updates

refactor

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

@@ -32,16 +32,20 @@ inputs:
     description: 'Cache destinations'
     required: false
   retry-wait-time:
-    description: 'Time to wait before retry in seconds'
+    description: 'Time to wait before attempt 2 in seconds'
     required: false
-    default: '5'
+    default: '60'
+  retry-wait-time-2:
+    description: 'Time to wait before attempt 3 in seconds'
+    required: false
+    default: '120'
 
 runs:
   using: "composite"
   steps:
-    - name: Build and push Docker image (First Attempt)
+    - name: Build and push Docker image (Attempt 1 of 3)
       id: buildx1
-      uses: docker/build-push-action@v5
+      uses: docker/build-push-action@v6
       continue-on-error: true
       with:
         context: ${{ inputs.context }}
@@ -54,16 +58,17 @@ runs:
         cache-from: ${{ inputs.cache-from }}
         cache-to: ${{ inputs.cache-to }}
 
-    - name: Wait to retry
+    - name: Wait before attempt 2
       if: steps.buildx1.outcome != 'success'
       run: |
         echo "First attempt failed. Waiting ${{ inputs.retry-wait-time }} seconds before retry..."
         sleep ${{ inputs.retry-wait-time }}
       shell: bash
 
-    - name: Build and push Docker image (Retry Attempt)
+    - name: Build and push Docker image (Attempt 2 of 3)
+      id: buildx2
       if: steps.buildx1.outcome != 'success'
-      uses: docker/build-push-action@v5
+      uses: docker/build-push-action@v6
       with:
         context: ${{ inputs.context }}
         file: ${{ inputs.file }}
@@ -74,3 +79,31 @@ 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

@@ -6,20 +6,24 @@
 [Describe the tests you ran to verify your changes]
 
 
-## Accepted Risk
-[Any know risks or failure modes to point out to reviewers]
+## Accepted Risk (provide if relevant)
+N/A
 
 
-## Related Issue(s)
-[If applicable, link to the issue(s) this PR addresses]
+## Related Issue(s) (provide if relevant)
+N/A
 
 
-## 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
+## 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)

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

@@ -3,52 +3,61 @@ name: Build and Push Backend Image on Tag
 on:
   push:
     tags:
-      - '*'
+      - "*"
 
 env:
-  REGISTRY_IMAGE: danswer/danswer-backend
+  REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'danswer/danswer-backend-cloud' || '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@v2
+      - 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: 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 }}
-          ${{ env.REGISTRY_IMAGE }}:latest
-        build-args: |
-          DANSWER_VERSION=${{ github.ref_name }}
+      - name: Install build-essential
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y build-essential
 
-    - name: Run Trivy vulnerability scanner
-      uses: aquasecurity/trivy-action@master
-      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
+      - 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

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

@@ -0,0 +1,137 @@
+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

@@ -3,41 +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' }}
+  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@v2
+      - 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: |
-          danswer/danswer-model-server:${{ github.ref_name }}
-          danswer/danswer-model-server:latest
-        build-args: |
-          DANSWER_VERSION=${{ 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 }}
 
-    - name: Run Trivy vulnerability scanner
-      uses: aquasecurity/trivy-action@master
-      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

@@ -7,11 +7,15 @@ on:
 
 env:
   REGISTRY_IMAGE: danswer/danswer-web-server
-
+  LATEST_TAG: ${{ contains(github.ref_name, 'latest') }}
+  
 jobs:
   build:
-    runs-on: 
-      group: ${{ matrix.platform == 'linux/amd64' && 'amd64-image-builders' || 'arm64-image-builders' }}
+    runs-on:
+      - runs-on
+      - runner=${{ matrix.platform == 'linux/amd64' && '8cpu-linux-x64' || '8cpu-linux-arm64' }}
+      - run-id=${{ github.run_id }}
+      - tag=platform-${{ matrix.platform }}
     strategy:
       fail-fast: false
       matrix:
@@ -35,7 +39,7 @@ jobs:
           images: ${{ env.REGISTRY_IMAGE }}
           tags: |
             type=raw,value=${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
-            type=raw,value=${{ env.REGISTRY_IMAGE }}:latest
+            type=raw,value=${{ env.LATEST_TAG == 'true' && format('{0}:latest', env.REGISTRY_IMAGE) || '' }}
       
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
@@ -112,8 +116,16 @@ jobs:
         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-tag-latest.yml

@@ -1,3 +1,6 @@
+# This workflow is set up to be manually triggered via the GitHub Action tab.
+# Given a version, it will tag those backend and webserver images as "latest".
+
 name: Tag Latest Version
 
 on:

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

@@ -0,0 +1,172 @@
+# 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

@@ -0,0 +1,23 @@
+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

@@ -0,0 +1,76 @@
+# 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-backport-autotrigger.yml

@@ -0,0 +1,124 @@
+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

@@ -0,0 +1,225 @@
+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

@@ -0,0 +1,72 @@
+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

@@ -1,68 +0,0 @@
-# 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-integration-tests.yml

@@ -0,0 +1,243 @@
+name: Run Integration Tests v2
+concurrency:
+  group: Run-Integration-Tests-${{ github.workflow }}-${{ github.head_ref || github.event.workflow_run.head_branch || github.run_id }}
+  cancel-in-progress: true
+
+on:
+  merge_group:
+  pull_request:
+    branches:
+      - main
+      - 'release/**'
+
+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: ${{ secrets.CONFLUENCE_ACCESS_TOKEN }}
+  
+jobs:
+  integration-tests:
+    # See https://runs-on.com/runners/linux/
+    runs-on: [runs-on,runner=8cpu-linux-x64,ram=16,"run-id=${{ github.run_id }}"]
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - 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: ${{ secrets.DOCKER_TOKEN }}
+
+      # tag every docker image with "test" so that we can spin up the correct set
+      # of images during testing
+      
+      # We don't need to build the Web Docker image since it's not yet used
+      # in the integration tests. We have a separate action to verify that it builds 
+      # successfully.
+      - name: Pull Web Docker image
+        run: |
+          docker pull danswer/danswer-web-server:latest
+          docker tag danswer/danswer-web-server:latest danswer/danswer-web-server:test
+
+      # we use the runs-on cache for docker builds
+      # in conjunction with runs-on runners, it has better speed and unlimited caching
+      # https://runs-on.com/caching/s3-cache-for-github-actions/
+      # https://runs-on.com/caching/docker/
+      # https://github.com/moby/buildkit#s3-cache-experimental
+      
+      # images are built and run locally for testing purposes. Not pushed.
+      - name: Build Backend Docker image
+        uses: ./.github/actions/custom-build-and-push
+        with:
+          context: ./backend
+          file: ./backend/Dockerfile
+          platforms: linux/amd64
+          tags: danswer/danswer-backend:test
+          push: false
+          load: true
+          cache-from: type=s3,prefix=cache/${{ 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=${{ env.RUNS_ON_S3_BUCKET_CACHE }},mode=max
+      
+      - name: Build integration test Docker image
+        uses: ./.github/actions/custom-build-and-push
+        with:
+          context: ./backend
+          file: ./backend/tests/integration/Dockerfile
+          platforms: linux/amd64
+          tags: danswer/danswer-integration:test
+          push: false
+          load: true
+          cache-from: type=s3,prefix=cache/${{ 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 [ ${{ steps.run_tests.outcome }} == 'failure' ]; then
+            echo "Integration tests failed. Exiting with error."
+            exit 1
+          else
+            echo "All integration tests passed successfully."
+          fi 
+
+      - name: Stop multi-tenant Docker containers
+        run: |
+          cd deployment/docker_compose
+          docker compose -f docker-compose.dev.yml -p danswer-stack down -v
+
+
+      - name: Start Docker containers 
+        run: |
+          cd deployment/docker_compose
+          ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true \
+          AUTH_TYPE=basic \
+          REQUIRE_EMAIL_VERIFICATION=false \
+          DISABLE_TELEMETRY=true \
+          IMAGE_TAG=test \
+          docker compose -f docker-compose.dev.yml -p danswer-stack up -d
+        id: start_docker
+
+      - name: Wait for service to be ready
+        run: |
+          echo "Starting wait-for-service script..."
+          
+          docker logs -f danswer-stack-api_server-1 &
+
+          start_time=$(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 $response). Retrying in 5 seconds..."
+            fi
+            
+            sleep 5
+          done
+          echo "Finished waiting for service."
+
+      - name: Run Standard Integration Tests
+        run: |
+          echo "Running integration tests..."
+          docker run --rm --network danswer-stack_default \
+            --name test-runner \
+            -e POSTGRES_HOST=relational_db \
+            -e POSTGRES_USER=postgres \
+            -e POSTGRES_PASSWORD=password \
+            -e POSTGRES_DB=postgres \
+            -e VESPA_HOST=index \
+            -e REDIS_HOST=cache \
+            -e API_SERVER_HOST=api_server \
+            -e OPENAI_API_KEY=${OPENAI_API_KEY} \
+            -e SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN} \
+            -e CONFLUENCE_TEST_SPACE_URL=${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
+        continue-on-error: true
+        id: run_tests
+
+      - name: Check 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
+
+      # 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
+        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

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

@@ -3,7 +3,9 @@ name: Python Checks
 on:
   merge_group:
   pull_request:
-    branches: [ main ]
+    branches:
+      - main
+      - 'release/**'
 
 jobs:
   mypy-check:
@@ -12,10 +14,10 @@ jobs:
 
     steps:
     - name: Checkout code
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: '3.11'
         cache: 'pip'

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

@@ -15,6 +15,17 @@ env:
   CONFLUENCE_TEST_PAGE_ID: ${{ secrets.CONFLUENCE_TEST_PAGE_ID }}
   CONFLUENCE_USER_NAME: ${{ secrets.CONFLUENCE_USER_NAME }}
   CONFLUENCE_ACCESS_TOKEN: ${{ secrets.CONFLUENCE_ACCESS_TOKEN }}
+  # 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:
@@ -29,7 +40,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.11"
           cache: "pip"

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

@@ -0,0 +1,58 @@
+name: Connector Tests
+
+on:
+  schedule:
+    # This cron expression runs the job daily at 16:00 UTC (9am PT)
+    - cron: "0 16 * * *"
+
+env:
+  # Bedrock
+  AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
+  AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+  AWS_REGION_NAME: ${{ secrets.AWS_REGION_NAME }}
+
+  # OpenAI
+  OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+
+jobs:
+  model-check:
+    # See https://runs-on.com/runners/linux/
+    runs-on: [runs-on,runner=8cpu-linux-x64,"run-id=${{ github.run_id }}"]
+
+    env:
+      PYTHONPATH: ./backend
+
+    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
+
+      - name: Install 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
+
+      - name: Run Tests
+        shell: script -q -e -c "bash --noprofile --norc -eo pipefail {0}"
+        run: |
+          py.test -o junit_family=xunit2 -xv --ff backend/tests/daily/llm
+          py.test -o junit_family=xunit2 -xv --ff backend/tests/daily/embedding
+
+      - name: Alert on Failure
+        if: failure() && github.event_name == 'schedule'
+        env:
+          SLACK_WEBHOOK: ${{ secrets.SLACK_WEBHOOK }}
+        run: |
+          curl -X POST \
+            -H 'Content-type: application/json' \
+            --data '{"text":"Scheduled Model Tests failed! Check the run at: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}"}' \
+            $SLACK_WEBHOOK

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

@@ -3,7 +3,9 @@ name: Python Unit Tests
 on:
   merge_group:
   pull_request:
-    branches: [ main ]
+    branches:
+      - main
+      - 'release/**'
 
 jobs:
   backend-check:
@@ -19,7 +21,7 @@ jobs:
       uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       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@v3.0.0
+      - uses: pre-commit/action@v3.0.1
         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) || '' }}

.github/workflows/run-it.yml

@@ -1,162 +0,0 @@
-name: Run Integration Tests
-concurrency:
-  group: Run-Integration-Tests-${{ github.workflow }}-${{ github.head_ref || github.event.workflow_run.head_branch || github.run_id }}
-  cancel-in-progress: true
-
-on:
-  merge_group:
-  pull_request:
-    branches: [ main ]
-
-env:
-  OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-
-jobs:
-  integration-tests:
-    # See https://runs-on.com/runners/linux/
-    runs-on: [runs-on,runner=8cpu-linux-x64,ram=32,"run-id=${{ github.run_id }}"]
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - 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 }}
-
-      # NOTE: we don't need to build the Web Docker image since it's not used
-      # during the IT for now. We have a separate action to verify it builds 
-      # succesfully
-      - name: Pull Web Docker image
-        run: |
-          docker pull danswer/danswer-web-server:latest
-          docker tag danswer/danswer-web-server:latest danswer/danswer-web-server:it
-
-      - 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:it
-          cache-from: type=registry,ref=danswer/danswer-backend:it
-          cache-to: |
-            type=registry,ref=danswer/danswer-backend:it,mode=max
-            type=inline
-
-      - 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:it
-          cache-from: type=registry,ref=danswer/danswer-model-server:it
-          cache-to: |
-            type=registry,ref=danswer/danswer-model-server:it,mode=max
-            type=inline
-
-      - name: Build integration test Docker image
-        uses: ./.github/actions/custom-build-and-push
-        with:
-          context: ./backend
-          file: ./backend/tests/integration/Dockerfile
-          platforms: linux/amd64
-          tags: danswer/integration-test-runner:it
-          cache-from: type=registry,ref=danswer/integration-test-runner:it
-          cache-to: |
-            type=registry,ref=danswer/integration-test-runner:it,mode=max
-            type=inline
-
-      - 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=it \
-          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 $response). Retrying in 5 seconds..."
-            fi
-            
-            sleep 5
-          done
-          echo "Finished waiting for service."
-
-      - name: Run integration tests
-        run: |
-          echo "Running integration tests..."
-          docker run --rm --network danswer-stack_default \
-            -e POSTGRES_HOST=relational_db \
-            -e POSTGRES_USER=postgres \
-            -e POSTGRES_PASSWORD=password \
-            -e POSTGRES_DB=postgres \
-            -e VESPA_HOST=index \
-            -e REDIS_HOST=cache \
-            -e API_SERVER_HOST=api_server \
-            -e OPENAI_API_KEY=${OPENAI_API_KEY} \
-            danswer/integration-test-runner:it
-        continue-on-error: true
-        id: run_tests
-
-      - name: Check 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: 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@v3
-        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

.github/workflows/tag-nightly.yml

@@ -0,0 +1,54 @@
+name: Nightly Tag Push
+
+on:
+  schedule:
+    - cron: '0 10 * * *' # Runs every day at 2 AM PST / 3 AM PDT / 10 AM UTC
+
+permissions:
+  contents: write  # Allows pushing tags to the repository
+
+jobs:
+  create-and-push-tag:
+    runs-on: [runs-on,runner=2cpu-linux-x64,"run-id=${{ github.run_id }}"]
+
+    steps:
+    # actions using GITHUB_TOKEN cannot trigger another workflow, but we do want this to trigger docker pushes
+    # see https://github.com/orgs/community/discussions/27028#discussioncomment-3254367 for the workaround we
+    # implement here which needs an actual user's deploy key
+    - name: Checkout code
+      uses: actions/checkout@v4
+      with:
+        ssh-key: "${{ secrets.RKUO_DEPLOY_KEY }}"
+
+    - name: Set up Git user
+      run: |
+        git config user.name "Richard Kuo [bot]"
+        git config user.email "rkuo[bot]@danswer.ai"
+
+    - name: Check for existing nightly tag
+      id: check_tag
+      run: |
+        if git tag --points-at HEAD --list "nightly-latest*" | grep -q .; then
+          echo "A tag starting with 'nightly-latest' already exists on HEAD."
+          echo "tag_exists=true" >> $GITHUB_OUTPUT
+        else
+          echo "No tag starting with 'nightly-latest' exists on HEAD."
+          echo "tag_exists=false" >> $GITHUB_OUTPUT
+        fi
+        
+    # don't tag again if HEAD already has a nightly-latest tag on it
+    - name: Create Nightly Tag
+      if: steps.check_tag.outputs.tag_exists == 'false'
+      env:
+        DATE: ${{ github.run_id }}
+      run: |
+        TAG_NAME="nightly-latest-$(date +'%Y%m%d')"
+        echo "Creating tag: $TAG_NAME"
+        git tag $TAG_NAME
+
+    - name: Push Tag
+      if: steps.check_tag.outputs.tag_exists == 'false'
+      run: |
+        TAG_NAME="nightly-latest-$(date +'%Y%m%d')"
+        git push origin $TAG_NAME
+        

.gitignore

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

.prettierignore

@@ -0,0 +1 @@
+backend/tests/integration/tests/pruning/website

.vscode/launch.template.jsonc

@@ -6,19 +6,69 @@
     // 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",
-                "Indexing",
-                "Background Jobs",
-                "Slack Bot"
-            ]
-        }
+                "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",
+			 }
+		}
     ],
     "configurations": [
+		{
+	    	// Dummy entry used to label the group
+			"name": "--- Individual ---",
+			"type": "node",
+			"request": "launch",
+			"presentation": {
+				"group": "2",
+				"order": 0
+			}
+		},
         {
             "name": "Web Server",
             "type": "node",
@@ -29,7 +79,11 @@
             "runtimeArgs": [
                 "run", "dev"
             ],
-            "console": "integratedTerminal"
+            "presentation": {
+				 "group": "2",
+			 },
+            "console": "integratedTerminal",
+            "consoleTitle": "Web Server Console"
         },
         {
             "name": "Model Server",
@@ -48,7 +102,11 @@
                 "--reload",
                 "--port",
                 "9000"
-            ]
+            ],
+            "presentation": {
+				 "group": "2",
+			},
+            "consoleTitle": "Model Server Console"
         },
         {
             "name": "API Server",
@@ -68,43 +126,13 @@
                 "--reload",
                 "--port",
                 "8080"
-            ]
+            ],
+            "presentation": {
+				 "group": "2",
+			 },
+            "consoleTitle": "API Server Console"
         },
-        {
-            "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,
+        // For the listener 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",
@@ -118,7 +146,151 @@
                 "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",
@@ -137,8 +309,22 @@
                 "-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",
@@ -147,7 +333,27 @@
             "runtimeArgs": ["${workspaceFolder}/backend/scripts/restart_containers.sh"],
             "cwd": "${workspaceFolder}",
             "console": "integratedTerminal",
-            "stopOnEntry": true
-        }
+            "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": "."
+            },
+        },
     ]
 }

CONTRIBUTING.md

@@ -22,7 +22,7 @@ Your input is vital to making sure that Danswer moves in the right direction.
 Before starting on implementation, please raise a GitHub issue.
 
 And always feel free to message us (Chris Weaver / Yuhong Sun) on 
-[Slack](https://join.slack.com/t/danswer/shared_invite/zt-2afut44lv-Rw3kSWu6_OmdAXRpCv80DQ) / 
+[Slack](https://join.slack.com/t/danswer/shared_invite/zt-2lcmqw703-071hBuZBfNEOGUsLa5PXvQ) / 
 [Discord](https://discord.gg/TDJ59cGV2X) directly about anything at all. 
 
 
@@ -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

@@ -1,4 +1,5 @@
 <!-- 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>
@@ -11,7 +12,7 @@
 <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-2lcmqw703-071hBuZBfNEOGUsLa5PXvQ" target="_blank">
+<a href="https://join.slack.com/t/danswer/shared_invite/zt-2twesxdr6-5iQitKZQpgq~hYIZ~dv3KA" 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">
@@ -68,13 +69,13 @@ 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.
-* Multi-Model model support, chat with images, video etc.
+* Multimodal 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 Noteable Benefits of Danswer
+## Other Notable 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.
@@ -127,3 +128,19 @@ 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

@@ -8,10 +8,11 @@ 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=0.3-dev
+ARG DANSWER_VERSION=0.8-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)
@@ -36,6 +37,8 @@ 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
@@ -70,11 +73,11 @@ 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); \
@@ -92,6 +95,7 @@ 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
 
@@ -101,7 +105,7 @@ 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=0.3-dev
+ARG DANSWER_VERSION=0.8-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.
 
-[alembic]
+[DEFAULT]
 # path to migration scripts
 script_location = alembic
 
@@ -47,7 +47,8 @@ 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
@@ -106,3 +107,12 @@ 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

@@ -1,107 +1,215 @@
+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 sqlalchemy.schema import SchemaItem
+from danswer.db.engine import get_all_tenant_ids
+from shared_configs.configs import POSTGRES_DEFAULT_SCHEMA
 
-# this is the Alembic Config object, which provides
-# access to the values within the .ini file in use.
+# 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
-# from myapp import mymodel
-# target_metadata = mymodel.Base.metadata
+# Add your model's MetaData object here for 'autogenerate' support
 target_metadata = [Base.metadata, ResultModelBase.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"}
 
+# Set up logging
+logger = logging.getLogger(__name__)
+
 
 def include_object(
     object: SchemaItem,
-    name: str,
-    type_: str,
+    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 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.
-
+def get_schema_options() -> tuple[str, bool, bool]:
     """
-    url = build_connection_string()
-    context.configure(
-        url=url,
-        target_metadata=target_metadata,  # type: ignore
-        literal_binds=True,
-        dialect_opts={"paramstyle": "named"},
-    )
+    Parses command-line options passed via '-x' in Alembic commands.
+    Recognizes 'schema', 'create_schema', and 'upgrade_all_tenants' options.
+    """
+    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)
+    create_schema = x_args.get("create_schema", "true").lower() == "true"
+    upgrade_all_tenants = x_args.get("upgrade_all_tenants", "false").lower() == "true"
 
-    with context.begin_transaction():
-        context.run_migrations()
+    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
 
 
-def do_run_migrations(connection: Connection) -> None:
+def do_run_migrations(
+    connection: Connection, schema_name: str, create_schema: bool
+) -> None:
+    """
+    Executes migrations in the specified schema.
+    """
+    logger.info(f"About to migrate schema: {schema_name}")
+
+    if create_schema:
+        connection.execute(text(f'CREATE SCHEMA IF NOT EXISTS "{schema_name}"'))
+        connection.execute(text("COMMIT"))
+
+    # Set search_path to the target schema
+    connection.execute(text(f'SET search_path TO "{schema_name}"'))
+
     context.configure(
         connection=connection,
         target_metadata=target_metadata,  # type: ignore
         include_object=include_object,
-    )  # type: ignore
+        version_table_schema=schema_name,
+        include_schemas=True,
+        compare_type=True,
+        compare_server_default=True,
+        script_location=config.get_main_option("script_location"),
+    )
 
     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.
-
     """
+    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()
 
-    connectable = create_async_engine(
+    engine = create_async_engine(
         build_connection_string(),
         poolclass=pool.NullPool,
     )
 
-    async with connectable.connect() as connection:
-        await connection.run_sync(do_run_migrations)
+    if upgrade_all_tenants:
+        # Run migrations for all tenant schemas sequentially
+        tenant_schemas = get_all_tenant_ids()
 
-    await connectable.dispose()
+        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()
 
 
 def run_migrations_online() -> None:
-    """Run migrations in 'online' mode."""
-
+    """
+    Runs migrations in 'online' mode using an asynchronous engine.
+    """
     asyncio.run(run_async_migrations())
 
 

backend/alembic/versions/177de57c21c9_display_custom_llm_models.py

@@ -0,0 +1,59 @@
+"""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

@@ -0,0 +1,26 @@
+"""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

@@ -0,0 +1,68 @@
+"""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

@@ -0,0 +1,30 @@
+"""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

@@ -0,0 +1,50 @@
+"""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/46b7a812670f_fix_user__external_user_group_id_fk.py

@@ -0,0 +1,46 @@
+"""fix_user__external_user_group_id_fk
+
+Revision ID: 46b7a812670f
+Revises: f32615f71aeb
+Create Date: 2024-09-23 12:58:03.894038
+
+"""
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision = "46b7a812670f"
+down_revision = "f32615f71aeb"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Drop the existing primary key
+    op.drop_constraint(
+        "user__external_user_group_id_pkey",
+        "user__external_user_group_id",
+        type_="primary",
+    )
+
+    # Add the new composite primary key
+    op.create_primary_key(
+        "user__external_user_group_id_pkey",
+        "user__external_user_group_id",
+        ["user_id", "external_user_group_id", "cc_pair_id"],
+    )
+
+
+def downgrade() -> None:
+    # Drop the composite primary key
+    op.drop_constraint(
+        "user__external_user_group_id_pkey",
+        "user__external_user_group_id",
+        type_="primary",
+    )
+    # Delete all entries from the table
+    op.execute("DELETE FROM user__external_user_group_id")
+
+    # Recreate the original primary key on user_id
+    op.create_primary_key(
+        "user__external_user_group_id_pkey", "user__external_user_group_id", ["user_id"]
+    )

backend/alembic/versions/47e5bef3a1d7_add_persona_categories.py

@@ -0,0 +1,45 @@
+"""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

@@ -0,0 +1,280 @@
+"""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

@@ -0,0 +1,70 @@
+"""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

@@ -0,0 +1,30 @@
+"""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

@@ -0,0 +1,153 @@
+"""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

@@ -0,0 +1,45 @@
+"""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/703313b75876_add_tokenratelimit_tables.py

@@ -9,7 +9,7 @@ import json
 from typing import cast
 from alembic import op
 import sqlalchemy as sa
-from danswer.dynamic_configs.factory import get_dynamic_config_store
+from danswer.key_value_store.factory import get_kv_store
 
 # revision identifiers, used by Alembic.
 revision = "703313b75876"
@@ -54,9 +54,7 @@ def upgrade() -> None:
     )
 
     try:
-        settings_json = cast(
-            str, get_dynamic_config_store().load("token_budget_settings")
-        )
+        settings_json = cast(str, get_kv_store().load("token_budget_settings"))
         settings = json.loads(settings_json)
 
         is_enabled = settings.get("enable_token_budget", False)
@@ -71,7 +69,7 @@ def upgrade() -> None:
             )
 
         # Delete the dynamic config
-        get_dynamic_config_store().delete("token_budget_settings")
+        get_kv_store().delete("token_budget_settings")
 
     except Exception:
         # Ignore if the dynamic config is not found

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.search.enums import RecencyBiasSetting
-from danswer.search.enums import SearchType
+from danswer.context.search.enums import RecencyBiasSetting
+from danswer.context.search.enums import SearchType
 
 # revision identifiers, used by Alembic.
 revision = "776b3bbe9092"

backend/alembic/versions/93560ba1b118_add_web_ui_option_to_slack_config.py

@@ -0,0 +1,35 @@
+"""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

@@ -0,0 +1,72 @@
+"""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

@@ -0,0 +1,30 @@
+"""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

@@ -0,0 +1,36 @@
+"""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

@@ -0,0 +1,27 @@
+"""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

@@ -0,0 +1,30 @@
+"""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/ac5eaac849f9_add_last_pruned_to_connector_table.py

@@ -0,0 +1,27 @@
+"""add last_pruned to the connector_credential_pair table
+
+Revision ID: ac5eaac849f9
+Revises: 52a219fb5233
+Create Date: 2024-09-10 15:04:26.437118
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+# revision identifiers, used by Alembic.
+revision = "ac5eaac849f9"
+down_revision = "46b7a812670f"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # last pruned represents the last time the connector was pruned
+    op.add_column(
+        "connector_credential_pair",
+        sa.Column("last_pruned", sa.DateTime(timezone=True), nullable=True),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("connector_credential_pair", "last_pruned")

backend/alembic/versions/b082fec533f0_make_last_attempt_status_nullable.py

@@ -31,6 +31,12 @@ 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

@@ -288,6 +288,15 @@ 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

@@ -0,0 +1,48 @@
+"""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

@@ -0,0 +1,29 @@
+"""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

@@ -23,6 +23,56 @@ 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 public.user")
+        sa.text('select id, chosen_assistants from "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 public.user set chosen_assistants = :chosen_assistants where id = :id"
+                'update "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 public.user")
+        sa.text('select id, chosen_assistants from "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 public.user set chosen_assistants = :chosen_assistants where id = :id"
+                'update "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

@@ -0,0 +1,42 @@
+"""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

@@ -0,0 +1,26 @@
+"""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

@@ -0,0 +1,3 @@
+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

@@ -0,0 +1,119 @@
+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

@@ -0,0 +1,24 @@
+"""${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

@@ -0,0 +1,24 @@
+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 "0.3-dev"
+__version__ = os.environ.get("DANSWER_VERSION", "") or "Development"

backend/danswer/access/models.py

@@ -16,6 +16,46 @@ 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
@@ -70,3 +110,12 @@ 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

@@ -0,0 +1,100 @@
+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

@@ -0,0 +1,30 @@
+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

@@ -0,0 +1,32 @@
+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

@@ -0,0 +1,16 @@
+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

@@ -0,0 +1,45 @@
+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

@@ -0,0 +1,15 @@
+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

@@ -0,0 +1,114 @@
+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

@@ -0,0 +1,78 @@
+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

@@ -0,0 +1,40 @@
+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

@@ -0,0 +1,30 @@
+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

@@ -0,0 +1,19 @@
+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

@@ -0,0 +1,44 @@
+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

@@ -0,0 +1,88 @@
+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

@@ -0,0 +1,11 @@
+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

@@ -0,0 +1,47 @@
+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

@@ -0,0 +1,60 @@
+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

@@ -0,0 +1,27 @@
+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

@@ -0,0 +1,36 @@
+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

@@ -0,0 +1,61 @@
+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

@@ -0,0 +1,98 @@
+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

@@ -0,0 +1,31 @@
+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

@@ -0,0 +1,53 @@
+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

@@ -0,0 +1,37 @@
+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

@@ -0,0 +1,27 @@
+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

@@ -0,0 +1,12 @@
+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

@@ -0,0 +1,9 @@
+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

@@ -0,0 +1,427 @@
+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

@@ -0,0 +1,101 @@
+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

@@ -0,0 +1,89 @@
+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

@@ -1,20 +1,21 @@
 from typing import cast
 
 from danswer.configs.constants import KV_USER_STORE_KEY
-from danswer.dynamic_configs.factory import get_dynamic_config_store
-from danswer.dynamic_configs.interface import ConfigNotFoundError
-from danswer.dynamic_configs.interface import JSON_ro
+from danswer.key_value_store.factory import get_kv_store
+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_dynamic_config_store()
+        store = get_kv_store()
+
         return cast(list, store.load(KV_USER_STORE_KEY))
-    except ConfigNotFoundError:
+    except KvKeyNotFoundError:
         return list()
 
 
 def write_invited_users(emails: list[str]) -> int:
-    store = get_dynamic_config_store()
+    store = get_kv_store()
     store.store(KV_USER_STORE_KEY, cast(JSON_ro, emails))
     return len(emails)