minor typing

.github/pull_request_template.md

@@ -6,24 +6,20 @@
 [Describe the tests you ran to verify your changes]
 
 
-## Accepted Risk (provide if relevant)
-N/A
+## Accepted Risk
+[Any know risks or failure modes to point out to reviewers]
 
 
-## Related Issue(s) (provide if relevant)
-N/A
+## Related Issue(s)
+[If applicable, link to the issue(s) this PR addresses]
 
 
-## Mental Checklist:
-- All of the automated tests pass
-- All PR comments are addressed and marked resolved
-- If there are migrations, they have been rebased to latest main
-- If there are new dependencies, they are added to the requirements
-- If there are new environment variables, they are added to all of the deployment methods
-- If there are new APIs that don't require auth, they are added to PUBLIC_ENDPOINT_SPECS
-- Docker images build and basic functionalities work
-- Author has done a final read through of the PR right before merge
-
-## Backporting (check the box to trigger backport action)
-Note: You have to check that the action passes, otherwise resolve the conflicts manually and tag the patches.
-- [ ] This PR should be backported (make sure to check that the backport attempt succeeds)
+## Checklist:
+- [ ] All of the automated tests pass
+- [ ] All PR comments are addressed and marked resolved
+- [ ] If there are migrations, they have been rebased to latest main
+- [ ] If there are new dependencies, they are added to the requirements
+- [ ] If there are new environment variables, they are added to all of the deployment methods
+- [ ] If there are new APIs that don't require auth, they are added to PUBLIC_ENDPOINT_SPECS
+- [ ] Docker images build and basic functionalities work
+- [ ] Author has done a final read through of the PR right before merge

.github/workflows/docker-build-push-backend-container-on-tag.yml

@@ -3,61 +3,61 @@ name: Build and Push Backend Image on Tag
 on:
   push:
     tags:
-      - "*"
+      - '*'
 
 env:
-  REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'danswer/danswer-backend-cloud' || 'danswer/danswer-backend' }}
+  REGISTRY_IMAGE: danswer/danswer-backend
   LATEST_TAG: ${{ contains(github.ref_name, 'latest') }}
-
+  
 jobs:
   build-and-push:
-    # TODO: investigate a matrix build like the web container
+    # TODO: investigate a matrix build like the web container 
     # See https://runs-on.com/runners/linux/
-    runs-on: [runs-on, runner=8cpu-linux-x64, "run-id=${{ github.run_id }}"]
+    runs-on: [runs-on,runner=8cpu-linux-x64,"run-id=${{ github.run_id }}"]
 
     steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
+    - name: Checkout code
+      uses: actions/checkout@v4
 
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+    - name: Set up Docker Buildx
+      uses: docker/setup-buildx-action@v3
 
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_USERNAME }}
-          password: ${{ secrets.DOCKER_TOKEN }}
+    - name: Login to Docker Hub
+      uses: docker/login-action@v3
+      with:
+        username: ${{ secrets.DOCKER_USERNAME }}
+        password: ${{ secrets.DOCKER_TOKEN }}
 
-      - name: Install build-essential
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y build-essential
+    - name: Install build-essential
+      run: |
+        sudo apt-get update
+        sudo apt-get install -y build-essential
+          
+    - name: Backend Image Docker Build and Push
+      uses: docker/build-push-action@v5
+      with:
+        context: ./backend
+        file: ./backend/Dockerfile
+        platforms: linux/amd64,linux/arm64
+        push: true
+        tags: |
+          ${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
+          ${{ env.LATEST_TAG == 'true' && format('{0}:latest', env.REGISTRY_IMAGE) || '' }}
+        build-args: |
+          DANSWER_VERSION=${{ github.ref_name }}
 
-      - name: Backend Image Docker Build and Push
-        uses: docker/build-push-action@v5
-        with:
-          context: ./backend
-          file: ./backend/Dockerfile
-          platforms: linux/amd64,linux/arm64
-          push: true
-          tags: |
-            ${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
-            ${{ env.LATEST_TAG == 'true' && format('{0}:latest', env.REGISTRY_IMAGE) || '' }}
-          build-args: |
-            DANSWER_VERSION=${{ github.ref_name }}
-
-      # trivy has their own rate limiting issues causing this action to flake
-      # we worked around it by hardcoding to different db repos in env
-      # can re-enable when they figure it out
-      # https://github.com/aquasecurity/trivy/discussions/7538
-      # https://github.com/aquasecurity/trivy-action/issues/389
-      - name: Run Trivy vulnerability scanner
-        uses: aquasecurity/trivy-action@master
-        env:
-          TRIVY_DB_REPOSITORY: "public.ecr.aws/aquasecurity/trivy-db:2"
-          TRIVY_JAVA_DB_REPOSITORY: "public.ecr.aws/aquasecurity/trivy-java-db:1"
-        with:
-          # To run locally: trivy image --severity HIGH,CRITICAL danswer/danswer-backend
-          image-ref: docker.io/${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
-          severity: "CRITICAL,HIGH"
-          trivyignores: ./backend/.trivyignore
+    # trivy has their own rate limiting issues causing this action to flake
+    # we worked around it by hardcoding to different db repos in env
+    # can re-enable when they figure it out
+    # https://github.com/aquasecurity/trivy/discussions/7538
+    # https://github.com/aquasecurity/trivy-action/issues/389
+    - name: Run Trivy vulnerability scanner
+      uses: aquasecurity/trivy-action@master
+      env:
+        TRIVY_DB_REPOSITORY: 'public.ecr.aws/aquasecurity/trivy-db:2'
+        TRIVY_JAVA_DB_REPOSITORY: 'public.ecr.aws/aquasecurity/trivy-java-db:1'
+      with:
+        # To run locally: trivy image --severity HIGH,CRITICAL danswer/danswer-backend
+        image-ref: docker.io/${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
+        severity: 'CRITICAL,HIGH'
+        trivyignores: ./backend/.trivyignore

.github/workflows/docker-build-push-cloud-web-container-on-tag.yml

@@ -1,137 +0,0 @@
-name: Build and Push Cloud Web Image on Tag
-# Identical to the web container build, but with correct image tag and build args
-
-on:
-  push:
-    tags:
-      - "*"
-
-env:
-  REGISTRY_IMAGE: danswer/danswer-web-server-cloud
-  LATEST_TAG: ${{ contains(github.ref_name, 'latest') }}
-
-jobs:
-  build:
-    runs-on:
-      - runs-on
-      - runner=${{ matrix.platform == 'linux/amd64' && '8cpu-linux-x64' || '8cpu-linux-arm64' }}
-      - run-id=${{ github.run_id }}
-      - tag=platform-${{ matrix.platform }}
-    strategy:
-      fail-fast: false
-      matrix:
-        platform:
-          - linux/amd64
-          - linux/arm64
-
-    steps:
-      - name: Prepare
-        run: |
-          platform=${{ matrix.platform }}
-          echo "PLATFORM_PAIR=${platform//\//-}" >> $GITHUB_ENV
-
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Docker meta
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.REGISTRY_IMAGE }}
-          tags: |
-            type=raw,value=${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
-            type=raw,value=${{ env.LATEST_TAG == 'true' && format('{0}:latest', env.REGISTRY_IMAGE) || '' }}
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_USERNAME }}
-          password: ${{ secrets.DOCKER_TOKEN }}
-
-      - name: Build and push by digest
-        id: build
-        uses: docker/build-push-action@v5
-        with:
-          context: ./web
-          file: ./web/Dockerfile
-          platforms: ${{ matrix.platform }}
-          push: true
-          build-args: |
-            DANSWER_VERSION=${{ github.ref_name }}
-            NEXT_PUBLIC_CLOUD_ENABLED=true
-            NEXT_PUBLIC_POSTHOG_KEY=${{ secrets.POSTHOG_KEY }}
-            NEXT_PUBLIC_POSTHOG_HOST=${{ secrets.POSTHOG_HOST }}
-            NEXT_PUBLIC_SENTRY_DSN=${{ secrets.SENTRY_DSN }}
-            NEXT_PUBLIC_GTM_ENABLED=true
-          # needed due to weird interactions with the builds for different platforms
-          no-cache: true
-          labels: ${{ steps.meta.outputs.labels }}
-          outputs: type=image,name=${{ env.REGISTRY_IMAGE }},push-by-digest=true,name-canonical=true,push=true
-
-      - name: Export digest
-        run: |
-          mkdir -p /tmp/digests
-          digest="${{ steps.build.outputs.digest }}"
-          touch "/tmp/digests/${digest#sha256:}"
-
-      - name: Upload digest
-        uses: actions/upload-artifact@v4
-        with:
-          name: digests-${{ env.PLATFORM_PAIR }}
-          path: /tmp/digests/*
-          if-no-files-found: error
-          retention-days: 1
-
-  merge:
-    runs-on: ubuntu-latest
-    needs:
-      - build
-    steps:
-      - name: Download digests
-        uses: actions/download-artifact@v4
-        with:
-          path: /tmp/digests
-          pattern: digests-*
-          merge-multiple: true
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Docker meta
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.REGISTRY_IMAGE }}
-
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_USERNAME }}
-          password: ${{ secrets.DOCKER_TOKEN }}
-
-      - name: Create manifest list and push
-        working-directory: /tmp/digests
-        run: |
-          docker buildx imagetools create $(jq -cr '.tags | map("-t " + .) | join(" ")' <<< "$DOCKER_METADATA_OUTPUT_JSON") \
-            $(printf '${{ env.REGISTRY_IMAGE }}@sha256:%s ' *)
-
-      - name: Inspect image
-        run: |
-          docker buildx imagetools inspect ${{ env.REGISTRY_IMAGE }}:${{ steps.meta.outputs.version }}
-
-      # trivy has their own rate limiting issues causing this action to flake
-      # we worked around it by hardcoding to different db repos in env
-      # can re-enable when they figure it out
-      # https://github.com/aquasecurity/trivy/discussions/7538
-      # https://github.com/aquasecurity/trivy-action/issues/389
-      - name: Run Trivy vulnerability scanner
-        uses: aquasecurity/trivy-action@master
-        env:
-          TRIVY_DB_REPOSITORY: "public.ecr.aws/aquasecurity/trivy-db:2"
-          TRIVY_JAVA_DB_REPOSITORY: "public.ecr.aws/aquasecurity/trivy-java-db:1"
-        with:
-          image-ref: docker.io/${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
-          severity: "CRITICAL,HIGH"

.github/workflows/docker-build-push-model-server-container-on-tag.yml

@@ -3,53 +3,53 @@ name: Build and Push Model Server Image on Tag
 on:
   push:
     tags:
-      - "*"
+      - '*'
 
 env:
-  REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'danswer/danswer-model-server-cloud' || 'danswer/danswer-model-server' }}
+  REGISTRY_IMAGE: danswer/danswer-model-server
   LATEST_TAG: ${{ contains(github.ref_name, 'latest') }}
-
+  
 jobs:
   build-and-push:
     # See https://runs-on.com/runners/linux/
-    runs-on: [runs-on, runner=8cpu-linux-x64, "run-id=${{ github.run_id }}"]
+    runs-on: [runs-on,runner=8cpu-linux-x64,"run-id=${{ github.run_id }}"]
 
     steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
+    - name: Checkout code
+      uses: actions/checkout@v4
 
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+    - name: Set up Docker Buildx
+      uses: docker/setup-buildx-action@v3
 
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_USERNAME }}
-          password: ${{ secrets.DOCKER_TOKEN }}
+    - name: Login to Docker Hub
+      uses: docker/login-action@v3
+      with:
+        username: ${{ secrets.DOCKER_USERNAME }}
+        password: ${{ secrets.DOCKER_TOKEN }}
 
-      - name: Model Server Image Docker Build and Push
-        uses: docker/build-push-action@v5
-        with:
-          context: ./backend
-          file: ./backend/Dockerfile.model_server
-          platforms: linux/amd64,linux/arm64
-          push: true
-          tags: |
-            ${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
-            ${{ env.LATEST_TAG == 'true' && format('{0}:latest', env.REGISTRY_IMAGE) || '' }}
-          build-args: |
-            DANSWER_VERSION=${{ github.ref_name }}
+    - name: Model Server Image Docker Build and Push
+      uses: docker/build-push-action@v5
+      with:
+        context: ./backend
+        file: ./backend/Dockerfile.model_server
+        platforms: linux/amd64,linux/arm64
+        push: true
+        tags: |
+          ${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
+          ${{ env.LATEST_TAG == 'true' && format('{0}:latest', env.REGISTRY_IMAGE) || '' }}
+        build-args: |
+          DANSWER_VERSION=${{ github.ref_name }}
 
-      # trivy has their own rate limiting issues causing this action to flake
-      # we worked around it by hardcoding to different db repos in env
-      # can re-enable when they figure it out
-      # https://github.com/aquasecurity/trivy/discussions/7538
-      # https://github.com/aquasecurity/trivy-action/issues/389
-      - name: Run Trivy vulnerability scanner
-        uses: aquasecurity/trivy-action@master
-        env:
-          TRIVY_DB_REPOSITORY: "public.ecr.aws/aquasecurity/trivy-db:2"
-          TRIVY_JAVA_DB_REPOSITORY: "public.ecr.aws/aquasecurity/trivy-java-db:1"
-        with:
-          image-ref: docker.io/danswer/danswer-model-server:${{ github.ref_name }}
-          severity: "CRITICAL,HIGH"
+    # trivy has their own rate limiting issues causing this action to flake
+    # we worked around it by hardcoding to different db repos in env
+    # can re-enable when they figure it out
+    # https://github.com/aquasecurity/trivy/discussions/7538
+    # https://github.com/aquasecurity/trivy-action/issues/389
+    - name: Run Trivy vulnerability scanner
+      uses: aquasecurity/trivy-action@master
+      env:
+        TRIVY_DB_REPOSITORY: 'public.ecr.aws/aquasecurity/trivy-db:2'
+        TRIVY_JAVA_DB_REPOSITORY: 'public.ecr.aws/aquasecurity/trivy-java-db:1'
+      with:
+        image-ref: docker.io/danswer/danswer-model-server:${{ github.ref_name }}
+        severity: 'CRITICAL,HIGH'

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

@@ -1,23 +0,0 @@
-name: 'Nightly - Close stale issues and PRs'
-on:
-  schedule:
-    - cron: '0 11 * * *' # Runs every day at 3 AM PST / 4 AM PDT / 11 AM UTC
-
-permissions:
-  # contents: write # only for delete-branch option
-  issues: write
-  pull-requests: write
-  
-jobs:
-  stale:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: 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.'
-          close-issue-message: 'This issue was closed because it has been stalled for 90 days with no activity.'
-          close-pr-message: 'This PR was closed because it has been stalled for 90 days with no activity.'
-          days-before-stale: 75
-#           days-before-close: 90  # uncomment after we test stale behavior
-          

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

@@ -1,76 +0,0 @@
-# Scan for problematic software licenses
-
-# trivy has their own rate limiting issues causing this action to flake
-# we worked around it by hardcoding to different db repos in env
-# can re-enable when they figure it out
-# https://github.com/aquasecurity/trivy/discussions/7538
-# https://github.com/aquasecurity/trivy-action/issues/389
-
-name: 'Nightly - Scan licenses'
-on:
-#   schedule:
-#     - cron: '0 14 * * *'  # Runs every day at 6 AM PST / 7 AM PDT / 2 PM UTC
-  workflow_dispatch:  # Allows manual triggering
-
-permissions:
-  actions: read
-  contents: read
-  security-events: write
-  
-jobs:
-  scan-licenses:
-    # See https://runs-on.com/runners/linux/
-    runs-on: [runs-on,runner=2cpu-linux-x64,"run-id=${{ github.run_id }}"]
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-          cache: 'pip'
-          cache-dependency-path: |
-            backend/requirements/default.txt
-            backend/requirements/dev.txt
-            backend/requirements/model_server.txt
-      
-      - name: Get explicit and transitive dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install --retries 5 --timeout 30 -r backend/requirements/default.txt
-          pip install --retries 5 --timeout 30 -r backend/requirements/dev.txt
-          pip install --retries 5 --timeout 30 -r backend/requirements/model_server.txt
-          pip freeze > requirements-all.txt
-                    
-      - name: Check python
-        id: license_check_report
-        uses: pilosus/action-pip-license-checker@v2
-        with:
-          requirements: 'requirements-all.txt'
-          fail: 'Copyleft'
-          exclude: '(?i)^(pylint|aio[-_]*).*'
-          
-      - name: Print report
-        if: ${{ always() }}
-        run: echo "${{ steps.license_check_report.outputs.report }}"
-      
-      - name: Install npm dependencies
-        working-directory: ./web
-        run: npm ci
-        
-      - name: Run Trivy vulnerability scanner in repo mode
-        uses: aquasecurity/trivy-action@0.28.0
-        with:
-          scan-type: fs
-          scanners: license
-          format: table
-#           format: sarif
-#           output: trivy-results.sarif
-          severity: HIGH,CRITICAL
-
-#       - name: Upload Trivy scan results to GitHub Security tab
-#         uses: github/codeql-action/upload-sarif@v3
-#         with:
-#           sarif_file: trivy-results.sarif

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

@@ -13,10 +13,7 @@ on:
 env:
   OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
   SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }}
-  CONFLUENCE_TEST_SPACE_URL: ${{ secrets.CONFLUENCE_TEST_SPACE_URL }}
-  CONFLUENCE_USER_NAME: ${{ secrets.CONFLUENCE_USER_NAME }}
-  CONFLUENCE_ACCESS_TOKEN: ${{ secrets.CONFLUENCE_ACCESS_TOKEN }}
-  
+
 jobs:
   integration-tests:
     # See https://runs-on.com/runners/linux/
@@ -75,7 +72,7 @@ jobs:
           load: true
           cache-from: type=s3,prefix=cache/${{ github.repository }}/integration-tests/model-server/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }}
           cache-to: type=s3,prefix=cache/${{ github.repository }}/integration-tests/model-server/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }},mode=max
-      
+
       - name: Build integration test Docker image
         uses: ./.github/actions/custom-build-and-push
         with:
@@ -88,58 +85,7 @@ jobs:
           cache-from: type=s3,prefix=cache/${{ github.repository }}/integration-tests/integration/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }}
           cache-to: type=s3,prefix=cache/${{ github.repository }}/integration-tests/integration/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }},mode=max
 
-      # Start containers for multi-tenant tests
-      - name: Start Docker containers for multi-tenant tests
-        run: |
-          cd deployment/docker_compose
-          ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true \
-          MULTI_TENANT=true \
-          AUTH_TYPE=basic \
-          REQUIRE_EMAIL_VERIFICATION=false \
-          DISABLE_TELEMETRY=true \
-          IMAGE_TAG=test \
-          docker compose -f docker-compose.dev.yml -p danswer-stack up -d
-        id: start_docker_multi_tenant
-
-      # In practice, `cloud` Auth type would require OAUTH credentials to be set.
-      - name: Run Multi-Tenant Integration Tests
-        run: |
-          echo "Running integration tests..."
-          docker run --rm --network danswer-stack_default \
-            --name test-runner \
-            -e POSTGRES_HOST=relational_db \
-            -e POSTGRES_USER=postgres \
-            -e POSTGRES_PASSWORD=password \
-            -e POSTGRES_DB=postgres \
-            -e VESPA_HOST=index \
-            -e REDIS_HOST=cache \
-            -e API_SERVER_HOST=api_server \
-            -e OPENAI_API_KEY=${OPENAI_API_KEY} \
-            -e SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN} \
-            -e TEST_WEB_HOSTNAME=test-runner \
-            -e AUTH_TYPE=cloud \
-            -e MULTI_TENANT=true \
-            danswer/danswer-integration:test \
-            /app/tests/integration/multitenant_tests
-        continue-on-error: true
-        id: run_multitenant_tests
-
-      - name: Check multi-tenant test results
-        run: |
-          if [ ${{ steps.run_tests.outcome }} == 'failure' ]; then
-            echo "Integration tests failed. Exiting with error."
-            exit 1
-          else
-            echo "All integration tests passed successfully."
-          fi 
-
-      - name: Stop multi-tenant Docker containers
-        run: |
-          cd deployment/docker_compose
-          docker compose -f docker-compose.dev.yml -p danswer-stack down -v
-
-
-      - name: Start Docker containers 
+      - name: Start Docker containers
         run: |
           cd deployment/docker_compose
           ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true \
@@ -184,7 +130,7 @@ jobs:
           done
           echo "Finished waiting for service."
 
-      - name: Run Standard Integration Tests
+      - name: Run integration tests
         run: |
           echo "Running integration tests..."
           docker run --rm --network danswer-stack_default \
@@ -198,13 +144,8 @@ jobs:
             -e API_SERVER_HOST=api_server \
             -e OPENAI_API_KEY=${OPENAI_API_KEY} \
             -e SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN} \
-            -e CONFLUENCE_TEST_SPACE_URL=${CONFLUENCE_TEST_SPACE_URL} \
-            -e CONFLUENCE_USER_NAME=${CONFLUENCE_USER_NAME} \
-            -e CONFLUENCE_ACCESS_TOKEN=${CONFLUENCE_ACCESS_TOKEN} \
             -e TEST_WEB_HOSTNAME=test-runner \
-            danswer/danswer-integration:test \
-            /app/tests/integration/tests \
-            /app/tests/integration/connector_job_tests
+            danswer/danswer-integration:test
         continue-on-error: true
         id: run_tests
 
@@ -217,18 +158,12 @@ jobs:
             echo "All integration tests passed successfully."
           fi
 
-      # save before stopping the containers so the logs can be captured
       - name: Save Docker logs
         if: success() || failure()
         run: |
           cd deployment/docker_compose
           docker compose -f docker-compose.dev.yml -p danswer-stack logs > docker-compose.log
           mv docker-compose.log ${{ github.workspace }}/docker-compose.log
-
-      - name: Stop Docker containers
-        run: |
-          cd deployment/docker_compose
-          docker compose -f docker-compose.dev.yml -p danswer-stack down -v
       
       - name: Upload logs
         if: success() || failure()

.github/workflows/pr-backport-autotrigger.yml

@@ -1,124 +0,0 @@
-name: Backport on Merge
-
-# Note this workflow does not trigger the builds, be sure to manually tag the branches to trigger the builds
-
-on:
-  pull_request:
-    types: [closed]  # Later we check for merge so only PRs that go in can get backported
-
-permissions:
-  contents: write
-  actions: write
-
-jobs:
-  backport:
-    if: github.event.pull_request.merged == true
-    runs-on: ubuntu-latest
-    env:
-      GITHUB_TOKEN: ${{ secrets.YUHONG_GH_ACTIONS }}
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          ssh-key: "${{ secrets.RKUO_DEPLOY_KEY }}"
-          fetch-depth: 0
-
-      - name: Set up Git user
-        run: |
-          git config user.name "Richard Kuo [bot]"
-          git config user.email "rkuo[bot]@danswer.ai"
-          git fetch --prune
-      
-      - name: Check for Backport Checkbox
-        id: checkbox-check
-        run: |
-          PR_BODY="${{ github.event.pull_request.body }}"
-          if [[ "$PR_BODY" == *"[x] This PR should be backported"* ]]; then
-            echo "backport=true" >> $GITHUB_OUTPUT
-          else
-            echo "backport=false" >> $GITHUB_OUTPUT
-          fi
-
-      - name: List and sort release branches
-        id: list-branches
-        run: |
-          git fetch --all --tags
-          BRANCHES=$(git for-each-ref --format='%(refname:short)' refs/remotes/origin/release/* | sed 's|origin/release/||' | sort -Vr)
-          BETA=$(echo "$BRANCHES" | head -n 1)
-          STABLE=$(echo "$BRANCHES" | head -n 2 | tail -n 1)
-          echo "beta=release/$BETA" >> $GITHUB_OUTPUT
-          echo "stable=release/$STABLE" >> $GITHUB_OUTPUT
-          # Fetch latest tags for beta and stable
-          LATEST_BETA_TAG=$(git tag -l "v[0-9]*.[0-9]*.[0-9]*-beta.[0-9]*" | grep -E "^v[0-9]+\.[0-9]+\.[0-9]+-beta\.[0-9]+$" | grep -v -- "-cloud" | sort -Vr | head -n 1)
-          LATEST_STABLE_TAG=$(git tag -l "v[0-9]*.[0-9]*.[0-9]*" | grep -E "^v[0-9]+\.[0-9]+\.[0-9]+$" | sort -Vr | head -n 1)
-          
-          # Handle case where no beta tags exist
-          if [[ -z "$LATEST_BETA_TAG" ]]; then
-            NEW_BETA_TAG="v1.0.0-beta.1"
-          else
-            NEW_BETA_TAG=$(echo $LATEST_BETA_TAG | awk -F '[.-]' '{print $1 "." $2 "." $3 "-beta." ($NF+1)}')
-          fi
-          
-          # Increment latest stable tag
-          NEW_STABLE_TAG=$(echo $LATEST_STABLE_TAG | awk -F '.' '{print $1 "." $2 "." ($3+1)}')
-          echo "latest_beta_tag=$LATEST_BETA_TAG" >> $GITHUB_OUTPUT
-          echo "latest_stable_tag=$LATEST_STABLE_TAG" >> $GITHUB_OUTPUT
-          echo "new_beta_tag=$NEW_BETA_TAG" >> $GITHUB_OUTPUT
-          echo "new_stable_tag=$NEW_STABLE_TAG" >> $GITHUB_OUTPUT
-
-      - name: Echo branch and tag information
-        run: |
-          echo "Beta branch: ${{ steps.list-branches.outputs.beta }}"
-          echo "Stable branch: ${{ steps.list-branches.outputs.stable }}"
-          echo "Latest beta tag: ${{ steps.list-branches.outputs.latest_beta_tag }}"
-          echo "Latest stable tag: ${{ steps.list-branches.outputs.latest_stable_tag }}"
-          echo "New beta tag: ${{ steps.list-branches.outputs.new_beta_tag }}"
-          echo "New stable tag: ${{ steps.list-branches.outputs.new_stable_tag }}"
-
-      - name: Trigger Backport
-        if: steps.checkbox-check.outputs.backport == 'true'
-        run: |
-          set -e
-          echo "Backporting to beta ${{ steps.list-branches.outputs.beta }} and stable ${{ steps.list-branches.outputs.stable }}"
-          
-          # Echo the merge commit SHA
-          echo "Merge commit SHA: ${{ github.event.pull_request.merge_commit_sha }}"
-          
-          # Fetch all history for all branches and tags
-          git fetch --prune
-
-          # Reset and prepare the beta branch
-          git checkout ${{ steps.list-branches.outputs.beta }}
-          echo "Last 5 commits on beta branch:"
-          git log -n 5 --pretty=format:"%H"
-          echo ""  # Newline for formatting
-
-          # Cherry-pick the merge commit from the merged PR
-          git cherry-pick -m 1 ${{ github.event.pull_request.merge_commit_sha }} || {
-            echo "Cherry-pick to beta failed due to conflicts."
-            exit 1
-          }
-          
-          # Create new beta branch/tag
-          git tag ${{ steps.list-branches.outputs.new_beta_tag }}
-          # Push the changes and tag to the beta branch using PAT
-          git push origin ${{ steps.list-branches.outputs.beta }}
-          git push origin ${{ steps.list-branches.outputs.new_beta_tag }}
-
-          # Reset and prepare the stable branch
-          git checkout ${{ steps.list-branches.outputs.stable }}
-          echo "Last 5 commits on stable branch:"
-          git log -n 5 --pretty=format:"%H"
-          echo ""  # Newline for formatting
-          
-          # Cherry-pick the merge commit from the merged PR
-          git cherry-pick -m 1 ${{ github.event.pull_request.merge_commit_sha }} || {
-            echo "Cherry-pick to stable failed due to conflicts."
-            exit 1
-          }
-          
-          # Create new stable branch/tag
-          git tag ${{ steps.list-branches.outputs.new_stable_tag }}
-          # Push the changes and tag to the stable branch using PAT
-          git push origin ${{ steps.list-branches.outputs.stable }}
-          git push origin ${{ steps.list-branches.outputs.new_stable_tag }}

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

@@ -1,225 +0,0 @@
-name: Run Chromatic Tests
-concurrency:
-  group: Run-Chromatic-Tests-${{ github.workflow }}-${{ github.head_ref || github.event.workflow_run.head_branch || github.run_id }}
-  cancel-in-progress: true
-
-on: push
-
-env:
-  OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-  SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }}
-
-jobs:
-  playwright-tests:
-    name: Playwright Tests
-
-    # See https://runs-on.com/runners/linux/
-    runs-on: [runs-on,runner=8cpu-linux-x64,ram=16,"run-id=${{ github.run_id }}"]
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-          cache: 'pip'
-          cache-dependency-path: |
-            backend/requirements/default.txt
-            backend/requirements/dev.txt
-            backend/requirements/model_server.txt
-      - run: |
-          python -m pip install --upgrade pip
-          pip install --retries 5 --timeout 30 -r backend/requirements/default.txt
-          pip install --retries 5 --timeout 30 -r backend/requirements/dev.txt
-          pip install --retries 5 --timeout 30 -r backend/requirements/model_server.txt
-        
-      - name: Setup node
-        uses: actions/setup-node@v4
-        with:
-          node-version: 22
-
-      - name: Install node dependencies
-        working-directory: ./web
-        run: npm ci
-
-      - name: Install playwright browsers
-        working-directory: ./web
-        run: npx playwright install --with-deps
-        
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_USERNAME }}
-          password: ${{ secrets.DOCKER_TOKEN }}
-
-      # tag every docker image with "test" so that we can spin up the correct set
-      # of images during testing
-      
-      # we use the runs-on cache for docker builds
-      # in conjunction with runs-on runners, it has better speed and unlimited caching
-      # https://runs-on.com/caching/s3-cache-for-github-actions/
-      # https://runs-on.com/caching/docker/
-      # https://github.com/moby/buildkit#s3-cache-experimental
-      
-      # images are built and run locally for testing purposes. Not pushed.
-
-      - name: Build Web Docker image
-        uses: ./.github/actions/custom-build-and-push
-        with:
-          context: ./web
-          file: ./web/Dockerfile
-          platforms: linux/amd64
-          tags: danswer/danswer-web-server:test
-          push: false
-          load: true
-          cache-from: type=s3,prefix=cache/${{ github.repository }}/integration-tests/web-server/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }}
-          cache-to: type=s3,prefix=cache/${{ github.repository }}/integration-tests/web-server/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }},mode=max
-
-      - name: Build Backend Docker image
-        uses: ./.github/actions/custom-build-and-push
-        with:
-          context: ./backend
-          file: ./backend/Dockerfile
-          platforms: linux/amd64
-          tags: danswer/danswer-backend:test
-          push: false
-          load: true
-          cache-from: type=s3,prefix=cache/${{ github.repository }}/integration-tests/backend/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }}
-          cache-to: type=s3,prefix=cache/${{ github.repository }}/integration-tests/backend/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }},mode=max
-
-      - name: Build Model Server Docker image
-        uses: ./.github/actions/custom-build-and-push
-        with:
-          context: ./backend
-          file: ./backend/Dockerfile.model_server
-          platforms: linux/amd64
-          tags: danswer/danswer-model-server:test
-          push: false
-          load: true
-          cache-from: type=s3,prefix=cache/${{ github.repository }}/integration-tests/model-server/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }}
-          cache-to: type=s3,prefix=cache/${{ github.repository }}/integration-tests/model-server/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }},mode=max
-
-      - name: Start Docker containers
-        run: |
-          cd deployment/docker_compose
-          ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true \
-          AUTH_TYPE=basic \
-          REQUIRE_EMAIL_VERIFICATION=false \
-          DISABLE_TELEMETRY=true \
-          IMAGE_TAG=test \
-          docker compose -f docker-compose.dev.yml -p danswer-stack up -d
-        id: start_docker
-
-      - name: Wait for service to be ready
-        run: |
-          echo "Starting wait-for-service script..."
-          
-          docker logs -f danswer-stack-api_server-1 &
-
-          start_time=$(date +%s)
-          timeout=300  # 5 minutes in seconds
-          
-          while true; do
-            current_time=$(date +%s)
-            elapsed_time=$((current_time - start_time))
-            
-            if [ $elapsed_time -ge $timeout ]; then
-              echo "Timeout reached. Service did not become ready in 5 minutes."
-              exit 1
-            fi
-            
-            # Use curl with error handling to ignore specific exit code 56
-            response=$(curl -s -o /dev/null -w "%{http_code}" http://localhost:8080/health || echo "curl_error")
-            
-            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
-            
-            sleep 5
-          done
-          echo "Finished waiting for service."
-
-      - name: Run pytest playwright test init
-        working-directory: ./backend
-        env: 
-          PYTEST_IGNORE_SKIP: true
-        run: pytest -s tests/integration/tests/playwright/test_playwright.py
-
-      - name: Run Playwright tests
-        working-directory: ./web
-        run: npx playwright test
-
-      - uses: actions/upload-artifact@v4
-        if: always()
-        with:
-          # Chromatic automatically defaults to the test-results directory.
-          # Replace with the path to your custom directory and adjust the CHROMATIC_ARCHIVE_LOCATION environment variable accordingly.
-          name: test-results
-          path: ./web/test-results
-          retention-days: 30
-                    
-      # save before stopping the containers so the logs can be captured
-      - name: Save Docker logs
-        if: success() || failure()
-        run: |
-          cd deployment/docker_compose
-          docker compose -f docker-compose.dev.yml -p danswer-stack logs > docker-compose.log
-          mv docker-compose.log ${{ github.workspace }}/docker-compose.log
-      
-      - name: Upload logs
-        if: success() || failure()
-        uses: actions/upload-artifact@v4
-        with:
-          name: docker-logs
-          path: ${{ github.workspace }}/docker-compose.log
-
-      - name: Stop Docker containers
-        run: |
-          cd deployment/docker_compose
-          docker compose -f docker-compose.dev.yml -p danswer-stack down -v
-
-  chromatic-tests:
-    name: Chromatic Tests
-    
-    needs: playwright-tests
-    runs-on: [runs-on,runner=8cpu-linux-x64,ram=16,"run-id=${{ github.run_id }}"]
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          
-      - name: Setup node
-        uses: actions/setup-node@v4
-        with:
-          node-version: 22
-          
-      - name: Install node dependencies
-        working-directory: ./web
-        run: npm ci
-        
-      - name: Download Playwright test results
-        uses: actions/download-artifact@v4
-        with:
-          name: test-results
-          path: ./web/test-results
-          
-      - name: Run Chromatic
-        uses: chromaui/action@latest
-        with:
-          playwright: true
-          projectToken: ${{ secrets.CHROMATIC_PROJECT_TOKEN }}
-          workingDir: ./web
-        env: 
-          CHROMATIC_ARCHIVE_LOCATION: ./test-results

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

@@ -1,72 +0,0 @@
-name: Helm - Lint and Test Charts
-
-on:
-  merge_group:
-  pull_request:
-    branches: [ main ]
-  workflow_dispatch:  # Allows manual triggering
-  
-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 }}"]
-
-    # fetch-depth 0 is required for helm/chart-testing-action
-    steps:
-    - name: Checkout code
-      uses: actions/checkout@v4
-      with:
-        fetch-depth: 0
-        
-    - name: Set up Helm
-      uses: azure/setup-helm@v4.2.0
-      with:
-        version: v3.14.4
-      
-    - name: Set up chart-testing
-      uses: helm/chart-testing-action@v2.6.1
-
-    # even though we specify chart-dirs in ct.yaml, it isn't used by ct for the list-changed command...
-    - name: Run chart-testing (list-changed)
-      id: list-changed
-      run: |
-        echo "default_branch: ${{ github.event.repository.default_branch }}"
-        changed=$(ct list-changed --remote origin --target-branch ${{ github.event.repository.default_branch }} --chart-dirs deployment/helm/charts)
-        echo "list-changed output: $changed"
-        if [[ -n "$changed" ]]; then
-          echo "changed=true" >> "$GITHUB_OUTPUT"
-        fi
-
-#     rkuo: I don't think we need python?
-#     - name: Set up Python
-#       uses: actions/setup-python@v5
-#       with:
-#         python-version: '3.11'
-#         cache: 'pip'
-#         cache-dependency-path: |
-#           backend/requirements/default.txt
-#           backend/requirements/dev.txt
-#           backend/requirements/model_server.txt
-#     - run: |
-#         python -m pip install --upgrade pip
-#         pip install --retries 5 --timeout 30 -r backend/requirements/default.txt
-#         pip install --retries 5 --timeout 30 -r backend/requirements/dev.txt
-#         pip install --retries 5 --timeout 30 -r backend/requirements/model_server.txt
-
-    # lint all charts if any changes were detected
-    - name: Run chart-testing (lint)
-      if: steps.list-changed.outputs.changed == 'true'
-      run: ct lint --config ct.yaml --all
-      # the following would lint only changed charts, but linting isn't expensive
-      # run: ct lint --config ct.yaml --target-branch ${{ github.event.repository.default_branch }}
-
-    - name: Create kind cluster
-      if: steps.list-changed.outputs.changed == 'true'
-      uses: helm/kind-action@v1.10.0
-
-    - name: Run chart-testing (install)
-      if: steps.list-changed.outputs.changed == 'true'
-      run: ct install --all --helm-extra-set-args="--set=nginx.enabled=false" --debug --config ct.yaml
-      # the following would install only changed charts, but we only have one chart so 
-      # don't worry about that for now
-      # run: ct install --target-branch ${{ github.event.repository.default_branch }}

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

@@ -0,0 +1,68 @@
+# This workflow is intentionally disabled while we're still working on it
+# It's close to ready, but a race condition needs to be fixed with
+# API server and Vespa startup, and it needs to have a way to build/test against
+# local containers
+
+name: Helm - Lint and Test Charts
+
+on:
+  merge_group:
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  lint-test:
+    # See https://runs-on.com/runners/linux/
+    runs-on: [runs-on,runner=8cpu-linux-x64,hdd=256,"run-id=${{ github.run_id }}"]
+
+    # fetch-depth 0 is required for helm/chart-testing-action
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 0
+        
+    - name: Set up Helm
+      uses: azure/setup-helm@v4.2.0
+      with:
+        version: v3.14.4
+      
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.11'
+        cache: 'pip'
+        cache-dependency-path: |
+          backend/requirements/default.txt
+          backend/requirements/dev.txt
+          backend/requirements/model_server.txt
+    - run: |
+        python -m pip install --upgrade pip
+        pip install --retries 5 --timeout 30 -r backend/requirements/default.txt
+        pip install --retries 5 --timeout 30 -r backend/requirements/dev.txt
+        pip install --retries 5 --timeout 30 -r backend/requirements/model_server.txt
+
+    - name: Set up chart-testing
+      uses: helm/chart-testing-action@v2.6.1
+
+    - name: Run chart-testing (list-changed)
+      id: list-changed
+      run: |
+        changed=$(ct list-changed --target-branch ${{ github.event.repository.default_branch }})
+        if [[ -n "$changed" ]]; then
+          echo "changed=true" >> "$GITHUB_OUTPUT"
+        fi
+
+    - name: Run chart-testing (lint)
+#       if: steps.list-changed.outputs.changed == 'true'
+      run: ct lint --all --config ct.yaml --target-branch ${{ github.event.repository.default_branch }}
+
+    - name: Create kind cluster
+#       if: steps.list-changed.outputs.changed == 'true'
+      uses: helm/kind-action@v1.10.0
+
+    - name: Run chart-testing (install)
+#       if: steps.list-changed.outputs.changed == 'true'
+      run: ct install --all --config ct.yaml
+#       run: ct install --target-branch ${{ github.event.repository.default_branch }}
+      

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

@@ -18,14 +18,6 @@ env:
   # Jira
   JIRA_USER_EMAIL: ${{ secrets.JIRA_USER_EMAIL }}
   JIRA_API_TOKEN: ${{ secrets.JIRA_API_TOKEN }}
-  # Google
-  GOOGLE_DRIVE_SERVICE_ACCOUNT_JSON_STR: ${{ secrets.GOOGLE_DRIVE_SERVICE_ACCOUNT_JSON_STR }}
-  GOOGLE_DRIVE_OAUTH_CREDENTIALS_JSON_STR_TEST_USER_1: ${{ secrets.GOOGLE_DRIVE_OAUTH_CREDENTIALS_JSON_STR_TEST_USER_1 }}
-  GOOGLE_DRIVE_OAUTH_CREDENTIALS_JSON_STR: ${{ secrets.GOOGLE_DRIVE_OAUTH_CREDENTIALS_JSON_STR }}
-  GOOGLE_GMAIL_SERVICE_ACCOUNT_JSON_STR: ${{ secrets.GOOGLE_GMAIL_SERVICE_ACCOUNT_JSON_STR }}
-  GOOGLE_GMAIL_OAUTH_CREDENTIALS_JSON_STR: ${{ secrets.GOOGLE_GMAIL_OAUTH_CREDENTIALS_JSON_STR }}
-  # Slab
-  SLAB_BOT_TOKEN: ${{ secrets.SLAB_BOT_TOKEN }}
 
 jobs:
   connectors-check:

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

@@ -15,7 +15,7 @@ env:
   OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
 
 jobs:
-  model-check:
+  connectors-check:
     # See https://runs-on.com/runners/linux/
     runs-on: [runs-on,runner=8cpu-linux-x64,"run-id=${{ github.run_id }}"]
 

.gitignore

@@ -7,4 +7,3 @@
 .vscode/
 *.sw?
 /backend/tests/regression/answer_quality/search_test_config.yaml
-/web/test-results/

.vscode/launch.template.jsonc

@@ -6,69 +6,19 @@
     // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
     "version": "0.2.0",
     "compounds": [
-		{
-			// Dummy entry used to label the group
-			"name": "--- Compound ---",
-		  	"configurations": [
-		  		"--- Individual ---"
-		  	],
-			"presentation": {
-				 "group": "1",
-			 }
-		},
         {
             "name": "Run All Danswer Services",
             "configurations": [
                 "Web Server",
                 "Model Server",
                 "API Server",
-                "Slack Bot",
-		  		"Celery primary", 
-		  		"Celery light", 
-		  		"Celery heavy", 
-		  		"Celery indexing", 
-		  		"Celery beat",
-            ],
-			"presentation": {
-				 "group": "1",
-			 }
-        },
-		{
-			"name": "Web / Model / API",
-		  	"configurations": [
-		  		"Web Server", 
-		  		"Model Server", 
-		  		"API Server",
-		  	],
-			"presentation": {
-				 "group": "1",
-			 }
-		},
-		{
-			"name": "Celery (all)",
-		  	"configurations": [
-		  		"Celery primary", 
-		  		"Celery light", 
-		  		"Celery heavy", 
-		  		"Celery indexing", 
-		  		"Celery beat"
-		  	],
-			"presentation": {
-				 "group": "1",
-			 }
-		}
+                "Indexing",
+                "Background Jobs",
+                "Slack Bot"
+            ]
+        }
     ],
     "configurations": [
-		{
-	    	// Dummy entry used to label the group
-			"name": "--- Individual ---",
-			"type": "node",
-			"request": "launch",
-			"presentation": {
-				"group": "2",
-				"order": 0
-			}
-		},
         {
             "name": "Web Server",
             "type": "node",
@@ -79,11 +29,7 @@
             "runtimeArgs": [
                 "run", "dev"
             ],
-            "presentation": {
-				 "group": "2",
-			 },
-            "console": "integratedTerminal",
-            "consoleTitle": "Web Server Console"
+            "console": "integratedTerminal"
         },
         {
             "name": "Model Server",
@@ -102,11 +48,7 @@
                 "--reload",
                 "--port",
                 "9000"
-            ],
-            "presentation": {
-				 "group": "2",
-			},
-            "consoleTitle": "Model Server Console"
+            ]
         },
         {
             "name": "API Server",
@@ -126,13 +68,43 @@
                 "--reload",
                 "--port",
                 "8080"
-            ],
-            "presentation": {
-				 "group": "2",
-			 },
-            "consoleTitle": "API Server Console"
+            ]
         },
-        // For the listener to access the Slack API,
+        {
+            "name": "Indexing",
+            "consoleName": "Indexing",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "danswer/background/update.py",
+            "cwd": "${workspaceFolder}/backend",
+            "envFile": "${workspaceFolder}/.vscode/.env",
+            "env": {
+                "ENABLE_MULTIPASS_INDEXING": "false",
+                "LOG_LEVEL": "DEBUG",
+                "PYTHONUNBUFFERED": "1",
+                "PYTHONPATH": "."
+            }
+        },
+        // Celery and all async jobs, usually would include indexing as well but this is handled separately above for dev
+        {
+            "name": "Background Jobs",
+            "consoleName": "Background Jobs",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "scripts/dev_run_background_jobs.py",
+            "cwd": "${workspaceFolder}/backend",
+            "envFile": "${workspaceFolder}/.vscode/.env",
+            "env": {
+                "LOG_DANSWER_MODEL_INTERACTIONS": "True",
+                "LOG_LEVEL": "DEBUG",
+                "PYTHONUNBUFFERED": "1",
+                "PYTHONPATH": "."
+            },
+            "args": [
+                "--no-indexing"
+            ]
+        },
+        // For the listner to access the Slack API,
         // DANSWER_BOT_SLACK_APP_TOKEN & DANSWER_BOT_SLACK_BOT_TOKEN need to be set in .env file located in the root of the project
         {
             "name": "Slack Bot",
@@ -146,151 +118,7 @@
                 "LOG_LEVEL": "DEBUG",
                 "PYTHONUNBUFFERED": "1",
                 "PYTHONPATH": "."
-            },
-            "presentation": {
-				 "group": "2",
-			},
-            "consoleTitle": "Slack Bot Console"
-        },
-        {
-            "name": "Celery primary",
-            "type": "debugpy",
-            "request": "launch",
-            "module": "celery",
-            "cwd": "${workspaceFolder}/backend",
-            "envFile": "${workspaceFolder}/.vscode/.env",
-            "env": {
-                "LOG_LEVEL": "INFO",
-                "PYTHONUNBUFFERED": "1",
-                "PYTHONPATH": "."
-            },
-            "args": [
-                "-A",
-                "danswer.background.celery.versioned_apps.primary",
-                "worker",
-                "--pool=threads",
-                "--concurrency=4",
-                "--prefetch-multiplier=1",
-                "--loglevel=INFO",
-                "--hostname=primary@%n",
-                "-Q",
-                "celery",
-            ],
-            "presentation": {
-				 "group": "2",
-			 },
-            "consoleTitle": "Celery primary Console"
-        },
-        {
-            "name": "Celery light",
-            "type": "debugpy",
-            "request": "launch",
-            "module": "celery",
-            "cwd": "${workspaceFolder}/backend",
-            "envFile": "${workspaceFolder}/.vscode/.env",
-            "env": {
-                "LOG_LEVEL": "INFO",
-                "PYTHONUNBUFFERED": "1",
-                "PYTHONPATH": "."
-            },
-            "args": [
-                "-A",
-                "danswer.background.celery.versioned_apps.light",
-                "worker",
-                "--pool=threads",
-                "--concurrency=64",
-                "--prefetch-multiplier=8",
-                "--loglevel=INFO",
-                "--hostname=light@%n",
-                "-Q",
-                "vespa_metadata_sync,connector_deletion,doc_permissions_upsert",
-            ],
-            "presentation": {
-				 "group": "2",
-			 },
-            "consoleTitle": "Celery light Console"
-        },
-        {
-            "name": "Celery heavy",
-            "type": "debugpy",
-            "request": "launch",
-            "module": "celery",
-            "cwd": "${workspaceFolder}/backend",
-            "envFile": "${workspaceFolder}/.vscode/.env",
-            "env": {
-                "LOG_LEVEL": "INFO",
-                "PYTHONUNBUFFERED": "1",
-                "PYTHONPATH": "."
-            },
-            "args": [
-                "-A",
-                "danswer.background.celery.versioned_apps.heavy",
-                "worker",
-                "--pool=threads",
-                "--concurrency=4",
-                "--prefetch-multiplier=1",
-                "--loglevel=INFO",
-                "--hostname=heavy@%n",
-                "-Q",
-                "connector_pruning,connector_doc_permissions_sync,connector_external_group_sync",
-            ],
-            "presentation": {
-				 "group": "2",
-			 },
-            "consoleTitle": "Celery heavy Console"
-        },
-        {
-            "name": "Celery indexing",
-            "type": "debugpy",
-            "request": "launch",
-            "module": "celery",
-            "cwd": "${workspaceFolder}/backend",
-            "envFile": "${workspaceFolder}/.vscode/.env",
-            "env": {
-                "ENABLE_MULTIPASS_INDEXING": "false",
-                "LOG_LEVEL": "DEBUG",
-                "PYTHONUNBUFFERED": "1",
-                "PYTHONPATH": "."
-            },
-            "args": [
-                "-A",
-                "danswer.background.celery.versioned_apps.indexing",
-                "worker",
-                "--pool=threads",
-                "--concurrency=1",
-                "--prefetch-multiplier=1",
-                "--loglevel=INFO",
-                "--hostname=indexing@%n",
-                "-Q",
-                "connector_indexing",
-            ],
-            "presentation": {
-				 "group": "2",
-			 },
-            "consoleTitle": "Celery indexing Console"
-        },
-        {
-            "name": "Celery beat",
-            "type": "debugpy",
-            "request": "launch",
-            "module": "celery",
-            "cwd": "${workspaceFolder}/backend",
-            "envFile": "${workspaceFolder}/.vscode/.env",
-            "env": {
-                "LOG_LEVEL": "DEBUG",
-                "PYTHONUNBUFFERED": "1",
-                "PYTHONPATH": "."
-            },
-            "args": [
-                "-A",
-                "danswer.background.celery.versioned_apps.beat",
-                "beat",
-                "--loglevel=INFO",
-            ],
-            "presentation": {
-				 "group": "2",
-			 },
-            "consoleTitle": "Celery beat Console"
+            }
         },
         {
             "name": "Pytest",
@@ -309,22 +137,8 @@
                 "-v"
                 // Specify a sepcific module/test to run or provide nothing to run all tests
                 //"tests/unit/danswer/llm/answering/test_prune_and_merge.py"
-            ],
-            "presentation": {
-				 "group": "2",
-			 },
-            "consoleTitle": "Pytest Console"
+            ]
         },
-		{
-	    	// Dummy entry used to label the group
-			"name": "--- Tasks ---",
-			"type": "node",
-			"request": "launch",
-			"presentation": {
-				"group": "3",
-				"order": 0
-			}
-		},
         {
             "name": "Clear and Restart External Volumes and Containers",
             "type": "node",
@@ -333,27 +147,7 @@
             "runtimeArgs": ["${workspaceFolder}/backend/scripts/restart_containers.sh"],
             "cwd": "${workspaceFolder}",
             "console": "integratedTerminal",
-            "stopOnEntry": true,
-            "presentation": {
-				 "group": "3",
-			 },
-        },
-        {
-	        // Celery jobs launched through a single background script (legacy)
-	        // Recommend using the "Celery (all)" compound launch instead.
-            "name": "Background Jobs",
-            "consoleName": "Background Jobs",
-            "type": "debugpy",
-            "request": "launch",
-            "program": "scripts/dev_run_background_jobs.py",
-            "cwd": "${workspaceFolder}/backend",
-            "envFile": "${workspaceFolder}/.vscode/.env",
-            "env": {
-                "LOG_DANSWER_MODEL_INTERACTIONS": "True",
-                "LOG_LEVEL": "DEBUG",
-                "PYTHONUNBUFFERED": "1",
-                "PYTHONPATH": "."
-            },
-        },
+            "stopOnEntry": true
+        }
     ]
 }

CONTRIBUTING.md

@@ -32,7 +32,7 @@ To contribute to this project, please follow the
 When opening a pull request, mention related issues and feel free to tag relevant maintainers.
 
 Before creating a pull request please make sure that the new changes conform to the formatting and linting requirements.
-See the [Formatting and Linting](#formatting-and-linting) section for how to run these checks locally.
+See the [Formatting and Linting](#-formatting-and-linting) section for how to run these checks locally.
 
 
 ### Getting Help 🙋

README.md

@@ -1,5 +1,4 @@
 <!-- DANSWER_METADATA={"link": "https://github.com/danswer-ai/danswer/blob/main/README.md"} -->
-<a name="readme-top"></a>
 
 <h2 align="center">
 <a href="https://www.danswer.ai/"> <img width="50%" src="https://github.com/danswer-owners/danswer/blob/1fabd9372d66cd54238847197c33f091a724803b/DanswerWithName.png?raw=true)" /></a>
@@ -12,7 +11,7 @@
 <a href="https://docs.danswer.dev/" target="_blank">
     <img src="https://img.shields.io/badge/docs-view-blue" alt="Documentation">
 </a>
-<a href="https://join.slack.com/t/danswer/shared_invite/zt-2twesxdr6-5iQitKZQpgq~hYIZ~dv3KA" target="_blank">
+<a href="https://join.slack.com/t/danswer/shared_invite/zt-2lcmqw703-071hBuZBfNEOGUsLa5PXvQ" target="_blank">
     <img src="https://img.shields.io/badge/slack-join-blue.svg?logo=slack" alt="Slack">
 </a>
 <a href="https://discord.gg/TDJ59cGV2X" target="_blank">
@@ -69,7 +68,7 @@ We also have built-in support for deployment on Kubernetes. Files for that can b
 
 ## 🚧 Roadmap
 * Chat/Prompt sharing with specific teammates and user groups.
-* Multimodal model support, chat with images, video etc.
+* Multi-Model model support, chat with images, video etc.
 * Choosing between LLMs and parameters during chat session.
 * Tool calling and agent configurations options.
 * Organizational understanding and ability to locate and suggest experts from your team.
@@ -128,19 +127,3 @@ To try the Danswer Enterprise Edition:
 
 ## 💡 Contributing
 Looking to contribute? Please check out the [Contribution Guide](CONTRIBUTING.md) for more details.
-
-## ⭐Star History
-
-[![Star History Chart](https://api.star-history.com/svg?repos=danswer-ai/danswer&type=Date)](https://star-history.com/#danswer-ai/danswer&Date)
-
-## ✨Contributors
-
-<a href="https://github.com/danswer-ai/danswer/graphs/contributors">
-  <img alt="contributors" src="https://contrib.rocks/image?repo=danswer-ai/danswer"/>
-</a>
-
-<p align="right" style="font-size: 14px; color: #555; margin-top: 20px;">
-    <a href="#readme-top" style="text-decoration: none; color: #007bff; font-weight: bold;">
-        ↑ Back to Top ↑
-    </a>
-</p>

backend/Dockerfile

@@ -12,6 +12,7 @@ ARG DANSWER_VERSION=0.8-dev
 ENV DANSWER_VERSION=${DANSWER_VERSION} \
     DANSWER_RUNNING_IN_DOCKER="true"
 
+ARG CA_CERT_CONTENT=""
 
 RUN echo "DANSWER_VERSION: ${DANSWER_VERSION}"
 # Install system dependencies
@@ -38,6 +39,15 @@ RUN apt-get update && \
     apt-get clean
 
 
+# Conditionally write the CA certificate and update certificates
+RUN if [ -n "$CA_CERT_CONTENT" ]; then \
+    echo "Adding custom CA certificate"; \
+    echo "$CA_CERT_CONTENT" > /usr/local/share/ca-certificates/my-ca.crt && \
+    chmod 644 /usr/local/share/ca-certificates/my-ca.crt && \
+    update-ca-certificates; \
+else \
+    echo "No custom CA certificate provided"; \
+fi
 
 # Install Python dependencies
 # Remove py which is pulled in by retry, py is not needed and is a CVE
@@ -73,11 +83,11 @@ RUN apt-get update && \
     rm -rf /var/lib/apt/lists/* && \
     rm -f /usr/local/lib/python3.11/site-packages/tornado/test/test.key
 
-
 # Pre-downloading models for setups with limited egress
 RUN python -c "from tokenizers import Tokenizer; \
 Tokenizer.from_pretrained('nomic-ai/nomic-embed-text-v1')"
 
+
 # Pre-downloading NLTK for setups with limited egress
 RUN python -c "import nltk; \
 nltk.download('stopwords', quiet=True); \

backend/alembic/env.py

@@ -1,5 +1,5 @@
 from sqlalchemy.engine.base import Connection
-from typing import Literal
+from typing import Any
 import asyncio
 from logging.config import fileConfig
 import logging
@@ -8,14 +8,12 @@ from alembic import context
 from sqlalchemy import pool
 from sqlalchemy.ext.asyncio import create_async_engine
 from sqlalchemy.sql import text
-from sqlalchemy.sql.schema import SchemaItem
 
-from shared_configs.configs import MULTI_TENANT
+from danswer.configs.app_configs import MULTI_TENANT
 from danswer.db.engine import build_connection_string
 from danswer.db.models import Base
 from celery.backends.database.session import ResultModelBase  # type: ignore
-from danswer.db.engine import get_all_tenant_ids
-from shared_configs.configs import POSTGRES_DEFAULT_SCHEMA
+from danswer.background.celery.celery_app import get_all_tenant_ids
 
 # Alembic Config object
 config = context.config
@@ -36,18 +34,7 @@ logger = logging.getLogger(__name__)
 
 
 def include_object(
-    object: SchemaItem,
-    name: str | None,
-    type_: Literal[
-        "schema",
-        "table",
-        "column",
-        "index",
-        "unique_constraint",
-        "foreign_key_constraint",
-    ],
-    reflected: bool,
-    compare_to: SchemaItem | None,
+    object: Any, name: str, type_: str, reflected: bool, compare_to: Any
 ) -> bool:
     """
     Determines whether a database object should be included in migrations.
@@ -70,15 +57,11 @@ def get_schema_options() -> tuple[str, bool, bool]:
             if "=" in pair:
                 key, value = pair.split("=", 1)
                 x_args[key.strip()] = value.strip()
-    schema_name = x_args.get("schema", POSTGRES_DEFAULT_SCHEMA)
+    schema_name = x_args.get("schema", "public")
     create_schema = x_args.get("create_schema", "true").lower() == "true"
     upgrade_all_tenants = x_args.get("upgrade_all_tenants", "false").lower() == "true"
 
-    if (
-        MULTI_TENANT
-        and schema_name == POSTGRES_DEFAULT_SCHEMA
-        and not upgrade_all_tenants
-    ):
+    if MULTI_TENANT and schema_name == "public":
         raise ValueError(
             "Cannot run default migrations in public schema when multi-tenancy is enabled. "
             "Please specify a tenant-specific schema."

backend/alembic/versions/177de57c21c9_display_custom_llm_models.py

@@ -1,59 +0,0 @@
-"""display custom llm models
-
-Revision ID: 177de57c21c9
-Revises: 4ee1287bd26a
-Create Date: 2024-11-21 11:49:04.488677
-
-"""
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-from sqlalchemy import and_
-
-revision = "177de57c21c9"
-down_revision = "4ee1287bd26a"
-branch_labels = None
-depends_on = None
-depends_on = None
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-    llm_provider = sa.table(
-        "llm_provider",
-        sa.column("id", sa.Integer),
-        sa.column("provider", sa.String),
-        sa.column("model_names", postgresql.ARRAY(sa.String)),
-        sa.column("display_model_names", postgresql.ARRAY(sa.String)),
-    )
-
-    excluded_providers = ["openai", "bedrock", "anthropic", "azure"]
-
-    providers_to_update = sa.select(
-        llm_provider.c.id,
-        llm_provider.c.model_names,
-        llm_provider.c.display_model_names,
-    ).where(
-        and_(
-            ~llm_provider.c.provider.in_(excluded_providers),
-            llm_provider.c.model_names.isnot(None),
-        )
-    )
-
-    results = conn.execute(providers_to_update).fetchall()
-
-    for provider_id, model_names, display_model_names in results:
-        if display_model_names is None:
-            display_model_names = []
-
-        combined_model_names = list(set(display_model_names + model_names))
-        update_stmt = (
-            llm_provider.update()
-            .where(llm_provider.c.id == provider_id)
-            .values(display_model_names=combined_model_names)
-        )
-        conn.execute(update_stmt)
-
-
-def downgrade() -> None:
-    pass

backend/alembic/versions/26b931506ecb_default_chosen_assistants_to_none.py

@@ -1,68 +0,0 @@
-"""default chosen assistants to none
-
-Revision ID: 26b931506ecb
-Revises: 2daa494a0851
-Create Date: 2024-11-12 13:23:29.858995
-
-"""
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision = "26b931506ecb"
-down_revision = "2daa494a0851"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "user", sa.Column("chosen_assistants_new", postgresql.JSONB(), nullable=True)
-    )
-
-    op.execute(
-        """
-    UPDATE "user"
-    SET chosen_assistants_new =
-        CASE
-            WHEN chosen_assistants = '[-2, -1, 0]' THEN NULL
-            ELSE chosen_assistants
-        END
-    """
-    )
-
-    op.drop_column("user", "chosen_assistants")
-
-    op.alter_column(
-        "user", "chosen_assistants_new", new_column_name="chosen_assistants"
-    )
-
-
-def downgrade() -> None:
-    op.add_column(
-        "user",
-        sa.Column(
-            "chosen_assistants_old",
-            postgresql.JSONB(),
-            nullable=False,
-            server_default="[-2, -1, 0]",
-        ),
-    )
-
-    op.execute(
-        """
-    UPDATE "user"
-    SET chosen_assistants_old =
-        CASE
-            WHEN chosen_assistants IS NULL THEN '[-2, -1, 0]'::jsonb
-            ELSE chosen_assistants
-        END
-    """
-    )
-
-    op.drop_column("user", "chosen_assistants")
-
-    op.alter_column(
-        "user", "chosen_assistants_old", new_column_name="chosen_assistants"
-    )

backend/alembic/versions/2daa494a0851_add_group_sync_time.py

@@ -1,30 +0,0 @@
-"""add-group-sync-time
-
-Revision ID: 2daa494a0851
-Revises: c0fd6e4da83a
-Create Date: 2024-11-11 10:57:22.991157
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "2daa494a0851"
-down_revision = "c0fd6e4da83a"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "connector_credential_pair",
-        sa.Column(
-            "last_time_external_group_sync",
-            sa.DateTime(timezone=True),
-            nullable=True,
-        ),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("connector_credential_pair", "last_time_external_group_sync")

backend/alembic/versions/33cb72ea4d80_single_tool_call_per_message.py

@@ -1,50 +0,0 @@
-"""single tool call per message
-
-Revision ID: 33cb72ea4d80
-Revises: 5b29123cd710
-Create Date: 2024-11-01 12:51:01.535003
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "33cb72ea4d80"
-down_revision = "5b29123cd710"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Step 1: Delete extraneous ToolCall entries
-    # Keep only the ToolCall with the smallest 'id' for each 'message_id'
-    op.execute(
-        sa.text(
-            """
-            DELETE FROM tool_call
-            WHERE id NOT IN (
-                SELECT MIN(id)
-                FROM tool_call
-                WHERE message_id IS NOT NULL
-                GROUP BY message_id
-            );
-        """
-        )
-    )
-
-    # Step 2: Add a unique constraint on message_id
-    op.create_unique_constraint(
-        constraint_name="uq_tool_call_message_id",
-        table_name="tool_call",
-        columns=["message_id"],
-    )
-
-
-def downgrade() -> None:
-    # Step 1: Drop the unique constraint on message_id
-    op.drop_constraint(
-        constraint_name="uq_tool_call_message_id",
-        table_name="tool_call",
-        type_="unique",
-    )

backend/alembic/versions/47e5bef3a1d7_add_persona_categories.py

@@ -1,45 +0,0 @@
-"""add persona categories
-
-Revision ID: 47e5bef3a1d7
-Revises: dfbe9e93d3c7
-Create Date: 2024-11-05 18:55:02.221064
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "47e5bef3a1d7"
-down_revision = "dfbe9e93d3c7"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Create the persona_category table
-    op.create_table(
-        "persona_category",
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column("name", sa.String(), nullable=False),
-        sa.Column("description", sa.String(), nullable=True),
-        sa.PrimaryKeyConstraint("id"),
-        sa.UniqueConstraint("name"),
-    )
-
-    # Add category_id to persona table
-    op.add_column("persona", sa.Column("category_id", sa.Integer(), nullable=True))
-    op.create_foreign_key(
-        "fk_persona_category",
-        "persona",
-        "persona_category",
-        ["category_id"],
-        ["id"],
-        ondelete="SET NULL",
-    )
-
-
-def downgrade() -> None:
-    op.drop_constraint("fk_persona_category", "persona", type_="foreignkey")
-    op.drop_column("persona", "category_id")
-    op.drop_table("persona_category")

backend/alembic/versions/4ee1287bd26a_add_multiple_slack_bot_support.py

@@ -1,280 +0,0 @@
-"""add_multiple_slack_bot_support
-
-Revision ID: 4ee1287bd26a
-Revises: 47e5bef3a1d7
-Create Date: 2024-11-06 13:15:53.302644
-
-"""
-import logging
-from typing import cast
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.orm import Session
-from danswer.key_value_store.factory import get_kv_store
-from danswer.db.models import SlackBot
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision = "4ee1287bd26a"
-down_revision = "47e5bef3a1d7"
-branch_labels: None = None
-depends_on: None = None
-
-# Configure logging
-logger = logging.getLogger("alembic.runtime.migration")
-logger.setLevel(logging.INFO)
-
-
-def upgrade() -> None:
-    logger.info(f"{revision}: create_table: slack_bot")
-    # Create new slack_bot table
-    op.create_table(
-        "slack_bot",
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column("name", sa.String(), nullable=False),
-        sa.Column("enabled", sa.Boolean(), nullable=False, server_default="true"),
-        sa.Column("bot_token", sa.LargeBinary(), nullable=False),
-        sa.Column("app_token", sa.LargeBinary(), nullable=False),
-        sa.PrimaryKeyConstraint("id"),
-        sa.UniqueConstraint("bot_token"),
-        sa.UniqueConstraint("app_token"),
-    )
-
-    # # Create new slack_channel_config table
-    op.create_table(
-        "slack_channel_config",
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column("slack_bot_id", sa.Integer(), nullable=True),
-        sa.Column("persona_id", sa.Integer(), nullable=True),
-        sa.Column("channel_config", postgresql.JSONB(), nullable=False),
-        sa.Column("response_type", sa.String(), nullable=False),
-        sa.Column(
-            "enable_auto_filters", sa.Boolean(), nullable=False, server_default="false"
-        ),
-        sa.ForeignKeyConstraint(
-            ["slack_bot_id"],
-            ["slack_bot.id"],
-        ),
-        sa.ForeignKeyConstraint(
-            ["persona_id"],
-            ["persona.id"],
-        ),
-        sa.PrimaryKeyConstraint("id"),
-    )
-
-    # Handle existing Slack bot tokens first
-    logger.info(f"{revision}: Checking for existing Slack bot.")
-    bot_token = None
-    app_token = None
-    first_row_id = None
-
-    try:
-        tokens = cast(dict, get_kv_store().load("slack_bot_tokens_config_key"))
-    except Exception:
-        logger.warning("No existing Slack bot tokens found.")
-        tokens = {}
-
-    bot_token = tokens.get("bot_token")
-    app_token = tokens.get("app_token")
-
-    if bot_token and app_token:
-        logger.info(f"{revision}: Found bot and app tokens.")
-
-        session = Session(bind=op.get_bind())
-        new_slack_bot = SlackBot(
-            name="Slack Bot (Migrated)",
-            enabled=True,
-            bot_token=bot_token,
-            app_token=app_token,
-        )
-        session.add(new_slack_bot)
-        session.commit()
-        first_row_id = new_slack_bot.id
-
-    # Create a default bot if none exists
-    # This is in case there are no slack tokens but there are channels configured
-    op.execute(
-        sa.text(
-            """
-            INSERT INTO slack_bot (name, enabled, bot_token, app_token)
-            SELECT 'Default Bot', true, '', ''
-            WHERE NOT EXISTS (SELECT 1 FROM slack_bot)
-            RETURNING id;
-            """
-        )
-    )
-
-    # Get the bot ID to use (either from existing migration or newly created)
-    bot_id_query = sa.text(
-        """
-        SELECT COALESCE(
-            :first_row_id,
-            (SELECT id FROM slack_bot ORDER BY id ASC LIMIT 1)
-        ) as bot_id;
-        """
-    )
-    result = op.get_bind().execute(bot_id_query, {"first_row_id": first_row_id})
-    bot_id = result.scalar()
-
-    # CTE (Common Table Expression) that transforms the old slack_bot_config table data
-    # This splits up the channel_names into their own rows
-    channel_names_cte = """
-        WITH channel_names AS (
-            SELECT
-                sbc.id as config_id,
-                sbc.persona_id,
-                sbc.response_type,
-                sbc.enable_auto_filters,
-                jsonb_array_elements_text(sbc.channel_config->'channel_names') as channel_name,
-                sbc.channel_config->>'respond_tag_only' as respond_tag_only,
-                sbc.channel_config->>'respond_to_bots' as respond_to_bots,
-                sbc.channel_config->'respond_member_group_list' as respond_member_group_list,
-                sbc.channel_config->'answer_filters' as answer_filters,
-                sbc.channel_config->'follow_up_tags' as follow_up_tags
-            FROM slack_bot_config sbc
-        )
-    """
-
-    # Insert the channel names into the new slack_channel_config table
-    insert_statement = """
-        INSERT INTO slack_channel_config (
-            slack_bot_id,
-            persona_id,
-            channel_config,
-            response_type,
-            enable_auto_filters
-        )
-        SELECT
-            :bot_id,
-            channel_name.persona_id,
-            jsonb_build_object(
-                'channel_name', channel_name.channel_name,
-                'respond_tag_only',
-                COALESCE((channel_name.respond_tag_only)::boolean, false),
-                'respond_to_bots',
-                COALESCE((channel_name.respond_to_bots)::boolean, false),
-                'respond_member_group_list',
-                COALESCE(channel_name.respond_member_group_list, '[]'::jsonb),
-                'answer_filters',
-                COALESCE(channel_name.answer_filters, '[]'::jsonb),
-                'follow_up_tags',
-                COALESCE(channel_name.follow_up_tags, '[]'::jsonb)
-            ),
-            channel_name.response_type,
-            channel_name.enable_auto_filters
-        FROM channel_names channel_name;
-    """
-
-    op.execute(sa.text(channel_names_cte + insert_statement).bindparams(bot_id=bot_id))
-
-    # Clean up old tokens if they existed
-    try:
-        if bot_token and app_token:
-            logger.info(f"{revision}: Removing old bot and app tokens.")
-            get_kv_store().delete("slack_bot_tokens_config_key")
-    except Exception:
-        logger.warning("tried to delete tokens in dynamic config but failed")
-    # Rename the table
-    op.rename_table(
-        "slack_bot_config__standard_answer_category",
-        "slack_channel_config__standard_answer_category",
-    )
-
-    # Rename the column
-    op.alter_column(
-        "slack_channel_config__standard_answer_category",
-        "slack_bot_config_id",
-        new_column_name="slack_channel_config_id",
-    )
-
-    # Drop the table with CASCADE to handle dependent objects
-    op.execute("DROP TABLE slack_bot_config CASCADE")
-
-    logger.info(f"{revision}: Migration complete.")
-
-
-def downgrade() -> None:
-    # Recreate the old slack_bot_config table
-    op.create_table(
-        "slack_bot_config",
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column("persona_id", sa.Integer(), nullable=True),
-        sa.Column("channel_config", postgresql.JSONB(), nullable=False),
-        sa.Column("response_type", sa.String(), nullable=False),
-        sa.Column("enable_auto_filters", sa.Boolean(), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["persona_id"],
-            ["persona.id"],
-        ),
-        sa.PrimaryKeyConstraint("id"),
-    )
-
-    # Migrate data back to the old format
-    # Group by persona_id to combine channel names back into arrays
-    op.execute(
-        sa.text(
-            """
-            INSERT INTO slack_bot_config (
-                persona_id,
-                channel_config,
-                response_type,
-                enable_auto_filters
-            )
-            SELECT DISTINCT ON (persona_id)
-                persona_id,
-                jsonb_build_object(
-                    'channel_names', (
-                        SELECT jsonb_agg(c.channel_config->>'channel_name')
-                        FROM slack_channel_config c
-                        WHERE c.persona_id = scc.persona_id
-                    ),
-                    'respond_tag_only', (channel_config->>'respond_tag_only')::boolean,
-                    'respond_to_bots', (channel_config->>'respond_to_bots')::boolean,
-                    'respond_member_group_list', channel_config->'respond_member_group_list',
-                    'answer_filters', channel_config->'answer_filters',
-                    'follow_up_tags', channel_config->'follow_up_tags'
-                ),
-                response_type,
-                enable_auto_filters
-            FROM slack_channel_config scc
-            WHERE persona_id IS NOT NULL;
-            """
-        )
-    )
-
-    # Rename the table back
-    op.rename_table(
-        "slack_channel_config__standard_answer_category",
-        "slack_bot_config__standard_answer_category",
-    )
-
-    # Rename the column back
-    op.alter_column(
-        "slack_bot_config__standard_answer_category",
-        "slack_channel_config_id",
-        new_column_name="slack_bot_config_id",
-    )
-
-    # Try to save the first bot's tokens back to KV store
-    try:
-        first_bot = (
-            op.get_bind()
-            .execute(
-                sa.text(
-                    "SELECT bot_token, app_token FROM slack_bot ORDER BY id LIMIT 1"
-                )
-            )
-            .first()
-        )
-        if first_bot and first_bot.bot_token and first_bot.app_token:
-            tokens = {
-                "bot_token": first_bot.bot_token,
-                "app_token": first_bot.app_token,
-            }
-            get_kv_store().store("slack_bot_tokens_config_key", tokens)
-    except Exception:
-        logger.warning("Failed to save tokens back to KV store")
-
-    # Drop the new tables in reverse order
-    op.drop_table("slack_channel_config")
-    op.drop_table("slack_bot")

backend/alembic/versions/5b29123cd710_nullable_search_settings_for_historic_.py

@@ -1,70 +0,0 @@
-"""nullable search settings for historic index attempts
-
-Revision ID: 5b29123cd710
-Revises: 949b4a92a401
-Create Date: 2024-10-30 19:37:59.630704
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "5b29123cd710"
-down_revision = "949b4a92a401"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Drop the existing foreign key constraint
-    op.drop_constraint(
-        "fk_index_attempt_search_settings", "index_attempt", type_="foreignkey"
-    )
-
-    # Modify the column to be nullable
-    op.alter_column(
-        "index_attempt", "search_settings_id", existing_type=sa.INTEGER(), nullable=True
-    )
-
-    # Add back the foreign key with ON DELETE SET NULL
-    op.create_foreign_key(
-        "fk_index_attempt_search_settings",
-        "index_attempt",
-        "search_settings",
-        ["search_settings_id"],
-        ["id"],
-        ondelete="SET NULL",
-    )
-
-
-def downgrade() -> None:
-    # Warning: This will delete all index attempts that don't have search settings
-    op.execute(
-        """
-        DELETE FROM index_attempt
-        WHERE search_settings_id IS NULL
-    """
-    )
-
-    # Drop foreign key constraint
-    op.drop_constraint(
-        "fk_index_attempt_search_settings", "index_attempt", type_="foreignkey"
-    )
-
-    # Modify the column to be not nullable
-    op.alter_column(
-        "index_attempt",
-        "search_settings_id",
-        existing_type=sa.INTEGER(),
-        nullable=False,
-    )
-
-    # Add back the foreign key without ON DELETE SET NULL
-    op.create_foreign_key(
-        "fk_index_attempt_search_settings",
-        "index_attempt",
-        "search_settings",
-        ["search_settings_id"],
-        ["id"],
-    )

backend/alembic/versions/6756efa39ada_id_uuid_for_chat_session.py

@@ -1,9 +1,7 @@
-"""Migrate chat_session and chat_message tables to use UUID primary keys
-
+"""
 Revision ID: 6756efa39ada
 Revises: 5d12a446f5c0
 Create Date: 2024-10-15 17:47:44.108537
-
 """
 from alembic import op
 import sqlalchemy as sa
@@ -14,6 +12,8 @@ branch_labels = None
 depends_on = None
 
 """
+Migrate chat_session and chat_message tables to use UUID primary keys.
+
 This script:
 1. Adds UUID columns to chat_session and chat_message
 2. Populates new columns with UUIDs

backend/alembic/versions/6d562f86c78b_remove_default_bot.py

@@ -1,45 +0,0 @@
-"""remove default bot
-
-Revision ID: 6d562f86c78b
-Revises: 177de57c21c9
-Create Date: 2024-11-22 11:51:29.331336
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "6d562f86c78b"
-down_revision = "177de57c21c9"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.execute(
-        sa.text(
-            """
-            DELETE FROM slack_bot
-            WHERE name = 'Default Bot'
-            AND bot_token = ''
-            AND app_token = ''
-            AND NOT EXISTS (
-                SELECT 1 FROM slack_channel_config
-                WHERE slack_channel_config.slack_bot_id = slack_bot.id
-            )
-            """
-        )
-    )
-
-
-def downgrade() -> None:
-    op.execute(
-        sa.text(
-            """
-            INSERT INTO slack_bot (name, enabled, bot_token, app_token)
-            SELECT 'Default Bot', true, '', ''
-            WHERE NOT EXISTS (SELECT 1 FROM slack_bot)
-            RETURNING id;
-            """
-        )
-    )

backend/alembic/versions/776b3bbe9092_remove_remaining_enums.py

@@ -9,8 +9,8 @@ from alembic import op
 import sqlalchemy as sa
 
 from danswer.db.models import IndexModelStatus
-from danswer.context.search.enums import RecencyBiasSetting
-from danswer.context.search.enums import SearchType
+from danswer.search.enums import RecencyBiasSetting
+from danswer.search.enums import SearchType
 
 # revision identifiers, used by Alembic.
 revision = "776b3bbe9092"

backend/alembic/versions/93560ba1b118_add_web_ui_option_to_slack_config.py

@@ -1,35 +0,0 @@
-"""add web ui option to slack config
-
-Revision ID: 93560ba1b118
-Revises: 6d562f86c78b
-Create Date: 2024-11-24 06:36:17.490612
-
-"""
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision = "93560ba1b118"
-down_revision = "6d562f86c78b"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Add show_continue_in_web_ui with default False to all existing channel_configs
-    op.execute(
-        """
-        UPDATE slack_channel_config
-        SET channel_config = channel_config || '{"show_continue_in_web_ui": false}'::jsonb
-        WHERE NOT channel_config ? 'show_continue_in_web_ui'
-        """
-    )
-
-
-def downgrade() -> None:
-    # Remove show_continue_in_web_ui from all channel_configs
-    op.execute(
-        """
-        UPDATE slack_channel_config
-        SET channel_config = channel_config - 'show_continue_in_web_ui'
-        """
-    )

backend/alembic/versions/949b4a92a401_remove_rt.py

@@ -1,72 +0,0 @@
-"""remove rt
-
-Revision ID: 949b4a92a401
-Revises: 1b10e1fda030
-Create Date: 2024-10-26 13:06:06.937969
-
-"""
-from alembic import op
-from sqlalchemy.orm import Session
-from sqlalchemy import text
-
-# Import your models and constants
-from danswer.db.models import (
-    Connector,
-    ConnectorCredentialPair,
-    Credential,
-    IndexAttempt,
-)
-
-
-# revision identifiers, used by Alembic.
-revision = "949b4a92a401"
-down_revision = "1b10e1fda030"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Deletes all RequestTracker connectors and associated data
-    bind = op.get_bind()
-    session = Session(bind=bind)
-
-    # Get connectors using raw SQL
-    result = bind.execute(
-        text("SELECT id FROM connector WHERE source = 'requesttracker'")
-    )
-    connector_ids = [row[0] for row in result]
-
-    if connector_ids:
-        cc_pairs_to_delete = (
-            session.query(ConnectorCredentialPair)
-            .filter(ConnectorCredentialPair.connector_id.in_(connector_ids))
-            .all()
-        )
-
-        cc_pair_ids = [cc_pair.id for cc_pair in cc_pairs_to_delete]
-
-        if cc_pair_ids:
-            session.query(IndexAttempt).filter(
-                IndexAttempt.connector_credential_pair_id.in_(cc_pair_ids)
-            ).delete(synchronize_session=False)
-
-            session.query(ConnectorCredentialPair).filter(
-                ConnectorCredentialPair.id.in_(cc_pair_ids)
-            ).delete(synchronize_session=False)
-
-        credential_ids = [cc_pair.credential_id for cc_pair in cc_pairs_to_delete]
-        if credential_ids:
-            session.query(Credential).filter(Credential.id.in_(credential_ids)).delete(
-                synchronize_session=False
-            )
-
-        session.query(Connector).filter(Connector.id.in_(connector_ids)).delete(
-            synchronize_session=False
-        )
-
-    session.commit()
-
-
-def downgrade() -> None:
-    # No-op downgrade as we cannot restore deleted data
-    pass

backend/alembic/versions/9cf5c00f72fe_add_creator_to_cc_pair.py

@@ -1,30 +0,0 @@
-"""add creator to cc pair
-
-Revision ID: 9cf5c00f72fe
-Revises: 26b931506ecb
-Create Date: 2024-11-12 15:16:42.682902
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "9cf5c00f72fe"
-down_revision = "26b931506ecb"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "connector_credential_pair",
-        sa.Column(
-            "creator_id",
-            sa.UUID(as_uuid=True),
-            nullable=True,
-        ),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("connector_credential_pair", "creator_id")

backend/alembic/versions/9f696734098f_combine_search_and_chat.py

@@ -1,36 +0,0 @@
-"""Combine Search and Chat
-
-Revision ID: 9f696734098f
-Revises: a8c2065484e6
-Create Date: 2024-11-27 15:32:19.694972
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "9f696734098f"
-down_revision = "a8c2065484e6"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.alter_column("chat_session", "description", nullable=True)
-    op.drop_column("chat_session", "one_shot")
-    op.drop_column("slack_channel_config", "response_type")
-
-
-def downgrade() -> None:
-    op.execute("UPDATE chat_session SET description = '' WHERE description IS NULL")
-    op.alter_column("chat_session", "description", nullable=False)
-    op.add_column(
-        "chat_session",
-        sa.Column("one_shot", sa.Boolean(), nullable=False, server_default=sa.false()),
-    )
-    op.add_column(
-        "slack_channel_config",
-        sa.Column(
-            "response_type", sa.String(), nullable=False, server_default="citations"
-        ),
-    )

backend/alembic/versions/a8c2065484e6_add_auto_scroll_to_user_model.py

@@ -1,27 +0,0 @@
-"""add auto scroll to user model
-
-Revision ID: a8c2065484e6
-Revises: abe7378b8217
-Create Date: 2024-11-22 17:34:09.690295
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "a8c2065484e6"
-down_revision = "abe7378b8217"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "user",
-        sa.Column("auto_scroll", sa.Boolean(), nullable=True, server_default=None),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("user", "auto_scroll")

backend/alembic/versions/abe7378b8217_add_indexing_trigger_to_cc_pair.py

@@ -1,30 +0,0 @@
-"""add indexing trigger to cc_pair
-
-Revision ID: abe7378b8217
-Revises: 6d562f86c78b
-Create Date: 2024-11-26 19:09:53.481171
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "abe7378b8217"
-down_revision = "93560ba1b118"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "connector_credential_pair",
-        sa.Column(
-            "indexing_trigger",
-            sa.Enum("UPDATE", "REINDEX", name="indexingmode", native_enum=False),
-            nullable=True,
-        ),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("connector_credential_pair", "indexing_trigger")

backend/alembic/versions/b082fec533f0_make_last_attempt_status_nullable.py

@@ -31,12 +31,6 @@ def upgrade() -> None:
 
 
 def downgrade() -> None:
-    # First, update any null values to a default value
-    op.execute(
-        "UPDATE connector_credential_pair SET last_attempt_status = 'NOT_STARTED' WHERE last_attempt_status IS NULL"
-    )
-
-    # Then, make the column non-nullable
     op.alter_column(
         "connector_credential_pair",
         "last_attempt_status",

backend/alembic/versions/b156fa702355_chat_reworked.py

@@ -288,15 +288,6 @@ def upgrade() -> None:
 
 
 def downgrade() -> None:
-    # NOTE: you will lose all chat history. This is to satisfy the non-nullable constraints
-    # below
-    op.execute("DELETE FROM chat_feedback")
-    op.execute("DELETE FROM chat_message__search_doc")
-    op.execute("DELETE FROM document_retrieval_feedback")
-    op.execute("DELETE FROM document_retrieval_feedback")
-    op.execute("DELETE FROM chat_message")
-    op.execute("DELETE FROM chat_session")
-
     op.drop_constraint(
         "chat_feedback__chat_message_fk", "chat_feedback", type_="foreignkey"
     )

backend/alembic/versions/b72ed7a5db0e_remove_description_from_starter_messages.py

@@ -1,48 +0,0 @@
-"""remove description from starter messages
-
-Revision ID: b72ed7a5db0e
-Revises: 33cb72ea4d80
-Create Date: 2024-11-03 15:55:28.944408
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "b72ed7a5db0e"
-down_revision = "33cb72ea4d80"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.execute(
-        sa.text(
-            """
-            UPDATE persona
-            SET starter_messages = (
-                SELECT jsonb_agg(elem - 'description')
-                FROM jsonb_array_elements(starter_messages) elem
-            )
-            WHERE starter_messages IS NOT NULL
-              AND jsonb_typeof(starter_messages) = 'array'
-            """
-        )
-    )
-
-
-def downgrade() -> None:
-    op.execute(
-        sa.text(
-            """
-            UPDATE persona
-            SET starter_messages = (
-                SELECT jsonb_agg(elem || '{"description": ""}')
-                FROM jsonb_array_elements(starter_messages) elem
-            )
-            WHERE starter_messages IS NOT NULL
-              AND jsonb_typeof(starter_messages) = 'array'
-            """
-        )
-    )

backend/alembic/versions/c0fd6e4da83a_add_recent_assistants.py

@@ -1,29 +0,0 @@
-"""add recent assistants
-
-Revision ID: c0fd6e4da83a
-Revises: b72ed7a5db0e
-Create Date: 2024-11-03 17:28:54.916618
-
-"""
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision = "c0fd6e4da83a"
-down_revision = "b72ed7a5db0e"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "user",
-        sa.Column(
-            "recent_assistants", postgresql.JSONB(), server_default="[]", nullable=False
-        ),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("user", "recent_assistants")

backend/alembic/versions/c99d76fcd298_add_nullable_to_persona_id_in_chat_.py

@@ -23,56 +23,6 @@ def upgrade() -> None:
 
 
 def downgrade() -> None:
-    # Delete chat messages and feedback first since they reference chat sessions
-    # Get chat messages from sessions with null persona_id
-    chat_messages_query = """
-        SELECT id
-        FROM chat_message
-        WHERE chat_session_id IN (
-            SELECT id
-            FROM chat_session
-            WHERE persona_id IS NULL
-        )
-    """
-
-    # Delete dependent records first
-    op.execute(
-        f"""
-        DELETE FROM document_retrieval_feedback
-        WHERE chat_message_id IN (
-            {chat_messages_query}
-        )
-    """
-    )
-    op.execute(
-        f"""
-        DELETE FROM chat_message__search_doc
-        WHERE chat_message_id IN (
-            {chat_messages_query}
-        )
-    """
-    )
-
-    # Delete chat messages
-    op.execute(
-        """
-        DELETE FROM chat_message
-        WHERE chat_session_id IN (
-            SELECT id
-            FROM chat_session
-            WHERE persona_id IS NULL
-        )
-    """
-    )
-
-    # Now we can safely delete the chat sessions
-    op.execute(
-        """
-        DELETE FROM chat_session
-        WHERE persona_id IS NULL
-    """
-    )
-
     op.alter_column(
         "chat_session",
         "persona_id",

backend/alembic/versions/dfbe9e93d3c7_extended_role_for_non_web.py

@@ -1,42 +0,0 @@
-"""extended_role_for_non_web
-
-Revision ID: dfbe9e93d3c7
-Revises: 9cf5c00f72fe
-Create Date: 2024-11-16 07:54:18.727906
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "dfbe9e93d3c7"
-down_revision = "9cf5c00f72fe"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.execute(
-        """
-        UPDATE "user"
-        SET role = 'EXT_PERM_USER'
-        WHERE has_web_login = false
-    """
-    )
-    op.drop_column("user", "has_web_login")
-
-
-def downgrade() -> None:
-    op.add_column(
-        "user",
-        sa.Column("has_web_login", sa.Boolean(), nullable=False, server_default="true"),
-    )
-
-    op.execute(
-        """
-        UPDATE "user"
-        SET has_web_login = false,
-            role = 'BASIC'
-        WHERE role IN ('SLACK_USER', 'EXT_PERM_USER')
-    """
-    )

backend/alembic_tenants/env.py

@@ -1,6 +1,5 @@
 import asyncio
 from logging.config import fileConfig
-from typing import Literal
 
 from sqlalchemy import pool
 from sqlalchemy.engine import Connection
@@ -38,15 +37,8 @@ EXCLUDE_TABLES = {"kombu_queue", "kombu_message"}
 
 def include_object(
     object: SchemaItem,
-    name: str | None,
-    type_: Literal[
-        "schema",
-        "table",
-        "column",
-        "index",
-        "unique_constraint",
-        "foreign_key_constraint",
-    ],
+    name: str,
+    type_: str,
     reflected: bool,
     compare_to: SchemaItem | None,
 ) -> bool:

backend/danswer/__init__.py

@@ -1,3 +1,3 @@
 import os
 
-__version__ = os.environ.get("DANSWER_VERSION", "") or "Development"
+__version__ = os.environ.get("DANSWER_VERSION", "") or "0.3-dev"

backend/danswer/access/models.py

@@ -16,46 +16,6 @@ class ExternalAccess:
     is_public: bool
 
 
-@dataclass(frozen=True)
-class DocExternalAccess:
-    """
-    This is just a class to wrap the external access and the document ID
-    together. It's used for syncing document permissions to Redis.
-    """
-
-    external_access: ExternalAccess
-    # The document ID
-    doc_id: str
-
-    def to_dict(self) -> dict:
-        return {
-            "external_access": {
-                "external_user_emails": list(self.external_access.external_user_emails),
-                "external_user_group_ids": list(
-                    self.external_access.external_user_group_ids
-                ),
-                "is_public": self.external_access.is_public,
-            },
-            "doc_id": self.doc_id,
-        }
-
-    @classmethod
-    def from_dict(cls, data: dict) -> "DocExternalAccess":
-        external_access = ExternalAccess(
-            external_user_emails=set(
-                data["external_access"].get("external_user_emails", [])
-            ),
-            external_user_group_ids=set(
-                data["external_access"].get("external_user_group_ids", [])
-            ),
-            is_public=data["external_access"]["is_public"],
-        )
-        return cls(
-            external_access=external_access,
-            doc_id=data["doc_id"],
-        )
-
-
 @dataclass(frozen=True)
 class DocumentAccess(ExternalAccess):
     # User emails for Danswer users, None indicates admin
@@ -110,12 +70,3 @@ class DocumentAccess(ExternalAccess):
             user_groups=set(user_groups),
             is_public=is_public,
         )
-
-
-default_public_access = DocumentAccess(
-    external_user_emails=set(),
-    external_user_group_ids=set(),
-    user_emails=set(),
-    user_groups=set(),
-    is_public=True,
-)

backend/danswer/agent_search/answer_query/graph_builder.py

@@ -1,100 +0,0 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
-
-from danswer.agent_search.answer_query.nodes.answer_check import answer_check
-from danswer.agent_search.answer_query.nodes.answer_generation import answer_generation
-from danswer.agent_search.answer_query.nodes.format_answer import format_answer
-from danswer.agent_search.answer_query.states import AnswerQueryInput
-from danswer.agent_search.answer_query.states import AnswerQueryOutput
-from danswer.agent_search.answer_query.states import AnswerQueryState
-from danswer.agent_search.expanded_retrieval.graph_builder import (
-    expanded_retrieval_graph_builder,
-)
-
-
-def answer_query_graph_builder() -> StateGraph:
-    graph = StateGraph(
-        state_schema=AnswerQueryState,
-        input=AnswerQueryInput,
-        output=AnswerQueryOutput,
-    )
-
-    ### Add nodes ###
-
-    expanded_retrieval = expanded_retrieval_graph_builder().compile()
-    graph.add_node(
-        node="expanded_retrieval_for_initial_decomp",
-        action=expanded_retrieval,
-    )
-    graph.add_node(
-        node="answer_check",
-        action=answer_check,
-    )
-    graph.add_node(
-        node="answer_generation",
-        action=answer_generation,
-    )
-    graph.add_node(
-        node="format_answer",
-        action=format_answer,
-    )
-
-    ### Add edges ###
-
-    graph.add_edge(
-        start_key=START,
-        end_key="expanded_retrieval_for_initial_decomp",
-    )
-    graph.add_edge(
-        start_key="expanded_retrieval_for_initial_decomp",
-        end_key="answer_generation",
-    )
-    graph.add_edge(
-        start_key="answer_generation",
-        end_key="answer_check",
-    )
-    graph.add_edge(
-        start_key="answer_check",
-        end_key="format_answer",
-    )
-    graph.add_edge(
-        start_key="format_answer",
-        end_key=END,
-    )
-
-    return graph
-
-
-if __name__ == "__main__":
-    from danswer.db.engine import get_session_context_manager
-    from danswer.llm.factory import get_default_llms
-    from danswer.context.search.models import SearchRequest
-
-    graph = answer_query_graph_builder()
-    compiled_graph = graph.compile()
-    primary_llm, fast_llm = get_default_llms()
-    search_request = SearchRequest(
-        query="Who made Excel and what other products did they make?",
-    )
-    with get_session_context_manager() as db_session:
-        inputs = AnswerQueryInput(
-            search_request=search_request,
-            primary_llm=primary_llm,
-            fast_llm=fast_llm,
-            db_session=db_session,
-            query_to_answer="Who made Excel?",
-        )
-        output = compiled_graph.invoke(
-            input=inputs,
-            # debug=True,
-            # subgraphs=True,
-        )
-        print(output)
-        # for namespace, chunk in compiled_graph.stream(
-        #     input=inputs,
-        #     # debug=True,
-        #     subgraphs=True,
-        # ):
-        #     print(namespace)
-        #     print(chunk)

backend/danswer/agent_search/answer_query/nodes/answer_check.py

@@ -1,30 +0,0 @@
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_message_runs
-
-from danswer.agent_search.answer_query.states import AnswerQueryState
-from danswer.agent_search.answer_query.states import QACheckOutput
-from danswer.agent_search.shared_graph_utils.prompts import BASE_CHECK_PROMPT
-
-
-def answer_check(state: AnswerQueryState) -> QACheckOutput:
-    msg = [
-        HumanMessage(
-            content=BASE_CHECK_PROMPT.format(
-                question=state["search_request"].query,
-                base_answer=state["answer"],
-            )
-        )
-    ]
-
-    fast_llm = state["fast_llm"]
-    response = list(
-        fast_llm.stream(
-            prompt=msg,
-        )
-    )
-
-    response_str = merge_message_runs(response, chunk_separator="")[0].content
-
-    return QACheckOutput(
-        answer_quality=response_str,
-    )

backend/danswer/agent_search/answer_query/nodes/answer_generation.py

@@ -1,32 +0,0 @@
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_message_runs
-
-from danswer.agent_search.answer_query.states import AnswerQueryState
-from danswer.agent_search.answer_query.states import QAGenerationOutput
-from danswer.agent_search.shared_graph_utils.prompts import BASE_RAG_PROMPT
-from danswer.agent_search.shared_graph_utils.utils import format_docs
-
-
-def answer_generation(state: AnswerQueryState) -> QAGenerationOutput:
-    query = state["query_to_answer"]
-    docs = state["reranked_documents"]
-
-    print(f"Number of verified retrieval docs: {len(docs)}")
-
-    msg = [
-        HumanMessage(
-            content=BASE_RAG_PROMPT.format(question=query, context=format_docs(docs))
-        )
-    ]
-
-    fast_llm = state["fast_llm"]
-    response = list(
-        fast_llm.stream(
-            prompt=msg,
-        )
-    )
-
-    answer_str = merge_message_runs(response, chunk_separator="")[0].content
-    return QAGenerationOutput(
-        answer=answer_str,
-    )

backend/danswer/agent_search/answer_query/nodes/format_answer.py

@@ -1,16 +0,0 @@
-from danswer.agent_search.answer_query.states import AnswerQueryOutput
-from danswer.agent_search.answer_query.states import AnswerQueryState
-from danswer.agent_search.answer_query.states import SearchAnswerResults
-
-
-def format_answer(state: AnswerQueryState) -> AnswerQueryOutput:
-    return AnswerQueryOutput(
-        decomp_answer_results=[
-            SearchAnswerResults(
-                query=state["query_to_answer"],
-                quality=state["answer_quality"],
-                answer=state["answer"],
-                documents=state["reranked_documents"],
-            )
-        ],
-    )

backend/danswer/agent_search/answer_query/states.py

@@ -1,48 +0,0 @@
-from typing import Annotated
-from typing import TypedDict
-
-from pydantic import BaseModel
-
-from danswer.agent_search.core_state import PrimaryState
-from danswer.agent_search.shared_graph_utils.operators import dedup_inference_sections
-from danswer.context.search.models import InferenceSection
-
-
-class SearchAnswerResults(BaseModel):
-    query: str
-    answer: str
-    quality: str
-    documents: Annotated[list[InferenceSection], dedup_inference_sections]
-
-
-class QACheckOutput(TypedDict, total=False):
-    answer_quality: str
-
-
-class QAGenerationOutput(TypedDict, total=False):
-    answer: str
-
-
-class ExpandedRetrievalOutput(TypedDict):
-    reranked_documents: Annotated[list[InferenceSection], dedup_inference_sections]
-
-
-class AnswerQueryState(
-    PrimaryState,
-    QACheckOutput,
-    QAGenerationOutput,
-    ExpandedRetrievalOutput,
-    total=True,
-):
-    query_to_answer: str
-    retrieved_documents: Annotated[list[InferenceSection], dedup_inference_sections]
-    verified_documents: Annotated[list[InferenceSection], dedup_inference_sections]
-    reranked_documents: Annotated[list[InferenceSection], dedup_inference_sections]
-
-
-class AnswerQueryInput(PrimaryState, total=True):
-    query_to_answer: str
-
-
-class AnswerQueryOutput(TypedDict):
-    decomp_answer_results: list[SearchAnswerResults]

backend/danswer/agent_search/core_state.py

@@ -1,15 +0,0 @@
-from typing import TypedDict
-
-from sqlalchemy.orm import Session
-
-from danswer.context.search.models import SearchRequest
-from danswer.llm.interfaces import LLM
-
-
-class PrimaryState(TypedDict, total=False):
-    search_request: SearchRequest
-    primary_llm: LLM
-    fast_llm: LLM
-    # a single session for the entire agent search
-    # is fine if we are only reading
-    db_session: Session

backend/danswer/agent_search/deep_answer/nodes/answer_generation.py

@@ -1,114 +0,0 @@
-from typing import Any
-
-from langchain_core.messages import HumanMessage
-
-from danswer.agent_search.main.states import MainState
-from danswer.agent_search.shared_graph_utils.prompts import COMBINED_CONTEXT
-from danswer.agent_search.shared_graph_utils.prompts import MODIFIED_RAG_PROMPT
-from danswer.agent_search.shared_graph_utils.utils import format_docs
-from danswer.agent_search.shared_graph_utils.utils import normalize_whitespace
-
-
-# aggregate sub questions and answers
-def deep_answer_generation(state: MainState) -> dict[str, Any]:
-    """
-    Generate answer
-
-    Args:
-        state (messages): The current state
-
-    Returns:
-         dict: The updated state with re-phrased question
-    """
-    print("---DEEP GENERATE---")
-
-    question = state["original_question"]
-    docs = state["deduped_retrieval_docs"]
-
-    deep_answer_context = state["core_answer_dynamic_context"]
-
-    print(f"Number of verified retrieval docs - deep: {len(docs)}")
-
-    combined_context = normalize_whitespace(
-        COMBINED_CONTEXT.format(
-            deep_answer_context=deep_answer_context, formated_docs=format_docs(docs)
-        )
-    )
-
-    msg = [
-        HumanMessage(
-            content=MODIFIED_RAG_PROMPT.format(
-                question=question, combined_context=combined_context
-            )
-        )
-    ]
-
-    # Grader
-    model = state["fast_llm"]
-    response = model.invoke(msg)
-
-    return {
-        "deep_answer": response.content,
-    }
-
-
-def final_stuff(state: MainState) -> dict[str, Any]:
-    """
-    Invokes the agent model to generate a response based on the current state. Given
-    the question, it will decide to retrieve using the retriever tool, or simply end.
-
-    Args:
-        state (messages): The current state
-
-    Returns:
-        dict: The updated state with the agent response appended to messages
-    """
-    print("---FINAL---")
-
-    messages = state["log_messages"]
-    time_ordered_messages = [x.pretty_repr() for x in messages]
-    time_ordered_messages.sort()
-
-    print("Message Log:")
-    print("\n".join(time_ordered_messages))
-
-    initial_sub_qas = state["initial_sub_qas"]
-    initial_sub_qa_list = []
-    for initial_sub_qa in initial_sub_qas:
-        if initial_sub_qa["sub_answer_check"] == "yes":
-            initial_sub_qa_list.append(
-                f'  Question:\n  {initial_sub_qa["sub_question"]}\n  --\n  Answer:\n  {initial_sub_qa["sub_answer"]}\n  -----'
-            )
-
-    initial_sub_qa_context = "\n".join(initial_sub_qa_list)
-
-    base_answer = state["base_answer"]
-
-    print(f"Final Base Answer:\n{base_answer}")
-    print("--------------------------------")
-    print(f"Initial Answered Sub Questions:\n{initial_sub_qa_context}")
-    print("--------------------------------")
-
-    if not state.get("deep_answer"):
-        print("No Deep Answer was required")
-        return {}
-
-    deep_answer = state["deep_answer"]
-    sub_qas = state["sub_qas"]
-    sub_qa_list = []
-    for sub_qa in sub_qas:
-        if sub_qa["sub_answer_check"] == "yes":
-            sub_qa_list.append(
-                f'  Question:\n  {sub_qa["sub_question"]}\n  --\n  Answer:\n  {sub_qa["sub_answer"]}\n  -----'
-            )
-
-    sub_qa_context = "\n".join(sub_qa_list)
-
-    print(f"Final Base Answer:\n{base_answer}")
-    print("--------------------------------")
-    print(f"Final Deep Answer:\n{deep_answer}")
-    print("--------------------------------")
-    print("Sub Questions and Answers:")
-    print(sub_qa_context)
-
-    return {}

backend/danswer/agent_search/deep_answer/nodes/deep_decomp.py

@@ -1,78 +0,0 @@
-import json
-import re
-from datetime import datetime
-from typing import Any
-
-from langchain_core.messages import HumanMessage
-
-from danswer.agent_search.main.states import MainState
-from danswer.agent_search.shared_graph_utils.prompts import DEEP_DECOMPOSE_PROMPT
-from danswer.agent_search.shared_graph_utils.utils import format_entity_term_extraction
-from danswer.agent_search.shared_graph_utils.utils import generate_log_message
-
-
-def decompose(state: MainState) -> dict[str, Any]:
-    """ """
-
-    node_start_time = datetime.now()
-
-    question = state["original_question"]
-    base_answer = state["base_answer"]
-
-    # get the entity term extraction dict and properly format it
-    entity_term_extraction_dict = state["retrieved_entities_relationships"][
-        "retrieved_entities_relationships"
-    ]
-
-    entity_term_extraction_str = format_entity_term_extraction(
-        entity_term_extraction_dict
-    )
-
-    initial_question_answers = state["initial_sub_qas"]
-
-    addressed_question_list = [
-        x["sub_question"]
-        for x in initial_question_answers
-        if x["sub_answer_check"] == "yes"
-    ]
-    failed_question_list = [
-        x["sub_question"]
-        for x in initial_question_answers
-        if x["sub_answer_check"] == "no"
-    ]
-
-    msg = [
-        HumanMessage(
-            content=DEEP_DECOMPOSE_PROMPT.format(
-                question=question,
-                entity_term_extraction_str=entity_term_extraction_str,
-                base_answer=base_answer,
-                answered_sub_questions="\n - ".join(addressed_question_list),
-                failed_sub_questions="\n - ".join(failed_question_list),
-            ),
-        )
-    ]
-
-    # Grader
-    model = state["fast_llm"]
-    response = model.invoke(msg)
-
-    cleaned_response = re.sub(r"```json\n|\n```", "", response.pretty_repr())
-    parsed_response = json.loads(cleaned_response)
-
-    sub_questions_dict = {}
-    for sub_question_nr, sub_question_dict in enumerate(
-        parsed_response["sub_questions"]
-    ):
-        sub_question_dict["answered"] = False
-        sub_question_dict["verified"] = False
-        sub_questions_dict[sub_question_nr] = sub_question_dict
-
-    return {
-        "decomposed_sub_questions_dict": sub_questions_dict,
-        "log_messages": generate_log_message(
-            message="deep - decompose",
-            node_start_time=node_start_time,
-            graph_start_time=state["graph_start_time"],
-        ),
-    }

backend/danswer/agent_search/deep_answer/nodes/entity_term_extraction.py

@@ -1,40 +0,0 @@
-import json
-import re
-from typing import Any
-
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_message_runs
-
-from danswer.agent_search.main.states import MainState
-from danswer.agent_search.shared_graph_utils.prompts import ENTITY_TERM_PROMPT
-from danswer.agent_search.shared_graph_utils.utils import format_docs
-
-
-def entity_term_extraction(state: MainState) -> dict[str, Any]:
-    """Extract entities and terms from the question and context"""
-
-    question = state["original_question"]
-    docs = state["deduped_retrieval_docs"]
-
-    doc_context = format_docs(docs)
-
-    msg = [
-        HumanMessage(
-            content=ENTITY_TERM_PROMPT.format(question=question, context=doc_context),
-        )
-    ]
-    fast_llm = state["fast_llm"]
-    # Grader
-    llm_response_list = list(
-        fast_llm.stream(
-            prompt=msg,
-        )
-    )
-    llm_response = merge_message_runs(llm_response_list, chunk_separator="")[0].content
-
-    cleaned_response = re.sub(r"```json\n|\n```", "", llm_response)
-    parsed_response = json.loads(cleaned_response)
-
-    return {
-        "retrieved_entities_relationships": parsed_response,
-    }

backend/danswer/agent_search/deep_answer/nodes/sub_qa_level_aggregator.py

@@ -1,30 +0,0 @@
-from typing import Any
-
-from danswer.agent_search.main.states import MainState
-
-
-# aggregate sub questions and answers
-def sub_qa_level_aggregator(state: MainState) -> dict[str, Any]:
-    sub_qas = state["sub_qas"]
-
-    dynamic_context_list = [
-        "Below you will find useful information to answer the original question:"
-    ]
-    checked_sub_qas = []
-
-    for core_answer_sub_qa in sub_qas:
-        question = core_answer_sub_qa["sub_question"]
-        answer = core_answer_sub_qa["sub_answer"]
-        verified = core_answer_sub_qa["sub_answer_check"]
-
-        if verified == "yes":
-            dynamic_context_list.append(
-                f"Question:\n{question}\n\nAnswer:\n{answer}\n\n---\n\n"
-            )
-            checked_sub_qas.append({"sub_question": question, "sub_answer": answer})
-    dynamic_context = "\n".join(dynamic_context_list)
-
-    return {
-        "core_answer_dynamic_context": dynamic_context,
-        "checked_sub_qas": checked_sub_qas,
-    }

backend/danswer/agent_search/deep_answer/nodes/sub_qa_manager.py

@@ -1,19 +0,0 @@
-from typing import Any
-
-from danswer.agent_search.main.states import MainState
-
-
-def sub_qa_manager(state: MainState) -> dict[str, Any]:
-    """ """
-
-    sub_questions_dict = state["decomposed_sub_questions_dict"]
-
-    sub_questions = {}
-
-    for sub_question_nr, sub_question_dict in sub_questions_dict.items():
-        sub_questions[sub_question_nr] = sub_question_dict["sub_question"]
-
-    return {
-        "sub_questions": sub_questions,
-        "num_new_question_iterations": 0,
-    }

backend/danswer/agent_search/expanded_retrieval/edges.py

@@ -1,83 +0,0 @@
-from collections.abc import Hashable
-
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_message_runs
-from langgraph.types import Send
-
-from danswer.agent_search.expanded_retrieval.nodes.doc_retrieval import RetrieveInput
-from danswer.agent_search.expanded_retrieval.states import DocRetrievalOutput
-from danswer.agent_search.expanded_retrieval.states import DocVerificationInput
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalInput
-from danswer.agent_search.shared_graph_utils.prompts import (
-    REWRITE_PROMPT_MULTI_ORIGINAL,
-)
-from danswer.llm.interfaces import LLM
-
-
-def parallel_retrieval_edge(state: ExpandedRetrievalInput) -> list[Send | Hashable]:
-    # print(f"parallel_retrieval_edge state: {state.keys()}")
-    print("parallel_retrieval_edge state")
-
-    # This should be better...
-    question = state.get("query_to_answer") or state["search_request"].query
-    llm: LLM = state["fast_llm"]
-
-    """
-    msg = [
-        HumanMessage(
-            content=REWRITE_PROMPT_MULTI.format(question=question),
-        )
-    ]
-    """
-    msg = [
-        HumanMessage(
-            content=REWRITE_PROMPT_MULTI_ORIGINAL.format(question=question),
-        )
-    ]
-
-    llm_response_list = list(
-        llm.stream(
-            prompt=msg,
-        )
-    )
-    llm_response = merge_message_runs(llm_response_list, chunk_separator="")[0].content
-
-    # print(f"llm_response: {llm_response}")
-
-    rewritten_queries = [
-        rewritten_query.strip() for rewritten_query in llm_response.split("--")
-    ]
-
-    # Add the original sub-question as one of the 'rewritten' queries
-    rewritten_queries = [question] + rewritten_queries
-
-    print(f"rewritten_queries: {rewritten_queries}")
-
-    return [
-        Send(
-            "doc_retrieval",
-            RetrieveInput(query_to_retrieve=query, **state),
-        )
-        for query in rewritten_queries
-    ]
-
-
-def parallel_verification_edge(state: DocRetrievalOutput) -> list[Send | Hashable]:
-    # print(f"parallel_retrieval_edge state: {state.keys()}")
-    print("parallel_retrieval_edge state")
-
-    retrieved_docs = state["retrieved_documents"]
-
-    return [
-        Send(
-            "doc_verification",
-            DocVerificationInput(doc_to_verify=doc, **state),
-        )
-        for doc in retrieved_docs
-    ]
-
-
-# this is not correct - remove
-# def conditionally_rerank_edge(state: ExpandedRetrievalState) -> bool:
-#    print(f"conditionally_rerank_edge state: {state.keys()}")
-#    return bool(state["search_request"].rerank_settings)

backend/danswer/agent_search/expanded_retrieval/graph_builder.py

@@ -1,129 +0,0 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
-
-from danswer.agent_search.expanded_retrieval.edges import parallel_retrieval_edge
-from danswer.agent_search.expanded_retrieval.edges import parallel_verification_edge
-from danswer.agent_search.expanded_retrieval.nodes.doc_reranking import doc_reranking
-from danswer.agent_search.expanded_retrieval.nodes.doc_retrieval import doc_retrieval
-from danswer.agent_search.expanded_retrieval.nodes.doc_verification import (
-    doc_verification,
-)
-from danswer.agent_search.expanded_retrieval.nodes.dummy_node import dummy_node
-from danswer.agent_search.expanded_retrieval.nodes.verification_kickoff import (
-    verification_kickoff,
-)
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalInput
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalOutput
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalState
-
-# from danswer.agent_search.expanded_retrieval.edges import conditionally_rerank_edge
-
-
-def expanded_retrieval_graph_builder() -> StateGraph:
-    graph = StateGraph(
-        state_schema=ExpandedRetrievalState,
-        input=ExpandedRetrievalInput,
-        output=ExpandedRetrievalOutput,
-    )
-
-    ### Add nodes ###
-
-    graph.add_node(
-        node="doc_retrieval",
-        action=doc_retrieval,
-    )
-    graph.add_node(
-        node="verification_kickoff",
-        action=verification_kickoff,
-    )
-    graph.add_node(
-        node="doc_verification",
-        action=doc_verification,
-    )
-    graph.add_node(
-        node="doc_reranking",
-        action=doc_reranking,
-    )
-
-    graph.add_node(
-        node="post_retrieval_dummy_node",
-        action=dummy_node,
-    )
-
-    graph.add_node(
-        node="dummy_node",
-        action=dummy_node,
-    )
-
-    ### Add edges ###
-
-    graph.add_conditional_edges(
-        source=START,
-        path=parallel_retrieval_edge,
-        path_map=["doc_retrieval"],
-    )
-
-    graph.add_edge(
-        start_key="doc_retrieval",
-        end_key="verification_kickoff",
-    )
-
-    graph.add_conditional_edges(
-        source="verification_kickoff",
-        path=parallel_verification_edge,
-        path_map=["doc_verification"],
-    )
-
-    # graph.add_edge(
-    #    start_key="doc_verification",
-    #    end_key="post_retrieval_dummy_node",
-    # )
-
-    graph.add_edge(
-        start_key="doc_verification",
-        end_key="doc_reranking",
-    )
-
-    graph.add_edge(
-        start_key="doc_reranking",
-        end_key="dummy_node",
-    )
-
-    # graph.add_conditional_edges(
-    #    source="doc_verification",
-    #    path=conditionally_rerank_edge,
-    #    path_map={
-    #        True: "doc_reranking",
-    #        False: END,
-    #    },
-    # )
-    graph.add_edge(
-        start_key="dummy_node",
-        end_key=END,
-    )
-
-    return graph
-
-
-if __name__ == "__main__":
-    from danswer.db.engine import get_session_context_manager
-    from danswer.llm.factory import get_default_llms
-    from danswer.context.search.models import SearchRequest
-
-    graph = expanded_retrieval_graph_builder()
-    compiled_graph = graph.compile()
-    primary_llm, fast_llm = get_default_llms()
-    search_request = SearchRequest(
-        query="Who made Excel and what other products did they make?",
-    )
-    with get_session_context_manager() as db_session:
-        inputs = ExpandedRetrievalInput(
-            search_request=search_request,
-            primary_llm=primary_llm,
-            fast_llm=fast_llm,
-            db_session=db_session,
-            query_to_answer="Who made Excel?",
-        )
-        for thing in compiled_graph.stream(inputs, debug=True):
-            print(thing)

backend/danswer/agent_search/expanded_retrieval/nodes/doc_reranking.py

@@ -1,13 +0,0 @@
-import datetime
-
-from danswer.agent_search.expanded_retrieval.states import DocRerankingOutput
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalState
-
-
-def doc_reranking(state: ExpandedRetrievalState) -> DocRerankingOutput:
-    print(f"doc_reranking state: {datetime.datetime.now()}")
-
-    verified_documents = state["verified_documents"]
-    reranked_documents = verified_documents
-
-    return DocRerankingOutput(reranked_documents=reranked_documents)

backend/danswer/agent_search/expanded_retrieval/nodes/doc_retrieval.py

@@ -1,75 +0,0 @@
-import datetime
-
-from danswer.agent_search.expanded_retrieval.states import DocRetrievalOutput
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalState
-from danswer.context.search.models import InferenceSection
-from danswer.context.search.models import SearchRequest
-from danswer.context.search.pipeline import SearchPipeline
-
-
-class RetrieveInput(ExpandedRetrievalState):
-    query_to_retrieve: str
-
-
-def doc_retrieval(state: RetrieveInput) -> DocRetrievalOutput:
-    # def doc_retrieval(state: RetrieveInput) -> Command[Literal["doc_verification"]]:
-    """
-    Retrieve documents
-
-    Args:
-        state (dict): The current graph state
-
-    Returns:
-        state (dict): New key added to state, documents, that contains retrieved documents
-    """
-    # print(f"doc_retrieval state: {state.keys()}")
-
-    if "query_to_answer" in state.keys():
-        query_question = state["query_to_answer"]
-    else:
-        query_question = state["search_request"].query
-
-    query_to_retrieve = state["query_to_retrieve"]
-
-    print(f"\ndoc_retrieval state: {datetime.datetime.now()}")
-    print(f"  -- search_request: {query_question[:100]}")
-    # print(f"       -- query_to_retrieve: {query_to_retrieve[:100]}")
-
-    documents: list[InferenceSection] = []
-    llm = state["primary_llm"]
-    fast_llm = state["fast_llm"]
-    # db_session = state["db_session"]
-
-    documents = SearchPipeline(
-        search_request=SearchRequest(
-            query=query_to_retrieve,
-        ),
-        user=None,
-        llm=llm,
-        fast_llm=fast_llm,
-        db_session=state["db_session"],
-    ).reranked_sections
-
-    top_1_score = documents[0].center_chunk.score
-    top_5_score = sum([doc.center_chunk.score for doc in documents[:5]]) / 5
-    top_10_score = sum([doc.center_chunk.score for doc in documents[:10]]) / 10
-
-    fit_score = 1 / 3 * (top_1_score + top_5_score + top_10_score)
-
-    # temp - limit the number of documents to 5
-    documents = documents[:5]
-
-    """
-    chunk_ids = {
-        "query": query_to_retrieve,
-        "chunk_ids": [doc.center_chunk.chunk_id for doc in documents],
-    }
-    """
-
-    print(f"sub_query: {query_to_retrieve[:50]}")
-    print(f"retrieved documents: {len(documents)}")
-    print(f"fit score: {fit_score}")
-    print()
-    return DocRetrievalOutput(
-        retrieved_documents=documents,
-    )

backend/danswer/agent_search/expanded_retrieval/nodes/doc_verification.py

@@ -1,63 +0,0 @@
-import datetime
-
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_message_runs
-
-from danswer.agent_search.expanded_retrieval.states import DocRetrievalOutput
-from danswer.agent_search.expanded_retrieval.states import DocVerificationOutput
-from danswer.agent_search.shared_graph_utils.models import BinaryDecision
-from danswer.agent_search.shared_graph_utils.prompts import VERIFIER_PROMPT
-
-
-def doc_verification(state: DocRetrievalOutput) -> DocVerificationOutput:
-    """
-    Check whether the document is relevant for the original user question
-
-    Args:
-        state (VerifierState): The current state
-
-    Returns:
-        dict: ict: The updated state with the final decision
-    """
-
-    # print(f"--- doc_verification state ---")
-
-    if "query_to_answer" in state.keys():
-        query_to_answer = state["query_to_answer"]
-    else:
-        query_to_answer = state["search_request"].query
-
-    doc_to_verify = state["doc_to_verify"]
-    document_content = doc_to_verify.combined_content
-
-    msg = [
-        HumanMessage(
-            content=VERIFIER_PROMPT.format(
-                question=query_to_answer, document_content=document_content
-            )
-        )
-    ]
-
-    fast_llm = state["fast_llm"]
-    response = list(
-        fast_llm.stream(
-            prompt=msg,
-        )
-    )
-
-    response_string = merge_message_runs(response, chunk_separator="")[0].content
-    # Convert string response to proper dictionary format
-    decision_dict = {"decision": response_string.lower()}
-    formatted_response = BinaryDecision.model_validate(decision_dict)
-
-    verified_documents = []
-    if formatted_response.decision == "yes":
-        verified_documents.append(doc_to_verify)
-
-        print(
-            f"Verdict & Completion: {formatted_response.decision} -- {datetime.datetime.now()}"
-        )
-
-    return DocVerificationOutput(
-        verified_documents=verified_documents,
-    )

backend/danswer/agent_search/expanded_retrieval/nodes/dummy_node.py

@@ -1,9 +0,0 @@
-def dummy_node(state):
-    """
-    This node is a dummy node that does not change the state but allows to inspect the state.
-    """
-    print(f"doc_reranking state: {state.keys()}")
-
-    state["verified_documents"]
-
-    return {}

backend/danswer/agent_search/expanded_retrieval/nodes/verification_kickoff.py

@@ -1,28 +0,0 @@
-import datetime
-from typing import Literal
-
-from langgraph.types import Command
-from langgraph.types import Send
-
-from danswer.agent_search.expanded_retrieval.states import (
-    DocVerificationInput,
-)
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalState
-
-
-def verification_kickoff(
-    state: ExpandedRetrievalState,
-) -> Command[Literal["doc_verification"]]:
-    print(f"verification_kickoff state: {datetime.datetime.now()}")
-
-    documents = state["retrieved_documents"]
-    return Command(
-        update={},
-        goto=[
-            Send(
-                node="doc_verification",
-                arg=DocVerificationInput(doc_to_verify=doc, **state),
-            )
-            for doc in documents
-        ],
-    )

backend/danswer/agent_search/expanded_retrieval/states.py

@@ -1,42 +0,0 @@
-from typing import Annotated
-from typing import TypedDict
-
-from danswer.agent_search.core_state import PrimaryState
-from danswer.agent_search.shared_graph_utils.operators import dedup_inference_sections
-from danswer.context.search.models import InferenceSection
-
-
-class DocRetrievalOutput(TypedDict, total=False):
-    retrieved_documents: Annotated[list[InferenceSection], dedup_inference_sections]
-    query_to_answer: str
-
-
-class DocVerificationInput(TypedDict, total=True):
-    query_to_answer: str
-    doc_to_verify: InferenceSection
-
-
-class DocVerificationOutput(TypedDict, total=False):
-    verified_documents: Annotated[list[InferenceSection], dedup_inference_sections]
-
-
-class DocRerankingOutput(TypedDict, total=False):
-    reranked_documents: Annotated[list[InferenceSection], dedup_inference_sections]
-
-
-class ExpandedRetrievalState(
-    PrimaryState,
-    DocRetrievalOutput,
-    DocVerificationOutput,
-    DocRerankingOutput,
-    total=True,
-):
-    query_to_answer: str
-
-
-class ExpandedRetrievalInput(PrimaryState, total=True):
-    query_to_answer: str
-
-
-class ExpandedRetrievalOutput(TypedDict):
-    reranked_documents: Annotated[list[InferenceSection], dedup_inference_sections]

backend/danswer/agent_search/main/edges.py

@@ -1,61 +0,0 @@
-from collections.abc import Hashable
-
-from langgraph.types import Send
-
-from danswer.agent_search.answer_query.states import AnswerQueryInput
-from danswer.agent_search.main.states import MainState
-
-
-def parallelize_decompozed_answer_queries(state: MainState) -> list[Send | Hashable]:
-    return [
-        Send(
-            "answer_query",
-            AnswerQueryInput(
-                **state,
-                query_to_answer=query,
-            ),
-        )
-        for query in state["initial_decomp_queries"]
-    ]
-
-
-# def continue_to_answer_sub_questions(state: QAState) -> Union[Hashable, list[Hashable]]:
-#     # Routes re-written queries to the (parallel) retrieval steps
-#     # Notice the 'Send()' API that takes care of the parallelization
-#     return [
-#         Send(
-#             "sub_answers_graph",
-#             ResearchQAState(
-#                 sub_question=sub_question["sub_question_str"],
-#                 sub_question_nr=sub_question["sub_question_nr"],
-#                 graph_start_time=state["graph_start_time"],
-#                 primary_llm=state["primary_llm"],
-#                 fast_llm=state["fast_llm"],
-#             ),
-#         )
-#         for sub_question in state["sub_questions"]
-#     ]
-
-
-# def continue_to_deep_answer(state: QAState) -> Union[Hashable, list[Hashable]]:
-#     print("---GO TO DEEP ANSWER OR END---")
-
-#     base_answer = state["base_answer"]
-
-#     question = state["original_question"]
-
-#     BASE_CHECK_MESSAGE = [
-#         HumanMessage(
-#             content=BASE_CHECK_PROMPT.format(question=question, base_answer=base_answer)
-#         )
-#     ]
-
-#     model = state["fast_llm"]
-#     response = model.invoke(BASE_CHECK_MESSAGE)
-
-#     print(f"CAN WE CONTINUE W/O GENERATING A DEEP ANSWER? - {response.pretty_repr()}")
-
-#     if response.pretty_repr() == "no":
-#         return "decompose"
-#     else:
-#         return "end"

backend/danswer/agent_search/main/graph_builder.py

@@ -1,125 +0,0 @@
-import datetime
-
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
-
-from danswer.agent_search.answer_query.graph_builder import answer_query_graph_builder
-from danswer.agent_search.expanded_retrieval.graph_builder import (
-    expanded_retrieval_graph_builder,
-)
-from danswer.agent_search.main.edges import parallelize_decompozed_answer_queries
-from danswer.agent_search.main.nodes.base_decomp import main_decomp_base
-from danswer.agent_search.main.nodes.dummy_node import dummy_node
-from danswer.agent_search.main.nodes.generate_initial_answer import (
-    generate_initial_answer,
-)
-from danswer.agent_search.main.states import MainInput
-from danswer.agent_search.main.states import MainState
-
-
-def main_graph_builder() -> StateGraph:
-    graph = StateGraph(
-        state_schema=MainState,
-        input=MainInput,
-    )
-
-    ### Add nodes ###
-
-    graph.add_node(
-        node="dummy_node_start",
-        action=dummy_node,
-    )
-
-    graph.add_node(
-        node="dummy_node_right",
-        action=dummy_node,
-    )
-
-    graph.add_node(
-        node="base_decomp",
-        action=main_decomp_base,
-    )
-    answer_query_subgraph = answer_query_graph_builder().compile()
-    graph.add_node(
-        node="answer_query",
-        action=answer_query_subgraph,
-    )
-    expanded_retrieval_subgraph = expanded_retrieval_graph_builder().compile()
-    graph.add_node(
-        node="expanded_retrieval",
-        action=expanded_retrieval_subgraph,
-    )
-    graph.add_node(
-        node="generate_initial_answer",
-        action=generate_initial_answer,
-    )
-
-    ### Add edges ###
-
-    graph.add_edge(
-        start_key=START,
-        end_key="dummy_node_start",
-    )
-
-    graph.add_edge(
-        start_key="dummy_node_start",
-        end_key="dummy_node_right",
-    )
-    graph.add_edge(
-        start_key="dummy_node_right",
-        end_key="expanded_retrieval",
-    )
-    # graph.add_edge(
-    #    start_key="expanded_retrieval",
-    #    end_key="generate_initial_answer",
-    # )
-
-    graph.add_edge(
-        start_key="dummy_node_start",
-        end_key="base_decomp",
-    )
-    graph.add_conditional_edges(
-        source="base_decomp",
-        path=parallelize_decompozed_answer_queries,
-        path_map=["answer_query"],
-    )
-    graph.add_edge(
-        start_key=["answer_query", "expanded_retrieval"],
-        end_key="generate_initial_answer",
-    )
-    graph.add_edge(
-        start_key="generate_initial_answer",
-        end_key=END,
-    )
-
-    return graph
-
-
-if __name__ == "__main__":
-    from danswer.db.engine import get_session_context_manager
-    from danswer.llm.factory import get_default_llms
-    from danswer.context.search.models import SearchRequest
-
-    graph = main_graph_builder()
-    compiled_graph = graph.compile()
-    primary_llm, fast_llm = get_default_llms()
-    search_request = SearchRequest(
-        query="Who made Excel and what other products did they make?",
-    )
-    with get_session_context_manager() as db_session:
-        inputs = MainInput(
-            search_request=search_request,
-            primary_llm=primary_llm,
-            fast_llm=fast_llm,
-            db_session=db_session,
-        )
-
-        print(f"START: {datetime.datetime.now()}")
-
-        output = compiled_graph.invoke(
-            input=inputs,
-            # debug=True,
-            # subgraphs=True,
-        )
-        print(output)

backend/danswer/agent_search/main/nodes/base_decomp.py

@@ -1,35 +0,0 @@
-import datetime
-
-from langchain_core.messages import HumanMessage
-
-from danswer.agent_search.main.states import BaseDecompOutput
-from danswer.agent_search.main.states import MainState
-from danswer.agent_search.shared_graph_utils.prompts import INITIAL_DECOMPOSITION_PROMPT
-from danswer.agent_search.shared_graph_utils.utils import clean_and_parse_list_string
-
-
-def main_decomp_base(state: MainState) -> BaseDecompOutput:
-    print(f"main_decomp_base state: {datetime.datetime.now()}")
-    question = state["search_request"].query
-
-    msg = [
-        HumanMessage(
-            content=INITIAL_DECOMPOSITION_PROMPT.format(question=question),
-        )
-    ]
-
-    # Get the rewritten queries in a defined format
-    model = state["fast_llm"]
-    response = model.invoke(msg)
-
-    content = response.pretty_repr()
-    list_of_subquestions = clean_and_parse_list_string(content)
-
-    decomp_list: list[str] = [
-        sub_question["sub_question"].strip() for sub_question in list_of_subquestions
-    ]
-
-    print(f"Decomp Questions: {decomp_list}")
-    return BaseDecompOutput(
-        initial_decomp_queries=decomp_list,
-    )

backend/danswer/agent_search/main/nodes/dummy_node.py

@@ -1,10 +0,0 @@
-import datetime
-
-
-def dummy_node(state):
-    """
-    This node is a dummy node that does not change the state but allows to inspect the state.
-    """
-    print(f"DUMMY NODE: {datetime.datetime.now()}")
-
-    return {}

backend/danswer/agent_search/main/nodes/generate_initial_answer.py

@@ -1,51 +0,0 @@
-from langchain_core.messages import HumanMessage
-
-from danswer.agent_search.main.states import InitialAnswerOutput
-from danswer.agent_search.main.states import MainState
-from danswer.agent_search.shared_graph_utils.prompts import INITIAL_RAG_PROMPT
-from danswer.agent_search.shared_graph_utils.utils import format_docs
-
-
-def generate_initial_answer(state: MainState) -> InitialAnswerOutput:
-    print("---GENERATE INITIAL---")
-
-    question = state["search_request"].query
-    docs = state["documents"]
-
-    decomp_answer_results = state["decomp_answer_results"]
-
-    good_qa_list: list[str] = []
-
-    _SUB_QUESTION_ANSWER_TEMPLATE = """
-    Sub-Question:\n  - {sub_question}\n  --\nAnswer:\n  - {sub_answer}\n\n
-    """
-    for decomp_answer_result in decomp_answer_results:
-        if (
-            decomp_answer_result.quality == "yes"
-            and len(decomp_answer_result.answer) > 0
-            and decomp_answer_result.answer != "I don't know"
-        ):
-            good_qa_list.append(
-                _SUB_QUESTION_ANSWER_TEMPLATE.format(
-                    sub_question=decomp_answer_result.query,
-                    sub_answer=decomp_answer_result.answer,
-                )
-            )
-
-    sub_question_answer_str = "\n\n------\n\n".join(good_qa_list)
-
-    msg = [
-        HumanMessage(
-            content=INITIAL_RAG_PROMPT.format(
-                question=question,
-                context=format_docs(docs),
-                answered_sub_questions=sub_question_answer_str,
-            )
-        )
-    ]
-
-    # Grader
-    model = state["fast_llm"]
-    response = model.invoke(msg)
-
-    return InitialAnswerOutput(initial_answer=response.pretty_repr())

backend/danswer/agent_search/main/states.py

@@ -1,37 +0,0 @@
-from operator import add
-from typing import Annotated
-from typing import TypedDict
-
-from danswer.agent_search.answer_query.states import SearchAnswerResults
-from danswer.agent_search.core_state import PrimaryState
-from danswer.agent_search.shared_graph_utils.operators import dedup_inference_sections
-from danswer.context.search.models import InferenceSection
-
-
-class BaseDecompOutput(TypedDict, total=False):
-    initial_decomp_queries: list[str]
-
-
-class InitialAnswerOutput(TypedDict, total=False):
-    initial_answer: str
-
-
-class MainState(
-    PrimaryState,
-    BaseDecompOutput,
-    InitialAnswerOutput,
-    total=True,
-):
-    documents: Annotated[list[InferenceSection], dedup_inference_sections]
-    decomp_answer_results: Annotated[list[SearchAnswerResults], add]
-
-
-class MainInput(PrimaryState, total=True):
-    pass
-
-
-class MainOutput(TypedDict):
-    """
-    This is not used because defining the output only matters for filtering the output of
-      a .invoke() call but we are streaming so we just yield the entire state.
-    """

backend/danswer/agent_search/run_graph.py

@@ -1,27 +0,0 @@
-from danswer.agent_search.primary_graph.graph_builder import build_core_graph
-from danswer.llm.answering.answer import AnswerStream
-from danswer.llm.interfaces import LLM
-from danswer.tools.tool import Tool
-
-
-def run_graph(
-    query: str,
-    llm: LLM,
-    tools: list[Tool],
-) -> AnswerStream:
-    graph = build_core_graph()
-
-    inputs = {
-        "original_query": query,
-        "messages": [],
-        "tools": tools,
-        "llm": llm,
-    }
-    compiled_graph = graph.compile()
-    output = compiled_graph.invoke(input=inputs)
-    yield from output
-
-
-if __name__ == "__main__":
-    pass
-    # run_graph("What is the capital of France?", llm, [])

backend/danswer/agent_search/shared_graph_utils/models.py

@@ -1,12 +0,0 @@
-from typing import Literal
-
-from pydantic import BaseModel
-
-
-# Pydantic models for structured outputs
-class RewrittenQueries(BaseModel):
-    rewritten_queries: list[str]
-
-
-class BinaryDecision(BaseModel):
-    decision: Literal["yes", "no"]

backend/danswer/agent_search/shared_graph_utils/operators.py

@@ -1,9 +0,0 @@
-from danswer.context.search.models import InferenceSection
-from danswer.llm.answering.prune_and_merge import _merge_sections
-
-
-def dedup_inference_sections(
-    list1: list[InferenceSection], list2: list[InferenceSection]
-) -> list[InferenceSection]:
-    deduped = _merge_sections(list1 + list2)
-    return deduped

backend/danswer/agent_search/shared_graph_utils/prompts.py

@@ -1,427 +0,0 @@
-REWRITE_PROMPT_MULTI_ORIGINAL = """ \n
-    Please convert an initial user question into a 2-3 more appropriate short and pointed search queries for retrievel from a
-    document store. Particularly, try to think about resolving ambiguities and make the search queries more specific,
-    enabling the system to search more broadly.
-    Also, try to make the search queries not redundant, i.e. not too similar! \n\n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-    Formulate the queries separated by '--' (Do not say 'Query 1: ...', just write the querytext): """
-
-REWRITE_PROMPT_MULTI = """ \n
-    Please create a list of 2-3 sample documents that could answer an original question. Each document
-    should be about as long as the original question. \n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-    Formulate the sample documents separated by '--' (Do not say 'Document 1: ...', just write the text): """
-
-BASE_RAG_PROMPT = """ \n
-    You are an assistant for question-answering tasks. Use the context provided below - and only the
-    provided context - to answer the question. If you don't know the answer or if the provided context is
-    empty, just say "I don't know". Do not use your internal knowledge!
-
-    Again, only use the provided context and do not use your internal knowledge! If you cannot answer the
-    question based on the context, say "I don't know". It is a matter of life and death that you do NOT
-    use your internal knowledge, just the provided information!
-
-    Use three sentences maximum and keep the answer concise.
-    answer concise.\nQuestion:\n {question} \nContext:\n {context} \n\n
-    \n\n
-    Answer:"""
-
-BASE_CHECK_PROMPT = """ \n
-    Please check whether 1) the suggested answer seems to fully address the original question AND 2)the
-    original question requests a simple, factual answer, and there are no ambiguities, judgements,
-    aggregations, or any other complications that may require extra context. (I.e., if the question is
-    somewhat addressed, but the answer would benefit from more context, then answer with 'no'.)
-
-    Please only answer with 'yes' or 'no' \n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-    Here is the proposed answer:
-    \n ------- \n
-    {base_answer}
-    \n ------- \n
-    Please answer with yes or no:"""
-
-VERIFIER_PROMPT = """ \n
-    Please check whether the document seems to be relevant for the answer of the question. Please
-    only answer with 'yes' or 'no' \n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-    Here is the document text:
-    \n ------- \n
-    {document_content}
-    \n ------- \n
-    Please answer with yes or no:"""
-
-INITIAL_DECOMPOSITION_PROMPT_BASIC = """ \n
-    Please decompose an initial user question into not more than 4 appropriate sub-questions that help to
-    answer the original question. The purpose for this decomposition is to isolate individulal entities
-    (i.e., 'compare sales of company A and company B' -> 'what are sales for company A' + 'what are sales
-    for company B'), split ambiguous terms (i.e., 'what is our success with company A' -> 'what are our
-    sales with company A' + 'what is our market share with company A' + 'is company A a reference customer
-    for us'), etc. Each sub-question should be realistically be answerable by a good RAG system. \n
-
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-
-    Please formulate your answer as a list of subquestions:
-
-    Answer:
-    """
-
-REWRITE_PROMPT_SINGLE = """ \n
-    Please convert an initial user question into a more appropriate search query for retrievel from a
-    document store. \n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-
-    Formulate the query: """
-
-MODIFIED_RAG_PROMPT = """You are an assistant for question-answering tasks. Use the context provided below
-    - and only this context - to answer the question. If you don't know the answer, just say "I don't know".
-    Use three sentences maximum and keep the answer concise.
-    Pay also particular attention to the sub-questions and their answers, at least it may enrich the answer.
-    Again, only use the provided context and do not use your internal knowledge! If you cannot answer the
-    question based on the context, say "I don't know". It is a matter of life and death that you do NOT
-    use your internal knowledge, just the provided information!
-
-    \nQuestion: {question}
-    \nContext: {combined_context} \n
-
-    Answer:"""
-
-ORIG_DEEP_DECOMPOSE_PROMPT = """ \n
-    An initial user question needs to be answered. An initial answer has been provided but it wasn't quite
-    good enough. Also, some sub-questions had been answered and this information has been used to provide
-    the initial answer. Some other subquestions may have been suggested based on little knowledge, but they
-    were not directly answerable. Also, some entities, relationships and terms are givenm to you so that
-    you have an idea of how the avaiolable data looks like.
-
-    Your role is to generate 3-5 new sub-questions that would help to answer the initial question,
-    considering:
-
-    1) The initial question
-    2) The initial answer that was found to be unsatisfactory
-    3) The sub-questions that were answered
-    4) The sub-questions that were suggested but not answered
-    5) The entities, relationships and terms that were extracted from the context
-
-    The individual questions should be answerable by a good RAG system.
-    So a good idea would be to use the sub-questions to resolve ambiguities and/or to separate the
-    question for different entities that may be involved in the original question, but in a way that does
-    not duplicate questions that were already tried.
-
-    Additional Guidelines:
-    - The sub-questions should be specific to the question and provide richer context for the question,
-    resolve ambiguities, or address shortcoming of the initial answer
-    - Each sub-question - when answered - should be relevant for the answer to the original question
-    - The sub-questions should be free from comparisions, ambiguities,judgements, aggregations, or any
-    other complications that may require extra context.
-    - The sub-questions MUST have the full context of the original question so that it can be executed by
-    a RAG system independently without the original question available
-      (Example:
-        - initial question: "What is the capital of France?"
-        - bad sub-question: "What is the name of the river there?"
-        - good sub-question: "What is the name of the river that flows through Paris?"
-    - For each sub-question, please provide a short explanation for why it is a good sub-question. So
-    generate a list of dictionaries with the following format:
-      [{{"sub_question": <sub-question>, "explanation": <explanation>, "search_term": <rewrite the
-      sub-question using as a search phrase for the document store>}}, ...]
-
-    \n\n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-
-    Here is the initial sub-optimal answer:
-    \n ------- \n
-    {base_answer}
-    \n ------- \n
-
-    Here are the sub-questions that were answered:
-    \n ------- \n
-    {answered_sub_questions}
-    \n ------- \n
-
-    Here are the sub-questions that were suggested but not answered:
-    \n ------- \n
-    {failed_sub_questions}
-    \n ------- \n
-
-    And here are the entities, relationships and terms extracted from the context:
-    \n ------- \n
-    {entity_term_extraction_str}
-    \n ------- \n
-
-   Please generate the list of good, fully contextualized sub-questions that would help to address the
-   main question. Again, please find questions that are NOT overlapping too much with the already answered
-   sub-questions or those that already were suggested and failed.
-   In other words - what can we try in addition to what has been tried so far?
-
-   Please think through it step by step and then generate the list of json dictionaries with the following
-   format:
-
-   {{"sub_questions": [{{"sub_question": <sub-question>,
-        "explanation": <explanation>,
-        "search_term": <rewrite the sub-question using as a search phrase for the document store>}},
-        ...]}} """
-
-DEEP_DECOMPOSE_PROMPT = """ \n
-    An initial user question needs to be answered. An initial answer has been provided but it wasn't quite
-    good enough. Also, some sub-questions had been answered and this information has been used to provide
-    the initial answer. Some other subquestions may have been suggested based on little knowledge, but they
-    were not directly answerable. Also, some entities, relationships and terms are givenm to you so that
-    you have an idea of how the avaiolable data looks like.
-
-    Your role is to generate 4-6 new sub-questions that would help to answer the initial question,
-    considering:
-
-    1) The initial question
-    2) The initial answer that was found to be unsatisfactory
-    3) The sub-questions that were answered
-    4) The sub-questions that were suggested but not answered
-    5) The entities, relationships and terms that were extracted from the context
-
-    The individual questions should be answerable by a good RAG system.
-    So a good idea would be to use the sub-questions to resolve ambiguities and/or to separate the
-    question for different entities that may be involved in the original question, but in a way that does
-    not duplicate questions that were already tried.
-
-    Additional Guidelines:
-    - The sub-questions should be specific to the question and provide richer context for the question,
-    resolve ambiguities, or address shortcoming of the initial answer
-    - Each sub-question - when answered - should be relevant for the answer to the original question
-    - The sub-questions should be free from comparisions, ambiguities,judgements, aggregations, or any
-    other complications that may require extra context.
-    - The sub-questions MUST have the full context of the original question so that it can be executed by
-    a RAG system independently without the original question available
-      (Example:
-        - initial question: "What is the capital of France?"
-        - bad sub-question: "What is the name of the river there?"
-        - good sub-question: "What is the name of the river that flows through Paris?"
-    - For each sub-question, please also provide a search term that can be used to retrieve relevant
-    documents from a document store.
-    \n\n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-
-    Here is the initial sub-optimal answer:
-    \n ------- \n
-    {base_answer}
-    \n ------- \n
-
-    Here are the sub-questions that were answered:
-    \n ------- \n
-    {answered_sub_questions}
-    \n ------- \n
-
-    Here are the sub-questions that were suggested but not answered:
-    \n ------- \n
-    {failed_sub_questions}
-    \n ------- \n
-
-    And here are the entities, relationships and terms extracted from the context:
-    \n ------- \n
-    {entity_term_extraction_str}
-    \n ------- \n
-
-   Please generate the list of good, fully contextualized sub-questions that would help to address the
-   main question. Again, please find questions that are NOT overlapping too much with the already answered
-   sub-questions or those that already were suggested and failed.
-   In other words - what can we try in addition to what has been tried so far?
-
-   Generate the list of json dictionaries with the following format:
-
-   {{"sub_questions": [{{"sub_question": <sub-question>,
-        "search_term": <rewrite the sub-question using as a search phrase for the document store>}},
-        ...]}} """
-
-DECOMPOSE_PROMPT = """ \n
-    For an initial user question, please generate at 5-10 individual sub-questions whose answers would help
-    \n to answer the initial question. The individual questions should be answerable by a good RAG system.
-    So a good idea would be to \n use the sub-questions to resolve ambiguities and/or to separate the
-    question for different entities that may be involved in the original question.
-
-    In order to arrive at meaningful sub-questions, please also consider the context retrieved from the
-    document store, expressed as entities, relationships and terms. You can also think about the types
-    mentioned in brackets
-
-    Guidelines:
-    - The sub-questions should be specific to the question and provide richer context for the question,
-    and or resolve ambiguities
-    - Each sub-question - when answered - should be relevant for the answer to the original question
-    - The sub-questions should be free from comparisions, ambiguities,judgements, aggregations, or any
-    other complications that may require extra context.
-    - The sub-questions MUST have the full context of the original question so that it can be executed by
-    a RAG system independently without the original question available
-      (Example:
-        - initial question: "What is the capital of France?"
-        - bad sub-question: "What is the name of the river there?"
-        - good sub-question: "What is the name of the river that flows through Paris?"
-    - For each sub-question, please provide a short explanation for why it is a good sub-question. So
-    generate a list of dictionaries with the following format:
-      [{{"sub_question": <sub-question>, "explanation": <explanation>, "search_term": <rewrite the
-      sub-question using as a search phrase for the document store>}}, ...]
-
-    \n\n
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-
-    And here are the entities, relationships and terms extracted from the context:
-    \n ------- \n
-    {entity_term_extraction_str}
-    \n ------- \n
-
-   Please generate the list of good, fully contextualized sub-questions that would help to address the
-   main question. Don't be too specific unless the original question is specific.
-   Please think through it step by step and then generate the list of json dictionaries with the following
-   format:
-   {{"sub_questions": [{{"sub_question": <sub-question>,
-        "explanation": <explanation>,
-        "search_term": <rewrite the sub-question using as a search phrase for the document store>}},
-        ...]}} """
-
-#### Consolidations
-COMBINED_CONTEXT = """-------
-    Below you will find useful information to answer the original question. First, you see a number of
-    sub-questions with their answers. This information should be considered to be more focussed and
-    somewhat more specific to the original question as it tries to contextualized facts.
-    After that will see the documents that were considered to be relevant to answer the original question.
-
-    Here are the sub-questions and their answers:
-    \n\n {deep_answer_context} \n\n
-    \n\n Here are the documents that were considered to be relevant to answer the original question:
-    \n\n {formated_docs} \n\n
-    ----------------
-    """
-
-SUB_QUESTION_EXPLANATION_RANKER_PROMPT = """-------
-    Below you will find a question that we ultimately want to answer (the original question) and a list of
-    motivations in arbitrary order for generated sub-questions that are supposed to help us answering the
-    original question. The motivations are formatted as <motivation number>:  <motivation explanation>.
-    (Again, the numbering is arbitrary and does not necessarily mean that 1 is the most relevant
-    motivation and 2 is less relevant.)
-
-    Please rank the motivations in order of relevance for answering the original question. Also, try to
-    ensure that the top questions do not duplicate too much, i.e. that they are not too similar.
-    Ultimately, create a list with the motivation numbers where the number of the most relevant
-    motivations comes first.
-
-    Here is the original question:
-    \n\n {original_question} \n\n
-    \n\n Here is the list of sub-question motivations:
-    \n\n {sub_question_explanations} \n\n
-    ----------------
-
-    Please think step by step and then generate the ranked list of motivations.
-
-    Please format your answer as a json object in the following format:
-    {{"reasonning": <explain your reasoning for the ranking>,
-      "ranked_motivations": <ranked list of motivation numbers>}}
-    """
-
-
-INITIAL_DECOMPOSITION_PROMPT = """ \n
-    Please decompose an initial user question into 2 or 3 appropriate sub-questions that help to
-    answer the original question. The purpose for this decomposition is to isolate individulal entities
-    (i.e., 'compare sales of company A and company B' -> 'what are sales for company A' + 'what are sales
-    for company B'), split ambiguous terms (i.e., 'what is our success with company A' -> 'what are our
-    sales with company A' + 'what is our market share with company A' + 'is company A a reference customer
-    for us'), etc. Each sub-question should be realistically be answerable by a good RAG system. \n
-
-    For each sub-question, please also create one search term that can be used to retrieve relevant
-    documents from a document store.
-
-    Here is the initial question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-
-    Please formulate your answer as a list of json objects with the following format:
-
-   [{{"sub_question": <sub-question>, "search_term": <search term>}}, ...]
-
-    Answer:
-    """
-
-INITIAL_RAG_PROMPT = """ \n
-    You are an assistant for question-answering tasks. Use the information provided below - and only the
-    provided information - to answer the provided question.
-
-    The information provided below consists of:
-     1) a number of answered sub-questions - these are very important(!) and definitely should be
-     considered to answer the question.
-     2) a number of documents that were also deemed relevant for the question.
-
-    If you don't know the answer or if the provided information is empty or insufficient, just say
-    "I don't know". Do not use your internal knowledge!
-
-    Again, only use the provided informationand do not use your internal knowledge! It is a matter of life
-    and death that you do NOT use your internal knowledge, just the provided information!
-
-    Try to keep your answer concise.
-
-    And here is the question and the provided information:
-    \n
-    \nQuestion:\n {question}
-
-    \nAnswered Sub-questions:\n {answered_sub_questions}
-
-    \nContext:\n {context} \n\n
-    \n\n
-
-    Answer:"""
-
-ENTITY_TERM_PROMPT = """ \n
-    Based on the original question and the context retieved from a dataset, please generate a list of
-    entities (e.g. companies, organizations, industries, products, locations, etc.), terms and concepts
-    (e.g. sales, revenue, etc.) that are relevant for the question, plus their relations to each other.
-
-    \n\n
-    Here is the original question:
-    \n ------- \n
-    {question}
-    \n ------- \n
-   And here is the context retrieved:
-    \n ------- \n
-    {context}
-    \n ------- \n
-
-    Please format your answer as a json object in the following format:
-
-    {{"retrieved_entities_relationships": {{
-        "entities": [{{
-            "entity_name": <assign a name for the entity>,
-            "entity_type": <specify a short type name for the entity, such as 'company', 'location',...>
-        }}],
-        "relationships": [{{
-            "name": <assign a name for the relationship>,
-            "type": <specify a short type name for the relationship, such as 'sales_to', 'is_location_of',...>,
-            "entities": [<related entity name 1>, <related entity name 2>]
-        }}],
-        "terms": [{{
-            "term_name": <assign a name for the term>,
-            "term_type": <specify a short type name for the term, such as 'revenue', 'market_share',...>,
-            "similar_to": <list terms that are similar to this term>
-        }}]
-    }}
-    }}
-   """

backend/danswer/agent_search/shared_graph_utils/utils.py

@@ -1,101 +0,0 @@
-import ast
-import json
-import re
-from collections.abc import Sequence
-from datetime import datetime
-from datetime import timedelta
-from typing import Any
-
-from danswer.context.search.models import InferenceSection
-
-
-def normalize_whitespace(text: str) -> str:
-    """Normalize whitespace in text to single spaces and strip leading/trailing whitespace."""
-    import re
-
-    return re.sub(r"\s+", " ", text.strip())
-
-
-# Post-processing
-def format_docs(docs: Sequence[InferenceSection]) -> str:
-    return "\n\n".join(doc.combined_content for doc in docs)
-
-
-def clean_and_parse_list_string(json_string: str) -> list[dict]:
-    # Remove any prefixes/labels before the actual JSON content
-    json_string = re.sub(r"^.*?(?=\[)", "", json_string, flags=re.DOTALL)
-
-    # Remove markdown code block markers and any newline prefixes
-    cleaned_string = re.sub(r"```json\n|\n```", "", json_string)
-    cleaned_string = cleaned_string.replace("\\n", " ").replace("\n", " ")
-    cleaned_string = " ".join(cleaned_string.split())
-
-    # Try parsing with json.loads first, fall back to ast.literal_eval
-    try:
-        return json.loads(cleaned_string)
-    except json.JSONDecodeError:
-        try:
-            return ast.literal_eval(cleaned_string)
-        except (ValueError, SyntaxError) as e:
-            raise ValueError(f"Failed to parse JSON string: {cleaned_string}") from e
-
-
-def clean_and_parse_json_string(json_string: str) -> dict[str, Any]:
-    # Remove markdown code block markers and any newline prefixes
-    cleaned_string = re.sub(r"```json\n|\n```", "", json_string)
-    cleaned_string = cleaned_string.replace("\\n", " ").replace("\n", " ")
-    cleaned_string = " ".join(cleaned_string.split())
-    # Parse the cleaned string into a Python dictionary
-    return json.loads(cleaned_string)
-
-
-def format_entity_term_extraction(entity_term_extraction_dict: dict[str, Any]) -> str:
-    entities = entity_term_extraction_dict["entities"]
-    terms = entity_term_extraction_dict["terms"]
-    relationships = entity_term_extraction_dict["relationships"]
-
-    entity_strs = ["\nEntities:\n"]
-    for entity in entities:
-        entity_str = f"{entity['entity_name']} ({entity['entity_type']})"
-        entity_strs.append(entity_str)
-
-    entity_str = "\n - ".join(entity_strs)
-
-    relationship_strs = ["\n\nRelationships:\n"]
-    for relationship in relationships:
-        relationship_str = f"{relationship['name']} ({relationship['type']}): {relationship['entities']}"
-        relationship_strs.append(relationship_str)
-
-    relationship_str = "\n - ".join(relationship_strs)
-
-    term_strs = ["\n\nTerms:\n"]
-    for term in terms:
-        term_str = f"{term['term_name']} ({term['term_type']}): similar to {term['similar_to']}"
-        term_strs.append(term_str)
-
-    term_str = "\n - ".join(term_strs)
-
-    return "\n".join(entity_strs + relationship_strs + term_strs)
-
-
-def _format_time_delta(time: timedelta) -> str:
-    seconds_from_start = f"{((time).seconds):03d}"
-    microseconds_from_start = f"{((time).microseconds):06d}"
-    return f"{seconds_from_start}.{microseconds_from_start}"
-
-
-def generate_log_message(
-    message: str,
-    node_start_time: datetime,
-    graph_start_time: datetime | None = None,
-) -> str:
-    current_time = datetime.now()
-
-    if graph_start_time is not None:
-        graph_time_str = _format_time_delta(current_time - graph_start_time)
-    else:
-        graph_time_str = "N/A"
-
-    node_time_str = _format_time_delta(current_time - node_start_time)
-
-    return f"{graph_time_str} ({node_time_str} s): {message}"

backend/danswer/auth/api_key.py

@@ -1,89 +0,0 @@
-import secrets
-import uuid
-from urllib.parse import quote
-from urllib.parse import unquote
-
-from fastapi import Request
-from passlib.hash import sha256_crypt
-from pydantic import BaseModel
-
-from danswer.auth.schemas import UserRole
-from danswer.configs.app_configs import API_KEY_HASH_ROUNDS
-
-
-_API_KEY_HEADER_NAME = "Authorization"
-# NOTE for others who are curious: In the context of a header, "X-" often refers
-# to non-standard, experimental, or custom headers in HTTP or other protocols. It
-# indicates that the header is not part of the official standards defined by
-# organizations like the Internet Engineering Task Force (IETF).
-_API_KEY_HEADER_ALTERNATIVE_NAME = "X-Danswer-Authorization"
-_BEARER_PREFIX = "Bearer "
-_API_KEY_PREFIX = "dn_"
-_API_KEY_LEN = 192
-
-
-class ApiKeyDescriptor(BaseModel):
-    api_key_id: int
-    api_key_display: str
-    api_key: str | None = None  # only present on initial creation
-    api_key_name: str | None = None
-    api_key_role: UserRole
-
-    user_id: uuid.UUID
-
-
-def generate_api_key(tenant_id: str | None = None) -> str:
-    # For backwards compatibility, if no tenant_id, generate old style key
-    if not tenant_id:
-        return _API_KEY_PREFIX + secrets.token_urlsafe(_API_KEY_LEN)
-
-    encoded_tenant = quote(tenant_id)  # URL encode the tenant ID
-    return f"{_API_KEY_PREFIX}{encoded_tenant}.{secrets.token_urlsafe(_API_KEY_LEN)}"
-
-
-def extract_tenant_from_api_key_header(request: Request) -> str | None:
-    """Extract tenant ID from request. Returns None if auth is disabled or invalid format."""
-    raw_api_key_header = request.headers.get(
-        _API_KEY_HEADER_ALTERNATIVE_NAME
-    ) or request.headers.get(_API_KEY_HEADER_NAME)
-
-    if not raw_api_key_header or not raw_api_key_header.startswith(_BEARER_PREFIX):
-        return None
-
-    api_key = raw_api_key_header[len(_BEARER_PREFIX) :].strip()
-
-    if not api_key.startswith(_API_KEY_PREFIX):
-        return None
-
-    parts = api_key[len(_API_KEY_PREFIX) :].split(".", 1)
-    if len(parts) != 2:
-        return None
-
-    tenant_id = parts[0]
-    return unquote(tenant_id) if tenant_id else None
-
-
-def hash_api_key(api_key: str) -> str:
-    # NOTE: no salt is needed, as the API key is randomly generated
-    # and overlaps are impossible
-    return sha256_crypt.hash(api_key, salt="", rounds=API_KEY_HASH_ROUNDS)
-
-
-def build_displayable_api_key(api_key: str) -> str:
-    if api_key.startswith(_API_KEY_PREFIX):
-        api_key = api_key[len(_API_KEY_PREFIX) :]
-
-    return _API_KEY_PREFIX + api_key[:4] + "********" + api_key[-4:]
-
-
-def get_hashed_api_key_from_request(request: Request) -> str | None:
-    raw_api_key_header = request.headers.get(
-        _API_KEY_HEADER_ALTERNATIVE_NAME
-    ) or request.headers.get(_API_KEY_HEADER_NAME)
-    if raw_api_key_header is None:
-        return None
-
-    if raw_api_key_header.startswith(_BEARER_PREFIX):
-        raw_api_key_header = raw_api_key_header[len(_BEARER_PREFIX) :].strip()
-
-    return hash_api_key(raw_api_key_header)

backend/danswer/auth/invited_users.py

@@ -2,14 +2,13 @@ from typing import cast
 
 from danswer.configs.constants import KV_USER_STORE_KEY
 from danswer.key_value_store.factory import get_kv_store
+from danswer.key_value_store.interface import JSON_ro
 from danswer.key_value_store.interface import KvKeyNotFoundError
-from danswer.utils.special_types import JSON_ro
 
 
 def get_invited_users() -> list[str]:
     try:
         store = get_kv_store()
-
         return cast(list, store.load(KV_USER_STORE_KEY))
     except KvKeyNotFoundError:
         return list()

backend/danswer/auth/noauth_user.py

@@ -23,9 +23,7 @@ def load_no_auth_user_preferences(store: KeyValueStore) -> UserPreferences:
         )
         return UserPreferences(**preferences_data)
     except KvKeyNotFoundError:
-        return UserPreferences(
-            chosen_assistants=None, default_model=None, auto_scroll=True
-        )
+        return UserPreferences(chosen_assistants=None, default_model=None)
 
 
 def fetch_no_auth_user(store: KeyValueStore) -> UserInfo:

backend/danswer/auth/schemas.py

@@ -13,24 +13,12 @@ class UserRole(str, Enum):
         groups they are curators of
     - Global Curator can perform admin actions
         for all groups they are a member of
-    - Limited can access a limited set of basic api endpoints
-    - Slack are users that have used danswer via slack but dont have a web login
-    - External permissioned users that have been picked up during the external permissions sync process but don't have a web login
     """
 
-    LIMITED = "limited"
     BASIC = "basic"
     ADMIN = "admin"
     CURATOR = "curator"
     GLOBAL_CURATOR = "global_curator"
-    SLACK_USER = "slack_user"
-    EXT_PERM_USER = "ext_perm_user"
-
-    def is_web_login(self) -> bool:
-        return self not in [
-            UserRole.SLACK_USER,
-            UserRole.EXT_PERM_USER,
-        ]
 
 
 class UserStatus(str, Enum):
@@ -45,8 +33,10 @@ class UserRead(schemas.BaseUser[uuid.UUID]):
 
 class UserCreate(schemas.BaseUserCreate):
     role: UserRole = UserRole.BASIC
+    has_web_login: bool | None = True
     tenant_id: str | None = None
 
 
 class UserUpdate(schemas.BaseUserUpdate):
     role: UserRole
+    has_web_login: bool | None = True

backend/danswer/auth/users.py

@@ -48,19 +48,20 @@ from httpx_oauth.integrations.fastapi import OAuth2AuthorizeCallback
 from httpx_oauth.oauth2 import BaseOAuth2
 from httpx_oauth.oauth2 import OAuth2Token
 from pydantic import BaseModel
-from sqlalchemy import text
-from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select
+from sqlalchemy.orm import attributes
+from sqlalchemy.orm import Session
 
-from danswer.auth.api_key import get_hashed_api_key_from_request
 from danswer.auth.invited_users import get_invited_users
 from danswer.auth.schemas import UserCreate
 from danswer.auth.schemas import UserRole
 from danswer.auth.schemas import UserUpdate
 from danswer.configs.app_configs import AUTH_TYPE
 from danswer.configs.app_configs import DISABLE_AUTH
-from danswer.configs.app_configs import DISABLE_VERIFICATION
 from danswer.configs.app_configs import EMAIL_FROM
+from danswer.configs.app_configs import MULTI_TENANT
 from danswer.configs.app_configs import REQUIRE_EMAIL_VERIFICATION
+from danswer.configs.app_configs import SECRET_JWT_KEY
 from danswer.configs.app_configs import SESSION_EXPIRE_TIME_SECONDS
 from danswer.configs.app_configs import SMTP_PASS
 from danswer.configs.app_configs import SMTP_PORT
@@ -74,28 +75,25 @@ from danswer.configs.constants import AuthType
 from danswer.configs.constants import DANSWER_API_KEY_DUMMY_EMAIL_DOMAIN
 from danswer.configs.constants import DANSWER_API_KEY_PREFIX
 from danswer.configs.constants import UNNAMED_KEY_PLACEHOLDER
-from danswer.db.api_key import fetch_user_for_api_key
 from danswer.db.auth import get_access_token_db
 from danswer.db.auth import get_default_admin_user_emails
 from danswer.db.auth import get_user_count
 from danswer.db.auth import get_user_db
 from danswer.db.auth import SQLAlchemyUserAdminDB
-from danswer.db.engine import get_async_session
 from danswer.db.engine import get_async_session_with_tenant
+from danswer.db.engine import get_session
 from danswer.db.engine import get_session_with_tenant
+from danswer.db.engine import get_sqlalchemy_engine
 from danswer.db.models import AccessToken
 from danswer.db.models import OAuthAccount
 from danswer.db.models import User
+from danswer.db.models import UserTenantMapping
 from danswer.db.users import get_user_by_email
-from danswer.server.utils import BasicAuthenticationError
 from danswer.utils.logger import setup_logger
 from danswer.utils.telemetry import optional_telemetry
 from danswer.utils.telemetry import RecordType
-from danswer.utils.variable_functionality import fetch_ee_implementation_or_noop
 from danswer.utils.variable_functionality import fetch_versioned_implementation
-from shared_configs.configs import async_return_default_schema
-from shared_configs.configs import MULTI_TENANT
-from shared_configs.contextvars import CURRENT_TENANT_ID_CONTEXTVAR
+from shared_configs.configs import current_tenant_id
 
 logger = setup_logger()
 
@@ -134,9 +132,7 @@ def get_display_email(email: str | None, space_less: bool = False) -> str:
 def user_needs_to_be_verified() -> bool:
     # all other auth types besides basic should require users to be
     # verified
-    return not DISABLE_VERIFICATION and (
-        AUTH_TYPE != AuthType.BASIC or REQUIRE_EMAIL_VERIFICATION
-    )
+    return AUTH_TYPE != AuthType.BASIC or REQUIRE_EMAIL_VERIFICATION
 
 
 def verify_email_is_invited(email: str) -> None:
@@ -189,6 +185,20 @@ def verify_email_domain(email: str) -> None:
             )
 
 
+def get_tenant_id_for_email(email: str) -> str:
+    if not MULTI_TENANT:
+        return "public"
+    # Implement logic to get tenant_id from the mapping table
+    with Session(get_sqlalchemy_engine()) as db_session:
+        result = db_session.execute(
+            select(UserTenantMapping.tenant_id).where(UserTenantMapping.email == email)
+        )
+        tenant_id = result.scalar_one_or_none()
+    if tenant_id is None:
+        raise exceptions.UserNotExists()
+    return tenant_id
+
+
 def send_user_verification_email(
     user_email: str,
     token: str,
@@ -217,36 +227,31 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
     reset_password_token_secret = USER_AUTH_SECRET
     verification_token_secret = USER_AUTH_SECRET
 
-    user_db: SQLAlchemyUserDatabase[User, uuid.UUID]
-
     async def create(
         self,
         user_create: schemas.UC | UserCreate,
         safe: bool = False,
         request: Optional[Request] = None,
     ) -> User:
-        referral_source = None
-        if request is not None:
-            referral_source = request.cookies.get("referral_source", None)
+        try:
+            tenant_id = (
+                get_tenant_id_for_email(user_create.email) if MULTI_TENANT else "public"
+            )
+        except exceptions.UserNotExists:
+            raise HTTPException(status_code=401, detail="User not found")
 
-        tenant_id = await fetch_ee_implementation_or_noop(
-            "danswer.server.tenants.provisioning",
-            "get_or_create_tenant_id",
-            async_return_default_schema,
-        )(
-            email=user_create.email,
-            referral_source=referral_source,
-        )
+        if not tenant_id:
+            raise HTTPException(
+                status_code=401, detail="User does not belong to an organization"
+            )
 
         async with get_async_session_with_tenant(tenant_id) as db_session:
-            token = CURRENT_TENANT_ID_CONTEXTVAR.set(tenant_id)
+            token = current_tenant_id.set(tenant_id)
 
             verify_email_is_invited(user_create.email)
             verify_email_domain(user_create.email)
             if MULTI_TENANT:
-                tenant_user_db = SQLAlchemyUserAdminDB[User, uuid.UUID](
-                    db_session, User, OAuthAccount
-                )
+                tenant_user_db = SQLAlchemyUserAdminDB(db_session, User, OAuthAccount)
                 self.user_db = tenant_user_db
                 self.database = tenant_user_db
 
@@ -259,15 +264,20 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
                     user_create.role = UserRole.ADMIN
                 else:
                     user_create.role = UserRole.BASIC
-
+            user = None
             try:
                 user = await super().create(user_create, safe=safe, request=request)  # type: ignore
             except exceptions.UserAlreadyExists:
                 user = await self.get_by_email(user_create.email)
                 # Handle case where user has used product outside of web and is now creating an account through web
-                if not user.role.is_web_login() and user_create.role.is_web_login():
+                if (
+                    not user.has_web_login
+                    and hasattr(user_create, "has_web_login")
+                    and user_create.has_web_login
+                ):
                     user_update = UserUpdate(
                         password=user_create.password,
+                        has_web_login=True,
                         role=user_create.role,
                         is_verified=user_create.is_verified,
                     )
@@ -275,13 +285,34 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
                 else:
                     raise exceptions.UserAlreadyExists()
 
-            finally:
-                CURRENT_TENANT_ID_CONTEXTVAR.reset(token)
-
+            current_tenant_id.reset(token)
             return user
 
-    async def oauth_callback(
+    async def on_after_login(
         self,
+        user: User,
+        request: Request | None = None,
+        response: Response | None = None,
+    ) -> None:
+        if response is None or not MULTI_TENANT:
+            return
+
+        tenant_id = get_tenant_id_for_email(user.email)
+
+        tenant_token = jwt.encode(
+            {"tenant_id": tenant_id}, SECRET_JWT_KEY, algorithm="HS256"
+        )
+
+        response.set_cookie(
+            key="tenant_details",
+            value=tenant_token,
+            httponly=True,
+            secure=WEB_DOMAIN.startswith("https"),
+            samesite="lax",
+        )
+
+    async def oauth_callback(
+        self: "BaseUserManager[models.UOAP, models.ID]",
         oauth_name: str,
         access_token: str,
         account_id: str,
@@ -292,37 +323,28 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
         *,
         associate_by_email: bool = False,
         is_verified_by_default: bool = False,
-    ) -> User:
-        referral_source = None
-        if request:
-            referral_source = getattr(request.state, "referral_source", None)
-
-        tenant_id = await fetch_ee_implementation_or_noop(
-            "danswer.server.tenants.provisioning",
-            "get_or_create_tenant_id",
-            async_return_default_schema,
-        )(
-            email=account_email,
-            referral_source=referral_source,
-        )
+    ) -> models.UOAP:
+        # Get tenant_id from mapping table
+        try:
+            tenant_id = (
+                get_tenant_id_for_email(account_email) if MULTI_TENANT else "public"
+            )
+        except exceptions.UserNotExists:
+            raise HTTPException(status_code=401, detail="User not found")
 
         if not tenant_id:
             raise HTTPException(status_code=401, detail="User not found")
 
-        # Proceed with the tenant context
         token = None
         async with get_async_session_with_tenant(tenant_id) as db_session:
-            token = CURRENT_TENANT_ID_CONTEXTVAR.set(tenant_id)
+            token = current_tenant_id.set(tenant_id)
 
             verify_email_in_whitelist(account_email, tenant_id)
             verify_email_domain(account_email)
-
             if MULTI_TENANT:
-                tenant_user_db = SQLAlchemyUserAdminDB[User, uuid.UUID](
-                    db_session, User, OAuthAccount
-                )
+                tenant_user_db = SQLAlchemyUserAdminDB(db_session, User, OAuthAccount)
                 self.user_db = tenant_user_db
-                self.database = tenant_user_db
+                self.database = tenant_user_db  # type: ignore
 
             oauth_account_dict = {
                 "oauth_name": oauth_name,
@@ -358,13 +380,9 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
                     }
 
                     user = await self.user_db.create(user_dict)
-
-                    # Explicitly set the Postgres schema for this session to ensure
-                    # OAuth account creation happens in the correct tenant schema
-                    await db_session.execute(text(f'SET search_path = "{tenant_id}"'))
-
-                    # Add OAuth account
-                    await self.user_db.add_oauth_account(user, oauth_account_dict)
+                    user = await self.user_db.add_oauth_account(
+                        user, oauth_account_dict
+                    )
                     await self.on_after_register(user, request)
 
             else:
@@ -374,11 +392,7 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
                         and existing_oauth_account.oauth_name == oauth_name
                     ):
                         user = await self.user_db.update_oauth_account(
-                            user,
-                            # NOTE: OAuthAccount DOES implement the OAuthAccountProtocol
-                            # but the type checker doesn't know that :(
-                            existing_oauth_account,  # type: ignore
-                            oauth_account_dict,
+                            user, existing_oauth_account, oauth_account_dict
                         )
 
             # NOTE: Most IdPs have very short expiry times, and we don't want to force the user to
@@ -391,15 +405,16 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
                 )
 
             # Handle case where user has used product outside of web and is now creating an account through web
-            if not user.role.is_web_login():
+            if not user.has_web_login:  # type: ignore
                 await self.user_db.update(
                     user,
                     {
                         "is_verified": is_verified_by_default,
-                        "role": UserRole.BASIC,
+                        "has_web_login": True,
                     },
                 )
                 user.is_verified = is_verified_by_default
+                user.has_web_login = True  # type: ignore
 
             # this is needed if an organization goes from `TRACK_EXTERNAL_IDP_EXPIRY=true` to `false`
             # otherwise, the oidc expiry will always be old, and the user will never be able to login
@@ -411,7 +426,7 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
                 user.oidc_expiry = None  # type: ignore
 
             if token:
-                CURRENT_TENANT_ID_CONTEXTVAR.reset(token)
+                current_tenant_id.reset(token)
 
             return user
 
@@ -447,13 +462,7 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
         email = credentials.username
 
         # Get tenant_id from mapping table
-        tenant_id = await fetch_ee_implementation_or_noop(
-            "danswer.server.tenants.provisioning",
-            "get_or_create_tenant_id",
-            async_return_default_schema,
-        )(
-            email=email,
-        )
+        tenant_id = get_tenant_id_for_email(email)
         if not tenant_id:
             # User not found in mapping
             self.password_helper.hash(credentials.password)
@@ -474,8 +483,11 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
                 self.password_helper.hash(credentials.password)
                 return None
 
-            if not user.role.is_web_login():
-                raise BasicAuthenticationError(
+            has_web_login = attributes.get_attribute(user, "has_web_login")
+
+            if not has_web_login:
+                raise HTTPException(
+                    status_code=status.HTTP_403_FORBIDDEN,
                     detail="NO_WEB_LOGIN_AND_HAS_NO_PASSWORD",
                 )
 
@@ -505,33 +517,8 @@ cookie_transport = CookieTransport(
 )
 
 
-# This strategy is used to add tenant_id to the JWT token
-class TenantAwareJWTStrategy(JWTStrategy):
-    async def _create_token_data(self, user: User, impersonate: bool = False) -> dict:
-        tenant_id = await fetch_ee_implementation_or_noop(
-            "danswer.server.tenants.provisioning",
-            "get_or_create_tenant_id",
-            async_return_default_schema,
-        )(
-            email=user.email,
-        )
-
-        data = {
-            "sub": str(user.id),
-            "aud": self.token_audience,
-            "tenant_id": tenant_id,
-        }
-        return data
-
-    async def write_token(self, user: User) -> str:
-        data = await self._create_token_data(user)
-        return generate_jwt(
-            data, self.encode_key, self.lifetime_seconds, algorithm=self.algorithm
-        )
-
-
-def get_jwt_strategy() -> TenantAwareJWTStrategy:
-    return TenantAwareJWTStrategy(
+def get_jwt_strategy() -> JWTStrategy:
+    return JWTStrategy(
         secret=USER_AUTH_SECRET,
         lifetime_seconds=SESSION_EXPIRE_TIME_SECONDS,
     )
@@ -605,7 +592,7 @@ optional_fastapi_current_user = fastapi_users.current_user(active=True, optional
 async def optional_user_(
     request: Request,
     user: User | None,
-    async_db_session: AsyncSession,
+    db_session: Session,
 ) -> User | None:
     """NOTE: `request` and `db_session` are not used here, but are included
     for the EE version of this function."""
@@ -614,21 +601,13 @@ async def optional_user_(
 
 async def optional_user(
     request: Request,
-    async_db_session: AsyncSession = Depends(get_async_session),
+    db_session: Session = Depends(get_session),
     user: User | None = Depends(optional_fastapi_current_user),
 ) -> User | None:
     versioned_fetch_user = fetch_versioned_implementation(
         "danswer.auth.users", "optional_user_"
     )
-    user = await versioned_fetch_user(request, user, async_db_session)
-
-    # check if an API key is present
-    if user is None:
-        hashed_api_key = get_hashed_api_key_from_request(request)
-        if hashed_api_key:
-            user = await fetch_user_for_api_key(hashed_api_key, async_db_session)
-
-    return user
+    return await versioned_fetch_user(request, user, db_session)
 
 
 async def double_check_user(
@@ -640,12 +619,14 @@ async def double_check_user(
         return None
 
     if user is None:
-        raise BasicAuthenticationError(
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
             detail="Access denied. User is not authenticated.",
         )
 
     if user_needs_to_be_verified() and not user.is_verified:
-        raise BasicAuthenticationError(
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
             detail="Access denied. User is not verified.",
         )
 
@@ -654,7 +635,8 @@ async def double_check_user(
         and user.oidc_expiry < datetime.now(timezone.utc)
         and not include_expired
     ):
-        raise BasicAuthenticationError(
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
             detail="Access denied. User's OIDC token has expired.",
         )
 
@@ -667,24 +649,10 @@ async def current_user_with_expired_token(
     return await double_check_user(user, include_expired=True)
 
 
-async def current_limited_user(
-    user: User | None = Depends(optional_user),
-) -> User | None:
-    return await double_check_user(user)
-
-
 async def current_user(
     user: User | None = Depends(optional_user),
 ) -> User | None:
-    user = await double_check_user(user)
-    if not user:
-        return None
-
-    if user.role == UserRole.LIMITED:
-        raise BasicAuthenticationError(
-            detail="Access denied. User role is LIMITED. BASIC or higher permissions are required.",
-        )
-    return user
+    return await double_check_user(user)
 
 
 async def current_curator_or_admin_user(
@@ -694,13 +662,15 @@ async def current_curator_or_admin_user(
         return None
 
     if not user or not hasattr(user, "role"):
-        raise BasicAuthenticationError(
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
             detail="Access denied. User is not authenticated or lacks role information.",
         )
 
     allowed_roles = {UserRole.GLOBAL_CURATOR, UserRole.CURATOR, UserRole.ADMIN}
     if user.role not in allowed_roles:
-        raise BasicAuthenticationError(
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
             detail="Access denied. User is not a curator or admin.",
         )
 
@@ -712,7 +682,8 @@ async def current_admin_user(user: User | None = Depends(current_user)) -> User
         return None
 
     if not user or not hasattr(user, "role") or user.role != UserRole.ADMIN:
-        raise BasicAuthenticationError(
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
             detail="Access denied. User must be an admin to perform this action.",
         )
 
@@ -740,6 +711,8 @@ def generate_state_token(
 
 
 # refer to https://github.com/fastapi-users/fastapi-users/blob/42ddc241b965475390e2bce887b084152ae1a2cd/fastapi_users/fastapi_users.py#L91
+
+
 def create_danswer_oauth_router(
     oauth_client: BaseOAuth2,
     backend: AuthenticationBackend,
@@ -789,22 +762,15 @@ def get_oauth_router(
         response_model=OAuth2AuthorizeResponse,
     )
     async def authorize(
-        request: Request,
-        scopes: List[str] = Query(None),
+        request: Request, scopes: List[str] = Query(None)
     ) -> OAuth2AuthorizeResponse:
-        referral_source = request.cookies.get("referral_source", None)
-
         if redirect_url is not None:
             authorize_redirect_url = redirect_url
         else:
             authorize_redirect_url = str(request.url_for(callback_route_name))
 
         next_url = request.query_params.get("next", "/")
-
-        state_data: Dict[str, str] = {
-            "next_url": next_url,
-            "referral_source": referral_source or "default_referral",
-        }
+        state_data: Dict[str, str] = {"next_url": next_url}
         state = generate_state_token(state_data, state_secret)
         authorization_url = await oauth_client.get_authorization_url(
             authorize_redirect_url,
@@ -863,11 +829,8 @@ def get_oauth_router(
             raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST)
 
         next_url = state_data.get("next_url", "/")
-        referral_source = state_data.get("referral_source", None)
 
-        request.state.referral_source = referral_source
-
-        # Proceed to authenticate or create the user
+        # Authenticate user
         try:
             user = await user_manager.oauth_callback(
                 oauth_client.name,
@@ -909,25 +872,7 @@ def get_oauth_router(
             redirect_response.status_code = response.status_code
         if hasattr(response, "media_type"):
             redirect_response.media_type = response.media_type
+
         return redirect_response
 
     return router
-
-
-async def api_key_dep(
-    request: Request, async_db_session: AsyncSession = Depends(get_async_session)
-) -> User | None:
-    if AUTH_TYPE == AuthType.DISABLED:
-        return None
-
-    hashed_api_key = get_hashed_api_key_from_request(request)
-    if not hashed_api_key:
-        raise HTTPException(status_code=401, detail="Missing API key")
-
-    if hashed_api_key:
-        user = await fetch_user_for_api_key(hashed_api_key, async_db_session)
-
-    if user is None:
-        raise HTTPException(status_code=401, detail="Invalid API key")
-
-    return user

backend/danswer/background/celery/apps/app_base.py

@@ -1,403 +0,0 @@
-import logging
-import multiprocessing
-import time
-from typing import Any
-
-import requests
-import sentry_sdk
-from celery import Task
-from celery.app import trace
-from celery.exceptions import WorkerShutdown
-from celery.states import READY_STATES
-from celery.utils.log import get_task_logger
-from celery.worker import strategy  # type: ignore
-from redis.lock import Lock as RedisLock
-from sentry_sdk.integrations.celery import CeleryIntegration
-from sqlalchemy import text
-from sqlalchemy.orm import Session
-
-from danswer.background.celery.apps.task_formatters import CeleryTaskColoredFormatter
-from danswer.background.celery.apps.task_formatters import CeleryTaskPlainFormatter
-from danswer.background.celery.celery_utils import celery_is_worker_primary
-from danswer.configs.constants import DanswerRedisLocks
-from danswer.db.engine import get_sqlalchemy_engine
-from danswer.document_index.vespa_constants import VESPA_CONFIG_SERVER_URL
-from danswer.redis.redis_connector import RedisConnector
-from danswer.redis.redis_connector_credential_pair import RedisConnectorCredentialPair
-from danswer.redis.redis_connector_delete import RedisConnectorDelete
-from danswer.redis.redis_connector_doc_perm_sync import RedisConnectorPermissionSync
-from danswer.redis.redis_connector_ext_group_sync import RedisConnectorExternalGroupSync
-from danswer.redis.redis_connector_prune import RedisConnectorPrune
-from danswer.redis.redis_document_set import RedisDocumentSet
-from danswer.redis.redis_pool import get_redis_client
-from danswer.redis.redis_usergroup import RedisUserGroup
-from danswer.utils.logger import ColoredFormatter
-from danswer.utils.logger import PlainFormatter
-from danswer.utils.logger import setup_logger
-from shared_configs.configs import SENTRY_DSN
-
-
-logger = setup_logger()
-
-task_logger = get_task_logger(__name__)
-
-if SENTRY_DSN:
-    sentry_sdk.init(
-        dsn=SENTRY_DSN,
-        integrations=[CeleryIntegration()],
-        traces_sample_rate=0.1,
-    )
-    logger.info("Sentry initialized")
-else:
-    logger.debug("Sentry DSN not provided, skipping Sentry initialization")
-
-
-def on_task_prerun(
-    sender: Any | None = None,
-    task_id: str | None = None,
-    task: Task | None = None,
-    args: tuple | None = None,
-    kwargs: dict | None = None,
-    **kwds: Any,
-) -> None:
-    pass
-
-
-def on_task_postrun(
-    sender: Any | None = None,
-    task_id: str | None = None,
-    task: Task | None = None,
-    args: tuple | None = None,
-    kwargs: dict[str, Any] | None = None,
-    retval: Any | None = None,
-    state: str | None = None,
-    **kwds: Any,
-) -> None:
-    """We handle this signal in order to remove completed tasks
-    from their respective tasksets. This allows us to track the progress of document set
-    and user group syncs.
-
-    This function runs after any task completes (both success and failure)
-    Note that this signal does not fire on a task that failed to complete and is going
-    to be retried.
-
-    This also does not fire if a worker with acks_late=False crashes (which all of our
-    long running workers are)
-    """
-    if not task:
-        return
-
-    task_logger.debug(f"Task {task.name} (ID: {task_id}) completed with state: {state}")
-
-    if state not in READY_STATES:
-        return
-
-    if not task_id:
-        return
-
-    # Get tenant_id directly from kwargs- each celery task has a tenant_id kwarg
-    if not kwargs:
-        logger.error(f"Task {task.name} (ID: {task_id}) is missing kwargs")
-        tenant_id = None
-    else:
-        tenant_id = kwargs.get("tenant_id")
-
-    task_logger.debug(
-        f"Task {task.name} (ID: {task_id}) completed with state: {state} "
-        f"{f'for tenant_id={tenant_id}' if tenant_id else ''}"
-    )
-
-    r = get_redis_client(tenant_id=tenant_id)
-
-    if task_id.startswith(RedisConnectorCredentialPair.PREFIX):
-        r.srem(RedisConnectorCredentialPair.get_taskset_key(), task_id)
-        return
-
-    if task_id.startswith(RedisDocumentSet.PREFIX):
-        document_set_id = RedisDocumentSet.get_id_from_task_id(task_id)
-        if document_set_id is not None:
-            rds = RedisDocumentSet(tenant_id, int(document_set_id))
-            r.srem(rds.taskset_key, task_id)
-        return
-
-    if task_id.startswith(RedisUserGroup.PREFIX):
-        usergroup_id = RedisUserGroup.get_id_from_task_id(task_id)
-        if usergroup_id is not None:
-            rug = RedisUserGroup(tenant_id, int(usergroup_id))
-            r.srem(rug.taskset_key, task_id)
-        return
-
-    if task_id.startswith(RedisConnectorDelete.PREFIX):
-        cc_pair_id = RedisConnector.get_id_from_task_id(task_id)
-        if cc_pair_id is not None:
-            RedisConnectorDelete.remove_from_taskset(int(cc_pair_id), task_id, r)
-        return
-
-    if task_id.startswith(RedisConnectorPrune.SUBTASK_PREFIX):
-        cc_pair_id = RedisConnector.get_id_from_task_id(task_id)
-        if cc_pair_id is not None:
-            RedisConnectorPrune.remove_from_taskset(int(cc_pair_id), task_id, r)
-        return
-
-    if task_id.startswith(RedisConnectorPermissionSync.SUBTASK_PREFIX):
-        cc_pair_id = RedisConnector.get_id_from_task_id(task_id)
-        if cc_pair_id is not None:
-            RedisConnectorPermissionSync.remove_from_taskset(
-                int(cc_pair_id), task_id, r
-            )
-        return
-
-    if task_id.startswith(RedisConnectorExternalGroupSync.SUBTASK_PREFIX):
-        cc_pair_id = RedisConnector.get_id_from_task_id(task_id)
-        if cc_pair_id is not None:
-            RedisConnectorExternalGroupSync.remove_from_taskset(
-                int(cc_pair_id), task_id, r
-            )
-        return
-
-
-def on_celeryd_init(sender: Any = None, conf: Any = None, **kwargs: Any) -> None:
-    """The first signal sent on celery worker startup"""
-    multiprocessing.set_start_method("spawn")  # fork is unsafe, set to spawn
-
-
-def wait_for_redis(sender: Any, **kwargs: Any) -> None:
-    """Waits for redis to become ready subject to a hardcoded timeout.
-    Will raise WorkerShutdown to kill the celery worker if the timeout is reached."""
-
-    r = get_redis_client(tenant_id=None)
-
-    WAIT_INTERVAL = 5
-    WAIT_LIMIT = 60
-
-    ready = False
-    time_start = time.monotonic()
-    logger.info("Redis: Readiness probe starting.")
-    while True:
-        try:
-            if r.ping():
-                ready = True
-                break
-        except Exception:
-            pass
-
-        time_elapsed = time.monotonic() - time_start
-        if time_elapsed > WAIT_LIMIT:
-            break
-
-        logger.info(
-            f"Redis: Readiness probe ongoing. elapsed={time_elapsed:.1f} timeout={WAIT_LIMIT:.1f}"
-        )
-
-        time.sleep(WAIT_INTERVAL)
-
-    if not ready:
-        msg = (
-            f"Redis: Readiness probe did not succeed within the timeout "
-            f"({WAIT_LIMIT} seconds). Exiting..."
-        )
-        logger.error(msg)
-        raise WorkerShutdown(msg)
-
-    logger.info("Redis: Readiness probe succeeded. Continuing...")
-    return
-
-
-def wait_for_db(sender: Any, **kwargs: Any) -> None:
-    """Waits for the db to become ready subject to a hardcoded timeout.
-    Will raise WorkerShutdown to kill the celery worker if the timeout is reached."""
-
-    WAIT_INTERVAL = 5
-    WAIT_LIMIT = 60
-
-    ready = False
-    time_start = time.monotonic()
-    logger.info("Database: Readiness probe starting.")
-    while True:
-        try:
-            with Session(get_sqlalchemy_engine()) as db_session:
-                result = db_session.execute(text("SELECT NOW()")).scalar()
-                if result:
-                    ready = True
-                    break
-        except Exception:
-            pass
-
-        time_elapsed = time.monotonic() - time_start
-        if time_elapsed > WAIT_LIMIT:
-            break
-
-        logger.info(
-            f"Database: Readiness probe ongoing. elapsed={time_elapsed:.1f} timeout={WAIT_LIMIT:.1f}"
-        )
-
-        time.sleep(WAIT_INTERVAL)
-
-    if not ready:
-        msg = (
-            f"Database: Readiness probe did not succeed within the timeout "
-            f"({WAIT_LIMIT} seconds). Exiting..."
-        )
-        logger.error(msg)
-        raise WorkerShutdown(msg)
-
-    logger.info("Database: Readiness probe succeeded. Continuing...")
-    return
-
-
-def wait_for_vespa(sender: Any, **kwargs: Any) -> None:
-    """Waits for Vespa to become ready subject to a hardcoded timeout.
-    Will raise WorkerShutdown to kill the celery worker if the timeout is reached."""
-
-    WAIT_INTERVAL = 5
-    WAIT_LIMIT = 60
-
-    ready = False
-    time_start = time.monotonic()
-    logger.info("Vespa: Readiness probe starting.")
-    while True:
-        try:
-            response = requests.get(f"{VESPA_CONFIG_SERVER_URL}/state/v1/health")
-            response.raise_for_status()
-
-            response_dict = response.json()
-            if response_dict["status"]["code"] == "up":
-                ready = True
-                break
-        except Exception:
-            pass
-
-        time_elapsed = time.monotonic() - time_start
-        if time_elapsed > WAIT_LIMIT:
-            break
-
-        logger.info(
-            f"Vespa: Readiness probe ongoing. elapsed={time_elapsed:.1f} timeout={WAIT_LIMIT:.1f}"
-        )
-
-        time.sleep(WAIT_INTERVAL)
-
-    if not ready:
-        msg = (
-            f"Vespa: Readiness probe did not succeed within the timeout "
-            f"({WAIT_LIMIT} seconds). Exiting..."
-        )
-        logger.error(msg)
-        raise WorkerShutdown(msg)
-
-    logger.info("Vespa: Readiness probe succeeded. Continuing...")
-    return
-
-
-def on_secondary_worker_init(sender: Any, **kwargs: Any) -> None:
-    logger.info("Running as a secondary celery worker.")
-
-    # Set up variables for waiting on primary worker
-    WAIT_INTERVAL = 5
-    WAIT_LIMIT = 60
-    r = get_redis_client(tenant_id=None)
-    time_start = time.monotonic()
-
-    logger.info("Waiting for primary worker to be ready...")
-    while True:
-        if r.exists(DanswerRedisLocks.PRIMARY_WORKER):
-            break
-
-        time_elapsed = time.monotonic() - time_start
-        logger.info(
-            f"Primary worker is not ready yet. elapsed={time_elapsed:.1f} timeout={WAIT_LIMIT:.1f}"
-        )
-        if time_elapsed > WAIT_LIMIT:
-            msg = (
-                f"Primary worker was not ready within the timeout. "
-                f"({WAIT_LIMIT} seconds). Exiting..."
-            )
-            logger.error(msg)
-            raise WorkerShutdown(msg)
-
-        time.sleep(WAIT_INTERVAL)
-
-    logger.info("Wait for primary worker completed successfully. Continuing...")
-    return
-
-
-def on_worker_ready(sender: Any, **kwargs: Any) -> None:
-    task_logger.info("worker_ready signal received.")
-
-
-def on_worker_shutdown(sender: Any, **kwargs: Any) -> None:
-    if not celery_is_worker_primary(sender):
-        return
-
-    if not sender.primary_worker_lock:
-        return
-
-    logger.info("Releasing primary worker lock.")
-    lock: RedisLock = sender.primary_worker_lock
-    try:
-        if lock.owned():
-            try:
-                lock.release()
-                sender.primary_worker_lock = None
-            except Exception:
-                logger.exception("Failed to release primary worker lock")
-    except Exception:
-        logger.exception("Failed to check if primary worker lock is owned")
-
-
-def on_setup_logging(
-    loglevel: Any, logfile: Any, format: Any, colorize: Any, **kwargs: Any
-) -> None:
-    # TODO: could unhardcode format and colorize and accept these as options from
-    # celery's config
-
-    # reformats the root logger
-    root_logger = logging.getLogger()
-
-    root_handler = logging.StreamHandler()  # Set up a handler for the root logger
-    root_formatter = ColoredFormatter(
-        "%(asctime)s %(filename)30s %(lineno)4s: %(message)s",
-        datefmt="%m/%d/%Y %I:%M:%S %p",
-    )
-    root_handler.setFormatter(root_formatter)
-    root_logger.addHandler(root_handler)  # Apply the handler to the root logger
-
-    if logfile:
-        root_file_handler = logging.FileHandler(logfile)
-        root_file_formatter = PlainFormatter(
-            "%(asctime)s %(filename)30s %(lineno)4s: %(message)s",
-            datefmt="%m/%d/%Y %I:%M:%S %p",
-        )
-        root_file_handler.setFormatter(root_file_formatter)
-        root_logger.addHandler(root_file_handler)
-
-    root_logger.setLevel(loglevel)
-
-    # reformats celery's task logger
-    task_formatter = CeleryTaskColoredFormatter(
-        "%(asctime)s %(filename)30s %(lineno)4s: %(message)s",
-        datefmt="%m/%d/%Y %I:%M:%S %p",
-    )
-    task_handler = logging.StreamHandler()  # Set up a handler for the task logger
-    task_handler.setFormatter(task_formatter)
-    task_logger.addHandler(task_handler)  # Apply the handler to the task logger
-
-    if logfile:
-        task_file_handler = logging.FileHandler(logfile)
-        task_file_formatter = CeleryTaskPlainFormatter(
-            "%(asctime)s %(filename)30s %(lineno)4s: %(message)s",
-            datefmt="%m/%d/%Y %I:%M:%S %p",
-        )
-        task_file_handler.setFormatter(task_file_formatter)
-        task_logger.addHandler(task_file_handler)
-
-    task_logger.setLevel(loglevel)
-    task_logger.propagate = False
-
-    # hide celery task received spam
-    # e.g. "Task check_for_pruning[a1e96171-0ba8-4e00-887b-9fbf7442eab3] received"
-    strategy.logger.setLevel(logging.WARNING)
-
-    # hide celery task succeeded/failed spam
-    # e.g. "Task check_for_pruning[a1e96171-0ba8-4e00-887b-9fbf7442eab3] succeeded in 0.03137450001668185s: None"
-    trace.logger.setLevel(logging.WARNING)

backend/danswer/background/celery/apps/beat.py

@@ -1,172 +0,0 @@
-from datetime import timedelta
-from typing import Any
-
-from celery import Celery
-from celery import signals
-from celery.beat import PersistentScheduler  # type: ignore
-from celery.signals import beat_init
-
-import danswer.background.celery.apps.app_base as app_base
-from danswer.configs.constants import POSTGRES_CELERY_BEAT_APP_NAME
-from danswer.db.engine import get_all_tenant_ids
-from danswer.db.engine import SqlEngine
-from danswer.utils.logger import setup_logger
-from danswer.utils.variable_functionality import fetch_versioned_implementation
-from shared_configs.configs import IGNORED_SYNCING_TENANT_LIST
-from shared_configs.configs import MULTI_TENANT
-
-logger = setup_logger(__name__)
-
-celery_app = Celery(__name__)
-celery_app.config_from_object("danswer.background.celery.configs.beat")
-
-
-class DynamicTenantScheduler(PersistentScheduler):
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        logger.info("Initializing DynamicTenantScheduler")
-        super().__init__(*args, **kwargs)
-        self._reload_interval = timedelta(minutes=2)
-        self._last_reload = self.app.now() - self._reload_interval
-        # Let the parent class handle store initialization
-        self.setup_schedule()
-        self._update_tenant_tasks()
-        logger.info(f"Set reload interval to {self._reload_interval}")
-
-    def setup_schedule(self) -> None:
-        logger.info("Setting up initial schedule")
-        super().setup_schedule()
-        logger.info("Initial schedule setup complete")
-
-    def tick(self) -> float:
-        retval = super().tick()
-        now = self.app.now()
-        if (
-            self._last_reload is None
-            or (now - self._last_reload) > self._reload_interval
-        ):
-            logger.info("Reload interval reached, initiating tenant task update")
-            self._update_tenant_tasks()
-            self._last_reload = now
-            logger.info("Tenant task update completed, reset reload timer")
-        return retval
-
-    def _update_tenant_tasks(self) -> None:
-        logger.info("Starting tenant task update process")
-        try:
-            logger.info("Fetching all tenant IDs")
-            tenant_ids = get_all_tenant_ids()
-            logger.info(f"Found {len(tenant_ids)} tenants")
-
-            logger.info("Fetching tasks to schedule")
-            tasks_to_schedule = fetch_versioned_implementation(
-                "danswer.background.celery.tasks.beat_schedule", "get_tasks_to_schedule"
-            )
-
-            new_beat_schedule: dict[str, dict[str, Any]] = {}
-
-            current_schedule = self.schedule.items()
-
-            existing_tenants = set()
-            for task_name, _ in current_schedule:
-                if "-" in task_name:
-                    existing_tenants.add(task_name.split("-")[-1])
-            logger.info(f"Found {len(existing_tenants)} existing tenants in schedule")
-
-            for tenant_id in tenant_ids:
-                if (
-                    IGNORED_SYNCING_TENANT_LIST
-                    and tenant_id in IGNORED_SYNCING_TENANT_LIST
-                ):
-                    logger.info(
-                        f"Skipping tenant {tenant_id} as it is in the ignored syncing list"
-                    )
-                    continue
-
-                if tenant_id not in existing_tenants:
-                    logger.info(f"Processing new tenant: {tenant_id}")
-
-                for task in tasks_to_schedule():
-                    task_name = f"{task['name']}-{tenant_id}"
-                    logger.debug(f"Creating task configuration for {task_name}")
-                    new_task = {
-                        "task": task["task"],
-                        "schedule": task["schedule"],
-                        "kwargs": {"tenant_id": tenant_id},
-                    }
-                    if options := task.get("options"):
-                        logger.debug(f"Adding options to task {task_name}: {options}")
-                        new_task["options"] = options
-                    new_beat_schedule[task_name] = new_task
-
-            if self._should_update_schedule(current_schedule, new_beat_schedule):
-                logger.info(
-                    "Schedule update required",
-                    extra={
-                        "new_tasks": len(new_beat_schedule),
-                        "current_tasks": len(current_schedule),
-                    },
-                )
-
-                # Create schedule entries
-                entries = {}
-                for name, entry in new_beat_schedule.items():
-                    entries[name] = self.Entry(
-                        name=name,
-                        app=self.app,
-                        task=entry["task"],
-                        schedule=entry["schedule"],
-                        options=entry.get("options", {}),
-                        kwargs=entry.get("kwargs", {}),
-                    )
-
-                # Update the schedule using the scheduler's methods
-                self.schedule.clear()
-                self.schedule.update(entries)
-
-                # Ensure changes are persisted
-                self.sync()
-
-                logger.info("Schedule update completed successfully")
-            else:
-                logger.info("Schedule is up to date, no changes needed")
-
-        except (AttributeError, KeyError):
-            logger.exception("Failed to process task configuration")
-        except Exception:
-            logger.exception("Unexpected error updating tenant tasks")
-
-    def _should_update_schedule(
-        self, current_schedule: dict, new_schedule: dict
-    ) -> bool:
-        """Compare schedules to determine if an update is needed."""
-        logger.debug("Comparing current and new schedules")
-        current_tasks = set(name for name, _ in current_schedule)
-        new_tasks = set(new_schedule.keys())
-        needs_update = current_tasks != new_tasks
-        logger.debug(f"Schedule update needed: {needs_update}")
-        return needs_update
-
-
-@beat_init.connect
-def on_beat_init(sender: Any, **kwargs: Any) -> None:
-    logger.info("beat_init signal received.")
-
-    # Celery beat shouldn't touch the db at all. But just setting a low minimum here.
-    SqlEngine.set_app_name(POSTGRES_CELERY_BEAT_APP_NAME)
-    SqlEngine.init_engine(pool_size=2, max_overflow=0)
-
-    # Startup checks are not needed in multi-tenant case
-    if MULTI_TENANT:
-        return
-
-    app_base.wait_for_redis(sender, **kwargs)
-
-
-@signals.setup_logging.connect
-def on_setup_logging(
-    loglevel: Any, logfile: Any, format: Any, colorize: Any, **kwargs: Any
-) -> None:
-    app_base.on_setup_logging(loglevel, logfile, format, colorize, **kwargs)
-
-
-celery_app.conf.beat_scheduler = DynamicTenantScheduler

backend/danswer/background/celery/apps/heavy.py

@@ -1,97 +0,0 @@
-import multiprocessing
-from typing import Any
-
-from celery import Celery
-from celery import signals
-from celery import Task
-from celery.signals import celeryd_init
-from celery.signals import worker_init
-from celery.signals import worker_ready
-from celery.signals import worker_shutdown
-
-import danswer.background.celery.apps.app_base as app_base
-from danswer.configs.constants import POSTGRES_CELERY_WORKER_HEAVY_APP_NAME
-from danswer.db.engine import SqlEngine
-from danswer.utils.logger import setup_logger
-from shared_configs.configs import MULTI_TENANT
-
-
-logger = setup_logger()
-
-celery_app = Celery(__name__)
-celery_app.config_from_object("danswer.background.celery.configs.heavy")
-
-
-@signals.task_prerun.connect
-def on_task_prerun(
-    sender: Any | None = None,
-    task_id: str | None = None,
-    task: Task | None = None,
-    args: tuple | None = None,
-    kwargs: dict | None = None,
-    **kwds: Any,
-) -> None:
-    app_base.on_task_prerun(sender, task_id, task, args, kwargs, **kwds)
-
-
-@signals.task_postrun.connect
-def on_task_postrun(
-    sender: Any | None = None,
-    task_id: str | None = None,
-    task: Task | None = None,
-    args: tuple | None = None,
-    kwargs: dict | None = None,
-    retval: Any | None = None,
-    state: str | None = None,
-    **kwds: Any,
-) -> None:
-    app_base.on_task_postrun(sender, task_id, task, args, kwargs, retval, state, **kwds)
-
-
-@celeryd_init.connect
-def on_celeryd_init(sender: Any = None, conf: Any = None, **kwargs: Any) -> None:
-    app_base.on_celeryd_init(sender, conf, **kwargs)
-
-
-@worker_init.connect
-def on_worker_init(sender: Any, **kwargs: Any) -> None:
-    logger.info("worker_init signal received.")
-    logger.info(f"Multiprocessing start method: {multiprocessing.get_start_method()}")
-
-    SqlEngine.set_app_name(POSTGRES_CELERY_WORKER_HEAVY_APP_NAME)
-    SqlEngine.init_engine(pool_size=4, max_overflow=12)
-
-    # Startup checks are not needed in multi-tenant case
-    if MULTI_TENANT:
-        return
-
-    app_base.wait_for_redis(sender, **kwargs)
-    app_base.wait_for_db(sender, **kwargs)
-    app_base.wait_for_vespa(sender, **kwargs)
-    app_base.on_secondary_worker_init(sender, **kwargs)
-
-
-@worker_ready.connect
-def on_worker_ready(sender: Any, **kwargs: Any) -> None:
-    app_base.on_worker_ready(sender, **kwargs)
-
-
-@worker_shutdown.connect
-def on_worker_shutdown(sender: Any, **kwargs: Any) -> None:
-    app_base.on_worker_shutdown(sender, **kwargs)
-
-
-@signals.setup_logging.connect
-def on_setup_logging(
-    loglevel: Any, logfile: Any, format: Any, colorize: Any, **kwargs: Any
-) -> None:
-    app_base.on_setup_logging(loglevel, logfile, format, colorize, **kwargs)
-
-
-celery_app.autodiscover_tasks(
-    [
-        "danswer.background.celery.tasks.pruning",
-        "danswer.background.celery.tasks.doc_permission_syncing",
-        "danswer.background.celery.tasks.external_group_syncing",
-    ]
-)

backend/danswer/background/celery/apps/indexing.py

@@ -1,101 +0,0 @@
-import multiprocessing
-from typing import Any
-
-from celery import Celery
-from celery import signals
-from celery import Task
-from celery.signals import celeryd_init
-from celery.signals import worker_init
-from celery.signals import worker_process_init
-from celery.signals import worker_ready
-from celery.signals import worker_shutdown
-
-import danswer.background.celery.apps.app_base as app_base
-from danswer.configs.constants import POSTGRES_CELERY_WORKER_INDEXING_APP_NAME
-from danswer.db.engine import SqlEngine
-from danswer.utils.logger import setup_logger
-from shared_configs.configs import MULTI_TENANT
-
-
-logger = setup_logger()
-
-celery_app = Celery(__name__)
-celery_app.config_from_object("danswer.background.celery.configs.indexing")
-
-
-@signals.task_prerun.connect
-def on_task_prerun(
-    sender: Any | None = None,
-    task_id: str | None = None,
-    task: Task | None = None,
-    args: tuple | None = None,
-    kwargs: dict | None = None,
-    **kwds: Any,
-) -> None:
-    app_base.on_task_prerun(sender, task_id, task, args, kwargs, **kwds)
-
-
-@signals.task_postrun.connect
-def on_task_postrun(
-    sender: Any | None = None,
-    task_id: str | None = None,
-    task: Task | None = None,
-    args: tuple | None = None,
-    kwargs: dict | None = None,
-    retval: Any | None = None,
-    state: str | None = None,
-    **kwds: Any,
-) -> None:
-    app_base.on_task_postrun(sender, task_id, task, args, kwargs, retval, state, **kwds)
-
-
-@celeryd_init.connect
-def on_celeryd_init(sender: Any = None, conf: Any = None, **kwargs: Any) -> None:
-    app_base.on_celeryd_init(sender, conf, **kwargs)
-
-
-@worker_init.connect
-def on_worker_init(sender: Any, **kwargs: Any) -> None:
-    logger.info("worker_init signal received.")
-    logger.info(f"Multiprocessing start method: {multiprocessing.get_start_method()}")
-
-    SqlEngine.set_app_name(POSTGRES_CELERY_WORKER_INDEXING_APP_NAME)
-    SqlEngine.init_engine(pool_size=sender.concurrency, max_overflow=sender.concurrency)
-
-    # Startup checks are not needed in multi-tenant case
-    if MULTI_TENANT:
-        return
-
-    app_base.wait_for_redis(sender, **kwargs)
-    app_base.wait_for_db(sender, **kwargs)
-    app_base.wait_for_vespa(sender, **kwargs)
-    app_base.on_secondary_worker_init(sender, **kwargs)
-
-
-@worker_ready.connect
-def on_worker_ready(sender: Any, **kwargs: Any) -> None:
-    app_base.on_worker_ready(sender, **kwargs)
-
-
-@worker_shutdown.connect
-def on_worker_shutdown(sender: Any, **kwargs: Any) -> None:
-    app_base.on_worker_shutdown(sender, **kwargs)
-
-
-@worker_process_init.connect
-def init_worker(**kwargs: Any) -> None:
-    SqlEngine.reset_engine()
-
-
-@signals.setup_logging.connect
-def on_setup_logging(
-    loglevel: Any, logfile: Any, format: Any, colorize: Any, **kwargs: Any
-) -> None:
-    app_base.on_setup_logging(loglevel, logfile, format, colorize, **kwargs)
-
-
-celery_app.autodiscover_tasks(
-    [
-        "danswer.background.celery.tasks.indexing",
-    ]
-)

backend/danswer/background/celery/apps/light.py

@@ -1,97 +0,0 @@
-import multiprocessing
-from typing import Any
-
-from celery import Celery
-from celery import signals
-from celery import Task
-from celery.signals import celeryd_init
-from celery.signals import worker_init
-from celery.signals import worker_ready
-from celery.signals import worker_shutdown
-
-import danswer.background.celery.apps.app_base as app_base
-from danswer.configs.constants import POSTGRES_CELERY_WORKER_LIGHT_APP_NAME
-from danswer.db.engine import SqlEngine
-from danswer.utils.logger import setup_logger
-from shared_configs.configs import MULTI_TENANT
-
-
-logger = setup_logger()
-
-celery_app = Celery(__name__)
-celery_app.config_from_object("danswer.background.celery.configs.light")
-
-
-@signals.task_prerun.connect
-def on_task_prerun(
-    sender: Any | None = None,
-    task_id: str | None = None,
-    task: Task | None = None,
-    args: tuple | None = None,
-    kwargs: dict | None = None,
-    **kwds: Any,
-) -> None:
-    app_base.on_task_prerun(sender, task_id, task, args, kwargs, **kwds)
-
-
-@signals.task_postrun.connect
-def on_task_postrun(
-    sender: Any | None = None,
-    task_id: str | None = None,
-    task: Task | None = None,
-    args: tuple | None = None,
-    kwargs: dict | None = None,
-    retval: Any | None = None,
-    state: str | None = None,
-    **kwds: Any,
-) -> None:
-    app_base.on_task_postrun(sender, task_id, task, args, kwargs, retval, state, **kwds)
-
-
-@celeryd_init.connect
-def on_celeryd_init(sender: Any = None, conf: Any = None, **kwargs: Any) -> None:
-    app_base.on_celeryd_init(sender, conf, **kwargs)
-
-
-@worker_init.connect
-def on_worker_init(sender: Any, **kwargs: Any) -> None:
-    logger.info("worker_init signal received.")
-    logger.info(f"Multiprocessing start method: {multiprocessing.get_start_method()}")
-
-    SqlEngine.set_app_name(POSTGRES_CELERY_WORKER_LIGHT_APP_NAME)
-    SqlEngine.init_engine(pool_size=sender.concurrency, max_overflow=8)
-    # Startup checks are not needed in multi-tenant case
-    if MULTI_TENANT:
-        return
-
-    app_base.wait_for_redis(sender, **kwargs)
-    app_base.wait_for_db(sender, **kwargs)
-    app_base.wait_for_vespa(sender, **kwargs)
-    app_base.on_secondary_worker_init(sender, **kwargs)
-
-
-@worker_ready.connect
-def on_worker_ready(sender: Any, **kwargs: Any) -> None:
-    app_base.on_worker_ready(sender, **kwargs)
-
-
-@worker_shutdown.connect
-def on_worker_shutdown(sender: Any, **kwargs: Any) -> None:
-    app_base.on_worker_shutdown(sender, **kwargs)
-
-
-@signals.setup_logging.connect
-def on_setup_logging(
-    loglevel: Any, logfile: Any, format: Any, colorize: Any, **kwargs: Any
-) -> None:
-    app_base.on_setup_logging(loglevel, logfile, format, colorize, **kwargs)
-
-
-celery_app.autodiscover_tasks(
-    [
-        "danswer.background.celery.tasks.shared",
-        "danswer.background.celery.tasks.vespa",
-        "danswer.background.celery.tasks.connector_deletion",
-        "danswer.background.celery.tasks.doc_permission_syncing",
-    ]
-)

backend/danswer/background/celery/apps/primary.py

@@ -1,285 +0,0 @@
-import multiprocessing
-from typing import Any
-from typing import cast
-
-from celery import bootsteps  # type: ignore
-from celery import Celery
-from celery import signals
-from celery import Task
-from celery.exceptions import WorkerShutdown
-from celery.signals import celeryd_init
-from celery.signals import worker_init
-from celery.signals import worker_ready
-from celery.signals import worker_shutdown
-from redis.lock import Lock as RedisLock
-
-import danswer.background.celery.apps.app_base as app_base
-from danswer.background.celery.apps.app_base import task_logger
-from danswer.background.celery.celery_utils import celery_is_worker_primary
-from danswer.background.celery.tasks.indexing.tasks import (
-    get_unfenced_index_attempt_ids,
-)
-from danswer.configs.constants import CELERY_PRIMARY_WORKER_LOCK_TIMEOUT
-from danswer.configs.constants import DanswerRedisLocks
-from danswer.configs.constants import POSTGRES_CELERY_WORKER_PRIMARY_APP_NAME
-from danswer.db.engine import get_session_with_default_tenant
-from danswer.db.engine import SqlEngine
-from danswer.db.index_attempt import get_index_attempt
-from danswer.db.index_attempt import mark_attempt_canceled
-from danswer.redis.redis_connector_credential_pair import RedisConnectorCredentialPair
-from danswer.redis.redis_connector_delete import RedisConnectorDelete
-from danswer.redis.redis_connector_doc_perm_sync import RedisConnectorPermissionSync
-from danswer.redis.redis_connector_ext_group_sync import RedisConnectorExternalGroupSync
-from danswer.redis.redis_connector_index import RedisConnectorIndex
-from danswer.redis.redis_connector_prune import RedisConnectorPrune
-from danswer.redis.redis_connector_stop import RedisConnectorStop
-from danswer.redis.redis_document_set import RedisDocumentSet
-from danswer.redis.redis_pool import get_redis_client
-from danswer.redis.redis_usergroup import RedisUserGroup
-from danswer.utils.logger import setup_logger
-from shared_configs.configs import MULTI_TENANT
-
-logger = setup_logger()
-
-celery_app = Celery(__name__)
-celery_app.config_from_object("danswer.background.celery.configs.primary")
-
-
-@signals.task_prerun.connect
-def on_task_prerun(
-    sender: Any | None = None,
-    task_id: str | None = None,
-    task: Task | None = None,
-    args: tuple | None = None,
-    kwargs: dict | None = None,
-    **kwds: Any,
-) -> None:
-    app_base.on_task_prerun(sender, task_id, task, args, kwargs, **kwds)
-
-
-@signals.task_postrun.connect
-def on_task_postrun(
-    sender: Any | None = None,
-    task_id: str | None = None,
-    task: Task | None = None,
-    args: tuple | None = None,
-    kwargs: dict | None = None,
-    retval: Any | None = None,
-    state: str | None = None,
-    **kwds: Any,
-) -> None:
-    app_base.on_task_postrun(sender, task_id, task, args, kwargs, retval, state, **kwds)
-
-
-@celeryd_init.connect
-def on_celeryd_init(sender: Any = None, conf: Any = None, **kwargs: Any) -> None:
-    app_base.on_celeryd_init(sender, conf, **kwargs)
-
-
-@worker_init.connect
-def on_worker_init(sender: Any, **kwargs: Any) -> None:
-    logger.info("worker_init signal received.")
-    logger.info(f"Multiprocessing start method: {multiprocessing.get_start_method()}")
-
-    SqlEngine.set_app_name(POSTGRES_CELERY_WORKER_PRIMARY_APP_NAME)
-    SqlEngine.init_engine(pool_size=8, max_overflow=0)
-
-    # Startup checks are not needed in multi-tenant case
-    if MULTI_TENANT:
-        return
-
-    app_base.wait_for_redis(sender, **kwargs)
-    app_base.wait_for_db(sender, **kwargs)
-    app_base.wait_for_vespa(sender, **kwargs)
-
-    logger.info("Running as the primary celery worker.")
-
-    # This is singleton work that should be done on startup exactly once
-    # by the primary worker. This is unnecessary in the multi tenant scenario
-    r = get_redis_client(tenant_id=None)
-
-    # Log the role and slave count - being connected to a slave or slave count > 0 could be problematic
-    info: dict[str, Any] = cast(dict, r.info("replication"))
-    role: str = cast(str, info.get("role"))
-    connected_slaves: int = info.get("connected_slaves", 0)
-
-    logger.info(
-        f"Redis INFO REPLICATION: role={role} connected_slaves={connected_slaves}"
-    )
-
-    # For the moment, we're assuming that we are the only primary worker
-    # that should be running.
-    # TODO: maybe check for or clean up another zombie primary worker if we detect it
-    r.delete(DanswerRedisLocks.PRIMARY_WORKER)
-
-    # this process wide lock is taken to help other workers start up in order.
-    # it is planned to use this lock to enforce singleton behavior on the primary
-    # worker, since the primary worker does redis cleanup on startup, but this isn't
-    # implemented yet.
-
-    # set thread_local=False since we don't control what thread the periodic task might
-    # reacquire the lock with
-    lock: RedisLock = r.lock(
-        DanswerRedisLocks.PRIMARY_WORKER,
-        timeout=CELERY_PRIMARY_WORKER_LOCK_TIMEOUT,
-        thread_local=False,
-    )
-
-    logger.info("Primary worker lock: Acquire starting.")
-    acquired = lock.acquire(blocking_timeout=CELERY_PRIMARY_WORKER_LOCK_TIMEOUT / 2)
-    if acquired:
-        logger.info("Primary worker lock: Acquire succeeded.")
-    else:
-        logger.error("Primary worker lock: Acquire failed!")
-        raise WorkerShutdown("Primary worker lock could not be acquired!")
-
-    # tacking on our own user data to the sender
-    sender.primary_worker_lock = lock
-
-    # As currently designed, when this worker starts as "primary", we reinitialize redis
-    # to a clean state (for our purposes, anyway)
-    r.delete(DanswerRedisLocks.CHECK_VESPA_SYNC_BEAT_LOCK)
-    r.delete(DanswerRedisLocks.MONITOR_VESPA_SYNC_BEAT_LOCK)
-
-    r.delete(RedisConnectorCredentialPair.get_taskset_key())
-    r.delete(RedisConnectorCredentialPair.get_fence_key())
-
-    RedisDocumentSet.reset_all(r)
-
-    RedisUserGroup.reset_all(r)
-
-    RedisConnectorDelete.reset_all(r)
-
-    RedisConnectorPrune.reset_all(r)
-
-    RedisConnectorIndex.reset_all(r)
-
-    RedisConnectorStop.reset_all(r)
-
-    RedisConnectorPermissionSync.reset_all(r)
-
-    RedisConnectorExternalGroupSync.reset_all(r)
-
-    # mark orphaned index attempts as failed
-    with get_session_with_default_tenant() as db_session:
-        unfenced_attempt_ids = get_unfenced_index_attempt_ids(db_session, r)
-        for attempt_id in unfenced_attempt_ids:
-            attempt = get_index_attempt(db_session, attempt_id)
-            if not attempt:
-                continue
-
-            failure_reason = (
-                f"Canceling leftover index attempt found on startup: "
-                f"index_attempt={attempt.id} "
-                f"cc_pair={attempt.connector_credential_pair_id} "
-                f"search_settings={attempt.search_settings_id}"
-            )
-            logger.warning(failure_reason)
-            mark_attempt_canceled(attempt.id, db_session, failure_reason)
-
-
-@worker_ready.connect
-def on_worker_ready(sender: Any, **kwargs: Any) -> None:
-    app_base.on_worker_ready(sender, **kwargs)
-
-
-@worker_shutdown.connect
-def on_worker_shutdown(sender: Any, **kwargs: Any) -> None:
-    app_base.on_worker_shutdown(sender, **kwargs)
-
-
-@signals.setup_logging.connect
-def on_setup_logging(
-    loglevel: Any, logfile: Any, format: Any, colorize: Any, **kwargs: Any
-) -> None:
-    app_base.on_setup_logging(loglevel, logfile, format, colorize, **kwargs)
-
-
-class HubPeriodicTask(bootsteps.StartStopStep):
-    """Regularly reacquires the primary worker lock outside of the task queue.
-    Use the task_logger in this class to avoid double logging.
-
-    This cannot be done inside a regular beat task because it must run on schedule and
-    a queue of existing work would starve the task from running.
-    """
-
-    # it's unclear to me whether using the hub's timer or the bootstep timer is better
-    requires = {"celery.worker.components:Hub"}
-
-    def __init__(self, worker: Any, **kwargs: Any) -> None:
-        self.interval = CELERY_PRIMARY_WORKER_LOCK_TIMEOUT / 8  # Interval in seconds
-        self.task_tref = None
-
-    def start(self, worker: Any) -> None:
-        if not celery_is_worker_primary(worker):
-            return
-
-        # Access the worker's event loop (hub)
-        hub = worker.consumer.controller.hub
-
-        # Schedule the periodic task
-        self.task_tref = hub.call_repeatedly(
-            self.interval, self.run_periodic_task, worker
-        )
-        task_logger.info("Scheduled periodic task with hub.")
-
-    def run_periodic_task(self, worker: Any) -> None:
-        try:
-            if not celery_is_worker_primary(worker):
-                return
-
-            if not hasattr(worker, "primary_worker_lock"):
-                return
-
-            lock: RedisLock = worker.primary_worker_lock
-
-            r = get_redis_client(tenant_id=None)
-
-            if lock.owned():
-                task_logger.debug("Reacquiring primary worker lock.")
-                lock.reacquire()
-            else:
-                task_logger.warning(
-                    "Full acquisition of primary worker lock. "
-                    "Reasons could be worker restart or lock expiration."
-                )
-                lock = r.lock(
-                    DanswerRedisLocks.PRIMARY_WORKER,
-                    timeout=CELERY_PRIMARY_WORKER_LOCK_TIMEOUT,
-                )
-
-                task_logger.info("Primary worker lock: Acquire starting.")
-                acquired = lock.acquire(
-                    blocking_timeout=CELERY_PRIMARY_WORKER_LOCK_TIMEOUT / 2
-                )
-                if acquired:
-                    task_logger.info("Primary worker lock: Acquire succeeded.")
-                    worker.primary_worker_lock = lock
-                else:
-                    task_logger.error("Primary worker lock: Acquire failed!")
-                    raise TimeoutError("Primary worker lock could not be acquired!")
-
-        except Exception:
-            task_logger.exception("Periodic task failed.")
-
-    def stop(self, worker: Any) -> None:
-        # Cancel the scheduled task when the worker stops
-        if self.task_tref:
-            self.task_tref.cancel()
-            task_logger.info("Canceled periodic task with hub.")
-
-
-celery_app.steps["worker"].add(HubPeriodicTask)
-
-celery_app.autodiscover_tasks(
-    [
-        "danswer.background.celery.tasks.connector_deletion",
-        "danswer.background.celery.tasks.indexing",
-        "danswer.background.celery.tasks.periodic",
-        "danswer.background.celery.tasks.doc_permission_syncing",
-        "danswer.background.celery.tasks.external_group_syncing",
-        "danswer.background.celery.tasks.pruning",
-        "danswer.background.celery.tasks.shared",
-        "danswer.background.celery.tasks.vespa",
-    ]
-)

backend/danswer/background/celery/apps/task_formatters.py

@@ -1,26 +0,0 @@
-import logging
-
-from celery import current_task
-
-from danswer.utils.logger import ColoredFormatter
-from danswer.utils.logger import PlainFormatter
-
-
-class CeleryTaskPlainFormatter(PlainFormatter):
-    def format(self, record: logging.LogRecord) -> str:
-        task = current_task
-        if task and task.request:
-            record.__dict__.update(task_id=task.request.id, task_name=task.name)
-            record.msg = f"[{task.name}({task.request.id})] {record.msg}"
-
-        return super().format(record)
-
-
-class CeleryTaskColoredFormatter(ColoredFormatter):
-    def format(self, record: logging.LogRecord) -> str:
-        task = current_task
-        if task and task.request:
-            record.__dict__.update(task_id=task.request.id, task_name=task.name)
-            record.msg = f"[{task.name}({task.request.id})] {record.msg}"
-
-        return super().format(record)

backend/danswer/background/celery/celery_app.py

@@ -0,0 +1,619 @@
+import logging
+import multiprocessing
+import time
+from datetime import timedelta
+from typing import Any
+
+import sentry_sdk
+from celery import bootsteps  # type: ignore
+from celery import Celery
+from celery import current_task
+from celery import signals
+from celery import Task
+from celery.exceptions import WorkerShutdown
+from celery.signals import beat_init
+from celery.signals import celeryd_init
+from celery.signals import worker_init
+from celery.signals import worker_ready
+from celery.signals import worker_shutdown
+from celery.states import READY_STATES
+from celery.utils.log import get_task_logger
+from sentry_sdk.integrations.celery import CeleryIntegration
+
+from danswer.background.celery.celery_redis import RedisConnectorCredentialPair
+from danswer.background.celery.celery_redis import RedisConnectorDeletion
+from danswer.background.celery.celery_redis import RedisConnectorIndexing
+from danswer.background.celery.celery_redis import RedisConnectorPruning
+from danswer.background.celery.celery_redis import RedisDocumentSet
+from danswer.background.celery.celery_redis import RedisUserGroup
+from danswer.background.celery.celery_utils import celery_is_worker_primary
+from danswer.background.celery.celery_utils import get_all_tenant_ids
+from danswer.configs.constants import CELERY_PRIMARY_WORKER_LOCK_TIMEOUT
+from danswer.configs.constants import DanswerCeleryPriority
+from danswer.configs.constants import DanswerRedisLocks
+from danswer.configs.constants import POSTGRES_CELERY_BEAT_APP_NAME
+from danswer.configs.constants import POSTGRES_CELERY_WORKER_HEAVY_APP_NAME
+from danswer.configs.constants import POSTGRES_CELERY_WORKER_INDEXING_APP_NAME
+from danswer.configs.constants import POSTGRES_CELERY_WORKER_LIGHT_APP_NAME
+from danswer.configs.constants import POSTGRES_CELERY_WORKER_PRIMARY_APP_NAME
+from danswer.db.engine import get_session_with_tenant
+from danswer.db.engine import SqlEngine
+from danswer.db.search_settings import get_current_search_settings
+from danswer.db.swap_index import check_index_swap
+from danswer.natural_language_processing.search_nlp_models import EmbeddingModel
+from danswer.natural_language_processing.search_nlp_models import warm_up_bi_encoder
+from danswer.redis.redis_pool import get_redis_client
+from danswer.utils.logger import ColoredFormatter
+from danswer.utils.logger import PlainFormatter
+from danswer.utils.logger import setup_logger
+from shared_configs.configs import INDEXING_MODEL_SERVER_HOST
+from shared_configs.configs import MODEL_SERVER_PORT
+from shared_configs.configs import SENTRY_DSN
+
+logger = setup_logger()
+
+# use this within celery tasks to get celery task specific logging
+task_logger = get_task_logger(__name__)
+
+if SENTRY_DSN:
+    sentry_sdk.init(
+        dsn=SENTRY_DSN,
+        integrations=[CeleryIntegration()],
+        traces_sample_rate=0.5,
+    )
+    logger.info("Sentry initialized")
+else:
+    logger.debug("Sentry DSN not provided, skipping Sentry initialization")
+
+
+celery_app = Celery(__name__)
+celery_app.config_from_object(
+    "danswer.background.celery.celeryconfig"
+)  # Load configuration from 'celeryconfig.py'
+
+
+@signals.task_prerun.connect
+def on_task_prerun(
+    sender: Any | None = None,
+    task_id: str | None = None,
+    task: Task | None = None,
+    args: tuple | None = None,
+    tenant_id: str | None = None,
+    kwargs: dict | None = None,
+    **kwds: Any,
+) -> None:
+    pass
+
+
+@signals.task_postrun.connect
+def on_task_postrun(
+    sender: Any | None = None,
+    task_id: str | None = None,
+    task: Task | None = None,
+    args: tuple | None = None,
+    kwargs: dict[str, Any] | None = None,
+    retval: Any | None = None,
+    state: str | None = None,
+    **kwds: Any,
+) -> None:
+    """We handle this signal in order to remove completed tasks
+    from their respective tasksets. This allows us to track the progress of document set
+    and user group syncs.
+
+    This function runs after any task completes (both success and failure)
+    Note that this signal does not fire on a task that failed to complete and is going
+    to be retried.
+
+    This also does not fire if a worker with acks_late=False crashes (which all of our
+    long running workers are)
+    """
+    if not task:
+        return
+
+    # Get tenant_id directly from kwargs- each celery task has a tenant_id kwarg
+    if not kwargs:
+        logger.error(f"Task {task.name} (ID: {task_id}) is missing kwargs")
+        tenant_id = None
+    else:
+        tenant_id = kwargs.get("tenant_id")
+
+    task_logger.debug(
+        f"Task {task.name} (ID: {task_id}) completed with state: {state} "
+        f"{f'for tenant_id={tenant_id}' if tenant_id else ''}"
+    )
+
+    if state not in READY_STATES:
+        return
+
+    if not task_id:
+        return
+
+    r = get_redis_client(tenant_id=tenant_id)
+
+    if task_id.startswith(RedisConnectorCredentialPair.PREFIX):
+        r.srem(RedisConnectorCredentialPair.get_taskset_key(), task_id)
+        return
+
+    if task_id.startswith(RedisDocumentSet.PREFIX):
+        document_set_id = RedisDocumentSet.get_id_from_task_id(task_id)
+        if document_set_id is not None:
+            rds = RedisDocumentSet(int(document_set_id))
+            r.srem(rds.taskset_key, task_id)
+        return
+
+    if task_id.startswith(RedisUserGroup.PREFIX):
+        usergroup_id = RedisUserGroup.get_id_from_task_id(task_id)
+        if usergroup_id is not None:
+            rug = RedisUserGroup(int(usergroup_id))
+            r.srem(rug.taskset_key, task_id)
+        return
+
+    if task_id.startswith(RedisConnectorDeletion.PREFIX):
+        cc_pair_id = RedisConnectorDeletion.get_id_from_task_id(task_id)
+        if cc_pair_id is not None:
+            rcd = RedisConnectorDeletion(int(cc_pair_id))
+            r.srem(rcd.taskset_key, task_id)
+        return
+
+    if task_id.startswith(RedisConnectorPruning.SUBTASK_PREFIX):
+        cc_pair_id = RedisConnectorPruning.get_id_from_task_id(task_id)
+        if cc_pair_id is not None:
+            rcp = RedisConnectorPruning(int(cc_pair_id))
+            r.srem(rcp.taskset_key, task_id)
+        return
+
+
+@celeryd_init.connect
+def on_celeryd_init(sender: Any = None, conf: Any = None, **kwargs: Any) -> None:
+    """The first signal sent on celery worker startup"""
+    multiprocessing.set_start_method("spawn")  # fork is unsafe, set to spawn
+
+
+@beat_init.connect
+def on_beat_init(sender: Any, **kwargs: Any) -> None:
+    SqlEngine.set_app_name(POSTGRES_CELERY_BEAT_APP_NAME)
+    SqlEngine.init_engine(pool_size=2, max_overflow=0)
+
+
+@worker_init.connect
+def on_worker_init(sender: Any, **kwargs: Any) -> None:
+    logger.info("worker_init signal received.")
+    logger.info(f"Multiprocessing start method: {multiprocessing.get_start_method()}")
+
+    # decide some initial startup settings based on the celery worker's hostname
+    # (set at the command line)'
+
+    hostname = sender.hostname
+    if hostname.startswith("light"):
+        SqlEngine.set_app_name(POSTGRES_CELERY_WORKER_LIGHT_APP_NAME)
+        SqlEngine.init_engine(pool_size=sender.concurrency, max_overflow=8)
+    elif hostname.startswith("heavy"):
+        SqlEngine.set_app_name(POSTGRES_CELERY_WORKER_HEAVY_APP_NAME)
+        SqlEngine.init_engine(pool_size=8, max_overflow=0)
+    elif hostname.startswith("indexing"):
+        SqlEngine.set_app_name(POSTGRES_CELERY_WORKER_INDEXING_APP_NAME)
+        SqlEngine.init_engine(pool_size=8, max_overflow=0)
+        tenant_ids = get_all_tenant_ids()
+
+        for tenant_id in tenant_ids:
+            # TODO: why is this necessary for the indexer to do?
+            with get_session_with_tenant(tenant_id) as db_session:
+                check_index_swap(db_session=db_session)
+                search_settings = get_current_search_settings(db_session)
+
+                # So that the first time users aren't surprised by really slow speed of first
+                # batch of documents indexed
+
+                if search_settings.provider_type is None:
+                    logger.notice(
+                        "Running a first inference to warm up embedding model"
+                    )
+                    embedding_model = EmbeddingModel.from_db_model(
+                        search_settings=search_settings,
+                        server_host=INDEXING_MODEL_SERVER_HOST,
+                        server_port=MODEL_SERVER_PORT,
+                    )
+
+                    warm_up_bi_encoder(
+                        embedding_model=embedding_model,
+                    )
+                    logger.notice("First inference complete.")
+    else:
+        SqlEngine.set_app_name(POSTGRES_CELERY_WORKER_PRIMARY_APP_NAME)
+        SqlEngine.init_engine(pool_size=8, max_overflow=0)
+
+    if not hasattr(sender, "primary_worker_locks"):
+        sender.primary_worker_locks = {}
+
+    tenant_ids = get_all_tenant_ids()
+
+    if not celery_is_worker_primary(sender):
+        logger.info("Running as a secondary celery worker.")
+        for tenant_id in tenant_ids:
+            r = get_redis_client(tenant_id=tenant_id)
+            WAIT_INTERVAL = 5
+            WAIT_LIMIT = 60
+            time_start = time.monotonic()
+            logger.notice("Redis: Readiness check starting.")
+            while True:
+                # Log all the locks in Redis
+                all_locks = r.keys("*")
+                logger.notice(f"Current Redis locks: {all_locks}")
+                if r.exists(DanswerRedisLocks.PRIMARY_WORKER):
+                    break
+                time_elapsed = time.monotonic() - time_start
+                logger.info(
+                    f"Redis: Ping failed. elapsed={time_elapsed:.1f} timeout={WAIT_LIMIT:.1f}"
+                )
+                if time_elapsed > WAIT_LIMIT:
+                    msg = (
+                        "Redis: Readiness check did not succeed within the timeout "
+                        f"({WAIT_LIMIT} seconds). Exiting..."
+                    )
+                    logger.error(msg)
+                    raise WorkerShutdown(msg)
+                time.sleep(WAIT_INTERVAL)
+            logger.info("Wait for primary worker completed successfully. Continuing...")
+        return  # Exit the function for secondary workers
+
+    for tenant_id in tenant_ids:
+        r = get_redis_client(tenant_id=tenant_id)
+
+        WAIT_INTERVAL = 5
+        WAIT_LIMIT = 60
+
+        time_start = time.monotonic()
+        logger.info("Running as the primary celery worker.")
+
+        # This is singleton work that should be done on startup exactly once
+        # by the primary worker
+        r = get_redis_client(tenant_id=tenant_id)
+
+        # For the moment, we're assuming that we are the only primary worker
+        # that should be running.
+        # TODO: maybe check for or clean up another zombie primary worker if we detect it
+        r.delete(DanswerRedisLocks.PRIMARY_WORKER)
+
+        # this process wide lock is taken to help other workers start up in order.
+        # it is planned to use this lock to enforce singleton behavior on the primary
+        # worker, since the primary worker does redis cleanup on startup, but this isn't
+        # implemented yet.
+        lock = r.lock(
+            DanswerRedisLocks.PRIMARY_WORKER,
+            timeout=CELERY_PRIMARY_WORKER_LOCK_TIMEOUT,
+        )
+
+        logger.info("Primary worker lock: Acquire starting.")
+        acquired = lock.acquire(blocking_timeout=CELERY_PRIMARY_WORKER_LOCK_TIMEOUT / 2)
+        if acquired:
+            logger.info("Primary worker lock: Acquire succeeded.")
+        else:
+            logger.error("Primary worker lock: Acquire failed!")
+            raise WorkerShutdown("Primary worker lock could not be acquired!")
+
+        sender.primary_worker_locks[tenant_id] = lock
+
+        # As currently designed, when this worker starts as "primary", we reinitialize redis
+        # to a clean state (for our purposes, anyway)
+        r.delete(DanswerRedisLocks.CHECK_VESPA_SYNC_BEAT_LOCK)
+        r.delete(DanswerRedisLocks.MONITOR_VESPA_SYNC_BEAT_LOCK)
+
+        r.delete(RedisConnectorCredentialPair.get_taskset_key())
+        r.delete(RedisConnectorCredentialPair.get_fence_key())
+
+        for key in r.scan_iter(RedisDocumentSet.TASKSET_PREFIX + "*"):
+            r.delete(key)
+
+        for key in r.scan_iter(RedisDocumentSet.FENCE_PREFIX + "*"):
+            r.delete(key)
+
+        for key in r.scan_iter(RedisUserGroup.TASKSET_PREFIX + "*"):
+            r.delete(key)
+
+        for key in r.scan_iter(RedisUserGroup.FENCE_PREFIX + "*"):
+            r.delete(key)
+
+        for key in r.scan_iter(RedisConnectorDeletion.TASKSET_PREFIX + "*"):
+            r.delete(key)
+
+        for key in r.scan_iter(RedisConnectorDeletion.FENCE_PREFIX + "*"):
+            r.delete(key)
+
+        for key in r.scan_iter(RedisConnectorPruning.TASKSET_PREFIX + "*"):
+            r.delete(key)
+
+        for key in r.scan_iter(RedisConnectorPruning.GENERATOR_COMPLETE_PREFIX + "*"):
+            r.delete(key)
+
+        for key in r.scan_iter(RedisConnectorPruning.GENERATOR_PROGRESS_PREFIX + "*"):
+            r.delete(key)
+
+        for key in r.scan_iter(RedisConnectorPruning.FENCE_PREFIX + "*"):
+            r.delete(key)
+
+        for key in r.scan_iter(RedisConnectorIndexing.TASKSET_PREFIX + "*"):
+            r.delete(key)
+
+        for key in r.scan_iter(RedisConnectorIndexing.GENERATOR_COMPLETE_PREFIX + "*"):
+            r.delete(key)
+
+        for key in r.scan_iter(RedisConnectorIndexing.GENERATOR_PROGRESS_PREFIX + "*"):
+            r.delete(key)
+
+        for key in r.scan_iter(RedisConnectorIndexing.FENCE_PREFIX + "*"):
+            r.delete(key)
+
+
+# @worker_process_init.connect
+# def on_worker_process_init(sender: Any, **kwargs: Any) -> None:
+#     """This only runs inside child processes when the worker is in pool=prefork mode.
+#     This may be technically unnecessary since we're finding prefork pools to be
+#     unstable and currently aren't planning on using them."""
+#     logger.info("worker_process_init signal received.")
+#     SqlEngine.set_app_name(POSTGRES_CELERY_WORKER_INDEXING_CHILD_APP_NAME)
+#     SqlEngine.init_engine(pool_size=5, max_overflow=0)
+
+#     # https://stackoverflow.com/questions/43944787/sqlalchemy-celery-with-scoped-session-error
+#     SqlEngine.get_engine().dispose(close=False)
+
+
+@worker_ready.connect
+def on_worker_ready(sender: Any, **kwargs: Any) -> None:
+    task_logger.info("worker_ready signal received.")
+
+
+@worker_shutdown.connect
+def on_worker_shutdown(sender: Any, **kwargs: Any) -> None:
+    if not celery_is_worker_primary(sender):
+        return
+
+    if not hasattr(sender, "primary_worker_locks"):
+        return
+
+    logger.info("Releasing primary worker lock.")
+    for tenant_id, lock in sender.primary_worker_locks.items():
+        logger.info(f"Releasing primary worker lock for tenant {tenant_id}.")
+        if lock.owned():
+            lock.release()
+    sender.primary_worker_locks = {}
+
+
+class CeleryTaskPlainFormatter(PlainFormatter):
+    def format(self, record: logging.LogRecord) -> str:
+        task = current_task
+        if task and task.request:
+            record.__dict__.update(task_id=task.request.id, task_name=task.name)
+            record.msg = f"[{task.name}({task.request.id})] {record.msg}"
+
+        return super().format(record)
+
+
+class CeleryTaskColoredFormatter(ColoredFormatter):
+    def format(self, record: logging.LogRecord) -> str:
+        task = current_task
+        if task and task.request:
+            record.__dict__.update(task_id=task.request.id, task_name=task.name)
+            record.msg = f"[{task.name}({task.request.id})] {record.msg}"
+
+        return super().format(record)
+
+
+@signals.setup_logging.connect
+def on_setup_logging(
+    loglevel: Any, logfile: Any, format: Any, colorize: Any, **kwargs: Any
+) -> None:
+    # TODO: could unhardcode format and colorize and accept these as options from
+    # celery's config
+
+    # reformats the root logger
+    root_logger = logging.getLogger()
+
+    root_handler = logging.StreamHandler()  # Set up a handler for the root logger
+    root_formatter = ColoredFormatter(
+        "%(asctime)s %(filename)30s %(lineno)4s: %(message)s",
+        datefmt="%m/%d/%Y %I:%M:%S %p",
+    )
+    root_handler.setFormatter(root_formatter)
+    root_logger.addHandler(root_handler)  # Apply the handler to the root logger
+
+    if logfile:
+        root_file_handler = logging.FileHandler(logfile)
+        root_file_formatter = PlainFormatter(
+            "%(asctime)s %(filename)30s %(lineno)4s: %(message)s",
+            datefmt="%m/%d/%Y %I:%M:%S %p",
+        )
+        root_file_handler.setFormatter(root_file_formatter)
+        root_logger.addHandler(root_file_handler)
+
+    root_logger.setLevel(loglevel)
+
+    # reformats celery's task logger
+    task_formatter = CeleryTaskColoredFormatter(
+        "%(asctime)s %(filename)30s %(lineno)4s: %(message)s",
+        datefmt="%m/%d/%Y %I:%M:%S %p",
+    )
+    task_handler = logging.StreamHandler()  # Set up a handler for the task logger
+    task_handler.setFormatter(task_formatter)
+    task_logger.addHandler(task_handler)  # Apply the handler to the task logger
+
+    if logfile:
+        task_file_handler = logging.FileHandler(logfile)
+        task_file_formatter = CeleryTaskPlainFormatter(
+            "%(asctime)s %(filename)30s %(lineno)4s: %(message)s",
+            datefmt="%m/%d/%Y %I:%M:%S %p",
+        )
+        task_file_handler.setFormatter(task_file_formatter)
+        task_logger.addHandler(task_file_handler)
+
+    task_logger.setLevel(loglevel)
+    task_logger.propagate = False
+
+
+class HubPeriodicTask(bootsteps.StartStopStep):
+    """Regularly reacquires the primary worker locks for all tenants outside of the task queue.
+    Use the task_logger in this class to avoid double logging.
+
+    This cannot be done inside a regular beat task because it must run on schedule and
+    a queue of existing work would starve the task from running.
+    """
+
+    # Requires the Hub component
+    requires = {"celery.worker.components:Hub"}
+
+    def __init__(self, worker: Any, **kwargs: Any) -> None:
+        super().__init__(worker, **kwargs)
+        self.interval = CELERY_PRIMARY_WORKER_LOCK_TIMEOUT / 8  # Interval in seconds
+        self.task_tref = None
+
+    def start(self, worker: Any) -> None:
+        if not celery_is_worker_primary(worker):
+            return
+
+        # Access the worker's event loop (hub)
+        hub = worker.consumer.controller.hub
+
+        # Schedule the periodic task
+        self.task_tref = hub.call_repeatedly(
+            self.interval, self.run_periodic_task, worker
+        )
+        task_logger.info("Scheduled periodic task with hub.")
+
+    def run_periodic_task(self, worker: Any) -> None:
+        try:
+            if not celery_is_worker_primary(worker):
+                return
+
+            if not hasattr(worker, "primary_worker_locks"):
+                return
+
+            # Retrieve all tenant IDs
+            tenant_ids = get_all_tenant_ids()
+
+            for tenant_id in tenant_ids:
+                lock = worker.primary_worker_locks.get(tenant_id)
+                if not lock:
+                    continue  # Skip if no lock for this tenant
+
+                r = get_redis_client(tenant_id=tenant_id)
+
+                if lock.owned():
+                    task_logger.debug(
+                        f"Reacquiring primary worker lock for tenant {tenant_id}."
+                    )
+                    lock.reacquire()
+                else:
+                    task_logger.warning(
+                        f"Full acquisition of primary worker lock for tenant {tenant_id}. "
+                        "Reasons could be worker restart or lock expiration."
+                    )
+                    lock = r.lock(
+                        DanswerRedisLocks.PRIMARY_WORKER,
+                        timeout=CELERY_PRIMARY_WORKER_LOCK_TIMEOUT,
+                    )
+
+                    task_logger.info(
+                        f"Primary worker lock for tenant {tenant_id}: Acquire starting."
+                    )
+                    acquired = lock.acquire(
+                        blocking_timeout=CELERY_PRIMARY_WORKER_LOCK_TIMEOUT / 2
+                    )
+                    if acquired:
+                        task_logger.info(
+                            f"Primary worker lock for tenant {tenant_id}: Acquire succeeded."
+                        )
+                        worker.primary_worker_locks[tenant_id] = lock
+                    else:
+                        task_logger.error(
+                            f"Primary worker lock for tenant {tenant_id}: Acquire failed!"
+                        )
+                        raise TimeoutError(
+                            f"Primary worker lock for tenant {tenant_id} could not be acquired!"
+                        )
+
+        except Exception as e:
+            task_logger.error(f"Error in periodic task: {e}")
+
+    def stop(self, worker: Any) -> None:
+        # Cancel the scheduled task when the worker stops
+        if self.task_tref:
+            self.task_tref.cancel()
+            task_logger.info("Canceled periodic task with hub.")
+
+
+celery_app.steps["worker"].add(HubPeriodicTask)
+
+celery_app.autodiscover_tasks(
+    [
+        "danswer.background.celery.tasks.connector_deletion",
+        "danswer.background.celery.tasks.indexing",
+        "danswer.background.celery.tasks.periodic",
+        "danswer.background.celery.tasks.pruning",
+        "danswer.background.celery.tasks.shared",
+        "danswer.background.celery.tasks.vespa",
+    ]
+)
+
+#####
+# Celery Beat (Periodic Tasks) Settings
+#####
+
+tenant_ids = get_all_tenant_ids()
+
+tasks_to_schedule = [
+    {
+        "name": "check-for-vespa-sync",
+        "task": "check_for_vespa_sync_task",
+        "schedule": timedelta(seconds=5),
+        "options": {"priority": DanswerCeleryPriority.HIGH},
+    },
+    {
+        "name": "check-for-connector-deletion",
+        "task": "check_for_connector_deletion_task",
+        "schedule": timedelta(seconds=60),
+        "options": {"priority": DanswerCeleryPriority.HIGH},
+    },
+    {
+        "name": "check-for-indexing",
+        "task": "check_for_indexing",
+        "schedule": timedelta(seconds=10),
+        "options": {"priority": DanswerCeleryPriority.HIGH},
+    },
+    {
+        "name": "check-for-prune",
+        "task": "check_for_pruning",
+        "schedule": timedelta(seconds=10),
+        "options": {"priority": DanswerCeleryPriority.HIGH},
+    },
+    {
+        "name": "kombu-message-cleanup",
+        "task": "kombu_message_cleanup_task",
+        "schedule": timedelta(seconds=3600),
+        "options": {"priority": DanswerCeleryPriority.LOWEST},
+    },
+    {
+        "name": "monitor-vespa-sync",
+        "task": "monitor_vespa_sync",
+        "schedule": timedelta(seconds=5),
+        "options": {"priority": DanswerCeleryPriority.HIGH},
+    },
+]
+
+# Build the celery beat schedule dynamically
+beat_schedule = {}
+
+for id in tenant_ids:
+    for task in tasks_to_schedule:
+        task_name = f"{task['name']}-{id}"  # Unique name for each scheduled task
+        beat_schedule[task_name] = {
+            "task": task["task"],
+            "schedule": task["schedule"],
+            "options": task["options"],
+            "kwargs": {"tenant_id": id},  # Must pass tenant_id as an argument
+        }
+
+# Include any existing beat schedules
+existing_beat_schedule = celery_app.conf.beat_schedule or {}
+beat_schedule.update(existing_beat_schedule)
+
+# Update the Celery app configuration once
+celery_app.conf.beat_schedule = beat_schedule

backend/danswer/background/celery/celery_redis.py

@@ -1,10 +1,542 @@
 # These are helper objects for tracking the keys we need to write in redis
+import time
+from abc import ABC
+from abc import abstractmethod
 from typing import cast
+from uuid import uuid4
 
+import redis
+from celery import Celery
 from redis import Redis
+from sqlalchemy.orm import Session
 
-from danswer.background.celery.configs.base import CELERY_SEPARATOR
+from danswer.background.celery.celeryconfig import CELERY_SEPARATOR
+from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DanswerCeleryPriority
+from danswer.configs.constants import DanswerCeleryQueues
+from danswer.db.connector_credential_pair import get_connector_credential_pair_from_id
+from danswer.db.document import construct_document_select_for_connector_credential_pair
+from danswer.db.document import (
+    construct_document_select_for_connector_credential_pair_by_needs_sync,
+)
+from danswer.db.document_set import construct_document_select_by_docset
+from danswer.utils.variable_functionality import fetch_versioned_implementation
+from danswer.utils.variable_functionality import global_version
+
+
+class RedisObjectHelper(ABC):
+    PREFIX = "base"
+    FENCE_PREFIX = PREFIX + "_fence"
+    TASKSET_PREFIX = PREFIX + "_taskset"
+
+    def __init__(self, id: str):
+        self._id: str = id
+
+    @property
+    def task_id_prefix(self) -> str:
+        return f"{self.PREFIX}_{self._id}"
+
+    @property
+    def fence_key(self) -> str:
+        # example: documentset_fence_1
+        return f"{self.FENCE_PREFIX}_{self._id}"
+
+    @property
+    def taskset_key(self) -> str:
+        # example: documentset_taskset_1
+        return f"{self.TASKSET_PREFIX}_{self._id}"
+
+    @staticmethod
+    def get_id_from_fence_key(key: str) -> str | None:
+        """
+        Extracts the object ID from a fence key in the format `PREFIX_fence_X`.
+
+        Args:
+            key (str): The fence key string.
+
+        Returns:
+            Optional[int]: The extracted ID if the key is in the correct format, otherwise None.
+        """
+        parts = key.split("_")
+        if len(parts) != 3:
+            return None
+
+        object_id = parts[2]
+        return object_id
+
+    @staticmethod
+    def get_id_from_task_id(task_id: str) -> str | None:
+        """
+        Extracts the object ID from a task ID string.
+
+        This method assumes the task ID is formatted as `prefix_objectid_suffix`, where:
+        - `prefix` is an arbitrary string (e.g., the name of the task or entity),
+        - `objectid` is the ID you want to extract,
+        - `suffix` is another arbitrary string (e.g., a UUID).
+
+        Example:
+            If the input `task_id` is `documentset_1_cbfdc96a-80ca-4312-a242-0bb68da3c1dc`,
+            this method will return the string `"1"`.
+
+        Args:
+            task_id (str): The task ID string from which to extract the object ID.
+
+        Returns:
+            str | None: The extracted object ID if the task ID is in the correct format, otherwise None.
+        """
+        # example: task_id=documentset_1_cbfdc96a-80ca-4312-a242-0bb68da3c1dc
+        parts = task_id.split("_")
+        if len(parts) != 3:
+            return None
+
+        object_id = parts[1]
+        return object_id
+
+    @abstractmethod
+    def generate_tasks(
+        self,
+        celery_app: Celery,
+        db_session: Session,
+        redis_client: Redis,
+        lock: redis.lock.Lock,
+        tenant_id: str | None,
+    ) -> int | None:
+        pass
+
+
+class RedisDocumentSet(RedisObjectHelper):
+    PREFIX = "documentset"
+    FENCE_PREFIX = PREFIX + "_fence"
+    TASKSET_PREFIX = PREFIX + "_taskset"
+
+    def __init__(self, id: int) -> None:
+        super().__init__(str(id))
+
+    def generate_tasks(
+        self,
+        celery_app: Celery,
+        db_session: Session,
+        redis_client: Redis,
+        lock: redis.lock.Lock,
+        tenant_id: str | None,
+    ) -> int | None:
+        last_lock_time = time.monotonic()
+
+        async_results = []
+        stmt = construct_document_select_by_docset(int(self._id), current_only=False)
+        for doc in db_session.scalars(stmt).yield_per(1):
+            current_time = time.monotonic()
+            if current_time - last_lock_time >= (
+                CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT / 4
+            ):
+                lock.reacquire()
+                last_lock_time = current_time
+
+            # celery's default task id format is "dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            # the key for the result is "celery-task-meta-dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            # we prefix the task id so it's easier to keep track of who created the task
+            # aka "documentset_1_6dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            custom_task_id = f"{self.task_id_prefix}_{uuid4()}"
+
+            # add to the set BEFORE creating the task.
+            redis_client.sadd(self.taskset_key, custom_task_id)
+
+            result = celery_app.send_task(
+                "vespa_metadata_sync_task",
+                kwargs=dict(document_id=doc.id, tenant_id=tenant_id),
+                queue=DanswerCeleryQueues.VESPA_METADATA_SYNC,
+                task_id=custom_task_id,
+                priority=DanswerCeleryPriority.LOW,
+            )
+
+            async_results.append(result)
+
+        return len(async_results)
+
+
+class RedisUserGroup(RedisObjectHelper):
+    PREFIX = "usergroup"
+    FENCE_PREFIX = PREFIX + "_fence"
+    TASKSET_PREFIX = PREFIX + "_taskset"
+
+    def __init__(self, id: int) -> None:
+        super().__init__(str(id))
+
+    def generate_tasks(
+        self,
+        celery_app: Celery,
+        db_session: Session,
+        redis_client: Redis,
+        lock: redis.lock.Lock,
+        tenant_id: str | None,
+    ) -> int | None:
+        last_lock_time = time.monotonic()
+
+        async_results = []
+
+        if not global_version.is_ee_version():
+            return 0
+
+        try:
+            construct_document_select_by_usergroup = fetch_versioned_implementation(
+                "danswer.db.user_group",
+                "construct_document_select_by_usergroup",
+            )
+        except ModuleNotFoundError:
+            return 0
+
+        stmt = construct_document_select_by_usergroup(int(self._id))
+        for doc in db_session.scalars(stmt).yield_per(1):
+            current_time = time.monotonic()
+            if current_time - last_lock_time >= (
+                CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT / 4
+            ):
+                lock.reacquire()
+                last_lock_time = current_time
+
+            # celery's default task id format is "dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            # the key for the result is "celery-task-meta-dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            # we prefix the task id so it's easier to keep track of who created the task
+            # aka "documentset_1_6dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            custom_task_id = f"{self.task_id_prefix}_{uuid4()}"
+
+            # add to the set BEFORE creating the task.
+            redis_client.sadd(self.taskset_key, custom_task_id)
+
+            result = celery_app.send_task(
+                "vespa_metadata_sync_task",
+                kwargs=dict(document_id=doc.id, tenant_id=tenant_id),
+                queue=DanswerCeleryQueues.VESPA_METADATA_SYNC,
+                task_id=custom_task_id,
+                priority=DanswerCeleryPriority.LOW,
+            )
+
+            async_results.append(result)
+
+        return len(async_results)
+
+
+class RedisConnectorCredentialPair(RedisObjectHelper):
+    """This class is used to scan documents by cc_pair in the db and collect them into
+    a unified set for syncing.
+
+    It differs from the other redis helpers in that the taskset used spans
+    all connectors and is not per connector."""
+
+    PREFIX = "connectorsync"
+    FENCE_PREFIX = PREFIX + "_fence"
+    TASKSET_PREFIX = PREFIX + "_taskset"
+
+    def __init__(self, id: int) -> None:
+        super().__init__(str(id))
+
+    @classmethod
+    def get_fence_key(cls) -> str:
+        return RedisConnectorCredentialPair.FENCE_PREFIX
+
+    @classmethod
+    def get_taskset_key(cls) -> str:
+        return RedisConnectorCredentialPair.TASKSET_PREFIX
+
+    @property
+    def taskset_key(self) -> str:
+        """Notice that this is intentionally reusing the same taskset for all
+        connector syncs"""
+        # example: connector_taskset
+        return f"{self.TASKSET_PREFIX}"
+
+    def generate_tasks(
+        self,
+        celery_app: Celery,
+        db_session: Session,
+        redis_client: Redis,
+        lock: redis.lock.Lock,
+        tenant_id: str | None,
+    ) -> int | None:
+        last_lock_time = time.monotonic()
+
+        async_results = []
+        cc_pair = get_connector_credential_pair_from_id(int(self._id), db_session)
+        if not cc_pair:
+            return None
+
+        stmt = construct_document_select_for_connector_credential_pair_by_needs_sync(
+            cc_pair.connector_id, cc_pair.credential_id
+        )
+        for doc in db_session.scalars(stmt).yield_per(1):
+            current_time = time.monotonic()
+            if current_time - last_lock_time >= (
+                CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT / 4
+            ):
+                lock.reacquire()
+                last_lock_time = current_time
+
+            # celery's default task id format is "dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            # the key for the result is "celery-task-meta-dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            # we prefix the task id so it's easier to keep track of who created the task
+            # aka "documentset_1_6dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            custom_task_id = f"{self.task_id_prefix}_{uuid4()}"
+
+            # add to the tracking taskset in redis BEFORE creating the celery task.
+            # note that for the moment we are using a single taskset key, not differentiated by cc_pair id
+            redis_client.sadd(
+                RedisConnectorCredentialPair.get_taskset_key(), custom_task_id
+            )
+
+            # Priority on sync's triggered by new indexing should be medium
+            result = celery_app.send_task(
+                "vespa_metadata_sync_task",
+                kwargs=dict(document_id=doc.id, tenant_id=tenant_id),
+                queue=DanswerCeleryQueues.VESPA_METADATA_SYNC,
+                task_id=custom_task_id,
+                priority=DanswerCeleryPriority.MEDIUM,
+            )
+
+            async_results.append(result)
+
+        return len(async_results)
+
+
+class RedisConnectorDeletion(RedisObjectHelper):
+    PREFIX = "connectordeletion"
+    FENCE_PREFIX = PREFIX + "_fence"
+    TASKSET_PREFIX = PREFIX + "_taskset"
+
+    def __init__(self, id: int) -> None:
+        super().__init__(str(id))
+
+    def generate_tasks(
+        self,
+        celery_app: Celery,
+        db_session: Session,
+        redis_client: Redis,
+        lock: redis.lock.Lock,
+        tenant_id: str | None,
+    ) -> int | None:
+        last_lock_time = time.monotonic()
+
+        async_results = []
+        cc_pair = get_connector_credential_pair_from_id(int(self._id), db_session)
+        if not cc_pair:
+            return None
+
+        stmt = construct_document_select_for_connector_credential_pair(
+            cc_pair.connector_id, cc_pair.credential_id
+        )
+        for doc in db_session.scalars(stmt).yield_per(1):
+            current_time = time.monotonic()
+            if current_time - last_lock_time >= (
+                CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT / 4
+            ):
+                lock.reacquire()
+                last_lock_time = current_time
+
+            # celery's default task id format is "dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            # the actual redis key is "celery-task-meta-dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            # we prefix the task id so it's easier to keep track of who created the task
+            # aka "documentset_1_6dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            custom_task_id = f"{self.task_id_prefix}_{uuid4()}"
+
+            # add to the tracking taskset in redis BEFORE creating the celery task.
+            # note that for the moment we are using a single taskset key, not differentiated by cc_pair id
+            redis_client.sadd(self.taskset_key, custom_task_id)
+
+            # Priority on sync's triggered by new indexing should be medium
+            result = celery_app.send_task(
+                "document_by_cc_pair_cleanup_task",
+                kwargs=dict(
+                    document_id=doc.id,
+                    connector_id=cc_pair.connector_id,
+                    credential_id=cc_pair.credential_id,
+                    tenant_id=tenant_id,
+                ),
+                queue=DanswerCeleryQueues.CONNECTOR_DELETION,
+                task_id=custom_task_id,
+                priority=DanswerCeleryPriority.MEDIUM,
+            )
+
+            async_results.append(result)
+
+        return len(async_results)
+
+
+class RedisConnectorPruning(RedisObjectHelper):
+    """Celery will kick off a long running generator task to crawl the connector and
+    find any missing docs, which will each then get a new cleanup task. The progress of
+    those tasks will then be monitored to completion.
+
+    Example rough happy path order:
+    Check connectorpruning_fence_1
+    Send generator task with id connectorpruning+generator_1_{uuid}
+
+    generator runs connector with callbacks that increment connectorpruning_generator_progress_1
+    generator creates many subtasks with id connectorpruning+sub_1_{uuid}
+      in taskset connectorpruning_taskset_1
+    on completion, generator sets connectorpruning_generator_complete_1
+
+    celery postrun removes subtasks from taskset
+    monitor beat task cleans up when taskset reaches 0 items
+    """
+
+    PREFIX = "connectorpruning"
+    FENCE_PREFIX = PREFIX + "_fence"  # a fence for the entire pruning process
+    GENERATOR_TASK_PREFIX = PREFIX + "+generator"
+
+    TASKSET_PREFIX = PREFIX + "_taskset"  # stores a list of prune tasks id's
+    SUBTASK_PREFIX = PREFIX + "+sub"
+
+    GENERATOR_PROGRESS_PREFIX = (
+        PREFIX + "_generator_progress"
+    )  # a signal that contains generator progress
+    GENERATOR_COMPLETE_PREFIX = (
+        PREFIX + "_generator_complete"
+    )  # a signal that the generator has finished
+
+    def __init__(self, id: int) -> None:
+        super().__init__(str(id))
+        self.documents_to_prune: set[str] = set()
+
+    @property
+    def generator_task_id_prefix(self) -> str:
+        return f"{self.GENERATOR_TASK_PREFIX}_{self._id}"
+
+    @property
+    def generator_progress_key(self) -> str:
+        # example: connectorpruning_generator_progress_1
+        return f"{self.GENERATOR_PROGRESS_PREFIX}_{self._id}"
+
+    @property
+    def generator_complete_key(self) -> str:
+        # example: connectorpruning_generator_complete_1
+        return f"{self.GENERATOR_COMPLETE_PREFIX}_{self._id}"
+
+    @property
+    def subtask_id_prefix(self) -> str:
+        return f"{self.SUBTASK_PREFIX}_{self._id}"
+
+    def generate_tasks(
+        self,
+        celery_app: Celery,
+        db_session: Session,
+        redis_client: Redis,
+        lock: redis.lock.Lock | None,
+        tenant_id: str | None,
+    ) -> int | None:
+        last_lock_time = time.monotonic()
+
+        async_results = []
+        cc_pair = get_connector_credential_pair_from_id(int(self._id), db_session)
+        if not cc_pair:
+            return None
+
+        for doc_id in self.documents_to_prune:
+            current_time = time.monotonic()
+            if lock and current_time - last_lock_time >= (
+                CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT / 4
+            ):
+                lock.reacquire()
+                last_lock_time = current_time
+
+            # celery's default task id format is "dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            # the actual redis key is "celery-task-meta-dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            # we prefix the task id so it's easier to keep track of who created the task
+            # aka "documentset_1_6dd32ded3-00aa-4884-8b21-42f8332e7fac"
+            custom_task_id = f"{self.subtask_id_prefix}_{uuid4()}"
+
+            # add to the tracking taskset in redis BEFORE creating the celery task.
+            # note that for the moment we are using a single taskset key, not differentiated by cc_pair id
+            redis_client.sadd(self.taskset_key, custom_task_id)
+
+            # Priority on sync's triggered by new indexing should be medium
+            result = celery_app.send_task(
+                "document_by_cc_pair_cleanup_task",
+                kwargs=dict(
+                    document_id=doc_id,
+                    connector_id=cc_pair.connector_id,
+                    credential_id=cc_pair.credential_id,
+                    tenant_id=tenant_id,
+                ),
+                queue=DanswerCeleryQueues.CONNECTOR_DELETION,
+                task_id=custom_task_id,
+                priority=DanswerCeleryPriority.MEDIUM,
+            )
+
+            async_results.append(result)
+
+        return len(async_results)
+
+    def is_pruning(self, db_session: Session, redis_client: Redis) -> bool:
+        """A single example of a helper method being refactored into the redis helper"""
+        cc_pair = get_connector_credential_pair_from_id(
+            cc_pair_id=int(self._id), db_session=db_session
+        )
+        if not cc_pair:
+            raise ValueError(f"cc_pair_id {self._id} does not exist.")
+
+        if redis_client.exists(self.fence_key):
+            return True
+
+        return False
+
+
+class RedisConnectorIndexing(RedisObjectHelper):
+    """Celery will kick off a long running indexing task to crawl the connector and
+    find any new or updated docs docs, which will each then get a new sync task or be
+    indexed inline.
+
+    ID should be a concatenation of cc_pair_id and search_setting_id, delimited by "/".
+    e.g. "2/5"
+    """
+
+    PREFIX = "connectorindexing"
+    FENCE_PREFIX = PREFIX + "_fence"  # a fence for the entire indexing process
+    GENERATOR_TASK_PREFIX = PREFIX + "+generator"
+
+    TASKSET_PREFIX = PREFIX + "_taskset"  # stores a list of prune tasks id's
+    SUBTASK_PREFIX = PREFIX + "+sub"
+
+    GENERATOR_LOCK_PREFIX = "da_lock:indexing"
+    GENERATOR_PROGRESS_PREFIX = (
+        PREFIX + "_generator_progress"
+    )  # a signal that contains generator progress
+    GENERATOR_COMPLETE_PREFIX = (
+        PREFIX + "_generator_complete"
+    )  # a signal that the generator has finished
+
+    def __init__(self, cc_pair_id: int, search_settings_id: int) -> None:
+        super().__init__(f"{cc_pair_id}/{search_settings_id}")
+
+    @property
+    def generator_lock_key(self) -> str:
+        return f"{self.GENERATOR_LOCK_PREFIX}_{self._id}"
+
+    @property
+    def generator_task_id_prefix(self) -> str:
+        return f"{self.GENERATOR_TASK_PREFIX}_{self._id}"
+
+    @property
+    def generator_progress_key(self) -> str:
+        # example: connectorpruning_generator_progress_1
+        return f"{self.GENERATOR_PROGRESS_PREFIX}_{self._id}"
+
+    @property
+    def generator_complete_key(self) -> str:
+        # example: connectorpruning_generator_complete_1
+        return f"{self.GENERATOR_COMPLETE_PREFIX}_{self._id}"
+
+    @property
+    def subtask_id_prefix(self) -> str:
+        return f"{self.SUBTASK_PREFIX}_{self._id}"
+
+    def generate_tasks(
+        self,
+        celery_app: Celery,
+        db_session: Session,
+        redis_client: Redis,
+        lock: redis.lock.Lock | None,
+        tenant_id: str | None,
+    ) -> int | None:
+        return None
 
 
 def celery_get_queue_length(queue: str, r: Redis) -> int:

backend/danswer/background/celery/celery_run.py

@@ -1,10 +1,9 @@
-"""Factory stub for running celery worker / celery beat."""
-from celery import Celery
-
+"""Entry point for running celery worker / celery beat."""
 from danswer.utils.variable_functionality import fetch_versioned_implementation
 from danswer.utils.variable_functionality import set_is_ee_based_on_env_variable
 
+
 set_is_ee_based_on_env_variable()
-app: Celery = fetch_versioned_implementation(
-    "danswer.background.celery.apps.primary", "celery_app"
+celery_app = fetch_versioned_implementation(
+    "danswer.background.celery.celery_app", "celery_app"
 )

backend/danswer/background/celery/celery_utils.py

@@ -1,23 +1,28 @@
+from collections.abc import Callable
 from datetime import datetime
 from datetime import timezone
 from typing import Any
 
+from sqlalchemy import text
 from sqlalchemy.orm import Session
 
+from danswer.background.celery.celery_redis import RedisConnectorDeletion
 from danswer.configs.app_configs import MAX_PRUNING_DOCUMENT_RETRIEVAL_PER_MINUTE
+from danswer.configs.app_configs import MULTI_TENANT
+from danswer.configs.constants import TENANT_ID_PREFIX
 from danswer.connectors.cross_connector_utils.rate_limit_wrapper import (
     rate_limit_builder,
 )
 from danswer.connectors.interfaces import BaseConnector
+from danswer.connectors.interfaces import IdConnector
 from danswer.connectors.interfaces import LoadConnector
 from danswer.connectors.interfaces import PollConnector
-from danswer.connectors.interfaces import SlimConnector
 from danswer.connectors.models import Document
 from danswer.db.connector_credential_pair import get_connector_credential_pair
+from danswer.db.engine import get_session_with_tenant
 from danswer.db.enums import TaskStatus
 from danswer.db.models import TaskQueueState
-from danswer.indexing.indexing_heartbeat import IndexingHeartbeatInterface
-from danswer.redis.redis_connector import RedisConnector
+from danswer.redis.redis_pool import get_redis_client
 from danswer.server.documents.models import DeletionAttemptSnapshot
 from danswer.utils.logger import setup_logger
 
@@ -40,14 +45,14 @@ def _get_deletion_status(
     if not cc_pair:
         return None
 
-    redis_connector = RedisConnector(tenant_id, cc_pair.id)
-    if not redis_connector.delete.fenced:
+    rcd = RedisConnectorDeletion(cc_pair.id)
+
+    r = get_redis_client(tenant_id=tenant_id)
+    if not r.exists(rcd.fence_key):
         return None
 
     return TaskQueueState(
-        task_id="",
-        task_name=redis_connector.delete.fence_key,
-        status=TaskStatus.STARTED,
+        task_id="", task_name=rcd.fence_key, status=TaskStatus.STARTED
     )
 
 
@@ -70,31 +75,26 @@ def get_deletion_attempt_snapshot(
     )
 
 
-def document_batch_to_ids(
-    doc_batch: list[Document],
-) -> set[str]:
+def document_batch_to_ids(doc_batch: list[Document]) -> set[str]:
     return {doc.id for doc in doc_batch}
 
 
 def extract_ids_from_runnable_connector(
     runnable_connector: BaseConnector,
-    callback: IndexingHeartbeatInterface | None = None,
+    progress_callback: Callable[[int], None] | None = None,
 ) -> set[str]:
     """
-    If the SlimConnector hasnt been implemented for the given connector, just pull
+    If the PruneConnector hasnt been implemented for the given connector, just pull
     all docs using the load_from_state and grab out the IDs.
 
     Optionally, a callback can be passed to handle the length of each document batch.
     """
     all_connector_doc_ids: set[str] = set()
 
-    if isinstance(runnable_connector, SlimConnector):
-        for metadata_batch in runnable_connector.retrieve_all_slim_documents():
-            all_connector_doc_ids.update({doc.id for doc in metadata_batch})
-
     doc_batch_generator = None
-
-    if isinstance(runnable_connector, LoadConnector):
+    if isinstance(runnable_connector, IdConnector):
+        all_connector_doc_ids = runnable_connector.retrieve_all_source_ids()
+    elif isinstance(runnable_connector, LoadConnector):
         doc_batch_generator = runnable_connector.load_from_state()
     elif isinstance(runnable_connector, PollConnector):
         start = datetime(1970, 1, 1, tzinfo=timezone.utc).timestamp()
@@ -103,22 +103,16 @@ def extract_ids_from_runnable_connector(
     else:
         raise RuntimeError("Pruning job could not find a valid runnable_connector.")
 
-    doc_batch_processing_func = document_batch_to_ids
-    if MAX_PRUNING_DOCUMENT_RETRIEVAL_PER_MINUTE:
-        doc_batch_processing_func = rate_limit_builder(
-            max_calls=MAX_PRUNING_DOCUMENT_RETRIEVAL_PER_MINUTE, period=60
-        )(document_batch_to_ids)
-    for doc_batch in doc_batch_generator:
-        if callback:
-            if callback.should_stop():
-                raise RuntimeError(
-                    "extract_ids_from_runnable_connector: Stop signal detected"
-                )
-
-        all_connector_doc_ids.update(doc_batch_processing_func(doc_batch))
-
-        if callback:
-            callback.progress("extract_ids_from_runnable_connector", len(doc_batch))
+    if doc_batch_generator:
+        doc_batch_processing_func = document_batch_to_ids
+        if MAX_PRUNING_DOCUMENT_RETRIEVAL_PER_MINUTE:
+            doc_batch_processing_func = rate_limit_builder(
+                max_calls=MAX_PRUNING_DOCUMENT_RETRIEVAL_PER_MINUTE, period=60
+            )(document_batch_to_ids)
+        for doc_batch in doc_batch_generator:
+            if progress_callback:
+                progress_callback(len(doc_batch))
+            all_connector_doc_ids.update(doc_batch_processing_func(doc_batch))
 
     return all_connector_doc_ids
 
@@ -139,10 +133,33 @@ def celery_is_listening_to_queue(worker: Any, name: str) -> bool:
 def celery_is_worker_primary(worker: Any) -> bool:
     """There are multiple approaches that could be taken to determine if a celery worker
     is 'primary', as defined by us. But the way we do it is to check the hostname set
-    for the celery worker, which can be done on the
+    for the celery worker, which can be done either in celeryconfig.py or on the
     command line with '--hostname'."""
     hostname = worker.hostname
     if hostname.startswith("primary"):
         return True
 
     return False
+
+
+def get_all_tenant_ids() -> list[str] | list[None]:
+    if not MULTI_TENANT:
+        return [None]
+    with get_session_with_tenant(tenant_id="public") as session:
+        result = session.execute(
+            text(
+                """
+            SELECT schema_name
+            FROM information_schema.schemata
+            WHERE schema_name NOT IN ('pg_catalog', 'information_schema', 'public')"""
+            )
+        )
+        tenant_ids = [row[0] for row in result]
+
+    valid_tenants = [
+        tenant
+        for tenant in tenant_ids
+        if tenant is None or tenant.startswith(TENANT_ID_PREFIX)
+    ]
+
+    return valid_tenants

backend/danswer/background/celery/celeryconfig.py

@@ -31,10 +31,21 @@ if REDIS_SSL:
     if REDIS_SSL_CA_CERTS:
         SSL_QUERY_PARAMS += f"&ssl_ca_certs={REDIS_SSL_CA_CERTS}"
 
-# region Broker settings
 # example celery_broker_url: "redis://:password@localhost:6379/15"
 broker_url = f"{REDIS_SCHEME}://{CELERY_PASSWORD_PART}{REDIS_HOST}:{REDIS_PORT}/{REDIS_DB_NUMBER_CELERY}{SSL_QUERY_PARAMS}"
 
+result_backend = f"{REDIS_SCHEME}://{CELERY_PASSWORD_PART}{REDIS_HOST}:{REDIS_PORT}/{REDIS_DB_NUMBER_CELERY_RESULT_BACKEND}{SSL_QUERY_PARAMS}"
+
+# NOTE: prefetch 4 is significantly faster than prefetch 1 for small tasks
+# however, prefetching is bad when tasks are lengthy as those tasks
+# can stall other tasks.
+worker_prefetch_multiplier = 4
+
+# Leaving this to the default of True may cause double logging since both our own app
+# and celery think they are controlling the logger.
+# TODO: Configure celery's logger entirely manually and set this to False
+# worker_hijack_root_logger = False
+
 broker_connection_retry_on_startup = True
 broker_pool_limit = CELERY_BROKER_POOL_LIMIT
 
@@ -49,7 +60,6 @@ broker_transport_options = {
     "socket_keepalive": True,
     "socket_keepalive_options": REDIS_SOCKET_KEEPALIVE_OPTIONS,
 }
-# endregion
 
 # redis backend settings
 # https://docs.celeryq.dev/en/stable/userguide/configuration.html#redis-backend-settings
@@ -63,19 +73,10 @@ redis_backend_health_check_interval = REDIS_HEALTH_CHECK_INTERVAL
 task_default_priority = DanswerCeleryPriority.MEDIUM
 task_acks_late = True
 
-# region Task result backend settings
 # It's possible we don't even need celery's result backend, in which case all of the optimization below
 # might be irrelevant
-result_backend = f"{REDIS_SCHEME}://{CELERY_PASSWORD_PART}{REDIS_HOST}:{REDIS_PORT}/{REDIS_DB_NUMBER_CELERY_RESULT_BACKEND}{SSL_QUERY_PARAMS}"
 result_expires = CELERY_RESULT_EXPIRES  # 86400 seconds is the default
-# endregion
 
-# Leaving this to the default of True may cause double logging since both our own app
-# and celery think they are controlling the logger.
-# TODO: Configure celery's logger entirely manually and set this to False
-# worker_hijack_root_logger = False
-
-# region Notes on serialization performance
 # Option 0: Defaults (json serializer, no compression)
 # about 1.5 KB per queued task. 1KB in queue, 400B for result, 100 as a child entry in generator result
 
@@ -101,4 +102,3 @@ result_expires = CELERY_RESULT_EXPIRES  # 86400 seconds is the default
 # task_serializer = "pickle-bzip2"
 # result_serializer = "pickle-bzip2"
 # accept_content=["pickle", "pickle-bzip2"]
-# endregion

backend/danswer/background/celery/configs/beat.py

@@ -1,14 +0,0 @@
-# docs: https://docs.celeryq.dev/en/stable/userguide/configuration.html
-import danswer.background.celery.configs.base as shared_config
-
-broker_url = shared_config.broker_url
-broker_connection_retry_on_startup = shared_config.broker_connection_retry_on_startup
-broker_pool_limit = shared_config.broker_pool_limit
-broker_transport_options = shared_config.broker_transport_options
-
-redis_socket_keepalive = shared_config.redis_socket_keepalive
-redis_retry_on_timeout = shared_config.redis_retry_on_timeout
-redis_backend_health_check_interval = shared_config.redis_backend_health_check_interval
-
-result_backend = shared_config.result_backend
-result_expires = shared_config.result_expires  # 86400 seconds is the default

backend/danswer/background/celery/configs/heavy.py

@@ -1,20 +0,0 @@
-import danswer.background.celery.configs.base as shared_config
-
-broker_url = shared_config.broker_url
-broker_connection_retry_on_startup = shared_config.broker_connection_retry_on_startup
-broker_pool_limit = shared_config.broker_pool_limit
-broker_transport_options = shared_config.broker_transport_options
-
-redis_socket_keepalive = shared_config.redis_socket_keepalive
-redis_retry_on_timeout = shared_config.redis_retry_on_timeout
-redis_backend_health_check_interval = shared_config.redis_backend_health_check_interval
-
-result_backend = shared_config.result_backend
-result_expires = shared_config.result_expires  # 86400 seconds is the default
-
-task_default_priority = shared_config.task_default_priority
-task_acks_late = shared_config.task_acks_late
-
-worker_concurrency = 4
-worker_pool = "threads"
-worker_prefetch_multiplier = 1

backend/danswer/background/celery/configs/indexing.py

@@ -1,21 +0,0 @@
-import danswer.background.celery.configs.base as shared_config
-from danswer.configs.app_configs import CELERY_WORKER_INDEXING_CONCURRENCY
-
-broker_url = shared_config.broker_url
-broker_connection_retry_on_startup = shared_config.broker_connection_retry_on_startup
-broker_pool_limit = shared_config.broker_pool_limit
-broker_transport_options = shared_config.broker_transport_options
-
-redis_socket_keepalive = shared_config.redis_socket_keepalive
-redis_retry_on_timeout = shared_config.redis_retry_on_timeout
-redis_backend_health_check_interval = shared_config.redis_backend_health_check_interval
-
-result_backend = shared_config.result_backend
-result_expires = shared_config.result_expires  # 86400 seconds is the default
-
-task_default_priority = shared_config.task_default_priority
-task_acks_late = shared_config.task_acks_late
-
-worker_concurrency = CELERY_WORKER_INDEXING_CONCURRENCY
-worker_pool = "threads"
-worker_prefetch_multiplier = 1

backend/danswer/background/celery/configs/light.py

@@ -1,22 +0,0 @@
-import danswer.background.celery.configs.base as shared_config
-from danswer.configs.app_configs import CELERY_WORKER_LIGHT_CONCURRENCY
-from danswer.configs.app_configs import CELERY_WORKER_LIGHT_PREFETCH_MULTIPLIER
-
-broker_url = shared_config.broker_url
-broker_connection_retry_on_startup = shared_config.broker_connection_retry_on_startup
-broker_pool_limit = shared_config.broker_pool_limit
-broker_transport_options = shared_config.broker_transport_options
-
-redis_socket_keepalive = shared_config.redis_socket_keepalive
-redis_retry_on_timeout = shared_config.redis_retry_on_timeout
-redis_backend_health_check_interval = shared_config.redis_backend_health_check_interval
-
-result_backend = shared_config.result_backend
-result_expires = shared_config.result_expires  # 86400 seconds is the default
-
-task_default_priority = shared_config.task_default_priority
-task_acks_late = shared_config.task_acks_late
-
-worker_concurrency = CELERY_WORKER_LIGHT_CONCURRENCY
-worker_pool = "threads"
-worker_prefetch_multiplier = CELERY_WORKER_LIGHT_PREFETCH_MULTIPLIER

backend/danswer/background/celery/configs/primary.py

@@ -1,20 +0,0 @@
-import danswer.background.celery.configs.base as shared_config
-
-broker_url = shared_config.broker_url
-broker_connection_retry_on_startup = shared_config.broker_connection_retry_on_startup
-broker_pool_limit = shared_config.broker_pool_limit
-broker_transport_options = shared_config.broker_transport_options
-
-redis_socket_keepalive = shared_config.redis_socket_keepalive
-redis_retry_on_timeout = shared_config.redis_retry_on_timeout
-redis_backend_health_check_interval = shared_config.redis_backend_health_check_interval
-
-result_backend = shared_config.result_backend
-result_expires = shared_config.result_expires  # 86400 seconds is the default
-
-task_default_priority = shared_config.task_default_priority
-task_acks_late = shared_config.task_acks_late
-
-worker_concurrency = 4
-worker_pool = "threads"
-worker_prefetch_multiplier = 1

backend/danswer/background/celery/tasks/beat_schedule.py

@@ -1,61 +0,0 @@
-from datetime import timedelta
-from typing import Any
-
-from danswer.configs.constants import DanswerCeleryPriority
-from danswer.configs.constants import DanswerCeleryTask
-
-
-tasks_to_schedule = [
-    {
-        "name": "check-for-vespa-sync",
-        "task": DanswerCeleryTask.CHECK_FOR_VESPA_SYNC_TASK,
-        "schedule": timedelta(seconds=20),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-    {
-        "name": "check-for-connector-deletion",
-        "task": DanswerCeleryTask.CHECK_FOR_CONNECTOR_DELETION,
-        "schedule": timedelta(seconds=20),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-    {
-        "name": "check-for-indexing",
-        "task": DanswerCeleryTask.CHECK_FOR_INDEXING,
-        "schedule": timedelta(seconds=15),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-    {
-        "name": "check-for-prune",
-        "task": DanswerCeleryTask.CHECK_FOR_PRUNING,
-        "schedule": timedelta(seconds=15),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-    {
-        "name": "kombu-message-cleanup",
-        "task": DanswerCeleryTask.KOMBU_MESSAGE_CLEANUP_TASK,
-        "schedule": timedelta(seconds=3600),
-        "options": {"priority": DanswerCeleryPriority.LOWEST},
-    },
-    {
-        "name": "monitor-vespa-sync",
-        "task": DanswerCeleryTask.MONITOR_VESPA_SYNC,
-        "schedule": timedelta(seconds=5),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-    {
-        "name": "check-for-doc-permissions-sync",
-        "task": DanswerCeleryTask.CHECK_FOR_DOC_PERMISSIONS_SYNC,
-        "schedule": timedelta(seconds=30),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-    {
-        "name": "check-for-external-group-sync",
-        "task": DanswerCeleryTask.CHECK_FOR_EXTERNAL_GROUP_SYNC,
-        "schedule": timedelta(seconds=20),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-]
-
-
-def get_tasks_to_schedule() -> list[dict[str, Any]]:
-    return tasks_to_schedule

backend/danswer/background/celery/tasks/connector_deletion/tasks.py

@@ -1,43 +1,32 @@
-from datetime import datetime
-from datetime import timezone
-
-from celery import Celery
+import redis
 from celery import shared_task
-from celery import Task
 from celery.exceptions import SoftTimeLimitExceeded
-from redis.lock import Lock as RedisLock
+from redis import Redis
 from sqlalchemy.orm import Session
+from sqlalchemy.orm.exc import ObjectDeletedError
 
-from danswer.background.celery.apps.app_base import task_logger
+from danswer.background.celery.celery_app import celery_app
+from danswer.background.celery.celery_app import task_logger
+from danswer.background.celery.celery_redis import RedisConnectorDeletion
 from danswer.configs.app_configs import JOB_TIMEOUT
 from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
-from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DanswerRedisLocks
-from danswer.db.connector_credential_pair import get_connector_credential_pair_from_id
 from danswer.db.connector_credential_pair import get_connector_credential_pairs
 from danswer.db.engine import get_session_with_tenant
 from danswer.db.enums import ConnectorCredentialPairStatus
-from danswer.db.search_settings import get_all_search_settings
-from danswer.redis.redis_connector import RedisConnector
-from danswer.redis.redis_connector_delete import RedisConnectorDeletePayload
+from danswer.db.models import ConnectorCredentialPair
 from danswer.redis.redis_pool import get_redis_client
 
 
-class TaskDependencyError(RuntimeError):
-    """Raised to the caller to indicate dependent tasks are running that would interfere
-    with connector deletion."""
-
-
 @shared_task(
-    name=DanswerCeleryTask.CHECK_FOR_CONNECTOR_DELETION,
+    name="check_for_connector_deletion_task",
     soft_time_limit=JOB_TIMEOUT,
     trail=False,
-    bind=True,
 )
-def check_for_connector_deletion_task(self: Task, *, tenant_id: str | None) -> None:
+def check_for_connector_deletion_task(*, tenant_id: str | None) -> None:
     r = get_redis_client(tenant_id=tenant_id)
 
-    lock_beat: RedisLock = r.lock(
+    lock_beat = r.lock(
         DanswerRedisLocks.CHECK_CONNECTOR_DELETION_BEAT_LOCK,
         timeout=CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT,
     )
@@ -47,140 +36,78 @@ def check_for_connector_deletion_task(self: Task, *, tenant_id: str | None) -> N
         if not lock_beat.acquire(blocking=False):
             return
 
-        # collect cc_pair_ids
-        cc_pair_ids: list[int] = []
         with get_session_with_tenant(tenant_id) as db_session:
             cc_pairs = get_connector_credential_pairs(db_session)
             for cc_pair in cc_pairs:
-                cc_pair_ids.append(cc_pair.id)
-
-        # try running cleanup on the cc_pair_ids
-        for cc_pair_id in cc_pair_ids:
-            with get_session_with_tenant(tenant_id) as db_session:
-                redis_connector = RedisConnector(tenant_id, cc_pair_id)
-                try:
-                    try_generate_document_cc_pair_cleanup_tasks(
-                        self.app, cc_pair_id, db_session, lock_beat, tenant_id
-                    )
-                except TaskDependencyError as e:
-                    # this means we wanted to start deleting but dependent tasks were running
-                    # Leave a stop signal to clear indexing and pruning tasks more quickly
-                    task_logger.info(str(e))
-                    redis_connector.stop.set_fence(True)
-                else:
-                    # clear the stop signal if it exists ... no longer needed
-                    redis_connector.stop.set_fence(False)
-
+                try_generate_document_cc_pair_cleanup_tasks(
+                    cc_pair, db_session, r, lock_beat, tenant_id
+                )
     except SoftTimeLimitExceeded:
         task_logger.info(
             "Soft time limit exceeded, task is being terminated gracefully."
         )
     except Exception:
-        task_logger.exception(f"Unexpected exception: tenant={tenant_id}")
+        task_logger.exception("Unexpected exception")
     finally:
         if lock_beat.owned():
             lock_beat.release()
 
 
 def try_generate_document_cc_pair_cleanup_tasks(
-    app: Celery,
-    cc_pair_id: int,
+    cc_pair: ConnectorCredentialPair,
     db_session: Session,
-    lock_beat: RedisLock,
+    r: Redis,
+    lock_beat: redis.lock.Lock,
     tenant_id: str | None,
 ) -> int | None:
     """Returns an int if syncing is needed. The int represents the number of sync tasks generated.
     Note that syncing can still be required even if the number of sync tasks generated is zero.
     Returns None if no syncing is required.
-
-    Will raise TaskDependencyError if dependent tasks such as indexing and pruning are
-    still running. In our case, the caller reacts by setting a stop signal in Redis to
-    exit those tasks as quickly as possible.
     """
 
     lock_beat.reacquire()
 
-    redis_connector = RedisConnector(tenant_id, cc_pair_id)
+    rcd = RedisConnectorDeletion(cc_pair.id)
 
     # don't generate sync tasks if tasks are still pending
-    if redis_connector.delete.fenced:
+    if r.exists(rcd.fence_key):
         return None
 
-    # we need to load the state of the object inside the fence
+    # we need to refresh the state of the object inside the fence
     # to avoid a race condition with db.commit/fence deletion
     # at the end of this taskset
-    cc_pair = get_connector_credential_pair_from_id(cc_pair_id, db_session)
-    if not cc_pair:
+    try:
+        db_session.refresh(cc_pair)
+    except ObjectDeletedError:
         return None
 
     if cc_pair.status != ConnectorCredentialPairStatus.DELETING:
         return None
 
-    # set a basic fence to start
-    fence_payload = RedisConnectorDeletePayload(
-        num_tasks=None,
-        submitted=datetime.now(timezone.utc),
+    # add tasks to celery and build up the task set to monitor in redis
+    r.delete(rcd.taskset_key)
+
+    # Add all documents that need to be updated into the queue
+    task_logger.info(
+        f"RedisConnectorDeletion.generate_tasks starting. cc_pair_id={cc_pair.id}"
+    )
+    tasks_generated = rcd.generate_tasks(
+        celery_app, db_session, r, lock_beat, tenant_id
+    )
+    if tasks_generated is None:
+        return None
+
+    # Currently we are allowing the sync to proceed with 0 tasks.
+    # It's possible for sets/groups to be generated initially with no entries
+    # and they still need to be marked as up to date.
+    # if tasks_generated == 0:
+    #     return 0
+
+    task_logger.info(
+        f"RedisConnectorDeletion.generate_tasks finished. "
+        f"cc_pair_id={cc_pair.id} tasks_generated={tasks_generated}"
     )
 
-    redis_connector.delete.set_fence(fence_payload)
-
-    try:
-        # do not proceed if connector indexing or connector pruning are running
-        search_settings_list = get_all_search_settings(db_session)
-        for search_settings in search_settings_list:
-            redis_connector_index = redis_connector.new_index(search_settings.id)
-            if redis_connector_index.fenced:
-                raise TaskDependencyError(
-                    f"Connector deletion - Delayed (indexing in progress): "
-                    f"cc_pair={cc_pair_id} "
-                    f"search_settings={search_settings.id}"
-                )
-
-        if redis_connector.prune.fenced:
-            raise TaskDependencyError(
-                f"Connector deletion - Delayed (pruning in progress): "
-                f"cc_pair={cc_pair_id}"
-            )
-
-        if redis_connector.permissions.fenced:
-            raise TaskDependencyError(
-                f"Connector deletion - Delayed (permissions in progress): "
-                f"cc_pair={cc_pair_id}"
-            )
-
-        # add tasks to celery and build up the task set to monitor in redis
-        redis_connector.delete.taskset_clear()
-
-        # Add all documents that need to be updated into the queue
-        task_logger.info(
-            f"RedisConnectorDeletion.generate_tasks starting. cc_pair={cc_pair_id}"
-        )
-        tasks_generated = redis_connector.delete.generate_tasks(
-            app, db_session, lock_beat
-        )
-        if tasks_generated is None:
-            raise ValueError("RedisConnectorDeletion.generate_tasks returned None")
-    except TaskDependencyError:
-        redis_connector.delete.set_fence(None)
-        raise
-    except Exception:
-        task_logger.exception("Unexpected exception")
-        redis_connector.delete.set_fence(None)
-        return None
-    else:
-        # Currently we are allowing the sync to proceed with 0 tasks.
-        # It's possible for sets/groups to be generated initially with no entries
-        # and they still need to be marked as up to date.
-        # if tasks_generated == 0:
-        #     return 0
-
-        task_logger.info(
-            f"RedisConnectorDeletion.generate_tasks finished. "
-            f"cc_pair={cc_pair_id} tasks_generated={tasks_generated}"
-        )
-
-        # set this only after all tasks have been added
-        fence_payload.num_tasks = tasks_generated
-        redis_connector.delete.set_fence(fence_payload)
-
+    # set this only after all tasks have been added
+    r.set(rcd.fence_key, tasks_generated)
     return tasks_generated