mypy

.github/actionlint.yml

@@ -17,7 +17,6 @@ self-hosted-runner:
     - runner=16cpu-linux-x64
     - ubuntu-slim # Currently in public preview
     - volume=40gb
-    - volume=50gb
 
 # Configuration variables in array of strings defined in your repository or
 # organization. `null` means disabling configuration variables check.

.github/actions/custom-build-and-push/action.yml

@@ -0,0 +1,135 @@
+name: 'Build and Push Docker Image with Retry'
+description: 'Attempts to build and push a Docker image, with a retry on failure'
+inputs:
+  context:
+    description: 'Build context'
+    required: true
+  file:
+    description: 'Dockerfile location'
+    required: true
+  platforms:
+    description: 'Target platforms'
+    required: true
+  pull:
+    description: 'Always attempt to pull a newer version of the image'
+    required: false
+    default: 'true'
+  push:
+    description: 'Push the image to registry'
+    required: false
+    default: 'true'
+  load:
+    description: 'Load the image into Docker daemon'
+    required: false
+    default: 'true'
+  tags:
+    description: 'Image tags'
+    required: true
+  no-cache:
+    description: 'Read from cache'
+    required: false
+    default: 'false'
+  cache-from:
+    description: 'Cache sources'
+    required: false
+  cache-to:
+    description: 'Cache destinations'
+    required: false
+  outputs:
+    description: 'Output destinations'
+    required: false
+  provenance:
+    description: 'Generate provenance attestation'
+    required: false
+    default: 'false'
+  build-args:
+    description: 'Build arguments'
+    required: false
+  retry-wait-time:
+    description: 'Time to wait before attempt 2 in seconds'
+    required: false
+    default: '60'
+  retry-wait-time-2:
+    description: 'Time to wait before attempt 3 in seconds'
+    required: false
+    default: '120'
+
+runs:
+  using: "composite"
+  steps:
+    - name: Build and push Docker image (Attempt 1 of 3)
+      id: buildx1
+      uses: docker/build-push-action@263435318d21b8e681c14492fe198d362a7d2c83 # ratchet:docker/build-push-action@v6
+      continue-on-error: true
+      with:
+        context: ${{ inputs.context }}
+        file: ${{ inputs.file }}
+        platforms: ${{ inputs.platforms }}
+        pull: ${{ inputs.pull }}
+        push: ${{ inputs.push }}
+        load: ${{ inputs.load }}
+        tags: ${{ inputs.tags }}
+        no-cache: ${{ inputs.no-cache }}
+        cache-from: ${{ inputs.cache-from }}
+        cache-to: ${{ inputs.cache-to }}
+        outputs: ${{ inputs.outputs }}
+        provenance: ${{ inputs.provenance }}
+        build-args: ${{ inputs.build-args }}
+
+    - name: Wait before attempt 2
+      if: steps.buildx1.outcome != 'success'
+      run: |
+        echo "First attempt failed. Waiting ${{ inputs.retry-wait-time }} seconds before retry..."
+        sleep ${{ inputs.retry-wait-time }}
+      shell: bash
+
+    - name: Build and push Docker image (Attempt 2 of 3)
+      id: buildx2
+      if: steps.buildx1.outcome != 'success'
+      uses: docker/build-push-action@263435318d21b8e681c14492fe198d362a7d2c83 # ratchet:docker/build-push-action@v6
+      with:
+        context: ${{ inputs.context }}
+        file: ${{ inputs.file }}
+        platforms: ${{ inputs.platforms }}
+        pull: ${{ inputs.pull }}
+        push: ${{ inputs.push }}
+        load: ${{ inputs.load }}
+        tags: ${{ inputs.tags }}
+        no-cache: ${{ inputs.no-cache }}
+        cache-from: ${{ inputs.cache-from }}
+        cache-to: ${{ inputs.cache-to }}
+        outputs: ${{ inputs.outputs }}
+        provenance: ${{ inputs.provenance }}
+        build-args: ${{ inputs.build-args }}
+
+    - name: Wait before attempt 3
+      if: steps.buildx1.outcome != 'success' && steps.buildx2.outcome != 'success'
+      run: |
+        echo "Second attempt failed. Waiting ${{ inputs.retry-wait-time-2 }} seconds before retry..."
+        sleep ${{ inputs.retry-wait-time-2 }}
+      shell: bash
+
+    - name: Build and push Docker image (Attempt 3 of 3)
+      id: buildx3
+      if: steps.buildx1.outcome != 'success' && steps.buildx2.outcome != 'success'
+      uses: docker/build-push-action@263435318d21b8e681c14492fe198d362a7d2c83 # ratchet:docker/build-push-action@v6
+      with:
+        context: ${{ inputs.context }}
+        file: ${{ inputs.file }}
+        platforms: ${{ inputs.platforms }}
+        pull: ${{ inputs.pull }}
+        push: ${{ inputs.push }}
+        load: ${{ inputs.load }}
+        tags: ${{ inputs.tags }}
+        no-cache: ${{ inputs.no-cache }}
+        cache-from: ${{ inputs.cache-from }}
+        cache-to: ${{ inputs.cache-to }}
+        outputs: ${{ inputs.outputs }}
+        provenance: ${{ inputs.provenance }}
+        build-args: ${{ inputs.build-args }}
+
+    - name: Report failure
+      if: steps.buildx1.outcome != 'success' && steps.buildx2.outcome != 'success' && steps.buildx3.outcome != 'success'
+      run: |
+        echo "All attempts failed. Possible transient infrastucture issues? Try again later or inspect logs for details."
+      shell: bash

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

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

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

@@ -1,9 +1,5 @@
 name: "Setup Python and Install Dependencies"
 description: "Sets up Python with uv and installs deps"
-inputs:
-  requirements:
-    description: "Newline-separated list of requirement files to install (relative to repo root)"
-    required: true
 runs:
   using: "composite"
   steps:
@@ -13,26 +9,11 @@ runs:
       # with:
       #   enable-cache: true
 
-    - name: Compute requirements hash
-      id: req-hash
-      shell: bash
-      env:
-        REQUIREMENTS: ${{ inputs.requirements }}
-      run: |
-        # Hash the contents of the specified requirement files
-        hash=""
-        while IFS= read -r req; do
-          if [ -n "$req" ] && [ -f "$req" ]; then
-            hash="$hash$(sha256sum "$req")"
-          fi
-        done <<< "$REQUIREMENTS"
-        echo "hash=$(echo "$hash" | sha256sum | cut -d' ' -f1)" >> "$GITHUB_OUTPUT"
-
     - name: Cache uv cache directory
       uses: runs-on/cache@50350ad4242587b6c8c2baa2e740b1bc11285ff4 # ratchet:runs-on/cache@v4
       with:
         path: ~/.cache/uv
-        key: ${{ runner.os }}-uv-${{ steps.req-hash.outputs.hash }}
+        key: ${{ runner.os }}-uv-${{ hashFiles('backend/requirements/*.txt', 'backend/pyproject.toml') }}
         restore-keys: |
           ${{ runner.os }}-uv-
 
@@ -43,30 +24,15 @@ runs:
 
     - name: Create virtual environment
       shell: bash
-      env:
-        VENV_DIR: ${{ runner.temp }}/venv
-      run: | # zizmor: ignore[github-env]
-        uv venv "$VENV_DIR"
-        # Validate path before adding to GITHUB_PATH to prevent code injection
-        if [ -d "$VENV_DIR/bin" ]; then
-          realpath "$VENV_DIR/bin" >> "$GITHUB_PATH"
-        else
-          echo "Error: $VENV_DIR/bin does not exist"
-          exit 1
-        fi
+      run: |
+        uv venv ${{ runner.temp }}/venv
+        echo "VENV_PATH=${{ runner.temp }}/venv" >> $GITHUB_ENV
+        echo "${{ runner.temp }}/venv/bin" >> $GITHUB_PATH
 
     - name: Install Python dependencies with uv
       shell: bash
-      env:
-        REQUIREMENTS: ${{ inputs.requirements }}
       run: |
-        # Build the uv pip install command with each requirement file as array elements
-        cmd=("uv" "pip" "install")
-        while IFS= read -r req; do
-          # Skip empty lines
-          if [ -n "$req" ]; then
-            cmd+=("-r" "$req")
-          fi
-        done <<< "$REQUIREMENTS"
-        echo "Running: ${cmd[*]}"
-        "${cmd[@]}"
+        uv pip install \
+          -r backend/requirements/default.txt \
+          -r backend/requirements/dev.txt \
+          -r backend/requirements/model_server.txt

.github/actions/slack-notify/action.yml

@@ -21,27 +21,26 @@ runs:
       shell: bash
       env:
         SLACK_WEBHOOK_URL: ${{ inputs.webhook-url }}
-        FAILED_JOBS: ${{ inputs.failed-jobs }}
-        TITLE: ${{ inputs.title }}
-        REF_NAME: ${{ inputs.ref-name }}
-        REPO: ${{ github.repository }}
-        WORKFLOW: ${{ github.workflow }}
-        RUN_NUMBER: ${{ github.run_number }}
-        RUN_ID: ${{ github.run_id }}
-        SERVER_URL: ${{ github.server_url }}
-        GITHUB_REF_NAME: ${{ github.ref_name }}
       run: |
         if [ -z "$SLACK_WEBHOOK_URL" ]; then
           echo "webhook-url input or SLACK_WEBHOOK_URL env var is not set, skipping notification"
           exit 0
         fi
 
-        # Build workflow URL
+        # Get inputs with defaults
+        FAILED_JOBS="${{ inputs.failed-jobs }}"
+        TITLE="${{ inputs.title }}"
+        REF_NAME="${{ inputs.ref-name }}"
+        REPO="${{ github.repository }}"
+        WORKFLOW="${{ github.workflow }}"
+        RUN_NUMBER="${{ github.run_number }}"
+        RUN_ID="${{ github.run_id }}"
+        SERVER_URL="${{ github.server_url }}"
         WORKFLOW_URL="${SERVER_URL}/${REPO}/actions/runs/${RUN_ID}"
 
         # Use ref_name from input or fall back to github.ref_name
         if [ -z "$REF_NAME" ]; then
-          REF_NAME="$GITHUB_REF_NAME"
+          REF_NAME="${{ github.ref_name }}"
         fi
 
         # Escape JSON special characters

.github/dependabot.yml

@@ -4,8 +4,6 @@ updates:
     directory: "/"
     schedule:
       interval: "weekly"
-    cooldown:
-      default-days: 7
     open-pull-requests-limit: 3
     assignees:
       - "jmelahman"
@@ -15,8 +13,6 @@ updates:
     directory: "/backend"
     schedule:
       interval: "weekly"
-    cooldown:
-      default-days: 7
     open-pull-requests-limit: 3
     assignees:
       - "jmelahman"

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

@@ -0,0 +1,32 @@
+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
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
+      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

@@ -18,7 +18,6 @@ jobs:
   determine-builds:
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
-    timeout-minutes: 90
     outputs:
       build-web: ${{ steps.check.outputs.build-web }}
       build-web-cloud: ${{ steps.check.outputs.build-web-cloud }}
@@ -83,47 +82,6 @@ jobs:
             echo "sanitized-tag=$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: ${{ github.ref_name }}
-
   build-web-amd64:
     needs: determine-builds
     if: needs.determine-builds.outputs.build-web == 'true'
@@ -132,7 +90,6 @@ jobs:
       - runner=4cpu-linux-x64
       - run-id=${{ github.run_id }}-web-amd64
       - extras=ecr-cache
-    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -141,7 +98,7 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -190,7 +147,6 @@ jobs:
       - runner=4cpu-linux-arm64
       - run-id=${{ github.run_id }}-web-arm64
       - extras=ecr-cache
-    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -199,7 +155,7 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -250,7 +206,6 @@ jobs:
       - runner=2cpu-linux-x64
       - run-id=${{ github.run_id }}-merge-web
       - extras=ecr-cache
-    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: onyxdotapp/onyx-web-server
     steps:
@@ -298,7 +253,6 @@ jobs:
       - runner=4cpu-linux-x64
       - run-id=${{ github.run_id }}-web-cloud-amd64
       - extras=ecr-cache
-    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -307,7 +261,7 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -364,7 +318,6 @@ jobs:
       - runner=4cpu-linux-arm64
       - run-id=${{ github.run_id }}-web-cloud-arm64
       - extras=ecr-cache
-    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -373,7 +326,7 @@ jobs:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -432,7 +385,6 @@ jobs:
       - runner=2cpu-linux-x64
       - run-id=${{ github.run_id }}-merge-web-cloud
       - extras=ecr-cache
-    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: onyxdotapp/onyx-web-server-cloud
     steps:
@@ -477,7 +429,6 @@ jobs:
       - runner=2cpu-linux-x64
       - run-id=${{ github.run_id }}-backend-amd64
       - extras=ecr-cache
-    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -486,7 +437,7 @@ 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@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -534,7 +485,6 @@ jobs:
       - runner=2cpu-linux-arm64
       - run-id=${{ github.run_id }}-backend-arm64
       - extras=ecr-cache
-    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -543,7 +493,7 @@ 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@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -593,7 +543,6 @@ jobs:
       - runner=2cpu-linux-x64
       - run-id=${{ github.run_id }}-merge-backend
       - extras=ecr-cache
-    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'onyxdotapp/onyx-backend-cloud' || 'onyxdotapp/onyx-backend' }}
     steps:
@@ -642,7 +591,6 @@ jobs:
       - run-id=${{ github.run_id }}-model-server-amd64
       - volume=40gb
       - extras=ecr-cache
-    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -651,7 +599,7 @@ 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@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -706,7 +654,6 @@ jobs:
       - run-id=${{ github.run_id }}-model-server-arm64
       - volume=40gb
       - extras=ecr-cache
-    timeout-minutes: 90
     outputs:
       digest: ${{ steps.build.outputs.digest }}
     env:
@@ -715,7 +662,7 @@ 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@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -771,7 +718,6 @@ jobs:
       - runner=2cpu-linux-x64
       - run-id=${{ github.run_id }}-merge-model-server
       - extras=ecr-cache
-    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'onyxdotapp/onyx-model-server-cloud' || 'onyxdotapp/onyx-model-server' }}
     steps:
@@ -821,7 +767,6 @@ jobs:
       - runner=2cpu-linux-arm64
       - run-id=${{ github.run_id }}-trivy-scan-web
       - extras=ecr-cache
-    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: onyxdotapp/onyx-web-server
     steps:
@@ -861,7 +806,6 @@ jobs:
       - runner=2cpu-linux-arm64
       - run-id=${{ github.run_id }}-trivy-scan-web-cloud
       - extras=ecr-cache
-    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: onyxdotapp/onyx-web-server-cloud
     steps:
@@ -901,14 +845,13 @@ jobs:
       - runner=2cpu-linux-arm64
       - run-id=${{ github.run_id }}-trivy-scan-backend
       - extras=ecr-cache
-    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'onyxdotapp/onyx-backend-cloud' || 'onyxdotapp/onyx-backend' }}
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -948,7 +891,6 @@ jobs:
       - runner=2cpu-linux-arm64
       - run-id=${{ github.run_id }}-trivy-scan-model-server
       - extras=ecr-cache
-    timeout-minutes: 90
     env:
       REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'onyxdotapp/onyx-model-server-cloud' || 'onyxdotapp/onyx-model-server' }}
     steps:
@@ -995,10 +937,9 @@ jobs:
     if: always() && (needs.build-web-amd64.result == 'failure' || needs.build-web-arm64.result == 'failure' || needs.merge-web.result == 'failure' || needs.build-web-cloud-amd64.result == 'failure' || needs.build-web-cloud-arm64.result == 'failure' || needs.merge-web-cloud.result == 'failure' || needs.build-backend-amd64.result == 'failure' || needs.build-backend-arm64.result == 'failure' || needs.merge-backend.result == 'failure' || needs.build-model-server-amd64.result == 'failure' || needs.build-model-server-arm64.result == 'failure' || needs.merge-model-server.result == 'failure') && github.event_name != 'workflow_dispatch'
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
-    timeout-minutes: 90
     steps:
       - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 

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

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

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

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

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

@@ -12,10 +12,9 @@ jobs:
     permissions:
       contents: write
     runs-on: ubuntu-latest
-    timeout-minutes: 45
     steps:
       - name: Checkout
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           fetch-depth: 0
           persist-credentials: false
@@ -32,7 +31,6 @@ 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-close-stale-issues.yml

@@ -11,9 +11,8 @@ permissions:
 jobs:
   stale:
     runs-on: ubuntu-latest
-    timeout-minutes: 45
     steps:
-      - uses: actions/stale@5f858e3efba33a5ca4407a664cc011ad407f2008 # ratchet:actions/stale@v10
+      - uses: actions/stale@5bef64f19d7facfb25b37b414482c7164d639639 # ratchet:actions/stale@v9
         with:
           stale-issue-message: 'This issue is stale because it has been open 75 days with no activity. Remove stale label or comment or this will be closed in 15 days.'
           stale-pr-message: 'This PR is stale because it has been open 75 days with no activity. Remove stale label or comment or this will be closed in 15 days.'

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

@@ -20,7 +20,6 @@ jobs:
   scan-licenses:
     # See https://runs-on.com/runners/linux/
     runs-on: [runs-on,runner=2cpu-linux-x64,"run-id=${{ github.run_id }}-scan-licenses"]
-    timeout-minutes: 45
     permissions:
       actions: read
       contents: read
@@ -28,12 +27,12 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         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'
@@ -90,7 +89,6 @@ jobs:
   scan-trivy:
     # See https://runs-on.com/runners/linux/
     runs-on: [runs-on,runner=2cpu-linux-x64,"run-id=${{ github.run_id }}-scan-trivy"]
-    timeout-minutes: 45
 
     steps:
     - name: Set up Docker Buildx

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

@@ -7,22 +7,17 @@ on:
   merge_group:
   pull_request:
     branches: [main]
-  push:
-    tags:
-      - "v*.*.*"
 
 permissions:
   contents: read
 
 env:
-  # AWS credentials for S3-specific test
-  S3_AWS_ACCESS_KEY_ID_FOR_TEST: ${{ secrets.S3_AWS_ACCESS_KEY_ID }}
-  S3_AWS_SECRET_ACCESS_KEY_FOR_TEST: ${{ secrets.S3_AWS_SECRET_ACCESS_KEY }}
+  # AWS
+  S3_AWS_ACCESS_KEY_ID: ${{ secrets.S3_AWS_ACCESS_KEY_ID }}
+  S3_AWS_SECRET_ACCESS_KEY: ${{ secrets.S3_AWS_SECRET_ACCESS_KEY }}
 
   # MinIO
   S3_ENDPOINT_URL: "http://localhost:9004"
-  S3_AWS_ACCESS_KEY_ID: "minioadmin"
-  S3_AWS_SECRET_ACCESS_KEY: "minioadmin"
 
   # Confluence
   CONFLUENCE_TEST_SPACE_URL: ${{ vars.CONFLUENCE_TEST_SPACE_URL }}
@@ -32,27 +27,19 @@ 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 }}
 
-  # Code Interpreter
-  # TODO: debug why this is failing and enable
-  CODE_INTERPRETER_BASE_URL: http://localhost:8000
-
 jobs:
   discover-test-dirs:
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
-    timeout-minutes: 45
     outputs:
       test-dirs: ${{ steps.set-matrix.outputs.test-dirs }}
     steps:
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -71,7 +58,6 @@ jobs:
       - runner=2cpu-linux-arm64
       - ${{ format('run-id={0}-external-dependency-unit-tests-job-{1}', github.run_id, strategy['job-index']) }}
       - extras=s3-cache
-    timeout-minutes: 45
     strategy:
       fail-fast: false
       matrix:
@@ -80,23 +66,17 @@ 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@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
       - name: Setup Python and Install Dependencies
         uses: ./.github/actions/setup-python-and-install-dependencies
-        with:
-          requirements: |
-            backend/requirements/default.txt
-            backend/requirements/dev.txt
-            backend/requirements/ee.txt
 
       - name: Setup Playwright
         uses: ./.github/actions/setup-playwright
@@ -110,25 +90,10 @@ jobs:
           username: ${{ secrets.DOCKER_USERNAME }}
           password: ${{ secrets.DOCKER_TOKEN }}
 
-      - name: Create .env file for Docker Compose
-        run: |
-          cat <<EOF > deployment/docker_compose/.env
-          CODE_INTERPRETER_BETA_ENABLED=true
-          DISABLE_TELEMETRY=true
-          EOF
-
       - name: Set up Standard Dependencies
         run: |
           cd deployment/docker_compose
-          docker compose \
-            -f docker-compose.yml \
-            -f docker-compose.dev.yml \
-            up -d \
-            minio \
-            relational_db \
-            cache \
-            index \
-            code-interpreter
+          docker compose -f docker-compose.yml -f docker-compose.dev.yml up -d minio relational_db cache index
 
       - name: Run migrations
         run: |
@@ -148,30 +113,3 @@ jobs:
             -xv \
             --ff \
             backend/tests/external_dependency_unit/${TEST_DIR}
-
-      - name: Collect Docker logs on failure
-        if: failure()
-        run: |
-          mkdir -p docker-logs
-          cd deployment/docker_compose
-
-          # Get list of running containers
-          containers=$(docker compose -f docker-compose.yml -f docker-compose.dev.yml ps -q)
-
-          # Collect logs from each container
-          for container in $containers; do
-            container_name=$(docker inspect --format='{{.Name}}' $container | sed 's/^\///')
-            echo "Collecting logs from $container_name..."
-            docker logs $container > ../../docker-logs/${container_name}.log 2>&1
-          done
-
-          cd ../..
-          echo "Docker logs collected in docker-logs directory"
-
-      - name: Upload Docker logs
-        if: failure()
-        uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # ratchet:actions/upload-artifact@v5
-        with:
-          name: docker-logs-${{ matrix.test-dir }}
-          path: docker-logs/
-          retention-days: 7

.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:
@@ -19,12 +16,11 @@ jobs:
   helm-chart-check:
     # See https://runs-on.com/runners/linux/
     runs-on: [runs-on,runner=8cpu-linux-x64,hdd=256,"run-id=${{ github.run_id }}-helm-chart-check"]
-    timeout-minutes: 45
 
     # fetch-depth 0 is required for helm/chart-testing-action
     steps:
     - name: Checkout code
-      uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+      uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
       with:
         fetch-depth: 0
         persist-credentials: false
@@ -83,7 +79,6 @@ 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
@@ -38,12 +35,11 @@ jobs:
   discover-test-dirs:
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
-    timeout-minutes: 45
     outputs:
       test-dirs: ${{ steps.set-matrix.outputs.test-dirs }}
     steps:
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -70,28 +66,13 @@ jobs:
 
   build-backend-image:
     runs-on: [runs-on, runner=1cpu-linux-arm64, "run-id=${{ github.run_id }}-build-backend-image", "extras=ecr-cache"]
-    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         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
 
@@ -112,41 +93,22 @@ 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' }}
 
 
   build-model-server-image:
     runs-on: [runs-on, runner=1cpu-linux-arm64, "run-id=${{ github.run_id }}-build-model-server-image", "extras=ecr-cache"]
-    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         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
 
@@ -167,23 +129,17 @@ 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"]
-    timeout-minutes: 45
     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@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -199,40 +155,16 @@ 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
 
@@ -249,7 +181,6 @@ jobs:
       - runner=4cpu-linux-arm64
       - ${{ format('run-id={0}-integration-tests-job-{1}', github.run_id, strategy['job-index']) }}
       - extras=ecr-cache
-    timeout-minutes: 45
 
     strategy:
       fail-fast: false
@@ -259,7 +190,7 @@ 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@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -274,28 +205,22 @@ 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 \
           docker compose -f docker-compose.yml -f docker-compose.dev.yml up \
             relational_db \
             index \
@@ -304,56 +229,43 @@ jobs:
             api_server \
             inference_model_server \
             indexing_model_server \
-            mcp_server \
             background \
             -d
         id: start_docker
 
-      - name: Wait for services to be ready
+      - name: Wait for service to be ready
         run: |
           echo "Starting wait-for-service script..."
 
-          wait_for_service() {
-            local url=$1
-            local label=$2
-            local timeout=${3:-300}  # default 5 minutes
-            local start_time
-            start_time=$(date +%s)
+          docker logs -f onyx-api_server-1 &
 
-            while true; do
-              local current_time
-              current_time=$(date +%s)
-              local elapsed_time=$((current_time - start_time))
+          start_time=$(date +%s)
+          timeout=300  # 5 minutes in seconds
 
-              if [ $elapsed_time -ge $timeout ]; then
-                echo "Timeout reached. ${label} did not become ready in $timeout seconds."
-                exit 1
-              fi
+          while true; do
+            current_time=$(date +%s)
+            elapsed_time=$((current_time - start_time))
 
-              local response
-              response=$(curl -s -o /dev/null -w "%{http_code}" "$url" || echo "curl_error")
+            if [ $elapsed_time -ge $timeout ]; then
+              echo "Timeout reached. Service did not become ready in 5 minutes."
+              exit 1
+            fi
 
-              if [ "$response" = "200" ]; then
-                echo "${label} is ready!"
-                break
-              elif [ "$response" = "curl_error" ]; then
-                echo "Curl encountered an error while checking ${label}. Retrying in 5 seconds..."
-              else
-                echo "${label} not ready yet (HTTP status $response). Retrying in 5 seconds..."
-              fi
+            # Use curl with error handling to ignore specific exit code 56
+            response=$(curl -s -o /dev/null -w "%{http_code}" http://localhost:8080/health || echo "curl_error")
 
-              sleep 5
-            done
-          }
+            if [ "$response" = "200" ]; then
+              echo "Service is ready!"
+              break
+            elif [ "$response" = "curl_error" ]; then
+              echo "Curl encountered an error, possibly exit code 56. Continuing to retry..."
+            else
+              echo "Service not ready yet (HTTP status $response). Retrying in 5 seconds..."
+            fi
 
-          wait_for_service "http://localhost:8080/health" "API server"
-          test_dir="${{ matrix.test-dir.path }}"
-          if [ "$test_dir" = "tests/mcp" ]; then
-            wait_for_service "http://localhost:8090/health" "MCP server"
-          else
-            echo "Skipping MCP server wait for non-MCP suite: $test_dir"
-          fi
-          echo "Finished waiting for services."
+            sleep 5
+          done
+          echo "Finished waiting for service."
 
       - name: Start Mock Services
         run: |
@@ -382,8 +294,6 @@ jobs:
               -e VESPA_HOST=index \
               -e REDIS_HOST=cache \
               -e API_SERVER_HOST=api_server \
-              -e MCP_SERVER_HOST=mcp_server \
-              -e MCP_SERVER_PORT=8090 \
               -e OPENAI_API_KEY=${OPENAI_API_KEY} \
               -e EXA_API_KEY=${EXA_API_KEY} \
               -e SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN} \
@@ -436,12 +346,11 @@ jobs:
         build-integration-image,
       ]
     runs-on: [runs-on, runner=8cpu-linux-arm64, "run-id=${{ github.run_id }}-multitenant-tests", "extras=ecr-cache"]
-    timeout-minutes: 45
 
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -465,7 +374,6 @@ jobs:
           ONYX_BACKEND_IMAGE=${ECR_CACHE}:integration-test-backend-test-${RUN_ID} \
           ONYX_MODEL_SERVER_IMAGE=${ECR_CACHE}:integration-test-model-server-test-${RUN_ID} \
           DEV_MODE=true \
-          MCP_SERVER_ENABLED=true \
           docker compose -f docker-compose.multitenant-dev.yml up \
             relational_db \
             index \
@@ -474,7 +382,6 @@ jobs:
             api_server \
             inference_model_server \
             indexing_model_server \
-            mcp_server \
             background \
             -d
         id: start_docker_multi_tenant
@@ -523,8 +430,6 @@ jobs:
             -e VESPA_HOST=index \
             -e REDIS_HOST=cache \
             -e API_SERVER_HOST=api_server \
-            -e MCP_SERVER_HOST=mcp_server \
-            -e MCP_SERVER_PORT=8090 \
             -e OPENAI_API_KEY=${OPENAI_API_KEY} \
             -e EXA_API_KEY=${EXA_API_KEY} \
             -e SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN} \
@@ -566,7 +471,6 @@ jobs:
   required:
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
-    timeout-minutes: 45
     needs: [integration-tests, multitenant-tests]
     if: ${{ always() }}
     steps:

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

@@ -3,8 +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:
-  push:
+on: push
 
 permissions:
   contents: read
@@ -13,18 +12,17 @@ jobs:
   jest-tests:
     name: Jest Tests
     runs-on: ubuntu-latest
-    timeout-minutes: 45
     steps:
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         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-labeler.yml

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

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

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

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

@@ -6,9 +6,6 @@ concurrency:
 on:
   merge_group:
     types: [checks_requested]
-  push:
-    tags:
-      - "v*.*.*"
 
 permissions:
   contents: read
@@ -17,7 +14,6 @@ env:
   # Test Environment Variables
   OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
   SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }}
-  EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
   CONFLUENCE_TEST_SPACE_URL: ${{ vars.CONFLUENCE_TEST_SPACE_URL }}
   CONFLUENCE_USER_NAME: ${{ vars.CONFLUENCE_USER_NAME }}
   CONFLUENCE_ACCESS_TOKEN: ${{ secrets.CONFLUENCE_ACCESS_TOKEN }}
@@ -35,12 +31,11 @@ jobs:
   discover-test-dirs:
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
-    timeout-minutes: 45
     outputs:
       test-dirs: ${{ steps.set-matrix.outputs.test-dirs }}
     steps:
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -66,28 +61,13 @@ jobs:
 
   build-backend-image:
     runs-on: [runs-on, runner=1cpu-linux-arm64, "run-id=${{ github.run_id }}-build-backend-image", "extras=ecr-cache"]
-    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         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
 
@@ -108,40 +88,20 @@ 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:
     runs-on: [runs-on, runner=1cpu-linux-arm64, "run-id=${{ github.run_id }}-build-model-server-image", "extras=ecr-cache"]
-    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         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
 
@@ -162,39 +122,19 @@ 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"]
-    timeout-minutes: 45
     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@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         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 +151,12 @@ 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
 
@@ -243,7 +173,6 @@ jobs:
       - runner=4cpu-linux-arm64
       - ${{ format('run-id={0}-integration-tests-mit-job-{1}', github.run_id, strategy['job-index']) }}
       - extras=ecr-cache
-    timeout-minutes: 45
 
     strategy:
       fail-fast: false
@@ -253,7 +182,7 @@ 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@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -268,26 +197,20 @@ 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 \
           docker compose -f docker-compose.yml -f docker-compose.dev.yml up \
             relational_db \
             index \
@@ -296,56 +219,43 @@ jobs:
             api_server \
             inference_model_server \
             indexing_model_server \
-            mcp_server \
             background \
             -d
         id: start_docker
 
-      - name: Wait for services to be ready
+      - name: Wait for service to be ready
         run: |
           echo "Starting wait-for-service script..."
 
-          wait_for_service() {
-            local url=$1
-            local label=$2
-            local timeout=${3:-300}  # default 5 minutes
-            local start_time
-            start_time=$(date +%s)
+          docker logs -f onyx-api_server-1 &
 
-            while true; do
-              local current_time
-              current_time=$(date +%s)
-              local elapsed_time=$((current_time - start_time))
+          start_time=$(date +%s)
+          timeout=300  # 5 minutes in seconds
 
-              if [ $elapsed_time -ge $timeout ]; then
-                echo "Timeout reached. ${label} did not become ready in $timeout seconds."
-                exit 1
-              fi
+          while true; do
+            current_time=$(date +%s)
+            elapsed_time=$((current_time - start_time))
 
-              local response
-              response=$(curl -s -o /dev/null -w "%{http_code}" "$url" || echo "curl_error")
+            if [ $elapsed_time -ge $timeout ]; then
+              echo "Timeout reached. Service did not become ready in 5 minutes."
+              exit 1
+            fi
 
-              if [ "$response" = "200" ]; then
-                echo "${label} is ready!"
-                break
-              elif [ "$response" = "curl_error" ]; then
-                echo "Curl encountered an error while checking ${label}. Retrying in 5 seconds..."
-              else
-                echo "${label} not ready yet (HTTP status $response). Retrying in 5 seconds..."
-              fi
+            # Use curl with error handling to ignore specific exit code 56
+            response=$(curl -s -o /dev/null -w "%{http_code}" http://localhost:8080/health || echo "curl_error")
 
-              sleep 5
-            done
-          }
+            if [ "$response" = "200" ]; then
+              echo "Service is ready!"
+              break
+            elif [ "$response" = "curl_error" ]; then
+              echo "Curl encountered an error, possibly exit code 56. Continuing to retry..."
+            else
+              echo "Service not ready yet (HTTP status $response). Retrying in 5 seconds..."
+            fi
 
-          wait_for_service "http://localhost:8080/health" "API server"
-          test_dir="${{ matrix.test-dir.path }}"
-          if [ "$test_dir" = "tests/mcp" ]; then
-            wait_for_service "http://localhost:8090/health" "MCP server"
-          else
-            echo "Skipping MCP server wait for non-MCP suite: $test_dir"
-          fi
-          echo "Finished waiting for services."
+            sleep 5
+          done
+          echo "Finished waiting for service."
 
       - name: Start Mock Services
         run: |
@@ -375,10 +285,7 @@ jobs:
               -e VESPA_HOST=index \
               -e REDIS_HOST=cache \
               -e API_SERVER_HOST=api_server \
-              -e MCP_SERVER_HOST=mcp_server \
-              -e MCP_SERVER_PORT=8090 \
               -e OPENAI_API_KEY=${OPENAI_API_KEY} \
-              -e EXA_API_KEY=${EXA_API_KEY} \
               -e SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN} \
               -e CONFLUENCE_TEST_SPACE_URL=${CONFLUENCE_TEST_SPACE_URL} \
               -e CONFLUENCE_USER_NAME=${CONFLUENCE_USER_NAME} \
@@ -424,7 +331,6 @@ jobs:
   required:
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
-    timeout-minutes: 45
     needs: [integration-tests-mit]
     if: ${{ always() }}
     steps:

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

@@ -3,8 +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:
-  push:
+on: push
 
 permissions:
   contents: read
@@ -28,13 +27,6 @@ env:
   MCP_OAUTH_USERNAME: ${{ vars.MCP_OAUTH_USERNAME }}
   MCP_OAUTH_PASSWORD: ${{ secrets.MCP_OAUTH_PASSWORD }}
 
-  # for MCP API Key tests
-  MCP_API_KEY: test-api-key-12345
-  MCP_API_KEY_TEST_PORT: 8005
-  MCP_API_KEY_TEST_URL: http://host.docker.internal:8005/mcp
-  MCP_API_KEY_SERVER_HOST: 0.0.0.0
-  MCP_API_KEY_SERVER_PUBLIC_HOST: host.docker.internal
-
   MOCK_LLM_RESPONSE: true
   MCP_TEST_SERVER_PORT: 8004
   MCP_TEST_SERVER_URL: http://host.docker.internal:8004/mcp
@@ -48,29 +40,14 @@ env:
 jobs:
   build-web-image:
     runs-on: [runs-on, runner=4cpu-linux-arm64, "run-id=${{ github.run_id }}-build-web-image", "extras=ecr-cache"]
-    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         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
 
@@ -91,41 +68,21 @@ 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:
     runs-on: [runs-on, runner=1cpu-linux-arm64, "run-id=${{ github.run_id }}-build-backend-image", "extras=ecr-cache"]
-    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         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
 
@@ -146,41 +103,22 @@ 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' }}
 
   build-model-server-image:
     runs-on: [runs-on, runner=1cpu-linux-arm64, "run-id=${{ github.run_id }}-build-model-server-image", "extras=ecr-cache"]
-    timeout-minutes: 45
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
 
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         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
 
@@ -201,26 +139,15 @@ 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:
     needs: [build-web-image, build-backend-image, build-model-server-image]
     name: Playwright Tests (${{ matrix.project }})
-    runs-on:
-      - runs-on
-      - runner=8cpu-linux-arm64
-      - "run-id=${{ github.run_id }}-playwright-tests-${{ matrix.project }}"
-      - "extras=ecr-cache"
-      - volume=50gb
-    timeout-minutes: 45
+    runs-on: [runs-on, runner=8cpu-linux-arm64, "run-id=${{ github.run_id }}-playwright-tests-${{ matrix.project }}", "extras=ecr-cache"]
     strategy:
       fail-fast: false
       matrix:
@@ -229,13 +156,13 @@ 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@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         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'
@@ -291,7 +218,7 @@ jobs:
       - name: Start Docker containers
         run: |
           cd deployment/docker_compose
-          docker compose -f docker-compose.yml -f docker-compose.dev.yml -f docker-compose.mcp-oauth-test.yml -f docker-compose.mcp-api-key-test.yml up -d
+          docker compose -f docker-compose.yml -f docker-compose.dev.yml -f docker-compose.mcp-oauth-test.yml up -d
         id: start_docker
 
       - name: Wait for service to be ready
@@ -351,54 +278,6 @@ jobs:
             sleep 3
           done
 
-      - name: Wait for MCP API Key mock server
-        run: |
-          echo "Waiting for MCP API Key mock server on port ${MCP_API_KEY_TEST_PORT:-8005}..."
-          start_time=$(date +%s)
-          timeout=120
-
-          while true; do
-            current_time=$(date +%s)
-            elapsed_time=$((current_time - start_time))
-
-            if [ $elapsed_time -ge $timeout ]; then
-              echo "Timeout reached. MCP API Key mock server did not become ready in ${timeout}s."
-              exit 1
-            fi
-
-            if curl -sf "http://localhost:${MCP_API_KEY_TEST_PORT:-8005}/healthz" > /dev/null; then
-              echo "MCP API Key mock server is ready!"
-              break
-            fi
-
-            sleep 3
-          done
-
-      - name: Wait for web server to be ready
-        run: |
-          echo "Waiting for web server on port 3000..."
-          start_time=$(date +%s)
-          timeout=120
-
-          while true; do
-            current_time=$(date +%s)
-            elapsed_time=$((current_time - start_time))
-
-            if [ $elapsed_time -ge $timeout ]; then
-              echo "Timeout reached. Web server did not become ready in ${timeout}s."
-              exit 1
-            fi
-
-            if curl -sf "http://localhost:3000/api/health" > /dev/null 2>&1 || \
-               curl -sf "http://localhost:3000/" > /dev/null 2>&1; then
-              echo "Web server is ready!"
-              break
-            fi
-
-            echo "Web server not ready yet. Retrying in 3 seconds..."
-            sleep 3
-          done
-
       - name: Run Playwright tests
         working-directory: ./web
         env:
@@ -439,7 +318,6 @@ jobs:
   playwright-required:
     # NOTE: Github-hosted runners have about 20s faster queue times and are preferred here.
     runs-on: ubuntu-slim
-    timeout-minutes: 45
     needs: [playwright-tests]
     if: ${{ always() }}
     steps:
@@ -465,12 +343,12 @@ jobs:
 #     ]
 #   steps:
 #     - name: Checkout code
-#       uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+#       uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
 #       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

@@ -9,9 +9,6 @@ on:
     branches:
       - main
       - 'release/**'
-  push:
-    tags:
-      - "v*.*.*"
 
 permissions:
   contents: read
@@ -22,28 +19,28 @@ jobs:
     # Note: Mypy seems quite optimized for x64 compared to arm64.
     # Similarly, mypy is single-threaded and incremental, so 2cpu is sufficient.
     runs-on: [runs-on, runner=2cpu-linux-x64, "run-id=${{ github.run_id }}-mypy-check", "extras=s3-cache"]
-    timeout-minutes: 45
 
     steps:
       - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
-      - name: Setup Python and Install Dependencies
-        uses: ./.github/actions/setup-python-and-install-dependencies
+      # 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:
-          requirements: |
-            backend/requirements/default.txt
-            backend/requirements/dev.txt
-            backend/requirements/model_server.txt
-            backend/requirements/ee.txt
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_TOKEN }}
 
-      - name: Generate OpenAPI schema and Python client
-        shell: bash
-        run: |
-          ods openapi all
+      - name: Prepare build
+        uses: ./.github/actions/prepare-build
+        with:
+          docker-username: ${{ secrets.DOCKER_USERNAME }}
+          docker-password: ${{ secrets.DOCKER_TOKEN }}
 
       - name: Cache mypy cache
         if: ${{ vars.DISABLE_MYPY_CACHE != 'true' }}
@@ -61,8 +58,11 @@ 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 * * *"
@@ -129,26 +126,20 @@ jobs:
   connectors-check:
     # See https://runs-on.com/runners/linux/
     runs-on: [runs-on, runner=8cpu-linux-x64, "run-id=${{ github.run_id }}-connectors-check", "extras=s3-cache"]
-    timeout-minutes: 45
 
     env:
       PYTHONPATH: ./backend
-      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@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
       - name: Setup Python and Install Dependencies
         uses: ./.github/actions/setup-python-and-install-dependencies
-        with:
-          requirements: |
-            backend/requirements/default.txt
-            backend/requirements/dev.txt
 
       - name: Setup Playwright
         uses: ./.github/actions/setup-playwright
@@ -161,20 +152,16 @@ 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 +174,7 @@ 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

@@ -32,14 +32,13 @@ jobs:
   model-check:
     # See https://runs-on.com/runners/linux/
     runs-on: [runs-on,runner=8cpu-linux-x64,"run-id=${{ github.run_id }}-model-check"]
-    timeout-minutes: 45
 
     env:
       PYTHONPATH: ./backend
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           persist-credentials: false
 
@@ -61,7 +60,7 @@ 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
@@ -20,30 +17,25 @@ jobs:
   backend-check:
     # See https://runs-on.com/runners/linux/
     runs-on: [runs-on, runner=2cpu-linux-arm64, "run-id=${{ github.run_id }}-backend-check"]
-    timeout-minutes: 45
 
 
     env:
       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@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
       with:
         persist-credentials: false
 
     - name: Setup Python and Install Dependencies
       uses: ./.github/actions/setup-python-and-install-dependencies
-      with:
-        requirements: |
-          backend/requirements/default.txt
-          backend/requirements/dev.txt
-          backend/requirements/model_server.txt
-          backend/requirements/ee.txt
 
     - name: Run Tests
       shell: script -q -e -c "bash --noprofile --norc -eo pipefail {0}"

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

@@ -6,44 +6,25 @@ concurrency:
 on:
   merge_group:
   pull_request: null
-  push:
-    branches:
-      - main
-    tags:
-      - "v*.*.*"
 
 permissions:
   contents: read
 
 jobs:
   quality-checks:
-    runs-on: ubuntu-latest
-    timeout-minutes: 45
+    # See https://runs-on.com/runners/linux/
+    runs-on: [runs-on, runner=1cpu-linux-arm64, "run-id=${{ github.run_id }}-quality-checks"]
     steps:
-      - uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+      - uses: runs-on/action@cd2b598b0515d39d78c38a02d529db87d2196d1e # ratchet:runs-on/action@v2
+      - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         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
         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' || '' }}
-      - name: Check Actions
-        uses: giner/check-actions@28d366c7cbbe235f9624a88aa31a628167eee28c # ratchet:giner/check-actions@v1.0.1
-        with:
-          check_permissions: false
-          check_versions: false
+          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/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

@@ -9,12 +9,11 @@ on:
 jobs:
   sync-foss:
     runs-on: ubuntu-latest
-    timeout-minutes: 45
     permissions:
       contents: read
     steps:
       - name: Checkout main Onyx repo
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6
+        uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           fetch-depth: 0
           persist-credentials: false

.github/workflows/tag-nightly.yml

@@ -11,14 +11,13 @@ permissions:
 jobs:
   create-and-push-tag:
     runs-on: ubuntu-slim
-    timeout-minutes: 45
 
     steps:
       # actions using GITHUB_TOKEN cannot trigger another workflow, but we do want this to trigger docker pushes
       # 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@08eba0b27e820071cde6df949e0beb9ba4906955 # ratchet:actions/checkout@v4
         with:
           ssh-key: "${{ secrets.DEPLOY_KEY }}"
           persist-credentials: true

.github/workflows/zizmor.yml

@@ -12,22 +12,19 @@ jobs:
   zizmor:
     name: zizmor
     runs-on: ubuntu-slim
-    timeout-minutes: 45
     permissions:
       security-events: write # needed for SARIF uploads
     steps:
       - name: Checkout repository
-        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # ratchet:actions/checkout@v6.0.1
+        uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # ratchet:actions/checkout@v5.0.1
         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

@@ -1,7 +1,6 @@
 # editors
 .vscode
 .zed
-.cursor
 
 # macos
 .DS_store
@@ -29,8 +28,6 @@ settings.json
 
 # others
 /deployment/data/nginx/app.conf
-/deployment/data/nginx/mcp.conf.inc
-/deployment/data/nginx/mcp_upstream.conf.inc
 *.sw?
 /backend/tests/regression/answer_quality/search_test_config.yaml
 *.egg-info
@@ -49,10 +46,5 @@ CLAUDE.md
 # Local .terraform.lock.hcl file
 .terraform.lock.hcl
 
-node_modules
-
 # MCP configs
 .playwright-mcp
-
-# plans
-plans/

.mcp.json.template

@@ -0,0 +1,8 @@
+{
+  "mcpServers": {
+    "onyx-mcp": {
+      "type": "http",
+      "url": "http://localhost:8000/mcp"
+    }
+  }
+}

.pre-commit-config.yaml

@@ -1,64 +1,24 @@
-default_install_hook_types:
-  - pre-commit
-  - post-checkout
-  - post-merge
-  - 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
-    rev: d30b4298e4fb63ce8609e29acdbcf4c9018a483c
-    hooks:
-      - id: uv-run
-        name: Check lazy imports
-        args: ["--with=onyx-devtools", "ods", "check-lazy-imports"]
-        files: ^backend/(?!\.venv/).*\.py$
-      - 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)$
-      # 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$
-
   - 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 +30,26 @@ 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 +66,36 @@ 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$
+
+  # We would like to have a mypy pre-commit hook, but due to the fact that
+  # pre-commit runs in it's own isolated environment, we would need to install
+  # and keep in sync all dependencies so mypy has access to the appropriate type
+  # stubs. This does not seem worth it at the moment, so for now we will stick to
+  # having mypy run via Github Actions / manually by contributors
+  # - repo: https://github.com/pre-commit/mirrors-mypy
+  #   rev: v1.1.1
+  #   hooks:
+  #     - id: mypy
+  #       exclude: ^tests/
+  #       # below are needed for type stubs since pre-commit runs in it's own
+  #       # isolated environment. Unfortunately, this needs to be kept in sync
+  #       # with requirements/dev.txt + requirements/default.txt
+  #       additional_dependencies: [
+  #         alembic==1.10.4,
+  #         types-beautifulsoup4==4.12.0.3,
+  #         types-html5lib==1.1.11.13,
+  #         types-oauthlib==3.2.0.9,
+  #         types-psycopg2==2.9.21.10,
+  #         types-python-dateutil==2.8.19.13,
+  #         types-regex==2023.3.23.1,
+  #         types-requests==2.28.11.17,
+  #         types-retry==0.9.9.3,
+  #         types-urllib3==1.26.25.11
+  #       ]
+  #       # TODO: add back once errors are addressed
+  #       # args: [--strict]

.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

@@ -20,7 +20,6 @@
         "Web Server",
         "Model Server",
         "API Server",
-        "MCP Server",
         "Slack Bot",
         "Celery primary",
         "Celery light",
@@ -133,6 +132,8 @@
       },
       "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",
@@ -151,34 +152,6 @@
       },
       "consoleTitle": "Slack Bot Console"
     },
-    {
-      "name": "MCP Server",
-      "consoleName": "MCP Server",
-      "type": "debugpy",
-      "request": "launch",
-      "module": "uvicorn",
-      "cwd": "${workspaceFolder}/backend",
-      "envFile": "${workspaceFolder}/.vscode/.env",
-      "env": {
-        "MCP_SERVER_ENABLED": "true",
-        "MCP_SERVER_PORT": "8090",
-        "MCP_SERVER_CORS_ORIGINS": "http://localhost:*",
-        "LOG_LEVEL": "DEBUG",
-        "PYTHONUNBUFFERED": "1"
-      },
-      "args": [
-        "onyx.mcp_server.api:mcp_app",
-        "--reload",
-        "--port",
-        "8090",
-        "--timeout-graceful-shutdown",
-        "0"
-      ],
-      "presentation": {
-        "group": "2"
-      },
-      "consoleTitle": "MCP Server Console"
-    },
     {
       "name": "Celery primary",
       "type": "debugpy",
@@ -508,6 +481,7 @@
       ],
       "cwd": "${workspaceFolder}",
       "console": "integratedTerminal",
+      "stopOnEntry": true,
       "presentation": {
         "group": "3"
       }
@@ -553,10 +527,10 @@
       "name": "Install Python Requirements",
       "type": "node",
       "request": "launch",
-      "runtimeExecutable": "uv",
+      "runtimeExecutable": "bash",
       "runtimeArgs": [
-        "sync",
-        "--all-extras"
+        "-c",
+        "pip install -r backend/requirements/default.txt && pip install -r backend/requirements/dev.txt && pip install -r backend/requirements/ee.txt && pip install -r backend/requirements/model_server.txt"
       ],
       "cwd": "${workspaceFolder}",
       "console": "integratedTerminal",
@@ -569,14 +543,14 @@
       "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/Dockerfile

@@ -12,13 +12,6 @@ ENV DANSWER_RUNNING_IN_DOCKER="true" \
     DO_NOT_TRACK="true" \
     PLAYWRIGHT_BROWSERS_PATH="/app/.cache/ms-playwright"
 
-# Create non-root user for security best practices
-RUN groupadd -g 1001 onyx && \
-    useradd -u 1001 -g onyx -m -s /bin/bash onyx && \
-    mkdir -p /var/log/onyx && \
-    chmod 755 /var/log/onyx && \
-    chown onyx:onyx /var/log/onyx
-
 COPY --from=ghcr.io/astral-sh/uv:0.9.9 /uv /uvx /bin/
 
 # Install system dependencies
@@ -58,7 +51,6 @@ RUN uv pip install --system --no-cache-dir --upgrade \
     pip uninstall -y py && \
     playwright install chromium && \
     playwright install-deps chromium && \
-    chown -R onyx:onyx /app && \
     ln -s /usr/local/bin/supervisord /usr/bin/supervisord && \
     # Cleanup for CVEs and size reduction
     # https://github.com/tornadoweb/tornado/issues/3107
@@ -102,6 +94,13 @@ tiktoken.get_encoding('cl100k_base')"
 # Set up application files
 WORKDIR /app
 
+# Create non-root user for security best practices
+RUN groupadd -g 1001 onyx && \
+    useradd -u 1001 -g onyx -m -s /bin/bash onyx && \
+    mkdir -p /var/log/onyx && \
+    chmod 755 /var/log/onyx && \
+    chown onyx:onyx /var/log/onyx
+
 # Enterprise Version Files
 COPY --chown=onyx:onyx ./ee /app/ee
 COPY supervisord.conf /etc/supervisor/conf.d/supervisord.conf

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/4f8a2b3c1d9e_add_open_url_tool.py

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

backend/alembic/versions/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/6436661d5b65_add_created_at_in_project_userfile.py

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

backend/alembic/versions/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/a852cbe15577_new_chat_history.py

@@ -1,417 +0,0 @@
-"""New Chat History
-
-Revision ID: a852cbe15577
-Revises: 6436661d5b65
-Create Date: 2025-11-08 15:16:37.781308
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision = "a852cbe15577"
-down_revision = "6436661d5b65"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # 1. Drop old research/agent tables (CASCADE handles dependencies)
-    op.execute("DROP TABLE IF EXISTS research_agent_iteration_sub_step CASCADE")
-    op.execute("DROP TABLE IF EXISTS research_agent_iteration CASCADE")
-    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"],
-    )
-
-    # Add new column
-    op.add_column(
-        "chat_message", sa.Column("reasoning_tokens", sa.Text(), nullable=True)
-    )
-
-    # 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")
-
-    # 3. ToolCall table changes
-    # Drop the unique constraint first
-    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(
-        """
-        UPDATE tool_call
-        SET chat_session_id = chat_message.chat_session_id
-        FROM chat_message
-        WHERE tool_call.message_id = chat_message.id
-    """
-    )
-
-    # 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",
-    )
-
-    # 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",
-    )
-
-    # Add parent_tool_call_id with FK
-    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"],
-        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(
-        """
-        ALTER TABLE tool_call
-        ALTER COLUMN tool_call_response TYPE TEXT
-        USING tool_call_response::text
-    """
-    )
-
-    # 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"),
-    )
-
-    # 5. Persona table change
-    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")
-
-    # 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.execute(
-        """
-        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),
-    )
-    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),
-    )
-    op.add_column(
-        "chat_message",
-        sa.Column("refined_answer_improvement", sa.Boolean(), nullable=True),
-    )
-    op.add_column(
-        "chat_message",
-        sa.Column("is_agentic", sa.Boolean(), nullable=False, server_default="false"),
-    )
-    op.add_column(
-        "chat_message", sa.Column("overridden_model", sa.String(), nullable=True)
-    )
-    op.add_column(
-        "chat_message", sa.Column("alternate_assistant_id", sa.Integer(), nullable=True)
-    )
-    op.add_column(
-        "chat_message", sa.Column("rephrased_query", sa.Text(), nullable=True)
-    )
-
-    op.drop_column("chat_message", "reasoning_tokens")
-
-    op.drop_constraint(
-        "fk_chat_message_latest_child_message_id", "chat_message", type_="foreignkey"
-    )
-    op.alter_column(
-        "chat_message",
-        "latest_child_message_id",
-        new_column_name="latest_child_message",
-    )
-
-    op.drop_constraint(
-        "fk_chat_message_parent_message_id", "chat_message", type_="foreignkey"
-    )
-    op.alter_column(
-        "chat_message", "parent_message_id", new_column_name="parent_message"
-    )
-
-    # Recreate agent sub question and sub query tables
-    op.create_table(
-        "agent__sub_question",
-        sa.Column("id", sa.Integer(), primary_key=True),
-        sa.Column("primary_question_id", sa.Integer(), nullable=False),
-        sa.Column("chat_session_id", postgresql.UUID(as_uuid=True), nullable=False),
-        sa.Column("sub_question", sa.Text(), nullable=False),
-        sa.Column("level", sa.Integer(), nullable=False),
-        sa.Column("level_question_num", sa.Integer(), nullable=False),
-        sa.Column(
-            "time_created",
-            sa.DateTime(timezone=True),
-            server_default=sa.text("now()"),
-            nullable=False,
-        ),
-        sa.Column("sub_answer", sa.Text(), nullable=False),
-        sa.Column("sub_question_doc_results", postgresql.JSONB(), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["primary_question_id"], ["chat_message.id"], ondelete="CASCADE"
-        ),
-        sa.ForeignKeyConstraint(["chat_session_id"], ["chat_session.id"]),
-        sa.PrimaryKeyConstraint("id"),
-    )
-
-    op.create_table(
-        "agent__sub_query",
-        sa.Column("id", sa.Integer(), primary_key=True),
-        sa.Column("parent_question_id", sa.Integer(), nullable=False),
-        sa.Column("chat_session_id", postgresql.UUID(as_uuid=True), nullable=False),
-        sa.Column("sub_query", sa.Text(), nullable=False),
-        sa.Column(
-            "time_created",
-            sa.DateTime(timezone=True),
-            server_default=sa.text("now()"),
-            nullable=False,
-        ),
-        sa.ForeignKeyConstraint(
-            ["parent_question_id"], ["agent__sub_question.id"], ondelete="CASCADE"
-        ),
-        sa.ForeignKeyConstraint(["chat_session_id"], ["chat_session.id"]),
-        sa.PrimaryKeyConstraint("id"),
-    )
-
-    op.create_table(
-        "agent__sub_query__search_doc",
-        sa.Column("sub_query_id", sa.Integer(), nullable=False),
-        sa.Column("search_doc_id", sa.Integer(), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["sub_query_id"], ["agent__sub_query.id"], ondelete="CASCADE"
-        ),
-        sa.ForeignKeyConstraint(["search_doc_id"], ["search_doc.id"]),
-        sa.PrimaryKeyConstraint("sub_query_id", "search_doc_id"),
-    )
-
-    # Recreate research agent tables
-    op.create_table(
-        "research_agent_iteration",
-        sa.Column("id", sa.Integer(), autoincrement=True, nullable=False),
-        sa.Column("primary_question_id", sa.Integer(), nullable=False),
-        sa.Column("iteration_nr", sa.Integer(), nullable=False),
-        sa.Column(
-            "created_at",
-            sa.DateTime(timezone=True),
-            server_default=sa.text("now()"),
-            nullable=False,
-        ),
-        sa.Column("purpose", sa.String(), nullable=True),
-        sa.Column("reasoning", sa.String(), nullable=True),
-        sa.ForeignKeyConstraint(
-            ["primary_question_id"], ["chat_message.id"], ondelete="CASCADE"
-        ),
-        sa.PrimaryKeyConstraint("id"),
-        sa.UniqueConstraint(
-            "primary_question_id",
-            "iteration_nr",
-            name="_research_agent_iteration_unique_constraint",
-        ),
-    )
-
-    op.create_table(
-        "research_agent_iteration_sub_step",
-        sa.Column("id", sa.Integer(), autoincrement=True, nullable=False),
-        sa.Column("primary_question_id", sa.Integer(), nullable=False),
-        sa.Column("iteration_nr", sa.Integer(), nullable=False),
-        sa.Column("iteration_sub_step_nr", sa.Integer(), nullable=False),
-        sa.Column(
-            "created_at",
-            sa.DateTime(timezone=True),
-            server_default=sa.text("now()"),
-            nullable=False,
-        ),
-        sa.Column("sub_step_instructions", sa.String(), nullable=True),
-        sa.Column("sub_step_tool_id", sa.Integer(), nullable=True),
-        sa.Column("reasoning", sa.String(), nullable=True),
-        sa.Column("sub_answer", sa.String(), nullable=True),
-        sa.Column("cited_doc_results", postgresql.JSONB(), nullable=False),
-        sa.Column("claims", postgresql.JSONB(), nullable=True),
-        sa.Column("is_web_fetch", sa.Boolean(), nullable=True),
-        sa.Column("queries", postgresql.JSONB(), nullable=True),
-        sa.Column("generated_images", postgresql.JSONB(), nullable=True),
-        sa.Column("additional_data", postgresql.JSONB(), nullable=True),
-        sa.Column("file_ids", postgresql.JSONB(), nullable=True),
-        sa.ForeignKeyConstraint(
-            ["primary_question_id", "iteration_nr"],
-            [
-                "research_agent_iteration.primary_question_id",
-                "research_agent_iteration.iteration_nr",
-            ],
-            ondelete="CASCADE",
-        ),
-        sa.ForeignKeyConstraint(["sub_step_tool_id"], ["tool.id"], ondelete="SET NULL"),
-        sa.PrimaryKeyConstraint("id"),
-    )

backend/alembic/versions/c7e9f4a3b2d1_add_python_tool.py

@@ -1,73 +0,0 @@
-"""add_python_tool
-
-Revision ID: c7e9f4a3b2d1
-Revises: 3c9a65f1207f
-Create Date: 2025-11-08 00:00:00.000000
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-
-# revision identifiers, used by Alembic.
-revision = "c7e9f4a3b2d1"
-down_revision = "3c9a65f1207f"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    """Add PythonTool to built-in tools"""
-    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, :enabled)
-            """
-        ),
-        {
-            "name": "PythonTool",
-            # in the UI, call it `Code Interpreter` since this is a well known term for this tool
-            "display_name": "Code Interpreter",
-            "description": (
-                "The Code Interpreter Action allows the assistant to execute "
-                "Python code in a secure, isolated environment for data analysis, "
-                "computation, visualization, and file processing."
-            ),
-            "in_code_tool_id": "PythonTool",
-            "enabled": True,
-        },
-    )
-
-    # needed to store files generated by the python tool
-    op.add_column(
-        "research_agent_iteration_sub_step",
-        sa.Column(
-            "file_ids",
-            postgresql.JSONB(astext_type=sa.Text()),
-            nullable=True,
-        ),
-    )
-
-
-def downgrade() -> None:
-    """Remove PythonTool from built-in tools"""
-    conn = op.get_bind()
-
-    conn.execute(
-        sa.text(
-            """
-            DELETE FROM tool
-            WHERE in_code_tool_id = :in_code_tool_id
-            """
-        ),
-        {
-            "in_code_tool_id": "PythonTool",
-        },
-    )
-
-    op.drop_column("research_agent_iteration_sub_step", "file_ids")

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/analytics.py

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

backend/ee/onyx/db/usage_export.py

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

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(
@@ -597,48 +581,6 @@ def update_user_curator_relationship(
     db_session.commit()
 
 
-def add_users_to_user_group(
-    db_session: Session,
-    user: User | None,
-    user_group_id: int,
-    user_ids: list[UUID],
-) -> UserGroup:
-    db_user_group = fetch_user_group(db_session=db_session, user_group_id=user_group_id)
-    if db_user_group is None:
-        raise ValueError(f"UserGroup with id '{user_group_id}' not found")
-
-    missing_users = [
-        user_id for user_id in user_ids if fetch_user_by_id(db_session, user_id) is None
-    ]
-    if missing_users:
-        raise ValueError(
-            f"User(s) not found: {', '.join(str(user_id) for user_id in missing_users)}"
-        )
-
-    _check_user_group_is_modifiable(db_user_group)
-
-    current_user_ids = [user.id for user in db_user_group.users]
-    current_user_ids_set = set(current_user_ids)
-    new_user_ids = [
-        user_id for user_id in user_ids if user_id not in current_user_ids_set
-    ]
-
-    if not new_user_ids:
-        return db_user_group
-
-    user_group_update = UserGroupUpdate(
-        user_ids=current_user_ids + new_user_ids,
-        cc_pair_ids=[cc_pair.id for cc_pair in db_user_group.cc_pairs],
-    )
-
-    return update_user_group(
-        db_session=db_session,
-        user=user,
-        user_group_id=user_group_id,
-        user_group_update=user_group_update,
-    )
-
-
 def update_user_group(
     db_session: Session,
     user: User | None,
@@ -661,17 +603,6 @@ def update_user_group(
     added_user_ids = list(updated_user_ids - current_user_ids)
     removed_user_ids = list(current_user_ids - updated_user_ids)
 
-    if added_user_ids:
-        missing_users = [
-            user_id
-            for user_id in added_user_ids
-            if fetch_user_by_id(db_session, user_id) is None
-        ]
-        if missing_users:
-            raise ValueError(
-                f"User(s) not found: {', '.join(str(user_id) for user_id in missing_users)}"
-            )
-
     # LEAVING THIS HERE FOR NOW FOR GIVING DIFFERENT ROLES
     # ACCESS TO DIFFERENT PERMISSIONS
     # if (removed_user_ids or added_user_ids) and (

backend/ee/onyx/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

@@ -23,7 +23,7 @@ from ee.onyx.server.query_and_chat.chat_backend import (
     router as chat_router,
 )
 from ee.onyx.server.query_and_chat.query_backend import (
-    basic_router as ee_query_router,
+    basic_router as query_router,
 )
 from ee.onyx.server.query_history.api import router as query_history_router
 from ee.onyx.server.reporting.usage_export_api import router as usage_export_router
@@ -48,9 +48,6 @@ from onyx.main import include_auth_router_with_prefix
 from onyx.main import include_router_with_global_prefix_prepended
 from onyx.main import lifespan as lifespan_base
 from onyx.main import use_route_function_names_as_operation_ids
-from onyx.server.query_and_chat.query_backend import (
-    basic_router as query_router,
-)
 from onyx.utils.logger import setup_logger
 from onyx.utils.variable_functionality import global_version
 from shared_configs.configs import MULTI_TENANT
@@ -122,7 +119,6 @@ def get_application() -> FastAPI:
     include_router_with_global_prefix_prepended(application, query_history_router)
     # EE only backend APIs
     include_router_with_global_prefix_prepended(application, query_router)
-    include_router_with_global_prefix_prepended(application, ee_query_router)
     include_router_with_global_prefix_prepended(application, chat_router)
     include_router_with_global_prefix_prepended(application, standard_answer_router)
     include_router_with_global_prefix_prepended(application, ee_oauth_router)

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 create_chat_history_chain
+from onyx.chat.chat_utils import combine_message_thread
+from onyx.chat.chat_utils import create_chat_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
@@ -22,6 +24,7 @@ from onyx.db.engine.sql_engine import get_session
 from onyx.db.models import User
 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_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
@@ -165,6 +168,8 @@ def handle_send_message_simple_with_history(
         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/query_and_chat/models.py

@@ -6,14 +6,18 @@ from pydantic import BaseModel
 from pydantic import Field
 from pydantic import model_validator
 
+from onyx.chat.models import PersonaOverrideConfig
+from onyx.chat.models import QADocsResponse
 from onyx.chat.models import ThreadMessage
 from onyx.configs.constants import DocumentSource
-from onyx.context.search.models import BaseFilters
-from onyx.context.search.models import BasicChunkRequest
+from onyx.context.search.enums import LLMEvaluationType
+from onyx.context.search.enums import SearchType
 from onyx.context.search.models import ChunkContext
-from onyx.context.search.models import InferenceChunk
+from onyx.context.search.models import RerankingDetails
 from onyx.context.search.models import RetrievalDetails
 from onyx.server.manage.models import StandardAnswer
+from onyx.server.query_and_chat.streaming_models import CitationInfo
+from onyx.server.query_and_chat.streaming_models import SubQuestionIdentifier
 
 
 class StandardAnswerRequest(BaseModel):
@@ -25,12 +29,14 @@ class StandardAnswerResponse(BaseModel):
     standard_answers: list[StandardAnswer] = Field(default_factory=list)
 
 
-class DocumentSearchRequest(BasicChunkRequest):
-    user_selected_filters: BaseFilters | None = None
-
-
-class DocumentSearchResponse(BaseModel):
-    top_documents: list[InferenceChunk]
+class DocumentSearchRequest(ChunkContext):
+    message: str
+    search_type: SearchType
+    retrieval_options: RetrievalDetails
+    recency_bias_multiplier: float = 1.0
+    evaluation_type: LLMEvaluationType
+    # None to use system defaults for reranking
+    rerank_settings: RerankingDetails | None = None
 
 
 class BasicCreateChatMessageRequest(ChunkContext):
@@ -90,17 +96,17 @@ class SimpleDoc(BaseModel):
     metadata: dict | None
 
 
-class AgentSubQuestion(BaseModel):
+class AgentSubQuestion(SubQuestionIdentifier):
     sub_question: str
     document_ids: list[str]
 
 
-class AgentAnswer(BaseModel):
+class AgentAnswer(SubQuestionIdentifier):
     answer: str
     answer_type: Literal["agent_sub_answer", "agent_level_answer"]
 
 
-class AgentSubQuery(BaseModel):
+class AgentSubQuery(SubQuestionIdentifier):
     sub_query: str
     query_id: int
 
@@ -146,3 +152,45 @@ class AgentSubQuery(BaseModel):
             sorted(level_question_dict.items(), key=lambda x: (x is None, x))
         )
         return sorted_dict
+
+
+class OneShotQARequest(ChunkContext):
+    # Supports simplier APIs that don't deal with chat histories or message edits
+    # Easier APIs to work with for developers
+    persona_override_config: PersonaOverrideConfig | None = None
+    persona_id: int | None = None
+
+    messages: list[ThreadMessage]
+    retrieval_options: RetrievalDetails = Field(default_factory=RetrievalDetails)
+    rerank_settings: RerankingDetails | None = None
+
+    # allows the caller to specify the exact search query they want to use
+    # can be used if the message sent to the LLM / query should not be the same
+    # will also disable Thread-based Rewording if specified
+    query_override: str | None = None
+
+    # If True, skips generating an AI response to the search query
+    skip_gen_ai_answer_generation: bool = False
+
+    # If True, uses agentic search instead of basic search
+    use_agentic_search: bool = False
+
+    @model_validator(mode="after")
+    def check_persona_fields(self) -> "OneShotQARequest":
+        if self.persona_override_config is None and self.persona_id is None:
+            raise ValueError("Exactly one of persona_config or persona_id must be set")
+        elif self.persona_override_config is not None and (self.persona_id is not None):
+            raise ValueError(
+                "If persona_override_config is set, persona_id cannot be set"
+            )
+        return self
+
+
+class OneShotQAResponse(BaseModel):
+    # This is built piece by piece, any of these can be None as the flow could break
+    answer: str | None = None
+    rephrase: str | None = None
+    citations: list[CitationInfo] | None = None
+    docs: QADocsResponse | None = None
+    error_msg: str | None = None
+    chat_message_id: int | None = None

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

@@ -1,23 +1,316 @@
+import json
+from collections.abc import Generator
+
 from fastapi import APIRouter
 from fastapi import Depends
 from fastapi import HTTPException
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
 from sqlalchemy.orm import Session
 
 from ee.onyx.onyxbot.slack.handlers.handle_standard_answers import (
     oneoff_standard_answers,
 )
+from ee.onyx.server.query_and_chat.models import DocumentSearchRequest
+from ee.onyx.server.query_and_chat.models import OneShotQARequest
+from ee.onyx.server.query_and_chat.models import OneShotQAResponse
 from ee.onyx.server.query_and_chat.models import StandardAnswerRequest
 from ee.onyx.server.query_and_chat.models import StandardAnswerResponse
 from onyx.auth.users import current_user
+from onyx.chat.chat_utils import combine_message_thread
+from onyx.chat.chat_utils import prepare_chat_message_request
+from onyx.chat.models import AnswerStream
+from onyx.chat.models import PersonaOverrideConfig
+from onyx.chat.models import QADocsResponse
+from onyx.chat.process_message import gather_stream
+from onyx.chat.process_message import stream_chat_message_objects
+from onyx.configs.chat_configs import NUM_RETURNED_HITS
+from onyx.configs.onyxbot_configs import MAX_THREAD_CONTEXT_PERCENTAGE
+from onyx.context.search.models import SavedSearchDocWithContent
+from onyx.context.search.models import SearchRequest
+from onyx.context.search.pipeline import SearchPipeline
+from onyx.context.search.utils import dedupe_documents
+from onyx.context.search.utils import drop_llm_indices
+from onyx.context.search.utils import relevant_sections_to_indices
 from onyx.db.engine.sql_engine import get_session
+from onyx.db.models import Persona
 from onyx.db.models import User
+from onyx.db.persona import get_persona_by_id
+from onyx.llm.factory import get_default_llms
+from onyx.llm.factory import get_llms_for_persona
+from onyx.llm.factory import get_main_llm_from_tuple
+from onyx.natural_language_processing.utils import get_tokenizer
+from onyx.server.query_and_chat.streaming_models import CitationInfo
+from onyx.server.utils import get_json_line
 from onyx.utils.logger import setup_logger
 
-logger = setup_logger()
 
+logger = setup_logger()
 basic_router = APIRouter(prefix="/query")
 
 
+class DocumentSearchPagination(BaseModel):
+    offset: int
+    limit: int
+    returned_count: int
+    has_more: bool
+    next_offset: int | None = None
+
+
+class DocumentSearchResponse(BaseModel):
+    top_documents: list[SavedSearchDocWithContent]
+    llm_indices: list[int]
+    pagination: DocumentSearchPagination
+
+
+def _normalize_pagination(limit: int | None, offset: int | None) -> tuple[int, int]:
+    if limit is None:
+        resolved_limit = NUM_RETURNED_HITS
+    else:
+        resolved_limit = limit
+
+    if resolved_limit <= 0:
+        raise HTTPException(
+            status_code=400, detail="retrieval_options.limit must be positive"
+        )
+
+    if offset is None:
+        resolved_offset = 0
+    else:
+        resolved_offset = offset
+
+    if resolved_offset < 0:
+        raise HTTPException(
+            status_code=400, detail="retrieval_options.offset cannot be negative"
+        )
+
+    return resolved_limit, resolved_offset
+
+
+@basic_router.post("/document-search")
+def handle_search_request(
+    search_request: DocumentSearchRequest,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> DocumentSearchResponse:
+    """Simple search endpoint, does not create a new message or records in the DB"""
+    query = search_request.message
+    logger.notice(f"Received document search query: {query}")
+
+    llm, fast_llm = get_default_llms()
+    pagination_limit, pagination_offset = _normalize_pagination(
+        limit=search_request.retrieval_options.limit,
+        offset=search_request.retrieval_options.offset,
+    )
+
+    search_pipeline = SearchPipeline(
+        search_request=SearchRequest(
+            query=query,
+            search_type=search_request.search_type,
+            human_selected_filters=search_request.retrieval_options.filters,
+            enable_auto_detect_filters=search_request.retrieval_options.enable_auto_detect_filters,
+            persona=None,  # For simplicity, default settings should be good for this search
+            offset=pagination_offset,
+            limit=pagination_limit + 1,
+            rerank_settings=search_request.rerank_settings,
+            evaluation_type=search_request.evaluation_type,
+            chunks_above=search_request.chunks_above,
+            chunks_below=search_request.chunks_below,
+            full_doc=search_request.full_doc,
+        ),
+        user=user,
+        llm=llm,
+        fast_llm=fast_llm,
+        skip_query_analysis=False,
+        db_session=db_session,
+        bypass_acl=False,
+    )
+    top_sections = search_pipeline.reranked_sections
+    relevance_sections = search_pipeline.section_relevance
+    top_docs = [
+        SavedSearchDocWithContent(
+            document_id=section.center_chunk.document_id,
+            chunk_ind=section.center_chunk.chunk_id,
+            content=section.center_chunk.content,
+            semantic_identifier=section.center_chunk.semantic_identifier or "Unknown",
+            link=(
+                section.center_chunk.source_links.get(0)
+                if section.center_chunk.source_links
+                else None
+            ),
+            blurb=section.center_chunk.blurb,
+            source_type=section.center_chunk.source_type,
+            boost=section.center_chunk.boost,
+            hidden=section.center_chunk.hidden,
+            metadata=section.center_chunk.metadata,
+            score=section.center_chunk.score or 0.0,
+            match_highlights=section.center_chunk.match_highlights,
+            updated_at=section.center_chunk.updated_at,
+            primary_owners=section.center_chunk.primary_owners,
+            secondary_owners=section.center_chunk.secondary_owners,
+            is_internet=False,
+            db_doc_id=0,
+        )
+        for section in top_sections
+    ]
+
+    # Track whether the underlying retrieval produced more items than requested
+    has_more_results = len(top_docs) > pagination_limit
+
+    # Deduping happens at the last step to avoid harming quality by dropping content early on
+    deduped_docs = top_docs
+    dropped_inds = None
+
+    if search_request.retrieval_options.dedupe_docs:
+        deduped_docs, dropped_inds = dedupe_documents(top_docs)
+
+    llm_indices = relevant_sections_to_indices(
+        relevance_sections=relevance_sections, items=deduped_docs
+    )
+
+    if dropped_inds:
+        llm_indices = drop_llm_indices(
+            llm_indices=llm_indices,
+            search_docs=deduped_docs,
+            dropped_indices=dropped_inds,
+        )
+
+    paginated_docs = deduped_docs[:pagination_limit]
+    llm_indices = [index for index in llm_indices if index < len(paginated_docs)]
+    has_more = has_more_results
+    pagination = DocumentSearchPagination(
+        offset=pagination_offset,
+        limit=pagination_limit,
+        returned_count=len(paginated_docs),
+        has_more=has_more,
+        next_offset=(pagination_offset + pagination_limit) if has_more else None,
+    )
+
+    return DocumentSearchResponse(
+        top_documents=paginated_docs,
+        llm_indices=llm_indices,
+        pagination=pagination,
+    )
+
+
+def get_answer_stream(
+    query_request: OneShotQARequest,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> AnswerStream:
+    query = query_request.messages[0].message
+    logger.notice(f"Received query for Answer API: {query}")
+
+    if (
+        query_request.persona_override_config is None
+        and query_request.persona_id is None
+    ):
+        raise KeyError("Must provide persona ID or Persona Config")
+
+    persona_info: Persona | PersonaOverrideConfig | None = None
+    if query_request.persona_override_config is not None:
+        persona_info = query_request.persona_override_config
+    elif query_request.persona_id is not None:
+        persona_info = get_persona_by_id(
+            persona_id=query_request.persona_id,
+            user=user,
+            db_session=db_session,
+            is_for_edit=False,
+        )
+
+    llm = get_main_llm_from_tuple(get_llms_for_persona(persona=persona_info, 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 * MAX_THREAD_CONTEXT_PERCENTAGE
+    )
+
+    combined_message = combine_message_thread(
+        messages=query_request.messages,
+        max_tokens=max_history_tokens,
+        llm_tokenizer=llm_tokenizer,
+    )
+
+    # Also creates a new chat session
+    request = prepare_chat_message_request(
+        message_text=combined_message,
+        user=user,
+        persona_id=query_request.persona_id,
+        persona_override_config=query_request.persona_override_config,
+        message_ts_to_respond_to=None,
+        retrieval_details=query_request.retrieval_options,
+        rerank_settings=query_request.rerank_settings,
+        db_session=db_session,
+        use_agentic_search=query_request.use_agentic_search,
+        skip_gen_ai_answer_generation=query_request.skip_gen_ai_answer_generation,
+    )
+
+    packets = stream_chat_message_objects(
+        new_msg_req=request,
+        user=user,
+        db_session=db_session,
+    )
+
+    return packets
+
+
+@basic_router.post("/answer-with-citation")
+def get_answer_with_citation(
+    request: OneShotQARequest,
+    db_session: Session = Depends(get_session),
+    user: User | None = Depends(current_user),
+) -> OneShotQAResponse:
+    try:
+        packets = get_answer_stream(request, user, db_session)
+        answer = gather_stream(packets)
+
+        if answer.error_msg:
+            raise RuntimeError(answer.error_msg)
+
+        return OneShotQAResponse(
+            answer=answer.answer,
+            chat_message_id=answer.message_id,
+            error_msg=answer.error_msg,
+            citations=[
+                CitationInfo(citation_num=i, document_id=doc_id)
+                for i, doc_id in answer.cited_documents.items()
+            ],
+            docs=QADocsResponse(
+                top_documents=answer.top_documents,
+                predicted_flow=None,
+                predicted_search=None,
+                applied_source_filters=None,
+                applied_time_cutoff=None,
+                recency_bias_multiplier=0.0,
+            ),
+        )
+    except Exception as e:
+        logger.error(f"Error in get_answer_with_citation: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail="An internal server error occurred")
+
+
+@basic_router.post("/stream-answer-with-citation")
+def stream_answer_with_citation(
+    request: OneShotQARequest,
+    db_session: Session = Depends(get_session),
+    user: User | None = Depends(current_user),
+) -> StreamingResponse:
+    def stream_generator() -> Generator[str, None, None]:
+        try:
+            for packet in get_answer_stream(request, user, db_session):
+                serialized = get_json_line(packet.model_dump())
+                yield serialized
+        except Exception as e:
+            logger.exception("Error in answer streaming")
+            yield json.dumps({"error": str(e)})
+
+    return StreamingResponse(stream_generator(), media_type="application/json")
+
+
 @basic_router.get("/standard-answer")
 def get_standard_answer(
     request: StandardAnswerRequest,

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

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

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

@@ -38,8 +38,10 @@ 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
@@ -273,7 +275,7 @@ def configure_default_api_keys(db_session: Session) -> None:
             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()
@@ -300,10 +302,10 @@ def configure_default_api_keys(db_session: Session) -> None:
             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,
         )

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

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

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

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

backend/onyx/agents/agent_framework/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 +1,365 @@
+import json
+from collections.abc import Callable
+from collections.abc import Iterator
+from collections.abc import Sequence
+from dataclasses import dataclass
+from typing import Any
+
+import onyx.tracing.framework._error_tracing as _error_tracing
+from onyx.agents.agent_framework.models import RunItemStreamEvent
+from onyx.agents.agent_framework.models import StreamEvent
+from onyx.agents.agent_framework.models import ToolCallOutputStreamItem
+from onyx.agents.agent_framework.models import ToolCallStreamItem
+from onyx.llm.interfaces import LanguageModelInput
+from onyx.llm.interfaces import LLM
+from onyx.llm.interfaces import ToolChoiceOptions
+from onyx.llm.message_types import ChatCompletionMessage
+from onyx.llm.message_types import ToolCall
+from onyx.llm.model_response import ModelResponseStream
+from onyx.tools.tool import RunContextWrapper
+from onyx.tools.tool import Tool
+from onyx.tracing.framework.create import agent_span
+from onyx.tracing.framework.create import function_span
+from onyx.tracing.framework.create import generation_span
+from onyx.tracing.framework.spans import SpanError
+
+
+@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] = []
+            tool_outputs: dict[str, str] = {}
+
+            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:
+                    tool = tools_by_name[name]
+                    arguments = 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.
+                    with function_span(tool.name) as span_fn:
+                        span_fn.span_data.input = arguments
+                        try:
+                            output = tool.run_v2(run_context, **arguments)
+                            tool_outputs[call_id] = _serialize_tool_output(output)
+                            span_fn.span_data.output = output
+                        except Exception as e:
+                            _error_tracing.attach_error_to_current_span(
+                                SpanError(
+                                    message="Error running tool",
+                                    data={"tool_name": tool.name, "error": str(e)},
+                                )
+                            )
+                            # Treat the error as the tool output so the framework can continue
+                            error_output = f"Error: {str(e)}"
+                            tool_outputs[call_id] = error_output
+                            output = error_output
+
+                    yield RunItemStreamEvent(
+                        type="tool_call_output",
+                        details=ToolCallOutputStreamItem(
+                            call_id=call_id,
+                            output=output,
+                        ),
+                    )
+                else:
+                    not_found_output = f"Tool {name} not found"
+                    tool_outputs[call_id] = _serialize_tool_output(not_found_output)
+                    yield RunItemStreamEvent(
+                        type="tool_call_output",
+                        details=ToolCallOutputStreamItem(
+                            call_id=call_id,
+                            output=not_found_output,
+                        ),
+                    )
+
+            new_messages_stateful.append(
+                {
+                    "role": "assistant",
+                    "content": None,
+                    "tool_calls": assistant_tool_calls,
+                }
+            )
+
+            for _, tool_call_data in sorted_tool_calls:
+                call_id = tool_call_data["id"]
+
+                if call_id in tool_outputs:
+                    new_messages_stateful.append(
+                        {
+                            "role": "tool",
+                            "content": tool_outputs[call_id],
+                            "tool_call_id": call_id,
+                        }
+                    )
+        current_span.finish(reset_current=True)
+
+    return QueryResult(
+        stream=stream_generator(),
+        new_messages_stateful=new_messages_stateful,
+    )

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 "web_search"
+        if tool_choice == "image_generation":
+            return "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 +1,61 @@
+from datetime import datetime
+from typing import cast
+
+from onyx.chat.models import LlmDoc
+from onyx.configs.constants import DocumentSource
+from onyx.context.search.models import InferenceSection
+from onyx.db.engine.sql_engine import get_session_with_current_tenant
+from onyx.tools.models import SearchToolOverrideKwargs
+from onyx.tools.tool_implementations.search.search_tool import (
+    FINAL_CONTEXT_DOCUMENTS_ID,
+)
+from onyx.tools.tool_implementations.search.search_tool import SearchTool
+
+
+def research(
+    question: str,
+    search_tool: SearchTool,
+    document_sources: list[DocumentSource] | None = None,
+    time_cutoff: datetime | None = None,
+) -> list[LlmDoc]:
+    # new db session to avoid concurrency issues
+
+    callback_container: list[list[InferenceSection]] = []
+    retrieved_docs: list[LlmDoc] = []
+
+    with get_session_with_current_tenant() as db_session:
+        for tool_response in search_tool.run(
+            query=question,
+            override_kwargs=SearchToolOverrideKwargs(
+                force_no_rerank=False,
+                alternate_db_session=db_session,
+                retrieved_sections_callback=callback_container.append,
+                skip_query_analysis=True,
+                document_sources=document_sources,
+                time_cutoff=time_cutoff,
+            ),
+        ):
+            # get retrieved docs to send to the rest of the graph
+            if tool_response.id == FINAL_CONTEXT_DOCUMENTS_ID:
+                retrieved_docs = cast(list[LlmDoc], tool_response.response)[:10]
+                break
+    return retrieved_docs
+
+
+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 +1,31 @@
+from onyx.agents.agent_search.dr.enums import DRPath
+from onyx.agents.agent_search.dr.enums import ResearchType
+
+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,
+}
+
+DR_TIME_BUDGET_BY_TYPE = {
+    ResearchType.THOUGHTFUL: 3.0,
+    ResearchType.DEEP: 12.0,
+    ResearchType.FAST: 0.5,
+}

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

@@ -0,0 +1,112 @@
+from datetime import datetime
+
+from onyx.agents.agent_search.dr.enums import DRPath
+from onyx.agents.agent_search.dr.enums import ResearchType
+from onyx.agents.agent_search.dr.models import DRPromptPurpose
+from onyx.agents.agent_search.dr.models import OrchestratorTool
+from onyx.prompts.dr_prompts import GET_CLARIFICATION_PROMPT
+from onyx.prompts.dr_prompts import KG_TYPES_DESCRIPTIONS
+from onyx.prompts.dr_prompts import ORCHESTRATOR_DEEP_INITIAL_PLAN_PROMPT
+from onyx.prompts.dr_prompts import ORCHESTRATOR_DEEP_ITERATIVE_DECISION_PROMPT
+from onyx.prompts.dr_prompts import ORCHESTRATOR_FAST_ITERATIVE_DECISION_PROMPT
+from onyx.prompts.dr_prompts import ORCHESTRATOR_FAST_ITERATIVE_REASONING_PROMPT
+from onyx.prompts.dr_prompts import ORCHESTRATOR_NEXT_STEP_PURPOSE_PROMPT
+from onyx.prompts.dr_prompts import TOOL_DIFFERENTIATION_HINTS
+from onyx.prompts.dr_prompts import TOOL_QUESTION_HINTS
+from onyx.prompts.prompt_template import PromptTemplate
+
+
+def get_dr_prompt_orchestration_templates(
+    purpose: DRPromptPurpose,
+    research_type: ResearchType,
+    available_tools: dict[str, OrchestratorTool],
+    entity_types_string: str | None = None,
+    relationship_types_string: str | None = None,
+    reasoning_result: str | None = None,
+    tool_calls_string: str | None = None,
+) -> PromptTemplate:
+    available_tools = available_tools or {}
+    tool_names = list(available_tools.keys())
+    tool_description_str = "\n\n".join(
+        f"- {tool_name}: {tool.description}"
+        for tool_name, tool in available_tools.items()
+    )
+    tool_cost_str = "\n".join(
+        f"{tool_name}: {tool.cost}" for tool_name, tool in available_tools.items()
+    )
+
+    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 research_type == ResearchType.THOUGHTFUL:
+            raise ValueError("plan generation is not supported for FAST time budget")
+        base_template = ORCHESTRATOR_DEEP_INITIAL_PLAN_PROMPT
+
+    elif purpose == DRPromptPurpose.NEXT_STEP_REASONING:
+        if research_type == ResearchType.THOUGHTFUL:
+            base_template = ORCHESTRATOR_FAST_ITERATIVE_REASONING_PROMPT
+        else:
+            raise ValueError(
+                "reasoning is not separately required for DEEP time budget"
+            )
+
+    elif purpose == DRPromptPurpose.NEXT_STEP_PURPOSE:
+        base_template = ORCHESTRATOR_NEXT_STEP_PURPOSE_PROMPT
+
+    elif purpose == DRPromptPurpose.NEXT_STEP:
+        if research_type == ResearchType.THOUGHTFUL:
+            base_template = ORCHESTRATOR_FAST_ITERATIVE_DECISION_PROMPT
+        else:
+            base_template = ORCHESTRATOR_DEEP_ITERATIVE_DECISION_PROMPT
+
+    elif purpose == DRPromptPurpose.CLARIFICATION:
+        if research_type == ResearchType.THOUGHTFUL:
+            raise ValueError("clarification is not supported for FAST time budget")
+        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 +1,32 @@
+from enum import Enum
+
+
+class ResearchType(str, Enum):
+    """Research type options for agent search operations"""
+
+    # BASIC = "BASIC"
+    LEGACY_AGENTIC = "LEGACY_AGENTIC"  # only used for legacy agentic search migrations
+    THOUGHTFUL = "THOUGHTFUL"
+    DEEP = "DEEP"
+    FAST = "FAST"
+
+
+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 pydantic import ConfigDict
+
+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
+
+    model_config = ConfigDict(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 +1,624 @@
+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_BY_TYPE
+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.enums import ResearchType
+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
+
+    research_type = graph_config.behavior.research_type
+    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 research_type not in ResearchType:
+        raise ValueError(f"Invalid research type: {research_type}")
+
+    if research_type in [ResearchType.THOUGHTFUL, ResearchType.FAST]:
+        if iteration_nr == 1:
+            remaining_time_budget = DR_TIME_BUDGET_BY_TYPE[research_type]
+
+        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,
+                ResearchType.THOUGHTFUL,
+                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,
+            ResearchType.THOUGHTFUL,
+            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
+
+    elif research_type == ResearchType.DEEP:
+        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_BY_TYPE[ResearchType.DEEP]
+
+            base_plan_prompt = get_dr_prompt_orchestration_templates(
+                DRPromptPurpose.PLAN,
+                ResearchType.DEEP,
+                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,
+            ResearchType.DEEP,
+            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
+
+    else:
+        raise NotImplementedError(f"Research type {research_type} is not implemented.")
+
+    base_next_step_purpose_prompt = get_dr_prompt_orchestration_templates(
+        DRPromptPurpose.NEXT_STEP_PURPOSE,
+        ResearchType.DEEP,
+        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 research_type in [ResearchType.THOUGHTFUL, ResearchType.DEEP]:
+
+        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))
+
+    elif research_type == ResearchType.FAST:
+        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 +1,423 @@
+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.enums import ResearchType
+from onyx.agents.agent_search.dr.models import AggregatedDRContext
+from onyx.agents.agent_search.dr.models import TestInfoCompleteResponse
+from onyx.agents.agent_search.dr.states import FinalUpdate
+from onyx.agents.agent_search.dr.states import MainState
+from onyx.agents.agent_search.dr.states import OrchestrationUpdate
+from onyx.agents.agent_search.dr.sub_agents.image_generation.models import (
+    GeneratedImageFullResult,
+)
+from onyx.agents.agent_search.dr.utils import aggregate_context
+from onyx.agents.agent_search.dr.utils import convert_inference_sections_to_search_docs
+from onyx.agents.agent_search.dr.utils import get_chat_history_string
+from onyx.agents.agent_search.dr.utils import get_prompt_question
+from onyx.agents.agent_search.dr.utils import parse_plan_to_dict
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.llm import invoke_llm_json
+from onyx.agents.agent_search.shared_graph_utils.llm import stream_llm_answer
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    get_langgraph_node_log_string,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+from onyx.agents.agent_search.utils import create_question_prompt
+from onyx.chat.chat_utils import llm_doc_from_inference_section
+from onyx.configs.agent_configs import TF_DR_TIMEOUT_LONG
+from onyx.context.search.models import InferenceSection
+from onyx.db.chat import create_search_doc_from_inference_section
+from onyx.db.chat import update_db_session_with_messages
+from onyx.db.models import ChatMessage__SearchDoc
+from onyx.db.models import ResearchAgentIteration
+from onyx.db.models import ResearchAgentIterationSubStep
+from onyx.db.models import SearchDoc as DbSearchDoc
+from onyx.llm.utils import check_number_of_tokens
+from onyx.prompts.chat_prompts import PROJECT_INSTRUCTIONS_SEPARATOR
+from onyx.prompts.dr_prompts import FINAL_ANSWER_PROMPT_W_SUB_ANSWERS
+from onyx.prompts.dr_prompts import FINAL_ANSWER_PROMPT_WITHOUT_SUB_ANSWERS
+from onyx.prompts.dr_prompts import TEST_INFO_COMPLETE_PROMPT
+from onyx.server.query_and_chat.streaming_models import CitationDelta
+from onyx.server.query_and_chat.streaming_models import CitationStart
+from onyx.server.query_and_chat.streaming_models import MessageStart
+from onyx.server.query_and_chat.streaming_models import SectionEnd
+from onyx.server.query_and_chat.streaming_models import StreamingType
+from onyx.utils.logger import setup_logger
+from onyx.utils.threadpool_concurrency import run_with_timeout
+
+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
+    research_type = graph_config.behavior.research_type
+    db_session = graph_config.persistence.db_session
+
+    # 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=research_type,
+        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")
+
+    research_type = graph_config.behavior.research_type
+
+    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 research_type == ResearchType.DEEP
+    ):
+        test_info_complete_prompt = TEST_INFO_COMPLETE_PROMPT.build(
+            base_question=prompt_question,
+            questions_answers_claims=iteration_responses_wo_docs_string,
+            chat_history_string=chat_history_string,
+            high_level_plan=(
+                state.plan_of_record.plan
+                if state.plan_of_record
+                else "No plan available"
+            ),
+        )
+
+        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 research_type in [ResearchType.THOUGHTFUL, ResearchType.FAST]:
+        final_answer_base_prompt = FINAL_ANSWER_PROMPT_WITHOUT_SUB_ANSWERS
+    elif research_type == ResearchType.DEEP:
+        final_answer_base_prompt = FINAL_ANSWER_PROMPT_W_SUB_ANSWERS
+    else:
+        raise ValueError(f"Invalid research type: {research_type}")
+
+    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 research_type == ResearchType.DEEP
+    ):
+        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 +1,248 @@
+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
+    research_type = graph_config.behavior.research_type
+    db_session = graph_config.persistence.db_session
+
+    # 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=research_type,
+        research_plan=plan_of_record_dict,
+        final_documents=search_docs,
+        update_parent_message=True,
+        research_answer_purpose=ResearchAnswerPurpose.ANSWER,
+        token_count=num_tokens,
+    )
+
+    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 +1,132 @@
+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.agents.agent_search.shared_graph_utils.utils import write_custom_event
+from onyx.chat.chat_utils import saved_search_docs_from_llm_docs
+from onyx.chat.models import AgentAnswerPiece
+from onyx.chat.models import CitationInfo
+from onyx.chat.models import LlmDoc
+from onyx.chat.models import OnyxAnswerPiece
+from onyx.chat.stream_processing.answer_response_handler import AnswerResponseHandler
+from onyx.chat.stream_processing.answer_response_handler import CitationResponseHandler
+from onyx.chat.stream_processing.answer_response_handler import (
+    PassThroughAnswerResponseHandler,
+)
+from onyx.chat.stream_processing.utils import map_document_id_order
+from onyx.context.search.models import InferenceSection
+from onyx.server.query_and_chat.streaming_models import CitationDelta
+from onyx.server.query_and_chat.streaming_models import CitationStart
+from onyx.server.query_and_chat.streaming_models import MessageDelta
+from onyx.server.query_and_chat.streaming_models import MessageStart
+from onyx.server.query_and_chat.streaming_models import SectionEnd
+from onyx.utils.logger import setup_logger
+
+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 +1,286 @@
+import re
+from datetime import datetime
+from typing import cast
+from uuid import UUID
+
+from langchain_core.runnables import RunnableConfig
+from langgraph.types import StreamWriter
+
+from onyx.agents.agent_search.dr.enums import ResearchType
+from onyx.agents.agent_search.dr.models import BaseSearchProcessingResponse
+from onyx.agents.agent_search.dr.models import IterationAnswer
+from onyx.agents.agent_search.dr.models import SearchAnswer
+from onyx.agents.agent_search.dr.sub_agents.states import BranchInput
+from onyx.agents.agent_search.dr.sub_agents.states import BranchUpdate
+from onyx.agents.agent_search.dr.utils import convert_inference_sections_to_search_docs
+from onyx.agents.agent_search.dr.utils import extract_document_citations
+from onyx.agents.agent_search.kb_search.graph_utils import build_document_context
+from onyx.agents.agent_search.models import GraphConfig
+from onyx.agents.agent_search.shared_graph_utils.llm import invoke_llm_json
+from onyx.agents.agent_search.shared_graph_utils.utils import (
+    get_langgraph_node_log_string,
+)
+from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
+from onyx.agents.agent_search.utils import create_question_prompt
+from onyx.chat.models import LlmDoc
+from onyx.configs.agent_configs import TF_DR_TIMEOUT_LONG
+from onyx.configs.agent_configs import TF_DR_TIMEOUT_SHORT
+from onyx.context.search.models import InferenceSection
+from onyx.db.connector import DocumentSource
+from onyx.db.engine.sql_engine import get_session_with_current_tenant
+from onyx.prompts.dr_prompts import BASE_SEARCH_PROCESSING_PROMPT
+from onyx.prompts.dr_prompts import INTERNAL_SEARCH_PROMPTS
+from onyx.secondary_llm_flows.source_filter import strings_to_document_sources
+from onyx.server.query_and_chat.streaming_models import SearchToolDelta
+from onyx.tools.models import SearchToolOverrideKwargs
+from onyx.tools.tool_implementations.search.search_tool import (
+    SEARCH_RESPONSE_SUMMARY_ID,
+)
+from onyx.tools.tool_implementations.search.search_tool import SearchResponseSummary
+from onyx.tools.tool_implementations.search.search_tool import SearchTool
+from onyx.utils.logger import setup_logger
+
+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
+    research_type = graph_config.behavior.research_type
+
+    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)
+    force_use_tool = 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] = []
+    callback_container: list[list[InferenceSection]] = []
+
+    user_file_ids: list[UUID] | None = None
+    project_id: int | None = None
+    if force_use_tool.override_kwargs and isinstance(
+        force_use_tool.override_kwargs, SearchToolOverrideKwargs
+    ):
+        override_kwargs = force_use_tool.override_kwargs
+        user_file_ids = override_kwargs.user_file_ids
+        project_id = override_kwargs.project_id
+
+    # new db session to avoid concurrency issues
+    with get_session_with_current_tenant() as search_db_session:
+        for tool_response in search_tool.run(
+            query=rewritten_query,
+            document_sources=specified_source_types,
+            time_filter=implied_time_filter,
+            override_kwargs=SearchToolOverrideKwargs(
+                force_no_rerank=True,
+                alternate_db_session=search_db_session,
+                retrieved_sections_callback=callback_container.append,
+                skip_query_analysis=True,
+                original_query=rewritten_query,
+                user_file_ids=user_file_ids,
+                project_id=project_id,
+            ),
+        ):
+            # get retrieved docs to send to the rest of the graph
+            if tool_response.id == SEARCH_RESPONSE_SUMMARY_ID:
+                response = cast(SearchResponseSummary, tool_response.response)
+                retrieved_docs = response.top_sections
+
+                break
+
+    # 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 research_type == ResearchType.DEEP:
+        search_prompt = INTERNAL_SEARCH_PROMPTS[research_type].build(
+            search_query=branch_query,
+            base_question=base_question,
+            document_text=document_texts,
+        )
+
+        # Run LLM
+
+        # 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,
+            )
+        ],
+    )