+# def basic_search(
+#     state: BranchInput,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> BranchUpdate:
+#     """
+#     LangGraph node to perform a standard search as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
-    iteration_nr = state.iteration_nr
-    parallelization_nr = state.parallelization_nr
-    current_step_nr = state.current_step_nr
-    assistant_system_prompt = state.assistant_system_prompt
-    assistant_task_prompt = state.assistant_task_prompt
+#     node_start_time = datetime.now()
+#     iteration_nr = state.iteration_nr
+#     parallelization_nr = state.parallelization_nr
+#     current_step_nr = state.current_step_nr
+#     assistant_system_prompt = state.assistant_system_prompt
+#     assistant_task_prompt = state.assistant_task_prompt
 
-    branch_query = state.branch_question
-    if not branch_query:
-        raise ValueError("branch_query is not set")
+#     branch_query = state.branch_question
+#     if not branch_query:
+#         raise ValueError("branch_query is not set")
 
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    base_question = graph_config.inputs.prompt_builder.raw_user_query
-    research_type = graph_config.behavior.research_type
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
+#     base_question = graph_config.inputs.prompt_builder.raw_user_query
+#     use_agentic_search = graph_config.behavior.use_agentic_search
 
-    if not state.available_tools:
-        raise ValueError("available_tools is not set")
+#     if not state.available_tools:
+#         raise ValueError("available_tools is not set")
 
-    elif len(state.tools_used) == 0:
-        raise ValueError("tools_used is empty")
+#     elif len(state.tools_used) == 0:
+#         raise ValueError("tools_used is empty")
 
-    search_tool_info = state.available_tools[state.tools_used[-1]]
-    search_tool = cast(SearchTool, search_tool_info.tool_object)
-    force_use_tool = graph_config.tooling.force_use_tool
+#     search_tool_info = state.available_tools[state.tools_used[-1]]
+#     search_tool = cast(SearchTool, search_tool_info.tool_object)
+#     graph_config.tooling.force_use_tool
 
-    # sanity check
-    if search_tool != graph_config.tooling.search_tool:
-        raise ValueError("search_tool does not match the configured search tool")
+#     # sanity check
+#     if search_tool != graph_config.tooling.search_tool:
+#         raise ValueError("search_tool does not match the configured search tool")
 
-    # rewrite query and identify source types
-    active_source_types_str = ", ".join(
-        [source.value for source in state.active_source_types or []]
-    )
+#     # rewrite query and identify source types
+#     active_source_types_str = ", ".join(
+#         [source.value for source in state.active_source_types or []]
+#     )
 
-    base_search_processing_prompt = BASE_SEARCH_PROCESSING_PROMPT.build(
-        active_source_types_str=active_source_types_str,
-        branch_query=branch_query,
-        current_time=datetime.now().strftime("%Y-%m-%d %H:%M"),
-    )
+#     base_search_processing_prompt = BASE_SEARCH_PROCESSING_PROMPT.build(
+#         active_source_types_str=active_source_types_str,
+#         branch_query=branch_query,
+#         current_time=datetime.now().strftime("%Y-%m-%d %H:%M"),
+#     )
 
-    try:
-        search_processing = invoke_llm_json(
-            llm=graph_config.tooling.primary_llm,
-            prompt=create_question_prompt(
-                assistant_system_prompt, base_search_processing_prompt
-            ),
-            schema=BaseSearchProcessingResponse,
-            timeout_override=TF_DR_TIMEOUT_SHORT,
-            # max_tokens=100,
-        )
-    except Exception as e:
-        logger.error(f"Could not process query: {e}")
-        raise e
+#     try:
+#         search_processing = invoke_llm_json(
+#             llm=graph_config.tooling.primary_llm,
+#             prompt=create_question_prompt(
+#                 assistant_system_prompt, base_search_processing_prompt
+#             ),
+#             schema=BaseSearchProcessingResponse,
+#             timeout_override=TF_DR_TIMEOUT_SHORT,
+#             # max_tokens=100,
+#         )
+#     except Exception as e:
+#         logger.error(f"Could not process query: {e}")
+#         raise e
 
-    rewritten_query = search_processing.rewritten_query
+#     rewritten_query = search_processing.rewritten_query
 
-    # give back the query so we can render it in the UI
-    write_custom_event(
-        current_step_nr,
-        SearchToolDelta(
-            queries=[rewritten_query],
-            documents=[],
-        ),
-        writer,
-    )
+#     # give back the query so we can render it in the UI
+#     write_custom_event(
+#         current_step_nr,
+#         SearchToolDelta(
+#             queries=[rewritten_query],
+#             documents=[],
+#         ),
+#         writer,
+#     )
 
-    implied_start_date = search_processing.time_filter
+#     implied_start_date = search_processing.time_filter
 
-    # Validate time_filter format if it exists
-    implied_time_filter = None
-    if implied_start_date:
+#     # Validate time_filter format if it exists
+#     implied_time_filter = None
+#     if implied_start_date:
 
-        # Check if time_filter is in YYYY-MM-DD format
-        date_pattern = r"^\d{4}-\d{2}-\d{2}$"
-        if re.match(date_pattern, implied_start_date):
-            implied_time_filter = datetime.strptime(implied_start_date, "%Y-%m-%d")
+#         # Check if time_filter is in YYYY-MM-DD format
+#         date_pattern = r"^\d{4}-\d{2}-\d{2}$"
+#         if re.match(date_pattern, implied_start_date):
+#             implied_time_filter = datetime.strptime(implied_start_date, "%Y-%m-%d")
 
-    specified_source_types: list[DocumentSource] | None = (
-        strings_to_document_sources(search_processing.specified_source_types)
-        if search_processing.specified_source_types
-        else None
-    )
+#     specified_source_types: list[DocumentSource] | None = (
+#         strings_to_document_sources(search_processing.specified_source_types)
+#         if search_processing.specified_source_types
+#         else None
+#     )
 
-    if specified_source_types is not None and len(specified_source_types) == 0:
-        specified_source_types = None
+#     if specified_source_types is not None and len(specified_source_types) == 0:
+#         specified_source_types = None
 
-    logger.debug(
-        f"Search start for Standard Search {iteration_nr}.{parallelization_nr} at {datetime.now()}"
-    )
+#     logger.debug(
+#         f"Search start for Standard Search {iteration_nr}.{parallelization_nr} at {datetime.now()}"
+#     )
 
-    retrieved_docs: list[InferenceSection] = []
-    callback_container: list[list[InferenceSection]] = []
+#     retrieved_docs: list[InferenceSection] = []
 
-    user_file_ids: list[UUID] | None = None
-    project_id: int | None = None
-    if force_use_tool.override_kwargs and isinstance(
-        force_use_tool.override_kwargs, SearchToolOverrideKwargs
-    ):
-        override_kwargs = force_use_tool.override_kwargs
-        user_file_ids = override_kwargs.user_file_ids
-        project_id = override_kwargs.project_id
+#     for tool_response in search_tool.run(
+#         query=rewritten_query,
+#         document_sources=specified_source_types,
+#         time_filter=implied_time_filter,
+#         override_kwargs=SearchToolOverrideKwargs(original_query=rewritten_query),
+#     ):
+#         # get retrieved docs to send to the rest of the graph
+#         if tool_response.id == SEARCH_INFERENCE_SECTIONS_ID:
+#             retrieved_docs = cast(list[InferenceSection], tool_response.response)
 
-    # new db session to avoid concurrency issues
-    with get_session_with_current_tenant() as search_db_session:
-        for tool_response in search_tool.run(
-            query=rewritten_query,
-            document_sources=specified_source_types,
-            time_filter=implied_time_filter,
-            override_kwargs=SearchToolOverrideKwargs(
-                force_no_rerank=True,
-                alternate_db_session=search_db_session,
-                retrieved_sections_callback=callback_container.append,
-                skip_query_analysis=True,
-                original_query=rewritten_query,
-                user_file_ids=user_file_ids,
-                project_id=project_id,
-            ),
-        ):
-            # get retrieved docs to send to the rest of the graph
-            if tool_response.id == SEARCH_RESPONSE_SUMMARY_ID:
-                response = cast(SearchResponseSummary, tool_response.response)
-                retrieved_docs = response.top_sections
+#             break
 
-                break
+#     # render the retrieved docs in the UI
+#     write_custom_event(
+#         current_step_nr,
+#         SearchToolDelta(
+#             queries=[],
+#             documents=convert_inference_sections_to_search_docs(
+#                 retrieved_docs, is_internet=False
+#             ),
+#         ),
+#         writer,
+#     )
 
-    # render the retrieved docs in the UI
-    write_custom_event(
-        current_step_nr,
-        SearchToolDelta(
-            queries=[],
-            documents=convert_inference_sections_to_search_docs(
-                retrieved_docs, is_internet=False
-            ),
-        ),
-        writer,
-    )
+#     document_texts_list = []
 
-    document_texts_list = []
+#     for doc_num, retrieved_doc in enumerate(retrieved_docs[:15]):
+#         if not isinstance(retrieved_doc, (InferenceSection, LlmDoc)):
+#             raise ValueError(f"Unexpected document type: {type(retrieved_doc)}")
+#         chunk_text = build_document_context(retrieved_doc, doc_num + 1)
+#         document_texts_list.append(chunk_text)
 
-    for doc_num, retrieved_doc in enumerate(retrieved_docs[:15]):
-        if not isinstance(retrieved_doc, (InferenceSection, LlmDoc)):
-            raise ValueError(f"Unexpected document type: {type(retrieved_doc)}")
-        chunk_text = build_document_context(retrieved_doc, doc_num + 1)
-        document_texts_list.append(chunk_text)
+#     document_texts = "\n\n".join(document_texts_list)
 
-    document_texts = "\n\n".join(document_texts_list)
+#     logger.debug(
+#         f"Search end/LLM start for Standard Search {iteration_nr}.{parallelization_nr} at {datetime.now()}"
+#     )
 
-    logger.debug(
-        f"Search end/LLM start for Standard Search {iteration_nr}.{parallelization_nr} at {datetime.now()}"
-    )
+#     # Built prompt
 
-    # Built prompt
+#     if use_agentic_search:
+#         search_prompt = INTERNAL_SEARCH_PROMPTS[use_agentic_search].build(
+#             search_query=branch_query,
+#             base_question=base_question,
+#             document_text=document_texts,
+#         )
 
-    if research_type == ResearchType.DEEP:
-        search_prompt = INTERNAL_SEARCH_PROMPTS[research_type].build(
-            search_query=branch_query,
-            base_question=base_question,
-            document_text=document_texts,
-        )
+#         # Run LLM
 
-        # Run LLM
+#         # search_answer_json = None
+#         search_answer_json = invoke_llm_json(
+#             llm=graph_config.tooling.primary_llm,
+#             prompt=create_question_prompt(
+#                 assistant_system_prompt, search_prompt + (assistant_task_prompt or "")
+#             ),
+#             schema=SearchAnswer,
+#             timeout_override=TF_DR_TIMEOUT_LONG,
+#             # max_tokens=1500,
+#         )
 
-        # search_answer_json = None
-        search_answer_json = invoke_llm_json(
-            llm=graph_config.tooling.primary_llm,
-            prompt=create_question_prompt(
-                assistant_system_prompt, search_prompt + (assistant_task_prompt or "")
-            ),
-            schema=SearchAnswer,
-            timeout_override=TF_DR_TIMEOUT_LONG,
-            # max_tokens=1500,
-        )
+#         logger.debug(
+#             f"LLM/all done for Standard Search {iteration_nr}.{parallelization_nr} at {datetime.now()}"
+#         )
 
-        logger.debug(
-            f"LLM/all done for Standard Search {iteration_nr}.{parallelization_nr} at {datetime.now()}"
-        )
+#         # get cited documents
+#         answer_string = search_answer_json.answer
+#         claims = search_answer_json.claims or []
+#         reasoning = search_answer_json.reasoning
+#         # answer_string = ""
+#         # claims = []
 
-        # get cited documents
-        answer_string = search_answer_json.answer
-        claims = search_answer_json.claims or []
-        reasoning = search_answer_json.reasoning
-        # answer_string = ""
-        # claims = []
+#         (
+#             citation_numbers,
+#             answer_string,
+#             claims,
+#         ) = extract_document_citations(answer_string, claims)
 
-        (
-            citation_numbers,
-            answer_string,
-            claims,
-        ) = extract_document_citations(answer_string, claims)
+#         if citation_numbers and (
+#             (max(citation_numbers) > len(retrieved_docs)) or min(citation_numbers) < 1
+#         ):
+#             raise ValueError("Citation numbers are out of range for retrieved docs.")
 
-        if citation_numbers and (
-            (max(citation_numbers) > len(retrieved_docs)) or min(citation_numbers) < 1
-        ):
-            raise ValueError("Citation numbers are out of range for retrieved docs.")
+#         cited_documents = {
+#             citation_number: retrieved_docs[citation_number - 1]
+#             for citation_number in citation_numbers
+#         }
 
-        cited_documents = {
-            citation_number: retrieved_docs[citation_number - 1]
-            for citation_number in citation_numbers
-        }
+#     else:
+#         answer_string = ""
+#         claims = []
+#         cited_documents = {
+#             doc_num + 1: retrieved_doc
+#             for doc_num, retrieved_doc in enumerate(retrieved_docs[:15])
+#         }
+#         reasoning = ""
 
-    else:
-        answer_string = ""
-        claims = []
-        cited_documents = {
-            doc_num + 1: retrieved_doc
-            for doc_num, retrieved_doc in enumerate(retrieved_docs[:15])
-        }
-        reasoning = ""
-
-    return BranchUpdate(
-        branch_iteration_responses=[
-            IterationAnswer(
-                tool=search_tool_info.llm_path,
-                tool_id=search_tool_info.tool_id,
-                iteration_nr=iteration_nr,
-                parallelization_nr=parallelization_nr,
-                question=branch_query,
-                answer=answer_string,
-                claims=claims,
-                cited_documents=cited_documents,
-                reasoning=reasoning,
-                additional_data=None,
-            )
-        ],
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="basic_search",
-                node_name="searching",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return BranchUpdate(
+#         branch_iteration_responses=[
+#             IterationAnswer(
+#                 tool=search_tool_info.llm_path,
+#                 tool_id=search_tool_info.tool_id,
+#                 iteration_nr=iteration_nr,
+#                 parallelization_nr=parallelization_nr,
+#                 question=branch_query,
+#                 answer=answer_string,
+#                 claims=claims,
+#                 cited_documents=cited_documents,
+#                 reasoning=reasoning,
+#                 additional_data=None,
+#             )
+#         ],
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="basic_search",
+#                 node_name="searching",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/basic_search/dr_basic_search_3_reduce.py

@@ -1,77 +1,77 @@
-from datetime import datetime
+# from datetime import datetime
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentUpdate
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.context.search.models import SavedSearchDoc
-from onyx.context.search.models import SearchDoc
-from onyx.server.query_and_chat.streaming_models import SectionEnd
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentUpdate
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.context.search.models import SavedSearchDoc
+# from onyx.context.search.models import SearchDoc
+# from onyx.server.query_and_chat.streaming_models import SectionEnd
+# from onyx.utils.logger import setup_logger
 
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def is_reducer(
-    state: SubAgentMainState,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> SubAgentUpdate:
-    """
-    LangGraph node to perform a standard search as part of the DR process.
-    """
+# def is_reducer(
+#     state: SubAgentMainState,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> SubAgentUpdate:
+#     """
+#     LangGraph node to perform a standard search as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
+#     node_start_time = datetime.now()
 
-    branch_updates = state.branch_iteration_responses
-    current_iteration = state.iteration_nr
-    current_step_nr = state.current_step_nr
+#     branch_updates = state.branch_iteration_responses
+#     current_iteration = state.iteration_nr
+#     current_step_nr = state.current_step_nr
 
-    new_updates = [
-        update for update in branch_updates if update.iteration_nr == current_iteration
-    ]
+#     new_updates = [
+#         update for update in branch_updates if update.iteration_nr == current_iteration
+#     ]
 
-    [update.question for update in new_updates]
-    doc_lists = [list(update.cited_documents.values()) for update in new_updates]
+#     [update.question for update in new_updates]
+#     doc_lists = [list(update.cited_documents.values()) for update in new_updates]
 
-    doc_list = []
+#     doc_list = []
 
-    for xs in doc_lists:
-        for x in xs:
-            doc_list.append(x)
+#     for xs in doc_lists:
+#         for x in xs:
+#             doc_list.append(x)
 
-    # Convert InferenceSections to SavedSearchDocs
-    search_docs = SearchDoc.from_chunks_or_sections(doc_list)
-    retrieved_saved_search_docs = [
-        SavedSearchDoc.from_search_doc(search_doc, db_doc_id=0)
-        for search_doc in search_docs
-    ]
+#     # Convert InferenceSections to SavedSearchDocs
+#     search_docs = SearchDoc.from_chunks_or_sections(doc_list)
+#     retrieved_saved_search_docs = [
+#         SavedSearchDoc.from_search_doc(search_doc, db_doc_id=0)
+#         for search_doc in search_docs
+#     ]
 
-    for retrieved_saved_search_doc in retrieved_saved_search_docs:
-        retrieved_saved_search_doc.is_internet = False
+#     for retrieved_saved_search_doc in retrieved_saved_search_docs:
+#         retrieved_saved_search_doc.is_internet = False
 
-    write_custom_event(
-        current_step_nr,
-        SectionEnd(),
-        writer,
-    )
+#     write_custom_event(
+#         current_step_nr,
+#         SectionEnd(),
+#         writer,
+#     )
 
-    current_step_nr += 1
+#     current_step_nr += 1
 
-    return SubAgentUpdate(
-        iteration_responses=new_updates,
-        current_step_nr=current_step_nr,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="basic_search",
-                node_name="consolidation",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return SubAgentUpdate(
+#         iteration_responses=new_updates,
+#         current_step_nr=current_step_nr,
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="basic_search",
+#                 node_name="consolidation",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/basic_search/dr_basic_search_graph_builder.py

@@ -1,50 +1,50 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
+# from langgraph.graph import END
+# from langgraph.graph import START
+# from langgraph.graph import StateGraph
 
-from onyx.agents.agent_search.dr.sub_agents.basic_search.dr_basic_search_1_branch import (
-    basic_search_branch,
-)
-from onyx.agents.agent_search.dr.sub_agents.basic_search.dr_basic_search_2_act import (
-    basic_search,
-)
-from onyx.agents.agent_search.dr.sub_agents.basic_search.dr_basic_search_3_reduce import (
-    is_reducer,
-)
-from onyx.agents.agent_search.dr.sub_agents.basic_search.dr_image_generation_conditional_edges import (
-    branching_router,
-)
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.sub_agents.basic_search.dr_basic_search_1_branch import (
+#     basic_search_branch,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.basic_search.dr_basic_search_2_act import (
+#     basic_search,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.basic_search.dr_basic_search_3_reduce import (
+#     is_reducer,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.basic_search.dr_image_generation_conditional_edges import (
+#     branching_router,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
+# from onyx.utils.logger import setup_logger
 
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def dr_basic_search_graph_builder() -> StateGraph:
-    """
-    LangGraph graph builder for Web Search Sub-Agent
-    """
+# def dr_basic_search_graph_builder() -> StateGraph:
+#     """
+#     LangGraph graph builder for Web Search Sub-Agent
+#     """
 
-    graph = StateGraph(state_schema=SubAgentMainState, input=SubAgentInput)
+#     graph = StateGraph(state_schema=SubAgentMainState, input=SubAgentInput)
 
-    ### Add nodes ###
+#     ### Add nodes ###
 
-    graph.add_node("branch", basic_search_branch)
+#     graph.add_node("branch", basic_search_branch)
 
-    graph.add_node("act", basic_search)
+#     graph.add_node("act", basic_search)
 
-    graph.add_node("reducer", is_reducer)
+#     graph.add_node("reducer", is_reducer)
 
-    ### Add edges ###
+#     ### Add edges ###
 
-    graph.add_edge(start_key=START, end_key="branch")
+#     graph.add_edge(start_key=START, end_key="branch")
 
-    graph.add_conditional_edges("branch", branching_router)
+#     graph.add_conditional_edges("branch", branching_router)
 
-    graph.add_edge(start_key="act", end_key="reducer")
+#     graph.add_edge(start_key="act", end_key="reducer")
 
-    graph.add_edge(start_key="reducer", end_key=END)
+#     graph.add_edge(start_key="reducer", end_key=END)
 
-    return graph
+#     return graph

backend/onyx/agents/agent_search/dr/sub_agents/basic_search/dr_image_generation_conditional_edges.py

@@ -1,30 +1,30 @@
-from collections.abc import Hashable
+# from collections.abc import Hashable
 
-from langgraph.types import Send
+# from langgraph.types import Send
 
-from onyx.agents.agent_search.dr.constants import MAX_DR_PARALLEL_SEARCH
-from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.dr.constants import MAX_DR_PARALLEL_SEARCH
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
 
 
-def branching_router(state: SubAgentInput) -> list[Send | Hashable]:
-    return [
-        Send(
-            "act",
-            BranchInput(
-                iteration_nr=state.iteration_nr,
-                parallelization_nr=parallelization_nr,
-                branch_question=query,
-                current_step_nr=state.current_step_nr,
-                context="",
-                active_source_types=state.active_source_types,
-                tools_used=state.tools_used,
-                available_tools=state.available_tools,
-                assistant_system_prompt=state.assistant_system_prompt,
-                assistant_task_prompt=state.assistant_task_prompt,
-            ),
-        )
-        for parallelization_nr, query in enumerate(
-            state.query_list[:MAX_DR_PARALLEL_SEARCH]
-        )
-    ]
+# def branching_router(state: SubAgentInput) -> list[Send | Hashable]:
+#     return [
+#         Send(
+#             "act",
+#             BranchInput(
+#                 iteration_nr=state.iteration_nr,
+#                 parallelization_nr=parallelization_nr,
+#                 branch_question=query,
+#                 current_step_nr=state.current_step_nr,
+#                 context="",
+#                 active_source_types=state.active_source_types,
+#                 tools_used=state.tools_used,
+#                 available_tools=state.available_tools,
+#                 assistant_system_prompt=state.assistant_system_prompt,
+#                 assistant_task_prompt=state.assistant_task_prompt,
+#             ),
+#         )
+#         for parallelization_nr, query in enumerate(
+#             state.query_list[:MAX_DR_PARALLEL_SEARCH]
+#         )
+#     ]

backend/onyx/agents/agent_search/dr/sub_agents/custom_tool/dr_custom_tool_1_branch.py

@@ -1,36 +1,36 @@
-from datetime import datetime
+# from datetime import datetime
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.states import LoggerUpdate
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.states import LoggerUpdate
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def custom_tool_branch(
-    state: SubAgentInput, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> LoggerUpdate:
-    """
-    LangGraph node to perform a generic tool call as part of the DR process.
-    """
+# def custom_tool_branch(
+#     state: SubAgentInput, config: RunnableConfig, writer: StreamWriter = lambda _: None
+# ) -> LoggerUpdate:
+#     """
+#     LangGraph node to perform a generic tool call as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
-    iteration_nr = state.iteration_nr
+#     node_start_time = datetime.now()
+#     iteration_nr = state.iteration_nr
 
-    logger.debug(f"Search start for Generic Tool {iteration_nr} at {datetime.now()}")
+#     logger.debug(f"Search start for Generic Tool {iteration_nr} at {datetime.now()}")
 
-    return LoggerUpdate(
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="custom_tool",
-                node_name="branching",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return LoggerUpdate(
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="custom_tool",
+#                 node_name="branching",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/custom_tool/dr_custom_tool_2_act.py

@@ -1,169 +1,164 @@
-import json
-from datetime import datetime
-from typing import cast
+# import json
+# from datetime import datetime
+# from typing import cast
 
-from langchain_core.messages import AIMessage
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.messages import AIMessage
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
-from onyx.agents.agent_search.dr.sub_agents.states import BranchUpdate
-from onyx.agents.agent_search.dr.sub_agents.states import IterationAnswer
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.configs.agent_configs import TF_DR_TIMEOUT_LONG
-from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
-from onyx.prompts.dr_prompts import CUSTOM_TOOL_PREP_PROMPT
-from onyx.prompts.dr_prompts import CUSTOM_TOOL_USE_PROMPT
-from onyx.tools.tool_implementations.custom.custom_tool import CUSTOM_TOOL_RESPONSE_ID
-from onyx.tools.tool_implementations.custom.custom_tool import CustomTool
-from onyx.tools.tool_implementations.custom.custom_tool import CustomToolCallSummary
-from onyx.tools.tool_implementations.mcp.mcp_tool import MCP_TOOL_RESPONSE_ID
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchUpdate
+# from onyx.agents.agent_search.dr.sub_agents.states import IterationAnswer
+# from onyx.agents.agent_search.models import GraphConfig
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.configs.agent_configs import TF_DR_TIMEOUT_LONG
+# from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
+# from onyx.prompts.dr_prompts import CUSTOM_TOOL_PREP_PROMPT
+# from onyx.prompts.dr_prompts import CUSTOM_TOOL_USE_PROMPT
+# from onyx.tools.tool_implementations.custom.custom_tool import CUSTOM_TOOL_RESPONSE_ID
+# from onyx.tools.tool_implementations.custom.custom_tool import CustomTool
+# from onyx.tools.tool_implementations.custom.custom_tool import CustomToolCallSummary
+# from onyx.tools.tool_implementations.mcp.mcp_tool import MCP_TOOL_RESPONSE_ID
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def custom_tool_act(
-    state: BranchInput,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> BranchUpdate:
-    """
-    LangGraph node to perform a generic tool call as part of the DR process.
-    """
+# def custom_tool_act(
+#     state: BranchInput,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> BranchUpdate:
+#     """
+#     LangGraph node to perform a generic tool call as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
-    iteration_nr = state.iteration_nr
-    parallelization_nr = state.parallelization_nr
+#     node_start_time = datetime.now()
+#     iteration_nr = state.iteration_nr
+#     parallelization_nr = state.parallelization_nr
 
-    if not state.available_tools:
-        raise ValueError("available_tools is not set")
+#     if not state.available_tools:
+#         raise ValueError("available_tools is not set")
 
-    custom_tool_info = state.available_tools[state.tools_used[-1]]
-    custom_tool_name = custom_tool_info.name
-    custom_tool = cast(CustomTool, custom_tool_info.tool_object)
+#     custom_tool_info = state.available_tools[state.tools_used[-1]]
+#     custom_tool_name = custom_tool_info.name
+#     custom_tool = cast(CustomTool, custom_tool_info.tool_object)
 
-    branch_query = state.branch_question
-    if not branch_query:
-        raise ValueError("branch_query is not set")
+#     branch_query = state.branch_question
+#     if not branch_query:
+#         raise ValueError("branch_query is not set")
 
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    base_question = graph_config.inputs.prompt_builder.raw_user_query
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
+#     base_question = graph_config.inputs.prompt_builder.raw_user_query
 
-    logger.debug(
-        f"Tool call start for {custom_tool_name} {iteration_nr}.{parallelization_nr} at {datetime.now()}"
-    )
+#     logger.debug(
+#         f"Tool call start for {custom_tool_name} {iteration_nr}.{parallelization_nr} at {datetime.now()}"
+#     )
 
-    # get tool call args
-    tool_args: dict | None = None
-    if graph_config.tooling.using_tool_calling_llm:
-        # get tool call args from tool-calling LLM
-        tool_use_prompt = CUSTOM_TOOL_PREP_PROMPT.build(
-            query=branch_query,
-            base_question=base_question,
-            tool_description=custom_tool_info.description,
-        )
-        tool_calling_msg = graph_config.tooling.primary_llm.invoke_langchain(
-            tool_use_prompt,
-            tools=[custom_tool.tool_definition()],
-            tool_choice="required",
-            timeout_override=TF_DR_TIMEOUT_LONG,
-        )
+# # get tool call args
+# tool_args: dict | None = None
+# if graph_config.tooling.using_tool_calling_llm:
+#     # get tool call args from tool-calling LLM
+#     tool_use_prompt = CUSTOM_TOOL_PREP_PROMPT.build(
+#         query=branch_query,
+#         base_question=base_question,
+#         tool_description=custom_tool_info.description,
+#     )
+#     tool_calling_msg = graph_config.tooling.primary_llm.invoke_langchain(
+#         tool_use_prompt,
+#         tools=[custom_tool.tool_definition()],
+#         tool_choice="required",
+#         timeout_override=TF_DR_TIMEOUT_LONG,
+#     )
 
-        # make sure we got a tool call
-        if (
-            isinstance(tool_calling_msg, AIMessage)
-            and len(tool_calling_msg.tool_calls) == 1
-        ):
-            tool_args = tool_calling_msg.tool_calls[0]["args"]
-        else:
-            logger.warning("Tool-calling LLM did not emit a tool call")
+#         # make sure we got a tool call
+#         if (
+#             isinstance(tool_calling_msg, AIMessage)
+#             and len(tool_calling_msg.tool_calls) == 1
+#         ):
+#             tool_args = tool_calling_msg.tool_calls[0]["args"]
+#         else:
+#             logger.warning("Tool-calling LLM did not emit a tool call")
 
-    if tool_args is None:
-        # get tool call args from non-tool-calling LLM or for failed tool-calling LLM
-        tool_args = custom_tool.get_args_for_non_tool_calling_llm(
-            query=branch_query,
-            history=[],
-            llm=graph_config.tooling.primary_llm,
-            force_run=True,
-        )
+#     if tool_args is None:
+#         raise ValueError(
+#             "Failed to obtain tool arguments from LLM - tool calling is required"
+#         )
 
-    if tool_args is None:
-        raise ValueError("Failed to obtain tool arguments from LLM")
+#     # run the tool
+#     response_summary: CustomToolCallSummary | None = None
+#     for tool_response in custom_tool.run(**tool_args):
+#         if tool_response.id in {CUSTOM_TOOL_RESPONSE_ID, MCP_TOOL_RESPONSE_ID}:
+#             response_summary = cast(CustomToolCallSummary, tool_response.response)
+#             break
 
-    # run the tool
-    response_summary: CustomToolCallSummary | None = None
-    for tool_response in custom_tool.run(**tool_args):
-        if tool_response.id in {CUSTOM_TOOL_RESPONSE_ID, MCP_TOOL_RESPONSE_ID}:
-            response_summary = cast(CustomToolCallSummary, tool_response.response)
-            break
+#     if not response_summary:
+#         raise ValueError("Custom tool did not return a valid response summary")
 
-    if not response_summary:
-        raise ValueError("Custom tool did not return a valid response summary")
+#     # summarise tool result
+#     if not response_summary.response_type:
+#         raise ValueError("Response type is not returned.")
 
-    # summarise tool result
-    if not response_summary.response_type:
-        raise ValueError("Response type is not returned.")
+#     if response_summary.response_type == "json":
+#         tool_result_str = json.dumps(response_summary.tool_result, ensure_ascii=False)
+#     elif response_summary.response_type in {"image", "csv"}:
+#         tool_result_str = f"{response_summary.response_type} files: {response_summary.tool_result.file_ids}"
+#     else:
+#         tool_result_str = str(response_summary.tool_result)
 
-    if response_summary.response_type == "json":
-        tool_result_str = json.dumps(response_summary.tool_result, ensure_ascii=False)
-    elif response_summary.response_type in {"image", "csv"}:
-        tool_result_str = f"{response_summary.response_type} files: {response_summary.tool_result.file_ids}"
-    else:
-        tool_result_str = str(response_summary.tool_result)
+#     tool_str = (
+#         f"Tool used: {custom_tool_name}\n"
+#         f"Description: {custom_tool_info.description}\n"
+#         f"Result: {tool_result_str}"
+#     )
 
-    tool_str = (
-        f"Tool used: {custom_tool_name}\n"
-        f"Description: {custom_tool_info.description}\n"
-        f"Result: {tool_result_str}"
-    )
+#     tool_summary_prompt = CUSTOM_TOOL_USE_PROMPT.build(
+#         query=branch_query, base_question=base_question, tool_response=tool_str
+#     )
+#     answer_string = str(
+#         graph_config.tooling.primary_llm.invoke(
+#             tool_summary_prompt, timeout_override=TF_DR_TIMEOUT_SHORT
+#         ).content
+#     ).strip()
 
-    tool_summary_prompt = CUSTOM_TOOL_USE_PROMPT.build(
-        query=branch_query, base_question=base_question, tool_response=tool_str
-    )
-    answer_string = str(
-        graph_config.tooling.primary_llm.invoke_langchain(
-            tool_summary_prompt, timeout_override=TF_DR_TIMEOUT_SHORT
-        ).content
-    ).strip()
+# tool_summary_prompt = CUSTOM_TOOL_USE_PROMPT.build(
+#     query=branch_query, base_question=base_question, tool_response=tool_str
+# )
+# answer_string = str(
+#     graph_config.tooling.primary_llm.invoke_langchain(
+#         tool_summary_prompt, timeout_override=TF_DR_TIMEOUT_SHORT
+#     ).content
+# ).strip()
 
-    # get file_ids:
-    file_ids = None
-    if response_summary.response_type in {"image", "csv"} and hasattr(
-        response_summary.tool_result, "file_ids"
-    ):
-        file_ids = response_summary.tool_result.file_ids
+#     logger.debug(
+#         f"Tool call end for {custom_tool_name} {iteration_nr}.{parallelization_nr} at {datetime.now()}"
+#     )
 
-    logger.debug(
-        f"Tool call end for {custom_tool_name} {iteration_nr}.{parallelization_nr} at {datetime.now()}"
-    )
-
-    return BranchUpdate(
-        branch_iteration_responses=[
-            IterationAnswer(
-                tool=custom_tool_name,
-                tool_id=custom_tool_info.tool_id,
-                iteration_nr=iteration_nr,
-                parallelization_nr=parallelization_nr,
-                question=branch_query,
-                answer=answer_string,
-                claims=[],
-                cited_documents={},
-                reasoning="",
-                additional_data=None,
-                response_type=response_summary.response_type,
-                data=response_summary.tool_result,
-                file_ids=file_ids,
-            )
-        ],
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="custom_tool",
-                node_name="tool_calling",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return BranchUpdate(
+#         branch_iteration_responses=[
+#             IterationAnswer(
+#                 tool=custom_tool_name,
+#                 tool_id=custom_tool_info.tool_id,
+#                 iteration_nr=iteration_nr,
+#                 parallelization_nr=parallelization_nr,
+#                 question=branch_query,
+#                 answer=answer_string,
+#                 claims=[],
+#                 cited_documents={},
+#                 reasoning="",
+#                 additional_data=None,
+#                 response_type=response_summary.response_type,
+#                 data=response_summary.tool_result,
+#                 file_ids=file_ids,
+#             )
+#         ],
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="custom_tool",
+#                 node_name="tool_calling",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/custom_tool/dr_custom_tool_3_reduce.py

@@ -1,82 +1,82 @@
-from datetime import datetime
+# from datetime import datetime
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentUpdate
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.server.query_and_chat.streaming_models import CustomToolDelta
-from onyx.server.query_and_chat.streaming_models import CustomToolStart
-from onyx.server.query_and_chat.streaming_models import SectionEnd
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentUpdate
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.server.query_and_chat.streaming_models import CustomToolDelta
+# from onyx.server.query_and_chat.streaming_models import CustomToolStart
+# from onyx.server.query_and_chat.streaming_models import SectionEnd
+# from onyx.utils.logger import setup_logger
 
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def custom_tool_reducer(
-    state: SubAgentMainState,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> SubAgentUpdate:
-    """
-    LangGraph node to perform a generic tool call as part of the DR process.
-    """
+# def custom_tool_reducer(
+#     state: SubAgentMainState,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> SubAgentUpdate:
+#     """
+#     LangGraph node to perform a generic tool call as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
+#     node_start_time = datetime.now()
 
-    current_step_nr = state.current_step_nr
+#     current_step_nr = state.current_step_nr
 
-    branch_updates = state.branch_iteration_responses
-    current_iteration = state.iteration_nr
+#     branch_updates = state.branch_iteration_responses
+#     current_iteration = state.iteration_nr
 
-    new_updates = [
-        update for update in branch_updates if update.iteration_nr == current_iteration
-    ]
+#     new_updates = [
+#         update for update in branch_updates if update.iteration_nr == current_iteration
+#     ]
 
-    for new_update in new_updates:
+#     for new_update in new_updates:
 
-        if not new_update.response_type:
-            raise ValueError("Response type is not returned.")
+#         if not new_update.response_type:
+#             raise ValueError("Response type is not returned.")
 
-        write_custom_event(
-            current_step_nr,
-            CustomToolStart(
-                tool_name=new_update.tool,
-            ),
-            writer,
-        )
+#         write_custom_event(
+#             current_step_nr,
+#             CustomToolStart(
+#                 tool_name=new_update.tool,
+#             ),
+#             writer,
+#         )
 
-        write_custom_event(
-            current_step_nr,
-            CustomToolDelta(
-                tool_name=new_update.tool,
-                response_type=new_update.response_type,
-                data=new_update.data,
-                file_ids=new_update.file_ids,
-            ),
-            writer,
-        )
+#         write_custom_event(
+#             current_step_nr,
+#             CustomToolDelta(
+#                 tool_name=new_update.tool,
+#                 response_type=new_update.response_type,
+#                 data=new_update.data,
+#                 file_ids=new_update.file_ids,
+#             ),
+#             writer,
+#         )
 
-        write_custom_event(
-            current_step_nr,
-            SectionEnd(),
-            writer,
-        )
+#         write_custom_event(
+#             current_step_nr,
+#             SectionEnd(),
+#             writer,
+#         )
 
-        current_step_nr += 1
+#         current_step_nr += 1
 
-    return SubAgentUpdate(
-        iteration_responses=new_updates,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="custom_tool",
-                node_name="consolidation",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return SubAgentUpdate(
+#         iteration_responses=new_updates,
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="custom_tool",
+#                 node_name="consolidation",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/custom_tool/dr_custom_tool_conditional_edges.py

@@ -1,28 +1,28 @@
-from collections.abc import Hashable
+# from collections.abc import Hashable
 
-from langgraph.types import Send
+# from langgraph.types import Send
 
-from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
-from onyx.agents.agent_search.dr.sub_agents.states import (
-    SubAgentInput,
-)
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
+# from onyx.agents.agent_search.dr.sub_agents.states import (
+#     SubAgentInput,
+# )
 
 
-def branching_router(state: SubAgentInput) -> list[Send | Hashable]:
-    return [
-        Send(
-            "act",
-            BranchInput(
-                iteration_nr=state.iteration_nr,
-                parallelization_nr=parallelization_nr,
-                branch_question=query,
-                context="",
-                active_source_types=state.active_source_types,
-                tools_used=state.tools_used,
-                available_tools=state.available_tools,
-            ),
-        )
-        for parallelization_nr, query in enumerate(
-            state.query_list[:1]  # no parallel call for now
-        )
-    ]
+# def branching_router(state: SubAgentInput) -> list[Send | Hashable]:
+#     return [
+#         Send(
+#             "act",
+#             BranchInput(
+#                 iteration_nr=state.iteration_nr,
+#                 parallelization_nr=parallelization_nr,
+#                 branch_question=query,
+#                 context="",
+#                 active_source_types=state.active_source_types,
+#                 tools_used=state.tools_used,
+#                 available_tools=state.available_tools,
+#             ),
+#         )
+#         for parallelization_nr, query in enumerate(
+#             state.query_list[:1]  # no parallel call for now
+#         )
+#     ]

backend/onyx/agents/agent_search/dr/sub_agents/custom_tool/dr_custom_tool_graph_builder.py

@@ -1,50 +1,50 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
+# from langgraph.graph import END
+# from langgraph.graph import START
+# from langgraph.graph import StateGraph
 
-from onyx.agents.agent_search.dr.sub_agents.custom_tool.dr_custom_tool_1_branch import (
-    custom_tool_branch,
-)
-from onyx.agents.agent_search.dr.sub_agents.custom_tool.dr_custom_tool_2_act import (
-    custom_tool_act,
-)
-from onyx.agents.agent_search.dr.sub_agents.custom_tool.dr_custom_tool_3_reduce import (
-    custom_tool_reducer,
-)
-from onyx.agents.agent_search.dr.sub_agents.custom_tool.dr_custom_tool_conditional_edges import (
-    branching_router,
-)
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.sub_agents.custom_tool.dr_custom_tool_1_branch import (
+#     custom_tool_branch,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.custom_tool.dr_custom_tool_2_act import (
+#     custom_tool_act,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.custom_tool.dr_custom_tool_3_reduce import (
+#     custom_tool_reducer,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.custom_tool.dr_custom_tool_conditional_edges import (
+#     branching_router,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
+# from onyx.utils.logger import setup_logger
 
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def dr_custom_tool_graph_builder() -> StateGraph:
-    """
-    LangGraph graph builder for Generic Tool Sub-Agent
-    """
+# def dr_custom_tool_graph_builder() -> StateGraph:
+#     """
+#     LangGraph graph builder for Generic Tool Sub-Agent
+#     """
 
-    graph = StateGraph(state_schema=SubAgentMainState, input=SubAgentInput)
+#     graph = StateGraph(state_schema=SubAgentMainState, input=SubAgentInput)
 
-    ### Add nodes ###
+#     ### Add nodes ###
 
-    graph.add_node("branch", custom_tool_branch)
+#     graph.add_node("branch", custom_tool_branch)
 
-    graph.add_node("act", custom_tool_act)
+#     graph.add_node("act", custom_tool_act)
 
-    graph.add_node("reducer", custom_tool_reducer)
+#     graph.add_node("reducer", custom_tool_reducer)
 
-    ### Add edges ###
+#     ### Add edges ###
 
-    graph.add_edge(start_key=START, end_key="branch")
+#     graph.add_edge(start_key=START, end_key="branch")
 
-    graph.add_conditional_edges("branch", branching_router)
+#     graph.add_conditional_edges("branch", branching_router)
 
-    graph.add_edge(start_key="act", end_key="reducer")
+#     graph.add_edge(start_key="act", end_key="reducer")
 
-    graph.add_edge(start_key="reducer", end_key=END)
+#     graph.add_edge(start_key="reducer", end_key=END)
 
-    return graph
+#     return graph

backend/onyx/agents/agent_search/dr/sub_agents/generic_internal_tool/dr_generic_internal_tool_1_branch.py

@@ -1,36 +1,36 @@
-from datetime import datetime
+# from datetime import datetime
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.states import LoggerUpdate
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.states import LoggerUpdate
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def generic_internal_tool_branch(
-    state: SubAgentInput, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> LoggerUpdate:
-    """
-    LangGraph node to perform a generic tool call as part of the DR process.
-    """
+# def generic_internal_tool_branch(
+#     state: SubAgentInput, config: RunnableConfig, writer: StreamWriter = lambda _: None
+# ) -> LoggerUpdate:
+#     """
+#     LangGraph node to perform a generic tool call as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
-    iteration_nr = state.iteration_nr
+#     node_start_time = datetime.now()
+#     iteration_nr = state.iteration_nr
 
-    logger.debug(f"Search start for Generic Tool {iteration_nr} at {datetime.now()}")
+#     logger.debug(f"Search start for Generic Tool {iteration_nr} at {datetime.now()}")
 
-    return LoggerUpdate(
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="generic_internal_tool",
-                node_name="branching",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return LoggerUpdate(
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="generic_internal_tool",
+#                 node_name="branching",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/generic_internal_tool/dr_generic_internal_tool_2_act.py

@@ -1,149 +1,147 @@
-import json
-from datetime import datetime
-from typing import cast
+# import json
+# from datetime import datetime
+# from typing import cast
 
-from langchain_core.messages import AIMessage
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.messages import AIMessage
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
-from onyx.agents.agent_search.dr.sub_agents.states import BranchUpdate
-from onyx.agents.agent_search.dr.sub_agents.states import IterationAnswer
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
-from onyx.prompts.dr_prompts import CUSTOM_TOOL_PREP_PROMPT
-from onyx.prompts.dr_prompts import CUSTOM_TOOL_USE_PROMPT
-from onyx.prompts.dr_prompts import OKTA_TOOL_USE_SPECIAL_PROMPT
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchUpdate
+# from onyx.agents.agent_search.dr.sub_agents.states import IterationAnswer
+# from onyx.agents.agent_search.models import GraphConfig
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
+# from onyx.prompts.dr_prompts import CUSTOM_TOOL_PREP_PROMPT
+# from onyx.prompts.dr_prompts import CUSTOM_TOOL_USE_PROMPT
+# from onyx.prompts.dr_prompts import OKTA_TOOL_USE_SPECIAL_PROMPT
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def generic_internal_tool_act(
-    state: BranchInput,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> BranchUpdate:
-    """
-    LangGraph node to perform a generic tool call as part of the DR process.
-    """
+# def generic_internal_tool_act(
+#     state: BranchInput,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> BranchUpdate:
+#     """
+#     LangGraph node to perform a generic tool call as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
-    iteration_nr = state.iteration_nr
-    parallelization_nr = state.parallelization_nr
+#     node_start_time = datetime.now()
+#     iteration_nr = state.iteration_nr
+#     parallelization_nr = state.parallelization_nr
 
-    if not state.available_tools:
-        raise ValueError("available_tools is not set")
+#     if not state.available_tools:
+#         raise ValueError("available_tools is not set")
 
-    generic_internal_tool_info = state.available_tools[state.tools_used[-1]]
-    generic_internal_tool_name = generic_internal_tool_info.llm_path
-    generic_internal_tool = generic_internal_tool_info.tool_object
+#     generic_internal_tool_info = state.available_tools[state.tools_used[-1]]
+#     generic_internal_tool_name = generic_internal_tool_info.llm_path
+#     generic_internal_tool = generic_internal_tool_info.tool_object
 
-    if generic_internal_tool is None:
-        raise ValueError("generic_internal_tool is not set")
+#     if generic_internal_tool is None:
+#         raise ValueError("generic_internal_tool is not set")
 
-    branch_query = state.branch_question
-    if not branch_query:
-        raise ValueError("branch_query is not set")
+#     branch_query = state.branch_question
+#     if not branch_query:
+#         raise ValueError("branch_query is not set")
 
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    base_question = graph_config.inputs.prompt_builder.raw_user_query
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
+#     base_question = graph_config.inputs.prompt_builder.raw_user_query
 
-    logger.debug(
-        f"Tool call start for {generic_internal_tool_name} {iteration_nr}.{parallelization_nr} at {datetime.now()}"
-    )
+#     logger.debug(
+#         f"Tool call start for {generic_internal_tool_name} {iteration_nr}.{parallelization_nr} at {datetime.now()}"
+#     )
 
-    # get tool call args
-    tool_args: dict | None = None
-    if graph_config.tooling.using_tool_calling_llm:
-        # get tool call args from tool-calling LLM
-        tool_use_prompt = CUSTOM_TOOL_PREP_PROMPT.build(
-            query=branch_query,
-            base_question=base_question,
-            tool_description=generic_internal_tool_info.description,
-        )
-        tool_calling_msg = graph_config.tooling.primary_llm.invoke_langchain(
-            tool_use_prompt,
-            tools=[generic_internal_tool.tool_definition()],
-            tool_choice="required",
-            timeout_override=TF_DR_TIMEOUT_SHORT,
-        )
+# # get tool call args
+# tool_args: dict | None = None
+# if graph_config.tooling.using_tool_calling_llm:
+#     # get tool call args from tool-calling LLM
+#     tool_use_prompt = CUSTOM_TOOL_PREP_PROMPT.build(
+#         query=branch_query,
+#         base_question=base_question,
+#         tool_description=generic_internal_tool_info.description,
+#     )
+#     tool_calling_msg = graph_config.tooling.primary_llm.invoke_langchain(
+#         tool_use_prompt,
+#         tools=[generic_internal_tool.tool_definition()],
+#         tool_choice="required",
+#         timeout_override=TF_DR_TIMEOUT_SHORT,
+#     )
 
-        # make sure we got a tool call
-        if (
-            isinstance(tool_calling_msg, AIMessage)
-            and len(tool_calling_msg.tool_calls) == 1
-        ):
-            tool_args = tool_calling_msg.tool_calls[0]["args"]
-        else:
-            logger.warning("Tool-calling LLM did not emit a tool call")
+#         # make sure we got a tool call
+#         if (
+#             isinstance(tool_calling_msg, AIMessage)
+#             and len(tool_calling_msg.tool_calls) == 1
+#         ):
+#             tool_args = tool_calling_msg.tool_calls[0]["args"]
+#         else:
+#             logger.warning("Tool-calling LLM did not emit a tool call")
 
-    if tool_args is None:
-        # get tool call args from non-tool-calling LLM or for failed tool-calling LLM
-        tool_args = generic_internal_tool.get_args_for_non_tool_calling_llm(
-            query=branch_query,
-            history=[],
-            llm=graph_config.tooling.primary_llm,
-            force_run=True,
-        )
+#     if tool_args is None:
+#         raise ValueError(
+#             "Failed to obtain tool arguments from LLM - tool calling is required"
+#         )
 
-    if tool_args is None:
-        raise ValueError("Failed to obtain tool arguments from LLM")
+#     # run the tool
+#     tool_responses = list(generic_internal_tool.run(**tool_args))
+#     final_data = generic_internal_tool.final_result(*tool_responses)
+#     tool_result_str = json.dumps(final_data, ensure_ascii=False)
 
-    # run the tool
-    tool_responses = list(generic_internal_tool.run(**tool_args))
-    final_data = generic_internal_tool.final_result(*tool_responses)
-    tool_result_str = json.dumps(final_data, ensure_ascii=False)
+#     tool_str = (
+#         f"Tool used: {generic_internal_tool.display_name}\n"
+#         f"Description: {generic_internal_tool_info.description}\n"
+#         f"Result: {tool_result_str}"
+#     )
 
-    tool_str = (
-        f"Tool used: {generic_internal_tool.display_name}\n"
-        f"Description: {generic_internal_tool_info.description}\n"
-        f"Result: {tool_result_str}"
-    )
+#     if generic_internal_tool.display_name == "Okta Profile":
+#         tool_prompt = OKTA_TOOL_USE_SPECIAL_PROMPT
+#     else:
+#         tool_prompt = CUSTOM_TOOL_USE_PROMPT
 
-    if generic_internal_tool.display_name == "Okta Profile":
-        tool_prompt = OKTA_TOOL_USE_SPECIAL_PROMPT
-    else:
-        tool_prompt = CUSTOM_TOOL_USE_PROMPT
+#     tool_summary_prompt = tool_prompt.build(
+#         query=branch_query, base_question=base_question, tool_response=tool_str
+#     )
+#     answer_string = str(
+#         graph_config.tooling.primary_llm.invoke(
+#             tool_summary_prompt, timeout_override=TF_DR_TIMEOUT_SHORT
+#         ).content
+#     ).strip()
 
-    tool_summary_prompt = tool_prompt.build(
-        query=branch_query, base_question=base_question, tool_response=tool_str
-    )
-    answer_string = str(
-        graph_config.tooling.primary_llm.invoke_langchain(
-            tool_summary_prompt, timeout_override=TF_DR_TIMEOUT_SHORT
-        ).content
-    ).strip()
+# tool_summary_prompt = tool_prompt.build(
+#     query=branch_query, base_question=base_question, tool_response=tool_str
+# )
+# answer_string = str(
+#     graph_config.tooling.primary_llm.invoke_langchain(
+#         tool_summary_prompt, timeout_override=TF_DR_TIMEOUT_SHORT
+#     ).content
+# ).strip()
 
-    logger.debug(
-        f"Tool call end for {generic_internal_tool_name} {iteration_nr}.{parallelization_nr} at {datetime.now()}"
-    )
-
-    return BranchUpdate(
-        branch_iteration_responses=[
-            IterationAnswer(
-                tool=generic_internal_tool.llm_name,
-                tool_id=generic_internal_tool_info.tool_id,
-                iteration_nr=iteration_nr,
-                parallelization_nr=parallelization_nr,
-                question=branch_query,
-                answer=answer_string,
-                claims=[],
-                cited_documents={},
-                reasoning="",
-                additional_data=None,
-                response_type="text",  # TODO: convert all response types to enums
-                data=answer_string,
-            )
-        ],
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="custom_tool",
-                node_name="tool_calling",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return BranchUpdate(
+#         branch_iteration_responses=[
+#             IterationAnswer(
+#                 tool=generic_internal_tool.name,
+#                 tool_id=generic_internal_tool_info.tool_id,
+#                 iteration_nr=iteration_nr,
+#                 parallelization_nr=parallelization_nr,
+#                 question=branch_query,
+#                 answer=answer_string,
+#                 claims=[],
+#                 cited_documents={},
+#                 reasoning="",
+#                 additional_data=None,
+#                 response_type="text",  # TODO: convert all response types to enums
+#                 data=answer_string,
+#             )
+#         ],
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="custom_tool",
+#                 node_name="tool_calling",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/generic_internal_tool/dr_generic_internal_tool_3_reduce.py

@@ -1,82 +1,82 @@
-from datetime import datetime
+# from datetime import datetime
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentUpdate
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.server.query_and_chat.streaming_models import CustomToolDelta
-from onyx.server.query_and_chat.streaming_models import CustomToolStart
-from onyx.server.query_and_chat.streaming_models import SectionEnd
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentUpdate
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.server.query_and_chat.streaming_models import CustomToolDelta
+# from onyx.server.query_and_chat.streaming_models import CustomToolStart
+# from onyx.server.query_and_chat.streaming_models import SectionEnd
+# from onyx.utils.logger import setup_logger
 
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def generic_internal_tool_reducer(
-    state: SubAgentMainState,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> SubAgentUpdate:
-    """
-    LangGraph node to perform a generic tool call as part of the DR process.
-    """
+# def generic_internal_tool_reducer(
+#     state: SubAgentMainState,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> SubAgentUpdate:
+#     """
+#     LangGraph node to perform a generic tool call as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
+#     node_start_time = datetime.now()
 
-    current_step_nr = state.current_step_nr
+#     current_step_nr = state.current_step_nr
 
-    branch_updates = state.branch_iteration_responses
-    current_iteration = state.iteration_nr
+#     branch_updates = state.branch_iteration_responses
+#     current_iteration = state.iteration_nr
 
-    new_updates = [
-        update for update in branch_updates if update.iteration_nr == current_iteration
-    ]
+#     new_updates = [
+#         update for update in branch_updates if update.iteration_nr == current_iteration
+#     ]
 
-    for new_update in new_updates:
+#     for new_update in new_updates:
 
-        if not new_update.response_type:
-            raise ValueError("Response type is not returned.")
+#         if not new_update.response_type:
+#             raise ValueError("Response type is not returned.")
 
-        write_custom_event(
-            current_step_nr,
-            CustomToolStart(
-                tool_name=new_update.tool,
-            ),
-            writer,
-        )
+#         write_custom_event(
+#             current_step_nr,
+#             CustomToolStart(
+#                 tool_name=new_update.tool,
+#             ),
+#             writer,
+#         )
 
-        write_custom_event(
-            current_step_nr,
-            CustomToolDelta(
-                tool_name=new_update.tool,
-                response_type=new_update.response_type,
-                data=new_update.data,
-                file_ids=[],
-            ),
-            writer,
-        )
+#         write_custom_event(
+#             current_step_nr,
+#             CustomToolDelta(
+#                 tool_name=new_update.tool,
+#                 response_type=new_update.response_type,
+#                 data=new_update.data,
+#                 file_ids=[],
+#             ),
+#             writer,
+#         )
 
-        write_custom_event(
-            current_step_nr,
-            SectionEnd(),
-            writer,
-        )
+#         write_custom_event(
+#             current_step_nr,
+#             SectionEnd(),
+#             writer,
+#         )
 
-        current_step_nr += 1
+#         current_step_nr += 1
 
-    return SubAgentUpdate(
-        iteration_responses=new_updates,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="custom_tool",
-                node_name="consolidation",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return SubAgentUpdate(
+#         iteration_responses=new_updates,
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="custom_tool",
+#                 node_name="consolidation",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/generic_internal_tool/dr_generic_internal_tool_conditional_edges.py

@@ -1,28 +1,28 @@
-from collections.abc import Hashable
+# from collections.abc import Hashable
 
-from langgraph.types import Send
+# from langgraph.types import Send
 
-from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
-from onyx.agents.agent_search.dr.sub_agents.states import (
-    SubAgentInput,
-)
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
+# from onyx.agents.agent_search.dr.sub_agents.states import (
+#     SubAgentInput,
+# )
 
 
-def branching_router(state: SubAgentInput) -> list[Send | Hashable]:
-    return [
-        Send(
-            "act",
-            BranchInput(
-                iteration_nr=state.iteration_nr,
-                parallelization_nr=parallelization_nr,
-                branch_question=query,
-                context="",
-                active_source_types=state.active_source_types,
-                tools_used=state.tools_used,
-                available_tools=state.available_tools,
-            ),
-        )
-        for parallelization_nr, query in enumerate(
-            state.query_list[:1]  # no parallel call for now
-        )
-    ]
+# def branching_router(state: SubAgentInput) -> list[Send | Hashable]:
+#     return [
+#         Send(
+#             "act",
+#             BranchInput(
+#                 iteration_nr=state.iteration_nr,
+#                 parallelization_nr=parallelization_nr,
+#                 branch_question=query,
+#                 context="",
+#                 active_source_types=state.active_source_types,
+#                 tools_used=state.tools_used,
+#                 available_tools=state.available_tools,
+#             ),
+#         )
+#         for parallelization_nr, query in enumerate(
+#             state.query_list[:1]  # no parallel call for now
+#         )
+#     ]

backend/onyx/agents/agent_search/dr/sub_agents/generic_internal_tool/dr_generic_internal_tool_graph_builder.py

@@ -1,50 +1,50 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
+# from langgraph.graph import END
+# from langgraph.graph import START
+# from langgraph.graph import StateGraph
 
-from onyx.agents.agent_search.dr.sub_agents.generic_internal_tool.dr_generic_internal_tool_1_branch import (
-    generic_internal_tool_branch,
-)
-from onyx.agents.agent_search.dr.sub_agents.generic_internal_tool.dr_generic_internal_tool_2_act import (
-    generic_internal_tool_act,
-)
-from onyx.agents.agent_search.dr.sub_agents.generic_internal_tool.dr_generic_internal_tool_3_reduce import (
-    generic_internal_tool_reducer,
-)
-from onyx.agents.agent_search.dr.sub_agents.generic_internal_tool.dr_generic_internal_tool_conditional_edges import (
-    branching_router,
-)
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.sub_agents.generic_internal_tool.dr_generic_internal_tool_1_branch import (
+#     generic_internal_tool_branch,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.generic_internal_tool.dr_generic_internal_tool_2_act import (
+#     generic_internal_tool_act,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.generic_internal_tool.dr_generic_internal_tool_3_reduce import (
+#     generic_internal_tool_reducer,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.generic_internal_tool.dr_generic_internal_tool_conditional_edges import (
+#     branching_router,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
+# from onyx.utils.logger import setup_logger
 
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def dr_generic_internal_tool_graph_builder() -> StateGraph:
-    """
-    LangGraph graph builder for Generic Tool Sub-Agent
-    """
+# def dr_generic_internal_tool_graph_builder() -> StateGraph:
+#     """
+#     LangGraph graph builder for Generic Tool Sub-Agent
+#     """
 
-    graph = StateGraph(state_schema=SubAgentMainState, input=SubAgentInput)
+#     graph = StateGraph(state_schema=SubAgentMainState, input=SubAgentInput)
 
-    ### Add nodes ###
+#     ### Add nodes ###
 
-    graph.add_node("branch", generic_internal_tool_branch)
+#     graph.add_node("branch", generic_internal_tool_branch)
 
-    graph.add_node("act", generic_internal_tool_act)
+#     graph.add_node("act", generic_internal_tool_act)
 
-    graph.add_node("reducer", generic_internal_tool_reducer)
+#     graph.add_node("reducer", generic_internal_tool_reducer)
 
-    ### Add edges ###
+#     ### Add edges ###
 
-    graph.add_edge(start_key=START, end_key="branch")
+#     graph.add_edge(start_key=START, end_key="branch")
 
-    graph.add_conditional_edges("branch", branching_router)
+#     graph.add_conditional_edges("branch", branching_router)
 
-    graph.add_edge(start_key="act", end_key="reducer")
+#     graph.add_edge(start_key="act", end_key="reducer")
 
-    graph.add_edge(start_key="reducer", end_key=END)
+#     graph.add_edge(start_key="reducer", end_key=END)
 
-    return graph
+#     return graph

backend/onyx/agents/agent_search/dr/sub_agents/image_generation/dr_image_generation_1_branch.py

@@ -1,45 +1,45 @@
-from datetime import datetime
+# from datetime import datetime
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.states import LoggerUpdate
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.server.query_and_chat.streaming_models import ImageGenerationToolStart
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.states import LoggerUpdate
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.server.query_and_chat.streaming_models import ImageGenerationToolStart
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def image_generation_branch(
-    state: SubAgentInput, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> LoggerUpdate:
-    """
-    LangGraph node to perform a image generation as part of the DR process.
-    """
+# def image_generation_branch(
+#     state: SubAgentInput, config: RunnableConfig, writer: StreamWriter = lambda _: None
+# ) -> LoggerUpdate:
+#     """
+#     LangGraph node to perform a image generation as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
-    iteration_nr = state.iteration_nr
+#     node_start_time = datetime.now()
+#     iteration_nr = state.iteration_nr
 
-    logger.debug(f"Image generation start {iteration_nr} at {datetime.now()}")
+#     logger.debug(f"Image generation start {iteration_nr} at {datetime.now()}")
 
-    # tell frontend that we are starting the image generation tool
-    write_custom_event(
-        state.current_step_nr,
-        ImageGenerationToolStart(),
-        writer,
-    )
+#     # tell frontend that we are starting the image generation tool
+#     write_custom_event(
+#         state.current_step_nr,
+#         ImageGenerationToolStart(),
+#         writer,
+#     )
 
-    return LoggerUpdate(
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="image_generation",
-                node_name="branching",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return LoggerUpdate(
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="image_generation",
+#                 node_name="branching",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/image_generation/dr_image_generation_2_act.py

@@ -1,189 +1,187 @@
-import json
-from datetime import datetime
-from typing import cast
+# import json
+# from datetime import datetime
+# from typing import cast
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.models import GeneratedImage
-from onyx.agents.agent_search.dr.models import IterationAnswer
-from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
-from onyx.agents.agent_search.dr.sub_agents.states import BranchUpdate
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.file_store.utils import build_frontend_file_url
-from onyx.file_store.utils import save_files
-from onyx.server.query_and_chat.streaming_models import ImageGenerationToolHeartbeat
-from onyx.tools.tool_implementations.images.image_generation_tool import (
-    IMAGE_GENERATION_HEARTBEAT_ID,
-)
-from onyx.tools.tool_implementations.images.image_generation_tool import (
-    IMAGE_GENERATION_RESPONSE_ID,
-)
-from onyx.tools.tool_implementations.images.image_generation_tool import (
-    ImageGenerationResponse,
-)
-from onyx.tools.tool_implementations.images.image_generation_tool import (
-    ImageGenerationTool,
-)
-from onyx.tools.tool_implementations.images.image_generation_tool import ImageShape
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.models import GeneratedImage
+# from onyx.agents.agent_search.dr.models import IterationAnswer
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchUpdate
+# from onyx.agents.agent_search.models import GraphConfig
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.file_store.utils import build_frontend_file_url
+# from onyx.file_store.utils import save_files
+# from onyx.server.query_and_chat.streaming_models import ImageGenerationToolHeartbeat
+# from onyx.tools.tool_implementations.images.image_generation_tool import (
+#     IMAGE_GENERATION_HEARTBEAT_ID,
+# )
+# from onyx.tools.tool_implementations.images.image_generation_tool import (
+#     IMAGE_GENERATION_RESPONSE_ID,
+# )
+# from onyx.tools.tool_implementations.images.image_generation_tool import (
+#     ImageGenerationResponse,
+# )
+# from onyx.tools.tool_implementations.images.image_generation_tool import (
+#     ImageGenerationTool,
+# )
+# from onyx.tools.tool_implementations.images.image_generation_tool import ImageShape
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def image_generation(
-    state: BranchInput,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> BranchUpdate:
-    """
-    LangGraph node to perform a standard search as part of the DR process.
-    """
+# def image_generation(
+#     state: BranchInput,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> BranchUpdate:
+#     """
+#     LangGraph node to perform a standard search as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
-    iteration_nr = state.iteration_nr
-    parallelization_nr = state.parallelization_nr
-    state.assistant_system_prompt
-    state.assistant_task_prompt
+#     node_start_time = datetime.now()
+#     iteration_nr = state.iteration_nr
+#     parallelization_nr = state.parallelization_nr
+#     state.assistant_system_prompt
+#     state.assistant_task_prompt
 
-    branch_query = state.branch_question
-    if not branch_query:
-        raise ValueError("branch_query is not set")
+#     branch_query = state.branch_question
+#     if not branch_query:
+#         raise ValueError("branch_query is not set")
 
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    graph_config.inputs.prompt_builder.raw_user_query
-    graph_config.behavior.research_type
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
 
-    if not state.available_tools:
-        raise ValueError("available_tools is not set")
+#     if not state.available_tools:
+#         raise ValueError("available_tools is not set")
 
-    image_tool_info = state.available_tools[state.tools_used[-1]]
-    image_tool = cast(ImageGenerationTool, image_tool_info.tool_object)
+#     image_tool_info = state.available_tools[state.tools_used[-1]]
+#     image_tool = cast(ImageGenerationTool, image_tool_info.tool_object)
 
-    image_prompt = branch_query
-    requested_shape: ImageShape | None = None
+#     image_prompt = branch_query
+#     requested_shape: ImageShape | None = None
 
-    try:
-        parsed_query = json.loads(branch_query)
-    except json.JSONDecodeError:
-        parsed_query = None
+#     try:
+#         parsed_query = json.loads(branch_query)
+#     except json.JSONDecodeError:
+#         parsed_query = None
 
-    if isinstance(parsed_query, dict):
-        prompt_from_llm = parsed_query.get("prompt")
-        if isinstance(prompt_from_llm, str) and prompt_from_llm.strip():
-            image_prompt = prompt_from_llm.strip()
+#     if isinstance(parsed_query, dict):
+#         prompt_from_llm = parsed_query.get("prompt")
+#         if isinstance(prompt_from_llm, str) and prompt_from_llm.strip():
+#             image_prompt = prompt_from_llm.strip()
 
-        raw_shape = parsed_query.get("shape")
-        if isinstance(raw_shape, str):
-            try:
-                requested_shape = ImageShape(raw_shape)
-            except ValueError:
-                logger.warning(
-                    "Received unsupported image shape '%s' from LLM. Falling back to square.",
-                    raw_shape,
-                )
+#         raw_shape = parsed_query.get("shape")
+#         if isinstance(raw_shape, str):
+#             try:
+#                 requested_shape = ImageShape(raw_shape)
+#             except ValueError:
+#                 logger.warning(
+#                     "Received unsupported image shape '%s' from LLM. Falling back to square.",
+#                     raw_shape,
+#                 )
 
-    logger.debug(
-        f"Image generation start for {iteration_nr}.{parallelization_nr} at {datetime.now()}"
-    )
+#     logger.debug(
+#         f"Image generation start for {iteration_nr}.{parallelization_nr} at {datetime.now()}"
+#     )
 
-    # Generate images using the image generation tool
-    image_generation_responses: list[ImageGenerationResponse] = []
+#     # Generate images using the image generation tool
+#     image_generation_responses: list[ImageGenerationResponse] = []
 
-    if requested_shape is not None:
-        tool_iterator = image_tool.run(
-            prompt=image_prompt,
-            shape=requested_shape.value,
-        )
-    else:
-        tool_iterator = image_tool.run(prompt=image_prompt)
+#     if requested_shape is not None:
+#         tool_iterator = image_tool.run(
+#             prompt=image_prompt,
+#             shape=requested_shape.value,
+#         )
+#     else:
+#         tool_iterator = image_tool.run(prompt=image_prompt)
 
-    for tool_response in tool_iterator:
-        if tool_response.id == IMAGE_GENERATION_HEARTBEAT_ID:
-            # Stream heartbeat to frontend
-            write_custom_event(
-                state.current_step_nr,
-                ImageGenerationToolHeartbeat(),
-                writer,
-            )
-        elif tool_response.id == IMAGE_GENERATION_RESPONSE_ID:
-            response = cast(list[ImageGenerationResponse], tool_response.response)
-            image_generation_responses = response
-            break
+#     for tool_response in tool_iterator:
+#         if tool_response.id == IMAGE_GENERATION_HEARTBEAT_ID:
+#             # Stream heartbeat to frontend
+#             write_custom_event(
+#                 state.current_step_nr,
+#                 ImageGenerationToolHeartbeat(),
+#                 writer,
+#             )
+#         elif tool_response.id == IMAGE_GENERATION_RESPONSE_ID:
+#             response = cast(list[ImageGenerationResponse], tool_response.response)
+#             image_generation_responses = response
+#             break
 
-    # save images to file store
-    file_ids = save_files(
-        urls=[],
-        base64_files=[img.image_data for img in image_generation_responses],
-    )
+#     # save images to file store
+#     file_ids = save_files(
+#         urls=[],
+#         base64_files=[img.image_data for img in image_generation_responses],
+#     )
 
-    final_generated_images = [
-        GeneratedImage(
-            file_id=file_id,
-            url=build_frontend_file_url(file_id),
-            revised_prompt=img.revised_prompt,
-            shape=(requested_shape or ImageShape.SQUARE).value,
-        )
-        for file_id, img in zip(file_ids, image_generation_responses)
-    ]
+#     final_generated_images = [
+#         GeneratedImage(
+#             file_id=file_id,
+#             url=build_frontend_file_url(file_id),
+#             revised_prompt=img.revised_prompt,
+#             shape=(requested_shape or ImageShape.SQUARE).value,
+#         )
+#         for file_id, img in zip(file_ids, image_generation_responses)
+#     ]
 
-    logger.debug(
-        f"Image generation complete for {iteration_nr}.{parallelization_nr} at {datetime.now()}"
-    )
+#     logger.debug(
+#         f"Image generation complete for {iteration_nr}.{parallelization_nr} at {datetime.now()}"
+#     )
 
-    # Create answer string describing the generated images
-    if final_generated_images:
-        image_descriptions = []
-        for i, img in enumerate(final_generated_images, 1):
-            if img.shape and img.shape != ImageShape.SQUARE.value:
-                image_descriptions.append(
-                    f"Image {i}: {img.revised_prompt} (shape: {img.shape})"
-                )
-            else:
-                image_descriptions.append(f"Image {i}: {img.revised_prompt}")
+#     # Create answer string describing the generated images
+#     if final_generated_images:
+#         image_descriptions = []
+#         for i, img in enumerate(final_generated_images, 1):
+#             if img.shape and img.shape != ImageShape.SQUARE.value:
+#                 image_descriptions.append(
+#                     f"Image {i}: {img.revised_prompt} (shape: {img.shape})"
+#                 )
+#             else:
+#                 image_descriptions.append(f"Image {i}: {img.revised_prompt}")
 
-        answer_string = (
-            f"Generated {len(final_generated_images)} image(s) based on the request: {image_prompt}\n\n"
-            + "\n".join(image_descriptions)
-        )
-        if requested_shape:
-            reasoning = (
-                "Used image generation tool to create "
-                f"{len(final_generated_images)} image(s) in {requested_shape.value} orientation."
-            )
-        else:
-            reasoning = (
-                "Used image generation tool to create "
-                f"{len(final_generated_images)} image(s) based on the user's request."
-            )
-    else:
-        answer_string = f"Failed to generate images for request: {image_prompt}"
-        reasoning = "Image generation tool did not return any results."
+#         answer_string = (
+#             f"Generated {len(final_generated_images)} image(s) based on the request: {image_prompt}\n\n"
+#             + "\n".join(image_descriptions)
+#         )
+#         if requested_shape:
+#             reasoning = (
+#                 "Used image generation tool to create "
+#                 f"{len(final_generated_images)} image(s) in {requested_shape.value} orientation."
+#             )
+#         else:
+#             reasoning = (
+#                 "Used image generation tool to create "
+#                 f"{len(final_generated_images)} image(s) based on the user's request."
+#             )
+#     else:
+#         answer_string = f"Failed to generate images for request: {image_prompt}"
+#         reasoning = "Image generation tool did not return any results."
 
-    return BranchUpdate(
-        branch_iteration_responses=[
-            IterationAnswer(
-                tool=image_tool_info.llm_path,
-                tool_id=image_tool_info.tool_id,
-                iteration_nr=iteration_nr,
-                parallelization_nr=parallelization_nr,
-                question=branch_query,
-                answer=answer_string,
-                claims=[],
-                cited_documents={},
-                reasoning=reasoning,
-                generated_images=final_generated_images,
-            )
-        ],
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="image_generation",
-                node_name="generating",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return BranchUpdate(
+#         branch_iteration_responses=[
+#             IterationAnswer(
+#                 tool=image_tool_info.llm_path,
+#                 tool_id=image_tool_info.tool_id,
+#                 iteration_nr=iteration_nr,
+#                 parallelization_nr=parallelization_nr,
+#                 question=branch_query,
+#                 answer=answer_string,
+#                 claims=[],
+#                 cited_documents={},
+#                 reasoning=reasoning,
+#                 generated_images=final_generated_images,
+#             )
+#         ],
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="image_generation",
+#                 node_name="generating",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/image_generation/dr_image_generation_3_reduce.py

@@ -1,71 +1,71 @@
-from datetime import datetime
+# from datetime import datetime
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.models import GeneratedImage
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentUpdate
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.server.query_and_chat.streaming_models import ImageGenerationToolDelta
-from onyx.server.query_and_chat.streaming_models import SectionEnd
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.models import GeneratedImage
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentUpdate
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.server.query_and_chat.streaming_models import ImageGenerationFinal
+# from onyx.server.query_and_chat.streaming_models import SectionEnd
+# from onyx.utils.logger import setup_logger
 
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def is_reducer(
-    state: SubAgentMainState,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> SubAgentUpdate:
-    """
-    LangGraph node to perform a standard search as part of the DR process.
-    """
+# def is_reducer(
+#     state: SubAgentMainState,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> SubAgentUpdate:
+#     """
+#     LangGraph node to perform a standard search as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
+#     node_start_time = datetime.now()
 
-    branch_updates = state.branch_iteration_responses
-    current_iteration = state.iteration_nr
-    current_step_nr = state.current_step_nr
+#     branch_updates = state.branch_iteration_responses
+#     current_iteration = state.iteration_nr
+#     current_step_nr = state.current_step_nr
 
-    new_updates = [
-        update for update in branch_updates if update.iteration_nr == current_iteration
-    ]
-    generated_images: list[GeneratedImage] = []
-    for update in new_updates:
-        if update.generated_images:
-            generated_images.extend(update.generated_images)
+#     new_updates = [
+#         update for update in branch_updates if update.iteration_nr == current_iteration
+#     ]
+#     generated_images: list[GeneratedImage] = []
+#     for update in new_updates:
+#         if update.generated_images:
+#             generated_images.extend(update.generated_images)
 
-    # Write the results to the stream
-    write_custom_event(
-        current_step_nr,
-        ImageGenerationToolDelta(
-            images=generated_images,
-        ),
-        writer,
-    )
+#     # Write the results to the stream
+#     write_custom_event(
+#         current_step_nr,
+#         ImageGenerationFinal(
+#             images=generated_images,
+#         ),
+#         writer,
+#     )
 
-    write_custom_event(
-        current_step_nr,
-        SectionEnd(),
-        writer,
-    )
+#     write_custom_event(
+#         current_step_nr,
+#         SectionEnd(),
+#         writer,
+#     )
 
-    current_step_nr += 1
+#     current_step_nr += 1
 
-    return SubAgentUpdate(
-        iteration_responses=new_updates,
-        current_step_nr=current_step_nr,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="image_generation",
-                node_name="consolidation",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return SubAgentUpdate(
+#         iteration_responses=new_updates,
+#         current_step_nr=current_step_nr,
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="image_generation",
+#                 node_name="consolidation",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/image_generation/dr_image_generation_conditional_edges.py

@@ -1,29 +1,29 @@
-from collections.abc import Hashable
+# from collections.abc import Hashable
 
-from langgraph.types import Send
+# from langgraph.types import Send
 
-from onyx.agents.agent_search.dr.constants import MAX_DR_PARALLEL_SEARCH
-from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.dr.constants import MAX_DR_PARALLEL_SEARCH
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
 
 
-def branching_router(state: SubAgentInput) -> list[Send | Hashable]:
-    return [
-        Send(
-            "act",
-            BranchInput(
-                iteration_nr=state.iteration_nr,
-                parallelization_nr=parallelization_nr,
-                branch_question=query,
-                context="",
-                active_source_types=state.active_source_types,
-                tools_used=state.tools_used,
-                available_tools=state.available_tools,
-                assistant_system_prompt=state.assistant_system_prompt,
-                assistant_task_prompt=state.assistant_task_prompt,
-            ),
-        )
-        for parallelization_nr, query in enumerate(
-            state.query_list[:MAX_DR_PARALLEL_SEARCH]
-        )
-    ]
+# def branching_router(state: SubAgentInput) -> list[Send | Hashable]:
+#     return [
+#         Send(
+#             "act",
+#             BranchInput(
+#                 iteration_nr=state.iteration_nr,
+#                 parallelization_nr=parallelization_nr,
+#                 branch_question=query,
+#                 context="",
+#                 active_source_types=state.active_source_types,
+#                 tools_used=state.tools_used,
+#                 available_tools=state.available_tools,
+#                 assistant_system_prompt=state.assistant_system_prompt,
+#                 assistant_task_prompt=state.assistant_task_prompt,
+#             ),
+#         )
+#         for parallelization_nr, query in enumerate(
+#             state.query_list[:MAX_DR_PARALLEL_SEARCH]
+#         )
+#     ]

backend/onyx/agents/agent_search/dr/sub_agents/image_generation/dr_image_generation_graph_builder.py

@@ -1,50 +1,50 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
+# from langgraph.graph import END
+# from langgraph.graph import START
+# from langgraph.graph import StateGraph
 
-from onyx.agents.agent_search.dr.sub_agents.image_generation.dr_image_generation_1_branch import (
-    image_generation_branch,
-)
-from onyx.agents.agent_search.dr.sub_agents.image_generation.dr_image_generation_2_act import (
-    image_generation,
-)
-from onyx.agents.agent_search.dr.sub_agents.image_generation.dr_image_generation_3_reduce import (
-    is_reducer,
-)
-from onyx.agents.agent_search.dr.sub_agents.image_generation.dr_image_generation_conditional_edges import (
-    branching_router,
-)
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.sub_agents.image_generation.dr_image_generation_1_branch import (
+#     image_generation_branch,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.image_generation.dr_image_generation_2_act import (
+#     image_generation,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.image_generation.dr_image_generation_3_reduce import (
+#     is_reducer,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.image_generation.dr_image_generation_conditional_edges import (
+#     branching_router,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
+# from onyx.utils.logger import setup_logger
 
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def dr_image_generation_graph_builder() -> StateGraph:
-    """
-    LangGraph graph builder for Image Generation Sub-Agent
-    """
+# def dr_image_generation_graph_builder() -> StateGraph:
+#     """
+#     LangGraph graph builder for Image Generation Sub-Agent
+#     """
 
-    graph = StateGraph(state_schema=SubAgentMainState, input=SubAgentInput)
+#     graph = StateGraph(state_schema=SubAgentMainState, input=SubAgentInput)
 
-    ### Add nodes ###
+#     ### Add nodes ###
 
-    graph.add_node("branch", image_generation_branch)
+#     graph.add_node("branch", image_generation_branch)
 
-    graph.add_node("act", image_generation)
+#     graph.add_node("act", image_generation)
 
-    graph.add_node("reducer", is_reducer)
+#     graph.add_node("reducer", is_reducer)
 
-    ### Add edges ###
+#     ### Add edges ###
 
-    graph.add_edge(start_key=START, end_key="branch")
+#     graph.add_edge(start_key=START, end_key="branch")
 
-    graph.add_conditional_edges("branch", branching_router)
+#     graph.add_conditional_edges("branch", branching_router)
 
-    graph.add_edge(start_key="act", end_key="reducer")
+#     graph.add_edge(start_key="act", end_key="reducer")
 
-    graph.add_edge(start_key="reducer", end_key=END)
+#     graph.add_edge(start_key="reducer", end_key=END)
 
-    return graph
+#     return graph

backend/onyx/agents/agent_search/dr/sub_agents/image_generation/models.py

@@ -1,13 +1,13 @@
-from pydantic import BaseModel
+# from pydantic import BaseModel
 
 
-class GeneratedImage(BaseModel):
-    file_id: str
-    url: str
-    revised_prompt: str
-    shape: str | None = None
+# class GeneratedImage(BaseModel):
+#     file_id: str
+#     url: str
+#     revised_prompt: str
+#     shape: str | None = None
 
 
-# Needed for PydanticType
-class GeneratedImageFullResult(BaseModel):
-    images: list[GeneratedImage]
+# # Needed for PydanticType
+# class GeneratedImageFullResult(BaseModel):
+#     images: list[GeneratedImage]

backend/onyx/agents/agent_search/dr/sub_agents/kg_search/dr_kg_search_1_branch.py

@@ -1,36 +1,36 @@
-from datetime import datetime
+# from datetime import datetime
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.states import LoggerUpdate
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.states import LoggerUpdate
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def kg_search_branch(
-    state: SubAgentInput, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> LoggerUpdate:
-    """
-    LangGraph node to perform a KG search as part of the DR process.
-    """
+# def kg_search_branch(
+#     state: SubAgentInput, config: RunnableConfig, writer: StreamWriter = lambda _: None
+# ) -> LoggerUpdate:
+#     """
+#     LangGraph node to perform a KG search as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
-    iteration_nr = state.iteration_nr
+#     node_start_time = datetime.now()
+#     iteration_nr = state.iteration_nr
 
-    logger.debug(f"Search start for KG Search {iteration_nr} at {datetime.now()}")
+#     logger.debug(f"Search start for KG Search {iteration_nr} at {datetime.now()}")
 
-    return LoggerUpdate(
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="kg_search",
-                node_name="branching",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return LoggerUpdate(
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="kg_search",
+#                 node_name="branching",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/kg_search/dr_kg_search_2_act.py

@@ -1,97 +1,97 @@
-from datetime import datetime
+# from datetime import datetime
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.models import IterationAnswer
-from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
-from onyx.agents.agent_search.dr.sub_agents.states import BranchUpdate
-from onyx.agents.agent_search.dr.utils import extract_document_citations
-from onyx.agents.agent_search.kb_search.graph_builder import kb_graph_builder
-from onyx.agents.agent_search.kb_search.states import MainInput as KbMainInput
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.context.search.models import InferenceSection
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.models import IterationAnswer
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchUpdate
+# from onyx.agents.agent_search.dr.utils import extract_document_citations
+# from onyx.agents.agent_search.kb_search.graph_builder import kb_graph_builder
+# from onyx.agents.agent_search.kb_search.states import MainInput as KbMainInput
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.context.search.models import InferenceSection
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def kg_search(
-    state: BranchInput, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> BranchUpdate:
-    """
-    LangGraph node to perform a KG search as part of the DR process.
-    """
+# def kg_search(
+#     state: BranchInput, config: RunnableConfig, writer: StreamWriter = lambda _: None
+# ) -> BranchUpdate:
+#     """
+#     LangGraph node to perform a KG search as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
-    iteration_nr = state.iteration_nr
-    state.current_step_nr
-    parallelization_nr = state.parallelization_nr
+#     node_start_time = datetime.now()
+#     iteration_nr = state.iteration_nr
+#     state.current_step_nr
+#     parallelization_nr = state.parallelization_nr
 
-    search_query = state.branch_question
-    if not search_query:
-        raise ValueError("search_query is not set")
+#     search_query = state.branch_question
+#     if not search_query:
+#         raise ValueError("search_query is not set")
 
-    logger.debug(
-        f"Search start for KG Search {iteration_nr}.{parallelization_nr} at {datetime.now()}"
-    )
+#     logger.debug(
+#         f"Search start for KG Search {iteration_nr}.{parallelization_nr} at {datetime.now()}"
+#     )
 
-    if not state.available_tools:
-        raise ValueError("available_tools is not set")
+#     if not state.available_tools:
+#         raise ValueError("available_tools is not set")
 
-    kg_tool_info = state.available_tools[state.tools_used[-1]]
+#     kg_tool_info = state.available_tools[state.tools_used[-1]]
 
-    kb_graph = kb_graph_builder().compile()
+#     kb_graph = kb_graph_builder().compile()
 
-    kb_results = kb_graph.invoke(
-        input=KbMainInput(question=search_query, individual_flow=False),
-        config=config,
-    )
+#     kb_results = kb_graph.invoke(
+#         input=KbMainInput(question=search_query, individual_flow=False),
+#         config=config,
+#     )
 
-    # get cited documents
-    answer_string = kb_results.get("final_answer") or "No answer provided"
-    claims: list[str] = []
-    retrieved_docs: list[InferenceSection] = kb_results.get("retrieved_documents", [])
+#     # get cited documents
+#     answer_string = kb_results.get("final_answer") or "No answer provided"
+#     claims: list[str] = []
+#     retrieved_docs: list[InferenceSection] = kb_results.get("retrieved_documents", [])
 
-    (
-        citation_numbers,
-        answer_string,
-        claims,
-    ) = extract_document_citations(answer_string, claims)
+#     (
+#         citation_numbers,
+#         answer_string,
+#         claims,
+#     ) = extract_document_citations(answer_string, claims)
 
-    # if citation is empty, the answer must have come from the KG rather than a doc
-    # in that case, simply cite the docs returned by the KG
-    if not citation_numbers:
-        citation_numbers = [i + 1 for i in range(len(retrieved_docs))]
+#     # if citation is empty, the answer must have come from the KG rather than a doc
+#     # in that case, simply cite the docs returned by the KG
+#     if not citation_numbers:
+#         citation_numbers = [i + 1 for i in range(len(retrieved_docs))]
 
-    cited_documents = {
-        citation_number: retrieved_docs[citation_number - 1]
-        for citation_number in citation_numbers
-        if citation_number <= len(retrieved_docs)
-    }
+#     cited_documents = {
+#         citation_number: retrieved_docs[citation_number - 1]
+#         for citation_number in citation_numbers
+#         if citation_number <= len(retrieved_docs)
+#     }
 
-    return BranchUpdate(
-        branch_iteration_responses=[
-            IterationAnswer(
-                tool=kg_tool_info.llm_path,
-                tool_id=kg_tool_info.tool_id,
-                iteration_nr=iteration_nr,
-                parallelization_nr=parallelization_nr,
-                question=search_query,
-                answer=answer_string,
-                claims=claims,
-                cited_documents=cited_documents,
-                reasoning=None,
-                additional_data=None,
-            )
-        ],
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="kg_search",
-                node_name="searching",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return BranchUpdate(
+#         branch_iteration_responses=[
+#             IterationAnswer(
+#                 tool=kg_tool_info.llm_path,
+#                 tool_id=kg_tool_info.tool_id,
+#                 iteration_nr=iteration_nr,
+#                 parallelization_nr=parallelization_nr,
+#                 question=search_query,
+#                 answer=answer_string,
+#                 claims=claims,
+#                 cited_documents=cited_documents,
+#                 reasoning=None,
+#                 additional_data=None,
+#             )
+#         ],
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="kg_search",
+#                 node_name="searching",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/kg_search/dr_kg_search_3_reduce.py

@@ -1,121 +1,121 @@
-from datetime import datetime
+# from datetime import datetime
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentUpdate
-from onyx.agents.agent_search.dr.utils import convert_inference_sections_to_search_docs
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.server.query_and_chat.streaming_models import ReasoningDelta
-from onyx.server.query_and_chat.streaming_models import ReasoningStart
-from onyx.server.query_and_chat.streaming_models import SearchToolDelta
-from onyx.server.query_and_chat.streaming_models import SearchToolStart
-from onyx.server.query_and_chat.streaming_models import SectionEnd
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentUpdate
+# from onyx.agents.agent_search.dr.utils import convert_inference_sections_to_search_docs
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.server.query_and_chat.streaming_models import ReasoningDelta
+# from onyx.server.query_and_chat.streaming_models import ReasoningStart
+# from onyx.server.query_and_chat.streaming_models import SearchToolDelta
+# from onyx.server.query_and_chat.streaming_models import SearchToolStart
+# from onyx.server.query_and_chat.streaming_models import SectionEnd
+# from onyx.utils.logger import setup_logger
 
 
-logger = setup_logger()
+# logger = setup_logger()
 
-_MAX_KG_STEAMED_ANSWER_LENGTH = 1000  # num characters
+# _MAX_KG_STEAMED_ANSWER_LENGTH = 1000  # num characters
 
 
-def kg_search_reducer(
-    state: SubAgentMainState,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> SubAgentUpdate:
-    """
-    LangGraph node to perform a KG search as part of the DR process.
-    """
+# def kg_search_reducer(
+#     state: SubAgentMainState,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> SubAgentUpdate:
+#     """
+#     LangGraph node to perform a KG search as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
+#     node_start_time = datetime.now()
 
-    branch_updates = state.branch_iteration_responses
-    current_iteration = state.iteration_nr
-    current_step_nr = state.current_step_nr
+#     branch_updates = state.branch_iteration_responses
+#     current_iteration = state.iteration_nr
+#     current_step_nr = state.current_step_nr
 
-    new_updates = [
-        update for update in branch_updates if update.iteration_nr == current_iteration
-    ]
+#     new_updates = [
+#         update for update in branch_updates if update.iteration_nr == current_iteration
+#     ]
 
-    queries = [update.question for update in new_updates]
-    doc_lists = [list(update.cited_documents.values()) for update in new_updates]
+#     queries = [update.question for update in new_updates]
+#     doc_lists = [list(update.cited_documents.values()) for update in new_updates]
 
-    doc_list = []
+#     doc_list = []
 
-    for xs in doc_lists:
-        for x in xs:
-            doc_list.append(x)
+#     for xs in doc_lists:
+#         for x in xs:
+#             doc_list.append(x)
 
-    retrieved_search_docs = convert_inference_sections_to_search_docs(doc_list)
-    kg_answer = (
-        "The Knowledge Graph Answer:\n\n" + new_updates[0].answer
-        if len(queries) == 1
-        else None
-    )
+#     retrieved_search_docs = convert_inference_sections_to_search_docs(doc_list)
+#     kg_answer = (
+#         "The Knowledge Graph Answer:\n\n" + new_updates[0].answer
+#         if len(queries) == 1
+#         else None
+#     )
 
-    if len(retrieved_search_docs) > 0:
-        write_custom_event(
-            current_step_nr,
-            SearchToolStart(
-                is_internet_search=False,
-            ),
-            writer,
-        )
-        write_custom_event(
-            current_step_nr,
-            SearchToolDelta(
-                queries=queries,
-                documents=retrieved_search_docs,
-            ),
-            writer,
-        )
-        write_custom_event(
-            current_step_nr,
-            SectionEnd(),
-            writer,
-        )
+#     if len(retrieved_search_docs) > 0:
+#         write_custom_event(
+#             current_step_nr,
+#             SearchToolStart(
+#                 is_internet_search=False,
+#             ),
+#             writer,
+#         )
+#         write_custom_event(
+#             current_step_nr,
+#             SearchToolDelta(
+#                 queries=queries,
+#                 documents=retrieved_search_docs,
+#             ),
+#             writer,
+#         )
+#         write_custom_event(
+#             current_step_nr,
+#             SectionEnd(),
+#             writer,
+#         )
 
-        current_step_nr += 1
+#         current_step_nr += 1
 
-    if kg_answer is not None:
+#     if kg_answer is not None:
 
-        kg_display_answer = (
-            f"{kg_answer[:_MAX_KG_STEAMED_ANSWER_LENGTH]}..."
-            if len(kg_answer) > _MAX_KG_STEAMED_ANSWER_LENGTH
-            else kg_answer
-        )
+#         kg_display_answer = (
+#             f"{kg_answer[:_MAX_KG_STEAMED_ANSWER_LENGTH]}..."
+#             if len(kg_answer) > _MAX_KG_STEAMED_ANSWER_LENGTH
+#             else kg_answer
+#         )
 
-        write_custom_event(
-            current_step_nr,
-            ReasoningStart(),
-            writer,
-        )
-        write_custom_event(
-            current_step_nr,
-            ReasoningDelta(reasoning=kg_display_answer),
-            writer,
-        )
-        write_custom_event(
-            current_step_nr,
-            SectionEnd(),
-            writer,
-        )
+#         write_custom_event(
+#             current_step_nr,
+#             ReasoningStart(),
+#             writer,
+#         )
+#         write_custom_event(
+#             current_step_nr,
+#             ReasoningDelta(reasoning=kg_display_answer),
+#             writer,
+#         )
+#         write_custom_event(
+#             current_step_nr,
+#             SectionEnd(),
+#             writer,
+#         )
 
-        current_step_nr += 1
+#         current_step_nr += 1
 
-    return SubAgentUpdate(
-        iteration_responses=new_updates,
-        current_step_nr=current_step_nr,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="kg_search",
-                node_name="consolidation",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return SubAgentUpdate(
+#         iteration_responses=new_updates,
+#         current_step_nr=current_step_nr,
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="kg_search",
+#                 node_name="consolidation",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/kg_search/dr_kg_search_conditional_edges.py

@@ -1,27 +1,27 @@
-from collections.abc import Hashable
+# from collections.abc import Hashable
 
-from langgraph.types import Send
+# from langgraph.types import Send
 
-from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
 
 
-def branching_router(state: SubAgentInput) -> list[Send | Hashable]:
-    return [
-        Send(
-            "act",
-            BranchInput(
-                iteration_nr=state.iteration_nr,
-                parallelization_nr=parallelization_nr,
-                branch_question=query,
-                context="",
-                tools_used=state.tools_used,
-                available_tools=state.available_tools,
-                assistant_system_prompt=state.assistant_system_prompt,
-                assistant_task_prompt=state.assistant_task_prompt,
-            ),
-        )
-        for parallelization_nr, query in enumerate(
-            state.query_list[:1]  # no parallel search for now
-        )
-    ]
+# def branching_router(state: SubAgentInput) -> list[Send | Hashable]:
+#     return [
+#         Send(
+#             "act",
+#             BranchInput(
+#                 iteration_nr=state.iteration_nr,
+#                 parallelization_nr=parallelization_nr,
+#                 branch_question=query,
+#                 context="",
+#                 tools_used=state.tools_used,
+#                 available_tools=state.available_tools,
+#                 assistant_system_prompt=state.assistant_system_prompt,
+#                 assistant_task_prompt=state.assistant_task_prompt,
+#             ),
+#         )
+#         for parallelization_nr, query in enumerate(
+#             state.query_list[:1]  # no parallel search for now
+#         )
+#     ]

backend/onyx/agents/agent_search/dr/sub_agents/kg_search/dr_kg_search_graph_builder.py

@@ -1,50 +1,50 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
+# from langgraph.graph import END
+# from langgraph.graph import START
+# from langgraph.graph import StateGraph
 
-from onyx.agents.agent_search.dr.sub_agents.kg_search.dr_kg_search_1_branch import (
-    kg_search_branch,
-)
-from onyx.agents.agent_search.dr.sub_agents.kg_search.dr_kg_search_2_act import (
-    kg_search,
-)
-from onyx.agents.agent_search.dr.sub_agents.kg_search.dr_kg_search_3_reduce import (
-    kg_search_reducer,
-)
-from onyx.agents.agent_search.dr.sub_agents.kg_search.dr_kg_search_conditional_edges import (
-    branching_router,
-)
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.sub_agents.kg_search.dr_kg_search_1_branch import (
+#     kg_search_branch,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.kg_search.dr_kg_search_2_act import (
+#     kg_search,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.kg_search.dr_kg_search_3_reduce import (
+#     kg_search_reducer,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.kg_search.dr_kg_search_conditional_edges import (
+#     branching_router,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
+# from onyx.utils.logger import setup_logger
 
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def dr_kg_search_graph_builder() -> StateGraph:
-    """
-    LangGraph graph builder for KG Search Sub-Agent
-    """
+# def dr_kg_search_graph_builder() -> StateGraph:
+#     """
+#     LangGraph graph builder for KG Search Sub-Agent
+#     """
 
-    graph = StateGraph(state_schema=SubAgentMainState, input=SubAgentInput)
+#     graph = StateGraph(state_schema=SubAgentMainState, input=SubAgentInput)
 
-    ### Add nodes ###
+#     ### Add nodes ###
 
-    graph.add_node("branch", kg_search_branch)
+#     graph.add_node("branch", kg_search_branch)
 
-    graph.add_node("act", kg_search)
+#     graph.add_node("act", kg_search)
 
-    graph.add_node("reducer", kg_search_reducer)
+#     graph.add_node("reducer", kg_search_reducer)
 
-    ### Add edges ###
+#     ### Add edges ###
 
-    graph.add_edge(start_key=START, end_key="branch")
+#     graph.add_edge(start_key=START, end_key="branch")
 
-    graph.add_conditional_edges("branch", branching_router)
+#     graph.add_conditional_edges("branch", branching_router)
 
-    graph.add_edge(start_key="act", end_key="reducer")
+#     graph.add_edge(start_key="act", end_key="reducer")
 
-    graph.add_edge(start_key="reducer", end_key=END)
+#     graph.add_edge(start_key="reducer", end_key=END)
 
-    return graph
+#     return graph

backend/onyx/agents/agent_search/dr/sub_agents/states.py

@@ -1,46 +1,46 @@
-from operator import add
-from typing import Annotated
+# from operator import add
+# from typing import Annotated
 
-from onyx.agents.agent_search.dr.models import IterationAnswer
-from onyx.agents.agent_search.dr.models import OrchestratorTool
-from onyx.agents.agent_search.dr.states import LoggerUpdate
-from onyx.db.connector import DocumentSource
+# from onyx.agents.agent_search.dr.models import IterationAnswer
+# from onyx.agents.agent_search.dr.models import OrchestratorTool
+# from onyx.agents.agent_search.dr.states import LoggerUpdate
+# from onyx.db.connector import DocumentSource
 
 
-class SubAgentUpdate(LoggerUpdate):
-    iteration_responses: Annotated[list[IterationAnswer], add] = []
-    current_step_nr: int = 1
+# class SubAgentUpdate(LoggerUpdate):
+#     iteration_responses: Annotated[list[IterationAnswer], add] = []
+#     current_step_nr: int = 1
 
 
-class BranchUpdate(LoggerUpdate):
-    branch_iteration_responses: Annotated[list[IterationAnswer], add] = []
+# class BranchUpdate(LoggerUpdate):
+#     branch_iteration_responses: Annotated[list[IterationAnswer], add] = []
 
 
-class SubAgentInput(LoggerUpdate):
-    iteration_nr: int = 0
-    current_step_nr: int = 1
-    query_list: list[str] = []
-    context: str | None = None
-    active_source_types: list[DocumentSource] | None = None
-    tools_used: Annotated[list[str], add] = []
-    available_tools: dict[str, OrchestratorTool] | None = None
-    assistant_system_prompt: str | None = None
-    assistant_task_prompt: str | None = None
+# class SubAgentInput(LoggerUpdate):
+#     iteration_nr: int = 0
+#     current_step_nr: int = 1
+#     query_list: list[str] = []
+#     context: str | None = None
+#     active_source_types: list[DocumentSource] | None = None
+#     tools_used: Annotated[list[str], add] = []
+#     available_tools: dict[str, OrchestratorTool] | None = None
+#     assistant_system_prompt: str | None = None
+#     assistant_task_prompt: str | None = None
 
 
-class SubAgentMainState(
-    # This includes the core state
-    SubAgentInput,
-    SubAgentUpdate,
-    BranchUpdate,
-):
-    pass
+# class SubAgentMainState(
+#     # This includes the core state
+#     SubAgentInput,
+#     SubAgentUpdate,
+#     BranchUpdate,
+# ):
+#     pass
 
 
-class BranchInput(SubAgentInput):
-    parallelization_nr: int = 0
-    branch_question: str
+# class BranchInput(SubAgentInput):
+#     parallelization_nr: int = 0
+#     branch_question: str
 
 
-class CustomToolBranchInput(LoggerUpdate):
-    tool_info: OrchestratorTool
+# class CustomToolBranchInput(LoggerUpdate):
+#     tool_info: OrchestratorTool

backend/onyx/agents/agent_search/dr/sub_agents/web_search/clients/exa_client.py

@@ -1,70 +1,71 @@
-from collections.abc import Sequence
+# from collections.abc import Sequence
 
-from exa_py import Exa
-from exa_py.api import HighlightsContentsOptions
+# from exa_py import Exa
+# from exa_py.api import HighlightsContentsOptions
 
-from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
-    WebContent,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
-    WebSearchProvider,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
-    WebSearchResult,
-)
-from onyx.connectors.cross_connector_utils.miscellaneous_utils import time_str_to_utc
-from onyx.utils.retry_wrapper import retry_builder
+# from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
+#     WebContent,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
+#     WebSearchProvider,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
+#     WebSearchResult,
+# )
+# from onyx.configs.chat_configs import EXA_API_KEY
+# from onyx.connectors.cross_connector_utils.miscellaneous_utils import time_str_to_utc
+# from onyx.utils.retry_wrapper import retry_builder
 
 
-class ExaClient(WebSearchProvider):
-    def __init__(self, api_key: str) -> None:
-        self.exa = Exa(api_key=api_key)
+# class ExaClient(WebSearchProvider):
+#     def __init__(self, api_key: str | None = EXA_API_KEY) -> None:
+#         self.exa = Exa(api_key=api_key)
 
-    @retry_builder(tries=3, delay=1, backoff=2)
-    def search(self, query: str) -> list[WebSearchResult]:
-        response = self.exa.search_and_contents(
-            query,
-            type="auto",
-            highlights=HighlightsContentsOptions(
-                num_sentences=2,
-                highlights_per_url=1,
-            ),
-            num_results=10,
-        )
+#     @retry_builder(tries=3, delay=1, backoff=2)
+#     def search(self, query: str) -> list[WebSearchResult]:
+#         response = self.exa.search_and_contents(
+#             query,
+#             type="auto",
+#             highlights=HighlightsContentsOptions(
+#                 num_sentences=2,
+#                 highlights_per_url=1,
+#             ),
+#             num_results=10,
+#         )
 
-        return [
-            WebSearchResult(
-                title=result.title or "",
-                link=result.url,
-                snippet=result.highlights[0] if result.highlights else "",
-                author=result.author,
-                published_date=(
-                    time_str_to_utc(result.published_date)
-                    if result.published_date
-                    else None
-                ),
-            )
-            for result in response.results
-        ]
+#         return [
+#             WebSearchResult(
+#                 title=result.title or "",
+#                 link=result.url,
+#                 snippet=result.highlights[0] if result.highlights else "",
+#                 author=result.author,
+#                 published_date=(
+#                     time_str_to_utc(result.published_date)
+#                     if result.published_date
+#                     else None
+#                 ),
+#             )
+#             for result in response.results
+#         ]
 
-    @retry_builder(tries=3, delay=1, backoff=2)
-    def contents(self, urls: Sequence[str]) -> list[WebContent]:
-        response = self.exa.get_contents(
-            urls=list(urls),
-            text=True,
-            livecrawl="preferred",
-        )
+#     @retry_builder(tries=3, delay=1, backoff=2)
+#     def contents(self, urls: Sequence[str]) -> list[WebContent]:
+#         response = self.exa.get_contents(
+#             urls=list(urls),
+#             text=True,
+#             livecrawl="preferred",
+#         )
 
-        return [
-            WebContent(
-                title=result.title or "",
-                link=result.url,
-                full_content=result.text or "",
-                published_date=(
-                    time_str_to_utc(result.published_date)
-                    if result.published_date
-                    else None
-                ),
-            )
-            for result in response.results
-        ]
+#         return [
+#             WebContent(
+#                 title=result.title or "",
+#                 link=result.url,
+#                 full_content=result.text or "",
+#                 published_date=(
+#                     time_str_to_utc(result.published_date)
+#                     if result.published_date
+#                     else None
+#                 ),
+#             )
+#             for result in response.results
+#         ]

backend/onyx/agents/agent_search/dr/sub_agents/web_search/clients/serper_client.py

@@ -1,159 +1,148 @@
-import json
-from collections.abc import Sequence
-from concurrent.futures import ThreadPoolExecutor
+# import json
+# from collections.abc import Sequence
+# from concurrent.futures import ThreadPoolExecutor
 
-import requests
+# import requests
 
-from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
-    WebContent,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
-    WebSearchProvider,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
-    WebSearchResult,
-)
-from onyx.connectors.cross_connector_utils.miscellaneous_utils import time_str_to_utc
-from onyx.utils.retry_wrapper import retry_builder
+# from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
+#     WebContent,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
+#     WebSearchProvider,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
+#     WebSearchResult,
+# )
+# from onyx.configs.chat_configs import SERPER_API_KEY
+# from onyx.connectors.cross_connector_utils.miscellaneous_utils import time_str_to_utc
+# from onyx.utils.retry_wrapper import retry_builder
 
-SERPER_SEARCH_URL = "https://google.serper.dev/search"
-SERPER_CONTENTS_URL = "https://scrape.serper.dev"
+# SERPER_SEARCH_URL = "https://google.serper.dev/search"
+# SERPER_CONTENTS_URL = "https://scrape.serper.dev"
 
 
-class SerperClient(WebSearchProvider):
-    def __init__(self, api_key: str) -> None:
-        self.headers = {
-            "X-API-KEY": api_key,
-            "Content-Type": "application/json",
-        }
+# class SerperClient(WebSearchProvider):
+#     def __init__(self, api_key: str | None = SERPER_API_KEY) -> None:
+#         self.headers = {
+#             "X-API-KEY": api_key,
+#             "Content-Type": "application/json",
+#         }
 
-    @retry_builder(tries=3, delay=1, backoff=2)
-    def search(self, query: str) -> list[WebSearchResult]:
-        payload = {
-            "q": query,
-        }
+#     @retry_builder(tries=3, delay=1, backoff=2)
+#     def search(self, query: str) -> list[WebSearchResult]:
+#         payload = {
+#             "q": query,
+#         }
 
-        response = requests.post(
-            SERPER_SEARCH_URL,
-            headers=self.headers,
-            data=json.dumps(payload),
-        )
+#         response = requests.post(
+#             SERPER_SEARCH_URL,
+#             headers=self.headers,
+#             data=json.dumps(payload),
+#         )
 
-        try:
-            response.raise_for_status()
-        except Exception:
-            # Avoid leaking API keys/URLs
-            raise ValueError(
-                "Serper search failed. Check credentials or quota."
-            ) from None
+#         response.raise_for_status()
 
-        results = response.json()
-        organic_results = results["organic"]
+#         results = response.json()
+#         organic_results = results["organic"]
 
-        return [
-            WebSearchResult(
-                title=result["title"],
-                link=result["link"],
-                snippet=result["snippet"],
-                author=None,
-                published_date=None,
-            )
-            for result in organic_results
-        ]
+#         return [
+#             WebSearchResult(
+#                 title=result["title"],
+#                 link=result["link"],
+#                 snippet=result["snippet"],
+#                 author=None,
+#                 published_date=None,
+#             )
+#             for result in organic_results
+#         ]
 
-    def contents(self, urls: Sequence[str]) -> list[WebContent]:
-        if not urls:
-            return []
+#     def contents(self, urls: Sequence[str]) -> list[WebContent]:
+#         if not urls:
+#             return []
 
-        # Serper can responds with 500s regularly. We want to retry,
-        # but in the event of failure, return an unsuccesful scrape.
-        def safe_get_webpage_content(url: str) -> WebContent:
-            try:
-                return self._get_webpage_content(url)
-            except Exception:
-                return WebContent(
-                    title="",
-                    link=url,
-                    full_content="",
-                    published_date=None,
-                    scrape_successful=False,
-                )
+#         # Serper can responds with 500s regularly. We want to retry,
+#         # but in the event of failure, return an unsuccesful scrape.
+#         def safe_get_webpage_content(url: str) -> WebContent:
+#             try:
+#                 return self._get_webpage_content(url)
+#             except Exception:
+#                 return WebContent(
+#                     title="",
+#                     link=url,
+#                     full_content="",
+#                     published_date=None,
+#                     scrape_successful=False,
+#                 )
 
-        with ThreadPoolExecutor(max_workers=min(8, len(urls))) as e:
-            return list(e.map(safe_get_webpage_content, urls))
+#         with ThreadPoolExecutor(max_workers=min(8, len(urls))) as e:
+#             return list(e.map(safe_get_webpage_content, urls))
 
-    @retry_builder(tries=3, delay=1, backoff=2)
-    def _get_webpage_content(self, url: str) -> WebContent:
-        payload = {
-            "url": url,
-        }
+#     @retry_builder(tries=3, delay=1, backoff=2)
+#     def _get_webpage_content(self, url: str) -> WebContent:
+#         payload = {
+#             "url": url,
+#         }
 
-        response = requests.post(
-            SERPER_CONTENTS_URL,
-            headers=self.headers,
-            data=json.dumps(payload),
-        )
+#         response = requests.post(
+#             SERPER_CONTENTS_URL,
+#             headers=self.headers,
+#             data=json.dumps(payload),
+#         )
 
-        # 400 returned when serper cannot scrape
-        if response.status_code == 400:
-            return WebContent(
-                title="",
-                link=url,
-                full_content="",
-                published_date=None,
-                scrape_successful=False,
-            )
+#         # 400 returned when serper cannot scrape
+#         if response.status_code == 400:
+#             return WebContent(
+#                 title="",
+#                 link=url,
+#                 full_content="",
+#                 published_date=None,
+#                 scrape_successful=False,
+#             )
 
-        try:
-            response.raise_for_status()
-        except Exception:
-            # Avoid leaking API keys/URLs
-            raise ValueError(
-                "Serper content fetch failed. Check credentials."
-            ) from None
+#         response.raise_for_status()
 
-        response_json = response.json()
+#         response_json = response.json()
 
-        # Response only guarantees text
-        text = response_json["text"]
+#         # Response only guarantees text
+#         text = response_json["text"]
 
-        # metadata & jsonld is not guaranteed to be present
-        metadata = response_json.get("metadata", {})
-        jsonld = response_json.get("jsonld", {})
+#         # metadata & jsonld is not guaranteed to be present
+#         metadata = response_json.get("metadata", {})
+#         jsonld = response_json.get("jsonld", {})
 
-        title = extract_title_from_metadata(metadata)
+#         title = extract_title_from_metadata(metadata)
 
-        # Serper does not provide a reliable mechanism to extract the url
-        response_url = url
-        published_date_str = extract_published_date_from_jsonld(jsonld)
-        published_date = None
+#         # Serper does not provide a reliable mechanism to extract the url
+#         response_url = url
+#         published_date_str = extract_published_date_from_jsonld(jsonld)
+#         published_date = None
 
-        if published_date_str:
-            try:
-                published_date = time_str_to_utc(published_date_str)
-            except Exception:
-                published_date = None
+#         if published_date_str:
+#             try:
+#                 published_date = time_str_to_utc(published_date_str)
+#             except Exception:
+#                 published_date = None
 
-        return WebContent(
-            title=title or "",
-            link=response_url,
-            full_content=text or "",
-            published_date=published_date,
-        )
+#         return WebContent(
+#             title=title or "",
+#             link=response_url,
+#             full_content=text or "",
+#             published_date=published_date,
+#         )
 
 
-def extract_title_from_metadata(metadata: dict[str, str]) -> str | None:
-    keys = ["title", "og:title"]
-    return extract_value_from_dict(metadata, keys)
+# def extract_title_from_metadata(metadata: dict[str, str]) -> str | None:
+#     keys = ["title", "og:title"]
+#     return extract_value_from_dict(metadata, keys)
 
 
-def extract_published_date_from_jsonld(jsonld: dict[str, str]) -> str | None:
-    keys = ["dateModified"]
-    return extract_value_from_dict(jsonld, keys)
+# def extract_published_date_from_jsonld(jsonld: dict[str, str]) -> str | None:
+#     keys = ["dateModified"]
+#     return extract_value_from_dict(jsonld, keys)
 
 
-def extract_value_from_dict(data: dict[str, str], keys: list[str]) -> str | None:
-    for key in keys:
-        if key in data:
-            return data[key]
-    return None
+# def extract_value_from_dict(data: dict[str, str], keys: list[str]) -> str | None:
+#     for key in keys:
+#         if key in data:
+#             return data[key]
+#     return None

backend/onyx/agents/agent_search/dr/sub_agents/web_search/dr_ws_1_branch.py

@@ -1,47 +1,47 @@
-from datetime import datetime
+# from datetime import datetime
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.states import LoggerUpdate
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.server.query_and_chat.streaming_models import SearchToolStart
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.states import LoggerUpdate
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.server.query_and_chat.streaming_models import SearchToolStart
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def is_branch(
-    state: SubAgentInput, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> LoggerUpdate:
-    """
-    LangGraph node to perform a web search as part of the DR process.
-    """
+# def is_branch(
+#     state: SubAgentInput, config: RunnableConfig, writer: StreamWriter = lambda _: None
+# ) -> LoggerUpdate:
+#     """
+#     LangGraph node to perform a web search as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
-    iteration_nr = state.iteration_nr
-    current_step_nr = state.current_step_nr
+#     node_start_time = datetime.now()
+#     iteration_nr = state.iteration_nr
+#     current_step_nr = state.current_step_nr
 
-    logger.debug(f"Search start for Web Search {iteration_nr} at {datetime.now()}")
+#     logger.debug(f"Search start for Web Search {iteration_nr} at {datetime.now()}")
 
-    write_custom_event(
-        current_step_nr,
-        SearchToolStart(
-            is_internet_search=True,
-        ),
-        writer,
-    )
+#     write_custom_event(
+#         current_step_nr,
+#         SearchToolStart(
+#             is_internet_search=True,
+#         ),
+#         writer,
+#     )
 
-    return LoggerUpdate(
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="internet_search",
-                node_name="branching",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return LoggerUpdate(
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="internet_search",
+#                 node_name="branching",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/web_search/dr_ws_2_search.py

@@ -1,137 +1,128 @@
-from datetime import datetime
-from typing import cast
+# from datetime import datetime
+# from typing import cast
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
-from langsmith import traceable
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
+# from langsmith import traceable
 
-from onyx.agents.agent_search.dr.models import WebSearchAnswer
-from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
-    WebSearchResult,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.providers import (
-    get_default_provider,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.states import (
-    InternetSearchInput,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.states import (
-    InternetSearchUpdate,
-)
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.llm import invoke_llm_json
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.agents.agent_search.utils import create_question_prompt
-from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
-from onyx.prompts.dr_prompts import WEB_SEARCH_URL_SELECTION_PROMPT
-from onyx.server.query_and_chat.streaming_models import SearchToolDelta
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.models import WebSearchAnswer
+# from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
+#     WebSearchResult,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.providers import (
+#     get_default_provider,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.states import (
+#     InternetSearchInput,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.states import (
+#     InternetSearchUpdate,
+# )
+# from onyx.agents.agent_search.models import GraphConfig
+# from onyx.agents.agent_search.shared_graph_utils.llm import invoke_llm_json
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.agents.agent_search.utils import create_question_prompt
+# from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
+# from onyx.prompts.dr_prompts import WEB_SEARCH_URL_SELECTION_PROMPT
+# from onyx.server.query_and_chat.streaming_models import SearchToolDelta
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def web_search(
-    state: InternetSearchInput,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> InternetSearchUpdate:
-    """
-    LangGraph node to perform internet search and decide which URLs to fetch.
-    """
+# def web_search(
+#     state: InternetSearchInput,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> InternetSearchUpdate:
+#     """
+#     LangGraph node to perform internet search and decide which URLs to fetch.
+#     """
 
-    node_start_time = datetime.now()
-    current_step_nr = state.current_step_nr
+#     node_start_time = datetime.now()
+#     current_step_nr = state.current_step_nr
 
-    if not current_step_nr:
-        raise ValueError("Current step number is not set. This should not happen.")
+#     if not current_step_nr:
+#         raise ValueError("Current step number is not set. This should not happen.")
 
-    assistant_system_prompt = state.assistant_system_prompt
-    assistant_task_prompt = state.assistant_task_prompt
+#     assistant_system_prompt = state.assistant_system_prompt
+#     assistant_task_prompt = state.assistant_task_prompt
 
-    if not state.available_tools:
-        raise ValueError("available_tools is not set")
-    search_query = state.branch_question
+#     if not state.available_tools:
+#         raise ValueError("available_tools is not set")
+#     search_query = state.branch_question
 
-    write_custom_event(
-        current_step_nr,
-        SearchToolDelta(
-            queries=[search_query],
-            documents=[],
-        ),
-        writer,
-    )
+#     write_custom_event(
+#         current_step_nr,
+#         SearchToolDelta(
+#             queries=[search_query],
+#             documents=[],
+#         ),
+#         writer,
+#     )
 
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    base_question = graph_config.inputs.prompt_builder.raw_user_query
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
+#     base_question = graph_config.inputs.prompt_builder.raw_user_query
 
-    if graph_config.inputs.persona is None:
-        raise ValueError("persona is not set")
+#     if graph_config.inputs.persona is None:
+#         raise ValueError("persona is not set")
 
-    provider = get_default_provider()
-    if not provider:
-        raise ValueError("No internet search provider found")
+#     provider = get_default_provider()
+#     if not provider:
+#         raise ValueError("No internet search provider found")
 
-    # Log which provider type is being used
-    provider_type = type(provider).__name__
-    logger.info(
-        f"Performing web search with {provider_type} for query: '{search_query}'"
-    )
+#     @traceable(name="Search Provider API Call")
+#     def _search(search_query: str) -> list[WebSearchResult]:
+#         search_results: list[WebSearchResult] = []
+#         try:
+#             search_results = list(provider.search(search_query))
+#         except Exception as e:
+#             logger.error(f"Error performing search: {e}")
+#         return search_results
 
-    @traceable(name="Search Provider API Call")
-    def _search(search_query: str) -> list[WebSearchResult]:
-        search_results: list[WebSearchResult] = []
-        try:
-            search_results = list(provider.search(search_query))
-            logger.info(
-                f"Search returned {len(search_results)} results using {provider_type}"
-            )
-        except Exception as e:
-            logger.error(f"Error performing search with {provider_type}: {e}")
-        return search_results
-
-    search_results: list[WebSearchResult] = _search(search_query)
-    search_results_text = "\n\n".join(
-        [
-            f"{i}. {result.title}\n   URL: {result.link}\n"
-            + (f"   Author: {result.author}\n" if result.author else "")
-            + (
-                f"   Date: {result.published_date.strftime('%Y-%m-%d')}\n"
-                if result.published_date
-                else ""
-            )
-            + (f"   Snippet: {result.snippet}\n" if result.snippet else "")
-            for i, result in enumerate(search_results)
-        ]
-    )
-    agent_decision_prompt = WEB_SEARCH_URL_SELECTION_PROMPT.build(
-        search_query=search_query,
-        base_question=base_question,
-        search_results_text=search_results_text,
-    )
-    agent_decision = invoke_llm_json(
-        llm=graph_config.tooling.fast_llm,
-        prompt=create_question_prompt(
-            assistant_system_prompt,
-            agent_decision_prompt + (assistant_task_prompt or ""),
-        ),
-        schema=WebSearchAnswer,
-        timeout_override=TF_DR_TIMEOUT_SHORT,
-    )
-    results_to_open = [
-        (search_query, search_results[i])
-        for i in agent_decision.urls_to_open_indices
-        if i < len(search_results) and i >= 0
-    ]
-    return InternetSearchUpdate(
-        results_to_open=results_to_open,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="internet_search",
-                node_name="searching",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     search_results: list[WebSearchResult] = _search(search_query)
+#     search_results_text = "\n\n".join(
+#         [
+#             f"{i}. {result.title}\n   URL: {result.link}\n"
+#             + (f"   Author: {result.author}\n" if result.author else "")
+#             + (
+#                 f"   Date: {result.published_date.strftime('%Y-%m-%d')}\n"
+#                 if result.published_date
+#                 else ""
+#             )
+#             + (f"   Snippet: {result.snippet}\n" if result.snippet else "")
+#             for i, result in enumerate(search_results)
+#         ]
+#     )
+#     agent_decision_prompt = WEB_SEARCH_URL_SELECTION_PROMPT.build(
+#         search_query=search_query,
+#         base_question=base_question,
+#         search_results_text=search_results_text,
+#     )
+#     agent_decision = invoke_llm_json(
+#         llm=graph_config.tooling.fast_llm,
+#         prompt=create_question_prompt(
+#             assistant_system_prompt,
+#             agent_decision_prompt + (assistant_task_prompt or ""),
+#         ),
+#         schema=WebSearchAnswer,
+#         timeout_override=TF_DR_TIMEOUT_SHORT,
+#     )
+#     results_to_open = [
+#         (search_query, search_results[i])
+#         for i in agent_decision.urls_to_open_indices
+#         if i < len(search_results) and i >= 0
+#     ]
+#     return InternetSearchUpdate(
+#         results_to_open=results_to_open,
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="internet_search",
+#                 node_name="searching",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/web_search/dr_ws_3_dedup_urls.py

@@ -1,52 +1,52 @@
-from collections import defaultdict
+# from collections import defaultdict
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
-    WebSearchResult,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.states import (
-    InternetSearchInput,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.utils import (
-    dummy_inference_section_from_internet_search_result,
-)
-from onyx.agents.agent_search.dr.utils import convert_inference_sections_to_search_docs
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.server.query_and_chat.streaming_models import SearchToolDelta
+# from onyx.agents.agent_search.dr.sub_agents.web_search.models import (
+#     WebSearchResult,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.states import (
+#     InternetSearchInput,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.utils import (
+#     dummy_inference_section_from_internet_search_result,
+# )
+# from onyx.agents.agent_search.dr.utils import convert_inference_sections_to_search_docs
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.server.query_and_chat.streaming_models import SearchToolDelta
 
 
-def dedup_urls(
-    state: InternetSearchInput,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> InternetSearchInput:
-    branch_questions_to_urls: dict[str, list[str]] = defaultdict(list)
-    unique_results_by_link: dict[str, WebSearchResult] = {}
-    for query, result in state.results_to_open:
-        branch_questions_to_urls[query].append(result.link)
-        if result.link not in unique_results_by_link:
-            unique_results_by_link[result.link] = result
+# def dedup_urls(
+#     state: InternetSearchInput,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> InternetSearchInput:
+#     branch_questions_to_urls: dict[str, list[str]] = defaultdict(list)
+#     unique_results_by_link: dict[str, WebSearchResult] = {}
+#     for query, result in state.results_to_open:
+#         branch_questions_to_urls[query].append(result.link)
+#         if result.link not in unique_results_by_link:
+#             unique_results_by_link[result.link] = result
 
-    unique_results = list(unique_results_by_link.values())
-    dummy_docs_inference_sections = [
-        dummy_inference_section_from_internet_search_result(doc)
-        for doc in unique_results
-    ]
+#     unique_results = list(unique_results_by_link.values())
+#     dummy_docs_inference_sections = [
+#         dummy_inference_section_from_internet_search_result(doc)
+#         for doc in unique_results
+#     ]
 
-    write_custom_event(
-        state.current_step_nr,
-        SearchToolDelta(
-            queries=[],
-            documents=convert_inference_sections_to_search_docs(
-                dummy_docs_inference_sections, is_internet=True
-            ),
-        ),
-        writer,
-    )
+#     write_custom_event(
+#         state.current_step_nr,
+#         SearchToolDelta(
+#             queries=[],
+#             documents=convert_inference_sections_to_search_docs(
+#                 dummy_docs_inference_sections, is_internet=True
+#             ),
+#         ),
+#         writer,
+#     )
 
-    return InternetSearchInput(
-        results_to_open=[],
-        branch_questions_to_urls=branch_questions_to_urls,
-    )
+#     return InternetSearchInput(
+#         results_to_open=[],
+#         branch_questions_to_urls=branch_questions_to_urls,
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/web_search/dr_ws_4_fetch.py

@@ -1,69 +1,69 @@
-from datetime import datetime
-from typing import cast
+# from datetime import datetime
+# from typing import cast
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.sub_agents.web_search.providers import (
-    get_default_content_provider,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.states import FetchInput
-from onyx.agents.agent_search.dr.sub_agents.web_search.states import FetchUpdate
-from onyx.agents.agent_search.dr.sub_agents.web_search.utils import (
-    dummy_inference_section_from_internet_content,
-)
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.context.search.models import InferenceSection
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.sub_agents.web_search.providers import (
+#     get_default_provider,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.states import FetchInput
+# from onyx.agents.agent_search.dr.sub_agents.web_search.states import FetchUpdate
+# from onyx.agents.agent_search.dr.sub_agents.web_search.utils import (
+#     inference_section_from_internet_page_scrape,
+# )
+# from onyx.agents.agent_search.models import GraphConfig
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.context.search.models import InferenceSection
+# from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def web_fetch(
-    state: FetchInput,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> FetchUpdate:
-    """
-    LangGraph node to fetch content from URLs and process the results.
-    """
+# def web_fetch(
+#     state: FetchInput,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> FetchUpdate:
+#     """
+#     LangGraph node to fetch content from URLs and process the results.
+#     """
 
-    node_start_time = datetime.now()
+#     node_start_time = datetime.now()
 
-    if not state.available_tools:
-        raise ValueError("available_tools is not set")
+#     if not state.available_tools:
+#         raise ValueError("available_tools is not set")
 
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
 
-    if graph_config.inputs.persona is None:
-        raise ValueError("persona is not set")
+#     if graph_config.inputs.persona is None:
+#         raise ValueError("persona is not set")
 
-    provider = get_default_content_provider()
-    if provider is None:
-        raise ValueError("No web content provider found")
+#     provider = get_default_provider()
+#     if provider is None:
+#         raise ValueError("No web search provider found")
 
-    retrieved_docs: list[InferenceSection] = []
-    try:
-        retrieved_docs = [
-            dummy_inference_section_from_internet_content(result)
-            for result in provider.contents(state.urls_to_open)
-        ]
-    except Exception as e:
-        logger.exception(e)
+#     retrieved_docs: list[InferenceSection] = []
+#     try:
+#         retrieved_docs = [
+#             inference_section_from_internet_page_scrape(result)
+#             for result in provider.contents(state.urls_to_open)
+#         ]
+#     except Exception as e:
+#         logger.error(f"Error fetching URLs: {e}")
 
-    if not retrieved_docs:
-        logger.warning("No content retrieved from URLs")
+#     if not retrieved_docs:
+#         logger.warning("No content retrieved from URLs")
 
-    return FetchUpdate(
-        raw_documents=retrieved_docs,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="internet_search",
-                node_name="fetching",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return FetchUpdate(
+#         raw_documents=retrieved_docs,
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="internet_search",
+#                 node_name="fetching",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/web_search/dr_ws_5_collect_raw_docs.py

@@ -1,19 +1,19 @@
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.sub_agents.web_search.states import FetchInput
-from onyx.agents.agent_search.dr.sub_agents.web_search.states import (
-    InternetSearchInput,
-)
+# from onyx.agents.agent_search.dr.sub_agents.web_search.states import FetchInput
+# from onyx.agents.agent_search.dr.sub_agents.web_search.states import (
+#     InternetSearchInput,
+# )
 
 
-def collect_raw_docs(
-    state: FetchInput,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> InternetSearchInput:
-    raw_documents = state.raw_documents
+# def collect_raw_docs(
+#     state: FetchInput,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> InternetSearchInput:
+#     raw_documents = state.raw_documents
 
-    return InternetSearchInput(
-        raw_documents=raw_documents,
-    )
+#     return InternetSearchInput(
+#         raw_documents=raw_documents,
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/web_search/dr_ws_6_summarize.py

@@ -1,133 +1,132 @@
-from datetime import datetime
-from typing import cast
+# from datetime import datetime
+# from typing import cast
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.enums import ResearchType
-from onyx.agents.agent_search.dr.models import IterationAnswer
-from onyx.agents.agent_search.dr.models import SearchAnswer
-from onyx.agents.agent_search.dr.sub_agents.states import BranchUpdate
-from onyx.agents.agent_search.dr.sub_agents.web_search.states import SummarizeInput
-from onyx.agents.agent_search.dr.utils import extract_document_citations
-from onyx.agents.agent_search.kb_search.graph_utils import build_document_context
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.llm import invoke_llm_json
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.utils import create_question_prompt
-from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
-from onyx.context.search.models import InferenceSection
-from onyx.prompts.dr_prompts import INTERNAL_SEARCH_PROMPTS
-from onyx.utils.logger import setup_logger
-from onyx.utils.url import normalize_url
+# from onyx.agents.agent_search.dr.models import IterationAnswer
+# from onyx.agents.agent_search.dr.models import SearchAnswer
+# from onyx.agents.agent_search.dr.sub_agents.states import BranchUpdate
+# from onyx.agents.agent_search.dr.sub_agents.web_search.states import SummarizeInput
+# from onyx.agents.agent_search.dr.utils import extract_document_citations
+# from onyx.agents.agent_search.kb_search.graph_utils import build_document_context
+# from onyx.agents.agent_search.models import GraphConfig
+# from onyx.agents.agent_search.shared_graph_utils.llm import invoke_llm_json
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.utils import create_question_prompt
+# from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
+# from onyx.context.search.models import InferenceSection
+# from onyx.prompts.dr_prompts import INTERNAL_SEARCH_PROMPTS
+# from onyx.utils.logger import setup_logger
+# from onyx.utils.url import normalize_url
 
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def is_summarize(
-    state: SummarizeInput,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> BranchUpdate:
-    """
-    LangGraph node to perform a internet search as part of the DR process.
-    """
+# def is_summarize(
+#     state: SummarizeInput,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> BranchUpdate:
+#     """
+#     LangGraph node to perform a internet search as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
+#     node_start_time = datetime.now()
 
-    # build branch iterations from fetch inputs
-    # Normalize URLs to handle mismatches from query parameters (e.g., ?activeTab=explore)
-    url_to_raw_document: dict[str, InferenceSection] = {}
-    for raw_document in state.raw_documents:
-        normalized_url = normalize_url(raw_document.center_chunk.semantic_identifier)
-        url_to_raw_document[normalized_url] = raw_document
+#     # build branch iterations from fetch inputs
+#     # Normalize URLs to handle mismatches from query parameters (e.g., ?activeTab=explore)
+#     url_to_raw_document: dict[str, InferenceSection] = {}
+#     for raw_document in state.raw_documents:
+#         normalized_url = normalize_url(raw_document.center_chunk.semantic_identifier)
+#         url_to_raw_document[normalized_url] = raw_document
 
-    # Normalize the URLs from branch_questions_to_urls as well
-    urls = [
-        normalize_url(url)
-        for url in state.branch_questions_to_urls[state.branch_question]
-    ]
-    current_iteration = state.iteration_nr
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    research_type = graph_config.behavior.research_type
-    if not state.available_tools:
-        raise ValueError("available_tools is not set")
-    is_tool_info = state.available_tools[state.tools_used[-1]]
+#     # Normalize the URLs from branch_questions_to_urls as well
+#     urls = [
+#         normalize_url(url)
+#         for url in state.branch_questions_to_urls[state.branch_question]
+#     ]
+#     current_iteration = state.iteration_nr
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
+#     use_agentic_search = graph_config.behavior.use_agentic_search
+#     if not state.available_tools:
+#         raise ValueError("available_tools is not set")
+#     is_tool_info = state.available_tools[state.tools_used[-1]]
 
-    if research_type == ResearchType.DEEP:
-        cited_raw_documents = [url_to_raw_document[url] for url in urls]
-        document_texts = _create_document_texts(cited_raw_documents)
-        search_prompt = INTERNAL_SEARCH_PROMPTS[research_type].build(
-            search_query=state.branch_question,
-            base_question=graph_config.inputs.prompt_builder.raw_user_query,
-            document_text=document_texts,
-        )
-        assistant_system_prompt = state.assistant_system_prompt
-        assistant_task_prompt = state.assistant_task_prompt
-        search_answer_json = invoke_llm_json(
-            llm=graph_config.tooling.primary_llm,
-            prompt=create_question_prompt(
-                assistant_system_prompt, search_prompt + (assistant_task_prompt or "")
-            ),
-            schema=SearchAnswer,
-            timeout_override=TF_DR_TIMEOUT_SHORT,
-        )
-        answer_string = search_answer_json.answer
-        claims = search_answer_json.claims or []
-        reasoning = search_answer_json.reasoning or ""
-        (
-            citation_numbers,
-            answer_string,
-            claims,
-        ) = extract_document_citations(answer_string, claims)
-        cited_documents = {
-            citation_number: cited_raw_documents[citation_number - 1]
-            for citation_number in citation_numbers
-        }
+#     if use_agentic_search:
+#         cited_raw_documents = [url_to_raw_document[url] for url in urls]
+#         document_texts = _create_document_texts(cited_raw_documents)
+#         search_prompt = INTERNAL_SEARCH_PROMPTS[use_agentic_search].build(
+#             search_query=state.branch_question,
+#             base_question=graph_config.inputs.prompt_builder.raw_user_query,
+#             document_text=document_texts,
+#         )
+#         assistant_system_prompt = state.assistant_system_prompt
+#         assistant_task_prompt = state.assistant_task_prompt
+#         search_answer_json = invoke_llm_json(
+#             llm=graph_config.tooling.primary_llm,
+#             prompt=create_question_prompt(
+#                 assistant_system_prompt, search_prompt + (assistant_task_prompt or "")
+#             ),
+#             schema=SearchAnswer,
+#             timeout_override=TF_DR_TIMEOUT_SHORT,
+#         )
+#         answer_string = search_answer_json.answer
+#         claims = search_answer_json.claims or []
+#         reasoning = search_answer_json.reasoning or ""
+#         (
+#             citation_numbers,
+#             answer_string,
+#             claims,
+#         ) = extract_document_citations(answer_string, claims)
+#         cited_documents = {
+#             citation_number: cited_raw_documents[citation_number - 1]
+#             for citation_number in citation_numbers
+#         }
 
-    else:
-        answer_string = ""
-        reasoning = ""
-        claims = []
-        cited_raw_documents = [url_to_raw_document[url] for url in urls]
-        cited_documents = {
-            doc_num + 1: retrieved_doc
-            for doc_num, retrieved_doc in enumerate(cited_raw_documents)
-        }
+#     else:
+#         answer_string = ""
+#         reasoning = ""
+#         claims = []
+#         cited_raw_documents = [url_to_raw_document[url] for url in urls]
+#         cited_documents = {
+#             doc_num + 1: retrieved_doc
+#             for doc_num, retrieved_doc in enumerate(cited_raw_documents)
+#         }
 
-    return BranchUpdate(
-        branch_iteration_responses=[
-            IterationAnswer(
-                tool=is_tool_info.llm_path,
-                tool_id=is_tool_info.tool_id,
-                iteration_nr=current_iteration,
-                parallelization_nr=0,
-                question=state.branch_question,
-                answer=answer_string,
-                claims=claims,
-                cited_documents=cited_documents,
-                reasoning=reasoning,
-                additional_data=None,
-            )
-        ],
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="internet_search",
-                node_name="summarizing",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return BranchUpdate(
+#         branch_iteration_responses=[
+#             IterationAnswer(
+#                 tool=is_tool_info.llm_path,
+#                 tool_id=is_tool_info.tool_id,
+#                 iteration_nr=current_iteration,
+#                 parallelization_nr=0,
+#                 question=state.branch_question,
+#                 answer=answer_string,
+#                 claims=claims,
+#                 cited_documents=cited_documents,
+#                 reasoning=reasoning,
+#                 additional_data=None,
+#             )
+#         ],
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="internet_search",
+#                 node_name="summarizing",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )
 
 
-def _create_document_texts(raw_documents: list[InferenceSection]) -> str:
-    document_texts_list = []
-    for doc_num, retrieved_doc in enumerate(raw_documents):
-        if not isinstance(retrieved_doc, InferenceSection):
-            raise ValueError(f"Unexpected document type: {type(retrieved_doc)}")
-        chunk_text = build_document_context(retrieved_doc, doc_num + 1)
-        document_texts_list.append(chunk_text)
-    return "\n\n".join(document_texts_list)
+# def _create_document_texts(raw_documents: list[InferenceSection]) -> str:
+#     document_texts_list = []
+#     for doc_num, retrieved_doc in enumerate(raw_documents):
+#         if not isinstance(retrieved_doc, InferenceSection):
+#             raise ValueError(f"Unexpected document type: {type(retrieved_doc)}")
+#         chunk_text = build_document_context(retrieved_doc, doc_num + 1)
+#         document_texts_list.append(chunk_text)
+#     return "\n\n".join(document_texts_list)

backend/onyx/agents/agent_search/dr/sub_agents/web_search/dr_ws_7_reduce.py

@@ -1,56 +1,56 @@
-from datetime import datetime
+# from datetime import datetime
 
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
 
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentUpdate
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.server.query_and_chat.streaming_models import SectionEnd
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentUpdate
+# from onyx.agents.agent_search.shared_graph_utils.utils import (
+#     get_langgraph_node_log_string,
+# )
+# from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+# from onyx.server.query_and_chat.streaming_models import SectionEnd
+# from onyx.utils.logger import setup_logger
 
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def is_reducer(
-    state: SubAgentMainState,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> SubAgentUpdate:
-    """
-    LangGraph node to perform a internet search as part of the DR process.
-    """
+# def is_reducer(
+#     state: SubAgentMainState,
+#     config: RunnableConfig,
+#     writer: StreamWriter = lambda _: None,
+# ) -> SubAgentUpdate:
+#     """
+#     LangGraph node to perform a internet search as part of the DR process.
+#     """
 
-    node_start_time = datetime.now()
+#     node_start_time = datetime.now()
 
-    branch_updates = state.branch_iteration_responses
-    current_iteration = state.iteration_nr
-    current_step_nr = state.current_step_nr
+#     branch_updates = state.branch_iteration_responses
+#     current_iteration = state.iteration_nr
+#     current_step_nr = state.current_step_nr
 
-    new_updates = [
-        update for update in branch_updates if update.iteration_nr == current_iteration
-    ]
+#     new_updates = [
+#         update for update in branch_updates if update.iteration_nr == current_iteration
+#     ]
 
-    write_custom_event(
-        current_step_nr,
-        SectionEnd(),
-        writer,
-    )
+#     write_custom_event(
+#         current_step_nr,
+#         SectionEnd(),
+#         writer,
+#     )
 
-    current_step_nr += 1
+#     current_step_nr += 1
 
-    return SubAgentUpdate(
-        iteration_responses=new_updates,
-        current_step_nr=current_step_nr,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="internet_search",
-                node_name="consolidation",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
+#     return SubAgentUpdate(
+#         iteration_responses=new_updates,
+#         current_step_nr=current_step_nr,
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="internet_search",
+#                 node_name="consolidation",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#     )

backend/onyx/agents/agent_search/dr/sub_agents/web_search/dr_ws_conditional_edges.py

@@ -1,79 +1,79 @@
-from collections.abc import Hashable
+# from collections.abc import Hashable
 
-from langgraph.types import Send
+# from langgraph.types import Send
 
-from onyx.agents.agent_search.dr.constants import MAX_DR_PARALLEL_SEARCH
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
-from onyx.agents.agent_search.dr.sub_agents.web_search.states import FetchInput
-from onyx.agents.agent_search.dr.sub_agents.web_search.states import (
-    InternetSearchInput,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.states import SummarizeInput
+# from onyx.agents.agent_search.dr.constants import MAX_DR_PARALLEL_SEARCH
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.dr.sub_agents.web_search.states import FetchInput
+# from onyx.agents.agent_search.dr.sub_agents.web_search.states import (
+#     InternetSearchInput,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.states import SummarizeInput
 
 
-def branching_router(state: SubAgentInput) -> list[Send | Hashable]:
-    return [
-        Send(
-            "search",
-            InternetSearchInput(
-                iteration_nr=state.iteration_nr,
-                current_step_nr=state.current_step_nr,
-                parallelization_nr=parallelization_nr,
-                query_list=[query],
-                branch_question=query,
-                context="",
-                tools_used=state.tools_used,
-                available_tools=state.available_tools,
-                assistant_system_prompt=state.assistant_system_prompt,
-                assistant_task_prompt=state.assistant_task_prompt,
-                results_to_open=[],
-            ),
-        )
-        for parallelization_nr, query in enumerate(
-            state.query_list[:MAX_DR_PARALLEL_SEARCH]
-        )
-    ]
+# def branching_router(state: SubAgentInput) -> list[Send | Hashable]:
+#     return [
+#         Send(
+#             "search",
+#             InternetSearchInput(
+#                 iteration_nr=state.iteration_nr,
+#                 current_step_nr=state.current_step_nr,
+#                 parallelization_nr=parallelization_nr,
+#                 query_list=[query],
+#                 branch_question=query,
+#                 context="",
+#                 tools_used=state.tools_used,
+#                 available_tools=state.available_tools,
+#                 assistant_system_prompt=state.assistant_system_prompt,
+#                 assistant_task_prompt=state.assistant_task_prompt,
+#                 results_to_open=[],
+#             ),
+#         )
+#         for parallelization_nr, query in enumerate(
+#             state.query_list[:MAX_DR_PARALLEL_SEARCH]
+#         )
+#     ]
 
 
-def fetch_router(state: InternetSearchInput) -> list[Send | Hashable]:
-    branch_questions_to_urls = state.branch_questions_to_urls
-    return [
-        Send(
-            "fetch",
-            FetchInput(
-                iteration_nr=state.iteration_nr,
-                urls_to_open=[url],
-                tools_used=state.tools_used,
-                available_tools=state.available_tools,
-                assistant_system_prompt=state.assistant_system_prompt,
-                assistant_task_prompt=state.assistant_task_prompt,
-                current_step_nr=state.current_step_nr,
-                branch_questions_to_urls=branch_questions_to_urls,
-                raw_documents=state.raw_documents,
-            ),
-        )
-        for url in set(
-            url for urls in branch_questions_to_urls.values() for url in urls
-        )
-    ]
+# def fetch_router(state: InternetSearchInput) -> list[Send | Hashable]:
+#     branch_questions_to_urls = state.branch_questions_to_urls
+#     return [
+#         Send(
+#             "fetch",
+#             FetchInput(
+#                 iteration_nr=state.iteration_nr,
+#                 urls_to_open=[url],
+#                 tools_used=state.tools_used,
+#                 available_tools=state.available_tools,
+#                 assistant_system_prompt=state.assistant_system_prompt,
+#                 assistant_task_prompt=state.assistant_task_prompt,
+#                 current_step_nr=state.current_step_nr,
+#                 branch_questions_to_urls=branch_questions_to_urls,
+#                 raw_documents=state.raw_documents,
+#             ),
+#         )
+#         for url in set(
+#             url for urls in branch_questions_to_urls.values() for url in urls
+#         )
+#     ]
 
 
-def summarize_router(state: InternetSearchInput) -> list[Send | Hashable]:
-    branch_questions_to_urls = state.branch_questions_to_urls
-    return [
-        Send(
-            "summarize",
-            SummarizeInput(
-                iteration_nr=state.iteration_nr,
-                raw_documents=state.raw_documents,
-                branch_questions_to_urls=branch_questions_to_urls,
-                branch_question=branch_question,
-                tools_used=state.tools_used,
-                available_tools=state.available_tools,
-                assistant_system_prompt=state.assistant_system_prompt,
-                assistant_task_prompt=state.assistant_task_prompt,
-                current_step_nr=state.current_step_nr,
-            ),
-        )
-        for branch_question in branch_questions_to_urls.keys()
-    ]
+# def summarize_router(state: InternetSearchInput) -> list[Send | Hashable]:
+#     branch_questions_to_urls = state.branch_questions_to_urls
+#     return [
+#         Send(
+#             "summarize",
+#             SummarizeInput(
+#                 iteration_nr=state.iteration_nr,
+#                 raw_documents=state.raw_documents,
+#                 branch_questions_to_urls=branch_questions_to_urls,
+#                 branch_question=branch_question,
+#                 tools_used=state.tools_used,
+#                 available_tools=state.available_tools,
+#                 assistant_system_prompt=state.assistant_system_prompt,
+#                 assistant_task_prompt=state.assistant_task_prompt,
+#                 current_step_nr=state.current_step_nr,
+#             ),
+#         )
+#         for branch_question in branch_questions_to_urls.keys()
+#     ]

backend/onyx/agents/agent_search/dr/sub_agents/web_search/dr_ws_graph_builder.py

@@ -1,84 +1,84 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
+# from langgraph.graph import END
+# from langgraph.graph import START
+# from langgraph.graph import StateGraph
 
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
-from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
-from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_1_branch import (
-    is_branch,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_2_search import (
-    web_search,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_3_dedup_urls import (
-    dedup_urls,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_4_fetch import (
-    web_fetch,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_5_collect_raw_docs import (
-    collect_raw_docs,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_6_summarize import (
-    is_summarize,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_7_reduce import (
-    is_reducer,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_conditional_edges import (
-    branching_router,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_conditional_edges import (
-    fetch_router,
-)
-from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_conditional_edges import (
-    summarize_router,
-)
-from onyx.utils.logger import setup_logger
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentInput
+# from onyx.agents.agent_search.dr.sub_agents.states import SubAgentMainState
+# from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_1_branch import (
+#     is_branch,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_2_search import (
+#     web_search,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_3_dedup_urls import (
+#     dedup_urls,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_4_fetch import (
+#     web_fetch,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_5_collect_raw_docs import (
+#     collect_raw_docs,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_6_summarize import (
+#     is_summarize,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_7_reduce import (
+#     is_reducer,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_conditional_edges import (
+#     branching_router,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_conditional_edges import (
+#     fetch_router,
+# )
+# from onyx.agents.agent_search.dr.sub_agents.web_search.dr_ws_conditional_edges import (
+#     summarize_router,
+# )
+# from onyx.utils.logger import setup_logger
 
 
-logger = setup_logger()
+# logger = setup_logger()
 
 
-def dr_ws_graph_builder() -> StateGraph:
-    """
-    LangGraph graph builder for Internet Search Sub-Agent
-    """
+# def dr_ws_graph_builder() -> StateGraph:
+#     """
+#     LangGraph graph builder for Internet Search Sub-Agent
+#     """
 
-    graph = StateGraph(state_schema=SubAgentMainState, input=SubAgentInput)
+#     graph = StateGraph(state_schema=SubAgentMainState, input=SubAgentInput)
 
-    ### Add nodes ###
+#     ### Add nodes ###
 
-    graph.add_node("branch", is_branch)
+#     graph.add_node("branch", is_branch)
 
-    graph.add_node("search", web_search)
+#     graph.add_node("search", web_search)
 
-    graph.add_node("dedup_urls", dedup_urls)
+#     graph.add_node("dedup_urls", dedup_urls)
 
-    graph.add_node("fetch", web_fetch)
+#     graph.add_node("fetch", web_fetch)
 
-    graph.add_node("collect_raw_docs", collect_raw_docs)
+#     graph.add_node("collect_raw_docs", collect_raw_docs)
 
-    graph.add_node("summarize", is_summarize)
+#     graph.add_node("summarize", is_summarize)
 
-    graph.add_node("reducer", is_reducer)
+#     graph.add_node("reducer", is_reducer)
 
-    ### Add edges ###
+#     ### Add edges ###
 
-    graph.add_edge(start_key=START, end_key="branch")
+#     graph.add_edge(start_key=START, end_key="branch")
 
-    graph.add_conditional_edges("branch", branching_router)
+#     graph.add_conditional_edges("branch", branching_router)
 
-    graph.add_edge(start_key="search", end_key="dedup_urls")
+#     graph.add_edge(start_key="search", end_key="dedup_urls")
 
-    graph.add_conditional_edges("dedup_urls", fetch_router)
+#     graph.add_conditional_edges("dedup_urls", fetch_router)
 
-    graph.add_edge(start_key="fetch", end_key="collect_raw_docs")
+#     graph.add_edge(start_key="fetch", end_key="collect_raw_docs")
 
-    graph.add_conditional_edges("collect_raw_docs", summarize_router)
+#     graph.add_conditional_edges("collect_raw_docs", summarize_router)
 
-    graph.add_edge(start_key="summarize", end_key="reducer")
+#     graph.add_edge(start_key="summarize", end_key="reducer")
 
-    graph.add_edge(start_key="reducer", end_key=END)
+#     graph.add_edge(start_key="reducer", end_key=END)
 
-    return graph
+#     return graph