functioning well

- may need tweaking

.github/pull_request_template.md

@@ -6,24 +6,6 @@
 [Describe the tests you ran to verify your changes]
 
 
-## Accepted Risk (provide if relevant)
-N/A
-
-
-## Related Issue(s) (provide if relevant)
-N/A
-
-
-## 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

@@ -6,7 +6,7 @@ on:
       - "*"
 
 env:
-  REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'danswer/danswer-backend-cloud' || 'danswer/danswer-backend' }}
+  REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'onyxdotapp/onyx-backend-cloud' || 'onyxdotapp/onyx-backend' }}
   LATEST_TAG: ${{ contains(github.ref_name, 'latest') }}
 
 jobs:
@@ -44,7 +44,7 @@ jobs:
             ${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
             ${{ env.LATEST_TAG == 'true' && format('{0}:latest', env.REGISTRY_IMAGE) || '' }}
           build-args: |
-            DANSWER_VERSION=${{ github.ref_name }}
+            ONYX_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
@@ -57,7 +57,7 @@ jobs:
           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
+          # To run locally: trivy image --severity HIGH,CRITICAL onyxdotapp/onyx-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

@@ -7,7 +7,7 @@ on:
       - "*"
 
 env:
-  REGISTRY_IMAGE: danswer/danswer-web-server-cloud
+  REGISTRY_IMAGE: onyxdotapp/onyx-web-server-cloud
   LATEST_TAG: ${{ contains(github.ref_name, 'latest') }}
 
 jobs:
@@ -60,11 +60,13 @@ jobs:
           platforms: ${{ matrix.platform }}
           push: true
           build-args: |
-            DANSWER_VERSION=${{ github.ref_name }}
+            ONYX_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
+            NEXT_PUBLIC_FORGOT_PASSWORD_ENABLED=true
           # needed due to weird interactions with the builds for different platforms
           no-cache: true
           labels: ${{ steps.meta.outputs.labels }}

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

@@ -6,20 +6,31 @@ on:
       - "*"
 
 env:
-  REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'danswer/danswer-model-server-cloud' || 'danswer/danswer-model-server' }}
+  REGISTRY_IMAGE: ${{ contains(github.ref_name, 'cloud') && 'onyxdotapp/onyx-model-server-cloud' || 'onyxdotapp/onyx-model-server' }}
   LATEST_TAG: ${{ contains(github.ref_name, 'latest') }}
+  DOCKER_BUILDKIT: 1
+  BUILDKIT_PROGRESS: plain
 
 jobs:
-  build-and-push:
-    # See https://runs-on.com/runners/linux/
-    runs-on: [runs-on, runner=8cpu-linux-x64, "run-id=${{ github.run_id }}"]
-
+  build-amd64:
+    runs-on:
+      [runs-on, runner=8cpu-linux-x64, "run-id=${{ github.run_id }}-amd64"]
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
 
+      - name: System Info
+        run: |
+          df -h
+          free -h
+          docker system prune -af --volumes
+
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
+        with:
+          driver-opts: |
+            image=moby/buildkit:latest
+            network=host
 
       - name: Login to Docker Hub
         uses: docker/login-action@v3
@@ -27,29 +38,86 @@ jobs:
           username: ${{ secrets.DOCKER_USERNAME }}
           password: ${{ secrets.DOCKER_TOKEN }}
 
-      - name: Model Server Image Docker Build and Push
+      - name: Build and Push AMD64
         uses: docker/build-push-action@v5
         with:
           context: ./backend
           file: ./backend/Dockerfile.model_server
-          platforms: linux/amd64,linux/arm64
+          platforms: linux/amd64
           push: true
-          tags: |
-            ${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
-            ${{ env.LATEST_TAG == 'true' && format('{0}:latest', env.REGISTRY_IMAGE) || '' }}
+          tags: ${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}-amd64
           build-args: |
             DANSWER_VERSION=${{ github.ref_name }}
+          outputs: type=registry
+          provenance: false
+
+  build-arm64:
+    runs-on:
+      [runs-on, runner=8cpu-linux-x64, "run-id=${{ github.run_id }}-arm64"]
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: System Info
+        run: |
+          df -h
+          free -h
+          docker system prune -af --volumes
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+        with:
+          driver-opts: |
+            image=moby/buildkit:latest
+            network=host
+
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_TOKEN }}
+
+      - name: Build and Push ARM64
+        uses: docker/build-push-action@v5
+        with:
+          context: ./backend
+          file: ./backend/Dockerfile.model_server
+          platforms: linux/arm64
+          push: true
+          tags: ${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}-arm64
+          build-args: |
+            DANSWER_VERSION=${{ github.ref_name }}
+          outputs: type=registry
+          provenance: false
+
+  merge-and-scan:
+    needs: [build-amd64, build-arm64]
+    runs-on: ubuntu-latest
+    steps:
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_TOKEN }}
+
+      - name: Create and Push Multi-arch Manifest
+        run: |
+          docker buildx create --use
+          docker buildx imagetools create -t ${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }} \
+            ${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}-amd64 \
+            ${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}-arm64
+          if [[ "${{ env.LATEST_TAG }}" == "true" ]]; then
+            docker buildx imagetools create -t ${{ env.REGISTRY_IMAGE }}:latest \
+              ${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}-amd64 \
+              ${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}-arm64
+          fi
 
-      # 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:${{ github.ref_name }}
+          image-ref: docker.io/${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
           severity: "CRITICAL,HIGH"
+          timeout: "10m"

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

@@ -3,12 +3,12 @@ name: Build and Push Web Image on Tag
 on:
   push:
     tags:
-      - '*'
+      - "*"
 
 env:
-  REGISTRY_IMAGE: danswer/danswer-web-server
+  REGISTRY_IMAGE: onyxdotapp/onyx-web-server
   LATEST_TAG: ${{ contains(github.ref_name, 'latest') }}
-  
+
 jobs:
   build:
     runs-on:
@@ -27,11 +27,11 @@ jobs:
       - name: Prepare
         run: |
           platform=${{ matrix.platform }}
-          echo "PLATFORM_PAIR=${platform//\//-}" >> $GITHUB_ENV          
-      
+          echo "PLATFORM_PAIR=${platform//\//-}" >> $GITHUB_ENV
+
       - name: Checkout
         uses: actions/checkout@v4
-      
+
       - name: Docker meta
         id: meta
         uses: docker/metadata-action@v5
@@ -40,16 +40,16 @@ jobs:
           tags: |
             type=raw,value=${{ 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
@@ -59,18 +59,18 @@ jobs:
           platforms: ${{ matrix.platform }}
           push: true
           build-args: |
-            DANSWER_VERSION=${{ github.ref_name }}
-          # needed due to weird interactions with the builds for different platforms  
+            ONYX_VERSION=${{ github.ref_name }}
+          # 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:}"          
-      
+          touch "/tmp/digests/${digest#sha256:}"
+
       - name: Upload digest
         uses: actions/upload-artifact@v4
         with:
@@ -90,42 +90,42 @@ jobs:
           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 ' *)          
-      
+            $(printf '${{ 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
+      # 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'
+          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'
+          severity: "CRITICAL,HIGH"

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

@@ -7,31 +7,31 @@ on:
   workflow_dispatch:
     inputs:
       version:
-        description: 'The version (ie v0.0.1) to tag as latest'
+        description: "The version (ie v0.0.1) to tag as latest"
         required: true
 
 jobs:
   tag:
     # 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=${{ github.run_id }}"]
+    runs-on: [runs-on, runner=2cpu-linux-x64, "run-id=${{ github.run_id }}"]
     steps:
-    - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v1
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v1
 
-    - name: Login to Docker Hub
-      uses: docker/login-action@v1
-      with:
-        username: ${{ secrets.DOCKER_USERNAME }}
-        password: ${{ secrets.DOCKER_TOKEN }}
+      - name: Login to Docker Hub
+        uses: docker/login-action@v1
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_TOKEN }}
 
-    - name: Enable Docker CLI experimental features
-      run: echo "DOCKER_CLI_EXPERIMENTAL=enabled" >> $GITHUB_ENV
+      - name: Enable Docker CLI experimental features
+        run: echo "DOCKER_CLI_EXPERIMENTAL=enabled" >> $GITHUB_ENV
 
-    - name: Pull, Tag and Push Web Server Image
-      run: |
-        docker buildx imagetools create -t danswer/danswer-web-server:latest danswer/danswer-web-server:${{ github.event.inputs.version }}
+      - name: Pull, Tag and Push Web Server Image
+        run: |
+          docker buildx imagetools create -t onyxdotapp/onyx-web-server:latest onyxdotapp/onyx-web-server:${{ github.event.inputs.version }}
 
-    - name: Pull, Tag and Push API Server Image
-      run: |
-        docker buildx imagetools create -t danswer/danswer-backend:latest danswer/danswer-backend:${{ github.event.inputs.version }}
+      - name: Pull, Tag and Push API Server Image
+        run: |
+          docker buildx imagetools create -t onyxdotapp/onyx-backend:latest onyxdotapp/onyx-backend:${{ github.event.inputs.version }}

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

@@ -8,43 +8,42 @@ on:
   workflow_dispatch:
     inputs:
       hotfix_commit:
-        description: 'Hotfix commit hash'
+        description: "Hotfix commit hash"
         required: true
       hotfix_suffix:
-        description: 'Hotfix branch suffix (e.g. hotfix/v0.8-{suffix})'
+        description: "Hotfix branch suffix (e.g. hotfix/v0.8-{suffix})"
         required: true
       release_branch_pattern:
-        description: 'Release branch pattern (regex)'
+        description: "Release branch pattern (regex)"
         required: true
-        default: 'release/.*'
+        default: "release/.*"
       auto_merge:
-        description: 'Automatically merge the hotfix PRs'
+        description: "Automatically merge the hotfix PRs"
         required: true
         type: choice
-        default: 'true'
+        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=${{ github.run_id }}"]
+    runs-on: [runs-on, runner=2cpu-linux-x64, "run-id=${{ 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"
+          git config user.email "rkuo[bot]@onyx.app"
 
       - name: Fetch All Branches
         run: |
@@ -62,10 +61,10 @@ jobs:
             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/,$//')
 
@@ -169,4 +168,4 @@ jobs:
                 echo "Failed to merge pull request #$PR_NUMBER."
               fi
             fi
-          done
+          done

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

@@ -4,7 +4,7 @@ name: Backport on Merge
 
 on:
   pull_request:
-    types: [closed]  # Later we check for merge so only PRs that go in can get backported
+    types: [closed] # Later we check for merge so only PRs that go in can get backported
 
 permissions:
   contents: write
@@ -26,9 +26,9 @@ jobs:
       - name: Set up Git user
         run: |
           git config user.name "Richard Kuo [bot]"
-          git config user.email "rkuo[bot]@danswer.ai"
+          git config user.email "rkuo[bot]@onyx.app"
           git fetch --prune
-      
+
       - name: Check for Backport Checkbox
         id: checkbox-check
         run: |
@@ -51,14 +51,14 @@ jobs:
           # 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
@@ -80,10 +80,10 @@ jobs:
         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
 
@@ -98,7 +98,7 @@ jobs:
             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
@@ -110,13 +110,13 @@ jobs:
           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

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

@@ -0,0 +1,238 @@
+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: ${{ secrets.SLACK_BOT_TOKEN }}
+
+jobs:
+  playwright-tests:
+    name: Playwright Tests
+
+    # See https://runs-on.com/runners/linux/
+    runs-on:
+      [
+        runs-on,
+        runner=32cpu-linux-x64,
+        disk=large,
+        "run-id=${{ 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: ${{ secrets.DOCKER_TOKEN }}
+
+      # tag every docker image with "test" so that we can spin up the correct set
+      # of images during testing
+
+      # we use the runs-on cache for docker builds
+      # in conjunction with runs-on runners, it has better speed and unlimited caching
+      # https://runs-on.com/caching/s3-cache-for-github-actions/
+      # https://runs-on.com/caching/docker/
+      # https://github.com/moby/buildkit#s3-cache-experimental
+
+      # images are built and run locally for testing purposes. Not pushed.
+
+      - name: Build Web Docker image
+        uses: ./.github/actions/custom-build-and-push
+        with:
+          context: ./web
+          file: ./web/Dockerfile
+          platforms: linux/amd64
+          tags: onyxdotapp/onyx-web-server:test
+          push: false
+          load: true
+          cache-from: type=s3,prefix=cache/${{ 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=${{ env.RUNS_ON_S3_BUCKET_CACHE }},mode=max
+
+      - name: Build Backend Docker image
+        uses: ./.github/actions/custom-build-and-push
+        with:
+          context: ./backend
+          file: ./backend/Dockerfile
+          platforms: linux/amd64
+          tags: onyxdotapp/onyx-backend:test
+          push: false
+          load: true
+          cache-from: type=s3,prefix=cache/${{ 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=${{ env.RUNS_ON_S3_BUCKET_CACHE }},mode=max
+
+      - name: Build Model Server Docker image
+        uses: ./.github/actions/custom-build-and-push
+        with:
+          context: ./backend
+          file: ./backend/Dockerfile.model_server
+          platforms: linux/amd64
+          tags: onyxdotapp/onyx-model-server:test
+          push: false
+          load: true
+          cache-from: type=s3,prefix=cache/${{ 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=${{ env.RUNS_ON_S3_BUCKET_CACHE }},mode=max
+
+      - name: Start Docker containers
+        run: |
+          cd deployment/docker_compose
+          ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true \
+          AUTH_TYPE=basic \
+          GEN_AI_API_KEY=${{ secrets.OPENAI_API_KEY }} \
+          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: ${{ github.workspace }}/docker-compose.log
+
+      - name: Stop Docker containers
+        run: |
+          cd deployment/docker_compose
+          docker compose -f docker-compose.dev.yml -p danswer-stack down -v
+
+  chromatic-tests:
+    name: Chromatic Tests
+
+    needs: playwright-tests
+    runs-on:
+      [
+        runs-on,
+        runner=32cpu-linux-x64,
+        disk=large,
+        "run-id=${{ 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

@@ -23,21 +23,6 @@ jobs:
       with:
         version: v3.14.4
       
-    - 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: Set up chart-testing
       uses: helm/chart-testing-action@v2.6.1
 
@@ -52,6 +37,22 @@ jobs:
           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'

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

@@ -8,16 +8,19 @@ on:
   pull_request:
     branches:
       - main
-      - 'release/**'
+      - "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=${{ github.run_id }}"]
+    runs-on: [runs-on, runner=32cpu-linux-x64, "run-id=${{ github.run_id }}"]
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
@@ -33,21 +36,21 @@ jobs:
 
       # 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 
+      # 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
+          docker pull onyxdotapp/onyx-web-server:latest
+          docker tag onyxdotapp/onyx-web-server:latest onyxdotapp/onyx-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
@@ -55,7 +58,7 @@ jobs:
           context: ./backend
           file: ./backend/Dockerfile
           platforms: linux/amd64
-          tags: danswer/danswer-backend:test
+          tags: onyxdotapp/onyx-backend:test
           push: false
           load: true
           cache-from: type=s3,prefix=cache/${{ github.repository }}/integration-tests/backend/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }}
@@ -67,19 +70,19 @@ jobs:
           context: ./backend
           file: ./backend/Dockerfile.model_server
           platforms: linux/amd64
-          tags: danswer/danswer-model-server:test
+          tags: onyxdotapp/onyx-model-server:test
           push: false
           load: true
           cache-from: type=s3,prefix=cache/${{ 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=${{ env.RUNS_ON_S3_BUCKET_CACHE }},mode=max
-      
+
       - name: Build integration test Docker image
         uses: ./.github/actions/custom-build-and-push
         with:
           context: ./backend
           file: ./backend/tests/integration/Dockerfile
           platforms: linux/amd64
-          tags: danswer/danswer-integration:test
+          tags: onyxdotapp/onyx-integration:test
           push: false
           load: true
           cache-from: type=s3,prefix=cache/${{ github.repository }}/integration-tests/integration/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }}
@@ -116,7 +119,7 @@ jobs:
             -e TEST_WEB_HOSTNAME=test-runner \
             -e AUTH_TYPE=cloud \
             -e MULTI_TENANT=true \
-            danswer/danswer-integration:test \
+            onyxdotapp/onyx-integration:test \
             /app/tests/integration/multitenant_tests
         continue-on-error: true
         id: run_multitenant_tests
@@ -128,15 +131,14 @@ jobs:
             exit 1
           else
             echo "All integration tests passed successfully."
-          fi 
+          fi
 
       - name: Stop multi-tenant Docker containers
         run: |
           cd deployment/docker_compose
           docker compose -f docker-compose.dev.yml -p danswer-stack down -v
 
-
-      - name: Start Docker containers 
+      - name: Start Docker containers
         run: |
           cd deployment/docker_compose
           ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true \
@@ -150,12 +152,12 @@ jobs:
       - 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=$((current_time - start_time))
@@ -195,9 +197,13 @@ jobs:
             -e API_SERVER_HOST=api_server \
             -e OPENAI_API_KEY=${OPENAI_API_KEY} \
             -e SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN} \
+            -e CONFLUENCE_TEST_SPACE_URL=${CONFLUENCE_TEST_SPACE_URL} \
+            -e CONFLUENCE_USER_NAME=${CONFLUENCE_USER_NAME} \
+            -e CONFLUENCE_ACCESS_TOKEN=${CONFLUENCE_ACCESS_TOKEN} \
             -e TEST_WEB_HOSTNAME=test-runner \
-            danswer/danswer-integration:test \
-            /app/tests/integration/tests
+            onyxdotapp/onyx-integration:test \
+            /app/tests/integration/tests \
+            /app/tests/integration/connector_job_tests
         continue-on-error: true
         id: run_tests
 
@@ -222,7 +228,7 @@ jobs:
         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

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

@@ -20,10 +20,25 @@ env:
   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 }}
+  # Zendesk
+  ZENDESK_SUBDOMAIN: ${{ secrets.ZENDESK_SUBDOMAIN }}
+  ZENDESK_EMAIL: ${{ secrets.ZENDESK_EMAIL }}
+  ZENDESK_TOKEN: ${{ secrets.ZENDESK_TOKEN }}
+  # Salesforce
+  SF_USERNAME: ${{ secrets.SF_USERNAME }}
+  SF_PASSWORD: ${{ secrets.SF_PASSWORD }}
+  SF_SECURITY_TOKEN: ${{ secrets.SF_SECURITY_TOKEN }}
+  # Airtable
+  AIRTABLE_TEST_BASE_ID: ${{ secrets.AIRTABLE_TEST_BASE_ID }}
+  AIRTABLE_TEST_TABLE_ID: ${{ secrets.AIRTABLE_TEST_TABLE_ID }}
+  AIRTABLE_TEST_TABLE_NAME: ${{ secrets.AIRTABLE_TEST_TABLE_NAME }}
+  AIRTABLE_ACCESS_TOKEN: ${{ secrets.AIRTABLE_ACCESS_TOKEN }}
 jobs:
   connectors-check:
     # See https://runs-on.com/runners/linux/

.github/workflows/tag-nightly.yml

@@ -2,53 +2,52 @@ name: Nightly Tag Push
 
 on:
   schedule:
-    - cron: '0 10 * * *' # Runs every day at 2 AM PST / 3 AM PDT / 10 AM UTC
+    - 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
+  contents: write # Allows pushing tags to the repository
 
 jobs:
   create-and-push-tag:
-    runs-on: [runs-on,runner=2cpu-linux-x64,"run-id=${{ github.run_id }}"]
+    runs-on: [runs-on, runner=2cpu-linux-x64, "run-id=${{ 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: "${{ secrets.RKUO_DEPLOY_KEY }}"
+      # actions using GITHUB_TOKEN cannot trigger another workflow, but we do want this to trigger docker pushes
+      # see https://github.com/orgs/community/discussions/27028#discussioncomment-3254367 for the workaround we
+      # implement here which needs an actual user's deploy key
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          ssh-key: "${{ 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: Set up Git user
+        run: |
+          git config user.name "Richard Kuo [bot]"
+          git config user.email "rkuo[bot]@onyx.app"
 
-    - 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: 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
 
-    - 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
-        
+      # 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 +7,6 @@
 .vscode/
 *.sw?
 /backend/tests/regression/answer_quality/search_test_config.yaml
+/web/test-results/
+backend/onyx/agent_search/main/test_data.json
+backend/tests/regression/answer_quality/test_data.json

.vscode/env_template.txt

@@ -5,6 +5,8 @@
 # For local dev, often user Authentication is not needed
 AUTH_TYPE=disabled
 
+# Skip warm up for dev
+SKIP_WARM_UP=True
 
 # Always keep these on for Dev
 # Logs all model prompts to stdout
@@ -49,3 +51,9 @@ BING_API_KEY=<REPLACE THIS>
 # Enable the full set of Danswer Enterprise Edition features
 # NOTE: DO NOT ENABLE THIS UNLESS YOU HAVE A PAID ENTERPRISE LICENSE (or if you are using this for local testing/development)
 ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=False
+
+# Agent Search configs  # TODO: Remove give proper namings
+AGENT_RETRIEVAL_STATS=False   # Note: This setting will incur substantial re-ranking effort
+AGENT_RERANKING_STATS=True
+AGENT_MAX_QUERY_RETRIEVAL_RESULTS=20
+AGENT_RERANKING_MAX_QUERY_RETRIEVAL_RESULTS=20

.vscode/launch.template.jsonc

@@ -17,7 +17,7 @@
 			 }
 		},
         {
-            "name": "Run All Danswer Services",
+            "name": "Run All Onyx Services",
             "configurations": [
                 "Web Server",
                 "Model Server",
@@ -122,7 +122,7 @@
                 "PYTHONUNBUFFERED": "1"
             },
             "args": [
-                "danswer.main:app",
+                "onyx.main:app",
                 "--reload",
                 "--port",
                 "8080"
@@ -139,7 +139,7 @@
             "consoleName": "Slack Bot",
             "type": "debugpy",
             "request": "launch",
-            "program": "danswer/danswerbot/slack/listener.py",
+            "program": "onyx/onyxbot/slack/listener.py",
             "cwd": "${workspaceFolder}/backend",
             "envFile": "${workspaceFolder}/.vscode/.env",
             "env": {
@@ -166,7 +166,7 @@
             },
             "args": [
                 "-A",
-                "danswer.background.celery.versioned_apps.primary",
+                "onyx.background.celery.versioned_apps.primary",
                 "worker",
                 "--pool=threads",
                 "--concurrency=4",
@@ -195,7 +195,7 @@
             },
             "args": [
                 "-A",
-                "danswer.background.celery.versioned_apps.light",
+                "onyx.background.celery.versioned_apps.light",
                 "worker",
                 "--pool=threads",
                 "--concurrency=64",
@@ -203,7 +203,7 @@
                 "--loglevel=INFO",
                 "--hostname=light@%n",
                 "-Q",
-                "vespa_metadata_sync,connector_deletion",
+                "vespa_metadata_sync,connector_deletion,doc_permissions_upsert",
             ],
             "presentation": {
 				 "group": "2",
@@ -224,7 +224,7 @@
             },
             "args": [
                 "-A",
-                "danswer.background.celery.versioned_apps.heavy",
+                "onyx.background.celery.versioned_apps.heavy",
                 "worker",
                 "--pool=threads",
                 "--concurrency=4",
@@ -232,7 +232,7 @@
                 "--loglevel=INFO",
                 "--hostname=heavy@%n",
                 "-Q",
-                "connector_pruning",
+                "connector_pruning,connector_doc_permissions_sync,connector_external_group_sync",
             ],
             "presentation": {
 				 "group": "2",
@@ -254,7 +254,7 @@
             },
             "args": [
                 "-A",
-                "danswer.background.celery.versioned_apps.indexing",
+                "onyx.background.celery.versioned_apps.indexing",
                 "worker",
                 "--pool=threads",
                 "--concurrency=1",
@@ -283,7 +283,7 @@
             },
             "args": [
                 "-A",
-                "danswer.background.celery.versioned_apps.beat",
+                "onyx.background.celery.versioned_apps.beat",
                 "beat",
                 "--loglevel=INFO",
             ],
@@ -308,7 +308,7 @@
             "args": [
                 "-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"
+                //"tests/unit/onyx/llm/answering/test_prune_and_merge.py"
             ],
             "presentation": {
 				 "group": "2",
@@ -355,5 +355,20 @@
                 "PYTHONPATH": "."
             },
         },
+        {
+            "name": "Install Python Requirements",
+            "type": "node",
+            "request": "launch",
+            "runtimeExecutable": "bash",
+            "runtimeArgs": [
+                "-c",
+                "pip install -r backend/requirements/default.txt && pip install -r backend/requirements/dev.txt && pip install -r backend/requirements/ee.txt && pip install -r backend/requirements/model_server.txt"
+            ],
+            "cwd": "${workspaceFolder}",
+            "console": "integratedTerminal",
+            "presentation": {
+                 "group": "3"
+            }
+        },
     ]
 }

CONTRIBUTING.md

@@ -1,105 +1,117 @@
-<!-- DANSWER_METADATA={"link": "https://github.com/danswer-ai/danswer/blob/main/CONTRIBUTING.md"} -->
+<!-- DANSWER_METADATA={"link": "https://github.com/onyx-dot-app/onyx/blob/main/CONTRIBUTING.md"} -->
 
-# Contributing to Danswer
-Hey there! We are so excited that you're interested in Danswer.
+# Contributing to Onyx
+
+Hey there! We are so excited that you're interested in Onyx.
 
 As an open source project in a rapidly changing space, we welcome all contributions.
 
-
 ## 💃 Guidelines
+
 ### Contribution Opportunities
-The [GitHub Issues](https://github.com/danswer-ai/danswer/issues) page is a great place to start for contribution ideas.
+
+The [GitHub Issues](https://github.com/onyx-dot-app/onyx/issues) page is a great place to start for contribution ideas.
+
+To ensure that your contribution is aligned with the project's direction, please reach out to Hagen (or any other maintainer) on the Onyx team
+via [Slack](https://join.slack.com/t/onyx-dot-app/shared_invite/zt-2twesxdr6-5iQitKZQpgq~hYIZ~dv3KA) /
+[Discord](https://discord.gg/TDJ59cGV2X) or [email](mailto:founders@onyx.app).
 
 Issues that have been explicitly approved by the maintainers (aligned with the direction of the project)
 will be marked with the `approved by maintainers` label.
 Issues marked `good first issue` are an especially great place to start.
 
 **Connectors** to other tools are another great place to contribute. For details on how, refer to this
-[README.md](https://github.com/danswer-ai/danswer/blob/main/backend/danswer/connectors/README.md).
+[README.md](https://github.com/onyx-dot-app/onyx/blob/main/backend/onyx/connectors/README.md).
 
 If you have a new/different contribution in mind, we'd love to hear about it!
-Your input is vital to making sure that Danswer moves in the right direction.
+Your input is vital to making sure that Onyx 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-2lcmqw703-071hBuZBfNEOGUsLa5PXvQ) / 
-[Discord](https://discord.gg/TDJ59cGV2X) directly about anything at all. 
-
+Also, always feel free to message the founders (Chris Weaver / Yuhong Sun) on
+[Slack](https://join.slack.com/t/onyx-dot-app/shared_invite/zt-2twesxdr6-5iQitKZQpgq~hYIZ~dv3KA) /
+[Discord](https://discord.gg/TDJ59cGV2X) directly about anything at all.
 
 ### Contributing Code
+
 To contribute to this project, please follow the
 ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
 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 🙋
+
 Our goal is to make contributing as easy as possible. If you run into any issues please don't hesitate to reach out.
 That way we can help future contributors and users can avoid the same issue.
 
 We also have support channels and generally interesting discussions on our
-[Slack](https://join.slack.com/t/danswer/shared_invite/zt-2afut44lv-Rw3kSWu6_OmdAXRpCv80DQ)
-and 
+[Slack](https://join.slack.com/t/onyx-dot-app/shared_invite/zt-2twesxdr6-5iQitKZQpgq~hYIZ~dv3KA)
+and
 [Discord](https://discord.gg/TDJ59cGV2X).
 
 We would love to see you there!
 
-
 ## Get Started 🚀
-Danswer being a fully functional app, relies on some external software, specifically:
+
+Onyx being a fully functional app, relies on some external software, specifically:
+
 - [Postgres](https://www.postgresql.org/) (Relational DB)
 - [Vespa](https://vespa.ai/) (Vector DB/Search Engine)
 - [Redis](https://redis.io/) (Cache)
 - [Nginx](https://nginx.org/) (Not needed for development flows generally)
 
-
 > **Note:**
-> This guide provides instructions to build and run Danswer locally from source with Docker containers providing the above external software. We believe this combination is easier for
-> development purposes. If you prefer to use pre-built container images, we provide instructions on running the full Danswer stack within Docker below.
-
+> This guide provides instructions to build and run Onyx locally from source with Docker containers providing the above external software. We believe this combination is easier for
+> development purposes. If you prefer to use pre-built container images, we provide instructions on running the full Onyx stack within Docker below.
 
 ### Local Set Up
+
 Be sure to use Python version 3.11. For instructions on installing Python 3.11 on macOS, refer to the [CONTRIBUTING_MACOS.md](./CONTRIBUTING_MACOS.md) readme.
 
 If using a lower version, modifications will have to be made to the code.
 If using a higher version, sometimes some libraries will not be available (i.e. we had problems with Tensorflow in the past with higher versions of python).
 
-
 #### Backend: Python requirements
+
 Currently, we use pip and recommend creating a virtual environment.
 
 For convenience here's a command for it:
+
 ```bash
 python -m venv .venv
 source .venv/bin/activate
 ```
 
 > **Note:**
-> This virtual environment MUST NOT be set up WITHIN the danswer directory if you plan on using mypy within certain IDEs.
-> For simplicity, we recommend setting up the virtual environment outside of the danswer directory.
+> This virtual environment MUST NOT be set up WITHIN the onyx directory if you plan on using mypy within certain IDEs.
+> For simplicity, we recommend setting up the virtual environment outside of the onyx directory.
 
 _For Windows, activate the virtual environment using Command Prompt:_
+
 ```bash
 .venv\Scripts\activate
 ```
+
 If using PowerShell, the command slightly differs:
+
 ```powershell
 .venv\Scripts\Activate.ps1
 ```
 
 Install the required python dependencies:
+
 ```bash
-pip install -r danswer/backend/requirements/default.txt
-pip install -r danswer/backend/requirements/dev.txt
-pip install -r danswer/backend/requirements/ee.txt
-pip install -r danswer/backend/requirements/model_server.txt
+pip install -r onyx/backend/requirements/default.txt
+pip install -r onyx/backend/requirements/dev.txt
+pip install -r onyx/backend/requirements/ee.txt
+pip install -r onyx/backend/requirements/model_server.txt
 ```
 
 Install Playwright for Python (headless browser required by the Web Connector)
 
 In the activated Python virtualenv, install Playwright for Python by running:
+
 ```bash
 playwright install
 ```
@@ -109,42 +121,90 @@ You may have to deactivate and reactivate your virtualenv for `playwright` to ap
 #### Frontend: Node dependencies
 
 Install [Node.js and npm](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) for the frontend.
-Once the above is done, navigate to `danswer/web` run:
+Once the above is done, navigate to `onyx/web` run:
+
 ```bash
 npm i
 ```
 
-#### Docker containers for external software
+## Formatting and Linting
+
+### Backend
+
+For the backend, you'll need to setup pre-commit hooks (black / reorder-python-imports).
+First, install pre-commit (if you don't have it already) following the instructions
+[here](https://pre-commit.com/#installation).
+
+With the virtual environment active, install the pre-commit library with:
+
+```bash
+pip install pre-commit
+```
+
+Then, from the `onyx/backend` directory, run:
+
+```bash
+pre-commit install
+```
+
+Additionally, we use `mypy` for static type checking.
+Onyx is fully type-annotated, and we want to keep it that way!
+To run the mypy checks manually, run `python -m mypy .` from the `onyx/backend` directory.
+
+### Web
+
+We use `prettier` for formatting. The desired version (2.8.8) will be installed via a `npm i` from the `onyx/web` directory.
+To run the formatter, use `npx prettier --write .` from the `onyx/web` directory.
+Please double check that prettier passes before creating a pull request.
+
+# Running the application for development
+
+## Developing using VSCode Debugger (recommended)
+
+We highly recommend using VSCode debugger for development.
+See [CONTRIBUTING_VSCODE.md](./CONTRIBUTING_VSCODE.md) for more details.
+
+Otherwise, you can follow the instructions below to run the application for development.
+
+## Manually running the application for development
+### Docker containers for external software
+
 You will need Docker installed to run these containers.
 
-First navigate to `danswer/deployment/docker_compose`, then start up Postgres/Vespa/Redis with:
+First navigate to `onyx/deployment/docker_compose`, then start up Postgres/Vespa/Redis with:
+
 ```bash
-docker compose -f docker-compose.dev.yml -p danswer-stack up -d index relational_db cache
+docker compose -f docker-compose.dev.yml -p onyx-stack up -d index relational_db cache
 ```
+
 (index refers to Vespa, relational_db refers to Postgres, and cache refers to Redis)
 
+### Running Onyx locally
+
+To start the frontend, navigate to `onyx/web` and run:
 
-#### Running Danswer locally
-To start the frontend, navigate to `danswer/web` and run:
 ```bash
 npm run dev
 ```
 
 Next, start the model server which runs the local NLP models.
-Navigate to `danswer/backend` and run:
+Navigate to `onyx/backend` and run:
+
 ```bash
 uvicorn model_server.main:app --reload --port 9000
 ```
 
 _For Windows (for compatibility with both PowerShell and Command Prompt):_
+
 ```bash
 powershell -Command "uvicorn model_server.main:app --reload --port 9000"
 ```
 
-The first time running Danswer, you will need to run the DB migrations for Postgres.
+The first time running Onyx, you will need to run the DB migrations for Postgres.
 After the first time, this is no longer required unless the DB models change.
 
-Navigate to `danswer/backend` and with the venv active, run:
+Navigate to `onyx/backend` and with the venv active, run:
+
 ```bash
 alembic upgrade head
 ```
@@ -152,21 +212,24 @@ alembic upgrade head
 Next, start the task queue which orchestrates the background jobs.
 Jobs that take more time are run async from the API server.
 
-Still in `danswer/backend`, run:
+Still in `onyx/backend`, run:
+
 ```bash
 python ./scripts/dev_run_background_jobs.py
 ```
 
-To run the backend API server, navigate back to `danswer/backend` and run:
+To run the backend API server, navigate back to `onyx/backend` and run:
+
 ```bash
-AUTH_TYPE=disabled uvicorn danswer.main:app --reload --port 8080
+AUTH_TYPE=disabled uvicorn onyx.main:app --reload --port 8080
 ```
 
 _For Windows (for compatibility with both PowerShell and Command Prompt):_
+
 ```bash
 powershell -Command "
     $env:AUTH_TYPE='disabled'
-    uvicorn danswer.main:app --reload --port 8080 
+    uvicorn onyx.main:app --reload --port 8080
 "
 ```
 
@@ -182,57 +245,32 @@ You should now have 4 servers running:
 - Model server
 - Background jobs
 
-Now, visit `http://localhost:3000` in your browser. You should see the Danswer onboarding wizard where you can connect your external LLM provider to Danswer.
+Now, visit `http://localhost:3000` in your browser. You should see the Onyx onboarding wizard where you can connect your external LLM provider to Onyx.
 
-You've successfully set up a local Danswer instance! 🏁
+You've successfully set up a local Onyx instance! 🏁
 
-#### Running the Danswer application in a container
+#### Running the Onyx application in a container
 
-You can run the full Danswer application stack from pre-built images including all external software dependencies.
+You can run the full Onyx application stack from pre-built images including all external software dependencies.
 
-Navigate to `danswer/deployment/docker_compose` and run:
+Navigate to `onyx/deployment/docker_compose` and run:
 
 ```bash
-docker compose -f docker-compose.dev.yml -p danswer-stack up -d
+docker compose -f docker-compose.dev.yml -p onyx-stack up -d
 ```
 
-After Docker pulls and starts these containers, navigate to `http://localhost:3000` to use Danswer.
+After Docker pulls and starts these containers, navigate to `http://localhost:3000` to use Onyx.
 
-If you want to make changes to Danswer and run those changes in Docker, you can also build a local version of the Danswer container images that incorporates your changes like so:
+If you want to make changes to Onyx and run those changes in Docker, you can also build a local version of the Onyx container images that incorporates your changes like so:
 
 ```bash
-docker compose -f docker-compose.dev.yml -p danswer-stack up -d --build
+docker compose -f docker-compose.dev.yml -p onyx-stack up -d --build
 ```
 
-### Formatting and Linting
-#### Backend
-For the backend, you'll need to setup pre-commit hooks (black / reorder-python-imports).
-First, install pre-commit (if you don't have it already) following the instructions
-[here](https://pre-commit.com/#installation).
-
-With the virtual environment active, install the pre-commit library with:
-```bash
-pip install pre-commit
-```
-
-Then, from the `danswer/backend` directory, run:
-```bash
-pre-commit install
-```
-
-Additionally, we use `mypy` for static type checking.
-Danswer is fully type-annotated, and we want to keep it that way! 
-To run the mypy checks manually, run `python -m mypy .` from the `danswer/backend` directory.
-
-
-#### Web
-We use `prettier` for formatting. The desired version (2.8.8) will be installed via a `npm i` from the `danswer/web` directory. 
-To run the formatter, use `npx prettier --write .` from the `danswer/web` directory.
-Please double check that prettier passes before creating a pull request.
-
 
 ### Release Process
-Danswer loosely follows the SemVer versioning standard.
+
+Onyx loosely follows the SemVer versioning standard.
 Major changes are released with a "minor" version bump. Currently we use patch release versions to indicate small feature changes.
 A set of Docker containers will be pushed automatically to DockerHub with every tag.
-You can see the containers [here](https://hub.docker.com/search?q=danswer%2F).
+You can see the containers [here](https://hub.docker.com/search?q=onyx%2F).

CONTRIBUTING_MACOS.md

@@ -1,15 +1,19 @@
 ## Some additional notes for Mac Users
-The base instructions to set up the development environment are located in [CONTRIBUTING.md](https://github.com/danswer-ai/danswer/blob/main/CONTRIBUTING.md).
+
+The base instructions to set up the development environment are located in [CONTRIBUTING.md](https://github.com/onyx-dot-app/onyx/blob/main/CONTRIBUTING.md).
 
 ### Setting up Python
+
 Ensure [Homebrew](https://brew.sh/) is already set up.
 
 Then install python 3.11.
+
 ```bash
 brew install python@3.11
 ```
 
 Add python 3.11 to your path: add the following line to ~/.zshrc
+
 ```
 export PATH="$(brew --prefix)/opt/python@3.11/libexec/bin:$PATH"
 ```
@@ -17,15 +21,16 @@ export PATH="$(brew --prefix)/opt/python@3.11/libexec/bin:$PATH"
 > **Note:**
 > You will need to open a new terminal for the path change above to take effect.
 
-
 ### Setting up Docker
-On macOS, you will need to install [Docker Desktop](https://www.docker.com/products/docker-desktop/) and 
+
+On macOS, you will need to install [Docker Desktop](https://www.docker.com/products/docker-desktop/) and
 ensure it is running before continuing with the docker commands.
 
-
 ### Formatting and Linting
+
 MacOS will likely require you to remove some quarantine attributes on some of the hooks for them to execute properly.
 After installing pre-commit, run the following command:
+
 ```bash
 sudo xattr -r -d com.apple.quarantine ~/.cache/pre-commit
-```
+```

CONTRIBUTING_VSCODE.md

@@ -0,0 +1,29 @@
+# VSCode Debugging Setup
+
+This guide explains how to set up and use VSCode's debugging capabilities with this project.
+
+## Initial Setup
+
+1. **Environment Setup**:
+   - Copy `.vscode/.env.template` to `.vscode/.env`
+   - Fill in the necessary environment variables in `.vscode/.env`
+2. **launch.json**:
+   - Copy `.vscode/launch.template.jsonc` to `.vscode/launch.json`
+
+## Using the Debugger
+
+Before starting, make sure the Docker Daemon is running.
+
+1. Open the Debug view in VSCode (Cmd+Shift+D on macOS)
+2. From the dropdown at the top, select "Clear and Restart External Volumes and Containers" and press the green play button
+3. From the dropdown at the top, select "Run All Onyx Services" and press the green play button
+4. Now, you can navigate to onyx in your browser (default is http://localhost:3000) and start using the app
+5. You can set breakpoints by clicking to the left of line numbers to help debug while the app is running
+6. Use the debug toolbar to step through code, inspect variables, etc.
+
+## Features
+
+- Hot reload is enabled for the web server and API servers
+- Python debugging is configured with debugpy
+- Environment variables are loaded from `.vscode/.env`
+- Console output is organized in the integrated terminal with labeled tabs

LICENSE

@@ -2,9 +2,9 @@ Copyright (c) 2023-present DanswerAI, Inc.
 
 Portions of this software are licensed as follows:
 
-* All content that resides under "ee" directories of this repository, if that directory exists, is licensed under the license defined in "backend/ee/LICENSE". Specifically all content under "backend/ee" and "web/src/app/ee" is licensed under the license defined in "backend/ee/LICENSE".
-* All third party components incorporated into the Danswer Software are licensed under the original license provided by the owner of the applicable component.
-* Content outside of the above mentioned directories or restrictions above is available under the "MIT Expat" license as defined below.
+- All content that resides under "ee" directories of this repository, if that directory exists, is licensed under the license defined in "backend/ee/LICENSE". Specifically all content under "backend/ee" and "web/src/app/ee" is licensed under the license defined in "backend/ee/LICENSE".
+- All third party components incorporated into the Onyx Software are licensed under the original license provided by the owner of the applicable component.
+- Content outside of the above mentioned directories or restrictions above is available under the "MIT Expat" license as defined below.
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -1,146 +1,135 @@
-<!-- DANSWER_METADATA={"link": "https://github.com/danswer-ai/danswer/blob/main/README.md"} -->
+<!-- DANSWER_METADATA={"link": "https://github.com/onyx-dot-app/onyx/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>
+<a href="https://www.onyx.app/"> <img width="50%" src="https://github.com/onyx-dot-app/onyx/blob/logo/OnyxLogoCropped.jpg?raw=true)" /></a>
 </h2>
 
 <p align="center">
-<p align="center">Open Source Gen-AI Chat + Unified Search.</p>
+<p align="center">Open Source Gen-AI + Enterprise Search.</p>
 
 <p align="center">
-<a href="https://docs.danswer.dev/" target="_blank">
+<a href="https://docs.onyx.app/" 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/onyx-dot-app/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">
     <img src="https://img.shields.io/badge/discord-join-blue.svg?logo=discord&logoColor=white" alt="Discord">
 </a>
-<a href="https://github.com/danswer-ai/danswer/blob/main/README.md" target="_blank">
+<a href="https://github.com/onyx-dot-app/onyx/blob/main/README.md" target="_blank">
     <img src="https://img.shields.io/static/v1?label=license&message=MIT&color=blue" alt="License">
 </a>
 </p>
 
-<strong>[Danswer](https://www.danswer.ai/)</strong> is the AI Assistant connected to your company's docs, apps, and people. 
-Danswer provides a Chat interface and plugs into any LLM of your choice. Danswer can be deployed anywhere and for any 
-scale - on a laptop, on-premise, or to cloud. Since you own the deployment, your user data and chats are fully in your 
-own control. Danswer is MIT licensed and designed to be modular and easily extensible. The system also comes fully ready 
-for production usage with user authentication, role management (admin/basic users), chat persistence, and a UI for 
-configuring Personas (AI Assistants) and their Prompts.
+<strong>[Onyx](https://www.onyx.app/)</strong> (formerly Danswer) is the AI Assistant connected to your company's docs, apps, and people.
+Onyx provides a Chat interface and plugs into any LLM of your choice. Onyx can be deployed anywhere and for any
+scale - on a laptop, on-premise, or to cloud. Since you own the deployment, your user data and chats are fully in your
+own control. Onyx is dual Licensed with most of it under MIT license and designed to be modular and easily extensible. The system also comes fully ready
+for production usage with user authentication, role management (admin/basic users), chat persistence, and a UI for
+configuring AI Assistants.
 
-Danswer also serves as a Unified Search across all common workplace tools such as Slack, Google Drive, Confluence, etc.
-By combining LLMs and team specific knowledge, Danswer becomes a subject matter expert for the team. Imagine ChatGPT if
+Onyx also serves as a Enterprise Search across all common workplace tools such as Slack, Google Drive, Confluence, etc.
+By combining LLMs and team specific knowledge, Onyx becomes a subject matter expert for the team. Imagine ChatGPT if
 it had access to your team's unique knowledge! It enables questions such as "A customer wants feature X, is this already
 supported?" or "Where's the pull request for feature Y?"
 
 <h3>Usage</h3>
 
-Danswer Web App:
+Onyx Web App:
 
-https://github.com/danswer-ai/danswer/assets/32520769/563be14c-9304-47b5-bf0a-9049c2b6f410
+https://github.com/onyx-dot-app/onyx/assets/32520769/563be14c-9304-47b5-bf0a-9049c2b6f410
 
+Or, plug Onyx into your existing Slack workflows (more integrations to come 😁):
 
-Or, plug Danswer into your existing Slack workflows (more integrations to come 😁):
+https://github.com/onyx-dot-app/onyx/assets/25087905/3e19739b-d178-4371-9a38-011430bdec1b
 
-https://github.com/danswer-ai/danswer/assets/25087905/3e19739b-d178-4371-9a38-011430bdec1b
-
-
-For more details on the Admin UI to manage connectors and users, check out our 
+For more details on the Admin UI to manage connectors and users, check out our
 <strong><a href="https://www.youtube.com/watch?v=geNzY1nbCnU">Full Video Demo</a></strong>!
 
 ## Deployment
 
-Danswer can easily be run locally (even on a laptop) or deployed on a virtual machine with a single
-`docker compose` command. Checkout our [docs](https://docs.danswer.dev/quickstart) to learn more.
+Onyx can easily be run locally (even on a laptop) or deployed on a virtual machine with a single
+`docker compose` command. Checkout our [docs](https://docs.onyx.app/quickstart) to learn more.
 
-We also have built-in support for deployment on Kubernetes. Files for that can be found [here](https://github.com/danswer-ai/danswer/tree/main/deployment/kubernetes).
+We also have built-in support for deployment on Kubernetes. Files for that can be found [here](https://github.com/onyx-dot-app/onyx/tree/main/deployment/kubernetes).
 
+## 💃 Main Features
 
-## 💃 Main Features 
-* Chat UI with the ability to select documents to chat with.
-* Create custom AI Assistants with different prompts and backing knowledge sets.
-* Connect Danswer with LLM of your choice (self-host for a fully airgapped solution).
-* Document Search + AI Answers for natural language queries.
-* Connectors to all common workplace tools like Google Drive, Confluence, Slack, etc.
-* Slack integration to get answers and search results directly in Slack.
-
+- Chat UI with the ability to select documents to chat with.
+- Create custom AI Assistants with different prompts and backing knowledge sets.
+- Connect Onyx with LLM of your choice (self-host for a fully airgapped solution).
+- Document Search + AI Answers for natural language queries.
+- Connectors to all common workplace tools like Google Drive, Confluence, Slack, etc.
+- Slack integration to get answers and search results directly in Slack.
 
 ## 🚧 Roadmap
-* Chat/Prompt sharing with specific teammates and user groups.
-* 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.
 
+- Chat/Prompt sharing with specific teammates and user groups.
+- 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 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.
-* Custom deep learning models + learn from user feedback.
-* Easy deployment and ability to host Danswer anywhere of your choosing.
+## Other Notable Benefits of Onyx
 
+- 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.
+- Custom deep learning models + learn from user feedback.
+- Easy deployment and ability to host Onyx anywhere of your choosing.
 
 ## 🔌 Connectors
+
 Efficiently pulls the latest changes from:
-  * Slack
-  * GitHub
-  * Google Drive
-  * Confluence
-  * Jira
-  * Zendesk
-  * Gmail
-  * Notion
-  * Gong
-  * Slab
-  * Linear
-  * Productboard
-  * Guru
-  * Bookstack
-  * Document360
-  * Sharepoint
-  * Hubspot
-  * Local Files
-  * Websites
-  * And more ...
+
+- Slack
+- GitHub
+- Google Drive
+- Confluence
+- Jira
+- Zendesk
+- Gmail
+- Notion
+- Gong
+- Slab
+- Linear
+- Productboard
+- Guru
+- Bookstack
+- Document360
+- Sharepoint
+- Hubspot
+- Local Files
+- Websites
+- And more ...
 
 ## 📚 Editions
 
-There are two editions of Danswer:
+There are two editions of Onyx:
 
-  * Danswer Community Edition (CE) is available freely under the MIT Expat license. This version has ALL the core features discussed above. This is the version of Danswer you will get if you follow the Deployment guide above.
-  * Danswer Enterprise Edition (EE) includes extra features that are primarily useful for larger organizations. Specifically, this includes:
-    * Single Sign-On (SSO), with support for both SAML and OIDC
-    * Role-based access control
-    * Document permission inheritance from connected sources
-    * Usage analytics and query history accessible to admins
-    * Whitelabeling
-    * API key authentication
-    * Encryption of secrets
-    * Any many more! Checkout [our website](https://www.danswer.ai/) for the latest.
+- Onyx Community Edition (CE) is available freely under the MIT Expat license. This version has ALL the core features discussed above. This is the version of Onyx you will get if you follow the Deployment guide above.
+- Onyx Enterprise Edition (EE) includes extra features that are primarily useful for larger organizations. Specifically, this includes:
+  - Single Sign-On (SSO), with support for both SAML and OIDC
+  - Role-based access control
+  - Document permission inheritance from connected sources
+  - Usage analytics and query history accessible to admins
+  - Whitelabeling
+  - API key authentication
+  - Encryption of secrets
+  - Any many more! Checkout [our website](https://www.onyx.app/) for the latest.
 
-To try the Danswer Enterprise Edition: 
+To try the Onyx Enterprise Edition:
 
-  1. Checkout our [Cloud product](https://app.danswer.ai/signup).
-  2. For self-hosting, contact us at [founders@danswer.ai](mailto:founders@danswer.ai) or book a call with us on our [Cal](https://cal.com/team/danswer/founders).
+1. Checkout our [Cloud product](https://cloud.onyx.app/signup).
+2. For self-hosting, contact us at [founders@onyx.app](mailto:founders@onyx.app) or book a call with us on our [Cal](https://cal.com/team/danswer/founders).
 
 ## 💡 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/aryn-ai/sycamore/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>
+[![Star History Chart](https://api.star-history.com/svg?repos=onyx-dot-app/onyx&type=Date)](https://star-history.com/#onyx-dot-app/onyx&Date)

backend/.gitignore

@@ -9,3 +9,4 @@ api_keys.py
 vespa-app.zip
 dynamic_config_storage/
 celerybeat-schedule*
+onyx/connectors/salesforce/data/

backend/Dockerfile

@@ -1,19 +1,19 @@
 FROM python:3.11.7-slim-bookworm
 
-LABEL com.danswer.maintainer="founders@danswer.ai"
-LABEL com.danswer.description="This image is the web/frontend container of Danswer which \
-contains code for both the Community and Enterprise editions of Danswer. If you do not \
+LABEL com.danswer.maintainer="founders@onyx.app"
+LABEL com.danswer.description="This image is the web/frontend container of Onyx which \
+contains code for both the Community and Enterprise editions of Onyx. If you do not \
 have a contract or agreement with DanswerAI, you are not permitted to use the Enterprise \
 Edition features outside of personal development or testing purposes. Please reach out to \
-founders@danswer.ai for more information. Please visit https://github.com/danswer-ai/danswer"
+founders@onyx.app for more information. Please visit https://github.com/onyx-dot-app/onyx"
 
-# Default DANSWER_VERSION, typically overriden during builds by GitHub Actions.
-ARG DANSWER_VERSION=0.8-dev
-ENV DANSWER_VERSION=${DANSWER_VERSION} \
+# Default ONYX_VERSION, typically overriden during builds by GitHub Actions.
+ARG ONYX_VERSION=0.8-dev
+ENV ONYX_VERSION=${ONYX_VERSION} \
     DANSWER_RUNNING_IN_DOCKER="true"
 
 
-RUN echo "DANSWER_VERSION: ${DANSWER_VERSION}"
+RUN echo "ONYX_VERSION: ${ONYX_VERSION}"
 # Install system dependencies
 # cmake needed for psycopg (postgres)
 # libpq-dev needed for psycopg (postgres)
@@ -56,7 +56,7 @@ RUN pip install --no-cache-dir --upgrade \
 # Cleanup for CVEs and size reduction
 # https://github.com/tornadoweb/tornado/issues/3107
 # xserver-common and xvfb included by playwright installation but not needed after
-# perl-base is part of the base Python Debian image but not needed for Danswer functionality
+# perl-base is part of the base Python Debian image but not needed for Onyx functionality
 # perl-base could only be removed with --allow-remove-essential
 RUN apt-get update && \
     apt-get remove -y --allow-remove-essential \
@@ -73,6 +73,7 @@ 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')"
@@ -91,7 +92,7 @@ COPY ./ee /app/ee
 COPY supervisord.conf /etc/supervisor/conf.d/supervisord.conf
 
 # Set up application files
-COPY ./danswer /app/danswer
+COPY ./onyx /app/onyx
 COPY ./shared_configs /app/shared_configs
 COPY ./alembic /app/alembic
 COPY ./alembic_tenants /app/alembic_tenants

backend/Dockerfile.model_server

@@ -1,18 +1,18 @@
 FROM python:3.11.7-slim-bookworm
 
-LABEL com.danswer.maintainer="founders@danswer.ai"
-LABEL com.danswer.description="This image is for the Danswer model server which runs all of the \
-AI models for Danswer. This container and all the code is MIT Licensed and free for all to use. \
-You can find it at https://hub.docker.com/r/danswer/danswer-model-server. For more details, \
-visit https://github.com/danswer-ai/danswer."
+LABEL com.danswer.maintainer="founders@onyx.app"
+LABEL com.danswer.description="This image is for the Onyx model server which runs all of the \
+AI models for Onyx. This container and all the code is MIT Licensed and free for all to use. \
+You can find it at https://hub.docker.com/r/onyx/onyx-model-server. For more details, \
+visit https://github.com/onyx-dot-app/onyx."
 
-# Default DANSWER_VERSION, typically overriden during builds by GitHub Actions.
-ARG DANSWER_VERSION=0.8-dev
-ENV DANSWER_VERSION=${DANSWER_VERSION} \
+# Default ONYX_VERSION, typically overriden during builds by GitHub Actions.
+ARG ONYX_VERSION=0.8-dev
+ENV ONYX_VERSION=${ONYX_VERSION} \
     DANSWER_RUNNING_IN_DOCKER="true"
 
 
-RUN echo "DANSWER_VERSION: ${DANSWER_VERSION}"
+RUN echo "ONYX_VERSION: ${ONYX_VERSION}"
 
 COPY ./requirements/model_server.txt /tmp/requirements.txt
 RUN pip install --no-cache-dir --upgrade \
@@ -20,11 +20,11 @@ RUN pip install --no-cache-dir --upgrade \
         --timeout 30 \
         -r /tmp/requirements.txt
 
-RUN apt-get remove -y --allow-remove-essential perl-base && \
+RUN apt-get remove -y --allow-remove-essential perl-base && \ 
     apt-get autoremove -y
 
 # Pre-downloading models for setups with limited egress
-# Download tokenizers, distilbert for the Danswer model
+# Download tokenizers, distilbert for the Onyx model
 # Download model weights
 # Run Nomic to pull in the custom architecture and have it cached locally
 RUN python -c "from transformers import AutoTokenizer; \
@@ -38,18 +38,18 @@ from sentence_transformers import SentenceTransformer; \
 SentenceTransformer(model_name_or_path='nomic-ai/nomic-embed-text-v1', trust_remote_code=True);"
 
 # In case the user has volumes mounted to /root/.cache/huggingface that they've downloaded while
-# running Danswer, don't overwrite it with the built in cache folder
+# running Onyx, don't overwrite it with the built in cache folder
 RUN mv /root/.cache/huggingface /root/.cache/temp_huggingface
 
 WORKDIR /app
 
 # Utils used by model server
-COPY ./danswer/utils/logger.py /app/danswer/utils/logger.py
+COPY ./onyx/utils/logger.py /app/onyx/utils/logger.py
 
 # Place to fetch version information
-COPY ./danswer/__init__.py /app/danswer/__init__.py
+COPY ./onyx/__init__.py /app/onyx/__init__.py
 
-# Shared between Danswer Backend and Model Server
+# Shared between Onyx Backend and Model Server
 COPY ./shared_configs /app/shared_configs
 
 # Model Server main code

backend/alembic/README.md

@@ -1,19 +1,22 @@
-<!-- DANSWER_METADATA={"link": "https://github.com/danswer-ai/danswer/blob/main/backend/alembic/README.md"} -->
+<!-- DANSWER_METADATA={"link": "https://github.com/onyx-dot-app/onyx/blob/main/backend/alembic/README.md"} -->
 
 # Alembic DB Migrations
-These files are for creating/updating the tables in the Relational DB (Postgres).
-Danswer migrations use a generic single-database configuration with an async dbapi.
 
-## To generate new migrations: 
-run from danswer/backend:
+These files are for creating/updating the tables in the Relational DB (Postgres).
+Onyx migrations use a generic single-database configuration with an async dbapi.
+
+## To generate new migrations:
+
+run from onyx/backend:
 `alembic revision --autogenerate -m <DESCRIPTION_OF_MIGRATION>`
 
 More info can be found here: https://alembic.sqlalchemy.org/en/latest/autogenerate.html
 
 ## Running migrations
+
 To run all un-applied migrations:
 `alembic upgrade head`
 
 To undo migrations:
-`alembic downgrade -X` 
+`alembic downgrade -X`
 where X is the number of migrations you want to undo from the current state

backend/alembic/env.py

@@ -1,56 +1,70 @@
+from typing import Any, Literal
+from onyx.db.engine import get_iam_auth_token
+from onyx.configs.app_configs import USE_IAM_AUTH
+from onyx.configs.app_configs import POSTGRES_HOST
+from onyx.configs.app_configs import POSTGRES_PORT
+from onyx.configs.app_configs import POSTGRES_USER
+from onyx.configs.app_configs import AWS_REGION_NAME
+from onyx.db.engine import build_connection_string
+from onyx.db.engine import get_all_tenant_ids
+from sqlalchemy import event
+from sqlalchemy import pool
+from sqlalchemy import text
 from sqlalchemy.engine.base import Connection
-from typing import Any
+import os
+import ssl
 import asyncio
-from logging.config import fileConfig
 import logging
+from logging.config import fileConfig
 
 from alembic import context
-from sqlalchemy import pool
 from sqlalchemy.ext.asyncio import create_async_engine
-from sqlalchemy.sql import text
-
-from shared_configs.configs import MULTI_TENANT
-from danswer.db.engine import build_connection_string
-from danswer.db.models import Base
+from sqlalchemy.sql.schema import SchemaItem
+from onyx.configs.constants import SSL_CERT_FILE
+from shared_configs.configs import MULTI_TENANT, POSTGRES_DEFAULT_SCHEMA
+from onyx.db.models import Base
 from celery.backends.database.session import ResultModelBase  # type: ignore
-from danswer.db.engine import get_all_tenant_ids
-from shared_configs.configs import POSTGRES_DEFAULT_SCHEMA
 
 # Alembic Config object
 config = context.config
 
-# Interpret the config file for Python logging.
 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
 target_metadata = [Base.metadata, ResultModelBase.metadata]
 
 EXCLUDE_TABLES = {"kombu_queue", "kombu_message"}
-
-# Set up logging
 logger = logging.getLogger(__name__)
 
+ssl_context: ssl.SSLContext | None = None
+if USE_IAM_AUTH:
+    if not os.path.exists(SSL_CERT_FILE):
+        raise FileNotFoundError(f"Expected {SSL_CERT_FILE} when USE_IAM_AUTH is true.")
+    ssl_context = ssl.create_default_context(cafile=SSL_CERT_FILE)
+
 
 def include_object(
-    object: Any, name: str, type_: str, reflected: bool, compare_to: Any
+    object: SchemaItem,
+    name: str | None,
+    type_: Literal[
+        "schema",
+        "table",
+        "column",
+        "index",
+        "unique_constraint",
+        "foreign_key_constraint",
+    ],
+    reflected: bool,
+    compare_to: SchemaItem | None,
 ) -> bool:
-    """
-    Determines whether a database object should be included in migrations.
-    Excludes specified tables from migrations.
-    """
     if type_ == "table" and name in EXCLUDE_TABLES:
         return False
     return True
 
 
 def get_schema_options() -> tuple[str, bool, bool]:
-    """
-    Parses command-line options passed via '-x' in Alembic commands.
-    Recognizes 'schema', 'create_schema', and 'upgrade_all_tenants' options.
-    """
     x_args_raw = context.get_x_argument()
     x_args = {}
     for arg in x_args_raw:
@@ -78,16 +92,12 @@ def get_schema_options() -> tuple[str, bool, bool]:
 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(
@@ -105,11 +115,25 @@ def do_run_migrations(
         context.run_migrations()
 
 
+def provide_iam_token_for_alembic(
+    dialect: Any, conn_rec: Any, cargs: Any, cparams: Any
+) -> None:
+    if USE_IAM_AUTH:
+        # Database connection settings
+        region = AWS_REGION_NAME
+        host = POSTGRES_HOST
+        port = POSTGRES_PORT
+        user = POSTGRES_USER
+
+        # Get IAM authentication token
+        token = get_iam_auth_token(host, port, user, region)
+
+        # For Alembic / SQLAlchemy in this context, set SSL and password
+        cparams["password"] = token
+        cparams["ssl"] = ssl_context
+
+
 async def run_async_migrations() -> None:
-    """
-    Determines whether to run migrations for a single schema or all schemas,
-    and executes migrations accordingly.
-    """
     schema_name, create_schema, upgrade_all_tenants = get_schema_options()
 
     engine = create_async_engine(
@@ -117,10 +141,16 @@ async def run_async_migrations() -> None:
         poolclass=pool.NullPool,
     )
 
-    if upgrade_all_tenants:
-        # Run migrations for all tenant schemas sequentially
-        tenant_schemas = get_all_tenant_ids()
+    if USE_IAM_AUTH:
 
+        @event.listens_for(engine.sync_engine, "do_connect")
+        def event_provide_iam_token_for_alembic(
+            dialect: Any, conn_rec: Any, cargs: Any, cparams: Any
+        ) -> None:
+            provide_iam_token_for_alembic(dialect, conn_rec, cargs, cparams)
+
+    if upgrade_all_tenants:
+        tenant_schemas = get_all_tenant_ids()
         for schema in tenant_schemas:
             try:
                 logger.info(f"Migrating schema: {schema}")
@@ -150,15 +180,20 @@ async def run_async_migrations() -> None:
 
 
 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)
+
+        if USE_IAM_AUTH:
+
+            @event.listens_for(engine.sync_engine, "do_connect")
+            def event_provide_iam_token_for_alembic_offline(
+                dialect: Any, conn_rec: Any, cargs: Any, cparams: Any
+            ) -> None:
+                provide_iam_token_for_alembic(dialect, conn_rec, cargs, cparams)
+
         tenant_schemas = get_all_tenant_ids()
         engine.sync_engine.dispose()
 
@@ -195,9 +230,6 @@ def run_migrations_offline() -> None:
 
 
 def run_migrations_online() -> None:
-    """
-    Runs migrations in 'online' mode using an asynchronous engine.
-    """
     asyncio.run(run_async_migrations())
 
 

backend/alembic/versions/027381bce97c_add_shortcut_option_for_users.py

@@ -0,0 +1,29 @@
+"""add shortcut option for users
+
+Revision ID: 027381bce97c
+Revises: 6fc7886d665d
+Create Date: 2025-01-14 12:14:00.814390
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "027381bce97c"
+down_revision = "6fc7886d665d"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "user",
+        sa.Column(
+            "shortcut_enabled", sa.Boolean(), nullable=False, server_default="true"
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("user", "shortcut_enabled")

backend/alembic/versions/0a98909f2757_enable_encrypted_fields.py

@@ -11,7 +11,7 @@ from sqlalchemy.sql import table
 from sqlalchemy.dialects import postgresql
 import json
 
-from danswer.utils.encryption import encrypt_string_to_bytes
+from onyx.utils.encryption import encrypt_string_to_bytes
 
 # revision identifiers, used by Alembic.
 revision = "0a98909f2757"

backend/alembic/versions/15326fcec57e_introduce_onyx_apis.py

@@ -1,4 +1,4 @@
-"""Introduce Danswer APIs
+"""Introduce Onyx APIs
 
 Revision ID: 15326fcec57e
 Revises: 77d07dffae64
@@ -8,7 +8,7 @@ Create Date: 2023-11-11 20:51:24.228999
 from alembic import op
 import sqlalchemy as sa
 
-from danswer.configs.constants import DocumentSource
+from onyx.configs.constants import DocumentSource
 
 # revision identifiers, used by Alembic.
 revision = "15326fcec57e"

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/1adf5ea20d2b_agent_doc_result_col.py

@@ -0,0 +1,29 @@
+"""agent_doc_result_col
+
+Revision ID: 1adf5ea20d2b
+Revises: e9cf2bd7baed
+Create Date: 2025-01-05 13:14:58.344316
+
+"""
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = "1adf5ea20d2b"
+down_revision = "e9cf2bd7baed"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Add the new column with JSONB type
+    op.add_column(
+        "sub_question",
+        sa.Column("sub_question_doc_results", postgresql.JSONB(), nullable=True),
+    )
+
+
+def downgrade() -> None:
+    # Drop the column
+    op.drop_column("sub_question", "sub_question_doc_results")

backend/alembic/versions/1f60f60c3401_embedding_model_search_settings.py

@@ -10,7 +10,7 @@ from alembic import op
 import sqlalchemy as sa
 from sqlalchemy.dialects import postgresql
 
-from danswer.configs.chat_configs import NUM_POSTPROCESSED_RESULTS
+from onyx.configs.chat_configs import NUM_POSTPROCESSED_RESULTS
 
 # revision identifiers, used by Alembic.
 revision = "1f60f60c3401"

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/2955778aa44c_add_chunk_count_to_document.py

@@ -0,0 +1,24 @@
+"""add chunk count to document
+
+Revision ID: 2955778aa44c
+Revises: c0aab6edb6dd
+Create Date: 2025-01-04 11:39:43.268612
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "2955778aa44c"
+down_revision = "c0aab6edb6dd"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column("document", sa.Column("chunk_count", sa.Integer(), nullable=True))
+
+
+def downgrade() -> None:
+    op.drop_column("document", "chunk_count")

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/35e518e0ddf4_properly_cascade.py

@@ -0,0 +1,121 @@
+"""properly_cascade
+
+Revision ID: 35e518e0ddf4
+Revises: 91a0a4d62b14
+Create Date: 2024-09-20 21:24:04.891018
+
+"""
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "35e518e0ddf4"
+down_revision = "91a0a4d62b14"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Update chat_message foreign key constraint
+    op.drop_constraint(
+        "chat_message_chat_session_id_fkey", "chat_message", type_="foreignkey"
+    )
+    op.create_foreign_key(
+        "chat_message_chat_session_id_fkey",
+        "chat_message",
+        "chat_session",
+        ["chat_session_id"],
+        ["id"],
+        ondelete="CASCADE",
+    )
+
+    # Update chat_message__search_doc foreign key constraints
+    op.drop_constraint(
+        "chat_message__search_doc_chat_message_id_fkey",
+        "chat_message__search_doc",
+        type_="foreignkey",
+    )
+    op.drop_constraint(
+        "chat_message__search_doc_search_doc_id_fkey",
+        "chat_message__search_doc",
+        type_="foreignkey",
+    )
+
+    op.create_foreign_key(
+        "chat_message__search_doc_chat_message_id_fkey",
+        "chat_message__search_doc",
+        "chat_message",
+        ["chat_message_id"],
+        ["id"],
+        ondelete="CASCADE",
+    )
+    op.create_foreign_key(
+        "chat_message__search_doc_search_doc_id_fkey",
+        "chat_message__search_doc",
+        "search_doc",
+        ["search_doc_id"],
+        ["id"],
+        ondelete="CASCADE",
+    )
+
+    # Add CASCADE delete for tool_call foreign key
+    op.drop_constraint("tool_call_message_id_fkey", "tool_call", type_="foreignkey")
+    op.create_foreign_key(
+        "tool_call_message_id_fkey",
+        "tool_call",
+        "chat_message",
+        ["message_id"],
+        ["id"],
+        ondelete="CASCADE",
+    )
+
+
+def downgrade() -> None:
+    # Revert chat_message foreign key constraint
+    op.drop_constraint(
+        "chat_message_chat_session_id_fkey", "chat_message", type_="foreignkey"
+    )
+    op.create_foreign_key(
+        "chat_message_chat_session_id_fkey",
+        "chat_message",
+        "chat_session",
+        ["chat_session_id"],
+        ["id"],
+    )
+
+    # Revert chat_message__search_doc foreign key constraints
+    op.drop_constraint(
+        "chat_message__search_doc_chat_message_id_fkey",
+        "chat_message__search_doc",
+        type_="foreignkey",
+    )
+    op.drop_constraint(
+        "chat_message__search_doc_search_doc_id_fkey",
+        "chat_message__search_doc",
+        type_="foreignkey",
+    )
+
+    op.create_foreign_key(
+        "chat_message__search_doc_chat_message_id_fkey",
+        "chat_message__search_doc",
+        "chat_message",
+        ["chat_message_id"],
+        ["id"],
+    )
+    op.create_foreign_key(
+        "chat_message__search_doc_search_doc_id_fkey",
+        "chat_message__search_doc",
+        "search_doc",
+        ["search_doc_id"],
+        ["id"],
+    )
+
+    # Revert tool_call foreign key constraint
+    op.drop_constraint("tool_call_message_id_fkey", "tool_call", type_="foreignkey")
+    op.create_foreign_key(
+        "tool_call_message_id_fkey",
+        "tool_call",
+        "chat_message",
+        ["message_id"],
+        ["id"],
+    )

backend/alembic/versions/369644546676_add_composite_index_for_index_attempt_.py

@@ -0,0 +1,35 @@
+"""add composite index for index attempt time updated
+
+Revision ID: 369644546676
+Revises: 2955778aa44c
+Create Date: 2025-01-08 15:38:17.224380
+
+"""
+from alembic import op
+from sqlalchemy import text
+
+# revision identifiers, used by Alembic.
+revision = "369644546676"
+down_revision = "2955778aa44c"
+branch_labels: None = None
+depends_on: None = None
+
+
+def upgrade() -> None:
+    op.create_index(
+        "ix_index_attempt_ccpair_search_settings_time_updated",
+        "index_attempt",
+        [
+            "connector_credential_pair_id",
+            "search_settings_id",
+            text("time_updated DESC"),
+        ],
+        unique=False,
+    )
+
+
+def downgrade() -> None:
+    op.drop_index(
+        "ix_index_attempt_ccpair_search_settings_time_updated",
+        table_name="index_attempt",
+    )

backend/alembic/versions/3c6531f32351_add_back_input_prompts.py

@@ -0,0 +1,58 @@
+"""add back input prompts
+
+Revision ID: 3c6531f32351
+Revises: aeda5f2df4f6
+Create Date: 2025-01-13 12:49:51.705235
+
+"""
+from alembic import op
+import sqlalchemy as sa
+import fastapi_users_db_sqlalchemy
+
+# revision identifiers, used by Alembic.
+revision = "3c6531f32351"
+down_revision = "aeda5f2df4f6"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "inputprompt",
+        sa.Column("id", sa.Integer(), autoincrement=True, nullable=False),
+        sa.Column("prompt", sa.String(), nullable=False),
+        sa.Column("content", sa.String(), nullable=False),
+        sa.Column("active", sa.Boolean(), nullable=False),
+        sa.Column("is_public", sa.Boolean(), nullable=False),
+        sa.Column(
+            "user_id",
+            fastapi_users_db_sqlalchemy.generics.GUID(),
+            nullable=True,
+        ),
+        sa.ForeignKeyConstraint(
+            ["user_id"],
+            ["user.id"],
+        ),
+        sa.PrimaryKeyConstraint("id"),
+    )
+    op.create_table(
+        "inputprompt__user",
+        sa.Column("input_prompt_id", sa.Integer(), nullable=False),
+        sa.Column(
+            "user_id", fastapi_users_db_sqlalchemy.generics.GUID(), nullable=False
+        ),
+        sa.ForeignKeyConstraint(
+            ["input_prompt_id"],
+            ["inputprompt.id"],
+        ),
+        sa.ForeignKeyConstraint(
+            ["user_id"],
+            ["user.id"],
+        ),
+        sa.PrimaryKeyConstraint("input_prompt_id", "user_id"),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("inputprompt__user")
+    op.drop_table("inputprompt")

backend/alembic/versions/46625e4745d4_remove_native_enum.py

@@ -17,7 +17,7 @@ depends_on: None = None
 
 def upgrade() -> None:
     # At this point, we directly changed some previous migrations,
-    # https://github.com/danswer-ai/danswer/pull/637
+    # https://github.com/onyx-dot-app/onyx/pull/637
     # Due to using Postgres native Enums, it caused some complications for first time users.
     # To remove those complications, all Enums are only handled application side moving forward.
     # This migration exists to ensure that existing users don't run into upgrade issues.

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 onyx.key_value_store.factory import get_kv_store
+from onyx.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/54a74a0417fc_danswerbot_onyxbot.py

@@ -0,0 +1,23 @@
+"""danswerbot -> onyxbot
+
+Revision ID: 54a74a0417fc
+Revises: 94dc3d0236f8
+Create Date: 2024-12-11 18:05:05.490737
+
+"""
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "54a74a0417fc"
+down_revision = "94dc3d0236f8"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.alter_column("chat_session", "danswerbot_flow", new_column_name="onyxbot_flow")
+
+
+def downgrade() -> None:
+    op.alter_column("chat_session", "onyxbot_flow", new_column_name="danswerbot_flow")

backend/alembic/versions/570282d33c49_track_onyxbot_explicitly.py

@@ -1,4 +1,4 @@
-"""Track Danswerbot Explicitly
+"""Track Onyxbot Explicitly
 
 Revision ID: 570282d33c49
 Revises: 7547d982db8f

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/6fc7886d665d_make_categories_labels_and_many_to_many.py

@@ -0,0 +1,79 @@
+"""make categories labels and many to many
+
+Revision ID: 6fc7886d665d
+Revises: 3c6531f32351
+Create Date: 2025-01-13 18:12:18.029112
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "6fc7886d665d"
+down_revision = "3c6531f32351"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Rename persona_category table to persona_label
+    op.rename_table("persona_category", "persona_label")
+
+    # Create the new association table
+    op.create_table(
+        "persona__persona_label",
+        sa.Column("persona_id", sa.Integer(), nullable=False),
+        sa.Column("persona_label_id", sa.Integer(), nullable=False),
+        sa.ForeignKeyConstraint(
+            ["persona_id"],
+            ["persona.id"],
+        ),
+        sa.ForeignKeyConstraint(
+            ["persona_label_id"],
+            ["persona_label.id"],
+        ),
+        sa.PrimaryKeyConstraint("persona_id", "persona_label_id"),
+    )
+
+    # Copy existing relationships to the new table
+    op.execute(
+        """
+        INSERT INTO persona__persona_label (persona_id, persona_label_id)
+        SELECT id, category_id FROM persona WHERE category_id IS NOT NULL
+    """
+    )
+
+    # Remove the old category_id column from persona table
+    op.drop_column("persona", "category_id")
+
+
+def downgrade() -> None:
+    # Rename persona_label table back to persona_category
+    op.rename_table("persona_label", "persona_category")
+
+    # Add back the category_id column to persona table
+    op.add_column("persona", sa.Column("category_id", sa.Integer(), nullable=True))
+    op.create_foreign_key(
+        "persona_category_id_fkey",
+        "persona",
+        "persona_category",
+        ["category_id"],
+        ["id"],
+    )
+
+    # Copy the first label relationship back to the persona table
+    op.execute(
+        """
+        UPDATE persona
+        SET category_id = (
+            SELECT persona_label_id
+            FROM persona__persona_label
+            WHERE persona__persona_label.persona_id = persona.id
+            LIMIT 1
+        )
+    """
+    )
+
+    # Drop the association table
+    op.drop_table("persona__persona_label")

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.key_value_store.factory import get_kv_store
+from onyx.key_value_store.factory import get_kv_store
 
 # revision identifiers, used by Alembic.
 revision = "703313b75876"

backend/alembic/versions/776b3bbe9092_remove_remaining_enums.py

@@ -8,9 +8,9 @@ Create Date: 2024-03-22 21:34:27.629444
 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 onyx.db.models import IndexModelStatus
+from onyx.context.search.enums import RecencyBiasSetting
+from onyx.context.search.enums import SearchType
 
 # revision identifiers, used by Alembic.
 revision = "776b3bbe9092"

backend/alembic/versions/77d07dffae64_forcibly_remove_more_enum_types_from_.py

@@ -18,7 +18,7 @@ depends_on: None = None
 
 def upgrade() -> None:
     # In a PR:
-    # https://github.com/danswer-ai/danswer/pull/397/files#diff-f05fb341f6373790b91852579631b64ca7645797a190837156a282b67e5b19c2
+    # https://github.com/onyx-dot-app/onyx/pull/397/files#diff-f05fb341f6373790b91852579631b64ca7645797a190837156a282b67e5b19c2
     # we directly changed some previous migrations. This caused some users to have native enums
     # while others wouldn't. This has caused some issues when adding new fields to these enums.
     # This migration manually changes the enum types to ensure that nobody uses native enums.

backend/alembic/versions/91a0a4d62b14_milestone.py

@@ -0,0 +1,45 @@
+"""Milestone
+
+Revision ID: 91a0a4d62b14
+Revises: dab04867cd88
+Create Date: 2024-12-13 19:03:30.947551
+
+"""
+from alembic import op
+import sqlalchemy as sa
+import fastapi_users_db_sqlalchemy
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = "91a0a4d62b14"
+down_revision = "dab04867cd88"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "milestone",
+        sa.Column("id", sa.UUID(), nullable=False),
+        sa.Column("tenant_id", sa.String(), nullable=True),
+        sa.Column(
+            "user_id",
+            fastapi_users_db_sqlalchemy.generics.GUID(),
+            nullable=True,
+        ),
+        sa.Column("event_type", sa.String(), nullable=False),
+        sa.Column(
+            "time_created",
+            sa.DateTime(timezone=True),
+            server_default=sa.text("now()"),
+            nullable=False,
+        ),
+        sa.Column("event_tracker", postgresql.JSONB(), nullable=True),
+        sa.ForeignKeyConstraint(["user_id"], ["user.id"], ondelete="CASCADE"),
+        sa.PrimaryKeyConstraint("id"),
+        sa.UniqueConstraint("event_type", name="uq_milestone_event_type"),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("milestone")

backend/alembic/versions/91fd3b470d1a_remove_documentsource_from_tag.py

@@ -7,7 +7,7 @@ Create Date: 2024-03-21 12:05:23.956734
 """
 from alembic import op
 import sqlalchemy as sa
-from danswer.configs.constants import DocumentSource
+from onyx.configs.constants import DocumentSource
 
 # revision identifiers, used by Alembic.
 revision = "91fd3b470d1a"

backend/alembic/versions/925b58bd75b6_agent_metric_col_rename__s.py

@@ -0,0 +1,35 @@
+"""agent_metric_col_rename__s
+
+Revision ID: 925b58bd75b6
+Revises: 9787be927e58
+Create Date: 2025-01-06 11:20:26.752441
+
+"""
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "925b58bd75b6"
+down_revision = "9787be927e58"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Rename columns using PostgreSQL syntax
+    op.alter_column(
+        "agent__search_metrics", "base_duration_s", new_column_name="base_duration__s"
+    )
+    op.alter_column(
+        "agent__search_metrics", "full_duration_s", new_column_name="full_duration__s"
+    )
+
+
+def downgrade() -> None:
+    # Revert the column renames
+    op.alter_column(
+        "agent__search_metrics", "base_duration__s", new_column_name="base_duration_s"
+    )
+    op.alter_column(
+        "agent__search_metrics", "full_duration__s", new_column_name="full_duration_s"
+    )

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

@@ -7,15 +7,15 @@ 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 (
+from onyx.db.models import (
     Connector,
     ConnectorCredentialPair,
     Credential,
     IndexAttempt,
 )
-from danswer.configs.constants import DocumentSource
 
 
 # revision identifiers, used by Alembic.
@@ -30,13 +30,11 @@ def upgrade() -> None:
     bind = op.get_bind()
     session = Session(bind=bind)
 
-    connectors_to_delete = (
-        session.query(Connector)
-        .filter(Connector.source == DocumentSource.REQUESTTRACKER)
-        .all()
+    # Get connectors using raw SQL
+    result = bind.execute(
+        text("SELECT id FROM connector WHERE source = 'requesttracker'")
     )
-
-    connector_ids = [connector.id for connector in connectors_to_delete]
+    connector_ids = [row[0] for row in result]
 
     if connector_ids:
         cc_pairs_to_delete = (

backend/alembic/versions/94dc3d0236f8_make_document_set_description_optional.py

@@ -0,0 +1,30 @@
+"""make document set description optional
+
+Revision ID: 94dc3d0236f8
+Revises: bf7a81109301
+Create Date: 2024-12-11 11:26:10.616722
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "94dc3d0236f8"
+down_revision = "bf7a81109301"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Make document_set.description column nullable
+    op.alter_column(
+        "document_set", "description", existing_type=sa.String(), nullable=True
+    )
+
+
+def downgrade() -> None:
+    # Revert document_set.description column to non-nullable
+    op.alter_column(
+        "document_set", "description", existing_type=sa.String(), nullable=False
+    )

backend/alembic/versions/9787be927e58_agent_metric_table_renames__agent__.py

@@ -0,0 +1,25 @@
+"""agent_metric_table_renames__agent__
+
+Revision ID: 9787be927e58
+Revises: bceb76d618ec
+Create Date: 2025-01-06 11:01:44.210160
+
+"""
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "9787be927e58"
+down_revision = "bceb76d618ec"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Rename table from agent_search_metrics to agent__search_metrics
+    op.rename_table("agent_search_metrics", "agent__search_metrics")
+
+
+def downgrade() -> None:
+    # Rename table back from agent__search_metrics to agent_search_metrics
+    op.rename_table("agent__search_metrics", "agent_search_metrics")

backend/alembic/versions/98a5008d8711_agent_tracking.py

@@ -0,0 +1,42 @@
+"""agent_tracking
+
+Revision ID: 98a5008d8711
+Revises: 027381bce97c
+Create Date: 2025-01-04 14:41:52.732238
+
+"""
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = "98a5008d8711"
+down_revision = "027381bce97c"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "agent_search_metrics",
+        sa.Column("id", sa.Integer(), nullable=False),
+        sa.Column("user_id", postgresql.UUID(as_uuid=True), nullable=True),
+        sa.Column("persona_id", sa.Integer(), nullable=True),
+        sa.Column("agent_type", sa.String(), nullable=False),
+        sa.Column("start_time", sa.DateTime(timezone=True), nullable=False),
+        sa.Column("base_duration_s", sa.Float(), nullable=False),
+        sa.Column("full_duration_s", sa.Float(), nullable=False),
+        sa.Column("base_metrics", postgresql.JSONB(), nullable=True),
+        sa.Column("refined_metrics", postgresql.JSONB(), nullable=True),
+        sa.Column("all_metrics", postgresql.JSONB(), nullable=True),
+        sa.ForeignKeyConstraint(
+            ["persona_id"],
+            ["persona.id"],
+        ),
+        sa.ForeignKeyConstraint(["user_id"], ["user.id"], ondelete="CASCADE"),
+        sa.PrimaryKeyConstraint("id"),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("agent_search_metrics")

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/aeda5f2df4f6_add_pinned_assistants.py

@@ -0,0 +1,27 @@
+"""add pinned assistants
+
+Revision ID: aeda5f2df4f6
+Revises: 369644546676
+Create Date: 2025-01-09 16:04:10.770636
+
+"""
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = "aeda5f2df4f6"
+down_revision = "369644546676"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "user", sa.Column("pinned_assistants", postgresql.JSONB(), nullable=True)
+    )
+    op.execute('UPDATE "user" SET pinned_assistants = chosen_assistants')
+
+
+def downgrade() -> None:
+    op.drop_column("user", "pinned_assistants")

backend/alembic/versions/b156fa702355_chat_reworked.py

@@ -10,7 +10,7 @@ from alembic import op
 import sqlalchemy as sa
 from sqlalchemy.dialects import postgresql
 from sqlalchemy.dialects.postgresql import ENUM
-from danswer.configs.constants import DocumentSource
+from onyx.configs.constants import DocumentSource
 
 # revision identifiers, used by Alembic.
 revision = "b156fa702355"
@@ -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/bceb76d618ec_agent_table_renames__agent__.py

@@ -0,0 +1,84 @@
+"""agent_table_renames__agent__
+
+Revision ID: bceb76d618ec
+Revises: c0132518a25b
+Create Date: 2025-01-06 10:50:48.109285
+
+"""
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "bceb76d618ec"
+down_revision = "c0132518a25b"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.drop_constraint(
+        "sub_query__search_doc_sub_query_id_fkey",
+        "sub_query__search_doc",
+        type_="foreignkey",
+    )
+    op.drop_constraint(
+        "sub_query__search_doc_search_doc_id_fkey",
+        "sub_query__search_doc",
+        type_="foreignkey",
+    )
+    # Rename tables
+    op.rename_table("sub_query", "agent__sub_query")
+    op.rename_table("sub_question", "agent__sub_question")
+    op.rename_table("sub_query__search_doc", "agent__sub_query__search_doc")
+
+    # Update both foreign key constraints for agent__sub_query__search_doc
+
+    # Create new foreign keys with updated names
+    op.create_foreign_key(
+        "agent__sub_query__search_doc_sub_query_id_fkey",
+        "agent__sub_query__search_doc",
+        "agent__sub_query",
+        ["sub_query_id"],
+        ["id"],
+    )
+    op.create_foreign_key(
+        "agent__sub_query__search_doc_search_doc_id_fkey",
+        "agent__sub_query__search_doc",
+        "search_doc",  # This table name doesn't change
+        ["search_doc_id"],
+        ["id"],
+    )
+
+
+def downgrade() -> None:
+    # Update foreign key constraints for sub_query__search_doc
+    op.drop_constraint(
+        "agent__sub_query__search_doc_sub_query_id_fkey",
+        "agent__sub_query__search_doc",
+        type_="foreignkey",
+    )
+    op.drop_constraint(
+        "agent__sub_query__search_doc_search_doc_id_fkey",
+        "agent__sub_query__search_doc",
+        type_="foreignkey",
+    )
+
+    # Rename tables back
+    op.rename_table("agent__sub_query__search_doc", "sub_query__search_doc")
+    op.rename_table("agent__sub_question", "sub_question")
+    op.rename_table("agent__sub_query", "sub_query")
+
+    op.create_foreign_key(
+        "sub_query__search_doc_sub_query_id_fkey",
+        "sub_query__search_doc",
+        "sub_query",
+        ["sub_query_id"],
+        ["id"],
+    )
+    op.create_foreign_key(
+        "sub_query__search_doc_search_doc_id_fkey",
+        "sub_query__search_doc",
+        "search_doc",  # This table name doesn't change
+        ["search_doc_id"],
+        ["id"],
+    )

backend/alembic/versions/bf7a81109301_delete_input_prompts.py

@@ -0,0 +1,57 @@
+"""delete_input_prompts
+
+Revision ID: bf7a81109301
+Revises: f7a894b06d02
+Create Date: 2024-12-09 12:00:49.884228
+
+"""
+from alembic import op
+import sqlalchemy as sa
+import fastapi_users_db_sqlalchemy
+
+
+# revision identifiers, used by Alembic.
+revision = "bf7a81109301"
+down_revision = "f7a894b06d02"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.drop_table("inputprompt__user")
+    op.drop_table("inputprompt")
+
+
+def downgrade() -> None:
+    op.create_table(
+        "inputprompt",
+        sa.Column("id", sa.Integer(), autoincrement=True, nullable=False),
+        sa.Column("prompt", sa.String(), nullable=False),
+        sa.Column("content", sa.String(), nullable=False),
+        sa.Column("active", sa.Boolean(), nullable=False),
+        sa.Column("is_public", sa.Boolean(), nullable=False),
+        sa.Column(
+            "user_id",
+            fastapi_users_db_sqlalchemy.generics.GUID(),
+            nullable=True,
+        ),
+        sa.ForeignKeyConstraint(
+            ["user_id"],
+            ["user.id"],
+        ),
+        sa.PrimaryKeyConstraint("id"),
+    )
+    op.create_table(
+        "inputprompt__user",
+        sa.Column("input_prompt_id", sa.Integer(), nullable=False),
+        sa.Column("user_id", sa.Integer(), nullable=False),
+        sa.ForeignKeyConstraint(
+            ["input_prompt_id"],
+            ["inputprompt.id"],
+        ),
+        sa.ForeignKeyConstraint(
+            ["user_id"],
+            ["inputprompt.id"],
+        ),
+        sa.PrimaryKeyConstraint("input_prompt_id", "user_id"),
+    )

backend/alembic/versions/c0132518a25b_agent_table_changes_rename_level.py

@@ -0,0 +1,40 @@
+"""agent_table_changes_rename_level
+
+Revision ID: c0132518a25b
+Revises: 1adf5ea20d2b
+Create Date: 2025-01-05 16:38:37.660152
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "c0132518a25b"
+down_revision = "1adf5ea20d2b"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Add level and level_question_nr columns with NOT NULL constraint
+    op.add_column(
+        "sub_question",
+        sa.Column("level", sa.Integer(), nullable=False, server_default="0"),
+    )
+    op.add_column(
+        "sub_question",
+        sa.Column(
+            "level_question_nr", sa.Integer(), nullable=False, server_default="0"
+        ),
+    )
+
+    # Remove the server_default after the columns are created
+    op.alter_column("sub_question", "level", server_default=None)
+    op.alter_column("sub_question", "level_question_nr", server_default=None)
+
+
+def downgrade() -> None:
+    # Remove the columns
+    op.drop_column("sub_question", "level_question_nr")
+    op.drop_column("sub_question", "level")

backend/alembic/versions/c0aab6edb6dd_delete_workspace.py

@@ -0,0 +1,87 @@
+"""delete workspace
+
+Revision ID: c0aab6edb6dd
+Revises: 35e518e0ddf4
+Create Date: 2024-12-17 14:37:07.660631
+
+"""
+
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "c0aab6edb6dd"
+down_revision = "35e518e0ddf4"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.execute(
+        """
+    UPDATE connector
+    SET connector_specific_config = connector_specific_config - 'workspace'
+    WHERE source = 'SLACK'
+    """
+    )
+
+
+def downgrade() -> None:
+    import json
+    from sqlalchemy import text
+    from slack_sdk import WebClient
+
+    conn = op.get_bind()
+
+    # Fetch all Slack credentials
+    creds_result = conn.execute(
+        text("SELECT id, credential_json FROM credential WHERE source = 'SLACK'")
+    )
+    all_slack_creds = creds_result.fetchall()
+    if not all_slack_creds:
+        return
+
+    for cred_row in all_slack_creds:
+        credential_id, credential_json = cred_row
+
+        credential_json = (
+            credential_json.tobytes().decode("utf-8")
+            if isinstance(credential_json, memoryview)
+            else credential_json.decode("utf-8")
+        )
+        credential_data = json.loads(credential_json)
+        slack_bot_token = credential_data.get("slack_bot_token")
+        if not slack_bot_token:
+            print(
+                f"No slack_bot_token found for credential {credential_id}. "
+                "Your Slack connector will not function until you upgrade and provide a valid token."
+            )
+            continue
+
+        client = WebClient(token=slack_bot_token)
+        try:
+            auth_response = client.auth_test()
+            workspace = auth_response["url"].split("//")[1].split(".")[0]
+
+            # Update only the connectors linked to this credential
+            # (and which are Slack connectors).
+            op.execute(
+                f"""
+                UPDATE connector AS c
+                SET connector_specific_config = jsonb_set(
+                    connector_specific_config,
+                    '{{workspace}}',
+                    to_jsonb('{workspace}'::text)
+                )
+                FROM connector_credential_pair AS ccp
+                WHERE ccp.connector_id = c.id
+                  AND c.source = 'SLACK'
+                  AND ccp.credential_id = {credential_id}
+            """
+            )
+        except Exception:
+            print(
+                f"We were unable to get the workspace url for your Slack Connector with id {credential_id}."
+            )
+            print("This connector will no longer work until you upgrade.")
+            continue

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/dab04867cd88_add_composite_index_to_document_by_.py

@@ -0,0 +1,32 @@
+"""Add composite index to document_by_connector_credential_pair
+
+Revision ID: dab04867cd88
+Revises: 54a74a0417fc
+Create Date: 2024-12-13 22:43:20.119990
+
+"""
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "dab04867cd88"
+down_revision = "54a74a0417fc"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Composite index on (connector_id, credential_id)
+    op.create_index(
+        "idx_document_cc_pair_connector_credential",
+        "document_by_connector_credential_pair",
+        ["connector_id", "credential_id"],
+        unique=False,
+    )
+
+
+def downgrade() -> None:
+    op.drop_index(
+        "idx_document_cc_pair_connector_credential",
+        table_name="document_by_connector_credential_pair",
+    )

backend/alembic/versions/dba7f71618f5_onyx_custom_tool_flow.py

@@ -1,4 +1,4 @@
-"""Danswer Custom Tool Flow
+"""Onyx Custom Tool Flow
 
 Revision ID: dba7f71618f5
 Revises: d5645c915d0e

backend/alembic/versions/dbaa756c2ccf_embedding_models.py

@@ -9,12 +9,12 @@ from alembic import op
 import sqlalchemy as sa
 from sqlalchemy import table, column, String, Integer, Boolean
 
-from danswer.db.search_settings import (
+from onyx.db.search_settings import (
     get_new_default_embedding_model,
     get_old_default_embedding_model,
     user_has_overridden_embedding_model,
 )
-from danswer.db.models import IndexModelStatus
+from onyx.db.models import IndexModelStatus
 
 # revision identifiers, used by Alembic.
 revision = "dbaa756c2ccf"

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/e50154680a5c_no_source_enum.py

@@ -8,7 +8,7 @@ Create Date: 2024-03-14 18:06:08.523106
 from alembic import op
 import sqlalchemy as sa
 
-from danswer.configs.constants import DocumentSource
+from onyx.configs.constants import DocumentSource
 
 # revision identifiers, used by Alembic.
 revision = "e50154680a5c"

backend/alembic/versions/e9cf2bd7baed_create_pro_search_persistence_tables.py

@@ -0,0 +1,68 @@
+"""create pro search persistence tables
+
+Revision ID: e9cf2bd7baed
+Revises: 98a5008d8711
+Create Date: 2025-01-02 17:55:56.544246
+
+"""
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects.postgresql import UUID
+
+
+# revision identifiers, used by Alembic.
+revision = "e9cf2bd7baed"
+down_revision = "98a5008d8711"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Create sub_question table
+    op.create_table(
+        "sub_question",
+        sa.Column("id", sa.Integer, primary_key=True),
+        sa.Column("primary_question_id", sa.Integer, sa.ForeignKey("chat_message.id")),
+        sa.Column(
+            "chat_session_id", UUID(as_uuid=True), sa.ForeignKey("chat_session.id")
+        ),
+        sa.Column("sub_question", sa.Text),
+        sa.Column(
+            "time_created", sa.DateTime(timezone=True), server_default=sa.func.now()
+        ),
+        sa.Column("sub_answer", sa.Text),
+    )
+
+    # Create sub_query table
+    op.create_table(
+        "sub_query",
+        sa.Column("id", sa.Integer, primary_key=True),
+        sa.Column("parent_question_id", sa.Integer, sa.ForeignKey("sub_question.id")),
+        sa.Column(
+            "chat_session_id", UUID(as_uuid=True), sa.ForeignKey("chat_session.id")
+        ),
+        sa.Column("sub_query", sa.Text),
+        sa.Column(
+            "time_created", sa.DateTime(timezone=True), server_default=sa.func.now()
+        ),
+    )
+
+    # Create sub_query__search_doc association table
+    op.create_table(
+        "sub_query__search_doc",
+        sa.Column(
+            "sub_query_id", sa.Integer, sa.ForeignKey("sub_query.id"), primary_key=True
+        ),
+        sa.Column(
+            "search_doc_id",
+            sa.Integer,
+            sa.ForeignKey("search_doc.id"),
+            primary_key=True,
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("sub_query__search_doc")
+    op.drop_table("sub_query")
+    op.drop_table("sub_question")

backend/alembic/versions/f7a894b06d02_non_nullbale_slack_bot_id_in_channel_.py

@@ -0,0 +1,40 @@
+"""non-nullbale slack bot id in channel config
+
+Revision ID: f7a894b06d02
+Revises: 9f696734098f
+Create Date: 2024-12-06 12:55:42.845723
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "f7a894b06d02"
+down_revision = "9f696734098f"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Delete all rows with null slack_bot_id
+    op.execute("DELETE FROM slack_channel_config WHERE slack_bot_id IS NULL")
+
+    # Make slack_bot_id non-nullable
+    op.alter_column(
+        "slack_channel_config",
+        "slack_bot_id",
+        existing_type=sa.Integer(),
+        nullable=False,
+    )
+
+
+def downgrade() -> None:
+    # Make slack_bot_id nullable again
+    op.alter_column(
+        "slack_channel_config",
+        "slack_bot_id",
+        existing_type=sa.Integer(),
+        nullable=True,
+    )

backend/alembic_tenants/README.md

@@ -1,3 +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.
+If you are not a Onyx developer, you can ignore this directory entirely.

backend/alembic_tenants/env.py

@@ -1,5 +1,6 @@
 import asyncio
 from logging.config import fileConfig
+from typing import Literal
 
 from sqlalchemy import pool
 from sqlalchemy.engine import Connection
@@ -7,8 +8,8 @@ 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
+from onyx.db.engine import build_connection_string
+from onyx.db.models import PublicBase
 
 # this is the Alembic Config object, which provides
 # access to the values within the .ini file in use.
@@ -37,8 +38,15 @@ EXCLUDE_TABLES = {"kombu_queue", "kombu_message"}
 
 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:

backend/alembic_tenants/versions/a4f6ee863c47_mapping_for_anonymous_user_path.py

@@ -0,0 +1,31 @@
+"""mapping for anonymous user path
+
+Revision ID: a4f6ee863c47
+Revises: 14a83a331951
+Create Date: 2025-01-04 14:16:58.697451
+
+"""
+import sqlalchemy as sa
+
+from alembic import op
+
+
+# revision identifiers, used by Alembic.
+revision = "a4f6ee863c47"
+down_revision = "14a83a331951"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "tenant_anonymous_user_path",
+        sa.Column("tenant_id", sa.String(), primary_key=True, nullable=False),
+        sa.Column("anonymous_user_path", sa.String(), nullable=False),
+        sa.PrimaryKeyConstraint("tenant_id"),
+        sa.UniqueConstraint("anonymous_user_path"),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("tenant_anonymous_user_path")

backend/chatt.txt

@@ -0,0 +1,536 @@
+"{\"user_message_id\": 475, \"reserved_assistant_message_id\": 476}\n"
+"{\"sub_question\": \"What\", \"level\": 0, \"level_question_nr\": 1}\n"
+"{\"sub_query\": \"ony\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 0}\n"
+"{\"sub_question\": \" is\", \"level\": 0, \"level_question_nr\": 1}\n"
+"{\"sub_query\": \"x\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 0}\n"
+"{\"sub_question\": \" On\", \"level\": 0, \"level_question_nr\": 1}\n"
+"{\"sub_query\": \" \", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 0}\n"
+"{\"sub_question\": \"yx\", \"level\": 0, \"level_question_nr\": 1}\n"
+"{\"sub_question\": \" \", \"level\": 0, \"level_question_nr\": 1}\n"
+"{\"sub_query\": \"1\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 0}\n"
+"{\"sub_question\": \"1\", \"level\": 0, \"level_question_nr\": 1}\n"
+"{\"sub_query\": \" features\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 0}\n"
+"{\"sub_query\": \" and\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 0}\n"
+"{\"sub_question\": \"?\", \"level\": 0, \"level_question_nr\": 1}\n"
+"{\"sub_question\": \"  \", \"level\": 0, \"level_question_nr\": 1}\n"
+"{\"sub_question\": \"\", \"level\": 0, \"level_question_nr\": 2}\n"
+"{\"sub_question\": \"What\", \"level\": 0, \"level_question_nr\": 2}\n"
+"{\"sub_question\": \" is\", \"level\": 0, \"level_question_nr\": 2}\n"
+"{\"sub_query\": \" specifications\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 0}\n"
+"{\"sub_question\": \" On\", \"level\": 0, \"level_question_nr\": 2}\n"
+"{\"sub_query\": \"  \", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 0}\n"
+"{\"sub_query\": \"\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 1}\n"
+"{\"sub_question\": \"yx\", \"level\": 0, \"level_question_nr\": 2}\n"
+"{\"sub_question\": \" \", \"level\": 0, \"level_question_nr\": 2}\n"
+"{\"sub_question\": \"2\", \"level\": 0, \"level_question_nr\": 2}\n"
+"{\"sub_query\": \"ony\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 1}\n"
+"{\"sub_question\": \"?\", \"level\": 0, \"level_question_nr\": 2}\n"
+"{\"sub_query\": \"x\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 1}\n"
+"{\"sub_question\": \"  \", \"level\": 0, \"level_question_nr\": 2}\n"
+"{\"sub_query\": \" \", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 1}\n"
+"{\"sub_question\": \"\", \"level\": 0, \"level_question_nr\": 3}\n"
+"{\"sub_query\": \"2\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 1}\n"
+"{\"sub_question\": \"What\", \"level\": 0, \"level_question_nr\": 3}\n"
+"{\"sub_query\": \" applications\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 1}\n"
+"{\"sub_question\": \" is\", \"level\": 0, \"level_question_nr\": 3}\n"
+"{\"sub_query\": \" and\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 1}\n"
+"{\"sub_question\": \" On\", \"level\": 0, \"level_question_nr\": 3}\n"
+"{\"sub_query\": \" use\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 1}\n"
+"{\"sub_question\": \"yx\", \"level\": 0, \"level_question_nr\": 3}\n"
+"{\"sub_query\": \" cases\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 1}\n"
+"{\"sub_question\": \" \", \"level\": 0, \"level_question_nr\": 3}\n"
+"{\"sub_question\": \"3\", \"level\": 0, \"level_question_nr\": 3}\n"
+"{\"sub_question\": \"?\", \"level\": 0, \"level_question_nr\": 3}\n"
+"{\"sub_question\": \"  \", \"level\": 0, \"level_question_nr\": 3}\n"
+"{\"sub_question\": \"\", \"level\": 0, \"level_question_nr\": 4}\n"
+"{\"sub_question\": \"What\", \"level\": 0, \"level_question_nr\": 4}\n"
+"{\"sub_question\": \" is\", \"level\": 0, \"level_question_nr\": 4}\n"
+"{\"sub_query\": \"  \", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 1}\n"
+"{\"sub_question\": \" On\", \"level\": 0, \"level_question_nr\": 4}\n"
+"{\"sub_query\": \"\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 2}\n"
+"{\"sub_question\": \"yx\", \"level\": 0, \"level_question_nr\": 4}\n"
+"{\"sub_question\": \" \", \"level\": 0, \"level_question_nr\": 4}\n"
+"{\"sub_query\": \"ony\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 2}\n"
+"{\"sub_question\": \"4\", \"level\": 0, \"level_question_nr\": 4}\n"
+"{\"sub_question\": \"?\", \"level\": 0, \"level_question_nr\": 4}\n"
+"{\"sub_query\": \"x\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 2}\n"
+"{\"sub_query\": \" \", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 2}\n"
+"{\"sub_question\": \"  \", \"level\": 0, \"level_question_nr\": 4}\n"
+"{\"sub_question\": \"\", \"level\": 0, \"level_question_nr\": 4}\n"
+"{\"sub_query\": \"3\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 2}\n"
+"{\"sub_query\": \" and\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 2}\n"
+"{\"sub_query\": \" \", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 2}\n"
+"{\"sub_query\": \"4\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 2}\n"
+"{\"sub_query\": \" comparison\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 2}\n"
+"{\"sub_query\": \" and\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 2}\n"
+"{\"sub_query\": \" differences\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 2}\n"
+"{\"sub_query\": \"  \", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 2}\n"
+"{\"sub_query\": \"\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 2}\n"
+"{\"sub_query\": \"On\", \"level\": 0, \"level_question_nr\": 3, \"query_id\": 0}\n"
+"{\"sub_query\": \"yx\", \"level\": 0, \"level_question_nr\": 3, \"query_id\": 0}\n"
+"{\"sub_query\": \"On\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 0}\n"
+"{\"sub_query\": \"yx\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 0}\n"
+"{\"sub_query\": \" \", \"level\": 0, \"level_question_nr\": 3, \"query_id\": 0}\n"
+"{\"sub_query\": \"4\", \"level\": 0, \"level_question_nr\": 3, \"query_id\": 0}\n"
+"{\"sub_query\": \"On\", \"level\": 0, \"level_question_nr\": 2, \"query_id\": 0}\n"
+"{\"sub_query\": \"yx\", \"level\": 0, \"level_question_nr\": 2, \"query_id\": 0}\n"
+"{\"sub_query\": \" \", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 0}\n"
+"{\"sub_query\": \"1\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 0}\n"
+"{\"sub_query\": \"On\", \"level\": 0, \"level_question_nr\": 1, \"query_id\": 0}\n"
+"{\"sub_query\": \" \", \"level\": 0, \"level_question_nr\": 2, \"query_id\": 0}\n"
+"{\"sub_query\": \"yx\", \"level\": 0, \"level_question_nr\": 1, \"query_id\": 0}\n"
+"{\"sub_query\": \" product\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 0}\n"
+"{\"sub_query\": \"3\", \"level\": 0, \"level_question_nr\": 2, \"query_id\": 0}\n"
+"{\"sub_query\": \" information\", \"level\": 0, \"level_question_nr\": 0, \"query_id\": 0}\n"
+"{\"sub_query\": \" software\", \"level\": 0, \"level_question_nr\": 3, \"query_id\": 0}\n"
+"{\"sub_query\": \" features\", \"level\": 0, \"level_question_nr\": 3, \"query_id\": 0}\n"
+"{\"sub_query\": \" \", \"level\": 0, \"level_question_nr\": 1, \"query_id\": 0}\n"
+"{\"sub_query\": \"  \", \"level\": 0, \"level_question_nr\": 3, \"query_id\": 0}\n"
+"{\"sub_query\": \" software\", \"level\": 0, \"level_question_nr\": 2, \"query_id\": 0}\n"
+"{\"sub_query\": \"2\", \"level\": 0, \"level_question_nr\": 1, \"query_id\": 0}\n"
+"{\"sub_query\": \"\", \"level\": 0, \"level_question_nr\": 3, \"query_id\": 1}\n"
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+"{\"answer_piece\": \" be\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" deployed\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" on\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" various\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" platforms\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" while\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" ensuring\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" user\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" data\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" control\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \".\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" It\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" also\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" serves\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" as\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" a\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" unified\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" search\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" tool\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" across\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" common\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" workplace\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" applications\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" like\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" Slack\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \",\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" Google\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" Drive\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \",\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" and\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" Con\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \"fluence\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \",\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" acting\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" as\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" a\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" subject\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" matter\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" expert\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" for\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" teams\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" [[1]]()\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \"{{1}}\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \"There\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" is\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" no\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" information\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" available\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" regarding\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" On\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \"yx\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" \", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \"2\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \",\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" \", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \"3\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \",\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" or\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" \", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \"4\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \",\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" so\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" I\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" cannot\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" provide\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" details\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" about\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \" them\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"answer_piece\": \".\", \"level\": 0, \"level_question_nr\": 0, \"answer_type\": \"agent_level_answer\"}\n"
+"{\"citations\": []}\n"
+"{\"message_id\": 476, \"parent_message\": 475, \"latest_child_message\": null, \"message\": \"I cannot reliably answer the question about Onyx 2, 3, and 4, as the provided information only describes Onyx 1, which is an AI Assistant formerly known as Danswer. Onyx 1 connects to a company's documents, applications, and personnel, providing a chat interface and integration with any large language model (LLM) of choice. It is designed to be modular, easily extensible, and can be deployed on various platforms while ensuring user data control. It also serves as a unified search tool across common workplace applications like Slack, Google Drive, and Confluence, acting as a subject matter expert for teams [[1]](){{1}}There is no information available regarding Onyx 2, 3, or 4, so I cannot provide details about them.\", \"rephrased_query\": \"what is onyx 1, 2, 3, 4\", \"context_docs\": {\"top_documents\": [{\"document_id\": \"https://docs.onyx.app/introduction\", \"chunk_ind\": 0, \"semantic_identifier\": \"Introduction - Onyx Documentation\", \"link\": \"https://docs.onyx.app/introduction\", \"blurb\": \"Onyx Documentation home page\\nSearch...\\nNavigation\\nWelcome to Onyx\\nIntroduction\\nWelcome to Onyx\\nIntroduction\\nOnyx Overview\\n\\nWhat is Onyx\\nOnyx (Formerly Danswer) is the AI Assistant connected to your companys docs, apps, and people. Onyx provides a Chat interface and plugs into any LLM of your choice. Onyx can be deployed anywhere and for any scale - on a laptop, on-premise, or to cloud. Since you own the deployment, your user data and chats are fully in your own control. Onyx is MIT licensed and designed to be modular and easily extensible.\", \"source_type\": \"web\", \"boost\": 0, \"hidden\": false, \"metadata\": {}, \"score\": 0.6275177643886491, \"is_relevant\": null, \"relevance_explanation\": null, \"match_highlights\": [\"\", \"such as A customer wants feature X, is this already supported? or Wheres the pull request for feature Y?\\n<hi>Onyx</hi> can also be plugged into existing tools like Slack to get answers and AI chats directly in Slack.\\n\\nDemo\\n\\nMain <hi>Features</hi> \\n- Chat UI with the ability to select documents to chat with.\\n- Create custom AI Assistants\", \"\"], \"updated_at\": null, \"primary_owners\": null, \"secondary_owners\": null, \"is_internet\": false, \"db_doc_id\": 35923}]}, \"message_type\": \"assistant\", \"time_sent\": \"2025-01-12T05:37:18.318251+00:00\", \"overridden_model\": \"gpt-4o\", \"alternate_assistant_id\": 0, \"chat_session_id\": \"40f91916-7419-48d1-9681-5882b0869d88\", \"citations\": {}, \"sub_questions\": [], \"files\": [], \"tool_call\": null}\n"

backend/danswer/__init__.py

@@ -1,3 +0,0 @@
-import os
-
-__version__ = os.environ.get("DANSWER_VERSION", "") or "Development"

backend/danswer/auth/noauth_user.py

@@ -1,38 +0,0 @@
-from collections.abc import Mapping
-from typing import Any
-from typing import cast
-
-from danswer.auth.schemas import UserRole
-from danswer.configs.constants import KV_NO_AUTH_USER_PREFERENCES_KEY
-from danswer.key_value_store.store import KeyValueStore
-from danswer.key_value_store.store import KvKeyNotFoundError
-from danswer.server.manage.models import UserInfo
-from danswer.server.manage.models import UserPreferences
-
-
-def set_no_auth_user_preferences(
-    store: KeyValueStore, preferences: UserPreferences
-) -> None:
-    store.store(KV_NO_AUTH_USER_PREFERENCES_KEY, preferences.model_dump())
-
-
-def load_no_auth_user_preferences(store: KeyValueStore) -> UserPreferences:
-    try:
-        preferences_data = cast(
-            Mapping[str, Any], store.load(KV_NO_AUTH_USER_PREFERENCES_KEY)
-        )
-        return UserPreferences(**preferences_data)
-    except KvKeyNotFoundError:
-        return UserPreferences(chosen_assistants=None, default_model=None)
-
-
-def fetch_no_auth_user(store: KeyValueStore) -> UserInfo:
-    return UserInfo(
-        id="__no_auth_user__",
-        email="anonymous@danswer.ai",
-        is_active=True,
-        is_superuser=False,
-        is_verified=True,
-        role=UserRole.ADMIN,
-        preferences=load_no_auth_user_preferences(store),
-    )

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

@@ -1,96 +0,0 @@
-from datetime import timedelta
-from typing import Any
-
-from celery.beat import PersistentScheduler  # type: ignore
-from celery.utils.log import get_task_logger
-
-from danswer.db.engine import get_all_tenant_ids
-from danswer.utils.variable_functionality import fetch_versioned_implementation
-
-logger = get_task_logger(__name__)
-
-
-class DynamicTenantScheduler(PersistentScheduler):
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(*args, **kwargs)
-        self._reload_interval = timedelta(minutes=1)
-        self._last_reload = self.app.now() - self._reload_interval
-
-    def setup_schedule(self) -> None:
-        super().setup_schedule()
-
-    def tick(self) -> float:
-        retval = super().tick()
-        now = self.app.now()
-        if (
-            self._last_reload is None
-            or (now - self._last_reload) > self._reload_interval
-        ):
-            logger.info("Reloading schedule to check for new tenants...")
-            self._update_tenant_tasks()
-            self._last_reload = now
-        return retval
-
-    def _update_tenant_tasks(self) -> None:
-        logger.info("Checking for tenant task updates...")
-        try:
-            tenant_ids = get_all_tenant_ids()
-            tasks_to_schedule = fetch_versioned_implementation(
-                "danswer.background.celery.tasks.beat_schedule", "get_tasks_to_schedule"
-            )
-
-            new_beat_schedule: dict[str, dict[str, Any]] = {}
-
-            current_schedule = getattr(self, "_store", {"entries": {}}).get(
-                "entries", {}
-            )
-
-            existing_tenants = set()
-            for task_name in current_schedule.keys():
-                if "-" in task_name:
-                    existing_tenants.add(task_name.split("-")[-1])
-
-            for tenant_id in tenant_ids:
-                if tenant_id not in existing_tenants:
-                    logger.info(f"Found new tenant: {tenant_id}")
-
-                for task in tasks_to_schedule():
-                    task_name = f"{task['name']}-{tenant_id}"
-                    new_task = {
-                        "task": task["task"],
-                        "schedule": task["schedule"],
-                        "kwargs": {"tenant_id": tenant_id},
-                    }
-                    if options := task.get("options"):
-                        new_task["options"] = options
-                    new_beat_schedule[task_name] = new_task
-
-            if self._should_update_schedule(current_schedule, new_beat_schedule):
-                logger.info(
-                    "Updating schedule",
-                    extra={
-                        "new_tasks": len(new_beat_schedule),
-                        "current_tasks": len(current_schedule),
-                    },
-                )
-                if not hasattr(self, "_store"):
-                    self._store: dict[str, dict] = {"entries": {}}
-                self.update_from_dict(new_beat_schedule)
-                logger.info(f"New schedule: {new_beat_schedule}")
-
-                logger.info("Tenant tasks updated successfully")
-            else:
-                logger.debug("No schedule updates needed")
-
-        except (AttributeError, KeyError):
-            logger.exception("Failed to process task configuration")
-        except Exception:
-            logger.exception("Unexpected error updating tenant tasks")
-
-    def _should_update_schedule(
-        self, current_schedule: dict, new_schedule: dict
-    ) -> bool:
-        """Compare schedules to determine if an update is needed."""
-        current_tasks = set(current_schedule.keys())
-        new_tasks = set(new_schedule.keys())
-        return current_tasks != new_tasks

backend/danswer/background/celery/celery_redis.py

@@ -1,25 +0,0 @@
-# These are helper objects for tracking the keys we need to write in redis
-from typing import cast
-
-from redis import Redis
-
-from danswer.background.celery.configs.base import CELERY_SEPARATOR
-from danswer.configs.constants import DanswerCeleryPriority
-
-
-def celery_get_queue_length(queue: str, r: Redis) -> int:
-    """This is a redis specific way to get the length of a celery queue.
-    It is priority aware and knows how to count across the multiple redis lists
-    used to implement task prioritization.
-    This operation is not atomic."""
-    total_length = 0
-    for i in range(len(DanswerCeleryPriority)):
-        queue_name = queue
-        if i > 0:
-            queue_name += CELERY_SEPARATOR
-            queue_name += str(i)
-
-        length = r.llen(queue_name)
-        total_length += cast(int, length)
-
-    return total_length

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

@@ -1,48 +0,0 @@
-from datetime import timedelta
-from typing import Any
-
-from danswer.configs.constants import DanswerCeleryPriority
-
-
-tasks_to_schedule = [
-    {
-        "name": "check-for-vespa-sync",
-        "task": "check_for_vespa_sync_task",
-        "schedule": timedelta(seconds=5),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-    {
-        "name": "check-for-connector-deletion",
-        "task": "check_for_connector_deletion_task",
-        "schedule": timedelta(seconds=20),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-    {
-        "name": "check-for-indexing",
-        "task": "check_for_indexing",
-        "schedule": timedelta(seconds=10),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-    {
-        "name": "check-for-prune",
-        "task": "check_for_pruning",
-        "schedule": timedelta(seconds=10),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-    {
-        "name": "kombu-message-cleanup",
-        "task": "kombu_message_cleanup_task",
-        "schedule": timedelta(seconds=3600),
-        "options": {"priority": DanswerCeleryPriority.LOWEST},
-    },
-    {
-        "name": "monitor-vespa-sync",
-        "task": "monitor_vespa_sync",
-        "schedule": timedelta(seconds=5),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-]
-
-
-def get_tasks_to_schedule() -> list[dict[str, Any]]:
-    return tasks_to_schedule

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

@@ -1,651 +0,0 @@
-from datetime import datetime
-from datetime import timezone
-from http import HTTPStatus
-from time import sleep
-
-import redis
-import sentry_sdk
-from celery import Celery
-from celery import shared_task
-from celery import Task
-from celery.exceptions import SoftTimeLimitExceeded
-from redis import Redis
-from sqlalchemy.orm import Session
-
-from danswer.background.celery.apps.app_base import task_logger
-from danswer.background.indexing.job_client import SimpleJobClient
-from danswer.background.indexing.run_indexing import run_indexing_entrypoint
-from danswer.background.indexing.run_indexing import RunIndexingCallbackInterface
-from danswer.configs.app_configs import DISABLE_INDEX_UPDATE_ON_SWAP
-from danswer.configs.constants import CELERY_INDEXING_LOCK_TIMEOUT
-from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
-from danswer.configs.constants import DANSWER_REDIS_FUNCTION_LOCK_PREFIX
-from danswer.configs.constants import DanswerCeleryPriority
-from danswer.configs.constants import DanswerCeleryQueues
-from danswer.configs.constants import DanswerRedisLocks
-from danswer.configs.constants import DocumentSource
-from danswer.db.connector_credential_pair import fetch_connector_credential_pairs
-from danswer.db.connector_credential_pair import get_connector_credential_pair_from_id
-from danswer.db.engine import get_db_current_time
-from danswer.db.engine import get_session_with_tenant
-from danswer.db.enums import ConnectorCredentialPairStatus
-from danswer.db.enums import IndexingStatus
-from danswer.db.enums import IndexModelStatus
-from danswer.db.index_attempt import create_index_attempt
-from danswer.db.index_attempt import get_index_attempt
-from danswer.db.index_attempt import get_last_attempt_for_cc_pair
-from danswer.db.index_attempt import mark_attempt_failed
-from danswer.db.models import ConnectorCredentialPair
-from danswer.db.models import IndexAttempt
-from danswer.db.models import SearchSettings
-from danswer.db.search_settings import get_current_search_settings
-from danswer.db.search_settings import get_secondary_search_settings
-from danswer.db.swap_index import check_index_swap
-from danswer.natural_language_processing.search_nlp_models import EmbeddingModel
-from danswer.natural_language_processing.search_nlp_models import warm_up_bi_encoder
-from danswer.redis.redis_connector import RedisConnector
-from danswer.redis.redis_connector_index import RedisConnectorIndexingFenceData
-from danswer.redis.redis_pool import get_redis_client
-from danswer.utils.logger import setup_logger
-from danswer.utils.variable_functionality import global_version
-from shared_configs.configs import INDEXING_MODEL_SERVER_HOST
-from shared_configs.configs import INDEXING_MODEL_SERVER_PORT
-from shared_configs.configs import MULTI_TENANT
-from shared_configs.configs import SENTRY_DSN
-
-logger = setup_logger()
-
-
-class RunIndexingCallback(RunIndexingCallbackInterface):
-    def __init__(
-        self,
-        stop_key: str,
-        generator_progress_key: str,
-        redis_lock: redis.lock.Lock,
-        redis_client: Redis,
-    ):
-        super().__init__()
-        self.redis_lock: redis.lock.Lock = redis_lock
-        self.stop_key: str = stop_key
-        self.generator_progress_key: str = generator_progress_key
-        self.redis_client = redis_client
-
-    def should_stop(self) -> bool:
-        if self.redis_client.exists(self.stop_key):
-            return True
-        return False
-
-    def progress(self, amount: int) -> None:
-        self.redis_lock.reacquire()
-        self.redis_client.incrby(self.generator_progress_key, amount)
-
-
-@shared_task(
-    name="check_for_indexing",
-    soft_time_limit=300,
-    bind=True,
-)
-def check_for_indexing(self: Task, *, tenant_id: str | None) -> int | None:
-    tasks_created = 0
-
-    r = get_redis_client(tenant_id=tenant_id)
-
-    lock_beat = r.lock(
-        DanswerRedisLocks.CHECK_INDEXING_BEAT_LOCK,
-        timeout=CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT,
-    )
-
-    try:
-        # these tasks should never overlap
-        if not lock_beat.acquire(blocking=False):
-            return None
-
-        with get_session_with_tenant(tenant_id=tenant_id) as db_session:
-            old_search_settings = check_index_swap(db_session=db_session)
-            current_search_settings = get_current_search_settings(db_session)
-            # So that the first time users aren't surprised by really slow speed of first
-            # batch of documents indexed
-            if current_search_settings.provider_type is None and not MULTI_TENANT:
-                if old_search_settings:
-                    embedding_model = EmbeddingModel.from_db_model(
-                        search_settings=current_search_settings,
-                        server_host=INDEXING_MODEL_SERVER_HOST,
-                        server_port=INDEXING_MODEL_SERVER_PORT,
-                    )
-
-                    # only warm up if search settings were changed
-                    warm_up_bi_encoder(
-                        embedding_model=embedding_model,
-                    )
-
-        cc_pair_ids: list[int] = []
-        with get_session_with_tenant(tenant_id) as db_session:
-            cc_pairs = fetch_connector_credential_pairs(db_session)
-            for cc_pair_entry in cc_pairs:
-                cc_pair_ids.append(cc_pair_entry.id)
-
-        for cc_pair_id in cc_pair_ids:
-            redis_connector = RedisConnector(tenant_id, cc_pair_id)
-            with get_session_with_tenant(tenant_id) as db_session:
-                # Get the primary search settings
-                primary_search_settings = get_current_search_settings(db_session)
-                search_settings = [primary_search_settings]
-
-                # Check for secondary search settings
-                secondary_search_settings = get_secondary_search_settings(db_session)
-                if secondary_search_settings is not None:
-                    # If secondary settings exist, add them to the list
-                    search_settings.append(secondary_search_settings)
-
-                for search_settings_instance in search_settings:
-                    redis_connector_index = redis_connector.new_index(
-                        search_settings_instance.id
-                    )
-                    if redis_connector_index.fenced:
-                        continue
-
-                    cc_pair = get_connector_credential_pair_from_id(
-                        cc_pair_id, db_session
-                    )
-                    if not cc_pair:
-                        continue
-
-                    last_attempt = get_last_attempt_for_cc_pair(
-                        cc_pair.id, search_settings_instance.id, db_session
-                    )
-                    if not _should_index(
-                        cc_pair=cc_pair,
-                        last_index=last_attempt,
-                        search_settings_instance=search_settings_instance,
-                        secondary_index_building=len(search_settings) > 1,
-                        db_session=db_session,
-                    ):
-                        continue
-
-                    # using a task queue and only allowing one task per cc_pair/search_setting
-                    # prevents us from starving out certain attempts
-                    attempt_id = try_creating_indexing_task(
-                        self.app,
-                        cc_pair,
-                        search_settings_instance,
-                        False,
-                        db_session,
-                        r,
-                        tenant_id,
-                    )
-                    if attempt_id:
-                        task_logger.info(
-                            f"Indexing queued: index_attempt={attempt_id} "
-                            f"cc_pair={cc_pair.id} "
-                            f"search_settings={search_settings_instance.id} "
-                        )
-                        tasks_created += 1
-    except SoftTimeLimitExceeded:
-        task_logger.info(
-            "Soft time limit exceeded, task is being terminated gracefully."
-        )
-    except Exception:
-        task_logger.exception(f"Unexpected exception: tenant={tenant_id}")
-    finally:
-        if lock_beat.owned():
-            lock_beat.release()
-
-    return tasks_created
-
-
-def _should_index(
-    cc_pair: ConnectorCredentialPair,
-    last_index: IndexAttempt | None,
-    search_settings_instance: SearchSettings,
-    secondary_index_building: bool,
-    db_session: Session,
-) -> bool:
-    """Checks various global settings and past indexing attempts to determine if
-    we should try to start indexing the cc pair / search setting combination.
-
-    Note that tactical checks such as preventing overlap with a currently running task
-    are not handled here.
-
-    Return True if we should try to index, False if not.
-    """
-    connector = cc_pair.connector
-
-    # uncomment for debugging
-    # task_logger.info(f"_should_index: "
-    #                  f"cc_pair={cc_pair.id} "
-    #                  f"connector={cc_pair.connector_id} "
-    #                  f"refresh_freq={connector.refresh_freq}")
-
-    # don't kick off indexing for `NOT_APPLICABLE` sources
-    if connector.source == DocumentSource.NOT_APPLICABLE:
-        return False
-
-    # User can still manually create single indexing attempts via the UI for the
-    # currently in use index
-    if DISABLE_INDEX_UPDATE_ON_SWAP:
-        if (
-            search_settings_instance.status == IndexModelStatus.PRESENT
-            and secondary_index_building
-        ):
-            return False
-
-    # When switching over models, always index at least once
-    if search_settings_instance.status == IndexModelStatus.FUTURE:
-        if last_index:
-            # No new index if the last index attempt succeeded
-            # Once is enough. The model will never be able to swap otherwise.
-            if last_index.status == IndexingStatus.SUCCESS:
-                return False
-
-            # No new index if the last index attempt is waiting to start
-            if last_index.status == IndexingStatus.NOT_STARTED:
-                return False
-
-            # No new index if the last index attempt is running
-            if last_index.status == IndexingStatus.IN_PROGRESS:
-                return False
-        else:
-            if (
-                connector.id == 0 or connector.source == DocumentSource.INGESTION_API
-            ):  # Ingestion API
-                return False
-        return True
-
-    # If the connector is paused or is the ingestion API, don't index
-    # NOTE: during an embedding model switch over, the following logic
-    # is bypassed by the above check for a future model
-    if (
-        not cc_pair.status.is_active()
-        or connector.id == 0
-        or connector.source == DocumentSource.INGESTION_API
-    ):
-        return False
-
-    # if no attempt has ever occurred, we should index regardless of refresh_freq
-    if not last_index:
-        return True
-
-    if connector.refresh_freq is None:
-        return False
-
-    current_db_time = get_db_current_time(db_session)
-    time_since_index = current_db_time - last_index.time_updated
-    if time_since_index.total_seconds() < connector.refresh_freq:
-        return False
-
-    return True
-
-
-def try_creating_indexing_task(
-    celery_app: Celery,
-    cc_pair: ConnectorCredentialPair,
-    search_settings: SearchSettings,
-    reindex: bool,
-    db_session: Session,
-    r: Redis,
-    tenant_id: str | None,
-) -> int | None:
-    """Checks for any conditions that should block the indexing task from being
-    created, then creates the task.
-
-    Does not check for scheduling related conditions as this function
-    is used to trigger indexing immediately.
-    """
-
-    LOCK_TIMEOUT = 30
-
-    # we need to serialize any attempt to trigger indexing since it can be triggered
-    # either via celery beat or manually (API call)
-    lock = r.lock(
-        DANSWER_REDIS_FUNCTION_LOCK_PREFIX + "try_creating_indexing_task",
-        timeout=LOCK_TIMEOUT,
-    )
-
-    acquired = lock.acquire(blocking_timeout=LOCK_TIMEOUT / 2)
-    if not acquired:
-        return None
-
-    try:
-        redis_connector = RedisConnector(tenant_id, cc_pair.id)
-        redis_connector_index = redis_connector.new_index(search_settings.id)
-
-        # skip if already indexing
-        if redis_connector_index.fenced:
-            return None
-
-        # skip indexing if the cc_pair is deleting
-        if redis_connector.delete.fenced:
-            return None
-
-        db_session.refresh(cc_pair)
-        if cc_pair.status == ConnectorCredentialPairStatus.DELETING:
-            return None
-
-        # add a long running generator task to the queue
-        redis_connector_index.generator_clear()
-
-        # set a basic fence to start
-        payload = RedisConnectorIndexingFenceData(
-            index_attempt_id=None,
-            started=None,
-            submitted=datetime.now(timezone.utc),
-            celery_task_id=None,
-        )
-
-        redis_connector_index.set_fence(payload)
-
-        # create the index attempt for tracking purposes
-        # code elsewhere checks for index attempts without an associated redis key
-        # and cleans them up
-        # therefore we must create the attempt and the task after the fence goes up
-        index_attempt_id = create_index_attempt(
-            cc_pair.id,
-            search_settings.id,
-            from_beginning=reindex,
-            db_session=db_session,
-        )
-
-        custom_task_id = redis_connector_index.generate_generator_task_id()
-
-        result = celery_app.send_task(
-            "connector_indexing_proxy_task",
-            kwargs=dict(
-                index_attempt_id=index_attempt_id,
-                cc_pair_id=cc_pair.id,
-                search_settings_id=search_settings.id,
-                tenant_id=tenant_id,
-            ),
-            queue=DanswerCeleryQueues.CONNECTOR_INDEXING,
-            task_id=custom_task_id,
-            priority=DanswerCeleryPriority.MEDIUM,
-        )
-        if not result:
-            raise RuntimeError("send_task for connector_indexing_proxy_task failed.")
-
-        # now fill out the fence with the rest of the data
-        payload.index_attempt_id = index_attempt_id
-        payload.celery_task_id = result.id
-        redis_connector_index.set_fence(payload)
-
-    except Exception:
-        redis_connector_index.set_fence(payload)
-        task_logger.exception(
-            f"Unexpected exception: "
-            f"tenant={tenant_id} "
-            f"cc_pair={cc_pair.id} "
-            f"search_settings={search_settings.id}"
-        )
-        return None
-    finally:
-        if lock.owned():
-            lock.release()
-
-    return index_attempt_id
-
-
-@shared_task(name="connector_indexing_proxy_task", acks_late=False, track_started=True)
-def connector_indexing_proxy_task(
-    index_attempt_id: int,
-    cc_pair_id: int,
-    search_settings_id: int,
-    tenant_id: str | None,
-) -> None:
-    """celery tasks are forked, but forking is unstable.  This proxies work to a spawned task."""
-    task_logger.info(
-        f"Indexing proxy - starting: attempt={index_attempt_id} "
-        f"tenant={tenant_id} "
-        f"cc_pair={cc_pair_id} "
-        f"search_settings={search_settings_id}"
-    )
-    client = SimpleJobClient()
-
-    job = client.submit(
-        connector_indexing_task,
-        index_attempt_id,
-        cc_pair_id,
-        search_settings_id,
-        tenant_id,
-        global_version.is_ee_version(),
-        pure=False,
-    )
-
-    if not job:
-        task_logger.info(
-            f"Indexing proxy - spawn failed: attempt={index_attempt_id} "
-            f"tenant={tenant_id} "
-            f"cc_pair={cc_pair_id} "
-            f"search_settings={search_settings_id}"
-        )
-        return
-
-    task_logger.info(
-        f"Indexing proxy - spawn succeeded: attempt={index_attempt_id} "
-        f"tenant={tenant_id} "
-        f"cc_pair={cc_pair_id} "
-        f"search_settings={search_settings_id}"
-    )
-
-    while True:
-        sleep(10)
-
-        # do nothing for ongoing jobs that haven't been stopped
-        if not job.done():
-            with get_session_with_tenant(tenant_id) as db_session:
-                index_attempt = get_index_attempt(
-                    db_session=db_session, index_attempt_id=index_attempt_id
-                )
-
-                if not index_attempt:
-                    continue
-
-                if not index_attempt.is_finished():
-                    continue
-
-        if job.status == "error":
-            task_logger.error(
-                f"Indexing proxy - spawned task exceptioned: "
-                f"attempt={index_attempt_id} "
-                f"tenant={tenant_id} "
-                f"cc_pair={cc_pair_id} "
-                f"search_settings={search_settings_id} "
-                f"error={job.exception()}"
-            )
-
-        job.release()
-        break
-
-    task_logger.info(
-        f"Indexing proxy - finished: attempt={index_attempt_id} "
-        f"tenant={tenant_id} "
-        f"cc_pair={cc_pair_id} "
-        f"search_settings={search_settings_id}"
-    )
-    return
-
-
-def connector_indexing_task(
-    index_attempt_id: int,
-    cc_pair_id: int,
-    search_settings_id: int,
-    tenant_id: str | None,
-    is_ee: bool,
-) -> int | None:
-    """Indexing task. For a cc pair, this task pulls all document IDs from the source
-    and compares those IDs to locally stored documents and deletes all locally stored IDs missing
-    from the most recently pulled document ID list
-
-    acks_late must be set to False. Otherwise, celery's visibility timeout will
-    cause any task that runs longer than the timeout to be redispatched by the broker.
-    There appears to be no good workaround for this, so we need to handle redispatching
-    manually.
-
-    Returns None if the task did not run (possibly due to a conflict).
-    Otherwise, returns an int >= 0 representing the number of indexed docs.
-
-    NOTE: if an exception is raised out of this task, the primary worker will detect
-    that the task transitioned to a "READY" state but the generator_complete_key doesn't exist.
-    This will cause the primary worker to abort the indexing attempt and clean up.
-    """
-
-    # Since connector_indexing_proxy_task spawns a new process using this function as
-    # the entrypoint, we init Sentry here.
-    if SENTRY_DSN:
-        sentry_sdk.init(
-            dsn=SENTRY_DSN,
-            traces_sample_rate=0.1,
-        )
-        logger.info("Sentry initialized")
-    else:
-        logger.debug("Sentry DSN not provided, skipping Sentry initialization")
-
-    logger.info(
-        f"Indexing spawned task starting: attempt={index_attempt_id} "
-        f"tenant={tenant_id} "
-        f"cc_pair={cc_pair_id} "
-        f"search_settings={search_settings_id}"
-    )
-
-    attempt_found = False
-    n_final_progress: int | None = None
-
-    redis_connector = RedisConnector(tenant_id, cc_pair_id)
-    redis_connector_index = redis_connector.new_index(search_settings_id)
-
-    r = get_redis_client(tenant_id=tenant_id)
-
-    if redis_connector.delete.fenced:
-        raise RuntimeError(
-            f"Indexing will not start because connector deletion is in progress: "
-            f"cc_pair={cc_pair_id} "
-            f"fence={redis_connector.delete.fence_key}"
-        )
-
-    if redis_connector.stop.fenced:
-        raise RuntimeError(
-            f"Indexing will not start because a connector stop signal was detected: "
-            f"cc_pair={cc_pair_id} "
-            f"fence={redis_connector.stop.fence_key}"
-        )
-
-    while True:
-        # wait for the fence to come up
-        if not redis_connector_index.fenced:
-            raise ValueError(
-                f"connector_indexing_task - fence not found: fence={redis_connector_index.fence_key}"
-            )
-
-        payload = redis_connector_index.payload
-        if not payload:
-            raise ValueError("connector_indexing_task: payload invalid or not found")
-
-        if payload.index_attempt_id is None or payload.celery_task_id is None:
-            logger.info(
-                f"connector_indexing_task - Waiting for fence: fence={redis_connector_index.fence_key}"
-            )
-            sleep(1)
-            continue
-
-        if payload.index_attempt_id != index_attempt_id:
-            raise ValueError(
-                f"connector_indexing_task - id mismatch. Task may be left over from previous run.: "
-                f"task_index_attempt={index_attempt_id} "
-                f"payload_index_attempt={payload.index_attempt_id}"
-            )
-
-        logger.info(
-            f"connector_indexing_task - Fence found, continuing...: fence={redis_connector_index.fence_key}"
-        )
-        break
-
-    lock = r.lock(
-        redis_connector_index.generator_lock_key,
-        timeout=CELERY_INDEXING_LOCK_TIMEOUT,
-    )
-
-    acquired = lock.acquire(blocking=False)
-    if not acquired:
-        logger.warning(
-            f"Indexing task already running, exiting...: "
-            f"cc_pair={cc_pair_id} search_settings={search_settings_id}"
-        )
-        return None
-
-    payload.started = datetime.now(timezone.utc)
-    redis_connector_index.set_fence(payload)
-
-    try:
-        with get_session_with_tenant(tenant_id) as db_session:
-            attempt = get_index_attempt(db_session, index_attempt_id)
-            if not attempt:
-                raise ValueError(
-                    f"Index attempt not found: index_attempt={index_attempt_id}"
-                )
-            attempt_found = True
-
-            cc_pair = get_connector_credential_pair_from_id(
-                cc_pair_id=cc_pair_id,
-                db_session=db_session,
-            )
-
-            if not cc_pair:
-                raise ValueError(f"cc_pair not found: cc_pair={cc_pair_id}")
-
-            if not cc_pair.connector:
-                raise ValueError(
-                    f"Connector not found: cc_pair={cc_pair_id} connector={cc_pair.connector_id}"
-                )
-
-            if not cc_pair.credential:
-                raise ValueError(
-                    f"Credential not found: cc_pair={cc_pair_id} credential={cc_pair.credential_id}"
-                )
-
-        # define a callback class
-        callback = RunIndexingCallback(
-            redis_connector.stop.fence_key,
-            redis_connector_index.generator_progress_key,
-            lock,
-            r,
-        )
-
-        logger.info(
-            f"Indexing spawned task running entrypoint: attempt={index_attempt_id} "
-            f"tenant={tenant_id} "
-            f"cc_pair={cc_pair_id} "
-            f"search_settings={search_settings_id}"
-        )
-
-        run_indexing_entrypoint(
-            index_attempt_id,
-            tenant_id,
-            cc_pair_id,
-            is_ee,
-            callback=callback,
-        )
-
-        # get back the total number of indexed docs and return it
-        n_final_progress = redis_connector_index.get_progress()
-        redis_connector_index.set_generator_complete(HTTPStatus.OK.value)
-    except Exception as e:
-        logger.exception(
-            f"Indexing spawned task failed: attempt={index_attempt_id} "
-            f"tenant={tenant_id} "
-            f"cc_pair={cc_pair_id} "
-            f"search_settings={search_settings_id}"
-        )
-        if attempt_found:
-            with get_session_with_tenant(tenant_id) as db_session:
-                mark_attempt_failed(index_attempt_id, db_session, failure_reason=str(e))
-
-        raise e
-    finally:
-        if lock.owned():
-            lock.release()
-
-    logger.info(
-        f"Indexing spawned task finished: attempt={index_attempt_id} "
-        f"tenant={tenant_id} "
-        f"cc_pair={cc_pair_id} "
-        f"search_settings={search_settings_id}"
-    )
-    return n_final_progress

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

@@ -1,6 +0,0 @@
-"""Factory stub for running celery worker / celery beat."""
-from danswer.background.celery.apps.beat import celery_app
-from danswer.utils.variable_functionality import set_is_ee_based_on_env_variable
-
-set_is_ee_based_on_env_variable()
-app = celery_app

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

@@ -1,8 +0,0 @@
-"""Factory stub for running celery worker / celery beat."""
-from danswer.utils.variable_functionality import fetch_versioned_implementation
-from danswer.utils.variable_functionality import set_is_ee_based_on_env_variable
-
-set_is_ee_based_on_env_variable()
-app = fetch_versioned_implementation(
-    "danswer.background.celery.apps.primary", "celery_app"
-)

backend/danswer/background/task_name_builders.py

@@ -1,4 +0,0 @@
-def name_sync_external_doc_permissions_task(
-    cc_pair_id: int, tenant_id: str | None = None
-) -> str:
-    return f"sync_external_doc_permissions_task__{cc_pair_id}"

backend/danswer/chat/models.py

@@ -1,185 +0,0 @@
-from collections.abc import Iterator
-from datetime import datetime
-from enum import Enum
-from typing import Any
-
-from pydantic import BaseModel
-
-from danswer.configs.constants import DocumentSource
-from danswer.search.enums import QueryFlow
-from danswer.search.enums import SearchType
-from danswer.search.models import RetrievalDocs
-from danswer.search.models import SearchResponse
-from danswer.tools.tool_implementations.custom.base_tool_types import ToolResultType
-
-
-class LlmDoc(BaseModel):
-    """This contains the minimal set information for the LLM portion including citations"""
-
-    document_id: str
-    content: str
-    blurb: str
-    semantic_identifier: str
-    source_type: DocumentSource
-    metadata: dict[str, str | list[str]]
-    updated_at: datetime | None
-    link: str | None
-    source_links: dict[int, str] | None
-
-
-# First chunk of info for streaming QA
-class QADocsResponse(RetrievalDocs):
-    rephrased_query: str | None = None
-    predicted_flow: QueryFlow | None
-    predicted_search: SearchType | None
-    applied_source_filters: list[DocumentSource] | None
-    applied_time_cutoff: datetime | None
-    recency_bias_multiplier: float
-
-    def model_dump(self, *args: list, **kwargs: dict[str, Any]) -> dict[str, Any]:  # type: ignore
-        initial_dict = super().model_dump(mode="json", *args, **kwargs)  # type: ignore
-        initial_dict["applied_time_cutoff"] = (
-            self.applied_time_cutoff.isoformat() if self.applied_time_cutoff else None
-        )
-
-        return initial_dict
-
-
-class StreamStopReason(Enum):
-    CONTEXT_LENGTH = "context_length"
-    CANCELLED = "cancelled"
-
-
-class StreamStopInfo(BaseModel):
-    stop_reason: StreamStopReason
-
-    def model_dump(self, *args: list, **kwargs: dict[str, Any]) -> dict[str, Any]:  # type: ignore
-        data = super().model_dump(mode="json", *args, **kwargs)  # type: ignore
-        data["stop_reason"] = self.stop_reason.name
-        return data
-
-
-class LLMRelevanceFilterResponse(BaseModel):
-    llm_selected_doc_indices: list[int]
-
-
-class FinalUsedContextDocsResponse(BaseModel):
-    final_context_docs: list[LlmDoc]
-
-
-class RelevanceAnalysis(BaseModel):
-    relevant: bool
-    content: str | None = None
-
-
-class SectionRelevancePiece(RelevanceAnalysis):
-    """LLM analysis mapped to an Inference Section"""
-
-    document_id: str
-    chunk_id: int  # ID of the center chunk for a given inference section
-
-
-class DocumentRelevance(BaseModel):
-    """Contains all relevance information for a given search"""
-
-    relevance_summaries: dict[str, RelevanceAnalysis]
-
-
-class DanswerAnswerPiece(BaseModel):
-    # A small piece of a complete answer. Used for streaming back answers.
-    answer_piece: str | None  # if None, specifies the end of an Answer
-
-
-# An intermediate representation of citations, later translated into
-# a mapping of the citation [n] number to SearchDoc
-class CitationInfo(BaseModel):
-    citation_num: int
-    document_id: str
-
-
-class AllCitations(BaseModel):
-    citations: list[CitationInfo]
-
-
-# This is a mapping of the citation number to the document index within
-# the result search doc set
-class MessageSpecificCitations(BaseModel):
-    citation_map: dict[int, int]
-
-
-class MessageResponseIDInfo(BaseModel):
-    user_message_id: int | None
-    reserved_assistant_message_id: int
-
-
-class StreamingError(BaseModel):
-    error: str
-    stack_trace: str | None = None
-
-
-class DanswerQuote(BaseModel):
-    # This is during inference so everything is a string by this point
-    quote: str
-    document_id: str
-    link: str | None
-    source_type: str
-    semantic_identifier: str
-    blurb: str
-
-
-class DanswerQuotes(BaseModel):
-    quotes: list[DanswerQuote]
-
-
-class DanswerContext(BaseModel):
-    content: str
-    document_id: str
-    semantic_identifier: str
-    blurb: str
-
-
-class DanswerContexts(BaseModel):
-    contexts: list[DanswerContext]
-
-
-class DanswerAnswer(BaseModel):
-    answer: str | None
-
-
-class QAResponse(SearchResponse, DanswerAnswer):
-    quotes: list[DanswerQuote] | None
-    contexts: list[DanswerContexts] | None
-    predicted_flow: QueryFlow
-    predicted_search: SearchType
-    eval_res_valid: bool | None = None
-    llm_selected_doc_indices: list[int] | None = None
-    error_msg: str | None = None
-
-
-class FileChatDisplay(BaseModel):
-    file_ids: list[str]
-
-
-class CustomToolResponse(BaseModel):
-    response: ToolResultType
-    tool_name: str
-
-
-AnswerQuestionPossibleReturn = (
-    DanswerAnswerPiece
-    | DanswerQuotes
-    | CitationInfo
-    | DanswerContexts
-    | FileChatDisplay
-    | CustomToolResponse
-    | StreamingError
-    | StreamStopInfo
-)
-
-
-AnswerQuestionStreamReturn = Iterator[AnswerQuestionPossibleReturn]
-
-
-class LLMMetricsContainer(BaseModel):
-    prompt_tokens: int
-    response_tokens: int

backend/danswer/chat/process_message.py

@@ -1,934 +0,0 @@
-import traceback
-from collections.abc import Callable
-from collections.abc import Iterator
-from functools import partial
-from typing import cast
-
-from sqlalchemy.orm import Session
-
-from danswer.chat.chat_utils import create_chat_chain
-from danswer.chat.models import AllCitations
-from danswer.chat.models import CitationInfo
-from danswer.chat.models import CustomToolResponse
-from danswer.chat.models import DanswerAnswerPiece
-from danswer.chat.models import FileChatDisplay
-from danswer.chat.models import FinalUsedContextDocsResponse
-from danswer.chat.models import LLMRelevanceFilterResponse
-from danswer.chat.models import MessageResponseIDInfo
-from danswer.chat.models import MessageSpecificCitations
-from danswer.chat.models import QADocsResponse
-from danswer.chat.models import StreamingError
-from danswer.chat.models import StreamStopInfo
-from danswer.configs.app_configs import AZURE_DALLE_API_BASE
-from danswer.configs.app_configs import AZURE_DALLE_API_KEY
-from danswer.configs.app_configs import AZURE_DALLE_API_VERSION
-from danswer.configs.app_configs import AZURE_DALLE_DEPLOYMENT_NAME
-from danswer.configs.chat_configs import BING_API_KEY
-from danswer.configs.chat_configs import CHAT_TARGET_CHUNK_PERCENTAGE
-from danswer.configs.chat_configs import DISABLE_LLM_CHOOSE_SEARCH
-from danswer.configs.chat_configs import MAX_CHUNKS_FED_TO_CHAT
-from danswer.configs.constants import MessageType
-from danswer.configs.model_configs import GEN_AI_TEMPERATURE
-from danswer.db.chat import attach_files_to_chat_message
-from danswer.db.chat import create_db_search_doc
-from danswer.db.chat import create_new_chat_message
-from danswer.db.chat import get_chat_message
-from danswer.db.chat import get_chat_session_by_id
-from danswer.db.chat import get_db_search_doc_by_id
-from danswer.db.chat import get_doc_query_identifiers_from_model
-from danswer.db.chat import get_or_create_root_message
-from danswer.db.chat import reserve_message_id
-from danswer.db.chat import translate_db_message_to_chat_message_detail
-from danswer.db.chat import translate_db_search_doc_to_server_search_doc
-from danswer.db.engine import get_session_context_manager
-from danswer.db.llm import fetch_existing_llm_providers
-from danswer.db.models import SearchDoc as DbSearchDoc
-from danswer.db.models import ToolCall
-from danswer.db.models import User
-from danswer.db.persona import get_persona_by_id
-from danswer.db.search_settings import get_current_search_settings
-from danswer.document_index.factory import get_default_document_index
-from danswer.file_store.models import ChatFileType
-from danswer.file_store.models import FileDescriptor
-from danswer.file_store.utils import load_all_chat_files
-from danswer.file_store.utils import save_files_from_urls
-from danswer.llm.answering.answer import Answer
-from danswer.llm.answering.models import AnswerStyleConfig
-from danswer.llm.answering.models import CitationConfig
-from danswer.llm.answering.models import DocumentPruningConfig
-from danswer.llm.answering.models import PreviousMessage
-from danswer.llm.answering.models import PromptConfig
-from danswer.llm.exceptions import GenAIDisabledException
-from danswer.llm.factory import get_llms_for_persona
-from danswer.llm.factory import get_main_llm_from_tuple
-from danswer.llm.interfaces import LLMConfig
-from danswer.llm.utils import litellm_exception_to_error_msg
-from danswer.natural_language_processing.utils import get_tokenizer
-from danswer.search.enums import LLMEvaluationType
-from danswer.search.enums import OptionalSearchSetting
-from danswer.search.enums import QueryFlow
-from danswer.search.enums import SearchType
-from danswer.search.models import InferenceSection
-from danswer.search.retrieval.search_runner import inference_sections_from_ids
-from danswer.search.utils import chunks_or_sections_to_search_docs
-from danswer.search.utils import dedupe_documents
-from danswer.search.utils import drop_llm_indices
-from danswer.search.utils import relevant_sections_to_indices
-from danswer.server.query_and_chat.models import ChatMessageDetail
-from danswer.server.query_and_chat.models import CreateChatMessageRequest
-from danswer.server.utils import get_json_line
-from danswer.tools.built_in_tools import get_built_in_tool_by_id
-from danswer.tools.force import ForceUseTool
-from danswer.tools.models import DynamicSchemaInfo
-from danswer.tools.models import ToolResponse
-from danswer.tools.tool import Tool
-from danswer.tools.tool_implementations.custom.custom_tool import (
-    build_custom_tools_from_openapi_schema_and_headers,
-)
-from danswer.tools.tool_implementations.custom.custom_tool import (
-    CUSTOM_TOOL_RESPONSE_ID,
-)
-from danswer.tools.tool_implementations.custom.custom_tool import CustomToolCallSummary
-from danswer.tools.tool_implementations.images.image_generation_tool import (
-    IMAGE_GENERATION_RESPONSE_ID,
-)
-from danswer.tools.tool_implementations.images.image_generation_tool import (
-    ImageGenerationResponse,
-)
-from danswer.tools.tool_implementations.images.image_generation_tool import (
-    ImageGenerationTool,
-)
-from danswer.tools.tool_implementations.internet_search.internet_search_tool import (
-    INTERNET_SEARCH_RESPONSE_ID,
-)
-from danswer.tools.tool_implementations.internet_search.internet_search_tool import (
-    internet_search_response_to_search_docs,
-)
-from danswer.tools.tool_implementations.internet_search.internet_search_tool import (
-    InternetSearchResponse,
-)
-from danswer.tools.tool_implementations.internet_search.internet_search_tool import (
-    InternetSearchTool,
-)
-from danswer.tools.tool_implementations.search.search_tool import (
-    FINAL_CONTEXT_DOCUMENTS_ID,
-)
-from danswer.tools.tool_implementations.search.search_tool import (
-    SEARCH_RESPONSE_SUMMARY_ID,
-)
-from danswer.tools.tool_implementations.search.search_tool import SearchResponseSummary
-from danswer.tools.tool_implementations.search.search_tool import SearchTool
-from danswer.tools.tool_implementations.search.search_tool import (
-    SECTION_RELEVANCE_LIST_ID,
-)
-from danswer.tools.tool_runner import ToolCallFinalResult
-from danswer.tools.utils import compute_all_tool_tokens
-from danswer.tools.utils import explicit_tool_calling_supported
-from danswer.utils.headers import header_dict_to_header_list
-from danswer.utils.logger import setup_logger
-from danswer.utils.timing import log_generator_function_time
-
-logger = setup_logger()
-
-
-def _translate_citations(
-    citations_list: list[CitationInfo], db_docs: list[DbSearchDoc]
-) -> MessageSpecificCitations:
-    """Always cites the first instance of the document_id, assumes the db_docs
-    are sorted in the order displayed in the UI"""
-    doc_id_to_saved_doc_id_map: dict[str, int] = {}
-    for db_doc in db_docs:
-        if db_doc.document_id not in doc_id_to_saved_doc_id_map:
-            doc_id_to_saved_doc_id_map[db_doc.document_id] = db_doc.id
-
-    citation_to_saved_doc_id_map: dict[int, int] = {}
-    for citation in citations_list:
-        if citation.citation_num not in citation_to_saved_doc_id_map:
-            citation_to_saved_doc_id_map[
-                citation.citation_num
-            ] = doc_id_to_saved_doc_id_map[citation.document_id]
-
-    return MessageSpecificCitations(citation_map=citation_to_saved_doc_id_map)
-
-
-def _handle_search_tool_response_summary(
-    packet: ToolResponse,
-    db_session: Session,
-    selected_search_docs: list[DbSearchDoc] | None,
-    dedupe_docs: bool = False,
-) -> tuple[QADocsResponse, list[DbSearchDoc], list[int] | None]:
-    response_sumary = cast(SearchResponseSummary, packet.response)
-
-    dropped_inds = None
-    if not selected_search_docs:
-        top_docs = chunks_or_sections_to_search_docs(response_sumary.top_sections)
-
-        deduped_docs = top_docs
-        if dedupe_docs:
-            deduped_docs, dropped_inds = dedupe_documents(top_docs)
-
-        reference_db_search_docs = [
-            create_db_search_doc(server_search_doc=doc, db_session=db_session)
-            for doc in deduped_docs
-        ]
-    else:
-        reference_db_search_docs = selected_search_docs
-
-    response_docs = [
-        translate_db_search_doc_to_server_search_doc(db_search_doc)
-        for db_search_doc in reference_db_search_docs
-    ]
-    return (
-        QADocsResponse(
-            rephrased_query=response_sumary.rephrased_query,
-            top_documents=response_docs,
-            predicted_flow=response_sumary.predicted_flow,
-            predicted_search=response_sumary.predicted_search,
-            applied_source_filters=response_sumary.final_filters.source_type,
-            applied_time_cutoff=response_sumary.final_filters.time_cutoff,
-            recency_bias_multiplier=response_sumary.recency_bias_multiplier,
-        ),
-        reference_db_search_docs,
-        dropped_inds,
-    )
-
-
-def _handle_internet_search_tool_response_summary(
-    packet: ToolResponse,
-    db_session: Session,
-) -> tuple[QADocsResponse, list[DbSearchDoc]]:
-    internet_search_response = cast(InternetSearchResponse, packet.response)
-    server_search_docs = internet_search_response_to_search_docs(
-        internet_search_response
-    )
-
-    reference_db_search_docs = [
-        create_db_search_doc(server_search_doc=doc, db_session=db_session)
-        for doc in server_search_docs
-    ]
-    response_docs = [
-        translate_db_search_doc_to_server_search_doc(db_search_doc)
-        for db_search_doc in reference_db_search_docs
-    ]
-    return (
-        QADocsResponse(
-            rephrased_query=internet_search_response.revised_query,
-            top_documents=response_docs,
-            predicted_flow=QueryFlow.QUESTION_ANSWER,
-            predicted_search=SearchType.SEMANTIC,
-            applied_source_filters=[],
-            applied_time_cutoff=None,
-            recency_bias_multiplier=1.0,
-        ),
-        reference_db_search_docs,
-    )
-
-
-def _get_force_search_settings(
-    new_msg_req: CreateChatMessageRequest, tools: list[Tool]
-) -> ForceUseTool:
-    internet_search_available = any(
-        isinstance(tool, InternetSearchTool) for tool in tools
-    )
-    search_tool_available = any(isinstance(tool, SearchTool) for tool in tools)
-
-    if not internet_search_available and not search_tool_available:
-        # Does not matter much which tool is set here as force is false and neither tool is available
-        return ForceUseTool(force_use=False, tool_name=SearchTool._NAME)
-
-    tool_name = SearchTool._NAME if search_tool_available else InternetSearchTool._NAME
-    # Currently, the internet search tool does not support query override
-    args = (
-        {"query": new_msg_req.query_override}
-        if new_msg_req.query_override and tool_name == SearchTool._NAME
-        else None
-    )
-
-    if new_msg_req.file_descriptors:
-        # If user has uploaded files they're using, don't run any of the search tools
-        return ForceUseTool(force_use=False, tool_name=tool_name)
-
-    should_force_search = any(
-        [
-            new_msg_req.retrieval_options
-            and new_msg_req.retrieval_options.run_search
-            == OptionalSearchSetting.ALWAYS,
-            new_msg_req.search_doc_ids,
-            DISABLE_LLM_CHOOSE_SEARCH,
-        ]
-    )
-
-    if should_force_search:
-        # If we are using selected docs, just put something here so the Tool doesn't need to build its own args via an LLM call
-        args = {"query": new_msg_req.message} if new_msg_req.search_doc_ids else args
-        return ForceUseTool(force_use=True, tool_name=tool_name, args=args)
-
-    return ForceUseTool(force_use=False, tool_name=tool_name, args=args)
-
-
-ChatPacket = (
-    StreamingError
-    | QADocsResponse
-    | LLMRelevanceFilterResponse
-    | FinalUsedContextDocsResponse
-    | ChatMessageDetail
-    | DanswerAnswerPiece
-    | AllCitations
-    | CitationInfo
-    | FileChatDisplay
-    | CustomToolResponse
-    | MessageSpecificCitations
-    | MessageResponseIDInfo
-    | StreamStopInfo
-)
-ChatPacketStream = Iterator[ChatPacket]
-
-
-def stream_chat_message_objects(
-    new_msg_req: CreateChatMessageRequest,
-    user: User | None,
-    db_session: Session,
-    # Needed to translate persona num_chunks to tokens to the LLM
-    default_num_chunks: float = MAX_CHUNKS_FED_TO_CHAT,
-    # For flow with search, don't include as many chunks as possible since we need to leave space
-    # for the chat history, for smaller models, we likely won't get MAX_CHUNKS_FED_TO_CHAT chunks
-    max_document_percentage: float = CHAT_TARGET_CHUNK_PERCENTAGE,
-    # if specified, uses the last user message and does not create a new user message based
-    # on the `new_msg_req.message`. Currently, requires a state where the last message is a
-    use_existing_user_message: bool = False,
-    litellm_additional_headers: dict[str, str] | None = None,
-    custom_tool_additional_headers: dict[str, str] | None = None,
-    is_connected: Callable[[], bool] | None = None,
-    enforce_chat_session_id_for_search_docs: bool = True,
-) -> ChatPacketStream:
-    """Streams in order:
-    1. [conditional] Retrieved documents if a search needs to be run
-    2. [conditional] LLM selected chunk indices if LLM chunk filtering is turned on
-    3. [always] A set of streamed LLM tokens or an error anywhere along the line if something fails
-    4. [always] Details on the final AI response message that is created
-    """
-    # Currently surrounding context is not supported for chat
-    # Chat is already token heavy and harder for the model to process plus it would roll history over much faster
-    new_msg_req.chunks_above = 0
-    new_msg_req.chunks_below = 0
-
-    try:
-        user_id = user.id if user is not None else None
-
-        chat_session = get_chat_session_by_id(
-            chat_session_id=new_msg_req.chat_session_id,
-            user_id=user_id,
-            db_session=db_session,
-        )
-
-        message_text = new_msg_req.message
-        chat_session_id = new_msg_req.chat_session_id
-        parent_id = new_msg_req.parent_message_id
-        reference_doc_ids = new_msg_req.search_doc_ids
-        retrieval_options = new_msg_req.retrieval_options
-        alternate_assistant_id = new_msg_req.alternate_assistant_id
-
-        # use alternate persona if alternative assistant id is passed in
-        if alternate_assistant_id is not None:
-            persona = get_persona_by_id(
-                alternate_assistant_id,
-                user=user,
-                db_session=db_session,
-                is_for_edit=False,
-            )
-        else:
-            persona = chat_session.persona
-
-        prompt_id = new_msg_req.prompt_id
-        if prompt_id is None and persona.prompts:
-            prompt_id = sorted(persona.prompts, key=lambda x: x.id)[-1].id
-
-        if reference_doc_ids is None and retrieval_options is None:
-            raise RuntimeError(
-                "Must specify a set of documents for chat or specify search options"
-            )
-
-        try:
-            llm, fast_llm = get_llms_for_persona(
-                persona=persona,
-                llm_override=new_msg_req.llm_override or chat_session.llm_override,
-                additional_headers=litellm_additional_headers,
-            )
-        except GenAIDisabledException:
-            raise RuntimeError("LLM is disabled. Can't use chat flow without LLM.")
-
-        llm_provider = llm.config.model_provider
-        llm_model_name = llm.config.model_name
-
-        llm_tokenizer = get_tokenizer(
-            model_name=llm_model_name,
-            provider_type=llm_provider,
-        )
-        llm_tokenizer_encode_func = cast(
-            Callable[[str], list[int]], llm_tokenizer.encode
-        )
-
-        search_settings = get_current_search_settings(db_session)
-        document_index = get_default_document_index(
-            primary_index_name=search_settings.index_name, secondary_index_name=None
-        )
-
-        # Every chat Session begins with an empty root message
-        root_message = get_or_create_root_message(
-            chat_session_id=chat_session_id, db_session=db_session
-        )
-
-        if parent_id is not None:
-            parent_message = get_chat_message(
-                chat_message_id=parent_id,
-                user_id=user_id,
-                db_session=db_session,
-            )
-        else:
-            parent_message = root_message
-
-        user_message = None
-
-        if new_msg_req.regenerate:
-            final_msg, history_msgs = create_chat_chain(
-                stop_at_message_id=parent_id,
-                chat_session_id=chat_session_id,
-                db_session=db_session,
-            )
-
-        elif not use_existing_user_message:
-            # Create new message at the right place in the tree and update the parent's child pointer
-            # Don't commit yet until we verify the chat message chain
-            user_message = create_new_chat_message(
-                chat_session_id=chat_session_id,
-                parent_message=parent_message,
-                prompt_id=prompt_id,
-                message=message_text,
-                token_count=len(llm_tokenizer_encode_func(message_text)),
-                message_type=MessageType.USER,
-                files=None,  # Need to attach later for optimization to only load files once in parallel
-                db_session=db_session,
-                commit=False,
-            )
-            # re-create linear history of messages
-            final_msg, history_msgs = create_chat_chain(
-                chat_session_id=chat_session_id, db_session=db_session
-            )
-            if final_msg.id != user_message.id:
-                db_session.rollback()
-                raise RuntimeError(
-                    "The new message was not on the mainline. "
-                    "Be sure to update the chat pointers before calling this."
-                )
-
-            # NOTE: do not commit user message - it will be committed when the
-            # assistant message is successfully generated
-        else:
-            # re-create linear history of messages
-            final_msg, history_msgs = create_chat_chain(
-                chat_session_id=chat_session_id, db_session=db_session
-            )
-            if final_msg.message_type != MessageType.USER:
-                raise RuntimeError(
-                    "The last message was not a user message. Cannot call "
-                    "`stream_chat_message_objects` with `is_regenerate=True` "
-                    "when the last message is not a user message."
-                )
-
-        # Disable Query Rephrasing for the first message
-        # This leads to a better first response since the LLM rephrasing the question
-        # leads to worst search quality
-        if not history_msgs:
-            new_msg_req.query_override = (
-                new_msg_req.query_override or new_msg_req.message
-            )
-
-        # load all files needed for this chat chain in memory
-        files = load_all_chat_files(
-            history_msgs, new_msg_req.file_descriptors, db_session
-        )
-        latest_query_files = [
-            file
-            for file in files
-            if file.file_id in [f["id"] for f in new_msg_req.file_descriptors]
-        ]
-
-        if user_message:
-            attach_files_to_chat_message(
-                chat_message=user_message,
-                files=[
-                    new_file.to_file_descriptor() for new_file in latest_query_files
-                ],
-                db_session=db_session,
-                commit=False,
-            )
-
-        selected_db_search_docs = None
-        selected_sections: list[InferenceSection] | None = None
-        if reference_doc_ids:
-            identifier_tuples = get_doc_query_identifiers_from_model(
-                search_doc_ids=reference_doc_ids,
-                chat_session=chat_session,
-                user_id=user_id,
-                db_session=db_session,
-                enforce_chat_session_id_for_search_docs=enforce_chat_session_id_for_search_docs,
-            )
-
-            # Generates full documents currently
-            # May extend to use sections instead in the future
-            selected_sections = inference_sections_from_ids(
-                doc_identifiers=identifier_tuples,
-                document_index=document_index,
-            )
-            document_pruning_config = DocumentPruningConfig(
-                is_manually_selected_docs=True
-            )
-
-            # In case the search doc is deleted, just don't include it
-            # though this should never happen
-            db_search_docs_or_none = [
-                get_db_search_doc_by_id(doc_id=doc_id, db_session=db_session)
-                for doc_id in reference_doc_ids
-            ]
-
-            selected_db_search_docs = [
-                db_sd for db_sd in db_search_docs_or_none if db_sd
-            ]
-
-        else:
-            document_pruning_config = DocumentPruningConfig(
-                max_chunks=int(
-                    persona.num_chunks
-                    if persona.num_chunks is not None
-                    else default_num_chunks
-                ),
-                max_window_percentage=max_document_percentage,
-            )
-        reserved_message_id = reserve_message_id(
-            db_session=db_session,
-            chat_session_id=chat_session_id,
-            parent_message=user_message.id
-            if user_message is not None
-            else parent_message.id,
-            message_type=MessageType.ASSISTANT,
-        )
-        yield MessageResponseIDInfo(
-            user_message_id=user_message.id if user_message else None,
-            reserved_assistant_message_id=reserved_message_id,
-        )
-
-        overridden_model = (
-            new_msg_req.llm_override.model_version if new_msg_req.llm_override else None
-        )
-
-        # Cannot determine these without the LLM step or breaking out early
-        partial_response = partial(
-            create_new_chat_message,
-            chat_session_id=chat_session_id,
-            parent_message=final_msg,
-            prompt_id=prompt_id,
-            overridden_model=overridden_model,
-            # message=,
-            # rephrased_query=,
-            # token_count=,
-            message_type=MessageType.ASSISTANT,
-            alternate_assistant_id=new_msg_req.alternate_assistant_id,
-            # error=,
-            # reference_docs=,
-            db_session=db_session,
-            commit=False,
-        )
-
-        if not final_msg.prompt:
-            raise RuntimeError("No Prompt found")
-
-        prompt_config = (
-            PromptConfig.from_model(
-                final_msg.prompt,
-                prompt_override=(
-                    new_msg_req.prompt_override or chat_session.prompt_override
-                ),
-            )
-            if not persona
-            else PromptConfig.from_model(persona.prompts[0])
-        )
-        answer_style_config = AnswerStyleConfig(
-            citation_config=CitationConfig(
-                all_docs_useful=selected_db_search_docs is not None
-            ),
-            document_pruning_config=document_pruning_config,
-            structured_response_format=new_msg_req.structured_response_format,
-        )
-
-        # find out what tools to use
-        search_tool: SearchTool | None = None
-        tool_dict: dict[int, list[Tool]] = {}  # tool_id to tool
-        for db_tool_model in persona.tools:
-            # handle in-code tools specially
-            if db_tool_model.in_code_tool_id:
-                tool_cls = get_built_in_tool_by_id(db_tool_model.id, db_session)
-                if tool_cls.__name__ == SearchTool.__name__ and not latest_query_files:
-                    search_tool = SearchTool(
-                        db_session=db_session,
-                        user=user,
-                        persona=persona,
-                        retrieval_options=retrieval_options,
-                        prompt_config=prompt_config,
-                        llm=llm,
-                        fast_llm=fast_llm,
-                        pruning_config=document_pruning_config,
-                        answer_style_config=answer_style_config,
-                        selected_sections=selected_sections,
-                        chunks_above=new_msg_req.chunks_above,
-                        chunks_below=new_msg_req.chunks_below,
-                        full_doc=new_msg_req.full_doc,
-                        evaluation_type=(
-                            LLMEvaluationType.BASIC
-                            if persona.llm_relevance_filter
-                            else LLMEvaluationType.SKIP
-                        ),
-                    )
-                    tool_dict[db_tool_model.id] = [search_tool]
-                elif tool_cls.__name__ == ImageGenerationTool.__name__:
-                    img_generation_llm_config: LLMConfig | None = None
-                    if (
-                        llm
-                        and llm.config.api_key
-                        and llm.config.model_provider == "openai"
-                    ):
-                        img_generation_llm_config = LLMConfig(
-                            model_provider=llm.config.model_provider,
-                            model_name="dall-e-3",
-                            temperature=GEN_AI_TEMPERATURE,
-                            api_key=llm.config.api_key,
-                            api_base=llm.config.api_base,
-                            api_version=llm.config.api_version,
-                        )
-                    elif (
-                        llm.config.model_provider == "azure"
-                        and AZURE_DALLE_API_KEY is not None
-                    ):
-                        img_generation_llm_config = LLMConfig(
-                            model_provider="azure",
-                            model_name=f"azure/{AZURE_DALLE_DEPLOYMENT_NAME}",
-                            temperature=GEN_AI_TEMPERATURE,
-                            api_key=AZURE_DALLE_API_KEY,
-                            api_base=AZURE_DALLE_API_BASE,
-                            api_version=AZURE_DALLE_API_VERSION,
-                        )
-                    else:
-                        llm_providers = fetch_existing_llm_providers(db_session)
-                        openai_provider = next(
-                            iter(
-                                [
-                                    llm_provider
-                                    for llm_provider in llm_providers
-                                    if llm_provider.provider == "openai"
-                                ]
-                            ),
-                            None,
-                        )
-                        if not openai_provider or not openai_provider.api_key:
-                            raise ValueError(
-                                "Image generation tool requires an OpenAI API key"
-                            )
-                        img_generation_llm_config = LLMConfig(
-                            model_provider=openai_provider.provider,
-                            model_name="dall-e-3",
-                            temperature=GEN_AI_TEMPERATURE,
-                            api_key=openai_provider.api_key,
-                            api_base=openai_provider.api_base,
-                            api_version=openai_provider.api_version,
-                        )
-                    tool_dict[db_tool_model.id] = [
-                        ImageGenerationTool(
-                            api_key=cast(str, img_generation_llm_config.api_key),
-                            api_base=img_generation_llm_config.api_base,
-                            api_version=img_generation_llm_config.api_version,
-                            additional_headers=litellm_additional_headers,
-                            model=img_generation_llm_config.model_name,
-                        )
-                    ]
-                elif tool_cls.__name__ == InternetSearchTool.__name__:
-                    bing_api_key = BING_API_KEY
-                    if not bing_api_key:
-                        raise ValueError(
-                            "Internet search tool requires a Bing API key, please contact your Danswer admin to get it added!"
-                        )
-                    tool_dict[db_tool_model.id] = [
-                        InternetSearchTool(
-                            api_key=bing_api_key,
-                            answer_style_config=answer_style_config,
-                            prompt_config=prompt_config,
-                        )
-                    ]
-
-                continue
-
-            # handle all custom tools
-            if db_tool_model.openapi_schema:
-                tool_dict[db_tool_model.id] = cast(
-                    list[Tool],
-                    build_custom_tools_from_openapi_schema_and_headers(
-                        db_tool_model.openapi_schema,
-                        dynamic_schema_info=DynamicSchemaInfo(
-                            chat_session_id=chat_session_id,
-                            message_id=user_message.id if user_message else None,
-                        ),
-                        custom_headers=(db_tool_model.custom_headers or [])
-                        + (
-                            header_dict_to_header_list(
-                                custom_tool_additional_headers or {}
-                            )
-                        ),
-                    ),
-                )
-
-        tools: list[Tool] = []
-        for tool_list in tool_dict.values():
-            tools.extend(tool_list)
-
-        # factor in tool definition size when pruning
-        document_pruning_config.tool_num_tokens = compute_all_tool_tokens(
-            tools, llm_tokenizer
-        )
-        document_pruning_config.using_tool_message = explicit_tool_calling_supported(
-            llm_provider, llm_model_name
-        )
-
-        # LLM prompt building, response capturing, etc.
-        answer = Answer(
-            is_connected=is_connected,
-            question=final_msg.message,
-            latest_query_files=latest_query_files,
-            answer_style_config=answer_style_config,
-            prompt_config=prompt_config,
-            llm=(
-                llm
-                or get_main_llm_from_tuple(
-                    get_llms_for_persona(
-                        persona=persona,
-                        llm_override=(
-                            new_msg_req.llm_override or chat_session.llm_override
-                        ),
-                        additional_headers=litellm_additional_headers,
-                    )
-                )
-            ),
-            message_history=[
-                PreviousMessage.from_chat_message(msg, files) for msg in history_msgs
-            ],
-            tools=tools,
-            force_use_tool=_get_force_search_settings(new_msg_req, tools),
-        )
-
-        reference_db_search_docs = None
-        qa_docs_response = None
-        ai_message_files = None  # any files to associate with the AI message e.g. dall-e generated images
-        dropped_indices = None
-        tool_result = None
-
-        for packet in answer.processed_streamed_output:
-            if isinstance(packet, ToolResponse):
-                if packet.id == SEARCH_RESPONSE_SUMMARY_ID:
-                    (
-                        qa_docs_response,
-                        reference_db_search_docs,
-                        dropped_indices,
-                    ) = _handle_search_tool_response_summary(
-                        packet=packet,
-                        db_session=db_session,
-                        selected_search_docs=selected_db_search_docs,
-                        # Deduping happens at the last step to avoid harming quality by dropping content early on
-                        dedupe_docs=(
-                            retrieval_options.dedupe_docs
-                            if retrieval_options
-                            else False
-                        ),
-                    )
-                    yield qa_docs_response
-                elif packet.id == SECTION_RELEVANCE_LIST_ID:
-                    relevance_sections = packet.response
-
-                    if reference_db_search_docs is not None:
-                        llm_indices = relevant_sections_to_indices(
-                            relevance_sections=relevance_sections,
-                            items=[
-                                translate_db_search_doc_to_server_search_doc(doc)
-                                for doc in reference_db_search_docs
-                            ],
-                        )
-
-                        if dropped_indices:
-                            llm_indices = drop_llm_indices(
-                                llm_indices=llm_indices,
-                                search_docs=reference_db_search_docs,
-                                dropped_indices=dropped_indices,
-                            )
-
-                        yield LLMRelevanceFilterResponse(
-                            llm_selected_doc_indices=llm_indices
-                        )
-                elif packet.id == FINAL_CONTEXT_DOCUMENTS_ID:
-                    yield FinalUsedContextDocsResponse(
-                        final_context_docs=packet.response
-                    )
-
-                elif packet.id == IMAGE_GENERATION_RESPONSE_ID:
-                    img_generation_response = cast(
-                        list[ImageGenerationResponse], packet.response
-                    )
-
-                    file_ids = save_files_from_urls(
-                        [img.url for img in img_generation_response]
-                    )
-                    ai_message_files = [
-                        FileDescriptor(id=str(file_id), type=ChatFileType.IMAGE)
-                        for file_id in file_ids
-                    ]
-                    yield FileChatDisplay(
-                        file_ids=[str(file_id) for file_id in file_ids]
-                    )
-                elif packet.id == INTERNET_SEARCH_RESPONSE_ID:
-                    (
-                        qa_docs_response,
-                        reference_db_search_docs,
-                    ) = _handle_internet_search_tool_response_summary(
-                        packet=packet,
-                        db_session=db_session,
-                    )
-                    yield qa_docs_response
-                elif packet.id == CUSTOM_TOOL_RESPONSE_ID:
-                    custom_tool_response = cast(CustomToolCallSummary, packet.response)
-
-                    if (
-                        custom_tool_response.response_type == "image"
-                        or custom_tool_response.response_type == "csv"
-                    ):
-                        file_ids = custom_tool_response.tool_result.file_ids
-                        ai_message_files = [
-                            FileDescriptor(
-                                id=str(file_id),
-                                type=ChatFileType.IMAGE
-                                if custom_tool_response.response_type == "image"
-                                else ChatFileType.CSV,
-                            )
-                            for file_id in file_ids
-                        ]
-                        yield FileChatDisplay(
-                            file_ids=[str(file_id) for file_id in file_ids]
-                        )
-                    else:
-                        yield CustomToolResponse(
-                            response=custom_tool_response.tool_result,
-                            tool_name=custom_tool_response.tool_name,
-                        )
-
-            elif isinstance(packet, StreamStopInfo):
-                pass
-            else:
-                if isinstance(packet, ToolCallFinalResult):
-                    tool_result = packet
-                yield cast(ChatPacket, packet)
-        logger.debug("Reached end of stream")
-    except ValueError as e:
-        logger.exception("Failed to process chat message.")
-
-        error_msg = str(e)
-        yield StreamingError(error=error_msg)
-        db_session.rollback()
-        return
-
-    except Exception as e:
-        logger.exception("Failed to process chat message.")
-
-        error_msg = str(e)
-        stack_trace = traceback.format_exc()
-        client_error_msg = litellm_exception_to_error_msg(e, llm)
-        if llm.config.api_key and len(llm.config.api_key) > 2:
-            error_msg = error_msg.replace(llm.config.api_key, "[REDACTED_API_KEY]")
-            stack_trace = stack_trace.replace(llm.config.api_key, "[REDACTED_API_KEY]")
-
-        yield StreamingError(error=client_error_msg, stack_trace=stack_trace)
-        db_session.rollback()
-        return
-
-    # Post-LLM answer processing
-    try:
-        logger.debug("Post-LLM answer processing")
-        message_specific_citations: MessageSpecificCitations | None = None
-        if reference_db_search_docs:
-            message_specific_citations = _translate_citations(
-                citations_list=answer.citations,
-                db_docs=reference_db_search_docs,
-            )
-            yield AllCitations(citations=answer.citations)
-
-        # Saving Gen AI answer and responding with message info
-        tool_name_to_tool_id: dict[str, int] = {}
-        for tool_id, tool_list in tool_dict.items():
-            for tool in tool_list:
-                tool_name_to_tool_id[tool.name] = tool_id
-
-        gen_ai_response_message = partial_response(
-            reserved_message_id=reserved_message_id,
-            message=answer.llm_answer,
-            rephrased_query=(
-                qa_docs_response.rephrased_query if qa_docs_response else None
-            ),
-            reference_docs=reference_db_search_docs,
-            files=ai_message_files,
-            token_count=len(llm_tokenizer_encode_func(answer.llm_answer)),
-            citations=message_specific_citations.citation_map
-            if message_specific_citations
-            else None,
-            error=None,
-            tool_call=(
-                ToolCall(
-                    tool_id=tool_name_to_tool_id[tool_result.tool_name],
-                    tool_name=tool_result.tool_name,
-                    tool_arguments=tool_result.tool_args,
-                    tool_result=tool_result.tool_result,
-                )
-                if tool_result
-                else None
-            ),
-        )
-
-        logger.debug("Committing messages")
-        db_session.commit()  # actually save user / assistant message
-
-        msg_detail_response = translate_db_message_to_chat_message_detail(
-            gen_ai_response_message
-        )
-
-        yield msg_detail_response
-    except Exception as e:
-        error_msg = str(e)
-        logger.exception(error_msg)
-
-        # Frontend will erase whatever answer and show this instead
-        yield StreamingError(error="Failed to parse LLM output")
-
-
-@log_generator_function_time()
-def stream_chat_message(
-    new_msg_req: CreateChatMessageRequest,
-    user: User | None,
-    use_existing_user_message: bool = False,
-    litellm_additional_headers: dict[str, str] | None = None,
-    custom_tool_additional_headers: dict[str, str] | None = None,
-    is_connected: Callable[[], bool] | None = None,
-) -> Iterator[str]:
-    with get_session_context_manager() as db_session:
-        objects = stream_chat_message_objects(
-            new_msg_req=new_msg_req,
-            user=user,
-            db_session=db_session,
-            use_existing_user_message=use_existing_user_message,
-            litellm_additional_headers=litellm_additional_headers,
-            custom_tool_additional_headers=custom_tool_additional_headers,
-            is_connected=is_connected,
-        )
-        for obj in objects:
-            yield get_json_line(obj.model_dump())

backend/danswer/chat/tools.py

@@ -1,115 +0,0 @@
-from typing_extensions import TypedDict  # noreorder
-
-from pydantic import BaseModel
-
-from danswer.prompts.chat_tools import DANSWER_TOOL_DESCRIPTION
-from danswer.prompts.chat_tools import DANSWER_TOOL_NAME
-from danswer.prompts.chat_tools import TOOL_FOLLOWUP
-from danswer.prompts.chat_tools import TOOL_LESS_FOLLOWUP
-from danswer.prompts.chat_tools import TOOL_LESS_PROMPT
-from danswer.prompts.chat_tools import TOOL_TEMPLATE
-from danswer.prompts.chat_tools import USER_INPUT
-
-
-class ToolInfo(TypedDict):
-    name: str
-    description: str
-
-
-class DanswerChatModelOut(BaseModel):
-    model_raw: str
-    action: str
-    action_input: str
-
-
-def call_tool(
-    model_actions: DanswerChatModelOut,
-) -> str:
-    raise NotImplementedError("There are no additional tool integrations right now")
-
-
-def form_user_prompt_text(
-    query: str,
-    tool_text: str | None,
-    hint_text: str | None,
-    user_input_prompt: str = USER_INPUT,
-    tool_less_prompt: str = TOOL_LESS_PROMPT,
-) -> str:
-    user_prompt = tool_text or tool_less_prompt
-
-    user_prompt += user_input_prompt.format(user_input=query)
-
-    if hint_text:
-        if user_prompt[-1] != "\n":
-            user_prompt += "\n"
-        user_prompt += "\nHint: " + hint_text
-
-    return user_prompt.strip()
-
-
-def form_tool_section_text(
-    tools: list[ToolInfo] | None, retrieval_enabled: bool, template: str = TOOL_TEMPLATE
-) -> str | None:
-    if not tools and not retrieval_enabled:
-        return None
-
-    if retrieval_enabled and tools:
-        tools.append(
-            {"name": DANSWER_TOOL_NAME, "description": DANSWER_TOOL_DESCRIPTION}
-        )
-
-    tools_intro = []
-    if tools:
-        num_tools = len(tools)
-        for tool in tools:
-            description_formatted = tool["description"].replace("\n", " ")
-            tools_intro.append(f"> {tool['name']}: {description_formatted}")
-
-        prefix = "Must be one of " if num_tools > 1 else "Must be "
-
-        tools_intro_text = "\n".join(tools_intro)
-        tool_names_text = prefix + ", ".join([tool["name"] for tool in tools])
-
-    else:
-        return None
-
-    return template.format(
-        tool_overviews=tools_intro_text, tool_names=tool_names_text
-    ).strip()
-
-
-def form_tool_followup_text(
-    tool_output: str,
-    query: str,
-    hint_text: str | None,
-    tool_followup_prompt: str = TOOL_FOLLOWUP,
-    ignore_hint: bool = False,
-) -> str:
-    # If multi-line query, it likely confuses the model more than helps
-    if "\n" not in query:
-        optional_reminder = f"\nAs a reminder, my query was: {query}\n"
-    else:
-        optional_reminder = ""
-
-    if not ignore_hint and hint_text:
-        hint_text_spaced = f"\nHint: {hint_text}\n"
-    else:
-        hint_text_spaced = ""
-
-    return tool_followup_prompt.format(
-        tool_output=tool_output,
-        optional_reminder=optional_reminder,
-        hint=hint_text_spaced,
-    ).strip()
-
-
-def form_tool_less_followup_text(
-    tool_output: str,
-    query: str,
-    hint_text: str | None,
-    tool_followup_prompt: str = TOOL_LESS_FOLLOWUP,
-) -> str:
-    hint = f"Hint: {hint_text}" if hint_text else ""
-    return tool_followup_prompt.format(
-        context_str=tool_output, user_query=query, hint_text=hint
-    ).strip()

backend/danswer/connectors/confluence/connector.py

@@ -1,302 +0,0 @@
-from datetime import datetime
-from datetime import timezone
-from typing import Any
-from urllib.parse import quote
-
-from danswer.configs.app_configs import CONFLUENCE_CONNECTOR_LABELS_TO_SKIP
-from danswer.configs.app_configs import CONTINUE_ON_CONNECTOR_FAILURE
-from danswer.configs.app_configs import INDEX_BATCH_SIZE
-from danswer.configs.constants import DocumentSource
-from danswer.connectors.confluence.onyx_confluence import OnyxConfluence
-from danswer.connectors.confluence.utils import attachment_to_content
-from danswer.connectors.confluence.utils import build_confluence_client
-from danswer.connectors.confluence.utils import build_confluence_document_id
-from danswer.connectors.confluence.utils import datetime_from_string
-from danswer.connectors.confluence.utils import extract_text_from_confluence_html
-from danswer.connectors.interfaces import GenerateDocumentsOutput
-from danswer.connectors.interfaces import GenerateSlimDocumentOutput
-from danswer.connectors.interfaces import LoadConnector
-from danswer.connectors.interfaces import PollConnector
-from danswer.connectors.interfaces import SecondsSinceUnixEpoch
-from danswer.connectors.interfaces import SlimConnector
-from danswer.connectors.models import BasicExpertInfo
-from danswer.connectors.models import ConnectorMissingCredentialError
-from danswer.connectors.models import Document
-from danswer.connectors.models import Section
-from danswer.connectors.models import SlimDocument
-from danswer.utils.logger import setup_logger
-
-logger = setup_logger()
-
-# Potential Improvements
-# 1. Include attachments, etc
-# 2. Segment into Sections for more accurate linking, can split by headers but make sure no text/ordering is lost
-
-_COMMENT_EXPANSION_FIELDS = ["body.storage.value"]
-_PAGE_EXPANSION_FIELDS = [
-    "body.storage.value",
-    "version",
-    "space",
-    "metadata.labels",
-]
-_ATTACHMENT_EXPANSION_FIELDS = [
-    "version",
-    "space",
-    "metadata.labels",
-]
-
-_RESTRICTIONS_EXPANSION_FIELDS = [
-    "space",
-    "restrictions.read.restrictions.user",
-    "restrictions.read.restrictions.group",
-]
-
-
-class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
-    def __init__(
-        self,
-        wiki_base: str,
-        is_cloud: bool,
-        space: str = "",
-        page_id: str = "",
-        index_recursively: bool = True,
-        cql_query: str | None = None,
-        batch_size: int = INDEX_BATCH_SIZE,
-        continue_on_failure: bool = CONTINUE_ON_CONNECTOR_FAILURE,
-        # if a page has one of the labels specified in this list, we will just
-        # skip it. This is generally used to avoid indexing extra sensitive
-        # pages.
-        labels_to_skip: list[str] = CONFLUENCE_CONNECTOR_LABELS_TO_SKIP,
-    ) -> None:
-        self.batch_size = batch_size
-        self.continue_on_failure = continue_on_failure
-        self.confluence_client: OnyxConfluence | None = None
-        self.is_cloud = is_cloud
-
-        # Remove trailing slash from wiki_base if present
-        self.wiki_base = wiki_base.rstrip("/")
-
-        # if nothing is provided, we will fetch all pages
-        cql_page_query = "type=page"
-        if cql_query:
-            # if a cql_query is provided, we will use it to fetch the pages
-            cql_page_query = cql_query
-        elif space:
-            # if no cql_query is provided, we will use the space to fetch the pages
-            cql_page_query += f" and space='{quote(space)}'"
-        elif page_id:
-            if index_recursively:
-                cql_page_query += f" and ancestor='{page_id}'"
-            else:
-                # if neither a space nor a cql_query is provided, we will use the page_id to fetch the page
-                cql_page_query += f" and id='{page_id}'"
-
-        self.cql_page_query = cql_page_query
-        self.cql_time_filter = ""
-
-        self.cql_label_filter = ""
-        if labels_to_skip:
-            labels_to_skip = list(set(labels_to_skip))
-            comma_separated_labels = ",".join(f"'{label}'" for label in labels_to_skip)
-            self.cql_label_filter = f" and label not in ({comma_separated_labels})"
-
-    def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
-        # see https://github.com/atlassian-api/atlassian-python-api/blob/master/atlassian/rest_client.py
-        # for a list of other hidden constructor args
-        self.confluence_client = build_confluence_client(
-            credentials_json=credentials,
-            is_cloud=self.is_cloud,
-            wiki_base=self.wiki_base,
-        )
-        return None
-
-    def _get_comment_string_for_page_id(self, page_id: str) -> str:
-        if self.confluence_client is None:
-            raise ConnectorMissingCredentialError("Confluence")
-
-        comment_string = ""
-
-        comment_cql = f"type=comment and container='{page_id}'"
-        comment_cql += self.cql_label_filter
-
-        expand = ",".join(_COMMENT_EXPANSION_FIELDS)
-        for comments in self.confluence_client.paginated_cql_page_retrieval(
-            cql=comment_cql,
-            expand=expand,
-        ):
-            for comment in comments:
-                comment_string += "\nComment:\n"
-                comment_string += extract_text_from_confluence_html(
-                    confluence_client=self.confluence_client,
-                    confluence_object=comment,
-                )
-
-        return comment_string
-
-    def _convert_object_to_document(
-        self, confluence_object: dict[str, Any]
-    ) -> Document | None:
-        """
-        Takes in a confluence object, extracts all metadata, and converts it into a document.
-        If its a page, it extracts the text, adds the comments for the document text.
-        If its an attachment, it just downloads the attachment and converts that into a document.
-        """
-        if self.confluence_client is None:
-            raise ConnectorMissingCredentialError("Confluence")
-
-        # The url and the id are the same
-        object_url = build_confluence_document_id(
-            self.wiki_base, confluence_object["_links"]["webui"]
-        )
-
-        object_text = None
-        # Extract text from page
-        if confluence_object["type"] == "page":
-            object_text = extract_text_from_confluence_html(
-                self.confluence_client, confluence_object
-            )
-            # Add comments to text
-            object_text += self._get_comment_string_for_page_id(confluence_object["id"])
-        elif confluence_object["type"] == "attachment":
-            object_text = attachment_to_content(
-                self.confluence_client, confluence_object
-            )
-
-        if object_text is None:
-            return None
-
-        # Get space name
-        doc_metadata: dict[str, str | list[str]] = {
-            "Wiki Space Name": confluence_object["space"]["name"]
-        }
-
-        # Get labels
-        label_dicts = confluence_object["metadata"]["labels"]["results"]
-        page_labels = [label["name"] for label in label_dicts]
-        if page_labels:
-            doc_metadata["labels"] = page_labels
-
-        # Get last modified and author email
-        last_modified = datetime_from_string(confluence_object["version"]["when"])
-        author_email = confluence_object["version"].get("by", {}).get("email")
-
-        return Document(
-            id=object_url,
-            sections=[Section(link=object_url, text=object_text)],
-            source=DocumentSource.CONFLUENCE,
-            semantic_identifier=confluence_object["title"],
-            doc_updated_at=last_modified,
-            primary_owners=(
-                [BasicExpertInfo(email=author_email)] if author_email else None
-            ),
-            metadata=doc_metadata,
-        )
-
-    def _fetch_document_batches(self) -> GenerateDocumentsOutput:
-        if self.confluence_client is None:
-            raise ConnectorMissingCredentialError("Confluence")
-
-        doc_batch: list[Document] = []
-        confluence_page_ids: list[str] = []
-
-        page_query = self.cql_page_query + self.cql_label_filter + self.cql_time_filter
-        # Fetch pages as Documents
-        for page_batch in self.confluence_client.paginated_cql_page_retrieval(
-            cql=page_query,
-            expand=",".join(_PAGE_EXPANSION_FIELDS),
-            limit=self.batch_size,
-        ):
-            for page in page_batch:
-                confluence_page_ids.append(page["id"])
-                doc = self._convert_object_to_document(page)
-                if doc is not None:
-                    doc_batch.append(doc)
-                if len(doc_batch) >= self.batch_size:
-                    yield doc_batch
-                    doc_batch = []
-
-        # Fetch attachments as Documents
-        for confluence_page_id in confluence_page_ids:
-            attachment_cql = f"type=attachment and container='{confluence_page_id}'"
-            attachment_cql += self.cql_label_filter
-            # TODO: maybe should add time filter as well?
-            for attachments in self.confluence_client.paginated_cql_page_retrieval(
-                cql=attachment_cql,
-                expand=",".join(_ATTACHMENT_EXPANSION_FIELDS),
-            ):
-                for attachment in attachments:
-                    doc = self._convert_object_to_document(attachment)
-                    if doc is not None:
-                        doc_batch.append(doc)
-                    if len(doc_batch) >= self.batch_size:
-                        yield doc_batch
-                        doc_batch = []
-
-        if doc_batch:
-            yield doc_batch
-
-    def load_from_state(self) -> GenerateDocumentsOutput:
-        return self._fetch_document_batches()
-
-    def poll_source(self, start: float, end: float) -> GenerateDocumentsOutput:
-        # Add time filters
-        formatted_start_time = datetime.fromtimestamp(start, tz=timezone.utc).strftime(
-            "%Y-%m-%d %H:%M"
-        )
-        formatted_end_time = datetime.fromtimestamp(end, tz=timezone.utc).strftime(
-            "%Y-%m-%d %H:%M"
-        )
-        self.cql_time_filter = f" and lastmodified >= '{formatted_start_time}'"
-        self.cql_time_filter += f" and lastmodified <= '{formatted_end_time}'"
-        return self._fetch_document_batches()
-
-    def retrieve_all_slim_documents(
-        self,
-        start: SecondsSinceUnixEpoch | None = None,
-        end: SecondsSinceUnixEpoch | None = None,
-    ) -> GenerateSlimDocumentOutput:
-        if self.confluence_client is None:
-            raise ConnectorMissingCredentialError("Confluence")
-
-        doc_metadata_list: list[SlimDocument] = []
-
-        restrictions_expand = ",".join(_RESTRICTIONS_EXPANSION_FIELDS)
-
-        page_query = self.cql_page_query + self.cql_label_filter
-        for pages in self.confluence_client.cql_paginate_all_expansions(
-            cql=page_query,
-            expand=restrictions_expand,
-        ):
-            for page in pages:
-                # If the page has restrictions, add them to the perm_sync_data
-                # These will be used by doc_sync.py to sync permissions
-                perm_sync_data = {
-                    "restrictions": page.get("restrictions", {}),
-                    "space_key": page.get("space", {}).get("key"),
-                }
-
-                doc_metadata_list.append(
-                    SlimDocument(
-                        id=build_confluence_document_id(
-                            self.wiki_base, page["_links"]["webui"]
-                        ),
-                        perm_sync_data=perm_sync_data,
-                    )
-                )
-                attachment_cql = f"type=attachment and container='{page['id']}'"
-                attachment_cql += self.cql_label_filter
-                for attachments in self.confluence_client.cql_paginate_all_expansions(
-                    cql=attachment_cql,
-                    expand=restrictions_expand,
-                ):
-                    for attachment in attachments:
-                        doc_metadata_list.append(
-                            SlimDocument(
-                                id=build_confluence_document_id(
-                                    self.wiki_base, attachment["_links"]["webui"]
-                                ),
-                                perm_sync_data=perm_sync_data,
-                            )
-                        )
-                yield doc_metadata_list
-                doc_metadata_list = []

backend/danswer/connectors/confluence/onyx_confluence.py

@@ -1,226 +0,0 @@
-import math
-import time
-from collections.abc import Callable
-from collections.abc import Iterator
-from typing import Any
-from typing import cast
-from typing import TypeVar
-from urllib.parse import quote
-
-from atlassian import Confluence  # type:ignore
-from requests import HTTPError
-
-from danswer.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-F = TypeVar("F", bound=Callable[..., Any])
-
-
-RATE_LIMIT_MESSAGE_LOWERCASE = "Rate limit exceeded".lower()
-
-
-class ConfluenceRateLimitError(Exception):
-    pass
-
-
-def _handle_http_error(e: HTTPError, attempt: int) -> int:
-    MIN_DELAY = 2
-    MAX_DELAY = 60
-    STARTING_DELAY = 5
-    BACKOFF = 2
-
-    # Check if the response or headers are None to avoid potential AttributeError
-    if e.response is None or e.response.headers is None:
-        logger.warning("HTTPError with `None` as response or as headers")
-        raise e
-
-    if (
-        e.response.status_code != 429
-        and RATE_LIMIT_MESSAGE_LOWERCASE not in e.response.text.lower()
-    ):
-        raise e
-
-    retry_after = None
-
-    retry_after_header = e.response.headers.get("Retry-After")
-    if retry_after_header is not None:
-        try:
-            retry_after = int(retry_after_header)
-            if retry_after > MAX_DELAY:
-                logger.warning(
-                    f"Clamping retry_after from {retry_after} to {MAX_DELAY} seconds..."
-                )
-                retry_after = MAX_DELAY
-            if retry_after < MIN_DELAY:
-                retry_after = MIN_DELAY
-        except ValueError:
-            pass
-
-    if retry_after is not None:
-        logger.warning(
-            f"Rate limiting with retry header. Retrying after {retry_after} seconds..."
-        )
-        delay = retry_after
-    else:
-        logger.warning(
-            "Rate limiting without retry header. Retrying with exponential backoff..."
-        )
-        delay = min(STARTING_DELAY * (BACKOFF**attempt), MAX_DELAY)
-
-    delay_until = math.ceil(time.monotonic() + delay)
-    return delay_until
-
-
-# https://developer.atlassian.com/cloud/confluence/rate-limiting/
-# this uses the native rate limiting option provided by the
-# confluence client and otherwise applies a simpler set of error handling
-def handle_confluence_rate_limit(confluence_call: F) -> F:
-    def wrapped_call(*args: list[Any], **kwargs: Any) -> Any:
-        MAX_RETRIES = 5
-
-        TIMEOUT = 3600
-        timeout_at = time.monotonic() + TIMEOUT
-
-        for attempt in range(MAX_RETRIES):
-            if time.monotonic() > timeout_at:
-                raise TimeoutError(
-                    f"Confluence call attempts took longer than {TIMEOUT} seconds."
-                )
-
-            try:
-                # we're relying more on the client to rate limit itself
-                # and applying our own retries in a more specific set of circumstances
-                return confluence_call(*args, **kwargs)
-            except HTTPError as e:
-                delay_until = _handle_http_error(e, attempt)
-                while time.monotonic() < delay_until:
-                    # in the future, check a signal here to exit
-                    time.sleep(1)
-            except AttributeError as e:
-                # Some error within the Confluence library, unclear why it fails.
-                # Users reported it to be intermittent, so just retry
-                if attempt == MAX_RETRIES - 1:
-                    raise e
-
-                logger.exception(
-                    "Confluence Client raised an AttributeError. Retrying..."
-                )
-                time.sleep(5)
-
-    return cast(F, wrapped_call)
-
-
-_DEFAULT_PAGINATION_LIMIT = 100
-
-
-class OnyxConfluence(Confluence):
-    """
-    This is a custom Confluence class that overrides the default Confluence class to add a custom CQL method.
-    This is necessary because the default Confluence class does not properly support cql expansions.
-    All methods are automatically wrapped with handle_confluence_rate_limit.
-    """
-
-    def __init__(self, url: str, *args: Any, **kwargs: Any) -> None:
-        super(OnyxConfluence, self).__init__(url, *args, **kwargs)
-        self._wrap_methods()
-
-    def _wrap_methods(self) -> None:
-        """
-        For each attribute that is callable (i.e., a method) and doesn't start with an underscore,
-        wrap it with handle_confluence_rate_limit.
-        """
-        for attr_name in dir(self):
-            if callable(getattr(self, attr_name)) and not attr_name.startswith("_"):
-                setattr(
-                    self,
-                    attr_name,
-                    handle_confluence_rate_limit(getattr(self, attr_name)),
-                )
-
-    def _paginate_url(
-        self, url_suffix: str, limit: int | None = None
-    ) -> Iterator[list[dict[str, Any]]]:
-        """
-        This will paginate through the top level query.
-        """
-        if not limit:
-            limit = _DEFAULT_PAGINATION_LIMIT
-
-        connection_char = "&" if "?" in url_suffix else "?"
-        url_suffix += f"{connection_char}limit={limit}"
-
-        while url_suffix:
-            try:
-                next_response = self.get(url_suffix)
-            except Exception as e:
-                logger.exception("Error in danswer_cql: \n")
-                raise e
-            yield next_response.get("results", [])
-            url_suffix = next_response.get("_links", {}).get("next")
-
-    def paginated_groups_retrieval(
-        self,
-        limit: int | None = None,
-    ) -> Iterator[list[dict[str, Any]]]:
-        return self._paginate_url("rest/api/group", limit)
-
-    def paginated_group_members_retrieval(
-        self,
-        group_name: str,
-        limit: int | None = None,
-    ) -> Iterator[list[dict[str, Any]]]:
-        group_name = quote(group_name)
-        return self._paginate_url(f"rest/api/group/{group_name}/member", limit)
-
-    def paginated_cql_user_retrieval(
-        self,
-        cql: str,
-        expand: str | None = None,
-        limit: int | None = None,
-    ) -> Iterator[list[dict[str, Any]]]:
-        expand_string = f"&expand={expand}" if expand else ""
-        return self._paginate_url(
-            f"rest/api/search/user?cql={cql}{expand_string}", limit
-        )
-
-    def paginated_cql_page_retrieval(
-        self,
-        cql: str,
-        expand: str | None = None,
-        limit: int | None = None,
-    ) -> Iterator[list[dict[str, Any]]]:
-        expand_string = f"&expand={expand}" if expand else ""
-        return self._paginate_url(
-            f"rest/api/content/search?cql={cql}{expand_string}", limit
-        )
-
-    def cql_paginate_all_expansions(
-        self,
-        cql: str,
-        expand: str | None = None,
-        limit: int | None = None,
-    ) -> Iterator[list[dict[str, Any]]]:
-        """
-        This function will paginate through the top level query first, then
-        paginate through all of the expansions.
-        The limit only applies to the top level query.
-        All expansion paginations use default pagination limit (defined by Atlassian).
-        """
-
-        def _traverse_and_update(data: dict | list) -> None:
-            if isinstance(data, dict):
-                next_url = data.get("_links", {}).get("next")
-                if next_url and "results" in data:
-                    data["results"].extend(self._paginate_url(next_url))
-
-                for value in data.values():
-                    _traverse_and_update(value)
-            elif isinstance(data, list):
-                for item in data:
-                    _traverse_and_update(item)
-
-        for results in self.paginated_cql_page_retrieval(cql, expand, limit):
-            _traverse_and_update(results)
-            yield results

backend/danswer/connectors/danswer_jira/connector.py

@@ -1,321 +0,0 @@
-import os
-from datetime import datetime
-from datetime import timezone
-from typing import Any
-from urllib.parse import urlparse
-
-from jira import JIRA
-from jira.resources import Issue
-
-from danswer.configs.app_configs import INDEX_BATCH_SIZE
-from danswer.configs.app_configs import JIRA_CONNECTOR_LABELS_TO_SKIP
-from danswer.configs.app_configs import JIRA_CONNECTOR_MAX_TICKET_SIZE
-from danswer.configs.constants import DocumentSource
-from danswer.connectors.cross_connector_utils.miscellaneous_utils import time_str_to_utc
-from danswer.connectors.interfaces import GenerateDocumentsOutput
-from danswer.connectors.interfaces import LoadConnector
-from danswer.connectors.interfaces import PollConnector
-from danswer.connectors.interfaces import SecondsSinceUnixEpoch
-from danswer.connectors.models import BasicExpertInfo
-from danswer.connectors.models import ConnectorMissingCredentialError
-from danswer.connectors.models import Document
-from danswer.connectors.models import Section
-from danswer.utils.logger import setup_logger
-
-
-logger = setup_logger()
-PROJECT_URL_PAT = "projects"
-JIRA_API_VERSION = os.environ.get("JIRA_API_VERSION") or "2"
-
-
-def extract_jira_project(url: str) -> tuple[str, str]:
-    parsed_url = urlparse(url)
-    jira_base = parsed_url.scheme + "://" + parsed_url.netloc
-
-    # Split the path by '/' and find the position of 'projects' to get the project name
-    split_path = parsed_url.path.split("/")
-    if PROJECT_URL_PAT in split_path:
-        project_pos = split_path.index(PROJECT_URL_PAT)
-        if len(split_path) > project_pos + 1:
-            jira_project = split_path[project_pos + 1]
-        else:
-            raise ValueError("No project name found in the URL")
-    else:
-        raise ValueError("'projects' not found in the URL")
-
-    return jira_base, jira_project
-
-
-def extract_text_from_adf(adf: dict | None) -> str:
-    """Extracts plain text from Atlassian Document Format:
-    https://developer.atlassian.com/cloud/jira/platform/apis/document/structure/
-
-    WARNING: This function is incomplete and will e.g. skip lists!
-    """
-    texts = []
-    if adf is not None and "content" in adf:
-        for block in adf["content"]:
-            if "content" in block:
-                for item in block["content"]:
-                    if item["type"] == "text":
-                        texts.append(item["text"])
-    return " ".join(texts)
-
-
-def best_effort_get_field_from_issue(jira_issue: Issue, field: str) -> Any:
-    if hasattr(jira_issue.fields, field):
-        return getattr(jira_issue.fields, field)
-
-    try:
-        return jira_issue.raw["fields"][field]
-    except Exception:
-        return None
-
-
-def _get_comment_strs(
-    jira: Issue, comment_email_blacklist: tuple[str, ...] = ()
-) -> list[str]:
-    comment_strs = []
-    for comment in jira.fields.comment.comments:
-        try:
-            body_text = (
-                comment.body
-                if JIRA_API_VERSION == "2"
-                else extract_text_from_adf(comment.raw["body"])
-            )
-
-            if (
-                hasattr(comment, "author")
-                and hasattr(comment.author, "emailAddress")
-                and comment.author.emailAddress in comment_email_blacklist
-            ):
-                continue  # Skip adding comment if author's email is in blacklist
-
-            comment_strs.append(body_text)
-        except Exception as e:
-            logger.error(f"Failed to process comment due to an error: {e}")
-            continue
-
-    return comment_strs
-
-
-def fetch_jira_issues_batch(
-    jql: str,
-    start_index: int,
-    jira_client: JIRA,
-    batch_size: int = INDEX_BATCH_SIZE,
-    comment_email_blacklist: tuple[str, ...] = (),
-    labels_to_skip: set[str] | None = None,
-) -> tuple[list[Document], int]:
-    doc_batch = []
-
-    batch = jira_client.search_issues(
-        jql,
-        startAt=start_index,
-        maxResults=batch_size,
-    )
-
-    for jira in batch:
-        if type(jira) != Issue:
-            logger.warning(f"Found Jira object not of type Issue {jira}")
-            continue
-
-        if labels_to_skip and any(
-            label in jira.fields.labels for label in labels_to_skip
-        ):
-            logger.info(
-                f"Skipping {jira.key} because it has a label to skip. Found "
-                f"labels: {jira.fields.labels}. Labels to skip: {labels_to_skip}."
-            )
-            continue
-
-        description = (
-            jira.fields.description
-            if JIRA_API_VERSION == "2"
-            else extract_text_from_adf(jira.raw["fields"]["description"])
-        )
-        comments = _get_comment_strs(jira, comment_email_blacklist)
-        ticket_content = f"{description}\n" + "\n".join(
-            [f"Comment: {comment}" for comment in comments if comment]
-        )
-
-        # Check ticket size
-        if len(ticket_content.encode("utf-8")) > JIRA_CONNECTOR_MAX_TICKET_SIZE:
-            logger.info(
-                f"Skipping {jira.key} because it exceeds the maximum size of "
-                f"{JIRA_CONNECTOR_MAX_TICKET_SIZE} bytes."
-            )
-            continue
-
-        page_url = f"{jira_client.client_info()}/browse/{jira.key}"
-
-        people = set()
-        try:
-            people.add(
-                BasicExpertInfo(
-                    display_name=jira.fields.creator.displayName,
-                    email=jira.fields.creator.emailAddress,
-                )
-            )
-        except Exception:
-            # Author should exist but if not, doesn't matter
-            pass
-
-        try:
-            people.add(
-                BasicExpertInfo(
-                    display_name=jira.fields.assignee.displayName,  # type: ignore
-                    email=jira.fields.assignee.emailAddress,  # type: ignore
-                )
-            )
-        except Exception:
-            # Author should exist but if not, doesn't matter
-            pass
-
-        metadata_dict = {}
-        priority = best_effort_get_field_from_issue(jira, "priority")
-        if priority:
-            metadata_dict["priority"] = priority.name
-        status = best_effort_get_field_from_issue(jira, "status")
-        if status:
-            metadata_dict["status"] = status.name
-        resolution = best_effort_get_field_from_issue(jira, "resolution")
-        if resolution:
-            metadata_dict["resolution"] = resolution.name
-        labels = best_effort_get_field_from_issue(jira, "labels")
-        if labels:
-            metadata_dict["label"] = labels
-
-        doc_batch.append(
-            Document(
-                id=page_url,
-                sections=[Section(link=page_url, text=ticket_content)],
-                source=DocumentSource.JIRA,
-                semantic_identifier=jira.fields.summary,
-                doc_updated_at=time_str_to_utc(jira.fields.updated),
-                primary_owners=list(people) or None,
-                # TODO add secondary_owners (commenters) if needed
-                metadata=metadata_dict,
-            )
-        )
-    return doc_batch, len(batch)
-
-
-class JiraConnector(LoadConnector, PollConnector):
-    def __init__(
-        self,
-        jira_project_url: str,
-        comment_email_blacklist: list[str] | None = None,
-        batch_size: int = INDEX_BATCH_SIZE,
-        # if a ticket has one of the labels specified in this list, we will just
-        # skip it. This is generally used to avoid indexing extra sensitive
-        # tickets.
-        labels_to_skip: list[str] = JIRA_CONNECTOR_LABELS_TO_SKIP,
-    ) -> None:
-        self.batch_size = batch_size
-        self.jira_base, self.jira_project = extract_jira_project(jira_project_url)
-        self.jira_client: JIRA | None = None
-        self._comment_email_blacklist = comment_email_blacklist or []
-
-        self.labels_to_skip = set(labels_to_skip)
-
-    @property
-    def comment_email_blacklist(self) -> tuple:
-        return tuple(email.strip() for email in self._comment_email_blacklist)
-
-    def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
-        api_token = credentials["jira_api_token"]
-        # if user provide an email we assume it's cloud
-        if "jira_user_email" in credentials:
-            email = credentials["jira_user_email"]
-            self.jira_client = JIRA(
-                basic_auth=(email, api_token),
-                server=self.jira_base,
-                options={"rest_api_version": JIRA_API_VERSION},
-            )
-        else:
-            self.jira_client = JIRA(
-                token_auth=api_token,
-                server=self.jira_base,
-                options={"rest_api_version": JIRA_API_VERSION},
-            )
-        return None
-
-    def load_from_state(self) -> GenerateDocumentsOutput:
-        if self.jira_client is None:
-            raise ConnectorMissingCredentialError("Jira")
-
-        # Quote the project name to handle reserved words
-        quoted_project = f'"{self.jira_project}"'
-        start_ind = 0
-        while True:
-            doc_batch, fetched_batch_size = fetch_jira_issues_batch(
-                jql=f"project = {quoted_project}",
-                start_index=start_ind,
-                jira_client=self.jira_client,
-                batch_size=self.batch_size,
-                comment_email_blacklist=self.comment_email_blacklist,
-                labels_to_skip=self.labels_to_skip,
-            )
-
-            if doc_batch:
-                yield doc_batch
-
-            start_ind += fetched_batch_size
-            if fetched_batch_size < self.batch_size:
-                break
-
-    def poll_source(
-        self, start: SecondsSinceUnixEpoch, end: SecondsSinceUnixEpoch
-    ) -> GenerateDocumentsOutput:
-        if self.jira_client is None:
-            raise ConnectorMissingCredentialError("Jira")
-
-        start_date_str = datetime.fromtimestamp(start, tz=timezone.utc).strftime(
-            "%Y-%m-%d %H:%M"
-        )
-        end_date_str = datetime.fromtimestamp(end, tz=timezone.utc).strftime(
-            "%Y-%m-%d %H:%M"
-        )
-
-        # Quote the project name to handle reserved words
-        quoted_project = f'"{self.jira_project}"'
-        jql = (
-            f"project = {quoted_project} AND "
-            f"updated >= '{start_date_str}' AND "
-            f"updated <= '{end_date_str}'"
-        )
-
-        start_ind = 0
-        while True:
-            doc_batch, fetched_batch_size = fetch_jira_issues_batch(
-                jql=jql,
-                start_index=start_ind,
-                jira_client=self.jira_client,
-                batch_size=self.batch_size,
-                comment_email_blacklist=self.comment_email_blacklist,
-                labels_to_skip=self.labels_to_skip,
-            )
-
-            if doc_batch:
-                yield doc_batch
-
-            start_ind += fetched_batch_size
-            if fetched_batch_size < self.batch_size:
-                break
-
-
-if __name__ == "__main__":
-    import os
-
-    connector = JiraConnector(
-        os.environ["JIRA_PROJECT_URL"], comment_email_blacklist=[]
-    )
-    connector.load_credentials(
-        {
-            "jira_user_email": os.environ["JIRA_USER_EMAIL"],
-            "jira_api_token": os.environ["JIRA_API_TOKEN"],
-        }
-    )
-    document_batches = connector.load_from_state()
-    print(next(document_batches))

backend/danswer/connectors/danswer_jira/utils.py

@@ -1,92 +0,0 @@
-"""Module with custom fields processing functions"""
-from typing import Any
-from typing import List
-
-from jira import JIRA
-from jira.resources import CustomFieldOption
-from jira.resources import Issue
-from jira.resources import User
-
-from danswer.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-class CustomFieldExtractor:
-    @staticmethod
-    def _process_custom_field_value(value: Any) -> str:
-        """
-        Process a custom field value to a string
-        """
-        try:
-            if isinstance(value, str):
-                return value
-            elif isinstance(value, CustomFieldOption):
-                return value.value
-            elif isinstance(value, User):
-                return value.displayName
-            elif isinstance(value, List):
-                return " ".join(
-                    [CustomFieldExtractor._process_custom_field_value(v) for v in value]
-                )
-            else:
-                return str(value)
-        except Exception as e:
-            logger.error(f"Error processing custom field value {value}: {e}")
-            return ""
-
-    @staticmethod
-    def get_issue_custom_fields(
-        jira: Issue, custom_fields: dict, max_value_length: int = 250
-    ) -> dict:
-        """
-        Process all custom fields of an issue to a dictionary of strings
-        :param jira: jira_issue, bug or similar
-        :param custom_fields: custom fields dictionary
-        :param max_value_length: maximum length of the value to be processed, if exceeded, it will be truncated
-        """
-
-        issue_custom_fields = {
-            custom_fields[key]: value
-            for key, value in jira.fields.__dict__.items()
-            if value and key in custom_fields.keys()
-        }
-
-        processed_fields = {}
-
-        if issue_custom_fields:
-            for key, value in issue_custom_fields.items():
-                processed = CustomFieldExtractor._process_custom_field_value(value)
-                # We need max length  parameter, because there are some plugins that often has very long description
-                # and there is just a technical information so we just avoid long values
-                if len(processed) < max_value_length:
-                    processed_fields[key] = processed
-
-        return processed_fields
-
-    @staticmethod
-    def get_all_custom_fields(jira_client: JIRA) -> dict:
-        """Get all custom fields from Jira"""
-        fields = jira_client.fields()
-        fields_dct = {
-            field["id"]: field["name"] for field in fields if field["custom"] is True
-        }
-        return fields_dct
-
-
-class CommonFieldExtractor:
-    @staticmethod
-    def get_issue_common_fields(jira: Issue) -> dict:
-        return {
-            "Priority": jira.fields.priority.name if jira.fields.priority else None,
-            "Reporter": jira.fields.reporter.displayName
-            if jira.fields.reporter
-            else None,
-            "Assignee": jira.fields.assignee.displayName
-            if jira.fields.assignee
-            else None,
-            "Status": jira.fields.status.name if jira.fields.status else None,
-            "Resolution": jira.fields.resolution.name
-            if jira.fields.resolution
-            else None,
-        }

backend/danswer/connectors/google_drive/connector.py

@@ -1,400 +0,0 @@
-from collections.abc import Callable
-from collections.abc import Iterator
-from concurrent.futures import as_completed
-from concurrent.futures import ThreadPoolExecutor
-from functools import partial
-from typing import Any
-
-from google.oauth2.credentials import Credentials as OAuthCredentials  # type: ignore
-from google.oauth2.service_account import Credentials as ServiceAccountCredentials  # type: ignore
-
-from danswer.configs.app_configs import INDEX_BATCH_SIZE
-from danswer.configs.constants import DocumentSource
-from danswer.connectors.google_drive.doc_conversion import build_slim_document
-from danswer.connectors.google_drive.doc_conversion import (
-    convert_drive_item_to_document,
-)
-from danswer.connectors.google_drive.file_retrieval import crawl_folders_for_files
-from danswer.connectors.google_drive.file_retrieval import get_all_files_in_my_drive
-from danswer.connectors.google_drive.file_retrieval import get_files_in_shared_drive
-from danswer.connectors.google_drive.models import GoogleDriveFileType
-from danswer.connectors.google_utils.google_auth import get_google_creds
-from danswer.connectors.google_utils.google_utils import execute_paginated_retrieval
-from danswer.connectors.google_utils.resources import get_admin_service
-from danswer.connectors.google_utils.resources import get_drive_service
-from danswer.connectors.google_utils.resources import get_google_docs_service
-from danswer.connectors.google_utils.shared_constants import (
-    DB_CREDENTIALS_PRIMARY_ADMIN_KEY,
-)
-from danswer.connectors.google_utils.shared_constants import MISSING_SCOPES_ERROR_STR
-from danswer.connectors.google_utils.shared_constants import ONYX_SCOPE_INSTRUCTIONS
-from danswer.connectors.google_utils.shared_constants import SCOPE_DOC_URL
-from danswer.connectors.google_utils.shared_constants import SLIM_BATCH_SIZE
-from danswer.connectors.google_utils.shared_constants import USER_FIELDS
-from danswer.connectors.interfaces import GenerateDocumentsOutput
-from danswer.connectors.interfaces import GenerateSlimDocumentOutput
-from danswer.connectors.interfaces import LoadConnector
-from danswer.connectors.interfaces import PollConnector
-from danswer.connectors.interfaces import SecondsSinceUnixEpoch
-from danswer.connectors.interfaces import SlimConnector
-from danswer.utils.logger import setup_logger
-
-logger = setup_logger()
-# TODO: Improve this by using the batch utility: https://googleapis.github.io/google-api-python-client/docs/batch.html
-# All file retrievals could be batched and made at once
-
-
-def _extract_str_list_from_comma_str(string: str | None) -> list[str]:
-    if not string:
-        return []
-    return [s.strip() for s in string.split(",") if s.strip()]
-
-
-def _extract_ids_from_urls(urls: list[str]) -> list[str]:
-    return [url.split("/")[-1] for url in urls]
-
-
-def _convert_single_file(
-    creds: Any, primary_admin_email: str, file: dict[str, Any]
-) -> Any:
-    user_email = file.get("owners", [{}])[0].get("emailAddress") or primary_admin_email
-    user_drive_service = get_drive_service(creds, user_email=user_email)
-    docs_service = get_google_docs_service(creds, user_email=user_email)
-    return convert_drive_item_to_document(
-        file=file,
-        drive_service=user_drive_service,
-        docs_service=docs_service,
-    )
-
-
-def _process_files_batch(
-    files: list[GoogleDriveFileType], convert_func: Callable, batch_size: int
-) -> GenerateDocumentsOutput:
-    doc_batch = []
-    with ThreadPoolExecutor(max_workers=min(16, len(files))) as executor:
-        for doc in executor.map(convert_func, files):
-            if doc:
-                doc_batch.append(doc)
-                if len(doc_batch) >= batch_size:
-                    yield doc_batch
-                    doc_batch = []
-    if doc_batch:
-        yield doc_batch
-
-
-class GoogleDriveConnector(LoadConnector, PollConnector, SlimConnector):
-    def __init__(
-        self,
-        include_shared_drives: bool = True,
-        shared_drive_urls: str | None = None,
-        include_my_drives: bool = True,
-        my_drive_emails: str | None = None,
-        shared_folder_urls: str | None = None,
-        batch_size: int = INDEX_BATCH_SIZE,
-        # OLD PARAMETERS
-        folder_paths: list[str] | None = None,
-        include_shared: bool | None = None,
-        follow_shortcuts: bool | None = None,
-        only_org_public: bool | None = None,
-        continue_on_failure: bool | None = None,
-    ) -> None:
-        # Check for old input parameters
-        if (
-            folder_paths is not None
-            or include_shared is not None
-            or follow_shortcuts is not None
-            or only_org_public is not None
-            or continue_on_failure is not None
-        ):
-            logger.exception(
-                "Google Drive connector received old input parameters. "
-                "Please visit the docs for help with the new setup: "
-                f"{SCOPE_DOC_URL}"
-            )
-            raise ValueError(
-                "Google Drive connector received old input parameters. "
-                "Please visit the docs for help with the new setup: "
-                f"{SCOPE_DOC_URL}"
-            )
-
-        if (
-            not include_shared_drives
-            and not include_my_drives
-            and not shared_folder_urls
-        ):
-            raise ValueError(
-                "At least one of include_shared_drives, include_my_drives,"
-                " or shared_folder_urls must be true"
-            )
-
-        self.batch_size = batch_size
-
-        self.include_shared_drives = include_shared_drives
-        shared_drive_url_list = _extract_str_list_from_comma_str(shared_drive_urls)
-        self._requested_shared_drive_ids = set(
-            _extract_ids_from_urls(shared_drive_url_list)
-        )
-
-        self.include_my_drives = include_my_drives
-        self._requested_my_drive_emails = set(
-            _extract_str_list_from_comma_str(my_drive_emails)
-        )
-
-        shared_folder_url_list = _extract_str_list_from_comma_str(shared_folder_urls)
-        self._requested_folder_ids = set(_extract_ids_from_urls(shared_folder_url_list))
-
-        self._primary_admin_email: str | None = None
-
-        self._creds: OAuthCredentials | ServiceAccountCredentials | None = None
-
-        self._retrieved_ids: set[str] = set()
-
-    @property
-    def primary_admin_email(self) -> str:
-        if self._primary_admin_email is None:
-            raise RuntimeError(
-                "Primary admin email missing, "
-                "should not call this property "
-                "before calling load_credentials"
-            )
-        return self._primary_admin_email
-
-    @property
-    def google_domain(self) -> str:
-        if self._primary_admin_email is None:
-            raise RuntimeError(
-                "Primary admin email missing, "
-                "should not call this property "
-                "before calling load_credentials"
-            )
-        return self._primary_admin_email.split("@")[-1]
-
-    @property
-    def creds(self) -> OAuthCredentials | ServiceAccountCredentials:
-        if self._creds is None:
-            raise RuntimeError(
-                "Creds missing, "
-                "should not call this property "
-                "before calling load_credentials"
-            )
-        return self._creds
-
-    def load_credentials(self, credentials: dict[str, Any]) -> dict[str, str] | None:
-        primary_admin_email = credentials[DB_CREDENTIALS_PRIMARY_ADMIN_KEY]
-        self._primary_admin_email = primary_admin_email
-
-        self._creds, new_creds_dict = get_google_creds(
-            credentials=credentials,
-            source=DocumentSource.GOOGLE_DRIVE,
-        )
-        return new_creds_dict
-
-    def _update_traversed_parent_ids(self, folder_id: str) -> None:
-        self._retrieved_ids.add(folder_id)
-
-    def _get_all_user_emails(self, admins_only: bool) -> list[str]:
-        admin_service = get_admin_service(
-            creds=self.creds,
-            user_email=self.primary_admin_email,
-        )
-        query = "isAdmin=true" if admins_only else "isAdmin=false"
-        emails = []
-        for user in execute_paginated_retrieval(
-            retrieval_function=admin_service.users().list,
-            list_key="users",
-            fields=USER_FIELDS,
-            domain=self.google_domain,
-            query=query,
-        ):
-            if email := user.get("primaryEmail"):
-                emails.append(email)
-        return emails
-
-    def _get_all_drive_ids(self) -> set[str]:
-        primary_drive_service = get_drive_service(
-            creds=self.creds,
-            user_email=self.primary_admin_email,
-        )
-        all_drive_ids = set()
-        for drive in execute_paginated_retrieval(
-            retrieval_function=primary_drive_service.drives().list,
-            list_key="drives",
-            useDomainAdminAccess=True,
-            fields="drives(id)",
-        ):
-            all_drive_ids.add(drive["id"])
-        return all_drive_ids
-
-    def _initialize_all_class_variables(self) -> None:
-        # Get all user emails
-        # Get admins first becuase they are more likely to have access to the most files
-        user_emails = [self.primary_admin_email]
-        for admins_only in [True, False]:
-            for email in self._get_all_user_emails(admins_only=admins_only):
-                if email not in user_emails:
-                    user_emails.append(email)
-        self._all_org_emails = user_emails
-
-        self._all_drive_ids: set[str] = self._get_all_drive_ids()
-
-        # remove drive ids from the folder ids because they are queried differently
-        self._requested_folder_ids -= self._all_drive_ids
-
-        # Remove drive_ids that are not in the all_drive_ids and check them as folders instead
-        invalid_drive_ids = self._requested_shared_drive_ids - self._all_drive_ids
-        if invalid_drive_ids:
-            logger.warning(
-                f"Some shared drive IDs were not found. IDs: {invalid_drive_ids}"
-            )
-            logger.warning("Checking for folder access instead...")
-            self._requested_folder_ids.update(invalid_drive_ids)
-
-        if not self.include_shared_drives:
-            self._requested_shared_drive_ids = set()
-        elif not self._requested_shared_drive_ids:
-            self._requested_shared_drive_ids = self._all_drive_ids
-
-    def _impersonate_user_for_retrieval(
-        self,
-        user_email: str,
-        is_slim: bool,
-        start: SecondsSinceUnixEpoch | None = None,
-        end: SecondsSinceUnixEpoch | None = None,
-    ) -> Iterator[GoogleDriveFileType]:
-        drive_service = get_drive_service(self.creds, user_email)
-        if self.include_my_drives and (
-            not self._requested_my_drive_emails
-            or user_email in self._requested_my_drive_emails
-        ):
-            yield from get_all_files_in_my_drive(
-                service=drive_service,
-                update_traversed_ids_func=self._update_traversed_parent_ids,
-                is_slim=is_slim,
-                start=start,
-                end=end,
-            )
-
-        remaining_drive_ids = self._requested_shared_drive_ids - self._retrieved_ids
-        for drive_id in remaining_drive_ids:
-            yield from get_files_in_shared_drive(
-                service=drive_service,
-                drive_id=drive_id,
-                is_slim=is_slim,
-                update_traversed_ids_func=self._update_traversed_parent_ids,
-                start=start,
-                end=end,
-            )
-
-        remaining_folders = self._requested_folder_ids - self._retrieved_ids
-        for folder_id in remaining_folders:
-            yield from crawl_folders_for_files(
-                service=drive_service,
-                parent_id=folder_id,
-                traversed_parent_ids=self._retrieved_ids,
-                update_traversed_ids_func=self._update_traversed_parent_ids,
-                start=start,
-                end=end,
-            )
-
-    def _fetch_drive_items(
-        self,
-        is_slim: bool,
-        start: SecondsSinceUnixEpoch | None = None,
-        end: SecondsSinceUnixEpoch | None = None,
-    ) -> Iterator[GoogleDriveFileType]:
-        self._initialize_all_class_variables()
-
-        # Process users in parallel using ThreadPoolExecutor
-        with ThreadPoolExecutor(max_workers=10) as executor:
-            future_to_email = {
-                executor.submit(
-                    self._impersonate_user_for_retrieval, email, is_slim, start, end
-                ): email
-                for email in self._all_org_emails
-            }
-
-            # Yield results as they complete
-            for future in as_completed(future_to_email):
-                yield from future.result()
-
-        remaining_folders = self._requested_folder_ids - self._retrieved_ids
-        if remaining_folders:
-            logger.warning(
-                f"Some folders/drives were not retrieved. IDs: {remaining_folders}"
-            )
-
-    def _extract_docs_from_google_drive(
-        self,
-        start: SecondsSinceUnixEpoch | None = None,
-        end: SecondsSinceUnixEpoch | None = None,
-    ) -> GenerateDocumentsOutput:
-        # Create a larger process pool for file conversion
-        convert_func = partial(
-            _convert_single_file, self.creds, self.primary_admin_email
-        )
-
-        # Process files in larger batches
-        LARGE_BATCH_SIZE = self.batch_size * 4
-        files_to_process = []
-        # Gather the files into batches to be processed in parallel
-        for file in self._fetch_drive_items(is_slim=False, start=start, end=end):
-            files_to_process.append(file)
-            if len(files_to_process) >= LARGE_BATCH_SIZE:
-                yield from _process_files_batch(
-                    files_to_process, convert_func, self.batch_size
-                )
-                files_to_process = []
-
-        # Process any remaining files
-        if files_to_process:
-            yield from _process_files_batch(
-                files_to_process, convert_func, self.batch_size
-            )
-
-    def load_from_state(self) -> GenerateDocumentsOutput:
-        try:
-            yield from self._extract_docs_from_google_drive()
-        except Exception as e:
-            if MISSING_SCOPES_ERROR_STR in str(e):
-                raise PermissionError(ONYX_SCOPE_INSTRUCTIONS) from e
-            raise e
-
-    def poll_source(
-        self, start: SecondsSinceUnixEpoch, end: SecondsSinceUnixEpoch
-    ) -> GenerateDocumentsOutput:
-        try:
-            yield from self._extract_docs_from_google_drive(start, end)
-        except Exception as e:
-            if MISSING_SCOPES_ERROR_STR in str(e):
-                raise PermissionError(ONYX_SCOPE_INSTRUCTIONS) from e
-            raise e
-
-    def _extract_slim_docs_from_google_drive(
-        self,
-        start: SecondsSinceUnixEpoch | None = None,
-        end: SecondsSinceUnixEpoch | None = None,
-    ) -> GenerateSlimDocumentOutput:
-        slim_batch = []
-        for file in self._fetch_drive_items(
-            is_slim=True,
-            start=start,
-            end=end,
-        ):
-            if doc := build_slim_document(file):
-                slim_batch.append(doc)
-            if len(slim_batch) >= SLIM_BATCH_SIZE:
-                yield slim_batch
-                slim_batch = []
-        yield slim_batch
-
-    def retrieve_all_slim_documents(
-        self,
-        start: SecondsSinceUnixEpoch | None = None,
-        end: SecondsSinceUnixEpoch | None = None,
-    ) -> GenerateSlimDocumentOutput:
-        try:
-            yield from self._extract_slim_docs_from_google_drive(start, end)
-        except Exception as e:
-            if MISSING_SCOPES_ERROR_STR in str(e):
-                raise PermissionError(ONYX_SCOPE_INSTRUCTIONS) from e
-            raise e

backend/danswer/connectors/google_utils/google_auth.py

@@ -1,107 +0,0 @@
-import json
-from typing import cast
-
-from google.auth.transport.requests import Request  # type: ignore
-from google.oauth2.credentials import Credentials as OAuthCredentials  # type: ignore
-from google.oauth2.service_account import Credentials as ServiceAccountCredentials  # type: ignore
-
-from danswer.configs.constants import DocumentSource
-from danswer.connectors.google_utils.shared_constants import (
-    DB_CREDENTIALS_DICT_SERVICE_ACCOUNT_KEY,
-)
-from danswer.connectors.google_utils.shared_constants import (
-    DB_CREDENTIALS_DICT_TOKEN_KEY,
-)
-from danswer.connectors.google_utils.shared_constants import (
-    DB_CREDENTIALS_PRIMARY_ADMIN_KEY,
-)
-from danswer.connectors.google_utils.shared_constants import (
-    GOOGLE_SCOPES,
-)
-from danswer.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def get_google_oauth_creds(
-    token_json_str: str, source: DocumentSource
-) -> OAuthCredentials | None:
-    creds_json = json.loads(token_json_str)
-    creds = OAuthCredentials.from_authorized_user_info(
-        info=creds_json,
-        scopes=GOOGLE_SCOPES[source],
-    )
-    if creds.valid:
-        return creds
-
-    if creds.expired and creds.refresh_token:
-        try:
-            creds.refresh(Request())
-            if creds.valid:
-                logger.notice("Refreshed Google Drive tokens.")
-                return creds
-        except Exception:
-            logger.exception("Failed to refresh google drive access token due to:")
-            return None
-
-    return None
-
-
-def get_google_creds(
-    credentials: dict[str, str],
-    source: DocumentSource,
-) -> tuple[ServiceAccountCredentials | OAuthCredentials, dict[str, str] | None]:
-    """Checks for two different types of credentials.
-    (1) A credential which holds a token acquired via a user going thorough
-    the Google OAuth flow.
-    (2) A credential which holds a service account key JSON file, which
-    can then be used to impersonate any user in the workspace.
-    """
-    oauth_creds = None
-    service_creds = None
-    new_creds_dict = None
-    if DB_CREDENTIALS_DICT_TOKEN_KEY in credentials:
-        # OAUTH
-        access_token_json_str = cast(str, credentials[DB_CREDENTIALS_DICT_TOKEN_KEY])
-        oauth_creds = get_google_oauth_creds(
-            token_json_str=access_token_json_str, source=source
-        )
-
-        # tell caller to update token stored in DB if it has changed
-        # (e.g. the token has been refreshed)
-        new_creds_json_str = oauth_creds.to_json() if oauth_creds else ""
-        if new_creds_json_str != access_token_json_str:
-            new_creds_dict = {
-                DB_CREDENTIALS_DICT_TOKEN_KEY: new_creds_json_str,
-                DB_CREDENTIALS_PRIMARY_ADMIN_KEY: credentials[
-                    DB_CREDENTIALS_PRIMARY_ADMIN_KEY
-                ],
-            }
-    elif DB_CREDENTIALS_DICT_SERVICE_ACCOUNT_KEY in credentials:
-        # SERVICE ACCOUNT
-        service_account_key_json_str = credentials[
-            DB_CREDENTIALS_DICT_SERVICE_ACCOUNT_KEY
-        ]
-        service_account_key = json.loads(service_account_key_json_str)
-
-        service_creds = ServiceAccountCredentials.from_service_account_info(
-            service_account_key, scopes=GOOGLE_SCOPES[source]
-        )
-
-        if not service_creds.valid or not service_creds.expired:
-            service_creds.refresh(Request())
-
-        if not service_creds.valid:
-            raise PermissionError(
-                f"Unable to access {source} - service account credentials are invalid."
-            )
-
-    creds: ServiceAccountCredentials | OAuthCredentials | None = (
-        oauth_creds or service_creds
-    )
-    if creds is None:
-        raise PermissionError(
-            f"Unable to access {source} - unknown credential structure."
-        )
-
-    return creds, new_creds_dict

backend/danswer/connectors/salesforce/connector.py

@@ -1,289 +0,0 @@
-import os
-from collections.abc import Iterator
-from datetime import datetime
-from datetime import timezone
-from typing import Any
-
-from simple_salesforce import Salesforce
-from simple_salesforce import SFType
-
-from danswer.configs.app_configs import INDEX_BATCH_SIZE
-from danswer.configs.constants import DocumentSource
-from danswer.connectors.cross_connector_utils.miscellaneous_utils import time_str_to_utc
-from danswer.connectors.interfaces import GenerateDocumentsOutput
-from danswer.connectors.interfaces import GenerateSlimDocumentOutput
-from danswer.connectors.interfaces import LoadConnector
-from danswer.connectors.interfaces import PollConnector
-from danswer.connectors.interfaces import SecondsSinceUnixEpoch
-from danswer.connectors.interfaces import SlimConnector
-from danswer.connectors.models import BasicExpertInfo
-from danswer.connectors.models import ConnectorMissingCredentialError
-from danswer.connectors.models import Document
-from danswer.connectors.models import Section
-from danswer.connectors.models import SlimDocument
-from danswer.connectors.salesforce.utils import extract_dict_text
-from danswer.utils.logger import setup_logger
-
-
-# TODO: this connector does not work well at large scales
-# the large query against a large Salesforce instance has been reported to take 1.5 hours.
-# Additionally it seems to eat up more memory over time if the connection is long running (again a scale issue).
-
-
-DEFAULT_PARENT_OBJECT_TYPES = ["Account"]
-MAX_QUERY_LENGTH = 10000  # max query length is 20,000 characters
-ID_PREFIX = "SALESFORCE_"
-
-logger = setup_logger()
-
-
-class SalesforceConnector(LoadConnector, PollConnector, SlimConnector):
-    def __init__(
-        self,
-        batch_size: int = INDEX_BATCH_SIZE,
-        requested_objects: list[str] = [],
-    ) -> None:
-        self.batch_size = batch_size
-        self.sf_client: Salesforce | None = None
-        self.parent_object_list = (
-            [obj.capitalize() for obj in requested_objects]
-            if requested_objects
-            else DEFAULT_PARENT_OBJECT_TYPES
-        )
-
-    def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
-        self.sf_client = Salesforce(
-            username=credentials["sf_username"],
-            password=credentials["sf_password"],
-            security_token=credentials["sf_security_token"],
-        )
-
-        return None
-
-    def _get_sf_type_object_json(self, type_name: str) -> Any:
-        if self.sf_client is None:
-            raise ConnectorMissingCredentialError("Salesforce")
-        sf_object = SFType(
-            type_name, self.sf_client.session_id, self.sf_client.sf_instance
-        )
-        return sf_object.describe()
-
-    def _get_name_from_id(self, id: str) -> str:
-        if self.sf_client is None:
-            raise ConnectorMissingCredentialError("Salesforce")
-        try:
-            user_object_info = self.sf_client.query(
-                f"SELECT Name FROM User WHERE Id = '{id}'"
-            )
-            name = user_object_info.get("Records", [{}])[0].get("Name", "Null User")
-            return name
-        except Exception:
-            logger.warning(f"Couldnt find name for object id: {id}")
-            return "Null User"
-
-    def _convert_object_instance_to_document(
-        self, object_dict: dict[str, Any]
-    ) -> Document:
-        if self.sf_client is None:
-            raise ConnectorMissingCredentialError("Salesforce")
-
-        salesforce_id = object_dict["Id"]
-        danswer_salesforce_id = f"{ID_PREFIX}{salesforce_id}"
-        extracted_link = f"https://{self.sf_client.sf_instance}/{salesforce_id}"
-        extracted_doc_updated_at = time_str_to_utc(object_dict["LastModifiedDate"])
-        extracted_object_text = extract_dict_text(object_dict)
-        extracted_semantic_identifier = object_dict.get("Name", "Unknown Object")
-        extracted_primary_owners = [
-            BasicExpertInfo(
-                display_name=self._get_name_from_id(object_dict["LastModifiedById"])
-            )
-        ]
-
-        doc = Document(
-            id=danswer_salesforce_id,
-            sections=[Section(link=extracted_link, text=extracted_object_text)],
-            source=DocumentSource.SALESFORCE,
-            semantic_identifier=extracted_semantic_identifier,
-            doc_updated_at=extracted_doc_updated_at,
-            primary_owners=extracted_primary_owners,
-            metadata={},
-        )
-        return doc
-
-    def _is_valid_child_object(self, child_relationship: dict) -> bool:
-        if self.sf_client is None:
-            raise ConnectorMissingCredentialError("Salesforce")
-
-        if not child_relationship["childSObject"]:
-            return False
-        if not child_relationship["relationshipName"]:
-            return False
-
-        sf_type = child_relationship["childSObject"]
-        object_description = self._get_sf_type_object_json(sf_type)
-        if not object_description["queryable"]:
-            return False
-
-        try:
-            query = f"SELECT Count() FROM {sf_type} LIMIT 1"
-            result = self.sf_client.query(query)
-            if result["totalSize"] == 0:
-                return False
-        except Exception as e:
-            logger.warning(f"Object type {sf_type} doesn't support query: {e}")
-            return False
-
-        if child_relationship["field"]:
-            if child_relationship["field"] == "RelatedToId":
-                return False
-        else:
-            return False
-
-        return True
-
-    def _get_all_children_of_sf_type(self, sf_type: str) -> list[dict]:
-        if self.sf_client is None:
-            raise ConnectorMissingCredentialError("Salesforce")
-
-        object_description = self._get_sf_type_object_json(sf_type)
-
-        children_objects: list[dict] = []
-        for child_relationship in object_description["childRelationships"]:
-            if self._is_valid_child_object(child_relationship):
-                children_objects.append(
-                    {
-                        "relationship_name": child_relationship["relationshipName"],
-                        "object_type": child_relationship["childSObject"],
-                    }
-                )
-        return children_objects
-
-    def _get_all_fields_for_sf_type(self, sf_type: str) -> list[str]:
-        if self.sf_client is None:
-            raise ConnectorMissingCredentialError("Salesforce")
-
-        object_description = self._get_sf_type_object_json(sf_type)
-
-        fields = [
-            field.get("name")
-            for field in object_description["fields"]
-            if field.get("type", "base64") != "base64"
-        ]
-
-        return fields
-
-    def _generate_query_per_parent_type(self, parent_sf_type: str) -> Iterator[str]:
-        """
-        This function takes in an object_type and generates query(s) designed to grab
-        information associated to objects of that type.
-        It does that by getting all the fields of the parent object type.
-        Then it gets all the child objects of that object type and all the fields of
-        those children as well.
-        """
-        parent_fields = self._get_all_fields_for_sf_type(parent_sf_type)
-        child_sf_types = self._get_all_children_of_sf_type(parent_sf_type)
-
-        query = f"SELECT {', '.join(parent_fields)}"
-        for child_object_dict in child_sf_types:
-            fields = self._get_all_fields_for_sf_type(child_object_dict["object_type"])
-            query_addition = f", \n(SELECT {', '.join(fields)} FROM {child_object_dict['relationship_name']})"
-
-            if len(query_addition) + len(query) > MAX_QUERY_LENGTH:
-                query += f"\n FROM {parent_sf_type}"
-                yield query
-                query = "SELECT Id" + query_addition
-            else:
-                query += query_addition
-
-        query += f"\n FROM {parent_sf_type}"
-
-        yield query
-
-    def _fetch_from_salesforce(
-        self,
-        start: datetime | None = None,
-        end: datetime | None = None,
-    ) -> GenerateDocumentsOutput:
-        if self.sf_client is None:
-            raise ConnectorMissingCredentialError("Salesforce")
-
-        doc_batch: list[Document] = []
-        for parent_object_type in self.parent_object_list:
-            logger.debug(f"Processing: {parent_object_type}")
-
-            query_results: dict = {}
-            for query in self._generate_query_per_parent_type(parent_object_type):
-                if start is not None and end is not None:
-                    if start and start.tzinfo is None:
-                        start = start.replace(tzinfo=timezone.utc)
-                    if end and end.tzinfo is None:
-                        end = end.replace(tzinfo=timezone.utc)
-                    query += f" WHERE LastModifiedDate > {start.isoformat()} AND LastModifiedDate < {end.isoformat()}"
-
-                query_result = self.sf_client.query_all(query)
-
-                for record_dict in query_result["records"]:
-                    query_results.setdefault(record_dict["Id"], {}).update(record_dict)
-
-            logger.info(
-                f"Number of {parent_object_type} Objects processed: {len(query_results)}"
-            )
-
-            for combined_object_dict in query_results.values():
-                doc_batch.append(
-                    self._convert_object_instance_to_document(combined_object_dict)
-                )
-
-                if len(doc_batch) > self.batch_size:
-                    yield doc_batch
-                    doc_batch = []
-        yield doc_batch
-
-    def load_from_state(self) -> GenerateDocumentsOutput:
-        return self._fetch_from_salesforce()
-
-    def poll_source(
-        self, start: SecondsSinceUnixEpoch, end: SecondsSinceUnixEpoch
-    ) -> GenerateDocumentsOutput:
-        if self.sf_client is None:
-            raise ConnectorMissingCredentialError("Salesforce")
-        start_datetime = datetime.utcfromtimestamp(start)
-        end_datetime = datetime.utcfromtimestamp(end)
-        return self._fetch_from_salesforce(start=start_datetime, end=end_datetime)
-
-    def retrieve_all_slim_documents(
-        self,
-        start: SecondsSinceUnixEpoch | None = None,
-        end: SecondsSinceUnixEpoch | None = None,
-    ) -> GenerateSlimDocumentOutput:
-        if self.sf_client is None:
-            raise ConnectorMissingCredentialError("Salesforce")
-        doc_metadata_list: list[SlimDocument] = []
-        for parent_object_type in self.parent_object_list:
-            query = f"SELECT Id FROM {parent_object_type}"
-            query_result = self.sf_client.query_all(query)
-            doc_metadata_list.extend(
-                SlimDocument(
-                    id=f"{ID_PREFIX}{instance_dict.get('Id', '')}",
-                    perm_sync_data={},
-                )
-                for instance_dict in query_result["records"]
-            )
-
-        yield doc_metadata_list
-
-
-if __name__ == "__main__":
-    connector = SalesforceConnector(
-        requested_objects=os.environ["REQUESTED_OBJECTS"].split(",")
-    )
-
-    connector.load_credentials(
-        {
-            "sf_username": os.environ["SF_USERNAME"],
-            "sf_password": os.environ["SF_PASSWORD"],
-            "sf_security_token": os.environ["SF_SECURITY_TOKEN"],
-        }
-    )
-    document_batches = connector.load_from_state()
-    print(next(document_batches))

backend/danswer/connectors/salesforce/utils.py

@@ -1,66 +0,0 @@
-import re
-from typing import Union
-
-SF_JSON_FILTER = r"Id$|Date$|stamp$|url$"
-
-
-def _clean_salesforce_dict(data: Union[dict, list]) -> Union[dict, list]:
-    if isinstance(data, dict):
-        if "records" in data.keys():
-            data = data["records"]
-    if isinstance(data, dict):
-        if "attributes" in data.keys():
-            if isinstance(data["attributes"], dict):
-                data.update(data.pop("attributes"))
-
-    if isinstance(data, dict):
-        filtered_dict = {}
-        for key, value in data.items():
-            if not re.search(SF_JSON_FILTER, key, re.IGNORECASE):
-                if "__c" in key:  # remove the custom object indicator for display
-                    key = key[:-3]
-                if isinstance(value, (dict, list)):
-                    filtered_value = _clean_salesforce_dict(value)
-                    if filtered_value:  # Only add non-empty dictionaries or lists
-                        filtered_dict[key] = filtered_value
-                elif value is not None:
-                    filtered_dict[key] = value
-        return filtered_dict
-    elif isinstance(data, list):
-        filtered_list = []
-        for item in data:
-            if isinstance(item, (dict, list)):
-                filtered_item = _clean_salesforce_dict(item)
-                if filtered_item:  # Only add non-empty dictionaries or lists
-                    filtered_list.append(filtered_item)
-            elif item is not None:
-                filtered_list.append(filtered_item)
-        return filtered_list
-    else:
-        return data
-
-
-def _json_to_natural_language(data: Union[dict, list], indent: int = 0) -> str:
-    result = []
-    indent_str = " " * indent
-
-    if isinstance(data, dict):
-        for key, value in data.items():
-            if isinstance(value, (dict, list)):
-                result.append(f"{indent_str}{key}:")
-                result.append(_json_to_natural_language(value, indent + 2))
-            else:
-                result.append(f"{indent_str}{key}: {value}")
-    elif isinstance(data, list):
-        for item in data:
-            result.append(_json_to_natural_language(item, indent))
-    else:
-        result.append(f"{indent_str}{data}")
-
-    return "\n".join(result)
-
-
-def extract_dict_text(raw_dict: dict) -> str:
-    processed_dict = _clean_salesforce_dict(raw_dict)
-    natural_language_dict = _json_to_natural_language(processed_dict)
-    return natural_language_dict

backend/danswer/connectors/slack/load_connector.py

@@ -1,140 +0,0 @@
-import json
-import os
-from datetime import datetime
-from datetime import timezone
-from pathlib import Path
-from typing import Any
-from typing import cast
-
-from danswer.configs.app_configs import INDEX_BATCH_SIZE
-from danswer.configs.constants import DocumentSource
-from danswer.connectors.interfaces import GenerateDocumentsOutput
-from danswer.connectors.interfaces import LoadConnector
-from danswer.connectors.models import Document
-from danswer.connectors.models import Section
-from danswer.connectors.slack.connector import filter_channels
-from danswer.connectors.slack.utils import get_message_link
-from danswer.utils.logger import setup_logger
-
-
-logger = setup_logger()
-
-
-def get_event_time(event: dict[str, Any]) -> datetime | None:
-    ts = event.get("ts")
-    if not ts:
-        return None
-    return datetime.fromtimestamp(float(ts), tz=timezone.utc)
-
-
-class SlackLoadConnector(LoadConnector):
-    # WARNING: DEPRECATED, DO NOT USE
-    def __init__(
-        self,
-        workspace: str,
-        export_path_str: str,
-        channels: list[str] | None = None,
-        # if specified, will treat the specified channel strings as
-        # regexes, and will only index channels that fully match the regexes
-        channel_regex_enabled: bool = False,
-        batch_size: int = INDEX_BATCH_SIZE,
-    ) -> None:
-        self.workspace = workspace
-        self.channels = channels
-        self.channel_regex_enabled = channel_regex_enabled
-        self.export_path_str = export_path_str
-        self.batch_size = batch_size
-
-    def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
-        if credentials:
-            logger.warning("Unexpected credentials provided for Slack Load Connector")
-        return None
-
-    @staticmethod
-    def _process_batch_event(
-        slack_event: dict[str, Any],
-        channel: dict[str, Any],
-        matching_doc: Document | None,
-        workspace: str,
-    ) -> Document | None:
-        if (
-            slack_event["type"] == "message"
-            and slack_event.get("subtype") != "channel_join"
-        ):
-            if matching_doc:
-                return Document(
-                    id=matching_doc.id,
-                    sections=matching_doc.sections
-                    + [
-                        Section(
-                            link=get_message_link(
-                                event=slack_event,
-                                workspace=workspace,
-                                channel_id=channel["id"],
-                            ),
-                            text=slack_event["text"],
-                        )
-                    ],
-                    source=matching_doc.source,
-                    semantic_identifier=matching_doc.semantic_identifier,
-                    title="",  # slack docs don't really have a "title"
-                    doc_updated_at=get_event_time(slack_event),
-                    metadata=matching_doc.metadata,
-                )
-
-            return Document(
-                id=slack_event["ts"],
-                sections=[
-                    Section(
-                        link=get_message_link(
-                            event=slack_event,
-                            workspace=workspace,
-                            channel_id=channel["id"],
-                        ),
-                        text=slack_event["text"],
-                    )
-                ],
-                source=DocumentSource.SLACK,
-                semantic_identifier=channel["name"],
-                title="",  # slack docs don't really have a "title"
-                doc_updated_at=get_event_time(slack_event),
-                metadata={},
-            )
-
-        return None
-
-    def load_from_state(self) -> GenerateDocumentsOutput:
-        export_path = Path(self.export_path_str)
-
-        with open(export_path / "channels.json") as f:
-            all_channels = json.load(f)
-
-        filtered_channels = filter_channels(
-            all_channels, self.channels, self.channel_regex_enabled
-        )
-
-        document_batch: dict[str, Document] = {}
-        for channel_info in filtered_channels:
-            channel_dir_path = export_path / cast(str, channel_info["name"])
-            channel_file_paths = [
-                channel_dir_path / file_name
-                for file_name in os.listdir(channel_dir_path)
-            ]
-            for path in channel_file_paths:
-                with open(path) as f:
-                    events = cast(list[dict[str, Any]], json.load(f))
-                for slack_event in events:
-                    doc = self._process_batch_event(
-                        slack_event=slack_event,
-                        channel=channel_info,
-                        matching_doc=document_batch.get(
-                            slack_event.get("thread_ts", "")
-                        ),
-                        workspace=self.workspace,
-                    )
-                    if doc:
-                        document_batch[doc.id] = doc
-                        if len(document_batch) >= self.batch_size:
-                            yield list(document_batch.values())
-
-        yield list(document_batch.values())