error -> warning + clean

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

@@ -32,20 +32,16 @@ inputs:
     description: 'Cache destinations'
     required: false
   retry-wait-time:
-    description: 'Time to wait before attempt 2 in seconds'
+    description: 'Time to wait before retry in seconds'
     required: false
-    default: '60'
-  retry-wait-time-2:
-    description: 'Time to wait before attempt 3 in seconds'
-    required: false
-    default: '120'
+    default: '5'
 
 runs:
   using: "composite"
   steps:
-    - name: Build and push Docker image (Attempt 1 of 3)
+    - name: Build and push Docker image (First Attempt)
       id: buildx1
-      uses: docker/build-push-action@v6
+      uses: docker/build-push-action@v5
       continue-on-error: true
       with:
         context: ${{ inputs.context }}
@@ -58,17 +54,16 @@ runs:
         cache-from: ${{ inputs.cache-from }}
         cache-to: ${{ inputs.cache-to }}
 
-    - name: Wait before attempt 2
+    - name: Wait to retry
       if: steps.buildx1.outcome != 'success'
       run: |
         echo "First attempt failed. Waiting ${{ inputs.retry-wait-time }} seconds before retry..."
         sleep ${{ inputs.retry-wait-time }}
       shell: bash
 
-    - name: Build and push Docker image (Attempt 2 of 3)
-      id: buildx2
+    - name: Build and push Docker image (Retry Attempt)
       if: steps.buildx1.outcome != 'success'
-      uses: docker/build-push-action@v6
+      uses: docker/build-push-action@v5
       with:
         context: ${{ inputs.context }}
         file: ${{ inputs.file }}
@@ -79,31 +74,3 @@ runs:
         tags: ${{ inputs.tags }}
         cache-from: ${{ inputs.cache-from }}
         cache-to: ${{ inputs.cache-to }}
-
-    - name: Wait before attempt 3
-      if: steps.buildx1.outcome != 'success' && steps.buildx2.outcome != 'success'
-      run: |
-        echo "Second attempt failed. Waiting ${{ inputs.retry-wait-time-2 }} seconds before retry..."
-        sleep ${{ inputs.retry-wait-time-2 }}
-      shell: bash
-
-    - name: Build and push Docker image (Attempt 3 of 3)
-      id: buildx3
-      if: steps.buildx1.outcome != 'success' && steps.buildx2.outcome != 'success'
-      uses: docker/build-push-action@v6
-      with:
-        context: ${{ inputs.context }}
-        file: ${{ inputs.file }}
-        platforms: ${{ inputs.platforms }}
-        pull: ${{ inputs.pull }}
-        push: ${{ inputs.push }}
-        load: ${{ inputs.load }}
-        tags: ${{ inputs.tags }}
-        cache-from: ${{ inputs.cache-from }}
-        cache-to: ${{ inputs.cache-to }}
-
-    - name: Report failure
-      if: steps.buildx1.outcome != 'success' && steps.buildx2.outcome != 'success' && steps.buildx3.outcome != 'success'
-      run: |
-        echo "All attempts failed. Possible transient infrastucture issues? Try again later or inspect logs for details."
-      shell: bash

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

@@ -11,11 +11,8 @@ env:
   
 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 }}
+    runs-on: 
+      group: ${{ matrix.platform == 'linux/amd64' && 'amd64-image-builders' || 'arm64-image-builders' }}
     strategy:
       fail-fast: false
       matrix:

.github/workflows/hotfix-release-branches.yml

@@ -1,172 +0,0 @@
-# This workflow is intended to be manually triggered via the GitHub Action tab.
-# Given a hotfix branch, it will attempt to open a PR to all release branches and
-# by default auto merge them
-
-name: Hotfix release branches
-
-on:
-  workflow_dispatch:
-    inputs:
-      hotfix_commit:
-        description: 'Hotfix commit hash'
-        required: true
-      hotfix_suffix:
-        description: 'Hotfix branch suffix (e.g. hotfix/v0.8-{suffix})'
-        required: true
-      release_branch_pattern:
-        description: 'Release branch pattern (regex)'
-        required: true
-        default: 'release/.*'
-      auto_merge:
-        description: 'Automatically merge the hotfix PRs'
-        required: true
-        type: choice
-        default: 'true'
-        options:
-          - true
-          - false
-          
-jobs:
-  hotfix_release_branches:
-    permissions: write-all
-    # See https://runs-on.com/runners/linux/
-    # use a lower powered instance since this just does i/o to docker hub
-    runs-on: [runs-on,runner=2cpu-linux-x64,"run-id=${{ github.run_id }}"]
-    steps:
-    
-      # needs RKUO_DEPLOY_KEY for write access to merge PR's
-      - name: Checkout Repository
-        uses: actions/checkout@v4
-        with:
-          ssh-key: "${{ 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"
-
-      - name: Fetch All Branches
-        run: |
-          git fetch --all --prune
-
-      - name: Verify Hotfix Commit Exists
-        run: |
-          git rev-parse --verify "${{ github.event.inputs.hotfix_commit }}" || { echo "Commit not found: ${{ github.event.inputs.hotfix_commit }}"; exit 1; }
-
-      - name: Get Release Branches
-        id: get_release_branches
-        run: |
-          BRANCHES=$(git branch -r | grep -E "${{ github.event.inputs.release_branch_pattern }}" | sed 's|origin/||' | tr -d ' ')
-          if [ -z "$BRANCHES" ]; then
-            echo "No release branches found matching pattern '${{ github.event.inputs.release_branch_pattern }}'."
-            exit 1
-          fi
-          
-          echo "Found release branches:"
-          echo "$BRANCHES"
-          
-          # Join the branches into a single line separated by commas
-          BRANCHES_JOINED=$(echo "$BRANCHES" | tr '\n' ',' | sed 's/,$//')
-
-          # Set the branches as an output
-          echo "branches=$BRANCHES_JOINED" >> $GITHUB_OUTPUT
-
-      # notes on all the vagaries of wiring up automated PR's
-      # https://github.com/peter-evans/create-pull-request/blob/main/docs/concepts-guidelines.md#triggering-further-workflow-runs
-      # we must use a custom token for GH_TOKEN to trigger the subsequent PR checks
-      - name: Create and Merge Pull Requests to Matching Release Branches
-        env:
-          HOTFIX_COMMIT: ${{ github.event.inputs.hotfix_commit }}
-          HOTFIX_SUFFIX: ${{ github.event.inputs.hotfix_suffix }}
-          AUTO_MERGE: ${{ github.event.inputs.auto_merge }}
-          GH_TOKEN: ${{ secrets.RKUO_PERSONAL_ACCESS_TOKEN }}
-        run: |
-          # Get the branches from the previous step
-          BRANCHES="${{ steps.get_release_branches.outputs.branches }}"
-
-          # Convert BRANCHES to an array
-          IFS=$',' read -ra BRANCH_ARRAY <<< "$BRANCHES"
-
-          # Loop through each release branch and create and merge a PR
-          for RELEASE_BRANCH in "${BRANCH_ARRAY[@]}"; do
-            echo "Processing $RELEASE_BRANCH..."
-            
-            # Parse out the release version by removing "release/" from the branch name
-            RELEASE_VERSION=${RELEASE_BRANCH#release/}
-            echo "Release version parsed: $RELEASE_VERSION"
-            
-            HOTFIX_BRANCH="hotfix/${RELEASE_VERSION}-${HOTFIX_SUFFIX}"
-            echo "Creating PR from $HOTFIX_BRANCH to $RELEASE_BRANCH"
-
-            # Checkout the release branch
-            echo "Checking out $RELEASE_BRANCH"
-            git checkout "$RELEASE_BRANCH"
-
-            # Create the new hotfix branch
-            if git rev-parse --verify "$HOTFIX_BRANCH" >/dev/null 2>&1; then
-              echo "Hotfix branch $HOTFIX_BRANCH already exists. Skipping branch creation."
-            else
-              echo "Branching $RELEASE_BRANCH to $HOTFIX_BRANCH"
-              git checkout -b "$HOTFIX_BRANCH"
-            fi
-            
-            # Check if the hotfix commit is a merge commit
-            if git rev-list --merges -n 1 "$HOTFIX_COMMIT" >/dev/null 2>&1; then
-              # -m 1 uses the target branch as the base (which is what we want)
-              echo "Hotfix commit $HOTFIX_COMMIT is a merge commit, using -m 1 for cherry-pick"
-              CHERRY_PICK_CMD="git cherry-pick -m 1 $HOTFIX_COMMIT"
-            else
-              CHERRY_PICK_CMD="git cherry-pick $HOTFIX_COMMIT"
-            fi
-
-            # Perform the cherry-pick
-            echo "Executing: $CHERRY_PICK_CMD"
-            eval "$CHERRY_PICK_CMD"
-
-            if [ $? -ne 0 ]; then
-              echo "Cherry-pick failed for $HOTFIX_COMMIT on $HOTFIX_BRANCH. Aborting..."
-              git cherry-pick --abort
-              continue
-            fi
-
-            # Push the hotfix branch to the remote
-            echo "Pushing $HOTFIX_BRANCH..."
-            git push origin "$HOTFIX_BRANCH"
-            echo "Hotfix branch $HOTFIX_BRANCH created and pushed."
-            
-            # Check if PR already exists
-            EXISTING_PR=$(gh pr list --head "$HOTFIX_BRANCH" --base "$RELEASE_BRANCH" --state open --json number --jq '.[0].number')
-
-            if [ -n "$EXISTING_PR" ]; then
-              echo "An open PR already exists: #$EXISTING_PR. Skipping..."
-              continue
-            fi
-            
-            # Create a new PR and capture the output
-            PR_OUTPUT=$(gh pr create --title "Merge $HOTFIX_BRANCH into $RELEASE_BRANCH" \
-              --body "Automated PR to merge \`$HOTFIX_BRANCH\` into \`$RELEASE_BRANCH\`." \
-              --head "$HOTFIX_BRANCH" --base "$RELEASE_BRANCH")
-
-            # Extract the URL from the output
-            PR_URL=$(echo "$PR_OUTPUT" | grep -Eo 'https://github.com/[^ ]+')
-            echo "Pull request created: $PR_URL"
-
-            # Extract PR number from URL
-            PR_NUMBER=$(basename "$PR_URL")
-            echo "Pull request created: $PR_NUMBER"
-
-            if [ "$AUTO_MERGE" == "true" ]; then
-              echo "Attempting to merge pull request #$PR_NUMBER"
-
-              # Attempt to merge the PR
-              gh pr merge "$PR_NUMBER" --merge --auto --delete-branch
-
-              if [ $? -eq 0 ]; then
-                echo "Pull request #$PR_NUMBER merged successfully."
-              else
-                # Optionally, handle the error or continue
-                echo "Failed to merge pull request #$PR_NUMBER."
-              fi
-            fi
-          done

.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
 
@@ -217,22 +158,16 @@ 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()
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v3
         with:
           name: docker-logs
           path: ${{ github.workspace }}/docker-compose.log

.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-checks.yml

@@ -14,10 +14,10 @@ jobs:
 
     steps:
     - name: Checkout code
-      uses: actions/checkout@v4
+      uses: actions/checkout@v3
 
     - name: Set up Python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v4
       with:
         python-version: '3.11'
         cache: 'pip'

.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:
@@ -40,7 +32,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v4
         with:
           python-version: "3.11"
           cache: "pip"

.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 }}"]
 
@@ -27,7 +27,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v4
         with:
           python-version: "3.11"
           cache: "pip"

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

@@ -21,7 +21,7 @@ jobs:
       uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v4
       with:
         python-version: '3.11'
         cache: 'pip'

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

@@ -18,6 +18,6 @@ jobs:
       - uses: actions/setup-python@v5
         with:
           python-version: "3.11"
-      - uses: pre-commit/action@v3.0.1
+      - uses: pre-commit/action@v3.0.0
         with:
           extra_args: ${{ github.event_name == 'pull_request' && format('--from-ref {0} --to-ref {1}', github.event.pull_request.base.sha, github.event.pull_request.head.sha) || '' }}

.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">
@@ -69,13 +68,13 @@ 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.
 
 
-## Other Notable Benefits of Danswer
+## Other Noteable Benefits of Danswer
 * User Authentication with document level access management.
 * Best in class Hybrid Search across all sources (BM-25 + prefix aware embedding models).
 * Admin Dashboard to configure connectors, document-sets, access, etc.
@@ -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

@@ -8,11 +8,10 @@ Edition features outside of personal development or testing purposes. Please rea
 founders@danswer.ai for more information. Please visit https://github.com/danswer-ai/danswer"
 
 # Default DANSWER_VERSION, typically overriden during builds by GitHub Actions.
-ARG DANSWER_VERSION=0.8-dev
+ARG DANSWER_VERSION=0.3-dev
 ENV DANSWER_VERSION=${DANSWER_VERSION} \
     DANSWER_RUNNING_IN_DOCKER="true"
 
-
 RUN echo "DANSWER_VERSION: ${DANSWER_VERSION}"
 # Install system dependencies
 # cmake needed for psycopg (postgres)
@@ -37,8 +36,6 @@ RUN apt-get update && \
     rm -rf /var/lib/apt/lists/* && \
     apt-get clean
 
-
-
 # Install Python dependencies
 # Remove py which is pulled in by retry, py is not needed and is a CVE
 COPY ./requirements/default.txt /tmp/requirements.txt
@@ -73,11 +70,11 @@ 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); \
@@ -95,7 +92,6 @@ COPY supervisord.conf /etc/supervisor/conf.d/supervisord.conf
 COPY ./danswer /app/danswer
 COPY ./shared_configs /app/shared_configs
 COPY ./alembic /app/alembic
-COPY ./alembic_tenants /app/alembic_tenants
 COPY ./alembic.ini /app/alembic.ini
 COPY supervisord.conf /usr/etc/supervisord.conf
 
@@ -105,7 +101,7 @@ COPY ./scripts/force_delete_connector_by_id.py /app/scripts/force_delete_connect
 # Put logo in assets
 COPY ./assets /app/assets
 
-ENV PYTHONPATH=/app
+ENV PYTHONPATH /app
 
 # Default command which does nothing
 # This container is used by api server and background which specify their own CMD

backend/Dockerfile.model_server

@@ -7,7 +7,7 @@ You can find it at https://hub.docker.com/r/danswer/danswer-model-server. For mo
 visit https://github.com/danswer-ai/danswer."
 
 # Default DANSWER_VERSION, typically overriden during builds by GitHub Actions.
-ARG DANSWER_VERSION=0.8-dev
+ARG DANSWER_VERSION=0.3-dev
 ENV DANSWER_VERSION=${DANSWER_VERSION} \
     DANSWER_RUNNING_IN_DOCKER="true"
 
@@ -55,6 +55,6 @@ COPY ./shared_configs /app/shared_configs
 # Model Server main code
 COPY ./model_server /app/model_server
 
-ENV PYTHONPATH=/app
+ENV PYTHONPATH /app
 
 CMD ["uvicorn", "model_server.main:app", "--host", "0.0.0.0", "--port", "9000"]

backend/alembic.ini

@@ -1,6 +1,6 @@
 # A generic, single database configuration.
 
-[DEFAULT]
+[alembic]
 # path to migration scripts
 script_location = alembic
 
@@ -47,8 +47,7 @@ prepend_sys_path = .
 # version_path_separator = :
 # version_path_separator = ;
 # version_path_separator = space
-version_path_separator = os  
-# Use os.pathsep. Default configuration used for new projects.
+version_path_separator = os  # Use os.pathsep. Default configuration used for new projects.
 
 # set to 'true' to search source files recursively
 # in each "version_locations" directory
@@ -107,12 +106,3 @@ formatter = generic
 [formatter_generic]
 format = %(levelname)-5.5s [%(name)s] %(message)s
 datefmt = %H:%M:%S
-
-
-[alembic]
-script_location = alembic
-version_locations = %(script_location)s/versions
-
-[schema_private]
-script_location = alembic_tenants
-version_locations = %(script_location)s/versions

backend/alembic/env.py

@@ -1,116 +1,104 @@
-from sqlalchemy.engine.base import Connection
-from typing import Literal
 import asyncio
 from logging.config import fileConfig
-import logging
 
 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.db.engine import build_connection_string
 from danswer.db.models import Base
+from sqlalchemy import pool
+from sqlalchemy.engine import Connection
+from sqlalchemy.ext.asyncio import create_async_engine
 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 sqlalchemy.schema import SchemaItem
+from sqlalchemy.sql import text
 
 # Alembic Config object
 config = context.config
 
 # Interpret the config file for Python logging.
+# This line sets up loggers basically.
 if config.config_file_name is not None and config.attributes.get(
     "configure_logger", True
 ):
     fileConfig(config.config_file_name)
 
-# Add your model's MetaData object here for 'autogenerate' support
+# Add your model's MetaData object here
+# for 'autogenerate' support
+# from myapp import mymodel
+# target_metadata = mymodel.Base.metadata
 target_metadata = [Base.metadata, ResultModelBase.metadata]
 
-EXCLUDE_TABLES = {"kombu_queue", "kombu_message"}
 
-# Set up logging
-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,
-) -> bool:
-    """
-    Determines whether a database object should be included in migrations.
-    Excludes specified tables from migrations.
-    """
-    if type_ == "table" and name in EXCLUDE_TABLES:
-        return False
-    return True
-
-
-def get_schema_options() -> tuple[str, bool, bool]:
-    """
-    Parses command-line options passed via '-x' in Alembic commands.
-    Recognizes 'schema', 'create_schema', and 'upgrade_all_tenants' options.
-    """
+def get_schema_options() -> tuple[str, bool]:
     x_args_raw = context.get_x_argument()
     x_args = {}
     for arg in x_args_raw:
         for pair in arg.split(","):
             if "=" in pair:
                 key, value = pair.split("=", 1)
-                x_args[key.strip()] = value.strip()
-    schema_name = x_args.get("schema", POSTGRES_DEFAULT_SCHEMA)
+                x_args[key] = value
+
+    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
-    ):
-        raise ValueError(
-            "Cannot run default migrations in public schema when multi-tenancy is enabled. "
-            "Please specify a tenant-specific schema."
-        )
-
-    return schema_name, create_schema, upgrade_all_tenants
+    return schema_name, create_schema
 
 
-def do_run_migrations(
-    connection: Connection, schema_name: str, create_schema: bool
-) -> None:
+EXCLUDE_TABLES = {"kombu_queue", "kombu_message"}
+
+
+def include_object(
+    object: SchemaItem,
+    name: str,
+    type_: str,
+    reflected: bool,
+    compare_to: SchemaItem | None,
+) -> bool:
+    if type_ == "table" and name in EXCLUDE_TABLES:
+        return False
+    return True
+
+
+def run_migrations_offline() -> None:
+    """Run migrations in 'offline' mode.
+
+    This configures the context with just a URL
+    and not an Engine, though an Engine is acceptable
+    here as well.  By skipping the Engine creation
+    we don't even need a DBAPI to be available.
+    Calls to context.execute() here emit the given string to the
+    script output.
     """
-    Executes migrations in the specified schema.
-    """
-    logger.info(f"About to migrate schema: {schema_name}")
+    url = build_connection_string()
+    schema, _ = get_schema_options()
 
+    context.configure(
+        url=url,
+        target_metadata=target_metadata,  # type: ignore
+        literal_binds=True,
+        include_object=include_object,
+        dialect_opts={"paramstyle": "named"},
+        version_table_schema=schema,
+        include_schemas=True,
+    )
+
+    with context.begin_transaction():
+        context.run_migrations()
+
+
+def do_run_migrations(connection: Connection) -> None:
+    schema, create_schema = get_schema_options()
     if create_schema:
-        connection.execute(text(f'CREATE SCHEMA IF NOT EXISTS "{schema_name}"'))
+        connection.execute(text(f'CREATE SCHEMA IF NOT EXISTS "{schema}"'))
         connection.execute(text("COMMIT"))
 
-    # Set search_path to the target schema
-    connection.execute(text(f'SET search_path TO "{schema_name}"'))
+    connection.execute(text(f'SET search_path TO "{schema}"'))
 
     context.configure(
         connection=connection,
         target_metadata=target_metadata,  # type: ignore
-        include_object=include_object,
-        version_table_schema=schema_name,
+        version_table_schema=schema,
         include_schemas=True,
         compare_type=True,
         compare_server_default=True,
-        script_location=config.get_main_option("script_location"),
     )
 
     with context.begin_transaction():
@@ -118,98 +106,20 @@ def do_run_migrations(
 
 
 async def run_async_migrations() -> None:
-    """
-    Determines whether to run migrations for a single schema or all schemas,
-    and executes migrations accordingly.
-    """
-    schema_name, create_schema, upgrade_all_tenants = get_schema_options()
-
-    engine = create_async_engine(
+    """Run migrations in 'online' mode."""
+    connectable = create_async_engine(
         build_connection_string(),
         poolclass=pool.NullPool,
     )
 
-    if upgrade_all_tenants:
-        # Run migrations for all tenant schemas sequentially
-        tenant_schemas = get_all_tenant_ids()
+    async with connectable.connect() as connection:
+        await connection.run_sync(do_run_migrations)
 
-        for schema in tenant_schemas:
-            try:
-                logger.info(f"Migrating schema: {schema}")
-                async with engine.connect() as connection:
-                    await connection.run_sync(
-                        do_run_migrations,
-                        schema_name=schema,
-                        create_schema=create_schema,
-                    )
-            except Exception as e:
-                logger.error(f"Error migrating schema {schema}: {e}")
-                raise
-    else:
-        try:
-            logger.info(f"Migrating schema: {schema_name}")
-            async with engine.connect() as connection:
-                await connection.run_sync(
-                    do_run_migrations,
-                    schema_name=schema_name,
-                    create_schema=create_schema,
-                )
-        except Exception as e:
-            logger.error(f"Error migrating schema {schema_name}: {e}")
-            raise
-
-    await engine.dispose()
-
-
-def run_migrations_offline() -> None:
-    """
-    Run migrations in 'offline' mode.
-    """
-    schema_name, _, upgrade_all_tenants = get_schema_options()
-    url = build_connection_string()
-
-    if upgrade_all_tenants:
-        # Run offline migrations for all tenant schemas
-        engine = create_async_engine(url)
-        tenant_schemas = get_all_tenant_ids()
-        engine.sync_engine.dispose()
-
-        for schema in tenant_schemas:
-            logger.info(f"Migrating schema: {schema}")
-            context.configure(
-                url=url,
-                target_metadata=target_metadata,  # type: ignore
-                literal_binds=True,
-                include_object=include_object,
-                version_table_schema=schema,
-                include_schemas=True,
-                script_location=config.get_main_option("script_location"),
-                dialect_opts={"paramstyle": "named"},
-            )
-
-            with context.begin_transaction():
-                context.run_migrations()
-    else:
-        logger.info(f"Migrating schema: {schema_name}")
-        context.configure(
-            url=url,
-            target_metadata=target_metadata,  # type: ignore
-            literal_binds=True,
-            include_object=include_object,
-            version_table_schema=schema_name,
-            include_schemas=True,
-            script_location=config.get_main_option("script_location"),
-            dialect_opts={"paramstyle": "named"},
-        )
-
-        with context.begin_transaction():
-            context.run_migrations()
+    await connectable.dispose()
 
 
 def run_migrations_online() -> None:
-    """
-    Runs migrations in 'online' mode using an asynchronous engine.
-    """
+    """Run migrations in 'online' mode."""
     asyncio.run(run_async_migrations())
 
 

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/1b10e1fda030_add_additional_data_to_notifications.py

@@ -1,26 +0,0 @@
-"""add additional data to notifications
-
-Revision ID: 1b10e1fda030
-Revises: 6756efa39ada
-Create Date: 2024-10-15 19:26:44.071259
-
-"""
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision = "1b10e1fda030"
-down_revision = "6756efa39ada"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "notification", sa.Column("additional_data", postgresql.JSONB(), nullable=True)
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("notification", "additional_data")

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/5d12a446f5c0_add_api_version_and_deployment_name_to_.py

@@ -1,30 +0,0 @@
-"""add api_version and deployment_name to search settings
-
-Revision ID: 5d12a446f5c0
-Revises: e4334d5b33ba
-Create Date: 2024-10-08 15:56:07.975636
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "5d12a446f5c0"
-down_revision = "e4334d5b33ba"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "embedding_provider", sa.Column("api_version", sa.String(), nullable=True)
-    )
-    op.add_column(
-        "embedding_provider", sa.Column("deployment_name", sa.String(), nullable=True)
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("embedding_provider", "deployment_name")
-    op.drop_column("embedding_provider", "api_version")

backend/alembic/versions/6756efa39ada_id_uuid_for_chat_session.py

@@ -1,153 +0,0 @@
-"""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
-
-revision = "6756efa39ada"
-down_revision = "5d12a446f5c0"
-branch_labels = None
-depends_on = None
-
-"""
-This script:
-1. Adds UUID columns to chat_session and chat_message
-2. Populates new columns with UUIDs
-3. Updates foreign key relationships
-4. Removes old integer ID columns
-
-Note: Downgrade will assign new integer IDs, not restore original ones.
-"""
-
-
-def upgrade() -> None:
-    op.execute("CREATE EXTENSION IF NOT EXISTS pgcrypto;")
-
-    op.add_column(
-        "chat_session",
-        sa.Column(
-            "new_id",
-            sa.UUID(as_uuid=True),
-            server_default=sa.text("gen_random_uuid()"),
-            nullable=False,
-        ),
-    )
-
-    op.execute("UPDATE chat_session SET new_id = gen_random_uuid();")
-
-    op.add_column(
-        "chat_message",
-        sa.Column("new_chat_session_id", sa.UUID(as_uuid=True), nullable=True),
-    )
-
-    op.execute(
-        """
-        UPDATE chat_message
-        SET new_chat_session_id = cs.new_id
-        FROM chat_session cs
-        WHERE chat_message.chat_session_id = cs.id;
-        """
-    )
-
-    op.drop_constraint(
-        "chat_message_chat_session_id_fkey", "chat_message", type_="foreignkey"
-    )
-
-    op.drop_column("chat_message", "chat_session_id")
-    op.alter_column(
-        "chat_message", "new_chat_session_id", new_column_name="chat_session_id"
-    )
-
-    op.drop_constraint("chat_session_pkey", "chat_session", type_="primary")
-    op.drop_column("chat_session", "id")
-    op.alter_column("chat_session", "new_id", new_column_name="id")
-
-    op.create_primary_key("chat_session_pkey", "chat_session", ["id"])
-
-    op.create_foreign_key(
-        "chat_message_chat_session_id_fkey",
-        "chat_message",
-        "chat_session",
-        ["chat_session_id"],
-        ["id"],
-        ondelete="CASCADE",
-    )
-
-
-def downgrade() -> None:
-    op.drop_constraint(
-        "chat_message_chat_session_id_fkey", "chat_message", type_="foreignkey"
-    )
-
-    op.add_column(
-        "chat_session",
-        sa.Column("old_id", sa.Integer, autoincrement=True, nullable=True),
-    )
-
-    op.execute("CREATE SEQUENCE chat_session_old_id_seq OWNED BY chat_session.old_id;")
-    op.execute(
-        "ALTER TABLE chat_session ALTER COLUMN old_id SET DEFAULT nextval('chat_session_old_id_seq');"
-    )
-
-    op.execute(
-        "UPDATE chat_session SET old_id = nextval('chat_session_old_id_seq') WHERE old_id IS NULL;"
-    )
-
-    op.alter_column("chat_session", "old_id", nullable=False)
-
-    op.drop_constraint("chat_session_pkey", "chat_session", type_="primary")
-    op.create_primary_key("chat_session_pkey", "chat_session", ["old_id"])
-
-    op.add_column(
-        "chat_message",
-        sa.Column("old_chat_session_id", sa.Integer, nullable=True),
-    )
-
-    op.execute(
-        """
-        UPDATE chat_message
-        SET old_chat_session_id = cs.old_id
-        FROM chat_session cs
-        WHERE chat_message.chat_session_id = cs.id;
-        """
-    )
-
-    op.drop_column("chat_message", "chat_session_id")
-    op.alter_column(
-        "chat_message", "old_chat_session_id", new_column_name="chat_session_id"
-    )
-
-    op.create_foreign_key(
-        "chat_message_chat_session_id_fkey",
-        "chat_message",
-        "chat_session",
-        ["chat_session_id"],
-        ["old_id"],
-        ondelete="CASCADE",
-    )
-
-    op.drop_column("chat_session", "id")
-    op.alter_column("chat_session", "old_id", new_column_name="id")
-
-    op.alter_column(
-        "chat_session",
-        "id",
-        type_=sa.Integer(),
-        existing_type=sa.Integer(),
-        existing_nullable=False,
-        existing_server_default=False,
-    )
-
-    # Rename the sequence
-    op.execute("ALTER SEQUENCE chat_session_old_id_seq RENAME TO chat_session_id_seq;")
-
-    # Update the default value to use the renamed sequence
-    op.alter_column(
-        "chat_session",
-        "id",
-        server_default=sa.text("nextval('chat_session_id_seq'::regclass)"),
-    )

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/da4c21c69164_chosen_assistants_changed_to_jsonb.py

@@ -20,7 +20,7 @@ depends_on: None = None
 def upgrade() -> None:
     conn = op.get_bind()
     existing_ids_and_chosen_assistants = conn.execute(
-        sa.text('select id, chosen_assistants from "user"')
+        sa.text("select id, chosen_assistants from public.user")
     )
     op.drop_column(
         "user",
@@ -37,7 +37,7 @@ def upgrade() -> None:
     for id, chosen_assistants in existing_ids_and_chosen_assistants:
         conn.execute(
             sa.text(
-                'update "user" set chosen_assistants = :chosen_assistants where id = :id'
+                "update public.user set chosen_assistants = :chosen_assistants where id = :id"
             ),
             {"chosen_assistants": json.dumps(chosen_assistants), "id": id},
         )
@@ -46,7 +46,7 @@ def upgrade() -> None:
 def downgrade() -> None:
     conn = op.get_bind()
     existing_ids_and_chosen_assistants = conn.execute(
-        sa.text('select id, chosen_assistants from "user"')
+        sa.text("select id, chosen_assistants from public.user")
     )
     op.drop_column(
         "user",
@@ -59,7 +59,7 @@ def downgrade() -> None:
     for id, chosen_assistants in existing_ids_and_chosen_assistants:
         conn.execute(
             sa.text(
-                'update "user" set chosen_assistants = :chosen_assistants where id = :id'
+                "update public.user set chosen_assistants = :chosen_assistants where id = :id"
             ),
             {"chosen_assistants": chosen_assistants, "id": 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/versions/e4334d5b33ba_add_deployment_name_to_llmprovider.py

@@ -1,26 +0,0 @@
-"""add_deployment_name_to_llmprovider
-
-Revision ID: e4334d5b33ba
-Revises: ac5eaac849f9
-Create Date: 2024-10-04 09:52:34.896867
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "e4334d5b33ba"
-down_revision = "ac5eaac849f9"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "llm_provider", sa.Column("deployment_name", sa.String(), nullable=True)
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("llm_provider", "deployment_name")

backend/alembic_tenants/README.md

@@ -1,3 +0,0 @@
-These files are for public table migrations when operating with multi tenancy.
-
-If you are not a Danswer developer, you can ignore this directory entirely.

backend/alembic_tenants/env.py

@@ -1,119 +0,0 @@
-import asyncio
-from logging.config import fileConfig
-from typing import Literal
-
-from sqlalchemy import pool
-from sqlalchemy.engine import Connection
-from sqlalchemy.ext.asyncio import create_async_engine
-from sqlalchemy.schema import SchemaItem
-
-from alembic import context
-from danswer.db.engine import build_connection_string
-from danswer.db.models import PublicBase
-
-# this is the Alembic Config object, which provides
-# access to the values within the .ini file in use.
-config = context.config
-
-# Interpret the config file for Python logging.
-# This line sets up loggers basically.
-if config.config_file_name is not None and config.attributes.get(
-    "configure_logger", True
-):
-    fileConfig(config.config_file_name)
-
-# add your model's MetaData object here
-# for 'autogenerate' support
-# from myapp import mymodel
-# target_metadata = mymodel.Base.metadata
-target_metadata = [PublicBase.metadata]
-
-# other values from the config, defined by the needs of env.py,
-# can be acquired:
-# my_important_option = config.get_main_option("my_important_option")
-# ... etc.
-
-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",
-    ],
-    reflected: bool,
-    compare_to: SchemaItem | None,
-) -> bool:
-    if type_ == "table" and name in EXCLUDE_TABLES:
-        return False
-    return True
-
-
-def run_migrations_offline() -> None:
-    """Run migrations in 'offline' mode.
-
-    This configures the context with just a URL
-    and not an Engine, though an Engine is acceptable
-    here as well.  By skipping the Engine creation
-    we don't even need a DBAPI to be available.
-
-    Calls to context.execute() here emit the given string to the
-    script output.
-
-    """
-    url = build_connection_string()
-    context.configure(
-        url=url,
-        target_metadata=target_metadata,  # type: ignore
-        literal_binds=True,
-        dialect_opts={"paramstyle": "named"},
-    )
-
-    with context.begin_transaction():
-        context.run_migrations()
-
-
-def do_run_migrations(connection: Connection) -> None:
-    context.configure(
-        connection=connection,
-        target_metadata=target_metadata,  # type: ignore
-        include_object=include_object,
-    )  # type: ignore
-
-    with context.begin_transaction():
-        context.run_migrations()
-
-
-async def run_async_migrations() -> None:
-    """In this scenario we need to create an Engine
-    and associate a connection with the context.
-
-    """
-
-    connectable = create_async_engine(
-        build_connection_string(),
-        poolclass=pool.NullPool,
-    )
-
-    async with connectable.connect() as connection:
-        await connection.run_sync(do_run_migrations)
-
-    await connectable.dispose()
-
-
-def run_migrations_online() -> None:
-    """Run migrations in 'online' mode."""
-
-    asyncio.run(run_async_migrations())
-
-
-if context.is_offline_mode():
-    run_migrations_offline()
-else:
-    run_migrations_online()

backend/alembic_tenants/script.py.mako

@@ -1,24 +0,0 @@
-"""${message}
-
-Revision ID: ${up_revision}
-Revises: ${down_revision | comma,n}
-Create Date: ${create_date}
-
-"""
-from alembic import op
-import sqlalchemy as sa
-${imports if imports else ""}
-
-# revision identifiers, used by Alembic.
-revision = ${repr(up_revision)}
-down_revision = ${repr(down_revision)}
-branch_labels = ${repr(branch_labels)}
-depends_on = ${repr(depends_on)}
-
-
-def upgrade() -> None:
-    ${upgrades if upgrades else "pass"}
-
-
-def downgrade() -> None:
-    ${downgrades if downgrades else "pass"}

backend/alembic_tenants/versions/14a83a331951_create_usertenantmapping_table.py

@@ -1,24 +0,0 @@
-import sqlalchemy as sa
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision = "14a83a331951"
-down_revision = None
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.create_table(
-        "user_tenant_mapping",
-        sa.Column("email", sa.String(), nullable=False),
-        sa.Column("tenant_id", sa.String(), nullable=False),
-        sa.UniqueConstraint("email", "tenant_id", name="uq_user_tenant"),
-        sa.UniqueConstraint("email", name="uq_email"),
-        schema="public",
-    )
-
-
-def downgrade() -> None:
-    op.drop_table("user_tenant_mapping", schema="public")

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["reordered_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["reordered_documents"],
-            )
-        ],
-    )

backend/danswer/agent_search/answer_query/states.py

@@ -1,45 +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):
-    reordered_documents: Annotated[list[InferenceSection], dedup_inference_sections]
-
-
-class AnswerQueryState(
-    PrimaryState,
-    QACheckOutput,
-    QAGenerationOutput,
-    ExpandedRetrievalOutput,
-    total=True,
-):
-    query_to_answer: str
-
-
-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

@@ -1,44 +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 ExpandedRetrievalInput
-from danswer.agent_search.shared_graph_utils.prompts import REWRITE_PROMPT_MULTI
-from danswer.llm.interfaces import LLM
-
-
-def parallel_retrieval_edge(state: ExpandedRetrievalInput) -> list[Send | Hashable]:
-    print(f"parallel_retrieval_edge state: {state.keys()}")
-
-    # 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),
-        )
-    ]
-    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 = llm_response.split("\n")
-
-    print(f"rewritten_queries: {rewritten_queries}")
-
-    return [
-        Send(
-            "doc_retrieval",
-            RetrieveInput(query_to_retrieve=query, **state),
-        )
-        for query in rewritten_queries
-    ]

backend/danswer/agent_search/expanded_retrieval/graph_builder.py

@@ -1,88 +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.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.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
-
-
-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,
-    )
-
-    ### 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_edge(
-        start_key="doc_verification",
-        end_key="doc_reranking",
-    )
-    graph.add_edge(
-        start_key="doc_reranking",
-        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

@@ -1,11 +0,0 @@
-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: {state.keys()}")
-
-    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

@@ -1,47 +0,0 @@
-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
-from danswer.db.engine import get_session_context_manager
-
-
-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()}")
-
-    state["query_to_retrieve"]
-
-    documents: list[InferenceSection] = []
-    llm = state["primary_llm"]
-    fast_llm = state["fast_llm"]
-    # db_session = state["db_session"]
-    query_to_retrieve = state["search_request"].query
-    with get_session_context_manager() as db_session1:
-        documents = SearchPipeline(
-            search_request=SearchRequest(
-                query=query_to_retrieve,
-            ),
-            user=None,
-            llm=llm,
-            fast_llm=fast_llm,
-            db_session=db_session1,
-        ).reranked_sections
-
-    print(f"retrieved documents: {len(documents)}")
-    return DocRetrievalOutput(
-        retrieved_documents=documents,
-    )

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

@@ -1,60 +0,0 @@
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_message_runs
-
-from danswer.agent_search.expanded_retrieval.states import DocVerificationOutput
-from danswer.agent_search.expanded_retrieval.states import ExpandedRetrievalState
-from danswer.agent_search.shared_graph_utils.models import BinaryDecision
-from danswer.agent_search.shared_graph_utils.prompts import VERIFIER_PROMPT
-from danswer.context.search.models import InferenceSection
-
-
-class DocVerificationInput(ExpandedRetrievalState, total=True):
-    doc_to_verify: InferenceSection
-
-
-def doc_verification(state: DocVerificationInput) -> 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: {state.keys()}")
-
-    original_query = 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=original_query, 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)
-
-    print(f"Verdict: {formatted_response.decision}")
-
-    verified_documents = []
-    if formatted_response.decision == "yes":
-        verified_documents.append(doc_to_verify)
-
-    return DocVerificationOutput(
-        verified_documents=verified_documents,
-    )

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

@@ -1,27 +0,0 @@
-from typing import Literal
-
-from langgraph.types import Command
-from langgraph.types import Send
-
-from danswer.agent_search.expanded_retrieval.nodes.doc_verification 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: {state.keys()}")
-
-    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

@@ -1,36 +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]
-
-
-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):
-    reordered_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

@@ -1,98 +0,0 @@
-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.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="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="expanded_retrieval",
-    )
-
-    graph.add_edge(
-        start_key=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="If i am familiar with the function that I need, how can I type it into a cell?",
-    )
-    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,
-        )
-        for thing in compiled_graph.stream(
-            input=inputs,
-            # stream_mode="debug",
-            # debug=True,
-            subgraphs=True,
-        ):
-            # print(thing)
-            print()
-            print()

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

@@ -1,31 +0,0 @@
-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:
-    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
-    ]
-
-    return BaseDecompOutput(
-        initial_decomp_queries=decomp_list,
-    )

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

@@ -1,53 +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.lower() == "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)
-    answer = response.pretty_repr()
-
-    print(answer)
-    return InitialAnswerOutput(initial_answer=answer)

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 +33,9 @@ class UserRead(schemas.BaseUser[uuid.UUID]):
 
 class UserCreate(schemas.BaseUserCreate):
     role: UserRole = UserRole.BASIC
-    tenant_id: str | None = None
+    has_web_login: bool | None = True
 
 
 class UserUpdate(schemas.BaseUserUpdate):
     role: UserRole
+    has_web_login: bool | None = True

backend/danswer/auth/users.py

@@ -5,23 +5,18 @@ from datetime import datetime
 from datetime import timezone
 from email.mime.multipart import MIMEMultipart
 from email.mime.text import MIMEText
-from typing import Dict
-from typing import List
 from typing import Optional
 from typing import Tuple
 
 import jwt
 from email_validator import EmailNotValidError
-from email_validator import EmailUndeliverableError
 from email_validator import validate_email
 from fastapi import APIRouter
 from fastapi import Depends
 from fastapi import HTTPException
-from fastapi import Query
 from fastapi import Request
 from fastapi import Response
 from fastapi import status
-from fastapi.responses import RedirectResponse
 from fastapi.security import OAuth2PasswordRequestForm
 from fastapi_users import BaseUserManager
 from fastapi_users import exceptions
@@ -31,35 +26,22 @@ from fastapi_users import schemas
 from fastapi_users import UUIDIDMixin
 from fastapi_users.authentication import AuthenticationBackend
 from fastapi_users.authentication import CookieTransport
-from fastapi_users.authentication import JWTStrategy
 from fastapi_users.authentication import Strategy
 from fastapi_users.authentication.strategy.db import AccessTokenDatabase
 from fastapi_users.authentication.strategy.db import DatabaseStrategy
-from fastapi_users.exceptions import UserAlreadyExists
-from fastapi_users.jwt import decode_jwt
-from fastapi_users.jwt import generate_jwt
-from fastapi_users.jwt import SecretType
-from fastapi_users.manager import UserManagerDependency
 from fastapi_users.openapi import OpenAPIResponseType
-from fastapi_users.router.common import ErrorCode
-from fastapi_users.router.common import ErrorModel
 from fastapi_users_db_sqlalchemy import SQLAlchemyUserDatabase
-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.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 DATA_PLANE_SECRET
 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 EXPECTED_API_KEY
 from danswer.configs.app_configs import REQUIRE_EMAIL_VERIFICATION
 from danswer.configs.app_configs import SESSION_EXPIRE_TIME_SECONDS
 from danswer.configs.app_configs import SMTP_PASS
@@ -74,28 +56,19 @@ 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_with_tenant
+from danswer.db.engine import get_session
+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.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
 
 logger = setup_logger()
 
@@ -134,9 +107,7 @@ 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:
@@ -147,10 +118,7 @@ def verify_email_is_invited(email: str) -> None:
     if not email:
         raise PermissionError("Email must be specified")
 
-    try:
-        email_info = validate_email(email)
-    except EmailUndeliverableError:
-        raise PermissionError("Email is not valid")
+    email_info = validate_email(email)  # can raise EmailNotValidError
 
     for email_whitelist in whitelist:
         try:
@@ -168,8 +136,8 @@ def verify_email_is_invited(email: str) -> None:
     raise PermissionError("User not on allowed user whitelist")
 
 
-def verify_email_in_whitelist(email: str, tenant_id: str | None = None) -> None:
-    with get_session_with_tenant(tenant_id) as db_session:
+def verify_email_in_whitelist(email: str) -> None:
+    with Session(get_sqlalchemy_engine()) as db_session:
         if not get_user_by_email(email, db_session):
             verify_email_is_invited(email)
 
@@ -217,71 +185,44 @@ 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)
-
-        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,
-        )
-
-        async with get_async_session_with_tenant(tenant_id) as db_session:
-            token = CURRENT_TENANT_ID_CONTEXTVAR.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
+        verify_email_is_invited(user_create.email)
+        verify_email_domain(user_create.email)
+        if hasattr(user_create, "role"):
+            user_count = await get_user_count()
+            if user_count == 0 or user_create.email in get_default_admin_user_emails():
+                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.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,
                 )
-                self.user_db = tenant_user_db
-                self.database = tenant_user_db
-
-            if hasattr(user_create, "role"):
-                user_count = await get_user_count()
-                if (
-                    user_count == 0
-                    or user_create.email in get_default_admin_user_emails()
-                ):
-                    user_create.role = UserRole.ADMIN
-                else:
-                    user_create.role = UserRole.BASIC
-
-            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():
-                    user_update = UserUpdate(
-                        password=user_create.password,
-                        role=user_create.role,
-                        is_verified=user_create.is_verified,
-                    )
-                    user = await self.update(user_update, user)
-                else:
-                    raise exceptions.UserAlreadyExists()
-
-            finally:
-                CURRENT_TENANT_ID_CONTEXTVAR.reset(token)
-
-            return user
+                user = await self.update(user_update, user)
+            else:
+                raise exceptions.UserAlreadyExists()
+        return user
 
     async def oauth_callback(
-        self,
+        self: "BaseUserManager[models.UOAP, models.ID]",
         oauth_name: str,
         access_token: str,
         account_id: str,
@@ -292,128 +233,46 @@ 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)
+    ) -> models.UOAP:
+        verify_email_in_whitelist(account_email)
+        verify_email_domain(account_email)
 
-        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,
+        user = await super().oauth_callback(  # type: ignore
+            oauth_name=oauth_name,
+            access_token=access_token,
+            account_id=account_id,
+            account_email=account_email,
+            expires_at=expires_at,
+            refresh_token=refresh_token,
+            request=request,
+            associate_by_email=associate_by_email,
+            is_verified_by_default=is_verified_by_default,
         )
 
-        if not tenant_id:
-            raise HTTPException(status_code=401, detail="User not found")
+        # NOTE: Most IdPs have very short expiry times, and we don't want to force the user to
+        # re-authenticate that frequently, so by default this is disabled
+        if expires_at and TRACK_EXTERNAL_IDP_EXPIRY:
+            oidc_expiry = datetime.fromtimestamp(expires_at, tz=timezone.utc)
+            await self.user_db.update(user, update_dict={"oidc_expiry": oidc_expiry})
 
-        # 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)
+        # 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
+        if user.oidc_expiry and not TRACK_EXTERNAL_IDP_EXPIRY:
+            await self.user_db.update(user, update_dict={"oidc_expiry": None})
 
-            verify_email_in_whitelist(account_email, tenant_id)
-            verify_email_domain(account_email)
+        # Handle case where user has used product outside of web and is now creating an account through web
+        if not user.has_web_login:
+            await self.user_db.update(
+                user,
+                update_dict={
+                    "is_verified": is_verified_by_default,
+                    "has_web_login": True,
+                },
+            )
+            user.is_verified = is_verified_by_default
+            user.has_web_login = True
 
-            if MULTI_TENANT:
-                tenant_user_db = SQLAlchemyUserAdminDB[User, uuid.UUID](
-                    db_session, User, OAuthAccount
-                )
-                self.user_db = tenant_user_db
-                self.database = tenant_user_db
-
-            oauth_account_dict = {
-                "oauth_name": oauth_name,
-                "access_token": access_token,
-                "account_id": account_id,
-                "account_email": account_email,
-                "expires_at": expires_at,
-                "refresh_token": refresh_token,
-            }
-
-            try:
-                # Attempt to get user by OAuth account
-                user = await self.get_by_oauth_account(oauth_name, account_id)
-
-            except exceptions.UserNotExists:
-                try:
-                    # Attempt to get user by email
-                    user = await self.get_by_email(account_email)
-                    if not associate_by_email:
-                        raise exceptions.UserAlreadyExists()
-
-                    user = await self.user_db.add_oauth_account(
-                        user, oauth_account_dict
-                    )
-
-                    # If user not found by OAuth account or email, create a new user
-                except exceptions.UserNotExists:
-                    password = self.password_helper.generate()
-                    user_dict = {
-                        "email": account_email,
-                        "hashed_password": self.password_helper.hash(password),
-                        "is_verified": is_verified_by_default,
-                    }
-
-                    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)
-                    await self.on_after_register(user, request)
-
-            else:
-                for existing_oauth_account in user.oauth_accounts:
-                    if (
-                        existing_oauth_account.account_id == account_id
-                        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,
-                        )
-
-            # NOTE: Most IdPs have very short expiry times, and we don't want to force the user to
-            # re-authenticate that frequently, so by default this is disabled
-
-            if expires_at and TRACK_EXTERNAL_IDP_EXPIRY:
-                oidc_expiry = datetime.fromtimestamp(expires_at, tz=timezone.utc)
-                await self.user_db.update(
-                    user, update_dict={"oidc_expiry": oidc_expiry}
-                )
-
-            # 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():
-                await self.user_db.update(
-                    user,
-                    {
-                        "is_verified": is_verified_by_default,
-                        "role": UserRole.BASIC,
-                    },
-                )
-                user.is_verified = is_verified_by_default
-
-            # 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
-            if (
-                user.oidc_expiry is not None  # type: ignore
-                and not TRACK_EXTERNAL_IDP_EXPIRY
-            ):
-                await self.user_db.update(user, {"oidc_expiry": None})
-                user.oidc_expiry = None  # type: ignore
-
-            if token:
-                CURRENT_TENANT_ID_CONTEXTVAR.reset(token)
-
-            return user
+        return user
 
     async def on_after_register(
         self, user: User, request: Optional[Request] = None
@@ -444,53 +303,28 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
     async def authenticate(
         self, credentials: OAuth2PasswordRequestForm
     ) -> Optional[User]:
-        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,
-        )
-        if not tenant_id:
-            # User not found in mapping
+        try:
+            user = await self.get_by_email(credentials.username)
+        except exceptions.UserNotExists:
             self.password_helper.hash(credentials.password)
             return None
 
-        # Create a tenant-specific session
-        async with get_async_session_with_tenant(tenant_id) as tenant_session:
-            tenant_user_db: SQLAlchemyUserDatabase = SQLAlchemyUserDatabase(
-                tenant_session, User
+        if not user.has_web_login:
+            raise HTTPException(
+                status_code=status.HTTP_403_FORBIDDEN,
+                detail="NO_WEB_LOGIN_AND_HAS_NO_PASSWORD",
             )
-            self.user_db = tenant_user_db
 
-            # Proceed with authentication
-            try:
-                user = await self.get_by_email(email)
+        verified, updated_password_hash = self.password_helper.verify_and_update(
+            credentials.password, user.hashed_password
+        )
+        if not verified:
+            return None
 
-            except exceptions.UserNotExists:
-                self.password_helper.hash(credentials.password)
-                return None
+        if updated_password_hash is not None:
+            await self.user_db.update(user, {"hashed_password": updated_password_hash})
 
-            if not user.role.is_web_login():
-                raise BasicAuthenticationError(
-                    detail="NO_WEB_LOGIN_AND_HAS_NO_PASSWORD",
-                )
-
-            verified, updated_password_hash = self.password_helper.verify_and_update(
-                credentials.password, user.hashed_password
-            )
-            if not verified:
-                return None
-
-            if updated_password_hash is not None:
-                await self.user_db.update(
-                    user, {"hashed_password": updated_password_hash}
-                )
-
-            return user
+        return user
 
 
 async def get_user_manager(
@@ -505,51 +339,20 @@ 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(
-        secret=USER_AUTH_SECRET,
-        lifetime_seconds=SESSION_EXPIRE_TIME_SECONDS,
-    )
-
-
 def get_database_strategy(
     access_token_db: AccessTokenDatabase[AccessToken] = Depends(get_access_token_db),
 ) -> DatabaseStrategy:
-    return DatabaseStrategy(
+    strategy = DatabaseStrategy(
         access_token_db, lifetime_seconds=SESSION_EXPIRE_TIME_SECONDS  # type: ignore
     )
+    return strategy
 
 
 auth_backend = AuthenticationBackend(
-    name="jwt" if MULTI_TENANT else "database",
+    name="database",
     transport=cookie_transport,
-    get_strategy=get_jwt_strategy if MULTI_TENANT else get_database_strategy,  # type: ignore
-)  # type: ignore
+    get_strategy=get_database_strategy,
+)
 
 
 class FastAPIUserWithLogoutRouter(FastAPIUsers[models.UP, models.ID]):
@@ -563,11 +366,9 @@ class FastAPIUserWithLogoutRouter(FastAPIUsers[models.UP, models.ID]):
         This way the login router does not need to be included
         """
         router = APIRouter()
-
         get_current_user_token = self.authenticator.current_user_token(
             active=True, verified=requires_verification
         )
-
         logout_responses: OpenAPIResponseType = {
             **{
                 status.HTTP_401_UNAUTHORIZED: {
@@ -605,7 +406,7 @@ 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 +415,13 @@ async def optional_user_(
 
 async def optional_user(
     request: Request,
-    async_db_session: AsyncSession = Depends(get_async_session),
     user: User | None = Depends(optional_fastapi_current_user),
+    db_session: Session = Depends(get_session),
 ) -> 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 +433,14 @@ 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 +449,8 @@ 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 +463,10 @@ 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 +476,15 @@ 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 +496,8 @@ 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.",
         )
 
@@ -724,210 +509,26 @@ def get_default_admin_user_emails_() -> list[str]:
     return []
 
 
-STATE_TOKEN_AUDIENCE = "fastapi-users:oauth-state"
-
-
-class OAuth2AuthorizeResponse(BaseModel):
-    authorization_url: str
-
-
-def generate_state_token(
-    data: Dict[str, str], secret: SecretType, lifetime_seconds: int = 3600
-) -> str:
-    data["aud"] = STATE_TOKEN_AUDIENCE
-
-    return generate_jwt(data, secret, lifetime_seconds)
-
-
-# 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,
-    state_secret: SecretType,
-    redirect_url: Optional[str] = None,
-    associate_by_email: bool = False,
-    is_verified_by_default: bool = False,
-) -> APIRouter:
-    return get_oauth_router(
-        oauth_client,
-        backend,
-        get_user_manager,
-        state_secret,
-        redirect_url,
-        associate_by_email,
-        is_verified_by_default,
-    )
-
-
-def get_oauth_router(
-    oauth_client: BaseOAuth2,
-    backend: AuthenticationBackend,
-    get_user_manager: UserManagerDependency[models.UP, models.ID],
-    state_secret: SecretType,
-    redirect_url: Optional[str] = None,
-    associate_by_email: bool = False,
-    is_verified_by_default: bool = False,
-) -> APIRouter:
-    """Generate a router with the OAuth routes."""
-    router = APIRouter()
-    callback_route_name = f"oauth:{oauth_client.name}.{backend.name}.callback"
-
-    if redirect_url is not None:
-        oauth2_authorize_callback = OAuth2AuthorizeCallback(
-            oauth_client,
-            redirect_url=redirect_url,
-        )
-    else:
-        oauth2_authorize_callback = OAuth2AuthorizeCallback(
-            oauth_client,
-            route_name=callback_route_name,
-        )
-
-    @router.get(
-        "/authorize",
-        name=f"oauth:{oauth_client.name}.{backend.name}.authorize",
-        response_model=OAuth2AuthorizeResponse,
-    )
-    async def authorize(
-        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 = generate_state_token(state_data, state_secret)
-        authorization_url = await oauth_client.get_authorization_url(
-            authorize_redirect_url,
-            state,
-            scopes,
-        )
-
-        return OAuth2AuthorizeResponse(authorization_url=authorization_url)
-
-    @router.get(
-        "/callback",
-        name=callback_route_name,
-        description="The response varies based on the authentication backend used.",
-        responses={
-            status.HTTP_400_BAD_REQUEST: {
-                "model": ErrorModel,
-                "content": {
-                    "application/json": {
-                        "examples": {
-                            "INVALID_STATE_TOKEN": {
-                                "summary": "Invalid state token.",
-                                "value": None,
-                            },
-                            ErrorCode.LOGIN_BAD_CREDENTIALS: {
-                                "summary": "User is inactive.",
-                                "value": {"detail": ErrorCode.LOGIN_BAD_CREDENTIALS},
-                            },
-                        }
-                    }
-                },
-            },
-        },
-    )
-    async def callback(
-        request: Request,
-        access_token_state: Tuple[OAuth2Token, str] = Depends(
-            oauth2_authorize_callback
-        ),
-        user_manager: BaseUserManager[models.UP, models.ID] = Depends(get_user_manager),
-        strategy: Strategy[models.UP, models.ID] = Depends(backend.get_strategy),
-    ) -> RedirectResponse:
-        token, state = access_token_state
-        account_id, account_email = await oauth_client.get_id_email(
-            token["access_token"]
-        )
-
-        if account_email is None:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=ErrorCode.OAUTH_NOT_AVAILABLE_EMAIL,
-            )
-
-        try:
-            state_data = decode_jwt(state, state_secret, [STATE_TOKEN_AUDIENCE])
-        except jwt.DecodeError:
-            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
-        try:
-            user = await user_manager.oauth_callback(
-                oauth_client.name,
-                token["access_token"],
-                account_id,
-                account_email,
-                token.get("expires_at"),
-                token.get("refresh_token"),
-                request,
-                associate_by_email=associate_by_email,
-                is_verified_by_default=is_verified_by_default,
-            )
-        except UserAlreadyExists:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=ErrorCode.OAUTH_USER_ALREADY_EXISTS,
-            )
-
-        if not user.is_active:
-            raise HTTPException(
-                status_code=status.HTTP_400_BAD_REQUEST,
-                detail=ErrorCode.LOGIN_BAD_CREDENTIALS,
-            )
-
-        # Login user
-        response = await backend.login(strategy, user)
-        await user_manager.on_after_login(user, request, response)
-
-        # Prepare redirect response
-        redirect_response = RedirectResponse(next_url, status_code=302)
-
-        # Copy headers and other attributes from 'response' to 'redirect_response'
-        for header_name, header_value in response.headers.items():
-            redirect_response.headers[header_name] = header_value
-
-        if hasattr(response, "body"):
-            redirect_response.body = response.body
-        if hasattr(response, "status_code"):
-            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:
+async def control_plane_dep(request: Request) -> None:
+    api_key = request.headers.get("X-API-KEY")
+    if api_key != EXPECTED_API_KEY:
+        logger.warning("Invalid API key")
         raise HTTPException(status_code=401, detail="Invalid API key")
 
-    return user
+    auth_header = request.headers.get("Authorization")
+    if not auth_header or not auth_header.startswith("Bearer "):
+        logger.warning("Invalid authorization header")
+        raise HTTPException(status_code=401, detail="Invalid authorization header")
+
+    token = auth_header.split(" ")[1]
+    try:
+        payload = jwt.decode(token, DATA_PLANE_SECRET, algorithms=["HS256"])
+        if payload.get("scope") != "tenant:create":
+            logger.warning("Insufficient permissions")
+            raise HTTPException(status_code=403, detail="Insufficient permissions")
+    except jwt.ExpiredSignatureError:
+        logger.warning("Token has expired")
+        raise HTTPException(status_code=401, detail="Token has expired")
+    except jwt.InvalidTokenError:
+        logger.warning("Invalid token")
+        raise HTTPException(status_code=401, detail="Invalid token")

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",
-    ]
-)