update folder logic

Update gmail test configuration

.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:
@@ -60,7 +60,7 @@ 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 }}

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

@@ -6,7 +6,7 @@ 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') }}
 
 jobs:
@@ -38,7 +38,7 @@ 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
@@ -51,5 +51,5 @@ jobs:
           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:${{ github.ref_name }}
+          image-ref: docker.io/onyxdotapp/onyx-model-server:${{ github.ref_name }}
           severity: "CRITICAL,HIGH"

.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

@@ -14,18 +14,19 @@ jobs:
     name: Playwright Tests
 
     # See https://runs-on.com/runners/linux/
-    runs-on: [runs-on,runner=8cpu-linux-x64,ram=16,"run-id=${{ github.run_id }}"]
+    runs-on:
+      [runs-on, runner=8cpu-linux-x64, ram=16, "run-id=${{ github.run_id }}"]
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
         with:
           fetch-depth: 0
-          
+
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: '3.11'
-          cache: 'pip'
+          python-version: "3.11"
+          cache: "pip"
           cache-dependency-path: |
             backend/requirements/default.txt
             backend/requirements/dev.txt
@@ -35,7 +36,7 @@ jobs:
           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:
@@ -48,7 +49,7 @@ jobs:
       - name: Install playwright browsers
         working-directory: ./web
         run: npx playwright install --with-deps
-        
+
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
 
@@ -60,13 +61,13 @@ jobs:
 
       # 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
@@ -75,7 +76,7 @@ jobs:
           context: ./web
           file: ./web/Dockerfile
           platforms: linux/amd64
-          tags: danswer/danswer-web-server:test
+          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=${{ env.RUNS_ON_S3_BUCKET_CACHE }}
@@ -87,7 +88,7 @@ jobs:
           context: ./backend
           file: ./backend/Dockerfile
           platforms: linux/amd64
-          tags: danswer/danswer-backend:test
+          tags: onyxdotapp/onyx-backend:test
           push: false
           load: true
           cache-from: type=s3,prefix=cache/${{ github.repository }}/integration-tests/backend/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }}
@@ -99,7 +100,7 @@ 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=${{ env.RUNS_ON_S3_BUCKET_CACHE }}
@@ -110,6 +111,7 @@ jobs:
           cd deployment/docker_compose
           ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true \
           AUTH_TYPE=basic \
+          GEN_AI_API_KEY=${{ secrets.OPENAI_API_KEY }} \
           REQUIRE_EMAIL_VERIFICATION=false \
           DISABLE_TELEMETRY=true \
           IMAGE_TAG=test \
@@ -119,12 +121,12 @@ 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))
@@ -152,7 +154,7 @@ jobs:
 
       - name: Run pytest playwright test init
         working-directory: ./backend
-        env: 
+        env:
           PYTEST_IGNORE_SKIP: true
         run: pytest -s tests/integration/tests/playwright/test_playwright.py
 
@@ -168,7 +170,7 @@ jobs:
           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()
@@ -176,7 +178,7 @@ jobs:
           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
@@ -191,35 +193,36 @@ jobs:
 
   chromatic-tests:
     name: Chromatic Tests
-    
+
     needs: playwright-tests
-    runs-on: [runs-on,runner=8cpu-linux-x64,ram=16,"run-id=${{ github.run_id }}"]
+    runs-on:
+      [runs-on, runner=8cpu-linux-x64, ram=16, "run-id=${{ github.run_id }}"]
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
         with:
           fetch-depth: 0
-          
+
       - name: Setup node
         uses: actions/setup-node@v4
         with:
           node-version: 22
-          
+
       - name: Install node dependencies
         working-directory: ./web
         run: npm ci
-        
+
       - name: Download Playwright test results
         uses: actions/download-artifact@v4
         with:
           name: test-results
           path: ./web/test-results
-          
+
       - name: Run Chromatic
         uses: chromaui/action@latest
         with:
           playwright: true
           projectToken: ${{ secrets.CHROMATIC_PROJECT_TOKEN }}
           workingDir: ./web
-        env: 
+        env:
           CHROMATIC_ARCHIVE_LOCATION: ./test-results

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

@@ -8,7 +8,7 @@ on:
   pull_request:
     branches:
       - main
-      - 'release/**'
+      - "release/**"
 
 env:
   OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
@@ -16,11 +16,12 @@ env:
   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=8cpu-linux-x64, ram=16, "run-id=${{ github.run_id }}"]
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
@@ -36,21 +37,21 @@ 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
@@ -58,7 +59,7 @@ 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=${{ env.RUNS_ON_S3_BUCKET_CACHE }}
@@ -70,19 +71,19 @@ jobs:
           context: ./backend
           file: ./backend/Dockerfile.model_server
           platforms: linux/amd64
-          tags: danswer/danswer-model-server:test
+          tags: onyxdotapp/onyx-model-server:test
           push: false
           load: true
           cache-from: type=s3,prefix=cache/${{ 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 }}
@@ -119,7 +120,7 @@ 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
@@ -131,15 +132,14 @@ 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 \
@@ -153,12 +153,12 @@ 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=$((current_time - start_time))
@@ -202,7 +202,7 @@ jobs:
             -e CONFLUENCE_USER_NAME=${CONFLUENCE_USER_NAME} \
             -e CONFLUENCE_ACCESS_TOKEN=${CONFLUENCE_ACCESS_TOKEN} \
             -e TEST_WEB_HOSTNAME=test-runner \
-            danswer/danswer-integration:test \
+            onyxdotapp/onyx-integration:test \
             /app/tests/integration/tests \
             /app/tests/integration/connector_job_tests
         continue-on-error: true
@@ -229,7 +229,7 @@ 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

@@ -24,6 +24,8 @@ env:
   GOOGLE_DRIVE_OAUTH_CREDENTIALS_JSON_STR: ${{ secrets.GOOGLE_DRIVE_OAUTH_CREDENTIALS_JSON_STR }}
   GOOGLE_GMAIL_SERVICE_ACCOUNT_JSON_STR: ${{ secrets.GOOGLE_GMAIL_SERVICE_ACCOUNT_JSON_STR }}
   GOOGLE_GMAIL_OAUTH_CREDENTIALS_JSON_STR: ${{ secrets.GOOGLE_GMAIL_OAUTH_CREDENTIALS_JSON_STR }}
+  # Slab
+  SLAB_BOT_TOKEN: ${{ secrets.SLAB_BOT_TOKEN }}
 
 jobs:
   connectors-check:

.github/workflows/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

.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",
@@ -224,7 +224,7 @@
             },
             "args": [
                 "-A",
-                "danswer.background.celery.versioned_apps.heavy",
+                "onyx.background.celery.versioned_apps.heavy",
                 "worker",
                 "--pool=threads",
                 "--concurrency=4",
@@ -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",

CONTRIBUTING.md

@@ -1,32 +1,34 @@
-<!-- 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.
 
 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. 
-
+And always feel free to message us (Chris Weaver / Yuhong Sun) on
+[Slack](https://join.slack.com/t/danswer/shared_invite/zt-1w76msxmd-HJHLe3KNFIAIzk_0dSOKaQ) /
+[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.
@@ -34,72 +36,78 @@ When opening a pull request, mention related issues and feel free to tag relevan
 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.
 
-
 ### 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/danswer/shared_invite/zt-1w76msxmd-HJHLe3KNFIAIzk_0dSOKaQ)
+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 +117,50 @@ 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
+
 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 +168,24 @@ 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 +201,61 @@ 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:
+Then, from the `onyx/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.
-
+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 `danswer/web` directory. 
-To run the formatter, use `npx prettier --write .` from the `danswer/web` directory.
+
+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.
 
-
 ### 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
-```
+```

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

@@ -1,142 +1,143 @@
-<!-- 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/LogoOnyx.png?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-2twesxdr6-5iQitKZQpgq~hYIZ~dv3KA" target="_blank">
+<a href="https://join.slack.com/t/danswer/shared_invite/zt-1w76msxmd-HJHLe3KNFIAIzk_0dSOKaQ" 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)
+[![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)
 
 ## ✨Contributors
 
-<a href="https://github.com/danswer-ai/danswer/graphs/contributors">
-  <img alt="contributors" src="https://contrib.rocks/image?repo=danswer-ai/danswer"/>
+<a href="https://github.com/onyx-dot-app/onyx/graphs/contributors">
+  <img alt="contributors" src="https://contrib.rocks/image?repo=onyx-dot-app/onyx"/>
 </a>
 
 <p align="right" style="font-size: 14px; color: #555; margin-top: 20px;">

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

@@ -1,5 +1,5 @@
 from sqlalchemy.engine.base import Connection
-from typing import Any
+from typing import Literal
 import asyncio
 from logging.config import fileConfig
 import logging
@@ -8,12 +8,13 @@ from alembic import context
 from sqlalchemy import pool
 from sqlalchemy.ext.asyncio import create_async_engine
 from sqlalchemy.sql import text
+from sqlalchemy.sql.schema import SchemaItem
 
 from shared_configs.configs import MULTI_TENANT
-from danswer.db.engine import build_connection_string
-from danswer.db.models import Base
+from onyx.db.engine import build_connection_string
+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 onyx.db.engine import get_all_tenant_ids
 from shared_configs.configs import POSTGRES_DEFAULT_SCHEMA
 
 # Alembic Config object
@@ -35,7 +36,18 @@ logger = logging.getLogger(__name__)
 
 
 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.

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/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/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/4ee1287bd26a_add_multiple_slack_bot_support.py

@@ -10,8 +10,8 @@ from typing import cast
 from alembic import op
 import sqlalchemy as sa
 from sqlalchemy.orm import Session
-from danswer.key_value_store.factory import get_kv_store
-from danswer.db.models import SlackBot
+from 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.

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/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.context.search.enums import RecencyBiasSetting
-from danswer.context.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/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/949b4a92a401_remove_rt.py

@@ -10,7 +10,7 @@ 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,

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/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/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"

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/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/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/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/danswer/__init__.py

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

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

@@ -1,60 +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=20),
-        "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=15),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-    {
-        "name": "check-for-prune",
-        "task": "check_for_pruning",
-        "schedule": timedelta(seconds=15),
-        "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},
-    },
-    {
-        "name": "check-for-doc-permissions-sync",
-        "task": "check_for_doc_permissions_sync",
-        "schedule": timedelta(seconds=30),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-    {
-        "name": "check-for-external-group-sync",
-        "task": "check_for_external_group_sync",
-        "schedule": timedelta(seconds=20),
-        "options": {"priority": DanswerCeleryPriority.HIGH},
-    },
-]
-
-
-def get_tasks_to_schedule() -> list[dict[str, Any]]:
-    return tasks_to_schedule

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

@@ -1,10 +0,0 @@
-"""Factory stub for running celery worker / celery beat."""
-from celery import Celery
-
-from danswer.utils.variable_functionality import fetch_versioned_implementation
-from danswer.utils.variable_functionality import set_is_ee_based_on_env_variable
-
-set_is_ee_based_on_env_variable()
-app: Celery = fetch_versioned_implementation(
-    "danswer.background.celery.apps.primary", "celery_app"
-)

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.context.search.enums import QueryFlow
-from danswer.context.search.enums import SearchType
-from danswer.context.search.models import RetrievalDocs
-from danswer.context.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/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/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())

backend/danswer/danswerbot/slack/handlers/utils.py

@@ -1,19 +0,0 @@
-from slack_sdk import WebClient
-
-from danswer.danswerbot.slack.utils import respond_in_thread
-
-
-def send_team_member_message(
-    client: WebClient,
-    channel: str,
-    thread_ts: str,
-) -> None:
-    respond_in_thread(
-        client=client,
-        channel=channel,
-        text=(
-            "👋 Hi, we've just gathered and forwarded the relevant "
-            + "information to the team. They'll get back to you shortly!"
-        ),
-        thread_ts=thread_ts,
-    )

backend/danswer/danswerbot/slack/icons.py

@@ -1,58 +0,0 @@
-from danswer.configs.constants import DocumentSource
-
-
-def source_to_github_img_link(source: DocumentSource) -> str | None:
-    # TODO: store these images somewhere better
-    if source == DocumentSource.WEB.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/backend/slackbot_images/Web.png"
-    if source == DocumentSource.FILE.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/backend/slackbot_images/File.png"
-    if source == DocumentSource.GOOGLE_SITES.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/GoogleSites.png"
-    if source == DocumentSource.SLACK.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Slack.png"
-    if source == DocumentSource.GMAIL.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Gmail.png"
-    if source == DocumentSource.GOOGLE_DRIVE.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/GoogleDrive.png"
-    if source == DocumentSource.GITHUB.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Github.png"
-    if source == DocumentSource.GITLAB.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Gitlab.png"
-    if source == DocumentSource.CONFLUENCE.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/backend/slackbot_images/Confluence.png"
-    if source == DocumentSource.JIRA.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/backend/slackbot_images/Jira.png"
-    if source == DocumentSource.NOTION.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Notion.png"
-    if source == DocumentSource.ZENDESK.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/backend/slackbot_images/Zendesk.png"
-    if source == DocumentSource.GONG.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Gong.png"
-    if source == DocumentSource.LINEAR.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Linear.png"
-    if source == DocumentSource.PRODUCTBOARD.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Productboard.webp"
-    if source == DocumentSource.SLAB.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/SlabLogo.png"
-    if source == DocumentSource.ZULIP.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Zulip.png"
-    if source == DocumentSource.GURU.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/backend/slackbot_images/Guru.png"
-    if source == DocumentSource.HUBSPOT.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/HubSpot.png"
-    if source == DocumentSource.DOCUMENT360.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Document360.png"
-    if source == DocumentSource.BOOKSTACK.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Bookstack.png"
-    if source == DocumentSource.LOOPIO.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Loopio.png"
-    if source == DocumentSource.SHAREPOINT.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/web/public/Sharepoint.png"
-    if source == DocumentSource.REQUESTTRACKER.value:
-        # just use file icon for now
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/backend/slackbot_images/File.png"
-    if source == DocumentSource.INGESTION_API.value:
-        return "https://raw.githubusercontent.com/danswer-ai/danswer/main/backend/slackbot_images/File.png"
-
-    return "https://raw.githubusercontent.com/danswer-ai/danswer/main/backend/slackbot_images/File.png"

backend/danswer/db/input_prompt.py

@@ -1,202 +0,0 @@
-from uuid import UUID
-
-from fastapi import HTTPException
-from sqlalchemy import select
-from sqlalchemy.orm import Session
-
-from danswer.db.models import InputPrompt
-from danswer.db.models import User
-from danswer.server.features.input_prompt.models import InputPromptSnapshot
-from danswer.server.manage.models import UserInfo
-from danswer.utils.logger import setup_logger
-
-
-logger = setup_logger()
-
-
-def insert_input_prompt_if_not_exists(
-    user: User | None,
-    input_prompt_id: int | None,
-    prompt: str,
-    content: str,
-    active: bool,
-    is_public: bool,
-    db_session: Session,
-    commit: bool = True,
-) -> InputPrompt:
-    if input_prompt_id is not None:
-        input_prompt = (
-            db_session.query(InputPrompt).filter_by(id=input_prompt_id).first()
-        )
-    else:
-        query = db_session.query(InputPrompt).filter(InputPrompt.prompt == prompt)
-        if user:
-            query = query.filter(InputPrompt.user_id == user.id)
-        else:
-            query = query.filter(InputPrompt.user_id.is_(None))
-        input_prompt = query.first()
-
-    if input_prompt is None:
-        input_prompt = InputPrompt(
-            id=input_prompt_id,
-            prompt=prompt,
-            content=content,
-            active=active,
-            is_public=is_public or user is None,
-            user_id=user.id if user else None,
-        )
-        db_session.add(input_prompt)
-
-    if commit:
-        db_session.commit()
-
-    return input_prompt
-
-
-def insert_input_prompt(
-    prompt: str,
-    content: str,
-    is_public: bool,
-    user: User | None,
-    db_session: Session,
-) -> InputPrompt:
-    input_prompt = InputPrompt(
-        prompt=prompt,
-        content=content,
-        active=True,
-        is_public=is_public or user is None,
-        user_id=user.id if user is not None else None,
-    )
-    db_session.add(input_prompt)
-    db_session.commit()
-
-    return input_prompt
-
-
-def update_input_prompt(
-    user: User | None,
-    input_prompt_id: int,
-    prompt: str,
-    content: str,
-    active: bool,
-    db_session: Session,
-) -> InputPrompt:
-    input_prompt = db_session.scalar(
-        select(InputPrompt).where(InputPrompt.id == input_prompt_id)
-    )
-    if input_prompt is None:
-        raise ValueError(f"No input prompt with id {input_prompt_id}")
-
-    if not validate_user_prompt_authorization(user, input_prompt):
-        raise HTTPException(status_code=401, detail="You don't own this prompt")
-
-    input_prompt.prompt = prompt
-    input_prompt.content = content
-    input_prompt.active = active
-
-    db_session.commit()
-    return input_prompt
-
-
-def validate_user_prompt_authorization(
-    user: User | None, input_prompt: InputPrompt
-) -> bool:
-    prompt = InputPromptSnapshot.from_model(input_prompt=input_prompt)
-
-    if prompt.user_id is not None:
-        if user is None:
-            return False
-
-        user_details = UserInfo.from_model(user)
-        if str(user_details.id) != str(prompt.user_id):
-            return False
-    return True
-
-
-def remove_public_input_prompt(input_prompt_id: int, db_session: Session) -> None:
-    input_prompt = db_session.scalar(
-        select(InputPrompt).where(InputPrompt.id == input_prompt_id)
-    )
-
-    if input_prompt is None:
-        raise ValueError(f"No input prompt with id {input_prompt_id}")
-
-    if not input_prompt.is_public:
-        raise HTTPException(status_code=400, detail="This prompt is not public")
-
-    db_session.delete(input_prompt)
-    db_session.commit()
-
-
-def remove_input_prompt(
-    user: User | None, input_prompt_id: int, db_session: Session
-) -> None:
-    input_prompt = db_session.scalar(
-        select(InputPrompt).where(InputPrompt.id == input_prompt_id)
-    )
-    if input_prompt is None:
-        raise ValueError(f"No input prompt with id {input_prompt_id}")
-
-    if input_prompt.is_public:
-        raise HTTPException(
-            status_code=400, detail="Cannot delete public prompts with this method"
-        )
-
-    if not validate_user_prompt_authorization(user, input_prompt):
-        raise HTTPException(status_code=401, detail="You do not own this prompt")
-
-    db_session.delete(input_prompt)
-    db_session.commit()
-
-
-def fetch_input_prompt_by_id(
-    id: int, user_id: UUID | None, db_session: Session
-) -> InputPrompt:
-    query = select(InputPrompt).where(InputPrompt.id == id)
-
-    if user_id:
-        query = query.where(
-            (InputPrompt.user_id == user_id) | (InputPrompt.user_id is None)
-        )
-    else:
-        # If no user_id is provided, only fetch prompts without a user_id (aka public)
-        query = query.where(InputPrompt.user_id == None)  # noqa
-
-    result = db_session.scalar(query)
-
-    if result is None:
-        raise HTTPException(422, "No input prompt found")
-
-    return result
-
-
-def fetch_public_input_prompts(
-    db_session: Session,
-) -> list[InputPrompt]:
-    query = select(InputPrompt).where(InputPrompt.is_public)
-    return list(db_session.scalars(query).all())
-
-
-def fetch_input_prompts_by_user(
-    db_session: Session,
-    user_id: UUID | None,
-    active: bool | None = None,
-    include_public: bool = False,
-) -> list[InputPrompt]:
-    query = select(InputPrompt)
-
-    if user_id is not None:
-        if include_public:
-            query = query.where(
-                (InputPrompt.user_id == user_id) | InputPrompt.is_public
-            )
-        else:
-            query = query.where(InputPrompt.user_id == user_id)
-
-    elif include_public:
-        query = query.where(InputPrompt.is_public)
-
-    if active is not None:
-        query = query.where(InputPrompt.active == active)
-
-    return list(db_session.scalars(query).all())

backend/danswer/file_store/utils.py

@@ -1,85 +0,0 @@
-from collections.abc import Callable
-from io import BytesIO
-from typing import Any
-from typing import cast
-from uuid import uuid4
-
-import requests
-from sqlalchemy.orm import Session
-
-from danswer.configs.constants import FileOrigin
-from danswer.db.engine import get_session_with_tenant
-from danswer.db.models import ChatMessage
-from danswer.file_store.file_store import get_default_file_store
-from danswer.file_store.models import FileDescriptor
-from danswer.file_store.models import InMemoryChatFile
-from danswer.utils.threadpool_concurrency import run_functions_tuples_in_parallel
-from shared_configs.contextvars import CURRENT_TENANT_ID_CONTEXTVAR
-
-
-def load_chat_file(
-    file_descriptor: FileDescriptor, db_session: Session
-) -> InMemoryChatFile:
-    file_io = get_default_file_store(db_session).read_file(
-        file_descriptor["id"], mode="b"
-    )
-    return InMemoryChatFile(
-        file_id=file_descriptor["id"],
-        content=file_io.read(),
-        file_type=file_descriptor["type"],
-        filename=file_descriptor.get("name"),
-    )
-
-
-def load_all_chat_files(
-    chat_messages: list[ChatMessage],
-    file_descriptors: list[FileDescriptor],
-    db_session: Session,
-) -> list[InMemoryChatFile]:
-    file_descriptors_for_history: list[FileDescriptor] = []
-    for chat_message in chat_messages:
-        if chat_message.files:
-            file_descriptors_for_history.extend(chat_message.files)
-
-    files = cast(
-        list[InMemoryChatFile],
-        run_functions_tuples_in_parallel(
-            [
-                (load_chat_file, (file, db_session))
-                for file in file_descriptors + file_descriptors_for_history
-            ]
-        ),
-    )
-    return files
-
-
-def save_file_from_url(url: str, tenant_id: str) -> str:
-    """NOTE: using multiple sessions here, since this is often called
-    using multithreading. In practice, sharing a session has resulted in
-    weird errors."""
-    with get_session_with_tenant(tenant_id) as db_session:
-        response = requests.get(url)
-        response.raise_for_status()
-
-        unique_id = str(uuid4())
-
-        file_io = BytesIO(response.content)
-        file_store = get_default_file_store(db_session)
-        file_store.save_file(
-            file_name=unique_id,
-            content=file_io,
-            display_name="GeneratedImage",
-            file_origin=FileOrigin.CHAT_IMAGE_GEN,
-            file_type="image/png;base64",
-        )
-        return unique_id
-
-
-def save_files_from_urls(urls: list[str]) -> list[str]:
-    tenant_id = CURRENT_TENANT_ID_CONTEXTVAR.get()
-
-    funcs: list[tuple[Callable[..., Any], tuple[Any, ...]]] = [
-        (save_file_from_url, (url, tenant_id)) for url in urls
-    ]
-    # Must pass in tenant_id here, since this is called by multithreading
-    return run_functions_tuples_in_parallel(funcs)

backend/danswer/llm/answering/llm_response_handler.py

@@ -1,84 +0,0 @@
-from collections.abc import Callable
-from collections.abc import Generator
-from collections.abc import Iterator
-from typing import TYPE_CHECKING
-
-from langchain_core.messages import BaseMessage
-from pydantic.v1 import BaseModel as BaseModel__v1
-
-from danswer.chat.models import CitationInfo
-from danswer.chat.models import DanswerAnswerPiece
-from danswer.chat.models import DanswerQuotes
-from danswer.chat.models import StreamStopInfo
-from danswer.chat.models import StreamStopReason
-from danswer.file_store.models import InMemoryChatFile
-from danswer.llm.answering.prompts.build import AnswerPromptBuilder
-from danswer.tools.force import ForceUseTool
-from danswer.tools.models import ToolCallFinalResult
-from danswer.tools.models import ToolCallKickoff
-from danswer.tools.models import ToolResponse
-from danswer.tools.tool import Tool
-
-
-if TYPE_CHECKING:
-    from danswer.llm.answering.stream_processing.answer_response_handler import (
-        AnswerResponseHandler,
-    )
-    from danswer.llm.answering.tool.tool_response_handler import ToolResponseHandler
-
-
-ResponsePart = (
-    DanswerAnswerPiece
-    | CitationInfo
-    | DanswerQuotes
-    | ToolCallKickoff
-    | ToolResponse
-    | ToolCallFinalResult
-    | StreamStopInfo
-)
-
-
-class LLMCall(BaseModel__v1):
-    prompt_builder: AnswerPromptBuilder
-    tools: list[Tool]
-    force_use_tool: ForceUseTool
-    files: list[InMemoryChatFile]
-    tool_call_info: list[ToolCallKickoff | ToolResponse | ToolCallFinalResult]
-    using_tool_calling_llm: bool
-
-    class Config:
-        arbitrary_types_allowed = True
-
-
-class LLMResponseHandlerManager:
-    def __init__(
-        self,
-        tool_handler: "ToolResponseHandler",
-        answer_handler: "AnswerResponseHandler",
-        is_cancelled: Callable[[], bool],
-    ):
-        self.tool_handler = tool_handler
-        self.answer_handler = answer_handler
-        self.is_cancelled = is_cancelled
-
-    def handle_llm_response(
-        self,
-        stream: Iterator[BaseMessage],
-    ) -> Generator[ResponsePart, None, None]:
-        all_messages: list[BaseMessage] = []
-        for message in stream:
-            if self.is_cancelled():
-                yield StreamStopInfo(stop_reason=StreamStopReason.CANCELLED)
-                return
-            # tool handler doesn't do anything until the full message is received
-            # NOTE: still need to run list() to get this to run
-            list(self.tool_handler.handle_response_part(message, all_messages))
-            yield from self.answer_handler.handle_response_part(message, all_messages)
-            all_messages.append(message)
-
-        # potentially give back all info on the selected tool call + its result
-        yield from self.tool_handler.handle_response_part(None, all_messages)
-        yield from self.answer_handler.handle_response_part(None, all_messages)
-
-    def next_llm_call(self, llm_call: LLMCall) -> LLMCall | None:
-        return self.tool_handler.next_llm_call(llm_call)

backend/danswer/llm/answering/models.py

@@ -1,163 +0,0 @@
-from collections.abc import Callable
-from collections.abc import Iterator
-from typing import TYPE_CHECKING
-
-from langchain.schema.messages import AIMessage
-from langchain.schema.messages import BaseMessage
-from langchain.schema.messages import HumanMessage
-from langchain.schema.messages import SystemMessage
-from pydantic import BaseModel
-from pydantic import ConfigDict
-from pydantic import Field
-from pydantic import model_validator
-
-from danswer.chat.models import AnswerQuestionStreamReturn
-from danswer.configs.constants import MessageType
-from danswer.file_store.models import InMemoryChatFile
-from danswer.llm.override_models import PromptOverride
-from danswer.llm.utils import build_content_with_imgs
-from danswer.tools.models import ToolCallFinalResult
-
-if TYPE_CHECKING:
-    from danswer.db.models import ChatMessage
-    from danswer.db.models import Prompt
-
-
-StreamProcessor = Callable[[Iterator[str]], AnswerQuestionStreamReturn]
-
-
-class PreviousMessage(BaseModel):
-    """Simplified version of `ChatMessage`"""
-
-    message: str
-    token_count: int
-    message_type: MessageType
-    files: list[InMemoryChatFile]
-    tool_call: ToolCallFinalResult | None
-
-    @classmethod
-    def from_chat_message(
-        cls, chat_message: "ChatMessage", available_files: list[InMemoryChatFile]
-    ) -> "PreviousMessage":
-        message_file_ids = (
-            [file["id"] for file in chat_message.files] if chat_message.files else []
-        )
-        return cls(
-            message=chat_message.message,
-            token_count=chat_message.token_count,
-            message_type=chat_message.message_type,
-            files=[
-                file
-                for file in available_files
-                if str(file.file_id) in message_file_ids
-            ],
-            tool_call=ToolCallFinalResult(
-                tool_name=chat_message.tool_call.tool_name,
-                tool_args=chat_message.tool_call.tool_arguments,
-                tool_result=chat_message.tool_call.tool_result,
-            )
-            if chat_message.tool_call
-            else None,
-        )
-
-    def to_langchain_msg(self) -> BaseMessage:
-        content = build_content_with_imgs(self.message, self.files)
-        if self.message_type == MessageType.USER:
-            return HumanMessage(content=content)
-        elif self.message_type == MessageType.ASSISTANT:
-            return AIMessage(content=content)
-        else:
-            return SystemMessage(content=content)
-
-
-class DocumentPruningConfig(BaseModel):
-    max_chunks: int | None = None
-    max_window_percentage: float | None = None
-    max_tokens: int | None = None
-    # different pruning behavior is expected when the
-    # user manually selects documents they want to chat with
-    # e.g. we don't want to truncate each document to be no more
-    # than one chunk long
-    is_manually_selected_docs: bool = False
-    # If user specifies to include additional context Chunks for each match, then different pruning
-    # is used. As many Sections as possible are included, and the last Section is truncated
-    # If this is false, all of the Sections are truncated if they are longer than the expected Chunk size.
-    # Sections are often expected to be longer than the maximum Chunk size but Chunks should not be.
-    use_sections: bool = True
-    # If using tools, then we need to consider the tool length
-    tool_num_tokens: int = 0
-    # If using a tool message to represent the docs, then we have to JSON serialize
-    # the document content, which adds to the token count.
-    using_tool_message: bool = False
-
-
-class ContextualPruningConfig(DocumentPruningConfig):
-    num_chunk_multiple: int
-
-    @classmethod
-    def from_doc_pruning_config(
-        cls, num_chunk_multiple: int, doc_pruning_config: DocumentPruningConfig
-    ) -> "ContextualPruningConfig":
-        return cls(num_chunk_multiple=num_chunk_multiple, **doc_pruning_config.dict())
-
-
-class CitationConfig(BaseModel):
-    all_docs_useful: bool = False
-
-
-class QuotesConfig(BaseModel):
-    pass
-
-
-class AnswerStyleConfig(BaseModel):
-    citation_config: CitationConfig | None = None
-    quotes_config: QuotesConfig | None = None
-    document_pruning_config: DocumentPruningConfig = Field(
-        default_factory=DocumentPruningConfig
-    )
-    # forces the LLM to return a structured response, see
-    # https://platform.openai.com/docs/guides/structured-outputs/introduction
-    # right now, only used by the simple chat API
-    structured_response_format: dict | None = None
-
-    @model_validator(mode="after")
-    def check_quotes_and_citation(self) -> "AnswerStyleConfig":
-        if self.citation_config is None and self.quotes_config is None:
-            raise ValueError(
-                "One of `citation_config` or `quotes_config` must be provided"
-            )
-
-        if self.citation_config is not None and self.quotes_config is not None:
-            raise ValueError(
-                "Only one of `citation_config` or `quotes_config` must be provided"
-            )
-
-        return self
-
-
-class PromptConfig(BaseModel):
-    """Final representation of the Prompt configuration passed
-    into the `Answer` object."""
-
-    system_prompt: str
-    task_prompt: str
-    datetime_aware: bool
-    include_citations: bool
-
-    @classmethod
-    def from_model(
-        cls, model: "Prompt", prompt_override: PromptOverride | None = None
-    ) -> "PromptConfig":
-        override_system_prompt = (
-            prompt_override.system_prompt if prompt_override else None
-        )
-        override_task_prompt = prompt_override.task_prompt if prompt_override else None
-
-        return cls(
-            system_prompt=override_system_prompt or model.system_prompt,
-            task_prompt=override_task_prompt or model.task_prompt,
-            datetime_aware=model.datetime_aware,
-            include_citations=model.include_citations,
-        )
-
-    model_config = ConfigDict(frozen=True)

backend/danswer/llm/answering/prompts/utils.py

@@ -1,20 +0,0 @@
-from danswer.prompts.direct_qa_prompts import PARAMATERIZED_PROMPT
-from danswer.prompts.direct_qa_prompts import PARAMATERIZED_PROMPT_WITHOUT_CONTEXT
-
-
-def build_dummy_prompt(
-    system_prompt: str, task_prompt: str, retrieval_disabled: bool
-) -> str:
-    if retrieval_disabled:
-        return PARAMATERIZED_PROMPT_WITHOUT_CONTEXT.format(
-            user_query="<USER_QUERY>",
-            system_prompt=system_prompt,
-            task_prompt=task_prompt,
-        ).strip()
-
-    return PARAMATERIZED_PROMPT.format(
-        context_docs_str="<CONTEXT_DOCS>",
-        user_query="<USER_QUERY>",
-        system_prompt=system_prompt,
-        task_prompt=task_prompt,
-    ).strip()

backend/danswer/one_shot_answer/answer_question.py

@@ -1,456 +0,0 @@
-from collections.abc import Callable
-from collections.abc import Iterator
-from typing import cast
-
-from sqlalchemy.orm import Session
-
-from danswer.chat.chat_utils import reorganize_citations
-from danswer.chat.models import CitationInfo
-from danswer.chat.models import DanswerAnswerPiece
-from danswer.chat.models import DanswerContexts
-from danswer.chat.models import DanswerQuotes
-from danswer.chat.models import DocumentRelevance
-from danswer.chat.models import LLMRelevanceFilterResponse
-from danswer.chat.models import QADocsResponse
-from danswer.chat.models import RelevanceAnalysis
-from danswer.chat.models import StreamingError
-from danswer.configs.chat_configs import DISABLE_LLM_DOC_RELEVANCE
-from danswer.configs.chat_configs import MAX_CHUNKS_FED_TO_CHAT
-from danswer.configs.chat_configs import QA_TIMEOUT
-from danswer.configs.constants import MessageType
-from danswer.context.search.enums import LLMEvaluationType
-from danswer.context.search.models import RerankMetricsContainer
-from danswer.context.search.models import RetrievalMetricsContainer
-from danswer.context.search.utils import chunks_or_sections_to_search_docs
-from danswer.context.search.utils import dedupe_documents
-from danswer.db.chat import create_chat_session
-from danswer.db.chat import create_db_search_doc
-from danswer.db.chat import create_new_chat_message
-from danswer.db.chat import get_or_create_root_message
-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.chat import update_search_docs_table_with_relevance
-from danswer.db.engine import get_session_context_manager
-from danswer.db.models import Persona
-from danswer.db.models import User
-from danswer.db.persona import get_prompt_by_id
-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 PromptConfig
-from danswer.llm.answering.models import QuotesConfig
-from danswer.llm.factory import get_llms_for_persona
-from danswer.llm.factory import get_main_llm_from_tuple
-from danswer.natural_language_processing.utils import get_tokenizer
-from danswer.one_shot_answer.models import DirectQARequest
-from danswer.one_shot_answer.models import OneShotQAResponse
-from danswer.one_shot_answer.models import QueryRephrase
-from danswer.one_shot_answer.qa_utils import combine_message_thread
-from danswer.one_shot_answer.qa_utils import slackify_message_thread
-from danswer.secondary_llm_flows.answer_validation import get_answer_validity
-from danswer.secondary_llm_flows.query_expansion import thread_based_query_rephrase
-from danswer.server.query_and_chat.models import ChatMessageDetail
-from danswer.server.utils import get_json_line
-from danswer.tools.force import ForceUseTool
-from danswer.tools.models import ToolResponse
-from danswer.tools.tool_implementations.search.search_tool import SEARCH_DOC_CONTENT_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 ToolCallKickoff
-from danswer.utils.logger import setup_logger
-from danswer.utils.long_term_log import LongTermLogger
-from danswer.utils.timing import log_generator_function_time
-from danswer.utils.variable_functionality import fetch_ee_implementation_or_noop
-
-logger = setup_logger()
-
-AnswerObjectIterator = Iterator[
-    QueryRephrase
-    | QADocsResponse
-    | LLMRelevanceFilterResponse
-    | DanswerAnswerPiece
-    | DanswerQuotes
-    | DanswerContexts
-    | StreamingError
-    | ChatMessageDetail
-    | CitationInfo
-    | ToolCallKickoff
-    | DocumentRelevance
-]
-
-
-def stream_answer_objects(
-    query_req: DirectQARequest,
-    user: User | None,
-    # These need to be passed in because in Web UI one shot flow,
-    # we can have much more document as there is no history.
-    # For Slack flow, we need to save more tokens for the thread context
-    max_document_tokens: int | None,
-    max_history_tokens: int | None,
-    db_session: Session,
-    # Needed to translate persona num_chunks to tokens to the LLM
-    default_num_chunks: float = MAX_CHUNKS_FED_TO_CHAT,
-    timeout: int = QA_TIMEOUT,
-    bypass_acl: bool = False,
-    use_citations: bool = False,
-    danswerbot_flow: bool = False,
-    retrieval_metrics_callback: (
-        Callable[[RetrievalMetricsContainer], None] | None
-    ) = None,
-    rerank_metrics_callback: Callable[[RerankMetricsContainer], None] | None = None,
-) -> AnswerObjectIterator:
-    """Streams in order:
-    1. [always] Retrieved documents, stops flow if nothing is found
-    2. [conditional] LLM selected chunk indices if LLM chunk filtering is turned on
-    3. [always] A set of streamed DanswerAnswerPiece and DanswerQuotes at the end
-                or an error anywhere along the line if something fails
-    4. [always] Details on the final AI response message that is created
-    """
-    user_id = user.id if user is not None else None
-    query_msg = query_req.messages[-1]
-    history = query_req.messages[:-1]
-
-    chat_session = create_chat_session(
-        db_session=db_session,
-        description="",  # One shot queries don't need naming as it's never displayed
-        user_id=user_id,
-        persona_id=query_req.persona_id,
-        one_shot=True,
-        danswerbot_flow=danswerbot_flow,
-    )
-
-    # permanent "log" store, used primarily for debugging
-    long_term_logger = LongTermLogger(
-        metadata={"user_id": str(user_id), "chat_session_id": str(chat_session.id)}
-    )
-
-    temporary_persona: Persona | None = None
-
-    if query_req.persona_config is not None:
-        temporary_persona = fetch_ee_implementation_or_noop(
-            "danswer.server.query_and_chat.utils", "create_temporary_persona", None
-        )(db_session=db_session, persona_config=query_req.persona_config, user=user)
-
-    persona = temporary_persona if temporary_persona else chat_session.persona
-
-    try:
-        llm, fast_llm = get_llms_for_persona(
-            persona=persona, long_term_logger=long_term_logger
-        )
-    except ValueError as e:
-        logger.error(
-            f"Failed to initialize LLMs for persona '{persona.name}': {str(e)}"
-        )
-        if "No LLM provider" in str(e):
-            raise ValueError(
-                "Please configure a Generative AI model to use this feature."
-            ) from e
-        raise ValueError(
-            "Failed to initialize the AI model. Please check your configuration and try again."
-        ) from e
-
-    llm_tokenizer = get_tokenizer(
-        model_name=llm.config.model_name,
-        provider_type=llm.config.model_provider,
-    )
-
-    # Create a chat session which will just store the root message, the query, and the AI response
-    root_message = get_or_create_root_message(
-        chat_session_id=chat_session.id, db_session=db_session
-    )
-
-    history_str = combine_message_thread(
-        messages=history,
-        max_tokens=max_history_tokens,
-        llm_tokenizer=llm_tokenizer,
-    )
-
-    rephrased_query = query_req.query_override or thread_based_query_rephrase(
-        user_query=query_msg.message,
-        history_str=history_str,
-    )
-
-    # Given back ahead of the documents for latency reasons
-    # In chat flow it's given back along with the documents
-    yield QueryRephrase(rephrased_query=rephrased_query)
-
-    prompt = None
-    if query_req.prompt_id is not None:
-        # NOTE: let the user access any prompt as long as the Persona is shared
-        # with them
-        prompt = get_prompt_by_id(
-            prompt_id=query_req.prompt_id, user=None, db_session=db_session
-        )
-    if prompt is None:
-        if not persona.prompts:
-            raise RuntimeError(
-                "Persona does not have any prompts - this should never happen"
-            )
-        prompt = persona.prompts[0]
-
-    user_message_str = query_msg.message
-    # For this endpoint, we only save one user message to the chat session
-    # However, for slackbot, we want to include the history of the entire thread
-    if danswerbot_flow:
-        # Right now, we only support bringing over citations and search docs
-        # from the last message in the thread, not the entire thread
-        # in the future, we may want to retrieve the entire thread
-        user_message_str = slackify_message_thread(query_req.messages)
-
-    # Create the first User query message
-    new_user_message = create_new_chat_message(
-        chat_session_id=chat_session.id,
-        parent_message=root_message,
-        prompt_id=query_req.prompt_id,
-        message=user_message_str,
-        token_count=len(llm_tokenizer.encode(user_message_str)),
-        message_type=MessageType.USER,
-        db_session=db_session,
-        commit=True,
-    )
-
-    prompt_config = PromptConfig.from_model(prompt)
-    document_pruning_config = DocumentPruningConfig(
-        max_chunks=int(
-            persona.num_chunks if persona.num_chunks is not None else default_num_chunks
-        ),
-        max_tokens=max_document_tokens,
-    )
-
-    answer_config = AnswerStyleConfig(
-        citation_config=CitationConfig() if use_citations else None,
-        quotes_config=QuotesConfig() if not use_citations else None,
-        document_pruning_config=document_pruning_config,
-    )
-
-    search_tool = SearchTool(
-        db_session=db_session,
-        user=user,
-        evaluation_type=(
-            LLMEvaluationType.SKIP
-            if DISABLE_LLM_DOC_RELEVANCE
-            else query_req.evaluation_type
-        ),
-        persona=persona,
-        retrieval_options=query_req.retrieval_options,
-        prompt_config=prompt_config,
-        llm=llm,
-        fast_llm=fast_llm,
-        pruning_config=document_pruning_config,
-        answer_style_config=answer_config,
-        bypass_acl=bypass_acl,
-        chunks_above=query_req.chunks_above,
-        chunks_below=query_req.chunks_below,
-        full_doc=query_req.full_doc,
-    )
-
-    answer = Answer(
-        question=query_msg.message,
-        answer_style_config=answer_config,
-        prompt_config=PromptConfig.from_model(prompt),
-        llm=get_main_llm_from_tuple(
-            get_llms_for_persona(persona=persona, long_term_logger=long_term_logger)
-        ),
-        single_message_history=history_str,
-        tools=[search_tool] if search_tool else [],
-        force_use_tool=(
-            ForceUseTool(
-                tool_name=search_tool.name,
-                args={"query": rephrased_query},
-                force_use=True,
-            )
-        ),
-        # for now, don't use tool calling for this flow, as we haven't
-        # tested quotes with tool calling too much yet
-        skip_explicit_tool_calling=True,
-        return_contexts=query_req.return_contexts,
-        skip_gen_ai_answer_generation=query_req.skip_gen_ai_answer_generation,
-    )
-    # won't be any FileChatDisplay responses since that tool is never passed in
-    for packet in cast(AnswerObjectIterator, answer.processed_streamed_output):
-        # for one-shot flow, don't currently do anything with these
-        if isinstance(packet, ToolResponse):
-            # (likely fine that it comes after the initial creation of the search docs)
-            if packet.id == SEARCH_RESPONSE_SUMMARY_ID:
-                search_response_summary = cast(SearchResponseSummary, packet.response)
-
-                top_docs = chunks_or_sections_to_search_docs(
-                    search_response_summary.top_sections
-                )
-
-                # Deduping happens at the last step to avoid harming quality by dropping content early on
-                deduped_docs = top_docs
-                if query_req.retrieval_options.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
-                ]
-
-                response_docs = [
-                    translate_db_search_doc_to_server_search_doc(db_search_doc)
-                    for db_search_doc in reference_db_search_docs
-                ]
-
-                initial_response = QADocsResponse(
-                    rephrased_query=rephrased_query,
-                    top_documents=response_docs,
-                    predicted_flow=search_response_summary.predicted_flow,
-                    predicted_search=search_response_summary.predicted_search,
-                    applied_source_filters=search_response_summary.final_filters.source_type,
-                    applied_time_cutoff=search_response_summary.final_filters.time_cutoff,
-                    recency_bias_multiplier=search_response_summary.recency_bias_multiplier,
-                )
-
-                yield initial_response
-
-            elif packet.id == SEARCH_DOC_CONTENT_ID:
-                yield packet.response
-
-            elif packet.id == SECTION_RELEVANCE_LIST_ID:
-                document_based_response = {}
-
-                if packet.response is not None:
-                    for evaluation in packet.response:
-                        document_based_response[
-                            evaluation.document_id
-                        ] = RelevanceAnalysis(
-                            relevant=evaluation.relevant, content=evaluation.content
-                        )
-
-                evaluation_response = DocumentRelevance(
-                    relevance_summaries=document_based_response
-                )
-                if reference_db_search_docs is not None:
-                    update_search_docs_table_with_relevance(
-                        db_session=db_session,
-                        reference_db_search_docs=reference_db_search_docs,
-                        relevance_summary=evaluation_response,
-                    )
-                yield evaluation_response
-
-        else:
-            yield packet
-
-    # Saving Gen AI answer and responding with message info
-    gen_ai_response_message = create_new_chat_message(
-        chat_session_id=chat_session.id,
-        parent_message=new_user_message,
-        prompt_id=query_req.prompt_id,
-        message=answer.llm_answer,
-        token_count=len(llm_tokenizer.encode(answer.llm_answer)),
-        message_type=MessageType.ASSISTANT,
-        error=None,
-        reference_docs=reference_db_search_docs,
-        db_session=db_session,
-        commit=True,
-    )
-
-    msg_detail_response = translate_db_message_to_chat_message_detail(
-        gen_ai_response_message
-    )
-    yield msg_detail_response
-
-
-@log_generator_function_time()
-def stream_search_answer(
-    query_req: DirectQARequest,
-    user: User | None,
-    max_document_tokens: int | None,
-    max_history_tokens: int | None,
-) -> Iterator[str]:
-    with get_session_context_manager() as session:
-        objects = stream_answer_objects(
-            query_req=query_req,
-            user=user,
-            max_document_tokens=max_document_tokens,
-            max_history_tokens=max_history_tokens,
-            db_session=session,
-        )
-        for obj in objects:
-            yield get_json_line(obj.model_dump())
-
-
-def get_search_answer(
-    query_req: DirectQARequest,
-    user: User | None,
-    max_document_tokens: int | None,
-    max_history_tokens: int | None,
-    db_session: Session,
-    answer_generation_timeout: int = QA_TIMEOUT,
-    enable_reflexion: bool = False,
-    bypass_acl: bool = False,
-    use_citations: bool = False,
-    danswerbot_flow: bool = False,
-    retrieval_metrics_callback: (
-        Callable[[RetrievalMetricsContainer], None] | None
-    ) = None,
-    rerank_metrics_callback: Callable[[RerankMetricsContainer], None] | None = None,
-) -> OneShotQAResponse:
-    """Collects the streamed one shot answer responses into a single object"""
-    qa_response = OneShotQAResponse()
-
-    results = stream_answer_objects(
-        query_req=query_req,
-        user=user,
-        max_document_tokens=max_document_tokens,
-        max_history_tokens=max_history_tokens,
-        db_session=db_session,
-        bypass_acl=bypass_acl,
-        use_citations=use_citations,
-        danswerbot_flow=danswerbot_flow,
-        timeout=answer_generation_timeout,
-        retrieval_metrics_callback=retrieval_metrics_callback,
-        rerank_metrics_callback=rerank_metrics_callback,
-    )
-
-    answer = ""
-    for packet in results:
-        if isinstance(packet, QueryRephrase):
-            qa_response.rephrase = packet.rephrased_query
-        if isinstance(packet, DanswerAnswerPiece) and packet.answer_piece:
-            answer += packet.answer_piece
-        elif isinstance(packet, QADocsResponse):
-            qa_response.docs = packet
-        elif isinstance(packet, LLMRelevanceFilterResponse):
-            qa_response.llm_selected_doc_indices = packet.llm_selected_doc_indices
-        elif isinstance(packet, DanswerQuotes):
-            qa_response.quotes = packet
-        elif isinstance(packet, CitationInfo):
-            if qa_response.citations:
-                qa_response.citations.append(packet)
-            else:
-                qa_response.citations = [packet]
-        elif isinstance(packet, DanswerContexts):
-            qa_response.contexts = packet
-        elif isinstance(packet, StreamingError):
-            qa_response.error_msg = packet.error
-        elif isinstance(packet, ChatMessageDetail):
-            qa_response.chat_message_id = packet.message_id
-
-    if answer:
-        qa_response.answer = answer
-
-    if enable_reflexion:
-        # Because follow up messages are explicitly tagged, we don't need to verify the answer
-        if len(query_req.messages) == 1:
-            first_query = query_req.messages[0].message
-            qa_response.answer_valid = get_answer_validity(first_query, answer)
-        else:
-            qa_response.answer_valid = True
-
-    if use_citations and qa_response.answer and qa_response.citations:
-        # Reorganize citation nums to be in the same order as the answer
-        qa_response.answer, qa_response.citations = reorganize_citations(
-            qa_response.answer, qa_response.citations
-        )
-
-    return qa_response

backend/danswer/one_shot_answer/models.py

@@ -1,114 +0,0 @@
-from typing import Any
-
-from pydantic import BaseModel
-from pydantic import Field
-from pydantic import model_validator
-
-from danswer.chat.models import CitationInfo
-from danswer.chat.models import DanswerContexts
-from danswer.chat.models import DanswerQuotes
-from danswer.chat.models import QADocsResponse
-from danswer.configs.constants import MessageType
-from danswer.context.search.enums import LLMEvaluationType
-from danswer.context.search.enums import RecencyBiasSetting
-from danswer.context.search.enums import SearchType
-from danswer.context.search.models import ChunkContext
-from danswer.context.search.models import RerankingDetails
-from danswer.context.search.models import RetrievalDetails
-
-
-class QueryRephrase(BaseModel):
-    rephrased_query: str
-
-
-class ThreadMessage(BaseModel):
-    message: str
-    sender: str | None = None
-    role: MessageType = MessageType.USER
-
-
-class PromptConfig(BaseModel):
-    name: str
-    description: str = ""
-    system_prompt: str
-    task_prompt: str = ""
-    include_citations: bool = True
-    datetime_aware: bool = True
-
-
-class ToolConfig(BaseModel):
-    id: int
-
-
-class PersonaConfig(BaseModel):
-    name: str
-    description: str
-    search_type: SearchType = SearchType.SEMANTIC
-    num_chunks: float | None = None
-    llm_relevance_filter: bool = False
-    llm_filter_extraction: bool = False
-    recency_bias: RecencyBiasSetting = RecencyBiasSetting.AUTO
-    llm_model_provider_override: str | None = None
-    llm_model_version_override: str | None = None
-
-    prompts: list[PromptConfig] = Field(default_factory=list)
-    prompt_ids: list[int] = Field(default_factory=list)
-
-    document_set_ids: list[int] = Field(default_factory=list)
-    tools: list[ToolConfig] = Field(default_factory=list)
-    tool_ids: list[int] = Field(default_factory=list)
-    custom_tools_openapi: list[dict[str, Any]] = Field(default_factory=list)
-
-
-class DirectQARequest(ChunkContext):
-    persona_config: PersonaConfig | None = None
-    persona_id: int | None = None
-
-    messages: list[ThreadMessage]
-    prompt_id: int | None = None
-    multilingual_query_expansion: list[str] | None = None
-    retrieval_options: RetrievalDetails = Field(default_factory=RetrievalDetails)
-    rerank_settings: RerankingDetails | None = None
-    evaluation_type: LLMEvaluationType = LLMEvaluationType.UNSPECIFIED
-
-    chain_of_thought: bool = False
-    return_contexts: bool = False
-
-    # allows the caller to specify the exact search query they want to use
-    # can be used if the message sent to the LLM / query should not be the same
-    # will also disable Thread-based Rewording if specified
-    query_override: str | None = None
-
-    # If True, skips generative an AI response to the search query
-    skip_gen_ai_answer_generation: bool = False
-
-    @model_validator(mode="after")
-    def check_persona_fields(self) -> "DirectQARequest":
-        if (self.persona_config is None) == (self.persona_id is None):
-            raise ValueError("Exactly one of persona_config or persona_id must be set")
-        return self
-
-    @model_validator(mode="after")
-    def check_chain_of_thought_and_prompt_id(self) -> "DirectQARequest":
-        if self.chain_of_thought and self.prompt_id is not None:
-            raise ValueError(
-                "If chain_of_thought is True, prompt_id must be None"
-                "The chain of thought prompt is only for question "
-                "answering and does not accept customizing."
-            )
-
-        return self
-
-
-class OneShotQAResponse(BaseModel):
-    # This is built piece by piece, any of these can be None as the flow could break
-    answer: str | None = None
-    rephrase: str | None = None
-    quotes: DanswerQuotes | None = None
-    citations: list[CitationInfo] | None = None
-    docs: QADocsResponse | None = None
-    llm_selected_doc_indices: list[int] | None = None
-    error_msg: str | None = None
-    answer_valid: bool = True  # Reflexion result, default True if Reflexion not run
-    chat_message_id: int | None = None
-    contexts: DanswerContexts | None = None

backend/danswer/one_shot_answer/qa_utils.py

@@ -1,81 +0,0 @@
-from collections.abc import Generator
-
-from danswer.configs.constants import MessageType
-from danswer.natural_language_processing.utils import BaseTokenizer
-from danswer.one_shot_answer.models import ThreadMessage
-from danswer.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def simulate_streaming_response(model_out: str) -> Generator[str, None, None]:
-    """Mock streaming by generating the passed in model output, character by character"""
-    for token in model_out:
-        yield token
-
-
-def combine_message_thread(
-    messages: list[ThreadMessage],
-    max_tokens: int | None,
-    llm_tokenizer: BaseTokenizer,
-) -> str:
-    """Used to create a single combined message context from threads"""
-    if not messages:
-        return ""
-
-    message_strs: list[str] = []
-    total_token_count = 0
-
-    for message in reversed(messages):
-        if message.role == MessageType.USER:
-            role_str = message.role.value.upper()
-            if message.sender:
-                role_str += " " + message.sender
-            else:
-                # Since other messages might have the user identifying information
-                # better to use Unknown for symmetry
-                role_str += " Unknown"
-        else:
-            role_str = message.role.value.upper()
-
-        msg_str = f"{role_str}:\n{message.message}"
-        message_token_count = len(llm_tokenizer.encode(msg_str))
-
-        if (
-            max_tokens is not None
-            and total_token_count + message_token_count > max_tokens
-        ):
-            break
-
-        message_strs.insert(0, msg_str)
-        total_token_count += message_token_count
-
-    return "\n\n".join(message_strs)
-
-
-def slackify_message(message: ThreadMessage) -> str:
-    if message.role != MessageType.USER:
-        return message.message
-
-    return f"{message.sender or 'Unknown User'} said in Slack:\n{message.message}"
-
-
-def slackify_message_thread(messages: list[ThreadMessage]) -> str:
-    if not messages:
-        return ""
-
-    message_strs: list[str] = []
-    for message in messages:
-        if message.role == MessageType.USER:
-            message_text = (
-                f"{message.sender or 'Unknown User'} said in Slack:\n{message.message}"
-            )
-        elif message.role == MessageType.ASSISTANT:
-            message_text = f"DanswerBot said in Slack:\n{message.message}"
-        else:
-            message_text = (
-                f"{message.role.value.upper()} said in Slack:\n{message.message}"
-            )
-        message_strs.append(message_text)
-
-    return "\n\n".join(message_strs)

backend/danswer/seeding/initial_docs_cohere.json

@@ -1,44 +0,0 @@
-[
-  {
-    "url": "https://docs.danswer.dev/more/use_cases/overview",
-    "title": "Use Cases Overview",
-    "content": "How to leverage Danswer in your organization\n\nDanswer Overview\nDanswer is the AI Assistant connected to your organization's docs, apps, and people. Danswer makes Generative AI more versatile for work by enabling new types of questions like \"What is the most common feature request we've heard from customers this month\". Whereas other AI systems have no context of your team and are generally unhelpful with work related questions, Danswer makes it possible to ask these questions in natural language and get back answers in seconds.\n\nDanswer can connect to +30 different tools and the use cases are not limited to the ones in the following pages. The highlighted use cases are for inspiration and come from feedback gathered from our users and customers.\n\n\nCommon Getting Started Questions:\n\nWhy are these docs connected in my Danswer deployment?\nAnswer: This is just an example of how connectors work in Danswer. You can connect up your own team's knowledge and you will be able to ask questions unique to your organization. Danswer will keep all of the knowledge up to date and in sync with your connected applications.\n\nIs my data being sent anywhere when I connect it up to Danswer?\nAnswer: No! Danswer is built with data security as our highest priority. We open sourced it so our users can know exactly what is going on with their data. By default all of the document processing happens within Danswer. The only time it is sent outward is for the GenAI call to generate answers.\n\nWhere is the feature for auto sync-ing document level access permissions from all connected sources?\nAnswer: This falls under the Enterprise Edition set of Danswer features built on top of the MIT/community edition. If you are on Danswer Cloud, you have access to them by default. If you're running it yourself, reach out to the Danswer team to receive access.",
-    "chunk_ind": 0
-  },
-  {
-    "url": "https://docs.danswer.dev/more/use_cases/enterprise_search",
-    "title": "Enterprise Search",
-    "content": "Value of Enterprise Search with Danswer\n\nWhat is Enterprise Search and why is it Important?\nAn Enterprise Search system gives team members a single place to access all of the disparate knowledge of an organization. Critical information is saved across a host of channels like call transcripts with prospects, engineering design docs, IT runbooks, customer support email exchanges, project management tickets, and more. As fast moving teams scale up, information gets spread out and more disorganized.\n\nSince it quickly becomes infeasible to check across every source, decisions get made on incomplete information, employee satisfaction decreases, and the most valuable members of your team are tied up with constant distractions as junior teammates are unable to unblock themselves. Danswer solves this problem by letting anyone on the team access all of the knowledge across your organization in a permissioned and secure way. Users can ask questions in natural language and get back answers and documents across all of the connected sources instantly.\n\nWhat's the real cost?\nA typical knowledge worker spends over 2 hours a week on search, but more than that, the cost of incomplete or incorrect information can be extremely high. Customer support/success that isn't able to find the reference to similar cases could cause hours or even days of delay leading to lower customer satisfaction or in the worst case - churn. An account exec not realizing that a prospect had previously mentioned a specific need could lead to lost deals. An engineer not realizing a similar feature had previously been built could result in weeks of wasted development time and tech debt with duplicate implementation. With a lack of knowledge, your whole organization is navigating in the dark - inefficient and mistake prone.",
-    "chunk_ind": 0
-  },
-  {
-    "url": "https://docs.danswer.dev/more/use_cases/enterprise_search",
-    "title": "Enterprise Search",
-    "content": "More than Search\nWhen analyzing the entire corpus of knowledge within your company is as easy as asking a question in a search bar, your entire team can stay informed and up to date. Danswer also makes it trivial to identify where knowledge is well documented and where it is lacking. Team members who are centers of knowledge can begin to effectively document their expertise since it is no longer being thrown into a black hole. All of this allows the organization to achieve higher efficiency and drive business outcomes.\n\nWith Generative AI, the entire user experience has evolved as well. For example, instead of just finding similar cases for your customer support team to reference, Danswer breaks down the issue and explains it so that even the most junior members can understand it. This in turn lets them give the most holistic and technically accurate response possible to your customers. On the other end, even the super stars of your sales team will not be able to review 10 hours of transcripts before hopping on that critical call, but Danswer can easily parse through it in mere seconds and give crucial context to help your team close.",
-    "chunk_ind": 0
-  },
-  {
-    "url": "https://docs.danswer.dev/more/use_cases/ai_platform",
-    "title": "AI Platform",
-    "content": "Build AI Agents powered by the knowledge and workflows specific to your organization.\n\nBeyond Answers\nAgents enabled by generative AI and reasoning capable models are helping teams to automate their work. Danswer is helping teams make it happen. Danswer provides out of the box user chat sessions, attaching custom tools, handling LLM reasoning, code execution, data analysis, referencing internal knowledge, and much more.\n\nDanswer as a platform is not a no-code agent builder. We are made by developers for developers and this gives your team the full flexibility and power to create agents not constrained by blocks and simple logic paths.\n\nFlexibility and Extensibility\nDanswer is open source and completely whitebox. This not only gives transparency to what happens within the system but also means that your team can directly modify the source code to suit your unique needs.",
-    "chunk_ind": 0
-  },
-  {
-    "url": "https://docs.danswer.dev/more/use_cases/customer_support",
-    "title": "Customer Support",
-    "content": "Help your customer support team instantly answer any question across your entire product.\n\nAI Enabled Support\nCustomer support agents have one of the highest breadth jobs. They field requests that cover the entire surface area of the product and need to help your users find success on extremely short timelines. Because they're not the same people who designed or built the system, they often lack the depth of understanding needed - resulting in delays and escalations to other teams. Modern teams are leveraging AI to help their CS team optimize the speed and quality of these critical customer-facing interactions.\n\nThe Importance of Context\nThere are two critical components of AI copilots for customer support. The first is that the AI system needs to be connected with as much information as possible (not just support tools like Zendesk or Intercom) and that the knowledge needs to be as fresh as possible. Sometimes a fix might even be in places rarely checked by CS such as pull requests in a code repository. The second critical component is the ability of the AI system to break down difficult concepts and convoluted processes into more digestible descriptions and for your team members to be able to chat back and forth with the system to build a better understanding.\n\nDanswer takes care of both of these. The system connects up to over 30+ different applications and the knowledge is pulled in constantly so that the information access is always up to date.",
-    "chunk_ind": 0
-  },
-  {
-    "url": "https://docs.danswer.dev/more/use_cases/sales",
-    "title": "Sales",
-    "content": "Keep your team up to date on every conversation and update so they can close.\n\nRecall Every Detail\nBeing able to instantly revisit every detail of any call without reading transcripts is helping Sales teams provide more tailored pitches, build stronger relationships, and close more deals. Instead of searching and reading through hours of transcripts in preparation for a call, your team can now ask Danswer \"What specific features was ACME interested in seeing for the demo\". Since your team doesn't have time to read every transcript prior to a call, Danswer provides a more thorough summary because it can instantly parse hundreds of pages and distill out the relevant information. Even for fast lookups it becomes much more convenient - for example to brush up on connection building topics by asking \"What rapport building topic did we chat about in the last call with ACME\".\n\nKnow Every Product Update\nIt is impossible for Sales teams to keep up with every product update. Because of this, when a prospect has a question that the Sales team does not know, they have no choice but to rely on the Product and Engineering orgs to get an authoritative answer. Not only is this distracting to the other teams, it also slows down the time to respond to the prospect (and as we know, time is the biggest killer of deals). With Danswer, it is even possible to get answers live on call because of how fast accessing information becomes. A question like \"Have we shipped the Microsoft AD integration yet?\" can now be answered in seconds meaning that prospects can get answers while on the call instead of asynchronously and sales cycles are reduced as a result.",
-    "chunk_ind": 0
-  },
-  {
-    "url": "https://docs.danswer.dev/more/use_cases/operations",
-    "title": "Operations",
-    "content": "Double the productivity of your Ops teams like IT, HR, etc.\n\nAutomatically Resolve Tickets\nModern teams are leveraging AI to auto-resolve up to 50% of tickets. Whether it is an employee asking about benefits details or how to set up the VPN for remote work, Danswer can help your team help themselves. This frees up your team to do the real impactful work of landing star candidates or improving your internal processes.\n\nAI Aided Onboarding\nOne of the periods where your team needs the most help is when they're just ramping up. Instead of feeling lost in dozens of new tools, Danswer gives them a single place where they can ask about anything in natural language. Whether it's how to set up their work environment or what their onboarding goals are, Danswer can walk them through every step with the help of Generative AI. This lets your team feel more empowered and gives time back to the more seasoned members of your team to focus on moving the needle.",
-    "chunk_ind": 0
-  }
-]

backend/danswer/seeding/input_prompts.yaml

@@ -1,24 +0,0 @@
-input_prompts:
-  - id: -5
-    prompt: "Elaborate"
-    content: "Elaborate on the above, give me a more in depth explanation."
-    active: true
-    is_public: true
-
-  - id: -4
-    prompt: "Reword"
-    content: "Help me rewrite the following politely and concisely for professional communication:\n"
-    active: true
-    is_public: true
-
-  - id: -3
-    prompt: "Email"
-    content: "Write a professional email for me including a subject line, signature, etc. Template the parts that need editing with [ ]. The email should cover the following points:\n"
-    active: true
-    is_public: true
-
-  - id: -2
-    prompt: "Debug"
-    content: "Provide step-by-step troubleshooting instructions for the following issue:\n"
-    active: true
-    is_public: true

backend/danswer/server/features/input_prompt/api.py

@@ -1,134 +0,0 @@
-from fastapi import APIRouter
-from fastapi import Depends
-from fastapi import HTTPException
-from sqlalchemy.orm import Session
-
-from danswer.auth.users import current_admin_user
-from danswer.auth.users import current_user
-from danswer.db.engine import get_session
-from danswer.db.input_prompt import fetch_input_prompt_by_id
-from danswer.db.input_prompt import fetch_input_prompts_by_user
-from danswer.db.input_prompt import fetch_public_input_prompts
-from danswer.db.input_prompt import insert_input_prompt
-from danswer.db.input_prompt import remove_input_prompt
-from danswer.db.input_prompt import remove_public_input_prompt
-from danswer.db.input_prompt import update_input_prompt
-from danswer.db.models import User
-from danswer.server.features.input_prompt.models import CreateInputPromptRequest
-from danswer.server.features.input_prompt.models import InputPromptSnapshot
-from danswer.server.features.input_prompt.models import UpdateInputPromptRequest
-from danswer.utils.logger import setup_logger
-
-logger = setup_logger()
-
-basic_router = APIRouter(prefix="/input_prompt")
-admin_router = APIRouter(prefix="/admin/input_prompt")
-
-
-@basic_router.get("")
-def list_input_prompts(
-    user: User | None = Depends(current_user),
-    include_public: bool = False,
-    db_session: Session = Depends(get_session),
-) -> list[InputPromptSnapshot]:
-    user_prompts = fetch_input_prompts_by_user(
-        user_id=user.id if user is not None else None,
-        db_session=db_session,
-        include_public=include_public,
-    )
-    return [InputPromptSnapshot.from_model(prompt) for prompt in user_prompts]
-
-
-@basic_router.get("/{input_prompt_id}")
-def get_input_prompt(
-    input_prompt_id: int,
-    user: User | None = Depends(current_user),
-    db_session: Session = Depends(get_session),
-) -> InputPromptSnapshot:
-    input_prompt = fetch_input_prompt_by_id(
-        id=input_prompt_id,
-        user_id=user.id if user is not None else None,
-        db_session=db_session,
-    )
-    return InputPromptSnapshot.from_model(input_prompt=input_prompt)
-
-
-@basic_router.post("")
-def create_input_prompt(
-    create_input_prompt_request: CreateInputPromptRequest,
-    user: User | None = Depends(current_user),
-    db_session: Session = Depends(get_session),
-) -> InputPromptSnapshot:
-    input_prompt = insert_input_prompt(
-        prompt=create_input_prompt_request.prompt,
-        content=create_input_prompt_request.content,
-        is_public=create_input_prompt_request.is_public,
-        user=user,
-        db_session=db_session,
-    )
-    return InputPromptSnapshot.from_model(input_prompt)
-
-
-@basic_router.patch("/{input_prompt_id}")
-def patch_input_prompt(
-    input_prompt_id: int,
-    update_input_prompt_request: UpdateInputPromptRequest,
-    user: User | None = Depends(current_user),
-    db_session: Session = Depends(get_session),
-) -> InputPromptSnapshot:
-    try:
-        updated_input_prompt = update_input_prompt(
-            user=user,
-            input_prompt_id=input_prompt_id,
-            prompt=update_input_prompt_request.prompt,
-            content=update_input_prompt_request.content,
-            active=update_input_prompt_request.active,
-            db_session=db_session,
-        )
-    except ValueError as e:
-        error_msg = "Error occurred while updated input prompt"
-        logger.warn(f"{error_msg}. Stack trace: {e}")
-        raise HTTPException(status_code=404, detail=error_msg)
-
-    return InputPromptSnapshot.from_model(updated_input_prompt)
-
-
-@basic_router.delete("/{input_prompt_id}")
-def delete_input_prompt(
-    input_prompt_id: int,
-    user: User | None = Depends(current_user),
-    db_session: Session = Depends(get_session),
-) -> None:
-    try:
-        remove_input_prompt(user, input_prompt_id, db_session)
-
-    except ValueError as e:
-        error_msg = "Error occurred while deleting input prompt"
-        logger.warn(f"{error_msg}. Stack trace: {e}")
-        raise HTTPException(status_code=404, detail=error_msg)
-
-
-@admin_router.delete("/{input_prompt_id}")
-def delete_public_input_prompt(
-    input_prompt_id: int,
-    _: User | None = Depends(current_admin_user),
-    db_session: Session = Depends(get_session),
-) -> None:
-    try:
-        remove_public_input_prompt(input_prompt_id, db_session)
-
-    except ValueError as e:
-        error_msg = "Error occurred while deleting input prompt"
-        logger.warn(f"{error_msg}. Stack trace: {e}")
-        raise HTTPException(status_code=404, detail=error_msg)
-
-
-@admin_router.get("")
-def list_public_input_prompts(
-    _: User | None = Depends(current_admin_user),
-    db_session: Session = Depends(get_session),
-) -> list[InputPromptSnapshot]:
-    user_prompts = fetch_public_input_prompts(
-        db_session=db_session,
-    )
-    return [InputPromptSnapshot.from_model(prompt) for prompt in user_prompts]

backend/danswer/server/features/input_prompt/models.py

@@ -1,47 +0,0 @@
-from uuid import UUID
-
-from pydantic import BaseModel
-
-from danswer.db.models import InputPrompt
-from danswer.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-class CreateInputPromptRequest(BaseModel):
-    prompt: str
-    content: str
-    is_public: bool
-
-
-class UpdateInputPromptRequest(BaseModel):
-    prompt: str
-    content: str
-    active: bool
-
-
-class InputPromptResponse(BaseModel):
-    id: int
-    prompt: str
-    content: str
-    active: bool
-
-
-class InputPromptSnapshot(BaseModel):
-    id: int
-    prompt: str
-    content: str
-    active: bool
-    user_id: UUID | None
-    is_public: bool
-
-    @classmethod
-    def from_model(cls, input_prompt: InputPrompt) -> "InputPromptSnapshot":
-        return InputPromptSnapshot(
-            id=input_prompt.id,
-            prompt=input_prompt.prompt,
-            content=input_prompt.content,
-            active=input_prompt.active,
-            user_id=input_prompt.user_id,
-            is_public=input_prompt.is_public,
-        )

backend/ee/LICENSE

@@ -1,20 +1,20 @@
 The DanswerAI Enterprise license (the “Enterprise License”)
 Copyright (c) 2023-present DanswerAI, Inc.
 
-With regard to the Danswer Software:
+With regard to the Onyx Software:
 
 This software and associated documentation files (the "Software") may only be
 used in production, if you (and any entity that you represent) have agreed to,
 and are in compliance with, the DanswerAI Subscription Terms of Service, available
-at https://danswer.ai/terms (the “Enterprise Terms”), or other
+at https://onyx.app/terms (the “Enterprise Terms”), or other
 agreement governing the use of the Software, as agreed by you and DanswerAI,
-and otherwise have a valid Danswer Enterprise license for the
+and otherwise have a valid Onyx Enterprise license for the
 correct number of user seats. Subject to the foregoing sentence, you are free to
 modify this Software and publish patches to the Software. You agree that DanswerAI
 and/or its licensors (as applicable) retain all right, title and interest in and
 to all such modifications and/or patches, and all such modifications and/or
 patches may only be used, copied, modified, displayed, distributed, or otherwise
-exploited with a valid Danswer Enterprise license for the correct
+exploited with a valid Onyx Enterprise license for the correct
 number of user seats. Notwithstanding the foregoing, you may copy and modify
 the Software for development and testing purposes, without requiring a
 subscription. You agree that DanswerAI and/or its licensors (as applicable) retain
@@ -31,6 +31,6 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 
-For all third party components incorporated into the Danswer Software, those
+For all third party components incorporated into the Onyx Software, those
 components are licensed under the original license provided by the owner of the
 applicable component.

backend/ee/danswer/auth/users.py

@@ -1,64 +0,0 @@
-from fastapi import Depends
-from fastapi import HTTPException
-from fastapi import Request
-from fastapi import status
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from danswer.auth.users import current_admin_user
-from danswer.configs.app_configs import AUTH_TYPE
-from danswer.configs.app_configs import SUPER_CLOUD_API_KEY
-from danswer.configs.app_configs import SUPER_USERS
-from danswer.configs.constants import AuthType
-from danswer.db.models import User
-from danswer.utils.logger import setup_logger
-from ee.danswer.db.saml import get_saml_account
-from ee.danswer.server.seeding import get_seed_config
-from ee.danswer.utils.secrets import extract_hashed_cookie
-
-logger = setup_logger()
-
-
-def verify_auth_setting() -> None:
-    # All the Auth flows are valid for EE version
-    logger.notice(f"Using Auth Type: {AUTH_TYPE.value}")
-
-
-async def optional_user_(
-    request: Request,
-    user: User | None,
-    async_db_session: AsyncSession,
-) -> User | None:
-    # Check if the user has a session cookie from SAML
-    if AUTH_TYPE == AuthType.SAML:
-        saved_cookie = extract_hashed_cookie(request)
-
-        if saved_cookie:
-            saml_account = await get_saml_account(
-                cookie=saved_cookie, async_db_session=async_db_session
-            )
-            user = saml_account.user if saml_account else None
-
-    return user
-
-
-def get_default_admin_user_emails_() -> list[str]:
-    seed_config = get_seed_config()
-    if seed_config and seed_config.admin_user_emails:
-        return seed_config.admin_user_emails
-    return []
-
-
-async def current_cloud_superuser(
-    request: Request,
-    user: User | None = Depends(current_admin_user),
-) -> User | None:
-    api_key = request.headers.get("Authorization", "").replace("Bearer ", "")
-    if api_key != SUPER_CLOUD_API_KEY:
-        raise HTTPException(status_code=401, detail="Invalid API key")
-
-    if user and user.email not in SUPER_USERS:
-        raise HTTPException(
-            status_code=status.HTTP_403_FORBIDDEN,
-            detail="Access denied. User must be a cloud superuser to perform this action.",
-        )
-    return user

backend/ee/danswer/configs/app_configs.py

@@ -1,21 +0,0 @@
-import os
-
-# Applicable for OIDC Auth
-OPENID_CONFIG_URL = os.environ.get("OPENID_CONFIG_URL", "")
-
-# Applicable for SAML Auth
-SAML_CONF_DIR = os.environ.get("SAML_CONF_DIR") or "/app/ee/danswer/configs/saml_config"
-
-
-#####
-# Auto Permission Sync
-#####
-NUM_PERMISSION_WORKERS = int(os.environ.get("NUM_PERMISSION_WORKERS") or 2)
-
-
-STRIPE_SECRET_KEY = os.environ.get("STRIPE_SECRET_KEY")
-STRIPE_PRICE_ID = os.environ.get("STRIPE_PRICE")
-
-OPENAI_DEFAULT_API_KEY = os.environ.get("OPENAI_DEFAULT_API_KEY")
-ANTHROPIC_DEFAULT_API_KEY = os.environ.get("ANTHROPIC_DEFAULT_API_KEY")
-COHERE_DEFAULT_API_KEY = os.environ.get("COHERE_DEFAULT_API_KEY")

backend/ee/danswer/server/query_and_chat/utils.py

@@ -1,85 +0,0 @@
-from typing import cast
-
-from fastapi import HTTPException
-from sqlalchemy.orm import Session
-
-from danswer.auth.users import is_user_admin
-from danswer.db.llm import fetch_existing_doc_sets
-from danswer.db.llm import fetch_existing_tools
-from danswer.db.models import Persona
-from danswer.db.models import Prompt
-from danswer.db.models import Tool
-from danswer.db.models import User
-from danswer.db.persona import get_prompts_by_ids
-from danswer.one_shot_answer.models import PersonaConfig
-from danswer.tools.tool_implementations.custom.custom_tool import (
-    build_custom_tools_from_openapi_schema_and_headers,
-)
-
-
-def create_temporary_persona(
-    persona_config: PersonaConfig, db_session: Session, user: User | None = None
-) -> Persona:
-    if not is_user_admin(user):
-        raise HTTPException(
-            status_code=403,
-            detail="User is not authorized to create a persona in one shot queries",
-        )
-
-    """Create a temporary Persona object from the provided configuration."""
-    persona = Persona(
-        name=persona_config.name,
-        description=persona_config.description,
-        num_chunks=persona_config.num_chunks,
-        llm_relevance_filter=persona_config.llm_relevance_filter,
-        llm_filter_extraction=persona_config.llm_filter_extraction,
-        recency_bias=persona_config.recency_bias,
-        llm_model_provider_override=persona_config.llm_model_provider_override,
-        llm_model_version_override=persona_config.llm_model_version_override,
-    )
-
-    if persona_config.prompts:
-        persona.prompts = [
-            Prompt(
-                name=p.name,
-                description=p.description,
-                system_prompt=p.system_prompt,
-                task_prompt=p.task_prompt,
-                include_citations=p.include_citations,
-                datetime_aware=p.datetime_aware,
-            )
-            for p in persona_config.prompts
-        ]
-    elif persona_config.prompt_ids:
-        persona.prompts = get_prompts_by_ids(
-            db_session=db_session, prompt_ids=persona_config.prompt_ids
-        )
-
-    persona.tools = []
-    if persona_config.custom_tools_openapi:
-        for schema in persona_config.custom_tools_openapi:
-            tools = cast(
-                list[Tool],
-                build_custom_tools_from_openapi_schema_and_headers(schema),
-            )
-            persona.tools.extend(tools)
-
-    if persona_config.tools:
-        tool_ids = [tool.id for tool in persona_config.tools]
-        persona.tools.extend(
-            fetch_existing_tools(db_session=db_session, tool_ids=tool_ids)
-        )
-
-    if persona_config.tool_ids:
-        persona.tools.extend(
-            fetch_existing_tools(
-                db_session=db_session, tool_ids=persona_config.tool_ids
-            )
-        )
-
-    fetched_docs = fetch_existing_doc_sets(
-        db_session=db_session, doc_ids=persona_config.document_set_ids
-    )
-    persona.document_sets = fetched_docs
-
-    return persona

backend/ee/onyx/access/access.py

@@ -1,17 +1,17 @@
 from sqlalchemy.orm import Session
 
-from danswer.access.access import (
+from ee.onyx.db.external_perm import fetch_external_groups_for_user
+from ee.onyx.db.user_group import fetch_user_groups_for_documents
+from ee.onyx.db.user_group import fetch_user_groups_for_user
+from onyx.access.access import (
     _get_access_for_documents as get_access_for_documents_without_groups,
 )
-from danswer.access.access import _get_acl_for_user as get_acl_for_user_without_groups
-from danswer.access.models import DocumentAccess
-from danswer.access.utils import prefix_external_group
-from danswer.access.utils import prefix_user_group
-from danswer.db.document import get_documents_by_ids
-from danswer.db.models import User
-from ee.danswer.db.external_perm import fetch_external_groups_for_user
-from ee.danswer.db.user_group import fetch_user_groups_for_documents
-from ee.danswer.db.user_group import fetch_user_groups_for_user
+from onyx.access.access import _get_acl_for_user as get_acl_for_user_without_groups
+from onyx.access.models import DocumentAccess
+from onyx.access.utils import prefix_external_group
+from onyx.access.utils import prefix_user_group
+from onyx.db.document import get_documents_by_ids
+from onyx.db.models import User
 
 
 def _get_access_for_document(
@@ -69,7 +69,7 @@ def _get_access_for_documents(
         )
 
         # If the document is determined to be "public" externally (through a SYNC connector)
-        # then it's given the same access level as if it were marked public within Danswer
+        # then it's given the same access level as if it were marked public within Onyx
         is_public_anywhere = document.is_public or non_ee_access.is_public
 
         # To avoid collisions of group namings between connectors, they need to be prefixed
@@ -89,7 +89,7 @@ def _get_acl_for_user(user: User | None, db_session: Session) -> set[str]:
     user should have access to a document if at least one entry in the document's ACL
     matches one entry in the returned set.
 
-    NOTE: is imported in danswer.access.access by `fetch_versioned_implementation`
+    NOTE: is imported in onyx.access.access by `fetch_versioned_implementation`
     DO NOT REMOVE."""
     db_user_groups = fetch_user_groups_for_user(db_session, user.id) if user else []
     prefixed_user_groups = [

backend/ee/onyx/auth/users.py

@@ -0,0 +1,120 @@
+from functools import lru_cache
+
+import requests
+from fastapi import Depends
+from fastapi import HTTPException
+from fastapi import Request
+from fastapi import status
+from jwt import decode as jwt_decode
+from jwt import InvalidTokenError
+from jwt import PyJWTError
+from sqlalchemy import func
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from ee.onyx.configs.app_configs import JWT_PUBLIC_KEY_URL
+from ee.onyx.configs.app_configs import SUPER_CLOUD_API_KEY
+from ee.onyx.configs.app_configs import SUPER_USERS
+from ee.onyx.db.saml import get_saml_account
+from ee.onyx.server.seeding import get_seed_config
+from ee.onyx.utils.secrets import extract_hashed_cookie
+from onyx.auth.users import current_admin_user
+from onyx.configs.app_configs import AUTH_TYPE
+from onyx.configs.constants import AuthType
+from onyx.db.models import User
+from onyx.utils.logger import setup_logger
+
+
+logger = setup_logger()
+
+
+@lru_cache()
+def get_public_key() -> str | None:
+    if JWT_PUBLIC_KEY_URL is None:
+        logger.error("JWT_PUBLIC_KEY_URL is not set")
+        return None
+
+    response = requests.get(JWT_PUBLIC_KEY_URL)
+    response.raise_for_status()
+    return response.text
+
+
+async def verify_jwt_token(token: str, async_db_session: AsyncSession) -> User | None:
+    try:
+        public_key_pem = get_public_key()
+        if public_key_pem is None:
+            logger.error("Failed to retrieve public key")
+            return None
+
+        payload = jwt_decode(
+            token,
+            public_key_pem,
+            algorithms=["RS256"],
+            audience=None,
+        )
+        email = payload.get("email")
+        if email:
+            result = await async_db_session.execute(
+                select(User).where(func.lower(User.email) == func.lower(email))
+            )
+            return result.scalars().first()
+    except InvalidTokenError:
+        logger.error("Invalid JWT token")
+        get_public_key.cache_clear()
+    except PyJWTError as e:
+        logger.error(f"JWT decoding error: {str(e)}")
+        get_public_key.cache_clear()
+    return None
+
+
+def verify_auth_setting() -> None:
+    # All the Auth flows are valid for EE version
+    logger.notice(f"Using Auth Type: {AUTH_TYPE.value}")
+
+
+async def optional_user_(
+    request: Request,
+    user: User | None,
+    async_db_session: AsyncSession,
+) -> User | None:
+    # Check if the user has a session cookie from SAML
+    if AUTH_TYPE == AuthType.SAML:
+        saved_cookie = extract_hashed_cookie(request)
+
+        if saved_cookie:
+            saml_account = await get_saml_account(
+                cookie=saved_cookie, async_db_session=async_db_session
+            )
+            user = saml_account.user if saml_account else None
+
+    # If user is still None, check for JWT in Authorization header
+    if user is None and JWT_PUBLIC_KEY_URL is not None:
+        auth_header = request.headers.get("Authorization")
+        if auth_header and auth_header.startswith("Bearer "):
+            token = auth_header[len("Bearer ") :].strip()
+            user = await verify_jwt_token(token, async_db_session)
+
+    return user
+
+
+def get_default_admin_user_emails_() -> list[str]:
+    seed_config = get_seed_config()
+    if seed_config and seed_config.admin_user_emails:
+        return seed_config.admin_user_emails
+    return []
+
+
+async def current_cloud_superuser(
+    request: Request,
+    user: User | None = Depends(current_admin_user),
+) -> User | None:
+    api_key = request.headers.get("Authorization", "").replace("Bearer ", "")
+    if api_key != SUPER_CLOUD_API_KEY:
+        raise HTTPException(status_code=401, detail="Invalid API key")
+
+    if user and user.email not in SUPER_USERS:
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="Access denied. User must be a cloud superuser to perform this action.",
+        )
+    return user

backend/ee/onyx/background/celery/apps/primary.py

@@ -1,13 +1,13 @@
-from danswer.background.celery.apps.primary import celery_app
-from danswer.background.task_utils import build_celery_task_wrapper
-from danswer.configs.app_configs import JOB_TIMEOUT
-from danswer.db.chat import delete_chat_sessions_older_than
-from danswer.db.engine import get_session_with_tenant
-from danswer.server.settings.store import load_settings
-from danswer.utils.logger import setup_logger
-from ee.danswer.background.celery_utils import should_perform_chat_ttl_check
-from ee.danswer.background.task_name_builders import name_chat_ttl_task
-from ee.danswer.server.reporting.usage_export_generation import create_new_usage_report
+from ee.onyx.background.celery_utils import should_perform_chat_ttl_check
+from ee.onyx.background.task_name_builders import name_chat_ttl_task
+from ee.onyx.server.reporting.usage_export_generation import create_new_usage_report
+from onyx.background.celery.apps.primary import celery_app
+from onyx.background.task_utils import build_celery_task_wrapper
+from onyx.configs.app_configs import JOB_TIMEOUT
+from onyx.db.chat import delete_chat_sessions_older_than
+from onyx.db.engine import get_session_with_tenant
+from onyx.server.settings.store import load_settings
+from onyx.utils.logger import setup_logger
 from shared_configs.configs import MULTI_TENANT
 from shared_configs.contextvars import CURRENT_TENANT_ID_CONTEXTVAR
 

backend/ee/onyx/background/celery/tasks/beat_schedule.py

@@ -1,19 +1,20 @@
 from datetime import timedelta
 from typing import Any
 
-from danswer.background.celery.tasks.beat_schedule import (
+from onyx.background.celery.tasks.beat_schedule import (
     tasks_to_schedule as base_tasks_to_schedule,
 )
+from onyx.configs.constants import OnyxCeleryTask
 
 ee_tasks_to_schedule = [
     {
         "name": "autogenerate_usage_report",
-        "task": "autogenerate_usage_report_task",
+        "task": OnyxCeleryTask.AUTOGENERATE_USAGE_REPORT_TASK,
         "schedule": timedelta(days=30),  # TODO: change this to config flag
     },
     {
         "name": "check-ttl-management",
-        "task": "check_ttl_management_task",
+        "task": OnyxCeleryTask.CHECK_TTL_MANAGEMENT_TASK,
         "schedule": timedelta(hours=1),
     },
 ]

backend/ee/onyx/background/celery/tasks/vespa/tasks.py

@@ -3,12 +3,12 @@ from typing import cast
 from redis import Redis
 from sqlalchemy.orm import Session
 
-from danswer.background.celery.apps.app_base import task_logger
-from danswer.redis.redis_usergroup import RedisUserGroup
-from danswer.utils.logger import setup_logger
-from ee.danswer.db.user_group import delete_user_group
-from ee.danswer.db.user_group import fetch_user_group
-from ee.danswer.db.user_group import mark_user_group_as_synced
+from ee.onyx.db.user_group import delete_user_group
+from ee.onyx.db.user_group import fetch_user_group
+from ee.onyx.db.user_group import mark_user_group_as_synced
+from onyx.background.celery.apps.app_base import task_logger
+from onyx.redis.redis_usergroup import RedisUserGroup
+from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
 

backend/ee/onyx/background/celery_utils.py

@@ -1,9 +1,9 @@
 from sqlalchemy.orm import Session
 
-from danswer.db.tasks import check_task_is_live_and_not_timed_out
-from danswer.db.tasks import get_latest_task
-from danswer.utils.logger import setup_logger
-from ee.danswer.background.task_name_builders import name_chat_ttl_task
+from ee.onyx.background.task_name_builders import name_chat_ttl_task
+from onyx.db.tasks import check_task_is_live_and_not_timed_out
+from onyx.db.tasks import get_latest_task
+from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
 

backend/ee/onyx/chat/process_message.py

@@ -0,0 +1,41 @@
+from ee.onyx.server.query_and_chat.models import OneShotQAResponse
+from onyx.chat.models import AllCitations
+from onyx.chat.models import LLMRelevanceFilterResponse
+from onyx.chat.models import OnyxAnswerPiece
+from onyx.chat.models import OnyxContexts
+from onyx.chat.models import QADocsResponse
+from onyx.chat.models import StreamingError
+from onyx.chat.process_message import ChatPacketStream
+from onyx.server.query_and_chat.models import ChatMessageDetail
+from onyx.utils.timing import log_function_time
+
+
+@log_function_time()
+def gather_stream_for_answer_api(
+    packets: ChatPacketStream,
+) -> OneShotQAResponse:
+    response = OneShotQAResponse()
+
+    answer = ""
+    for packet in packets:
+        if isinstance(packet, OnyxAnswerPiece) and packet.answer_piece:
+            answer += packet.answer_piece
+        elif isinstance(packet, QADocsResponse):
+            response.docs = packet
+            # Extraneous, provided for backwards compatibility
+            response.rephrase = packet.rephrased_query
+        elif isinstance(packet, StreamingError):
+            response.error_msg = packet.error
+        elif isinstance(packet, ChatMessageDetail):
+            response.chat_message_id = packet.message_id
+        elif isinstance(packet, LLMRelevanceFilterResponse):
+            response.llm_selected_doc_indices = packet.llm_selected_doc_indices
+        elif isinstance(packet, AllCitations):
+            response.citations = packet.citations
+        elif isinstance(packet, OnyxContexts):
+            response.contexts = packet
+
+    if answer:
+        response.answer = answer
+
+    return response

backend/ee/onyx/configs/app_configs.py

@@ -0,0 +1,49 @@
+import json
+import os
+
+# Applicable for OIDC Auth
+OPENID_CONFIG_URL = os.environ.get("OPENID_CONFIG_URL", "")
+
+# Applicable for SAML Auth
+SAML_CONF_DIR = os.environ.get("SAML_CONF_DIR") or "/app/ee/onyx/configs/saml_config"
+
+
+#####
+# Auto Permission Sync
+#####
+# In seconds, default is 5 minutes
+CONFLUENCE_PERMISSION_GROUP_SYNC_FREQUENCY = int(
+    os.environ.get("CONFLUENCE_PERMISSION_GROUP_SYNC_FREQUENCY") or 5 * 60
+)
+# In seconds, default is 5 minutes
+CONFLUENCE_PERMISSION_DOC_SYNC_FREQUENCY = int(
+    os.environ.get("CONFLUENCE_PERMISSION_DOC_SYNC_FREQUENCY") or 5 * 60
+)
+NUM_PERMISSION_WORKERS = int(os.environ.get("NUM_PERMISSION_WORKERS") or 2)
+
+
+STRIPE_SECRET_KEY = os.environ.get("STRIPE_SECRET_KEY")
+STRIPE_PRICE_ID = os.environ.get("STRIPE_PRICE")
+
+OPENAI_DEFAULT_API_KEY = os.environ.get("OPENAI_DEFAULT_API_KEY")
+ANTHROPIC_DEFAULT_API_KEY = os.environ.get("ANTHROPIC_DEFAULT_API_KEY")
+COHERE_DEFAULT_API_KEY = os.environ.get("COHERE_DEFAULT_API_KEY")
+
+# JWT Public Key URL
+JWT_PUBLIC_KEY_URL: str | None = os.getenv("JWT_PUBLIC_KEY_URL", None)
+
+
+# Super Users
+SUPER_USERS = json.loads(os.environ.get("SUPER_USERS", "[]"))
+SUPER_CLOUD_API_KEY = os.environ.get("SUPER_CLOUD_API_KEY", "api_key")
+
+OAUTH_SLACK_CLIENT_ID = os.environ.get("OAUTH_SLACK_CLIENT_ID", "")
+OAUTH_SLACK_CLIENT_SECRET = os.environ.get("OAUTH_SLACK_CLIENT_SECRET", "")
+OAUTH_CONFLUENCE_CLIENT_ID = os.environ.get("OAUTH_CONFLUENCE_CLIENT_ID", "")
+OAUTH_CONFLUENCE_CLIENT_SECRET = os.environ.get("OAUTH_CONFLUENCE_CLIENT_SECRET", "")
+OAUTH_JIRA_CLIENT_ID = os.environ.get("OAUTH_JIRA_CLIENT_ID", "")
+OAUTH_JIRA_CLIENT_SECRET = os.environ.get("OAUTH_JIRA_CLIENT_SECRET", "")
+OAUTH_GOOGLE_DRIVE_CLIENT_ID = os.environ.get("OAUTH_GOOGLE_DRIVE_CLIENT_ID", "")
+OAUTH_GOOGLE_DRIVE_CLIENT_SECRET = os.environ.get(
+    "OAUTH_GOOGLE_DRIVE_CLIENT_SECRET", ""
+)

backend/ee/onyx/configs/saml_config/template.settings.json

@@ -4,7 +4,7 @@
   "idp": {
     "entityId": "<Provide This from IDP>",
     "singleSignOnService": {
-      "url": "<Replace this with your IDP URL> https://trial-1234567.okta.com/home/trial-1234567_danswer/somevalues/somevalues",
+      "url": "<Replace this with your IDP URL> https://trial-1234567.okta.com/home/trial-1234567_onyx/somevalues/somevalues",
       "binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
     },
     "x509cert": "<Provide this>"

backend/ee/onyx/db/analytics.py

@@ -10,10 +10,10 @@ from sqlalchemy import or_
 from sqlalchemy import select
 from sqlalchemy.orm import Session
 
-from danswer.configs.constants import MessageType
-from danswer.db.models import ChatMessage
-from danswer.db.models import ChatMessageFeedback
-from danswer.db.models import ChatSession
+from onyx.configs.constants import MessageType
+from onyx.db.models import ChatMessage
+from onyx.db.models import ChatMessageFeedback
+from onyx.db.models import ChatSession
 
 
 def fetch_query_analytics(
@@ -83,18 +83,18 @@ def fetch_per_user_query_analytics(
     return db_session.execute(stmt).all()  # type: ignore
 
 
-def fetch_danswerbot_analytics(
+def fetch_onyxbot_analytics(
     start: datetime.datetime,
     end: datetime.datetime,
     db_session: Session,
 ) -> Sequence[tuple[int, int, datetime.date]]:
     """Gets the:
     Date of each set of aggregated statistics
-    Number of DanswerBot Queries (Chat Sessions)
+    Number of OnyxBot Queries (Chat Sessions)
     Number of instances of Negative feedback OR Needing additional help
         (only counting the last feedback)
     """
-    # Get every chat session in the time range which is a Danswerbot flow
+    # Get every chat session in the time range which is a Onyxbot flow
     # along with the first Assistant message which is the response to the user question.
     # Generally there should not be more than one AI message per chat session of this type
     subquery_first_ai_response = (
@@ -106,7 +106,7 @@ def fetch_danswerbot_analytics(
         .where(
             ChatSession.time_created >= start,
             ChatSession.time_created <= end,
-            ChatSession.danswerbot_flow.is_(True),
+            ChatSession.onyxbot_flow.is_(True),
         )
         .where(
             ChatMessage.message_type == MessageType.ASSISTANT,
@@ -130,7 +130,7 @@ def fetch_danswerbot_analytics(
         db_session.query(
             func.count(ChatSession.id).label("total_sessions"),
             # Need to explicitly specify this as False to handle the NULL case so the cases without
-            # feedback aren't counted against Danswerbot
+            # feedback aren't counted against Onyxbot
             func.sum(
                 case(
                     (
@@ -150,7 +150,7 @@ def fetch_danswerbot_analytics(
             ChatSession.id == subquery_first_ai_response.c.chat_session_id,
         )
         # Combine the chat sessions with latest feedback to get the latest feedback for the first AI
-        # message of the chat session where the chat session is Danswerbot type and within the time
+        # message of the chat session where the chat session is Onyxbot type and within the time
         # range specified. Left/outer join used here to ensure that if no feedback, a null is used
         # for the feedback id
         .outerjoin(
@@ -170,3 +170,67 @@ def fetch_danswerbot_analytics(
     )
 
     return results
+
+
+def fetch_persona_message_analytics(
+    db_session: Session,
+    persona_id: int,
+    start: datetime.datetime,
+    end: datetime.datetime,
+) -> list[tuple[int, datetime.date]]:
+    """Gets the daily message counts for a specific persona within the given time range."""
+    query = (
+        select(
+            func.count(ChatMessage.id),
+            cast(ChatMessage.time_sent, Date),
+        )
+        .join(
+            ChatSession,
+            ChatMessage.chat_session_id == ChatSession.id,
+        )
+        .where(
+            or_(
+                ChatMessage.alternate_assistant_id == persona_id,
+                ChatSession.persona_id == persona_id,
+            ),
+            ChatMessage.time_sent >= start,
+            ChatMessage.time_sent <= end,
+            ChatMessage.message_type == MessageType.ASSISTANT,
+        )
+        .group_by(cast(ChatMessage.time_sent, Date))
+        .order_by(cast(ChatMessage.time_sent, Date))
+    )
+
+    return [tuple(row) for row in db_session.execute(query).all()]
+
+
+def fetch_persona_unique_users(
+    db_session: Session,
+    persona_id: int,
+    start: datetime.datetime,
+    end: datetime.datetime,
+) -> list[tuple[int, datetime.date]]:
+    """Gets the daily unique user counts for a specific persona within the given time range."""
+    query = (
+        select(
+            func.count(func.distinct(ChatSession.user_id)),
+            cast(ChatMessage.time_sent, Date),
+        )
+        .join(
+            ChatSession,
+            ChatMessage.chat_session_id == ChatSession.id,
+        )
+        .where(
+            or_(
+                ChatMessage.alternate_assistant_id == persona_id,
+                ChatSession.persona_id == persona_id,
+            ),
+            ChatMessage.time_sent >= start,
+            ChatMessage.time_sent <= end,
+            ChatMessage.message_type == MessageType.ASSISTANT,
+        )
+        .group_by(cast(ChatMessage.time_sent, Date))
+        .order_by(cast(ChatMessage.time_sent, Date))
+    )
+
+    return [tuple(row) for row in db_session.execute(query).all()]

backend/ee/onyx/db/connector.py

@@ -1,9 +1,9 @@
 from sqlalchemy import distinct
 from sqlalchemy.orm import Session
 
-from danswer.configs.constants import DocumentSource
-from danswer.db.models import Connector
-from danswer.utils.logger import setup_logger
+from onyx.configs.constants import DocumentSource
+from onyx.db.models import Connector
+from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
 

backend/ee/onyx/db/connector_credential_pair.py

@@ -1,13 +1,13 @@
 from sqlalchemy import delete
 from sqlalchemy.orm import Session
 
-from danswer.configs.constants import DocumentSource
-from danswer.db.connector_credential_pair import get_connector_credential_pair
-from danswer.db.enums import AccessType
-from danswer.db.models import Connector
-from danswer.db.models import ConnectorCredentialPair
-from danswer.db.models import UserGroup__ConnectorCredentialPair
-from danswer.utils.logger import setup_logger
+from onyx.configs.constants import DocumentSource
+from onyx.db.connector_credential_pair import get_connector_credential_pair
+from onyx.db.enums import AccessType
+from onyx.db.models import Connector
+from onyx.db.models import ConnectorCredentialPair
+from onyx.db.models import UserGroup__ConnectorCredentialPair
+from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
 
@@ -37,10 +37,15 @@ def get_cc_pairs_by_source(
     source_type: DocumentSource,
     only_sync: bool,
 ) -> list[ConnectorCredentialPair]:
+    """
+    Get all cc_pairs for a given source type (and optionally only sync)
+    result is sorted by cc_pair id
+    """
     query = (
         db_session.query(ConnectorCredentialPair)
         .join(ConnectorCredentialPair.connector)
         .filter(Connector.source == source_type)
+        .order_by(ConnectorCredentialPair.id)
     )
 
     if only_sync:

backend/ee/onyx/db/document.py

@@ -4,10 +4,10 @@ from datetime import timezone
 from sqlalchemy import select
 from sqlalchemy.orm import Session
 
-from danswer.access.models import ExternalAccess
-from danswer.access.utils import prefix_group_w_source
-from danswer.configs.constants import DocumentSource
-from danswer.db.models import Document as DbDocument
+from onyx.access.models import ExternalAccess
+from onyx.access.utils import prefix_group_w_source
+from onyx.configs.constants import DocumentSource
+from onyx.db.models import Document as DbDocument
 
 
 def upsert_document_external_perms__no_commit(
@@ -55,9 +55,10 @@ def upsert_document_external_perms(
     doc_id: str,
     external_access: ExternalAccess,
     source_type: DocumentSource,
-) -> None:
+) -> bool:
     """
-    This sets the permissions for a document in postgres.
+    This sets the permissions for a document in postgres. Returns True if the
+    a new document was created, False otherwise.
     NOTE: this will replace any existing external access, it will not do a union
     """
     document = db_session.scalars(
@@ -85,7 +86,7 @@ def upsert_document_external_perms(
         )
         db_session.add(document)
         db_session.commit()
-        return
+        return True
 
     # If the document exists, we need to check if the external access has changed
     if (
@@ -98,3 +99,5 @@ def upsert_document_external_perms(
         document.is_public = external_access.is_public
         document.last_modified = datetime.now(timezone.utc)
         db_session.commit()
+
+    return False

backend/ee/onyx/db/document_set.py

@@ -2,13 +2,13 @@ from uuid import UUID
 
 from sqlalchemy.orm import Session
 
-from danswer.db.models import ConnectorCredentialPair
-from danswer.db.models import DocumentSet
-from danswer.db.models import DocumentSet__ConnectorCredentialPair
-from danswer.db.models import DocumentSet__User
-from danswer.db.models import DocumentSet__UserGroup
-from danswer.db.models import User__UserGroup
-from danswer.db.models import UserGroup
+from onyx.db.models import ConnectorCredentialPair
+from onyx.db.models import DocumentSet
+from onyx.db.models import DocumentSet__ConnectorCredentialPair
+from onyx.db.models import DocumentSet__User
+from onyx.db.models import DocumentSet__UserGroup
+from onyx.db.models import User__UserGroup
+from onyx.db.models import UserGroup
 
 
 def make_doc_set_private(

backend/ee/onyx/db/external_perm.py

@@ -6,10 +6,13 @@ from sqlalchemy import delete
 from sqlalchemy import select
 from sqlalchemy.orm import Session
 
-from danswer.access.utils import prefix_group_w_source
-from danswer.configs.constants import DocumentSource
-from danswer.db.models import User__ExternalUserGroupId
-from danswer.db.users import batch_add_ext_perm_user_if_not_exists
+from onyx.access.utils import prefix_group_w_source
+from onyx.configs.constants import DocumentSource
+from onyx.db.models import User__ExternalUserGroupId
+from onyx.db.users import batch_add_ext_perm_user_if_not_exists
+from onyx.utils.logger import setup_logger
+
+logger = setup_logger()
 
 
 class ExternalUserGroup(BaseModel):
@@ -73,7 +76,13 @@ def replace_user__ext_group_for_cc_pair(
     new_external_permissions = []
     for external_group in group_defs:
         for user_email in external_group.user_emails:
-            user_id = email_id_map[user_email]
+            user_id = email_id_map.get(user_email.lower())
+            if user_id is None:
+                logger.warning(
+                    f"User in group {external_group.id}"
+                    f" with email {user_email} not found"
+                )
+                continue
             new_external_permissions.append(
                 User__ExternalUserGroupId(
                     user_id=user_id,

backend/ee/onyx/db/persona.py

@@ -2,8 +2,8 @@ from uuid import UUID
 
 from sqlalchemy.orm import Session
 
-from danswer.db.models import Persona__User
-from danswer.db.models import Persona__UserGroup
+from onyx.db.models import Persona__User
+from onyx.db.models import Persona__UserGroup
 
 
 def make_persona_private(

backend/ee/onyx/db/query_history.py

@@ -10,8 +10,8 @@ from sqlalchemy.orm import joinedload
 from sqlalchemy.orm import Session
 from sqlalchemy.sql.expression import UnaryExpression
 
-from danswer.db.models import ChatMessage
-from danswer.db.models import ChatSession
+from onyx.db.models import ChatMessage
+from onyx.db.models import ChatSession
 
 SortByOptions = Literal["time_sent"]
 

backend/ee/onyx/db/saml.py

@@ -9,8 +9,8 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy.orm import selectinload
 from sqlalchemy.orm import Session
 
-from danswer.configs.app_configs import SESSION_EXPIRE_TIME_SECONDS
-from danswer.db.models import SamlAccount
+from onyx.configs.app_configs import SESSION_EXPIRE_TIME_SECONDS
+from onyx.db.models import SamlAccount
 
 
 def upsert_saml_account(

backend/ee/onyx/db/standard_answer.py

@@ -5,9 +5,9 @@ from collections.abc import Sequence
 from sqlalchemy import select
 from sqlalchemy.orm import Session
 
-from danswer.db.models import StandardAnswer
-from danswer.db.models import StandardAnswerCategory
-from danswer.utils.logger import setup_logger
+from onyx.db.models import StandardAnswer
+from onyx.db.models import StandardAnswerCategory
+from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
 

backend/ee/onyx/db/token_limit.py

@@ -7,14 +7,14 @@ from sqlalchemy import select
 from sqlalchemy.orm import aliased
 from sqlalchemy.orm import Session
 
-from danswer.configs.constants import TokenRateLimitScope
-from danswer.db.models import TokenRateLimit
-from danswer.db.models import TokenRateLimit__UserGroup
-from danswer.db.models import User
-from danswer.db.models import User__UserGroup
-from danswer.db.models import UserGroup
-from danswer.db.models import UserRole
-from danswer.server.token_rate_limits.models import TokenRateLimitArgs
+from onyx.configs.constants import TokenRateLimitScope
+from onyx.db.models import TokenRateLimit
+from onyx.db.models import TokenRateLimit__UserGroup
+from onyx.db.models import User
+from onyx.db.models import User__UserGroup
+from onyx.db.models import UserGroup
+from onyx.db.models import UserRole
+from onyx.server.token_rate_limits.models import TokenRateLimitArgs
 
 
 def _add_user_filters(

backend/ee/onyx/db/usage_export.py

@@ -7,13 +7,13 @@ from typing import Optional
 from fastapi_users_db_sqlalchemy import UUID_ID
 from sqlalchemy.orm import Session
 
-from danswer.configs.constants import MessageType
-from danswer.db.models import UsageReport
-from danswer.file_store.file_store import get_default_file_store
-from ee.danswer.db.query_history import fetch_chat_sessions_eagerly_by_time
-from ee.danswer.server.reporting.usage_export_models import ChatMessageSkeleton
-from ee.danswer.server.reporting.usage_export_models import FlowType
-from ee.danswer.server.reporting.usage_export_models import UsageReportMetadata
+from ee.onyx.db.query_history import fetch_chat_sessions_eagerly_by_time
+from ee.onyx.server.reporting.usage_export_models import ChatMessageSkeleton
+from ee.onyx.server.reporting.usage_export_models import FlowType
+from ee.onyx.server.reporting.usage_export_models import UsageReportMetadata
+from onyx.configs.constants import MessageType
+from onyx.db.models import UsageReport
+from onyx.file_store.file_store import get_default_file_store
 
 
 # Gets skeletons of all message
@@ -33,12 +33,7 @@ def get_empty_chat_messages_entries__paginated(
 
     message_skeletons: list[ChatMessageSkeleton] = []
     for chat_session in chat_sessions:
-        if chat_session.one_shot:
-            flow_type = FlowType.SEARCH
-        elif chat_session.danswerbot_flow:
-            flow_type = FlowType.SLACK
-        else:
-            flow_type = FlowType.CHAT
+        flow_type = FlowType.SLACK if chat_session.onyxbot_flow else FlowType.CHAT
 
         for message in chat_session.messages:
             # Only count user messages

backend/ee/onyx/db/user_group.py

@@ -10,27 +10,27 @@ from sqlalchemy import select
 from sqlalchemy import update
 from sqlalchemy.orm import Session
 
-from danswer.db.connector_credential_pair import get_connector_credential_pair_from_id
-from danswer.db.enums import AccessType
-from danswer.db.enums import ConnectorCredentialPairStatus
-from danswer.db.models import ConnectorCredentialPair
-from danswer.db.models import Credential__UserGroup
-from danswer.db.models import Document
-from danswer.db.models import DocumentByConnectorCredentialPair
-from danswer.db.models import DocumentSet__UserGroup
-from danswer.db.models import LLMProvider__UserGroup
-from danswer.db.models import Persona__UserGroup
-from danswer.db.models import TokenRateLimit__UserGroup
-from danswer.db.models import User
-from danswer.db.models import User__UserGroup
-from danswer.db.models import UserGroup
-from danswer.db.models import UserGroup__ConnectorCredentialPair
-from danswer.db.models import UserRole
-from danswer.db.users import fetch_user_by_id
-from danswer.utils.logger import setup_logger
-from ee.danswer.server.user_group.models import SetCuratorRequest
-from ee.danswer.server.user_group.models import UserGroupCreate
-from ee.danswer.server.user_group.models import UserGroupUpdate
+from ee.onyx.server.user_group.models import SetCuratorRequest
+from ee.onyx.server.user_group.models import UserGroupCreate
+from ee.onyx.server.user_group.models import UserGroupUpdate
+from onyx.db.connector_credential_pair import get_connector_credential_pair_from_id
+from onyx.db.enums import AccessType
+from onyx.db.enums import ConnectorCredentialPairStatus
+from onyx.db.models import ConnectorCredentialPair
+from onyx.db.models import Credential__UserGroup
+from onyx.db.models import Document
+from onyx.db.models import DocumentByConnectorCredentialPair
+from onyx.db.models import DocumentSet__UserGroup
+from onyx.db.models import LLMProvider__UserGroup
+from onyx.db.models import Persona__UserGroup
+from onyx.db.models import TokenRateLimit__UserGroup
+from onyx.db.models import User
+from onyx.db.models import User__UserGroup
+from onyx.db.models import UserGroup
+from onyx.db.models import UserGroup__ConnectorCredentialPair
+from onyx.db.models import UserRole
+from onyx.db.users import fetch_user_by_id
+from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
 

backend/ee/onyx/external_permissions/confluence/doc_sync.py

@@ -4,14 +4,14 @@ https://confluence.atlassian.com/conf85/check-who-can-view-a-page-1283360557.htm
 """
 from typing import Any
 
-from danswer.access.models import DocExternalAccess
-from danswer.access.models import ExternalAccess
-from danswer.connectors.confluence.connector import ConfluenceConnector
-from danswer.connectors.confluence.onyx_confluence import OnyxConfluence
-from danswer.connectors.confluence.utils import get_user_email_from_username__server
-from danswer.connectors.models import SlimDocument
-from danswer.db.models import ConnectorCredentialPair
-from danswer.utils.logger import setup_logger
+from onyx.access.models import DocExternalAccess
+from onyx.access.models import ExternalAccess
+from onyx.connectors.confluence.connector import ConfluenceConnector
+from onyx.connectors.confluence.onyx_confluence import OnyxConfluence
+from onyx.connectors.confluence.utils import get_user_email_from_username__server
+from onyx.connectors.models import SlimDocument
+from onyx.db.models import ConnectorCredentialPair
+from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
 
@@ -195,6 +195,7 @@ def _fetch_all_page_restrictions_for_space(
     confluence_client: OnyxConfluence,
     slim_docs: list[SlimDocument],
     space_permissions_by_space_key: dict[str, ExternalAccess],
+    is_cloud: bool,
 ) -> list[DocExternalAccess]:
     """
     For all pages, if a page has restrictions, then use those restrictions.
@@ -222,27 +223,50 @@ def _fetch_all_page_restrictions_for_space(
             continue
 
         space_key = slim_doc.perm_sync_data.get("space_key")
-        if space_permissions := space_permissions_by_space_key.get(space_key):
-            # If there are no restrictions, then use the space's restrictions
-            document_restrictions.append(
-                DocExternalAccess(
-                    doc_id=slim_doc.id,
-                    external_access=space_permissions,
-                )
+        if not (space_permissions := space_permissions_by_space_key.get(space_key)):
+            logger.debug(
+                f"Individually fetching space permissions for space {space_key}"
             )
-            if (
-                not space_permissions.is_public
-                and not space_permissions.external_user_emails
-                and not space_permissions.external_user_group_ids
-            ):
+            try:
+                # If the space permissions are not in the cache, then fetch them
+                if is_cloud:
+                    retrieved_space_permissions = _get_cloud_space_permissions(
+                        confluence_client=confluence_client, space_key=space_key
+                    )
+                else:
+                    retrieved_space_permissions = _get_server_space_permissions(
+                        confluence_client=confluence_client, space_key=space_key
+                    )
+                space_permissions_by_space_key[space_key] = retrieved_space_permissions
+                space_permissions = retrieved_space_permissions
+            except Exception as e:
                 logger.warning(
-                    f"Permissions are empty for document: {slim_doc.id}\n"
-                    "This means space permissions are may be wrong for"
-                    f" Space key: {space_key}"
+                    f"Error fetching space permissions for space {space_key}: {e}"
                 )
+
+        if not space_permissions:
+            logger.warning(
+                f"No permissions found for document {slim_doc.id} in space {space_key}"
+            )
             continue
 
-        logger.warning(f"No permissions found for document {slim_doc.id}")
+        # If there are no restrictions, then use the space's restrictions
+        document_restrictions.append(
+            DocExternalAccess(
+                doc_id=slim_doc.id,
+                external_access=space_permissions,
+            )
+        )
+        if (
+            not space_permissions.is_public
+            and not space_permissions.external_user_emails
+            and not space_permissions.external_user_group_ids
+        ):
+            logger.warning(
+                f"Permissions are empty for document: {slim_doc.id}\n"
+                "This means space permissions are may be wrong for"
+                f" Space key: {space_key}"
+            )
 
     logger.debug("Finished fetching all page restrictions for space")
     return document_restrictions
@@ -281,4 +305,5 @@ def confluence_doc_sync(
         confluence_client=confluence_connector.confluence_client,
         slim_docs=slim_docs,
         space_permissions_by_space_key=space_permissions_by_space_key,
+        is_cloud=is_cloud,
     )

backend/ee/onyx/external_permissions/confluence/group_sync.py

@@ -1,9 +1,9 @@
-from danswer.connectors.confluence.onyx_confluence import build_confluence_client
-from danswer.connectors.confluence.onyx_confluence import OnyxConfluence
-from danswer.connectors.confluence.utils import get_user_email_from_username__server
-from danswer.db.models import ConnectorCredentialPair
-from danswer.utils.logger import setup_logger
-from ee.danswer.db.external_perm import ExternalUserGroup
+from ee.onyx.db.external_perm import ExternalUserGroup
+from onyx.connectors.confluence.onyx_confluence import build_confluence_client
+from onyx.connectors.confluence.onyx_confluence import OnyxConfluence
+from onyx.connectors.confluence.utils import get_user_email_from_username__server
+from onyx.db.models import ConnectorCredentialPair
+from onyx.utils.logger import setup_logger
 
 
 logger = setup_logger()
@@ -52,13 +52,13 @@ def confluence_group_sync(
     group_member_email_map = _build_group_member_email_map(
         confluence_client=confluence_client,
     )
-    danswer_groups: list[ExternalUserGroup] = []
+    onyx_groups: list[ExternalUserGroup] = []
     for group_id, group_member_emails in group_member_email_map.items():
-        danswer_groups.append(
+        onyx_groups.append(
             ExternalUserGroup(
                 id=group_id,
                 user_emails=list(group_member_emails),
             )
         )
 
-    return danswer_groups
+    return onyx_groups

backend/ee/onyx/external_permissions/gmail/doc_sync.py

@@ -1,12 +1,12 @@
 from datetime import datetime
 from datetime import timezone
 
-from danswer.access.models import DocExternalAccess
-from danswer.access.models import ExternalAccess
-from danswer.connectors.gmail.connector import GmailConnector
-from danswer.connectors.interfaces import GenerateSlimDocumentOutput
-from danswer.db.models import ConnectorCredentialPair
-from danswer.utils.logger import setup_logger
+from onyx.access.models import DocExternalAccess
+from onyx.access.models import ExternalAccess
+from onyx.connectors.gmail.connector import GmailConnector
+from onyx.connectors.interfaces import GenerateSlimDocumentOutput
+from onyx.db.models import ConnectorCredentialPair
+from onyx.utils.logger import setup_logger
 
 logger = setup_logger()