ok

.github/dependabot.yml

@@ -5,7 +5,7 @@ updates:
     schedule:
       interval: "weekly"
     cooldown:
-      default-days: 7
+      default-days: 4
     open-pull-requests-limit: 3
     assignees:
       - "jmelahman"
@@ -16,7 +16,7 @@ updates:
     schedule:
       interval: "weekly"
     cooldown:
-      default-days: 7
+      default-days: 4
     open-pull-requests-limit: 3
     assignees:
       - "jmelahman"

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

@@ -0,0 +1,33 @@
+name: Check Lazy Imports
+concurrency:
+  group: Check-Lazy-Imports-${{ github.workflow }}-${{ github.head_ref || github.event.workflow_run.head_branch || github.run_id }}
+  cancel-in-progress: true
+
+on:
+  merge_group:
+  pull_request:
+    branches:
+      - main
+      - 'release/**'
+
+permissions:
+  contents: read
+
+jobs:
+  check-lazy-imports:
+    runs-on: ubuntu-latest
+    timeout-minutes: 45
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
+      with:
+        persist-credentials: false
+
+    - name: Set up Python
+      uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # ratchet:actions/setup-python@v6
+      with:
+        python-version: '3.11'
+
+    - name: Check lazy imports
+      run: python3 backend/scripts/check_lazy_imports.py

.github/workflows/deployment.yml

@@ -83,47 +83,6 @@ jobs:
             echo "sanitized-tag=$SANITIZED_TAG"
           } >> "$GITHUB_OUTPUT"
 
-  check-version-tag:
-    runs-on: ubuntu-slim
-    timeout-minutes: 10
-    if: ${{ !startsWith(github.ref_name, 'nightly-latest') && github.event_name != 'workflow_dispatch' }}
-    steps:
-      - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
-        with:
-          persist-credentials: false
-          fetch-depth: 0
-
-      - name: Setup uv
-        uses: astral-sh/setup-uv@1e862dfacbd1d6d858c55d9b792c756523627244 # ratchet:astral-sh/setup-uv@v7.1.4
-        with:
-          # NOTE: This isn't caching much and zizmor suggests this could be poisoned, so disable.
-          enable-cache: false
-
-      - name: Validate tag is versioned correctly
-        run: |
-          uv run --no-sync --with release-tag tag --check
-
-  notify-slack-on-tag-check-failure:
-    needs:
-      - check-version-tag
-    if: always() && needs.check-version-tag.result == 'failure' && github.event_name != 'workflow_dispatch'
-    runs-on: ubuntu-slim
-    timeout-minutes: 10
-    steps:
-      - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
-        with:
-          persist-credentials: false
-
-      - name: Send Slack notification
-        uses: ./.github/actions/slack-notify
-        with:
-          webhook-url: ${{ secrets.MONITOR_DEPLOYMENTS_WEBHOOK }}
-          failed-jobs: "• check-version-tag"
-          title: "🚨 Version Tag Check Failed"
-          ref-name: ${{ github.ref_name }}
-
   build-web-amd64:
     needs: determine-builds
     if: needs.determine-builds.outputs.build-web == 'true'
@@ -141,7 +100,7 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -199,7 +158,7 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -307,7 +266,7 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -373,7 +332,7 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -486,7 +445,7 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -543,7 +502,7 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -651,7 +610,7 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -715,7 +674,7 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -908,7 +867,7 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -998,7 +957,7 @@ jobs:
     timeout-minutes: 90
     steps:
       - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 

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

@@ -15,7 +15,7 @@ jobs:
     timeout-minutes: 45
     steps:
       - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           fetch-depth: 0
           persist-credentials: false
@@ -32,7 +32,6 @@ jobs:
           helm repo add cloudnative-pg https://cloudnative-pg.github.io/charts
           helm repo add ot-container-kit https://ot-container-kit.github.io/helm-charts
           helm repo add minio https://charts.min.io/
-          helm repo add code-interpreter https://onyx-dot-app.github.io/code-interpreter/
           helm repo update
 
       - name: Build chart dependencies

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

@@ -28,12 +28,12 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
       - name: Set up Python
-        uses: actions/setup-python@83679a892e2d95755f2dac6acb0bfd1e9ac5d548 # ratchet:actions/setup-python@v6
+        uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # ratchet:actions/setup-python@v6
         with:
           python-version: '3.11'
           cache: 'pip'

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

@@ -7,9 +7,6 @@ on:
   merge_group:
   pull_request:
     branches: [main]
-  push:
-    tags:
-      - "v*.*.*"
 
 permissions:
   contents: read
@@ -32,9 +29,6 @@ env:
   CONFLUENCE_ACCESS_TOKEN: ${{ secrets.CONFLUENCE_ACCESS_TOKEN }}
   CONFLUENCE_ACCESS_TOKEN_SCOPED: ${{ secrets.CONFLUENCE_ACCESS_TOKEN_SCOPED }}
 
-  # Jira
-  JIRA_ADMIN_API_TOKEN: ${{ secrets.JIRA_ADMIN_API_TOKEN }}
-
   # LLMs
   OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
   ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
@@ -52,7 +46,7 @@ jobs:
       test-dirs: ${{ steps.set-matrix.outputs.test-dirs }}
     steps:
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -80,13 +74,12 @@ jobs:
     env:
       PYTHONPATH: ./backend
       MODEL_SERVER_HOST: "disabled"
-      DISABLE_TELEMETRY: "true"
 
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -114,7 +107,6 @@ jobs:
         run: |
           cat <<EOF > deployment/docker_compose/.env
           CODE_INTERPRETER_BETA_ENABLED=true
-          DISABLE_TELEMETRY=true
           EOF
 
       - name: Set up Standard Dependencies
@@ -170,7 +162,7 @@ jobs:
 
       - name: Upload Docker logs
         if: failure()
-        uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # ratchet:actions/upload-artifact@v5
+        uses: actions/upload-artifact@v4
         with:
           name: docker-logs-${{ matrix.test-dir }}
           path: docker-logs/

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

@@ -7,9 +7,6 @@ on:
   merge_group:
   pull_request:
     branches: [ main ]
-  push:
-    tags:
-      - "v*.*.*"
   workflow_dispatch:  # Allows manual triggering
 
 permissions:
@@ -24,7 +21,7 @@ jobs:
     # fetch-depth 0 is required for helm/chart-testing-action
     steps:
     - name: Checkout code
-      uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+      uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
       with:
         fetch-depth: 0
         persist-credentials: false
@@ -83,7 +80,6 @@ jobs:
         helm repo add cloudnative-pg https://cloudnative-pg.github.io/charts
         helm repo add ot-container-kit https://ot-container-kit.github.io/helm-charts
         helm repo add minio https://charts.min.io/
-        helm repo add code-interpreter https://onyx-dot-app.github.io/code-interpreter/
         helm repo update
 
     - name: Install Redis operator

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

@@ -9,9 +9,6 @@ on:
     branches:
       - main
       - "release/**"
-  push:
-    tags:
-      - "v*.*.*"
 
 permissions:
   contents: read
@@ -43,7 +40,7 @@ jobs:
       test-dirs: ${{ steps.set-matrix.outputs.test-dirs }}
     steps:
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -74,24 +71,10 @@ jobs:
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
-      - name: Format branch name for cache
-        id: format-branch
-        env:
-          PR_NUMBER: ${{ github.event.pull_request.number }}
-          REF_NAME: ${{ github.ref_name }}
-        run: |
-          if [ -n "${PR_NUMBER}" ]; then
-            CACHE_SUFFIX="${PR_NUMBER}"
-          else
-            # shellcheck disable=SC2001
-            CACHE_SUFFIX=$(echo "${REF_NAME}" | sed 's/[^A-Za-z0-9._-]/-/g')
-          fi
-          echo "cache-suffix=${CACHE_SUFFIX}" >> $GITHUB_OUTPUT
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@e468171a9de216ec08956ac3ada2f0791b6bd435 # ratchet:docker/setup-buildx-action@v3
 
@@ -112,13 +95,9 @@ jobs:
           push: true
           tags: ${{ env.RUNS_ON_ECR_CACHE }}:integration-test-backend-test-${{ github.run_id }}
           cache-from: |
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache-${{ github.event.pull_request.head.sha || github.sha }}
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache-${{ steps.format-branch.outputs.cache-suffix }}
             type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache
             type=registry,ref=onyxdotapp/onyx-backend:latest
           cache-to: |
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache-${{ github.event.pull_request.head.sha || github.sha }},mode=max
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache-${{ steps.format-branch.outputs.cache-suffix }},mode=max
             type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache,mode=max
           no-cache: ${{ vars.DOCKER_NO_CACHE == 'true' }}
 
@@ -129,24 +108,10 @@ jobs:
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
-      - name: Format branch name for cache
-        id: format-branch
-        env:
-          PR_NUMBER: ${{ github.event.pull_request.number }}
-          REF_NAME: ${{ github.ref_name }}
-        run: |
-          if [ -n "${PR_NUMBER}" ]; then
-            CACHE_SUFFIX="${PR_NUMBER}"
-          else
-            # shellcheck disable=SC2001
-            CACHE_SUFFIX=$(echo "${REF_NAME}" | sed 's/[^A-Za-z0-9._-]/-/g')
-          fi
-          echo "cache-suffix=${CACHE_SUFFIX}" >> $GITHUB_OUTPUT
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@e468171a9de216ec08956ac3ada2f0791b6bd435 # ratchet:docker/setup-buildx-action@v3
 
@@ -167,14 +132,9 @@ jobs:
           push: true
           tags: ${{ env.RUNS_ON_ECR_CACHE }}:integration-test-model-server-test-${{ github.run_id }}
           cache-from: |
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ github.event.pull_request.head.sha || github.sha }}
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ steps.format-branch.outputs.cache-suffix }}
             type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache
             type=registry,ref=onyxdotapp/onyx-model-server:latest
-          cache-to: |
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ github.event.pull_request.head.sha || github.sha }},mode=max
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ steps.format-branch.outputs.cache-suffix }},mode=max
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache,mode=max
+          cache-to: type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache,mode=max
 
 
   build-integration-image:
@@ -183,7 +143,7 @@ jobs:
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -199,40 +159,16 @@ jobs:
           username: ${{ secrets.DOCKER_USERNAME }}
           password: ${{ secrets.DOCKER_TOKEN }}
 
-      - name: Format branch name for cache
-        id: format-branch
-        env:
-          PR_NUMBER: ${{ github.event.pull_request.number }}
-          REF_NAME: ${{ github.ref_name }}
-        run: |
-          if [ -n "${PR_NUMBER}" ]; then
-            CACHE_SUFFIX="${PR_NUMBER}"
-          else
-            # shellcheck disable=SC2001
-            CACHE_SUFFIX=$(echo "${REF_NAME}" | sed 's/[^A-Za-z0-9._-]/-/g')
-          fi
-          echo "cache-suffix=${CACHE_SUFFIX}" >> $GITHUB_OUTPUT
-
       - name: Build and push integration test image with Docker Bake
         env:
           INTEGRATION_REPOSITORY: ${{ env.RUNS_ON_ECR_CACHE }}
           TAG: integration-test-${{ github.run_id }}
-          CACHE_SUFFIX: ${{ steps.format-branch.outputs.cache-suffix }}
-          HEAD_SHA: ${{ github.event.pull_request.head.sha || github.sha }}
         run: |
           cd backend && docker buildx bake --push \
-            --set backend.cache-from=type=registry,ref=${RUNS_ON_ECR_CACHE}:backend-cache-${HEAD_SHA} \
-            --set backend.cache-from=type=registry,ref=${RUNS_ON_ECR_CACHE}:backend-cache-${CACHE_SUFFIX} \
             --set backend.cache-from=type=registry,ref=${RUNS_ON_ECR_CACHE}:backend-cache \
             --set backend.cache-from=type=registry,ref=onyxdotapp/onyx-backend:latest \
-            --set backend.cache-to=type=registry,ref=${RUNS_ON_ECR_CACHE}:backend-cache-${HEAD_SHA},mode=max \
-            --set backend.cache-to=type=registry,ref=${RUNS_ON_ECR_CACHE}:backend-cache-${CACHE_SUFFIX},mode=max \
             --set backend.cache-to=type=registry,ref=${RUNS_ON_ECR_CACHE}:backend-cache,mode=max \
-            --set integration.cache-from=type=registry,ref=${RUNS_ON_ECR_CACHE}:integration-cache-${HEAD_SHA} \
-            --set integration.cache-from=type=registry,ref=${RUNS_ON_ECR_CACHE}:integration-cache-${CACHE_SUFFIX} \
             --set integration.cache-from=type=registry,ref=${RUNS_ON_ECR_CACHE}:integration-cache \
-            --set integration.cache-to=type=registry,ref=${RUNS_ON_ECR_CACHE}:integration-cache-${HEAD_SHA},mode=max \
-            --set integration.cache-to=type=registry,ref=${RUNS_ON_ECR_CACHE}:integration-cache-${CACHE_SUFFIX},mode=max \
             --set integration.cache-to=type=registry,ref=${RUNS_ON_ECR_CACHE}:integration-cache,mode=max \
             integration
 
@@ -259,7 +195,7 @@ jobs:
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -274,28 +210,23 @@ jobs:
 
       # NOTE: Use pre-ping/null pool to reduce flakiness due to dropped connections
       # NOTE: don't need web server for integration tests
-      - name: Create .env file for Docker Compose
+      - name: Start Docker containers
         env:
           ECR_CACHE: ${{ env.RUNS_ON_ECR_CACHE }}
           RUN_ID: ${{ github.run_id }}
-        run: |
-          cat <<EOF > deployment/docker_compose/.env
-          ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true
-          AUTH_TYPE=basic
-          POSTGRES_POOL_PRE_PING=true
-          POSTGRES_USE_NULL_POOL=true
-          REQUIRE_EMAIL_VERIFICATION=false
-          DISABLE_TELEMETRY=true
-          ONYX_BACKEND_IMAGE=${ECR_CACHE}:integration-test-backend-test-${RUN_ID}
-          ONYX_MODEL_SERVER_IMAGE=${ECR_CACHE}:integration-test-model-server-test-${RUN_ID}
-          INTEGRATION_TESTS_MODE=true
-          CHECK_TTL_MANAGEMENT_TASK_FREQUENCY_IN_HOURS=0.001
-          MCP_SERVER_ENABLED=true
-          EOF
-
-      - name: Start Docker containers
         run: |
           cd deployment/docker_compose
+          ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true \
+          AUTH_TYPE=basic \
+          POSTGRES_POOL_PRE_PING=true \
+          POSTGRES_USE_NULL_POOL=true \
+          REQUIRE_EMAIL_VERIFICATION=false \
+          DISABLE_TELEMETRY=true \
+          ONYX_BACKEND_IMAGE=${ECR_CACHE}:integration-test-backend-test-${RUN_ID} \
+          ONYX_MODEL_SERVER_IMAGE=${ECR_CACHE}:integration-test-model-server-test-${RUN_ID} \
+          INTEGRATION_TESTS_MODE=true \
+          CHECK_TTL_MANAGEMENT_TASK_FREQUENCY_IN_HOURS=0.001 \
+          MCP_SERVER_ENABLED=true \
           docker compose -f docker-compose.yml -f docker-compose.dev.yml up \
             relational_db \
             index \
@@ -441,7 +372,7 @@ jobs:
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 

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

@@ -3,15 +3,7 @@ concurrency:
   group: Run-Jest-Tests-${{ github.workflow }}-${{ github.head_ref || github.event.workflow_run.head_branch || github.run_id }}
   cancel-in-progress: true
 
-on:
-  merge_group:
-  pull_request:
-    branches:
-      - main
-      - "release/**"
-  push:
-    tags:
-      - "v*.*.*"
+on: push
 
 permissions:
   contents: read
@@ -23,15 +15,15 @@ jobs:
     timeout-minutes: 45
     steps:
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
       - name: Setup node
-        uses: actions/setup-node@395ad3262231945c25e8478fd5baf05154b1d79f # ratchet:actions/setup-node@v4
+        uses: actions/setup-node@2028fbc5c25fe9cf00d9f06a71cc4710d4507903 # ratchet:actions/setup-node@v4
         with:
           node-version: 22
-          cache: "npm"
+          cache: 'npm'
           cache-dependency-path: ./web/package-lock.json
 
       - name: Install node dependencies

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

@@ -6,9 +6,6 @@ concurrency:
 on:
   merge_group:
     types: [checks_requested]
-  push:
-    tags:
-      - "v*.*.*"
 
 permissions:
   contents: read
@@ -40,7 +37,7 @@ jobs:
       test-dirs: ${{ steps.set-matrix.outputs.test-dirs }}
     steps:
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -70,24 +67,10 @@ jobs:
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
-      - name: Format branch name for cache
-        id: format-branch
-        env:
-          PR_NUMBER: ${{ github.event.pull_request.number }}
-          REF_NAME: ${{ github.ref_name }}
-        run: |
-          if [ -n "${PR_NUMBER}" ]; then
-            CACHE_SUFFIX="${PR_NUMBER}"
-          else
-            # shellcheck disable=SC2001
-            CACHE_SUFFIX=$(echo "${REF_NAME}" | sed 's/[^A-Za-z0-9._-]/-/g')
-          fi
-          echo "cache-suffix=${CACHE_SUFFIX}" >> $GITHUB_OUTPUT
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@e468171a9de216ec08956ac3ada2f0791b6bd435 # ratchet:docker/setup-buildx-action@v3
 
@@ -108,14 +91,9 @@ jobs:
           push: true
           tags: ${{ env.RUNS_ON_ECR_CACHE }}:integration-test-backend-test-${{ github.run_id }}
           cache-from: |
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache-${{ github.event.pull_request.head.sha || github.sha }}
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache-${{ steps.format-branch.outputs.cache-suffix }}
             type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache
             type=registry,ref=onyxdotapp/onyx-backend:latest
-          cache-to: |
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache-${{ github.event.pull_request.head.sha || github.sha }},mode=max
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache-${{ steps.format-branch.outputs.cache-suffix }},mode=max
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache,mode=max
+          cache-to: type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache,mode=max
           no-cache: ${{ vars.DOCKER_NO_CACHE == 'true' }}
 
   build-model-server-image:
@@ -124,24 +102,10 @@ jobs:
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
-      - name: Format branch name for cache
-        id: format-branch
-        env:
-          PR_NUMBER: ${{ github.event.pull_request.number }}
-          REF_NAME: ${{ github.ref_name }}
-        run: |
-          if [ -n "${PR_NUMBER}" ]; then
-            CACHE_SUFFIX="${PR_NUMBER}"
-          else
-            # shellcheck disable=SC2001
-            CACHE_SUFFIX=$(echo "${REF_NAME}" | sed 's/[^A-Za-z0-9._-]/-/g')
-          fi
-          echo "cache-suffix=${CACHE_SUFFIX}" >> $GITHUB_OUTPUT
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@e468171a9de216ec08956ac3ada2f0791b6bd435 # ratchet:docker/setup-buildx-action@v3
 
@@ -162,14 +126,9 @@ jobs:
           push: true
           tags: ${{ env.RUNS_ON_ECR_CACHE }}:integration-test-model-server-test-${{ github.run_id }}
           cache-from: |
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ github.event.pull_request.head.sha || github.sha }}
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ steps.format-branch.outputs.cache-suffix }}
             type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache
             type=registry,ref=onyxdotapp/onyx-model-server:latest
-          cache-to: |
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ github.event.pull_request.head.sha || github.sha }},mode=max
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ steps.format-branch.outputs.cache-suffix }},mode=max
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache,mode=max
+          cache-to: type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache,mode=max
 
   build-integration-image:
     runs-on: [runs-on, runner=2cpu-linux-arm64, "run-id=${{ github.run_id }}-build-integration-image", "extras=ecr-cache"]
@@ -177,24 +136,10 @@ jobs:
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
-      - name: Format branch name for cache
-        id: format-branch
-        env:
-          PR_NUMBER: ${{ github.event.pull_request.number }}
-          REF_NAME: ${{ github.ref_name }}
-        run: |
-          if [ -n "${PR_NUMBER}" ]; then
-            CACHE_SUFFIX="${PR_NUMBER}"
-          else
-            # shellcheck disable=SC2001
-            CACHE_SUFFIX=$(echo "${REF_NAME}" | sed 's/[^A-Za-z0-9._-]/-/g')
-          fi
-          echo "cache-suffix=${CACHE_SUFFIX}" >> $GITHUB_OUTPUT
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@e468171a9de216ec08956ac3ada2f0791b6bd435 # ratchet:docker/setup-buildx-action@v3
 
@@ -211,22 +156,12 @@ jobs:
         env:
           INTEGRATION_REPOSITORY: ${{ env.RUNS_ON_ECR_CACHE }}
           TAG: integration-test-${{ github.run_id }}
-          CACHE_SUFFIX: ${{ steps.format-branch.outputs.cache-suffix }}
-          HEAD_SHA: ${{ github.event.pull_request.head.sha || github.sha }}
         run: |
           cd backend && docker buildx bake --push \
-            --set backend.cache-from=type=registry,ref=${RUNS_ON_ECR_CACHE}:backend-cache-${HEAD_SHA} \
-            --set backend.cache-from=type=registry,ref=${RUNS_ON_ECR_CACHE}:backend-cache-${CACHE_SUFFIX} \
             --set backend.cache-from=type=registry,ref=${RUNS_ON_ECR_CACHE}:backend-cache \
             --set backend.cache-from=type=registry,ref=onyxdotapp/onyx-backend:latest \
-            --set backend.cache-to=type=registry,ref=${RUNS_ON_ECR_CACHE}:backend-cache-${HEAD_SHA},mode=max \
-            --set backend.cache-to=type=registry,ref=${RUNS_ON_ECR_CACHE}:backend-cache-${CACHE_SUFFIX},mode=max \
             --set backend.cache-to=type=registry,ref=${RUNS_ON_ECR_CACHE}:backend-cache,mode=max \
-            --set integration.cache-from=type=registry,ref=${RUNS_ON_ECR_CACHE}:integration-cache-${HEAD_SHA} \
-            --set integration.cache-from=type=registry,ref=${RUNS_ON_ECR_CACHE}:integration-cache-${CACHE_SUFFIX} \
             --set integration.cache-from=type=registry,ref=${RUNS_ON_ECR_CACHE}:integration-cache \
-            --set integration.cache-to=type=registry,ref=${RUNS_ON_ECR_CACHE}:integration-cache-${HEAD_SHA},mode=max \
-            --set integration.cache-to=type=registry,ref=${RUNS_ON_ECR_CACHE}:integration-cache-${CACHE_SUFFIX},mode=max \
             --set integration.cache-to=type=registry,ref=${RUNS_ON_ECR_CACHE}:integration-cache,mode=max \
             integration
 
@@ -253,7 +188,7 @@ jobs:
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -268,26 +203,21 @@ jobs:
 
       # NOTE: Use pre-ping/null pool to reduce flakiness due to dropped connections
       # NOTE: don't need web server for integration tests
-      - name: Create .env file for Docker Compose
+      - name: Start Docker containers
         env:
           ECR_CACHE: ${{ env.RUNS_ON_ECR_CACHE }}
           RUN_ID: ${{ github.run_id }}
-        run: |
-          cat <<EOF > deployment/docker_compose/.env
-          AUTH_TYPE=basic
-          POSTGRES_POOL_PRE_PING=true
-          POSTGRES_USE_NULL_POOL=true
-          REQUIRE_EMAIL_VERIFICATION=false
-          DISABLE_TELEMETRY=true
-          ONYX_BACKEND_IMAGE=${ECR_CACHE}:integration-test-backend-test-${RUN_ID}
-          ONYX_MODEL_SERVER_IMAGE=${ECR_CACHE}:integration-test-model-server-test-${RUN_ID}
-          INTEGRATION_TESTS_MODE=true
-          MCP_SERVER_ENABLED=true
-          EOF
-
-      - name: Start Docker containers
         run: |
           cd deployment/docker_compose
+          AUTH_TYPE=basic \
+          POSTGRES_POOL_PRE_PING=true \
+          POSTGRES_USE_NULL_POOL=true \
+          REQUIRE_EMAIL_VERIFICATION=false \
+          DISABLE_TELEMETRY=true \
+          ONYX_BACKEND_IMAGE=${ECR_CACHE}:integration-test-backend-test-${RUN_ID} \
+          ONYX_MODEL_SERVER_IMAGE=${ECR_CACHE}:integration-test-model-server-test-${RUN_ID} \
+          INTEGRATION_TESTS_MODE=true \
+          MCP_SERVER_ENABLED=true \
           docker compose -f docker-compose.yml -f docker-compose.dev.yml up \
             relational_db \
             index \

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

@@ -3,15 +3,7 @@ concurrency:
   group: Run-Playwright-Tests-${{ github.workflow }}-${{ github.head_ref || github.event.workflow_run.head_branch || github.run_id }}
   cancel-in-progress: true
 
-on:
-  merge_group:
-  pull_request:
-    branches:
-      - main
-      - "release/**"
-  push:
-    tags:
-      - "v*.*.*"
+on: push
 
 permissions:
   contents: read
@@ -60,24 +52,10 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
-      - name: Format branch name for cache
-        id: format-branch
-        env:
-          PR_NUMBER: ${{ github.event.pull_request.number }}
-          REF_NAME: ${{ github.ref_name }}
-        run: |
-          if [ -n "${PR_NUMBER}" ]; then
-            CACHE_SUFFIX="${PR_NUMBER}"
-          else
-            # shellcheck disable=SC2001
-            CACHE_SUFFIX=$(echo "${REF_NAME}" | sed 's/[^A-Za-z0-9._-]/-/g')
-          fi
-          echo "cache-suffix=${CACHE_SUFFIX}" >> $GITHUB_OUTPUT
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@e468171a9de216ec08956ac3ada2f0791b6bd435 # ratchet:docker/setup-buildx-action@v3
 
@@ -98,14 +76,9 @@ jobs:
           tags: ${{ env.RUNS_ON_ECR_CACHE }}:playwright-test-web-${{ github.run_id }}
           push: true
           cache-from: |
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:web-cache-${{ github.event.pull_request.head.sha || github.sha }}
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:web-cache-${{ steps.format-branch.outputs.cache-suffix }}
             type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:web-cache
             type=registry,ref=onyxdotapp/onyx-web-server:latest
-          cache-to: |
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:web-cache-${{ github.event.pull_request.head.sha || github.sha }},mode=max
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:web-cache-${{ steps.format-branch.outputs.cache-suffix }},mode=max
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:web-cache,mode=max
+          cache-to: type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:web-cache,mode=max
           no-cache: ${{ vars.DOCKER_NO_CACHE == 'true' }}
 
   build-backend-image:
@@ -115,24 +88,10 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
-      - name: Format branch name for cache
-        id: format-branch
-        env:
-          PR_NUMBER: ${{ github.event.pull_request.number }}
-          REF_NAME: ${{ github.ref_name }}
-        run: |
-          if [ -n "${PR_NUMBER}" ]; then
-            CACHE_SUFFIX="${PR_NUMBER}"
-          else
-            # shellcheck disable=SC2001
-            CACHE_SUFFIX=$(echo "${REF_NAME}" | sed 's/[^A-Za-z0-9._-]/-/g')
-          fi
-          echo "cache-suffix=${CACHE_SUFFIX}" >> $GITHUB_OUTPUT
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@e468171a9de216ec08956ac3ada2f0791b6bd435 # ratchet:docker/setup-buildx-action@v3
 
@@ -153,13 +112,9 @@ jobs:
           tags: ${{ env.RUNS_ON_ECR_CACHE }}:playwright-test-backend-${{ github.run_id }}
           push: true
           cache-from: |
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache-${{ github.event.pull_request.head.sha || github.sha }}
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache-${{ steps.format-branch.outputs.cache-suffix }}
             type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache
             type=registry,ref=onyxdotapp/onyx-backend:latest
           cache-to: |
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache-${{ github.event.pull_request.head.sha || github.sha }},mode=max
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache-${{ steps.format-branch.outputs.cache-suffix }},mode=max
             type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:backend-cache,mode=max
           no-cache: ${{ vars.DOCKER_NO_CACHE == 'true' }}
 
@@ -170,24 +125,10 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
-      - name: Format branch name for cache
-        id: format-branch
-        env:
-          PR_NUMBER: ${{ github.event.pull_request.number }}
-          REF_NAME: ${{ github.ref_name }}
-        run: |
-          if [ -n "${PR_NUMBER}" ]; then
-            CACHE_SUFFIX="${PR_NUMBER}"
-          else
-            # shellcheck disable=SC2001
-            CACHE_SUFFIX=$(echo "${REF_NAME}" | sed 's/[^A-Za-z0-9._-]/-/g')
-          fi
-          echo "cache-suffix=${CACHE_SUFFIX}" >> $GITHUB_OUTPUT
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@e468171a9de216ec08956ac3ada2f0791b6bd435 # ratchet:docker/setup-buildx-action@v3
 
@@ -208,14 +149,9 @@ jobs:
           tags: ${{ env.RUNS_ON_ECR_CACHE }}:playwright-test-model-server-${{ github.run_id }}
           push: true
           cache-from: |
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ github.event.pull_request.head.sha || github.sha }}
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ steps.format-branch.outputs.cache-suffix }}
             type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache
             type=registry,ref=onyxdotapp/onyx-model-server:latest
-          cache-to: |
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ github.event.pull_request.head.sha || github.sha }},mode=max
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache-${{ steps.format-branch.outputs.cache-suffix }},mode=max
-            type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache,mode=max
+          cache-to: type=registry,ref=${{ env.RUNS_ON_ECR_CACHE }}:model-server-cache,mode=max
           no-cache: ${{ vars.DOCKER_NO_CACHE == 'true' }}
 
   playwright-tests:
@@ -236,13 +172,13 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           fetch-depth: 0
           persist-credentials: false
 
       - name: Setup node
-        uses: actions/setup-node@395ad3262231945c25e8478fd5baf05154b1d79f # ratchet:actions/setup-node@v4
+        uses: actions/setup-node@2028fbc5c25fe9cf00d9f06a71cc4710d4507903 # ratchet:actions/setup-node@v4
         with:
           node-version: 22
           cache: 'npm'
@@ -472,12 +408,12 @@ jobs:
 #     ]
 #   steps:
 #     - name: Checkout code
-#       uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+#       uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
 #       with:
 #         fetch-depth: 0
 
 #     - name: Setup node
-#       uses: actions/setup-node@395ad3262231945c25e8478fd5baf05154b1d79f # ratchet:actions/setup-node@v4
+#       uses: actions/setup-node@2028fbc5c25fe9cf00d9f06a71cc4710d4507903 # ratchet:actions/setup-node@v4
 #       with:
 #         node-version: 22
 

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

@@ -9,14 +9,29 @@ on:
     branches:
       - main
       - 'release/**'
-  push:
-    tags:
-      - "v*.*.*"
 
 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.
@@ -27,7 +42,7 @@ jobs:
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -40,10 +55,35 @@ jobs:
             backend/requirements/model_server.txt
             backend/requirements/ee.txt
 
-      - name: Generate OpenAPI schema and Python client
+      - 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: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_TOKEN }}
+
+      - name: Generate OpenAPI Python client
         shell: bash
         run: |
-          ods openapi all
+          docker run --rm \
+            -v "${{ 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' }}
@@ -61,8 +101,11 @@ jobs:
           TERM: xterm-256color
         run: mypy .
 
-      - name: Run MyPy (tools/)
-        env:
-          MYPY_FORCE_COLOR: 1
-          TERM: xterm-256color
-        run: mypy tools/
+      - name: Check import order with reorder-python-imports
+        working-directory: ./backend
+        run: |
+          find ./onyx -name "*.py" | xargs reorder-python-imports --py311-plus
+
+      - name: Check code formatting with Black
+        working-directory: ./backend
+        run: black --check .

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

@@ -7,9 +7,6 @@ on:
   merge_group:
   pull_request:
     branches: [main]
-  push:
-    tags:
-      - "v*.*.*"
   schedule:
     # This cron expression runs the job daily at 16:00 UTC (9am PT)
     - cron: "0 16 * * *"
@@ -133,13 +130,12 @@ jobs:
 
     env:
       PYTHONPATH: ./backend
-      DISABLE_TELEMETRY: "true"
 
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -161,20 +157,16 @@ jobs:
             hubspot:
               - 'backend/onyx/connectors/hubspot/**'
               - 'backend/tests/daily/connectors/hubspot/**'
-              - 'uv.lock'
             salesforce:
               - 'backend/onyx/connectors/salesforce/**'
               - 'backend/tests/daily/connectors/salesforce/**'
-              - 'uv.lock'
             github:
               - 'backend/onyx/connectors/github/**'
               - 'backend/tests/daily/connectors/github/**'
-              - 'uv.lock'
             file_processing:
               - 'backend/onyx/file_processing/**'
-              - 'uv.lock'
 
-      - name: Run Tests (excluding HubSpot, Salesforce, GitHub, and Coda)
+      - name: Run Tests (excluding HubSpot, Salesforce, and GitHub)
         shell: script -q -e -c "bash --noprofile --norc -eo pipefail {0}"
         run: |
           py.test \
@@ -187,8 +179,7 @@ jobs:
             backend/tests/daily/connectors \
             --ignore backend/tests/daily/connectors/hubspot \
             --ignore backend/tests/daily/connectors/salesforce \
-            --ignore backend/tests/daily/connectors/github \
-            --ignore backend/tests/daily/connectors/coda
+            --ignore backend/tests/daily/connectors/github
 
       - name: Run HubSpot Connector Tests
         if: ${{ github.event_name == 'schedule' || steps.changes.outputs.hubspot == 'true' || steps.changes.outputs.file_processing == 'true' }}

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

@@ -39,7 +39,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           persist-credentials: false
 
@@ -61,7 +61,7 @@ jobs:
           docker tag onyxdotapp/onyx-model-server:latest onyxdotapp/onyx-model-server:test
 
       - name: Set up Python
-        uses: actions/setup-python@83679a892e2d95755f2dac6acb0bfd1e9ac5d548 # ratchet:actions/setup-python@v6
+        uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # ratchet:actions/setup-python@v6
         with:
           python-version: "3.11"
           cache: "pip"

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

@@ -9,9 +9,6 @@ on:
     branches:
       - main
       - 'release/**'
-  push:
-    tags:
-      - "v*.*.*"
 
 permissions:
   contents: read
@@ -26,13 +23,15 @@ jobs:
     env:
       PYTHONPATH: ./backend
       REDIS_CLOUD_PYTEST_PASSWORD: ${{ secrets.REDIS_CLOUD_PYTEST_PASSWORD }}
-      DISABLE_TELEMETRY: "true"
+      SF_USERNAME: ${{ secrets.SF_USERNAME }}
+      SF_PASSWORD: ${{ secrets.SF_PASSWORD }}
+      SF_SECURITY_TOKEN: ${{ secrets.SF_SECURITY_TOKEN }}
 
     steps:
     - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
     - name: Checkout code
-      uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+      uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
       with:
         persist-credentials: false
 

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

@@ -6,42 +6,32 @@ concurrency:
 on:
   merge_group:
   pull_request: null
-  push:
-    branches:
-      - main
-    tags:
-      - "v*.*.*"
 
 permissions:
   contents: read
 
 jobs:
   quality-checks:
-    runs-on: ubuntu-latest
+    # 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: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+      - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
+      - uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           fetch-depth: 0
           persist-credentials: false
-      - uses: actions/setup-python@83679a892e2d95755f2dac6acb0bfd1e9ac5d548 # ratchet:actions/setup-python@v6
+      - uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # ratchet:actions/setup-python@v6
         with:
           python-version: "3.11"
       - name: Setup Terraform
         uses: hashicorp/setup-terraform@b9cd54a3c349d3f38e8881555d616ced269862dd # ratchet:hashicorp/setup-terraform@v3
-      - name: Setup node
-        uses: actions/setup-node@395ad3262231945c25e8478fd5baf05154b1d79f # ratchet:actions/setup-node@v6
-        with: # zizmor: ignore[cache-poisoning]
-          node-version: 22
-          cache: "npm"
-          cache-dependency-path: ./web/package-lock.json
-      - name: Install node dependencies
-        working-directory: ./web
-        run: npm ci
-      - uses: j178/prek-action@91fd7d7cf70ae1dee9f4f44e7dfa5d1073fe6623 # ratchet:j178/prek-action@v1
+      - 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:
-          prek-version: '0.2.21'
-          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) || github.event_name == 'merge_group' && format('--from-ref {0} --to-ref {1}', github.event.merge_group.base_sha, github.event.merge_group.head_sha) || github.ref_name == 'main' && '--all-files' || '' }}
+          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:

.github/workflows/release-devtools.yml

@@ -1,40 +0,0 @@
-name: Release Devtools
-
-on:
-  push:
-    tags:
-      - "ods/v*.*.*"
-
-jobs:
-  pypi:
-    runs-on: ubuntu-latest
-    environment:
-      name: release-devtools
-    permissions:
-      id-token: write
-    timeout-minutes: 10
-    strategy:
-      matrix:
-        os-arch:
-          - {goos: "linux", goarch: "amd64"}
-          - {goos: "linux", goarch: "arm64"}
-          - {goos: "windows", goarch: "amd64"}
-          - {goos: "windows", goarch: "arm64"}
-          - {goos: "darwin", goarch: "amd64"}
-          - {goos: "darwin", goarch: "arm64"}
-          - {goos: "", goarch: ""}
-    steps:
-      - uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
-        with:
-          persist-credentials: false
-          fetch-depth: 0
-      - uses: astral-sh/setup-uv@1e862dfacbd1d6d858c55d9b792c756523627244 # ratchet:astral-sh/setup-uv@v7
-        with:
-          enable-cache: false
-      - run: |
-          GOOS="${{ matrix.os-arch.goos }}" \
-          GOARCH="${{ matrix.os-arch.goarch }}" \
-          uv build --wheel
-        working-directory: tools/ods
-      - run: uv publish
-        working-directory: tools/ods

.github/workflows/sync_foss.yml

@@ -14,7 +14,7 @@ jobs:
       contents: read
     steps:
       - name: Checkout main Onyx repo
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           fetch-depth: 0
           persist-credentials: false

.github/workflows/tag-nightly.yml

@@ -18,7 +18,7 @@ jobs:
       # see https://github.com/orgs/community/discussions/27028#discussioncomment-3254367 for the workaround we
       # implement here which needs an actual user's deploy key
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6
         with:
           ssh-key: "${{ secrets.DEPLOY_KEY }}"
           persist-credentials: true

.github/workflows/zizmor.yml

@@ -17,17 +17,15 @@ jobs:
       security-events: write # needed for SARIF uploads
     steps:
       - name: Checkout repository
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6.0.1
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # ratchet:actions/checkout@v6.0.0
         with:
           persist-credentials: false
 
       - name: Install the latest version of uv
-        uses: astral-sh/setup-uv@1e862dfacbd1d6d858c55d9b792c756523627244 # ratchet:astral-sh/setup-uv@v7.1.4
-        with:
-          enable-cache: false
+        uses: astral-sh/setup-uv@5a7eac68fb9809dea845d802897dc5c723910fa3 # ratchet:astral-sh/setup-uv@v7.1.3
 
       - name: Run zizmor
-        run: uv run --no-sync --with zizmor zizmor --format=sarif . > results.sarif
+        run: uvx zizmor==1.16.3 --format=sarif . > results.sarif
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 

.gitignore

@@ -53,6 +53,3 @@ node_modules
 
 # MCP configs
 .playwright-mcp
-
-# plans
-plans/

.pre-commit-config.yaml

@@ -5,60 +5,36 @@ default_install_hook_types:
   - post-rewrite
 repos:
   - repo: https://github.com/astral-sh/uv-pre-commit
-    # From: https://github.com/astral-sh/uv-pre-commit/pull/53/commits/d30b4298e4fb63ce8609e29acdbcf4c9018a483c
+    # This revision is from https://github.com/astral-sh/uv-pre-commit/pull/53
     rev: d30b4298e4fb63ce8609e29acdbcf4c9018a483c
     hooks:
       - id: uv-sync
-        args: ["--locked", "--all-extras"]
-      - id: uv-lock
-        files: ^pyproject\.toml$
-      - id: uv-export
-        name: uv-export default.txt
-        args: ["--no-emit-project", "--no-default-groups", "--no-hashes", "--extra", "backend", "-o", "backend/requirements/default.txt"]
-        files: ^(pyproject\.toml|uv\.lock|backend/requirements/.*\.txt)$
-      - id: uv-export
-        name: uv-export dev.txt
-        args: ["--no-emit-project", "--no-default-groups", "--no-hashes", "--extra", "dev", "-o", "backend/requirements/dev.txt"]
-        files: ^(pyproject\.toml|uv\.lock|backend/requirements/.*\.txt)$
-      - id: uv-export
-        name: uv-export ee.txt
-        args: ["--no-emit-project", "--no-default-groups", "--no-hashes", "--extra", "ee", "-o", "backend/requirements/ee.txt"]
-        files: ^(pyproject\.toml|uv\.lock|backend/requirements/.*\.txt)$
-      - id: uv-export
-        name: uv-export model_server.txt
-        args: ["--no-emit-project", "--no-default-groups", "--no-hashes", "--extra", "model_server", "-o", "backend/requirements/model_server.txt"]
-        files: ^(pyproject\.toml|uv\.lock|backend/requirements/.*\.txt)$
       - id: uv-run
-        name: Check lazy imports
-        args: ["--with=onyx-devtools", "ods", "check-lazy-imports"]
-        files: ^backend/(?!\.venv/).*\.py$
-      # NOTE: This takes ~6s on a single, large module which is prohibitively slow.
-      # - id: uv-run
-      #   name: mypy
-      #   args: ["--all-extras", "mypy"]
-      #   pass_filenames: true
-      #   files: ^backend/.*\.py$
+        name: mypy
+        args: ["mypy"]
+        pass_filenames: true
+        files: ^backend/.*\.py$
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: 3e8a8703264a2f4a69428a0aa4dcb512790b2c8c  # frozen: v6.0.0
+    rev: v4.6.0
     hooks:
       - id: check-yaml
         files: ^.github/
 
   - repo: https://github.com/rhysd/actionlint
-    rev: a443f344ff32813837fa49f7aa6cbc478d770e62  # frozen: v1.7.9
+    rev: v1.7.8
     hooks:
       - id: actionlint
 
   - repo: https://github.com/psf/black
-    rev: 8a737e727ac5ab2f1d4cf5876720ed276dc8dc4b # frozen: 25.1.0
+    rev: 25.1.0
     hooks:
     - id: black
       language_version: python3.11
 
   # this is a fork which keeps compatibility with black
   - repo: https://github.com/wimglenn/reorder-python-imports-black
-    rev: f55cd27f90f0cf0ee775002c2383ce1c7820013d  # frozen: v3.14.0
+    rev: v3.14.0
     hooks:
     - id: reorder-python-imports
       args: ['--py311-plus', '--application-directories=backend/']
@@ -70,32 +46,26 @@ repos:
   # These settings will remove unused imports with side effects
   # Note: The repo currently does not and should not have imports with side effects
   - repo: https://github.com/PyCQA/autoflake
-    rev: 0544741e2b4a22b472d9d93e37d4ea9153820bb1  # frozen: v2.3.1
+    rev: v2.3.1
     hooks:
       - id: autoflake
         args: [ '--remove-all-unused-imports', '--remove-unused-variables', '--in-place' , '--recursive']
 
-  - repo: https://github.com/golangci/golangci-lint
-    rev: 9f61b0f53f80672872fced07b6874397c3ed197b  # frozen: v2.7.2
-    hooks:
-      - id: golangci-lint
-        entry: bash -c "find tools/ -name go.mod -print0 | xargs -0 -I{} bash -c 'cd \"$(dirname {})\" && golangci-lint run ./...'"
-
   - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: 971923581912ef60a6b70dbf0c3e9a39563c9d47  # frozen: v0.11.4
+    rev: v0.11.4
     hooks:
       - id: ruff
 
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: ffb6a759a979008c0e6dff86e39f4745a2d9eac4  # frozen: v3.1.0
+    rev: v3.1.0
     hooks:
     - id: prettier
       types_or: [html, css, javascript, ts, tsx]
       language_version: system
 
   - repo: https://github.com/sirwart/ripsecrets
-    rev: 7d94620933e79b8acaa0cd9e60e9864b07673d86  # frozen: v0.1.11
+    rev: v0.1.11
     hooks:
       - id: ripsecrets
         args:
@@ -112,9 +82,8 @@ repos:
         pass_filenames: false
         files: \.tf$
 
-      - id: typescript-check
-        name: TypeScript type check
-        entry: bash -c 'cd web && npm run types:check'
+      - id: check-lazy-imports
+        name: Check lazy imports
+        entry: python3 backend/scripts/check_lazy_imports.py
         language: system
-        pass_filenames: false
-        files: ^web/.*\.(ts|tsx)$
+        files: ^backend/(?!\.venv/).*\.py$

.vscode/env_template.txt

@@ -5,8 +5,11 @@
 # For local dev, often user Authentication is not needed
 AUTH_TYPE=disabled
 
+# Skip warm up for dev
+SKIP_WARM_UP=True
+
 # Always keep these on for Dev
-# Logs model prompts, reasoning, and answer to stdout
+# Logs all model prompts to stdout
 LOG_ONYX_MODEL_INTERACTIONS=True
 # More verbose logging
 LOG_LEVEL=debug
@@ -34,16 +37,31 @@ OPENAI_API_KEY=<REPLACE THIS>
 GEN_AI_MODEL_VERSION=gpt-4o
 FAST_GEN_AI_MODEL_VERSION=gpt-4o
 
+# For Onyx Slack Bot, overrides the UI values so no need to set this up via UI every time
+# Only needed if using OnyxBot
+#ONYX_BOT_SLACK_APP_TOKEN=<REPLACE THIS>
+#ONYX_BOT_SLACK_BOT_TOKEN=<REPLACE THIS>
+
 
 # Python stuff
 PYTHONPATH=../backend
 PYTHONUNBUFFERED=1
 
 
+# Internet Search
+EXA_API_KEY=<REPLACE THIS>
+
+
 # Enable the full set of Danswer Enterprise Edition features
 # NOTE: DO NOT ENABLE THIS UNLESS YOU HAVE A PAID ENTERPRISE LICENSE (or if you are using this for local testing/development)
 ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=False
 
+# Agent Search configs  # TODO: Remove give proper namings
+AGENT_RETRIEVAL_STATS=False   # Note: This setting will incur substantial re-ranking effort
+AGENT_RERANKING_STATS=True
+AGENT_MAX_QUERY_RETRIEVAL_RESULTS=20
+AGENT_RERANKING_MAX_QUERY_RETRIEVAL_RESULTS=20
+
 # S3 File Store Configuration (MinIO for local development)
 S3_ENDPOINT_URL=http://localhost:9004
 S3_FILE_STORE_BUCKET_NAME=onyx-file-store-bucket

.vscode/launch.template.jsonc

@@ -133,6 +133,8 @@
       },
       "consoleTitle": "API Server Console"
     },
+    // For the listener to access the Slack API,
+    // ONYX_BOT_SLACK_APP_TOKEN & ONYX_BOT_SLACK_BOT_TOKEN need to be set in .env file located in the root of the project
     {
       "name": "Slack Bot",
       "consoleName": "Slack Bot",
@@ -508,6 +510,7 @@
       ],
       "cwd": "${workspaceFolder}",
       "console": "integratedTerminal",
+      "stopOnEntry": true,
       "presentation": {
         "group": "3"
       }
@@ -553,10 +556,10 @@
       "name": "Install Python Requirements",
       "type": "node",
       "request": "launch",
-      "runtimeExecutable": "uv",
+      "runtimeExecutable": "bash",
       "runtimeArgs": [
-        "sync",
-        "--all-extras"
+        "-c",
+        "pip install -r backend/requirements/default.txt && pip install -r backend/requirements/dev.txt && pip install -r backend/requirements/ee.txt && pip install -r backend/requirements/model_server.txt"
       ],
       "cwd": "${workspaceFolder}",
       "console": "integratedTerminal",
@@ -569,14 +572,14 @@
       "name": "Onyx OpenAPI Schema Generator",
       "type": "debugpy",
       "request": "launch",
-      "program": "backend/scripts/onyx_openapi_schema.py",
-      "cwd": "${workspaceFolder}",
+      "program": "scripts/onyx_openapi_schema.py",
+      "cwd": "${workspaceFolder}/backend",
       "envFile": "${workspaceFolder}/.env",
       "env": {
         "PYTHONUNBUFFERED": "1",
-        "PYTHONPATH": "backend"
+        "PYTHONPATH": "."
       },
-      "args": ["--filename", "backend/generated/openapi.json", "--generate-python-client"]
+      "args": ["--filename", "generated/openapi.json"]
     },
     {
       // script to debug multi tenant db issues

CLAUDE.md.template

@@ -4,7 +4,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 ## KEY NOTES
 
-- If you run into any missing python dependency errors, try running your command with `source .venv/bin/activate` \
+- If you run into any missing python dependency errors, try running your command with `source backend/.venv/bin/activate` \
 to assume the python venv.
 - To make tests work, check the `.env` file at the root of the project to find an OpenAI key.
 - If using `playwright` to explore the frontend, you can usually log in with username `a@test.com` and password

CONTRIBUTING.md

@@ -71,12 +71,12 @@ If using a higher version, sometimes some libraries will not be available (i.e.
 
 #### Backend: Python requirements
 
-Currently, we use [uv](https://docs.astral.sh/uv/) and recommend creating a [virtual environment](https://docs.astral.sh/uv/pip/environments/#using-a-virtual-environment).
+Currently, we use pip and recommend creating a virtual environment.
 
 For convenience here's a command for it:
 
 ```bash
-uv venv .venv --python 3.11
+python -m venv .venv
 source .venv/bin/activate
 ```
 
@@ -95,15 +95,33 @@ If using PowerShell, the command slightly differs:
 Install the required python dependencies:
 
 ```bash
-uv sync --all-extras
+pip install -r backend/requirements/combined.txt
 ```
 
-Install Playwright for Python (headless browser required by the Web Connector):
+or
 
 ```bash
-uv run playwright install
+pip install -r backend/requirements/default.txt
+pip install -r backend/requirements/dev.txt
+pip install -r backend/requirements/ee.txt
+pip install -r backend/requirements/model_server.txt
 ```
 
+Fix vscode/cursor auto-imports:
+```bash
+pip install -e .
+```
+
+Install Playwright for Python (headless browser required by the Web Connector)
+
+In the activated Python virtualenv, install Playwright for Python by running:
+
+```bash
+playwright install
+```
+
+You may have to deactivate and reactivate your virtualenv for `playwright` to appear on your path.
+
 #### Frontend: Node dependencies
 
 Onyx uses Node v22.20.0. We highly recommend you use [Node Version Manager (nvm)](https://github.com/nvm-sh/nvm)
@@ -112,7 +130,7 @@ to manage your Node installations. Once installed, you can run
 ```bash
 nvm install 22 && nvm use 22
 node -v # verify your active version
-```
+``` 
 
 Navigate to `onyx/web` and run:
 
@@ -126,15 +144,21 @@ npm i
 
 For the backend, you'll need to setup pre-commit hooks (black / reorder-python-imports).
 
-Then run:
+With the virtual environment active, install the pre-commit library with:
 
 ```bash
-uv run pre-commit install
+pip install pre-commit
+```
+
+Then, from the `onyx/backend` directory, run:
+
+```bash
+pre-commit install
 ```
 
 Additionally, we use `mypy` for static type checking.
 Onyx is fully type-annotated, and we want to keep it that way!
-To run the mypy checks manually, run `uv run mypy .` from the `onyx/backend` directory.
+To run the mypy checks manually, run `python -m mypy .` from the `onyx/backend` directory.
 
 ### Web
 

backend/alembic/README.md

@@ -7,12 +7,8 @@ Onyx migrations use a generic single-database configuration with an async dbapi.
 
 ## To generate new migrations:
 
-From onyx/backend, run:
-`alembic revision -m <DESCRIPTION_OF_MIGRATION>`
-
-Note: you cannot use the `--autogenerate` flag as the automatic schema parsing does not work.
-
-Manually populate the upgrade and downgrade in your new migration.
+run from onyx/backend:
+`alembic revision --autogenerate -m <DESCRIPTION_OF_MIGRATION>`
 
 More info can be found here: https://alembic.sqlalchemy.org/en/latest/autogenerate.html
 

backend/alembic/versions/18b5b2524446_add_is_clarification_to_chat_message.py

@@ -1,29 +0,0 @@
-"""add is_clarification to chat_message
-
-Revision ID: 18b5b2524446
-Revises: 87c52ec39f84
-Create Date: 2025-01-16
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "18b5b2524446"
-down_revision = "87c52ec39f84"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "chat_message",
-        sa.Column(
-            "is_clarification", sa.Boolean(), nullable=False, server_default="false"
-        ),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("chat_message", "is_clarification")

backend/alembic/versions/2a391f840e85_add_last_refreshed_at_mcp_server.py

@@ -1,27 +0,0 @@
-"""add last refreshed at mcp server
-
-Revision ID: 2a391f840e85
-Revises: 4cebcbc9b2ae
-Create Date: 2025-12-06 15:19:59.766066
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembi.
-revision = "2a391f840e85"
-down_revision = "4cebcbc9b2ae"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "mcp_server",
-        sa.Column("last_refreshed_at", sa.DateTime(timezone=True), nullable=True),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("mcp_server", "last_refreshed_at")

backend/alembic/versions/4cebcbc9b2ae_add_tab_index_to_tool_call.py

@@ -1,27 +0,0 @@
-"""add tab_index to tool_call
-
-Revision ID: 4cebcbc9b2ae
-Revises: a1b2c3d4e5f6
-Create Date: 2025-12-16
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "4cebcbc9b2ae"
-down_revision = "a1b2c3d4e5f6"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "tool_call",
-        sa.Column("tab_index", sa.Integer(), nullable=False, server_default="0"),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("tool_call", "tab_index")

backend/alembic/versions/5e6f7a8b9c0d_update_default_persona_prompt.py

@@ -1,55 +0,0 @@
-"""update_default_persona_prompt
-
-Revision ID: 5e6f7a8b9c0d
-Revises: 4f8a2b3c1d9e
-Create Date: 2025-11-30 12:00:00.000000
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "5e6f7a8b9c0d"
-down_revision = "4f8a2b3c1d9e"
-branch_labels = None
-depends_on = None
-
-
-DEFAULT_PERSONA_ID = 0
-
-# ruff: noqa: E501, W605 start
-DEFAULT_SYSTEM_PROMPT = """
-You are a highly capable, thoughtful, and precise assistant. Your goal is to deeply understand the user's intent, ask clarifying questions when needed, think step-by-step through complex problems, provide clear and accurate answers, and proactively anticipate helpful follow-up information. Always prioritize being truthful, nuanced, insightful, and efficient.
-
-The current date is [[CURRENT_DATETIME]].{citation_reminder_or_empty}
-
-# Response Style
-You use different text styles, bolding, emojis (sparingly), block quotes, and other formatting to make your responses more readable and engaging.
-You use proper Markdown and LaTeX to format your responses for math, scientific, and chemical formulas, symbols, etc.: '$$\\n[expression]\\n$$' for standalone cases and '\\( [expression] \\)' when inline.
-For code you prefer to use Markdown and specify the language.
-You can use horizontal rules (---) to separate sections of your responses.
-You can use Markdown tables to format your responses for data, lists, and other structured information.
-""".lstrip()
-# ruff: noqa: E501, W605 end
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-    conn.execute(
-        sa.text(
-            """
-            UPDATE persona
-            SET system_prompt = :system_prompt
-            WHERE id = :persona_id
-            """
-        ),
-        {"system_prompt": DEFAULT_SYSTEM_PROMPT, "persona_id": DEFAULT_PERSONA_ID},
-    )
-
-
-def downgrade() -> None:
-    # We don't revert the system prompt on downgrade since we don't know
-    # what the previous value was. The new prompt is a reasonable default.
-    pass

backend/alembic/versions/7bd55f264e1b_add_display_name_to_model_configuration.py

@@ -1,27 +0,0 @@
-"""Add display_name to model_configuration
-
-Revision ID: 7bd55f264e1b
-Revises: e8f0d2a38171
-Create Date: 2025-12-04
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "7bd55f264e1b"
-down_revision = "e8f0d2a38171"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "model_configuration",
-        sa.Column("display_name", sa.String(), nullable=True),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("model_configuration", "display_name")

backend/alembic/versions/87c52ec39f84_update_default_system_prompt.py

@@ -1,55 +0,0 @@
-"""update_default_system_prompt
-
-Revision ID: 87c52ec39f84
-Revises: 7bd55f264e1b
-Create Date: 2025-12-05 15:54:06.002452
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "87c52ec39f84"
-down_revision = "7bd55f264e1b"
-branch_labels = None
-depends_on = None
-
-
-DEFAULT_PERSONA_ID = 0
-
-# ruff: noqa: E501, W605 start
-DEFAULT_SYSTEM_PROMPT = """
-You are a highly capable, thoughtful, and precise assistant. Your goal is to deeply understand the user's intent, ask clarifying questions when needed, think step-by-step through complex problems, provide clear and accurate answers, and proactively anticipate helpful follow-up information. Always prioritize being truthful, nuanced, insightful, and efficient.
-
-The current date is [[CURRENT_DATETIME]].[[CITATION_GUIDANCE]]
-
-# Response Style
-You use different text styles, bolding, emojis (sparingly), block quotes, and other formatting to make your responses more readable and engaging.
-You use proper Markdown and LaTeX to format your responses for math, scientific, and chemical formulas, symbols, etc.: '$$\\n[expression]\\n$$' for standalone cases and '\\( [expression] \\)' when inline.
-For code you prefer to use Markdown and specify the language.
-You can use horizontal rules (---) to separate sections of your responses.
-You can use Markdown tables to format your responses for data, lists, and other structured information.
-""".lstrip()
-# ruff: noqa: E501, W605 end
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-    conn.execute(
-        sa.text(
-            """
-            UPDATE persona
-            SET system_prompt = :system_prompt
-            WHERE id = :persona_id
-            """
-        ),
-        {"system_prompt": DEFAULT_SYSTEM_PROMPT, "persona_id": DEFAULT_PERSONA_ID},
-    )
-
-
-def downgrade() -> None:
-    # We don't revert the system prompt on downgrade since we don't know
-    # what the previous value was. The new prompt is a reasonable default.
-    pass

backend/alembic/versions/a01bf2971c5d_update_default_tool_descriptions.py

@@ -1,62 +0,0 @@
-"""update_default_tool_descriptions
-
-Revision ID: a01bf2971c5d
-Revises: 87c52ec39f84
-Create Date: 2025-12-16 15:21:25.656375
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "a01bf2971c5d"
-down_revision = "18b5b2524446"
-branch_labels = None
-depends_on = None
-
-# new tool descriptions (12/2025)
-TOOL_DESCRIPTIONS = {
-    "SearchTool": "The Search Action allows the agent to search through connected knowledge to help build an answer.",
-    "ImageGenerationTool": (
-        "The Image Generation Action allows the agent to use DALL-E 3 or GPT-IMAGE-1 to generate images. "
-        "The action will be used when the user asks the agent to generate an image."
-    ),
-    "WebSearchTool": (
-        "The Web Search Action allows the agent "
-        "to perform internet searches for up-to-date information."
-    ),
-    "KnowledgeGraphTool": (
-        "The Knowledge Graph Search Action allows the agent to search the "
-        "Knowledge Graph for information. This tool can (for now) only be active in the KG Beta Agent, "
-        "and it requires the Knowledge Graph to be enabled."
-    ),
-    "OktaProfileTool": (
-        "The Okta Profile Action allows the agent to fetch the current user's information from Okta. "
-        "This may include the user's name, email, phone number, address, and other details such as their "
-        "manager and direct reports."
-    ),
-}
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-    conn.execute(sa.text("BEGIN"))
-
-    try:
-        for tool_id, description in TOOL_DESCRIPTIONS.items():
-            conn.execute(
-                sa.text(
-                    "UPDATE tool SET description = :description WHERE in_code_tool_id = :tool_id"
-                ),
-                {"description": description, "tool_id": tool_id},
-            )
-        conn.execute(sa.text("COMMIT"))
-    except Exception as e:
-        conn.execute(sa.text("ROLLBACK"))
-        raise e
-
-
-def downgrade() -> None:
-    pass

backend/alembic/versions/a1b2c3d4e5f6_add_license_table.py

@@ -1,49 +0,0 @@
-"""add license table
-
-Revision ID: a1b2c3d4e5f6
-Revises: a01bf2971c5d
-Create Date: 2025-12-04 10:00:00.000000
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "a1b2c3d4e5f6"
-down_revision = "a01bf2971c5d"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.create_table(
-        "license",
-        sa.Column("id", sa.Integer(), primary_key=True),
-        sa.Column("license_data", sa.Text(), nullable=False),
-        sa.Column(
-            "created_at",
-            sa.DateTime(timezone=True),
-            server_default=sa.func.now(),
-            nullable=False,
-        ),
-        sa.Column(
-            "updated_at",
-            sa.DateTime(timezone=True),
-            server_default=sa.func.now(),
-            nullable=False,
-        ),
-    )
-
-    # Singleton pattern - only ever one row in this table
-    op.create_index(
-        "idx_license_singleton",
-        "license",
-        [sa.text("(true)")],
-        unique=True,
-    )
-
-
-def downgrade() -> None:
-    op.drop_index("idx_license_singleton", table_name="license")
-    op.drop_table("license")

backend/alembic/versions/a2b3c4d5e6f7_remove_fast_default_model_name.py

@@ -1,27 +0,0 @@
-"""Remove fast_default_model_name from llm_provider
-
-Revision ID: a2b3c4d5e6f7
-Revises: 2a391f840e85
-Create Date: 2024-12-17
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "a2b3c4d5e6f7"
-down_revision = "2a391f840e85"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.drop_column("llm_provider", "fast_default_model_name")
-
-
-def downgrade() -> None:
-    op.add_column(
-        "llm_provider",
-        sa.Column("fast_default_model_name", sa.String(), nullable=True),
-    )

backend/alembic/versions/a852cbe15577_new_chat_history.py

@@ -18,253 +18,412 @@ depends_on = None
 
 
 def upgrade() -> None:
-    # 1. Drop old research/agent tables (CASCADE handles dependencies)
+    # 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")
 
-    # 2. ChatMessage table changes
-    # Rename columns and add FKs
-    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"],
-    )
-    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"],
+    # 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"],
+        )
 
-    # Add new column
-    op.add_column(
-        "chat_message", sa.Column("reasoning_tokens", sa.Text(), nullable=True)
+    # 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"],
+        )
 
-    # Drop old columns
-    op.drop_column("chat_message", "rephrased_query")
-    op.drop_column("chat_message", "alternate_assistant_id")
-    op.drop_column("chat_message", "overridden_model")
-    op.drop_column("chat_message", "is_agentic")
-    op.drop_column("chat_message", "refined_answer_improvement")
-    op.drop_column("chat_message", "research_type")
-    op.drop_column("chat_message", "research_plan")
-    op.drop_column("chat_message", "research_answer_purpose")
+    # 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)
+        )
 
-    # 3. ToolCall table changes
-    # Drop the unique constraint first
+    # 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")
 
-    # Delete orphaned tool_call rows (those without valid chat_message)
-    op.execute(
-        "DELETE FROM tool_call WHERE message_id NOT IN (SELECT id FROM chat_message)"
-    )
-
-    # Add chat_session_id as nullable first, populate, then make NOT NULL
-    op.add_column(
-        "tool_call",
-        sa.Column("chat_session_id", postgresql.UUID(as_uuid=True), nullable=True),
-    )
-
-    # Populate chat_session_id from the related chat_message
-    op.execute(
+    # 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}'
         """
-        UPDATE tool_call
-        SET chat_session_id = chat_message.chat_session_id
-        FROM chat_message
-        WHERE tool_call.message_id = chat_message.id
+            )
+        )
+        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=""),
+        )
 
-    # Now make it NOT NULL and add FK
-    op.alter_column("tool_call", "chat_session_id", nullable=False)
-    op.create_foreign_key(
-        "fk_tool_call_chat_session_id",
-        "tool_call",
-        "chat_session",
-        ["chat_session_id"],
-        ["id"],
-        ondelete="CASCADE",
+    # 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 message_id and make nullable, recreate FK with CASCADE
-    op.drop_constraint("tool_call_message_id_fkey", "tool_call", type_="foreignkey")
-    op.alter_column(
-        "tool_call",
-        "message_id",
-        new_column_name="parent_chat_message_id",
-        nullable=True,
-    )
-    op.create_foreign_key(
-        "fk_tool_call_parent_chat_message_id",
-        "tool_call",
-        "chat_message",
-        ["parent_chat_message_id"],
-        ["id"],
-        ondelete="CASCADE",
+    # 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"
+        )
 
-    # Add parent_tool_call_id with FK
-    op.add_column(
-        "tool_call", sa.Column("parent_tool_call_id", sa.Integer(), nullable=True)
+    # 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'
+    """
+        )
     )
-    op.create_foreign_key(
-        "fk_tool_call_parent_tool_call_id",
-        "tool_call",
-        "tool_call",
-        ["parent_tool_call_id"],
-        ["id"],
-        ondelete="CASCADE",
-    )
-
-    # Add other new columns
-    op.add_column(
-        "tool_call",
-        sa.Column("turn_number", sa.Integer(), nullable=False, server_default="0"),
-    )
-    op.add_column(
-        "tool_call",
-        sa.Column("tool_call_id", sa.String(), nullable=False, server_default=""),
-    )
-    op.add_column("tool_call", sa.Column("reasoning_tokens", sa.Text(), nullable=True))
-    op.add_column(
-        "tool_call",
-        sa.Column("tool_call_tokens", sa.Integer(), nullable=False, server_default="0"),
-    )
-    op.add_column(
-        "tool_call",
-        sa.Column("generated_images", postgresql.JSONB(), nullable=True),
-    )
-
-    # Rename columns
-    op.alter_column(
-        "tool_call", "tool_arguments", new_column_name="tool_call_arguments"
-    )
-    op.alter_column("tool_call", "tool_result", new_column_name="tool_call_response")
-
-    # Change tool_call_response type from JSONB to Text
-    op.execute(
+    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
         """
-        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"
+            ),
+        )
 
-    # Drop old columns
-    op.drop_column("tool_call", "tool_name")
-
-    # 4. Create new association table
-    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 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),
+        )
 
-    # 5. Persona table change
-    op.add_column(
-        "persona",
-        sa.Column(
-            "replace_base_system_prompt",
-            sa.Boolean(),
-            nullable=False,
-            server_default="false",
-        ),
+    # 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 new association table
-    op.drop_table("tool_call__search_doc")
+    # 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, server_default=""),
-    )
-
-    # Change tool_call_response back to JSONB
+    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", "generated_images")
-    op.drop_column("tool_call", "tool_call_tokens")
     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.drop_constraint(
-        "fk_tool_call_parent_chat_message_id", "tool_call", type_="foreignkey"
-    )
     op.alter_column(
         "tool_call",
         "parent_chat_message_id",
         new_column_name="message_id",
         nullable=False,
     )
-    op.create_foreign_key(
-        "tool_call_message_id_fkey",
-        "tool_call",
-        "chat_message",
-        ["message_id"],
-        ["id"],
-    )
-
     op.drop_constraint("fk_tool_call_chat_session_id", "tool_call", type_="foreignkey")
     op.drop_column("tool_call", "chat_session_id")
 
-    op.create_unique_constraint("uq_tool_call_message_id", "tool_call", ["message_id"])
-
-    # Reverse ChatMessage changes
-    # Note: research_answer_purpose and research_type were originally String columns,
-    # not Enum types (see migrations 5ae8240accb3 and f8a9b2c3d4e5)
     op.add_column(
         "chat_message",
-        sa.Column("research_answer_purpose", sa.String(), nullable=True),
+        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.String(), nullable=True),
+        sa.Column(
+            "research_type",
+            sa.Enum("SIMPLE", "DEEP", name="researchtype"),
+            nullable=True,
+        ),
     )
     op.add_column(
         "chat_message",
@@ -283,9 +442,7 @@ def downgrade() -> None:
     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"
     )
@@ -294,7 +451,6 @@ def downgrade() -> None:
         "latest_child_message_id",
         new_column_name="latest_child_message",
     )
-
     op.drop_constraint(
         "fk_chat_message_parent_message_id", "chat_message", type_="foreignkey"
     )
@@ -403,7 +559,6 @@ def downgrade() -> None:
         sa.Column("queries", postgresql.JSONB(), nullable=True),
         sa.Column("generated_images", postgresql.JSONB(), nullable=True),
         sa.Column("additional_data", postgresql.JSONB(), nullable=True),
-        sa.Column("file_ids", postgresql.JSONB(), nullable=True),
         sa.ForeignKeyConstraint(
             ["primary_question_id", "iteration_nr"],
             [

backend/alembic/versions/b8c9d0e1f2a3_drop_milestone_table.py

@@ -1,46 +0,0 @@
-"""Drop milestone table
-
-Revision ID: b8c9d0e1f2a3
-Revises: a2b3c4d5e6f7
-Create Date: 2025-12-18
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-import fastapi_users_db_sqlalchemy
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision = "b8c9d0e1f2a3"
-down_revision = "a2b3c4d5e6f7"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.drop_table("milestone")
-
-
-def downgrade() -> None:
-    op.create_table(
-        "milestone",
-        sa.Column("id", sa.UUID(), nullable=False),
-        sa.Column("tenant_id", sa.String(), nullable=True),
-        sa.Column(
-            "user_id",
-            fastapi_users_db_sqlalchemy.generics.GUID(),
-            nullable=True,
-        ),
-        sa.Column("event_type", sa.String(), nullable=False),
-        sa.Column(
-            "time_created",
-            sa.DateTime(timezone=True),
-            server_default=sa.text("now()"),
-            nullable=False,
-        ),
-        sa.Column("event_tracker", postgresql.JSONB(), nullable=True),
-        sa.ForeignKeyConstraint(["user_id"], ["user.id"], ondelete="CASCADE"),
-        sa.PrimaryKeyConstraint("id"),
-        sa.UniqueConstraint("event_type", name="uq_milestone_event_type"),
-    )

backend/alembic/versions/c1d2e3f4a5b6_add_deep_research_tool.py

@@ -1,52 +0,0 @@
-"""add_deep_research_tool
-
-Revision ID: c1d2e3f4a5b6
-Revises: b8c9d0e1f2a3
-Create Date: 2025-12-18 16:00:00.000000
-
-"""
-
-from alembic import op
-from onyx.deep_research.dr_mock_tools import RESEARCH_AGENT_DB_NAME
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "c1d2e3f4a5b6"
-down_revision = "b8c9d0e1f2a3"
-branch_labels = None
-depends_on = None
-
-
-DEEP_RESEARCH_TOOL = {
-    "name": RESEARCH_AGENT_DB_NAME,
-    "display_name": "Research Agent",
-    "description": "The Research Agent is a sub-agent that conducts research on a specific topic.",
-    "in_code_tool_id": "ResearchAgent",
-}
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-    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, false)
-            """
-        ),
-        DEEP_RESEARCH_TOOL,
-    )
-
-
-def downgrade() -> None:
-    conn = op.get_bind()
-    conn.execute(
-        sa.text(
-            """
-            DELETE FROM tool
-            WHERE in_code_tool_id = :in_code_tool_id
-            """
-        ),
-        {"in_code_tool_id": DEEP_RESEARCH_TOOL["in_code_tool_id"]},
-    )

backend/alembic/versions/e8f0d2a38171_add_status_to_mcp_server_and_make_auth_.py

@@ -1,115 +0,0 @@
-"""add status to mcp server and make auth fields nullable
-
-Revision ID: e8f0d2a38171
-Revises: ed9e44312505
-Create Date: 2025-11-28 11:15:37.667340
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-from onyx.db.enums import (  # type: ignore[import-untyped]
-    MCPTransport,
-    MCPAuthenticationType,
-    MCPAuthenticationPerformer,
-    MCPServerStatus,
-)
-
-# revision identifiers, used by Alembic.
-revision = "e8f0d2a38171"
-down_revision = "ed9e44312505"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Make auth fields nullable
-    op.alter_column(
-        "mcp_server",
-        "transport",
-        existing_type=sa.Enum(MCPTransport, name="mcp_transport", native_enum=False),
-        nullable=True,
-    )
-
-    op.alter_column(
-        "mcp_server",
-        "auth_type",
-        existing_type=sa.Enum(
-            MCPAuthenticationType, name="mcp_authentication_type", native_enum=False
-        ),
-        nullable=True,
-    )
-
-    op.alter_column(
-        "mcp_server",
-        "auth_performer",
-        existing_type=sa.Enum(
-            MCPAuthenticationPerformer,
-            name="mcp_authentication_performer",
-            native_enum=False,
-        ),
-        nullable=True,
-    )
-
-    # Add status column with default
-    op.add_column(
-        "mcp_server",
-        sa.Column(
-            "status",
-            sa.Enum(MCPServerStatus, name="mcp_server_status", native_enum=False),
-            nullable=False,
-            server_default="CREATED",
-        ),
-    )
-
-    # For existing records, mark status as CONNECTED
-    bind = op.get_bind()
-    bind.execute(
-        sa.text(
-            """
-        UPDATE mcp_server
-        SET status = 'CONNECTED'
-        WHERE status != 'CONNECTED'
-        and admin_connection_config_id IS NOT NULL
-        """
-        )
-    )
-
-
-def downgrade() -> None:
-    # Remove status column
-    op.drop_column("mcp_server", "status")
-
-    # Make auth fields non-nullable (set defaults first)
-    op.execute(
-        "UPDATE mcp_server SET transport = 'STREAMABLE_HTTP' WHERE transport IS NULL"
-    )
-    op.execute("UPDATE mcp_server SET auth_type = 'NONE' WHERE auth_type IS NULL")
-    op.execute(
-        "UPDATE mcp_server SET auth_performer = 'ADMIN' WHERE auth_performer IS NULL"
-    )
-
-    op.alter_column(
-        "mcp_server",
-        "transport",
-        existing_type=sa.Enum(MCPTransport, name="mcp_transport", native_enum=False),
-        nullable=False,
-    )
-    op.alter_column(
-        "mcp_server",
-        "auth_type",
-        existing_type=sa.Enum(
-            MCPAuthenticationType, name="mcp_authentication_type", native_enum=False
-        ),
-        nullable=False,
-    )
-    op.alter_column(
-        "mcp_server",
-        "auth_performer",
-        existing_type=sa.Enum(
-            MCPAuthenticationPerformer,
-            name="mcp_authentication_performer",
-            native_enum=False,
-        ),
-        nullable=False,
-    )

backend/alembic/versions/ed9e44312505_add_icon_name_field.py

@@ -1,34 +0,0 @@
-"""Add icon_name field
-
-Revision ID: ed9e44312505
-Revises: 5e6f7a8b9c0d
-Create Date: 2025-12-03 16:35:07.828393
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "ed9e44312505"
-down_revision = "5e6f7a8b9c0d"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Add icon_name column
-    op.add_column("persona", sa.Column("icon_name", sa.String(), nullable=True))
-
-    # Remove old icon columns
-    op.drop_column("persona", "icon_shape")
-    op.drop_column("persona", "icon_color")
-
-
-def downgrade() -> None:
-    # Re-add old icon columns
-    op.add_column("persona", sa.Column("icon_color", sa.String(), nullable=True))
-    op.add_column("persona", sa.Column("icon_shape", sa.Integer(), nullable=True))
-
-    # Remove icon_name column
-    op.drop_column("persona", "icon_name")

backend/ee/onyx/configs/app_configs.py

@@ -41,10 +41,6 @@ CONFLUENCE_ANONYMOUS_ACCESS_IS_PUBLIC = (
 JIRA_PERMISSION_DOC_SYNC_FREQUENCY = int(
     os.environ.get("JIRA_PERMISSION_DOC_SYNC_FREQUENCY") or 30 * 60
 )
-# In seconds, default is 30 minutes
-JIRA_PERMISSION_GROUP_SYNC_FREQUENCY = int(
-    os.environ.get("JIRA_PERMISSION_GROUP_SYNC_FREQUENCY") or 30 * 60
-)
 
 
 #####

backend/ee/onyx/db/license.py

@@ -1,278 +0,0 @@
-"""Database and cache operations for the license table."""
-
-from datetime import datetime
-
-from sqlalchemy import func
-from sqlalchemy import select
-from sqlalchemy.orm import Session
-
-from ee.onyx.server.license.models import LicenseMetadata
-from ee.onyx.server.license.models import LicensePayload
-from ee.onyx.server.license.models import LicenseSource
-from onyx.db.models import License
-from onyx.db.models import User
-from onyx.redis.redis_pool import get_redis_client
-from onyx.redis.redis_pool import get_redis_replica_client
-from onyx.utils.logger import setup_logger
-from shared_configs.configs import MULTI_TENANT
-from shared_configs.contextvars import get_current_tenant_id
-
-logger = setup_logger()
-
-LICENSE_METADATA_KEY = "license:metadata"
-LICENSE_CACHE_TTL_SECONDS = 86400  # 24 hours
-
-
-# -----------------------------------------------------------------------------
-# Database CRUD Operations
-# -----------------------------------------------------------------------------
-
-
-def get_license(db_session: Session) -> License | None:
-    """
-    Get the current license (singleton pattern - only one row).
-
-    Args:
-        db_session: Database session
-
-    Returns:
-        License object if exists, None otherwise
-    """
-    return db_session.execute(select(License)).scalars().first()
-
-
-def upsert_license(db_session: Session, license_data: str) -> License:
-    """
-    Insert or update the license (singleton pattern).
-
-    Args:
-        db_session: Database session
-        license_data: Base64-encoded signed license blob
-
-    Returns:
-        The created or updated License object
-    """
-    existing = get_license(db_session)
-
-    if existing:
-        existing.license_data = license_data
-        db_session.commit()
-        db_session.refresh(existing)
-        logger.info("License updated")
-        return existing
-
-    new_license = License(license_data=license_data)
-    db_session.add(new_license)
-    db_session.commit()
-    db_session.refresh(new_license)
-    logger.info("License created")
-    return new_license
-
-
-def delete_license(db_session: Session) -> bool:
-    """
-    Delete the current license.
-
-    Args:
-        db_session: Database session
-
-    Returns:
-        True if deleted, False if no license existed
-    """
-    existing = get_license(db_session)
-    if existing:
-        db_session.delete(existing)
-        db_session.commit()
-        logger.info("License deleted")
-        return True
-    return False
-
-
-# -----------------------------------------------------------------------------
-# Seat Counting
-# -----------------------------------------------------------------------------
-
-
-def get_used_seats(tenant_id: str | None = None) -> int:
-    """
-    Get current seat usage.
-
-    For multi-tenant: counts users in UserTenantMapping for this tenant.
-    For self-hosted: counts all active users (includes both Onyx UI users
-    and Slack users who have been converted to Onyx users).
-    """
-    if MULTI_TENANT:
-        from ee.onyx.server.tenants.user_mapping import get_tenant_count
-
-        return get_tenant_count(tenant_id or get_current_tenant_id())
-    else:
-        # Self-hosted: count all active users (Onyx + converted Slack users)
-        from onyx.db.engine.sql_engine import get_session_with_current_tenant
-
-        with get_session_with_current_tenant() as db_session:
-            result = db_session.execute(
-                select(func.count()).select_from(User).where(User.is_active)  # type: ignore
-            )
-            return result.scalar() or 0
-
-
-# -----------------------------------------------------------------------------
-# Redis Cache Operations
-# -----------------------------------------------------------------------------
-
-
-def get_cached_license_metadata(tenant_id: str | None = None) -> LicenseMetadata | None:
-    """
-    Get license metadata from Redis cache.
-
-    Args:
-        tenant_id: Tenant ID (for multi-tenant deployments)
-
-    Returns:
-        LicenseMetadata if cached, None otherwise
-    """
-    tenant = tenant_id or get_current_tenant_id()
-    redis_client = get_redis_replica_client(tenant_id=tenant)
-
-    cached = redis_client.get(LICENSE_METADATA_KEY)
-    if cached:
-        try:
-            cached_str: str
-            if isinstance(cached, bytes):
-                cached_str = cached.decode("utf-8")
-            else:
-                cached_str = str(cached)
-            return LicenseMetadata.model_validate_json(cached_str)
-        except Exception as e:
-            logger.warning(f"Failed to parse cached license metadata: {e}")
-            return None
-    return None
-
-
-def invalidate_license_cache(tenant_id: str | None = None) -> None:
-    """
-    Invalidate the license metadata cache (not the license itself).
-
-    This deletes the cached LicenseMetadata from Redis. The actual license
-    in the database is not affected. Redis delete is idempotent - if the
-    key doesn't exist, this is a no-op.
-
-    Args:
-        tenant_id: Tenant ID (for multi-tenant deployments)
-    """
-    tenant = tenant_id or get_current_tenant_id()
-    redis_client = get_redis_client(tenant_id=tenant)
-    redis_client.delete(LICENSE_METADATA_KEY)
-    logger.info("License cache invalidated")
-
-
-def update_license_cache(
-    payload: LicensePayload,
-    source: LicenseSource | None = None,
-    grace_period_end: datetime | None = None,
-    tenant_id: str | None = None,
-) -> LicenseMetadata:
-    """
-    Update the Redis cache with license metadata.
-
-    We cache all license statuses (ACTIVE, GRACE_PERIOD, GATED_ACCESS) because:
-    1. Frontend needs status to show appropriate UI/banners
-    2. Caching avoids repeated DB + crypto verification on every request
-    3. Status enforcement happens at the feature level, not here
-
-    Args:
-        payload: Verified license payload
-        source: How the license was obtained
-        grace_period_end: Optional grace period end time
-        tenant_id: Tenant ID (for multi-tenant deployments)
-
-    Returns:
-        The cached LicenseMetadata
-    """
-    from ee.onyx.utils.license import get_license_status
-
-    tenant = tenant_id or get_current_tenant_id()
-    redis_client = get_redis_client(tenant_id=tenant)
-
-    used_seats = get_used_seats(tenant)
-    status = get_license_status(payload, grace_period_end)
-
-    metadata = LicenseMetadata(
-        tenant_id=payload.tenant_id,
-        organization_name=payload.organization_name,
-        seats=payload.seats,
-        used_seats=used_seats,
-        plan_type=payload.plan_type,
-        issued_at=payload.issued_at,
-        expires_at=payload.expires_at,
-        grace_period_end=grace_period_end,
-        status=status,
-        source=source,
-        stripe_subscription_id=payload.stripe_subscription_id,
-    )
-
-    redis_client.setex(
-        LICENSE_METADATA_KEY,
-        LICENSE_CACHE_TTL_SECONDS,
-        metadata.model_dump_json(),
-    )
-
-    logger.info(f"License cache updated: {metadata.seats} seats, status={status.value}")
-    return metadata
-
-
-def refresh_license_cache(
-    db_session: Session,
-    tenant_id: str | None = None,
-) -> LicenseMetadata | None:
-    """
-    Refresh the license cache from the database.
-
-    Args:
-        db_session: Database session
-        tenant_id: Tenant ID (for multi-tenant deployments)
-
-    Returns:
-        LicenseMetadata if license exists, None otherwise
-    """
-    from ee.onyx.utils.license import verify_license_signature
-
-    license_record = get_license(db_session)
-    if not license_record:
-        invalidate_license_cache(tenant_id)
-        return None
-
-    try:
-        payload = verify_license_signature(license_record.license_data)
-        return update_license_cache(
-            payload,
-            source=LicenseSource.AUTO_FETCH,
-            tenant_id=tenant_id,
-        )
-    except ValueError as e:
-        logger.error(f"Failed to verify license during cache refresh: {e}")
-        invalidate_license_cache(tenant_id)
-        return None
-
-
-def get_license_metadata(
-    db_session: Session,
-    tenant_id: str | None = None,
-) -> LicenseMetadata | None:
-    """
-    Get license metadata, using cache if available.
-
-    Args:
-        db_session: Database session
-        tenant_id: Tenant ID (for multi-tenant deployments)
-
-    Returns:
-        LicenseMetadata if license exists, None otherwise
-    """
-    # Try cache first
-    cached = get_cached_license_metadata(tenant_id)
-    if cached:
-        return cached
-
-    # Refresh from database
-    return refresh_license_cache(db_session, tenant_id)

backend/ee/onyx/db/user_group.py

@@ -8,7 +8,6 @@ from sqlalchemy import func
 from sqlalchemy import Select
 from sqlalchemy import select
 from sqlalchemy import update
-from sqlalchemy.dialects.postgresql import insert
 from sqlalchemy.orm import Session
 
 from ee.onyx.server.user_group.models import SetCuratorRequest
@@ -363,29 +362,14 @@ def _check_user_group_is_modifiable(user_group: UserGroup) -> None:
 
 def _add_user__user_group_relationships__no_commit(
     db_session: Session, user_group_id: int, user_ids: list[UUID]
-) -> None:
-    """NOTE: does not commit the transaction.
-
-    This function is idempotent - it will skip users who are already in the group
-    to avoid duplicate key violations during concurrent operations or re-syncs.
-    Uses ON CONFLICT DO NOTHING to keep inserts atomic under concurrency.
-    """
-    if not user_ids:
-        return
-
-    insert_stmt = (
-        insert(User__UserGroup)
-        .values(
-            [
-                {"user_id": user_id, "user_group_id": user_group_id}
-                for user_id in user_ids
-            ]
-        )
-        .on_conflict_do_nothing(
-            index_elements=[User__UserGroup.user_group_id, User__UserGroup.user_id]
-        )
-    )
-    db_session.execute(insert_stmt)
+) -> list[User__UserGroup]:
+    """NOTE: does not commit the transaction."""
+    relationships = [
+        User__UserGroup(user_id=user_id, user_group_id=user_group_id)
+        for user_id in user_ids
+    ]
+    db_session.add_all(relationships)
+    return relationships
 
 
 def _add_user_group__cc_pair_relationships__no_commit(

backend/ee/onyx/external_permissions/confluence/group_sync.py

@@ -3,15 +3,12 @@ from collections.abc import Generator
 from ee.onyx.db.external_perm import ExternalUserGroup
 from ee.onyx.external_permissions.confluence.constants import ALL_CONF_EMAILS_GROUP_NAME
 from onyx.background.error_logging import emit_background_error
-from onyx.configs.app_configs import CONFLUENCE_USE_ONYX_USERS_FOR_GROUP_SYNC
 from onyx.connectors.confluence.onyx_confluence import (
     get_user_email_from_username__server,
 )
 from onyx.connectors.confluence.onyx_confluence import OnyxConfluence
 from onyx.connectors.credentials_provider import OnyxDBCredentialsProvider
-from onyx.db.engine.sql_engine import get_session_with_current_tenant
 from onyx.db.models import ConnectorCredentialPair
-from onyx.db.users import get_all_users
 from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
@@ -22,7 +19,7 @@ def _build_group_member_email_map(
 ) -> dict[str, set[str]]:
     group_member_emails: dict[str, set[str]] = {}
     for user in confluence_client.paginated_cql_user_retrieval():
-        logger.info(f"Processing groups for user: {user}")
+        logger.debug(f"Processing groups for user: {user}")
 
         email = user.email
         if not email:
@@ -34,8 +31,6 @@ def _build_group_member_email_map(
                     confluence_client=confluence_client,
                     user_name=user_name,
                 )
-            else:
-                logger.error(f"user result missing username field: {user}")
 
         if not email:
             # If we still don't have an email, skip this user
@@ -69,92 +64,6 @@ def _build_group_member_email_map(
     return group_member_emails
 
 
-def _build_group_member_email_map_from_onyx_users(
-    confluence_client: OnyxConfluence,
-) -> dict[str, set[str]]:
-    """Hacky, but it's the only way to do this as long as the
-    Confluence APIs are broken.
-
-    This is fixed in Confluence Data Center 10.1.0, so first choice
-    is to tell users to upgrade to 10.1.0.
-    https://jira.atlassian.com/browse/CONFSERVER-95999
-    """
-    with get_session_with_current_tenant() as db_session:
-        # don't include external since they are handled by the "through confluence"
-        # user fetching mechanism
-        user_emails = [
-            user.email for user in get_all_users(db_session, include_external=False)
-        ]
-
-    def _infer_username_from_email(email: str) -> str:
-        return email.split("@")[0]
-
-    group_member_emails: dict[str, set[str]] = {}
-    for email in user_emails:
-        logger.info(f"Processing groups for user with email: {email}")
-        try:
-            user_name = _infer_username_from_email(email)
-            response = confluence_client.get_user_details_by_username(user_name)
-            user_key = response.get("userKey")
-            if not user_key:
-                logger.error(f"User key not found for user with email {email}")
-                continue
-
-            all_users_groups: set[str] = set()
-            for group in confluence_client.paginated_groups_by_user_retrieval(user_key):
-                # group name uniqueness is enforced by Confluence, so we can use it as a group ID
-                group_id = group["name"]
-                group_member_emails.setdefault(group_id, set()).add(email)
-                all_users_groups.add(group_id)
-
-            if not all_users_groups:
-                msg = f"No groups found for user with email: {email}"
-                logger.error(msg)
-            else:
-                logger.info(
-                    f"Found groups {all_users_groups} for user with email {email}"
-                )
-        except Exception:
-            logger.exception(f"Error getting user details for user with email {email}")
-
-    return group_member_emails
-
-
-def _build_final_group_to_member_email_map(
-    confluence_client: OnyxConfluence,
-    cc_pair_id: int,
-    # if set, will infer confluence usernames from onyx users in addition to using the
-    # confluence users API. This is a hacky workaround for the fact that the Confluence
-    # users API is broken before Confluence Data Center 10.1.0.
-    use_onyx_users: bool = CONFLUENCE_USE_ONYX_USERS_FOR_GROUP_SYNC,
-) -> dict[str, set[str]]:
-    group_to_member_email_map = _build_group_member_email_map(
-        confluence_client=confluence_client,
-        cc_pair_id=cc_pair_id,
-    )
-    group_to_member_email_map_from_onyx_users = (
-        (
-            _build_group_member_email_map_from_onyx_users(
-                confluence_client=confluence_client,
-            )
-        )
-        if use_onyx_users
-        else {}
-    )
-
-    all_group_ids = set(group_to_member_email_map.keys()) | set(
-        group_to_member_email_map_from_onyx_users.keys()
-    )
-    final_group_to_member_email_map = {}
-    for group_id in all_group_ids:
-        group_member_emails = group_to_member_email_map.get(
-            group_id, set()
-        ) | group_to_member_email_map_from_onyx_users.get(group_id, set())
-        final_group_to_member_email_map[group_id] = group_member_emails
-
-    return final_group_to_member_email_map
-
-
 def confluence_group_sync(
     tenant_id: str,
     cc_pair: ConnectorCredentialPair,
@@ -178,12 +87,13 @@ def confluence_group_sync(
     confluence_client._probe_connection(**probe_kwargs)
     confluence_client._initialize_connection(**final_kwargs)
 
-    group_to_member_email_map = _build_final_group_to_member_email_map(
-        confluence_client, cc_pair.id
+    group_member_email_map = _build_group_member_email_map(
+        confluence_client=confluence_client,
+        cc_pair_id=cc_pair.id,
     )
 
     all_found_emails = set()
-    for group_id, group_member_emails in group_to_member_email_map.items():
+    for group_id, group_member_emails in group_member_email_map.items():
         yield (
             ExternalUserGroup(
                 id=group_id,

backend/ee/onyx/external_permissions/jira/group_sync.py

@@ -1,136 +0,0 @@
-from collections.abc import Generator
-
-from jira import JIRA
-
-from ee.onyx.db.external_perm import ExternalUserGroup
-from onyx.connectors.jira.utils import build_jira_client
-from onyx.db.models import ConnectorCredentialPair
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def _get_jira_group_members_email(
-    jira_client: JIRA,
-    group_name: str,
-) -> list[str]:
-    """Get all member emails for a Jira group.
-
-    Filters out app accounts (bots, integrations) and only returns real user emails.
-    """
-    emails: list[str] = []
-
-    try:
-        # group_members returns an OrderedDict of account_id -> member_info
-        members = jira_client.group_members(group=group_name)
-
-        if not members:
-            logger.warning(f"No members found for group {group_name}")
-            return emails
-
-        for account_id, member_info in members.items():
-            # member_info is a dict with keys like 'fullname', 'email', 'active'
-            email = member_info.get("email")
-
-            # Skip "hidden" emails - these are typically app accounts
-            if email and email != "hidden":
-                emails.append(email)
-            else:
-                # For cloud, we might need to fetch user details separately
-                try:
-                    user = jira_client.user(id=account_id)
-
-                    # Skip app accounts (bots, integrations, etc.)
-                    if hasattr(user, "accountType") and user.accountType == "app":
-                        logger.info(
-                            f"Skipping app account {account_id} for group {group_name}"
-                        )
-                        continue
-
-                    if hasattr(user, "emailAddress") and user.emailAddress:
-                        emails.append(user.emailAddress)
-                    else:
-                        logger.warning(f"User {account_id} has no email address")
-                except Exception as e:
-                    logger.warning(
-                        f"Could not fetch email for user {account_id} in group {group_name}: {e}"
-                    )
-
-    except Exception as e:
-        logger.error(f"Error fetching members for group {group_name}: {e}")
-
-    return emails
-
-
-def _build_group_member_email_map(
-    jira_client: JIRA,
-) -> dict[str, set[str]]:
-    """Build a map of group names to member emails."""
-    group_member_emails: dict[str, set[str]] = {}
-
-    try:
-        # Get all groups from Jira - returns a list of group name strings
-        group_names = jira_client.groups()
-
-        if not group_names:
-            logger.warning("No groups found in Jira")
-            return group_member_emails
-
-        logger.info(f"Found {len(group_names)} groups in Jira")
-
-        for group_name in group_names:
-            if not group_name:
-                continue
-
-            member_emails = _get_jira_group_members_email(
-                jira_client=jira_client,
-                group_name=group_name,
-            )
-
-            if member_emails:
-                group_member_emails[group_name] = set(member_emails)
-                logger.debug(
-                    f"Found {len(member_emails)} members for group {group_name}"
-                )
-            else:
-                logger.debug(f"No members found for group {group_name}")
-
-    except Exception as e:
-        logger.error(f"Error building group member email map: {e}")
-
-    return group_member_emails
-
-
-def jira_group_sync(
-    tenant_id: str,
-    cc_pair: ConnectorCredentialPair,
-) -> Generator[ExternalUserGroup, None, None]:
-    """
-    Sync Jira groups and their members.
-
-    This function fetches all groups from Jira and yields ExternalUserGroup
-    objects containing the group ID and member emails.
-    """
-    jira_base_url = cc_pair.connector.connector_specific_config.get("jira_base_url", "")
-    scoped_token = cc_pair.connector.connector_specific_config.get(
-        "scoped_token", False
-    )
-
-    if not jira_base_url:
-        raise ValueError("No jira_base_url found in connector config")
-
-    jira_client = build_jira_client(
-        credentials=cc_pair.credential.credential_json,
-        jira_base=jira_base_url,
-        scoped_token=scoped_token,
-    )
-
-    group_member_email_map = _build_group_member_email_map(jira_client=jira_client)
-    if not group_member_email_map:
-        raise ValueError(f"No groups with members found for cc_pair_id={cc_pair.id}")
-
-    for group_id, group_member_emails in group_member_email_map.items():
-        yield ExternalUserGroup(
-            id=group_id,
-            user_emails=list(group_member_emails),
-        )

backend/ee/onyx/external_permissions/jira/page_access.py

@@ -16,10 +16,6 @@ HolderMap = dict[str, list[Holder]]
 logger = setup_logger()
 
 
-def _get_role_id(holder: Holder) -> str | None:
-    return holder.get("value") or holder.get("parameter")
-
-
 def _build_holder_map(permissions: list[dict]) -> dict[str, list[Holder]]:
     """
     A "Holder" in JIRA is a person / entity who "holds" the corresponding permission.
@@ -114,137 +110,80 @@ def _get_user_emails(user_holders: list[Holder]) -> list[str]:
     return emails
 
 
-def _get_user_emails_and_groups_from_project_roles(
+def _get_user_emails_from_project_roles(
     jira_client: JIRA,
     jira_project: str,
     project_role_holders: list[Holder],
-) -> tuple[list[str], list[str]]:
-    """
-    Get user emails and group names from project roles.
-    Returns a tuple of (emails, group_names).
-    """
-    # Get role IDs - Cloud uses "value", Data Center uses "parameter"
-    role_ids = []
-    for holder in project_role_holders:
-        role_id = _get_role_id(holder)
-        if role_id:
-            role_ids.append(role_id)
-        else:
-            logger.warning(f"No value or parameter in projectRole holder: {holder}")
-
+) -> list[str]:
+    # NOTE (@raunakab) a `parallel_yield` may be helpful here...?
     roles = [
-        jira_client.project_role(project=jira_project, id=role_id)
-        for role_id in role_ids
+        jira_client.project_role(project=jira_project, id=project_role_holder["value"])
+        for project_role_holder in project_role_holders
+        if "value" in project_role_holder
     ]
 
     emails = []
-    groups = []
 
     for role in roles:
         if not hasattr(role, "actors"):
-            logger.warning(f"Project role {role} has no actors attribute")
             continue
 
         for actor in role.actors:
-            # Handle group actors
-            if hasattr(actor, "actorGroup"):
-                group_name = getattr(actor.actorGroup, "name", None) or getattr(
-                    actor.actorGroup, "displayName", None
-                )
-                if group_name:
-                    groups.append(group_name)
+            if not hasattr(actor, "actorUser") or not hasattr(
+                actor.actorUser, "accountId"
+            ):
                 continue
 
-            # Handle user actors
-            if hasattr(actor, "actorUser"):
-                account_id = getattr(actor.actorUser, "accountId", None)
-                if not account_id:
-                    logger.error(f"No accountId in actorUser: {actor.actorUser}")
-                    continue
-
-                user = jira_client.user(id=account_id)
-                if not hasattr(user, "accountType") or user.accountType != "atlassian":
-                    logger.info(
-                        f"Skipping user {account_id} because it is not an atlassian user"
-                    )
-                    continue
-
-                if not hasattr(user, "emailAddress"):
-                    msg = f"User's email address was not able to be retrieved;  {actor.actorUser.accountId=}"
-                    if hasattr(user, "displayName"):
-                        msg += f" {actor.displayName=}"
-                    logger.warning(msg)
-                    continue
-
-                emails.append(user.emailAddress)
+            user = jira_client.user(id=actor.actorUser.accountId)
+            if not hasattr(user, "accountType") or user.accountType != "atlassian":
                 continue
 
-            logger.debug(f"Skipping actor type: {actor}")
+            if not hasattr(user, "emailAddress"):
+                msg = f"User's email address was not able to be retrieved;  {actor.actorUser.accountId=}"
+                if hasattr(user, "displayName"):
+                    msg += f" {actor.displayName=}"
+                logger.warn(msg)
+                continue
 
-    return emails, groups
+            emails.append(user.emailAddress)
+
+    return emails
 
 
 def _build_external_access_from_holder_map(
     jira_client: JIRA, jira_project: str, holder_map: HolderMap
 ) -> ExternalAccess:
     """
-    Build ExternalAccess from the holder map.
-
-    Holder types handled:
-        - "anyone": Public project, anyone can access
-        - "applicationRole": All users with a Jira license can access (treated as public)
-        - "user": Specific users with access
-        - "projectRole": Project roles containing users and/or groups
-        - "group": Groups directly assigned in the permission scheme
+    # Note:
+        If the `holder_map` contains an instance of "anyone", then this is a public JIRA project.
+        Otherwise, we fetch the "projectRole"s (i.e., the user-groups in JIRA speak), and the user emails.
     """
-    # Public access - anyone can view
+
     if "anyone" in holder_map:
         return ExternalAccess(
             external_user_emails=set(), external_user_group_ids=set(), is_public=True
         )
 
-    # applicationRole means all users with a Jira license can access - treat as public
-    if "applicationRole" in holder_map:
-        return ExternalAccess(
-            external_user_emails=set(), external_user_group_ids=set(), is_public=True
-        )
-
-    # Get emails from explicit user holders
     user_emails = (
         _get_user_emails(user_holders=holder_map["user"])
         if "user" in holder_map
         else []
     )
-
-    # Get emails and groups from project roles
-    project_role_user_emails: list[str] = []
-    project_role_groups: list[str] = []
-    if "projectRole" in holder_map:
-        project_role_user_emails, project_role_groups = (
-            _get_user_emails_and_groups_from_project_roles(
-                jira_client=jira_client,
-                jira_project=jira_project,
-                project_role_holders=holder_map["projectRole"],
-            )
+    project_role_user_emails = (
+        _get_user_emails_from_project_roles(
+            jira_client=jira_client,
+            jira_project=jira_project,
+            project_role_holders=holder_map["projectRole"],
         )
-
-    # Get groups directly assigned in permission scheme (common in Data Center)
-    # Format: {'type': 'group', 'parameter': 'group-name', 'expand': 'group'}
-    direct_groups: list[str] = []
-    if "group" in holder_map:
-        for group_holder in holder_map["group"]:
-            group_name = _get_role_id(group_holder)
-            if group_name:
-                direct_groups.append(group_name)
-            else:
-                logger.error(f"No parameter/value in group holder: {group_holder}")
+        if "projectRole" in holder_map
+        else []
+    )
 
     external_user_emails = set(user_emails + project_role_user_emails)
-    external_user_group_ids = set(project_role_groups + direct_groups)
 
     return ExternalAccess(
         external_user_emails=external_user_emails,
-        external_user_group_ids=external_user_group_ids,
+        external_user_group_ids=set(),
         is_public=False,
     )
 
@@ -258,11 +197,9 @@ def get_project_permissions(
     )
 
     if not hasattr(project_permissions, "permissions"):
-        logger.error(f"Project {jira_project} has no permissions attribute")
         return None
 
     if not isinstance(project_permissions.permissions, list):
-        logger.error(f"Project {jira_project} permissions is not a list")
         return None
 
     holder_map = _build_holder_map(permissions=project_permissions.permissions)

backend/ee/onyx/external_permissions/sharepoint/permission_utils.py

@@ -15,7 +15,6 @@ from ee.onyx.db.external_perm import ExternalUserGroup
 from onyx.access.models import ExternalAccess
 from onyx.access.utils import build_ext_group_name_for_onyx
 from onyx.configs.constants import DocumentSource
-from onyx.connectors.sharepoint.connector import SHARED_DOCUMENTS_MAP_REVERSE
 from onyx.connectors.sharepoint.connector import sleep_and_retry
 from onyx.utils.logger import setup_logger
 
@@ -512,8 +511,8 @@ def get_external_access_from_sharepoint(
                 f"Failed to get SharePoint list item ID for item {drive_item.id}"
             )
 
-        if drive_name in SHARED_DOCUMENTS_MAP_REVERSE:
-            drive_name = SHARED_DOCUMENTS_MAP_REVERSE[drive_name]
+        if drive_name == "Shared Documents":
+            drive_name = "Documents"
 
         item = client_context.web.lists.get_by_title(drive_name).items.get_by_id(
             item_id

backend/ee/onyx/external_permissions/sync_params.py

@@ -11,7 +11,6 @@ from ee.onyx.configs.app_configs import GITHUB_PERMISSION_DOC_SYNC_FREQUENCY
 from ee.onyx.configs.app_configs import GITHUB_PERMISSION_GROUP_SYNC_FREQUENCY
 from ee.onyx.configs.app_configs import GOOGLE_DRIVE_PERMISSION_GROUP_SYNC_FREQUENCY
 from ee.onyx.configs.app_configs import JIRA_PERMISSION_DOC_SYNC_FREQUENCY
-from ee.onyx.configs.app_configs import JIRA_PERMISSION_GROUP_SYNC_FREQUENCY
 from ee.onyx.configs.app_configs import SHAREPOINT_PERMISSION_DOC_SYNC_FREQUENCY
 from ee.onyx.configs.app_configs import SHAREPOINT_PERMISSION_GROUP_SYNC_FREQUENCY
 from ee.onyx.configs.app_configs import SLACK_PERMISSION_DOC_SYNC_FREQUENCY
@@ -24,7 +23,6 @@ from ee.onyx.external_permissions.gmail.doc_sync import gmail_doc_sync
 from ee.onyx.external_permissions.google_drive.doc_sync import gdrive_doc_sync
 from ee.onyx.external_permissions.google_drive.group_sync import gdrive_group_sync
 from ee.onyx.external_permissions.jira.doc_sync import jira_doc_sync
-from ee.onyx.external_permissions.jira.group_sync import jira_group_sync
 from ee.onyx.external_permissions.perm_sync_types import CensoringFuncType
 from ee.onyx.external_permissions.perm_sync_types import DocSyncFuncType
 from ee.onyx.external_permissions.perm_sync_types import FetchAllDocumentsFunction
@@ -112,11 +110,6 @@ _SOURCE_TO_SYNC_CONFIG: dict[DocumentSource, SyncConfig] = {
             doc_sync_func=jira_doc_sync,
             initial_index_should_sync=True,
         ),
-        group_sync_config=GroupSyncConfig(
-            group_sync_frequency=JIRA_PERMISSION_GROUP_SYNC_FREQUENCY,
-            group_sync_func=jira_group_sync,
-            group_sync_is_cc_pair_agnostic=True,
-        ),
     ),
     # Groups are not needed for Slack.
     # All channel access is done at the individual user level.

backend/ee/onyx/main.py

@@ -14,7 +14,6 @@ from ee.onyx.server.enterprise_settings.api import (
     basic_router as enterprise_settings_router,
 )
 from ee.onyx.server.evals.api import router as evals_router
-from ee.onyx.server.license.api import router as license_router
 from ee.onyx.server.manage.standard_answer import router as standard_answer_router
 from ee.onyx.server.middleware.tenant_tracking import (
     add_api_server_tenant_id_middleware,
@@ -140,8 +139,6 @@ def get_application() -> FastAPI:
     )
     include_router_with_global_prefix_prepended(application, enterprise_settings_router)
     include_router_with_global_prefix_prepended(application, usage_export_router)
-    # License management
-    include_router_with_global_prefix_prepended(application, license_router)
 
     if MULTI_TENANT:
         # Tenant management

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

@@ -1,246 +0,0 @@
-"""License API endpoints."""
-
-import requests
-from fastapi import APIRouter
-from fastapi import Depends
-from fastapi import File
-from fastapi import HTTPException
-from fastapi import UploadFile
-from sqlalchemy.orm import Session
-
-from ee.onyx.auth.users import current_admin_user
-from ee.onyx.db.license import delete_license as db_delete_license
-from ee.onyx.db.license import get_license_metadata
-from ee.onyx.db.license import invalidate_license_cache
-from ee.onyx.db.license import refresh_license_cache
-from ee.onyx.db.license import update_license_cache
-from ee.onyx.db.license import upsert_license
-from ee.onyx.server.license.models import LicenseResponse
-from ee.onyx.server.license.models import LicenseSource
-from ee.onyx.server.license.models import LicenseStatusResponse
-from ee.onyx.server.license.models import LicenseUploadResponse
-from ee.onyx.server.license.models import SeatUsageResponse
-from ee.onyx.server.tenants.access import generate_data_plane_token
-from ee.onyx.utils.license import verify_license_signature
-from onyx.auth.users import User
-from onyx.configs.app_configs import CONTROL_PLANE_API_BASE_URL
-from onyx.db.engine.sql_engine import get_session
-from onyx.utils.logger import setup_logger
-from shared_configs.contextvars import get_current_tenant_id
-
-logger = setup_logger()
-
-router = APIRouter(prefix="/license")
-
-
-@router.get("")
-async def get_license_status(
-    _: User = Depends(current_admin_user),
-    db_session: Session = Depends(get_session),
-) -> LicenseStatusResponse:
-    """Get current license status and seat usage."""
-    metadata = get_license_metadata(db_session)
-
-    if not metadata:
-        return LicenseStatusResponse(has_license=False)
-
-    return LicenseStatusResponse(
-        has_license=True,
-        seats=metadata.seats,
-        used_seats=metadata.used_seats,
-        plan_type=metadata.plan_type,
-        issued_at=metadata.issued_at,
-        expires_at=metadata.expires_at,
-        grace_period_end=metadata.grace_period_end,
-        status=metadata.status,
-        source=metadata.source,
-    )
-
-
-@router.get("/seats")
-async def get_seat_usage(
-    _: User = Depends(current_admin_user),
-    db_session: Session = Depends(get_session),
-) -> SeatUsageResponse:
-    """Get detailed seat usage information."""
-    metadata = get_license_metadata(db_session)
-
-    if not metadata:
-        return SeatUsageResponse(
-            total_seats=0,
-            used_seats=0,
-            available_seats=0,
-        )
-
-    return SeatUsageResponse(
-        total_seats=metadata.seats,
-        used_seats=metadata.used_seats,
-        available_seats=max(0, metadata.seats - metadata.used_seats),
-    )
-
-
-@router.post("/fetch")
-async def fetch_license(
-    _: User = Depends(current_admin_user),
-    db_session: Session = Depends(get_session),
-) -> LicenseResponse:
-    """
-    Fetch license from control plane.
-    Used after Stripe checkout completion to retrieve the new license.
-    """
-    tenant_id = get_current_tenant_id()
-
-    try:
-        token = generate_data_plane_token()
-    except ValueError as e:
-        logger.error(f"Failed to generate data plane token: {e}")
-        raise HTTPException(
-            status_code=500, detail="Authentication configuration error"
-        )
-
-    try:
-        headers = {
-            "Authorization": f"Bearer {token}",
-            "Content-Type": "application/json",
-        }
-        url = f"{CONTROL_PLANE_API_BASE_URL}/license/{tenant_id}"
-        response = requests.get(url, headers=headers, timeout=10)
-        response.raise_for_status()
-
-        data = response.json()
-        if not isinstance(data, dict) or "license" not in data:
-            raise HTTPException(
-                status_code=502, detail="Invalid response from control plane"
-            )
-
-        license_data = data["license"]
-        if not license_data:
-            raise HTTPException(status_code=404, detail="No license found")
-
-        # Verify signature before persisting
-        payload = verify_license_signature(license_data)
-
-        # Verify the fetched license is for this tenant
-        if payload.tenant_id != tenant_id:
-            logger.error(
-                f"License tenant mismatch: expected {tenant_id}, got {payload.tenant_id}"
-            )
-            raise HTTPException(
-                status_code=400,
-                detail="License tenant ID mismatch - control plane returned wrong license",
-            )
-
-        # Persist to DB and update cache atomically
-        upsert_license(db_session, license_data)
-        try:
-            update_license_cache(payload, source=LicenseSource.AUTO_FETCH)
-        except Exception as cache_error:
-            # Log but don't fail - DB is source of truth, cache will refresh on next read
-            logger.warning(f"Failed to update license cache: {cache_error}")
-
-        return LicenseResponse(success=True, license=payload)
-
-    except requests.HTTPError as e:
-        status_code = e.response.status_code if e.response is not None else 502
-        logger.error(f"Control plane returned error: {status_code}")
-        raise HTTPException(
-            status_code=status_code,
-            detail="Failed to fetch license from control plane",
-        )
-    except ValueError as e:
-        logger.error(f"License verification failed: {type(e).__name__}")
-        raise HTTPException(status_code=400, detail=str(e))
-    except requests.RequestException:
-        logger.exception("Failed to fetch license from control plane")
-        raise HTTPException(
-            status_code=502, detail="Failed to connect to control plane"
-        )
-
-
-@router.post("/upload")
-async def upload_license(
-    license_file: UploadFile = File(...),
-    _: User = Depends(current_admin_user),
-    db_session: Session = Depends(get_session),
-) -> LicenseUploadResponse:
-    """
-    Upload a license file manually.
-    Used for air-gapped deployments where control plane is not accessible.
-    """
-    try:
-        content = await license_file.read()
-        license_data = content.decode("utf-8").strip()
-    except UnicodeDecodeError:
-        raise HTTPException(status_code=400, detail="Invalid license file format")
-
-    try:
-        payload = verify_license_signature(license_data)
-    except ValueError as e:
-        raise HTTPException(status_code=400, detail=str(e))
-
-    tenant_id = get_current_tenant_id()
-    if payload.tenant_id != tenant_id:
-        raise HTTPException(
-            status_code=400,
-            detail=f"License tenant ID mismatch. Expected {tenant_id}, got {payload.tenant_id}",
-        )
-
-    # Persist to DB and update cache
-    upsert_license(db_session, license_data)
-    try:
-        update_license_cache(payload, source=LicenseSource.MANUAL_UPLOAD)
-    except Exception as cache_error:
-        # Log but don't fail - DB is source of truth, cache will refresh on next read
-        logger.warning(f"Failed to update license cache: {cache_error}")
-
-    return LicenseUploadResponse(
-        success=True,
-        message=f"License uploaded successfully. {payload.seats} seats, expires {payload.expires_at.date()}",
-    )
-
-
-@router.post("/refresh")
-async def refresh_license_cache_endpoint(
-    _: User = Depends(current_admin_user),
-    db_session: Session = Depends(get_session),
-) -> LicenseStatusResponse:
-    """
-    Force refresh the license cache from the database.
-    Useful after manual database changes or to verify license validity.
-    """
-    metadata = refresh_license_cache(db_session)
-
-    if not metadata:
-        return LicenseStatusResponse(has_license=False)
-
-    return LicenseStatusResponse(
-        has_license=True,
-        seats=metadata.seats,
-        used_seats=metadata.used_seats,
-        plan_type=metadata.plan_type,
-        issued_at=metadata.issued_at,
-        expires_at=metadata.expires_at,
-        grace_period_end=metadata.grace_period_end,
-        status=metadata.status,
-        source=metadata.source,
-    )
-
-
-@router.delete("")
-async def delete_license(
-    _: User = Depends(current_admin_user),
-    db_session: Session = Depends(get_session),
-) -> dict[str, bool]:
-    """
-    Delete the current license.
-    Admin only - removes license and invalidates cache.
-    """
-    # Invalidate cache first - if DB delete fails, stale cache is worse than no cache
-    try:
-        invalidate_license_cache()
-    except Exception as cache_error:
-        logger.warning(f"Failed to invalidate license cache: {cache_error}")
-
-    deleted = db_delete_license(db_session)
-
-    return {"deleted": deleted}

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

@@ -1,92 +0,0 @@
-from datetime import datetime
-from enum import Enum
-
-from pydantic import BaseModel
-
-from onyx.server.settings.models import ApplicationStatus
-
-
-class PlanType(str, Enum):
-    MONTHLY = "monthly"
-    ANNUAL = "annual"
-
-
-class LicenseSource(str, Enum):
-    AUTO_FETCH = "auto_fetch"
-    MANUAL_UPLOAD = "manual_upload"
-
-
-class LicensePayload(BaseModel):
-    """The payload portion of a signed license."""
-
-    version: str
-    tenant_id: str
-    organization_name: str | None = None
-    issued_at: datetime
-    expires_at: datetime
-    seats: int
-    plan_type: PlanType
-    billing_cycle: str | None = None
-    grace_period_days: int = 30
-    stripe_subscription_id: str | None = None
-    stripe_customer_id: str | None = None
-
-
-class LicenseData(BaseModel):
-    """Full signed license structure."""
-
-    payload: LicensePayload
-    signature: str
-
-
-class LicenseMetadata(BaseModel):
-    """Cached license metadata stored in Redis."""
-
-    tenant_id: str
-    organization_name: str | None = None
-    seats: int
-    used_seats: int
-    plan_type: PlanType
-    issued_at: datetime
-    expires_at: datetime
-    grace_period_end: datetime | None = None
-    status: ApplicationStatus
-    source: LicenseSource | None = None
-    stripe_subscription_id: str | None = None
-
-
-class LicenseStatusResponse(BaseModel):
-    """Response for license status API."""
-
-    has_license: bool
-    seats: int = 0
-    used_seats: int = 0
-    plan_type: PlanType | None = None
-    issued_at: datetime | None = None
-    expires_at: datetime | None = None
-    grace_period_end: datetime | None = None
-    status: ApplicationStatus | None = None
-    source: LicenseSource | None = None
-
-
-class LicenseResponse(BaseModel):
-    """Response after license fetch/upload."""
-
-    success: bool
-    message: str | None = None
-    license: LicensePayload | None = None
-
-
-class LicenseUploadResponse(BaseModel):
-    """Response after license upload."""
-
-    success: bool
-    message: str | None = None
-
-
-class SeatUsageResponse(BaseModel):
-    """Response for seat usage API."""
-
-    total_seats: int
-    used_seats: int
-    available_seats: int

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

@@ -8,10 +8,12 @@ from ee.onyx.server.query_and_chat.models import (
     BasicCreateChatMessageWithHistoryRequest,
 )
 from onyx.auth.users import current_user
+from onyx.chat.chat_utils import combine_message_thread
 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
+from onyx.configs.chat_configs import CHAT_TARGET_CHUNK_PERCENTAGE
 from onyx.configs.constants import MessageType
 from onyx.context.search.models import OptionalSearchSetting
 from onyx.context.search.models import RetrievalDetails
@@ -20,8 +22,9 @@ from onyx.db.chat import create_new_chat_message
 from onyx.db.chat import get_or_create_root_message
 from onyx.db.engine.sql_engine import get_session
 from onyx.db.models import User
-from onyx.llm.factory import get_llm_for_persona
+from onyx.llm.factory import get_llms_for_persona
 from onyx.natural_language_processing.utils import get_tokenizer
+from onyx.secondary_llm_flows.query_expansion import thread_based_query_rephrase
 from onyx.server.query_and_chat.models import CreateChatMessageRequest
 from onyx.utils.logger import setup_logger
 
@@ -66,9 +69,9 @@ def handle_simplified_chat_message(
         chat_session_id = chat_message_req.chat_session_id
 
     try:
-        parent_message = create_chat_history_chain(
+        parent_message, _ = create_chat_history_chain(
             chat_session_id=chat_session_id, db_session=db_session
-        )[-1]
+        )
     except Exception:
         parent_message = get_or_create_root_message(
             chat_session_id=chat_session_id, db_session=db_session
@@ -158,13 +161,15 @@ def handle_send_message_simple_with_history(
         persona_id=req.persona_id,
     )
 
-    llm = get_llm_for_persona(persona=chat_session.persona, user=user)
+    llm, _ = get_llms_for_persona(persona=chat_session.persona, user=user)
 
     llm_tokenizer = get_tokenizer(
         model_name=llm.config.model_name,
         provider_type=llm.config.model_provider,
     )
 
+    max_history_tokens = int(llm.config.max_input_tokens * CHAT_TARGET_CHUNK_PERCENTAGE)
+
     # Every chat Session begins with an empty root message
     root_message = get_or_create_root_message(
         chat_session_id=chat_session.id, db_session=db_session
@@ -183,6 +188,17 @@ def handle_send_message_simple_with_history(
         )
     db_session.commit()
 
+    history_str = combine_message_thread(
+        messages=msg_history,
+        max_tokens=max_history_tokens,
+        llm_tokenizer=llm_tokenizer,
+    )
+
+    rephrased_query = req.query_override or thread_based_query_rephrase(
+        user_query=query,
+        history_str=history_str,
+    )
+
     if req.retrieval_options is None and req.search_doc_ids is None:
         retrieval_options: RetrievalDetails | None = RetrievalDetails(
             run_search=OptionalSearchSetting.ALWAYS,
@@ -200,7 +216,7 @@ def handle_send_message_simple_with_history(
         retrieval_options=retrieval_options,
         # Simple API does not support reranking, hide complexity from user
         rerank_settings=None,
-        query_override=None,
+        query_override=rephrased_query,
         chunks_above=0,
         chunks_below=0,
         full_doc=req.full_doc,

backend/ee/onyx/server/tenants/provisioning.py

@@ -38,14 +38,16 @@ from onyx.db.models import IndexModelStatus
 from onyx.db.models import SearchSettings
 from onyx.db.models import UserTenantMapping
 from onyx.llm.llm_provider_options import ANTHROPIC_PROVIDER_NAME
+from onyx.llm.llm_provider_options import ANTHROPIC_VISIBLE_MODEL_NAMES
 from onyx.llm.llm_provider_options import get_anthropic_model_names
-from onyx.llm.llm_provider_options import get_openai_model_names
+from onyx.llm.llm_provider_options import OPEN_AI_MODEL_NAMES
+from onyx.llm.llm_provider_options import OPEN_AI_VISIBLE_MODEL_NAMES
 from onyx.llm.llm_provider_options import OPENAI_PROVIDER_NAME
 from onyx.server.manage.embedding.models import CloudEmbeddingProviderCreationRequest
 from onyx.server.manage.llm.models import LLMProviderUpsertRequest
 from onyx.server.manage.llm.models import ModelConfigurationUpsertRequest
 from onyx.setup import setup_onyx
-from onyx.utils.telemetry import mt_cloud_telemetry
+from onyx.utils.telemetry import create_milestone_and_report
 from shared_configs.configs import MULTI_TENANT
 from shared_configs.configs import POSTGRES_DEFAULT_SCHEMA
 from shared_configs.configs import TENANT_ID_PREFIX
@@ -269,10 +271,11 @@ def configure_default_api_keys(db_session: Session) -> None:
             provider=ANTHROPIC_PROVIDER_NAME,
             api_key=ANTHROPIC_DEFAULT_API_KEY,
             default_model_name="claude-3-7-sonnet-20250219",
+            fast_default_model_name="claude-3-5-sonnet-20241022",
             model_configurations=[
                 ModelConfigurationUpsertRequest(
                     name=name,
-                    is_visible=False,
+                    is_visible=name in ANTHROPIC_VISIBLE_MODEL_NAMES,
                     max_input_tokens=None,
                 )
                 for name in get_anthropic_model_names()
@@ -295,13 +298,14 @@ def configure_default_api_keys(db_session: Session) -> None:
             provider=OPENAI_PROVIDER_NAME,
             api_key=OPENAI_DEFAULT_API_KEY,
             default_model_name="gpt-4o",
+            fast_default_model_name="gpt-4o-mini",
             model_configurations=[
                 ModelConfigurationUpsertRequest(
                     name=model_name,
-                    is_visible=False,
+                    is_visible=model_name in OPEN_AI_VISIBLE_MODEL_NAMES,
                     max_input_tokens=None,
                 )
-                for model_name in get_openai_model_names()
+                for model_name in OPEN_AI_MODEL_NAMES
             ],
             api_key_changed=True,
         )
@@ -560,11 +564,17 @@ async def assign_tenant_to_user(
     try:
         add_users_to_tenant([email], tenant_id)
 
-        mt_cloud_telemetry(
-            tenant_id=tenant_id,
-            distinct_id=email,
-            event=MilestoneRecordType.TENANT_CREATED,
-        )
+        # Create milestone record in the same transaction context as the tenant assignment
+        with get_session_with_tenant(tenant_id=tenant_id) as db_session:
+            create_milestone_and_report(
+                user=None,
+                distinct_id=tenant_id,
+                event_type=MilestoneRecordType.TENANT_CREATED,
+                properties={
+                    "email": email,
+                },
+                db_session=db_session,
+            )
     except Exception:
         logger.exception(f"Failed to assign tenant {tenant_id} to user {email}")
         raise Exception("Failed to assign tenant to user")

backend/ee/onyx/server/tenants/user_mapping.py

@@ -249,17 +249,6 @@ def accept_user_invite(email: str, tenant_id: str) -> None:
             )
             raise
 
-    # Remove from invited users list since they've accepted
-    token = CURRENT_TENANT_ID_CONTEXTVAR.set(tenant_id)
-    try:
-        invited_users = get_invited_users()
-        if email in invited_users:
-            invited_users.remove(email)
-            write_invited_users(invited_users)
-            logger.info(f"Removed {email} from invited users list after acceptance")
-    finally:
-        CURRENT_TENANT_ID_CONTEXTVAR.reset(token)
-
 
 def deny_user_invite(email: str, tenant_id: str) -> None:
     """

backend/ee/onyx/utils/license.py

@@ -1,126 +0,0 @@
-"""RSA-4096 license signature verification utilities."""
-
-import base64
-import json
-import os
-from datetime import datetime
-from datetime import timezone
-
-from cryptography.exceptions import InvalidSignature
-from cryptography.hazmat.primitives import hashes
-from cryptography.hazmat.primitives import serialization
-from cryptography.hazmat.primitives.asymmetric import padding
-from cryptography.hazmat.primitives.asymmetric.rsa import RSAPublicKey
-
-from ee.onyx.server.license.models import LicenseData
-from ee.onyx.server.license.models import LicensePayload
-from onyx.server.settings.models import ApplicationStatus
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-# RSA-4096 Public Key for license verification
-# Load from environment variable - key is generated on the control plane
-# In production, inject via Kubernetes secrets or secrets manager
-LICENSE_PUBLIC_KEY_PEM = os.environ.get("LICENSE_PUBLIC_KEY_PEM", "")
-
-
-def _get_public_key() -> RSAPublicKey:
-    """Load the public key from environment variable."""
-    if not LICENSE_PUBLIC_KEY_PEM:
-        raise ValueError(
-            "LICENSE_PUBLIC_KEY_PEM environment variable not set. "
-            "License verification requires the control plane public key."
-        )
-    key = serialization.load_pem_public_key(LICENSE_PUBLIC_KEY_PEM.encode())
-    if not isinstance(key, RSAPublicKey):
-        raise ValueError("Expected RSA public key")
-    return key
-
-
-def verify_license_signature(license_data: str) -> LicensePayload:
-    """
-    Verify RSA-4096 signature and return payload if valid.
-
-    Args:
-        license_data: Base64-encoded JSON containing payload and signature
-
-    Returns:
-        LicensePayload if signature is valid
-
-    Raises:
-        ValueError: If license data is invalid or signature verification fails
-    """
-    try:
-        # Decode the license data
-        decoded = json.loads(base64.b64decode(license_data))
-        license_obj = LicenseData(**decoded)
-
-        payload_json = json.dumps(
-            license_obj.payload.model_dump(mode="json"), sort_keys=True
-        )
-        signature_bytes = base64.b64decode(license_obj.signature)
-
-        # Verify signature using PSS padding (modern standard)
-        public_key = _get_public_key()
-        public_key.verify(
-            signature_bytes,
-            payload_json.encode(),
-            padding.PSS(
-                mgf=padding.MGF1(hashes.SHA256()),
-                salt_length=padding.PSS.MAX_LENGTH,
-            ),
-            hashes.SHA256(),
-        )
-
-        return license_obj.payload
-
-    except InvalidSignature:
-        logger.error("License signature verification failed")
-        raise ValueError("Invalid license signature")
-    except json.JSONDecodeError:
-        logger.error("Failed to decode license JSON")
-        raise ValueError("Invalid license format: not valid JSON")
-    except (ValueError, KeyError, TypeError) as e:
-        logger.error(f"License data validation error: {type(e).__name__}")
-        raise ValueError(f"Invalid license format: {type(e).__name__}")
-    except Exception:
-        logger.exception("Unexpected error during license verification")
-        raise ValueError("License verification failed: unexpected error")
-
-
-def get_license_status(
-    payload: LicensePayload,
-    grace_period_end: datetime | None = None,
-) -> ApplicationStatus:
-    """
-    Determine current license status based on expiry.
-
-    Args:
-        payload: The verified license payload
-        grace_period_end: Optional grace period end datetime
-
-    Returns:
-        ApplicationStatus indicating current license state
-    """
-    now = datetime.now(timezone.utc)
-
-    # Check if grace period has expired
-    if grace_period_end and now > grace_period_end:
-        return ApplicationStatus.GATED_ACCESS
-
-    # Check if license has expired
-    if now > payload.expires_at:
-        if grace_period_end and now <= grace_period_end:
-            return ApplicationStatus.GRACE_PERIOD
-        return ApplicationStatus.GATED_ACCESS
-
-    # License is valid
-    return ApplicationStatus.ACTIVE
-
-
-def is_license_valid(payload: LicensePayload) -> bool:
-    """Check if a license is currently valid (not expired)."""
-    now = datetime.now(timezone.utc)
-    return now <= payload.expires_at

backend/onyx/agents/agent_framework/message_format.py

@@ -0,0 +1,73 @@
+import json
+from collections.abc import Sequence
+from typing import cast
+
+from langchain_core.messages import AIMessage
+from langchain_core.messages import BaseMessage
+from langchain_core.messages import FunctionMessage
+
+from onyx.llm.message_types import AssistantMessage
+from onyx.llm.message_types import ChatCompletionMessage
+from onyx.llm.message_types import FunctionCall
+from onyx.llm.message_types import SystemMessage
+from onyx.llm.message_types import ToolCall
+from onyx.llm.message_types import ToolMessage
+from onyx.llm.message_types import UserMessageWithText
+
+
+HUMAN = "human"
+SYSTEM = "system"
+AI = "ai"
+FUNCTION = "function"
+
+
+def base_messages_to_chat_completion_msgs(
+    msgs: Sequence[BaseMessage],
+) -> list[ChatCompletionMessage]:
+    return [_base_message_to_chat_completion_msg(msg) for msg in msgs]
+
+
+def _base_message_to_chat_completion_msg(
+    msg: BaseMessage,
+) -> ChatCompletionMessage:
+    if msg.type == HUMAN:
+        content = msg.content if isinstance(msg.content, str) else str(msg.content)
+        user_msg: UserMessageWithText = {"role": "user", "content": content}
+        return user_msg
+    if msg.type == SYSTEM:
+        content = msg.content if isinstance(msg.content, str) else str(msg.content)
+        system_msg: SystemMessage = {"role": "system", "content": content}
+        return system_msg
+    if msg.type == AI:
+        content = msg.content if isinstance(msg.content, str) else str(msg.content)
+        assistant_msg: AssistantMessage = {
+            "role": "assistant",
+            "content": content,
+        }
+        if isinstance(msg, AIMessage) and msg.tool_calls:
+            assistant_msg["tool_calls"] = [
+                ToolCall(
+                    id=tool_call.get("id") or "",
+                    type="function",
+                    function=FunctionCall(
+                        name=tool_call["name"],
+                        arguments=json.dumps(tool_call["args"]),
+                    ),
+                )
+                for tool_call in msg.tool_calls
+            ]
+        return assistant_msg
+    if msg.type == FUNCTION:
+        function_message = cast(FunctionMessage, msg)
+        content = (
+            function_message.content
+            if isinstance(function_message.content, str)
+            else str(function_message.content)
+        )
+        tool_msg: ToolMessage = {
+            "role": "tool",
+            "content": content,
+            "tool_call_id": function_message.name or "",
+        }
+        return tool_msg
+    raise ValueError(f"Unexpected message type: {msg.type}")

backend/onyx/agents/agent_framework/models.py

@@ -0,0 +1,47 @@
+from typing import Any
+from typing import Literal
+from typing import TypeAlias
+
+from pydantic import BaseModel
+
+from onyx.llm.model_response import ModelResponseStream
+
+
+class ToolCallStreamItem(BaseModel):
+    call_id: str | None = None
+
+    id: str | None = None
+
+    name: str | None = None
+
+    arguments: str | None = None
+
+    type: Literal["function_call"] = "function_call"
+
+    index: int | None = None
+
+
+class ToolCallOutputStreamItem(BaseModel):
+    call_id: str | None = None
+
+    output: Any
+
+    type: Literal["function_call_output"] = "function_call_output"
+
+
+RunItemStreamEventDetails: TypeAlias = ToolCallStreamItem | ToolCallOutputStreamItem
+
+
+class RunItemStreamEvent(BaseModel):
+    type: Literal[
+        "message_start",
+        "message_done",
+        "reasoning_start",
+        "reasoning_done",
+        "tool_call",
+        "tool_call_output",
+    ]
+    details: RunItemStreamEventDetails | None = None
+
+
+StreamEvent: TypeAlias = ModelResponseStream | RunItemStreamEvent

backend/onyx/agents/agent_framework/query.py

@@ -0,0 +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
+
+# 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]
+
+
+# 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 []
+
+#     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]] = []
+
+#     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(arguments, dict):
+#             continue
+
+#         call_id = candidate.get("id")
+#         arguments_str = json.dumps(arguments)
+#         tool_calls.append(
+#             {
+#                 "id": call_id,
+#                 "name": name,
+#                 "arguments": arguments_str,
+#             }
+#         )
+
+#     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
+
+#     tool_calls_from_content = _parse_tool_calls_from_message_content(
+#         "".join(content_parts)
+#     )
+
+#     if not tool_calls_from_content:
+#         return
+
+#     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"],
+#         }
+
+
+# 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 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.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}
+
+#     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]
+
+#     def stream_generator() -> Iterator[StreamEvent]:
+#         message_started = False
+#         reasoning_started = False
+
+#         tool_calls_in_progress: dict[int, dict[str, Any]] = {}
+
+#         content_parts: list[str] = []
+
+#         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
+
+#         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
+
+#                 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.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
+#                         )
+
+#                 yield chunk
+
+#                 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 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
+
+#         # 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] = []
+
+#             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
+
+#                 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,
+#                     ),
+#                 )
+
+#                 if name in tools_by_name:
+#                     tools_by_name[name]
+#                     json.loads(arguments_str)
+
+# 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.
+
+# TODO broken for now, no need for a run_v2
+# output = tool.run_v2(run_context, **arguments)
+
+# yield RunItemStreamEvent(
+#     type="tool_call_output",
+#     details=ToolCallOutputStreamItem(
+#         call_id=call_id,
+#         output=output,
+#     ),
+# )

backend/onyx/agents/agent_sdk/message_format.py

@@ -0,0 +1,167 @@
+from collections.abc import Sequence
+
+from langchain.schema.messages import BaseMessage
+
+from onyx.agents.agent_sdk.message_types import AgentSDKMessage
+from onyx.agents.agent_sdk.message_types import AssistantMessageWithContent
+from onyx.agents.agent_sdk.message_types import ImageContent
+from onyx.agents.agent_sdk.message_types import InputTextContent
+from onyx.agents.agent_sdk.message_types import SystemMessage
+from onyx.agents.agent_sdk.message_types import UserMessage
+
+
+# TODO: Currently, we only support native API input for images. For other
+# files, we process the content and share it as text in the message. In
+# the future, we might support native file uploads for other types of files.
+def base_messages_to_agent_sdk_msgs(
+    msgs: Sequence[BaseMessage],
+    is_responses_api: bool,
+) -> list[AgentSDKMessage]:
+    return [_base_message_to_agent_sdk_msg(msg, is_responses_api) for msg in msgs]
+
+
+def _base_message_to_agent_sdk_msg(
+    msg: BaseMessage, is_responses_api: bool
+) -> AgentSDKMessage:
+    message_type_to_agent_sdk_role = {
+        "human": "user",
+        "system": "system",
+        "ai": "assistant",
+    }
+    role = message_type_to_agent_sdk_role[msg.type]
+
+    # Convert content to Agent SDK format
+    content = msg.content
+
+    if isinstance(content, str):
+        # For system/user/assistant messages, use InputTextContent
+        if role in ("system", "user"):
+            input_text_content: list[InputTextContent | ImageContent] = [
+                InputTextContent(type="input_text", text=content)
+            ]
+            if role == "system":
+                # SystemMessage only accepts InputTextContent
+                system_msg: SystemMessage = {
+                    "role": "system",
+                    "content": [InputTextContent(type="input_text", text=content)],
+                }
+                return system_msg
+            else:  # user
+                user_msg: UserMessage = {
+                    "role": "user",
+                    "content": input_text_content,
+                }
+                return user_msg
+        else:  # assistant
+            assistant_msg: AssistantMessageWithContent
+            if is_responses_api:
+                from onyx.agents.agent_sdk.message_types import OutputTextContent
+
+                assistant_msg = {
+                    "role": "assistant",
+                    "content": [OutputTextContent(type="output_text", text=content)],
+                }
+            else:
+                assistant_msg = {
+                    "role": "assistant",
+                    "content": [InputTextContent(type="input_text", text=content)],
+                }
+            return assistant_msg
+    elif isinstance(content, list):
+        # For lists, we need to process based on the role
+        if role == "assistant":
+            # For responses API, use OutputTextContent; otherwise use InputTextContent
+            assistant_content: list[InputTextContent | OutputTextContent] = []
+
+            if is_responses_api:
+                from onyx.agents.agent_sdk.message_types import OutputTextContent
+
+                for item in content:
+                    if isinstance(item, str):
+                        assistant_content.append(
+                            OutputTextContent(type="output_text", text=item)
+                        )
+                    elif isinstance(item, dict) and item.get("type") == "text":
+                        assistant_content.append(
+                            OutputTextContent(
+                                type="output_text", text=item.get("text", "")
+                            )
+                        )
+                    else:
+                        raise ValueError(
+                            f"Unexpected item type for assistant message: {type(item)}. Item: {item}"
+                        )
+            else:
+                for item in content:
+                    if isinstance(item, str):
+                        assistant_content.append(
+                            InputTextContent(type="input_text", text=item)
+                        )
+                    elif isinstance(item, dict) and item.get("type") == "text":
+                        assistant_content.append(
+                            InputTextContent(
+                                type="input_text", text=item.get("text", "")
+                            )
+                        )
+                    else:
+                        raise ValueError(
+                            f"Unexpected item type for assistant message: {type(item)}. Item: {item}"
+                        )
+
+            assistant_msg_list: AssistantMessageWithContent = {
+                "role": "assistant",
+                "content": assistant_content,
+            }
+            return assistant_msg_list
+        else:  # system or user - use InputTextContent
+            input_content: list[InputTextContent | ImageContent] = []
+            for item in content:
+                if isinstance(item, str):
+                    input_content.append(InputTextContent(type="input_text", text=item))
+                elif isinstance(item, dict):
+                    item_type = item.get("type")
+                    if item_type == "text":
+                        input_content.append(
+                            InputTextContent(
+                                type="input_text", text=item.get("text", "")
+                            )
+                        )
+                    elif item_type == "image_url":
+                        # Convert image_url to input_image format
+                        image_url = item.get("image_url", {})
+                        if isinstance(image_url, dict):
+                            url = image_url.get("url", "")
+                        else:
+                            url = image_url
+                        input_content.append(
+                            ImageContent(
+                                type="input_image", image_url=url, detail="auto"
+                            )
+                        )
+                    else:
+                        raise ValueError(f"Unexpected item type: {item_type}")
+                else:
+                    raise ValueError(
+                        f"Unexpected item type: {type(item)}. Item: {item}"
+                    )
+
+            if role == "system":
+                # SystemMessage only accepts InputTextContent (no images)
+                text_only_content = [
+                    c for c in input_content if c["type"] == "input_text"
+                ]
+                system_msg_list: SystemMessage = {
+                    "role": "system",
+                    "content": text_only_content,  # type: ignore[typeddict-item]
+                }
+                return system_msg_list
+            else:  # user
+                user_msg_list: UserMessage = {
+                    "role": "user",
+                    "content": input_content,
+                }
+                return user_msg_list
+    else:
+        raise ValueError(
+            f"Unexpected content type: {type(content)}. Content: {content}"
+        )

backend/onyx/agents/agent_sdk/message_types.py

@@ -0,0 +1,125 @@
+"""Strongly typed message structures for Agent SDK messages."""
+
+from typing import Literal
+from typing import NotRequired
+
+from typing_extensions import TypedDict
+
+
+class InputTextContent(TypedDict):
+    type: Literal["input_text"]
+    text: str
+
+
+class OutputTextContent(TypedDict):
+    type: Literal["output_text"]
+    text: str
+
+
+TextContent = InputTextContent | OutputTextContent
+
+
+class ImageContent(TypedDict):
+    type: Literal["input_image"]
+    image_url: str
+    detail: str
+
+
+# Tool call structures
+class ToolCallFunction(TypedDict):
+    name: str
+    arguments: str
+
+
+class ToolCall(TypedDict):
+    id: str
+    type: Literal["function"]
+    function: ToolCallFunction
+
+
+# Message types
+class SystemMessage(TypedDict):
+    role: Literal["system"]
+    content: list[InputTextContent]  # System messages use input text
+
+
+class UserMessage(TypedDict):
+    role: Literal["user"]
+    content: list[
+        InputTextContent | ImageContent
+    ]  # User messages use input text or images
+
+
+class AssistantMessageWithContent(TypedDict):
+    role: Literal["assistant"]
+    content: list[
+        InputTextContent | OutputTextContent
+    ]  # Assistant messages use output_text for responses API compatibility
+
+
+class AssistantMessageWithToolCalls(TypedDict):
+    role: Literal["assistant"]
+    tool_calls: list[ToolCall]
+
+
+class AssistantMessageDuringAgentRun(TypedDict):
+    role: Literal["assistant"]
+    id: str
+    content: (
+        list[InputTextContent | OutputTextContent] | list[ToolCall]
+    )  # Assistant runtime messages receive output_text from agents SDK for responses API compatibility
+    status: Literal["completed", "failed", "in_progress"]
+    type: Literal["message"]
+
+
+class ToolMessage(TypedDict):
+    role: Literal["tool"]
+    content: str
+    tool_call_id: str
+
+
+class FunctionCallMessage(TypedDict):
+    """Agent SDK function call message format."""
+
+    type: Literal["function_call"]
+    id: NotRequired[str]
+    call_id: str
+    name: str
+    arguments: str
+
+
+class FunctionCallOutputMessage(TypedDict):
+    """Agent SDK function call output message format."""
+
+    type: Literal["function_call_output"]
+    call_id: str
+    output: str
+
+
+class SummaryText(TypedDict):
+    """Summary text item in reasoning messages."""
+
+    text: str
+    type: Literal["summary_text"]
+
+
+class ReasoningMessage(TypedDict):
+    """Agent SDK reasoning message format."""
+
+    id: str
+    type: Literal["reasoning"]
+    summary: list[SummaryText]
+
+
+# Union type for all Agent SDK messages
+AgentSDKMessage = (
+    SystemMessage
+    | UserMessage
+    | AssistantMessageWithContent
+    | AssistantMessageWithToolCalls
+    | AssistantMessageDuringAgentRun
+    | ToolMessage
+    | FunctionCallMessage
+    | FunctionCallOutputMessage
+    | ReasoningMessage
+)

backend/onyx/agents/agent_sdk/monkey_patches.py

@@ -0,0 +1,36 @@
+from typing import Any
+
+from agents.models.openai_responses import Converter as OpenAIResponsesConverter
+
+
+# TODO: I am very sad that I have to monkey patch this :(
+# Basically, OpenAI agents sdk doesn't convert the tool choice correctly
+# when they have a built-in tool in their framework, like they do for web_search
+# and image_generation.
+# Going to open up a thread with OpenAI agents team to see what they recommend
+# or what we can fix.
+# A discussion is warranted, but we likely want to just write our own LitellmModel for
+# the OpenAI agents SDK since they probably don't really care about Litellm and will
+# prioritize functionality for their own models.
+def monkey_patch_convert_tool_choice_to_ignore_openai_hosted_web_search() -> None:
+    if (
+        getattr(OpenAIResponsesConverter.convert_tool_choice, "__name__", "")
+        == "_patched_convert_tool_choice"
+    ):
+        return
+
+    orig_func = OpenAIResponsesConverter.convert_tool_choice.__func__  # type: ignore[attr-defined]
+
+    def _patched_convert_tool_choice(cls: type, tool_choice: Any) -> Any:
+        # Handle OpenAI hosted tools that we have custom implementations for
+        # Without this patch, the library uses special formatting that breaks our custom tools
+        # See: https://platform.openai.com/docs/api-reference/responses/create#responses_create-tool_choice-hosted_tool-type
+        if tool_choice == "web_search":
+            return {"type": "function", "name": "web_search"}
+        if tool_choice == "image_generation":
+            return {"type": "function", "name": "image_generation"}
+        return orig_func(cls, tool_choice)
+
+    OpenAIResponsesConverter.convert_tool_choice = classmethod(  # type: ignore[method-assign, assignment]
+        _patched_convert_tool_choice
+    )

backend/onyx/agents/agent_sdk/sync_agent_stream_adapter.py

@@ -0,0 +1,178 @@
+import asyncio
+import queue
+import threading
+from collections.abc import Iterator
+from collections.abc import Sequence
+from typing import Generic
+from typing import Optional
+from typing import TypeVar
+
+from agents import Agent
+from agents import RunResultStreaming
+from agents import TContext
+from agents.run import Runner
+
+from onyx.agents.agent_sdk.message_types import AgentSDKMessage
+from onyx.utils.threadpool_concurrency import run_in_background
+
+T = TypeVar("T")
+
+
+class SyncAgentStream(Generic[T]):
+    """
+    Convert an async streamed run into a sync iterator with cooperative cancellation.
+    Runs the Agent in a background thread.
+
+    Usage:
+        adapter = SyncStreamAdapter(
+            agent=agent,
+            input=input,
+            context=context,
+            max_turns=100,
+            queue_maxsize=0,         # optional backpressure
+        )
+        for ev in adapter:          # sync iteration
+            ...
+        # or cancel from elsewhere:
+        adapter.cancel()
+    """
+
+    _SENTINEL = object()
+
+    def __init__(
+        self,
+        *,
+        agent: Agent,
+        input: Sequence[AgentSDKMessage],
+        context: TContext | None = None,
+        max_turns: int = 100,
+        queue_maxsize: int = 0,
+    ) -> None:
+        self._agent = agent
+        self._input = input
+        self._context = context
+        self._max_turns = max_turns
+
+        self._q: "queue.Queue[object]" = queue.Queue(maxsize=queue_maxsize)
+        self._loop: Optional[asyncio.AbstractEventLoop] = None
+        self._thread: Optional[threading.Thread] = None
+        self.streamed: RunResultStreaming | None = None
+        self._exc: Optional[BaseException] = None
+        self._cancel_requested = threading.Event()
+        self._started = threading.Event()
+        self._done = threading.Event()
+
+        self._start_thread()
+
+    # ---------- public sync API ----------
+
+    def __iter__(self) -> Iterator[T]:
+        try:
+            while True:
+                item = self._q.get()
+                if item is self._SENTINEL:
+                    # If the consumer thread raised, surface it now
+                    if self._exc is not None:
+                        raise self._exc
+                    # Normal completion
+                    return
+                yield item  # type: ignore[misc,return-value]
+        finally:
+            # Ensure we fully clean up whether we exited due to exception,
+            # StopIteration, or external cancel.
+            self.close()
+
+    def cancel(self) -> bool:
+        """
+        Cooperatively cancel the underlying streamed run and shut down.
+        Safe to call multiple times and from any thread.
+        """
+        self._cancel_requested.set()
+        loop = self._loop
+        streamed = self.streamed
+        if loop is not None and streamed is not None and not self._done.is_set():
+            loop.call_soon_threadsafe(streamed.cancel)
+            return True
+        return False
+
+    def close(self, *, wait: bool = True) -> None:
+        """Idempotent shutdown."""
+        self.cancel()
+        # ask the loop to stop if it's still running
+        loop = self._loop
+        if loop is not None and loop.is_running():
+            try:
+                loop.call_soon_threadsafe(loop.stop)
+            except Exception:
+                pass
+        # join the thread
+        if wait and self._thread is not None and self._thread.is_alive():
+            self._thread.join(timeout=5.0)
+
+    # ---------- internals ----------
+
+    def _start_thread(self) -> None:
+        t = run_in_background(self._thread_main)
+        self._thread = t
+        # Optionally wait until the loop/worker is started so .cancel() is safe soon after init
+        self._started.wait(timeout=1.0)
+
+    def _thread_main(self) -> None:
+        loop = asyncio.new_event_loop()
+        self._loop = loop
+        asyncio.set_event_loop(loop)
+
+        async def worker() -> None:
+            try:
+                # Start the streamed run inside the loop thread
+                self.streamed = Runner.run_streamed(
+                    self._agent,
+                    self._input,  # type: ignore[arg-type]
+                    context=self._context,
+                    max_turns=self._max_turns,
+                )
+
+                # If cancel was requested before we created _streamed, honor it now
+                if self._cancel_requested.is_set():
+                    await self.streamed.cancel()  # type: ignore[func-returns-value]
+
+                # Consume async events and forward into the thread-safe queue
+                async for ev in self.streamed.stream_events():
+                    # Early exit if a late cancel arrives
+                    if self._cancel_requested.is_set():
+                        # Try to cancel gracefully; don't break until cancel takes effect
+                        try:
+                            await self.streamed.cancel()  # type: ignore[func-returns-value]
+                        except Exception:
+                            pass
+                        break
+                    # This put() may block if queue_maxsize > 0 (backpressure)
+                    self._q.put(ev)
+
+            except BaseException as e:
+                # Save exception to surface on the sync iterator side
+                self._exc = e
+            finally:
+                # Signal end-of-stream
+                self._q.put(self._SENTINEL)
+                self._done.set()
+
+        # Mark started and run the worker to completion
+        self._started.set()
+        try:
+            loop.run_until_complete(worker())
+        finally:
+            try:
+                # Drain pending tasks/callbacks safely
+                pending = asyncio.all_tasks(loop=loop)
+                for task in pending:
+                    task.cancel()
+                if pending:
+                    loop.run_until_complete(
+                        asyncio.gather(*pending, return_exceptions=True)
+                    )
+            except Exception:
+                pass
+            finally:
+                loop.close()
+                self._loop = None

backend/onyx/agents/agent_search/core_state.py

@@ -0,0 +1,21 @@
+# from operator import add
+# from typing import Annotated
+
+# from pydantic import BaseModel
+
+
+# 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
+
+
+# class SubgraphCoreState(BaseModel):
+#     """
+#     This is the core state that is shared across all subgraphs.
+#     """
+
+#     log_messages: Annotated[list[str], add] = []

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

@@ -0,0 +1,62 @@
+# from collections.abc import Hashable
+# from typing import cast
+
+# 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
+
+
+# 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
+
+#     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
+#     ]
+
+
+# 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
+#     ]

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

@@ -0,0 +1,103 @@
+# 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
+
+# logger = setup_logger()
+
+# test_mode = False
+
+
+# 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,
+#     )
+
+#     ### Add nodes ###
+
+#     graph.add_node(
+#         "search_objects",
+#         search_objects,
+#     )
+
+#     graph.add_node(
+#         "structure_research_by_source",
+#         structure_research_by_object,
+#     )
+
+#     graph.add_node(
+#         "research_object_source",
+#         research_object_source,
+#     )
+
+#     graph.add_node(
+#         "consolidate_object_research",
+#         consolidate_object_research,
+#     )
+
+#     graph.add_node(
+#         "consolidate_research",
+#         consolidate_research,
+#     )
+
+#     ### Add edges ###
+
+#     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_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_edge(
+#         start_key="consolidate_object_research",
+#         end_key="consolidate_research",
+#     )
+
+#     graph.add_edge(
+#         start_key="consolidate_research",
+#         end_key=END,
+#     )
+
+#     return graph

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

@@ -0,0 +1,146 @@
+# from typing import cast
+
+# 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
+
+# logger = setup_logger()
+
+
+# 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
+
+#     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 ""
+
+#         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_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")
+
+#         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")
+
+#     except Exception as e:
+#         raise ValueError(
+#             f"Agent 1 instructions not found or not formatted correctly: {e}"
+#         )
+
+#     # Extract objects
+
+#     if agent_1_base_data is None:
+#         # Retrieve chunks for objects
+
+#         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 = "\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,
+#         )
+
+# 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(";")]
+
+#     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"],
+#     )

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

@@ -0,0 +1,180 @@
+# 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 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()
+
+
+# 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
+
+#     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 ""
+
+#         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_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_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}"
+#         )
+
+#     # Populate prompt
+
+#     # 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
+
+#     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
+
+#     retrieved_docs = research(
+#         question=research_area,
+#         search_tool=search_tool,
+#         document_sources=document_sources,
+#         time_cutoff=agent_2_source_start_time,
+#     )
+
+#     # 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 = "\n\n".join(document_texts_list)
+
+#     # Built prompt
+
+#     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)
+#     )
+
+#     # 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,
+#     )
+
+#         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}")
+
+#     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"],
+#     )

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

@@ -0,0 +1,48 @@
+# from collections import defaultdict
+# from typing import Dict
+# from typing import List
+
+# 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
+
+# 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.
+#     """
+
+#     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)
+
+#     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}"
+#         )
+
+#     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")
+
+#     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

@@ -0,0 +1,103 @@
+# from typing import cast
+
+# 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
+
+# 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
+
+#     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 ""
+
+#     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 = object_information["object"]
+#     information = object_information["information"]
+
+#     # 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,
+#     )
+
+#     # 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,
+#     )
+
+#         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}")
+
+#     object_research_results = {
+#         "object": object,
+#         "research_result": consolidated_information,
+#     }
+
+#     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"],
+#     )

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

@@ -0,0 +1,127 @@
+# from typing import cast
+
+# 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
+
+# logger = setup_logger()
+
+
+# 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"])
+
+#     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")
+
+#     # 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}"
+#         )
+
+#     research_result_list = []
+
+#     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}")
+
+#         research_results = "\n\n".join(research_result_list)
+
+#     else:
+#         raise NotImplementedError("Only '*concatenate*' is currently supported")
+
+#     # 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,
+#         )
+
+#     # Run LLM
+
+#     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,
+#             ),
+#         )
+
+#     except Exception as e:
+#         raise ValueError(f"Error in consolidate_research: {e}")
+
+#     logger.debug("DivCon Step A5 - Final Generation - completed")
+
+#     return ResearchUpdate(
+#         research_results=research_results,
+#         log_messages=["Agent Source Consilidation done"],
+#     )

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

@@ -0,0 +1,50 @@
+# from datetime import datetime
+# from typing import cast
+
+# 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
+
+#     retrieved_docs: list[LlmDoc] = []
+
+#     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)
+
+#     if len(parts) == 1:
+#         return None
+
+#     after_start = parts[1].strip()
+
+#     if not end_marker:
+#         return after_start
+
+#     extract = after_start.split(end_marker)[0]
+
+#     return extract.strip()

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

@@ -0,0 +1,72 @@
+# from operator import add
+# from typing import Annotated
+# from typing import Dict
+# from typing import TypedDict
+
+# 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
+
+
+# ### States ###
+# class LoggerUpdate(BaseModel):
+#     log_messages: Annotated[list[str], add] = []
+
+
+# class SearchSourcesObjectsUpdate(LoggerUpdate):
+#     analysis_objects: list[str] = []
+#     analysis_sources: list[DocumentSource] = []
+
+
+# class ObjectSourceInput(LoggerUpdate):
+#     object_source_combination: tuple[str, DocumentSource]
+
+
+# class ObjectSourceResearchUpdate(LoggerUpdate):
+#     object_source_research_results: Annotated[list[Dict[str, str]], add] = []
+
+
+# class ObjectInformationInput(LoggerUpdate):
+#     object_information: Dict[str, str]
+
+
+# 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 ResearchUpdate(LoggerUpdate):
+#     research_results: str | None = None
+
+
+# ## 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 Output State - presently not used
+# class MainOutput(TypedDict):
+#     log_messages: list[str]

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

@@ -0,0 +1,36 @@
+# from pydantic import BaseModel
+
+
+# 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 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 AgentAdditionalMetrics(BaseModel):
+#     pass

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

@@ -0,0 +1,61 @@
+# from collections.abc import Hashable
+
+# 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
+
+
+# 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]
+
+#     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
+
+#     # 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
+
+#     return next_tool_path
+
+
+# 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]
+
+#     if next_path == DRPath.ORCHESTRATOR.value:
+#         return DRPath.ORCHESTRATOR
+#     return DRPath.LOGGER

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

@@ -0,0 +1,27 @@
+# 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_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
+# )
+
+# 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,
+# }
+
+# # 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

@@ -0,0 +1,111 @@
+# from datetime import datetime
+
+# 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,
+#     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_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
+#     ):
+
+#         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 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 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:
+#         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 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}")
+
+#     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

@@ -0,0 +1,22 @@
+# from enum import Enum
+
+
+# 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"

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

@@ -0,0 +1,88 @@
+# 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.sub_agents.basic_search.dr_basic_search_2_act import search
+
+
+# def dr_graph_builder() -> StateGraph:
+#     """
+#     LangGraph graph builder for the deep research agent.
+#     """
+
+#     graph = StateGraph(state_schema=MainState, input=MainInput)
+
+#     ### Add nodes ###
+
+#     graph.add_node(DRPath.CLARIFIER, clarifier)
+
+#     graph.add_node(DRPath.ORCHESTRATOR, orchestrator)
+
+#     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)
+
+#     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)
+
+#     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)
+
+#     graph.add_node(DRPath.CLOSER, closer)
+#     graph.add_node(DRPath.LOGGER, logging)
+
+#     ### Add edges ###
+
+#     graph.add_edge(start_key=START, end_key=DRPath.CLARIFIER)
+
+#     graph.add_conditional_edges(DRPath.CLARIFIER, 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_conditional_edges(DRPath.CLOSER, completeness_router)
+#     graph.add_edge(start_key=DRPath.LOGGER, end_key=END)
+
+#     return graph

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

@@ -0,0 +1,131 @@
+# from enum import Enum
+
+# 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
+
+
+# class OrchestratorStep(BaseModel):
+#     tool: str
+#     questions: list[str]
+
+
+# class OrchestratorDecisonsNoPlan(BaseModel):
+#     reasoning: str
+#     next_step: OrchestratorStep
+
+
+# class OrchestrationPlan(BaseModel):
+#     reasoning: str
+#     plan: str
+
+
+# class ClarificationGenerationResponse(BaseModel):
+#     clarification_needed: bool
+#     clarification_question: str
+
+
+# class DecisionResponse(BaseModel):
+#     reasoning: str
+#     decision: str
+
+
+# class QueryEvaluationResponse(BaseModel):
+#     reasoning: str
+#     query_permitted: bool
+
+
+# class OrchestrationClarificationInfo(BaseModel):
+#     clarification_question: str
+#     clarification_response: str | None = None
+
+
+# class WebSearchAnswer(BaseModel):
+#     urls_to_open_indices: list[int]
+
+
+# class SearchAnswer(BaseModel):
+#     reasoning: str
+#     answer: str
+#     claims: list[str] | None = None
+
+
+# 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
+
+#     class Config:
+#         arbitrary_types_allowed = True
+
+
+# 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 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 BaseSearchProcessingResponse(BaseModel):
+#     specified_source_types: list[str]
+#     rewritten_query: str
+#     time_filter: str

backend/onyx/agents/agent_search/dr/nodes/dr_a0_clarification.py

@@ -0,0 +1,918 @@
+# import re
+# from datetime import datetime
+# from typing import Any
+# from typing import cast
+
+# from braintrust import traced
+# from langchain_core.messages import HumanMessage
+# from langchain_core.messages import merge_content
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
+# from sqlalchemy.orm import Session
+
+# from onyx.agents.agent_search.dr.constants import AVERAGE_TOOL_COSTS
+# from onyx.agents.agent_search.dr.constants import MAX_CHAT_HISTORY_MESSAGES
+# from onyx.agents.agent_search.dr.dr_prompt_builder import (
+#     get_dr_prompt_orchestration_templates,
+# )
+# 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 ClarificationGenerationResponse
+# from onyx.agents.agent_search.dr.models import DecisionResponse
+# from onyx.agents.agent_search.dr.models import DRPromptPurpose
+# from onyx.agents.agent_search.dr.models import OrchestrationClarificationInfo
+# from onyx.agents.agent_search.dr.models import OrchestratorTool
+# from onyx.agents.agent_search.dr.process_llm_stream import (
+#     BasicSearchProcessedStreamResults,
+# )
+# from onyx.agents.agent_search.dr.process_llm_stream import process_llm_stream
+# from onyx.agents.agent_search.dr.states import MainState
+# from onyx.agents.agent_search.dr.states import OrchestrationSetup
+# from onyx.agents.agent_search.dr.utils import get_chat_history_string
+# 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 run_with_timeout
+# 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 build_citation_map_from_numbers
+# from onyx.chat.chat_utils import saved_search_docs_from_llm_docs
+# from onyx.chat.memories import get_memories
+# from onyx.chat.models import PromptConfig
+# from onyx.chat.prompt_builder.citations_prompt import build_citations_system_message
+# from onyx.chat.prompt_builder.citations_prompt import build_citations_user_message
+# from onyx.chat.stream_processing.citation_processing import (
+#     normalize_square_bracket_citations_to_double_with_links,
+# )
+# from onyx.configs.agent_configs import TF_DR_TIMEOUT_LONG
+# from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
+# from onyx.configs.constants import DocumentSource
+# from onyx.configs.constants import DocumentSourceDescription
+# from onyx.configs.constants import TMP_DRALPHA_PERSONA_NAME
+# from onyx.db.chat import create_search_doc_from_saved_search_doc
+# from onyx.db.chat import update_db_session_with_messages
+# from onyx.db.connector import fetch_unique_document_sources
+# from onyx.db.kg_config import get_kg_config_settings
+# from onyx.db.models import SearchDoc
+# from onyx.db.models import Tool
+# from onyx.db.tools import get_tools
+# from onyx.file_store.models import ChatFileType
+# from onyx.file_store.models import InMemoryChatFile
+# from onyx.kg.utils.extraction_utils import get_entity_types_str
+# from onyx.kg.utils.extraction_utils import get_relationship_types_str
+# from onyx.llm.utils import check_number_of_tokens
+# from onyx.llm.utils import get_max_input_tokens
+# from onyx.natural_language_processing.utils import get_tokenizer
+# from onyx.prompts.chat_prompts import PROJECT_INSTRUCTIONS_SEPARATOR
+# from onyx.prompts.dr_prompts import ANSWER_PROMPT_WO_TOOL_CALLING
+# from onyx.prompts.dr_prompts import DECISION_PROMPT_W_TOOL_CALLING
+# from onyx.prompts.dr_prompts import DECISION_PROMPT_WO_TOOL_CALLING
+# from onyx.prompts.dr_prompts import DEFAULT_DR_SYSTEM_PROMPT
+# from onyx.prompts.dr_prompts import REPEAT_PROMPT
+# from onyx.prompts.dr_prompts import TOOL_DESCRIPTION
+# from onyx.prompts.prompt_template import PromptTemplate
+# from onyx.prompts.prompt_utils import handle_company_awareness
+# from onyx.prompts.prompt_utils import handle_memories
+# from onyx.server.query_and_chat.streaming_models import MessageStart
+# from onyx.server.query_and_chat.streaming_models import OverallStop
+# from onyx.server.query_and_chat.streaming_models import SectionEnd
+# from onyx.server.query_and_chat.streaming_models import StreamingType
+# from onyx.tools.tool_implementations.images.image_generation_tool import (
+#     ImageGenerationTool,
+# )
+# from onyx.tools.tool_implementations.knowledge_graph.knowledge_graph_tool import (
+#     KnowledgeGraphTool,
+# )
+# from onyx.tools.tool_implementations.search.search_tool import SearchTool
+# from onyx.tools.tool_implementations.web_search.web_search_tool import (
+#     WebSearchTool,
+# )
+# from onyx.utils.b64 import get_image_type
+# from onyx.utils.b64 import get_image_type_from_bytes
+# from onyx.utils.logger import setup_logger
+
+# logger = setup_logger()
+
+
+# def _get_available_tools(
+#     db_session: Session,
+#     graph_config: GraphConfig,
+#     kg_enabled: bool,
+#     active_source_types: list[DocumentSource],
+# ) -> dict[str, OrchestratorTool]:
+
+#     available_tools: dict[str, OrchestratorTool] = {}
+
+#     kg_enabled = graph_config.behavior.kg_config_settings.KG_ENABLED
+#     persona = graph_config.inputs.persona
+
+#     if persona:
+#         include_kg = persona.name == TMP_DRALPHA_PERSONA_NAME and kg_enabled
+#     else:
+#         include_kg = False
+
+#     tool_dict: dict[int, Tool] = {
+#         tool.id: tool for tool in get_tools(db_session, only_enabled=True)
+#     }
+
+#     for tool in graph_config.tooling.tools:
+
+#         if not tool.is_available(db_session):
+#             logger.info(f"Tool {tool.name} is not available, skipping")
+#             continue
+
+#         tool_db_info = tool_dict.get(tool.id)
+#         if tool_db_info:
+#             incode_tool_id = tool_db_info.in_code_tool_id
+#         else:
+#             raise ValueError(f"Tool {tool.name} is not found in the database")
+
+#         if isinstance(tool, WebSearchTool):
+#             llm_path = DRPath.WEB_SEARCH.value
+#             path = DRPath.WEB_SEARCH
+#         elif isinstance(tool, SearchTool):
+#             llm_path = DRPath.INTERNAL_SEARCH.value
+#             path = DRPath.INTERNAL_SEARCH
+#         elif isinstance(tool, KnowledgeGraphTool) and include_kg:
+#             # TODO (chris): move this into the `is_available` check
+#             if len(active_source_types) == 0:
+#                 logger.error(
+#                     "No active source types found, skipping Knowledge Graph tool"
+#                 )
+#                 continue
+#             llm_path = DRPath.KNOWLEDGE_GRAPH.value
+#             path = DRPath.KNOWLEDGE_GRAPH
+#         elif isinstance(tool, ImageGenerationTool):
+#             llm_path = DRPath.IMAGE_GENERATION.value
+#             path = DRPath.IMAGE_GENERATION
+#         elif incode_tool_id:
+#             # if incode tool id is found, it is a generic internal tool
+#             llm_path = DRPath.GENERIC_INTERNAL_TOOL.value
+#             path = DRPath.GENERIC_INTERNAL_TOOL
+#         else:
+#             # otherwise it is a custom tool
+#             llm_path = DRPath.GENERIC_TOOL.value
+#             path = DRPath.GENERIC_TOOL
+
+#         if path not in {DRPath.GENERIC_INTERNAL_TOOL, DRPath.GENERIC_TOOL}:
+#             description = TOOL_DESCRIPTION.get(path, tool.description)
+#             cost = AVERAGE_TOOL_COSTS[path]
+#         else:
+#             description = tool.description
+#             cost = 1.0
+
+#         tool_info = OrchestratorTool(
+#             tool_id=tool.id,
+#             name=tool.llm_name,
+#             llm_path=llm_path,
+#             path=path,
+#             description=description,
+#             metadata={},
+#             cost=cost,
+#             tool_object=tool,
+#         )
+
+#         # TODO: handle custom tools with same name as other tools (e.g., CLOSER)
+#         available_tools[tool.llm_name] = tool_info
+
+#     available_tool_paths = [tool.path for tool in available_tools.values()]
+
+#     # make sure KG isn't enabled without internal search
+#     if (
+#         DRPath.KNOWLEDGE_GRAPH in available_tool_paths
+#         and DRPath.INTERNAL_SEARCH not in available_tool_paths
+#     ):
+#         raise ValueError(
+#             "The Knowledge Graph is not supported without internal search tool"
+#         )
+
+#     # add CLOSER tool, which is always available
+#     available_tools[DRPath.CLOSER.value] = OrchestratorTool(
+#         tool_id=-1,
+#         name=DRPath.CLOSER.value,
+#         llm_path=DRPath.CLOSER.value,
+#         path=DRPath.CLOSER,
+#         description=TOOL_DESCRIPTION[DRPath.CLOSER],
+#         metadata={},
+#         cost=0.0,
+#         tool_object=None,
+#     )
+
+#     return available_tools
+
+
+# def _construct_uploaded_text_context(files: list[InMemoryChatFile]) -> str:
+#     """Construct the uploaded context from the files."""
+#     file_contents = []
+#     for file in files:
+#         if file.file_type in (
+#             ChatFileType.DOC,
+#             ChatFileType.PLAIN_TEXT,
+#             ChatFileType.CSV,
+#         ):
+#             file_contents.append(file.content.decode("utf-8"))
+#     if len(file_contents) > 0:
+#         return "Uploaded context:\n\n\n" + "\n\n".join(file_contents)
+#     return ""
+
+
+# def _construct_uploaded_image_context(
+#     files: list[InMemoryChatFile] | None = None,
+#     img_urls: list[str] | None = None,
+#     b64_imgs: list[str] | None = None,
+# ) -> list[dict[str, Any]] | None:
+#     """Construct the uploaded image context from the files."""
+#     # Only include image files for user messages
+#     if files is None:
+#         return None
+
+#     img_files = [file for file in files if file.file_type == ChatFileType.IMAGE]
+
+#     img_urls = img_urls or []
+#     b64_imgs = b64_imgs or []
+
+#     if not (img_files or img_urls or b64_imgs):
+#         return None
+
+#     return cast(
+#         list[dict[str, Any]],
+#         [
+#             {
+#                 "type": "image_url",
+#                 "image_url": {
+#                     "url": (
+#                         f"data:{get_image_type_from_bytes(file.content)};"
+#                         f"base64,{file.to_base64()}"
+#                     ),
+#                 },
+#             }
+#             for file in img_files
+#         ]
+#         + [
+#             {
+#                 "type": "image_url",
+#                 "image_url": {
+#                     "url": f"data:{get_image_type(b64_img)};base64,{b64_img}",
+#                 },
+#             }
+#             for b64_img in b64_imgs
+#         ]
+#         + [
+#             {
+#                 "type": "image_url",
+#                 "image_url": {
+#                     "url": url,
+#                 },
+#             }
+#             for url in img_urls
+#         ],
+#     )
+
+
+# def _get_existing_clarification_request(
+#     graph_config: GraphConfig,
+# ) -> tuple[OrchestrationClarificationInfo, str, str] | None:
+#     """
+#     Returns the clarification info, original question, and updated chat history if
+#     a clarification request and response exists, otherwise returns None.
+#     """
+#     # check for clarification request and response in message history
+#     previous_raw_messages = graph_config.inputs.prompt_builder.raw_message_history
+
+#     if len(previous_raw_messages) == 0 or (
+#         previous_raw_messages[-1].research_answer_purpose
+#         != ResearchAnswerPurpose.CLARIFICATION_REQUEST
+#     ):
+#         return None
+
+#     # get the clarification request and response
+#     previous_messages = graph_config.inputs.prompt_builder.message_history
+#     last_message = previous_raw_messages[-1].message
+
+#     clarification = OrchestrationClarificationInfo(
+#         clarification_question=last_message.strip(),
+#         clarification_response=graph_config.inputs.prompt_builder.raw_user_query,
+#     )
+#     original_question = graph_config.inputs.prompt_builder.raw_user_query
+#     chat_history_string = "(No chat history yet available)"
+
+#     # get the original user query and chat history string before the original query
+#     # e.g., if history = [user query, assistant clarification request, user clarification response],
+#     # previous_messages = [user query, assistant clarification request], we want the user query
+#     for i, message in enumerate(reversed(previous_messages), 1):
+#         if (
+#             isinstance(message, HumanMessage)
+#             and message.content
+#             and isinstance(message.content, str)
+#         ):
+#             original_question = message.content
+#             chat_history_string = (
+#                 get_chat_history_string(
+#                     graph_config.inputs.prompt_builder.message_history[:-i],
+#                     MAX_CHAT_HISTORY_MESSAGES,
+#                 )
+#                 or "(No chat history yet available)"
+#             )
+#             break
+
+#     return clarification, original_question, chat_history_string
+
+
+# def _persist_final_docs_and_citations(
+#     db_session: Session,
+#     context_llm_docs: list[Any] | None,
+#     full_answer: str | None,
+# ) -> tuple[list[SearchDoc], dict[int, int] | None]:
+#     """Persist final documents from in-context docs and derive citation mapping.
+
+#     Returns the list of persisted `SearchDoc` records and an optional
+#     citation map translating inline [[n]] references to DB doc indices.
+#     """
+#     final_documents_db: list[SearchDoc] = []
+#     citations_map: dict[int, int] | None = None
+
+#     if not context_llm_docs:
+#         return final_documents_db, citations_map
+
+#     saved_search_docs = saved_search_docs_from_llm_docs(context_llm_docs)
+#     for saved_doc in saved_search_docs:
+#         db_doc = create_search_doc_from_saved_search_doc(saved_doc)
+#         db_session.add(db_doc)
+#         final_documents_db.append(db_doc)
+#     db_session.flush()
+
+#     cited_numbers: set[int] = set()
+#     try:
+#         # Match [[1]] or [[1, 2]] optionally followed by a link like ([[1]](http...))
+#         matches = re.findall(
+#             r"\[\[(\d+(?:,\s*\d+)*)\]\](?:\([^)]*\))?", full_answer or ""
+#         )
+#         for match in matches:
+#             for num_str in match.split(","):
+#                 num = int(num_str.strip())
+#                 cited_numbers.add(num)
+#     except Exception:
+#         cited_numbers = set()
+
+#     if cited_numbers and final_documents_db:
+#         translations = build_citation_map_from_numbers(
+#             cited_numbers=cited_numbers,
+#             db_docs=final_documents_db,
+#         )
+#         citations_map = translations or None
+
+#     return final_documents_db, citations_map
+
+
+# _ARTIFICIAL_ALL_ENCOMPASSING_TOOL = {
+#     "type": "function",
+#     "function": {
+#         "name": "run_any_knowledge_retrieval_and_any_action_tool",
+#         "description": "Use this tool to get ANY external information \
+# that is relevant to the question, or for any action to be taken, including image generation. In fact, \
+# ANY tool mentioned can be accessed through this generic tool. If in doubt, use this tool.",
+#         "parameters": {
+#             "type": "object",
+#             "properties": {
+#                 "request": {
+#                     "type": "string",
+#                     "description": "The request to be made to the tool",
+#                 },
+#             },
+#             "required": ["request"],
+#         },
+#     },
+# }
+
+
+# def clarifier(
+#     state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
+# ) -> OrchestrationSetup:
+#     """
+#     Perform a quick search on the question as is and see whether a set of clarification
+#     questions is needed. For now this is based on the models
+#     """
+
+#     node_start_time = datetime.now()
+#     current_step_nr = 0
+
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
+
+#     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,
+#     )
+
+#     max_input_tokens = get_max_input_tokens(
+#         model_name=llm_model_name,
+#         model_provider=llm_provider,
+#     )
+
+#     use_tool_calling_llm = graph_config.tooling.using_tool_calling_llm
+#     db_session = graph_config.persistence.db_session
+
+#     original_question = graph_config.inputs.prompt_builder.raw_user_query
+#     research_type = graph_config.behavior.research_type
+
+#     force_use_tool = graph_config.tooling.force_use_tool
+
+#     message_id = graph_config.persistence.message_id
+
+#     # Perform a commit to ensure the message_id is set and saved
+#     db_session.commit()
+
+#     # get the connected tools and format for the Deep Research flow
+#     kg_enabled = graph_config.behavior.kg_config_settings.KG_ENABLED
+#     db_session = graph_config.persistence.db_session
+#     active_source_types = fetch_unique_document_sources(db_session)
+
+#     available_tools = _get_available_tools(
+#         db_session, graph_config, kg_enabled, active_source_types
+#     )
+
+#     available_tool_descriptions_str = "\n -" + "\n -".join(
+#         [tool.description for tool in available_tools.values()]
+#     )
+
+#     kg_config = get_kg_config_settings()
+#     if kg_config.KG_ENABLED and kg_config.KG_EXPOSED:
+#         all_entity_types = get_entity_types_str(active=True)
+#         all_relationship_types = get_relationship_types_str(active=True)
+#     else:
+#         all_entity_types = ""
+#         all_relationship_types = ""
+
+#     # if not active_source_types:
+#     #    raise ValueError("No active source types found")
+
+#     active_source_types_descriptions = [
+#         DocumentSourceDescription[source_type] for source_type in active_source_types
+#     ]
+
+#     if len(active_source_types_descriptions) > 0:
+#         active_source_type_descriptions_str = "\n -" + "\n -".join(
+#             active_source_types_descriptions
+#         )
+#     else:
+#         active_source_type_descriptions_str = ""
+
+#     if graph_config.inputs.persona:
+#         assistant_system_prompt = PromptTemplate(
+#             graph_config.inputs.persona.system_prompt or DEFAULT_DR_SYSTEM_PROMPT
+#         ).build()
+#         if graph_config.inputs.persona.task_prompt:
+#             assistant_task_prompt = (
+#                 "\n\nHere are more specifications from the user:\n\n"
+#                 + PromptTemplate(graph_config.inputs.persona.task_prompt).build()
+#             )
+#         else:
+#             assistant_task_prompt = ""
+
+#     else:
+#         assistant_system_prompt = PromptTemplate(DEFAULT_DR_SYSTEM_PROMPT).build()
+#         assistant_task_prompt = ""
+
+#     if graph_config.inputs.project_instructions:
+#         assistant_system_prompt = (
+#             assistant_system_prompt
+#             + PROJECT_INSTRUCTIONS_SEPARATOR
+#             + graph_config.inputs.project_instructions
+#         )
+#     user = (
+#         graph_config.tooling.search_tool.user
+#         if graph_config.tooling.search_tool
+#         else None
+#     )
+#     memories = get_memories(user, db_session)
+#     assistant_system_prompt = handle_company_awareness(assistant_system_prompt)
+#     assistant_system_prompt = handle_memories(assistant_system_prompt, memories)
+
+#     chat_history_string = (
+#         get_chat_history_string(
+#             graph_config.inputs.prompt_builder.message_history,
+#             MAX_CHAT_HISTORY_MESSAGES,
+#         )
+#         or "(No chat history yet available)"
+#     )
+
+#     uploaded_text_context = (
+#         _construct_uploaded_text_context(graph_config.inputs.files)
+#         if graph_config.inputs.files
+#         else ""
+#     )
+
+#     uploaded_context_tokens = check_number_of_tokens(
+#         uploaded_text_context, llm_tokenizer.encode
+#     )
+
+#     if uploaded_context_tokens > 0.5 * max_input_tokens:
+#         raise ValueError(
+#             f"Uploaded context is too long. {uploaded_context_tokens} tokens, "
+#             f"but for this model we only allow {0.5 * max_input_tokens} tokens for uploaded context"
+#         )
+
+#     uploaded_image_context = _construct_uploaded_image_context(
+#         graph_config.inputs.files
+#     )
+
+#     # Use project/search context docs if available to enable citation mapping
+#     context_llm_docs = getattr(
+#         graph_config.inputs.prompt_builder, "context_llm_docs", None
+#     )
+
+#     if not (force_use_tool and force_use_tool.force_use):
+
+#         if not use_tool_calling_llm or len(available_tools) == 1:
+#             if len(available_tools) > 1:
+#                 decision_prompt = DECISION_PROMPT_WO_TOOL_CALLING.build(
+#                     question=original_question,
+#                     chat_history_string=chat_history_string,
+#                     uploaded_context=uploaded_text_context or "",
+#                     active_source_type_descriptions_str=active_source_type_descriptions_str,
+#                     available_tool_descriptions_str=available_tool_descriptions_str,
+#                 )
+
+#                 llm_decision = invoke_llm_json(
+#                     llm=graph_config.tooling.primary_llm,
+#                     prompt=create_question_prompt(
+#                         assistant_system_prompt,
+#                         decision_prompt,
+#                         uploaded_image_context=uploaded_image_context,
+#                     ),
+#                     schema=DecisionResponse,
+#                 )
+#             else:
+#                 # if there is only one tool (Closer), we don't need to decide. It's an LLM answer
+#                 llm_decision = DecisionResponse(decision="LLM", reasoning="")
+
+#             if llm_decision.decision == "LLM" and research_type != ResearchType.DEEP:
+
+#                 write_custom_event(
+#                     current_step_nr,
+#                     MessageStart(
+#                         content="",
+#                         final_documents=[],
+#                     ),
+#                     writer,
+#                 )
+
+#                 answer_prompt = ANSWER_PROMPT_WO_TOOL_CALLING.build(
+#                     question=original_question,
+#                     chat_history_string=chat_history_string,
+#                     uploaded_context=uploaded_text_context or "",
+#                     active_source_type_descriptions_str=active_source_type_descriptions_str,
+#                     available_tool_descriptions_str=available_tool_descriptions_str,
+#                 )
+
+#                 answer_tokens, _, _ = run_with_timeout(
+#                     TF_DR_TIMEOUT_LONG,
+#                     lambda: stream_llm_answer(
+#                         llm=graph_config.tooling.primary_llm,
+#                         prompt=create_question_prompt(
+#                             assistant_system_prompt,
+#                             answer_prompt + assistant_task_prompt,
+#                             uploaded_image_context=uploaded_image_context,
+#                         ),
+#                         event_name="basic_response",
+#                         writer=writer,
+#                         answer_piece=StreamingType.MESSAGE_DELTA.value,
+#                         agent_answer_level=0,
+#                         agent_answer_question_num=0,
+#                         agent_answer_type="agent_level_answer",
+#                         timeout_override=TF_DR_TIMEOUT_LONG,
+#                         ind=current_step_nr,
+#                         context_docs=None,
+#                         replace_citations=True,
+#                         max_tokens=None,
+#                     ),
+#                 )
+
+#                 write_custom_event(
+#                     current_step_nr,
+#                     SectionEnd(
+#                         type="section_end",
+#                     ),
+#                     writer,
+#                 )
+#                 current_step_nr += 1
+
+#                 answer_str = cast(str, merge_content(*answer_tokens))
+
+#                 write_custom_event(
+#                     current_step_nr,
+#                     OverallStop(),
+#                     writer,
+#                 )
+
+#                 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=answer_str,
+#                     update_parent_message=True,
+#                     research_answer_purpose=ResearchAnswerPurpose.ANSWER,
+#                 )
+#                 db_session.commit()
+
+#                 return OrchestrationSetup(
+#                     original_question=original_question,
+#                     chat_history_string="",
+#                     tools_used=[DRPath.END.value],
+#                     available_tools=available_tools,
+#                     query_list=[],
+#                     assistant_system_prompt=assistant_system_prompt,
+#                     assistant_task_prompt=assistant_task_prompt,
+#                 )
+
+#         else:
+
+#             decision_prompt = DECISION_PROMPT_W_TOOL_CALLING.build(
+#                 question=original_question,
+#                 chat_history_string=chat_history_string,
+#                 uploaded_context=uploaded_text_context or "",
+#                 active_source_type_descriptions_str=active_source_type_descriptions_str,
+#             )
+
+#             if context_llm_docs:
+#                 persona = graph_config.inputs.persona
+#                 if persona is not None:
+#                     prompt_config = PromptConfig.from_model(
+#                         persona, db_session=graph_config.persistence.db_session
+#                     )
+#                 else:
+#                     prompt_config = PromptConfig(
+#                         default_behavior_system_prompt=assistant_system_prompt,
+#                         custom_instructions=None,
+#                         reminder="",
+#                         datetime_aware=True,
+#                     )
+
+#                 system_prompt_to_use_content = build_citations_system_message(
+#                     prompt_config
+#                 ).content
+#                 system_prompt_to_use: str = cast(str, system_prompt_to_use_content)
+#                 if graph_config.inputs.project_instructions:
+#                     system_prompt_to_use = (
+#                         system_prompt_to_use
+#                         + PROJECT_INSTRUCTIONS_SEPARATOR
+#                         + graph_config.inputs.project_instructions
+#                     )
+#                 user_prompt_to_use = build_citations_user_message(
+#                     user_query=original_question,
+#                     files=[],
+#                     prompt_config=prompt_config,
+#                     context_docs=context_llm_docs,
+#                     all_doc_useful=False,
+#                     history_message=chat_history_string,
+#                     context_type="user files",
+#                 ).content
+#             else:
+#                 system_prompt_to_use = assistant_system_prompt
+#                 user_prompt_to_use = decision_prompt + assistant_task_prompt
+
+#             @traced(name="clarifier stream and process", type="llm")
+#             def stream_and_process() -> BasicSearchProcessedStreamResults:
+#                 stream = graph_config.tooling.primary_llm.stream_langchain(
+#                     prompt=create_question_prompt(
+#                         cast(str, system_prompt_to_use),
+#                         cast(str, user_prompt_to_use),
+#                         uploaded_image_context=uploaded_image_context,
+#                     ),
+#                     tools=([_ARTIFICIAL_ALL_ENCOMPASSING_TOOL]),
+#                     tool_choice=(None),
+#                     structured_response_format=graph_config.inputs.structured_response_format,
+#                 )
+#                 return process_llm_stream(
+#                     messages=stream,
+#                     should_stream_answer=True,
+#                     writer=writer,
+#                     ind=0,
+#                     search_results=context_llm_docs,
+#                     generate_final_answer=True,
+#                     chat_message_id=str(graph_config.persistence.chat_session_id),
+#                 )
+
+#             # Deep research always continues to clarification or search
+#             if research_type != ResearchType.DEEP:
+#                 full_response = stream_and_process()
+#                 if len(full_response.ai_message_chunk.tool_calls) == 0:
+
+#                     if isinstance(full_response.full_answer, str):
+#                         full_answer = (
+#                             normalize_square_bracket_citations_to_double_with_links(
+#                                 full_response.full_answer
+#                             )
+#                         )
+#                     else:
+#                         full_answer = None
+
+#                     # Persist final documents and derive citations when using in-context docs
+#                     final_documents_db, citations_map = (
+#                         _persist_final_docs_and_citations(
+#                             db_session=db_session,
+#                             context_llm_docs=context_llm_docs,
+#                             full_answer=full_answer,
+#                         )
+#                     )
+
+#                     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=full_answer,
+#                         token_count=len(llm_tokenizer.encode(full_answer or "")),
+#                         citations=citations_map,
+#                         final_documents=final_documents_db or None,
+#                         update_parent_message=True,
+#                         research_answer_purpose=ResearchAnswerPurpose.ANSWER,
+#                     )
+
+#                     db_session.commit()
+
+#                     return OrchestrationSetup(
+#                         original_question=original_question,
+#                         chat_history_string="",
+#                         tools_used=[DRPath.END.value],
+#                         query_list=[],
+#                         available_tools=available_tools,
+#                         assistant_system_prompt=assistant_system_prompt,
+#                         assistant_task_prompt=assistant_task_prompt,
+#                     )
+
+#         # Continue, as external knowledge is required.
+
+#     current_step_nr += 1
+
+#     clarification = None
+
+#     if research_type == ResearchType.DEEP:
+#         result = _get_existing_clarification_request(graph_config)
+#         if result is not None:
+#             clarification, original_question, chat_history_string = result
+#         else:
+#             # generate clarification questions if needed
+#             chat_history_string = (
+#                 get_chat_history_string(
+#                     graph_config.inputs.prompt_builder.message_history,
+#                     MAX_CHAT_HISTORY_MESSAGES,
+#                 )
+#                 or "(No chat history yet available)"
+#             )
+
+#             base_clarification_prompt = get_dr_prompt_orchestration_templates(
+#                 DRPromptPurpose.CLARIFICATION,
+#                 research_type,
+#                 entity_types_string=all_entity_types,
+#                 relationship_types_string=all_relationship_types,
+#                 available_tools=available_tools,
+#             )
+#             clarification_prompt = base_clarification_prompt.build(
+#                 question=original_question,
+#                 chat_history_string=chat_history_string,
+#             )
+
+#             try:
+#                 clarification_response = invoke_llm_json(
+#                     llm=graph_config.tooling.primary_llm,
+#                     prompt=create_question_prompt(
+#                         assistant_system_prompt,
+#                         clarification_prompt,
+#                         uploaded_image_context=uploaded_image_context,
+#                     ),
+#                     schema=ClarificationGenerationResponse,
+#                     timeout_override=TF_DR_TIMEOUT_SHORT,
+#                     # max_tokens=1500,
+#                 )
+#             except Exception as e:
+#                 logger.error(f"Error in clarification generation: {e}")
+#                 raise e
+
+#             if (
+#                 clarification_response.clarification_needed
+#                 and clarification_response.clarification_question
+#             ):
+#                 clarification = OrchestrationClarificationInfo(
+#                     clarification_question=clarification_response.clarification_question,
+#                     clarification_response=None,
+#                 )
+#                 write_custom_event(
+#                     current_step_nr,
+#                     MessageStart(
+#                         content="",
+#                         final_documents=None,
+#                     ),
+#                     writer,
+#                 )
+
+#                 repeat_prompt = REPEAT_PROMPT.build(
+#                     original_information=clarification_response.clarification_question
+#                 )
+
+#                 _, _, _ = run_with_timeout(
+#                     TF_DR_TIMEOUT_LONG,
+#                     lambda: stream_llm_answer(
+#                         llm=graph_config.tooling.primary_llm,
+#                         prompt=repeat_prompt,
+#                         event_name="basic_response",
+#                         writer=writer,
+#                         agent_answer_level=0,
+#                         agent_answer_question_num=0,
+#                         agent_answer_type="agent_level_answer",
+#                         timeout_override=TF_DR_TIMEOUT_LONG,
+#                         answer_piece=StreamingType.MESSAGE_DELTA.value,
+#                         ind=current_step_nr,
+#                         # max_tokens=None,
+#                     ),
+#                 )
+
+#                 write_custom_event(
+#                     current_step_nr,
+#                     SectionEnd(
+#                         type="section_end",
+#                     ),
+#                     writer,
+#                 )
+
+#                 write_custom_event(
+#                     1,
+#                     OverallStop(),
+#                     writer,
+#                 )
+
+#                 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=clarification_response.clarification_question,
+#                     update_parent_message=True,
+#                     research_type=research_type,
+#                     research_answer_purpose=ResearchAnswerPurpose.CLARIFICATION_REQUEST,
+#                 )
+
+#                 db_session.commit()
+
+#     else:
+#         chat_history_string = (
+#             get_chat_history_string(
+#                 graph_config.inputs.prompt_builder.message_history,
+#                 MAX_CHAT_HISTORY_MESSAGES,
+#             )
+#             or "(No chat history yet available)"
+#         )
+
+#     if (
+#         clarification
+#         and clarification.clarification_question
+#         and clarification.clarification_response is None
+#     ):
+
+#         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=clarification.clarification_question,
+#             update_parent_message=True,
+#             research_type=research_type,
+#             research_answer_purpose=ResearchAnswerPurpose.CLARIFICATION_REQUEST,
+#         )
+
+#         db_session.commit()
+
+#         next_tool = DRPath.END.value
+#     else:
+#         next_tool = DRPath.ORCHESTRATOR.value
+
+#     return OrchestrationSetup(
+#         original_question=original_question,
+#         chat_history_string=chat_history_string,
+#         tools_used=[next_tool],
+#         query_list=[],
+#         iteration_nr=0,
+#         current_step_nr=current_step_nr,
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="main",
+#                 node_name="clarifier",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#         clarification=clarification,
+#         available_tools=available_tools,
+#         active_source_types=active_source_types,
+#         active_source_types_descriptions="\n".join(active_source_types_descriptions),
+#         assistant_system_prompt=assistant_system_prompt,
+#         assistant_task_prompt=assistant_task_prompt,
+#         uploaded_test_context=uploaded_text_context,
+#         uploaded_image_context=uploaded_image_context,
+#     )

backend/onyx/agents/agent_search/dr/nodes/dr_a1_orchestrator.py

@@ -0,0 +1,617 @@
+# from datetime import datetime
+# from typing import cast
+
+# from langchain_core.messages import merge_content
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
+
+# from onyx.agents.agent_search.dr.constants import DR_TIME_BUDGET_DEFAULT
+# from onyx.agents.agent_search.dr.constants import HIGH_LEVEL_PLAN_PREFIX
+# from onyx.agents.agent_search.dr.dr_prompt_builder import (
+#     get_dr_prompt_orchestration_templates,
+# )
+# 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 OrchestrationPlan
+# from onyx.agents.agent_search.dr.models import OrchestratorDecisonsNoPlan
+# from onyx.agents.agent_search.dr.states import IterationInstructions
+# from onyx.agents.agent_search.dr.states import MainState
+# from onyx.agents.agent_search.dr.states import OrchestrationUpdate
+# from onyx.agents.agent_search.dr.utils import aggregate_context
+# from onyx.agents.agent_search.dr.utils import create_tool_call_string
+# from onyx.agents.agent_search.dr.utils import get_prompt_question
+# 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 run_with_timeout
+# 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_LONG
+# from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
+# from onyx.kg.utils.extraction_utils import get_entity_types_str
+# from onyx.kg.utils.extraction_utils import get_relationship_types_str
+# from onyx.prompts.dr_prompts import DEFAULLT_DECISION_PROMPT
+# from onyx.prompts.dr_prompts import REPEAT_PROMPT
+# from onyx.prompts.dr_prompts import SUFFICIENT_INFORMATION_STRING
+# from onyx.server.query_and_chat.streaming_models import ReasoningStart
+# 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
+
+# logger = setup_logger()
+
+# _DECISION_SYSTEM_PROMPT_PREFIX = "Here are general instructions by the user, which \
+# may or may not influence the decision what to do next:\n\n"
+
+
+# def _get_implied_next_tool_based_on_tool_call_history(
+#     tools_used: list[str],
+# ) -> str | None:
+#     """
+#     Identify the next tool based on the tool call history. Initially, we only support
+#     special handling of the image generation tool.
+#     """
+#     if tools_used[-1] == DRPath.IMAGE_GENERATION.value:
+#         return DRPath.LOGGER.value
+#     else:
+#         return None
+
+
+# def orchestrator(
+#     state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
+# ) -> OrchestrationUpdate:
+#     """
+#     LangGraph node to decide the next step in the DR process.
+#     """
+
+#     node_start_time = datetime.now()
+
+#     graph_config = cast(GraphConfig, config["metadata"]["config"])
+#     question = state.original_question
+#     if not question:
+#         raise ValueError("Question is required for orchestrator")
+
+#     state.original_question
+
+#     available_tools = state.available_tools
+
+#     plan_of_record = state.plan_of_record
+#     clarification = state.clarification
+#     assistant_system_prompt = state.assistant_system_prompt
+
+#     if assistant_system_prompt:
+#         decision_system_prompt: str = (
+#             DEFAULLT_DECISION_PROMPT
+#             + _DECISION_SYSTEM_PROMPT_PREFIX
+#             + assistant_system_prompt
+#         )
+#     else:
+#         decision_system_prompt = DEFAULLT_DECISION_PROMPT
+
+#     iteration_nr = state.iteration_nr + 1
+#     current_step_nr = state.current_step_nr
+
+#     use_agentic_search = graph_config.behavior.use_agentic_search
+#     remaining_time_budget = state.remaining_time_budget
+#     chat_history_string = state.chat_history_string or "(No chat history yet available)"
+#     answer_history_string = (
+#         aggregate_context(state.iteration_responses, include_documents=True).context
+#         or "(No answer history yet available)"
+#     )
+
+#     next_tool_name = None
+
+#     # Identify early exit condition based on tool call history
+
+#     next_tool_based_on_tool_call_history = (
+#         _get_implied_next_tool_based_on_tool_call_history(state.tools_used)
+#     )
+
+#     if next_tool_based_on_tool_call_history == DRPath.LOGGER.value:
+#         return OrchestrationUpdate(
+#             tools_used=[DRPath.LOGGER.value],
+#             query_list=[],
+#             iteration_nr=iteration_nr,
+#             current_step_nr=current_step_nr,
+#             log_messages=[
+#                 get_langgraph_node_log_string(
+#                     graph_component="main",
+#                     node_name="orchestrator",
+#                     node_start_time=node_start_time,
+#                 )
+#             ],
+#             plan_of_record=plan_of_record,
+#             remaining_time_budget=remaining_time_budget,
+#             iteration_instructions=[
+#                 IterationInstructions(
+#                     iteration_nr=iteration_nr,
+#                     plan=plan_of_record.plan if plan_of_record else None,
+#                     reasoning="",
+#                     purpose="",
+#                 )
+#             ],
+#         )
+
+#     # no early exit forced. Continue.
+
+#     available_tools = state.available_tools or {}
+
+#     uploaded_context = state.uploaded_test_context or ""
+#     uploaded_image_context = state.uploaded_image_context or []
+
+#     questions = [
+#         f"{iteration_response.tool}: {iteration_response.question}"
+#         for iteration_response in state.iteration_responses
+#         if len(iteration_response.question) > 0
+#     ]
+
+#     question_history_string = (
+#         "\n".join(f"  - {question}" for question in questions)
+#         if questions
+#         else "(No question history yet available)"
+#     )
+
+#     prompt_question = get_prompt_question(question, clarification)
+
+#     gaps_str = (
+#         ("\n  - " + "\n  - ".join(state.gaps))
+#         if state.gaps
+#         else "(No explicit gaps were pointed out so far)"
+#     )
+
+#     all_entity_types = get_entity_types_str(active=True)
+#     all_relationship_types = get_relationship_types_str(active=True)
+
+#     # default to closer
+#     query_list = ["Answer the question with the information you have."]
+#     decision_prompt = None
+
+#     reasoning_result = "(No reasoning result provided yet.)"
+#     tool_calls_string = "(No tool calls provided yet.)"
+
+#     if not use_agentic_search:
+#         if iteration_nr == 1:
+#             # Default time budget for non-agentic search
+#             remaining_time_budget = 3.0
+
+#         elif remaining_time_budget <= 0:
+
+#             write_custom_event(
+#                 current_step_nr,
+#                 SectionEnd(),
+#                 writer,
+#             )
+
+#             current_step_nr += 1
+
+#             return OrchestrationUpdate(
+#                 tools_used=[DRPath.CLOSER.value],
+#                 current_step_nr=current_step_nr,
+#                 query_list=[],
+#                 iteration_nr=iteration_nr,
+#                 log_messages=[
+#                     get_langgraph_node_log_string(
+#                         graph_component="main",
+#                         node_name="orchestrator",
+#                         node_start_time=node_start_time,
+#                     )
+#                 ],
+#                 plan_of_record=plan_of_record,
+#                 remaining_time_budget=remaining_time_budget,
+#                 iteration_instructions=[
+#                     IterationInstructions(
+#                         iteration_nr=iteration_nr,
+#                         plan=None,
+#                         reasoning="Time to wrap up.",
+#                         purpose="",
+#                     )
+#                 ],
+#             )
+
+#         elif iteration_nr > 1 and remaining_time_budget > 0:
+#             # for each iteration past the first one, we need to see whether we
+#             # have enough information to answer the question.
+#             # if we do, we can stop the iteration and return the answer.
+#             # if we do not, we need to continue the iteration.
+
+#             base_reasoning_prompt = get_dr_prompt_orchestration_templates(
+#                 DRPromptPurpose.NEXT_STEP_REASONING,
+#                 use_agentic_search,
+#                 entity_types_string=all_entity_types,
+#                 relationship_types_string=all_relationship_types,
+#                 available_tools=available_tools,
+#             )
+
+#             reasoning_prompt = base_reasoning_prompt.build(
+#                 question=question,
+#                 chat_history_string=chat_history_string,
+#                 answer_history_string=answer_history_string,
+#                 iteration_nr=str(iteration_nr),
+#                 remaining_time_budget=str(remaining_time_budget),
+#                 uploaded_context=uploaded_context,
+#             )
+
+#             reasoning_tokens: list[str] = [""]
+
+#             reasoning_tokens, _, _ = run_with_timeout(
+#                 TF_DR_TIMEOUT_LONG,
+#                 lambda: stream_llm_answer(
+#                     llm=graph_config.tooling.primary_llm,
+#                     prompt=create_question_prompt(
+#                         decision_system_prompt,
+#                         reasoning_prompt,
+#                         uploaded_image_context=uploaded_image_context,
+#                     ),
+#                     event_name="basic_response",
+#                     writer=writer,
+#                     agent_answer_level=0,
+#                     agent_answer_question_num=0,
+#                     agent_answer_type="agent_level_answer",
+#                     timeout_override=TF_DR_TIMEOUT_LONG,
+#                     answer_piece=StreamingType.REASONING_DELTA.value,
+#                     ind=current_step_nr,
+#                     # max_tokens=None,
+#                 ),
+#             )
+
+#             write_custom_event(
+#                 current_step_nr,
+#                 SectionEnd(),
+#                 writer,
+#             )
+
+#             current_step_nr += 1
+
+#             reasoning_result = cast(str, merge_content(*reasoning_tokens))
+
+#             if SUFFICIENT_INFORMATION_STRING in reasoning_result:
+#                 return OrchestrationUpdate(
+#                     tools_used=[DRPath.CLOSER.value],
+#                     current_step_nr=current_step_nr,
+#                     query_list=[],
+#                     iteration_nr=iteration_nr,
+#                     log_messages=[
+#                         get_langgraph_node_log_string(
+#                             graph_component="main",
+#                             node_name="orchestrator",
+#                             node_start_time=node_start_time,
+#                         )
+#                     ],
+#                     plan_of_record=plan_of_record,
+#                     remaining_time_budget=remaining_time_budget,
+#                     iteration_instructions=[
+#                         IterationInstructions(
+#                             iteration_nr=iteration_nr,
+#                             plan=None,
+#                             reasoning=reasoning_result,
+#                             purpose="",
+#                         )
+#                     ],
+#                 )
+
+#         # for Thoughtful mode, we force a tool if requested an available
+#         available_tools_for_decision = available_tools
+#         force_use_tool = graph_config.tooling.force_use_tool
+#         if iteration_nr == 1 and force_use_tool and force_use_tool.force_use:
+
+#             forced_tool_name = force_use_tool.tool_name
+
+#             available_tool_dict = {
+#                 available_tool.tool_object.name: available_tool
+#                 for _, available_tool in available_tools.items()
+#                 if available_tool.tool_object
+#             }
+
+#             if forced_tool_name in available_tool_dict.keys():
+#                 forced_tool = available_tool_dict[forced_tool_name]
+
+#                 available_tools_for_decision = {forced_tool.name: forced_tool}
+
+#         base_decision_prompt = get_dr_prompt_orchestration_templates(
+#             DRPromptPurpose.NEXT_STEP,
+#             use_agentic_search,
+#             entity_types_string=all_entity_types,
+#             relationship_types_string=all_relationship_types,
+#             available_tools=available_tools_for_decision,
+#         )
+#         decision_prompt = base_decision_prompt.build(
+#             question=question,
+#             chat_history_string=chat_history_string,
+#             answer_history_string=answer_history_string,
+#             iteration_nr=str(iteration_nr),
+#             remaining_time_budget=str(remaining_time_budget),
+#             reasoning_result=reasoning_result,
+#             uploaded_context=uploaded_context,
+#         )
+
+#         if remaining_time_budget > 0:
+#             try:
+#                 orchestrator_action = invoke_llm_json(
+#                     llm=graph_config.tooling.primary_llm,
+#                     prompt=create_question_prompt(
+#                         decision_system_prompt,
+#                         decision_prompt,
+#                         uploaded_image_context=uploaded_image_context,
+#                     ),
+#                     schema=OrchestratorDecisonsNoPlan,
+#                     timeout_override=TF_DR_TIMEOUT_SHORT,
+#                     # max_tokens=2500,
+#                 )
+#                 next_step = orchestrator_action.next_step
+#                 next_tool_name = next_step.tool
+#                 query_list = [q for q in (next_step.questions or [])]
+
+#                 tool_calls_string = create_tool_call_string(next_tool_name, query_list)
+
+#             except Exception as e:
+#                 logger.error(f"Error in approach extraction: {e}")
+#                 raise e
+
+#             if next_tool_name in available_tools.keys():
+#                 remaining_time_budget -= available_tools[next_tool_name].cost
+#             else:
+#                 logger.warning(f"Tool {next_tool_name} not found in available tools")
+#                 remaining_time_budget -= 1.0
+
+#         else:
+#             reasoning_result = "Time to wrap up."
+#             next_tool_name = DRPath.CLOSER.value
+
+#     else:  # use_agentic_search is True
+#         if iteration_nr == 1 and not plan_of_record:
+#             # by default, we start a new iteration, but if there is a feedback request,
+#             # we start a new iteration 0 again (set a bit later)
+
+#             remaining_time_budget = DR_TIME_BUDGET_DEFAULT
+
+#             base_plan_prompt = get_dr_prompt_orchestration_templates(
+#                 DRPromptPurpose.PLAN,
+#                 use_agentic_search,
+#                 entity_types_string=all_entity_types,
+#                 relationship_types_string=all_relationship_types,
+#                 available_tools=available_tools,
+#             )
+#             plan_generation_prompt = base_plan_prompt.build(
+#                 question=prompt_question,
+#                 chat_history_string=chat_history_string,
+#                 uploaded_context=uploaded_context,
+#             )
+
+#             try:
+#                 plan_of_record = invoke_llm_json(
+#                     llm=graph_config.tooling.primary_llm,
+#                     prompt=create_question_prompt(
+#                         decision_system_prompt,
+#                         plan_generation_prompt,
+#                         uploaded_image_context=uploaded_image_context,
+#                     ),
+#                     schema=OrchestrationPlan,
+#                     timeout_override=TF_DR_TIMEOUT_SHORT,
+#                     # max_tokens=3000,
+#                 )
+#             except Exception as e:
+#                 logger.error(f"Error in plan generation: {e}")
+#                 raise
+
+#             write_custom_event(
+#                 current_step_nr,
+#                 ReasoningStart(),
+#                 writer,
+#             )
+
+#             start_time = datetime.now()
+
+#             repeat_plan_prompt = REPEAT_PROMPT.build(
+#                 original_information=f"{HIGH_LEVEL_PLAN_PREFIX}\n\n {plan_of_record.plan}"
+#             )
+
+#             _, _, _ = run_with_timeout(
+#                 TF_DR_TIMEOUT_LONG,
+#                 lambda: stream_llm_answer(
+#                     llm=graph_config.tooling.primary_llm,
+#                     prompt=repeat_plan_prompt,
+#                     event_name="basic_response",
+#                     writer=writer,
+#                     agent_answer_level=0,
+#                     agent_answer_question_num=0,
+#                     agent_answer_type="agent_level_answer",
+#                     timeout_override=TF_DR_TIMEOUT_LONG,
+#                     answer_piece=StreamingType.REASONING_DELTA.value,
+#                     ind=current_step_nr,
+#                 ),
+#             )
+
+#             end_time = datetime.now()
+#             logger.debug(f"Time taken for plan streaming: {end_time - start_time}")
+
+#             write_custom_event(
+#                 current_step_nr,
+#                 SectionEnd(),
+#                 writer,
+#             )
+#             current_step_nr += 1
+
+#         if not plan_of_record:
+#             raise ValueError(
+#                 "Plan information is required for iterative decision making"
+#             )
+
+#         base_decision_prompt = get_dr_prompt_orchestration_templates(
+#             DRPromptPurpose.NEXT_STEP,
+#             use_agentic_search,
+#             entity_types_string=all_entity_types,
+#             relationship_types_string=all_relationship_types,
+#             available_tools=available_tools,
+#         )
+#         decision_prompt = base_decision_prompt.build(
+#             answer_history_string=answer_history_string,
+#             question_history_string=question_history_string,
+#             question=prompt_question,
+#             iteration_nr=str(iteration_nr),
+#             current_plan_of_record_string=plan_of_record.plan,
+#             chat_history_string=chat_history_string,
+#             remaining_time_budget=str(remaining_time_budget),
+#             gaps=gaps_str,
+#             uploaded_context=uploaded_context,
+#         )
+
+#         if remaining_time_budget > 0:
+#             try:
+#                 orchestrator_action = invoke_llm_json(
+#                     llm=graph_config.tooling.primary_llm,
+#                     prompt=create_question_prompt(
+#                         decision_system_prompt,
+#                         decision_prompt,
+#                         uploaded_image_context=uploaded_image_context,
+#                     ),
+#                     schema=OrchestratorDecisonsNoPlan,
+#                     timeout_override=TF_DR_TIMEOUT_LONG,
+#                     # max_tokens=1500,
+#                 )
+#                 next_step = orchestrator_action.next_step
+#                 next_tool_name = next_step.tool
+
+#                 query_list = [q for q in (next_step.questions or [])]
+#                 reasoning_result = orchestrator_action.reasoning
+
+#                 tool_calls_string = create_tool_call_string(next_tool_name, query_list)
+#             except Exception as e:
+#                 logger.error(f"Error in approach extraction: {e}")
+#                 raise e
+
+#             if next_tool_name in available_tools.keys():
+#                 remaining_time_budget -= available_tools[next_tool_name].cost
+#             else:
+#                 logger.warning(f"Tool {next_tool_name} not found in available tools")
+#                 remaining_time_budget -= 1.0
+#         else:
+#             reasoning_result = "Time to wrap up."
+#             next_tool_name = DRPath.CLOSER.value
+
+#         write_custom_event(
+#             current_step_nr,
+#             ReasoningStart(),
+#             writer,
+#         )
+
+#         repeat_reasoning_prompt = REPEAT_PROMPT.build(
+#             original_information=reasoning_result
+#         )
+
+#         _, _, _ = run_with_timeout(
+#             TF_DR_TIMEOUT_LONG,
+#             lambda: stream_llm_answer(
+#                 llm=graph_config.tooling.primary_llm,
+#                 prompt=repeat_reasoning_prompt,
+#                 event_name="basic_response",
+#                 writer=writer,
+#                 agent_answer_level=0,
+#                 agent_answer_question_num=0,
+#                 agent_answer_type="agent_level_answer",
+#                 timeout_override=TF_DR_TIMEOUT_LONG,
+#                 answer_piece=StreamingType.REASONING_DELTA.value,
+#                 ind=current_step_nr,
+#                 # max_tokens=None,
+#             ),
+#         )
+
+#         write_custom_event(
+#             current_step_nr,
+#             SectionEnd(),
+#             writer,
+#         )
+
+#         current_step_nr += 1
+
+#     base_next_step_purpose_prompt = get_dr_prompt_orchestration_templates(
+#         DRPromptPurpose.NEXT_STEP_PURPOSE,
+#         use_agentic_search,
+#         entity_types_string=all_entity_types,
+#         relationship_types_string=all_relationship_types,
+#         available_tools=available_tools,
+#     )
+#     orchestration_next_step_purpose_prompt = base_next_step_purpose_prompt.build(
+#         question=prompt_question,
+#         reasoning_result=reasoning_result,
+#         tool_calls=tool_calls_string,
+#     )
+
+#     purpose_tokens: list[str] = [""]
+#     purpose = ""
+
+#     if use_agentic_search:
+
+#         try:
+
+#             write_custom_event(
+#                 current_step_nr,
+#                 ReasoningStart(),
+#                 writer,
+#             )
+
+#             purpose_tokens, _, _ = run_with_timeout(
+#                 TF_DR_TIMEOUT_LONG,
+#                 lambda: stream_llm_answer(
+#                     llm=graph_config.tooling.primary_llm,
+#                     prompt=create_question_prompt(
+#                         decision_system_prompt,
+#                         orchestration_next_step_purpose_prompt,
+#                         uploaded_image_context=uploaded_image_context,
+#                     ),
+#                     event_name="basic_response",
+#                     writer=writer,
+#                     agent_answer_level=0,
+#                     agent_answer_question_num=0,
+#                     agent_answer_type="agent_level_answer",
+#                     timeout_override=TF_DR_TIMEOUT_LONG,
+#                     answer_piece=StreamingType.REASONING_DELTA.value,
+#                     ind=current_step_nr,
+#                     # max_tokens=None,
+#                 ),
+#             )
+
+#             write_custom_event(
+#                 current_step_nr,
+#                 SectionEnd(),
+#                 writer,
+#             )
+
+#             current_step_nr += 1
+
+#         except Exception as e:
+#             logger.error("Error in orchestration next step purpose.")
+#             raise e
+
+#         purpose = cast(str, merge_content(*purpose_tokens))
+#     else:
+#         purpose = f"Answering the question using the {next_tool_name}"
+
+#     if not next_tool_name:
+#         raise ValueError("The next step has not been defined. This should not happen.")
+
+#     return OrchestrationUpdate(
+#         tools_used=[next_tool_name],
+#         query_list=query_list or [],
+#         iteration_nr=iteration_nr,
+#         current_step_nr=current_step_nr,
+#         log_messages=[
+#             get_langgraph_node_log_string(
+#                 graph_component="main",
+#                 node_name="orchestrator",
+#                 node_start_time=node_start_time,
+#             )
+#         ],
+#         plan_of_record=plan_of_record,
+#         remaining_time_budget=remaining_time_budget,
+#         iteration_instructions=[
+#             IterationInstructions(
+#                 iteration_nr=iteration_nr,
+#                 plan=plan_of_record.plan if plan_of_record else None,
+#                 reasoning=reasoning_result,
+#                 purpose=purpose,
+#             )
+#         ],
+#     )

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

@@ -0,0 +1,418 @@
+# 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 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()
+
+
+# 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)
+
+#     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(",")]
+
+#     # 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)
+
+
+# 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)
+
+
+# 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
+#     ]
+
+#     # 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
+
+#     # 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=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_answer in aggregated_context.global_iteration_responses:
+
+#         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]
+
+#         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()
+
+
+# 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.
+
+#     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")
+
+#     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
+
+#     uploaded_context = state.uploaded_test_context or ""
+
+#     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)"
+#     )
+
+#     aggregated_context_w_docs = aggregate_context(
+#         state.iteration_responses, include_documents=True
+#     )
+
+#     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
+
+#     num_closer_suggestions = state.num_closer_suggestions
+
+#     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,
+#         )
+
+#         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,
+#             )
+
+#     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,
+#     )
+
+#     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,
+#         )
+#     )
+
+#     # 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
+
+#     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,
+#     )
+
+#     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
+#     ]
+
+#     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}")
+
+#     write_custom_event(current_step_nr, SectionEnd(), writer)
+
+#     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)
+
+#     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,
+#     # )
+
+#     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

@@ -0,0 +1,246 @@
+# 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 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()
+
+
+# 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)
+
+#     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(",")]
+
+#     # 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)
+
+
+# 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)
+
+
+# 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
+#     ]
+
+#     # 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
+
+#     # 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=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_answer in aggregated_context.global_iteration_responses:
+
+#         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]
+
+#         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()
+
+
+# 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.
+
+#     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")
+
+#     aggregated_context = aggregate_context(
+#         state.iteration_responses, include_documents=True
+#     )
+
+#     all_cited_documents = aggregated_context.cited_documents
+
+#     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
+
+#     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)
+
+#     # 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,
+#             )
+#         ],
+#     )

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

@@ -0,0 +1,131 @@
+# 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 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()
+
+
+# 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="")
+
+#     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] = []
+
+#     # 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
+
+#         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"
+#                         )
+
+#                     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)
+
+#     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)
+
+#     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

@@ -0,0 +1,82 @@
+# from operator import add
+# from typing import Annotated
+# from typing import Any
+# from typing import TypedDict
+
+# 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
+
+# ### States ###
+
+
+# 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 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
+
+
+# class AnswerUpdate(LoggerUpdate):
+#     iteration_responses: Annotated[list[IterationAnswer], add] = []
+
+
+# class FinalUpdate(LoggerUpdate):
+#     final_answer: str | None = None
+#     all_cited_documents: list[InferenceSection] = []
+
+
+# ## Graph Input State
+# class MainInput(CoreState):
+#     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]

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

@@ -0,0 +1,47 @@
+# from datetime import datetime
+
+# 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
+
+# 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.
+#     """
+
+#     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()}")
+
+#     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,
+#             )
+#         ],
+#     )

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

@@ -0,0 +1,261 @@
+# import re
+# from datetime import datetime
+# from typing import cast
+
+# from langchain_core.runnables import RunnableConfig
+# from langgraph.types import StreamWriter
+
+# 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()
+
+
+# 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
+
+#     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
+#     use_agentic_search = graph_config.behavior.use_agentic_search
+
+#     if not state.available_tools:
+#         raise ValueError("available_tools is not set")
+
+#     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)
+#     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")
+
+#     # 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"),
+#     )
+
+#     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
+
+#     # 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
+
+#     # 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")
+
+#     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
+
+#     logger.debug(
+#         f"Search start for Standard Search {iteration_nr}.{parallelization_nr} at {datetime.now()}"
+#     )
+
+#     retrieved_docs: list[InferenceSection] = []
+
+#     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)
+
+#             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,
+#     )
+
+#     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)
+
+#     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()}"
+#     )
+
+#     # 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,
+#         )
+
+#         # 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,
+#         )
+
+#         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 = []
+
+#         (
+#             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.")
+
+#         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 = ""
+
+#     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

@@ -0,0 +1,77 @@
+# from datetime import datetime
+
+# 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
+
+
+# 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.
+#     """
+
+#     node_start_time = datetime.now()
+
+#     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
+#     ]
+
+#     [update.question for update in new_updates]
+#     doc_lists = [list(update.cited_documents.values()) for update in new_updates]
+
+#     doc_list = []
+
+#     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
+#     ]
+
+#     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,
+#     )
+
+#     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,
+#             )
+#         ],
+#     )

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

@@ -0,0 +1,50 @@
+# 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
+
+
+# logger = setup_logger()
+
+
+# def dr_basic_search_graph_builder() -> StateGraph:
+#     """
+#     LangGraph graph builder for Web Search Sub-Agent
+#     """
+
+#     graph = StateGraph(state_schema=SubAgentMainState, input=SubAgentInput)
+
+#     ### Add nodes ###
+
+#     graph.add_node("branch", basic_search_branch)
+
+#     graph.add_node("act", basic_search)
+
+#     graph.add_node("reducer", is_reducer)
+
+#     ### Add edges ###
+
+#     graph.add_edge(start_key=START, end_key="branch")
+
+#     graph.add_conditional_edges("branch", branching_router)
+
+#     graph.add_edge(start_key="act", end_key="reducer")
+
+#     graph.add_edge(start_key="reducer", end_key=END)
+
+#     return graph

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

@@ -0,0 +1,30 @@
+# from collections.abc import Hashable
+
+# 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
+
+
+# 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

@@ -0,0 +1,36 @@
+# from datetime import datetime
+
+# 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
+
+# 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.
+#     """
+
+#     node_start_time = datetime.now()
+#     iteration_nr = state.iteration_nr
+
+#     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,
+#             )
+#         ],
+#     )

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

@@ -0,0 +1,164 @@
+# 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 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()
+
+
+# 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
+
+#     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)
+
+#     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
+
+#     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,
+#     )
+
+#         # 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:
+#         raise ValueError(
+#             "Failed to obtain tool arguments from LLM - tool calling is required"
+#         )
+
+#     # 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")
+
+#     # 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)
+
+#     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()
+
+#     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,
+#             )
+#         ],
+#     )

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

@@ -0,0 +1,82 @@
+# from datetime import datetime
+
+# 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
+
+
+# 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.
+#     """
+
+#     node_start_time = datetime.now()
+
+#     current_step_nr = state.current_step_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
+#     ]
+
+#     for new_update in new_updates:
+
+#         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,
+#             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,
+#         )
+
+#         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,
+#             )
+#         ],
+#     )

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

@@ -0,0 +1,28 @@
+# from collections.abc import Hashable
+
+# 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,
+# )
+
+
+# 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

@@ -0,0 +1,50 @@
+# 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
+
+
+# logger = setup_logger()
+
+
+# def dr_custom_tool_graph_builder() -> StateGraph:
+#     """
+#     LangGraph graph builder for Generic Tool Sub-Agent
+#     """
+
+#     graph = StateGraph(state_schema=SubAgentMainState, input=SubAgentInput)
+
+#     ### Add nodes ###
+
+#     graph.add_node("branch", custom_tool_branch)
+
+#     graph.add_node("act", custom_tool_act)
+
+#     graph.add_node("reducer", custom_tool_reducer)
+
+#     ### Add edges ###
+
+#     graph.add_edge(start_key=START, end_key="branch")
+
+#     graph.add_conditional_edges("branch", branching_router)
+
+#     graph.add_edge(start_key="act", end_key="reducer")
+
+#     graph.add_edge(start_key="reducer", end_key=END)
+
+#     return graph

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

@@ -0,0 +1,36 @@
+# from datetime import datetime
+
+# 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
+
+# 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.
+#     """
+
+#     node_start_time = datetime.now()
+#     iteration_nr = state.iteration_nr
+
+#     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,
+#             )
+#         ],
+#     )

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

@@ -0,0 +1,147 @@
+# 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 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()
+
+
+# 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
+
+#     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
+
+#     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")
+
+#     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()}"
+#     )
+
+# # 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")
+
+#     if tool_args is None:
+#         raise ValueError(
+#             "Failed to obtain tool arguments from LLM - tool calling is required"
+#         )
+
+#     # 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}"
+#     )
+
+#     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()
+
+#     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

@@ -0,0 +1,82 @@
+# from datetime import datetime
+
+# 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
+
+
+# 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.
+#     """
+
+#     node_start_time = datetime.now()
+
+#     current_step_nr = state.current_step_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
+#     ]
+
+#     for new_update in new_updates:
+
+#         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,
+#             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,
+#         )
+
+#         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,
+#             )
+#         ],
+#     )

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

@@ -0,0 +1,28 @@
+# from collections.abc import Hashable
+
+# 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,
+# )
+
+
+# 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
+#         )
+#     ]