fix model defaults

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

@@ -65,10 +65,8 @@ jobs:
             NEXT_PUBLIC_POSTHOG_KEY=${{ secrets.POSTHOG_KEY }}
             NEXT_PUBLIC_POSTHOG_HOST=${{ secrets.POSTHOG_HOST }}
             NEXT_PUBLIC_SENTRY_DSN=${{ secrets.SENTRY_DSN }}
-            NEXT_PUBLIC_STRIPE_PUBLISHABLE_KEY=${{ secrets.STRIPE_PUBLISHABLE_KEY }}
             NEXT_PUBLIC_GTM_ENABLED=true
             NEXT_PUBLIC_FORGOT_PASSWORD_ENABLED=true
-            NEXT_PUBLIC_INCLUDE_ERROR_POPUP_SUPPORT_LINK=true
             NODE_OPTIONS=--max-old-space-size=8192
           # needed due to weird interactions with the builds for different platforms
           no-cache: true

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

@@ -12,32 +12,7 @@ env:
   BUILDKIT_PROGRESS: plain
 
 jobs:
-  # 1) Preliminary job to check if the changed files are relevant
-  check_model_server_changes:
-    runs-on: ubuntu-latest
-    outputs:
-      changed: ${{ steps.check.outputs.changed }}
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Check if relevant files changed
-        id: check
-        run: |
-          # Default to "false"
-          echo "changed=false" >> $GITHUB_OUTPUT
-
-          # Compare the previous commit (github.event.before) to the current one (github.sha)
-          # If any file in backend/model_server/** or backend/Dockerfile.model_server is changed,
-          # set changed=true
-          if git diff --name-only ${{ github.event.before }} ${{ github.sha }} \
-             | grep -E '^backend/model_server/|^backend/Dockerfile.model_server'; then
-            echo "changed=true" >> $GITHUB_OUTPUT
-          fi
-
   build-amd64:
-    needs: [check_model_server_changes]
-    if: needs.check_model_server_changes.outputs.changed == 'true'
     runs-on:
       [runs-on, runner=8cpu-linux-x64, "run-id=${{ github.run_id }}-amd64"]
     steps:
@@ -77,8 +52,6 @@ jobs:
           provenance: false
 
   build-arm64:
-    needs: [check_model_server_changes]
-    if: needs.check_model_server_changes.outputs.changed == 'true'
     runs-on:
       [runs-on, runner=8cpu-linux-x64, "run-id=${{ github.run_id }}-arm64"]
     steps:
@@ -118,8 +91,7 @@ jobs:
           provenance: false
 
   merge-and-scan:
-    needs: [build-amd64, build-arm64, check_model_server_changes]
-    if: needs.check_model_server_changes.outputs.changed == 'true'
+    needs: [build-amd64, build-arm64]
     runs-on: ubuntu-latest
     steps:
       - name: Login to Docker Hub

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

@@ -1,6 +1,6 @@
-name: Run Playwright Tests
+name: Run Chromatic Tests
 concurrency:
-  group: Run-Playwright-Tests-${{ github.workflow }}-${{ github.head_ref || github.event.workflow_run.head_branch || github.run_id }}
+  group: Run-Chromatic-Tests-${{ github.workflow }}-${{ github.head_ref || github.event.workflow_run.head_branch || github.run_id }}
   cancel-in-progress: true
 
 on: push
@@ -8,8 +8,6 @@ on: push
 env:
   OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
   SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }}
-  GEN_AI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-  MOCK_LLM_RESPONSE: true
 
 jobs:
   playwright-tests:
@@ -198,47 +196,43 @@ jobs:
           cd deployment/docker_compose
           docker compose -f docker-compose.dev.yml -p danswer-stack down -v
 
-# NOTE: Chromatic UI diff testing is currently disabled.
-# We are using Playwright for local and CI testing without visual regression checks.
-# Chromatic may be reintroduced in the future for UI diff testing if needed.
+  chromatic-tests:
+    name: Chromatic Tests
 
-# chromatic-tests:
-#   name: Chromatic Tests
+    needs: playwright-tests
+    runs-on:
+      [
+        runs-on,
+        runner=32cpu-linux-x64,
+        disk=large,
+        "run-id=${{ github.run_id }}",
+      ]
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
 
-#   needs: playwright-tests
-#   runs-on:
-#     [
-#       runs-on,
-#       runner=32cpu-linux-x64,
-#       disk=large,
-#       "run-id=${{ github.run_id }}",
-#     ]
-#   steps:
-#     - name: Checkout code
-#       uses: actions/checkout@v4
-#       with:
-#         fetch-depth: 0
+      - name: Setup node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 22
 
-#     - name: Setup node
-#       uses: actions/setup-node@v4
-#       with:
-#         node-version: 22
+      - name: Install node dependencies
+        working-directory: ./web
+        run: npm ci
 
-#     - 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: Download Playwright test results
-#       uses: actions/download-artifact@v4
-#       with:
-#         name: test-results
-#         path: ./web/test-results
-
-#     - name: Run Chromatic
-#       uses: chromaui/action@latest
-#       with:
-#         playwright: true
-#         projectToken: ${{ secrets.CHROMATIC_PROJECT_TOKEN }}
-#         workingDir: ./web
-#       env:
-#         CHROMATIC_ARCHIVE_LOCATION: ./test-results
+      - name: Run Chromatic
+        uses: chromaui/action@latest
+        with:
+          playwright: true
+          projectToken: ${{ secrets.CHROMATIC_PROJECT_TOKEN }}
+          workingDir: ./web
+        env:
+          CHROMATIC_ARCHIVE_LOCATION: ./test-results

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

@@ -21,10 +21,10 @@ jobs:
     - name: Set up Helm
       uses: azure/setup-helm@v4.2.0
       with:
-        version: v3.17.0
+        version: v3.14.4
       
     - name: Set up chart-testing
-      uses: helm/chart-testing-action@v2.7.0
+      uses: helm/chart-testing-action@v2.6.1
 
     # even though we specify chart-dirs in ct.yaml, it isn't used by ct for the list-changed command...
     - name: Run chart-testing (list-changed)
@@ -37,6 +37,22 @@ jobs:
           echo "changed=true" >> "$GITHUB_OUTPUT"
         fi
 
+#     rkuo: I don't think we need python?
+#     - name: Set up Python
+#       uses: actions/setup-python@v5
+#       with:
+#         python-version: '3.11'
+#         cache: 'pip'
+#         cache-dependency-path: |
+#           backend/requirements/default.txt
+#           backend/requirements/dev.txt
+#           backend/requirements/model_server.txt
+#     - run: |
+#         python -m pip install --upgrade pip
+#         pip install --retries 5 --timeout 30 -r backend/requirements/default.txt
+#         pip install --retries 5 --timeout 30 -r backend/requirements/dev.txt
+#         pip install --retries 5 --timeout 30 -r backend/requirements/model_server.txt
+
     # lint all charts if any changes were detected
     - name: Run chart-testing (lint)
       if: steps.list-changed.outputs.changed == 'true'
@@ -46,7 +62,7 @@ jobs:
 
     - name: Create kind cluster
       if: steps.list-changed.outputs.changed == 'true'
-      uses: helm/kind-action@v1.12.0
+      uses: helm/kind-action@v1.10.0
 
     - name: Run chart-testing (install)
       if: steps.list-changed.outputs.changed == 'true'

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

@@ -94,27 +94,23 @@ jobs:
           cd deployment/docker_compose
           ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true \
           MULTI_TENANT=true \
-          AUTH_TYPE=cloud \
+          AUTH_TYPE=basic \
           REQUIRE_EMAIL_VERIFICATION=false \
           DISABLE_TELEMETRY=true \
           IMAGE_TAG=test \
-          DEV_MODE=true \
-          docker compose -f docker-compose.multitenant-dev.yml -p onyx-stack up -d
+          docker compose -f docker-compose.dev.yml -p danswer-stack up -d
         id: start_docker_multi_tenant
 
       # In practice, `cloud` Auth type would require OAUTH credentials to be set.
       - name: Run Multi-Tenant Integration Tests
         run: |
-          echo "Waiting for 3 minutes to ensure API server is ready..."
-          sleep 180
           echo "Running integration tests..."
-          docker run --rm --network onyx-stack_default \
+          docker run --rm --network danswer-stack_default \
             --name test-runner \
             -e POSTGRES_HOST=relational_db \
             -e POSTGRES_USER=postgres \
             -e POSTGRES_PASSWORD=password \
             -e POSTGRES_DB=postgres \
-            -e POSTGRES_USE_NULL_POOL=true \
             -e VESPA_HOST=index \
             -e REDIS_HOST=cache \
             -e API_SERVER_HOST=api_server \
@@ -123,10 +119,6 @@ jobs:
             -e TEST_WEB_HOSTNAME=test-runner \
             -e AUTH_TYPE=cloud \
             -e MULTI_TENANT=true \
-            -e REQUIRE_EMAIL_VERIFICATION=false \
-            -e DISABLE_TELEMETRY=true \
-            -e IMAGE_TAG=test \
-            -e DEV_MODE=true \
             onyxdotapp/onyx-integration:test \
             /app/tests/integration/multitenant_tests
         continue-on-error: true
@@ -134,37 +126,34 @@ jobs:
 
       - name: Check multi-tenant test results
         run: |
-          if [ ${{ steps.run_multitenant_tests.outcome }} == 'failure' ]; then
-            echo "Multi-tenant integration tests failed. Exiting with error."
+          if [ ${{ steps.run_tests.outcome }} == 'failure' ]; then
+            echo "Integration tests failed. Exiting with error."
             exit 1
           else
-            echo "All multi-tenant integration tests passed successfully."
+            echo "All integration tests passed successfully."
           fi
 
       - name: Stop multi-tenant Docker containers
         run: |
           cd deployment/docker_compose
-          docker compose -f docker-compose.multitenant-dev.yml -p onyx-stack down -v
-      
-      # NOTE: Use pre-ping/null pool to reduce flakiness due to dropped connections
+          docker compose -f docker-compose.dev.yml -p danswer-stack down -v
+
       - name: Start Docker containers
         run: |
           cd deployment/docker_compose
           ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true \
           AUTH_TYPE=basic \
-          POSTGRES_POOL_PRE_PING=true \
-          POSTGRES_USE_NULL_POOL=true \
           REQUIRE_EMAIL_VERIFICATION=false \
           DISABLE_TELEMETRY=true \
           IMAGE_TAG=test \
-          docker compose -f docker-compose.dev.yml -p onyx-stack up -d
+          docker compose -f docker-compose.dev.yml -p danswer-stack up -d
         id: start_docker
 
       - name: Wait for service to be ready
         run: |
           echo "Starting wait-for-service script..."
 
-          docker logs -f onyx-stack-api_server-1 &
+          docker logs -f danswer-stack-api_server-1 &
 
           start_time=$(date +%s)
           timeout=300  # 5 minutes in seconds
@@ -194,24 +183,15 @@ jobs:
           done
           echo "Finished waiting for service."
 
-      - name: Start Mock Services
-        run: |
-          cd backend/tests/integration/mock_services
-          docker compose -f docker-compose.mock-it-services.yml \
-            -p mock-it-services-stack up -d
-      
-      # NOTE: Use pre-ping/null to reduce flakiness due to dropped connections
       - name: Run Standard Integration Tests
         run: |
           echo "Running integration tests..."
-          docker run --rm --network onyx-stack_default \
+          docker run --rm --network danswer-stack_default \
             --name test-runner \
             -e POSTGRES_HOST=relational_db \
             -e POSTGRES_USER=postgres \
             -e POSTGRES_PASSWORD=password \
             -e POSTGRES_DB=postgres \
-            -e POSTGRES_POOL_PRE_PING=true \
-            -e POSTGRES_USE_NULL_POOL=true \
             -e VESPA_HOST=index \
             -e REDIS_HOST=cache \
             -e API_SERVER_HOST=api_server \
@@ -221,8 +201,6 @@ jobs:
             -e CONFLUENCE_USER_NAME=${CONFLUENCE_USER_NAME} \
             -e CONFLUENCE_ACCESS_TOKEN=${CONFLUENCE_ACCESS_TOKEN} \
             -e TEST_WEB_HOSTNAME=test-runner \
-            -e MOCK_CONNECTOR_SERVER_HOST=mock_connector_server \
-            -e MOCK_CONNECTOR_SERVER_PORT=8001 \
             onyxdotapp/onyx-integration:test \
             /app/tests/integration/tests \
             /app/tests/integration/connector_job_tests
@@ -238,30 +216,27 @@ jobs:
             echo "All integration tests passed successfully."
           fi
 
-      # ------------------------------------------------------------
-      # Always gather logs BEFORE "down":
-      - name: Dump API server logs
-        if: always()
+      # save before stopping the containers so the logs can be captured
+      - name: Save Docker logs
+        if: success() || failure()
         run: |
           cd deployment/docker_compose
-          docker compose -f docker-compose.dev.yml -p onyx-stack logs --no-color api_server > $GITHUB_WORKSPACE/api_server.log || true
-
-      - name: Dump all-container logs (optional)
-        if: always()
-        run: |
-          cd deployment/docker_compose
-          docker compose -f docker-compose.dev.yml -p onyx-stack logs --no-color > $GITHUB_WORKSPACE/docker-compose.log || true
-
-      - name: Upload logs
-        if: always()
-        uses: actions/upload-artifact@v4
-        with:
-          name: docker-all-logs
-          path: ${{ github.workspace }}/docker-compose.log
-      # ------------------------------------------------------------
+          docker compose -f docker-compose.dev.yml -p danswer-stack logs > docker-compose.log
+          mv docker-compose.log ${{ github.workspace }}/docker-compose.log
 
       - name: Stop Docker containers
-        if: always()
         run: |
           cd deployment/docker_compose
-          docker compose -f docker-compose.dev.yml -p onyx-stack down -v
+          docker compose -f docker-compose.dev.yml -p danswer-stack down -v
+
+      - name: Upload logs
+        if: success() || failure()
+        uses: actions/upload-artifact@v4
+        with:
+          name: docker-logs
+          path: ${{ github.workspace }}/docker-compose.log
+
+      - name: Stop Docker containers
+        run: |
+          cd deployment/docker_compose
+          docker compose -f docker-compose.dev.yml -p danswer-stack down -v

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

@@ -39,15 +39,6 @@ env:
   AIRTABLE_TEST_TABLE_ID: ${{ secrets.AIRTABLE_TEST_TABLE_ID }}
   AIRTABLE_TEST_TABLE_NAME: ${{ secrets.AIRTABLE_TEST_TABLE_NAME }}
   AIRTABLE_ACCESS_TOKEN: ${{ secrets.AIRTABLE_ACCESS_TOKEN }}
-  # Sharepoint
-  SHAREPOINT_CLIENT_ID: ${{ secrets.SHAREPOINT_CLIENT_ID }}
-  SHAREPOINT_CLIENT_SECRET: ${{ secrets.SHAREPOINT_CLIENT_SECRET }}
-  SHAREPOINT_CLIENT_DIRECTORY_ID: ${{ secrets.SHAREPOINT_CLIENT_DIRECTORY_ID }}
-  SHAREPOINT_SITE: ${{ secrets.SHAREPOINT_SITE }}
-  # Gitbook
-  GITBOOK_SPACE_ID: ${{ secrets.GITBOOK_SPACE_ID }}
-  GITBOOK_API_KEY: ${{ secrets.GITBOOK_API_KEY }}
-
 jobs:
   connectors-check:
     # See https://runs-on.com/runners/linux/
@@ -74,9 +65,7 @@ jobs:
           python -m pip install --upgrade pip
           pip install --retries 5 --timeout 30 -r backend/requirements/default.txt
           pip install --retries 5 --timeout 30 -r backend/requirements/dev.txt
-          playwright install chromium
-          playwright install-deps chromium
-          
+
       - name: Run Tests
         shell: script -q -e -c "bash --noprofile --norc -eo pipefail {0}"
         run: py.test -o junit_family=xunit2 -xv --ff backend/tests/daily/connectors

.gitignore

@@ -7,6 +7,4 @@
 .vscode/
 *.sw?
 /backend/tests/regression/answer_quality/search_test_config.yaml
-/web/test-results/
-backend/onyx/agent_search/main/test_data.json
-backend/tests/regression/answer_quality/test_data.json
+/web/test-results/

.vscode/env_template.txt

@@ -52,9 +52,3 @@ BING_API_KEY=<REPLACE THIS>
 # Enable the full set of Danswer Enterprise Edition features
 # NOTE: DO NOT ENABLE THIS UNLESS YOU HAVE A PAID ENTERPRISE LICENSE (or if you are using this for local testing/development)
 ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=False
-
-# Agent Search configs  # TODO: Remove give proper namings
-AGENT_RETRIEVAL_STATS=False   # Note: This setting will incur substantial re-ranking effort
-AGENT_RERANKING_STATS=True
-AGENT_MAX_QUERY_RETRIEVAL_RESULTS=20
-AGENT_RERANKING_MAX_QUERY_RETRIEVAL_RESULTS=20

.vscode/launch.template.jsonc

@@ -205,7 +205,7 @@
                 "--loglevel=INFO",
                 "--hostname=light@%n",
                 "-Q",
-                "vespa_metadata_sync,connector_deletion,doc_permissions_upsert,checkpoint_cleanup",
+                "vespa_metadata_sync,connector_deletion,doc_permissions_upsert",
             ],
             "presentation": {
 				 "group": "2",

README.md

@@ -24,94 +24,112 @@
 </a>
 </p>
 
-<strong>[Onyx](https://www.onyx.app/)</strong> (formerly Danswer) is the AI platform connected to your company's docs, apps, and people.
-Onyx provides a feature rich Chat interface and plugs into any LLM of your choice.
-There are over 40 supported connectors such as Google Drive, Slack, Confluence, Salesforce, etc. which keep knowledge and permissions up to date.
-Create custom AI agents with unique prompts, knowledge, and actions the agents can take.
-Onyx can be deployed securely anywhere and for any scale - on a laptop, on-premise, or to cloud.
+<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.
 
+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>Feature Showcase</h3>
+<h3>Usage</h3>
 
-**Deep research over your team's knowledge:**
+Onyx Web App:
 
-https://private-user-images.githubusercontent.com/32520769/414509312-48392e83-95d0-4fb5-8650-a396e05e0a32.mp4?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.a9D8A0sgKE9AoaoE-mfFbJ6_OKYeqaf7TZ4Han2JfW8
+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 😁):
 
-**Use Onyx as a secure AI Chat with any LLM:**
-
-![Onyx Chat Silent Demo](https://github.com/onyx-dot-app/onyx/releases/download/v0.21.1/OnyxChatSilentDemo.gif)
-
-
-**Easily set up connectors to your apps:**
-
-![Onyx Connector Silent Demo](https://github.com/onyx-dot-app/onyx/releases/download/v0.21.1/OnyxConnectorSilentDemo.gif)
-
-
-**Access Onyx where your team already works:**
-
-![Onyx Bot Demo](https://github.com/onyx-dot-app/onyx/releases/download/v0.21.1/OnyxBot.png)
+https://github.com/onyx-dot-app/onyx/assets/25087905/3e19739b-d178-4371-9a38-011430bdec1b
 
+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
-**To try it out for free and get started in seconds, check out [Onyx Cloud](https://cloud.onyx.app/signup)**.
 
-Onyx can also be run locally (even on a laptop) or deployed on a virtual machine with a single
+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 high-availability/scalable deployment on Kubernetes.
-References [here](https://github.com/onyx-dot-app/onyx/tree/main/deployment).
+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
+
+- 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
-- Extensions to the Chrome Plugin
-- Latest methods in information retrieval (StructRAG, LightGraphRAG, etc.)
-- Personalized Search
+
+- 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.
-- Code Search
-- SQL and Structured Query Language
 
+## Other Notable Benefits of Onyx
 
-## 🔍 Other Notable Benefits of Onyx
-- Custom deep learning models only through Onyx + learn from user feedback.
-- Flexible security features like SSO (OIDC/SAML/OAuth2), RBAC, encryption of credentials, etc.
-- Knowledge curation features like document-sets, query history, usage analytics, etc.
-- Scalable deployment options tested up to many tens of thousands users and hundreds of millions of documents.
-
+- 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
-Keep knowledge and access up to sync across 40+ connectors:
 
+Efficiently pulls the latest changes from:
+
+- Slack
+- GitHub
 - Google Drive
 - Confluence
-- Slack
-- Gmail
-- Salesforce
-- Microsoft Sharepoint
-- Github
 - Jira
 - Zendesk
+- Gmail
+- Notion
 - Gong
-- Microsoft Teams
-- Dropbox
+- Slab
+- Linear
+- Productboard
+- Guru
+- Bookstack
+- Document360
+- Sharepoint
+- Hubspot
 - Local Files
 - Websites
 - And more ...
 
-See the full list [here](https://docs.onyx.app/connectors).
+## 📚 Editions
 
-
-## 📚 Licensing
 There are two editions of Onyx:
 
-- Onyx Community Edition (CE) is available freely under the MIT Expat license. Simply follow the Deployment guide above.
-- Onyx Enterprise Edition (EE) includes extra features that are primarily useful for larger organizations.
-For feature details, check out [our website](https://www.onyx.app/pricing).
+- 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
+  - And many more! Checkout [our website](https://www.onyx.app/) for the latest.
 
 To try the Onyx Enterprise Edition:
-1. Checkout [Onyx Cloud](https://cloud.onyx.app/signup).
-2. For self-hosting the Enterprise Edition, contact us at [founders@onyx.app](mailto:founders@onyx.app) or book a call with us on our [Cal](https://cal.com/team/onyx/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=onyx-dot-app/onyx&type=Date)](https://star-history.com/#onyx-dot-app/onyx&Date)

backend/Dockerfile

@@ -28,16 +28,14 @@ RUN apt-get update && \
         curl \
         zip \
         ca-certificates \
-        libgnutls30 \
-        libblkid1 \
-        libmount1 \
-        libsmartcols1 \
-        libuuid1 \
+        libgnutls30=3.7.9-2+deb12u3 \
+        libblkid1=2.38.1-5+deb12u1 \
+        libmount1=2.38.1-5+deb12u1 \
+        libsmartcols1=2.38.1-5+deb12u1 \
+        libuuid1=2.38.1-5+deb12u1 \
         libxmlsec1-dev \
         pkg-config \
-        gcc \
-        nano \
-        vim && \
+        gcc && \
     rm -rf /var/lib/apt/lists/* && \
     apt-get clean
 
@@ -103,8 +101,7 @@ COPY ./alembic_tenants /app/alembic_tenants
 COPY ./alembic.ini /app/alembic.ini
 COPY supervisord.conf /usr/etc/supervisord.conf
 
-# Escape hatch scripts
-COPY ./scripts/debugging /app/scripts/debugging
+# Escape hatch
 COPY ./scripts/force_delete_connector_by_id.py /app/scripts/force_delete_connector_by_id.py
 
 # Put logo in assets

backend/alembic/versions/1a03d2c2856b_add_indexes_to_document__tag.py

@@ -1,27 +0,0 @@
-"""Add indexes to document__tag
-
-Revision ID: 1a03d2c2856b
-Revises: 9c00a2bccb83
-Create Date: 2025-02-18 10:45:13.957807
-
-"""
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision = "1a03d2c2856b"
-down_revision = "9c00a2bccb83"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.create_index(
-        op.f("ix_document__tag_tag_id"),
-        "document__tag",
-        ["tag_id"],
-        unique=False,
-    )
-
-
-def downgrade() -> None:
-    op.drop_index(op.f("ix_document__tag_tag_id"), table_name="document__tag")

backend/alembic/versions/2cdeff6d8c93_set_built_in_to_default.py

@@ -1,32 +0,0 @@
-"""set built in to default
-
-Revision ID: 2cdeff6d8c93
-Revises: f5437cc136c5
-Create Date: 2025-02-11 14:57:51.308775
-
-"""
-from alembic import op
-
-
-# revision identifiers, used by Alembic.
-revision = "2cdeff6d8c93"
-down_revision = "f5437cc136c5"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Prior to this migration / point in the codebase history,
-    # built in personas were implicitly treated as default personas (with no option to change this)
-    # This migration makes that explicit
-    op.execute(
-        """
-        UPDATE persona
-        SET is_default_persona = TRUE
-        WHERE builtin_persona = TRUE
-    """
-    )
-
-
-def downgrade() -> None:
-    pass

backend/alembic/versions/2f80c6a2550f_add_chat_session_specific_temperature_.py

@@ -1,36 +0,0 @@
-"""add chat session specific temperature override
-
-Revision ID: 2f80c6a2550f
-Revises: 33ea50e88f24
-Create Date: 2025-01-31 10:30:27.289646
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "2f80c6a2550f"
-down_revision = "33ea50e88f24"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "chat_session", sa.Column("temperature_override", sa.Float(), nullable=True)
-    )
-    op.add_column(
-        "user",
-        sa.Column(
-            "temperature_override_enabled",
-            sa.Boolean(),
-            nullable=False,
-            server_default=sa.false(),
-        ),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("chat_session", "temperature_override")
-    op.drop_column("user", "temperature_override_enabled")

backend/alembic/versions/33ea50e88f24_foreign_key_input_prompts.py

@@ -1,80 +0,0 @@
-"""foreign key input prompts
-
-Revision ID: 33ea50e88f24
-Revises: a6df6b88ef81
-Create Date: 2025-01-29 10:54:22.141765
-
-"""
-from alembic import op
-
-
-# revision identifiers, used by Alembic.
-revision = "33ea50e88f24"
-down_revision = "a6df6b88ef81"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Safely drop constraints if exists
-    op.execute(
-        """
-        ALTER TABLE inputprompt__user
-        DROP CONSTRAINT IF EXISTS inputprompt__user_input_prompt_id_fkey
-        """
-    )
-    op.execute(
-        """
-        ALTER TABLE inputprompt__user
-        DROP CONSTRAINT IF EXISTS inputprompt__user_user_id_fkey
-        """
-    )
-
-    # Recreate with ON DELETE CASCADE
-    op.create_foreign_key(
-        "inputprompt__user_input_prompt_id_fkey",
-        "inputprompt__user",
-        "inputprompt",
-        ["input_prompt_id"],
-        ["id"],
-        ondelete="CASCADE",
-    )
-
-    op.create_foreign_key(
-        "inputprompt__user_user_id_fkey",
-        "inputprompt__user",
-        "user",
-        ["user_id"],
-        ["id"],
-        ondelete="CASCADE",
-    )
-
-
-def downgrade() -> None:
-    # Drop the new FKs with ondelete
-    op.drop_constraint(
-        "inputprompt__user_input_prompt_id_fkey",
-        "inputprompt__user",
-        type_="foreignkey",
-    )
-    op.drop_constraint(
-        "inputprompt__user_user_id_fkey",
-        "inputprompt__user",
-        type_="foreignkey",
-    )
-
-    # Recreate them without cascading
-    op.create_foreign_key(
-        "inputprompt__user_input_prompt_id_fkey",
-        "inputprompt__user",
-        "inputprompt",
-        ["input_prompt_id"],
-        ["id"],
-    )
-    op.create_foreign_key(
-        "inputprompt__user_user_id_fkey",
-        "inputprompt__user",
-        "user",
-        ["user_id"],
-        ["id"],
-    )

backend/alembic/versions/4d58345da04a_lowercase_user_emails.py

@@ -1,37 +0,0 @@
-"""lowercase_user_emails
-
-Revision ID: 4d58345da04a
-Revises: f1ca58b2f2ec
-Create Date: 2025-01-29 07:48:46.784041
-
-"""
-from alembic import op
-from sqlalchemy.sql import text
-
-
-# revision identifiers, used by Alembic.
-revision = "4d58345da04a"
-down_revision = "f1ca58b2f2ec"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Get database connection
-    connection = op.get_bind()
-
-    # Update all user emails to lowercase
-    connection.execute(
-        text(
-            """
-            UPDATE "user"
-            SET email = LOWER(email)
-            WHERE email != LOWER(email)
-            """
-        )
-    )
-
-
-def downgrade() -> None:
-    # Cannot restore original case of emails
-    pass

backend/alembic/versions/4ee1287bd26a_add_multiple_slack_bot_support.py

@@ -5,6 +5,7 @@ Revises: 47e5bef3a1d7
 Create Date: 2024-11-06 13:15:53.302644
 
 """
+import logging
 from typing import cast
 from alembic import op
 import sqlalchemy as sa
@@ -19,8 +20,13 @@ down_revision = "47e5bef3a1d7"
 branch_labels: None = None
 depends_on: None = None
 
+# Configure logging
+logger = logging.getLogger("alembic.runtime.migration")
+logger.setLevel(logging.INFO)
+
 
 def upgrade() -> None:
+    logger.info(f"{revision}: create_table: slack_bot")
     # Create new slack_bot table
     op.create_table(
         "slack_bot",
@@ -57,6 +63,7 @@ def upgrade() -> None:
     )
 
     # Handle existing Slack bot tokens first
+    logger.info(f"{revision}: Checking for existing Slack bot.")
     bot_token = None
     app_token = None
     first_row_id = None
@@ -64,12 +71,15 @@ def upgrade() -> None:
     try:
         tokens = cast(dict, get_kv_store().load("slack_bot_tokens_config_key"))
     except Exception:
+        logger.warning("No existing Slack bot tokens found.")
         tokens = {}
 
     bot_token = tokens.get("bot_token")
     app_token = tokens.get("app_token")
 
     if bot_token and app_token:
+        logger.info(f"{revision}: Found bot and app tokens.")
+
         session = Session(bind=op.get_bind())
         new_slack_bot = SlackBot(
             name="Slack Bot (Migrated)",
@@ -160,9 +170,10 @@ def upgrade() -> None:
     # Clean up old tokens if they existed
     try:
         if bot_token and app_token:
+            logger.info(f"{revision}: Removing old bot and app tokens.")
             get_kv_store().delete("slack_bot_tokens_config_key")
     except Exception:
-        pass
+        logger.warning("tried to delete tokens in dynamic config but failed")
     # Rename the table
     op.rename_table(
         "slack_bot_config__standard_answer_category",
@@ -179,6 +190,8 @@ def upgrade() -> None:
     # Drop the table with CASCADE to handle dependent objects
     op.execute("DROP TABLE slack_bot_config CASCADE")
 
+    logger.info(f"{revision}: Migration complete.")
+
 
 def downgrade() -> None:
     # Recreate the old slack_bot_config table
@@ -260,7 +273,7 @@ def downgrade() -> None:
             }
             get_kv_store().store("slack_bot_tokens_config_key", tokens)
     except Exception:
-        pass
+        logger.warning("Failed to save tokens back to KV store")
 
     # Drop the new tables in reverse order
     op.drop_table("slack_channel_config")

backend/alembic/versions/98a5008d8711_agent_tracking.py

@@ -1,107 +0,0 @@
-"""agent_tracking
-
-Revision ID: 98a5008d8711
-Revises: 2f80c6a2550f
-Create Date: 2025-01-29 17:00:00.000001
-
-"""
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-from sqlalchemy.dialects.postgresql import UUID
-
-# revision identifiers, used by Alembic.
-revision = "98a5008d8711"
-down_revision = "2f80c6a2550f"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.create_table(
-        "agent__search_metrics",
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column("user_id", postgresql.UUID(as_uuid=True), nullable=True),
-        sa.Column("persona_id", sa.Integer(), nullable=True),
-        sa.Column("agent_type", sa.String(), nullable=False),
-        sa.Column("start_time", sa.DateTime(timezone=True), nullable=False),
-        sa.Column("base_duration_s", sa.Float(), nullable=False),
-        sa.Column("full_duration_s", sa.Float(), nullable=False),
-        sa.Column("base_metrics", postgresql.JSONB(), nullable=True),
-        sa.Column("refined_metrics", postgresql.JSONB(), nullable=True),
-        sa.Column("all_metrics", postgresql.JSONB(), nullable=True),
-        sa.ForeignKeyConstraint(
-            ["persona_id"],
-            ["persona.id"],
-        ),
-        sa.ForeignKeyConstraint(["user_id"], ["user.id"], ondelete="CASCADE"),
-        sa.PrimaryKeyConstraint("id"),
-    )
-
-    # Create sub_question table
-    op.create_table(
-        "agent__sub_question",
-        sa.Column("id", sa.Integer, primary_key=True),
-        sa.Column("primary_question_id", sa.Integer, sa.ForeignKey("chat_message.id")),
-        sa.Column(
-            "chat_session_id", UUID(as_uuid=True), sa.ForeignKey("chat_session.id")
-        ),
-        sa.Column("sub_question", sa.Text),
-        sa.Column(
-            "time_created", sa.DateTime(timezone=True), server_default=sa.func.now()
-        ),
-        sa.Column("sub_answer", sa.Text),
-        sa.Column("sub_question_doc_results", postgresql.JSONB(), nullable=True),
-        sa.Column("level", sa.Integer(), nullable=False),
-        sa.Column("level_question_num", sa.Integer(), nullable=False),
-    )
-
-    # Create sub_query table
-    op.create_table(
-        "agent__sub_query",
-        sa.Column("id", sa.Integer, primary_key=True),
-        sa.Column(
-            "parent_question_id", sa.Integer, sa.ForeignKey("agent__sub_question.id")
-        ),
-        sa.Column(
-            "chat_session_id", UUID(as_uuid=True), sa.ForeignKey("chat_session.id")
-        ),
-        sa.Column("sub_query", sa.Text),
-        sa.Column(
-            "time_created", sa.DateTime(timezone=True), server_default=sa.func.now()
-        ),
-    )
-
-    # Create sub_query__search_doc association table
-    op.create_table(
-        "agent__sub_query__search_doc",
-        sa.Column(
-            "sub_query_id",
-            sa.Integer,
-            sa.ForeignKey("agent__sub_query.id"),
-            primary_key=True,
-        ),
-        sa.Column(
-            "search_doc_id",
-            sa.Integer,
-            sa.ForeignKey("search_doc.id"),
-            primary_key=True,
-        ),
-    )
-
-    op.add_column(
-        "chat_message",
-        sa.Column(
-            "refined_answer_improvement",
-            sa.Boolean(),
-            nullable=True,
-        ),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("chat_message", "refined_answer_improvement")
-    op.drop_table("agent__sub_query__search_doc")
-    op.drop_table("agent__sub_query")
-    op.drop_table("agent__sub_question")
-    op.drop_table("agent__search_metrics")

backend/alembic/versions/9c00a2bccb83_chat_message_agentic.py

@@ -1,43 +0,0 @@
-"""chat_message_agentic
-
-Revision ID: 9c00a2bccb83
-Revises: b7a7eee5aa15
-Create Date: 2025-02-17 11:15:43.081150
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "9c00a2bccb83"
-down_revision = "b7a7eee5aa15"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # First add the column as nullable
-    op.add_column("chat_message", sa.Column("is_agentic", sa.Boolean(), nullable=True))
-
-    # Update existing rows based on presence of SubQuestions
-    op.execute(
-        """
-        UPDATE chat_message
-        SET is_agentic = EXISTS (
-            SELECT 1
-            FROM agent__sub_question
-            WHERE agent__sub_question.primary_question_id = chat_message.id
-        )
-        WHERE is_agentic IS NULL
-    """
-    )
-
-    # Make the column non-nullable with a default value of False
-    op.alter_column(
-        "chat_message", "is_agentic", nullable=False, server_default=sa.text("false")
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("chat_message", "is_agentic")

backend/alembic/versions/a6df6b88ef81_remove_recent_assistants.py

@@ -1,29 +0,0 @@
-"""remove recent assistants
-
-Revision ID: a6df6b88ef81
-Revises: 4d58345da04a
-Create Date: 2025-01-29 10:25:52.790407
-
-"""
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision = "a6df6b88ef81"
-down_revision = "4d58345da04a"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.drop_column("user", "recent_assistants")
-
-
-def downgrade() -> None:
-    op.add_column(
-        "user",
-        sa.Column(
-            "recent_assistants", postgresql.JSONB(), server_default="[]", nullable=False
-        ),
-    )

backend/alembic/versions/acaab4ef4507_remove_inactive_ccpair_status_on_.py

@@ -1,29 +0,0 @@
-"""remove inactive ccpair status on downgrade
-
-Revision ID: acaab4ef4507
-Revises: b388730a2899
-Create Date: 2025-02-16 18:21:41.330212
-
-"""
-from alembic import op
-from onyx.db.models import ConnectorCredentialPair
-from onyx.db.enums import ConnectorCredentialPairStatus
-from sqlalchemy import update
-
-# revision identifiers, used by Alembic.
-revision = "acaab4ef4507"
-down_revision = "b388730a2899"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    pass
-
-
-def downgrade() -> None:
-    op.execute(
-        update(ConnectorCredentialPair)
-        .where(ConnectorCredentialPair.status == ConnectorCredentialPairStatus.INVALID)
-        .values(status=ConnectorCredentialPairStatus.ACTIVE)
-    )

backend/alembic/versions/b388730a2899_nullable_preferences.py

@@ -1,31 +0,0 @@
-"""nullable preferences
-
-Revision ID: b388730a2899
-Revises: 1a03d2c2856b
-Create Date: 2025-02-17 18:49:22.643902
-
-"""
-from alembic import op
-
-
-# revision identifiers, used by Alembic.
-revision = "b388730a2899"
-down_revision = "1a03d2c2856b"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.alter_column("user", "temperature_override_enabled", nullable=True)
-    op.alter_column("user", "auto_scroll", nullable=True)
-
-
-def downgrade() -> None:
-    # Ensure no null values before making columns non-nullable
-    op.execute(
-        'UPDATE "user" SET temperature_override_enabled = false WHERE temperature_override_enabled IS NULL'
-    )
-    op.execute('UPDATE "user" SET auto_scroll = false WHERE auto_scroll IS NULL')
-
-    op.alter_column("user", "temperature_override_enabled", nullable=False)
-    op.alter_column("user", "auto_scroll", nullable=False)

backend/alembic/versions/b7a7eee5aa15_add_checkpointing_failure_handling.py

@@ -1,124 +0,0 @@
-"""Add checkpointing/failure handling
-
-Revision ID: b7a7eee5aa15
-Revises: f39c5794c10a
-Create Date: 2025-01-24 15:17:36.763172
-
-"""
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision = "b7a7eee5aa15"
-down_revision = "f39c5794c10a"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "index_attempt",
-        sa.Column("checkpoint_pointer", sa.String(), nullable=True),
-    )
-    op.add_column(
-        "index_attempt",
-        sa.Column("poll_range_start", sa.DateTime(timezone=True), nullable=True),
-    )
-    op.add_column(
-        "index_attempt",
-        sa.Column("poll_range_end", sa.DateTime(timezone=True), nullable=True),
-    )
-
-    op.create_index(
-        "ix_index_attempt_cc_pair_settings_poll",
-        "index_attempt",
-        [
-            "connector_credential_pair_id",
-            "search_settings_id",
-            "status",
-            sa.text("time_updated DESC"),
-        ],
-    )
-
-    # Drop the old IndexAttemptError table
-    op.drop_index("index_attempt_id", table_name="index_attempt_errors")
-    op.drop_table("index_attempt_errors")
-
-    # Create the new version of the table
-    op.create_table(
-        "index_attempt_errors",
-        sa.Column("id", sa.Integer(), primary_key=True),
-        sa.Column("index_attempt_id", sa.Integer(), nullable=False),
-        sa.Column("connector_credential_pair_id", sa.Integer(), nullable=False),
-        sa.Column("document_id", sa.String(), nullable=True),
-        sa.Column("document_link", sa.String(), nullable=True),
-        sa.Column("entity_id", sa.String(), nullable=True),
-        sa.Column("failed_time_range_start", sa.DateTime(timezone=True), nullable=True),
-        sa.Column("failed_time_range_end", sa.DateTime(timezone=True), nullable=True),
-        sa.Column("failure_message", sa.Text(), nullable=False),
-        sa.Column("is_resolved", sa.Boolean(), nullable=False, default=False),
-        sa.Column(
-            "time_created",
-            sa.DateTime(timezone=True),
-            server_default=sa.text("now()"),
-            nullable=False,
-        ),
-        sa.ForeignKeyConstraint(
-            ["index_attempt_id"],
-            ["index_attempt.id"],
-        ),
-        sa.ForeignKeyConstraint(
-            ["connector_credential_pair_id"],
-            ["connector_credential_pair.id"],
-        ),
-    )
-
-
-def downgrade() -> None:
-    op.execute("SET lock_timeout = '5s'")
-
-    # try a few times to drop the table, this has been observed to fail due to other locks
-    # blocking the drop
-    NUM_TRIES = 10
-    for i in range(NUM_TRIES):
-        try:
-            op.drop_table("index_attempt_errors")
-            break
-        except Exception as e:
-            if i == NUM_TRIES - 1:
-                raise e
-            print(f"Error dropping table: {e}. Retrying...")
-
-    op.execute("SET lock_timeout = DEFAULT")
-
-    # Recreate the old IndexAttemptError table
-    op.create_table(
-        "index_attempt_errors",
-        sa.Column("id", sa.Integer(), primary_key=True),
-        sa.Column("index_attempt_id", sa.Integer(), nullable=True),
-        sa.Column("batch", sa.Integer(), nullable=True),
-        sa.Column("doc_summaries", postgresql.JSONB(), nullable=False),
-        sa.Column("error_msg", sa.Text(), nullable=True),
-        sa.Column("traceback", sa.Text(), nullable=True),
-        sa.Column(
-            "time_created",
-            sa.DateTime(timezone=True),
-            server_default=sa.text("now()"),
-        ),
-        sa.ForeignKeyConstraint(
-            ["index_attempt_id"],
-            ["index_attempt.id"],
-        ),
-    )
-
-    op.create_index(
-        "index_attempt_id",
-        "index_attempt_errors",
-        ["time_created"],
-    )
-
-    op.drop_index("ix_index_attempt_cc_pair_settings_poll")
-    op.drop_column("index_attempt", "checkpoint_pointer")
-    op.drop_column("index_attempt", "poll_range_start")
-    op.drop_column("index_attempt", "poll_range_end")

backend/alembic/versions/eaa3b5593925_add_default_slack_channel_config.py

@@ -1,80 +0,0 @@
-"""add default slack channel config
-
-Revision ID: eaa3b5593925
-Revises: 98a5008d8711
-Create Date: 2025-02-03 18:07:56.552526
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "eaa3b5593925"
-down_revision = "98a5008d8711"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Add is_default column
-    op.add_column(
-        "slack_channel_config",
-        sa.Column("is_default", sa.Boolean(), nullable=False, server_default="false"),
-    )
-
-    op.create_index(
-        "ix_slack_channel_config_slack_bot_id_default",
-        "slack_channel_config",
-        ["slack_bot_id", "is_default"],
-        unique=True,
-        postgresql_where=sa.text("is_default IS TRUE"),
-    )
-
-    # Create default channel configs for existing slack bots without one
-    conn = op.get_bind()
-    slack_bots = conn.execute(sa.text("SELECT id FROM slack_bot")).fetchall()
-
-    for slack_bot in slack_bots:
-        slack_bot_id = slack_bot[0]
-        existing_default = conn.execute(
-            sa.text(
-                "SELECT id FROM slack_channel_config WHERE slack_bot_id = :bot_id AND is_default = TRUE"
-            ),
-            {"bot_id": slack_bot_id},
-        ).fetchone()
-
-        if not existing_default:
-            conn.execute(
-                sa.text(
-                    """
-                    INSERT INTO slack_channel_config (
-                        slack_bot_id, persona_id, channel_config, enable_auto_filters, is_default
-                    ) VALUES (
-                        :bot_id, NULL,
-                        '{"channel_name": null, '
-                        '"respond_member_group_list": [], '
-                        '"answer_filters": [], '
-                        '"follow_up_tags": [], '
-                        '"respond_tag_only": true}',
-                        FALSE, TRUE
-                    )
-                """
-                ),
-                {"bot_id": slack_bot_id},
-            )
-
-
-def downgrade() -> None:
-    # Delete default slack channel configs
-    conn = op.get_bind()
-    conn.execute(sa.text("DELETE FROM slack_channel_config WHERE is_default = TRUE"))
-
-    # Remove index
-    op.drop_index(
-        "ix_slack_channel_config_slack_bot_id_default",
-        table_name="slack_channel_config",
-    )
-
-    # Remove is_default column
-    op.drop_column("slack_channel_config", "is_default")

backend/alembic/versions/f39c5794c10a_add_background_errors_table.py

@@ -1,40 +0,0 @@
-"""Add background errors table
-
-Revision ID: f39c5794c10a
-Revises: 2cdeff6d8c93
-Create Date: 2025-02-12 17:11:14.527876
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "f39c5794c10a"
-down_revision = "2cdeff6d8c93"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.create_table(
-        "background_error",
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column("message", sa.String(), nullable=False),
-        sa.Column(
-            "time_created",
-            sa.DateTime(timezone=True),
-            server_default=sa.text("now()"),
-            nullable=False,
-        ),
-        sa.Column("cc_pair_id", sa.Integer(), nullable=True),
-        sa.PrimaryKeyConstraint("id"),
-        sa.ForeignKeyConstraint(
-            ["cc_pair_id"],
-            ["connector_credential_pair.id"],
-            ondelete="CASCADE",
-        ),
-    )
-
-
-def downgrade() -> None:
-    op.drop_table("background_error")

backend/alembic/versions/f5437cc136c5_delete_non_search_assistants.py

@@ -1,53 +0,0 @@
-"""delete non-search assistants
-
-Revision ID: f5437cc136c5
-Revises: eaa3b5593925
-Create Date: 2025-02-04 16:17:15.677256
-
-"""
-from alembic import op
-
-
-# revision identifiers, used by Alembic.
-revision = "f5437cc136c5"
-down_revision = "eaa3b5593925"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    pass
-
-
-def downgrade() -> None:
-    # Fix: split the statements into multiple op.execute() calls
-    op.execute(
-        """
-        WITH personas_without_search AS (
-            SELECT p.id
-            FROM persona p
-            LEFT JOIN persona__tool pt ON p.id = pt.persona_id
-            LEFT JOIN tool t ON pt.tool_id = t.id
-            GROUP BY p.id
-            HAVING COUNT(CASE WHEN t.in_code_tool_id = 'run_search' THEN 1 END) = 0
-        )
-        UPDATE slack_channel_config
-        SET persona_id = NULL
-        WHERE is_default = TRUE AND persona_id IN (SELECT id FROM personas_without_search)
-        """
-    )
-
-    op.execute(
-        """
-        WITH personas_without_search AS (
-            SELECT p.id
-            FROM persona p
-            LEFT JOIN persona__tool pt ON p.id = pt.persona_id
-            LEFT JOIN tool t ON pt.tool_id = t.id
-            GROUP BY p.id
-            HAVING COUNT(CASE WHEN t.in_code_tool_id = 'run_search' THEN 1 END) = 0
-        )
-        DELETE FROM slack_channel_config
-        WHERE is_default = FALSE AND persona_id IN (SELECT id FROM personas_without_search)
-        """
-    )

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

@@ -21,7 +21,7 @@ logger = setup_logger()
 def perform_ttl_management_task(
     retention_limit_days: int, *, tenant_id: str | None
 ) -> None:
-    with get_session_with_tenant(tenant_id=tenant_id) as db_session:
+    with get_session_with_tenant(tenant_id) as db_session:
         delete_chat_sessions_older_than(retention_limit_days, db_session)
 
 
@@ -44,7 +44,7 @@ def check_ttl_management_task(*, tenant_id: str | None) -> None:
 
     settings = load_settings()
     retention_limit_days = settings.maximum_chat_retention_days
-    with get_session_with_tenant(tenant_id=tenant_id) as db_session:
+    with get_session_with_tenant(tenant_id) as db_session:
         if should_perform_chat_ttl_check(retention_limit_days, db_session):
             perform_ttl_management_task.apply_async(
                 kwargs=dict(
@@ -62,7 +62,7 @@ def check_ttl_management_task(*, tenant_id: str | None) -> None:
 )
 def autogenerate_usage_report_task(*, tenant_id: str | None) -> None:
     """This generates usage report under the /admin/generate-usage/report endpoint"""
-    with get_session_with_tenant(tenant_id=tenant_id) as db_session:
+    with get_session_with_tenant(tenant_id) as db_session:
         create_new_usage_report(
             db_session=db_session,
             user_id=None,

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

@@ -1,46 +1,44 @@
 from datetime import timedelta
 from typing import Any
 
-from onyx.background.celery.tasks.beat_schedule import (
-    beat_cloud_tasks as base_beat_system_tasks,
-)
 from onyx.background.celery.tasks.beat_schedule import BEAT_EXPIRES_DEFAULT
 from onyx.background.celery.tasks.beat_schedule import (
-    beat_task_templates as base_beat_task_templates,
+    cloud_tasks_to_schedule as base_cloud_tasks_to_schedule,
 )
-from onyx.background.celery.tasks.beat_schedule import generate_cloud_tasks
 from onyx.background.celery.tasks.beat_schedule import (
-    get_tasks_to_schedule as base_get_tasks_to_schedule,
+    tasks_to_schedule as base_tasks_to_schedule,
 )
+from onyx.configs.constants import ONYX_CLOUD_CELERY_TASK_PREFIX
 from onyx.configs.constants import OnyxCeleryPriority
 from onyx.configs.constants import OnyxCeleryTask
 from shared_configs.configs import MULTI_TENANT
 
-ee_beat_system_tasks: list[dict] = []
-
-ee_beat_task_templates: list[dict] = []
-ee_beat_task_templates.extend(
-    [
-        {
-            "name": "autogenerate-usage-report",
-            "task": OnyxCeleryTask.AUTOGENERATE_USAGE_REPORT_TASK,
-            "schedule": timedelta(days=30),
-            "options": {
-                "priority": OnyxCeleryPriority.MEDIUM,
-                "expires": BEAT_EXPIRES_DEFAULT,
-            },
+ee_cloud_tasks_to_schedule = [
+    {
+        "name": f"{ONYX_CLOUD_CELERY_TASK_PREFIX}_autogenerate-usage-report",
+        "task": OnyxCeleryTask.CLOUD_BEAT_TASK_GENERATOR,
+        "schedule": timedelta(days=30),
+        "options": {
+            "priority": OnyxCeleryPriority.HIGHEST,
+            "expires": BEAT_EXPIRES_DEFAULT,
         },
-        {
-            "name": "check-ttl-management",
-            "task": OnyxCeleryTask.CHECK_TTL_MANAGEMENT_TASK,
-            "schedule": timedelta(hours=1),
-            "options": {
-                "priority": OnyxCeleryPriority.MEDIUM,
-                "expires": BEAT_EXPIRES_DEFAULT,
-            },
+        "kwargs": {
+            "task_name": OnyxCeleryTask.AUTOGENERATE_USAGE_REPORT_TASK,
         },
-    ]
-)
+    },
+    {
+        "name": f"{ONYX_CLOUD_CELERY_TASK_PREFIX}_check-ttl-management",
+        "task": OnyxCeleryTask.CLOUD_BEAT_TASK_GENERATOR,
+        "schedule": timedelta(hours=1),
+        "options": {
+            "priority": OnyxCeleryPriority.HIGHEST,
+            "expires": BEAT_EXPIRES_DEFAULT,
+        },
+        "kwargs": {
+            "task_name": OnyxCeleryTask.CHECK_TTL_MANAGEMENT_TASK,
+        },
+    },
+]
 
 ee_tasks_to_schedule: list[dict] = []
 
@@ -67,14 +65,9 @@ if not MULTI_TENANT:
     ]
 
 
-def get_cloud_tasks_to_schedule(beat_multiplier: float) -> list[dict[str, Any]]:
-    beat_system_tasks = ee_beat_system_tasks + base_beat_system_tasks
-    beat_task_templates = ee_beat_task_templates + base_beat_task_templates
-    cloud_tasks = generate_cloud_tasks(
-        beat_system_tasks, beat_task_templates, beat_multiplier
-    )
-    return cloud_tasks
+def get_cloud_tasks_to_schedule() -> list[dict[str, Any]]:
+    return ee_cloud_tasks_to_schedule + base_cloud_tasks_to_schedule
 
 
 def get_tasks_to_schedule() -> list[dict[str, Any]]:
-    return ee_tasks_to_schedule + base_get_tasks_to_schedule()
+    return ee_tasks_to_schedule + base_tasks_to_schedule

backend/ee/onyx/configs/app_configs.py

@@ -77,5 +77,3 @@ POSTHOG_HOST = os.environ.get("POSTHOG_HOST") or "https://us.i.posthog.com"
 HUBSPOT_TRACKING_URL = os.environ.get("HUBSPOT_TRACKING_URL")
 
 ANONYMOUS_USER_COOKIE_NAME = "onyx_anonymous_user"
-
-GATED_TENANTS_KEY = "gated_tenants"

backend/ee/onyx/db/persona.py

@@ -2,11 +2,8 @@ from uuid import UUID
 
 from sqlalchemy.orm import Session
 
-from onyx.configs.constants import NotificationType
 from onyx.db.models import Persona__User
 from onyx.db.models import Persona__UserGroup
-from onyx.db.notification import create_notification
-from onyx.server.features.persona.models import PersonaSharedNotificationData
 
 
 def make_persona_private(
@@ -15,9 +12,6 @@ def make_persona_private(
     group_ids: list[int] | None,
     db_session: Session,
 ) -> None:
-    """NOTE(rkuo): This function batches all updates into a single commit. If we don't
-    dedupe the inputs, the commit will exception."""
-
     db_session.query(Persona__User).filter(
         Persona__User.persona_id == persona_id
     ).delete(synchronize_session="fetch")
@@ -26,22 +20,11 @@ def make_persona_private(
     ).delete(synchronize_session="fetch")
 
     if user_ids:
-        user_ids_set = set(user_ids)
-        for user_id in user_ids_set:
-            db_session.add(Persona__User(persona_id=persona_id, user_id=user_id))
-
-            create_notification(
-                user_id=user_id,
-                notif_type=NotificationType.PERSONA_SHARED,
-                db_session=db_session,
-                additional_data=PersonaSharedNotificationData(
-                    persona_id=persona_id,
-                ).model_dump(),
-            )
+        for user_uuid in user_ids:
+            db_session.add(Persona__User(persona_id=persona_id, user_id=user_uuid))
 
     if group_ids:
-        group_ids_set = set(group_ids)
-        for group_id in group_ids_set:
+        for group_id in group_ids:
             db_session.add(
                 Persona__UserGroup(persona_id=persona_id, user_group_id=group_id)
             )

backend/ee/onyx/db/user_group.py

@@ -218,14 +218,14 @@ def fetch_user_groups_for_user(
     return db_session.scalars(stmt).all()
 
 
-def construct_document_id_select_by_usergroup(
+def construct_document_select_by_usergroup(
     user_group_id: int,
 ) -> Select:
     """This returns a statement that should be executed using
     .yield_per() to minimize overhead. The primary consumers of this function
     are background processing task generators."""
     stmt = (
-        select(Document.id)
+        select(Document)
         .join(
             DocumentByConnectorCredentialPair,
             Document.id == DocumentByConnectorCredentialPair.id,

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

@@ -13,7 +13,6 @@ 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.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
@@ -258,7 +257,6 @@ def _fetch_all_page_restrictions(
     slim_docs: list[SlimDocument],
     space_permissions_by_space_key: dict[str, ExternalAccess],
     is_cloud: bool,
-    callback: IndexingHeartbeatInterface | None,
 ) -> list[DocExternalAccess]:
     """
     For all pages, if a page has restrictions, then use those restrictions.
@@ -267,12 +265,6 @@ def _fetch_all_page_restrictions(
     document_restrictions: list[DocExternalAccess] = []
 
     for slim_doc in slim_docs:
-        if callback:
-            if callback.should_stop():
-                raise RuntimeError("confluence_doc_sync: Stop signal detected")
-
-            callback.progress("confluence_doc_sync:fetch_all_page_restrictions", 1)
-
         if slim_doc.perm_sync_data is None:
             raise ValueError(
                 f"No permission sync data found for document {slim_doc.id}"
@@ -342,7 +334,7 @@ def _fetch_all_page_restrictions(
 
 
 def confluence_doc_sync(
-    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
+    cc_pair: ConnectorCredentialPair,
 ) -> list[DocExternalAccess]:
     """
     Adds the external permissions to the documents in postgres
@@ -365,16 +357,8 @@ def confluence_doc_sync(
 
     slim_docs = []
     logger.debug("Fetching all slim documents from confluence")
-    for doc_batch in confluence_connector.retrieve_all_slim_documents(
-        callback=callback
-    ):
+    for doc_batch in confluence_connector.retrieve_all_slim_documents():
         logger.debug(f"Got {len(doc_batch)} slim documents from confluence")
-        if callback:
-            if callback.should_stop():
-                raise RuntimeError("confluence_doc_sync: Stop signal detected")
-
-            callback.progress("confluence_doc_sync", 1)
-
         slim_docs.extend(doc_batch)
 
     logger.debug("Fetching all page restrictions for space")
@@ -383,5 +367,4 @@ def confluence_doc_sync(
         slim_docs=slim_docs,
         space_permissions_by_space_key=space_permissions_by_space_key,
         is_cloud=is_cloud,
-        callback=callback,
     )

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

@@ -1,6 +1,5 @@
 from ee.onyx.db.external_perm import ExternalUserGroup
 from ee.onyx.external_permissions.confluence.constants import ALL_CONF_EMAILS_GROUP_NAME
-from onyx.background.error_logging import emit_background_error
 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
@@ -11,51 +10,33 @@ logger = setup_logger()
 
 
 def _build_group_member_email_map(
-    confluence_client: OnyxConfluence, cc_pair_id: int
+    confluence_client: OnyxConfluence,
 ) -> dict[str, set[str]]:
     group_member_emails: dict[str, set[str]] = {}
-    for user in confluence_client.paginated_cql_user_retrieval():
-        logger.debug(f"Processing groups for user: {user}")
-
-        email = user.email
+    for user_result in confluence_client.paginated_cql_user_retrieval():
+        user = user_result.get("user", {})
+        if not user:
+            logger.warning(f"user result missing user field: {user_result}")
+            continue
+        email = user.get("email")
         if not email:
             # This field is only present in Confluence Server
-            user_name = user.username
+            user_name = user.get("username")
             # If it is present, try to get the email using a Server-specific method
             if user_name:
                 email = get_user_email_from_username__server(
                     confluence_client=confluence_client,
                     user_name=user_name,
                 )
-
         if not email:
             # If we still don't have an email, skip this user
-            msg = f"user result missing email field: {user}"
-            if user.type == "app":
-                logger.warning(msg)
-            else:
-                emit_background_error(msg, cc_pair_id=cc_pair_id)
-                logger.error(msg)
+            logger.warning(f"user result missing email field: {user_result}")
             continue
 
-        all_users_groups: set[str] = set()
-        for group in confluence_client.paginated_groups_by_user_retrieval(user.user_id):
+        for group in confluence_client.paginated_groups_by_user_retrieval(user):
             # group name uniqueness is enforced by Confluence, so we can use it as a group ID
             group_id = group["name"]
             group_member_emails.setdefault(group_id, set()).add(email)
-            all_users_groups.add(group_id)
-
-        if not all_users_groups:
-            msg = f"No groups found for user with email: {email}"
-            emit_background_error(msg, cc_pair_id=cc_pair_id)
-            logger.error(msg)
-        else:
-            logger.debug(f"Found groups {all_users_groups} for user with email {email}")
-
-    if not group_member_emails:
-        msg = "No groups found for any users."
-        emit_background_error(msg, cc_pair_id=cc_pair_id)
-        logger.error(msg)
 
     return group_member_emails
 
@@ -71,7 +52,6 @@ def confluence_group_sync(
 
     group_member_email_map = _build_group_member_email_map(
         confluence_client=confluence_client,
-        cc_pair_id=cc_pair.id,
     )
     onyx_groups: list[ExternalUserGroup] = []
     all_found_emails = set()

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

@@ -6,7 +6,6 @@ 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.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
@@ -15,7 +14,6 @@ logger = setup_logger()
 def _get_slim_doc_generator(
     cc_pair: ConnectorCredentialPair,
     gmail_connector: GmailConnector,
-    callback: IndexingHeartbeatInterface | None = None,
 ) -> GenerateSlimDocumentOutput:
     current_time = datetime.now(timezone.utc)
     start_time = (
@@ -25,14 +23,12 @@ def _get_slim_doc_generator(
     )
 
     return gmail_connector.retrieve_all_slim_documents(
-        start=start_time,
-        end=current_time.timestamp(),
-        callback=callback,
+        start=start_time, end=current_time.timestamp()
     )
 
 
 def gmail_doc_sync(
-    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
+    cc_pair: ConnectorCredentialPair,
 ) -> list[DocExternalAccess]:
     """
     Adds the external permissions to the documents in postgres
@@ -43,19 +39,11 @@ def gmail_doc_sync(
     gmail_connector = GmailConnector(**cc_pair.connector.connector_specific_config)
     gmail_connector.load_credentials(cc_pair.credential.credential_json)
 
-    slim_doc_generator = _get_slim_doc_generator(
-        cc_pair, gmail_connector, callback=callback
-    )
+    slim_doc_generator = _get_slim_doc_generator(cc_pair, gmail_connector)
 
     document_external_access: list[DocExternalAccess] = []
     for slim_doc_batch in slim_doc_generator:
         for slim_doc in slim_doc_batch:
-            if callback:
-                if callback.should_stop():
-                    raise RuntimeError("gmail_doc_sync: Stop signal detected")
-
-                callback.progress("gmail_doc_sync", 1)
-
             if slim_doc.perm_sync_data is None:
                 logger.warning(f"No permissions found for document {slim_doc.id}")
                 continue

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

@@ -10,7 +10,6 @@ from onyx.connectors.google_utils.resources import get_drive_service
 from onyx.connectors.interfaces import GenerateSlimDocumentOutput
 from onyx.connectors.models import SlimDocument
 from onyx.db.models import ConnectorCredentialPair
-from onyx.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
@@ -21,7 +20,6 @@ _PERMISSION_ID_PERMISSION_MAP: dict[str, dict[str, Any]] = {}
 def _get_slim_doc_generator(
     cc_pair: ConnectorCredentialPair,
     google_drive_connector: GoogleDriveConnector,
-    callback: IndexingHeartbeatInterface | None = None,
 ) -> GenerateSlimDocumentOutput:
     current_time = datetime.now(timezone.utc)
     start_time = (
@@ -31,9 +29,7 @@ def _get_slim_doc_generator(
     )
 
     return google_drive_connector.retrieve_all_slim_documents(
-        start=start_time,
-        end=current_time.timestamp(),
-        callback=callback,
+        start=start_time, end=current_time.timestamp()
     )
 
 
@@ -132,7 +128,7 @@ def _get_permissions_from_slim_doc(
 
 
 def gdrive_doc_sync(
-    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
+    cc_pair: ConnectorCredentialPair,
 ) -> list[DocExternalAccess]:
     """
     Adds the external permissions to the documents in postgres
@@ -150,12 +146,6 @@ def gdrive_doc_sync(
     document_external_accesses = []
     for slim_doc_batch in slim_doc_generator:
         for slim_doc in slim_doc_batch:
-            if callback:
-                if callback.should_stop():
-                    raise RuntimeError("gdrive_doc_sync: Stop signal detected")
-
-                callback.progress("gdrive_doc_sync", 1)
-
             ext_access = _get_permissions_from_slim_doc(
                 google_drive_connector=google_drive_connector,
                 slim_doc=slim_doc,

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

@@ -5,9 +5,8 @@ from onyx.access.models import DocExternalAccess
 from onyx.access.models import ExternalAccess
 from onyx.connectors.slack.connector import get_channels
 from onyx.connectors.slack.connector import make_paginated_slack_api_call_w_retries
-from onyx.connectors.slack.connector import SlackConnector
+from onyx.connectors.slack.connector import SlackPollConnector
 from onyx.db.models import ConnectorCredentialPair
-from onyx.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 from onyx.utils.logger import setup_logger
 
 
@@ -15,12 +14,12 @@ logger = setup_logger()
 
 
 def _get_slack_document_ids_and_channels(
-    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
+    cc_pair: ConnectorCredentialPair,
 ) -> dict[str, list[str]]:
-    slack_connector = SlackConnector(**cc_pair.connector.connector_specific_config)
+    slack_connector = SlackPollConnector(**cc_pair.connector.connector_specific_config)
     slack_connector.load_credentials(cc_pair.credential.credential_json)
 
-    slim_doc_generator = slack_connector.retrieve_all_slim_documents(callback=callback)
+    slim_doc_generator = slack_connector.retrieve_all_slim_documents()
 
     channel_doc_map: dict[str, list[str]] = {}
     for doc_metadata_batch in slim_doc_generator:
@@ -32,14 +31,6 @@ def _get_slack_document_ids_and_channels(
                 channel_doc_map[channel_id] = []
             channel_doc_map[channel_id].append(doc_metadata.id)
 
-        if callback:
-            if callback.should_stop():
-                raise RuntimeError(
-                    "_get_slack_document_ids_and_channels: Stop signal detected"
-                )
-
-            callback.progress("_get_slack_document_ids_and_channels", 1)
-
     return channel_doc_map
 
 
@@ -123,7 +114,7 @@ def _fetch_channel_permissions(
 
 
 def slack_doc_sync(
-    cc_pair: ConnectorCredentialPair, callback: IndexingHeartbeatInterface | None
+    cc_pair: ConnectorCredentialPair,
 ) -> list[DocExternalAccess]:
     """
     Adds the external permissions to the documents in postgres
@@ -136,7 +127,7 @@ def slack_doc_sync(
     )
     user_id_to_email_map = fetch_user_id_to_email_map(slack_client)
     channel_doc_map = _get_slack_document_ids_and_channels(
-        cc_pair=cc_pair, callback=callback
+        cc_pair=cc_pair,
     )
     workspace_permissions = _fetch_workspace_permissions(
         user_id_to_email_map=user_id_to_email_map,

backend/ee/onyx/external_permissions/sync_params.py

@@ -15,13 +15,11 @@ from ee.onyx.external_permissions.slack.doc_sync import slack_doc_sync
 from onyx.access.models import DocExternalAccess
 from onyx.configs.constants import DocumentSource
 from onyx.db.models import ConnectorCredentialPair
-from onyx.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 
 # Defining the input/output types for the sync functions
 DocSyncFuncType = Callable[
     [
         ConnectorCredentialPair,
-        IndexingHeartbeatInterface | None,
     ],
     list[DocExternalAccess],
 ]

backend/ee/onyx/onyxbot/slack/handlers/handle_standard_answers.py

@@ -80,7 +80,7 @@ def oneoff_standard_answers(
 def _handle_standard_answers(
     message_info: SlackMessageInfo,
     receiver_ids: list[str] | None,
-    slack_channel_config: SlackChannelConfig,
+    slack_channel_config: SlackChannelConfig | None,
     prompt: Prompt | None,
     logger: OnyxLoggingAdapter,
     client: WebClient,
@@ -94,10 +94,13 @@ def _handle_standard_answers(
     Returns True if standard answers are found to match the user's message and therefore,
     we still need to respond to the users.
     """
+    # if no channel config, then no standard answers are configured
+    if not slack_channel_config:
+        return False
 
     slack_thread_id = message_info.thread_to_respond
     configured_standard_answer_categories = (
-        slack_channel_config.standard_answer_categories
+        slack_channel_config.standard_answer_categories if slack_channel_config else []
     )
     configured_standard_answers = set(
         [

backend/ee/onyx/server/middleware/tenant_tracking.py

@@ -10,7 +10,6 @@ from fastapi import Response
 from ee.onyx.auth.users import decode_anonymous_user_jwt_token
 from ee.onyx.configs.app_configs import ANONYMOUS_USER_COOKIE_NAME
 from onyx.auth.api_key import extract_tenant_from_api_key_header
-from onyx.configs.constants import TENANT_ID_COOKIE_NAME
 from onyx.db.engine import is_valid_schema_name
 from onyx.redis.redis_pool import retrieve_auth_token_data_from_redis
 from shared_configs.configs import MULTI_TENANT
@@ -33,7 +32,7 @@ def add_tenant_id_middleware(app: FastAPI, logger: logging.LoggerAdapter) -> Non
             return await call_next(request)
 
         except Exception as e:
-            logger.exception(f"Error in tenant ID middleware: {str(e)}")
+            logger.error(f"Error in tenant ID middleware: {str(e)}")
             raise
 
 
@@ -44,12 +43,11 @@ async def _get_tenant_id_from_request(
     Attempt to extract tenant_id from:
     1) The API key header
     2) The Redis-based token (stored in Cookie: fastapiusersauth)
-    3)  Reset token cookie
     Fallback: POSTGRES_DEFAULT_SCHEMA
     """
     # Check for API key
     tenant_id = extract_tenant_from_api_key_header(request)
-    if tenant_id is not None:
+    if tenant_id:
         return tenant_id
 
     # Check for anonymous user cookie
@@ -64,7 +62,6 @@ async def _get_tenant_id_from_request(
 
     try:
         # Look up token data in Redis
-
         token_data = await retrieve_auth_token_data_from_redis(request)
 
         if not token_data:
@@ -88,18 +85,8 @@ async def _get_tenant_id_from_request(
         if not is_valid_schema_name(tenant_id):
             raise HTTPException(status_code=400, detail="Invalid tenant ID format")
 
+        return tenant_id
+
     except Exception as e:
         logger.error(f"Unexpected error in _get_tenant_id_from_request: {str(e)}")
         raise HTTPException(status_code=500, detail="Internal server error")
-
-    finally:
-        if tenant_id:
-            return tenant_id
-
-        # As a final step, check for explicit tenant_id cookie
-        tenant_id_cookie = request.cookies.get(TENANT_ID_COOKIE_NAME)
-        if tenant_id_cookie and is_valid_schema_name(tenant_id_cookie):
-            return tenant_id_cookie
-
-        # If we've reached this point, return the default schema
-        return POSTGRES_DEFAULT_SCHEMA

backend/ee/onyx/server/oauth.py

@@ -36,12 +36,12 @@ from onyx.connectors.google_utils.shared_constants import (
     GoogleOAuthAuthenticationMethod,
 )
 from onyx.db.credentials import create_credential
+from onyx.db.engine import get_current_tenant_id
 from onyx.db.engine import get_session
 from onyx.db.models import User
 from onyx.redis.redis_pool import get_redis_client
 from onyx.server.documents.models import CredentialBase
 from onyx.utils.logger import setup_logger
-from shared_configs.contextvars import get_current_tenant_id
 
 
 logger = setup_logger()
@@ -271,12 +271,12 @@ def prepare_authorization_request(
     connector: DocumentSource,
     redirect_on_success: str | None,
     user: User = Depends(current_user),
+    tenant_id: str | None = Depends(get_current_tenant_id),
 ) -> JSONResponse:
     """Used by the frontend to generate the url for the user's browser during auth request.
 
     Example: https://www.oauth.com/oauth2-servers/authorization/the-authorization-request/
     """
-    tenant_id = get_current_tenant_id()
 
     # create random oauth state param for security and to retrieve user data later
     oauth_uuid = uuid.uuid4()
@@ -286,7 +286,6 @@ def prepare_authorization_request(
     oauth_state = (
         base64.urlsafe_b64encode(oauth_uuid.bytes).rstrip(b"=").decode("utf-8")
     )
-    session: str
 
     if connector == DocumentSource.SLACK:
         oauth_url = SlackOAuth.generate_oauth_url(oauth_state)
@@ -329,6 +328,7 @@ def handle_slack_oauth_callback(
     state: str,
     user: User = Depends(current_user),
     db_session: Session = Depends(get_session),
+    tenant_id: str | None = Depends(get_current_tenant_id),
 ) -> JSONResponse:
     if not SlackOAuth.CLIENT_ID or not SlackOAuth.CLIENT_SECRET:
         raise HTTPException(
@@ -336,7 +336,7 @@ def handle_slack_oauth_callback(
             detail="Slack client ID or client secret is not configured.",
         )
 
-    r = get_redis_client()
+    r = get_redis_client(tenant_id=tenant_id)
 
     # recover the state
     padded_state = state + "=" * (
@@ -522,6 +522,7 @@ def handle_google_drive_oauth_callback(
     state: str,
     user: User = Depends(current_user),
     db_session: Session = Depends(get_session),
+    tenant_id: str | None = Depends(get_current_tenant_id),
 ) -> JSONResponse:
     if not GoogleDriveOAuth.CLIENT_ID or not GoogleDriveOAuth.CLIENT_SECRET:
         raise HTTPException(
@@ -529,7 +530,7 @@ def handle_google_drive_oauth_callback(
             detail="Google Drive client ID or client secret is not configured.",
         )
 
-    r = get_redis_client()
+    r = get_redis_client(tenant_id=tenant_id)
 
     # recover the state
     padded_state = state + "=" * (
@@ -553,7 +554,6 @@ def handle_google_drive_oauth_callback(
         )
 
     session_json = session_json_bytes.decode("utf-8")
-    session: GoogleDriveOAuth.OAuthSession
     try:
         session = GoogleDriveOAuth.parse_session(session_json)
 

backend/ee/onyx/server/query_and_chat/chat_backend.py

@@ -179,7 +179,6 @@ def handle_simplified_chat_message(
         chunks_below=0,
         full_doc=chat_message_req.full_doc,
         structured_response_format=chat_message_req.structured_response_format,
-        use_agentic_search=chat_message_req.use_agentic_search,
     )
 
     packets = stream_chat_message_objects(
@@ -302,7 +301,6 @@ def handle_send_message_simple_with_history(
         chunks_below=0,
         full_doc=req.full_doc,
         structured_response_format=req.structured_response_format,
-        use_agentic_search=req.use_agentic_search,
     )
 
     packets = stream_chat_message_objects(

backend/ee/onyx/server/query_and_chat/models.py

@@ -57,9 +57,6 @@ class BasicCreateChatMessageRequest(ChunkContext):
     # https://platform.openai.com/docs/guides/structured-outputs/introduction
     structured_response_format: dict | None = None
 
-    # If True, uses agentic search instead of basic search
-    use_agentic_search: bool = False
-
 
 class BasicCreateChatMessageWithHistoryRequest(ChunkContext):
     # Last element is the new query. All previous elements are historical context
@@ -74,8 +71,6 @@ class BasicCreateChatMessageWithHistoryRequest(ChunkContext):
     # only works if using an OpenAI model. See the following for more details:
     # https://platform.openai.com/docs/guides/structured-outputs/introduction
     structured_response_format: dict | None = None
-    # If True, uses agentic search instead of basic search
-    use_agentic_search: bool = False
 
 
 class SimpleDoc(BaseModel):
@@ -125,12 +120,9 @@ class OneShotQARequest(ChunkContext):
     # will also disable Thread-based Rewording if specified
     query_override: str | None = None
 
-    # If True, skips generating an AI response to the search query
+    # If True, skips generative an AI response to the search query
     skip_gen_ai_answer_generation: bool = False
 
-    # If True, uses agentic search instead of basic search
-    use_agentic_search: bool = False
-
     @model_validator(mode="after")
     def check_persona_fields(self) -> "OneShotQARequest":
         if self.persona_override_config is None and self.persona_id is None:

backend/ee/onyx/server/query_and_chat/query_backend.py

@@ -83,7 +83,6 @@ def handle_search_request(
         user=user,
         llm=llm,
         fast_llm=fast_llm,
-        skip_query_analysis=False,
         db_session=db_session,
         bypass_acl=False,
     )
@@ -197,8 +196,6 @@ def get_answer_stream(
         retrieval_details=query_request.retrieval_options,
         rerank_settings=query_request.rerank_settings,
         db_session=db_session,
-        use_agentic_search=query_request.use_agentic_search,
-        skip_gen_ai_answer_generation=query_request.skip_gen_ai_answer_generation,
     )
 
     packets = stream_chat_message_objects(

backend/ee/onyx/server/query_and_chat/token_limit.py

@@ -28,7 +28,7 @@ from onyx.server.query_and_chat.token_limit import _user_is_rate_limited_by_glob
 from onyx.utils.threadpool_concurrency import run_functions_tuples_in_parallel
 
 
-def _check_token_rate_limits(user: User | None, tenant_id: str) -> None:
+def _check_token_rate_limits(user: User | None, tenant_id: str | None) -> None:
     if user is None:
         # Unauthenticated users are only rate limited by global settings
         _user_is_rate_limited_by_global(tenant_id)
@@ -52,8 +52,8 @@ User rate limits
 """
 
 
-def _user_is_rate_limited(user_id: UUID, tenant_id: str) -> None:
-    with get_session_with_tenant(tenant_id=tenant_id) as db_session:
+def _user_is_rate_limited(user_id: UUID, tenant_id: str | None) -> None:
+    with get_session_with_tenant(tenant_id) as db_session:
         user_rate_limits = fetch_all_user_token_rate_limits(
             db_session=db_session, enabled_only=True, ordered=False
         )
@@ -94,7 +94,7 @@ User Group rate limits
 
 
 def _user_is_rate_limited_by_group(user_id: UUID, tenant_id: str | None) -> None:
-    with get_session_with_tenant(tenant_id=tenant_id) as db_session:
+    with get_session_with_tenant(tenant_id) as db_session:
         group_rate_limits = _fetch_all_user_group_rate_limits(user_id, db_session)
 
         if group_rate_limits:

backend/ee/onyx/server/tenants/api.py

@@ -18,16 +18,11 @@ from ee.onyx.server.tenants.anonymous_user_path import (
 from ee.onyx.server.tenants.anonymous_user_path import modify_anonymous_user_path
 from ee.onyx.server.tenants.anonymous_user_path import validate_anonymous_user_path
 from ee.onyx.server.tenants.billing import fetch_billing_information
-from ee.onyx.server.tenants.billing import fetch_stripe_checkout_session
 from ee.onyx.server.tenants.billing import fetch_tenant_stripe_information
 from ee.onyx.server.tenants.models import AnonymousUserPath
 from ee.onyx.server.tenants.models import BillingInformation
 from ee.onyx.server.tenants.models import ImpersonateRequest
 from ee.onyx.server.tenants.models import ProductGatingRequest
-from ee.onyx.server.tenants.models import ProductGatingResponse
-from ee.onyx.server.tenants.models import SubscriptionSessionResponse
-from ee.onyx.server.tenants.models import SubscriptionStatusResponse
-from ee.onyx.server.tenants.product_gating import store_product_gating
 from ee.onyx.server.tenants.provisioning import delete_user_from_control_plane
 from ee.onyx.server.tenants.user_mapping import get_tenant_id_for_email
 from ee.onyx.server.tenants.user_mapping import remove_all_users_from_tenant
@@ -39,17 +34,18 @@ from onyx.auth.users import get_redis_strategy
 from onyx.auth.users import optional_user
 from onyx.auth.users import User
 from onyx.configs.app_configs import WEB_DOMAIN
-from onyx.configs.constants import FASTAPI_USERS_AUTH_COOKIE_NAME
 from onyx.db.auth import get_user_count
+from onyx.db.engine import get_current_tenant_id
 from onyx.db.engine import get_session
-from onyx.db.engine import get_session_with_shared_schema
 from onyx.db.engine import get_session_with_tenant
+from onyx.db.notification import create_notification
 from onyx.db.users import delete_user_from_db
 from onyx.db.users import get_user_by_email
 from onyx.server.manage.models import UserByEmail
+from onyx.server.settings.store import load_settings
+from onyx.server.settings.store import store_settings
 from onyx.utils.logger import setup_logger
 from shared_configs.contextvars import CURRENT_TENANT_ID_CONTEXTVAR
-from shared_configs.contextvars import get_current_tenant_id
 
 stripe.api_key = STRIPE_SECRET_KEY
 logger = setup_logger()
@@ -58,14 +54,13 @@ router = APIRouter(prefix="/tenants")
 
 @router.get("/anonymous-user-path")
 async def get_anonymous_user_path_api(
+    tenant_id: str | None = Depends(get_current_tenant_id),
     _: User | None = Depends(current_admin_user),
 ) -> AnonymousUserPath:
-    tenant_id = get_current_tenant_id()
-
     if tenant_id is None:
         raise HTTPException(status_code=404, detail="Tenant not found")
 
-    with get_session_with_shared_schema() as db_session:
+    with get_session_with_tenant(tenant_id=None) as db_session:
         current_path = get_anonymous_user_path(tenant_id, db_session)
 
     return AnonymousUserPath(anonymous_user_path=current_path)
@@ -74,15 +69,15 @@ async def get_anonymous_user_path_api(
 @router.post("/anonymous-user-path")
 async def set_anonymous_user_path_api(
     anonymous_user_path: str,
+    tenant_id: str = Depends(get_current_tenant_id),
     _: User | None = Depends(current_admin_user),
 ) -> None:
-    tenant_id = get_current_tenant_id()
     try:
         validate_anonymous_user_path(anonymous_user_path)
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
 
-    with get_session_with_shared_schema() as db_session:
+    with get_session_with_tenant(tenant_id=None) as db_session:
         try:
             modify_anonymous_user_path(tenant_id, anonymous_user_path, db_session)
         except IntegrityError:
@@ -103,7 +98,7 @@ async def login_as_anonymous_user(
     anonymous_user_path: str,
     _: User | None = Depends(optional_user),
 ) -> Response:
-    with get_session_with_shared_schema() as db_session:
+    with get_session_with_tenant(tenant_id=None) as db_session:
         tenant_id = get_tenant_id_for_anonymous_user_path(
             anonymous_user_path, db_session
         )
@@ -116,7 +111,6 @@ async def login_as_anonymous_user(
     token = generate_anonymous_user_jwt_token(tenant_id)
 
     response = Response()
-    response.delete_cookie(FASTAPI_USERS_AUTH_COOKIE_NAME)
     response.set_cookie(
         key=ANONYMOUS_USER_COOKIE_NAME,
         value=token,
@@ -130,48 +124,52 @@ async def login_as_anonymous_user(
 @router.post("/product-gating")
 def gate_product(
     product_gating_request: ProductGatingRequest, _: None = Depends(control_plane_dep)
-) -> ProductGatingResponse:
+) -> None:
     """
     Gating the product means that the product is not available to the tenant.
     They will be directed to the billing page.
-    We gate the product when their subscription has ended.
+    We gate the product when
+    1) User has ended free trial without adding payment method
+    2) User's card has declined
     """
-    try:
-        store_product_gating(
-            product_gating_request.tenant_id, product_gating_request.application_status
-        )
-        return ProductGatingResponse(updated=True, error=None)
+    tenant_id = product_gating_request.tenant_id
+    token = CURRENT_TENANT_ID_CONTEXTVAR.set(tenant_id)
 
-    except Exception as e:
-        logger.exception("Failed to gate product")
-        return ProductGatingResponse(updated=False, error=str(e))
+    settings = load_settings()
+    settings.product_gating = product_gating_request.product_gating
+    store_settings(settings)
+
+    if product_gating_request.notification:
+        with get_session_with_tenant(tenant_id) as db_session:
+            create_notification(None, product_gating_request.notification, db_session)
+
+    if token is not None:
+        CURRENT_TENANT_ID_CONTEXTVAR.reset(token)
 
 
-@router.get("/billing-information")
+@router.get("/billing-information", response_model=BillingInformation)
 async def billing_information(
     _: User = Depends(current_admin_user),
-) -> BillingInformation | SubscriptionStatusResponse:
+) -> BillingInformation:
     logger.info("Fetching billing information")
-    tenant_id = get_current_tenant_id()
-    return fetch_billing_information(tenant_id)
+    return BillingInformation(
+        **fetch_billing_information(CURRENT_TENANT_ID_CONTEXTVAR.get())
+    )
 
 
 @router.post("/create-customer-portal-session")
-async def create_customer_portal_session(
-    _: User = Depends(current_admin_user),
-) -> dict:
-    tenant_id = get_current_tenant_id()
-
+async def create_customer_portal_session(_: User = Depends(current_admin_user)) -> dict:
     try:
+        # Fetch tenant_id and current tenant's information
+        tenant_id = CURRENT_TENANT_ID_CONTEXTVAR.get()
         stripe_info = fetch_tenant_stripe_information(tenant_id)
         stripe_customer_id = stripe_info.get("stripe_customer_id")
         if not stripe_customer_id:
             raise HTTPException(status_code=400, detail="Stripe customer ID not found")
         logger.info(stripe_customer_id)
-
         portal_session = stripe.billing_portal.Session.create(
             customer=stripe_customer_id,
-            return_url=f"{WEB_DOMAIN}/admin/billing",
+            return_url=f"{WEB_DOMAIN}/admin/cloud-settings",
         )
         logger.info(portal_session)
         return {"url": portal_session.url}
@@ -180,22 +178,6 @@ async def create_customer_portal_session(
         raise HTTPException(status_code=500, detail=str(e))
 
 
-@router.post("/create-subscription-session")
-async def create_subscription_session(
-    _: User = Depends(current_admin_user),
-) -> SubscriptionSessionResponse:
-    try:
-        tenant_id = CURRENT_TENANT_ID_CONTEXTVAR.get()
-        if not tenant_id:
-            raise HTTPException(status_code=400, detail="Tenant ID not found")
-        session_id = fetch_stripe_checkout_session(tenant_id)
-        return SubscriptionSessionResponse(sessionId=session_id)
-
-    except Exception as e:
-        logger.exception("Failed to create resubscription session")
-        raise HTTPException(status_code=500, detail=str(e))
-
-
 @router.post("/impersonate")
 async def impersonate_user(
     impersonate_request: ImpersonateRequest,
@@ -204,7 +186,7 @@ async def impersonate_user(
     """Allows a cloud superuser to impersonate another user by generating an impersonation JWT token"""
     tenant_id = get_tenant_id_for_email(impersonate_request.email)
 
-    with get_session_with_tenant(tenant_id=tenant_id) as tenant_session:
+    with get_session_with_tenant(tenant_id) as tenant_session:
         user_to_impersonate = get_user_by_email(
             impersonate_request.email, tenant_session
         )
@@ -228,9 +210,8 @@ async def leave_organization(
     user_email: UserByEmail,
     current_user: User | None = Depends(current_admin_user),
     db_session: Session = Depends(get_session),
+    tenant_id: str = Depends(get_current_tenant_id),
 ) -> None:
-    tenant_id = get_current_tenant_id()
-
     if current_user is None or current_user.email != user_email.user_email:
         raise HTTPException(
             status_code=403, detail="You can only leave the organization as yourself"

backend/ee/onyx/server/tenants/billing.py

@@ -6,7 +6,6 @@ import stripe
 from ee.onyx.configs.app_configs import STRIPE_PRICE_ID
 from ee.onyx.configs.app_configs import STRIPE_SECRET_KEY
 from ee.onyx.server.tenants.access import generate_data_plane_token
-from ee.onyx.server.tenants.models import BillingInformation
 from onyx.configs.app_configs import CONTROL_PLANE_API_BASE_URL
 from onyx.utils.logger import setup_logger
 
@@ -15,19 +14,6 @@ stripe.api_key = STRIPE_SECRET_KEY
 logger = setup_logger()
 
 
-def fetch_stripe_checkout_session(tenant_id: str) -> str:
-    token = generate_data_plane_token()
-    headers = {
-        "Authorization": f"Bearer {token}",
-        "Content-Type": "application/json",
-    }
-    url = f"{CONTROL_PLANE_API_BASE_URL}/create-checkout-session"
-    params = {"tenant_id": tenant_id}
-    response = requests.post(url, headers=headers, params=params)
-    response.raise_for_status()
-    return response.json()["sessionId"]
-
-
 def fetch_tenant_stripe_information(tenant_id: str) -> dict:
     token = generate_data_plane_token()
     headers = {
@@ -41,7 +27,7 @@ def fetch_tenant_stripe_information(tenant_id: str) -> dict:
     return response.json()
 
 
-def fetch_billing_information(tenant_id: str) -> BillingInformation:
+def fetch_billing_information(tenant_id: str) -> dict:
     logger.info("Fetching billing information")
     token = generate_data_plane_token()
     headers = {
@@ -52,7 +38,7 @@ def fetch_billing_information(tenant_id: str) -> BillingInformation:
     params = {"tenant_id": tenant_id}
     response = requests.get(url, headers=headers, params=params)
     response.raise_for_status()
-    billing_info = BillingInformation(**response.json())
+    billing_info = response.json()
     return billing_info
 
 

backend/ee/onyx/server/tenants/models.py

@@ -1,8 +1,7 @@
-from datetime import datetime
-
 from pydantic import BaseModel
 
-from onyx.server.settings.models import ApplicationStatus
+from onyx.configs.constants import NotificationType
+from onyx.server.settings.models import GatingType
 
 
 class CheckoutSessionCreationRequest(BaseModel):
@@ -16,24 +15,15 @@ class CreateTenantRequest(BaseModel):
 
 class ProductGatingRequest(BaseModel):
     tenant_id: str
-    application_status: ApplicationStatus
-
-
-class SubscriptionStatusResponse(BaseModel):
-    subscribed: bool
+    product_gating: GatingType
+    notification: NotificationType | None = None
 
 
 class BillingInformation(BaseModel):
-    stripe_subscription_id: str
-    status: str
-    current_period_start: datetime
-    current_period_end: datetime
-    number_of_seats: int
-    cancel_at_period_end: bool
-    canceled_at: datetime | None
-    trial_start: datetime | None
-    trial_end: datetime | None
     seats: int
+    subscription_status: str
+    billing_start: str
+    billing_end: str
     payment_method_enabled: bool
 
 
@@ -58,12 +48,3 @@ class TenantDeletionPayload(BaseModel):
 
 class AnonymousUserPath(BaseModel):
     anonymous_user_path: str | None
-
-
-class ProductGatingResponse(BaseModel):
-    updated: bool
-    error: str | None
-
-
-class SubscriptionSessionResponse(BaseModel):
-    sessionId: str

backend/ee/onyx/server/tenants/product_gating.py

@@ -1,51 +0,0 @@
-from typing import cast
-
-from ee.onyx.configs.app_configs import GATED_TENANTS_KEY
-from onyx.configs.constants import ONYX_CLOUD_TENANT_ID
-from onyx.redis.redis_pool import get_redis_client
-from onyx.redis.redis_pool import get_redis_replica_client
-from onyx.server.settings.models import ApplicationStatus
-from onyx.server.settings.store import load_settings
-from onyx.server.settings.store import store_settings
-from onyx.setup import setup_logger
-from shared_configs.contextvars import CURRENT_TENANT_ID_CONTEXTVAR
-
-logger = setup_logger()
-
-
-def update_tenant_gating(tenant_id: str, status: ApplicationStatus) -> None:
-    redis_client = get_redis_client(tenant_id=ONYX_CLOUD_TENANT_ID)
-
-    # Store the full status
-    status_key = f"tenant:{tenant_id}:status"
-    redis_client.set(status_key, status.value)
-
-    # Maintain the GATED_ACCESS set
-    if status == ApplicationStatus.GATED_ACCESS:
-        redis_client.sadd(GATED_TENANTS_KEY, tenant_id)
-    else:
-        redis_client.srem(GATED_TENANTS_KEY, tenant_id)
-
-
-def store_product_gating(tenant_id: str, application_status: ApplicationStatus) -> None:
-    try:
-        token = CURRENT_TENANT_ID_CONTEXTVAR.set(tenant_id)
-
-        settings = load_settings()
-        settings.application_status = application_status
-        store_settings(settings)
-
-        # Store gated tenant information in Redis
-        update_tenant_gating(tenant_id, application_status)
-
-        if token is not None:
-            CURRENT_TENANT_ID_CONTEXTVAR.reset(token)
-
-    except Exception:
-        logger.exception("Failed to gate product")
-        raise
-
-
-def get_gated_tenants() -> set[str]:
-    redis_client = get_redis_replica_client(tenant_id=ONYX_CLOUD_TENANT_ID)
-    return cast(set[str], redis_client.smembers(GATED_TENANTS_KEY))

backend/ee/onyx/server/tenants/provisioning.py

@@ -24,7 +24,6 @@ from ee.onyx.server.tenants.user_mapping import get_tenant_id_for_email
 from ee.onyx.server.tenants.user_mapping import user_owns_a_tenant
 from onyx.auth.users import exceptions
 from onyx.configs.app_configs import CONTROL_PLANE_API_BASE_URL
-from onyx.configs.app_configs import DEV_MODE
 from onyx.configs.constants import MilestoneRecordType
 from onyx.db.engine import get_session_with_tenant
 from onyx.db.engine import get_sqlalchemy_engine
@@ -86,8 +85,7 @@ async def create_tenant(email: str, referral_source: str | None = None) -> str:
         # Provision tenant on data plane
         await provision_tenant(tenant_id, email)
         # Notify control plane
-        if not DEV_MODE:
-            await notify_control_plane(tenant_id, email, referral_source)
+        await notify_control_plane(tenant_id, email, referral_source)
     except Exception as e:
         logger.error(f"Tenant provisioning failed: {e}")
         await rollback_tenant_provisioning(tenant_id)
@@ -118,7 +116,7 @@ async def provision_tenant(tenant_id: str, email: str) -> None:
         # Await the Alembic migrations
         await asyncio.to_thread(run_alembic_migrations, tenant_id)
 
-        with get_session_with_tenant(tenant_id=tenant_id) as db_session:
+        with get_session_with_tenant(tenant_id) as db_session:
             configure_default_api_keys(db_session)
 
             current_search_settings = (
@@ -134,7 +132,7 @@ async def provision_tenant(tenant_id: str, email: str) -> None:
 
         add_users_to_tenant([email], tenant_id)
 
-        with get_session_with_tenant(tenant_id=tenant_id) as db_session:
+        with get_session_with_tenant(tenant_id) as db_session:
             create_milestone_and_report(
                 user=None,
                 distinct_id=tenant_id,

backend/ee/onyx/server/tenants/user_mapping.py

@@ -28,7 +28,7 @@ def get_tenant_id_for_email(email: str) -> str:
 
 
 def user_owns_a_tenant(email: str) -> bool:
-    with get_session_with_tenant(tenant_id=None) as db_session:
+    with get_session_with_tenant(POSTGRES_DEFAULT_SCHEMA) as db_session:
         result = (
             db_session.query(UserTenantMapping)
             .filter(UserTenantMapping.email == email)
@@ -38,7 +38,7 @@ def user_owns_a_tenant(email: str) -> bool:
 
 
 def add_users_to_tenant(emails: list[str], tenant_id: str) -> None:
-    with get_session_with_tenant(tenant_id=None) as db_session:
+    with get_session_with_tenant(POSTGRES_DEFAULT_SCHEMA) as db_session:
         try:
             for email in emails:
                 db_session.add(UserTenantMapping(email=email, tenant_id=tenant_id))
@@ -48,7 +48,7 @@ def add_users_to_tenant(emails: list[str], tenant_id: str) -> None:
 
 
 def remove_users_from_tenant(emails: list[str], tenant_id: str) -> None:
-    with get_session_with_tenant(tenant_id=None) as db_session:
+    with get_session_with_tenant(POSTGRES_DEFAULT_SCHEMA) as db_session:
         try:
             mappings_to_delete = (
                 db_session.query(UserTenantMapping)
@@ -71,7 +71,7 @@ def remove_users_from_tenant(emails: list[str], tenant_id: str) -> None:
 
 
 def remove_all_users_from_tenant(tenant_id: str) -> None:
-    with get_session_with_tenant(tenant_id=None) as db_session:
+    with get_session_with_tenant(POSTGRES_DEFAULT_SCHEMA) as db_session:
         db_session.query(UserTenantMapping).filter(
             UserTenantMapping.tenant_id == tenant_id
         ).delete()

backend/ee/onyx/server/user_group/models.py

@@ -58,7 +58,6 @@ class UserGroup(BaseModel):
                     credential=CredentialSnapshot.from_credential_db_model(
                         cc_pair_relationship.cc_pair.credential
                     ),
-                    access_type=cc_pair_relationship.cc_pair.access_type,
                 )
                 for cc_pair_relationship in user_group_model.cc_pair_relationships
                 if cc_pair_relationship.is_current

backend/model_server/constants.py

@@ -28,9 +28,3 @@ class EmbeddingModelTextType:
     @staticmethod
     def get_type(provider: EmbeddingProvider, text_type: EmbedTextType) -> str:
         return EmbeddingModelTextType.PROVIDER_TEXT_TYPE_MAP[provider][text_type]
-
-
-class GPUStatus:
-    CUDA = "cuda"
-    MAC_MPS = "mps"
-    NONE = "none"

backend/model_server/encoders.py

@@ -12,7 +12,6 @@ import voyageai  # type: ignore
 from cohere import AsyncClient as CohereAsyncClient
 from fastapi import APIRouter
 from fastapi import HTTPException
-from fastapi import Request
 from google.oauth2 import service_account  # type: ignore
 from litellm import aembedding
 from litellm.exceptions import RateLimitError
@@ -321,7 +320,6 @@ async def embed_text(
     prefix: str | None,
     api_url: str | None,
     api_version: str | None,
-    gpu_type: str = "UNKNOWN",
 ) -> list[Embedding]:
     if not all(texts):
         logger.error("Empty strings provided for embedding")
@@ -375,11 +373,8 @@ async def embed_text(
 
         elapsed = time.monotonic() - start
         logger.info(
-            f"event=embedding_provider "
-            f"texts={len(texts)} "
-            f"chars={total_chars} "
-            f"provider={provider_type} "
-            f"elapsed={elapsed:.2f}"
+            f"Successfully embedded {len(texts)} texts with {total_chars} total characters "
+            f"with provider {provider_type} in {elapsed:.2f}"
         )
     elif model_name is not None:
         logger.info(
@@ -408,14 +403,6 @@ async def embed_text(
             f"Successfully embedded {len(texts)} texts with {total_chars} total characters "
             f"with local model {model_name} in {elapsed:.2f}"
         )
-        logger.info(
-            f"event=embedding_model "
-            f"texts={len(texts)} "
-            f"chars={total_chars} "
-            f"model={model_name} "
-            f"gpu={gpu_type} "
-            f"elapsed={elapsed:.2f}"
-        )
     else:
         logger.error("Neither model name nor provider specified for embedding")
         raise ValueError(
@@ -468,15 +455,8 @@ async def litellm_rerank(
 
 
 @router.post("/bi-encoder-embed")
-async def route_bi_encoder_embed(
-    request: Request,
-    embed_request: EmbedRequest,
-) -> EmbedResponse:
-    return await process_embed_request(embed_request, request.app.state.gpu_type)
-
-
 async def process_embed_request(
-    embed_request: EmbedRequest, gpu_type: str = "UNKNOWN"
+    embed_request: EmbedRequest,
 ) -> EmbedResponse:
     if not embed_request.texts:
         raise HTTPException(status_code=400, detail="No texts to be embedded")
@@ -504,7 +484,6 @@ async def process_embed_request(
             api_url=embed_request.api_url,
             api_version=embed_request.api_version,
             prefix=prefix,
-            gpu_type=gpu_type,
         )
         return EmbedResponse(embeddings=embeddings)
     except RateLimitError as e:

backend/model_server/main.py

@@ -16,7 +16,6 @@ from model_server.custom_models import router as custom_models_router
 from model_server.custom_models import warm_up_intent_model
 from model_server.encoders import router as encoders_router
 from model_server.management_endpoints import router as management_router
-from model_server.utils import get_gpu_type
 from onyx import __version__
 from onyx.utils.logger import setup_logger
 from shared_configs.configs import INDEXING_ONLY
@@ -59,10 +58,12 @@ def _move_files_recursively(source: Path, dest: Path, overwrite: bool = False) -
 
 @asynccontextmanager
 async def lifespan(app: FastAPI) -> AsyncGenerator:
-    gpu_type = get_gpu_type()
-    logger.notice(f"Torch GPU Detection: gpu_type={gpu_type}")
-
-    app.state.gpu_type = gpu_type
+    if torch.cuda.is_available():
+        logger.notice("CUDA GPU is available")
+    elif torch.backends.mps.is_available():
+        logger.notice("Mac MPS is available")
+    else:
+        logger.notice("GPU is not available, using CPU")
 
     if TEMP_HF_CACHE_PATH.is_dir():
         logger.notice("Moving contents of temp_huggingface to huggingface cache.")

backend/model_server/management_endpoints.py

@@ -1,9 +1,7 @@
+import torch
 from fastapi import APIRouter
 from fastapi import Response
 
-from model_server.constants import GPUStatus
-from model_server.utils import get_gpu_type
-
 router = APIRouter(prefix="/api")
 
 
@@ -13,7 +11,10 @@ async def healthcheck() -> Response:
 
 
 @router.get("/gpu-status")
-async def route_gpu_status() -> dict[str, bool | str]:
-    gpu_type = get_gpu_type()
-    gpu_available = gpu_type != GPUStatus.NONE
-    return {"gpu_available": gpu_available, "type": gpu_type}
+async def gpu_status() -> dict[str, bool | str]:
+    if torch.cuda.is_available():
+        return {"gpu_available": True, "type": "cuda"}
+    elif torch.backends.mps.is_available():
+        return {"gpu_available": True, "type": "mps"}
+    else:
+        return {"gpu_available": False, "type": "none"}

backend/model_server/utils.py

@@ -8,9 +8,6 @@ from typing import Any
 from typing import cast
 from typing import TypeVar
 
-import torch
-
-from model_server.constants import GPUStatus
 from onyx.utils.logger import setup_logger
 
 logger = setup_logger()
@@ -61,12 +58,3 @@ def simple_log_function_time(
             return cast(F, wrapped_sync_func)
 
     return decorator
-
-
-def get_gpu_type() -> str:
-    if torch.cuda.is_available():
-        return GPUStatus.CUDA
-    if torch.backends.mps.is_available():
-        return GPUStatus.MAC_MPS
-
-    return GPUStatus.NONE

backend/onyx/agents/agent_search/basic/graph_builder.py

@@ -1,97 +0,0 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
-
-from onyx.agents.agent_search.basic.states import BasicInput
-from onyx.agents.agent_search.basic.states import BasicOutput
-from onyx.agents.agent_search.basic.states import BasicState
-from onyx.agents.agent_search.orchestration.nodes.call_tool import call_tool
-from onyx.agents.agent_search.orchestration.nodes.choose_tool import choose_tool
-from onyx.agents.agent_search.orchestration.nodes.prepare_tool_input import (
-    prepare_tool_input,
-)
-from onyx.agents.agent_search.orchestration.nodes.use_tool_response import (
-    basic_use_tool_response,
-)
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def basic_graph_builder() -> StateGraph:
-    graph = StateGraph(
-        state_schema=BasicState,
-        input=BasicInput,
-        output=BasicOutput,
-    )
-
-    ### Add nodes ###
-
-    graph.add_node(
-        node="prepare_tool_input",
-        action=prepare_tool_input,
-    )
-
-    graph.add_node(
-        node="choose_tool",
-        action=choose_tool,
-    )
-
-    graph.add_node(
-        node="call_tool",
-        action=call_tool,
-    )
-
-    graph.add_node(
-        node="basic_use_tool_response",
-        action=basic_use_tool_response,
-    )
-
-    ### Add edges ###
-
-    graph.add_edge(start_key=START, end_key="prepare_tool_input")
-
-    graph.add_edge(start_key="prepare_tool_input", end_key="choose_tool")
-
-    graph.add_conditional_edges("choose_tool", should_continue, ["call_tool", END])
-
-    graph.add_edge(
-        start_key="call_tool",
-        end_key="basic_use_tool_response",
-    )
-
-    graph.add_edge(
-        start_key="basic_use_tool_response",
-        end_key=END,
-    )
-
-    return graph
-
-
-def should_continue(state: BasicState) -> str:
-    return (
-        # If there are no tool calls, basic graph already streamed the answer
-        END
-        if state.tool_choice is None
-        else "call_tool"
-    )
-
-
-if __name__ == "__main__":
-    from onyx.db.engine import get_session_context_manager
-    from onyx.context.search.models import SearchRequest
-    from onyx.llm.factory import get_default_llms
-    from onyx.agents.agent_search.shared_graph_utils.utils import get_test_config
-
-    graph = basic_graph_builder()
-    compiled_graph = graph.compile()
-    input = BasicInput(unused=True)
-    primary_llm, fast_llm = get_default_llms()
-    with get_session_context_manager() as db_session:
-        config, _ = get_test_config(
-            db_session=db_session,
-            primary_llm=primary_llm,
-            fast_llm=fast_llm,
-            search_request=SearchRequest(query="How does onyx use FastAPI?"),
-        )
-        compiled_graph.invoke(input, config={"metadata": {"config": config}})

backend/onyx/agents/agent_search/basic/states.py

@@ -1,35 +0,0 @@
-from typing import TypedDict
-
-from langchain_core.messages import AIMessageChunk
-from pydantic import BaseModel
-
-from onyx.agents.agent_search.orchestration.states import ToolCallUpdate
-from onyx.agents.agent_search.orchestration.states import ToolChoiceInput
-from onyx.agents.agent_search.orchestration.states import ToolChoiceUpdate
-
-# States contain values that change over the course of graph execution,
-# Config is for values that are set at the start and never change.
-# If you are using a value from the config and realize it needs to change,
-# you should add it to the state and use/update the version in the state.
-
-
-## Graph Input State
-class BasicInput(BaseModel):
-    # Langgraph needs a nonempty input, but we pass in all static
-    # data through a RunnableConfig.
-    unused: bool = True
-
-
-## Graph Output State
-class BasicOutput(TypedDict):
-    tool_call_chunk: AIMessageChunk
-
-
-## Graph State
-class BasicState(
-    BasicInput,
-    ToolChoiceInput,
-    ToolCallUpdate,
-    ToolChoiceUpdate,
-):
-    pass

backend/onyx/agents/agent_search/basic/utils.py

@@ -1,64 +0,0 @@
-from collections.abc import Iterator
-from typing import cast
-
-from langchain_core.messages import AIMessageChunk
-from langchain_core.messages import BaseMessage
-from langgraph.types import StreamWriter
-
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.chat.models import LlmDoc
-from onyx.chat.models import OnyxContext
-from onyx.chat.stream_processing.answer_response_handler import AnswerResponseHandler
-from onyx.chat.stream_processing.answer_response_handler import CitationResponseHandler
-from onyx.chat.stream_processing.answer_response_handler import (
-    PassThroughAnswerResponseHandler,
-)
-from onyx.chat.stream_processing.utils import map_document_id_order
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def process_llm_stream(
-    messages: Iterator[BaseMessage],
-    should_stream_answer: bool,
-    writer: StreamWriter,
-    final_search_results: list[LlmDoc] | None = None,
-    displayed_search_results: list[OnyxContext] | list[LlmDoc] | None = None,
-) -> AIMessageChunk:
-    tool_call_chunk = AIMessageChunk(content="")
-
-    if final_search_results and displayed_search_results:
-        answer_handler: AnswerResponseHandler = CitationResponseHandler(
-            context_docs=final_search_results,
-            final_doc_id_to_rank_map=map_document_id_order(final_search_results),
-            display_doc_id_to_rank_map=map_document_id_order(displayed_search_results),
-        )
-    else:
-        answer_handler = PassThroughAnswerResponseHandler()
-
-    full_answer = ""
-    # This stream will be the llm answer if no tool is chosen. When a tool is chosen,
-    # the stream will contain AIMessageChunks with tool call information.
-    for message in messages:
-        answer_piece = message.content
-        if not isinstance(answer_piece, str):
-            # this is only used for logging, so fine to
-            # just add the string representation
-            answer_piece = str(answer_piece)
-        full_answer += answer_piece
-
-        if isinstance(message, AIMessageChunk) and (
-            message.tool_call_chunks or message.tool_calls
-        ):
-            tool_call_chunk += message  # type: ignore
-        elif should_stream_answer:
-            for response_part in answer_handler.handle_response_part(message, []):
-                write_custom_event(
-                    "basic_response",
-                    response_part,
-                    writer,
-                )
-
-    logger.debug(f"Full answer: {full_answer}")
-    return cast(AIMessageChunk, tool_call_chunk)

backend/onyx/agents/agent_search/core_state.py

@@ -1,20 +0,0 @@
-from operator import add
-from typing import Annotated
-
-from pydantic import BaseModel
-
-
-class CoreState(BaseModel):
-    """
-    This is the core state that is shared across all subgraphs.
-    """
-
-    log_messages: Annotated[list[str], add] = []
-
-
-class SubgraphCoreState(BaseModel):
-    """
-    This is the core state that is shared across all subgraphs.
-    """
-
-    log_messages: Annotated[list[str], add] = []

backend/onyx/agents/agent_search/deep_search/initial/generate_individual_sub_answer/edges.py

@@ -1,31 +0,0 @@
-from collections.abc import Hashable
-from datetime import datetime
-
-from langgraph.types import Send
-
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    SubQuestionAnsweringInput,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
-    ExpandedRetrievalInput,
-)
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def send_to_expanded_retrieval(state: SubQuestionAnsweringInput) -> Send | Hashable:
-    """
-    LangGraph edge to send a sub-question to the expanded retrieval.
-    """
-    edge_start_time = datetime.now()
-
-    return Send(
-        "initial_sub_question_expanded_retrieval",
-        ExpandedRetrievalInput(
-            question=state.question,
-            base_search=False,
-            sub_question_id=state.question_id,
-            log_messages=[f"{edge_start_time} -- Sending to expanded retrieval"],
-        ),
-    )

backend/onyx/agents/agent_search/deep_search/initial/generate_individual_sub_answer/graph_builder.py

@@ -1,137 +0,0 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
-
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.edges import (
-    send_to_expanded_retrieval,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.check_sub_answer import (
-    check_sub_answer,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.format_sub_answer import (
-    format_sub_answer,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.generate_sub_answer import (
-    generate_sub_answer,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.ingest_retrieved_documents import (
-    ingest_retrieved_documents,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    AnswerQuestionOutput,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    AnswerQuestionState,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    SubQuestionAnsweringInput,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.graph_builder import (
-    expanded_retrieval_graph_builder,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import get_test_config
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def answer_query_graph_builder() -> StateGraph:
-    """
-    LangGraph sub-graph builder for the initial individual sub-answer generation.
-    """
-    graph = StateGraph(
-        state_schema=AnswerQuestionState,
-        input=SubQuestionAnsweringInput,
-        output=AnswerQuestionOutput,
-    )
-
-    ### Add nodes ###
-
-    # The sub-graph that executes the expanded retrieval process for a sub-question
-    expanded_retrieval = expanded_retrieval_graph_builder().compile()
-    graph.add_node(
-        node="initial_sub_question_expanded_retrieval",
-        action=expanded_retrieval,
-    )
-
-    # The node that ingests the retrieved documents and puts them into the proper
-    # state keys.
-    graph.add_node(
-        node="ingest_retrieval",
-        action=ingest_retrieved_documents,
-    )
-
-    # The node that generates the sub-answer
-    graph.add_node(
-        node="generate_sub_answer",
-        action=generate_sub_answer,
-    )
-
-    # The node that checks the sub-answer
-    graph.add_node(
-        node="answer_check",
-        action=check_sub_answer,
-    )
-
-    # The node that formats the sub-answer for the following initial answer generation
-    graph.add_node(
-        node="format_answer",
-        action=format_sub_answer,
-    )
-
-    ### Add edges ###
-
-    graph.add_conditional_edges(
-        source=START,
-        path=send_to_expanded_retrieval,
-        path_map=["initial_sub_question_expanded_retrieval"],
-    )
-    graph.add_edge(
-        start_key="initial_sub_question_expanded_retrieval",
-        end_key="ingest_retrieval",
-    )
-    graph.add_edge(
-        start_key="ingest_retrieval",
-        end_key="generate_sub_answer",
-    )
-    graph.add_edge(
-        start_key="generate_sub_answer",
-        end_key="answer_check",
-    )
-    graph.add_edge(
-        start_key="answer_check",
-        end_key="format_answer",
-    )
-    graph.add_edge(
-        start_key="format_answer",
-        end_key=END,
-    )
-
-    return graph
-
-
-if __name__ == "__main__":
-    from onyx.db.engine import get_session_context_manager
-    from onyx.llm.factory import get_default_llms
-    from onyx.context.search.models import SearchRequest
-
-    graph = answer_query_graph_builder()
-    compiled_graph = graph.compile()
-    primary_llm, fast_llm = get_default_llms()
-    search_request = SearchRequest(
-        query="what can you do with onyx or danswer?",
-    )
-    with get_session_context_manager() as db_session:
-        graph_config, search_tool = get_test_config(
-            db_session, primary_llm, fast_llm, search_request
-        )
-        inputs = SubQuestionAnsweringInput(
-            question="what can you do with onyx?",
-            question_id="0_0",
-            log_messages=[],
-        )
-        for thing in compiled_graph.stream(
-            input=inputs,
-            config={"configurable": {"config": graph_config}},
-        ):
-            logger.debug(thing)

backend/onyx/agents/agent_search/deep_search/initial/generate_individual_sub_answer/nodes/check_sub_answer.py

@@ -1,134 +0,0 @@
-from datetime import datetime
-from typing import cast
-
-from langchain_core.messages import BaseMessage
-from langchain_core.messages import HumanMessage
-from langchain_core.runnables.config import RunnableConfig
-
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    AnswerQuestionState,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    SubQuestionAnswerCheckUpdate,
-)
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    binary_string_test,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_RATELIMIT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_TIMEOUT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_POSITIVE_VALUE_STR,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import AgentLLMErrorType
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
-from onyx.configs.agent_configs import AGENT_TIMEOUT_CONNECT_LLM_SUBANSWER_CHECK
-from onyx.configs.agent_configs import AGENT_TIMEOUT_LLM_SUBANSWER_CHECK
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
-from onyx.prompts.agent_search import SUB_ANSWER_CHECK_PROMPT
-from onyx.prompts.agent_search import UNKNOWN_ANSWER
-from onyx.utils.logger import setup_logger
-from onyx.utils.threadpool_concurrency import run_with_timeout
-from onyx.utils.timing import log_function_time
-
-logger = setup_logger()
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="LLM Timeout Error. The sub-answer will be treated as 'relevant'",
-    rate_limit="LLM Rate Limit Error. The sub-answer will be treated as 'relevant'",
-    general_error="General LLM Error. The sub-answer will be treated as 'relevant'",
-)
-
-
-@log_function_time(print_only=True)
-def check_sub_answer(
-    state: AnswerQuestionState, config: RunnableConfig
-) -> SubQuestionAnswerCheckUpdate:
-    """
-    LangGraph node to check the quality of the sub-answer. The answer
-    is represented as a boolean value.
-    """
-    node_start_time = datetime.now()
-
-    level, question_num = parse_question_id(state.question_id)
-    if state.answer == UNKNOWN_ANSWER:
-        return SubQuestionAnswerCheckUpdate(
-            answer_quality=False,
-            log_messages=[
-                get_langgraph_node_log_string(
-                    graph_component="initial  - generate individual sub answer",
-                    node_name="check sub answer",
-                    node_start_time=node_start_time,
-                    result="unknown answer",
-                )
-            ],
-        )
-    msg = [
-        HumanMessage(
-            content=SUB_ANSWER_CHECK_PROMPT.format(
-                question=state.question,
-                base_answer=state.answer,
-            )
-        )
-    ]
-
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    fast_llm = graph_config.tooling.fast_llm
-    agent_error: AgentErrorLog | None = None
-    response: BaseMessage | None = None
-    try:
-        response = run_with_timeout(
-            AGENT_TIMEOUT_LLM_SUBANSWER_CHECK,
-            fast_llm.invoke,
-            prompt=msg,
-            timeout_override=AGENT_TIMEOUT_CONNECT_LLM_SUBANSWER_CHECK,
-        )
-
-        quality_str: str = cast(str, response.content)
-        answer_quality = binary_string_test(
-            text=quality_str, positive_value=AGENT_POSITIVE_VALUE_STR
-        )
-        log_result = f"Answer quality: {quality_str}"
-
-    except (LLMTimeoutError, TimeoutError):
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.TIMEOUT,
-            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-            error_result=_llm_node_error_strings.timeout,
-        )
-        answer_quality = True
-        log_result = agent_error.error_result
-        logger.error("LLM Timeout Error - check sub answer")
-
-    except LLMRateLimitError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.RATE_LIMIT,
-            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
-            error_result=_llm_node_error_strings.rate_limit,
-        )
-
-        answer_quality = True
-        log_result = agent_error.error_result
-        logger.error("LLM Rate Limit Error - check sub answer")
-
-    return SubQuestionAnswerCheckUpdate(
-        answer_quality=answer_quality,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="initial  - generate individual sub answer",
-                node_name="check sub answer",
-                node_start_time=node_start_time,
-                result=log_result,
-            )
-        ],
-    )

backend/onyx/agents/agent_search/deep_search/initial/generate_individual_sub_answer/nodes/format_sub_answer.py

@@ -1,30 +0,0 @@
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    AnswerQuestionOutput,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    AnswerQuestionState,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import (
-    SubQuestionAnswerResults,
-)
-
-
-def format_sub_answer(state: AnswerQuestionState) -> AnswerQuestionOutput:
-    """
-    LangGraph node to generate the sub-answer format.
-    """
-    return AnswerQuestionOutput(
-        answer_results=[
-            SubQuestionAnswerResults(
-                question=state.question,
-                question_id=state.question_id,
-                verified_high_quality=state.answer_quality,
-                answer=state.answer,
-                sub_query_retrieval_results=state.expanded_retrieval_results,
-                verified_reranked_documents=state.verified_reranked_documents,
-                context_documents=state.context_documents,
-                cited_documents=state.cited_documents,
-                sub_question_retrieval_stats=state.sub_question_retrieval_stats,
-            )
-        ],
-    )

backend/onyx/agents/agent_search/deep_search/initial/generate_individual_sub_answer/nodes/generate_sub_answer.py

@@ -1,203 +0,0 @@
-from datetime import datetime
-from typing import cast
-
-from langchain_core.messages import merge_message_runs
-from langchain_core.runnables.config import RunnableConfig
-from langgraph.types import StreamWriter
-
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    AnswerQuestionState,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    SubQuestionAnswerGenerationUpdate,
-)
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    build_sub_question_answer_prompt,
-)
-from onyx.agents.agent_search.shared_graph_utils.calculations import (
-    dedup_sort_inference_section_list,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_RATELIMIT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_TIMEOUT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AgentLLMErrorType,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    LLM_ANSWER_ERROR_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
-from onyx.agents.agent_search.shared_graph_utils.utils import get_answer_citation_ids
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_persona_agent_prompt_expressions,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.chat.models import AgentAnswerPiece
-from onyx.chat.models import StreamStopInfo
-from onyx.chat.models import StreamStopReason
-from onyx.chat.models import StreamType
-from onyx.configs.agent_configs import AGENT_MAX_ANSWER_CONTEXT_DOCS
-from onyx.configs.agent_configs import AGENT_TIMEOUT_CONNECT_LLM_SUBANSWER_GENERATION
-from onyx.configs.agent_configs import AGENT_TIMEOUT_LLM_SUBANSWER_GENERATION
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
-from onyx.prompts.agent_search import NO_RECOVERED_DOCS
-from onyx.utils.logger import setup_logger
-from onyx.utils.threadpool_concurrency import run_with_timeout
-from onyx.utils.timing import log_function_time
-
-logger = setup_logger()
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="LLM Timeout Error. A sub-answer could not be constructed and the sub-question will be ignored.",
-    rate_limit="LLM Rate Limit Error. A sub-answer could not be constructed and the sub-question will be ignored.",
-    general_error="General LLM Error. A sub-answer could not be constructed and the sub-question will be ignored.",
-)
-
-
-@log_function_time(print_only=True)
-def generate_sub_answer(
-    state: AnswerQuestionState,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> SubQuestionAnswerGenerationUpdate:
-    """
-    LangGraph node to generate a sub-answer.
-    """
-    node_start_time = datetime.now()
-
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    question = state.question
-    state.verified_reranked_documents
-    level, question_num = parse_question_id(state.question_id)
-    context_docs = state.context_documents[:AGENT_MAX_ANSWER_CONTEXT_DOCS]
-
-    context_docs = dedup_sort_inference_section_list(context_docs)
-
-    persona_contextualized_prompt = get_persona_agent_prompt_expressions(
-        graph_config.inputs.search_request.persona
-    ).contextualized_prompt
-
-    if len(context_docs) == 0:
-        answer_str = NO_RECOVERED_DOCS
-        cited_documents: list = []
-        log_results = "No documents retrieved"
-        write_custom_event(
-            "sub_answers",
-            AgentAnswerPiece(
-                answer_piece=answer_str,
-                level=level,
-                level_question_num=question_num,
-                answer_type="agent_sub_answer",
-            ),
-            writer,
-        )
-    else:
-        fast_llm = graph_config.tooling.fast_llm
-        msg = build_sub_question_answer_prompt(
-            question=question,
-            original_question=graph_config.inputs.search_request.query,
-            docs=context_docs,
-            persona_specification=persona_contextualized_prompt,
-            config=fast_llm.config,
-        )
-
-        dispatch_timings: list[float] = []
-        agent_error: AgentErrorLog | None = None
-        response: list[str] = []
-
-        def stream_sub_answer() -> list[str]:
-            for message in fast_llm.stream(
-                prompt=msg,
-                timeout_override=AGENT_TIMEOUT_CONNECT_LLM_SUBANSWER_GENERATION,
-            ):
-                # TODO: in principle, the answer here COULD contain images, but we don't support that yet
-                content = message.content
-                if not isinstance(content, str):
-                    raise ValueError(
-                        f"Expected content to be a string, but got {type(content)}"
-                    )
-                start_stream_token = datetime.now()
-                write_custom_event(
-                    "sub_answers",
-                    AgentAnswerPiece(
-                        answer_piece=content,
-                        level=level,
-                        level_question_num=question_num,
-                        answer_type="agent_sub_answer",
-                    ),
-                    writer,
-                )
-                end_stream_token = datetime.now()
-                dispatch_timings.append(
-                    (end_stream_token - start_stream_token).microseconds
-                )
-                response.append(content)
-            return response
-
-        try:
-            response = run_with_timeout(
-                AGENT_TIMEOUT_LLM_SUBANSWER_GENERATION,
-                stream_sub_answer,
-            )
-
-        except (LLMTimeoutError, TimeoutError):
-            agent_error = AgentErrorLog(
-                error_type=AgentLLMErrorType.TIMEOUT,
-                error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-                error_result=_llm_node_error_strings.timeout,
-            )
-            logger.error("LLM Timeout Error - generate sub answer")
-        except LLMRateLimitError:
-            agent_error = AgentErrorLog(
-                error_type=AgentLLMErrorType.RATE_LIMIT,
-                error_message=AGENT_LLM_RATELIMIT_MESSAGE,
-                error_result=_llm_node_error_strings.rate_limit,
-            )
-            logger.error("LLM Rate Limit Error - generate sub answer")
-
-        if agent_error:
-            answer_str = LLM_ANSWER_ERROR_MESSAGE
-            cited_documents = []
-            log_results = (
-                agent_error.error_result
-                or "Sub-answer generation failed due to LLM error"
-            )
-
-        else:
-            answer_str = merge_message_runs(response, chunk_separator="")[0].content
-            answer_citation_ids = get_answer_citation_ids(answer_str)
-            cited_documents = [
-                context_docs[id] for id in answer_citation_ids if id < len(context_docs)
-            ]
-            log_results = None
-
-    stop_event = StreamStopInfo(
-        stop_reason=StreamStopReason.FINISHED,
-        stream_type=StreamType.SUB_ANSWER,
-        level=level,
-        level_question_num=question_num,
-    )
-    write_custom_event("stream_finished", stop_event, writer)
-
-    return SubQuestionAnswerGenerationUpdate(
-        answer=answer_str,
-        cited_documents=cited_documents,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="initial - generate individual sub answer",
-                node_name="generate sub answer",
-                node_start_time=node_start_time,
-                result=log_results or "",
-            )
-        ],
-    )

backend/onyx/agents/agent_search/deep_search/initial/generate_individual_sub_answer/nodes/ingest_retrieved_documents.py

@@ -1,25 +0,0 @@
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    SubQuestionRetrievalIngestionUpdate,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
-    ExpandedRetrievalOutput,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
-
-
-def ingest_retrieved_documents(
-    state: ExpandedRetrievalOutput,
-) -> SubQuestionRetrievalIngestionUpdate:
-    """
-    LangGraph node to ingest the retrieved documents to format it for the sub-answer.
-    """
-    sub_question_retrieval_stats = state.expanded_retrieval_result.retrieval_stats
-    if sub_question_retrieval_stats is None:
-        sub_question_retrieval_stats = [AgentChunkRetrievalStats()]
-
-    return SubQuestionRetrievalIngestionUpdate(
-        expanded_retrieval_results=state.expanded_retrieval_result.expanded_query_results,
-        verified_reranked_documents=state.expanded_retrieval_result.verified_reranked_documents,
-        context_documents=state.expanded_retrieval_result.context_documents,
-        sub_question_retrieval_stats=sub_question_retrieval_stats,
-    )

backend/onyx/agents/agent_search/deep_search/initial/generate_individual_sub_answer/states.py

@@ -1,73 +0,0 @@
-from operator import add
-from typing import Annotated
-
-from pydantic import BaseModel
-
-from onyx.agents.agent_search.core_state import SubgraphCoreState
-from onyx.agents.agent_search.deep_search.main.states import LoggerUpdate
-from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
-from onyx.agents.agent_search.shared_graph_utils.models import QueryRetrievalResult
-from onyx.agents.agent_search.shared_graph_utils.models import (
-    SubQuestionAnswerResults,
-)
-from onyx.agents.agent_search.shared_graph_utils.operators import (
-    dedup_inference_sections,
-)
-from onyx.context.search.models import InferenceSection
-
-
-## Update States
-class SubQuestionAnswerCheckUpdate(LoggerUpdate, BaseModel):
-    answer_quality: bool = False
-    log_messages: list[str] = []
-
-
-class SubQuestionAnswerGenerationUpdate(LoggerUpdate, BaseModel):
-    answer: str = ""
-    log_messages: list[str] = []
-    cited_documents: Annotated[list[InferenceSection], dedup_inference_sections] = []
-    # answer_stat: AnswerStats
-
-
-class SubQuestionRetrievalIngestionUpdate(LoggerUpdate, BaseModel):
-    expanded_retrieval_results: list[QueryRetrievalResult] = []
-    verified_reranked_documents: Annotated[
-        list[InferenceSection], dedup_inference_sections
-    ] = []
-    context_documents: Annotated[list[InferenceSection], dedup_inference_sections] = []
-    sub_question_retrieval_stats: AgentChunkRetrievalStats = AgentChunkRetrievalStats()
-
-
-## Graph Input State
-
-
-class SubQuestionAnsweringInput(SubgraphCoreState):
-    question: str
-    question_id: str
-    # level 0 is original question and first decomposition, level 1 is follow up, etc
-    # question_num is a unique number per original question per level.
-
-
-## Graph State
-
-
-class AnswerQuestionState(
-    SubQuestionAnsweringInput,
-    SubQuestionAnswerGenerationUpdate,
-    SubQuestionAnswerCheckUpdate,
-    SubQuestionRetrievalIngestionUpdate,
-):
-    pass
-
-
-## Graph Output State
-
-
-class AnswerQuestionOutput(LoggerUpdate, BaseModel):
-    """
-    This is a list of results even though each call of this subgraph only returns one result.
-    This is because if we parallelize the answer query subgraph, there will be multiple
-      results in a list so the add operator is used to add them together.
-    """
-
-    answer_results: Annotated[list[SubQuestionAnswerResults], add] = []

backend/onyx/agents/agent_search/deep_search/initial/generate_initial_answer/edges.py

@@ -1,50 +0,0 @@
-from collections.abc import Hashable
-from datetime import datetime
-
-from langgraph.types import Send
-
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    AnswerQuestionOutput,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    SubQuestionAnsweringInput,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.states import (
-    SubQuestionRetrievalState,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import make_question_id
-
-
-def parallelize_initial_sub_question_answering(
-    state: SubQuestionRetrievalState,
-) -> list[Send | Hashable]:
-    """
-    LangGraph edge to parallelize the initial sub-question answering. If there are no sub-questions,
-    we send empty answers to the initial answer generation, and that answer would be generated
-    solely based on the documents retrieved for the original question.
-    """
-    edge_start_time = datetime.now()
-    if len(state.initial_sub_questions) > 0:
-        return [
-            Send(
-                "answer_query_subgraph",
-                SubQuestionAnsweringInput(
-                    question=question,
-                    question_id=make_question_id(0, question_num + 1),
-                    log_messages=[
-                        f"{edge_start_time} -- Main Edge - Parallelize Initial Sub-question Answering"
-                    ],
-                ),
-            )
-            for question_num, question in enumerate(state.initial_sub_questions)
-        ]
-
-    else:
-        return [
-            Send(
-                "ingest_answers",
-                AnswerQuestionOutput(
-                    answer_results=[],
-                ),
-            )
-        ]

backend/onyx/agents/agent_search/deep_search/initial/generate_initial_answer/graph_builder.py

@@ -1,96 +0,0 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
-
-from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.nodes.generate_initial_answer import (
-    generate_initial_answer,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.nodes.validate_initial_answer import (
-    validate_initial_answer,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.states import (
-    SubQuestionRetrievalInput,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.states import (
-    SubQuestionRetrievalState,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_sub_answers.graph_builder import (
-    generate_sub_answers_graph_builder,
-)
-from onyx.agents.agent_search.deep_search.initial.retrieve_orig_question_docs.graph_builder import (
-    retrieve_orig_question_docs_graph_builder,
-)
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def generate_initial_answer_graph_builder(test_mode: bool = False) -> StateGraph:
-    """
-    LangGraph graph builder for the initial answer generation.
-    """
-    graph = StateGraph(
-        state_schema=SubQuestionRetrievalState,
-        input=SubQuestionRetrievalInput,
-    )
-
-    # The sub-graph that generates the initial sub-answers
-    generate_sub_answers = generate_sub_answers_graph_builder().compile()
-    graph.add_node(
-        node="generate_sub_answers_subgraph",
-        action=generate_sub_answers,
-    )
-
-    # The sub-graph that retrieves the original question documents. This is run
-    # in parallel with the sub-answer generation process
-    retrieve_orig_question_docs = retrieve_orig_question_docs_graph_builder().compile()
-    graph.add_node(
-        node="retrieve_orig_question_docs_subgraph_wrapper",
-        action=retrieve_orig_question_docs,
-    )
-
-    # Node that generates the initial answer using the results of the previous
-    # two sub-graphs
-    graph.add_node(
-        node="generate_initial_answer",
-        action=generate_initial_answer,
-    )
-
-    # Node that validates the initial answer
-    graph.add_node(
-        node="validate_initial_answer",
-        action=validate_initial_answer,
-    )
-
-    ### Add edges ###
-
-    graph.add_edge(
-        start_key=START,
-        end_key="retrieve_orig_question_docs_subgraph_wrapper",
-    )
-
-    graph.add_edge(
-        start_key=START,
-        end_key="generate_sub_answers_subgraph",
-    )
-
-    # Wait for both, the original question docs and the sub-answers to be generated before proceeding
-    graph.add_edge(
-        start_key=[
-            "retrieve_orig_question_docs_subgraph_wrapper",
-            "generate_sub_answers_subgraph",
-        ],
-        end_key="generate_initial_answer",
-    )
-
-    graph.add_edge(
-        start_key="generate_initial_answer",
-        end_key="validate_initial_answer",
-    )
-
-    graph.add_edge(
-        start_key="validate_initial_answer",
-        end_key=END,
-    )
-
-    return graph

backend/onyx/agents/agent_search/deep_search/initial/generate_initial_answer/nodes/generate_initial_answer.py

@@ -1,419 +0,0 @@
-from datetime import datetime
-from typing import cast
-
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_content
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
-
-from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.states import (
-    SubQuestionRetrievalState,
-)
-from onyx.agents.agent_search.deep_search.main.models import AgentBaseMetrics
-from onyx.agents.agent_search.deep_search.main.operations import (
-    calculate_initial_agent_stats,
-)
-from onyx.agents.agent_search.deep_search.main.operations import get_query_info
-from onyx.agents.agent_search.deep_search.main.operations import logger
-from onyx.agents.agent_search.deep_search.main.states import (
-    InitialAnswerUpdate,
-)
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    get_prompt_enrichment_components,
-)
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    trim_prompt_piece,
-)
-from onyx.agents.agent_search.shared_graph_utils.calculations import (
-    get_answer_generation_documents,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_RATELIMIT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_TIMEOUT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AgentLLMErrorType,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
-from onyx.agents.agent_search.shared_graph_utils.models import InitialAgentResultStats
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
-from onyx.agents.agent_search.shared_graph_utils.operators import (
-    dedup_inference_section_list,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    dispatch_main_answer_stop_info,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import format_docs
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_deduplicated_structured_subquestion_documents,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import relevance_from_docs
-from onyx.agents.agent_search.shared_graph_utils.utils import remove_document_citations
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.chat.models import AgentAnswerPiece
-from onyx.chat.models import ExtendedToolResponse
-from onyx.chat.models import StreamingError
-from onyx.configs.agent_configs import AGENT_ANSWER_GENERATION_BY_FAST_LLM
-from onyx.configs.agent_configs import AGENT_MAX_ANSWER_CONTEXT_DOCS
-from onyx.configs.agent_configs import AGENT_MAX_STREAMED_DOCS_FOR_INITIAL_ANSWER
-from onyx.configs.agent_configs import AGENT_MIN_ORIG_QUESTION_DOCS
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_CONNECT_LLM_INITIAL_ANSWER_GENERATION,
-)
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_LLM_INITIAL_ANSWER_GENERATION,
-)
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
-from onyx.prompts.agent_search import INITIAL_ANSWER_PROMPT_W_SUB_QUESTIONS
-from onyx.prompts.agent_search import (
-    INITIAL_ANSWER_PROMPT_WO_SUB_QUESTIONS,
-)
-from onyx.prompts.agent_search import (
-    SUB_QUESTION_ANSWER_TEMPLATE,
-)
-from onyx.prompts.agent_search import UNKNOWN_ANSWER
-from onyx.tools.tool_implementations.search.search_tool import yield_search_responses
-from onyx.utils.threadpool_concurrency import run_with_timeout
-from onyx.utils.timing import log_function_time
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="LLM Timeout Error. The initial answer could not be generated.",
-    rate_limit="LLM Rate Limit Error. The initial answer could not be generated.",
-    general_error="General LLM Error. The initial answer could not be generated.",
-)
-
-
-@log_function_time(print_only=True)
-def generate_initial_answer(
-    state: SubQuestionRetrievalState,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> InitialAnswerUpdate:
-    """
-    LangGraph node to generate the initial answer, using the initial sub-questions/sub-answers and the
-    documents retrieved for the original question.
-    """
-    node_start_time = datetime.now()
-
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    question = graph_config.inputs.search_request.query
-    prompt_enrichment_components = get_prompt_enrichment_components(graph_config)
-
-    # get all documents cited in sub-questions
-    structured_subquestion_docs = get_deduplicated_structured_subquestion_documents(
-        state.sub_question_results
-    )
-
-    orig_question_retrieval_documents = state.orig_question_retrieved_documents
-
-    consolidated_context_docs = structured_subquestion_docs.cited_documents
-    counter = 0
-    for original_doc_number, original_doc in enumerate(
-        orig_question_retrieval_documents
-    ):
-        if original_doc_number not in structured_subquestion_docs.cited_documents:
-            if (
-                counter <= AGENT_MIN_ORIG_QUESTION_DOCS
-                or len(consolidated_context_docs) < AGENT_MAX_ANSWER_CONTEXT_DOCS
-            ):
-                consolidated_context_docs.append(original_doc)
-                counter += 1
-
-    # sort docs by their scores - though the scores refer to different questions
-    relevant_docs = dedup_inference_section_list(consolidated_context_docs)
-
-    sub_questions: list[str] = []
-
-    # Create the list of documents to stream out. Start with the
-    # ones that wil be in the context (or, if len == 0, use docs
-    # that were retrieved for the original question)
-    answer_generation_documents = get_answer_generation_documents(
-        relevant_docs=relevant_docs,
-        context_documents=structured_subquestion_docs.context_documents,
-        original_question_docs=orig_question_retrieval_documents,
-        max_docs=AGENT_MAX_STREAMED_DOCS_FOR_INITIAL_ANSWER,
-    )
-
-    # Use the query info from the base document retrieval
-    query_info = get_query_info(state.orig_question_sub_query_retrieval_results)
-
-    assert (
-        graph_config.tooling.search_tool
-    ), "search_tool must be provided for agentic search"
-
-    relevance_list = relevance_from_docs(
-        answer_generation_documents.streaming_documents
-    )
-    for tool_response in yield_search_responses(
-        query=question,
-        reranked_sections=answer_generation_documents.streaming_documents,
-        final_context_sections=answer_generation_documents.context_documents,
-        search_query_info=query_info,
-        get_section_relevance=lambda: relevance_list,
-        search_tool=graph_config.tooling.search_tool,
-    ):
-        write_custom_event(
-            "tool_response",
-            ExtendedToolResponse(
-                id=tool_response.id,
-                response=tool_response.response,
-                level=0,
-                level_question_num=0,  # 0, 0 is the base question
-            ),
-            writer,
-        )
-
-    if len(answer_generation_documents.context_documents) == 0:
-        write_custom_event(
-            "initial_agent_answer",
-            AgentAnswerPiece(
-                answer_piece=UNKNOWN_ANSWER,
-                level=0,
-                level_question_num=0,
-                answer_type="agent_level_answer",
-            ),
-            writer,
-        )
-        dispatch_main_answer_stop_info(0, writer)
-
-        answer = UNKNOWN_ANSWER
-        initial_agent_stats = InitialAgentResultStats(
-            sub_questions={},
-            original_question={},
-            agent_effectiveness={},
-        )
-
-    else:
-        sub_question_answer_results = state.sub_question_results
-
-        # Collect the sub-questions and sub-answers and construct an appropriate
-        # prompt string.
-        # Consider replacing by a function.
-        answered_sub_questions: list[str] = []
-        all_sub_questions: list[str] = []  # Separate list for tracking all questions
-
-        for idx, sub_question_answer_result in enumerate(
-            sub_question_answer_results, start=1
-        ):
-            all_sub_questions.append(sub_question_answer_result.question)
-
-            is_valid_answer = (
-                sub_question_answer_result.verified_high_quality
-                and sub_question_answer_result.answer
-                and sub_question_answer_result.answer != UNKNOWN_ANSWER
-            )
-
-            if is_valid_answer:
-                answered_sub_questions.append(
-                    SUB_QUESTION_ANSWER_TEMPLATE.format(
-                        sub_question=sub_question_answer_result.question,
-                        sub_answer=sub_question_answer_result.answer,
-                        sub_question_num=idx,
-                    )
-                )
-
-        sub_question_answer_str = (
-            "\n\n------\n\n".join(answered_sub_questions)
-            if answered_sub_questions
-            else ""
-        )
-
-        # Use the appropriate prompt based on whether there are sub-questions.
-        base_prompt = (
-            INITIAL_ANSWER_PROMPT_W_SUB_QUESTIONS
-            if answered_sub_questions
-            else INITIAL_ANSWER_PROMPT_WO_SUB_QUESTIONS
-        )
-
-        sub_questions = all_sub_questions  # Replace the original assignment
-
-        model = (
-            graph_config.tooling.fast_llm
-            if AGENT_ANSWER_GENERATION_BY_FAST_LLM
-            else graph_config.tooling.primary_llm
-        )
-
-        doc_context = format_docs(answer_generation_documents.context_documents)
-        doc_context = trim_prompt_piece(
-            config=model.config,
-            prompt_piece=doc_context,
-            reserved_str=(
-                base_prompt
-                + sub_question_answer_str
-                + prompt_enrichment_components.persona_prompts.contextualized_prompt
-                + prompt_enrichment_components.history
-                + prompt_enrichment_components.date_str
-            ),
-        )
-
-        msg = [
-            HumanMessage(
-                content=base_prompt.format(
-                    question=question,
-                    answered_sub_questions=remove_document_citations(
-                        sub_question_answer_str
-                    ),
-                    relevant_docs=doc_context,
-                    persona_specification=prompt_enrichment_components.persona_prompts.contextualized_prompt,
-                    history=prompt_enrichment_components.history,
-                    date_prompt=prompt_enrichment_components.date_str,
-                )
-            )
-        ]
-
-        streamed_tokens: list[str] = [""]
-        dispatch_timings: list[float] = []
-
-        agent_error: AgentErrorLog | None = None
-
-        def stream_initial_answer() -> list[str]:
-            response: list[str] = []
-            for message in model.stream(
-                msg,
-                timeout_override=AGENT_TIMEOUT_CONNECT_LLM_INITIAL_ANSWER_GENERATION,
-            ):
-                # TODO: in principle, the answer here COULD contain images, but we don't support that yet
-                content = message.content
-                if not isinstance(content, str):
-                    raise ValueError(
-                        f"Expected content to be a string, but got {type(content)}"
-                    )
-                start_stream_token = datetime.now()
-
-                write_custom_event(
-                    "initial_agent_answer",
-                    AgentAnswerPiece(
-                        answer_piece=content,
-                        level=0,
-                        level_question_num=0,
-                        answer_type="agent_level_answer",
-                    ),
-                    writer,
-                )
-                end_stream_token = datetime.now()
-                dispatch_timings.append(
-                    (end_stream_token - start_stream_token).microseconds
-                )
-                response.append(content)
-            return response
-
-        try:
-            streamed_tokens = run_with_timeout(
-                AGENT_TIMEOUT_LLM_INITIAL_ANSWER_GENERATION,
-                stream_initial_answer,
-            )
-
-        except (LLMTimeoutError, TimeoutError):
-            agent_error = AgentErrorLog(
-                error_type=AgentLLMErrorType.TIMEOUT,
-                error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-                error_result=_llm_node_error_strings.timeout,
-            )
-            logger.error("LLM Timeout Error - generate initial answer")
-
-        except LLMRateLimitError:
-            agent_error = AgentErrorLog(
-                error_type=AgentLLMErrorType.RATE_LIMIT,
-                error_message=AGENT_LLM_RATELIMIT_MESSAGE,
-                error_result=_llm_node_error_strings.rate_limit,
-            )
-            logger.error("LLM Rate Limit Error - generate initial answer")
-
-        if agent_error:
-            write_custom_event(
-                "initial_agent_answer",
-                StreamingError(
-                    error=AGENT_LLM_TIMEOUT_MESSAGE,
-                ),
-                writer,
-            )
-            return InitialAnswerUpdate(
-                initial_answer=None,
-                answer_error=AgentErrorLog(
-                    error_message=agent_error.error_message or "An LLM error occurred",
-                    error_type=agent_error.error_type,
-                    error_result=agent_error.error_result,
-                ),
-                initial_agent_stats=None,
-                generated_sub_questions=sub_questions,
-                agent_base_end_time=None,
-                agent_base_metrics=None,
-                log_messages=[
-                    get_langgraph_node_log_string(
-                        graph_component="initial - generate initial answer",
-                        node_name="generate initial answer",
-                        node_start_time=node_start_time,
-                        result=agent_error.error_result or "An LLM error occurred",
-                    )
-                ],
-            )
-
-        logger.debug(
-            f"Average dispatch time for initial answer: {sum(dispatch_timings) / len(dispatch_timings)}"
-        )
-
-        dispatch_main_answer_stop_info(0, writer)
-        response = merge_content(*streamed_tokens)
-        answer = cast(str, response)
-
-        initial_agent_stats = calculate_initial_agent_stats(
-            state.sub_question_results, state.orig_question_retrieval_stats
-        )
-
-        logger.debug(
-            f"\n\nYYYYY--Sub-Questions:\n\n{sub_question_answer_str}\n\nStats:\n\n"
-        )
-
-        if initial_agent_stats:
-            logger.debug(initial_agent_stats.original_question)
-            logger.debug(initial_agent_stats.sub_questions)
-            logger.debug(initial_agent_stats.agent_effectiveness)
-
-    agent_base_end_time = datetime.now()
-
-    if agent_base_end_time and state.agent_start_time:
-        duration_s = (agent_base_end_time - state.agent_start_time).total_seconds()
-    else:
-        duration_s = None
-
-    agent_base_metrics = AgentBaseMetrics(
-        num_verified_documents_total=len(relevant_docs),
-        num_verified_documents_core=state.orig_question_retrieval_stats.verified_count,
-        verified_avg_score_core=state.orig_question_retrieval_stats.verified_avg_scores,
-        num_verified_documents_base=initial_agent_stats.sub_questions.get(
-            "num_verified_documents"
-        ),
-        verified_avg_score_base=initial_agent_stats.sub_questions.get(
-            "verified_avg_score"
-        ),
-        base_doc_boost_factor=initial_agent_stats.agent_effectiveness.get(
-            "utilized_chunk_ratio"
-        ),
-        support_boost_factor=initial_agent_stats.agent_effectiveness.get(
-            "support_ratio"
-        ),
-        duration_s=duration_s,
-    )
-
-    return InitialAnswerUpdate(
-        initial_answer=answer,
-        initial_agent_stats=initial_agent_stats,
-        generated_sub_questions=sub_questions,
-        agent_base_end_time=agent_base_end_time,
-        agent_base_metrics=agent_base_metrics,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="initial - generate initial answer",
-                node_name="generate initial answer",
-                node_start_time=node_start_time,
-                result="",
-            )
-        ],
-    )

backend/onyx/agents/agent_search/deep_search/initial/generate_initial_answer/nodes/validate_initial_answer.py

@@ -1,42 +0,0 @@
-from datetime import datetime
-
-from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.states import (
-    SubQuestionRetrievalState,
-)
-from onyx.agents.agent_search.deep_search.main.operations import logger
-from onyx.agents.agent_search.deep_search.main.states import (
-    InitialAnswerQualityUpdate,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.utils.timing import log_function_time
-
-
-@log_function_time(print_only=True)
-def validate_initial_answer(
-    state: SubQuestionRetrievalState,
-) -> InitialAnswerQualityUpdate:
-    """
-    Check whether the initial answer sufficiently addresses the original user question.
-    """
-
-    node_start_time = datetime.now()
-
-    logger.debug(
-        f"--------{node_start_time}--------Checking for base answer validity - for not set True/False manually"
-    )
-
-    verdict = True  # not actually required as already streamed out. Refinement will do similar
-
-    return InitialAnswerQualityUpdate(
-        initial_answer_quality_eval=verdict,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="initial - generate initial answer",
-                node_name="validate initial answer",
-                node_start_time=node_start_time,
-                result="",
-            )
-        ],
-    )

backend/onyx/agents/agent_search/deep_search/initial/generate_initial_answer/states.py

@@ -1,51 +0,0 @@
-from operator import add
-from typing import Annotated
-from typing import TypedDict
-
-from onyx.agents.agent_search.core_state import CoreState
-from onyx.agents.agent_search.deep_search.main.states import (
-    ExploratorySearchUpdate,
-)
-from onyx.agents.agent_search.deep_search.main.states import (
-    InitialAnswerQualityUpdate,
-)
-from onyx.agents.agent_search.deep_search.main.states import (
-    InitialAnswerUpdate,
-)
-from onyx.agents.agent_search.deep_search.main.states import (
-    InitialQuestionDecompositionUpdate,
-)
-from onyx.agents.agent_search.deep_search.main.states import (
-    OrigQuestionRetrievalUpdate,
-)
-from onyx.agents.agent_search.deep_search.main.states import (
-    SubQuestionResultsUpdate,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.models import (
-    QuestionRetrievalResult,
-)
-from onyx.context.search.models import InferenceSection
-
-
-### States ###
-class SubQuestionRetrievalInput(CoreState):
-    exploratory_search_results: list[InferenceSection]
-
-
-## Graph State
-class SubQuestionRetrievalState(
-    # This includes the core state
-    SubQuestionRetrievalInput,
-    InitialQuestionDecompositionUpdate,
-    InitialAnswerUpdate,
-    SubQuestionResultsUpdate,
-    OrigQuestionRetrievalUpdate,
-    InitialAnswerQualityUpdate,
-    ExploratorySearchUpdate,
-):
-    base_raw_search_result: Annotated[list[QuestionRetrievalResult], add]
-
-
-## Graph Output State
-class SubQuestionRetrievalOutput(TypedDict):
-    log_messages: list[str]

backend/onyx/agents/agent_search/deep_search/initial/generate_sub_answers/edges.py

@@ -1,48 +0,0 @@
-from collections.abc import Hashable
-from datetime import datetime
-
-from langgraph.types import Send
-
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    AnswerQuestionOutput,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    SubQuestionAnsweringInput,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.states import (
-    SubQuestionRetrievalState,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import make_question_id
-
-
-def parallelize_initial_sub_question_answering(
-    state: SubQuestionRetrievalState,
-) -> list[Send | Hashable]:
-    """
-    LangGraph edge to parallelize the initial sub-question answering.
-    """
-    edge_start_time = datetime.now()
-    if len(state.initial_sub_questions) > 0:
-        return [
-            Send(
-                "answer_sub_question_subgraphs",
-                SubQuestionAnsweringInput(
-                    question=question,
-                    question_id=make_question_id(0, question_num + 1),
-                    log_messages=[
-                        f"{edge_start_time} -- Main Edge - Parallelize Initial Sub-question Answering"
-                    ],
-                ),
-            )
-            for question_num, question in enumerate(state.initial_sub_questions)
-        ]
-
-    else:
-        return [
-            Send(
-                "ingest_answers",
-                AnswerQuestionOutput(
-                    answer_results=[],
-                ),
-            )
-        ]

backend/onyx/agents/agent_search/deep_search/initial/generate_sub_answers/graph_builder.py

@@ -1,81 +0,0 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
-
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.graph_builder import (
-    answer_query_graph_builder,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_sub_answers.edges import (
-    parallelize_initial_sub_question_answering,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_sub_answers.nodes.decompose_orig_question import (
-    decompose_orig_question,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_sub_answers.nodes.format_initial_sub_answers import (
-    format_initial_sub_answers,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_sub_answers.states import (
-    SubQuestionAnsweringInput,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_sub_answers.states import (
-    SubQuestionAnsweringState,
-)
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-test_mode = False
-
-
-def generate_sub_answers_graph_builder() -> StateGraph:
-    """
-    LangGraph graph builder for the initial sub-answer generation process.
-    It generates the initial sub-questions and produces the answers.
-    """
-
-    graph = StateGraph(
-        state_schema=SubQuestionAnsweringState,
-        input=SubQuestionAnsweringInput,
-    )
-
-    # Decompose the original question into sub-questions
-    graph.add_node(
-        node="decompose_orig_question",
-        action=decompose_orig_question,
-    )
-
-    # The sub-graph that executes the initial sub-question answering for
-    # each of the sub-questions.
-    answer_sub_question_subgraphs = answer_query_graph_builder().compile()
-    graph.add_node(
-        node="answer_sub_question_subgraphs",
-        action=answer_sub_question_subgraphs,
-    )
-
-    # Node that collects and formats the initial sub-question answers
-    graph.add_node(
-        node="format_initial_sub_question_answers",
-        action=format_initial_sub_answers,
-    )
-
-    graph.add_edge(
-        start_key=START,
-        end_key="decompose_orig_question",
-    )
-
-    graph.add_conditional_edges(
-        source="decompose_orig_question",
-        path=parallelize_initial_sub_question_answering,
-        path_map=["answer_sub_question_subgraphs"],
-    )
-    graph.add_edge(
-        start_key=["answer_sub_question_subgraphs"],
-        end_key="format_initial_sub_question_answers",
-    )
-
-    graph.add_edge(
-        start_key="format_initial_sub_question_answers",
-        end_key=END,
-    )
-
-    return graph

backend/onyx/agents/agent_search/deep_search/initial/generate_sub_answers/nodes/decompose_orig_question.py

@@ -1,188 +0,0 @@
-from datetime import datetime
-from typing import cast
-
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_content
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
-
-from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.states import (
-    SubQuestionRetrievalState,
-)
-from onyx.agents.agent_search.deep_search.main.models import (
-    AgentRefinedMetrics,
-)
-from onyx.agents.agent_search.deep_search.main.operations import dispatch_subquestion
-from onyx.agents.agent_search.deep_search.main.operations import (
-    dispatch_subquestion_sep,
-)
-from onyx.agents.agent_search.deep_search.main.states import (
-    InitialQuestionDecompositionUpdate,
-)
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    build_history_prompt,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import BaseMessage_Content
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
-from onyx.agents.agent_search.shared_graph_utils.utils import dispatch_separated
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.chat.models import StreamStopInfo
-from onyx.chat.models import StreamStopReason
-from onyx.chat.models import StreamType
-from onyx.chat.models import SubQuestionPiece
-from onyx.configs.agent_configs import AGENT_NUM_DOCS_FOR_DECOMPOSITION
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_CONNECT_LLM_SUBQUESTION_GENERATION,
-)
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_LLM_SUBQUESTION_GENERATION,
-)
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
-from onyx.prompts.agent_search import (
-    INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH_ASSUMING_REFINEMENT,
-)
-from onyx.prompts.agent_search import (
-    INITIAL_QUESTION_DECOMPOSITION_PROMPT_ASSUMING_REFINEMENT,
-)
-from onyx.utils.logger import setup_logger
-from onyx.utils.threadpool_concurrency import run_with_timeout
-from onyx.utils.timing import log_function_time
-
-logger = setup_logger()
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="LLM Timeout Error. Sub-questions could not be generated.",
-    rate_limit="LLM Rate Limit Error. Sub-questions could not be generated.",
-    general_error="General LLM Error. Sub-questions could not be generated.",
-)
-
-
-@log_function_time(print_only=True)
-def decompose_orig_question(
-    state: SubQuestionRetrievalState,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> InitialQuestionDecompositionUpdate:
-    """
-    LangGraph node to decompose the original question into sub-questions.
-    """
-    node_start_time = datetime.now()
-
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    question = graph_config.inputs.search_request.query
-    perform_initial_search_decomposition = (
-        graph_config.behavior.perform_initial_search_decomposition
-    )
-    # Get the rewritten queries in a defined format
-    model = graph_config.tooling.fast_llm
-
-    history = build_history_prompt(graph_config, question)
-
-    # Use the initial search results to inform the decomposition
-    agent_start_time = datetime.now()
-
-    # Initial search to inform decomposition. Just get top 3 fits
-
-    if perform_initial_search_decomposition:
-        # Due to unfortunate state representation in LangGraph, we need here to double check that the retrieval has
-        # happened prior to this point, allowing silent failure here since it is not critical for decomposition in
-        # all queries.
-        if not state.exploratory_search_results:
-            logger.error("Initial search for decomposition failed")
-
-        sample_doc_str = "\n\n".join(
-            [
-                doc.combined_content
-                for doc in state.exploratory_search_results[
-                    :AGENT_NUM_DOCS_FOR_DECOMPOSITION
-                ]
-            ]
-        )
-
-        decomposition_prompt = INITIAL_DECOMPOSITION_PROMPT_QUESTIONS_AFTER_SEARCH_ASSUMING_REFINEMENT.format(
-            question=question, sample_doc_str=sample_doc_str, history=history
-        )
-
-    else:
-        decomposition_prompt = (
-            INITIAL_QUESTION_DECOMPOSITION_PROMPT_ASSUMING_REFINEMENT.format(
-                question=question, history=history
-            )
-        )
-
-    # Start decomposition
-
-    msg = [HumanMessage(content=decomposition_prompt)]
-
-    # Send the initial question as a subquestion with number 0
-    write_custom_event(
-        "decomp_qs",
-        SubQuestionPiece(
-            sub_question=question,
-            level=0,
-            level_question_num=0,
-        ),
-        writer,
-    )
-
-    # dispatches custom events for subquestion tokens, adding in subquestion ids.
-
-    streamed_tokens: list[BaseMessage_Content] = []
-
-    try:
-        streamed_tokens = run_with_timeout(
-            AGENT_TIMEOUT_LLM_SUBQUESTION_GENERATION,
-            dispatch_separated,
-            model.stream(
-                msg,
-                timeout_override=AGENT_TIMEOUT_CONNECT_LLM_SUBQUESTION_GENERATION,
-            ),
-            dispatch_subquestion(0, writer),
-            sep_callback=dispatch_subquestion_sep(0, writer),
-        )
-
-        decomposition_response = merge_content(*streamed_tokens)
-
-        list_of_subqs = cast(str, decomposition_response).split("\n")
-
-        initial_sub_questions = [sq.strip() for sq in list_of_subqs if sq.strip() != ""]
-        log_result = f"decomposed original question into {len(initial_sub_questions)} subquestions"
-
-        stop_event = StreamStopInfo(
-            stop_reason=StreamStopReason.FINISHED,
-            stream_type=StreamType.SUB_QUESTIONS,
-            level=0,
-        )
-        write_custom_event("stream_finished", stop_event, writer)
-
-    except (LLMTimeoutError, TimeoutError) as e:
-        logger.error("LLM Timeout Error - decompose orig question")
-        raise e  # fail loudly on this critical step
-    except LLMRateLimitError as e:
-        logger.error("LLM Rate Limit Error - decompose orig question")
-        raise e
-
-    return InitialQuestionDecompositionUpdate(
-        initial_sub_questions=initial_sub_questions,
-        agent_start_time=agent_start_time,
-        agent_refined_start_time=None,
-        agent_refined_end_time=None,
-        agent_refined_metrics=AgentRefinedMetrics(
-            refined_doc_boost_factor=None,
-            refined_question_boost_factor=None,
-            duration_s=None,
-        ),
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="initial - generate sub answers",
-                node_name="decompose original question",
-                node_start_time=node_start_time,
-                result=log_result,
-            )
-        ],
-    )

backend/onyx/agents/agent_search/deep_search/initial/generate_sub_answers/nodes/format_initial_sub_answers.py

@@ -1,50 +0,0 @@
-from datetime import datetime
-
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    AnswerQuestionOutput,
-)
-from onyx.agents.agent_search.deep_search.main.states import (
-    SubQuestionResultsUpdate,
-)
-from onyx.agents.agent_search.shared_graph_utils.operators import (
-    dedup_inference_sections,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-
-
-def format_initial_sub_answers(
-    state: AnswerQuestionOutput,
-) -> SubQuestionResultsUpdate:
-    """
-    LangGraph node to format the answers to the initial sub-questions, including
-    deduping verified documents and context documents.
-    """
-    node_start_time = datetime.now()
-
-    documents = []
-    context_documents = []
-    cited_documents = []
-    answer_results = state.answer_results
-    for answer_result in answer_results:
-        documents.extend(answer_result.verified_reranked_documents)
-        context_documents.extend(answer_result.context_documents)
-        cited_documents.extend(answer_result.cited_documents)
-
-    return SubQuestionResultsUpdate(
-        # Deduping is done by the documents operator for the main graph
-        # so we might not need to dedup here
-        verified_reranked_documents=dedup_inference_sections(documents, []),
-        context_documents=dedup_inference_sections(context_documents, []),
-        cited_documents=dedup_inference_sections(cited_documents, []),
-        sub_question_results=answer_results,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="initial - generate sub answers",
-                node_name="format initial sub answers",
-                node_start_time=node_start_time,
-                result="",
-            )
-        ],
-    )

backend/onyx/agents/agent_search/deep_search/initial/generate_sub_answers/states.py

@@ -1,34 +0,0 @@
-from typing import TypedDict
-
-from onyx.agents.agent_search.core_state import CoreState
-from onyx.agents.agent_search.deep_search.main.states import (
-    InitialAnswerUpdate,
-)
-from onyx.agents.agent_search.deep_search.main.states import (
-    InitialQuestionDecompositionUpdate,
-)
-from onyx.agents.agent_search.deep_search.main.states import (
-    SubQuestionResultsUpdate,
-)
-from onyx.context.search.models import InferenceSection
-
-
-### States ###
-class SubQuestionAnsweringInput(CoreState):
-    exploratory_search_results: list[InferenceSection]
-
-
-## Graph State
-class SubQuestionAnsweringState(
-    # This includes the core state
-    SubQuestionAnsweringInput,
-    InitialQuestionDecompositionUpdate,
-    InitialAnswerUpdate,
-    SubQuestionResultsUpdate,
-):
-    pass
-
-
-## Graph Output State
-class SubQuestionAnsweringOutput(TypedDict):
-    log_messages: list[str]

backend/onyx/agents/agent_search/deep_search/initial/retrieve_orig_question_docs/graph_builder.py

@@ -1,81 +0,0 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
-
-from onyx.agents.agent_search.deep_search.initial.retrieve_orig_question_docs.nodes.format_orig_question_search_input import (
-    format_orig_question_search_input,
-)
-from onyx.agents.agent_search.deep_search.initial.retrieve_orig_question_docs.nodes.format_orig_question_search_output import (
-    format_orig_question_search_output,
-)
-from onyx.agents.agent_search.deep_search.initial.retrieve_orig_question_docs.states import (
-    BaseRawSearchInput,
-)
-from onyx.agents.agent_search.deep_search.initial.retrieve_orig_question_docs.states import (
-    BaseRawSearchOutput,
-)
-from onyx.agents.agent_search.deep_search.initial.retrieve_orig_question_docs.states import (
-    BaseRawSearchState,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.graph_builder import (
-    expanded_retrieval_graph_builder,
-)
-
-
-def retrieve_orig_question_docs_graph_builder() -> StateGraph:
-    """
-    LangGraph graph builder for the retrieval of documents
-    that are relevant to the original question. This is
-    largely a wrapper around the expanded retrieval process to
-    ensure parallelism with the sub-question answer process.
-    """
-    graph = StateGraph(
-        state_schema=BaseRawSearchState,
-        input=BaseRawSearchInput,
-        output=BaseRawSearchOutput,
-    )
-
-    ### Add nodes ###
-
-    # Format the original question search output
-    graph.add_node(
-        node="format_orig_question_search_output",
-        action=format_orig_question_search_output,
-    )
-
-    # The sub-graph that executes the expanded retrieval process
-    expanded_retrieval = expanded_retrieval_graph_builder().compile()
-    graph.add_node(
-        node="retrieve_orig_question_docs_subgraph",
-        action=expanded_retrieval,
-    )
-
-    # Format the original question search input
-    graph.add_node(
-        node="format_orig_question_search_input",
-        action=format_orig_question_search_input,
-    )
-
-    ### Add edges ###
-
-    graph.add_edge(start_key=START, end_key="format_orig_question_search_input")
-
-    graph.add_edge(
-        start_key="format_orig_question_search_input",
-        end_key="retrieve_orig_question_docs_subgraph",
-    )
-    graph.add_edge(
-        start_key="retrieve_orig_question_docs_subgraph",
-        end_key="format_orig_question_search_output",
-    )
-
-    graph.add_edge(
-        start_key="format_orig_question_search_output",
-        end_key=END,
-    )
-
-    return graph
-
-
-if __name__ == "__main__":
-    pass

backend/onyx/agents/agent_search/deep_search/initial/retrieve_orig_question_docs/nodes/format_orig_question_search_input.py

@@ -1,28 +0,0 @@
-from typing import cast
-
-from langchain_core.runnables.config import RunnableConfig
-
-from onyx.agents.agent_search.core_state import CoreState
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
-    ExpandedRetrievalInput,
-)
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def format_orig_question_search_input(
-    state: CoreState, config: RunnableConfig
-) -> ExpandedRetrievalInput:
-    """
-    LangGraph node to format the search input for the original question.
-    """
-    logger.debug("generate_raw_search_data")
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    return ExpandedRetrievalInput(
-        question=graph_config.inputs.search_request.query,
-        base_search=True,
-        sub_question_id=None,  # This graph is always and only used for the original question
-        log_messages=[],
-    )

backend/onyx/agents/agent_search/deep_search/initial/retrieve_orig_question_docs/nodes/format_orig_question_search_output.py

@@ -1,30 +0,0 @@
-from onyx.agents.agent_search.deep_search.main.states import OrigQuestionRetrievalUpdate
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
-    ExpandedRetrievalOutput,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def format_orig_question_search_output(
-    state: ExpandedRetrievalOutput,
-) -> OrigQuestionRetrievalUpdate:
-    """
-    LangGraph node to format the search result for the original question into the
-    proper format.
-    """
-    sub_question_retrieval_stats = state.expanded_retrieval_result.retrieval_stats
-    if sub_question_retrieval_stats is None:
-        sub_question_retrieval_stats = AgentChunkRetrievalStats()
-    else:
-        sub_question_retrieval_stats = sub_question_retrieval_stats
-
-    return OrigQuestionRetrievalUpdate(
-        orig_question_verified_reranked_documents=state.expanded_retrieval_result.verified_reranked_documents,
-        orig_question_sub_query_retrieval_results=state.expanded_retrieval_result.expanded_query_results,
-        orig_question_retrieved_documents=state.retrieved_documents,
-        orig_question_retrieval_stats=sub_question_retrieval_stats,
-        log_messages=[],
-    )

backend/onyx/agents/agent_search/deep_search/initial/retrieve_orig_question_docs/states.py

@@ -1,29 +0,0 @@
-from onyx.agents.agent_search.deep_search.main.states import (
-    OrigQuestionRetrievalUpdate,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
-    ExpandedRetrievalInput,
-)
-
-
-## Graph Input State
-class BaseRawSearchInput(ExpandedRetrievalInput):
-    pass
-
-
-## Graph Output State
-class BaseRawSearchOutput(OrigQuestionRetrievalUpdate):
-    """
-    This is a list of results even though each call of this subgraph only returns one result.
-    This is because if we parallelize the answer query subgraph, there will be multiple
-      results in a list so the add operator is used to add them together.
-    """
-
-    # base_expanded_retrieval_result: QuestionRetrievalResult = QuestionRetrievalResult()
-
-
-## Graph State
-class BaseRawSearchState(
-    BaseRawSearchInput, BaseRawSearchOutput, OrigQuestionRetrievalUpdate
-):
-    pass

backend/onyx/agents/agent_search/deep_search/main/edges.py

@@ -1,113 +0,0 @@
-from collections.abc import Hashable
-from datetime import datetime
-from typing import cast
-from typing import Literal
-
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import Send
-
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    AnswerQuestionOutput,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    SubQuestionAnsweringInput,
-)
-from onyx.agents.agent_search.deep_search.main.states import MainState
-from onyx.agents.agent_search.deep_search.main.states import (
-    RequireRefinemenEvalUpdate,
-)
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.utils import make_question_id
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def route_initial_tool_choice(
-    state: MainState, config: RunnableConfig
-) -> Literal["call_tool", "start_agent_search", "logging_node"]:
-    """
-    LangGraph edge to route to agent search.
-    """
-    agent_config = cast(GraphConfig, config["metadata"]["config"])
-    if state.tool_choice is not None:
-        if (
-            agent_config.behavior.use_agentic_search
-            and agent_config.tooling.search_tool is not None
-            and state.tool_choice.tool.name == agent_config.tooling.search_tool.name
-        ):
-            return "start_agent_search"
-        else:
-            return "call_tool"
-    else:
-        return "logging_node"
-
-
-def parallelize_initial_sub_question_answering(
-    state: MainState,
-) -> list[Send | Hashable]:
-    edge_start_time = datetime.now()
-    if len(state.initial_sub_questions) > 0:
-        return [
-            Send(
-                "answer_query_subgraph",
-                SubQuestionAnsweringInput(
-                    question=question,
-                    question_id=make_question_id(0, question_num + 1),
-                    log_messages=[
-                        f"{edge_start_time} -- Main Edge - Parallelize Initial Sub-question Answering"
-                    ],
-                ),
-            )
-            for question_num, question in enumerate(state.initial_sub_questions)
-        ]
-
-    else:
-        return [
-            Send(
-                "ingest_answers",
-                AnswerQuestionOutput(
-                    answer_results=[],
-                ),
-            )
-        ]
-
-
-# Define the function that determines whether to continue or not
-def continue_to_refined_answer_or_end(
-    state: RequireRefinemenEvalUpdate,
-) -> Literal["create_refined_sub_questions", "logging_node"]:
-    if state.require_refined_answer_eval:
-        return "create_refined_sub_questions"
-    else:
-        return "logging_node"
-
-
-def parallelize_refined_sub_question_answering(
-    state: MainState,
-) -> list[Send | Hashable]:
-    edge_start_time = datetime.now()
-    if len(state.refined_sub_questions) > 0:
-        return [
-            Send(
-                "answer_refined_question_subgraphs",
-                SubQuestionAnsweringInput(
-                    question=question_data.sub_question,
-                    question_id=make_question_id(1, question_num),
-                    log_messages=[
-                        f"{edge_start_time} -- Main Edge - Parallelize Refined Sub-question Answering"
-                    ],
-                ),
-            )
-            for question_num, question_data in state.refined_sub_questions.items()
-        ]
-
-    else:
-        return [
-            Send(
-                "ingest_refined_sub_answers",
-                AnswerQuestionOutput(
-                    answer_results=[],
-                ),
-            )
-        ]

backend/onyx/agents/agent_search/deep_search/main/graph_builder.py

@@ -1,263 +0,0 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
-
-from onyx.agents.agent_search.deep_search.initial.generate_initial_answer.graph_builder import (
-    generate_initial_answer_graph_builder,
-)
-from onyx.agents.agent_search.deep_search.main.edges import (
-    continue_to_refined_answer_or_end,
-)
-from onyx.agents.agent_search.deep_search.main.edges import (
-    parallelize_refined_sub_question_answering,
-)
-from onyx.agents.agent_search.deep_search.main.edges import (
-    route_initial_tool_choice,
-)
-from onyx.agents.agent_search.deep_search.main.nodes.compare_answers import (
-    compare_answers,
-)
-from onyx.agents.agent_search.deep_search.main.nodes.create_refined_sub_questions import (
-    create_refined_sub_questions,
-)
-from onyx.agents.agent_search.deep_search.main.nodes.decide_refinement_need import (
-    decide_refinement_need,
-)
-from onyx.agents.agent_search.deep_search.main.nodes.extract_entities_terms import (
-    extract_entities_terms,
-)
-from onyx.agents.agent_search.deep_search.main.nodes.generate_validate_refined_answer import (
-    generate_validate_refined_answer,
-)
-from onyx.agents.agent_search.deep_search.main.nodes.ingest_refined_sub_answers import (
-    ingest_refined_sub_answers,
-)
-from onyx.agents.agent_search.deep_search.main.nodes.persist_agent_results import (
-    persist_agent_results,
-)
-from onyx.agents.agent_search.deep_search.main.nodes.start_agent_search import (
-    start_agent_search,
-)
-from onyx.agents.agent_search.deep_search.main.states import MainInput
-from onyx.agents.agent_search.deep_search.main.states import MainState
-from onyx.agents.agent_search.deep_search.refinement.consolidate_sub_answers.graph_builder import (
-    answer_refined_query_graph_builder,
-)
-from onyx.agents.agent_search.orchestration.nodes.call_tool import call_tool
-from onyx.agents.agent_search.orchestration.nodes.choose_tool import choose_tool
-from onyx.agents.agent_search.orchestration.nodes.prepare_tool_input import (
-    prepare_tool_input,
-)
-from onyx.agents.agent_search.orchestration.nodes.use_tool_response import (
-    basic_use_tool_response,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import get_test_config
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-test_mode = False
-
-
-def main_graph_builder(test_mode: bool = False) -> StateGraph:
-    """
-    LangGraph graph builder for the main agent search process.
-    """
-    graph = StateGraph(
-        state_schema=MainState,
-        input=MainInput,
-    )
-
-    # Prepare the tool input
-    graph.add_node(
-        node="prepare_tool_input",
-        action=prepare_tool_input,
-    )
-
-    # Choose the initial tool
-    graph.add_node(
-        node="initial_tool_choice",
-        action=choose_tool,
-    )
-
-    # Call the tool, if required
-    graph.add_node(
-        node="call_tool",
-        action=call_tool,
-    )
-
-    # Use the tool response
-    graph.add_node(
-        node="basic_use_tool_response",
-        action=basic_use_tool_response,
-    )
-
-    # Start the agent search process
-    graph.add_node(
-        node="start_agent_search",
-        action=start_agent_search,
-    )
-
-    # The sub-graph for the initial answer generation
-    generate_initial_answer_subgraph = generate_initial_answer_graph_builder().compile()
-    graph.add_node(
-        node="generate_initial_answer_subgraph",
-        action=generate_initial_answer_subgraph,
-    )
-
-    # Create the refined sub-questions
-    graph.add_node(
-        node="create_refined_sub_questions",
-        action=create_refined_sub_questions,
-    )
-
-    # Subgraph for the refined sub-answer generation
-    answer_refined_question = answer_refined_query_graph_builder().compile()
-    graph.add_node(
-        node="answer_refined_question_subgraphs",
-        action=answer_refined_question,
-    )
-
-    # Ingest the refined sub-answers
-    graph.add_node(
-        node="ingest_refined_sub_answers",
-        action=ingest_refined_sub_answers,
-    )
-
-    # Node to generate the refined answer
-    graph.add_node(
-        node="generate_validate_refined_answer",
-        action=generate_validate_refined_answer,
-    )
-
-    # Early node to extract the entities and terms from the initial answer,
-    # This information is used to inform the creation the refined sub-questions
-    graph.add_node(
-        node="extract_entity_term",
-        action=extract_entities_terms,
-    )
-
-    # Decide if the answer needs to be refined (currently always true)
-    graph.add_node(
-        node="decide_refinement_need",
-        action=decide_refinement_need,
-    )
-
-    # Compare the initial and refined answers, and determine whether
-    # the refined answer is sufficiently better
-    graph.add_node(
-        node="compare_answers",
-        action=compare_answers,
-    )
-
-    # Log the results. This will log the stats as well as the answers, sub-questions, and sub-answers
-    graph.add_node(
-        node="logging_node",
-        action=persist_agent_results,
-    )
-
-    ### Add edges ###
-
-    graph.add_edge(start_key=START, end_key="prepare_tool_input")
-
-    graph.add_edge(
-        start_key="prepare_tool_input",
-        end_key="initial_tool_choice",
-    )
-
-    graph.add_conditional_edges(
-        "initial_tool_choice",
-        route_initial_tool_choice,
-        ["call_tool", "start_agent_search", "logging_node"],
-    )
-
-    graph.add_edge(
-        start_key="call_tool",
-        end_key="basic_use_tool_response",
-    )
-    graph.add_edge(
-        start_key="basic_use_tool_response",
-        end_key="logging_node",
-    )
-
-    graph.add_edge(
-        start_key="start_agent_search",
-        end_key="generate_initial_answer_subgraph",
-    )
-
-    graph.add_edge(
-        start_key="start_agent_search",
-        end_key="extract_entity_term",
-    )
-
-    # Wait for the initial answer generation and the entity/term extraction to be complete
-    # before deciding if a refinement is needed.
-    graph.add_edge(
-        start_key=["generate_initial_answer_subgraph", "extract_entity_term"],
-        end_key="decide_refinement_need",
-    )
-
-    graph.add_conditional_edges(
-        source="decide_refinement_need",
-        path=continue_to_refined_answer_or_end,
-        path_map=["create_refined_sub_questions", "logging_node"],
-    )
-
-    graph.add_conditional_edges(
-        source="create_refined_sub_questions",
-        path=parallelize_refined_sub_question_answering,
-        path_map=["answer_refined_question_subgraphs"],
-    )
-    graph.add_edge(
-        start_key="answer_refined_question_subgraphs",
-        end_key="ingest_refined_sub_answers",
-    )
-
-    graph.add_edge(
-        start_key="ingest_refined_sub_answers",
-        end_key="generate_validate_refined_answer",
-    )
-
-    graph.add_edge(
-        start_key="generate_validate_refined_answer",
-        end_key="compare_answers",
-    )
-    graph.add_edge(
-        start_key="compare_answers",
-        end_key="logging_node",
-    )
-
-    graph.add_edge(
-        start_key="logging_node",
-        end_key=END,
-    )
-
-    return graph
-
-
-if __name__ == "__main__":
-    pass
-
-    from onyx.db.engine import get_session_context_manager
-    from onyx.llm.factory import get_default_llms
-    from onyx.context.search.models import SearchRequest
-
-    graph = main_graph_builder()
-    compiled_graph = graph.compile()
-    primary_llm, fast_llm = get_default_llms()
-
-    with get_session_context_manager() as db_session:
-        search_request = SearchRequest(query="Who created Excel?")
-        graph_config = get_test_config(
-            db_session, primary_llm, fast_llm, search_request
-        )
-
-        inputs = MainInput(log_messages=[])
-
-        for thing in compiled_graph.stream(
-            input=inputs,
-            config={"configurable": {"config": graph_config}},
-            stream_mode="custom",
-            subgraphs=True,
-        ):
-            logger.debug(thing)

backend/onyx/agents/agent_search/deep_search/main/models.py

@@ -1,36 +0,0 @@
-from pydantic import BaseModel
-
-
-class RefinementSubQuestion(BaseModel):
-    sub_question: str
-    sub_question_id: str
-    verified: bool
-    answered: bool
-    answer: str
-
-
-class AgentTimings(BaseModel):
-    base_duration_s: float | None
-    refined_duration_s: float | None
-    full_duration_s: float | None
-
-
-class AgentBaseMetrics(BaseModel):
-    num_verified_documents_total: int | None
-    num_verified_documents_core: int | None
-    verified_avg_score_core: float | None
-    num_verified_documents_base: int | float | None
-    verified_avg_score_base: float | None = None
-    base_doc_boost_factor: float | None = None
-    support_boost_factor: float | None = None
-    duration_s: float | None = None
-
-
-class AgentRefinedMetrics(BaseModel):
-    refined_doc_boost_factor: float | None = None
-    refined_question_boost_factor: float | None = None
-    duration_s: float | None = None
-
-
-class AgentAdditionalMetrics(BaseModel):
-    pass

backend/onyx/agents/agent_search/deep_search/main/nodes/compare_answers.py

@@ -1,166 +0,0 @@
-from datetime import datetime
-from typing import cast
-
-from langchain_core.messages import BaseMessage
-from langchain_core.messages import HumanMessage
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
-
-from onyx.agents.agent_search.deep_search.main.states import (
-    InitialRefinedAnswerComparisonUpdate,
-)
-from onyx.agents.agent_search.deep_search.main.states import MainState
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    binary_string_test,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_RATELIMIT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_TIMEOUT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_POSITIVE_VALUE_STR,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AgentLLMErrorType,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.chat.models import RefinedAnswerImprovement
-from onyx.configs.agent_configs import AGENT_TIMEOUT_CONNECT_LLM_COMPARE_ANSWERS
-from onyx.configs.agent_configs import AGENT_TIMEOUT_LLM_COMPARE_ANSWERS
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
-from onyx.prompts.agent_search import (
-    INITIAL_REFINED_ANSWER_COMPARISON_PROMPT,
-)
-from onyx.utils.logger import setup_logger
-from onyx.utils.threadpool_concurrency import run_with_timeout
-from onyx.utils.timing import log_function_time
-
-logger = setup_logger()
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="The LLM timed out, and the answers could not be compared.",
-    rate_limit="The LLM encountered a rate limit, and the answers could not be compared.",
-    general_error="The LLM encountered an error, and the answers could not be compared.",
-)
-
-_ANSWER_QUALITY_NOT_SUFFICIENT_MESSAGE = (
-    "Answer quality is not sufficient, so stay with the initial answer."
-)
-
-
-@log_function_time(print_only=True)
-def compare_answers(
-    state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> InitialRefinedAnswerComparisonUpdate:
-    """
-    LangGraph node to compare the initial answer and the refined answer and determine if the
-    refined answer is sufficiently better than the initial answer.
-    """
-    node_start_time = datetime.now()
-
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    question = graph_config.inputs.search_request.query
-    initial_answer = state.initial_answer
-    refined_answer = state.refined_answer
-
-    # if answer quality is not sufficient, then stay with the initial answer
-    if not state.refined_answer_quality:
-        write_custom_event(
-            "refined_answer_improvement",
-            RefinedAnswerImprovement(
-                refined_answer_improvement=False,
-            ),
-            writer,
-        )
-
-        return InitialRefinedAnswerComparisonUpdate(
-            refined_answer_improvement_eval=False,
-            log_messages=[
-                get_langgraph_node_log_string(
-                    graph_component="main",
-                    node_name="compare answers",
-                    node_start_time=node_start_time,
-                    result=_ANSWER_QUALITY_NOT_SUFFICIENT_MESSAGE,
-                )
-            ],
-        )
-
-    compare_answers_prompt = INITIAL_REFINED_ANSWER_COMPARISON_PROMPT.format(
-        question=question, initial_answer=initial_answer, refined_answer=refined_answer
-    )
-
-    msg = [HumanMessage(content=compare_answers_prompt)]
-
-    agent_error: AgentErrorLog | None = None
-    # Get the rewritten queries in a defined format
-    model = graph_config.tooling.fast_llm
-    resp: BaseMessage | None = None
-    refined_answer_improvement: bool | None = None
-    # no need to stream this
-    try:
-        resp = run_with_timeout(
-            AGENT_TIMEOUT_LLM_COMPARE_ANSWERS,
-            model.invoke,
-            prompt=msg,
-            timeout_override=AGENT_TIMEOUT_CONNECT_LLM_COMPARE_ANSWERS,
-        )
-
-    except (LLMTimeoutError, TimeoutError):
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.TIMEOUT,
-            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-            error_result=_llm_node_error_strings.timeout,
-        )
-        logger.error("LLM Timeout Error - compare answers")
-        # continue as True in this support step
-    except LLMRateLimitError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.RATE_LIMIT,
-            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
-            error_result=_llm_node_error_strings.rate_limit,
-        )
-        logger.error("LLM Rate Limit Error - compare answers")
-        # continue as True in this support step
-
-    if agent_error or resp is None:
-        refined_answer_improvement = True
-        if agent_error:
-            log_result = agent_error.error_result
-        else:
-            log_result = "An answer could not be generated."
-
-    else:
-        refined_answer_improvement = binary_string_test(
-            text=cast(str, resp.content),
-            positive_value=AGENT_POSITIVE_VALUE_STR,
-        )
-        log_result = f"Answer comparison: {refined_answer_improvement}"
-
-    write_custom_event(
-        "refined_answer_improvement",
-        RefinedAnswerImprovement(
-            refined_answer_improvement=refined_answer_improvement,
-        ),
-        writer,
-    )
-
-    return InitialRefinedAnswerComparisonUpdate(
-        refined_answer_improvement_eval=refined_answer_improvement,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="main",
-                node_name="compare answers",
-                node_start_time=node_start_time,
-                result=log_result,
-            )
-        ],
-    )

backend/onyx/agents/agent_search/deep_search/main/nodes/create_refined_sub_questions.py

@@ -1,211 +0,0 @@
-from datetime import datetime
-from typing import cast
-
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_content
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
-
-from onyx.agents.agent_search.deep_search.main.models import (
-    RefinementSubQuestion,
-)
-from onyx.agents.agent_search.deep_search.main.operations import dispatch_subquestion
-from onyx.agents.agent_search.deep_search.main.operations import (
-    dispatch_subquestion_sep,
-)
-from onyx.agents.agent_search.deep_search.main.states import MainState
-from onyx.agents.agent_search.deep_search.main.states import (
-    RefinedQuestionDecompositionUpdate,
-)
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    build_history_prompt,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_RATELIMIT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_TIMEOUT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AgentLLMErrorType,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
-from onyx.agents.agent_search.shared_graph_utils.models import BaseMessage_Content
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
-from onyx.agents.agent_search.shared_graph_utils.utils import dispatch_separated
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    format_entity_term_extraction,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import make_question_id
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.chat.models import StreamingError
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_CONNECT_LLM_REFINED_SUBQUESTION_GENERATION,
-)
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_LLM_REFINED_SUBQUESTION_GENERATION,
-)
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
-from onyx.prompts.agent_search import (
-    REFINEMENT_QUESTION_DECOMPOSITION_PROMPT_W_INITIAL_SUBQUESTION_ANSWERS,
-)
-from onyx.tools.models import ToolCallKickoff
-from onyx.utils.logger import setup_logger
-from onyx.utils.threadpool_concurrency import run_with_timeout
-from onyx.utils.timing import log_function_time
-
-logger = setup_logger()
-
-_ANSWERED_SUBQUESTIONS_DIVIDER = "\n\n---\n\n"
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="The LLM timed out. The sub-questions could not be generated.",
-    rate_limit="The LLM encountered a rate limit. The sub-questions could not be generated.",
-    general_error="The LLM encountered an error. The sub-questions could not be generated.",
-)
-
-
-@log_function_time(print_only=True)
-def create_refined_sub_questions(
-    state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> RefinedQuestionDecompositionUpdate:
-    """
-    LangGraph node to create refined sub-questions based on the initial answer, the history,
-    the entity term extraction results found earlier, and the sub-questions that were answered and failed.
-    """
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    write_custom_event(
-        "start_refined_answer_creation",
-        ToolCallKickoff(
-            tool_name="agent_search_1",
-            tool_args={
-                "query": graph_config.inputs.search_request.query,
-                "answer": state.initial_answer,
-            },
-        ),
-        writer,
-    )
-
-    node_start_time = datetime.now()
-
-    agent_refined_start_time = datetime.now()
-
-    question = graph_config.inputs.search_request.query
-    base_answer = state.initial_answer
-    history = build_history_prompt(graph_config, question)
-    # get the entity term extraction dict and properly format it
-    entity_retlation_term_extractions = state.entity_relation_term_extractions
-
-    entity_term_extraction_str = format_entity_term_extraction(
-        entity_retlation_term_extractions
-    )
-
-    initial_question_answers = state.sub_question_results
-
-    addressed_subquestions_with_answers = [
-        f"Subquestion: {x.question}\nSubanswer:\n{x.answer}"
-        for x in initial_question_answers
-        if x.verified_high_quality and x.answer
-    ]
-
-    failed_question_list = [
-        x.question for x in initial_question_answers if not x.verified_high_quality
-    ]
-
-    msg = [
-        HumanMessage(
-            content=REFINEMENT_QUESTION_DECOMPOSITION_PROMPT_W_INITIAL_SUBQUESTION_ANSWERS.format(
-                question=question,
-                history=history,
-                entity_term_extraction_str=entity_term_extraction_str,
-                base_answer=base_answer,
-                answered_subquestions_with_answers=_ANSWERED_SUBQUESTIONS_DIVIDER.join(
-                    addressed_subquestions_with_answers
-                ),
-                failed_sub_questions="\n - ".join(failed_question_list),
-            ),
-        )
-    ]
-
-    # Grader
-    model = graph_config.tooling.fast_llm
-
-    agent_error: AgentErrorLog | None = None
-    streamed_tokens: list[BaseMessage_Content] = []
-    try:
-        streamed_tokens = run_with_timeout(
-            AGENT_TIMEOUT_LLM_REFINED_SUBQUESTION_GENERATION,
-            dispatch_separated,
-            model.stream(
-                msg,
-                timeout_override=AGENT_TIMEOUT_CONNECT_LLM_REFINED_SUBQUESTION_GENERATION,
-            ),
-            dispatch_subquestion(1, writer),
-            sep_callback=dispatch_subquestion_sep(1, writer),
-        )
-    except (LLMTimeoutError, TimeoutError):
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.TIMEOUT,
-            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-            error_result=_llm_node_error_strings.timeout,
-        )
-        logger.error("LLM Timeout Error - create refined sub questions")
-
-    except LLMRateLimitError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.RATE_LIMIT,
-            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
-            error_result=_llm_node_error_strings.rate_limit,
-        )
-        logger.error("LLM Rate Limit Error - create refined sub questions")
-
-    if agent_error:
-        refined_sub_question_dict: dict[int, RefinementSubQuestion] = {}
-        log_result = agent_error.error_result
-        write_custom_event(
-            "refined_sub_question_creation_error",
-            StreamingError(
-                error="Your LLM was not able to create refined sub questions in time and timed out. Please try again.",
-            ),
-            writer,
-        )
-
-    else:
-        response = merge_content(*streamed_tokens)
-
-        if isinstance(response, str):
-            parsed_response = [q for q in response.split("\n") if q.strip() != ""]
-        else:
-            raise ValueError("LLM response is not a string")
-
-        refined_sub_question_dict = {}
-        for sub_question_num, sub_question in enumerate(parsed_response):
-            refined_sub_question = RefinementSubQuestion(
-                sub_question=sub_question,
-                sub_question_id=make_question_id(1, sub_question_num + 1),
-                verified=False,
-                answered=False,
-                answer="",
-            )
-
-            refined_sub_question_dict[sub_question_num + 1] = refined_sub_question
-
-        log_result = f"Created {len(refined_sub_question_dict)} refined sub questions"
-
-    return RefinedQuestionDecompositionUpdate(
-        refined_sub_questions=refined_sub_question_dict,
-        agent_refined_start_time=agent_refined_start_time,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="main",
-                node_name="create refined sub questions",
-                node_start_time=node_start_time,
-                result=log_result,
-            )
-        ],
-    )

backend/onyx/agents/agent_search/deep_search/main/nodes/decide_refinement_need.py

@@ -1,62 +0,0 @@
-from datetime import datetime
-from typing import cast
-
-from langchain_core.runnables import RunnableConfig
-
-from onyx.agents.agent_search.deep_search.main.states import MainState
-from onyx.agents.agent_search.deep_search.main.states import (
-    RequireRefinemenEvalUpdate,
-)
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.utils.timing import log_function_time
-
-
-@log_function_time(print_only=True)
-def decide_refinement_need(
-    state: MainState, config: RunnableConfig
-) -> RequireRefinemenEvalUpdate:
-    """
-    LangGraph node to decide if refinement is needed based on the initial answer and the question.
-    At present, we always refine.
-    """
-    node_start_time = datetime.now()
-
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-
-    decision = True  # TODO: just for current testing purposes
-
-    if state.answer_error:
-        return RequireRefinemenEvalUpdate(
-            require_refined_answer_eval=False,
-            log_messages=[
-                get_langgraph_node_log_string(
-                    graph_component="main",
-                    node_name="decide refinement need",
-                    node_start_time=node_start_time,
-                    result="Timeout Error",
-                )
-            ],
-        )
-
-    log_messages = [
-        get_langgraph_node_log_string(
-            graph_component="main",
-            node_name="decide refinement need",
-            node_start_time=node_start_time,
-            result=f"Refinement decision: {decision}",
-        )
-    ]
-
-    if graph_config.behavior.allow_refinement:
-        return RequireRefinemenEvalUpdate(
-            require_refined_answer_eval=decision,
-            log_messages=log_messages,
-        )
-    else:
-        return RequireRefinemenEvalUpdate(
-            require_refined_answer_eval=False,
-            log_messages=log_messages,
-        )

backend/onyx/agents/agent_search/deep_search/main/nodes/extract_entities_terms.py

@@ -1,140 +0,0 @@
-from datetime import datetime
-from typing import cast
-
-from langchain_core.messages import HumanMessage
-from langchain_core.runnables import RunnableConfig
-
-from onyx.agents.agent_search.deep_search.main.operations import logger
-from onyx.agents.agent_search.deep_search.main.states import (
-    EntityTermExtractionUpdate,
-)
-from onyx.agents.agent_search.deep_search.main.states import MainState
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    trim_prompt_piece,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import EntityExtractionResult
-from onyx.agents.agent_search.shared_graph_utils.models import (
-    EntityRelationshipTermExtraction,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import format_docs
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_CONNECT_LLM_ENTITY_TERM_EXTRACTION,
-)
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_LLM_ENTITY_TERM_EXTRACTION,
-)
-from onyx.configs.constants import NUM_EXPLORATORY_DOCS
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
-from onyx.prompts.agent_search import ENTITY_TERM_EXTRACTION_PROMPT
-from onyx.prompts.agent_search import ENTITY_TERM_EXTRACTION_PROMPT_JSON_EXAMPLE
-from onyx.utils.threadpool_concurrency import run_with_timeout
-from onyx.utils.timing import log_function_time
-
-
-@log_function_time(print_only=True)
-def extract_entities_terms(
-    state: MainState, config: RunnableConfig
-) -> EntityTermExtractionUpdate:
-    """
-    LangGraph node to extract entities, relationships, and terms from the initial search results.
-    This data is used to inform particularly the sub-questions that are created for the refined answer.
-    """
-    node_start_time = datetime.now()
-
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    if not graph_config.behavior.allow_refinement:
-        return EntityTermExtractionUpdate(
-            entity_relation_term_extractions=EntityRelationshipTermExtraction(
-                entities=[],
-                relationships=[],
-                terms=[],
-            ),
-            log_messages=[
-                get_langgraph_node_log_string(
-                    graph_component="main",
-                    node_name="extract entities terms",
-                    node_start_time=node_start_time,
-                    result="Refinement is not allowed",
-                )
-            ],
-        )
-
-    # first four lines duplicates from generate_initial_answer
-    question = graph_config.inputs.search_request.query
-    initial_search_docs = state.exploratory_search_results[:NUM_EXPLORATORY_DOCS]
-
-    # start with the entity/term/extraction
-    doc_context = format_docs(initial_search_docs)
-
-    # Calculation here is only approximate
-    doc_context = trim_prompt_piece(
-        graph_config.tooling.fast_llm.config,
-        doc_context,
-        ENTITY_TERM_EXTRACTION_PROMPT
-        + question
-        + ENTITY_TERM_EXTRACTION_PROMPT_JSON_EXAMPLE,
-    )
-
-    msg = [
-        HumanMessage(
-            content=ENTITY_TERM_EXTRACTION_PROMPT.format(
-                question=question, context=doc_context
-            )
-            + ENTITY_TERM_EXTRACTION_PROMPT_JSON_EXAMPLE,
-        )
-    ]
-    fast_llm = graph_config.tooling.fast_llm
-    # Grader
-    try:
-        llm_response = run_with_timeout(
-            AGENT_TIMEOUT_LLM_ENTITY_TERM_EXTRACTION,
-            fast_llm.invoke,
-            prompt=msg,
-            timeout_override=AGENT_TIMEOUT_CONNECT_LLM_ENTITY_TERM_EXTRACTION,
-        )
-
-        cleaned_response = (
-            str(llm_response.content).replace("```json\n", "").replace("\n```", "")
-        )
-        first_bracket = cleaned_response.find("{")
-        last_bracket = cleaned_response.rfind("}")
-        cleaned_response = cleaned_response[first_bracket : last_bracket + 1]
-
-        try:
-            entity_extraction_result = EntityExtractionResult.model_validate_json(
-                cleaned_response
-            )
-        except ValueError:
-            logger.error(
-                "Failed to parse LLM response as JSON in Entity-Term Extraction"
-            )
-            entity_extraction_result = EntityExtractionResult(
-                retrieved_entities_relationships=EntityRelationshipTermExtraction(),
-            )
-    except (LLMTimeoutError, TimeoutError):
-        logger.error("LLM Timeout Error - extract entities terms")
-        entity_extraction_result = EntityExtractionResult(
-            retrieved_entities_relationships=EntityRelationshipTermExtraction(),
-        )
-
-    except LLMRateLimitError:
-        logger.error("LLM Rate Limit Error - extract entities terms")
-        entity_extraction_result = EntityExtractionResult(
-            retrieved_entities_relationships=EntityRelationshipTermExtraction(),
-        )
-
-    return EntityTermExtractionUpdate(
-        entity_relation_term_extractions=entity_extraction_result.retrieved_entities_relationships,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="main",
-                node_name="extract entities terms",
-                node_start_time=node_start_time,
-            )
-        ],
-    )

backend/onyx/agents/agent_search/deep_search/main/nodes/generate_validate_refined_answer.py

@@ -1,458 +0,0 @@
-from datetime import datetime
-from typing import cast
-
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_content
-from langchain_core.runnables import RunnableConfig
-from langgraph.types import StreamWriter
-
-from onyx.agents.agent_search.deep_search.main.models import (
-    AgentRefinedMetrics,
-)
-from onyx.agents.agent_search.deep_search.main.operations import get_query_info
-from onyx.agents.agent_search.deep_search.main.states import MainState
-from onyx.agents.agent_search.deep_search.main.states import (
-    RefinedAnswerUpdate,
-)
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    binary_string_test_after_answer_separator,
-)
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    get_prompt_enrichment_components,
-)
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    trim_prompt_piece,
-)
-from onyx.agents.agent_search.shared_graph_utils.calculations import (
-    get_answer_generation_documents,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import AGENT_ANSWER_SEPARATOR
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_RATELIMIT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_TIMEOUT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_POSITIVE_VALUE_STR,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AgentLLMErrorType,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
-from onyx.agents.agent_search.shared_graph_utils.models import RefinedAgentStats
-from onyx.agents.agent_search.shared_graph_utils.operators import (
-    dedup_inference_section_list,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    dispatch_main_answer_stop_info,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import format_docs
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_deduplicated_structured_subquestion_documents,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
-from onyx.agents.agent_search.shared_graph_utils.utils import relevance_from_docs
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    remove_document_citations,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.chat.models import AgentAnswerPiece
-from onyx.chat.models import ExtendedToolResponse
-from onyx.chat.models import StreamingError
-from onyx.configs.agent_configs import AGENT_ANSWER_GENERATION_BY_FAST_LLM
-from onyx.configs.agent_configs import AGENT_MAX_ANSWER_CONTEXT_DOCS
-from onyx.configs.agent_configs import AGENT_MAX_STREAMED_DOCS_FOR_REFINED_ANSWER
-from onyx.configs.agent_configs import AGENT_MIN_ORIG_QUESTION_DOCS
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_CONNECT_LLM_REFINED_ANSWER_GENERATION,
-)
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_CONNECT_LLM_REFINED_ANSWER_VALIDATION,
-)
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_LLM_REFINED_ANSWER_GENERATION,
-)
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_LLM_REFINED_ANSWER_VALIDATION,
-)
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
-from onyx.prompts.agent_search import (
-    REFINED_ANSWER_PROMPT_W_SUB_QUESTIONS,
-)
-from onyx.prompts.agent_search import (
-    REFINED_ANSWER_PROMPT_WO_SUB_QUESTIONS,
-)
-from onyx.prompts.agent_search import (
-    REFINED_ANSWER_VALIDATION_PROMPT,
-)
-from onyx.prompts.agent_search import (
-    SUB_QUESTION_ANSWER_TEMPLATE_REFINED,
-)
-from onyx.prompts.agent_search import UNKNOWN_ANSWER
-from onyx.tools.tool_implementations.search.search_tool import yield_search_responses
-from onyx.utils.logger import setup_logger
-from onyx.utils.threadpool_concurrency import run_with_timeout
-from onyx.utils.timing import log_function_time
-
-logger = setup_logger()
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="The LLM timed out. The refined answer could not be generated.",
-    rate_limit="The LLM encountered a rate limit. The refined answer could not be generated.",
-    general_error="The LLM encountered an error. The refined answer could not be generated.",
-)
-
-
-@log_function_time(print_only=True)
-def generate_validate_refined_answer(
-    state: MainState, config: RunnableConfig, writer: StreamWriter = lambda _: None
-) -> RefinedAnswerUpdate:
-    """
-    LangGraph node to generate the refined answer and validate it.
-    """
-
-    node_start_time = datetime.now()
-
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    question = graph_config.inputs.search_request.query
-    prompt_enrichment_components = get_prompt_enrichment_components(graph_config)
-
-    persona_contextualized_prompt = (
-        prompt_enrichment_components.persona_prompts.contextualized_prompt
-    )
-
-    verified_reranked_documents = state.verified_reranked_documents
-
-    # get all documents cited in sub-questions
-    structured_subquestion_docs = get_deduplicated_structured_subquestion_documents(
-        state.sub_question_results
-    )
-
-    original_question_verified_documents = (
-        state.orig_question_verified_reranked_documents
-    )
-    original_question_retrieved_documents = state.orig_question_retrieved_documents
-
-    consolidated_context_docs = structured_subquestion_docs.cited_documents
-
-    counter = 0
-    for original_doc_number, original_doc in enumerate(
-        original_question_verified_documents
-    ):
-        if original_doc_number not in structured_subquestion_docs.cited_documents:
-            if (
-                counter <= AGENT_MIN_ORIG_QUESTION_DOCS
-                or len(consolidated_context_docs)
-                < 1.5
-                * AGENT_MAX_ANSWER_CONTEXT_DOCS  # allow for larger context in refinement
-            ):
-                consolidated_context_docs.append(original_doc)
-                counter += 1
-
-    # sort docs by their scores - though the scores refer to different questions
-    relevant_docs = dedup_inference_section_list(consolidated_context_docs)
-
-    # Create the list of documents to stream out. Start with the
-    # ones that wil be in the context (or, if len == 0, use docs
-    # that were retrieved for the original question)
-    answer_generation_documents = get_answer_generation_documents(
-        relevant_docs=relevant_docs,
-        context_documents=structured_subquestion_docs.context_documents,
-        original_question_docs=original_question_retrieved_documents,
-        max_docs=AGENT_MAX_STREAMED_DOCS_FOR_REFINED_ANSWER,
-    )
-
-    query_info = get_query_info(state.orig_question_sub_query_retrieval_results)
-    assert (
-        graph_config.tooling.search_tool
-    ), "search_tool must be provided for agentic search"
-    # stream refined answer docs, or original question docs if no relevant docs are found
-    relevance_list = relevance_from_docs(
-        answer_generation_documents.streaming_documents
-    )
-    for tool_response in yield_search_responses(
-        query=question,
-        reranked_sections=answer_generation_documents.streaming_documents,
-        final_context_sections=answer_generation_documents.context_documents,
-        search_query_info=query_info,
-        get_section_relevance=lambda: relevance_list,
-        search_tool=graph_config.tooling.search_tool,
-    ):
-        write_custom_event(
-            "tool_response",
-            ExtendedToolResponse(
-                id=tool_response.id,
-                response=tool_response.response,
-                level=1,
-                level_question_num=0,  # 0, 0 is the base question
-            ),
-            writer,
-        )
-
-    if len(verified_reranked_documents) > 0:
-        refined_doc_effectiveness = len(relevant_docs) / len(
-            verified_reranked_documents
-        )
-    else:
-        refined_doc_effectiveness = 10.0
-
-    sub_question_answer_results = state.sub_question_results
-
-    answered_sub_question_answer_list: list[str] = []
-    sub_questions: list[str] = []
-    initial_answered_sub_questions: set[str] = set()
-    refined_answered_sub_questions: set[str] = set()
-
-    for i, result in enumerate(sub_question_answer_results, 1):
-        question_level, _ = parse_question_id(result.question_id)
-        sub_questions.append(result.question)
-
-        if (
-            result.verified_high_quality
-            and result.answer
-            and result.answer != UNKNOWN_ANSWER
-        ):
-            sub_question_type = "initial" if question_level == 0 else "refined"
-            question_set = (
-                initial_answered_sub_questions
-                if question_level == 0
-                else refined_answered_sub_questions
-            )
-            question_set.add(result.question)
-
-            answered_sub_question_answer_list.append(
-                SUB_QUESTION_ANSWER_TEMPLATE_REFINED.format(
-                    sub_question=result.question,
-                    sub_answer=result.answer,
-                    sub_question_num=i,
-                    sub_question_type=sub_question_type,
-                )
-            )
-
-    # Calculate efficiency
-    total_answered_questions = (
-        initial_answered_sub_questions | refined_answered_sub_questions
-    )
-    revision_question_efficiency = (
-        len(total_answered_questions) / len(initial_answered_sub_questions)
-        if initial_answered_sub_questions
-        else 10.0
-        if refined_answered_sub_questions
-        else 1.0
-    )
-
-    sub_question_answer_str = "\n\n------\n\n".join(
-        set(answered_sub_question_answer_list)
-    )
-    initial_answer = state.initial_answer or ""
-
-    # Choose appropriate prompt template
-    base_prompt = (
-        REFINED_ANSWER_PROMPT_W_SUB_QUESTIONS
-        if answered_sub_question_answer_list
-        else REFINED_ANSWER_PROMPT_WO_SUB_QUESTIONS
-    )
-
-    model = (
-        graph_config.tooling.fast_llm
-        if AGENT_ANSWER_GENERATION_BY_FAST_LLM
-        else graph_config.tooling.primary_llm
-    )
-
-    relevant_docs_str = format_docs(answer_generation_documents.context_documents)
-    relevant_docs_str = trim_prompt_piece(
-        model.config,
-        relevant_docs_str,
-        base_prompt
-        + question
-        + sub_question_answer_str
-        + initial_answer
-        + persona_contextualized_prompt
-        + prompt_enrichment_components.history,
-    )
-
-    msg = [
-        HumanMessage(
-            content=base_prompt.format(
-                question=question,
-                history=prompt_enrichment_components.history,
-                answered_sub_questions=remove_document_citations(
-                    sub_question_answer_str
-                ),
-                relevant_docs=relevant_docs_str,
-                initial_answer=remove_document_citations(initial_answer)
-                if initial_answer
-                else None,
-                persona_specification=persona_contextualized_prompt,
-                date_prompt=prompt_enrichment_components.date_str,
-            )
-        )
-    ]
-
-    streamed_tokens: list[str] = [""]
-    dispatch_timings: list[float] = []
-    agent_error: AgentErrorLog | None = None
-
-    def stream_refined_answer() -> list[str]:
-        for message in model.stream(
-            msg, timeout_override=AGENT_TIMEOUT_CONNECT_LLM_REFINED_ANSWER_GENERATION
-        ):
-            # TODO: in principle, the answer here COULD contain images, but we don't support that yet
-            content = message.content
-            if not isinstance(content, str):
-                raise ValueError(
-                    f"Expected content to be a string, but got {type(content)}"
-                )
-
-            start_stream_token = datetime.now()
-            write_custom_event(
-                "refined_agent_answer",
-                AgentAnswerPiece(
-                    answer_piece=content,
-                    level=1,
-                    level_question_num=0,
-                    answer_type="agent_level_answer",
-                ),
-                writer,
-            )
-            end_stream_token = datetime.now()
-            dispatch_timings.append(
-                (end_stream_token - start_stream_token).microseconds
-            )
-            streamed_tokens.append(content)
-        return streamed_tokens
-
-    try:
-        streamed_tokens = run_with_timeout(
-            AGENT_TIMEOUT_LLM_REFINED_ANSWER_GENERATION,
-            stream_refined_answer,
-        )
-
-    except (LLMTimeoutError, TimeoutError):
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.TIMEOUT,
-            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-            error_result=_llm_node_error_strings.timeout,
-        )
-        logger.error("LLM Timeout Error - generate refined answer")
-
-    except LLMRateLimitError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.RATE_LIMIT,
-            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
-            error_result=_llm_node_error_strings.rate_limit,
-        )
-        logger.error("LLM Rate Limit Error - generate refined answer")
-
-    if agent_error:
-        write_custom_event(
-            "initial_agent_answer",
-            StreamingError(
-                error=AGENT_LLM_TIMEOUT_MESSAGE,
-            ),
-            writer,
-        )
-
-        return RefinedAnswerUpdate(
-            refined_answer=None,
-            refined_answer_quality=False,  # TODO: replace this with the actual check value
-            refined_agent_stats=None,
-            agent_refined_end_time=None,
-            agent_refined_metrics=AgentRefinedMetrics(
-                refined_doc_boost_factor=0.0,
-                refined_question_boost_factor=0.0,
-                duration_s=None,
-            ),
-            log_messages=[
-                get_langgraph_node_log_string(
-                    graph_component="main",
-                    node_name="generate refined answer",
-                    node_start_time=node_start_time,
-                    result=agent_error.error_result or "An LLM error occurred",
-                )
-            ],
-        )
-
-    logger.debug(
-        f"Average dispatch time for refined answer: {sum(dispatch_timings) / len(dispatch_timings)}"
-    )
-    dispatch_main_answer_stop_info(1, writer)
-    response = merge_content(*streamed_tokens)
-    answer = cast(str, response)
-
-    # run a validation step for the refined answer only
-
-    msg = [
-        HumanMessage(
-            content=REFINED_ANSWER_VALIDATION_PROMPT.format(
-                question=question,
-                history=prompt_enrichment_components.history,
-                answered_sub_questions=sub_question_answer_str,
-                relevant_docs=relevant_docs_str,
-                proposed_answer=answer,
-                persona_specification=persona_contextualized_prompt,
-            )
-        )
-    ]
-
-    validation_model = graph_config.tooling.fast_llm
-    try:
-        validation_response = run_with_timeout(
-            AGENT_TIMEOUT_LLM_REFINED_ANSWER_VALIDATION,
-            validation_model.invoke,
-            prompt=msg,
-            timeout_override=AGENT_TIMEOUT_CONNECT_LLM_REFINED_ANSWER_VALIDATION,
-        )
-        refined_answer_quality = binary_string_test_after_answer_separator(
-            text=cast(str, validation_response.content),
-            positive_value=AGENT_POSITIVE_VALUE_STR,
-            separator=AGENT_ANSWER_SEPARATOR,
-        )
-    except (LLMTimeoutError, TimeoutError):
-        refined_answer_quality = True
-        logger.error("LLM Timeout Error - validate refined answer")
-
-    except LLMRateLimitError:
-        refined_answer_quality = True
-        logger.error("LLM Rate Limit Error - validate refined answer")
-
-    refined_agent_stats = RefinedAgentStats(
-        revision_doc_efficiency=refined_doc_effectiveness,
-        revision_question_efficiency=revision_question_efficiency,
-    )
-
-    agent_refined_end_time = datetime.now()
-    if state.agent_refined_start_time:
-        agent_refined_duration = (
-            agent_refined_end_time - state.agent_refined_start_time
-        ).total_seconds()
-    else:
-        agent_refined_duration = None
-
-    agent_refined_metrics = AgentRefinedMetrics(
-        refined_doc_boost_factor=refined_agent_stats.revision_doc_efficiency,
-        refined_question_boost_factor=refined_agent_stats.revision_question_efficiency,
-        duration_s=agent_refined_duration,
-    )
-
-    return RefinedAnswerUpdate(
-        refined_answer=answer,
-        refined_answer_quality=refined_answer_quality,
-        refined_agent_stats=refined_agent_stats,
-        agent_refined_end_time=agent_refined_end_time,
-        agent_refined_metrics=agent_refined_metrics,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="main",
-                node_name="generate refined answer",
-                node_start_time=node_start_time,
-            )
-        ],
-    )

backend/onyx/agents/agent_search/deep_search/main/nodes/ingest_refined_sub_answers.py

@@ -1,42 +0,0 @@
-from datetime import datetime
-
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    AnswerQuestionOutput,
-)
-from onyx.agents.agent_search.deep_search.main.states import (
-    SubQuestionResultsUpdate,
-)
-from onyx.agents.agent_search.shared_graph_utils.operators import (
-    dedup_inference_sections,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-
-
-def ingest_refined_sub_answers(
-    state: AnswerQuestionOutput,
-) -> SubQuestionResultsUpdate:
-    """
-    LangGraph node to ingest and format the refined sub-answers and retrieved documents.
-    """
-    node_start_time = datetime.now()
-
-    documents = []
-    answer_results = state.answer_results
-    for answer_result in answer_results:
-        documents.extend(answer_result.verified_reranked_documents)
-
-    return SubQuestionResultsUpdate(
-        # Deduping is done by the documents operator for the main graph
-        # so we might not need to dedup here
-        verified_reranked_documents=dedup_inference_sections(documents, []),
-        sub_question_results=answer_results,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="main",
-                node_name="ingest refined answers",
-                node_start_time=node_start_time,
-            )
-        ],
-    )

backend/onyx/agents/agent_search/deep_search/main/nodes/persist_agent_results.py

@@ -1,129 +0,0 @@
-from datetime import datetime
-from typing import cast
-
-from langchain_core.runnables import RunnableConfig
-
-from onyx.agents.agent_search.deep_search.main.models import (
-    AgentAdditionalMetrics,
-)
-from onyx.agents.agent_search.deep_search.main.models import AgentTimings
-from onyx.agents.agent_search.deep_search.main.operations import logger
-from onyx.agents.agent_search.deep_search.main.states import MainOutput
-from onyx.agents.agent_search.deep_search.main.states import MainState
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.models import CombinedAgentMetrics
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.db.chat import log_agent_metrics
-from onyx.db.chat import log_agent_sub_question_results
-
-
-def persist_agent_results(state: MainState, config: RunnableConfig) -> MainOutput:
-    """
-    LangGraph node to persist the agent results, including agent logging data.
-    """
-    node_start_time = datetime.now()
-
-    agent_start_time = state.agent_start_time
-    agent_base_end_time = state.agent_base_end_time
-    agent_refined_start_time = state.agent_refined_start_time
-    agent_refined_end_time = state.agent_refined_end_time
-    agent_end_time = agent_refined_end_time or agent_base_end_time
-
-    agent_base_duration = None
-    if agent_base_end_time and agent_start_time:
-        agent_base_duration = (agent_base_end_time - agent_start_time).total_seconds()
-
-    agent_refined_duration = None
-    if agent_refined_start_time and agent_refined_end_time:
-        agent_refined_duration = (
-            agent_refined_end_time - agent_refined_start_time
-        ).total_seconds()
-
-    agent_full_duration = None
-    if agent_end_time and agent_start_time:
-        agent_full_duration = (agent_end_time - agent_start_time).total_seconds()
-
-    agent_type = "refined" if agent_refined_duration else "base"
-
-    agent_base_metrics = state.agent_base_metrics
-    agent_refined_metrics = state.agent_refined_metrics
-
-    combined_agent_metrics = CombinedAgentMetrics(
-        timings=AgentTimings(
-            base_duration_s=agent_base_duration,
-            refined_duration_s=agent_refined_duration,
-            full_duration_s=agent_full_duration,
-        ),
-        base_metrics=agent_base_metrics,
-        refined_metrics=agent_refined_metrics,
-        additional_metrics=AgentAdditionalMetrics(),
-    )
-
-    persona_id = None
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    if graph_config.inputs.search_request.persona:
-        persona_id = graph_config.inputs.search_request.persona.id
-
-    user_id = None
-    assert (
-        graph_config.tooling.search_tool
-    ), "search_tool must be provided for agentic search"
-    user = graph_config.tooling.search_tool.user
-    if user:
-        user_id = user.id
-
-    # log the agent metrics
-    if graph_config.persistence:
-        if agent_base_duration is not None:
-            log_agent_metrics(
-                db_session=graph_config.persistence.db_session,
-                user_id=user_id,
-                persona_id=persona_id,
-                agent_type=agent_type,
-                start_time=agent_start_time,
-                agent_metrics=combined_agent_metrics,
-            )
-
-        # Persist the sub-answer in the database
-        db_session = graph_config.persistence.db_session
-        chat_session_id = graph_config.persistence.chat_session_id
-        primary_message_id = graph_config.persistence.message_id
-        sub_question_answer_results = state.sub_question_results
-
-        log_agent_sub_question_results(
-            db_session=db_session,
-            chat_session_id=chat_session_id,
-            primary_message_id=primary_message_id,
-            sub_question_answer_results=sub_question_answer_results,
-        )
-
-    main_output = MainOutput(
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="main",
-                node_name="persist agent results",
-                node_start_time=node_start_time,
-            )
-        ],
-    )
-
-    for log_message in state.log_messages:
-        logger.debug(log_message)
-
-    if state.agent_base_metrics:
-        logger.debug(f"Initial loop: {state.agent_base_metrics.duration_s}")
-    if state.agent_refined_metrics:
-        logger.debug(f"Refined loop: {state.agent_refined_metrics.duration_s}")
-    if (
-        state.agent_base_metrics
-        and state.agent_refined_metrics
-        and state.agent_base_metrics.duration_s
-        and state.agent_refined_metrics.duration_s
-    ):
-        logger.debug(
-            f"Total time: {float(state.agent_base_metrics.duration_s) + float(state.agent_refined_metrics.duration_s)}"
-        )
-
-    return main_output

backend/onyx/agents/agent_search/deep_search/main/nodes/start_agent_search.py

@@ -1,52 +0,0 @@
-from datetime import datetime
-from typing import cast
-
-from langchain_core.runnables import RunnableConfig
-
-from onyx.agents.agent_search.deep_search.main.states import (
-    ExploratorySearchUpdate,
-)
-from onyx.agents.agent_search.deep_search.main.states import MainState
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.agent_prompt_ops import (
-    build_history_prompt,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import retrieve_search_docs
-from onyx.configs.agent_configs import AGENT_EXPLORATORY_SEARCH_RESULTS
-from onyx.context.search.models import InferenceSection
-
-
-def start_agent_search(
-    state: MainState, config: RunnableConfig
-) -> ExploratorySearchUpdate:
-    """
-    LangGraph node to start the agentic search process.
-    """
-    node_start_time = datetime.now()
-
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    question = graph_config.inputs.search_request.query
-
-    history = build_history_prompt(graph_config, question)
-
-    # Initial search to inform decomposition. Just get top 3 fits
-    search_tool = graph_config.tooling.search_tool
-    assert search_tool, "search_tool must be provided for agentic search"
-    retrieved_docs: list[InferenceSection] = retrieve_search_docs(search_tool, question)
-
-    exploratory_search_results = retrieved_docs[:AGENT_EXPLORATORY_SEARCH_RESULTS]
-
-    return ExploratorySearchUpdate(
-        exploratory_search_results=exploratory_search_results,
-        previous_history_summary=history,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="main",
-                node_name="start agent search",
-                node_start_time=node_start_time,
-            )
-        ],
-    )

backend/onyx/agents/agent_search/deep_search/main/operations.py

@@ -1,151 +0,0 @@
-from collections.abc import Callable
-
-from langgraph.types import StreamWriter
-
-from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
-from onyx.agents.agent_search.shared_graph_utils.models import InitialAgentResultStats
-from onyx.agents.agent_search.shared_graph_utils.models import QueryRetrievalResult
-from onyx.agents.agent_search.shared_graph_utils.models import (
-    SubQuestionAnswerResults,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import write_custom_event
-from onyx.chat.models import StreamStopInfo
-from onyx.chat.models import StreamStopReason
-from onyx.chat.models import StreamType
-from onyx.chat.models import SubQuestionPiece
-from onyx.context.search.models import IndexFilters
-from onyx.tools.models import SearchQueryInfo
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def dispatch_subquestion(
-    level: int, writer: StreamWriter
-) -> Callable[[str, int], None]:
-    def _helper(sub_question_part: str, sep_num: int) -> None:
-        write_custom_event(
-            "decomp_qs",
-            SubQuestionPiece(
-                sub_question=sub_question_part,
-                level=level,
-                level_question_num=sep_num,
-            ),
-            writer,
-        )
-
-    return _helper
-
-
-def dispatch_subquestion_sep(level: int, writer: StreamWriter) -> Callable[[int], None]:
-    def _helper(sep_num: int) -> None:
-        write_custom_event(
-            "stream_finished",
-            StreamStopInfo(
-                stop_reason=StreamStopReason.FINISHED,
-                stream_type=StreamType.SUB_QUESTIONS,
-                level=level,
-                level_question_num=sep_num,
-            ),
-            writer,
-        )
-
-    return _helper
-
-
-def calculate_initial_agent_stats(
-    decomp_answer_results: list[SubQuestionAnswerResults],
-    original_question_stats: AgentChunkRetrievalStats,
-) -> InitialAgentResultStats:
-    initial_agent_result_stats: InitialAgentResultStats = InitialAgentResultStats(
-        sub_questions={},
-        original_question={},
-        agent_effectiveness={},
-    )
-
-    orig_verified = original_question_stats.verified_count
-    orig_support_score = original_question_stats.verified_avg_scores
-
-    verified_document_chunk_ids = []
-    support_scores = 0.0
-
-    for decomp_answer_result in decomp_answer_results:
-        verified_document_chunk_ids += (
-            decomp_answer_result.sub_question_retrieval_stats.verified_doc_chunk_ids
-        )
-        if (
-            decomp_answer_result.sub_question_retrieval_stats.verified_avg_scores
-            is not None
-        ):
-            support_scores += (
-                decomp_answer_result.sub_question_retrieval_stats.verified_avg_scores
-            )
-
-    verified_document_chunk_ids = list(set(verified_document_chunk_ids))
-
-    # Calculate sub-question stats
-    if (
-        verified_document_chunk_ids
-        and len(verified_document_chunk_ids) > 0
-        and support_scores is not None
-    ):
-        sub_question_stats: dict[str, float | int | None] = {
-            "num_verified_documents": len(verified_document_chunk_ids),
-            "verified_avg_score": float(support_scores / len(decomp_answer_results)),
-        }
-    else:
-        sub_question_stats = {"num_verified_documents": 0, "verified_avg_score": None}
-
-    initial_agent_result_stats.sub_questions.update(sub_question_stats)
-
-    # Get original question stats
-    initial_agent_result_stats.original_question.update(
-        {
-            "num_verified_documents": original_question_stats.verified_count,
-            "verified_avg_score": original_question_stats.verified_avg_scores,
-        }
-    )
-
-    # Calculate chunk utilization ratio
-    sub_verified = initial_agent_result_stats.sub_questions["num_verified_documents"]
-
-    chunk_ratio: float | None = None
-    if sub_verified is not None and orig_verified is not None and orig_verified > 0:
-        chunk_ratio = (float(sub_verified) / orig_verified) if sub_verified > 0 else 0.0
-    elif sub_verified is not None and sub_verified > 0:
-        chunk_ratio = 10.0
-
-    initial_agent_result_stats.agent_effectiveness["utilized_chunk_ratio"] = chunk_ratio
-
-    if (
-        orig_support_score is None
-        or orig_support_score == 0.0
-        and initial_agent_result_stats.sub_questions["verified_avg_score"] is None
-    ):
-        initial_agent_result_stats.agent_effectiveness["support_ratio"] = None
-    elif orig_support_score is None or orig_support_score == 0.0:
-        initial_agent_result_stats.agent_effectiveness["support_ratio"] = 10
-    elif initial_agent_result_stats.sub_questions["verified_avg_score"] is None:
-        initial_agent_result_stats.agent_effectiveness["support_ratio"] = 0
-    else:
-        initial_agent_result_stats.agent_effectiveness["support_ratio"] = (
-            initial_agent_result_stats.sub_questions["verified_avg_score"]
-            / orig_support_score
-        )
-
-    return initial_agent_result_stats
-
-
-def get_query_info(results: list[QueryRetrievalResult]) -> SearchQueryInfo:
-    # Use the query info from the base document retrieval
-    # this is used for some fields that are the same across the searches done
-    query_info = None
-    for result in results:
-        if result.query_info is not None:
-            query_info = result.query_info
-            break
-    return query_info or SearchQueryInfo(
-        predicted_search=None,
-        final_filters=IndexFilters(access_control_list=None),
-        recency_bias_multiplier=1.0,
-    )

backend/onyx/agents/agent_search/deep_search/main/states.py

@@ -1,175 +0,0 @@
-from datetime import datetime
-from operator import add
-from typing import Annotated
-from typing import TypedDict
-
-from pydantic import BaseModel
-
-from onyx.agents.agent_search.core_state import CoreState
-from onyx.agents.agent_search.deep_search.main.models import AgentBaseMetrics
-from onyx.agents.agent_search.deep_search.main.models import (
-    AgentRefinedMetrics,
-)
-from onyx.agents.agent_search.deep_search.main.models import (
-    RefinementSubQuestion,
-)
-from onyx.agents.agent_search.orchestration.states import ToolCallUpdate
-from onyx.agents.agent_search.orchestration.states import ToolChoiceInput
-from onyx.agents.agent_search.orchestration.states import ToolChoiceUpdate
-from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
-from onyx.agents.agent_search.shared_graph_utils.models import (
-    EntityRelationshipTermExtraction,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import InitialAgentResultStats
-from onyx.agents.agent_search.shared_graph_utils.models import QueryRetrievalResult
-from onyx.agents.agent_search.shared_graph_utils.models import RefinedAgentStats
-from onyx.agents.agent_search.shared_graph_utils.models import (
-    SubQuestionAnswerResults,
-)
-from onyx.agents.agent_search.shared_graph_utils.operators import (
-    dedup_inference_sections,
-)
-from onyx.agents.agent_search.shared_graph_utils.operators import (
-    dedup_question_answer_results,
-)
-from onyx.context.search.models import InferenceSection
-
-
-### States ###
-class LoggerUpdate(BaseModel):
-    log_messages: Annotated[list[str], add] = []
-
-
-class RefinedAgentStartStats(BaseModel):
-    agent_refined_start_time: datetime | None = None
-
-
-class RefinedAgentEndStats(BaseModel):
-    agent_refined_end_time: datetime | None = None
-    agent_refined_metrics: AgentRefinedMetrics = AgentRefinedMetrics()
-
-
-class InitialQuestionDecompositionUpdate(
-    RefinedAgentStartStats, RefinedAgentEndStats, LoggerUpdate
-):
-    agent_start_time: datetime | None = None
-    previous_history: str | None = None
-    initial_sub_questions: list[str] = []
-
-
-class ExploratorySearchUpdate(LoggerUpdate):
-    exploratory_search_results: list[InferenceSection] = []
-    previous_history_summary: str | None = None
-
-
-class InitialRefinedAnswerComparisonUpdate(LoggerUpdate):
-    """
-    Evaluation of whether the refined answer is better than the initial answer
-    """
-
-    refined_answer_improvement_eval: bool = False
-
-
-class InitialAnswerUpdate(LoggerUpdate):
-    """
-    Initial answer information
-    """
-
-    initial_answer: str | None = None
-    answer_error: AgentErrorLog | None = None
-    initial_agent_stats: InitialAgentResultStats | None = None
-    generated_sub_questions: list[str] = []
-    agent_base_end_time: datetime | None = None
-    agent_base_metrics: AgentBaseMetrics | None = None
-
-
-class RefinedAnswerUpdate(RefinedAgentEndStats, LoggerUpdate):
-    """
-    Refined answer information
-    """
-
-    refined_answer: str | None = None
-    answer_error: AgentErrorLog | None = None
-    refined_agent_stats: RefinedAgentStats | None = None
-    refined_answer_quality: bool = False
-
-
-class InitialAnswerQualityUpdate(LoggerUpdate):
-    """
-    Initial answer quality evaluation
-    """
-
-    initial_answer_quality_eval: bool = False
-
-
-class RequireRefinemenEvalUpdate(LoggerUpdate):
-    require_refined_answer_eval: bool = True
-
-
-class SubQuestionResultsUpdate(LoggerUpdate):
-    verified_reranked_documents: Annotated[
-        list[InferenceSection], dedup_inference_sections
-    ] = []
-    context_documents: Annotated[list[InferenceSection], dedup_inference_sections] = []
-    cited_documents: Annotated[
-        list[InferenceSection], dedup_inference_sections
-    ] = []  # cited docs from sub-answers are used for answer context
-    sub_question_results: Annotated[
-        list[SubQuestionAnswerResults], dedup_question_answer_results
-    ] = []
-
-
-class OrigQuestionRetrievalUpdate(LoggerUpdate):
-    orig_question_retrieved_documents: Annotated[
-        list[InferenceSection], dedup_inference_sections
-    ]
-    orig_question_verified_reranked_documents: Annotated[
-        list[InferenceSection], dedup_inference_sections
-    ]
-    orig_question_sub_query_retrieval_results: list[QueryRetrievalResult] = []
-    orig_question_retrieval_stats: AgentChunkRetrievalStats = AgentChunkRetrievalStats()
-
-
-class EntityTermExtractionUpdate(LoggerUpdate):
-    entity_relation_term_extractions: EntityRelationshipTermExtraction = (
-        EntityRelationshipTermExtraction()
-    )
-
-
-class RefinedQuestionDecompositionUpdate(RefinedAgentStartStats, LoggerUpdate):
-    refined_sub_questions: dict[int, RefinementSubQuestion] = {}
-
-
-## Graph Input State
-class MainInput(CoreState):
-    pass
-
-
-## Graph State
-class MainState(
-    # This includes the core state
-    MainInput,
-    ToolChoiceInput,
-    ToolCallUpdate,
-    ToolChoiceUpdate,
-    InitialQuestionDecompositionUpdate,
-    InitialAnswerUpdate,
-    SubQuestionResultsUpdate,
-    OrigQuestionRetrievalUpdate,
-    EntityTermExtractionUpdate,
-    InitialAnswerQualityUpdate,
-    RequireRefinemenEvalUpdate,
-    RefinedQuestionDecompositionUpdate,
-    RefinedAnswerUpdate,
-    RefinedAgentStartStats,
-    RefinedAgentEndStats,
-    InitialRefinedAnswerComparisonUpdate,
-    ExploratorySearchUpdate,
-):
-    pass
-
-
-## Graph Output State - presently not used
-class MainOutput(TypedDict):
-    log_messages: list[str]

backend/onyx/agents/agent_search/deep_search/refinement/consolidate_sub_answers/edges.py

@@ -1,33 +0,0 @@
-from collections.abc import Hashable
-from datetime import datetime
-
-from langgraph.types import Send
-
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    SubQuestionAnsweringInput,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
-    ExpandedRetrievalInput,
-)
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def send_to_expanded_refined_retrieval(
-    state: SubQuestionAnsweringInput,
-) -> Send | Hashable:
-    """
-    LangGraph edge to sends a refined sub-question extended retrieval.
-    """
-    logger.debug("sending to expanded retrieval for follow up question via edge")
-    datetime.now()
-    return Send(
-        "refined_sub_question_expanded_retrieval",
-        ExpandedRetrievalInput(
-            question=state.question,
-            sub_question_id=state.question_id,
-            base_search=False,
-            log_messages=[f"{datetime.now()} -- Sending to expanded retrieval"],
-        ),
-    )

backend/onyx/agents/agent_search/deep_search/refinement/consolidate_sub_answers/graph_builder.py

@@ -1,132 +0,0 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
-
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.check_sub_answer import (
-    check_sub_answer,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.format_sub_answer import (
-    format_sub_answer,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.generate_sub_answer import (
-    generate_sub_answer,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.nodes.ingest_retrieved_documents import (
-    ingest_retrieved_documents,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    AnswerQuestionOutput,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    AnswerQuestionState,
-)
-from onyx.agents.agent_search.deep_search.initial.generate_individual_sub_answer.states import (
-    SubQuestionAnsweringInput,
-)
-from onyx.agents.agent_search.deep_search.refinement.consolidate_sub_answers.edges import (
-    send_to_expanded_refined_retrieval,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.graph_builder import (
-    expanded_retrieval_graph_builder,
-)
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def answer_refined_query_graph_builder() -> StateGraph:
-    """
-    LangGraph graph builder for the refined sub-answer generation process.
-    """
-    graph = StateGraph(
-        state_schema=AnswerQuestionState,
-        input=SubQuestionAnsweringInput,
-        output=AnswerQuestionOutput,
-    )
-
-    ### Add nodes ###
-
-    # Subgraph for the expanded retrieval process
-    expanded_retrieval = expanded_retrieval_graph_builder().compile()
-    graph.add_node(
-        node="refined_sub_question_expanded_retrieval",
-        action=expanded_retrieval,
-    )
-
-    # Ingest the retrieved documents
-    graph.add_node(
-        node="ingest_refined_retrieval",
-        action=ingest_retrieved_documents,
-    )
-
-    # Generate the refined sub-answer
-    graph.add_node(
-        node="generate_refined_sub_answer",
-        action=generate_sub_answer,
-    )
-
-    # Check if the refined sub-answer is correct
-    graph.add_node(
-        node="refined_sub_answer_check",
-        action=check_sub_answer,
-    )
-
-    # Format the refined sub-answer
-    graph.add_node(
-        node="format_refined_sub_answer",
-        action=format_sub_answer,
-    )
-
-    ### Add edges ###
-
-    graph.add_conditional_edges(
-        source=START,
-        path=send_to_expanded_refined_retrieval,
-        path_map=["refined_sub_question_expanded_retrieval"],
-    )
-    graph.add_edge(
-        start_key="refined_sub_question_expanded_retrieval",
-        end_key="ingest_refined_retrieval",
-    )
-    graph.add_edge(
-        start_key="ingest_refined_retrieval",
-        end_key="generate_refined_sub_answer",
-    )
-    graph.add_edge(
-        start_key="generate_refined_sub_answer",
-        end_key="refined_sub_answer_check",
-    )
-    graph.add_edge(
-        start_key="refined_sub_answer_check",
-        end_key="format_refined_sub_answer",
-    )
-    graph.add_edge(
-        start_key="format_refined_sub_answer",
-        end_key=END,
-    )
-
-    return graph
-
-
-if __name__ == "__main__":
-    from onyx.db.engine import get_session_context_manager
-    from onyx.llm.factory import get_default_llms
-    from onyx.context.search.models import SearchRequest
-
-    graph = answer_refined_query_graph_builder()
-    compiled_graph = graph.compile()
-    primary_llm, fast_llm = get_default_llms()
-    search_request = SearchRequest(
-        query="what can you do with onyx or danswer?",
-    )
-    with get_session_context_manager() as db_session:
-        inputs = SubQuestionAnsweringInput(
-            question="what can you do with onyx?",
-            question_id="0_0",
-            log_messages=[],
-        )
-        for thing in compiled_graph.stream(
-            input=inputs,
-            stream_mode="custom",
-        ):
-            logger.debug(thing)

backend/onyx/agents/agent_search/deep_search/shared/expanded_retrieval/edges.py

@@ -1,42 +0,0 @@
-from collections.abc import Hashable
-from typing import cast
-
-from langchain_core.runnables.config import RunnableConfig
-from langgraph.types import Send
-
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
-    ExpandedRetrievalState,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
-    RetrievalInput,
-)
-from onyx.agents.agent_search.models import GraphConfig
-
-
-def parallel_retrieval_edge(
-    state: ExpandedRetrievalState, config: RunnableConfig
-) -> list[Send | Hashable]:
-    """
-    LangGraph edge to parallelize the retrieval process for each of the
-    generated sub-queries and the original question.
-    """
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    question = (
-        state.question if state.question else graph_config.inputs.search_request.query
-    )
-
-    query_expansions = state.expanded_queries + [question]
-
-    return [
-        Send(
-            "retrieve_documents",
-            RetrievalInput(
-                query_to_retrieve=query,
-                question=question,
-                base_search=False,
-                sub_question_id=state.sub_question_id,
-                log_messages=[],
-            ),
-        )
-        for query in query_expansions
-    ]

backend/onyx/agents/agent_search/deep_search/shared/expanded_retrieval/graph_builder.py

@@ -1,161 +0,0 @@
-from langgraph.graph import END
-from langgraph.graph import START
-from langgraph.graph import StateGraph
-
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.edges import (
-    parallel_retrieval_edge,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.nodes.expand_queries import (
-    expand_queries,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.nodes.format_queries import (
-    format_queries,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.nodes.format_results import (
-    format_results,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.nodes.kickoff_verification import (
-    kickoff_verification,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.nodes.rerank_documents import (
-    rerank_documents,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.nodes.retrieve_documents import (
-    retrieve_documents,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.nodes.verify_documents import (
-    verify_documents,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
-    ExpandedRetrievalInput,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
-    ExpandedRetrievalOutput,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
-    ExpandedRetrievalState,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import get_test_config
-from onyx.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-def expanded_retrieval_graph_builder() -> StateGraph:
-    """
-    LangGraph graph builder for the expanded retrieval process.
-    """
-    graph = StateGraph(
-        state_schema=ExpandedRetrievalState,
-        input=ExpandedRetrievalInput,
-        output=ExpandedRetrievalOutput,
-    )
-
-    ### Add nodes ###
-
-    # Convert the question into multiple sub-queries
-    graph.add_node(
-        node="expand_queries",
-        action=expand_queries,
-    )
-
-    # Format the sub-queries into a list of strings
-    graph.add_node(
-        node="format_queries",
-        action=format_queries,
-    )
-
-    # Retrieve the documents for each sub-query
-    graph.add_node(
-        node="retrieve_documents",
-        action=retrieve_documents,
-    )
-
-    # Start verification process that the documents are relevant to the question (not the query)
-    graph.add_node(
-        node="kickoff_verification",
-        action=kickoff_verification,
-    )
-
-    # Verify that a given document is relevant to the question (not the query)
-    graph.add_node(
-        node="verify_documents",
-        action=verify_documents,
-    )
-
-    # Rerank the documents that have been verified
-    graph.add_node(
-        node="rerank_documents",
-        action=rerank_documents,
-    )
-
-    # Format the results into a list of strings
-    graph.add_node(
-        node="format_results",
-        action=format_results,
-    )
-
-    ### Add edges ###
-    graph.add_edge(
-        start_key=START,
-        end_key="expand_queries",
-    )
-    graph.add_edge(
-        start_key="expand_queries",
-        end_key="format_queries",
-    )
-
-    graph.add_conditional_edges(
-        source="format_queries",
-        path=parallel_retrieval_edge,
-        path_map=["retrieve_documents"],
-    )
-    graph.add_edge(
-        start_key="retrieve_documents",
-        end_key="kickoff_verification",
-    )
-    graph.add_edge(
-        start_key="verify_documents",
-        end_key="rerank_documents",
-    )
-    graph.add_edge(
-        start_key="rerank_documents",
-        end_key="format_results",
-    )
-    graph.add_edge(
-        start_key="format_results",
-        end_key=END,
-    )
-
-    return graph
-
-
-if __name__ == "__main__":
-    from onyx.db.engine import get_session_context_manager
-    from onyx.llm.factory import get_default_llms
-    from onyx.context.search.models import SearchRequest
-
-    graph = expanded_retrieval_graph_builder()
-    compiled_graph = graph.compile()
-    primary_llm, fast_llm = get_default_llms()
-    search_request = SearchRequest(
-        query="what can you do with onyx or danswer?",
-    )
-
-    with get_session_context_manager() as db_session:
-        graph_config, search_tool = get_test_config(
-            db_session, primary_llm, fast_llm, search_request
-        )
-        inputs = ExpandedRetrievalInput(
-            question="what can you do with onyx?",
-            base_search=False,
-            sub_question_id=None,
-            log_messages=[],
-        )
-        for thing in compiled_graph.stream(
-            input=inputs,
-            config={"configurable": {"config": graph_config}},
-            stream_mode="custom",
-            subgraphs=True,
-        ):
-            logger.debug(thing)

backend/onyx/agents/agent_search/deep_search/shared/expanded_retrieval/models.py

@@ -1,13 +0,0 @@
-from pydantic import BaseModel
-
-from onyx.agents.agent_search.shared_graph_utils.models import AgentChunkRetrievalStats
-from onyx.agents.agent_search.shared_graph_utils.models import QueryRetrievalResult
-from onyx.context.search.models import InferenceSection
-
-
-class QuestionRetrievalResult(BaseModel):
-    expanded_query_results: list[QueryRetrievalResult] = []
-    retrieved_documents: list[InferenceSection] = []
-    verified_reranked_documents: list[InferenceSection] = []
-    context_documents: list[InferenceSection] = []
-    retrieval_stats: AgentChunkRetrievalStats = AgentChunkRetrievalStats()

backend/onyx/agents/agent_search/deep_search/shared/expanded_retrieval/nodes/expand_queries.py

@@ -1,137 +0,0 @@
-from datetime import datetime
-from typing import cast
-
-from langchain_core.messages import HumanMessage
-from langchain_core.messages import merge_message_runs
-from langchain_core.runnables.config import RunnableConfig
-from langgraph.types import StreamWriter
-
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.operations import (
-    dispatch_subquery,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
-    ExpandedRetrievalInput,
-)
-from onyx.agents.agent_search.deep_search.shared.expanded_retrieval.states import (
-    QueryExpansionUpdate,
-)
-from onyx.agents.agent_search.models import GraphConfig
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_RATELIMIT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AGENT_LLM_TIMEOUT_MESSAGE,
-)
-from onyx.agents.agent_search.shared_graph_utils.constants import (
-    AgentLLMErrorType,
-)
-from onyx.agents.agent_search.shared_graph_utils.models import AgentErrorLog
-from onyx.agents.agent_search.shared_graph_utils.models import BaseMessage_Content
-from onyx.agents.agent_search.shared_graph_utils.models import LLMNodeErrorStrings
-from onyx.agents.agent_search.shared_graph_utils.utils import dispatch_separated
-from onyx.agents.agent_search.shared_graph_utils.utils import (
-    get_langgraph_node_log_string,
-)
-from onyx.agents.agent_search.shared_graph_utils.utils import parse_question_id
-from onyx.configs.agent_configs import (
-    AGENT_TIMEOUT_CONNECT_LLM_QUERY_REWRITING_GENERATION,
-)
-from onyx.configs.agent_configs import AGENT_TIMEOUT_LLM_QUERY_REWRITING_GENERATION
-from onyx.llm.chat_llm import LLMRateLimitError
-from onyx.llm.chat_llm import LLMTimeoutError
-from onyx.prompts.agent_search import (
-    QUERY_REWRITING_PROMPT,
-)
-from onyx.utils.logger import setup_logger
-from onyx.utils.threadpool_concurrency import run_with_timeout
-from onyx.utils.timing import log_function_time
-
-logger = setup_logger()
-
-_llm_node_error_strings = LLMNodeErrorStrings(
-    timeout="Query rewriting failed due to LLM timeout - the original question will be used.",
-    rate_limit="Query rewriting failed due to LLM rate limit - the original question will be used.",
-    general_error="Query rewriting failed due to LLM error - the original question will be used.",
-)
-
-
-@log_function_time(print_only=True)
-def expand_queries(
-    state: ExpandedRetrievalInput,
-    config: RunnableConfig,
-    writer: StreamWriter = lambda _: None,
-) -> QueryExpansionUpdate:
-    """
-    LangGraph node to expand a question into multiple search queries.
-    """
-    # Sometimes we want to expand the original question, sometimes we want to expand a sub-question.
-    # When we are running this node on the original question, no question is explictly passed in.
-    # Instead, we use the original question from the search request.
-    graph_config = cast(GraphConfig, config["metadata"]["config"])
-    node_start_time = datetime.now()
-    question = state.question
-
-    model = graph_config.tooling.fast_llm
-    sub_question_id = state.sub_question_id
-    if sub_question_id is None:
-        level, question_num = 0, 0
-    else:
-        level, question_num = parse_question_id(sub_question_id)
-
-    msg = [
-        HumanMessage(
-            content=QUERY_REWRITING_PROMPT.format(question=question),
-        )
-    ]
-
-    agent_error: AgentErrorLog | None = None
-    llm_response_list: list[BaseMessage_Content] = []
-    llm_response = ""
-    rewritten_queries = []
-
-    try:
-        llm_response_list = run_with_timeout(
-            AGENT_TIMEOUT_LLM_QUERY_REWRITING_GENERATION,
-            dispatch_separated,
-            model.stream(
-                prompt=msg,
-                timeout_override=AGENT_TIMEOUT_CONNECT_LLM_QUERY_REWRITING_GENERATION,
-            ),
-            dispatch_subquery(level, question_num, writer),
-        )
-        llm_response = merge_message_runs(llm_response_list, chunk_separator="")[
-            0
-        ].content
-        rewritten_queries = llm_response.split("\n")
-        log_result = f"Number of expanded queries: {len(rewritten_queries)}"
-
-    except (LLMTimeoutError, TimeoutError):
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.TIMEOUT,
-            error_message=AGENT_LLM_TIMEOUT_MESSAGE,
-            error_result=_llm_node_error_strings.timeout,
-        )
-        logger.error("LLM Timeout Error - expand queries")
-        log_result = agent_error.error_result
-
-    except LLMRateLimitError:
-        agent_error = AgentErrorLog(
-            error_type=AgentLLMErrorType.RATE_LIMIT,
-            error_message=AGENT_LLM_RATELIMIT_MESSAGE,
-            error_result=_llm_node_error_strings.rate_limit,
-        )
-        logger.error("LLM Rate Limit Error - expand queries")
-        log_result = agent_error.error_result
-    # use subquestion as query if query generation fails
-
-    return QueryExpansionUpdate(
-        expanded_queries=rewritten_queries,
-        log_messages=[
-            get_langgraph_node_log_string(
-                graph_component="shared - expanded retrieval",
-                node_name="expand queries",
-                node_start_time=node_start_time,
-                result=log_result,
-            )
-        ],
-    )