use redis completion signal to double check exit code

Update initial seeding for latency requirements

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

@@ -65,6 +65,7 @@ jobs:
             NEXT_PUBLIC_POSTHOG_KEY=${{ secrets.POSTHOG_KEY }}
             NEXT_PUBLIC_POSTHOG_HOST=${{ secrets.POSTHOG_HOST }}
             NEXT_PUBLIC_SENTRY_DSN=${{ secrets.SENTRY_DSN }}
+            NEXT_PUBLIC_GTM_ENABLED=true
           # needed due to weird interactions with the builds for different platforms
           no-cache: true
           labels: ${{ steps.meta.outputs.labels }}

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

@@ -0,0 +1,225 @@
+name: Run Chromatic Tests
+concurrency:
+  group: Run-Chromatic-Tests-${{ github.workflow }}-${{ github.head_ref || github.event.workflow_run.head_branch || github.run_id }}
+  cancel-in-progress: true
+
+on: push
+
+env:
+  OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+  SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }}
+
+jobs:
+  playwright-tests:
+    name: Playwright Tests
+
+    # See https://runs-on.com/runners/linux/
+    runs-on: [runs-on,runner=8cpu-linux-x64,ram=16,"run-id=${{ github.run_id }}"]
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+          cache: 'pip'
+          cache-dependency-path: |
+            backend/requirements/default.txt
+            backend/requirements/dev.txt
+            backend/requirements/model_server.txt
+      - run: |
+          python -m pip install --upgrade pip
+          pip install --retries 5 --timeout 30 -r backend/requirements/default.txt
+          pip install --retries 5 --timeout 30 -r backend/requirements/dev.txt
+          pip install --retries 5 --timeout 30 -r backend/requirements/model_server.txt
+        
+      - name: Setup node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 22
+
+      - name: Install node dependencies
+        working-directory: ./web
+        run: npm ci
+
+      - name: Install playwright browsers
+        working-directory: ./web
+        run: npx playwright install --with-deps
+        
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_TOKEN }}
+
+      # tag every docker image with "test" so that we can spin up the correct set
+      # of images during testing
+      
+      # we use the runs-on cache for docker builds
+      # in conjunction with runs-on runners, it has better speed and unlimited caching
+      # https://runs-on.com/caching/s3-cache-for-github-actions/
+      # https://runs-on.com/caching/docker/
+      # https://github.com/moby/buildkit#s3-cache-experimental
+      
+      # images are built and run locally for testing purposes. Not pushed.
+
+      - name: Build Web Docker image
+        uses: ./.github/actions/custom-build-and-push
+        with:
+          context: ./web
+          file: ./web/Dockerfile
+          platforms: linux/amd64
+          tags: danswer/danswer-web-server:test
+          push: false
+          load: true
+          cache-from: type=s3,prefix=cache/${{ github.repository }}/integration-tests/web-server/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }}
+          cache-to: type=s3,prefix=cache/${{ github.repository }}/integration-tests/web-server/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }},mode=max
+
+      - name: Build Backend Docker image
+        uses: ./.github/actions/custom-build-and-push
+        with:
+          context: ./backend
+          file: ./backend/Dockerfile
+          platforms: linux/amd64
+          tags: danswer/danswer-backend:test
+          push: false
+          load: true
+          cache-from: type=s3,prefix=cache/${{ github.repository }}/integration-tests/backend/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }}
+          cache-to: type=s3,prefix=cache/${{ github.repository }}/integration-tests/backend/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }},mode=max
+
+      - name: Build Model Server Docker image
+        uses: ./.github/actions/custom-build-and-push
+        with:
+          context: ./backend
+          file: ./backend/Dockerfile.model_server
+          platforms: linux/amd64
+          tags: danswer/danswer-model-server:test
+          push: false
+          load: true
+          cache-from: type=s3,prefix=cache/${{ github.repository }}/integration-tests/model-server/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }}
+          cache-to: type=s3,prefix=cache/${{ github.repository }}/integration-tests/model-server/,region=${{ env.RUNS_ON_AWS_REGION }},bucket=${{ env.RUNS_ON_S3_BUCKET_CACHE }},mode=max
+
+      - name: Start Docker containers
+        run: |
+          cd deployment/docker_compose
+          ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=true \
+          AUTH_TYPE=basic \
+          REQUIRE_EMAIL_VERIFICATION=false \
+          DISABLE_TELEMETRY=true \
+          IMAGE_TAG=test \
+          docker compose -f docker-compose.dev.yml -p danswer-stack up -d
+        id: start_docker
+
+      - name: Wait for service to be ready
+        run: |
+          echo "Starting wait-for-service script..."
+          
+          docker logs -f danswer-stack-api_server-1 &
+
+          start_time=$(date +%s)
+          timeout=300  # 5 minutes in seconds
+          
+          while true; do
+            current_time=$(date +%s)
+            elapsed_time=$((current_time - start_time))
+            
+            if [ $elapsed_time -ge $timeout ]; then
+              echo "Timeout reached. Service did not become ready in 5 minutes."
+              exit 1
+            fi
+            
+            # Use curl with error handling to ignore specific exit code 56
+            response=$(curl -s -o /dev/null -w "%{http_code}" http://localhost:8080/health || echo "curl_error")
+            
+            if [ "$response" = "200" ]; then
+              echo "Service is ready!"
+              break
+            elif [ "$response" = "curl_error" ]; then
+              echo "Curl encountered an error, possibly exit code 56. Continuing to retry..."
+            else
+              echo "Service not ready yet (HTTP status $response). Retrying in 5 seconds..."
+            fi
+            
+            sleep 5
+          done
+          echo "Finished waiting for service."
+
+      - name: Run pytest playwright test init
+        working-directory: ./backend
+        env: 
+          PYTEST_IGNORE_SKIP: true
+        run: pytest -s tests/integration/tests/playwright/test_playwright.py
+
+      - name: Run Playwright tests
+        working-directory: ./web
+        run: npx playwright test
+
+      - uses: actions/upload-artifact@v4
+        if: always()
+        with:
+          # Chromatic automatically defaults to the test-results directory.
+          # Replace with the path to your custom directory and adjust the CHROMATIC_ARCHIVE_LOCATION environment variable accordingly.
+          name: test-results
+          path: ./web/test-results
+          retention-days: 30
+                    
+      # save before stopping the containers so the logs can be captured
+      - name: Save Docker logs
+        if: success() || failure()
+        run: |
+          cd deployment/docker_compose
+          docker compose -f docker-compose.dev.yml -p danswer-stack logs > docker-compose.log
+          mv docker-compose.log ${{ github.workspace }}/docker-compose.log
+      
+      - name: Upload logs
+        if: success() || failure()
+        uses: actions/upload-artifact@v4
+        with:
+          name: docker-logs
+          path: ${{ github.workspace }}/docker-compose.log
+
+      - name: Stop Docker containers
+        run: |
+          cd deployment/docker_compose
+          docker compose -f docker-compose.dev.yml -p danswer-stack down -v
+
+  chromatic-tests:
+    name: Chromatic Tests
+    
+    needs: playwright-tests
+    runs-on: [runs-on,runner=8cpu-linux-x64,ram=16,"run-id=${{ github.run_id }}"]
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          
+      - name: Setup node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 22
+          
+      - name: Install node dependencies
+        working-directory: ./web
+        run: npm ci
+        
+      - name: Download Playwright test results
+        uses: actions/download-artifact@v4
+        with:
+          name: test-results
+          path: ./web/test-results
+          
+      - name: Run Chromatic
+        uses: chromaui/action@latest
+        with:
+          playwright: true
+          projectToken: ${{ secrets.CHROMATIC_PROJECT_TOKEN }}
+          workingDir: ./web
+        env: 
+          CHROMATIC_ARCHIVE_LOCATION: ./test-results

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

@@ -13,7 +13,10 @@ on:
 env:
   OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
   SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }}
-
+  CONFLUENCE_TEST_SPACE_URL: ${{ secrets.CONFLUENCE_TEST_SPACE_URL }}
+  CONFLUENCE_USER_NAME: ${{ secrets.CONFLUENCE_USER_NAME }}
+  CONFLUENCE_ACCESS_TOKEN: ${{ secrets.CONFLUENCE_ACCESS_TOKEN }}
+  
 jobs:
   integration-tests:
     # See https://runs-on.com/runners/linux/
@@ -195,6 +198,9 @@ jobs:
             -e API_SERVER_HOST=api_server \
             -e OPENAI_API_KEY=${OPENAI_API_KEY} \
             -e SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN} \
+            -e CONFLUENCE_TEST_SPACE_URL=${CONFLUENCE_TEST_SPACE_URL} \
+            -e CONFLUENCE_USER_NAME=${CONFLUENCE_USER_NAME} \
+            -e CONFLUENCE_ACCESS_TOKEN=${CONFLUENCE_ACCESS_TOKEN} \
             -e TEST_WEB_HOSTNAME=test-runner \
             danswer/danswer-integration:test \
             /app/tests/integration/tests \

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

@@ -20,9 +20,12 @@ env:
   JIRA_API_TOKEN: ${{ secrets.JIRA_API_TOKEN }}
   # Google
   GOOGLE_DRIVE_SERVICE_ACCOUNT_JSON_STR: ${{ secrets.GOOGLE_DRIVE_SERVICE_ACCOUNT_JSON_STR }}
+  GOOGLE_DRIVE_OAUTH_CREDENTIALS_JSON_STR_TEST_USER_1: ${{ secrets.GOOGLE_DRIVE_OAUTH_CREDENTIALS_JSON_STR_TEST_USER_1 }}
   GOOGLE_DRIVE_OAUTH_CREDENTIALS_JSON_STR: ${{ secrets.GOOGLE_DRIVE_OAUTH_CREDENTIALS_JSON_STR }}
   GOOGLE_GMAIL_SERVICE_ACCOUNT_JSON_STR: ${{ secrets.GOOGLE_GMAIL_SERVICE_ACCOUNT_JSON_STR }}
   GOOGLE_GMAIL_OAUTH_CREDENTIALS_JSON_STR: ${{ secrets.GOOGLE_GMAIL_OAUTH_CREDENTIALS_JSON_STR }}
+  # Slab
+  SLAB_BOT_TOKEN: ${{ secrets.SLAB_BOT_TOKEN }}
 
 jobs:
   connectors-check:

.gitignore

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

CONTRIBUTING.md

@@ -32,7 +32,7 @@ To contribute to this project, please follow the
 When opening a pull request, mention related issues and feel free to tag relevant maintainers.
 
 Before creating a pull request please make sure that the new changes conform to the formatting and linting requirements.
-See the [Formatting and Linting](#-formatting-and-linting) section for how to run these checks locally.
+See the [Formatting and Linting](#formatting-and-linting) section for how to run these checks locally.
 
 
 ### Getting Help 🙋

README.md

@@ -1,48 +1,48 @@
-<!-- DANSWER_METADATA={"link": "https://github.com/danswer-ai/danswer/blob/main/README.md"} -->
+<!-- DANSWER_METADATA={"link": "https://github.com/onyx-dot-app/onyx/blob/main/README.md"} -->
 <a name="readme-top"></a>
 
 <h2 align="center">
-<a href="https://www.danswer.ai/"> <img width="50%" src="https://github.com/danswer-owners/danswer/blob/1fabd9372d66cd54238847197c33f091a724803b/DanswerWithName.png?raw=true)" /></a>
+<a href="https://www.onyx.app/"> <img width="50%" src="https://github.com/onyx-dot-app/onyx/blob/logo/LogoOnyx.png?raw=true)" /></a>
 </h2>
 
 <p align="center">
-<p align="center">Open Source Gen-AI Chat + Unified Search.</p>
+<p align="center">Open Source Gen-AI + Enterprise Search.</p>
 
 <p align="center">
-<a href="https://docs.danswer.dev/" target="_blank">
+<a href="https://docs.onyx.app/" target="_blank">
     <img src="https://img.shields.io/badge/docs-view-blue" alt="Documentation">
 </a>
-<a href="https://join.slack.com/t/danswer/shared_invite/zt-2lcmqw703-071hBuZBfNEOGUsLa5PXvQ" target="_blank">
+<a href="https://join.slack.com/t/onyx-dot-app/shared_invite/zt-2sslpdbyq-iIbTaNIVPBw_i_4vrujLYQ" target="_blank">
     <img src="https://img.shields.io/badge/slack-join-blue.svg?logo=slack" alt="Slack">
 </a>
 <a href="https://discord.gg/TDJ59cGV2X" target="_blank">
     <img src="https://img.shields.io/badge/discord-join-blue.svg?logo=discord&logoColor=white" alt="Discord">
 </a>
-<a href="https://github.com/danswer-ai/danswer/blob/main/README.md" target="_blank">
+<a href="https://github.com/onyx-dot-app/onyx/blob/main/README.md" target="_blank">
     <img src="https://img.shields.io/static/v1?label=license&message=MIT&color=blue" alt="License">
 </a>
 </p>
 
-<strong>[Danswer](https://www.danswer.ai/)</strong> is the AI Assistant connected to your company's docs, apps, and people. 
-Danswer provides a Chat interface and plugs into any LLM of your choice. Danswer can be deployed anywhere and for any 
+<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. Danswer is MIT licensed and designed to be modular and easily extensible. The system also comes fully ready 
+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 Personas (AI Assistants) and their Prompts.
+configuring AI Assistants.
 
-Danswer also serves as a Unified Search across all common workplace tools such as Slack, Google Drive, Confluence, etc.
-By combining LLMs and team specific knowledge, Danswer becomes a subject matter expert for the team. Imagine ChatGPT if
+Onyx also serves as a Enterprise Search across all common workplace tools such as Slack, Google Drive, Confluence, etc.
+By combining LLMs and team specific knowledge, Onyx becomes a subject matter expert for the team. Imagine ChatGPT if
 it had access to your team's unique knowledge! It enables questions such as "A customer wants feature X, is this already
 supported?" or "Where's the pull request for feature Y?"
 
 <h3>Usage</h3>
 
-Danswer Web App:
+Onyx Web App:
 
 https://github.com/danswer-ai/danswer/assets/32520769/563be14c-9304-47b5-bf0a-9049c2b6f410
 
 
-Or, plug Danswer into your existing Slack workflows (more integrations to come 😁):
+Or, plug Onyx into your existing Slack workflows (more integrations to come 😁):
 
 https://github.com/danswer-ai/danswer/assets/25087905/3e19739b-d178-4371-9a38-011430bdec1b
 
@@ -52,16 +52,16 @@ For more details on the Admin UI to manage connectors and users, check out our
 
 ## Deployment
 
-Danswer can easily be run locally (even on a laptop) or deployed on a virtual machine with a single
-`docker compose` command. Checkout our [docs](https://docs.danswer.dev/quickstart) to learn more.
+Onyx can easily be run locally (even on a laptop) or deployed on a virtual machine with a single
+`docker compose` command. Checkout our [docs](https://docs.onyx.app/quickstart) to learn more.
 
-We also have built-in support for deployment on Kubernetes. Files for that can be found [here](https://github.com/danswer-ai/danswer/tree/main/deployment/kubernetes).
+We also have built-in support for deployment on Kubernetes. Files for that can be found [here](https://github.com/onyx-dot-app/onyx/tree/main/deployment/kubernetes).
 
 
 ## 💃 Main Features 
 * Chat UI with the ability to select documents to chat with.
 * Create custom AI Assistants with different prompts and backing knowledge sets.
-* Connect Danswer with LLM of your choice (self-host for a fully airgapped solution).
+* 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.
@@ -75,12 +75,12 @@ We also have built-in support for deployment on Kubernetes. Files for that can b
 * Organizational understanding and ability to locate and suggest experts from your team.
 
 
-## Other Notable Benefits of Danswer
+## Other Notable Benefits of Onyx
 * User Authentication with document level access management.
 * Best in class Hybrid Search across all sources (BM-25 + prefix aware embedding models).
 * Admin Dashboard to configure connectors, document-sets, access, etc.
 * Custom deep learning models + learn from user feedback.
-* Easy deployment and ability to host Danswer anywhere of your choosing.
+* Easy deployment and ability to host Onyx anywhere of your choosing.
 
 
 ## 🔌 Connectors
@@ -108,10 +108,10 @@ Efficiently pulls the latest changes from:
 
 ## 📚 Editions
 
-There are two editions of Danswer:
+There are two editions of Onyx:
 
-  * Danswer Community Edition (CE) is available freely under the MIT Expat license. This version has ALL the core features discussed above. This is the version of Danswer you will get if you follow the Deployment guide above.
-  * Danswer Enterprise Edition (EE) includes extra features that are primarily useful for larger organizations. Specifically, this includes:
+  * 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
@@ -119,24 +119,24 @@ There are two editions of Danswer:
     * Whitelabeling
     * API key authentication
     * Encryption of secrets
-    * Any many more! Checkout [our website](https://www.danswer.ai/) for the latest.
+    * Any many more! Checkout [our website](https://www.onyx.app/) for the latest.
 
-To try the Danswer Enterprise Edition: 
+To try the Onyx Enterprise Edition: 
 
-  1. Checkout our [Cloud product](https://app.danswer.ai/signup).
-  2. For self-hosting, contact us at [founders@danswer.ai](mailto:founders@danswer.ai) or book a call with us on our [Cal](https://cal.com/team/danswer/founders).
+  1. Checkout our [Cloud product](https://cloud.onyx.app/signup).
+  2. For self-hosting, contact us at [founders@onyx.app](mailto:founders@onyx.app) or book a call with us on our [Cal](https://cal.com/team/danswer/founders).
 
 ## 💡 Contributing
 Looking to contribute? Please check out the [Contribution Guide](CONTRIBUTING.md) for more details.
 
 ## ⭐Star History
 
-[![Star History Chart](https://api.star-history.com/svg?repos=danswer-ai/danswer&type=Date)](https://star-history.com/#danswer-ai/danswer&Date)
+[![Star History Chart](https://api.star-history.com/svg?repos=onyx-dot-app/onyx&type=Date)](https://star-history.com/#onyx-dot-app/onyx&Date)
 
 ## ✨Contributors
 
-<a href="https://github.com/aryn-ai/sycamore/graphs/contributors">
-  <img alt="contributors" src="https://contrib.rocks/image?repo=danswer-ai/danswer"/>
+<a href="https://github.com/onyx-dot-app/onyx/graphs/contributors">
+  <img alt="contributors" src="https://contrib.rocks/image?repo=onyx-dot-app/onyx"/>
 </a>
 
 <p align="right" style="font-size: 14px; color: #555; margin-top: 20px;">

backend/Dockerfile

@@ -73,6 +73,7 @@ RUN apt-get update && \
     rm -rf /var/lib/apt/lists/* && \
     rm -f /usr/local/lib/python3.11/site-packages/tornado/test/test.key
 
+
 # Pre-downloading models for setups with limited egress
 RUN python -c "from tokenizers import Tokenizer; \
 Tokenizer.from_pretrained('nomic-ai/nomic-embed-text-v1')"

backend/alembic/env.py

@@ -1,5 +1,5 @@
 from sqlalchemy.engine.base import Connection
-from typing import Any
+from typing import Literal
 import asyncio
 from logging.config import fileConfig
 import logging
@@ -8,6 +8,7 @@ from alembic import context
 from sqlalchemy import pool
 from sqlalchemy.ext.asyncio import create_async_engine
 from sqlalchemy.sql import text
+from sqlalchemy.sql.schema import SchemaItem
 
 from shared_configs.configs import MULTI_TENANT
 from danswer.db.engine import build_connection_string
@@ -35,7 +36,18 @@ logger = logging.getLogger(__name__)
 
 
 def include_object(
-    object: Any, name: str, type_: str, reflected: bool, compare_to: Any
+    object: SchemaItem,
+    name: str | None,
+    type_: Literal[
+        "schema",
+        "table",
+        "column",
+        "index",
+        "unique_constraint",
+        "foreign_key_constraint",
+    ],
+    reflected: bool,
+    compare_to: SchemaItem | None,
 ) -> bool:
     """
     Determines whether a database object should be included in migrations.

backend/alembic/versions/177de57c21c9_display_custom_llm_models.py

@@ -0,0 +1,59 @@
+"""display custom llm models
+
+Revision ID: 177de57c21c9
+Revises: 4ee1287bd26a
+Create Date: 2024-11-21 11:49:04.488677
+
+"""
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+from sqlalchemy import and_
+
+revision = "177de57c21c9"
+down_revision = "4ee1287bd26a"
+branch_labels = None
+depends_on = None
+depends_on = None
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+    llm_provider = sa.table(
+        "llm_provider",
+        sa.column("id", sa.Integer),
+        sa.column("provider", sa.String),
+        sa.column("model_names", postgresql.ARRAY(sa.String)),
+        sa.column("display_model_names", postgresql.ARRAY(sa.String)),
+    )
+
+    excluded_providers = ["openai", "bedrock", "anthropic", "azure"]
+
+    providers_to_update = sa.select(
+        llm_provider.c.id,
+        llm_provider.c.model_names,
+        llm_provider.c.display_model_names,
+    ).where(
+        and_(
+            ~llm_provider.c.provider.in_(excluded_providers),
+            llm_provider.c.model_names.isnot(None),
+        )
+    )
+
+    results = conn.execute(providers_to_update).fetchall()
+
+    for provider_id, model_names, display_model_names in results:
+        if display_model_names is None:
+            display_model_names = []
+
+        combined_model_names = list(set(display_model_names + model_names))
+        update_stmt = (
+            llm_provider.update()
+            .where(llm_provider.c.id == provider_id)
+            .values(display_model_names=combined_model_names)
+        )
+        conn.execute(update_stmt)
+
+
+def downgrade() -> None:
+    pass

backend/alembic/versions/47e5bef3a1d7_add_persona_categories.py

@@ -0,0 +1,45 @@
+"""add persona categories
+
+Revision ID: 47e5bef3a1d7
+Revises: dfbe9e93d3c7
+Create Date: 2024-11-05 18:55:02.221064
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "47e5bef3a1d7"
+down_revision = "dfbe9e93d3c7"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Create the persona_category table
+    op.create_table(
+        "persona_category",
+        sa.Column("id", sa.Integer(), nullable=False),
+        sa.Column("name", sa.String(), nullable=False),
+        sa.Column("description", sa.String(), nullable=True),
+        sa.PrimaryKeyConstraint("id"),
+        sa.UniqueConstraint("name"),
+    )
+
+    # Add category_id to persona table
+    op.add_column("persona", sa.Column("category_id", sa.Integer(), nullable=True))
+    op.create_foreign_key(
+        "fk_persona_category",
+        "persona",
+        "persona_category",
+        ["category_id"],
+        ["id"],
+        ondelete="SET NULL",
+    )
+
+
+def downgrade() -> None:
+    op.drop_constraint("fk_persona_category", "persona", type_="foreignkey")
+    op.drop_column("persona", "category_id")
+    op.drop_table("persona_category")

backend/alembic/versions/4ee1287bd26a_add_multiple_slack_bot_support.py

@@ -0,0 +1,280 @@
+"""add_multiple_slack_bot_support
+
+Revision ID: 4ee1287bd26a
+Revises: 47e5bef3a1d7
+Create Date: 2024-11-06 13:15:53.302644
+
+"""
+import logging
+from typing import cast
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.orm import Session
+from danswer.key_value_store.factory import get_kv_store
+from danswer.db.models import SlackBot
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = "4ee1287bd26a"
+down_revision = "47e5bef3a1d7"
+branch_labels: None = None
+depends_on: None = None
+
+# Configure logging
+logger = logging.getLogger("alembic.runtime.migration")
+logger.setLevel(logging.INFO)
+
+
+def upgrade() -> None:
+    logger.info(f"{revision}: create_table: slack_bot")
+    # Create new slack_bot table
+    op.create_table(
+        "slack_bot",
+        sa.Column("id", sa.Integer(), nullable=False),
+        sa.Column("name", sa.String(), nullable=False),
+        sa.Column("enabled", sa.Boolean(), nullable=False, server_default="true"),
+        sa.Column("bot_token", sa.LargeBinary(), nullable=False),
+        sa.Column("app_token", sa.LargeBinary(), nullable=False),
+        sa.PrimaryKeyConstraint("id"),
+        sa.UniqueConstraint("bot_token"),
+        sa.UniqueConstraint("app_token"),
+    )
+
+    # # Create new slack_channel_config table
+    op.create_table(
+        "slack_channel_config",
+        sa.Column("id", sa.Integer(), nullable=False),
+        sa.Column("slack_bot_id", sa.Integer(), nullable=True),
+        sa.Column("persona_id", sa.Integer(), nullable=True),
+        sa.Column("channel_config", postgresql.JSONB(), nullable=False),
+        sa.Column("response_type", sa.String(), nullable=False),
+        sa.Column(
+            "enable_auto_filters", sa.Boolean(), nullable=False, server_default="false"
+        ),
+        sa.ForeignKeyConstraint(
+            ["slack_bot_id"],
+            ["slack_bot.id"],
+        ),
+        sa.ForeignKeyConstraint(
+            ["persona_id"],
+            ["persona.id"],
+        ),
+        sa.PrimaryKeyConstraint("id"),
+    )
+
+    # Handle existing Slack bot tokens first
+    logger.info(f"{revision}: Checking for existing Slack bot.")
+    bot_token = None
+    app_token = None
+    first_row_id = None
+
+    try:
+        tokens = cast(dict, get_kv_store().load("slack_bot_tokens_config_key"))
+    except Exception:
+        logger.warning("No existing Slack bot tokens found.")
+        tokens = {}
+
+    bot_token = tokens.get("bot_token")
+    app_token = tokens.get("app_token")
+
+    if bot_token and app_token:
+        logger.info(f"{revision}: Found bot and app tokens.")
+
+        session = Session(bind=op.get_bind())
+        new_slack_bot = SlackBot(
+            name="Slack Bot (Migrated)",
+            enabled=True,
+            bot_token=bot_token,
+            app_token=app_token,
+        )
+        session.add(new_slack_bot)
+        session.commit()
+        first_row_id = new_slack_bot.id
+
+    # Create a default bot if none exists
+    # This is in case there are no slack tokens but there are channels configured
+    op.execute(
+        sa.text(
+            """
+            INSERT INTO slack_bot (name, enabled, bot_token, app_token)
+            SELECT 'Default Bot', true, '', ''
+            WHERE NOT EXISTS (SELECT 1 FROM slack_bot)
+            RETURNING id;
+            """
+        )
+    )
+
+    # Get the bot ID to use (either from existing migration or newly created)
+    bot_id_query = sa.text(
+        """
+        SELECT COALESCE(
+            :first_row_id,
+            (SELECT id FROM slack_bot ORDER BY id ASC LIMIT 1)
+        ) as bot_id;
+        """
+    )
+    result = op.get_bind().execute(bot_id_query, {"first_row_id": first_row_id})
+    bot_id = result.scalar()
+
+    # CTE (Common Table Expression) that transforms the old slack_bot_config table data
+    # This splits up the channel_names into their own rows
+    channel_names_cte = """
+        WITH channel_names AS (
+            SELECT
+                sbc.id as config_id,
+                sbc.persona_id,
+                sbc.response_type,
+                sbc.enable_auto_filters,
+                jsonb_array_elements_text(sbc.channel_config->'channel_names') as channel_name,
+                sbc.channel_config->>'respond_tag_only' as respond_tag_only,
+                sbc.channel_config->>'respond_to_bots' as respond_to_bots,
+                sbc.channel_config->'respond_member_group_list' as respond_member_group_list,
+                sbc.channel_config->'answer_filters' as answer_filters,
+                sbc.channel_config->'follow_up_tags' as follow_up_tags
+            FROM slack_bot_config sbc
+        )
+    """
+
+    # Insert the channel names into the new slack_channel_config table
+    insert_statement = """
+        INSERT INTO slack_channel_config (
+            slack_bot_id,
+            persona_id,
+            channel_config,
+            response_type,
+            enable_auto_filters
+        )
+        SELECT
+            :bot_id,
+            channel_name.persona_id,
+            jsonb_build_object(
+                'channel_name', channel_name.channel_name,
+                'respond_tag_only',
+                COALESCE((channel_name.respond_tag_only)::boolean, false),
+                'respond_to_bots',
+                COALESCE((channel_name.respond_to_bots)::boolean, false),
+                'respond_member_group_list',
+                COALESCE(channel_name.respond_member_group_list, '[]'::jsonb),
+                'answer_filters',
+                COALESCE(channel_name.answer_filters, '[]'::jsonb),
+                'follow_up_tags',
+                COALESCE(channel_name.follow_up_tags, '[]'::jsonb)
+            ),
+            channel_name.response_type,
+            channel_name.enable_auto_filters
+        FROM channel_names channel_name;
+    """
+
+    op.execute(sa.text(channel_names_cte + insert_statement).bindparams(bot_id=bot_id))
+
+    # Clean up old tokens if they existed
+    try:
+        if bot_token and app_token:
+            logger.info(f"{revision}: Removing old bot and app tokens.")
+            get_kv_store().delete("slack_bot_tokens_config_key")
+    except Exception:
+        logger.warning("tried to delete tokens in dynamic config but failed")
+    # Rename the table
+    op.rename_table(
+        "slack_bot_config__standard_answer_category",
+        "slack_channel_config__standard_answer_category",
+    )
+
+    # Rename the column
+    op.alter_column(
+        "slack_channel_config__standard_answer_category",
+        "slack_bot_config_id",
+        new_column_name="slack_channel_config_id",
+    )
+
+    # Drop the table with CASCADE to handle dependent objects
+    op.execute("DROP TABLE slack_bot_config CASCADE")
+
+    logger.info(f"{revision}: Migration complete.")
+
+
+def downgrade() -> None:
+    # Recreate the old slack_bot_config table
+    op.create_table(
+        "slack_bot_config",
+        sa.Column("id", sa.Integer(), nullable=False),
+        sa.Column("persona_id", sa.Integer(), nullable=True),
+        sa.Column("channel_config", postgresql.JSONB(), nullable=False),
+        sa.Column("response_type", sa.String(), nullable=False),
+        sa.Column("enable_auto_filters", sa.Boolean(), nullable=False),
+        sa.ForeignKeyConstraint(
+            ["persona_id"],
+            ["persona.id"],
+        ),
+        sa.PrimaryKeyConstraint("id"),
+    )
+
+    # Migrate data back to the old format
+    # Group by persona_id to combine channel names back into arrays
+    op.execute(
+        sa.text(
+            """
+            INSERT INTO slack_bot_config (
+                persona_id,
+                channel_config,
+                response_type,
+                enable_auto_filters
+            )
+            SELECT DISTINCT ON (persona_id)
+                persona_id,
+                jsonb_build_object(
+                    'channel_names', (
+                        SELECT jsonb_agg(c.channel_config->>'channel_name')
+                        FROM slack_channel_config c
+                        WHERE c.persona_id = scc.persona_id
+                    ),
+                    'respond_tag_only', (channel_config->>'respond_tag_only')::boolean,
+                    'respond_to_bots', (channel_config->>'respond_to_bots')::boolean,
+                    'respond_member_group_list', channel_config->'respond_member_group_list',
+                    'answer_filters', channel_config->'answer_filters',
+                    'follow_up_tags', channel_config->'follow_up_tags'
+                ),
+                response_type,
+                enable_auto_filters
+            FROM slack_channel_config scc
+            WHERE persona_id IS NOT NULL;
+            """
+        )
+    )
+
+    # Rename the table back
+    op.rename_table(
+        "slack_channel_config__standard_answer_category",
+        "slack_bot_config__standard_answer_category",
+    )
+
+    # Rename the column back
+    op.alter_column(
+        "slack_bot_config__standard_answer_category",
+        "slack_channel_config_id",
+        new_column_name="slack_bot_config_id",
+    )
+
+    # Try to save the first bot's tokens back to KV store
+    try:
+        first_bot = (
+            op.get_bind()
+            .execute(
+                sa.text(
+                    "SELECT bot_token, app_token FROM slack_bot ORDER BY id LIMIT 1"
+                )
+            )
+            .first()
+        )
+        if first_bot and first_bot.bot_token and first_bot.app_token:
+            tokens = {
+                "bot_token": first_bot.bot_token,
+                "app_token": first_bot.app_token,
+            }
+            get_kv_store().store("slack_bot_tokens_config_key", tokens)
+    except Exception:
+        logger.warning("Failed to save tokens back to KV store")
+
+    # Drop the new tables in reverse order
+    op.drop_table("slack_channel_config")
+    op.drop_table("slack_bot")

backend/alembic/versions/6d562f86c78b_remove_default_bot.py

@@ -0,0 +1,45 @@
+"""remove default bot
+
+Revision ID: 6d562f86c78b
+Revises: 177de57c21c9
+Create Date: 2024-11-22 11:51:29.331336
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+# revision identifiers, used by Alembic.
+revision = "6d562f86c78b"
+down_revision = "177de57c21c9"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.execute(
+        sa.text(
+            """
+            DELETE FROM slack_bot
+            WHERE name = 'Default Bot'
+            AND bot_token = ''
+            AND app_token = ''
+            AND NOT EXISTS (
+                SELECT 1 FROM slack_channel_config
+                WHERE slack_channel_config.slack_bot_id = slack_bot.id
+            )
+            """
+        )
+    )
+
+
+def downgrade() -> None:
+    op.execute(
+        sa.text(
+            """
+            INSERT INTO slack_bot (name, enabled, bot_token, app_token)
+            SELECT 'Default Bot', true, '', ''
+            WHERE NOT EXISTS (SELECT 1 FROM slack_bot)
+            RETURNING id;
+            """
+        )
+    )

backend/alembic/versions/776b3bbe9092_remove_remaining_enums.py

@@ -9,8 +9,8 @@ from alembic import op
 import sqlalchemy as sa
 
 from danswer.db.models import IndexModelStatus
-from danswer.search.enums import RecencyBiasSetting
-from danswer.search.enums import SearchType
+from danswer.context.search.enums import RecencyBiasSetting
+from danswer.context.search.enums import SearchType
 
 # revision identifiers, used by Alembic.
 revision = "776b3bbe9092"

backend/alembic/versions/93560ba1b118_add_web_ui_option_to_slack_config.py

@@ -0,0 +1,35 @@
+"""add web ui option to slack config
+
+Revision ID: 93560ba1b118
+Revises: 6d562f86c78b
+Create Date: 2024-11-24 06:36:17.490612
+
+"""
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision = "93560ba1b118"
+down_revision = "6d562f86c78b"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Add show_continue_in_web_ui with default False to all existing channel_configs
+    op.execute(
+        """
+        UPDATE slack_channel_config
+        SET channel_config = channel_config || '{"show_continue_in_web_ui": false}'::jsonb
+        WHERE NOT channel_config ? 'show_continue_in_web_ui'
+        """
+    )
+
+
+def downgrade() -> None:
+    # Remove show_continue_in_web_ui from all channel_configs
+    op.execute(
+        """
+        UPDATE slack_channel_config
+        SET channel_config = channel_config - 'show_continue_in_web_ui'
+        """
+    )

backend/alembic/versions/949b4a92a401_remove_rt.py

@@ -7,6 +7,7 @@ Create Date: 2024-10-26 13:06:06.937969
 """
 from alembic import op
 from sqlalchemy.orm import Session
+from sqlalchemy import text
 
 # Import your models and constants
 from danswer.db.models import (
@@ -15,7 +16,6 @@ from danswer.db.models import (
     Credential,
     IndexAttempt,
 )
-from danswer.configs.constants import DocumentSource
 
 
 # revision identifiers, used by Alembic.
@@ -30,13 +30,11 @@ def upgrade() -> None:
     bind = op.get_bind()
     session = Session(bind=bind)
 
-    connectors_to_delete = (
-        session.query(Connector)
-        .filter(Connector.source == DocumentSource.REQUESTTRACKER)
-        .all()
+    # Get connectors using raw SQL
+    result = bind.execute(
+        text("SELECT id FROM connector WHERE source = 'requesttracker'")
     )
-
-    connector_ids = [connector.id for connector in connectors_to_delete]
+    connector_ids = [row[0] for row in result]
 
     if connector_ids:
         cc_pairs_to_delete = (

backend/alembic/versions/9cf5c00f72fe_add_creator_to_cc_pair.py

@@ -1,7 +1,7 @@
 """add creator to cc pair
 
 Revision ID: 9cf5c00f72fe
-Revises: c0fd6e4da83a
+Revises: 26b931506ecb
 Create Date: 2024-11-12 15:16:42.682902
 
 """

backend/alembic/versions/9f696734098f_combine_search_and_chat.py

@@ -0,0 +1,36 @@
+"""Combine Search and Chat
+
+Revision ID: 9f696734098f
+Revises: a8c2065484e6
+Create Date: 2024-11-27 15:32:19.694972
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+# revision identifiers, used by Alembic.
+revision = "9f696734098f"
+down_revision = "a8c2065484e6"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.alter_column("chat_session", "description", nullable=True)
+    op.drop_column("chat_session", "one_shot")
+    op.drop_column("slack_channel_config", "response_type")
+
+
+def downgrade() -> None:
+    op.execute("UPDATE chat_session SET description = '' WHERE description IS NULL")
+    op.alter_column("chat_session", "description", nullable=False)
+    op.add_column(
+        "chat_session",
+        sa.Column("one_shot", sa.Boolean(), nullable=False, server_default=sa.false()),
+    )
+    op.add_column(
+        "slack_channel_config",
+        sa.Column(
+            "response_type", sa.String(), nullable=False, server_default="citations"
+        ),
+    )

backend/alembic/versions/a8c2065484e6_add_auto_scroll_to_user_model.py

@@ -0,0 +1,27 @@
+"""add auto scroll to user model
+
+Revision ID: a8c2065484e6
+Revises: abe7378b8217
+Create Date: 2024-11-22 17:34:09.690295
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "a8c2065484e6"
+down_revision = "abe7378b8217"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "user",
+        sa.Column("auto_scroll", sa.Boolean(), nullable=True, server_default=None),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("user", "auto_scroll")

backend/alembic/versions/abe7378b8217_add_indexing_trigger_to_cc_pair.py

@@ -0,0 +1,30 @@
+"""add indexing trigger to cc_pair
+
+Revision ID: abe7378b8217
+Revises: 6d562f86c78b
+Create Date: 2024-11-26 19:09:53.481171
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+# revision identifiers, used by Alembic.
+revision = "abe7378b8217"
+down_revision = "93560ba1b118"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "connector_credential_pair",
+        sa.Column(
+            "indexing_trigger",
+            sa.Enum("UPDATE", "REINDEX", name="indexingmode", native_enum=False),
+            nullable=True,
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("connector_credential_pair", "indexing_trigger")

backend/alembic/versions/bf7a81109301_delete_input_prompts.py

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

backend/alembic/versions/dfbe9e93d3c7_extended_role_for_non_web.py

@@ -0,0 +1,42 @@
+"""extended_role_for_non_web
+
+Revision ID: dfbe9e93d3c7
+Revises: 9cf5c00f72fe
+Create Date: 2024-11-16 07:54:18.727906
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+# revision identifiers, used by Alembic.
+revision = "dfbe9e93d3c7"
+down_revision = "9cf5c00f72fe"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.execute(
+        """
+        UPDATE "user"
+        SET role = 'EXT_PERM_USER'
+        WHERE has_web_login = false
+    """
+    )
+    op.drop_column("user", "has_web_login")
+
+
+def downgrade() -> None:
+    op.add_column(
+        "user",
+        sa.Column("has_web_login", sa.Boolean(), nullable=False, server_default="true"),
+    )
+
+    op.execute(
+        """
+        UPDATE "user"
+        SET has_web_login = false,
+            role = 'BASIC'
+        WHERE role IN ('SLACK_USER', 'EXT_PERM_USER')
+    """
+    )

backend/alembic/versions/f7a894b06d02_non_nullbale_slack_bot_id_in_channel_.py

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

backend/alembic_tenants/env.py

@@ -1,5 +1,6 @@
 import asyncio
 from logging.config import fileConfig
+from typing import Literal
 
 from sqlalchemy import pool
 from sqlalchemy.engine import Connection
@@ -37,8 +38,15 @@ EXCLUDE_TABLES = {"kombu_queue", "kombu_message"}
 
 def include_object(
     object: SchemaItem,
-    name: str,
-    type_: str,
+    name: str | None,
+    type_: Literal[
+        "schema",
+        "table",
+        "column",
+        "index",
+        "unique_constraint",
+        "foreign_key_constraint",
+    ],
     reflected: bool,
     compare_to: SchemaItem | None,
 ) -> bool:

backend/danswer/access/models.py

@@ -18,6 +18,11 @@ class ExternalAccess:
 
 @dataclass(frozen=True)
 class DocExternalAccess:
+    """
+    This is just a class to wrap the external access and the document ID
+    together. It's used for syncing document permissions to Redis.
+    """
+
     external_access: ExternalAccess
     # The document ID
     doc_id: str

backend/danswer/auth/api_key.py

@@ -1,3 +1,4 @@
+import hashlib
 import secrets
 import uuid
 from urllib.parse import quote
@@ -18,7 +19,8 @@ _API_KEY_HEADER_NAME = "Authorization"
 # organizations like the Internet Engineering Task Force (IETF).
 _API_KEY_HEADER_ALTERNATIVE_NAME = "X-Danswer-Authorization"
 _BEARER_PREFIX = "Bearer "
-_API_KEY_PREFIX = "dn_"
+_API_KEY_PREFIX = "on_"
+_DEPRECATED_API_KEY_PREFIX = "dn_"
 _API_KEY_LEN = 192
 
 
@@ -52,7 +54,9 @@ def extract_tenant_from_api_key_header(request: Request) -> str | None:
 
     api_key = raw_api_key_header[len(_BEARER_PREFIX) :].strip()
 
-    if not api_key.startswith(_API_KEY_PREFIX):
+    if not api_key.startswith(_API_KEY_PREFIX) and not api_key.startswith(
+        _DEPRECATED_API_KEY_PREFIX
+    ):
         return None
 
     parts = api_key[len(_API_KEY_PREFIX) :].split(".", 1)
@@ -63,10 +67,19 @@ def extract_tenant_from_api_key_header(request: Request) -> str | None:
     return unquote(tenant_id) if tenant_id else None
 
 
+def _deprecated_hash_api_key(api_key: str) -> str:
+    return sha256_crypt.hash(api_key, salt="", rounds=API_KEY_HASH_ROUNDS)
+
+
 def hash_api_key(api_key: str) -> str:
     # NOTE: no salt is needed, as the API key is randomly generated
     # and overlaps are impossible
-    return sha256_crypt.hash(api_key, salt="", rounds=API_KEY_HASH_ROUNDS)
+    if api_key.startswith(_API_KEY_PREFIX):
+        return hashlib.sha256(api_key.encode("utf-8")).hexdigest()
+    elif api_key.startswith(_DEPRECATED_API_KEY_PREFIX):
+        return _deprecated_hash_api_key(api_key)
+    else:
+        raise ValueError(f"Invalid API key prefix: {api_key[:3]}")
 
 
 def build_displayable_api_key(api_key: str) -> str:

backend/danswer/auth/invited_users.py

@@ -2,14 +2,13 @@ from typing import cast
 
 from danswer.configs.constants import KV_USER_STORE_KEY
 from danswer.key_value_store.factory import get_kv_store
-from danswer.key_value_store.interface import JSON_ro
 from danswer.key_value_store.interface import KvKeyNotFoundError
+from danswer.utils.special_types import JSON_ro
 
 
 def get_invited_users() -> list[str]:
     try:
         store = get_kv_store()
-
         return cast(list, store.load(KV_USER_STORE_KEY))
     except KvKeyNotFoundError:
         return list()

backend/danswer/auth/noauth_user.py

@@ -23,7 +23,9 @@ def load_no_auth_user_preferences(store: KeyValueStore) -> UserPreferences:
         )
         return UserPreferences(**preferences_data)
     except KvKeyNotFoundError:
-        return UserPreferences(chosen_assistants=None, default_model=None)
+        return UserPreferences(
+            chosen_assistants=None, default_model=None, auto_scroll=True
+        )
 
 
 def fetch_no_auth_user(store: KeyValueStore) -> UserInfo:

backend/danswer/auth/schemas.py

@@ -13,6 +13,9 @@ class UserRole(str, Enum):
         groups they are curators of
     - Global Curator can perform admin actions
         for all groups they are a member of
+    - Limited can access a limited set of basic api endpoints
+    - Slack are users that have used danswer via slack but dont have a web login
+    - External permissioned users that have been picked up during the external permissions sync process but don't have a web login
     """
 
     LIMITED = "limited"
@@ -20,6 +23,14 @@ class UserRole(str, Enum):
     ADMIN = "admin"
     CURATOR = "curator"
     GLOBAL_CURATOR = "global_curator"
+    SLACK_USER = "slack_user"
+    EXT_PERM_USER = "ext_perm_user"
+
+    def is_web_login(self) -> bool:
+        return self not in [
+            UserRole.SLACK_USER,
+            UserRole.EXT_PERM_USER,
+        ]
 
 
 class UserStatus(str, Enum):
@@ -34,10 +45,8 @@ class UserRead(schemas.BaseUser[uuid.UUID]):
 
 class UserCreate(schemas.BaseUserCreate):
     role: UserRole = UserRole.BASIC
-    has_web_login: bool | None = True
     tenant_id: str | None = None
 
 
 class UserUpdate(schemas.BaseUserUpdate):
     role: UserRole
-    has_web_login: bool | None = True

backend/danswer/auth/users.py

@@ -49,8 +49,7 @@ from httpx_oauth.oauth2 import BaseOAuth2
 from httpx_oauth.oauth2 import OAuth2Token
 from pydantic import BaseModel
 from sqlalchemy import text
-from sqlalchemy.orm import attributes
-from sqlalchemy.orm import Session
+from sqlalchemy.ext.asyncio import AsyncSession
 
 from danswer.auth.api_key import get_hashed_api_key_from_request
 from danswer.auth.invited_users import get_invited_users
@@ -59,7 +58,6 @@ from danswer.auth.schemas import UserRole
 from danswer.auth.schemas import UserUpdate
 from danswer.configs.app_configs import AUTH_TYPE
 from danswer.configs.app_configs import DISABLE_AUTH
-from danswer.configs.app_configs import DISABLE_VERIFICATION
 from danswer.configs.app_configs import EMAIL_FROM
 from danswer.configs.app_configs import REQUIRE_EMAIL_VERIFICATION
 from danswer.configs.app_configs import SESSION_EXPIRE_TIME_SECONDS
@@ -81,13 +79,14 @@ from danswer.db.auth import get_default_admin_user_emails
 from danswer.db.auth import get_user_count
 from danswer.db.auth import get_user_db
 from danswer.db.auth import SQLAlchemyUserAdminDB
+from danswer.db.engine import get_async_session
 from danswer.db.engine import get_async_session_with_tenant
-from danswer.db.engine import get_session
 from danswer.db.engine import get_session_with_tenant
 from danswer.db.models import AccessToken
 from danswer.db.models import OAuthAccount
 from danswer.db.models import User
 from danswer.db.users import get_user_by_email
+from danswer.server.utils import BasicAuthenticationError
 from danswer.utils.logger import setup_logger
 from danswer.utils.telemetry import optional_telemetry
 from danswer.utils.telemetry import RecordType
@@ -100,11 +99,6 @@ from shared_configs.contextvars import CURRENT_TENANT_ID_CONTEXTVAR
 logger = setup_logger()
 
 
-class BasicAuthenticationError(HTTPException):
-    def __init__(self, detail: str):
-        super().__init__(status_code=status.HTTP_403_FORBIDDEN, detail=detail)
-
-
 def is_user_admin(user: User | None) -> bool:
     if AUTH_TYPE == AuthType.DISABLED:
         return True
@@ -137,11 +131,12 @@ def get_display_email(email: str | None, space_less: bool = False) -> str:
 
 
 def user_needs_to_be_verified() -> bool:
-    # all other auth types besides basic should require users to be
-    # verified
-    return not DISABLE_VERIFICATION and (
-        AUTH_TYPE != AuthType.BASIC or REQUIRE_EMAIL_VERIFICATION
-    )
+    if AUTH_TYPE == AuthType.BASIC or AUTH_TYPE == AuthType.CLOUD:
+        return REQUIRE_EMAIL_VERIFICATION
+
+    # For other auth types, if the user is authenticated it's assumed that
+    # the user is already verified via the external IDP
+    return False
 
 
 def verify_email_is_invited(email: str) -> None:
@@ -222,6 +217,8 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
     reset_password_token_secret = USER_AUTH_SECRET
     verification_token_secret = USER_AUTH_SECRET
 
+    user_db: SQLAlchemyUserDatabase[User, uuid.UUID]
+
     async def create(
         self,
         user_create: schemas.UC | UserCreate,
@@ -247,7 +244,9 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
             verify_email_is_invited(user_create.email)
             verify_email_domain(user_create.email)
             if MULTI_TENANT:
-                tenant_user_db = SQLAlchemyUserAdminDB(db_session, User, OAuthAccount)
+                tenant_user_db = SQLAlchemyUserAdminDB[User, uuid.UUID](
+                    db_session, User, OAuthAccount
+                )
                 self.user_db = tenant_user_db
                 self.database = tenant_user_db
 
@@ -266,14 +265,9 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
             except exceptions.UserAlreadyExists:
                 user = await self.get_by_email(user_create.email)
                 # Handle case where user has used product outside of web and is now creating an account through web
-                if (
-                    not user.has_web_login
-                    and hasattr(user_create, "has_web_login")
-                    and user_create.has_web_login
-                ):
+                if not user.role.is_web_login() and user_create.role.is_web_login():
                     user_update = UserUpdate(
                         password=user_create.password,
-                        has_web_login=True,
                         role=user_create.role,
                         is_verified=user_create.is_verified,
                     )
@@ -287,7 +281,7 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
             return user
 
     async def oauth_callback(
-        self: "BaseUserManager[models.UOAP, models.ID]",
+        self,
         oauth_name: str,
         access_token: str,
         account_id: str,
@@ -298,7 +292,7 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
         *,
         associate_by_email: bool = False,
         is_verified_by_default: bool = False,
-    ) -> models.UOAP:
+    ) -> User:
         referral_source = None
         if request:
             referral_source = getattr(request.state, "referral_source", None)
@@ -324,9 +318,11 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
             verify_email_domain(account_email)
 
             if MULTI_TENANT:
-                tenant_user_db = SQLAlchemyUserAdminDB(db_session, User, OAuthAccount)
+                tenant_user_db = SQLAlchemyUserAdminDB[User, uuid.UUID](
+                    db_session, User, OAuthAccount
+                )
                 self.user_db = tenant_user_db
-                self.database = tenant_user_db  # type: ignore
+                self.database = tenant_user_db
 
             oauth_account_dict = {
                 "oauth_name": oauth_name,
@@ -378,7 +374,11 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
                         and existing_oauth_account.oauth_name == oauth_name
                     ):
                         user = await self.user_db.update_oauth_account(
-                            user, existing_oauth_account, oauth_account_dict
+                            user,
+                            # NOTE: OAuthAccount DOES implement the OAuthAccountProtocol
+                            # but the type checker doesn't know that :(
+                            existing_oauth_account,  # type: ignore
+                            oauth_account_dict,
                         )
 
             # NOTE: Most IdPs have very short expiry times, and we don't want to force the user to
@@ -391,16 +391,15 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
                 )
 
             # Handle case where user has used product outside of web and is now creating an account through web
-            if not user.has_web_login:  # type: ignore
+            if not user.role.is_web_login():
                 await self.user_db.update(
                     user,
                     {
                         "is_verified": is_verified_by_default,
-                        "has_web_login": True,
+                        "role": UserRole.BASIC,
                     },
                 )
                 user.is_verified = is_verified_by_default
-                user.has_web_login = True  # type: ignore
 
             # this is needed if an organization goes from `TRACK_EXTERNAL_IDP_EXPIRY=true` to `false`
             # otherwise, the oidc expiry will always be old, and the user will never be able to login
@@ -475,9 +474,7 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
                 self.password_helper.hash(credentials.password)
                 return None
 
-            has_web_login = attributes.get_attribute(user, "has_web_login")
-
-            if not has_web_login:
+            if not user.role.is_web_login():
                 raise BasicAuthenticationError(
                     detail="NO_WEB_LOGIN_AND_HAS_NO_PASSWORD",
                 )
@@ -608,7 +605,7 @@ optional_fastapi_current_user = fastapi_users.current_user(active=True, optional
 async def optional_user_(
     request: Request,
     user: User | None,
-    db_session: Session,
+    async_db_session: AsyncSession,
 ) -> User | None:
     """NOTE: `request` and `db_session` are not used here, but are included
     for the EE version of this function."""
@@ -617,13 +614,21 @@ async def optional_user_(
 
 async def optional_user(
     request: Request,
-    db_session: Session = Depends(get_session),
+    async_db_session: AsyncSession = Depends(get_async_session),
     user: User | None = Depends(optional_fastapi_current_user),
 ) -> User | None:
     versioned_fetch_user = fetch_versioned_implementation(
         "danswer.auth.users", "optional_user_"
     )
-    return await versioned_fetch_user(request, user, db_session)
+    user = await versioned_fetch_user(request, user, async_db_session)
+
+    # check if an API key is present
+    if user is None:
+        hashed_api_key = get_hashed_api_key_from_request(request)
+        if hashed_api_key:
+            user = await fetch_user_for_api_key(hashed_api_key, async_db_session)
+
+    return user
 
 
 async def double_check_user(
@@ -909,8 +914,8 @@ def get_oauth_router(
     return router
 
 
-def api_key_dep(
-    request: Request, db_session: Session = Depends(get_session)
+async def api_key_dep(
+    request: Request, async_db_session: AsyncSession = Depends(get_async_session)
 ) -> User | None:
     if AUTH_TYPE == AuthType.DISABLED:
         return None
@@ -920,7 +925,7 @@ def api_key_dep(
         raise HTTPException(status_code=401, detail="Missing API key")
 
     if hashed_api_key:
-        user = fetch_user_for_api_key(hashed_api_key, db_session)
+        user = await fetch_user_for_api_key(hashed_api_key, async_db_session)
 
     if user is None:
         raise HTTPException(status_code=401, detail="Invalid API key")

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

@@ -11,6 +11,7 @@ from celery.exceptions import WorkerShutdown
 from celery.states import READY_STATES
 from celery.utils.log import get_task_logger
 from celery.worker import strategy  # type: ignore
+from redis.lock import Lock as RedisLock
 from sentry_sdk.integrations.celery import CeleryIntegration
 from sqlalchemy import text
 from sqlalchemy.orm import Session
@@ -332,16 +333,16 @@ def on_worker_shutdown(sender: Any, **kwargs: Any) -> None:
         return
 
     logger.info("Releasing primary worker lock.")
-    lock = sender.primary_worker_lock
+    lock: RedisLock = sender.primary_worker_lock
     try:
         if lock.owned():
             try:
                 lock.release()
                 sender.primary_worker_lock = None
-            except Exception as e:
-                logger.error(f"Failed to release primary worker lock: {e}")
-    except Exception as e:
-        logger.error(f"Failed to check if primary worker lock is owned: {e}")
+            except Exception:
+                logger.exception("Failed to release primary worker lock")
+    except Exception:
+        logger.exception("Failed to check if primary worker lock is owned")
 
 
 def on_setup_logging(

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

@@ -1,5 +1,6 @@
 import multiprocessing
 from typing import Any
+from typing import cast
 
 from celery import bootsteps  # type: ignore
 from celery import Celery
@@ -10,14 +11,21 @@ from celery.signals import celeryd_init
 from celery.signals import worker_init
 from celery.signals import worker_ready
 from celery.signals import worker_shutdown
+from redis.lock import Lock as RedisLock
 
 import danswer.background.celery.apps.app_base as app_base
 from danswer.background.celery.apps.app_base import task_logger
 from danswer.background.celery.celery_utils import celery_is_worker_primary
+from danswer.background.celery.tasks.indexing.tasks import (
+    get_unfenced_index_attempt_ids,
+)
 from danswer.configs.constants import CELERY_PRIMARY_WORKER_LOCK_TIMEOUT
 from danswer.configs.constants import DanswerRedisLocks
 from danswer.configs.constants import POSTGRES_CELERY_WORKER_PRIMARY_APP_NAME
+from danswer.db.engine import get_session_with_default_tenant
 from danswer.db.engine import SqlEngine
+from danswer.db.index_attempt import get_index_attempt
+from danswer.db.index_attempt import mark_attempt_canceled
 from danswer.redis.redis_connector_credential_pair import RedisConnectorCredentialPair
 from danswer.redis.redis_connector_delete import RedisConnectorDelete
 from danswer.redis.redis_connector_doc_perm_sync import RedisConnectorPermissionSync
@@ -31,7 +39,6 @@ from danswer.redis.redis_usergroup import RedisUserGroup
 from danswer.utils.logger import setup_logger
 from shared_configs.configs import MULTI_TENANT
 
-
 logger = setup_logger()
 
 celery_app = Celery(__name__)
@@ -91,6 +98,15 @@ def on_worker_init(sender: Any, **kwargs: Any) -> None:
     # by the primary worker. This is unnecessary in the multi tenant scenario
     r = get_redis_client(tenant_id=None)
 
+    # Log the role and slave count - being connected to a slave or slave count > 0 could be problematic
+    info: dict[str, Any] = cast(dict, r.info("replication"))
+    role: str = cast(str, info.get("role"))
+    connected_slaves: int = info.get("connected_slaves", 0)
+
+    logger.info(
+        f"Redis INFO REPLICATION: role={role} connected_slaves={connected_slaves}"
+    )
+
     # For the moment, we're assuming that we are the only primary worker
     # that should be running.
     # TODO: maybe check for or clean up another zombie primary worker if we detect it
@@ -100,9 +116,13 @@ def on_worker_init(sender: Any, **kwargs: Any) -> None:
     # it is planned to use this lock to enforce singleton behavior on the primary
     # worker, since the primary worker does redis cleanup on startup, but this isn't
     # implemented yet.
-    lock = r.lock(
+
+    # set thread_local=False since we don't control what thread the periodic task might
+    # reacquire the lock with
+    lock: RedisLock = r.lock(
         DanswerRedisLocks.PRIMARY_WORKER,
         timeout=CELERY_PRIMARY_WORKER_LOCK_TIMEOUT,
+        thread_local=False,
     )
 
     logger.info("Primary worker lock: Acquire starting.")
@@ -140,6 +160,23 @@ def on_worker_init(sender: Any, **kwargs: Any) -> None:
 
     RedisConnectorExternalGroupSync.reset_all(r)
 
+    # mark orphaned index attempts as failed
+    with get_session_with_default_tenant() as db_session:
+        unfenced_attempt_ids = get_unfenced_index_attempt_ids(db_session, r)
+        for attempt_id in unfenced_attempt_ids:
+            attempt = get_index_attempt(db_session, attempt_id)
+            if not attempt:
+                continue
+
+            failure_reason = (
+                f"Canceling leftover index attempt found on startup: "
+                f"index_attempt={attempt.id} "
+                f"cc_pair={attempt.connector_credential_pair_id} "
+                f"search_settings={attempt.search_settings_id}"
+            )
+            logger.warning(failure_reason)
+            mark_attempt_canceled(attempt.id, db_session, failure_reason)
+
 
 @worker_ready.connect
 def on_worker_ready(sender: Any, **kwargs: Any) -> None:
@@ -194,7 +231,7 @@ class HubPeriodicTask(bootsteps.StartStopStep):
             if not hasattr(worker, "primary_worker_lock"):
                 return
 
-            lock = worker.primary_worker_lock
+            lock: RedisLock = worker.primary_worker_lock
 
             r = get_redis_client(tenant_id=None)
 

backend/danswer/background/celery/celery_utils.py

@@ -4,7 +4,6 @@ from typing import Any
 
 from sqlalchemy.orm import Session
 
-from danswer.background.indexing.run_indexing import RunIndexingCallbackInterface
 from danswer.configs.app_configs import MAX_PRUNING_DOCUMENT_RETRIEVAL_PER_MINUTE
 from danswer.connectors.cross_connector_utils.rate_limit_wrapper import (
     rate_limit_builder,
@@ -17,6 +16,7 @@ from danswer.connectors.models import Document
 from danswer.db.connector_credential_pair import get_connector_credential_pair
 from danswer.db.enums import TaskStatus
 from danswer.db.models import TaskQueueState
+from danswer.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 from danswer.redis.redis_connector import RedisConnector
 from danswer.server.documents.models import DeletionAttemptSnapshot
 from danswer.utils.logger import setup_logger
@@ -78,7 +78,7 @@ def document_batch_to_ids(
 
 def extract_ids_from_runnable_connector(
     runnable_connector: BaseConnector,
-    callback: RunIndexingCallbackInterface | None = None,
+    callback: IndexingHeartbeatInterface | None = None,
 ) -> set[str]:
     """
     If the SlimConnector hasnt been implemented for the given connector, just pull
@@ -111,10 +111,15 @@ def extract_ids_from_runnable_connector(
     for doc_batch in doc_batch_generator:
         if callback:
             if callback.should_stop():
-                raise RuntimeError("Stop signal received")
-            callback.progress(len(doc_batch))
+                raise RuntimeError(
+                    "extract_ids_from_runnable_connector: Stop signal detected"
+                )
+
         all_connector_doc_ids.update(doc_batch_processing_func(doc_batch))
 
+        if callback:
+            callback.progress("extract_ids_from_runnable_connector", len(doc_batch))
+
     return all_connector_doc_ids
 
 

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

@@ -2,54 +2,55 @@ from datetime import timedelta
 from typing import Any
 
 from danswer.configs.constants import DanswerCeleryPriority
+from danswer.configs.constants import DanswerCeleryTask
 
 
 tasks_to_schedule = [
     {
         "name": "check-for-vespa-sync",
-        "task": "check_for_vespa_sync_task",
+        "task": DanswerCeleryTask.CHECK_FOR_VESPA_SYNC_TASK,
         "schedule": timedelta(seconds=20),
         "options": {"priority": DanswerCeleryPriority.HIGH},
     },
     {
         "name": "check-for-connector-deletion",
-        "task": "check_for_connector_deletion_task",
+        "task": DanswerCeleryTask.CHECK_FOR_CONNECTOR_DELETION,
         "schedule": timedelta(seconds=20),
         "options": {"priority": DanswerCeleryPriority.HIGH},
     },
     {
         "name": "check-for-indexing",
-        "task": "check_for_indexing",
+        "task": DanswerCeleryTask.CHECK_FOR_INDEXING,
         "schedule": timedelta(seconds=15),
         "options": {"priority": DanswerCeleryPriority.HIGH},
     },
     {
         "name": "check-for-prune",
-        "task": "check_for_pruning",
+        "task": DanswerCeleryTask.CHECK_FOR_PRUNING,
         "schedule": timedelta(seconds=15),
         "options": {"priority": DanswerCeleryPriority.HIGH},
     },
     {
         "name": "kombu-message-cleanup",
-        "task": "kombu_message_cleanup_task",
+        "task": DanswerCeleryTask.KOMBU_MESSAGE_CLEANUP_TASK,
         "schedule": timedelta(seconds=3600),
         "options": {"priority": DanswerCeleryPriority.LOWEST},
     },
     {
         "name": "monitor-vespa-sync",
-        "task": "monitor_vespa_sync",
+        "task": DanswerCeleryTask.MONITOR_VESPA_SYNC,
         "schedule": timedelta(seconds=5),
         "options": {"priority": DanswerCeleryPriority.HIGH},
     },
     {
         "name": "check-for-doc-permissions-sync",
-        "task": "check_for_doc_permissions_sync",
+        "task": DanswerCeleryTask.CHECK_FOR_DOC_PERMISSIONS_SYNC,
         "schedule": timedelta(seconds=30),
         "options": {"priority": DanswerCeleryPriority.HIGH},
     },
     {
         "name": "check-for-external-group-sync",
-        "task": "check_for_external_group_sync",
+        "task": DanswerCeleryTask.CHECK_FOR_EXTERNAL_GROUP_SYNC,
         "schedule": timedelta(seconds=20),
         "options": {"priority": DanswerCeleryPriority.HIGH},
     },

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

@@ -1,17 +1,17 @@
 from datetime import datetime
 from datetime import timezone
 
-import redis
 from celery import Celery
 from celery import shared_task
 from celery import Task
 from celery.exceptions import SoftTimeLimitExceeded
-from redis import Redis
+from redis.lock import Lock as RedisLock
 from sqlalchemy.orm import Session
 
 from danswer.background.celery.apps.app_base import task_logger
 from danswer.configs.app_configs import JOB_TIMEOUT
 from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
+from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DanswerRedisLocks
 from danswer.db.connector_credential_pair import get_connector_credential_pair_from_id
 from danswer.db.connector_credential_pair import get_connector_credential_pairs
@@ -19,7 +19,7 @@ from danswer.db.engine import get_session_with_tenant
 from danswer.db.enums import ConnectorCredentialPairStatus
 from danswer.db.search_settings import get_all_search_settings
 from danswer.redis.redis_connector import RedisConnector
-from danswer.redis.redis_connector_delete import RedisConnectorDeletionFenceData
+from danswer.redis.redis_connector_delete import RedisConnectorDeletePayload
 from danswer.redis.redis_pool import get_redis_client
 
 
@@ -29,7 +29,7 @@ class TaskDependencyError(RuntimeError):
 
 
 @shared_task(
-    name="check_for_connector_deletion_task",
+    name=DanswerCeleryTask.CHECK_FOR_CONNECTOR_DELETION,
     soft_time_limit=JOB_TIMEOUT,
     trail=False,
     bind=True,
@@ -37,7 +37,7 @@ class TaskDependencyError(RuntimeError):
 def check_for_connector_deletion_task(self: Task, *, tenant_id: str | None) -> None:
     r = get_redis_client(tenant_id=tenant_id)
 
-    lock_beat = r.lock(
+    lock_beat: RedisLock = r.lock(
         DanswerRedisLocks.CHECK_CONNECTOR_DELETION_BEAT_LOCK,
         timeout=CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT,
     )
@@ -60,7 +60,7 @@ def check_for_connector_deletion_task(self: Task, *, tenant_id: str | None) -> N
                 redis_connector = RedisConnector(tenant_id, cc_pair_id)
                 try:
                     try_generate_document_cc_pair_cleanup_tasks(
-                        self.app, cc_pair_id, db_session, r, lock_beat, tenant_id
+                        self.app, cc_pair_id, db_session, lock_beat, tenant_id
                     )
                 except TaskDependencyError as e:
                     # this means we wanted to start deleting but dependent tasks were running
@@ -86,8 +86,7 @@ def try_generate_document_cc_pair_cleanup_tasks(
     app: Celery,
     cc_pair_id: int,
     db_session: Session,
-    r: Redis,
-    lock_beat: redis.lock.Lock,
+    lock_beat: RedisLock,
     tenant_id: str | None,
 ) -> int | None:
     """Returns an int if syncing is needed. The int represents the number of sync tasks generated.
@@ -118,7 +117,7 @@ def try_generate_document_cc_pair_cleanup_tasks(
         return None
 
     # set a basic fence to start
-    fence_payload = RedisConnectorDeletionFenceData(
+    fence_payload = RedisConnectorDeletePayload(
         num_tasks=None,
         submitted=datetime.now(timezone.utc),
     )

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

@@ -8,6 +8,7 @@ from celery import shared_task
 from celery import Task
 from celery.exceptions import SoftTimeLimitExceeded
 from redis import Redis
+from redis.lock import Lock as RedisLock
 
 from danswer.access.models import DocExternalAccess
 from danswer.background.celery.apps.app_base import task_logger
@@ -17,17 +18,19 @@ from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DANSWER_REDIS_FUNCTION_LOCK_PREFIX
 from danswer.configs.constants import DanswerCeleryPriority
 from danswer.configs.constants import DanswerCeleryQueues
+from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DanswerRedisLocks
 from danswer.configs.constants import DocumentSource
 from danswer.db.connector_credential_pair import get_connector_credential_pair_from_id
+from danswer.db.document import upsert_document_by_connector_credential_pair
 from danswer.db.engine import get_session_with_tenant
 from danswer.db.enums import AccessType
 from danswer.db.enums import ConnectorCredentialPairStatus
 from danswer.db.models import ConnectorCredentialPair
-from danswer.db.users import batch_add_non_web_user_if_not_exists
+from danswer.db.users import batch_add_ext_perm_user_if_not_exists
 from danswer.redis.redis_connector import RedisConnector
 from danswer.redis.redis_connector_doc_perm_sync import (
-    RedisConnectorPermissionSyncData,
+    RedisConnectorPermissionSyncPayload,
 )
 from danswer.redis.redis_pool import get_redis_client
 from danswer.utils.logger import doc_permission_sync_ctx
@@ -81,7 +84,7 @@ def _is_external_doc_permissions_sync_due(cc_pair: ConnectorCredentialPair) -> b
 
 
 @shared_task(
-    name="check_for_doc_permissions_sync",
+    name=DanswerCeleryTask.CHECK_FOR_DOC_PERMISSIONS_SYNC,
     soft_time_limit=JOB_TIMEOUT,
     bind=True,
 )
@@ -138,7 +141,7 @@ def try_creating_permissions_sync_task(
 
     LOCK_TIMEOUT = 30
 
-    lock = r.lock(
+    lock: RedisLock = r.lock(
         DANSWER_REDIS_FUNCTION_LOCK_PREFIX + "try_generate_permissions_sync_tasks",
         timeout=LOCK_TIMEOUT,
     )
@@ -162,8 +165,8 @@ def try_creating_permissions_sync_task(
 
         custom_task_id = f"{redis_connector.permissions.generator_task_key}_{uuid4()}"
 
-        app.send_task(
-            "connector_permission_sync_generator_task",
+        result = app.send_task(
+            DanswerCeleryTask.CONNECTOR_PERMISSION_SYNC_GENERATOR_TASK,
             kwargs=dict(
                 cc_pair_id=cc_pair_id,
                 tenant_id=tenant_id,
@@ -174,8 +177,8 @@ def try_creating_permissions_sync_task(
         )
 
         # set a basic fence to start
-        payload = RedisConnectorPermissionSyncData(
-            started=None,
+        payload = RedisConnectorPermissionSyncPayload(
+            started=None, celery_task_id=result.id
         )
 
         redis_connector.permissions.set_fence(payload)
@@ -190,7 +193,7 @@ def try_creating_permissions_sync_task(
 
 
 @shared_task(
-    name="connector_permission_sync_generator_task",
+    name=DanswerCeleryTask.CONNECTOR_PERMISSION_SYNC_GENERATOR_TASK,
     acks_late=False,
     soft_time_limit=JOB_TIMEOUT,
     track_started=True,
@@ -216,7 +219,7 @@ def connector_permission_sync_generator_task(
 
     r = get_redis_client(tenant_id=tenant_id)
 
-    lock = r.lock(
+    lock: RedisLock = r.lock(
         DanswerRedisLocks.CONNECTOR_DOC_PERMISSIONS_SYNC_LOCK_PREFIX
         + f"_{redis_connector.id}",
         timeout=CELERY_PERMISSIONS_SYNC_LOCK_TIMEOUT,
@@ -241,13 +244,17 @@ def connector_permission_sync_generator_task(
 
             doc_sync_func = DOC_PERMISSIONS_FUNC_MAP.get(source_type)
             if doc_sync_func is None:
-                raise ValueError(f"No doc sync func found for {source_type}")
+                raise ValueError(
+                    f"No doc sync func found for {source_type} with cc_pair={cc_pair_id}"
+                )
 
-            logger.info(f"Syncing docs for {source_type}")
+            logger.info(f"Syncing docs for {source_type} with cc_pair={cc_pair_id}")
 
-            payload = RedisConnectorPermissionSyncData(
-                started=datetime.now(timezone.utc),
-            )
+            payload = redis_connector.permissions.payload
+            if not payload:
+                raise ValueError(f"No fence payload found: cc_pair={cc_pair_id}")
+
+            payload.started = datetime.now(timezone.utc)
             redis_connector.permissions.set_fence(payload)
 
             document_external_accesses: list[DocExternalAccess] = doc_sync_func(cc_pair)
@@ -256,7 +263,12 @@ def connector_permission_sync_generator_task(
                 f"RedisConnector.permissions.generate_tasks starting. cc_pair={cc_pair_id}"
             )
             tasks_generated = redis_connector.permissions.generate_tasks(
-                self.app, lock, document_external_accesses, source_type
+                celery_app=self.app,
+                lock=lock,
+                new_permissions=document_external_accesses,
+                source_string=source_type,
+                connector_id=cc_pair.connector.id,
+                credential_id=cc_pair.credential.id,
             )
             if tasks_generated is None:
                 return None
@@ -281,7 +293,7 @@ def connector_permission_sync_generator_task(
 
 
 @shared_task(
-    name="update_external_document_permissions_task",
+    name=DanswerCeleryTask.UPDATE_EXTERNAL_DOCUMENT_PERMISSIONS_TASK,
     soft_time_limit=LIGHT_SOFT_TIME_LIMIT,
     time_limit=LIGHT_TIME_LIMIT,
     max_retries=DOCUMENT_PERMISSIONS_UPDATE_MAX_RETRIES,
@@ -292,6 +304,8 @@ def update_external_document_permissions_task(
     tenant_id: str | None,
     serialized_doc_external_access: dict,
     source_string: str,
+    connector_id: int,
+    credential_id: int,
 ) -> bool:
     document_external_access = DocExternalAccess.from_dict(
         serialized_doc_external_access
@@ -300,18 +314,28 @@ def update_external_document_permissions_task(
     external_access = document_external_access.external_access
     try:
         with get_session_with_tenant(tenant_id) as db_session:
-            # Then we build the update requests to update vespa
-            batch_add_non_web_user_if_not_exists(
+            # Add the users to the DB if they don't exist
+            batch_add_ext_perm_user_if_not_exists(
                 db_session=db_session,
                 emails=list(external_access.external_user_emails),
             )
-            upsert_document_external_perms(
+            # Then we upsert the document's external permissions in postgres
+            created_new_doc = upsert_document_external_perms(
                 db_session=db_session,
                 doc_id=doc_id,
                 external_access=external_access,
                 source_type=DocumentSource(source_string),
             )
 
+            if created_new_doc:
+                # If a new document was created, we associate it with the cc_pair
+                upsert_document_by_connector_credential_pair(
+                    db_session=db_session,
+                    connector_id=connector_id,
+                    credential_id=credential_id,
+                    document_ids=[doc_id],
+                )
+
             logger.debug(
                 f"Successfully synced postgres document permissions for {doc_id}"
             )

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

@@ -8,6 +8,7 @@ from celery import shared_task
 from celery import Task
 from celery.exceptions import SoftTimeLimitExceeded
 from redis import Redis
+from redis.lock import Lock as RedisLock
 
 from danswer.background.celery.apps.app_base import task_logger
 from danswer.configs.app_configs import JOB_TIMEOUT
@@ -16,6 +17,7 @@ from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DANSWER_REDIS_FUNCTION_LOCK_PREFIX
 from danswer.configs.constants import DanswerCeleryPriority
 from danswer.configs.constants import DanswerCeleryQueues
+from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DanswerRedisLocks
 from danswer.db.connector import mark_cc_pair_as_external_group_synced
 from danswer.db.connector_credential_pair import get_connector_credential_pair_from_id
@@ -24,13 +26,20 @@ from danswer.db.enums import AccessType
 from danswer.db.enums import ConnectorCredentialPairStatus
 from danswer.db.models import ConnectorCredentialPair
 from danswer.redis.redis_connector import RedisConnector
+from danswer.redis.redis_connector_ext_group_sync import (
+    RedisConnectorExternalGroupSyncPayload,
+)
 from danswer.redis.redis_pool import get_redis_client
 from danswer.utils.logger import setup_logger
 from ee.danswer.db.connector_credential_pair import get_all_auto_sync_cc_pairs
+from ee.danswer.db.connector_credential_pair import get_cc_pairs_by_source
 from ee.danswer.db.external_perm import ExternalUserGroup
 from ee.danswer.db.external_perm import replace_user__ext_group_for_cc_pair
-from ee.danswer.external_permissions.sync_params import EXTERNAL_GROUP_SYNC_PERIOD
+from ee.danswer.external_permissions.sync_params import EXTERNAL_GROUP_SYNC_PERIODS
 from ee.danswer.external_permissions.sync_params import GROUP_PERMISSIONS_FUNC_MAP
+from ee.danswer.external_permissions.sync_params import (
+    GROUP_PERMISSIONS_IS_CC_PAIR_AGNOSTIC,
+)
 
 logger = setup_logger()
 
@@ -49,7 +58,7 @@ def _is_external_group_sync_due(cc_pair: ConnectorCredentialPair) -> bool:
     if cc_pair.access_type != AccessType.SYNC:
         return False
 
-    # skip pruning if not active
+    # skip external group sync if not active
     if cc_pair.status != ConnectorCredentialPairStatus.ACTIVE:
         return False
 
@@ -66,9 +75,9 @@ def _is_external_group_sync_due(cc_pair: ConnectorCredentialPair) -> bool:
     if last_ext_group_sync is None:
         return True
 
-    source_sync_period = EXTERNAL_GROUP_SYNC_PERIOD
+    source_sync_period = EXTERNAL_GROUP_SYNC_PERIODS.get(cc_pair.connector.source)
 
-    # If EXTERNAL_GROUP_SYNC_PERIOD is None, we always run the sync.
+    # If EXTERNAL_GROUP_SYNC_PERIODS is None, we always run the sync.
     if not source_sync_period:
         return True
 
@@ -81,7 +90,7 @@ def _is_external_group_sync_due(cc_pair: ConnectorCredentialPair) -> bool:
 
 
 @shared_task(
-    name="check_for_external_group_sync",
+    name=DanswerCeleryTask.CHECK_FOR_EXTERNAL_GROUP_SYNC,
     soft_time_limit=JOB_TIMEOUT,
     bind=True,
 )
@@ -102,12 +111,28 @@ def check_for_external_group_sync(self: Task, *, tenant_id: str | None) -> None:
         with get_session_with_tenant(tenant_id) as db_session:
             cc_pairs = get_all_auto_sync_cc_pairs(db_session)
 
+            # We only want to sync one cc_pair per source type in
+            # GROUP_PERMISSIONS_IS_CC_PAIR_AGNOSTIC
+            for source in GROUP_PERMISSIONS_IS_CC_PAIR_AGNOSTIC:
+                # These are ordered by cc_pair id so the first one is the one we want
+                cc_pairs_to_dedupe = get_cc_pairs_by_source(
+                    db_session, source, only_sync=True
+                )
+                # We only want to sync one cc_pair per source type
+                # in GROUP_PERMISSIONS_IS_CC_PAIR_AGNOSTIC so we dedupe here
+                for cc_pair_to_remove in cc_pairs_to_dedupe[1:]:
+                    cc_pairs = [
+                        cc_pair
+                        for cc_pair in cc_pairs
+                        if cc_pair.id != cc_pair_to_remove.id
+                    ]
+
             for cc_pair in cc_pairs:
                 if _is_external_group_sync_due(cc_pair):
                     cc_pair_ids_to_sync.append(cc_pair.id)
 
         for cc_pair_id in cc_pair_ids_to_sync:
-            tasks_created = try_creating_permissions_sync_task(
+            tasks_created = try_creating_external_group_sync_task(
                 self.app, cc_pair_id, r, tenant_id
             )
             if not tasks_created:
@@ -125,7 +150,7 @@ def check_for_external_group_sync(self: Task, *, tenant_id: str | None) -> None:
             lock_beat.release()
 
 
-def try_creating_permissions_sync_task(
+def try_creating_external_group_sync_task(
     app: Celery,
     cc_pair_id: int,
     r: Redis,
@@ -156,8 +181,8 @@ def try_creating_permissions_sync_task(
 
         custom_task_id = f"{redis_connector.external_group_sync.taskset_key}_{uuid4()}"
 
-        _ = app.send_task(
-            "connector_external_group_sync_generator_task",
+        result = app.send_task(
+            DanswerCeleryTask.CONNECTOR_EXTERNAL_GROUP_SYNC_GENERATOR_TASK,
             kwargs=dict(
                 cc_pair_id=cc_pair_id,
                 tenant_id=tenant_id,
@@ -166,8 +191,13 @@ def try_creating_permissions_sync_task(
             task_id=custom_task_id,
             priority=DanswerCeleryPriority.HIGH,
         )
-        # set a basic fence to start
-        redis_connector.external_group_sync.set_fence(True)
+
+        payload = RedisConnectorExternalGroupSyncPayload(
+            started=datetime.now(timezone.utc),
+            celery_task_id=result.id,
+        )
+
+        redis_connector.external_group_sync.set_fence(payload)
 
     except Exception:
         task_logger.exception(
@@ -182,7 +212,7 @@ def try_creating_permissions_sync_task(
 
 
 @shared_task(
-    name="connector_external_group_sync_generator_task",
+    name=DanswerCeleryTask.CONNECTOR_EXTERNAL_GROUP_SYNC_GENERATOR_TASK,
     acks_late=False,
     soft_time_limit=JOB_TIMEOUT,
     track_started=True,
@@ -195,7 +225,7 @@ def connector_external_group_sync_generator_task(
     tenant_id: str | None,
 ) -> None:
     """
-    Permission sync task that handles document permission syncing for a given connector credential pair
+    Permission sync task that handles external group syncing for a given connector credential pair
     This task assumes that the task has already been properly fenced
     """
 
@@ -203,7 +233,7 @@ def connector_external_group_sync_generator_task(
 
     r = get_redis_client(tenant_id=tenant_id)
 
-    lock = r.lock(
+    lock: RedisLock = r.lock(
         DanswerRedisLocks.CONNECTOR_EXTERNAL_GROUP_SYNC_LOCK_PREFIX
         + f"_{redis_connector.id}",
         timeout=CELERY_EXTERNAL_GROUP_SYNC_LOCK_TIMEOUT,
@@ -228,9 +258,13 @@ def connector_external_group_sync_generator_task(
 
             ext_group_sync_func = GROUP_PERMISSIONS_FUNC_MAP.get(source_type)
             if ext_group_sync_func is None:
-                raise ValueError(f"No external group sync func found for {source_type}")
+                raise ValueError(
+                    f"No external group sync func found for {source_type} for cc_pair: {cc_pair_id}"
+                )
 
-            logger.info(f"Syncing docs for {source_type}")
+            logger.info(
+                f"Syncing external groups for {source_type} for cc_pair: {cc_pair_id}"
+            )
 
             external_user_groups: list[ExternalUserGroup] = ext_group_sync_func(cc_pair)
 
@@ -249,7 +283,6 @@ def connector_external_group_sync_generator_task(
             )
 
             mark_cc_pair_as_external_group_synced(db_session, cc_pair.id)
-
     except Exception as e:
         task_logger.exception(
             f"Failed to run external group sync: cc_pair={cc_pair_id}"
@@ -260,6 +293,6 @@ def connector_external_group_sync_generator_task(
         raise e
     finally:
         # we always want to clear the fence after the task is done or failed so it doesn't get stuck
-        redis_connector.external_group_sync.set_fence(False)
+        redis_connector.external_group_sync.set_fence(None)
         if lock.owned():
             lock.release()

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

@@ -10,41 +10,50 @@ from celery import shared_task
 from celery import Task
 from celery.exceptions import SoftTimeLimitExceeded
 from redis import Redis
+from redis.exceptions import LockError
+from redis.lock import Lock as RedisLock
 from sqlalchemy.orm import Session
 
 from danswer.background.celery.apps.app_base import task_logger
 from danswer.background.indexing.job_client import SimpleJobClient
 from danswer.background.indexing.run_indexing import run_indexing_entrypoint
-from danswer.background.indexing.run_indexing import RunIndexingCallbackInterface
 from danswer.configs.app_configs import DISABLE_INDEX_UPDATE_ON_SWAP
 from danswer.configs.constants import CELERY_INDEXING_LOCK_TIMEOUT
 from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DANSWER_REDIS_FUNCTION_LOCK_PREFIX
 from danswer.configs.constants import DanswerCeleryPriority
 from danswer.configs.constants import DanswerCeleryQueues
+from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DanswerRedisLocks
 from danswer.configs.constants import DocumentSource
+from danswer.db.connector import mark_ccpair_with_indexing_trigger
 from danswer.db.connector_credential_pair import fetch_connector_credential_pairs
 from danswer.db.connector_credential_pair import get_connector_credential_pair_from_id
 from danswer.db.engine import get_db_current_time
 from danswer.db.engine import get_session_with_tenant
 from danswer.db.enums import ConnectorCredentialPairStatus
+from danswer.db.enums import IndexingMode
 from danswer.db.enums import IndexingStatus
 from danswer.db.enums import IndexModelStatus
 from danswer.db.index_attempt import create_index_attempt
+from danswer.db.index_attempt import delete_index_attempt
+from danswer.db.index_attempt import get_all_index_attempts_by_status
 from danswer.db.index_attempt import get_index_attempt
 from danswer.db.index_attempt import get_last_attempt_for_cc_pair
+from danswer.db.index_attempt import mark_attempt_canceled
 from danswer.db.index_attempt import mark_attempt_failed
 from danswer.db.models import ConnectorCredentialPair
 from danswer.db.models import IndexAttempt
 from danswer.db.models import SearchSettings
+from danswer.db.search_settings import get_active_search_settings
 from danswer.db.search_settings import get_current_search_settings
-from danswer.db.search_settings import get_secondary_search_settings
 from danswer.db.swap_index import check_index_swap
+from danswer.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 from danswer.natural_language_processing.search_nlp_models import EmbeddingModel
 from danswer.natural_language_processing.search_nlp_models import warm_up_bi_encoder
 from danswer.redis.redis_connector import RedisConnector
-from danswer.redis.redis_connector_index import RedisConnectorIndexingFenceData
+from danswer.redis.redis_connector_index import RedisConnectorIndex
+from danswer.redis.redis_connector_index import RedisConnectorIndexPayload
 from danswer.redis.redis_pool import get_redis_client
 from danswer.utils.logger import setup_logger
 from danswer.utils.variable_functionality import global_version
@@ -56,41 +65,108 @@ from shared_configs.configs import SENTRY_DSN
 logger = setup_logger()
 
 
-class RunIndexingCallback(RunIndexingCallbackInterface):
+class IndexingCallback(IndexingHeartbeatInterface):
     def __init__(
         self,
         stop_key: str,
         generator_progress_key: str,
-        redis_lock: redis.lock.Lock,
+        redis_lock: RedisLock,
         redis_client: Redis,
     ):
         super().__init__()
-        self.redis_lock: redis.lock.Lock = redis_lock
+        self.redis_lock: RedisLock = redis_lock
         self.stop_key: str = stop_key
         self.generator_progress_key: str = generator_progress_key
         self.redis_client = redis_client
+        self.started: datetime = datetime.now(timezone.utc)
+        self.redis_lock.reacquire()
+
+        self.last_tag: str = "IndexingCallback.__init__"
+        self.last_lock_reacquire: datetime = datetime.now(timezone.utc)
 
     def should_stop(self) -> bool:
         if self.redis_client.exists(self.stop_key):
             return True
         return False
 
-    def progress(self, amount: int) -> None:
-        self.redis_lock.reacquire()
+    def progress(self, tag: str, amount: int) -> None:
+        try:
+            self.redis_lock.reacquire()
+            self.last_tag = tag
+            self.last_lock_reacquire = datetime.now(timezone.utc)
+        except LockError:
+            logger.exception(
+                f"IndexingCallback - lock.reacquire exceptioned. "
+                f"lock_timeout={self.redis_lock.timeout} "
+                f"start={self.started} "
+                f"last_tag={self.last_tag} "
+                f"last_reacquired={self.last_lock_reacquire} "
+                f"now={datetime.now(timezone.utc)}"
+            )
+            raise
+
         self.redis_client.incrby(self.generator_progress_key, amount)
 
 
+def get_unfenced_index_attempt_ids(db_session: Session, r: redis.Redis) -> list[int]:
+    """Gets a list of unfenced index attempts. Should not be possible, so we'd typically
+    want to clean them up.
+
+    Unfenced = attempt not in terminal state and fence does not exist.
+    """
+    unfenced_attempts: list[int] = []
+
+    # inner/outer/inner double check pattern to avoid race conditions when checking for
+    # bad state
+    # inner = index_attempt in non terminal state
+    # outer = r.fence_key down
+
+    # check the db for index attempts in a non terminal state
+    attempts: list[IndexAttempt] = []
+    attempts.extend(
+        get_all_index_attempts_by_status(IndexingStatus.NOT_STARTED, db_session)
+    )
+    attempts.extend(
+        get_all_index_attempts_by_status(IndexingStatus.IN_PROGRESS, db_session)
+    )
+
+    for attempt in attempts:
+        fence_key = RedisConnectorIndex.fence_key_with_ids(
+            attempt.connector_credential_pair_id, attempt.search_settings_id
+        )
+
+        # if the fence is down / doesn't exist, possible error but not confirmed
+        if r.exists(fence_key):
+            continue
+
+        # Between the time the attempts are first looked up and the time we see the fence down,
+        # the attempt may have completed and taken down the fence normally.
+
+        # We need to double check that the index attempt is still in a non terminal state
+        # and matches the original state, which confirms we are really in a bad state.
+        attempt_2 = get_index_attempt(db_session, attempt.id)
+        if not attempt_2:
+            continue
+
+        if attempt.status != attempt_2.status:
+            continue
+
+        unfenced_attempts.append(attempt.id)
+
+    return unfenced_attempts
+
+
 @shared_task(
-    name="check_for_indexing",
+    name=DanswerCeleryTask.CHECK_FOR_INDEXING,
     soft_time_limit=300,
     bind=True,
 )
 def check_for_indexing(self: Task, *, tenant_id: str | None) -> int | None:
     tasks_created = 0
-
+    locked = False
     r = get_redis_client(tenant_id=tenant_id)
 
-    lock_beat = r.lock(
+    lock_beat: RedisLock = r.lock(
         DanswerRedisLocks.CHECK_INDEXING_BEAT_LOCK,
         timeout=CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT,
     )
@@ -100,6 +176,9 @@ def check_for_indexing(self: Task, *, tenant_id: str | None) -> int | None:
         if not lock_beat.acquire(blocking=False):
             return None
 
+        locked = True
+
+        # check for search settings swap
         with get_session_with_tenant(tenant_id=tenant_id) as db_session:
             old_search_settings = check_index_swap(db_session=db_session)
             current_search_settings = get_current_search_settings(db_session)
@@ -118,26 +197,24 @@ def check_for_indexing(self: Task, *, tenant_id: str | None) -> int | None:
                         embedding_model=embedding_model,
                     )
 
+        # gather cc_pair_ids
         cc_pair_ids: list[int] = []
         with get_session_with_tenant(tenant_id) as db_session:
+            lock_beat.reacquire()
             cc_pairs = fetch_connector_credential_pairs(db_session)
             for cc_pair_entry in cc_pairs:
                 cc_pair_ids.append(cc_pair_entry.id)
 
+        # kick off index attempts
         for cc_pair_id in cc_pair_ids:
+            lock_beat.reacquire()
+
             redis_connector = RedisConnector(tenant_id, cc_pair_id)
             with get_session_with_tenant(tenant_id) as db_session:
-                # Get the primary search settings
-                primary_search_settings = get_current_search_settings(db_session)
-                search_settings = [primary_search_settings]
-
-                # Check for secondary search settings
-                secondary_search_settings = get_secondary_search_settings(db_session)
-                if secondary_search_settings is not None:
-                    # If secondary settings exist, add them to the list
-                    search_settings.append(secondary_search_settings)
-
-                for search_settings_instance in search_settings:
+                search_settings_list: list[SearchSettings] = get_active_search_settings(
+                    db_session
+                )
+                for search_settings_instance in search_settings_list:
                     redis_connector_index = redis_connector.new_index(
                         search_settings_instance.id
                     )
@@ -153,33 +230,80 @@ def check_for_indexing(self: Task, *, tenant_id: str | None) -> int | None:
                     last_attempt = get_last_attempt_for_cc_pair(
                         cc_pair.id, search_settings_instance.id, db_session
                     )
+
+                    search_settings_primary = False
+                    if search_settings_instance.id == search_settings_list[0].id:
+                        search_settings_primary = True
+
                     if not _should_index(
                         cc_pair=cc_pair,
                         last_index=last_attempt,
                         search_settings_instance=search_settings_instance,
-                        secondary_index_building=len(search_settings) > 1,
+                        search_settings_primary=search_settings_primary,
+                        secondary_index_building=len(search_settings_list) > 1,
                         db_session=db_session,
                     ):
                         continue
 
+                    reindex = False
+                    if search_settings_instance.id == search_settings_list[0].id:
+                        # the indexing trigger is only checked and cleared with the primary search settings
+                        if cc_pair.indexing_trigger is not None:
+                            if cc_pair.indexing_trigger == IndexingMode.REINDEX:
+                                reindex = True
+
+                            task_logger.info(
+                                f"Connector indexing manual trigger detected: "
+                                f"cc_pair={cc_pair.id} "
+                                f"search_settings={search_settings_instance.id} "
+                                f"indexing_mode={cc_pair.indexing_trigger}"
+                            )
+
+                            mark_ccpair_with_indexing_trigger(
+                                cc_pair.id, None, db_session
+                            )
+
                     # using a task queue and only allowing one task per cc_pair/search_setting
                     # prevents us from starving out certain attempts
                     attempt_id = try_creating_indexing_task(
                         self.app,
                         cc_pair,
                         search_settings_instance,
-                        False,
+                        reindex,
                         db_session,
                         r,
                         tenant_id,
                     )
                     if attempt_id:
                         task_logger.info(
-                            f"Indexing queued: index_attempt={attempt_id} "
+                            f"Connector indexing queued: "
+                            f"index_attempt={attempt_id} "
                             f"cc_pair={cc_pair.id} "
-                            f"search_settings={search_settings_instance.id} "
+                            f"search_settings={search_settings_instance.id}"
                         )
                         tasks_created += 1
+
+        # Fail any index attempts in the DB that don't have fences
+        # This shouldn't ever happen!
+        with get_session_with_tenant(tenant_id) as db_session:
+            unfenced_attempt_ids = get_unfenced_index_attempt_ids(db_session, r)
+            for attempt_id in unfenced_attempt_ids:
+                lock_beat.reacquire()
+
+                attempt = get_index_attempt(db_session, attempt_id)
+                if not attempt:
+                    continue
+
+                failure_reason = (
+                    f"Unfenced index attempt found in DB: "
+                    f"index_attempt={attempt.id} "
+                    f"cc_pair={attempt.connector_credential_pair_id} "
+                    f"search_settings={attempt.search_settings_id}"
+                )
+                task_logger.error(failure_reason)
+                mark_attempt_failed(
+                    attempt.id, db_session, failure_reason=failure_reason
+                )
     except SoftTimeLimitExceeded:
         task_logger.info(
             "Soft time limit exceeded, task is being terminated gracefully."
@@ -187,8 +311,14 @@ def check_for_indexing(self: Task, *, tenant_id: str | None) -> int | None:
     except Exception:
         task_logger.exception(f"Unexpected exception: tenant={tenant_id}")
     finally:
-        if lock_beat.owned():
-            lock_beat.release()
+        if locked:
+            if lock_beat.owned():
+                lock_beat.release()
+            else:
+                task_logger.error(
+                    "check_for_indexing - Lock not owned on completion: "
+                    f"tenant={tenant_id}"
+                )
 
     return tasks_created
 
@@ -197,6 +327,7 @@ def _should_index(
     cc_pair: ConnectorCredentialPair,
     last_index: IndexAttempt | None,
     search_settings_instance: SearchSettings,
+    search_settings_primary: bool,
     secondary_index_building: bool,
     db_session: Session,
 ) -> bool:
@@ -261,6 +392,11 @@ def _should_index(
     ):
         return False
 
+    if search_settings_primary:
+        if cc_pair.indexing_trigger is not None:
+            # if a manual indexing trigger is on the cc pair, honor it for primary search settings
+            return True
+
     # if no attempt has ever occurred, we should index regardless of refresh_freq
     if not last_index:
         return True
@@ -293,10 +429,11 @@ def try_creating_indexing_task(
     """
 
     LOCK_TIMEOUT = 30
+    index_attempt_id: int | None = None
 
     # we need to serialize any attempt to trigger indexing since it can be triggered
     # either via celery beat or manually (API call)
-    lock = r.lock(
+    lock: RedisLock = r.lock(
         DANSWER_REDIS_FUNCTION_LOCK_PREFIX + "try_creating_indexing_task",
         timeout=LOCK_TIMEOUT,
     )
@@ -325,7 +462,7 @@ def try_creating_indexing_task(
         redis_connector_index.generator_clear()
 
         # set a basic fence to start
-        payload = RedisConnectorIndexingFenceData(
+        payload = RedisConnectorIndexPayload(
             index_attempt_id=None,
             started=None,
             submitted=datetime.now(timezone.utc),
@@ -347,8 +484,10 @@ def try_creating_indexing_task(
 
         custom_task_id = redis_connector_index.generate_generator_task_id()
 
+        # when the task is sent, we have yet to finish setting up the fence
+        # therefore, the task must contain code that blocks until the fence is ready
         result = celery_app.send_task(
-            "connector_indexing_proxy_task",
+            DanswerCeleryTask.CONNECTOR_INDEXING_PROXY_TASK,
             kwargs=dict(
                 index_attempt_id=index_attempt_id,
                 cc_pair_id=cc_pair.id,
@@ -366,15 +505,17 @@ def try_creating_indexing_task(
         payload.index_attempt_id = index_attempt_id
         payload.celery_task_id = result.id
         redis_connector_index.set_fence(payload)
-
     except Exception:
-        redis_connector_index.set_fence(payload)
         task_logger.exception(
-            f"Unexpected exception: "
+            f"try_creating_indexing_task - Unexpected exception: "
             f"tenant={tenant_id} "
             f"cc_pair={cc_pair.id} "
             f"search_settings={search_settings.id}"
         )
+
+        if index_attempt_id is not None:
+            delete_index_attempt(db_session, index_attempt_id)
+        redis_connector_index.set_fence(None)
         return None
     finally:
         if lock.owned():
@@ -383,8 +524,14 @@ def try_creating_indexing_task(
     return index_attempt_id
 
 
-@shared_task(name="connector_indexing_proxy_task", acks_late=False, track_started=True)
+@shared_task(
+    name=DanswerCeleryTask.CONNECTOR_INDEXING_PROXY_TASK,
+    bind=True,
+    acks_late=False,
+    track_started=True,
+)
 def connector_indexing_proxy_task(
+    self: Task,
     index_attempt_id: int,
     cc_pair_id: int,
     search_settings_id: int,
@@ -392,15 +539,19 @@ def connector_indexing_proxy_task(
 ) -> None:
     """celery tasks are forked, but forking is unstable.  This proxies work to a spawned task."""
     task_logger.info(
-        f"Indexing proxy - starting: attempt={index_attempt_id} "
+        f"Indexing watchdog - starting: attempt={index_attempt_id} "
         f"tenant={tenant_id} "
         f"cc_pair={cc_pair_id} "
         f"search_settings={search_settings_id}"
     )
+
+    if not self.request.id:
+        task_logger.error("self.request.id is None!")
+
     client = SimpleJobClient()
 
     job = client.submit(
-        connector_indexing_task,
+        connector_indexing_task_wrapper,
         index_attempt_id,
         cc_pair_id,
         search_settings_id,
@@ -411,7 +562,7 @@ def connector_indexing_proxy_task(
 
     if not job:
         task_logger.info(
-            f"Indexing proxy - spawn failed: attempt={index_attempt_id} "
+            f"Indexing watchdog - spawn failed: attempt={index_attempt_id} "
             f"tenant={tenant_id} "
             f"cc_pair={cc_pair_id} "
             f"search_settings={search_settings_id}"
@@ -419,43 +570,117 @@ def connector_indexing_proxy_task(
         return
 
     task_logger.info(
-        f"Indexing proxy - spawn succeeded: attempt={index_attempt_id} "
+        f"Indexing watchdog - spawn succeeded: attempt={index_attempt_id} "
         f"tenant={tenant_id} "
         f"cc_pair={cc_pair_id} "
         f"search_settings={search_settings_id}"
     )
 
+    redis_connector = RedisConnector(tenant_id, cc_pair_id)
+    redis_connector_index = redis_connector.new_index(search_settings_id)
+
     while True:
-        sleep(10)
+        sleep(5)
 
-        # do nothing for ongoing jobs that haven't been stopped
-        if not job.done():
-            with get_session_with_tenant(tenant_id) as db_session:
-                index_attempt = get_index_attempt(
-                    db_session=db_session, index_attempt_id=index_attempt_id
-                )
-
-                if not index_attempt:
-                    continue
-
-                if not index_attempt.is_finished():
-                    continue
-
-        if job.status == "error":
-            task_logger.error(
-                f"Indexing proxy - spawned task exceptioned: "
+        if self.request.id and redis_connector_index.terminating(self.request.id):
+            task_logger.warning(
+                "Indexing watchdog - termination signal detected: "
                 f"attempt={index_attempt_id} "
                 f"tenant={tenant_id} "
                 f"cc_pair={cc_pair_id} "
-                f"search_settings={search_settings_id} "
-                f"error={job.exception()}"
+                f"search_settings={search_settings_id}"
             )
 
+            try:
+                with get_session_with_tenant(tenant_id) as db_session:
+                    mark_attempt_canceled(
+                        index_attempt_id,
+                        db_session,
+                        "Connector termination signal detected",
+                    )
+            except Exception:
+                # if the DB exceptions, we'll just get an unfriendly failure message
+                # in the UI instead of the cancellation message
+                logger.exception(
+                    "Indexing watchdog - transient exception marking index attempt as canceled: "
+                    f"attempt={index_attempt_id} "
+                    f"tenant={tenant_id} "
+                    f"cc_pair={cc_pair_id} "
+                    f"search_settings={search_settings_id}"
+                )
+
+                job.cancel()
+
+            break
+
+        if not job.done():
+            # if the spawned task is still running, restart the check once again
+            # if the index attempt is not in a finished status
+            try:
+                with get_session_with_tenant(tenant_id) as db_session:
+                    index_attempt = get_index_attempt(
+                        db_session=db_session, index_attempt_id=index_attempt_id
+                    )
+
+                    if not index_attempt:
+                        continue
+
+                    if not index_attempt.is_finished():
+                        continue
+            except Exception:
+                # if the DB exceptioned, just restart the check.
+                # polling the index attempt status doesn't need to be strongly consistent
+                logger.exception(
+                    "Indexing watchdog - transient exception looking up index attempt: "
+                    f"attempt={index_attempt_id} "
+                    f"tenant={tenant_id} "
+                    f"cc_pair={cc_pair_id} "
+                    f"search_settings={search_settings_id}"
+                )
+                continue
+
+        if job.status == "error":
+            ignore_exitcode = False
+
+            exit_code: int | None = None
+            if job.process:
+                exit_code = job.process.exitcode
+
+            # seeing non-deterministic behavior where spawned tasks occasionally return exit code 1
+            # even though logging clearly indicates that they completed successfully
+            # to work around this, we ignore the job error state if the completion signal is OK
+            status_int = redis_connector_index.get_completion()
+            if status_int:
+                status_enum = HTTPStatus(status_int)
+                if status_enum == HTTPStatus.OK:
+                    ignore_exitcode = True
+
+            if ignore_exitcode:
+                task_logger.warning(
+                    "Indexing watchdog - spawned task has non-zero exit code "
+                    "but completion signal is OK. Continuing...: "
+                    f"attempt={index_attempt_id} "
+                    f"tenant={tenant_id} "
+                    f"cc_pair={cc_pair_id} "
+                    f"search_settings={search_settings_id} "
+                    f"exit_code={exit_code}"
+                )
+            else:
+                task_logger.error(
+                    "Indexing watchdog - spawned task exceptioned: "
+                    f"attempt={index_attempt_id} "
+                    f"tenant={tenant_id} "
+                    f"cc_pair={cc_pair_id} "
+                    f"search_settings={search_settings_id} "
+                    f"exit_code={exit_code} "
+                    f"error={job.exception()}"
+                )
+
         job.release()
         break
 
     task_logger.info(
-        f"Indexing proxy - finished: attempt={index_attempt_id} "
+        f"Indexing watchdog - finished: attempt={index_attempt_id} "
         f"tenant={tenant_id} "
         f"cc_pair={cc_pair_id} "
         f"search_settings={search_settings_id}"
@@ -463,6 +688,38 @@ def connector_indexing_proxy_task(
     return
 
 
+def connector_indexing_task_wrapper(
+    index_attempt_id: int,
+    cc_pair_id: int,
+    search_settings_id: int,
+    tenant_id: str | None,
+    is_ee: bool,
+) -> int | None:
+    """Just wraps connector_indexing_task so we can log any exceptions before
+    re-raising it."""
+    result: int | None = None
+
+    try:
+        result = connector_indexing_task(
+            index_attempt_id,
+            cc_pair_id,
+            search_settings_id,
+            tenant_id,
+            is_ee,
+        )
+    except:
+        logger.exception(
+            f"connector_indexing_task exceptioned: "
+            f"tenant={tenant_id} "
+            f"index_attempt={index_attempt_id} "
+            f"cc_pair={cc_pair_id} "
+            f"search_settings={search_settings_id}"
+        )
+        raise
+
+    return result
+
+
 def connector_indexing_task(
     index_attempt_id: int,
     cc_pair_id: int,
@@ -499,7 +756,8 @@ def connector_indexing_task(
         logger.debug("Sentry DSN not provided, skipping Sentry initialization")
 
     logger.info(
-        f"Indexing spawned task starting: attempt={index_attempt_id} "
+        f"Indexing spawned task starting: "
+        f"attempt={index_attempt_id} "
         f"tenant={tenant_id} "
         f"cc_pair={cc_pair_id} "
         f"search_settings={search_settings_id}"
@@ -516,6 +774,7 @@ def connector_indexing_task(
     if redis_connector.delete.fenced:
         raise RuntimeError(
             f"Indexing will not start because connector deletion is in progress: "
+            f"attempt={index_attempt_id} "
             f"cc_pair={cc_pair_id} "
             f"fence={redis_connector.delete.fence_key}"
         )
@@ -523,18 +782,18 @@ def connector_indexing_task(
     if redis_connector.stop.fenced:
         raise RuntimeError(
             f"Indexing will not start because a connector stop signal was detected: "
+            f"attempt={index_attempt_id} "
             f"cc_pair={cc_pair_id} "
             f"fence={redis_connector.stop.fence_key}"
         )
 
     while True:
-        # wait for the fence to come up
-        if not redis_connector_index.fenced:
+        if not redis_connector_index.fenced:  # The fence must exist
             raise ValueError(
                 f"connector_indexing_task - fence not found: fence={redis_connector_index.fence_key}"
             )
 
-        payload = redis_connector_index.payload
+        payload = redis_connector_index.payload  # The payload must exist
         if not payload:
             raise ValueError("connector_indexing_task: payload invalid or not found")
 
@@ -557,16 +816,19 @@ def connector_indexing_task(
         )
         break
 
-    lock = r.lock(
+    # set thread_local=False since we don't control what thread the indexing/pruning
+    # might run our callback with
+    lock: RedisLock = r.lock(
         redis_connector_index.generator_lock_key,
         timeout=CELERY_INDEXING_LOCK_TIMEOUT,
+        thread_local=False,
     )
 
     acquired = lock.acquire(blocking=False)
     if not acquired:
         logger.warning(
             f"Indexing task already running, exiting...: "
-            f"cc_pair={cc_pair_id} search_settings={search_settings_id}"
+            f"index_attempt={index_attempt_id} cc_pair={cc_pair_id} search_settings={search_settings_id}"
         )
         return None
 
@@ -601,7 +863,7 @@ def connector_indexing_task(
                 )
 
         # define a callback class
-        callback = RunIndexingCallback(
+        callback = IndexingCallback(
             redis_connector.stop.fence_key,
             redis_connector_index.generator_progress_key,
             lock,

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

@@ -13,12 +13,13 @@ from sqlalchemy.orm import Session
 
 from danswer.background.celery.apps.app_base import task_logger
 from danswer.configs.app_configs import JOB_TIMEOUT
+from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import PostgresAdvisoryLocks
 from danswer.db.engine import get_session_with_tenant
 
 
 @shared_task(
-    name="kombu_message_cleanup_task",
+    name=DanswerCeleryTask.KOMBU_MESSAGE_CLEANUP_TASK,
     soft_time_limit=JOB_TIMEOUT,
     bind=True,
     base=AbortableTask,

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

@@ -8,11 +8,12 @@ from celery import shared_task
 from celery import Task
 from celery.exceptions import SoftTimeLimitExceeded
 from redis import Redis
+from redis.lock import Lock as RedisLock
 from sqlalchemy.orm import Session
 
 from danswer.background.celery.apps.app_base import task_logger
 from danswer.background.celery.celery_utils import extract_ids_from_runnable_connector
-from danswer.background.celery.tasks.indexing.tasks import RunIndexingCallback
+from danswer.background.celery.tasks.indexing.tasks import IndexingCallback
 from danswer.configs.app_configs import ALLOW_SIMULTANEOUS_PRUNING
 from danswer.configs.app_configs import JOB_TIMEOUT
 from danswer.configs.constants import CELERY_PRUNING_LOCK_TIMEOUT
@@ -20,6 +21,7 @@ from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DANSWER_REDIS_FUNCTION_LOCK_PREFIX
 from danswer.configs.constants import DanswerCeleryPriority
 from danswer.configs.constants import DanswerCeleryQueues
+from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DanswerRedisLocks
 from danswer.connectors.factory import instantiate_connector
 from danswer.connectors.models import InputType
@@ -39,7 +41,14 @@ logger = setup_logger()
 
 
 def _is_pruning_due(cc_pair: ConnectorCredentialPair) -> bool:
-    """Returns boolean indicating if pruning is due."""
+    """Returns boolean indicating if pruning is due.
+
+    Next pruning time is calculated as a delta from the last successful prune, or the
+    last successful indexing if pruning has never succeeded.
+
+    TODO(rkuo): consider whether we should allow pruning to be immediately rescheduled
+    if pruning fails (which is what it does now). A backoff could be reasonable.
+    """
 
     # skip pruning if no prune frequency is set
     # pruning can still be forced via the API which will run a pruning task directly
@@ -68,7 +77,7 @@ def _is_pruning_due(cc_pair: ConnectorCredentialPair) -> bool:
 
 
 @shared_task(
-    name="check_for_pruning",
+    name=DanswerCeleryTask.CHECK_FOR_PRUNING,
     soft_time_limit=JOB_TIMEOUT,
     bind=True,
 )
@@ -177,7 +186,7 @@ def try_creating_prune_generator_task(
         custom_task_id = f"{redis_connector.prune.generator_task_key}_{uuid4()}"
 
         celery_app.send_task(
-            "connector_pruning_generator_task",
+            DanswerCeleryTask.CONNECTOR_PRUNING_GENERATOR_TASK,
             kwargs=dict(
                 cc_pair_id=cc_pair.id,
                 connector_id=cc_pair.connector_id,
@@ -202,7 +211,7 @@ def try_creating_prune_generator_task(
 
 
 @shared_task(
-    name="connector_pruning_generator_task",
+    name=DanswerCeleryTask.CONNECTOR_PRUNING_GENERATOR_TASK,
     acks_late=False,
     soft_time_limit=JOB_TIMEOUT,
     track_started=True,
@@ -225,13 +234,18 @@ def connector_pruning_generator_task(
     pruning_ctx_dict["request_id"] = self.request.id
     pruning_ctx.set(pruning_ctx_dict)
 
+    task_logger.info(f"Pruning generator starting: cc_pair={cc_pair_id}")
+
     redis_connector = RedisConnector(tenant_id, cc_pair_id)
 
     r = get_redis_client(tenant_id=tenant_id)
 
-    lock = r.lock(
+    # set thread_local=False since we don't control what thread the indexing/pruning
+    # might run our callback with
+    lock: RedisLock = r.lock(
         DanswerRedisLocks.PRUNING_LOCK_PREFIX + f"_{redis_connector.id}",
         timeout=CELERY_PRUNING_LOCK_TIMEOUT,
+        thread_local=False,
     )
 
     acquired = lock.acquire(blocking=False)
@@ -255,6 +269,11 @@ def connector_pruning_generator_task(
                 )
                 return
 
+            task_logger.info(
+                f"Pruning generator running connector: "
+                f"cc_pair={cc_pair_id} "
+                f"connector_source={cc_pair.connector.source}"
+            )
             runnable_connector = instantiate_connector(
                 db_session,
                 cc_pair.connector.source,
@@ -263,12 +282,13 @@ def connector_pruning_generator_task(
                 cc_pair.credential,
             )
 
-            callback = RunIndexingCallback(
+            callback = IndexingCallback(
                 redis_connector.stop.fence_key,
                 redis_connector.prune.generator_progress_key,
                 lock,
                 r,
             )
+
             # a list of docs in the source
             all_connector_doc_ids: set[str] = extract_ids_from_runnable_connector(
                 runnable_connector, callback
@@ -290,8 +310,8 @@ def connector_pruning_generator_task(
             task_logger.info(
                 f"Pruning set collected: "
                 f"cc_pair={cc_pair_id} "
-                f"docs_to_remove={len(doc_ids_to_remove)} "
-                f"doc_source={cc_pair.connector.source}"
+                f"connector_source={cc_pair.connector.source} "
+                f"docs_to_remove={len(doc_ids_to_remove)}"
             )
 
             task_logger.info(
@@ -314,10 +334,10 @@ def connector_pruning_generator_task(
             f"Failed to run pruning: cc_pair={cc_pair_id} connector={connector_id}"
         )
 
-        redis_connector.prune.generator_clear()
-        redis_connector.prune.taskset_clear()
-        redis_connector.prune.set_fence(False)
+        redis_connector.prune.reset()
         raise e
     finally:
         if lock.owned():
             lock.release()
+
+        task_logger.info(f"Pruning generator finished: cc_pair={cc_pair_id}")

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

@@ -9,6 +9,7 @@ from tenacity import RetryError
 from danswer.access.access import get_access_for_document
 from danswer.background.celery.apps.app_base import task_logger
 from danswer.background.celery.tasks.shared.RetryDocumentIndex import RetryDocumentIndex
+from danswer.configs.constants import DanswerCeleryTask
 from danswer.db.document import delete_document_by_connector_credential_pair__no_commit
 from danswer.db.document import delete_documents_complete__no_commit
 from danswer.db.document import get_document
@@ -31,7 +32,7 @@ LIGHT_TIME_LIMIT = LIGHT_SOFT_TIME_LIMIT + 15
 
 
 @shared_task(
-    name="document_by_cc_pair_cleanup_task",
+    name=DanswerCeleryTask.DOCUMENT_BY_CC_PAIR_CLEANUP_TASK,
     soft_time_limit=LIGHT_SOFT_TIME_LIMIT,
     time_limit=LIGHT_TIME_LIMIT,
     max_retries=DOCUMENT_BY_CC_PAIR_CLEANUP_MAX_RETRIES,
@@ -177,7 +178,17 @@ def document_by_cc_pair_cleanup_task(
                 f"Max celery task retries reached. Marking doc as dirty for reconciliation: "
                 f"tenant={tenant_id} doc={document_id}"
             )
-            with get_session_with_tenant(tenant_id):
+            with get_session_with_tenant(tenant_id) as db_session:
+                # delete the cc pair relationship now and let reconciliation clean it up
+                # in vespa
+                delete_document_by_connector_credential_pair__no_commit(
+                    db_session=db_session,
+                    document_id=document_id,
+                    connector_credential_pair_identifier=ConnectorCredentialPairIdentifier(
+                        connector_id=connector_id,
+                        credential_id=credential_id,
+                    ),
+                )
                 mark_document_as_modified(document_id, db_session)
         return False
 

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

@@ -5,7 +5,6 @@ from http import HTTPStatus
 from typing import cast
 
 import httpx
-import redis
 from celery import Celery
 from celery import shared_task
 from celery import Task
@@ -13,6 +12,7 @@ from celery.exceptions import SoftTimeLimitExceeded
 from celery.result import AsyncResult
 from celery.states import READY_STATES
 from redis import Redis
+from redis.lock import Lock as RedisLock
 from sqlalchemy.orm import Session
 from tenacity import RetryError
 
@@ -25,6 +25,7 @@ from danswer.background.celery.tasks.shared.tasks import LIGHT_TIME_LIMIT
 from danswer.configs.app_configs import JOB_TIMEOUT
 from danswer.configs.constants import CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT
 from danswer.configs.constants import DanswerCeleryQueues
+from danswer.configs.constants import DanswerCeleryTask
 from danswer.configs.constants import DanswerRedisLocks
 from danswer.db.connector import fetch_connector_by_id
 from danswer.db.connector import mark_cc_pair_as_permissions_synced
@@ -48,11 +49,9 @@ from danswer.db.document_set import mark_document_set_as_synced
 from danswer.db.engine import get_session_with_tenant
 from danswer.db.enums import IndexingStatus
 from danswer.db.index_attempt import delete_index_attempts
-from danswer.db.index_attempt import get_all_index_attempts_by_status
 from danswer.db.index_attempt import get_index_attempt
 from danswer.db.index_attempt import mark_attempt_failed
 from danswer.db.models import DocumentSet
-from danswer.db.models import IndexAttempt
 from danswer.document_index.document_index_utils import get_both_index_names
 from danswer.document_index.factory import get_default_document_index
 from danswer.document_index.interfaces import VespaDocumentFields
@@ -61,7 +60,7 @@ from danswer.redis.redis_connector_credential_pair import RedisConnectorCredenti
 from danswer.redis.redis_connector_delete import RedisConnectorDelete
 from danswer.redis.redis_connector_doc_perm_sync import RedisConnectorPermissionSync
 from danswer.redis.redis_connector_doc_perm_sync import (
-    RedisConnectorPermissionSyncData,
+    RedisConnectorPermissionSyncPayload,
 )
 from danswer.redis.redis_connector_index import RedisConnectorIndex
 from danswer.redis.redis_connector_prune import RedisConnectorPrune
@@ -82,7 +81,7 @@ logger = setup_logger()
 # celery auto associates tasks created inside another task,
 # which bloats the result metadata considerably. trail=False prevents this.
 @shared_task(
-    name="check_for_vespa_sync_task",
+    name=DanswerCeleryTask.CHECK_FOR_VESPA_SYNC_TASK,
     soft_time_limit=JOB_TIMEOUT,
     trail=False,
     bind=True,
@@ -167,7 +166,7 @@ def try_generate_stale_document_sync_tasks(
     celery_app: Celery,
     db_session: Session,
     r: Redis,
-    lock_beat: redis.lock.Lock,
+    lock_beat: RedisLock,
     tenant_id: str | None,
 ) -> int | None:
     # the fence is up, do nothing
@@ -185,7 +184,12 @@ def try_generate_stale_document_sync_tasks(
         f"Stale documents found (at least {stale_doc_count}). Generating sync tasks by cc pair."
     )
 
-    task_logger.info("RedisConnector.generate_tasks starting by cc_pair.")
+    task_logger.info(
+        "RedisConnector.generate_tasks starting by cc_pair. "
+        "Documents spanning multiple cc_pairs will only be synced once."
+    )
+
+    docs_to_skip: set[str] = set()
 
     # rkuo: we could technically sync all stale docs in one big pass.
     # but I feel it's more understandable to group the docs by cc_pair
@@ -193,22 +197,21 @@ def try_generate_stale_document_sync_tasks(
     cc_pairs = get_connector_credential_pairs(db_session)
     for cc_pair in cc_pairs:
         rc = RedisConnectorCredentialPair(tenant_id, cc_pair.id)
-        tasks_generated = rc.generate_tasks(
-            celery_app, db_session, r, lock_beat, tenant_id
-        )
+        rc.set_skip_docs(docs_to_skip)
+        result = rc.generate_tasks(celery_app, db_session, r, lock_beat, tenant_id)
 
-        if tasks_generated is None:
+        if result is None:
             continue
 
-        if tasks_generated == 0:
+        if result[1] == 0:
             continue
 
         task_logger.info(
             f"RedisConnector.generate_tasks finished for single cc_pair. "
-            f"cc_pair_id={cc_pair.id} tasks_generated={tasks_generated}"
+            f"cc_pair={cc_pair.id} tasks_generated={result[0]} tasks_possible={result[1]}"
         )
 
-        total_tasks_generated += tasks_generated
+        total_tasks_generated += result[0]
 
     task_logger.info(
         f"RedisConnector.generate_tasks finished for all cc_pairs. total_tasks_generated={total_tasks_generated}"
@@ -223,7 +226,7 @@ def try_generate_document_set_sync_tasks(
     document_set_id: int,
     db_session: Session,
     r: Redis,
-    lock_beat: redis.lock.Lock,
+    lock_beat: RedisLock,
     tenant_id: str | None,
 ) -> int | None:
     lock_beat.reacquire()
@@ -251,12 +254,11 @@ def try_generate_document_set_sync_tasks(
     )
 
     # Add all documents that need to be updated into the queue
-    tasks_generated = rds.generate_tasks(
-        celery_app, db_session, r, lock_beat, tenant_id
-    )
-    if tasks_generated is None:
+    result = rds.generate_tasks(celery_app, db_session, r, lock_beat, tenant_id)
+    if result is None:
         return None
 
+    tasks_generated = result[0]
     # Currently we are allowing the sync to proceed with 0 tasks.
     # It's possible for sets/groups to be generated initially with no entries
     # and they still need to be marked as up to date.
@@ -265,7 +267,7 @@ def try_generate_document_set_sync_tasks(
 
     task_logger.info(
         f"RedisDocumentSet.generate_tasks finished. "
-        f"document_set_id={document_set.id} tasks_generated={tasks_generated}"
+        f"document_set={document_set.id} tasks_generated={tasks_generated}"
     )
 
     # set this only after all tasks have been added
@@ -278,7 +280,7 @@ def try_generate_user_group_sync_tasks(
     usergroup_id: int,
     db_session: Session,
     r: Redis,
-    lock_beat: redis.lock.Lock,
+    lock_beat: RedisLock,
     tenant_id: str | None,
 ) -> int | None:
     lock_beat.reacquire()
@@ -307,12 +309,11 @@ def try_generate_user_group_sync_tasks(
     task_logger.info(
         f"RedisUserGroup.generate_tasks starting. usergroup_id={usergroup.id}"
     )
-    tasks_generated = rug.generate_tasks(
-        celery_app, db_session, r, lock_beat, tenant_id
-    )
-    if tasks_generated is None:
+    result = rug.generate_tasks(celery_app, db_session, r, lock_beat, tenant_id)
+    if result is None:
         return None
 
+    tasks_generated = result[0]
     # Currently we are allowing the sync to proceed with 0 tasks.
     # It's possible for sets/groups to be generated initially with no entries
     # and they still need to be marked as up to date.
@@ -321,7 +322,7 @@ def try_generate_user_group_sync_tasks(
 
     task_logger.info(
         f"RedisUserGroup.generate_tasks finished. "
-        f"usergroup_id={usergroup.id} tasks_generated={tasks_generated}"
+        f"usergroup={usergroup.id} tasks_generated={tasks_generated}"
     )
 
     # set this only after all tasks have been added
@@ -441,11 +442,22 @@ def monitor_connector_deletion_taskset(
                 db_session, cc_pair.connector_id, cc_pair.credential_id
             )
             if len(doc_ids) > 0:
-                # if this happens, documents somehow got added while deletion was in progress. Likely a bug
-                # gating off pruning and indexing work before deletion starts
+                # NOTE(rkuo): if this happens, documents somehow got added while
+                # deletion was in progress. Likely a bug gating off pruning and indexing
+                # work before deletion starts.
                 task_logger.warning(
-                    f"Connector deletion - documents still found after taskset completion: "
-                    f"cc_pair={cc_pair_id} num={len(doc_ids)}"
+                    "Connector deletion - documents still found after taskset completion. "
+                    "Clearing the current deletion attempt and allowing deletion to restart: "
+                    f"cc_pair={cc_pair_id} "
+                    f"docs_deleted={fence_data.num_tasks} "
+                    f"docs_remaining={len(doc_ids)}"
+                )
+
+                # We don't want to waive off why we get into this state, but resetting
+                # our attempt and letting the deletion restart is a good way to recover
+                redis_connector.delete.reset()
+                raise RuntimeError(
+                    "Connector deletion - documents still found after taskset completion"
                 )
 
             # clean up the rest of the related Postgres entities
@@ -509,8 +521,7 @@ def monitor_connector_deletion_taskset(
         f"docs_deleted={fence_data.num_tasks}"
     )
 
-    redis_connector.delete.taskset_clear()
-    redis_connector.delete.set_fence(None)
+    redis_connector.delete.reset()
 
 
 def monitor_ccpair_pruning_taskset(
@@ -579,7 +590,7 @@ def monitor_ccpair_permissions_taskset(
     if remaining > 0:
         return
 
-    payload: RedisConnectorPermissionSyncData | None = (
+    payload: RedisConnectorPermissionSyncPayload | None = (
         redis_connector.permissions.payload
     )
     start_time: datetime | None = payload.started if payload else None
@@ -587,9 +598,7 @@ def monitor_ccpair_permissions_taskset(
     mark_cc_pair_as_permissions_synced(db_session, int(cc_pair_id), start_time)
     task_logger.info(f"Successfully synced permissions for cc_pair={cc_pair_id}")
 
-    redis_connector.permissions.taskset_clear()
-    redis_connector.permissions.generator_clear()
-    redis_connector.permissions.set_fence(None)
+    redis_connector.permissions.reset()
 
 
 def monitor_ccpair_indexing_taskset(
@@ -626,8 +635,8 @@ def monitor_ccpair_indexing_taskset(
     progress = redis_connector_index.get_progress()
     if progress is not None:
         task_logger.info(
-            f"Connector indexing progress: cc_pair_id={cc_pair_id} "
-            f"search_settings_id={search_settings_id} "
+            f"Connector indexing progress: cc_pair={cc_pair_id} "
+            f"search_settings={search_settings_id} "
             f"progress={progress} "
             f"elapsed_submitted={elapsed_submitted.total_seconds():.2f}"
         )
@@ -636,39 +645,73 @@ def monitor_ccpair_indexing_taskset(
         # the task is still setting up
         return
 
-    # Read result state BEFORE generator_complete_key to avoid a race condition
     # never use any blocking methods on the result from inside a task!
     result: AsyncResult = AsyncResult(payload.celery_task_id)
-    result_state = result.state
 
+    # inner/outer/inner double check pattern to avoid race conditions when checking for
+    # bad state
+
+    # inner = get_completion / generator_complete not signaled
+    # outer = result.state in READY state
     status_int = redis_connector_index.get_completion()
-    if status_int is None:
-        if result_state in READY_STATES:
-            # IF the task state is READY, THEN generator_complete should be set
-            # if it isn't, then the worker crashed
-            task_logger.info(
-                f"Connector indexing aborted: "
-                f"cc_pair_id={cc_pair_id} "
-                f"search_settings_id={search_settings_id} "
-                f"elapsed_submitted={elapsed_submitted.total_seconds():.2f}"
-            )
+    if status_int is None:  # inner signal not set ... possible error
+        task_state = result.state
+        if (
+            task_state in READY_STATES
+        ):  # outer signal in terminal state ... possible error
+            # Now double check!
+            if redis_connector_index.get_completion() is None:
+                # inner signal still not set (and cannot change when outer result_state is READY)
+                # Task is finished but generator complete isn't set.
+                # We have a problem! Worker may have crashed.
+                task_result = str(result.result)
+                task_traceback = str(result.traceback)
 
-            index_attempt = get_index_attempt(db_session, payload.index_attempt_id)
-            if index_attempt:
-                mark_attempt_failed(
-                    index_attempt_id=payload.index_attempt_id,
-                    db_session=db_session,
-                    failure_reason="Connector indexing aborted or exceptioned.",
+                msg = (
+                    f"Connector indexing aborted or exceptioned: "
+                    f"attempt={payload.index_attempt_id} "
+                    f"celery_task={payload.celery_task_id} "
+                    f"cc_pair={cc_pair_id} "
+                    f"search_settings={search_settings_id} "
+                    f"elapsed_submitted={elapsed_submitted.total_seconds():.2f} "
+                    f"result.state={task_state} "
+                    f"result.result={task_result} "
+                    f"result.traceback={task_traceback}"
                 )
+                task_logger.warning(msg)
 
-            redis_connector_index.reset()
+                try:
+                    index_attempt = get_index_attempt(
+                        db_session, payload.index_attempt_id
+                    )
+                    if index_attempt:
+                        if (
+                            index_attempt.status != IndexingStatus.CANCELED
+                            and index_attempt.status != IndexingStatus.FAILED
+                        ):
+                            mark_attempt_failed(
+                                index_attempt_id=payload.index_attempt_id,
+                                db_session=db_session,
+                                failure_reason=msg,
+                            )
+                except Exception:
+                    task_logger.exception(
+                        "monitor_ccpair_indexing_taskset - transient exception marking index attempt as failed: "
+                        f"attempt={payload.index_attempt_id} "
+                        f"tenant={tenant_id} "
+                        f"cc_pair={cc_pair_id} "
+                        f"search_settings={search_settings_id}"
+                    )
+
+                redis_connector_index.reset()
         return
 
     status_enum = HTTPStatus(status_int)
 
     task_logger.info(
-        f"Connector indexing finished: cc_pair_id={cc_pair_id} "
-        f"search_settings_id={search_settings_id} "
+        f"Connector indexing finished: cc_pair={cc_pair_id} "
+        f"search_settings={search_settings_id} "
+        f"progress={progress} "
         f"status={status_enum.name} "
         f"elapsed_submitted={elapsed_submitted.total_seconds():.2f}"
     )
@@ -676,7 +719,7 @@ def monitor_ccpair_indexing_taskset(
     redis_connector_index.reset()
 
 
-@shared_task(name="monitor_vespa_sync", soft_time_limit=300, bind=True)
+@shared_task(name=DanswerCeleryTask.MONITOR_VESPA_SYNC, soft_time_limit=300, bind=True)
 def monitor_vespa_sync(self: Task, tenant_id: str | None) -> bool:
     """This is a celery beat task that monitors and finalizes metadata sync tasksets.
     It scans for fence values and then gets the counts of any associated tasksets.
@@ -689,7 +732,7 @@ def monitor_vespa_sync(self: Task, tenant_id: str | None) -> bool:
     """
     r = get_redis_client(tenant_id=tenant_id)
 
-    lock_beat: redis.lock.Lock = r.lock(
+    lock_beat: RedisLock = r.lock(
         DanswerRedisLocks.MONITOR_VESPA_SYNC_BEAT_LOCK,
         timeout=CELERY_VESPA_SYNC_BEAT_LOCK_TIMEOUT,
     )
@@ -701,7 +744,7 @@ def monitor_vespa_sync(self: Task, tenant_id: str | None) -> bool:
 
         # print current queue lengths
         r_celery = self.app.broker_connection().channel().client  # type: ignore
-        n_celery = celery_get_queue_length("celery", r)
+        n_celery = celery_get_queue_length("celery", r_celery)
         n_indexing = celery_get_queue_length(
             DanswerCeleryQueues.CONNECTOR_INDEXING, r_celery
         )
@@ -727,34 +770,6 @@ def monitor_vespa_sync(self: Task, tenant_id: str | None) -> bool:
             f"permissions_sync={n_permissions_sync} "
         )
 
-        # do some cleanup before clearing fences
-        # check the db for any outstanding index attempts
-        with get_session_with_tenant(tenant_id) as db_session:
-            attempts: list[IndexAttempt] = []
-            attempts.extend(
-                get_all_index_attempts_by_status(IndexingStatus.NOT_STARTED, db_session)
-            )
-            attempts.extend(
-                get_all_index_attempts_by_status(IndexingStatus.IN_PROGRESS, db_session)
-            )
-
-            for attempt in attempts:
-                # if attempts exist in the db but we don't detect them in redis, mark them as failed
-                fence_key = RedisConnectorIndex.fence_key_with_ids(
-                    attempt.connector_credential_pair_id, attempt.search_settings_id
-                )
-                if not r.exists(fence_key):
-                    failure_reason = (
-                        f"Unknown index attempt. Might be left over from a process restart: "
-                        f"index_attempt={attempt.id} "
-                        f"cc_pair={attempt.connector_credential_pair_id} "
-                        f"search_settings={attempt.search_settings_id}"
-                    )
-                    task_logger.warning(failure_reason)
-                    mark_attempt_failed(
-                        attempt.id, db_session, failure_reason=failure_reason
-                    )
-
         lock_beat.reacquire()
         if r.exists(RedisConnectorCredentialPair.get_fence_key()):
             monitor_connector_taskset(r)
@@ -815,7 +830,7 @@ def monitor_vespa_sync(self: Task, tenant_id: str | None) -> bool:
 
 
 @shared_task(
-    name="vespa_metadata_sync_task",
+    name=DanswerCeleryTask.VESPA_METADATA_SYNC_TASK,
     bind=True,
     soft_time_limit=LIGHT_SOFT_TIME_LIMIT,
     time_limit=LIGHT_TIME_LIMIT,

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

@@ -1,6 +1,8 @@
 """Factory stub for running celery worker / celery beat."""
+from celery import Celery
+
 from danswer.background.celery.apps.beat import celery_app
 from danswer.utils.variable_functionality import set_is_ee_based_on_env_variable
 
 set_is_ee_based_on_env_variable()
-app = celery_app
+app: Celery = celery_app

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

@@ -1,8 +1,10 @@
 """Factory stub for running celery worker / celery beat."""
+from celery import Celery
+
 from danswer.utils.variable_functionality import fetch_versioned_implementation
 from danswer.utils.variable_functionality import set_is_ee_based_on_env_variable
 
 set_is_ee_based_on_env_variable()
-app = fetch_versioned_implementation(
+app: Celery = fetch_versioned_implementation(
     "danswer.background.celery.apps.primary", "celery_app"
 )

backend/danswer/background/indexing/job_client.py

@@ -82,7 +82,7 @@ class SimpleJob:
             return "running"
         elif self.process.exitcode is None:
             return "cancelled"
-        elif self.process.exitcode > 0:
+        elif self.process.exitcode != 0:
             return "error"
         else:
             return "finished"
@@ -123,7 +123,8 @@ class SimpleJobClient:
         self._cleanup_completed_jobs()
         if len(self.jobs) >= self.n_workers:
             logger.debug(
-                f"No available workers to run job. Currently running '{len(self.jobs)}' jobs, with a limit of '{self.n_workers}'."
+                f"No available workers to run job. "
+                f"Currently running '{len(self.jobs)}' jobs, with a limit of '{self.n_workers}'."
             )
             return None
 

backend/danswer/background/indexing/run_indexing.py

@@ -1,7 +1,5 @@
 import time
 import traceback
-from abc import ABC
-from abc import abstractmethod
 from datetime import datetime
 from datetime import timedelta
 from datetime import timezone
@@ -21,6 +19,7 @@ from danswer.db.connector_credential_pair import get_last_successful_attempt_tim
 from danswer.db.connector_credential_pair import update_connector_credential_pair
 from danswer.db.engine import get_session_with_tenant
 from danswer.db.enums import ConnectorCredentialPairStatus
+from danswer.db.index_attempt import mark_attempt_canceled
 from danswer.db.index_attempt import mark_attempt_failed
 from danswer.db.index_attempt import mark_attempt_partially_succeeded
 from danswer.db.index_attempt import mark_attempt_succeeded
@@ -31,7 +30,7 @@ from danswer.db.models import IndexingStatus
 from danswer.db.models import IndexModelStatus
 from danswer.document_index.factory import get_default_document_index
 from danswer.indexing.embedder import DefaultIndexingEmbedder
-from danswer.indexing.indexing_heartbeat import IndexingHeartbeat
+from danswer.indexing.indexing_heartbeat import IndexingHeartbeatInterface
 from danswer.indexing.indexing_pipeline import build_indexing_pipeline
 from danswer.utils.logger import setup_logger
 from danswer.utils.logger import TaskAttemptSingleton
@@ -42,19 +41,6 @@ logger = setup_logger()
 INDEXING_TRACER_NUM_PRINT_ENTRIES = 5
 
 
-class RunIndexingCallbackInterface(ABC):
-    """Defines a callback interface to be passed to
-    to run_indexing_entrypoint."""
-
-    @abstractmethod
-    def should_stop(self) -> bool:
-        """Signal to stop the looping function in flight."""
-
-    @abstractmethod
-    def progress(self, amount: int) -> None:
-        """Send progress updates to the caller."""
-
-
 def _get_connector_runner(
     db_session: Session,
     attempt: IndexAttempt,
@@ -102,11 +88,15 @@ def _get_connector_runner(
     )
 
 
+class ConnectorStopSignal(Exception):
+    """A custom exception used to signal a stop in processing."""
+
+
 def _run_indexing(
     db_session: Session,
     index_attempt: IndexAttempt,
     tenant_id: str | None,
-    callback: RunIndexingCallbackInterface | None = None,
+    callback: IndexingHeartbeatInterface | None = None,
 ) -> None:
     """
     1. Get documents which are either new or updated from specified application
@@ -138,13 +128,7 @@ def _run_indexing(
 
     embedding_model = DefaultIndexingEmbedder.from_db_search_settings(
         search_settings=search_settings,
-        heartbeat=IndexingHeartbeat(
-            index_attempt_id=index_attempt.id,
-            db_session=db_session,
-            # let the world know we're still making progress after
-            # every 10 batches
-            freq=10,
-        ),
+        callback=callback,
     )
 
     indexing_pipeline = build_indexing_pipeline(
@@ -157,6 +141,7 @@ def _run_indexing(
         ),
         db_session=db_session,
         tenant_id=tenant_id,
+        callback=callback,
     )
 
     db_cc_pair = index_attempt.connector_credential_pair
@@ -228,7 +213,7 @@ def _run_indexing(
                 # contents still need to be initially pulled.
                 if callback:
                     if callback.should_stop():
-                        raise RuntimeError("Connector stop signal detected")
+                        raise ConnectorStopSignal("Connector stop signal detected")
 
                 # TODO: should we move this into the above callback instead?
                 db_session.refresh(db_cc_pair)
@@ -289,7 +274,7 @@ def _run_indexing(
                 db_session.commit()
 
                 if callback:
-                    callback.progress(len(doc_batch))
+                    callback.progress("_run_indexing", len(doc_batch))
 
                 # This new value is updated every batch, so UI can refresh per batch update
                 update_docs_indexed(
@@ -322,26 +307,16 @@ def _run_indexing(
                 )
         except Exception as e:
             logger.exception(
-                f"Connector run ran into exception after elapsed time: {time.time() - start_time} seconds"
+                f"Connector run exceptioned after elapsed time: {time.time() - start_time} seconds"
             )
-            # Only mark the attempt as a complete failure if this is the first indexing window.
-            # Otherwise, some progress was made - the next run will not start from the beginning.
-            # In this case, it is not accurate to mark it as a failure. When the next run begins,
-            # if that fails immediately, it will be marked as a failure.
-            #
-            # NOTE: if the connector is manually disabled, we should mark it as a failure regardless
-            # to give better clarity in the UI, as the next run will never happen.
-            if (
-                ind == 0
-                or not db_cc_pair.status.is_active()
-                or index_attempt.status != IndexingStatus.IN_PROGRESS
-            ):
-                mark_attempt_failed(
+
+            if isinstance(e, ConnectorStopSignal):
+                mark_attempt_canceled(
                     index_attempt.id,
                     db_session,
-                    failure_reason=str(e),
-                    full_exception_trace=traceback.format_exc(),
+                    reason=str(e),
                 )
+
                 if is_primary:
                     update_connector_credential_pair(
                         db_session=db_session,
@@ -353,6 +328,37 @@ def _run_indexing(
                 if INDEXING_TRACER_INTERVAL > 0:
                     tracer.stop()
                 raise e
+            else:
+                # Only mark the attempt as a complete failure if this is the first indexing window.
+                # Otherwise, some progress was made - the next run will not start from the beginning.
+                # In this case, it is not accurate to mark it as a failure. When the next run begins,
+                # if that fails immediately, it will be marked as a failure.
+                #
+                # NOTE: if the connector is manually disabled, we should mark it as a failure regardless
+                # to give better clarity in the UI, as the next run will never happen.
+                if (
+                    ind == 0
+                    or not db_cc_pair.status.is_active()
+                    or index_attempt.status != IndexingStatus.IN_PROGRESS
+                ):
+                    mark_attempt_failed(
+                        index_attempt.id,
+                        db_session,
+                        failure_reason=str(e),
+                        full_exception_trace=traceback.format_exc(),
+                    )
+
+                    if is_primary:
+                        update_connector_credential_pair(
+                            db_session=db_session,
+                            connector_id=db_connector.id,
+                            credential_id=db_credential.id,
+                            net_docs=net_doc_change,
+                        )
+
+                    if INDEXING_TRACER_INTERVAL > 0:
+                        tracer.stop()
+                    raise e
 
             # break => similar to success case. As mentioned above, if the next run fails for the same
             # reason it will then be marked as a failure
@@ -419,7 +425,7 @@ def run_indexing_entrypoint(
     tenant_id: str | None,
     connector_credential_pair_id: int,
     is_ee: bool = False,
-    callback: RunIndexingCallbackInterface | None = None,
+    callback: IndexingHeartbeatInterface | None = None,
 ) -> None:
     try:
         if is_ee:
@@ -433,11 +439,13 @@ def run_indexing_entrypoint(
         with get_session_with_tenant(tenant_id) as db_session:
             attempt = transition_attempt_to_in_progress(index_attempt_id, db_session)
 
+            tenant_str = ""
+            if tenant_id is not None:
+                tenant_str = f" for tenant {tenant_id}"
+
             logger.info(
-                f"Indexing starting for tenant {tenant_id}: "
-                if tenant_id is not None
-                else ""
-                + f"connector='{attempt.connector_credential_pair.connector.name}' "
+                f"Indexing starting{tenant_str}: "
+                f"connector='{attempt.connector_credential_pair.connector.name}' "
                 f"config='{attempt.connector_credential_pair.connector.connector_specific_config}' "
                 f"credentials='{attempt.connector_credential_pair.connector_id}'"
             )
@@ -445,10 +453,8 @@ def run_indexing_entrypoint(
             _run_indexing(db_session, attempt, tenant_id, callback)
 
             logger.info(
-                f"Indexing finished for tenant {tenant_id}: "
-                if tenant_id is not None
-                else ""
-                + f"connector='{attempt.connector_credential_pair.connector.name}' "
+                f"Indexing finished{tenant_str}: "
+                f"connector='{attempt.connector_credential_pair.connector.name}' "
                 f"config='{attempt.connector_credential_pair.connector.connector_specific_config}' "
                 f"credentials='{attempt.connector_credential_pair.connector_id}'"
             )

backend/danswer/chat/answer.py

@@ -6,33 +6,27 @@ from langchain.schema.messages import BaseMessage
 from langchain_core.messages import AIMessageChunk
 from langchain_core.messages import ToolCall
 
+from danswer.chat.llm_response_handler import LLMResponseHandlerManager
 from danswer.chat.models import AnswerQuestionPossibleReturn
+from danswer.chat.models import AnswerStyleConfig
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import DanswerAnswerPiece
-from danswer.file_store.utils import InMemoryChatFile
-from danswer.llm.answering.llm_response_handler import LLMCall
-from danswer.llm.answering.llm_response_handler import LLMResponseHandlerManager
-from danswer.llm.answering.models import AnswerStyleConfig
-from danswer.llm.answering.models import PreviousMessage
-from danswer.llm.answering.models import PromptConfig
-from danswer.llm.answering.prompts.build import AnswerPromptBuilder
-from danswer.llm.answering.prompts.build import default_build_system_message
-from danswer.llm.answering.prompts.build import default_build_user_message
-from danswer.llm.answering.stream_processing.answer_response_handler import (
-    AnswerResponseHandler,
-)
-from danswer.llm.answering.stream_processing.answer_response_handler import (
+from danswer.chat.models import PromptConfig
+from danswer.chat.prompt_builder.build import AnswerPromptBuilder
+from danswer.chat.prompt_builder.build import default_build_system_message
+from danswer.chat.prompt_builder.build import default_build_user_message
+from danswer.chat.prompt_builder.build import LLMCall
+from danswer.chat.stream_processing.answer_response_handler import (
     CitationResponseHandler,
 )
-from danswer.llm.answering.stream_processing.answer_response_handler import (
+from danswer.chat.stream_processing.answer_response_handler import (
     DummyAnswerResponseHandler,
 )
-from danswer.llm.answering.stream_processing.answer_response_handler import (
-    QuotesResponseHandler,
-)
-from danswer.llm.answering.stream_processing.utils import map_document_id_order
-from danswer.llm.answering.tool.tool_response_handler import ToolResponseHandler
+from danswer.chat.stream_processing.utils import map_document_id_order
+from danswer.chat.tool_handling.tool_response_handler import ToolResponseHandler
+from danswer.file_store.utils import InMemoryChatFile
 from danswer.llm.interfaces import LLM
+from danswer.llm.models import PreviousMessage
 from danswer.natural_language_processing.utils import get_tokenizer
 from danswer.tools.force import ForceUseTool
 from danswer.tools.models import ToolResponse
@@ -212,20 +206,28 @@ class Answer:
         # + figure out what the next LLM call should be
         tool_call_handler = ToolResponseHandler(current_llm_call.tools)
 
-        search_result = SearchTool.get_search_result(current_llm_call) or []
+        search_result, displayed_search_results_map = SearchTool.get_search_result(
+            current_llm_call
+        ) or ([], {})
 
-        answer_handler: AnswerResponseHandler
-        if self.answer_style_config.citation_config:
-            answer_handler = CitationResponseHandler(
-                context_docs=search_result,
-                doc_id_to_rank_map=map_document_id_order(search_result),
-            )
-        elif self.answer_style_config.quotes_config:
-            answer_handler = QuotesResponseHandler(
-                context_docs=search_result,
-            )
-        else:
-            raise ValueError("No answer style config provided")
+        # Quotes are no longer supported
+        # answer_handler: AnswerResponseHandler
+        # if self.answer_style_config.citation_config:
+        #     answer_handler = CitationResponseHandler(
+        #         context_docs=search_result,
+        #         doc_id_to_rank_map=map_document_id_order(search_result),
+        #     )
+        # elif self.answer_style_config.quotes_config:
+        #     answer_handler = QuotesResponseHandler(
+        #         context_docs=search_result,
+        #     )
+        # else:
+        #     raise ValueError("No answer style config provided")
+        answer_handler = CitationResponseHandler(
+            context_docs=search_result,
+            doc_id_to_rank_map=map_document_id_order(search_result),
+            display_doc_order_dict=displayed_search_results_map,
+        )
 
         response_handler_manager = LLMResponseHandlerManager(
             tool_call_handler, answer_handler, self.is_cancelled
@@ -233,6 +235,8 @@ class Answer:
 
         # DEBUG: good breakpoint
         stream = self.llm.stream(
+            # For tool calling LLMs, we want to insert the task prompt as part of this flow, this is because the LLM
+            # may choose to not call any tools and just generate the answer, in which case the task prompt is needed.
             prompt=current_llm_call.prompt_builder.build(),
             tools=[tool.tool_definition() for tool in current_llm_call.tools] or None,
             tool_choice=(
@@ -263,6 +267,7 @@ class Answer:
             message_history=self.message_history,
             llm_config=self.llm.config,
             single_message_history=self.single_message_history,
+            raw_user_text=self.question,
         )
         prompt_builder.update_system_prompt(
             default_build_system_message(self.prompt_config)

backend/danswer/chat/chat_utils.py

@@ -2,20 +2,79 @@ import re
 from typing import cast
 from uuid import UUID
 
+from fastapi import HTTPException
 from fastapi.datastructures import Headers
 from sqlalchemy.orm import Session
 
+from danswer.auth.users import is_user_admin
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import LlmDoc
+from danswer.chat.models import PersonaOverrideConfig
+from danswer.chat.models import ThreadMessage
+from danswer.configs.constants import DEFAULT_PERSONA_ID
+from danswer.configs.constants import MessageType
+from danswer.context.search.models import InferenceSection
+from danswer.context.search.models import RerankingDetails
+from danswer.context.search.models import RetrievalDetails
+from danswer.db.chat import create_chat_session
 from danswer.db.chat import get_chat_messages_by_session
+from danswer.db.llm import fetch_existing_doc_sets
+from danswer.db.llm import fetch_existing_tools
 from danswer.db.models import ChatMessage
-from danswer.llm.answering.models import PreviousMessage
-from danswer.search.models import InferenceSection
+from danswer.db.models import Persona
+from danswer.db.models import Prompt
+from danswer.db.models import Tool
+from danswer.db.models import User
+from danswer.db.persona import get_prompts_by_ids
+from danswer.llm.models import PreviousMessage
+from danswer.natural_language_processing.utils import BaseTokenizer
+from danswer.server.query_and_chat.models import CreateChatMessageRequest
+from danswer.tools.tool_implementations.custom.custom_tool import (
+    build_custom_tools_from_openapi_schema_and_headers,
+)
 from danswer.utils.logger import setup_logger
 
 logger = setup_logger()
 
 
+def prepare_chat_message_request(
+    message_text: str,
+    user: User | None,
+    persona_id: int | None,
+    # Does the question need to have a persona override
+    persona_override_config: PersonaOverrideConfig | None,
+    prompt: Prompt | None,
+    message_ts_to_respond_to: str | None,
+    retrieval_details: RetrievalDetails | None,
+    rerank_settings: RerankingDetails | None,
+    db_session: Session,
+) -> CreateChatMessageRequest:
+    # Typically used for one shot flows like SlackBot or non-chat API endpoint use cases
+    new_chat_session = create_chat_session(
+        db_session=db_session,
+        description=None,
+        user_id=user.id if user else None,
+        # If using an override, this id will be ignored later on
+        persona_id=persona_id or DEFAULT_PERSONA_ID,
+        danswerbot_flow=True,
+        slack_thread_id=message_ts_to_respond_to,
+    )
+
+    return CreateChatMessageRequest(
+        chat_session_id=new_chat_session.id,
+        parent_message_id=None,  # It's a standalone chat session each time
+        message=message_text,
+        file_descriptors=[],  # Currently SlackBot/answer api do not support files in the context
+        prompt_id=prompt.id if prompt else None,
+        # Can always override the persona for the single query, if it's a normal persona
+        # then it will be treated the same
+        persona_override_config=persona_override_config,
+        search_doc_ids=None,
+        retrieval_options=retrieval_details,
+        rerank_settings=rerank_settings,
+    )
+
+
 def llm_doc_from_inference_section(inference_section: InferenceSection) -> LlmDoc:
     return LlmDoc(
         document_id=inference_section.center_chunk.document_id,
@@ -31,9 +90,49 @@ def llm_doc_from_inference_section(inference_section: InferenceSection) -> LlmDo
         if inference_section.center_chunk.source_links
         else None,
         source_links=inference_section.center_chunk.source_links,
+        match_highlights=inference_section.center_chunk.match_highlights,
     )
 
 
+def combine_message_thread(
+    messages: list[ThreadMessage],
+    max_tokens: int | None,
+    llm_tokenizer: BaseTokenizer,
+) -> str:
+    """Used to create a single combined message context from threads"""
+    if not messages:
+        return ""
+
+    message_strs: list[str] = []
+    total_token_count = 0
+
+    for message in reversed(messages):
+        if message.role == MessageType.USER:
+            role_str = message.role.value.upper()
+            if message.sender:
+                role_str += " " + message.sender
+            else:
+                # Since other messages might have the user identifying information
+                # better to use Unknown for symmetry
+                role_str += " Unknown"
+        else:
+            role_str = message.role.value.upper()
+
+        msg_str = f"{role_str}:\n{message.message}"
+        message_token_count = len(llm_tokenizer.encode(msg_str))
+
+        if (
+            max_tokens is not None
+            and total_token_count + message_token_count > max_tokens
+        ):
+            break
+
+        message_strs.insert(0, msg_str)
+        total_token_count += message_token_count
+
+    return "\n\n".join(message_strs)
+
+
 def create_chat_chain(
     chat_session_id: UUID,
     db_session: Session,
@@ -196,3 +295,71 @@ def extract_headers(
             if lowercase_key in headers:
                 extracted_headers[lowercase_key] = headers[lowercase_key]
     return extracted_headers
+
+
+def create_temporary_persona(
+    persona_config: PersonaOverrideConfig, db_session: Session, user: User | None = None
+) -> Persona:
+    if not is_user_admin(user):
+        raise HTTPException(
+            status_code=403,
+            detail="User is not authorized to create a persona in one shot queries",
+        )
+
+    """Create a temporary Persona object from the provided configuration."""
+    persona = Persona(
+        name=persona_config.name,
+        description=persona_config.description,
+        num_chunks=persona_config.num_chunks,
+        llm_relevance_filter=persona_config.llm_relevance_filter,
+        llm_filter_extraction=persona_config.llm_filter_extraction,
+        recency_bias=persona_config.recency_bias,
+        llm_model_provider_override=persona_config.llm_model_provider_override,
+        llm_model_version_override=persona_config.llm_model_version_override,
+    )
+
+    if persona_config.prompts:
+        persona.prompts = [
+            Prompt(
+                name=p.name,
+                description=p.description,
+                system_prompt=p.system_prompt,
+                task_prompt=p.task_prompt,
+                include_citations=p.include_citations,
+                datetime_aware=p.datetime_aware,
+            )
+            for p in persona_config.prompts
+        ]
+    elif persona_config.prompt_ids:
+        persona.prompts = get_prompts_by_ids(
+            db_session=db_session, prompt_ids=persona_config.prompt_ids
+        )
+
+    persona.tools = []
+    if persona_config.custom_tools_openapi:
+        for schema in persona_config.custom_tools_openapi:
+            tools = cast(
+                list[Tool],
+                build_custom_tools_from_openapi_schema_and_headers(schema),
+            )
+            persona.tools.extend(tools)
+
+    if persona_config.tools:
+        tool_ids = [tool.id for tool in persona_config.tools]
+        persona.tools.extend(
+            fetch_existing_tools(db_session=db_session, tool_ids=tool_ids)
+        )
+
+    if persona_config.tool_ids:
+        persona.tools.extend(
+            fetch_existing_tools(
+                db_session=db_session, tool_ids=persona_config.tool_ids
+            )
+        )
+
+    fetched_docs = fetch_existing_doc_sets(
+        db_session=db_session, doc_ids=persona_config.document_set_ids
+    )
+    persona.document_sets = fetched_docs
+
+    return persona

backend/danswer/chat/input_prompts.yaml

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

backend/danswer/chat/llm_response_handler.py

@@ -1,60 +1,22 @@
 from collections.abc import Callable
 from collections.abc import Generator
 from collections.abc import Iterator
-from typing import TYPE_CHECKING
 
 from langchain_core.messages import BaseMessage
-from pydantic.v1 import BaseModel as BaseModel__v1
 
-from danswer.chat.models import CitationInfo
-from danswer.chat.models import DanswerAnswerPiece
-from danswer.chat.models import DanswerQuotes
+from danswer.chat.models import ResponsePart
 from danswer.chat.models import StreamStopInfo
 from danswer.chat.models import StreamStopReason
-from danswer.file_store.models import InMemoryChatFile
-from danswer.llm.answering.prompts.build import AnswerPromptBuilder
-from danswer.tools.force import ForceUseTool
-from danswer.tools.models import ToolCallFinalResult
-from danswer.tools.models import ToolCallKickoff
-from danswer.tools.models import ToolResponse
-from danswer.tools.tool import Tool
-
-
-if TYPE_CHECKING:
-    from danswer.llm.answering.stream_processing.answer_response_handler import (
-        AnswerResponseHandler,
-    )
-    from danswer.llm.answering.tool.tool_response_handler import ToolResponseHandler
-
-
-ResponsePart = (
-    DanswerAnswerPiece
-    | CitationInfo
-    | DanswerQuotes
-    | ToolCallKickoff
-    | ToolResponse
-    | ToolCallFinalResult
-    | StreamStopInfo
-)
-
-
-class LLMCall(BaseModel__v1):
-    prompt_builder: AnswerPromptBuilder
-    tools: list[Tool]
-    force_use_tool: ForceUseTool
-    files: list[InMemoryChatFile]
-    tool_call_info: list[ToolCallKickoff | ToolResponse | ToolCallFinalResult]
-    using_tool_calling_llm: bool
-
-    class Config:
-        arbitrary_types_allowed = True
+from danswer.chat.prompt_builder.build import LLMCall
+from danswer.chat.stream_processing.answer_response_handler import AnswerResponseHandler
+from danswer.chat.tool_handling.tool_response_handler import ToolResponseHandler
 
 
 class LLMResponseHandlerManager:
     def __init__(
         self,
-        tool_handler: "ToolResponseHandler",
-        answer_handler: "AnswerResponseHandler",
+        tool_handler: ToolResponseHandler,
+        answer_handler: AnswerResponseHandler,
         is_cancelled: Callable[[], bool],
     ):
         self.tool_handler = tool_handler

backend/danswer/chat/models.py

@@ -1,17 +1,30 @@
+from collections.abc import Callable
 from collections.abc import Iterator
 from datetime import datetime
 from enum import Enum
 from typing import Any
+from typing import TYPE_CHECKING
 
 from pydantic import BaseModel
+from pydantic import ConfigDict
+from pydantic import Field
+from pydantic import model_validator
 
 from danswer.configs.constants import DocumentSource
-from danswer.search.enums import QueryFlow
-from danswer.search.enums import SearchType
-from danswer.search.models import RetrievalDocs
-from danswer.search.models import SearchResponse
+from danswer.configs.constants import MessageType
+from danswer.context.search.enums import QueryFlow
+from danswer.context.search.enums import RecencyBiasSetting
+from danswer.context.search.enums import SearchType
+from danswer.context.search.models import RetrievalDocs
+from danswer.llm.override_models import PromptOverride
+from danswer.tools.models import ToolCallFinalResult
+from danswer.tools.models import ToolCallKickoff
+from danswer.tools.models import ToolResponse
 from danswer.tools.tool_implementations.custom.base_tool_types import ToolResultType
 
+if TYPE_CHECKING:
+    from danswer.db.models import Prompt
+
 
 class LlmDoc(BaseModel):
     """This contains the minimal set information for the LLM portion including citations"""
@@ -25,6 +38,7 @@ class LlmDoc(BaseModel):
     updated_at: datetime | None
     link: str | None
     source_links: dict[int, str] | None
+    match_highlights: list[str] | None
 
 
 # First chunk of info for streaming QA
@@ -117,20 +131,6 @@ class StreamingError(BaseModel):
     stack_trace: str | None = None
 
 
-class DanswerQuote(BaseModel):
-    # This is during inference so everything is a string by this point
-    quote: str
-    document_id: str
-    link: str | None
-    source_type: str
-    semantic_identifier: str
-    blurb: str
-
-
-class DanswerQuotes(BaseModel):
-    quotes: list[DanswerQuote]
-
-
 class DanswerContext(BaseModel):
     content: str
     document_id: str
@@ -146,14 +146,20 @@ class DanswerAnswer(BaseModel):
     answer: str | None
 
 
-class QAResponse(SearchResponse, DanswerAnswer):
-    quotes: list[DanswerQuote] | None
-    contexts: list[DanswerContexts] | None
-    predicted_flow: QueryFlow
-    predicted_search: SearchType
-    eval_res_valid: bool | None = None
+class ThreadMessage(BaseModel):
+    message: str
+    sender: str | None = None
+    role: MessageType = MessageType.USER
+
+
+class ChatDanswerBotResponse(BaseModel):
+    answer: str | None = None
+    citations: list[CitationInfo] | None = None
+    docs: QADocsResponse | None = None
     llm_selected_doc_indices: list[int] | None = None
     error_msg: str | None = None
+    chat_message_id: int | None = None
+    answer_valid: bool = True  # Reflexion result, default True if Reflexion not run
 
 
 class FileChatDisplay(BaseModel):
@@ -165,9 +171,41 @@ class CustomToolResponse(BaseModel):
     tool_name: str
 
 
+class ToolConfig(BaseModel):
+    id: int
+
+
+class PromptOverrideConfig(BaseModel):
+    name: str
+    description: str = ""
+    system_prompt: str
+    task_prompt: str = ""
+    include_citations: bool = True
+    datetime_aware: bool = True
+
+
+class PersonaOverrideConfig(BaseModel):
+    name: str
+    description: str
+    search_type: SearchType = SearchType.SEMANTIC
+    num_chunks: float | None = None
+    llm_relevance_filter: bool = False
+    llm_filter_extraction: bool = False
+    recency_bias: RecencyBiasSetting = RecencyBiasSetting.AUTO
+    llm_model_provider_override: str | None = None
+    llm_model_version_override: str | None = None
+
+    prompts: list[PromptOverrideConfig] = Field(default_factory=list)
+    prompt_ids: list[int] = Field(default_factory=list)
+
+    document_set_ids: list[int] = Field(default_factory=list)
+    tools: list[ToolConfig] = Field(default_factory=list)
+    tool_ids: list[int] = Field(default_factory=list)
+    custom_tools_openapi: list[dict[str, Any]] = Field(default_factory=list)
+
+
 AnswerQuestionPossibleReturn = (
     DanswerAnswerPiece
-    | DanswerQuotes
     | CitationInfo
     | DanswerContexts
     | FileChatDisplay
@@ -183,3 +221,109 @@ AnswerQuestionStreamReturn = Iterator[AnswerQuestionPossibleReturn]
 class LLMMetricsContainer(BaseModel):
     prompt_tokens: int
     response_tokens: int
+
+
+StreamProcessor = Callable[[Iterator[str]], AnswerQuestionStreamReturn]
+
+
+class DocumentPruningConfig(BaseModel):
+    max_chunks: int | None = None
+    max_window_percentage: float | None = None
+    max_tokens: int | None = None
+    # different pruning behavior is expected when the
+    # user manually selects documents they want to chat with
+    # e.g. we don't want to truncate each document to be no more
+    # than one chunk long
+    is_manually_selected_docs: bool = False
+    # If user specifies to include additional context Chunks for each match, then different pruning
+    # is used. As many Sections as possible are included, and the last Section is truncated
+    # If this is false, all of the Sections are truncated if they are longer than the expected Chunk size.
+    # Sections are often expected to be longer than the maximum Chunk size but Chunks should not be.
+    use_sections: bool = True
+    # If using tools, then we need to consider the tool length
+    tool_num_tokens: int = 0
+    # If using a tool message to represent the docs, then we have to JSON serialize
+    # the document content, which adds to the token count.
+    using_tool_message: bool = False
+
+
+class ContextualPruningConfig(DocumentPruningConfig):
+    num_chunk_multiple: int
+
+    @classmethod
+    def from_doc_pruning_config(
+        cls, num_chunk_multiple: int, doc_pruning_config: DocumentPruningConfig
+    ) -> "ContextualPruningConfig":
+        return cls(num_chunk_multiple=num_chunk_multiple, **doc_pruning_config.dict())
+
+
+class CitationConfig(BaseModel):
+    all_docs_useful: bool = False
+
+
+class QuotesConfig(BaseModel):
+    pass
+
+
+class AnswerStyleConfig(BaseModel):
+    citation_config: CitationConfig | None = None
+    quotes_config: QuotesConfig | None = None
+    document_pruning_config: DocumentPruningConfig = Field(
+        default_factory=DocumentPruningConfig
+    )
+    # forces the LLM to return a structured response, see
+    # https://platform.openai.com/docs/guides/structured-outputs/introduction
+    # right now, only used by the simple chat API
+    structured_response_format: dict | None = None
+
+    @model_validator(mode="after")
+    def check_quotes_and_citation(self) -> "AnswerStyleConfig":
+        if self.citation_config is None and self.quotes_config is None:
+            raise ValueError(
+                "One of `citation_config` or `quotes_config` must be provided"
+            )
+
+        if self.citation_config is not None and self.quotes_config is not None:
+            raise ValueError(
+                "Only one of `citation_config` or `quotes_config` must be provided"
+            )
+
+        return self
+
+
+class PromptConfig(BaseModel):
+    """Final representation of the Prompt configuration passed
+    into the `Answer` object."""
+
+    system_prompt: str
+    task_prompt: str
+    datetime_aware: bool
+    include_citations: bool
+
+    @classmethod
+    def from_model(
+        cls, model: "Prompt", prompt_override: PromptOverride | None = None
+    ) -> "PromptConfig":
+        override_system_prompt = (
+            prompt_override.system_prompt if prompt_override else None
+        )
+        override_task_prompt = prompt_override.task_prompt if prompt_override else None
+
+        return cls(
+            system_prompt=override_system_prompt or model.system_prompt,
+            task_prompt=override_task_prompt or model.task_prompt,
+            datetime_aware=model.datetime_aware,
+            include_citations=model.include_citations,
+        )
+
+    model_config = ConfigDict(frozen=True)
+
+
+ResponsePart = (
+    DanswerAnswerPiece
+    | CitationInfo
+    | ToolCallKickoff
+    | ToolResponse
+    | ToolCallFinalResult
+    | StreamStopInfo
+)

backend/danswer/chat/process_message.py

@@ -6,16 +6,24 @@ from typing import cast
 
 from sqlalchemy.orm import Session
 
+from danswer.chat.answer import Answer
 from danswer.chat.chat_utils import create_chat_chain
+from danswer.chat.chat_utils import create_temporary_persona
 from danswer.chat.models import AllCitations
+from danswer.chat.models import AnswerStyleConfig
+from danswer.chat.models import ChatDanswerBotResponse
+from danswer.chat.models import CitationConfig
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import CustomToolResponse
 from danswer.chat.models import DanswerAnswerPiece
+from danswer.chat.models import DanswerContexts
+from danswer.chat.models import DocumentPruningConfig
 from danswer.chat.models import FileChatDisplay
 from danswer.chat.models import FinalUsedContextDocsResponse
 from danswer.chat.models import LLMRelevanceFilterResponse
 from danswer.chat.models import MessageResponseIDInfo
 from danswer.chat.models import MessageSpecificCitations
+from danswer.chat.models import PromptConfig
 from danswer.chat.models import QADocsResponse
 from danswer.chat.models import StreamingError
 from danswer.chat.models import StreamStopInfo
@@ -23,6 +31,16 @@ from danswer.configs.chat_configs import CHAT_TARGET_CHUNK_PERCENTAGE
 from danswer.configs.chat_configs import DISABLE_LLM_CHOOSE_SEARCH
 from danswer.configs.chat_configs import MAX_CHUNKS_FED_TO_CHAT
 from danswer.configs.constants import MessageType
+from danswer.context.search.enums import OptionalSearchSetting
+from danswer.context.search.enums import QueryFlow
+from danswer.context.search.enums import SearchType
+from danswer.context.search.models import InferenceSection
+from danswer.context.search.models import RetrievalDetails
+from danswer.context.search.retrieval.search_runner import inference_sections_from_ids
+from danswer.context.search.utils import chunks_or_sections_to_search_docs
+from danswer.context.search.utils import dedupe_documents
+from danswer.context.search.utils import drop_llm_indices
+from danswer.context.search.utils import relevant_sections_to_indices
 from danswer.db.chat import attach_files_to_chat_message
 from danswer.db.chat import create_db_search_doc
 from danswer.db.chat import create_new_chat_message
@@ -44,28 +62,13 @@ from danswer.document_index.factory import get_default_document_index
 from danswer.file_store.models import ChatFileType
 from danswer.file_store.models import FileDescriptor
 from danswer.file_store.utils import load_all_chat_files
-from danswer.file_store.utils import save_files_from_urls
-from danswer.llm.answering.answer import Answer
-from danswer.llm.answering.models import AnswerStyleConfig
-from danswer.llm.answering.models import CitationConfig
-from danswer.llm.answering.models import DocumentPruningConfig
-from danswer.llm.answering.models import PreviousMessage
-from danswer.llm.answering.models import PromptConfig
+from danswer.file_store.utils import save_files
 from danswer.llm.exceptions import GenAIDisabledException
 from danswer.llm.factory import get_llms_for_persona
 from danswer.llm.factory import get_main_llm_from_tuple
+from danswer.llm.models import PreviousMessage
 from danswer.llm.utils import litellm_exception_to_error_msg
 from danswer.natural_language_processing.utils import get_tokenizer
-from danswer.search.enums import OptionalSearchSetting
-from danswer.search.enums import QueryFlow
-from danswer.search.enums import SearchType
-from danswer.search.models import InferenceSection
-from danswer.search.models import RetrievalDetails
-from danswer.search.retrieval.search_runner import inference_sections_from_ids
-from danswer.search.utils import chunks_or_sections_to_search_docs
-from danswer.search.utils import dedupe_documents
-from danswer.search.utils import drop_llm_indices
-from danswer.search.utils import relevant_sections_to_indices
 from danswer.server.query_and_chat.models import ChatMessageDetail
 from danswer.server.query_and_chat.models import CreateChatMessageRequest
 from danswer.server.utils import get_json_line
@@ -102,6 +105,7 @@ from danswer.tools.tool_implementations.internet_search.internet_search_tool imp
 from danswer.tools.tool_implementations.search.search_tool import (
     FINAL_CONTEXT_DOCUMENTS_ID,
 )
+from danswer.tools.tool_implementations.search.search_tool import SEARCH_DOC_CONTENT_ID
 from danswer.tools.tool_implementations.search.search_tool import (
     SEARCH_RESPONSE_SUMMARY_ID,
 )
@@ -112,7 +116,11 @@ from danswer.tools.tool_implementations.search.search_tool import (
 )
 from danswer.tools.tool_runner import ToolCallFinalResult
 from danswer.utils.logger import setup_logger
+from danswer.utils.long_term_log import LongTermLogger
+from danswer.utils.timing import log_function_time
 from danswer.utils.timing import log_generator_function_time
+from shared_configs.contextvars import CURRENT_TENANT_ID_CONTEXTVAR
+
 
 logger = setup_logger()
 
@@ -255,6 +263,7 @@ def _get_force_search_settings(
 ChatPacket = (
     StreamingError
     | QADocsResponse
+    | DanswerContexts
     | LLMRelevanceFilterResponse
     | FinalUsedContextDocsResponse
     | ChatMessageDetail
@@ -285,6 +294,8 @@ def stream_chat_message_objects(
     custom_tool_additional_headers: dict[str, str] | None = None,
     is_connected: Callable[[], bool] | None = None,
     enforce_chat_session_id_for_search_docs: bool = True,
+    bypass_acl: bool = False,
+    include_contexts: bool = False,
 ) -> ChatPacketStream:
     """Streams in order:
     1. [conditional] Retrieved documents if a search needs to be run
@@ -292,6 +303,7 @@ def stream_chat_message_objects(
     3. [always] A set of streamed LLM tokens or an error anywhere along the line if something fails
     4. [always] Details on the final AI response message that is created
     """
+    tenant_id = CURRENT_TENANT_ID_CONTEXTVAR.get()
     use_existing_user_message = new_msg_req.use_existing_user_message
     existing_assistant_message_id = new_msg_req.existing_assistant_message_id
 
@@ -316,17 +328,36 @@ def stream_chat_message_objects(
         retrieval_options = new_msg_req.retrieval_options
         alternate_assistant_id = new_msg_req.alternate_assistant_id
 
-        # use alternate persona if alternative assistant id is passed in
+        # permanent "log" store, used primarily for debugging
+        long_term_logger = LongTermLogger(
+            metadata={"user_id": str(user_id), "chat_session_id": str(chat_session_id)}
+        )
+
         if alternate_assistant_id is not None:
+            # Allows users to specify a temporary persona (assistant) in the chat session
+            # this takes highest priority since it's user specified
             persona = get_persona_by_id(
                 alternate_assistant_id,
                 user=user,
                 db_session=db_session,
                 is_for_edit=False,
             )
+        elif new_msg_req.persona_override_config:
+            # Certain endpoints allow users to specify arbitrary persona settings
+            # this should never conflict with the alternate_assistant_id
+            persona = persona = create_temporary_persona(
+                db_session=db_session,
+                persona_config=new_msg_req.persona_override_config,
+                user=user,
+            )
         else:
             persona = chat_session.persona
 
+        if not persona:
+            raise RuntimeError("No persona specified or found for chat session")
+
+        # If a prompt override is specified via the API, use that with highest priority
+        # but for saving it, we are just mapping it to an existing prompt
         prompt_id = new_msg_req.prompt_id
         if prompt_id is None and persona.prompts:
             prompt_id = sorted(persona.prompts, key=lambda x: x.id)[-1].id
@@ -341,6 +372,7 @@ def stream_chat_message_objects(
                 persona=persona,
                 llm_override=new_msg_req.llm_override or chat_session.llm_override,
                 additional_headers=litellm_additional_headers,
+                long_term_logger=long_term_logger,
             )
         except GenAIDisabledException:
             raise RuntimeError("LLM is disabled. Can't use chat flow without LLM.")
@@ -548,19 +580,34 @@ def stream_chat_message_objects(
             reserved_message_id=reserved_message_id,
         )
 
-        if not final_msg.prompt:
-            raise RuntimeError("No Prompt found")
-
-        prompt_config = (
-            PromptConfig.from_model(
-                final_msg.prompt,
-                prompt_override=(
-                    new_msg_req.prompt_override or chat_session.prompt_override
-                ),
+        prompt_override = new_msg_req.prompt_override or chat_session.prompt_override
+        if new_msg_req.persona_override_config:
+            prompt_config = PromptConfig(
+                system_prompt=new_msg_req.persona_override_config.prompts[
+                    0
+                ].system_prompt,
+                task_prompt=new_msg_req.persona_override_config.prompts[0].task_prompt,
+                datetime_aware=new_msg_req.persona_override_config.prompts[
+                    0
+                ].datetime_aware,
+                include_citations=new_msg_req.persona_override_config.prompts[
+                    0
+                ].include_citations,
             )
-            if not persona
-            else PromptConfig.from_model(persona.prompts[0])
-        )
+        elif prompt_override:
+            if not final_msg.prompt:
+                raise ValueError(
+                    "Prompt override cannot be applied, no base prompt found."
+                )
+            prompt_config = PromptConfig.from_model(
+                final_msg.prompt,
+                prompt_override=prompt_override,
+            )
+        elif final_msg.prompt:
+            prompt_config = PromptConfig.from_model(final_msg.prompt)
+        else:
+            prompt_config = PromptConfig.from_model(persona.prompts[0])
+
         answer_style_config = AnswerStyleConfig(
             citation_config=CitationConfig(
                 all_docs_useful=selected_db_search_docs is not None
@@ -580,11 +627,13 @@ def stream_chat_message_objects(
                 answer_style_config=answer_style_config,
                 document_pruning_config=document_pruning_config,
                 retrieval_options=retrieval_options or RetrievalDetails(),
+                rerank_settings=new_msg_req.rerank_settings,
                 selected_sections=selected_sections,
                 chunks_above=new_msg_req.chunks_above,
                 chunks_below=new_msg_req.chunks_below,
                 full_doc=new_msg_req.full_doc,
                 latest_query_files=latest_query_files,
+                bypass_acl=bypass_acl,
             ),
             internet_search_tool_config=InternetSearchToolConfig(
                 answer_style_config=answer_style_config,
@@ -598,6 +647,7 @@ def stream_chat_message_objects(
                 additional_headers=custom_tool_additional_headers,
             ),
         )
+
         tools: list[Tool] = []
         for tool_list in tool_dict.values():
             tools.extend(tool_list)
@@ -630,7 +680,8 @@ def stream_chat_message_objects(
 
         reference_db_search_docs = None
         qa_docs_response = None
-        ai_message_files = None  # any files to associate with the AI message e.g. dall-e generated images
+        # any files to associate with the AI message e.g. dall-e generated images
+        ai_message_files = []
         dropped_indices = None
         tool_result = None
 
@@ -685,8 +736,14 @@ def stream_chat_message_objects(
                         list[ImageGenerationResponse], packet.response
                     )
 
-                    file_ids = save_files_from_urls(
-                        [img.url for img in img_generation_response]
+                    file_ids = save_files(
+                        urls=[img.url for img in img_generation_response if img.url],
+                        base64_files=[
+                            img.image_data
+                            for img in img_generation_response
+                            if img.image_data
+                        ],
+                        tenant_id=tenant_id,
                     )
                     ai_message_files = [
                         FileDescriptor(id=str(file_id), type=ChatFileType.IMAGE)
@@ -712,15 +769,19 @@ def stream_chat_message_objects(
                         or custom_tool_response.response_type == "csv"
                     ):
                         file_ids = custom_tool_response.tool_result.file_ids
-                        ai_message_files = [
-                            FileDescriptor(
-                                id=str(file_id),
-                                type=ChatFileType.IMAGE
-                                if custom_tool_response.response_type == "image"
-                                else ChatFileType.CSV,
-                            )
-                            for file_id in file_ids
-                        ]
+                        ai_message_files.extend(
+                            [
+                                FileDescriptor(
+                                    id=str(file_id),
+                                    type=(
+                                        ChatFileType.IMAGE
+                                        if custom_tool_response.response_type == "image"
+                                        else ChatFileType.CSV
+                                    ),
+                                )
+                                for file_id in file_ids
+                            ]
+                        )
                         yield FileChatDisplay(
                             file_ids=[str(file_id) for file_id in file_ids]
                         )
@@ -729,6 +790,8 @@ def stream_chat_message_objects(
                             response=custom_tool_response.tool_result,
                             tool_name=custom_tool_response.tool_name,
                         )
+                elif packet.id == SEARCH_DOC_CONTENT_ID and include_contexts:
+                    yield cast(DanswerContexts, packet.response)
 
             elif isinstance(packet, StreamStopInfo):
                 pass
@@ -768,7 +831,8 @@ def stream_chat_message_objects(
                 citations_list=answer.citations,
                 db_docs=reference_db_search_docs,
             )
-            yield AllCitations(citations=answer.citations)
+            if not answer.is_cancelled():
+                yield AllCitations(citations=answer.citations)
 
         # Saving Gen AI answer and responding with message info
         tool_name_to_tool_id: dict[str, int] = {}
@@ -837,3 +901,30 @@ def stream_chat_message(
         )
         for obj in objects:
             yield get_json_line(obj.model_dump())
+
+
+@log_function_time()
+def gather_stream_for_slack(
+    packets: ChatPacketStream,
+) -> ChatDanswerBotResponse:
+    response = ChatDanswerBotResponse()
+
+    answer = ""
+    for packet in packets:
+        if isinstance(packet, DanswerAnswerPiece) and packet.answer_piece:
+            answer += packet.answer_piece
+        elif isinstance(packet, QADocsResponse):
+            response.docs = packet
+        elif isinstance(packet, StreamingError):
+            response.error_msg = packet.error
+        elif isinstance(packet, ChatMessageDetail):
+            response.chat_message_id = packet.message_id
+        elif isinstance(packet, LLMRelevanceFilterResponse):
+            response.llm_selected_doc_indices = packet.llm_selected_doc_indices
+        elif isinstance(packet, AllCitations):
+            response.citations = packet.citations
+
+    if answer:
+        response.answer = answer
+
+    return response

backend/danswer/chat/prompt_builder/build.py

@@ -4,20 +4,26 @@ from typing import cast
 from langchain_core.messages import BaseMessage
 from langchain_core.messages import HumanMessage
 from langchain_core.messages import SystemMessage
+from pydantic.v1 import BaseModel as BaseModel__v1
 
+from danswer.chat.models import PromptConfig
+from danswer.chat.prompt_builder.citations_prompt import compute_max_llm_input_tokens
+from danswer.chat.prompt_builder.utils import translate_history_to_basemessages
 from danswer.file_store.models import InMemoryChatFile
-from danswer.llm.answering.models import PreviousMessage
-from danswer.llm.answering.models import PromptConfig
-from danswer.llm.answering.prompts.citations_prompt import compute_max_llm_input_tokens
 from danswer.llm.interfaces import LLMConfig
+from danswer.llm.models import PreviousMessage
 from danswer.llm.utils import build_content_with_imgs
 from danswer.llm.utils import check_message_tokens
 from danswer.llm.utils import message_to_prompt_and_imgs
-from danswer.llm.utils import translate_history_to_basemessages
 from danswer.natural_language_processing.utils import get_tokenizer
 from danswer.prompts.chat_prompts import CHAT_USER_CONTEXT_FREE_PROMPT
 from danswer.prompts.prompt_utils import add_date_time_to_prompt
 from danswer.prompts.prompt_utils import drop_messages_history_overflow
+from danswer.tools.force import ForceUseTool
+from danswer.tools.models import ToolCallFinalResult
+from danswer.tools.models import ToolCallKickoff
+from danswer.tools.models import ToolResponse
+from danswer.tools.tool import Tool
 
 
 def default_build_system_message(
@@ -58,6 +64,7 @@ class AnswerPromptBuilder:
         user_message: HumanMessage,
         message_history: list[PreviousMessage],
         llm_config: LLMConfig,
+        raw_user_text: str,
         single_message_history: str | None = None,
     ) -> None:
         self.max_tokens = compute_max_llm_input_tokens(llm_config)
@@ -88,6 +95,8 @@ class AnswerPromptBuilder:
 
         self.new_messages_and_token_cnts: list[tuple[BaseMessage, int]] = []
 
+        self.raw_user_message = raw_user_text
+
     def update_system_prompt(self, system_message: SystemMessage | None) -> None:
         if not system_message:
             self.system_message_and_token_cnt = None
@@ -136,3 +145,15 @@ class AnswerPromptBuilder:
         return drop_messages_history_overflow(
             final_messages_with_tokens, self.max_tokens
         )
+
+
+class LLMCall(BaseModel__v1):
+    prompt_builder: AnswerPromptBuilder
+    tools: list[Tool]
+    force_use_tool: ForceUseTool
+    files: list[InMemoryChatFile]
+    tool_call_info: list[ToolCallKickoff | ToolResponse | ToolCallFinalResult]
+    using_tool_calling_llm: bool
+
+    class Config:
+        arbitrary_types_allowed = True

backend/danswer/chat/prompt_builder/citations_prompt.py

@@ -2,11 +2,12 @@ from langchain.schema.messages import HumanMessage
 from langchain.schema.messages import SystemMessage
 
 from danswer.chat.models import LlmDoc
+from danswer.chat.models import PromptConfig
 from danswer.configs.model_configs import GEN_AI_SINGLE_USER_MESSAGE_EXPECTED_MAX_TOKENS
+from danswer.context.search.models import InferenceChunk
 from danswer.db.models import Persona
 from danswer.db.persona import get_default_prompt__read_only
 from danswer.db.search_settings import get_multilingual_expansion
-from danswer.llm.answering.models import PromptConfig
 from danswer.llm.factory import get_llms_for_persona
 from danswer.llm.factory import get_main_llm_from_tuple
 from danswer.llm.interfaces import LLMConfig
@@ -29,7 +30,6 @@ from danswer.prompts.token_counts import (
 from danswer.prompts.token_counts import CITATION_REMINDER_TOKEN_CNT
 from danswer.prompts.token_counts import CITATION_STATEMENT_TOKEN_CNT
 from danswer.prompts.token_counts import LANGUAGE_HINT_TOKEN_CNT
-from danswer.search.models import InferenceChunk
 from danswer.utils.logger import setup_logger
 
 logger = setup_logger()

backend/danswer/chat/prompt_builder/quotes_prompt.py

@@ -1,46 +1,16 @@
 from langchain.schema.messages import HumanMessage
 
 from danswer.chat.models import LlmDoc
+from danswer.chat.models import PromptConfig
 from danswer.configs.chat_configs import LANGUAGE_HINT
-from danswer.configs.chat_configs import QA_PROMPT_OVERRIDE
+from danswer.context.search.models import InferenceChunk
 from danswer.db.search_settings import get_multilingual_expansion
-from danswer.llm.answering.models import PromptConfig
 from danswer.llm.utils import message_to_prompt_and_imgs
 from danswer.prompts.direct_qa_prompts import CONTEXT_BLOCK
 from danswer.prompts.direct_qa_prompts import HISTORY_BLOCK
 from danswer.prompts.direct_qa_prompts import JSON_PROMPT
-from danswer.prompts.direct_qa_prompts import WEAK_LLM_PROMPT
 from danswer.prompts.prompt_utils import add_date_time_to_prompt
 from danswer.prompts.prompt_utils import build_complete_context_str
-from danswer.search.models import InferenceChunk
-
-
-def _build_weak_llm_quotes_prompt(
-    question: str,
-    context_docs: list[LlmDoc] | list[InferenceChunk],
-    history_str: str,
-    prompt: PromptConfig,
-) -> HumanMessage:
-    """Since Danswer supports a variety of LLMs, this less demanding prompt is provided
-    as an option to use with weaker LLMs such as small version, low float precision, quantized,
-    or distilled models. It only uses one context document and has very weak requirements of
-    output format.
-    """
-    context_block = ""
-    if context_docs:
-        context_block = CONTEXT_BLOCK.format(context_docs_str=context_docs[0].content)
-
-    prompt_str = WEAK_LLM_PROMPT.format(
-        system_prompt=prompt.system_prompt,
-        context_block=context_block,
-        task_prompt=prompt.task_prompt,
-        user_query=question,
-    )
-
-    if prompt.datetime_aware:
-        prompt_str = add_date_time_to_prompt(prompt_str=prompt_str)
-
-    return HumanMessage(content=prompt_str)
 
 
 def _build_strong_llm_quotes_prompt(
@@ -81,15 +51,9 @@ def build_quotes_user_message(
     history_str: str,
     prompt: PromptConfig,
 ) -> HumanMessage:
-    prompt_builder = (
-        _build_weak_llm_quotes_prompt
-        if QA_PROMPT_OVERRIDE == "weak"
-        else _build_strong_llm_quotes_prompt
-    )
-
     query, _ = message_to_prompt_and_imgs(message)
 
-    return prompt_builder(
+    return _build_strong_llm_quotes_prompt(
         question=query,
         context_docs=context_docs,
         history_str=history_str,

backend/danswer/chat/prompt_builder/utils.py

@@ -0,0 +1,62 @@
+from langchain.schema.messages import AIMessage
+from langchain.schema.messages import BaseMessage
+from langchain.schema.messages import HumanMessage
+
+from danswer.configs.constants import MessageType
+from danswer.db.models import ChatMessage
+from danswer.file_store.models import InMemoryChatFile
+from danswer.llm.models import PreviousMessage
+from danswer.llm.utils import build_content_with_imgs
+from danswer.prompts.direct_qa_prompts import PARAMATERIZED_PROMPT
+from danswer.prompts.direct_qa_prompts import PARAMATERIZED_PROMPT_WITHOUT_CONTEXT
+
+
+def build_dummy_prompt(
+    system_prompt: str, task_prompt: str, retrieval_disabled: bool
+) -> str:
+    if retrieval_disabled:
+        return PARAMATERIZED_PROMPT_WITHOUT_CONTEXT.format(
+            user_query="<USER_QUERY>",
+            system_prompt=system_prompt,
+            task_prompt=task_prompt,
+        ).strip()
+
+    return PARAMATERIZED_PROMPT.format(
+        context_docs_str="<CONTEXT_DOCS>",
+        user_query="<USER_QUERY>",
+        system_prompt=system_prompt,
+        task_prompt=task_prompt,
+    ).strip()
+
+
+def translate_danswer_msg_to_langchain(
+    msg: ChatMessage | PreviousMessage,
+) -> BaseMessage:
+    files: list[InMemoryChatFile] = []
+
+    # If the message is a `ChatMessage`, it doesn't have the downloaded files
+    # attached. Just ignore them for now.
+    if not isinstance(msg, ChatMessage):
+        files = msg.files
+    content = build_content_with_imgs(msg.message, files, message_type=msg.message_type)
+
+    if msg.message_type == MessageType.SYSTEM:
+        raise ValueError("System messages are not currently part of history")
+    if msg.message_type == MessageType.ASSISTANT:
+        return AIMessage(content=content)
+    if msg.message_type == MessageType.USER:
+        return HumanMessage(content=content)
+
+    raise ValueError(f"New message type {msg.message_type} not handled")
+
+
+def translate_history_to_basemessages(
+    history: list[ChatMessage] | list["PreviousMessage"],
+) -> tuple[list[BaseMessage], list[int]]:
+    history_basemessages = [
+        translate_danswer_msg_to_langchain(msg)
+        for msg in history
+        if msg.token_count != 0
+    ]
+    history_token_counts = [msg.token_count for msg in history if msg.token_count != 0]
+    return history_basemessages, history_token_counts

backend/danswer/chat/prune_and_merge.py

@@ -5,20 +5,20 @@ from typing import TypeVar
 
 from pydantic import BaseModel
 
+from danswer.chat.models import ContextualPruningConfig
 from danswer.chat.models import (
     LlmDoc,
 )
+from danswer.chat.models import PromptConfig
+from danswer.chat.prompt_builder.citations_prompt import compute_max_document_tokens
 from danswer.configs.constants import IGNORE_FOR_QA
 from danswer.configs.model_configs import DOC_EMBEDDING_CONTEXT_SIZE
-from danswer.llm.answering.models import ContextualPruningConfig
-from danswer.llm.answering.models import PromptConfig
-from danswer.llm.answering.prompts.citations_prompt import compute_max_document_tokens
+from danswer.context.search.models import InferenceChunk
+from danswer.context.search.models import InferenceSection
 from danswer.llm.interfaces import LLMConfig
 from danswer.natural_language_processing.utils import get_tokenizer
 from danswer.natural_language_processing.utils import tokenizer_trim_content
 from danswer.prompts.prompt_utils import build_doc_context_str
-from danswer.search.models import InferenceChunk
-from danswer.search.models import InferenceSection
 from danswer.tools.tool_implementations.search.search_utils import section_to_dict
 from danswer.utils.logger import setup_logger
 

backend/danswer/chat/stream_processing/answer_response_handler.py

@@ -3,16 +3,14 @@ from collections.abc import Generator
 
 from langchain_core.messages import BaseMessage
 
+from danswer.chat.llm_response_handler import ResponsePart
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import LlmDoc
-from danswer.llm.answering.llm_response_handler import ResponsePart
-from danswer.llm.answering.stream_processing.citation_processing import (
-    CitationProcessor,
-)
-from danswer.llm.answering.stream_processing.quotes_processing import (
-    QuotesProcessor,
-)
-from danswer.llm.answering.stream_processing.utils import DocumentIdOrderMapping
+from danswer.chat.stream_processing.citation_processing import CitationProcessor
+from danswer.chat.stream_processing.utils import DocumentIdOrderMapping
+from danswer.utils.logger import setup_logger
+
+logger = setup_logger()
 
 
 class AnswerResponseHandler(abc.ABC):
@@ -37,17 +35,25 @@ class DummyAnswerResponseHandler(AnswerResponseHandler):
 
 class CitationResponseHandler(AnswerResponseHandler):
     def __init__(
-        self, context_docs: list[LlmDoc], doc_id_to_rank_map: DocumentIdOrderMapping
+        self,
+        context_docs: list[LlmDoc],
+        doc_id_to_rank_map: DocumentIdOrderMapping,
+        display_doc_order_dict: dict[str, int],
     ):
         self.context_docs = context_docs
         self.doc_id_to_rank_map = doc_id_to_rank_map
+        self.display_doc_order_dict = display_doc_order_dict
         self.citation_processor = CitationProcessor(
             context_docs=self.context_docs,
             doc_id_to_rank_map=self.doc_id_to_rank_map,
+            display_doc_order_dict=self.display_doc_order_dict,
         )
         self.processed_text = ""
         self.citations: list[CitationInfo] = []
 
+        # TODO remove this after citation issue is resolved
+        logger.debug(f"Document to ranking map {self.doc_id_to_rank_map}")
+
     def handle_response_part(
         self,
         response_item: BaseMessage | None,
@@ -64,28 +70,29 @@ class CitationResponseHandler(AnswerResponseHandler):
         yield from self.citation_processor.process_token(content)
 
 
-class QuotesResponseHandler(AnswerResponseHandler):
-    def __init__(
-        self,
-        context_docs: list[LlmDoc],
-        is_json_prompt: bool = True,
-    ):
-        self.quotes_processor = QuotesProcessor(
-            context_docs=context_docs,
-            is_json_prompt=is_json_prompt,
-        )
+# No longer in use, remove later
+# class QuotesResponseHandler(AnswerResponseHandler):
+#     def __init__(
+#         self,
+#         context_docs: list[LlmDoc],
+#         is_json_prompt: bool = True,
+#     ):
+#         self.quotes_processor = QuotesProcessor(
+#             context_docs=context_docs,
+#             is_json_prompt=is_json_prompt,
+#         )
 
-    def handle_response_part(
-        self,
-        response_item: BaseMessage | None,
-        previous_response_items: list[BaseMessage],
-    ) -> Generator[ResponsePart, None, None]:
-        if response_item is None:
-            yield from self.quotes_processor.process_token(None)
-            return
+#     def handle_response_part(
+#         self,
+#         response_item: BaseMessage | None,
+#         previous_response_items: list[BaseMessage],
+#     ) -> Generator[ResponsePart, None, None]:
+#         if response_item is None:
+#             yield from self.quotes_processor.process_token(None)
+#             return
 
-        content = (
-            response_item.content if isinstance(response_item.content, str) else ""
-        )
+#         content = (
+#             response_item.content if isinstance(response_item.content, str) else ""
+#         )
 
-        yield from self.quotes_processor.process_token(content)
+#         yield from self.quotes_processor.process_token(content)

backend/danswer/chat/stream_processing/citation_processing.py

@@ -4,8 +4,8 @@ from collections.abc import Generator
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import DanswerAnswerPiece
 from danswer.chat.models import LlmDoc
+from danswer.chat.stream_processing.utils import DocumentIdOrderMapping
 from danswer.configs.chat_configs import STOP_STREAM_PAT
-from danswer.llm.answering.stream_processing.utils import DocumentIdOrderMapping
 from danswer.prompts.constants import TRIPLE_BACKTICK
 from danswer.utils.logger import setup_logger
 
@@ -22,12 +22,16 @@ class CitationProcessor:
         self,
         context_docs: list[LlmDoc],
         doc_id_to_rank_map: DocumentIdOrderMapping,
+        display_doc_order_dict: dict[str, int],
         stop_stream: str | None = STOP_STREAM_PAT,
     ):
         self.context_docs = context_docs
         self.doc_id_to_rank_map = doc_id_to_rank_map
         self.stop_stream = stop_stream
         self.order_mapping = doc_id_to_rank_map.order_mapping
+        self.display_doc_order_dict = (
+            display_doc_order_dict  # original order of docs to displayed to user
+        )
         self.llm_out = ""
         self.max_citation_num = len(context_docs)
         self.citation_order: list[int] = []
@@ -67,9 +71,9 @@ class CitationProcessor:
                 if piece_that_comes_after == "\n" and in_code_block(self.llm_out):
                     self.curr_segment = self.curr_segment.replace("```", "```plaintext")
 
-        citation_pattern = r"\[(\d+)\]"
+        citation_pattern = r"\[(\d+)\]|\[\[(\d+)\]\]"  # [1], [[1]], etc.
         citations_found = list(re.finditer(citation_pattern, self.curr_segment))
-        possible_citation_pattern = r"(\[\d*$)"  # [1, [, etc
+        possible_citation_pattern = r"(\[+\d*$)"  # [1, [, [[, [[2, etc.
         possible_citation_found = re.search(
             possible_citation_pattern, self.curr_segment
         )
@@ -77,13 +81,15 @@ class CitationProcessor:
         if len(citations_found) == 0 and len(self.llm_out) - self.past_cite_count > 5:
             self.current_citations = []
 
-        result = ""  # Initialize result here
+        result = ""
         if citations_found and not in_code_block(self.llm_out):
             last_citation_end = 0
             length_to_add = 0
             while len(citations_found) > 0:
                 citation = citations_found.pop(0)
-                numerical_value = int(citation.group(1))
+                numerical_value = int(
+                    next(group for group in citation.groups() if group is not None)
+                )
 
                 if 1 <= numerical_value <= self.max_citation_num:
                     context_llm_doc = self.context_docs[numerical_value - 1]
@@ -96,6 +102,18 @@ class CitationProcessor:
                         self.citation_order.index(real_citation_num) + 1
                     )
 
+                    # get the value that was displayed to user, should always
+                    # be in the display_doc_order_dict. But check anyways
+                    if context_llm_doc.document_id in self.display_doc_order_dict:
+                        displayed_citation_num = self.display_doc_order_dict[
+                            context_llm_doc.document_id
+                        ]
+                    else:
+                        displayed_citation_num = real_citation_num
+                        logger.warning(
+                            f"Doc {context_llm_doc.document_id} not in display_doc_order_dict. Used LLM citation number instead."
+                        )
+
                     # Skip consecutive citations of the same work
                     if target_citation_num in self.current_citations:
                         start, end = citation.span()
@@ -116,6 +134,7 @@ class CitationProcessor:
                                 doc_id = int(match.group(1))
                                 context_llm_doc = self.context_docs[doc_id - 1]
                                 yield CitationInfo(
+                                    # stay with the original for now (order of LLM cites)
                                     citation_num=target_citation_num,
                                     document_id=context_llm_doc.document_id,
                                 )
@@ -131,29 +150,24 @@ class CitationProcessor:
 
                     link = context_llm_doc.link
 
-                    # Replace the citation in the current segment
-                    start, end = citation.span()
-                    self.curr_segment = (
-                        self.curr_segment[: start + length_to_add]
-                        + f"[{target_citation_num}]"
-                        + self.curr_segment[end + length_to_add :]
-                    )
-
                     self.past_cite_count = len(self.llm_out)
                     self.current_citations.append(target_citation_num)
 
                     if target_citation_num not in self.cited_inds:
                         self.cited_inds.add(target_citation_num)
                         yield CitationInfo(
+                            # stay with the original for now (order of LLM cites)
                             citation_num=target_citation_num,
                             document_id=context_llm_doc.document_id,
                         )
 
+                    start, end = citation.span()
                     if link:
                         prev_length = len(self.curr_segment)
                         self.curr_segment = (
                             self.curr_segment[: start + length_to_add]
-                            + f"[[{target_citation_num}]]({link})"
+                            + f"[[{displayed_citation_num}]]({link})"  # use the value that was displayed to user
+                            # + f"[[{target_citation_num}]]({link})"
                             + self.curr_segment[end + length_to_add :]
                         )
                         length_to_add += len(self.curr_segment) - prev_length
@@ -161,7 +175,8 @@ class CitationProcessor:
                         prev_length = len(self.curr_segment)
                         self.curr_segment = (
                             self.curr_segment[: start + length_to_add]
-                            + f"[[{target_citation_num}]]()"
+                            + f"[[{displayed_citation_num}]]()"  # use the value that was displayed to user
+                            # + f"[[{target_citation_num}]]()"
                             + self.curr_segment[end + length_to_add :]
                         )
                         length_to_add += len(self.curr_segment) - prev_length

backend/danswer/chat/stream_processing/quotes_processing.py

@@ -1,3 +1,4 @@
+# THIS IS NO LONGER IN USE
 import math
 import re
 from collections.abc import Generator
@@ -5,16 +6,15 @@ from json import JSONDecodeError
 from typing import Optional
 
 import regex
+from pydantic import BaseModel
 
 from danswer.chat.models import DanswerAnswer
 from danswer.chat.models import DanswerAnswerPiece
-from danswer.chat.models import DanswerQuote
-from danswer.chat.models import DanswerQuotes
 from danswer.chat.models import LlmDoc
 from danswer.configs.chat_configs import QUOTE_ALLOWED_ERROR_PERCENT
+from danswer.context.search.models import InferenceChunk
 from danswer.prompts.constants import ANSWER_PAT
 from danswer.prompts.constants import QUOTE_PAT
-from danswer.search.models import InferenceChunk
 from danswer.utils.logger import setup_logger
 from danswer.utils.text_processing import clean_model_quote
 from danswer.utils.text_processing import clean_up_code_blocks
@@ -26,6 +26,20 @@ logger = setup_logger()
 answer_pattern = re.compile(r'{\s*"answer"\s*:\s*"', re.IGNORECASE)
 
 
+class DanswerQuote(BaseModel):
+    # This is during inference so everything is a string by this point
+    quote: str
+    document_id: str
+    link: str | None
+    source_type: str
+    semantic_identifier: str
+    blurb: str
+
+
+class DanswerQuotes(BaseModel):
+    quotes: list[DanswerQuote]
+
+
 def _extract_answer_quotes_freeform(
     answer_raw: str,
 ) -> tuple[Optional[str], Optional[list[str]]]:
@@ -231,16 +245,16 @@ class QuotesProcessor:
 
         model_previous = self.model_output
         self.model_output += token
-
         if not self.found_answer_start:
             m = answer_pattern.search(self.model_output)
             if m:
                 self.found_answer_start = True
 
                 # Prevent heavy cases of hallucinations
-                if self.is_json_prompt and len(self.model_output) > 70:
-                    logger.warning("LLM did not produce json as prompted")
+                if self.is_json_prompt and len(self.model_output) > 400:
                     self.found_answer_end = True
+                    logger.warning("LLM did not produce json as prompted")
+                    logger.debug("Model output thus far:", self.model_output)
                     return
 
                 remaining = self.model_output[m.end() :]

backend/danswer/chat/stream_processing/utils.py

@@ -3,7 +3,7 @@ from collections.abc import Sequence
 from pydantic import BaseModel
 
 from danswer.chat.models import LlmDoc
-from danswer.search.models import InferenceChunk
+from danswer.context.search.models import InferenceChunk
 
 
 class DocumentIdOrderMapping(BaseModel):

backend/danswer/chat/tool_handling/tool_response_handler.py

@@ -4,8 +4,8 @@ from langchain_core.messages import AIMessageChunk
 from langchain_core.messages import BaseMessage
 from langchain_core.messages import ToolCall
 
-from danswer.llm.answering.llm_response_handler import LLMCall
-from danswer.llm.answering.llm_response_handler import ResponsePart
+from danswer.chat.models import ResponsePart
+from danswer.chat.prompt_builder.build import LLMCall
 from danswer.llm.interfaces import LLM
 from danswer.tools.force import ForceUseTool
 from danswer.tools.message import build_tool_message
@@ -62,7 +62,7 @@ class ToolResponseHandler:
                 llm_call.force_use_tool.args
                 if llm_call.force_use_tool.args is not None
                 else tool.get_args_for_non_tool_calling_llm(
-                    query=llm_call.prompt_builder.get_user_message_content(),
+                    query=llm_call.prompt_builder.raw_user_message,
                     history=llm_call.prompt_builder.raw_message_history,
                     llm=llm,
                     force_run=True,
@@ -76,7 +76,7 @@ class ToolResponseHandler:
         else:
             tool_options = check_which_tools_should_run_for_non_tool_calling_llm(
                 tools=llm_call.tools,
-                query=llm_call.prompt_builder.get_user_message_content(),
+                query=llm_call.prompt_builder.raw_user_message,
                 history=llm_call.prompt_builder.raw_message_history,
                 llm=llm,
             )
@@ -95,7 +95,7 @@ class ToolResponseHandler:
                 select_single_tool_for_non_tool_calling_llm(
                     tools_and_args=available_tools_and_args,
                     history=llm_call.prompt_builder.raw_message_history,
-                    query=llm_call.prompt_builder.get_user_message_content(),
+                    query=llm_call.prompt_builder.raw_user_message,
                     llm=llm,
                 )
                 if available_tools_and_args

backend/danswer/chat/tools.py

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

backend/danswer/configs/app_configs.py

@@ -43,9 +43,6 @@ WEB_DOMAIN = os.environ.get("WEB_DOMAIN") or "http://localhost:3000"
 AUTH_TYPE = AuthType((os.environ.get("AUTH_TYPE") or AuthType.DISABLED.value).lower())
 DISABLE_AUTH = AUTH_TYPE == AuthType.DISABLED
 
-# Necessary for cloud integration tests
-DISABLE_VERIFICATION = os.environ.get("DISABLE_VERIFICATION", "").lower() == "true"
-
 # Encryption key secret is used to encrypt connector credentials, api keys, and other sensitive
 # information. This provides an extra layer of security on top of Postgres access controls
 # and is available in Danswer EE
@@ -84,7 +81,14 @@ OAUTH_CLIENT_SECRET = (
     or ""
 )
 
+# for future OAuth connector support
+# OAUTH_CONFLUENCE_CLIENT_ID = os.environ.get("OAUTH_CONFLUENCE_CLIENT_ID", "")
+# OAUTH_CONFLUENCE_CLIENT_SECRET = os.environ.get("OAUTH_CONFLUENCE_CLIENT_SECRET", "")
+# OAUTH_JIRA_CLIENT_ID = os.environ.get("OAUTH_JIRA_CLIENT_ID", "")
+# OAUTH_JIRA_CLIENT_SECRET = os.environ.get("OAUTH_JIRA_CLIENT_SECRET", "")
+
 USER_AUTH_SECRET = os.environ.get("USER_AUTH_SECRET", "")
+
 # for basic auth
 REQUIRE_EMAIL_VERIFICATION = (
     os.environ.get("REQUIRE_EMAIL_VERIFICATION", "").lower() == "true"
@@ -118,6 +122,8 @@ VESPA_HOST = os.environ.get("VESPA_HOST") or "localhost"
 VESPA_CONFIG_SERVER_HOST = os.environ.get("VESPA_CONFIG_SERVER_HOST") or VESPA_HOST
 VESPA_PORT = os.environ.get("VESPA_PORT") or "8081"
 VESPA_TENANT_PORT = os.environ.get("VESPA_TENANT_PORT") or "19071"
+# the number of times to try and connect to vespa on startup before giving up
+VESPA_NUM_ATTEMPTS_ON_STARTUP = int(os.environ.get("NUM_RETRIES_ON_STARTUP") or 10)
 
 VESPA_CLOUD_URL = os.environ.get("VESPA_CLOUD_URL", "")
 
@@ -234,7 +240,7 @@ except ValueError:
         CELERY_WORKER_LIGHT_PREFETCH_MULTIPLIER_DEFAULT
     )
 
-CELERY_WORKER_INDEXING_CONCURRENCY_DEFAULT = 1
+CELERY_WORKER_INDEXING_CONCURRENCY_DEFAULT = 3
 try:
     env_value = os.environ.get("CELERY_WORKER_INDEXING_CONCURRENCY")
     if not env_value:
@@ -308,6 +314,22 @@ CONFLUENCE_CONNECTOR_ATTACHMENT_CHAR_COUNT_THRESHOLD = int(
     os.environ.get("CONFLUENCE_CONNECTOR_ATTACHMENT_CHAR_COUNT_THRESHOLD", 200_000)
 )
 
+# Due to breakages in the confluence API, the timezone offset must be specified client side
+# to match the user's specified timezone.
+
+# The current state of affairs:
+# CQL queries are parsed in the user's timezone and cannot be specified in UTC
+# no API retrieves the user's timezone
+# All data is returned in UTC, so we can't derive the user's timezone from that
+
+# https://community.developer.atlassian.com/t/confluence-cloud-time-zone-get-via-rest-api/35954/16
+# https://jira.atlassian.com/browse/CONFCLOUD-69670
+
+# enter as a floating point offset from UTC in hours (-24 < val < 24)
+# this will be applied globally, so it probably makes sense to transition this to per
+# connector as some point.
+CONFLUENCE_TIMEZONE_OFFSET = float(os.environ.get("CONFLUENCE_TIMEZONE_OFFSET", 0.0))
+
 JIRA_CONNECTOR_LABELS_TO_SKIP = [
     ignored_tag
     for ignored_tag in os.environ.get("JIRA_CONNECTOR_LABELS_TO_SKIP", "").split(",")
@@ -326,6 +348,12 @@ GITLAB_CONNECTOR_INCLUDE_CODE_FILES = (
     os.environ.get("GITLAB_CONNECTOR_INCLUDE_CODE_FILES", "").lower() == "true"
 )
 
+# Egnyte specific configs
+EGNYTE_LOCALHOST_OVERRIDE = os.getenv("EGNYTE_LOCALHOST_OVERRIDE")
+EGNYTE_BASE_DOMAIN = os.getenv("EGNYTE_DOMAIN")
+EGNYTE_CLIENT_ID = os.getenv("EGNYTE_CLIENT_ID")
+EGNYTE_CLIENT_SECRET = os.getenv("EGNYTE_CLIENT_SECRET")
+
 DASK_JOB_CLIENT_ENABLED = (
     os.environ.get("DASK_JOB_CLIENT_ENABLED", "").lower() == "true"
 )
@@ -389,21 +417,28 @@ LARGE_CHUNK_RATIO = 4
 # We don't want the metadata to overwhelm the actual contents of the chunk
 SKIP_METADATA_IN_CHUNK = os.environ.get("SKIP_METADATA_IN_CHUNK", "").lower() == "true"
 # Timeout to wait for job's last update before killing it, in hours
-CLEANUP_INDEXING_JOBS_TIMEOUT = int(os.environ.get("CLEANUP_INDEXING_JOBS_TIMEOUT", 3))
+CLEANUP_INDEXING_JOBS_TIMEOUT = int(
+    os.environ.get("CLEANUP_INDEXING_JOBS_TIMEOUT") or 3
+)
 
 # The indexer will warn in the logs whenver a document exceeds this threshold (in bytes)
 INDEXING_SIZE_WARNING_THRESHOLD = int(
-    os.environ.get("INDEXING_SIZE_WARNING_THRESHOLD", 100 * 1024 * 1024)
+    os.environ.get("INDEXING_SIZE_WARNING_THRESHOLD") or 100 * 1024 * 1024
 )
 
 # during indexing, will log verbose memory diff stats every x batches and at the end.
 # 0 disables this behavior and is the default.
-INDEXING_TRACER_INTERVAL = int(os.environ.get("INDEXING_TRACER_INTERVAL", 0))
+INDEXING_TRACER_INTERVAL = int(os.environ.get("INDEXING_TRACER_INTERVAL") or 0)
 
 # During an indexing attempt, specifies the number of batches which are allowed to
 # exception without aborting the attempt.
-INDEXING_EXCEPTION_LIMIT = int(os.environ.get("INDEXING_EXCEPTION_LIMIT", 0))
+INDEXING_EXCEPTION_LIMIT = int(os.environ.get("INDEXING_EXCEPTION_LIMIT") or 0)
 
+# Maximum file size in a document to be indexed
+MAX_DOCUMENT_CHARS = int(os.environ.get("MAX_DOCUMENT_CHARS") or 5_000_000)
+MAX_FILE_SIZE_BYTES = int(
+    os.environ.get("MAX_FILE_SIZE_BYTES") or 2 * 1024 * 1024 * 1024
+)  # 2GB in bytes
 
 #####
 # Miscellaneous
@@ -422,6 +457,9 @@ LOG_ALL_MODEL_INTERACTIONS = (
 LOG_DANSWER_MODEL_INTERACTIONS = (
     os.environ.get("LOG_DANSWER_MODEL_INTERACTIONS", "").lower() == "true"
 )
+LOG_INDIVIDUAL_MODEL_TOKENS = (
+    os.environ.get("LOG_INDIVIDUAL_MODEL_TOKENS", "").lower() == "true"
+)
 # If set to `true` will enable additional logs about Vespa query performance
 # (time spent on finding the right docs + time spent fetching summaries from disk)
 LOG_VESPA_TIMING_INFORMATION = (
@@ -490,10 +528,6 @@ CONTROL_PLANE_API_BASE_URL = os.environ.get(
 # JWT configuration
 JWT_ALGORITHM = "HS256"
 
-# Super Users
-SUPER_USERS = json.loads(os.environ.get("SUPER_USERS", '["pablo@danswer.ai"]'))
-SUPER_CLOUD_API_KEY = os.environ.get("SUPER_CLOUD_API_KEY", "api_key")
-
 
 #####
 # API Key Configs
@@ -507,3 +541,6 @@ API_KEY_HASH_ROUNDS = (
 
 POD_NAME = os.environ.get("POD_NAME")
 POD_NAMESPACE = os.environ.get("POD_NAMESPACE")
+
+
+DEV_MODE = os.environ.get("DEV_MODE", "").lower() == "true"

backend/danswer/configs/chat_configs.py

@@ -1,9 +1,8 @@
 import os
 
 
-PROMPTS_YAML = "./danswer/chat/prompts.yaml"
-PERSONAS_YAML = "./danswer/chat/personas.yaml"
-INPUT_PROMPT_YAML = "./danswer/chat/input_prompts.yaml"
+PROMPTS_YAML = "./danswer/seeding/prompts.yaml"
+PERSONAS_YAML = "./danswer/seeding/personas.yaml"
 
 NUM_RETURNED_HITS = 50
 # Used for LLM filtering and reranking
@@ -17,9 +16,6 @@ MAX_CHUNKS_FED_TO_CHAT = float(os.environ.get("MAX_CHUNKS_FED_TO_CHAT") or 10.0)
 # ~3k input, half for docs, half for chat history + prompts
 CHAT_TARGET_CHUNK_PERCENTAGE = 512 * 3 / 3072
 
-# For selecting a different LLM question-answering prompt format
-# Valid values: default, cot, weak
-QA_PROMPT_OVERRIDE = os.environ.get("QA_PROMPT_OVERRIDE") or None
 # 1 / (1 + DOC_TIME_DECAY * doc-age-in-years), set to 0 to have no decay
 # Capped in Vespa at 0.5
 DOC_TIME_DECAY = float(
@@ -27,8 +23,6 @@ DOC_TIME_DECAY = float(
 )
 BASE_RECENCY_DECAY = 0.5
 FAVOR_RECENT_DECAY_MULTIPLIER = 2.0
-# Currently this next one is not configurable via env
-DISABLE_LLM_QUERY_ANSWERABILITY = QA_PROMPT_OVERRIDE == "weak"
 # For the highest matching base size chunk, how many chunks above and below do we pull in by default
 # Note this is not in any of the deployment configs yet
 # Currently only applies to search flow not chat

backend/danswer/configs/constants.py

@@ -31,6 +31,8 @@ DISABLED_GEN_AI_MSG = (
     "You can still use Danswer as a search engine."
 )
 
+DEFAULT_PERSONA_ID = 0
+
 # Postgres connection constants for application_name
 POSTGRES_WEB_APP_NAME = "web"
 POSTGRES_INDEXER_APP_NAME = "indexer"
@@ -60,7 +62,6 @@ KV_GMAIL_CRED_KEY = "gmail_app_credential"
 KV_GMAIL_SERVICE_ACCOUNT_KEY = "gmail_service_account_key"
 KV_GOOGLE_DRIVE_CRED_KEY = "google_drive_app_credential"
 KV_GOOGLE_DRIVE_SERVICE_ACCOUNT_KEY = "google_drive_service_account_key"
-KV_SLACK_BOT_TOKENS_CONFIG_KEY = "slack_bot_tokens_config_key"
 KV_GEN_AI_KEY_CHECK_TIME = "genai_api_key_last_check_time"
 KV_SETTINGS_KEY = "danswer_settings"
 KV_CUSTOMER_UUID_KEY = "customer_uuid"
@@ -74,7 +75,7 @@ CELERY_PRIMARY_WORKER_LOCK_TIMEOUT = 120
 
 # needs to be long enough to cover the maximum time it takes to download an object
 # if we can get callbacks as object bytes download, we could lower this a lot.
-CELERY_INDEXING_LOCK_TIMEOUT = 60 * 60  # 60 min
+CELERY_INDEXING_LOCK_TIMEOUT = 3 * 60 * 60  # 60 min
 
 # needs to be long enough to cover the maximum time it takes to download an object
 # if we can get callbacks as object bytes download, we could lower this a lot.
@@ -131,6 +132,7 @@ class DocumentSource(str, Enum):
     NOT_APPLICABLE = "not_applicable"
     FRESHDESK = "freshdesk"
     FIREFLIES = "fireflies"
+    EGNYTE = "egnyte"
 
 
 DocumentSourceRequiringTenantContext: list[DocumentSource] = [DocumentSource.FILE]
@@ -260,6 +262,32 @@ class DanswerCeleryPriority(int, Enum):
     LOWEST = auto()
 
 
+class DanswerCeleryTask:
+    CHECK_FOR_CONNECTOR_DELETION = "check_for_connector_deletion_task"
+    CHECK_FOR_VESPA_SYNC_TASK = "check_for_vespa_sync_task"
+    CHECK_FOR_INDEXING = "check_for_indexing"
+    CHECK_FOR_PRUNING = "check_for_pruning"
+    CHECK_FOR_DOC_PERMISSIONS_SYNC = "check_for_doc_permissions_sync"
+    CHECK_FOR_EXTERNAL_GROUP_SYNC = "check_for_external_group_sync"
+    MONITOR_VESPA_SYNC = "monitor_vespa_sync"
+    KOMBU_MESSAGE_CLEANUP_TASK = "kombu_message_cleanup_task"
+    CONNECTOR_PERMISSION_SYNC_GENERATOR_TASK = (
+        "connector_permission_sync_generator_task"
+    )
+    UPDATE_EXTERNAL_DOCUMENT_PERMISSIONS_TASK = (
+        "update_external_document_permissions_task"
+    )
+    CONNECTOR_EXTERNAL_GROUP_SYNC_GENERATOR_TASK = (
+        "connector_external_group_sync_generator_task"
+    )
+    CONNECTOR_INDEXING_PROXY_TASK = "connector_indexing_proxy_task"
+    CONNECTOR_PRUNING_GENERATOR_TASK = "connector_pruning_generator_task"
+    DOCUMENT_BY_CC_PAIR_CLEANUP_TASK = "document_by_cc_pair_cleanup_task"
+    VESPA_METADATA_SYNC_TASK = "vespa_metadata_sync_task"
+    CHECK_TTL_MANAGEMENT_TASK = "check_ttl_management_task"
+    AUTOGENERATE_USAGE_REPORT_TASK = "autogenerate_usage_report_task"
+
+
 REDIS_SOCKET_KEEPALIVE_OPTIONS = {}
 REDIS_SOCKET_KEEPALIVE_OPTIONS[socket.TCP_KEEPINTVL] = 15
 REDIS_SOCKET_KEEPALIVE_OPTIONS[socket.TCP_KEEPCNT] = 3

backend/danswer/configs/danswerbot_configs.py

@@ -4,11 +4,8 @@ import os
 # Danswer Slack Bot Configs
 #####
 DANSWER_BOT_NUM_RETRIES = int(os.environ.get("DANSWER_BOT_NUM_RETRIES", "5"))
-DANSWER_BOT_ANSWER_GENERATION_TIMEOUT = int(
-    os.environ.get("DANSWER_BOT_ANSWER_GENERATION_TIMEOUT", "90")
-)
 # How much of the available input context can be used for thread context
-DANSWER_BOT_TARGET_CHUNK_PERCENTAGE = 512 * 2 / 3072
+MAX_THREAD_CONTEXT_PERCENTAGE = 512 * 2 / 3072
 # Number of docs to display in "Reference Documents"
 DANSWER_BOT_NUM_DOCS_TO_DISPLAY = int(
     os.environ.get("DANSWER_BOT_NUM_DOCS_TO_DISPLAY", "5")
@@ -47,17 +44,6 @@ DANSWER_BOT_DISPLAY_ERROR_MSGS = os.environ.get(
 DANSWER_BOT_RESPOND_EVERY_CHANNEL = (
     os.environ.get("DANSWER_BOT_RESPOND_EVERY_CHANNEL", "").lower() == "true"
 )
-# Add a second LLM call post Answer to verify if the Answer is valid
-# Throws out answers that don't directly or fully answer the user query
-# This is the default for all DanswerBot channels unless the channel is configured individually
-# Set/unset by "Hide Non Answers"
-ENABLE_DANSWERBOT_REFLEXION = (
-    os.environ.get("ENABLE_DANSWERBOT_REFLEXION", "").lower() == "true"
-)
-# Currently not support chain of thought, probably will add back later
-DANSWER_BOT_DISABLE_COT = True
-# if set, will default DanswerBot to use quotes and reference documents
-DANSWER_BOT_USE_QUOTES = os.environ.get("DANSWER_BOT_USE_QUOTES", "").lower() == "true"
 
 # Maximum Questions Per Minute, Default Uncapped
 DANSWER_BOT_MAX_QPM = int(os.environ.get("DANSWER_BOT_MAX_QPM") or 0) or None

backend/danswer/configs/model_configs.py

@@ -70,7 +70,9 @@ GEN_AI_NUM_RESERVED_OUTPUT_TOKENS = int(
 )
 
 # Typically, GenAI models nowadays are at least 4K tokens
-GEN_AI_MODEL_FALLBACK_MAX_TOKENS = 4096
+GEN_AI_MODEL_FALLBACK_MAX_TOKENS = int(
+    os.environ.get("GEN_AI_MODEL_FALLBACK_MAX_TOKENS") or 4096
+)
 
 # Number of tokens from chat history to include at maximum
 # 3000 should be enough context regardless of use, no need to include as much as possible

backend/danswer/configs/tool_configs.py

@@ -2,6 +2,8 @@ import json
 import os
 
 
+IMAGE_GENERATION_OUTPUT_FORMAT = os.environ.get("IMAGE_GENERATION_OUTPUT_FORMAT", "url")
+
 # if specified, will pass through request headers to the call to API calls made by custom tools
 CUSTOM_TOOL_PASS_THROUGH_HEADERS: list[str] | None = None
 _CUSTOM_TOOL_PASS_THROUGH_HEADERS_RAW = os.environ.get(

backend/danswer/connectors/README.md

@@ -11,11 +11,16 @@ Connectors come in 3 different flows:
 - Load Connector:
   - Bulk indexes documents to reflect a point in time. This type of connector generally works by either pulling all
   documents via a connector's API or loads the documents from some sort of a dump file.
-- Poll connector:
+- Poll Connector:
   - Incrementally updates documents based on a provided time range. It is used by the background job to pull the latest
   changes and additions since the last round of polling. This connector helps keep the document index up to date
   without needing to fetch/embed/index every document which would be too slow to do frequently on large sets of
   documents.
+- Slim Connector:
+  - This connector should be a lighter weight method of checking all documents in the source to see if they still exist.
+  - This connector should be identical to the Poll or Load Connector except that it only fetches the IDs of the documents, not the documents themselves.
+  - This is used by our pruning job which removes old documents from the index.
+  - The optional start and end datetimes can be ignored.
 - Event Based connectors:
   - Connectors that listen to events and update documents accordingly.
   - Currently not used by the background job, this exists for future design purposes.
@@ -26,8 +31,14 @@ Refer to [interfaces.py](https://github.com/danswer-ai/danswer/blob/main/backend
 and this first contributor created Pull Request for a new connector (Shoutout to Dan Brown):
 [Reference Pull Request](https://github.com/danswer-ai/danswer/pull/139)
 
+For implementing a Slim Connector, refer to the comments in this PR:
+[Slim Connector PR](https://github.com/danswer-ai/danswer/pull/3303/files)
+
+All new connectors should have tests added to the `backend/tests/daily/connectors` directory. Refer to the above PR for an example of adding tests for a new connector.
+
+
 #### Implementing the new Connector
-The connector must subclass one or more of LoadConnector, PollConnector, or EventConnector.
+The connector must subclass one or more of LoadConnector, PollConnector, SlimConnector, or EventConnector.
 
 The `__init__` should take arguments for configuring what documents the connector will and where it finds those
 documents. For example, if you have a wiki site, it may include the configuration for the team, topic, folder, etc. of

backend/danswer/connectors/blob/connector.py

@@ -5,9 +5,9 @@ from io import BytesIO
 from typing import Any
 from typing import Optional
 
-import boto3
-from botocore.client import Config
-from mypy_boto3_s3 import S3Client
+import boto3  # type: ignore
+from botocore.client import Config  # type: ignore
+from mypy_boto3_s3 import S3Client  # type: ignore
 
 from danswer.configs.app_configs import INDEX_BATCH_SIZE
 from danswer.configs.constants import BlobType

backend/danswer/connectors/confluence/connector.py

@@ -1,18 +1,21 @@
 from datetime import datetime
+from datetime import timedelta
 from datetime import timezone
 from typing import Any
 from urllib.parse import quote
 
 from danswer.configs.app_configs import CONFLUENCE_CONNECTOR_LABELS_TO_SKIP
+from danswer.configs.app_configs import CONFLUENCE_TIMEZONE_OFFSET
 from danswer.configs.app_configs import CONTINUE_ON_CONNECTOR_FAILURE
 from danswer.configs.app_configs import INDEX_BATCH_SIZE
 from danswer.configs.constants import DocumentSource
+from danswer.connectors.confluence.onyx_confluence import build_confluence_client
 from danswer.connectors.confluence.onyx_confluence import OnyxConfluence
 from danswer.connectors.confluence.utils import attachment_to_content
-from danswer.connectors.confluence.utils import build_confluence_client
 from danswer.connectors.confluence.utils import build_confluence_document_id
 from danswer.connectors.confluence.utils import datetime_from_string
 from danswer.connectors.confluence.utils import extract_text_from_confluence_html
+from danswer.connectors.confluence.utils import validate_attachment_filetype
 from danswer.connectors.interfaces import GenerateDocumentsOutput
 from danswer.connectors.interfaces import GenerateSlimDocumentOutput
 from danswer.connectors.interfaces import LoadConnector
@@ -51,6 +54,8 @@ _RESTRICTIONS_EXPANSION_FIELDS = [
     "restrictions.read.restrictions.group",
 ]
 
+_SLIM_DOC_BATCH_SIZE = 5000
+
 
 class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
     def __init__(
@@ -67,10 +72,11 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
         # skip it. This is generally used to avoid indexing extra sensitive
         # pages.
         labels_to_skip: list[str] = CONFLUENCE_CONNECTOR_LABELS_TO_SKIP,
+        timezone_offset: float = CONFLUENCE_TIMEZONE_OFFSET,
     ) -> None:
         self.batch_size = batch_size
         self.continue_on_failure = continue_on_failure
-        self.confluence_client: OnyxConfluence | None = None
+        self._confluence_client: OnyxConfluence | None = None
         self.is_cloud = is_cloud
 
         # Remove trailing slash from wiki_base if present
@@ -81,15 +87,15 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
         if cql_query:
             # if a cql_query is provided, we will use it to fetch the pages
             cql_page_query = cql_query
-        elif space:
-            # if no cql_query is provided, we will use the space to fetch the pages
-            cql_page_query += f" and space='{quote(space)}'"
         elif page_id:
+            # if a cql_query is not provided, we will use the page_id to fetch the page
             if index_recursively:
                 cql_page_query += f" and ancestor='{page_id}'"
             else:
-                # if neither a space nor a cql_query is provided, we will use the page_id to fetch the page
                 cql_page_query += f" and id='{page_id}'"
+        elif space:
+            # if no cql_query or page_id is provided, we will use the space to fetch the pages
+            cql_page_query += f" and space='{quote(space)}'"
 
         self.cql_page_query = cql_page_query
         self.cql_time_filter = ""
@@ -97,39 +103,46 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
         self.cql_label_filter = ""
         if labels_to_skip:
             labels_to_skip = list(set(labels_to_skip))
-            comma_separated_labels = ",".join(f"'{label}'" for label in labels_to_skip)
+            comma_separated_labels = ",".join(
+                f"'{quote(label)}'" for label in labels_to_skip
+            )
             self.cql_label_filter = f" and label not in ({comma_separated_labels})"
 
+        self.timezone: timezone = timezone(offset=timedelta(hours=timezone_offset))
+
+    @property
+    def confluence_client(self) -> OnyxConfluence:
+        if self._confluence_client is None:
+            raise ConnectorMissingCredentialError("Confluence")
+        return self._confluence_client
+
     def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
         # see https://github.com/atlassian-api/atlassian-python-api/blob/master/atlassian/rest_client.py
         # for a list of other hidden constructor args
-        self.confluence_client = build_confluence_client(
-            credentials_json=credentials,
+        self._confluence_client = build_confluence_client(
+            credentials=credentials,
             is_cloud=self.is_cloud,
             wiki_base=self.wiki_base,
         )
         return None
 
     def _get_comment_string_for_page_id(self, page_id: str) -> str:
-        if self.confluence_client is None:
-            raise ConnectorMissingCredentialError("Confluence")
-
         comment_string = ""
 
         comment_cql = f"type=comment and container='{page_id}'"
         comment_cql += self.cql_label_filter
 
         expand = ",".join(_COMMENT_EXPANSION_FIELDS)
-        for comments in self.confluence_client.paginated_cql_page_retrieval(
+        for comment in self.confluence_client.paginated_cql_retrieval(
             cql=comment_cql,
             expand=expand,
         ):
-            for comment in comments:
-                comment_string += "\nComment:\n"
-                comment_string += extract_text_from_confluence_html(
-                    confluence_client=self.confluence_client,
-                    confluence_object=comment,
-                )
+            comment_string += "\nComment:\n"
+            comment_string += extract_text_from_confluence_html(
+                confluence_client=self.confluence_client,
+                confluence_object=comment,
+                fetched_titles=set(),
+            )
 
         return comment_string
 
@@ -141,9 +154,6 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
         If its a page, it extracts the text, adds the comments for the document text.
         If its an attachment, it just downloads the attachment and converts that into a document.
         """
-        if self.confluence_client is None:
-            raise ConnectorMissingCredentialError("Confluence")
-
         # The url and the id are the same
         object_url = build_confluence_document_id(
             self.wiki_base, confluence_object["_links"]["webui"], self.is_cloud
@@ -153,16 +163,19 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
         # Extract text from page
         if confluence_object["type"] == "page":
             object_text = extract_text_from_confluence_html(
-                self.confluence_client, confluence_object
+                confluence_client=self.confluence_client,
+                confluence_object=confluence_object,
+                fetched_titles={confluence_object.get("title", "")},
             )
             # Add comments to text
             object_text += self._get_comment_string_for_page_id(confluence_object["id"])
         elif confluence_object["type"] == "attachment":
             object_text = attachment_to_content(
-                self.confluence_client, confluence_object
+                confluence_client=self.confluence_client, attachment=confluence_object
             )
 
         if object_text is None:
+            # This only happens for attachments that are not parseable
             return None
 
         # Get space name
@@ -193,44 +206,41 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
         )
 
     def _fetch_document_batches(self) -> GenerateDocumentsOutput:
-        if self.confluence_client is None:
-            raise ConnectorMissingCredentialError("Confluence")
-
         doc_batch: list[Document] = []
         confluence_page_ids: list[str] = []
 
         page_query = self.cql_page_query + self.cql_label_filter + self.cql_time_filter
+        logger.debug(f"page_query: {page_query}")
         # Fetch pages as Documents
-        for page_batch in self.confluence_client.paginated_cql_page_retrieval(
+        for page in self.confluence_client.paginated_cql_retrieval(
             cql=page_query,
             expand=",".join(_PAGE_EXPANSION_FIELDS),
             limit=self.batch_size,
         ):
-            for page in page_batch:
-                confluence_page_ids.append(page["id"])
-                doc = self._convert_object_to_document(page)
-                if doc is not None:
-                    doc_batch.append(doc)
-                if len(doc_batch) >= self.batch_size:
-                    yield doc_batch
-                    doc_batch = []
+            logger.debug(f"_fetch_document_batches: {page['id']}")
+            confluence_page_ids.append(page["id"])
+            doc = self._convert_object_to_document(page)
+            if doc is not None:
+                doc_batch.append(doc)
+            if len(doc_batch) >= self.batch_size:
+                yield doc_batch
+                doc_batch = []
 
         # Fetch attachments as Documents
         for confluence_page_id in confluence_page_ids:
             attachment_cql = f"type=attachment and container='{confluence_page_id}'"
             attachment_cql += self.cql_label_filter
             # TODO: maybe should add time filter as well?
-            for attachments in self.confluence_client.paginated_cql_page_retrieval(
+            for attachment in self.confluence_client.paginated_cql_retrieval(
                 cql=attachment_cql,
                 expand=",".join(_ATTACHMENT_EXPANSION_FIELDS),
             ):
-                for attachment in attachments:
-                    doc = self._convert_object_to_document(attachment)
-                    if doc is not None:
-                        doc_batch.append(doc)
-                    if len(doc_batch) >= self.batch_size:
-                        yield doc_batch
-                        doc_batch = []
+                doc = self._convert_object_to_document(attachment)
+                if doc is not None:
+                    doc_batch.append(doc)
+                if len(doc_batch) >= self.batch_size:
+                    yield doc_batch
+                    doc_batch = []
 
         if doc_batch:
             yield doc_batch
@@ -240,10 +250,10 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
 
     def poll_source(self, start: float, end: float) -> GenerateDocumentsOutput:
         # Add time filters
-        formatted_start_time = datetime.fromtimestamp(start, tz=timezone.utc).strftime(
+        formatted_start_time = datetime.fromtimestamp(start, tz=self.timezone).strftime(
             "%Y-%m-%d %H:%M"
         )
-        formatted_end_time = datetime.fromtimestamp(end, tz=timezone.utc).strftime(
+        formatted_end_time = datetime.fromtimestamp(end, tz=self.timezone).strftime(
             "%Y-%m-%d %H:%M"
         )
         self.cql_time_filter = f" and lastmodified >= '{formatted_start_time}'"
@@ -255,52 +265,69 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
         start: SecondsSinceUnixEpoch | None = None,
         end: SecondsSinceUnixEpoch | None = None,
     ) -> GenerateSlimDocumentOutput:
-        if self.confluence_client is None:
-            raise ConnectorMissingCredentialError("Confluence")
-
         doc_metadata_list: list[SlimDocument] = []
 
         restrictions_expand = ",".join(_RESTRICTIONS_EXPANSION_FIELDS)
 
         page_query = self.cql_page_query + self.cql_label_filter
-        for pages in self.confluence_client.cql_paginate_all_expansions(
+        for page in self.confluence_client.cql_paginate_all_expansions(
             cql=page_query,
             expand=restrictions_expand,
+            limit=_SLIM_DOC_BATCH_SIZE,
         ):
-            for page in pages:
-                # If the page has restrictions, add them to the perm_sync_data
-                # These will be used by doc_sync.py to sync permissions
-                perm_sync_data = {
-                    "restrictions": page.get("restrictions", {}),
-                    "space_key": page.get("space", {}).get("key"),
+            # If the page has restrictions, add them to the perm_sync_data
+            # These will be used by doc_sync.py to sync permissions
+            page_restrictions = page.get("restrictions")
+            page_space_key = page.get("space", {}).get("key")
+            page_perm_sync_data = {
+                "restrictions": page_restrictions or {},
+                "space_key": page_space_key,
+            }
+
+            doc_metadata_list.append(
+                SlimDocument(
+                    id=build_confluence_document_id(
+                        self.wiki_base,
+                        page["_links"]["webui"],
+                        self.is_cloud,
+                    ),
+                    perm_sync_data=page_perm_sync_data,
+                )
+            )
+            attachment_cql = f"type=attachment and container='{page['id']}'"
+            attachment_cql += self.cql_label_filter
+            for attachment in self.confluence_client.cql_paginate_all_expansions(
+                cql=attachment_cql,
+                expand=restrictions_expand,
+                limit=_SLIM_DOC_BATCH_SIZE,
+            ):
+                if not validate_attachment_filetype(attachment):
+                    continue
+                attachment_restrictions = attachment.get("restrictions")
+                if not attachment_restrictions:
+                    attachment_restrictions = page_restrictions
+
+                attachment_space_key = attachment.get("space", {}).get("key")
+                if not attachment_space_key:
+                    attachment_space_key = page_space_key
+
+                attachment_perm_sync_data = {
+                    "restrictions": attachment_restrictions or {},
+                    "space_key": attachment_space_key,
                 }
 
                 doc_metadata_list.append(
                     SlimDocument(
                         id=build_confluence_document_id(
                             self.wiki_base,
-                            page["_links"]["webui"],
+                            attachment["_links"]["webui"],
                             self.is_cloud,
                         ),
-                        perm_sync_data=perm_sync_data,
+                        perm_sync_data=attachment_perm_sync_data,
                     )
                 )
-                attachment_cql = f"type=attachment and container='{page['id']}'"
-                attachment_cql += self.cql_label_filter
-                for attachments in self.confluence_client.cql_paginate_all_expansions(
-                    cql=attachment_cql,
-                    expand=restrictions_expand,
-                ):
-                    for attachment in attachments:
-                        doc_metadata_list.append(
-                            SlimDocument(
-                                id=build_confluence_document_id(
-                                    self.wiki_base,
-                                    attachment["_links"]["webui"],
-                                    self.is_cloud,
-                                ),
-                                perm_sync_data=perm_sync_data,
-                            )
-                        )
-                yield doc_metadata_list
-                doc_metadata_list = []
+            if len(doc_metadata_list) > _SLIM_DOC_BATCH_SIZE:
+                yield doc_metadata_list[:_SLIM_DOC_BATCH_SIZE]
+                doc_metadata_list = doc_metadata_list[_SLIM_DOC_BATCH_SIZE:]
+
+        yield doc_metadata_list

backend/danswer/connectors/confluence/onyx_confluence.py

@@ -20,6 +20,10 @@ F = TypeVar("F", bound=Callable[..., Any])
 
 RATE_LIMIT_MESSAGE_LOWERCASE = "Rate limit exceeded".lower()
 
+# https://jira.atlassian.com/browse/CONFCLOUD-76433
+_PROBLEMATIC_EXPANSIONS = "body.storage.value"
+_REPLACEMENT_EXPANSIONS = "body.view.value"
+
 
 class ConfluenceRateLimitError(Exception):
     pass
@@ -80,7 +84,7 @@ def handle_confluence_rate_limit(confluence_call: F) -> F:
     def wrapped_call(*args: list[Any], **kwargs: Any) -> Any:
         MAX_RETRIES = 5
 
-        TIMEOUT = 3600
+        TIMEOUT = 600
         timeout_at = time.monotonic() + TIMEOUT
 
         for attempt in range(MAX_RETRIES):
@@ -95,6 +99,10 @@ def handle_confluence_rate_limit(confluence_call: F) -> F:
                 return confluence_call(*args, **kwargs)
             except HTTPError as e:
                 delay_until = _handle_http_error(e, attempt)
+                logger.warning(
+                    f"HTTPError in confluence call. "
+                    f"Retrying in {delay_until} seconds..."
+                )
                 while time.monotonic() < delay_until:
                     # in the future, check a signal here to exit
                     time.sleep(1)
@@ -112,7 +120,7 @@ def handle_confluence_rate_limit(confluence_call: F) -> F:
     return cast(F, wrapped_call)
 
 
-_DEFAULT_PAGINATION_LIMIT = 100
+_DEFAULT_PAGINATION_LIMIT = 1000
 
 
 class OnyxConfluence(Confluence):
@@ -126,6 +134,32 @@ class OnyxConfluence(Confluence):
         super(OnyxConfluence, self).__init__(url, *args, **kwargs)
         self._wrap_methods()
 
+    def get_current_user(self, expand: str | None = None) -> Any:
+        """
+        Implements a method that isn't in the third party client.
+
+        Get information about the current user
+        :param expand: OPTIONAL expand for get status of user.
+                Possible param is "status". Results are "Active, Deactivated"
+        :return: Returns the user details
+        """
+
+        from atlassian.errors import ApiPermissionError  # type:ignore
+
+        url = "rest/api/user/current"
+        params = {}
+        if expand:
+            params["expand"] = expand
+        try:
+            response = self.get(url, params=params)
+        except HTTPError as e:
+            if e.response.status_code == 403:
+                raise ApiPermissionError(
+                    "The calling user does not have permission", reason=e
+                )
+            raise
+        return response
+
     def _wrap_methods(self) -> None:
         """
         For each attribute that is callable (i.e., a method) and doesn't start with an underscore,
@@ -141,7 +175,7 @@ class OnyxConfluence(Confluence):
 
     def _paginate_url(
         self, url_suffix: str, limit: int | None = None
-    ) -> Iterator[list[dict[str, Any]]]:
+    ) -> Iterator[dict[str, Any]]:
         """
         This will paginate through the top level query.
         """
@@ -153,46 +187,43 @@ class OnyxConfluence(Confluence):
 
         while url_suffix:
             try:
+                logger.debug(f"Making confluence call to {url_suffix}")
                 next_response = self.get(url_suffix)
             except Exception as e:
-                logger.exception("Error in danswer_cql: \n")
-                raise e
-            yield next_response.get("results", [])
+                logger.warning(f"Error in confluence call to {url_suffix}")
+
+                # If the problematic expansion is in the url, replace it
+                # with the replacement expansion and try again
+                # If that fails, raise the error
+                if _PROBLEMATIC_EXPANSIONS not in url_suffix:
+                    logger.exception(f"Error in confluence call to {url_suffix}")
+                    raise e
+                logger.warning(
+                    f"Replacing {_PROBLEMATIC_EXPANSIONS} with {_REPLACEMENT_EXPANSIONS}"
+                    " and trying again."
+                )
+                url_suffix = url_suffix.replace(
+                    _PROBLEMATIC_EXPANSIONS,
+                    _REPLACEMENT_EXPANSIONS,
+                )
+                continue
+
+            # yield the results individually
+            yield from next_response.get("results", [])
+
             url_suffix = next_response.get("_links", {}).get("next")
 
-    def paginated_groups_retrieval(
-        self,
-        limit: int | None = None,
-    ) -> Iterator[list[dict[str, Any]]]:
-        return self._paginate_url("rest/api/group", limit)
-
-    def paginated_group_members_retrieval(
-        self,
-        group_name: str,
-        limit: int | None = None,
-    ) -> Iterator[list[dict[str, Any]]]:
-        group_name = quote(group_name)
-        return self._paginate_url(f"rest/api/group/{group_name}/member", limit)
-
-    def paginated_cql_user_retrieval(
+    def paginated_cql_retrieval(
         self,
         cql: str,
         expand: str | None = None,
         limit: int | None = None,
-    ) -> Iterator[list[dict[str, Any]]]:
+    ) -> Iterator[dict[str, Any]]:
+        """
+        The content/search endpoint can be used to fetch pages, attachments, and comments.
+        """
         expand_string = f"&expand={expand}" if expand else ""
-        return self._paginate_url(
-            f"rest/api/search/user?cql={cql}{expand_string}", limit
-        )
-
-    def paginated_cql_page_retrieval(
-        self,
-        cql: str,
-        expand: str | None = None,
-        limit: int | None = None,
-    ) -> Iterator[list[dict[str, Any]]]:
-        expand_string = f"&expand={expand}" if expand else ""
-        return self._paginate_url(
+        yield from self._paginate_url(
             f"rest/api/content/search?cql={cql}{expand_string}", limit
         )
 
@@ -201,7 +232,7 @@ class OnyxConfluence(Confluence):
         cql: str,
         expand: str | None = None,
         limit: int | None = None,
-    ) -> Iterator[list[dict[str, Any]]]:
+    ) -> Iterator[dict[str, Any]]:
         """
         This function will paginate through the top level query first, then
         paginate through all of the expansions.
@@ -221,6 +252,121 @@ class OnyxConfluence(Confluence):
                 for item in data:
                     _traverse_and_update(item)
 
-        for results in self.paginated_cql_page_retrieval(cql, expand, limit):
-            _traverse_and_update(results)
-            yield results
+        for confluence_object in self.paginated_cql_retrieval(cql, expand, limit):
+            _traverse_and_update(confluence_object)
+            yield confluence_object
+
+    def paginated_cql_user_retrieval(
+        self,
+        expand: str | None = None,
+        limit: int | None = None,
+    ) -> Iterator[dict[str, Any]]:
+        """
+        The search/user endpoint can be used to fetch users.
+        It's a seperate endpoint from the content/search endpoint used only for users.
+        Otherwise it's very similar to the content/search endpoint.
+        """
+        cql = "type=user"
+        url = "rest/api/search/user" if self.cloud else "rest/api/search"
+        expand_string = f"&expand={expand}" if expand else ""
+        url += f"?cql={cql}{expand_string}"
+        yield from self._paginate_url(url, limit)
+
+    def paginated_groups_by_user_retrieval(
+        self,
+        user: dict[str, Any],
+        limit: int | None = None,
+    ) -> Iterator[dict[str, Any]]:
+        """
+        This is not an SQL like query.
+        It's a confluence specific endpoint that can be used to fetch groups.
+        """
+        user_field = "accountId" if self.cloud else "key"
+        user_value = user["accountId"] if self.cloud else user["userKey"]
+        # Server uses userKey (but calls it key during the API call), Cloud uses accountId
+        user_query = f"{user_field}={quote(user_value)}"
+
+        url = f"rest/api/user/memberof?{user_query}"
+        yield from self._paginate_url(url, limit)
+
+    def paginated_groups_retrieval(
+        self,
+        limit: int | None = None,
+    ) -> Iterator[dict[str, Any]]:
+        """
+        This is not an SQL like query.
+        It's a confluence specific endpoint that can be used to fetch groups.
+        """
+        yield from self._paginate_url("rest/api/group", limit)
+
+    def paginated_group_members_retrieval(
+        self,
+        group_name: str,
+        limit: int | None = None,
+    ) -> Iterator[dict[str, Any]]:
+        """
+        This is not an SQL like query.
+        It's a confluence specific endpoint that can be used to fetch the members of a group.
+        THIS DOESN'T WORK FOR SERVER because it breaks when there is a slash in the group name.
+        E.g. neither "test/group" nor "test%2Fgroup" works for confluence.
+        """
+        group_name = quote(group_name)
+        yield from self._paginate_url(f"rest/api/group/{group_name}/member", limit)
+
+
+def _validate_connector_configuration(
+    credentials: dict[str, Any],
+    is_cloud: bool,
+    wiki_base: str,
+) -> None:
+    # test connection with direct client, no retries
+    confluence_client_with_minimal_retries = Confluence(
+        api_version="cloud" if is_cloud else "latest",
+        url=wiki_base.rstrip("/"),
+        username=credentials["confluence_username"] if is_cloud else None,
+        password=credentials["confluence_access_token"] if is_cloud else None,
+        token=credentials["confluence_access_token"] if not is_cloud else None,
+        backoff_and_retry=True,
+        max_backoff_retries=6,
+        max_backoff_seconds=10,
+    )
+    spaces = confluence_client_with_minimal_retries.get_all_spaces(limit=1)
+
+    # uncomment the following for testing
+    # the following is an attempt to retrieve the user's timezone
+    # Unfornately, all data is returned in UTC regardless of the user's time zone
+    # even tho CQL parses incoming times based on the user's time zone
+    # space_key = spaces["results"][0]["key"]
+    # space_details = confluence_client_with_minimal_retries.cql(f"space.key={space_key}+AND+type=space")
+
+    if not spaces:
+        raise RuntimeError(
+            f"No spaces found at {wiki_base}! "
+            "Check your credentials and wiki_base and make sure "
+            "is_cloud is set correctly."
+        )
+
+
+def build_confluence_client(
+    credentials: dict[str, Any],
+    is_cloud: bool,
+    wiki_base: str,
+) -> OnyxConfluence:
+    _validate_connector_configuration(
+        credentials=credentials,
+        is_cloud=is_cloud,
+        wiki_base=wiki_base,
+    )
+    return OnyxConfluence(
+        api_version="cloud" if is_cloud else "latest",
+        # Remove trailing slash from wiki_base if present
+        url=wiki_base.rstrip("/"),
+        # passing in username causes issues for Confluence data center
+        username=credentials["confluence_username"] if is_cloud else None,
+        password=credentials["confluence_access_token"] if is_cloud else None,
+        token=credentials["confluence_access_token"] if not is_cloud else None,
+        backoff_and_retry=True,
+        max_backoff_retries=10,
+        max_backoff_seconds=60,
+        cloud=is_cloud,
+    )

backend/danswer/connectors/confluence/utils.py

@@ -2,6 +2,7 @@ import io
 from datetime import datetime
 from datetime import timezone
 from typing import Any
+from urllib.parse import quote
 
 import bs4
 
@@ -31,7 +32,11 @@ def get_user_email_from_username__server(
             response = confluence_client.get_mobile_parameters(user_name)
             email = response.get("email")
         except Exception:
-            email = None
+            # For now, we'll just return a string that indicates failure
+            # We may want to revert to returning None in the future
+            # email = None
+            email = f"FAILED TO GET CONFLUENCE EMAIL FOR {user_name}"
+            logger.warning(f"failed to get confluence email for {user_name}")
         _USER_EMAIL_CACHE[user_name] = email
     return _USER_EMAIL_CACHE[user_name]
 
@@ -71,7 +76,9 @@ def _get_user(confluence_client: OnyxConfluence, user_id: str) -> str:
 
 
 def extract_text_from_confluence_html(
-    confluence_client: OnyxConfluence, confluence_object: dict[str, Any]
+    confluence_client: OnyxConfluence,
+    confluence_object: dict[str, Any],
+    fetched_titles: set[str],
 ) -> str:
     """Parse a Confluence html page and replace the 'user Id' by the real
         User Display Name
@@ -79,7 +86,7 @@ def extract_text_from_confluence_html(
     Args:
         confluence_object (dict): The confluence object as a dict
         confluence_client (Confluence): Confluence client
-
+        fetched_titles (set[str]): The titles of the pages that have already been fetched
     Returns:
         str: loaded and formated Confluence page
     """
@@ -101,54 +108,92 @@ def extract_text_from_confluence_html(
         # Include @ sign for tagging, more clear for LLM
         user.replaceWith("@" + _get_user(confluence_client, user_id))
 
-    for html_page_reference in soup.findAll("ri:page"):
+    for html_page_reference in soup.findAll("ac:structured-macro"):
+        # Here, we only want to process page within page macros
+        if html_page_reference.attrs.get("ac:name") != "include":
+            continue
+
+        page_data = html_page_reference.find("ri:page")
+        if not page_data:
+            logger.warning(
+                f"Skipping retrieval of {html_page_reference} because because page data is missing"
+            )
+            continue
+
+        page_title = page_data.attrs.get("ri:content-title")
+        if not page_title:
+            # only fetch pages that have a title
+            logger.warning(
+                f"Skipping retrieval of {html_page_reference} because it has no title"
+            )
+            continue
+
+        if page_title in fetched_titles:
+            # prevent recursive fetching of pages
+            logger.debug(f"Skipping {page_title} because it has already been fetched")
+            continue
+
+        fetched_titles.add(page_title)
+
         # Wrap this in a try-except because there are some pages that might not exist
         try:
-            page_title = html_page_reference.attrs["ri:content-title"]
-            if not page_title:
-                continue
-
-            page_query = f"type=page and title='{page_title}'"
+            page_query = f"type=page and title='{quote(page_title)}'"
 
             page_contents: dict[str, Any] | None = None
             # Confluence enforces title uniqueness, so we should only get one result here
-            for page_batch in confluence_client.paginated_cql_page_retrieval(
+            for page in confluence_client.paginated_cql_retrieval(
                 cql=page_query,
                 expand="body.storage.value",
                 limit=1,
             ):
-                page_contents = page_batch[0]
+                page_contents = page
                 break
-        except Exception:
+        except Exception as e:
             logger.warning(
-                f"Error getting page contents for object {confluence_object}"
+                f"Error getting page contents for object {confluence_object}: {e}"
             )
             continue
 
         if not page_contents:
             continue
+
         text_from_page = extract_text_from_confluence_html(
-            confluence_client, page_contents
+            confluence_client=confluence_client,
+            confluence_object=page_contents,
+            fetched_titles=fetched_titles,
         )
 
         html_page_reference.replaceWith(text_from_page)
 
+    for html_link_body in soup.findAll("ac:link-body"):
+        # This extracts the text from inline links in the page so they can be
+        # represented in the document text as plain text
+        try:
+            text_from_link = html_link_body.text
+            html_link_body.replaceWith(f"(LINK TEXT: {text_from_link})")
+        except Exception as e:
+            logger.warning(f"Error processing ac:link-body: {e}")
+
     return format_document_soup(soup)
 
 
+def validate_attachment_filetype(attachment: dict[str, Any]) -> bool:
+    return attachment["metadata"]["mediaType"] not in [
+        "image/jpeg",
+        "image/png",
+        "image/gif",
+        "image/svg+xml",
+        "video/mp4",
+        "video/quicktime",
+    ]
+
+
 def attachment_to_content(
     confluence_client: OnyxConfluence,
     attachment: dict[str, Any],
 ) -> str | None:
     """If it returns None, assume that we should skip this attachment."""
-    if attachment["metadata"]["mediaType"] in [
-        "image/jpeg",
-        "image/png",
-        "image/gif",
-        "image/svg+xml",
-        "video/mp4",
-        "video/quicktime",
-    ]:
+    if not validate_attachment_filetype(attachment):
         return None
 
     download_link = confluence_client.url + attachment["_links"]["download"]
@@ -204,7 +249,7 @@ def build_confluence_document_id(
     return f"{base_url}{content_url}"
 
 
-def extract_referenced_attachment_names(page_text: str) -> list[str]:
+def _extract_referenced_attachment_names(page_text: str) -> list[str]:
     """Parse a Confluence html page to generate a list of current
         attachments in use
 
@@ -232,20 +277,3 @@ def datetime_from_string(datetime_string: str) -> datetime:
         datetime_object = datetime_object.astimezone(timezone.utc)
 
     return datetime_object
-
-
-def build_confluence_client(
-    credentials_json: dict[str, Any], is_cloud: bool, wiki_base: str
-) -> OnyxConfluence:
-    return OnyxConfluence(
-        api_version="cloud" if is_cloud else "latest",
-        # Remove trailing slash from wiki_base if present
-        url=wiki_base.rstrip("/"),
-        # passing in username causes issues for Confluence data center
-        username=credentials_json["confluence_username"] if is_cloud else None,
-        password=credentials_json["confluence_access_token"] if is_cloud else None,
-        token=credentials_json["confluence_access_token"] if not is_cloud else None,
-        backoff_and_retry=True,
-        max_backoff_retries=60,
-        max_backoff_seconds=60,
-    )

backend/danswer/connectors/danswer_jira/connector.py

@@ -1,8 +1,8 @@
 import os
+from collections.abc import Iterable
 from datetime import datetime
 from datetime import timezone
 from typing import Any
-from urllib.parse import urlparse
 
 from jira import JIRA
 from jira.resources import Issue
@@ -12,129 +12,93 @@ from danswer.configs.app_configs import JIRA_CONNECTOR_LABELS_TO_SKIP
 from danswer.configs.app_configs import JIRA_CONNECTOR_MAX_TICKET_SIZE
 from danswer.configs.constants import DocumentSource
 from danswer.connectors.cross_connector_utils.miscellaneous_utils import time_str_to_utc
+from danswer.connectors.danswer_jira.utils import best_effort_basic_expert_info
+from danswer.connectors.danswer_jira.utils import best_effort_get_field_from_issue
+from danswer.connectors.danswer_jira.utils import build_jira_client
+from danswer.connectors.danswer_jira.utils import build_jira_url
+from danswer.connectors.danswer_jira.utils import extract_jira_project
+from danswer.connectors.danswer_jira.utils import extract_text_from_adf
+from danswer.connectors.danswer_jira.utils import get_comment_strs
 from danswer.connectors.interfaces import GenerateDocumentsOutput
+from danswer.connectors.interfaces import GenerateSlimDocumentOutput
 from danswer.connectors.interfaces import LoadConnector
 from danswer.connectors.interfaces import PollConnector
 from danswer.connectors.interfaces import SecondsSinceUnixEpoch
-from danswer.connectors.models import BasicExpertInfo
+from danswer.connectors.interfaces import SlimConnector
 from danswer.connectors.models import ConnectorMissingCredentialError
 from danswer.connectors.models import Document
 from danswer.connectors.models import Section
+from danswer.connectors.models import SlimDocument
 from danswer.utils.logger import setup_logger
 
 
 logger = setup_logger()
-PROJECT_URL_PAT = "projects"
+
 JIRA_API_VERSION = os.environ.get("JIRA_API_VERSION") or "2"
+_JIRA_SLIM_PAGE_SIZE = 500
+_JIRA_FULL_PAGE_SIZE = 50
 
 
-def extract_jira_project(url: str) -> tuple[str, str]:
-    parsed_url = urlparse(url)
-    jira_base = parsed_url.scheme + "://" + parsed_url.netloc
+def _paginate_jql_search(
+    jira_client: JIRA,
+    jql: str,
+    max_results: int,
+    fields: str | None = None,
+) -> Iterable[Issue]:
+    start = 0
+    while True:
+        logger.debug(
+            f"Fetching Jira issues with JQL: {jql}, "
+            f"starting at {start}, max results: {max_results}"
+        )
+        issues = jira_client.search_issues(
+            jql_str=jql,
+            startAt=start,
+            maxResults=max_results,
+            fields=fields,
+        )
 
-    # Split the path by '/' and find the position of 'projects' to get the project name
-    split_path = parsed_url.path.split("/")
-    if PROJECT_URL_PAT in split_path:
-        project_pos = split_path.index(PROJECT_URL_PAT)
-        if len(split_path) > project_pos + 1:
-            jira_project = split_path[project_pos + 1]
-        else:
-            raise ValueError("No project name found in the URL")
-    else:
-        raise ValueError("'projects' not found in the URL")
+        for issue in issues:
+            if isinstance(issue, Issue):
+                yield issue
+            else:
+                raise Exception(f"Found Jira object not of type Issue: {issue}")
 
-    return jira_base, jira_project
+        if len(issues) < max_results:
+            break
 
-
-def extract_text_from_adf(adf: dict | None) -> str:
-    """Extracts plain text from Atlassian Document Format:
-    https://developer.atlassian.com/cloud/jira/platform/apis/document/structure/
-
-    WARNING: This function is incomplete and will e.g. skip lists!
-    """
-    texts = []
-    if adf is not None and "content" in adf:
-        for block in adf["content"]:
-            if "content" in block:
-                for item in block["content"]:
-                    if item["type"] == "text":
-                        texts.append(item["text"])
-    return " ".join(texts)
-
-
-def best_effort_get_field_from_issue(jira_issue: Issue, field: str) -> Any:
-    if hasattr(jira_issue.fields, field):
-        return getattr(jira_issue.fields, field)
-
-    try:
-        return jira_issue.raw["fields"][field]
-    except Exception:
-        return None
-
-
-def _get_comment_strs(
-    jira: Issue, comment_email_blacklist: tuple[str, ...] = ()
-) -> list[str]:
-    comment_strs = []
-    for comment in jira.fields.comment.comments:
-        try:
-            body_text = (
-                comment.body
-                if JIRA_API_VERSION == "2"
-                else extract_text_from_adf(comment.raw["body"])
-            )
-
-            if (
-                hasattr(comment, "author")
-                and hasattr(comment.author, "emailAddress")
-                and comment.author.emailAddress in comment_email_blacklist
-            ):
-                continue  # Skip adding comment if author's email is in blacklist
-
-            comment_strs.append(body_text)
-        except Exception as e:
-            logger.error(f"Failed to process comment due to an error: {e}")
-            continue
-
-    return comment_strs
+        start += max_results
 
 
 def fetch_jira_issues_batch(
-    jql: str,
-    start_index: int,
     jira_client: JIRA,
-    batch_size: int = INDEX_BATCH_SIZE,
+    jql: str,
+    batch_size: int,
     comment_email_blacklist: tuple[str, ...] = (),
     labels_to_skip: set[str] | None = None,
-) -> tuple[list[Document], int]:
-    doc_batch = []
-
-    batch = jira_client.search_issues(
-        jql,
-        startAt=start_index,
-        maxResults=batch_size,
-    )
-
-    for jira in batch:
-        if type(jira) != Issue:
-            logger.warning(f"Found Jira object not of type Issue {jira}")
-            continue
-
-        if labels_to_skip and any(
-            label in jira.fields.labels for label in labels_to_skip
-        ):
-            logger.info(
-                f"Skipping {jira.key} because it has a label to skip. Found "
-                f"labels: {jira.fields.labels}. Labels to skip: {labels_to_skip}."
-            )
-            continue
+) -> Iterable[Document]:
+    for issue in _paginate_jql_search(
+        jira_client=jira_client,
+        jql=jql,
+        max_results=batch_size,
+    ):
+        if labels_to_skip:
+            if any(label in issue.fields.labels for label in labels_to_skip):
+                logger.info(
+                    f"Skipping {issue.key} because it has a label to skip. Found "
+                    f"labels: {issue.fields.labels}. Labels to skip: {labels_to_skip}."
+                )
+                continue
 
         description = (
-            jira.fields.description
+            issue.fields.description
             if JIRA_API_VERSION == "2"
-            else extract_text_from_adf(jira.raw["fields"]["description"])
+            else extract_text_from_adf(issue.raw["fields"]["description"])
+        )
+        comments = get_comment_strs(
+            issue=issue,
+            comment_email_blacklist=comment_email_blacklist,
         )
-        comments = _get_comment_strs(jira, comment_email_blacklist)
         ticket_content = f"{description}\n" + "\n".join(
             [f"Comment: {comment}" for comment in comments if comment]
         )
@@ -142,66 +106,53 @@ def fetch_jira_issues_batch(
         # Check ticket size
         if len(ticket_content.encode("utf-8")) > JIRA_CONNECTOR_MAX_TICKET_SIZE:
             logger.info(
-                f"Skipping {jira.key} because it exceeds the maximum size of "
+                f"Skipping {issue.key} because it exceeds the maximum size of "
                 f"{JIRA_CONNECTOR_MAX_TICKET_SIZE} bytes."
             )
             continue
 
-        page_url = f"{jira_client.client_info()}/browse/{jira.key}"
+        page_url = f"{jira_client.client_info()}/browse/{issue.key}"
 
         people = set()
         try:
-            people.add(
-                BasicExpertInfo(
-                    display_name=jira.fields.creator.displayName,
-                    email=jira.fields.creator.emailAddress,
-                )
-            )
+            creator = best_effort_get_field_from_issue(issue, "creator")
+            if basic_expert_info := best_effort_basic_expert_info(creator):
+                people.add(basic_expert_info)
         except Exception:
             # Author should exist but if not, doesn't matter
             pass
 
         try:
-            people.add(
-                BasicExpertInfo(
-                    display_name=jira.fields.assignee.displayName,  # type: ignore
-                    email=jira.fields.assignee.emailAddress,  # type: ignore
-                )
-            )
+            assignee = best_effort_get_field_from_issue(issue, "assignee")
+            if basic_expert_info := best_effort_basic_expert_info(assignee):
+                people.add(basic_expert_info)
         except Exception:
             # Author should exist but if not, doesn't matter
             pass
 
         metadata_dict = {}
-        priority = best_effort_get_field_from_issue(jira, "priority")
-        if priority:
+        if priority := best_effort_get_field_from_issue(issue, "priority"):
             metadata_dict["priority"] = priority.name
-        status = best_effort_get_field_from_issue(jira, "status")
-        if status:
+        if status := best_effort_get_field_from_issue(issue, "status"):
             metadata_dict["status"] = status.name
-        resolution = best_effort_get_field_from_issue(jira, "resolution")
-        if resolution:
+        if resolution := best_effort_get_field_from_issue(issue, "resolution"):
             metadata_dict["resolution"] = resolution.name
-        labels = best_effort_get_field_from_issue(jira, "labels")
-        if labels:
+        if labels := best_effort_get_field_from_issue(issue, "labels"):
             metadata_dict["label"] = labels
 
-        doc_batch.append(
-            Document(
-                id=page_url,
-                sections=[Section(link=page_url, text=ticket_content)],
-                source=DocumentSource.JIRA,
-                semantic_identifier=jira.fields.summary,
-                doc_updated_at=time_str_to_utc(jira.fields.updated),
-                primary_owners=list(people) or None,
-                # TODO add secondary_owners (commenters) if needed
-                metadata=metadata_dict,
-            )
+        yield Document(
+            id=page_url,
+            sections=[Section(link=page_url, text=ticket_content)],
+            source=DocumentSource.JIRA,
+            semantic_identifier=issue.fields.summary,
+            doc_updated_at=time_str_to_utc(issue.fields.updated),
+            primary_owners=list(people) or None,
+            # TODO add secondary_owners (commenters) if needed
+            metadata=metadata_dict,
         )
-    return doc_batch, len(batch)
 
 
-class JiraConnector(LoadConnector, PollConnector):
+class JiraConnector(LoadConnector, PollConnector, SlimConnector):
     def __init__(
         self,
         jira_project_url: str,
@@ -213,8 +164,8 @@ class JiraConnector(LoadConnector, PollConnector):
         labels_to_skip: list[str] = JIRA_CONNECTOR_LABELS_TO_SKIP,
     ) -> None:
         self.batch_size = batch_size
-        self.jira_base, self.jira_project = extract_jira_project(jira_project_url)
-        self.jira_client: JIRA | None = None
+        self.jira_base, self._jira_project = extract_jira_project(jira_project_url)
+        self._jira_client: JIRA | None = None
         self._comment_email_blacklist = comment_email_blacklist or []
 
         self.labels_to_skip = set(labels_to_skip)
@@ -223,54 +174,45 @@ class JiraConnector(LoadConnector, PollConnector):
     def comment_email_blacklist(self) -> tuple:
         return tuple(email.strip() for email in self._comment_email_blacklist)
 
+    @property
+    def jira_client(self) -> JIRA:
+        if self._jira_client is None:
+            raise ConnectorMissingCredentialError("Jira")
+        return self._jira_client
+
+    @property
+    def quoted_jira_project(self) -> str:
+        # Quote the project name to handle reserved words
+        return f'"{self._jira_project}"'
+
     def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
-        api_token = credentials["jira_api_token"]
-        # if user provide an email we assume it's cloud
-        if "jira_user_email" in credentials:
-            email = credentials["jira_user_email"]
-            self.jira_client = JIRA(
-                basic_auth=(email, api_token),
-                server=self.jira_base,
-                options={"rest_api_version": JIRA_API_VERSION},
-            )
-        else:
-            self.jira_client = JIRA(
-                token_auth=api_token,
-                server=self.jira_base,
-                options={"rest_api_version": JIRA_API_VERSION},
-            )
+        self._jira_client = build_jira_client(
+            credentials=credentials,
+            jira_base=self.jira_base,
+        )
         return None
 
     def load_from_state(self) -> GenerateDocumentsOutput:
-        if self.jira_client is None:
-            raise ConnectorMissingCredentialError("Jira")
+        jql = f"project = {self.quoted_jira_project}"
 
-        # Quote the project name to handle reserved words
-        quoted_project = f'"{self.jira_project}"'
-        start_ind = 0
-        while True:
-            doc_batch, fetched_batch_size = fetch_jira_issues_batch(
-                jql=f"project = {quoted_project}",
-                start_index=start_ind,
-                jira_client=self.jira_client,
-                batch_size=self.batch_size,
-                comment_email_blacklist=self.comment_email_blacklist,
-                labels_to_skip=self.labels_to_skip,
-            )
+        document_batch = []
+        for doc in fetch_jira_issues_batch(
+            jira_client=self.jira_client,
+            jql=jql,
+            batch_size=_JIRA_FULL_PAGE_SIZE,
+            comment_email_blacklist=self.comment_email_blacklist,
+            labels_to_skip=self.labels_to_skip,
+        ):
+            document_batch.append(doc)
+            if len(document_batch) >= self.batch_size:
+                yield document_batch
+                document_batch = []
 
-            if doc_batch:
-                yield doc_batch
-
-            start_ind += fetched_batch_size
-            if fetched_batch_size < self.batch_size:
-                break
+        yield document_batch
 
     def poll_source(
         self, start: SecondsSinceUnixEpoch, end: SecondsSinceUnixEpoch
     ) -> GenerateDocumentsOutput:
-        if self.jira_client is None:
-            raise ConnectorMissingCredentialError("Jira")
-
         start_date_str = datetime.fromtimestamp(start, tz=timezone.utc).strftime(
             "%Y-%m-%d %H:%M"
         )
@@ -278,31 +220,54 @@ class JiraConnector(LoadConnector, PollConnector):
             "%Y-%m-%d %H:%M"
         )
 
-        # Quote the project name to handle reserved words
-        quoted_project = f'"{self.jira_project}"'
         jql = (
-            f"project = {quoted_project} AND "
+            f"project = {self.quoted_jira_project} AND "
             f"updated >= '{start_date_str}' AND "
             f"updated <= '{end_date_str}'"
         )
 
-        start_ind = 0
-        while True:
-            doc_batch, fetched_batch_size = fetch_jira_issues_batch(
-                jql=jql,
-                start_index=start_ind,
-                jira_client=self.jira_client,
-                batch_size=self.batch_size,
-                comment_email_blacklist=self.comment_email_blacklist,
-                labels_to_skip=self.labels_to_skip,
+        document_batch = []
+        for doc in fetch_jira_issues_batch(
+            jira_client=self.jira_client,
+            jql=jql,
+            batch_size=_JIRA_FULL_PAGE_SIZE,
+            comment_email_blacklist=self.comment_email_blacklist,
+            labels_to_skip=self.labels_to_skip,
+        ):
+            document_batch.append(doc)
+            if len(document_batch) >= self.batch_size:
+                yield document_batch
+                document_batch = []
+
+        yield document_batch
+
+    def retrieve_all_slim_documents(
+        self,
+        start: SecondsSinceUnixEpoch | None = None,
+        end: SecondsSinceUnixEpoch | None = None,
+    ) -> GenerateSlimDocumentOutput:
+        jql = f"project = {self.quoted_jira_project}"
+
+        slim_doc_batch = []
+        for issue in _paginate_jql_search(
+            jira_client=self.jira_client,
+            jql=jql,
+            max_results=_JIRA_SLIM_PAGE_SIZE,
+            fields="key",
+        ):
+            issue_key = best_effort_get_field_from_issue(issue, "key")
+            id = build_jira_url(self.jira_client, issue_key)
+            slim_doc_batch.append(
+                SlimDocument(
+                    id=id,
+                    perm_sync_data=None,
+                )
             )
+            if len(slim_doc_batch) >= _JIRA_SLIM_PAGE_SIZE:
+                yield slim_doc_batch
+                slim_doc_batch = []
 
-            if doc_batch:
-                yield doc_batch
-
-            start_ind += fetched_batch_size
-            if fetched_batch_size < self.batch_size:
-                break
+        yield slim_doc_batch
 
 
 if __name__ == "__main__":

backend/danswer/connectors/danswer_jira/utils.py

@@ -1,17 +1,136 @@
 """Module with custom fields processing functions"""
+import os
 from typing import Any
 from typing import List
+from urllib.parse import urlparse
 
 from jira import JIRA
 from jira.resources import CustomFieldOption
 from jira.resources import Issue
 from jira.resources import User
 
+from danswer.connectors.models import BasicExpertInfo
 from danswer.utils.logger import setup_logger
 
 logger = setup_logger()
 
 
+PROJECT_URL_PAT = "projects"
+JIRA_API_VERSION = os.environ.get("JIRA_API_VERSION") or "2"
+
+
+def best_effort_basic_expert_info(obj: Any) -> BasicExpertInfo | None:
+    display_name = None
+    email = None
+    if hasattr(obj, "display_name"):
+        display_name = obj.display_name
+    else:
+        display_name = obj.get("displayName")
+
+    if hasattr(obj, "emailAddress"):
+        email = obj.emailAddress
+    else:
+        email = obj.get("emailAddress")
+
+    if not email and not display_name:
+        return None
+
+    return BasicExpertInfo(display_name=display_name, email=email)
+
+
+def best_effort_get_field_from_issue(jira_issue: Issue, field: str) -> Any:
+    if hasattr(jira_issue.fields, field):
+        return getattr(jira_issue.fields, field)
+
+    try:
+        return jira_issue.raw["fields"][field]
+    except Exception:
+        return None
+
+
+def extract_text_from_adf(adf: dict | None) -> str:
+    """Extracts plain text from Atlassian Document Format:
+    https://developer.atlassian.com/cloud/jira/platform/apis/document/structure/
+
+    WARNING: This function is incomplete and will e.g. skip lists!
+    """
+    texts = []
+    if adf is not None and "content" in adf:
+        for block in adf["content"]:
+            if "content" in block:
+                for item in block["content"]:
+                    if item["type"] == "text":
+                        texts.append(item["text"])
+    return " ".join(texts)
+
+
+def build_jira_url(jira_client: JIRA, issue_key: str) -> str:
+    return f"{jira_client.client_info()}/browse/{issue_key}"
+
+
+def build_jira_client(credentials: dict[str, Any], jira_base: str) -> JIRA:
+    api_token = credentials["jira_api_token"]
+    # if user provide an email we assume it's cloud
+    if "jira_user_email" in credentials:
+        email = credentials["jira_user_email"]
+        return JIRA(
+            basic_auth=(email, api_token),
+            server=jira_base,
+            options={"rest_api_version": JIRA_API_VERSION},
+        )
+    else:
+        return JIRA(
+            token_auth=api_token,
+            server=jira_base,
+            options={"rest_api_version": JIRA_API_VERSION},
+        )
+
+
+def extract_jira_project(url: str) -> tuple[str, str]:
+    parsed_url = urlparse(url)
+    jira_base = parsed_url.scheme + "://" + parsed_url.netloc
+
+    # Split the path by '/' and find the position of 'projects' to get the project name
+    split_path = parsed_url.path.split("/")
+    if PROJECT_URL_PAT in split_path:
+        project_pos = split_path.index(PROJECT_URL_PAT)
+        if len(split_path) > project_pos + 1:
+            jira_project = split_path[project_pos + 1]
+        else:
+            raise ValueError("No project name found in the URL")
+    else:
+        raise ValueError("'projects' not found in the URL")
+
+    return jira_base, jira_project
+
+
+def get_comment_strs(
+    issue: Issue, comment_email_blacklist: tuple[str, ...] = ()
+) -> list[str]:
+    comment_strs = []
+    for comment in issue.fields.comment.comments:
+        try:
+            body_text = (
+                comment.body
+                if JIRA_API_VERSION == "2"
+                else extract_text_from_adf(comment.raw["body"])
+            )
+
+            if (
+                hasattr(comment, "author")
+                and hasattr(comment.author, "emailAddress")
+                and comment.author.emailAddress in comment_email_blacklist
+            ):
+                continue  # Skip adding comment if author's email is in blacklist
+
+            comment_strs.append(body_text)
+        except Exception as e:
+            logger.error(f"Failed to process comment due to an error: {e}")
+            continue
+
+    return comment_strs
+
+
 class CustomFieldExtractor:
     @staticmethod
     def _process_custom_field_value(value: Any) -> str:

backend/danswer/connectors/egnyte/connector.py

@@ -0,0 +1,384 @@
+import io
+import os
+from collections.abc import Generator
+from datetime import datetime
+from datetime import timezone
+from logging import Logger
+from typing import Any
+from typing import cast
+from typing import IO
+
+import requests
+from retry import retry
+
+from danswer.configs.app_configs import EGNYTE_BASE_DOMAIN
+from danswer.configs.app_configs import EGNYTE_CLIENT_ID
+from danswer.configs.app_configs import EGNYTE_CLIENT_SECRET
+from danswer.configs.app_configs import EGNYTE_LOCALHOST_OVERRIDE
+from danswer.configs.app_configs import INDEX_BATCH_SIZE
+from danswer.configs.constants import DocumentSource
+from danswer.connectors.interfaces import GenerateDocumentsOutput
+from danswer.connectors.interfaces import LoadConnector
+from danswer.connectors.interfaces import OAuthConnector
+from danswer.connectors.interfaces import PollConnector
+from danswer.connectors.interfaces import SecondsSinceUnixEpoch
+from danswer.connectors.models import BasicExpertInfo
+from danswer.connectors.models import ConnectorMissingCredentialError
+from danswer.connectors.models import Document
+from danswer.connectors.models import Section
+from danswer.file_processing.extract_file_text import detect_encoding
+from danswer.file_processing.extract_file_text import extract_file_text
+from danswer.file_processing.extract_file_text import get_file_ext
+from danswer.file_processing.extract_file_text import is_text_file_extension
+from danswer.file_processing.extract_file_text import is_valid_file_ext
+from danswer.file_processing.extract_file_text import read_text_file
+from danswer.utils.logger import setup_logger
+
+
+logger = setup_logger()
+
+_EGNYTE_API_BASE = "https://{domain}.egnyte.com/pubapi/v1"
+_EGNYTE_APP_BASE = "https://{domain}.egnyte.com"
+_TIMEOUT = 60
+
+
+def _request_with_retries(
+    method: str,
+    url: str,
+    data: dict[str, Any] | None = None,
+    headers: dict[str, Any] | None = None,
+    params: dict[str, Any] | None = None,
+    timeout: int = _TIMEOUT,
+    stream: bool = False,
+    tries: int = 8,
+    delay: float = 1,
+    backoff: float = 2,
+) -> requests.Response:
+    @retry(tries=tries, delay=delay, backoff=backoff, logger=cast(Logger, logger))
+    def _make_request() -> requests.Response:
+        response = requests.request(
+            method,
+            url,
+            data=data,
+            headers=headers,
+            params=params,
+            timeout=timeout,
+            stream=stream,
+        )
+        try:
+            response.raise_for_status()
+        except requests.exceptions.HTTPError as e:
+            if e.response.status_code != 403:
+                logger.exception(
+                    f"Failed to call Egnyte API.\n"
+                    f"URL: {url}\n"
+                    f"Headers: {headers}\n"
+                    f"Data: {data}\n"
+                    f"Params: {params}"
+                )
+            raise e
+        return response
+
+    return _make_request()
+
+
+def _parse_last_modified(last_modified: str) -> datetime:
+    return datetime.strptime(last_modified, "%a, %d %b %Y %H:%M:%S %Z").replace(
+        tzinfo=timezone.utc
+    )
+
+
+def _process_egnyte_file(
+    file_metadata: dict[str, Any],
+    file_content: IO,
+    base_url: str,
+    folder_path: str | None = None,
+) -> Document | None:
+    """Process an Egnyte file into a Document object
+
+    Args:
+        file_data: The file data from Egnyte API
+        file_content: The raw content of the file in bytes
+        base_url: The base URL for the Egnyte instance
+        folder_path: Optional folder path to filter results
+    """
+    # Skip if file path doesn't match folder path filter
+    if folder_path and not file_metadata["path"].startswith(folder_path):
+        raise ValueError(
+            f"File path {file_metadata['path']} does not match folder path {folder_path}"
+        )
+
+    file_name = file_metadata["name"]
+    extension = get_file_ext(file_name)
+    if not is_valid_file_ext(extension):
+        logger.warning(f"Skipping file '{file_name}' with extension '{extension}'")
+        return None
+
+    # Extract text content based on file type
+    if is_text_file_extension(file_name):
+        encoding = detect_encoding(file_content)
+        file_content_raw, file_metadata = read_text_file(
+            file_content, encoding=encoding, ignore_danswer_metadata=False
+        )
+    else:
+        file_content_raw = extract_file_text(
+            file=file_content,
+            file_name=file_name,
+            break_on_unprocessable=True,
+        )
+
+    # Build the web URL for the file
+    web_url = f"{base_url}/navigate/file/{file_metadata['group_id']}"
+
+    # Create document metadata
+    metadata: dict[str, str | list[str]] = {
+        "file_path": file_metadata["path"],
+        "last_modified": file_metadata.get("last_modified", ""),
+    }
+
+    # Add lock info if present
+    if lock_info := file_metadata.get("lock_info"):
+        metadata[
+            "lock_owner"
+        ] = f"{lock_info.get('first_name', '')} {lock_info.get('last_name', '')}"
+
+    # Create the document owners
+    primary_owner = None
+    if uploaded_by := file_metadata.get("uploaded_by"):
+        primary_owner = BasicExpertInfo(
+            email=uploaded_by,  # Using username as email since that's what we have
+        )
+
+    # Create the document
+    return Document(
+        id=f"egnyte-{file_metadata['entry_id']}",
+        sections=[Section(text=file_content_raw.strip(), link=web_url)],
+        source=DocumentSource.EGNYTE,
+        semantic_identifier=file_name,
+        metadata=metadata,
+        doc_updated_at=(
+            _parse_last_modified(file_metadata["last_modified"])
+            if "last_modified" in file_metadata
+            else None
+        ),
+        primary_owners=[primary_owner] if primary_owner else None,
+    )
+
+
+class EgnyteConnector(LoadConnector, PollConnector, OAuthConnector):
+    def __init__(
+        self,
+        folder_path: str | None = None,
+        batch_size: int = INDEX_BATCH_SIZE,
+    ) -> None:
+        self.domain = ""  # will always be set in `load_credentials`
+        self.folder_path = folder_path or ""  # Root folder if not specified
+        self.batch_size = batch_size
+        self.access_token: str | None = None
+
+    @classmethod
+    def oauth_id(cls) -> DocumentSource:
+        return DocumentSource.EGNYTE
+
+    @classmethod
+    def oauth_authorization_url(cls, base_domain: str, state: str) -> str:
+        if not EGNYTE_CLIENT_ID:
+            raise ValueError("EGNYTE_CLIENT_ID environment variable must be set")
+        if not EGNYTE_BASE_DOMAIN:
+            raise ValueError("EGNYTE_DOMAIN environment variable must be set")
+
+        if EGNYTE_LOCALHOST_OVERRIDE:
+            base_domain = EGNYTE_LOCALHOST_OVERRIDE
+
+        callback_uri = f"{base_domain.strip('/')}/connector/oauth/callback/egnyte"
+        return (
+            f"https://{EGNYTE_BASE_DOMAIN}.egnyte.com/puboauth/token"
+            f"?client_id={EGNYTE_CLIENT_ID}"
+            f"&redirect_uri={callback_uri}"
+            f"&scope=Egnyte.filesystem"
+            f"&state={state}"
+            f"&response_type=code"
+        )
+
+    @classmethod
+    def oauth_code_to_token(cls, code: str) -> dict[str, Any]:
+        if not EGNYTE_CLIENT_ID:
+            raise ValueError("EGNYTE_CLIENT_ID environment variable must be set")
+        if not EGNYTE_CLIENT_SECRET:
+            raise ValueError("EGNYTE_CLIENT_SECRET environment variable must be set")
+        if not EGNYTE_BASE_DOMAIN:
+            raise ValueError("EGNYTE_DOMAIN environment variable must be set")
+
+        # Exchange code for token
+        url = f"https://{EGNYTE_BASE_DOMAIN}.egnyte.com/puboauth/token"
+        data = {
+            "client_id": EGNYTE_CLIENT_ID,
+            "client_secret": EGNYTE_CLIENT_SECRET,
+            "code": code,
+            "grant_type": "authorization_code",
+            "redirect_uri": f"{EGNYTE_LOCALHOST_OVERRIDE or ''}/connector/oauth/callback/egnyte",
+            "scope": "Egnyte.filesystem",
+        }
+        headers = {"Content-Type": "application/x-www-form-urlencoded"}
+
+        response = _request_with_retries(
+            method="POST",
+            url=url,
+            data=data,
+            headers=headers,
+            # try a lot faster since this is a realtime flow
+            backoff=0,
+            delay=0.1,
+        )
+        if not response.ok:
+            raise RuntimeError(f"Failed to exchange code for token: {response.text}")
+
+        token_data = response.json()
+        return {
+            "domain": EGNYTE_BASE_DOMAIN,
+            "access_token": token_data["access_token"],
+        }
+
+    def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
+        self.domain = credentials["domain"]
+        self.access_token = credentials["access_token"]
+        return None
+
+    def _get_files_list(
+        self,
+        path: str,
+    ) -> list[dict[str, Any]]:
+        if not self.access_token or not self.domain:
+            raise ConnectorMissingCredentialError("Egnyte")
+
+        headers = {
+            "Authorization": f"Bearer {self.access_token}",
+        }
+
+        params: dict[str, Any] = {
+            "list_content": True,
+        }
+
+        url = f"{_EGNYTE_API_BASE.format(domain=self.domain)}/fs/{path or ''}"
+        response = _request_with_retries(
+            method="GET", url=url, headers=headers, params=params, timeout=_TIMEOUT
+        )
+        if not response.ok:
+            raise RuntimeError(f"Failed to fetch files from Egnyte: {response.text}")
+
+        data = response.json()
+        all_files: list[dict[str, Any]] = []
+
+        # Add files from current directory
+        all_files.extend(data.get("files", []))
+
+        # Recursively traverse folders
+        for item in data.get("folders", []):
+            all_files.extend(self._get_files_list(item["path"]))
+
+        return all_files
+
+    def _filter_files(
+        self,
+        files: list[dict[str, Any]],
+        start_time: datetime | None = None,
+        end_time: datetime | None = None,
+    ) -> list[dict[str, Any]]:
+        filtered_files = []
+        for file in files:
+            if file["is_folder"]:
+                continue
+
+            file_modified = _parse_last_modified(file["last_modified"])
+            if start_time and file_modified < start_time:
+                continue
+            if end_time and file_modified > end_time:
+                continue
+
+            filtered_files.append(file)
+
+        return filtered_files
+
+    def _process_files(
+        self,
+        start_time: datetime | None = None,
+        end_time: datetime | None = None,
+    ) -> Generator[list[Document], None, None]:
+        files = self._get_files_list(self.folder_path)
+        files = self._filter_files(files, start_time, end_time)
+
+        current_batch: list[Document] = []
+        for file in files:
+            try:
+                # Set up request with streaming enabled
+                headers = {
+                    "Authorization": f"Bearer {self.access_token}",
+                }
+                url = f"{_EGNYTE_API_BASE.format(domain=self.domain)}/fs-content/{file['path']}"
+                response = _request_with_retries(
+                    method="GET",
+                    url=url,
+                    headers=headers,
+                    timeout=_TIMEOUT,
+                    stream=True,
+                )
+
+                if not response.ok:
+                    logger.error(
+                        f"Failed to fetch file content: {file['path']} (status code: {response.status_code})"
+                    )
+                    continue
+
+                # Stream the response content into a BytesIO buffer
+                buffer = io.BytesIO()
+                for chunk in response.iter_content(chunk_size=8192):
+                    if chunk:
+                        buffer.write(chunk)
+
+                # Reset buffer's position to the start
+                buffer.seek(0)
+
+                # Process the streamed file content
+                doc = _process_egnyte_file(
+                    file_metadata=file,
+                    file_content=buffer,
+                    base_url=_EGNYTE_APP_BASE.format(domain=self.domain),
+                    folder_path=self.folder_path,
+                )
+
+                if doc is not None:
+                    current_batch.append(doc)
+
+                    if len(current_batch) >= self.batch_size:
+                        yield current_batch
+                        current_batch = []
+
+            except Exception:
+                logger.exception(f"Failed to process file {file['path']}")
+                continue
+
+        if current_batch:
+            yield current_batch
+
+    def load_from_state(self) -> GenerateDocumentsOutput:
+        yield from self._process_files()
+
+    def poll_source(
+        self, start: SecondsSinceUnixEpoch, end: SecondsSinceUnixEpoch
+    ) -> GenerateDocumentsOutput:
+        start_time = datetime.fromtimestamp(start, tz=timezone.utc)
+        end_time = datetime.fromtimestamp(end, tz=timezone.utc)
+
+        yield from self._process_files(start_time=start_time, end_time=end_time)
+
+
+if __name__ == "__main__":
+    connector = EgnyteConnector()
+    connector.load_credentials(
+        {
+            "domain": os.environ["EGNYTE_DOMAIN"],
+            "access_token": os.environ["EGNYTE_ACCESS_TOKEN"],
+        }
+    )
+    document_batches = connector.load_from_state()
+    print(next(document_batches))

backend/danswer/connectors/factory.py

@@ -15,6 +15,7 @@ from danswer.connectors.danswer_jira.connector import JiraConnector
 from danswer.connectors.discourse.connector import DiscourseConnector
 from danswer.connectors.document360.connector import Document360Connector
 from danswer.connectors.dropbox.connector import DropboxConnector
+from danswer.connectors.egnyte.connector import EgnyteConnector
 from danswer.connectors.file.connector import LocalFileConnector
 from danswer.connectors.fireflies.connector import FirefliesConnector
 from danswer.connectors.freshdesk.connector import FreshdeskConnector
@@ -40,7 +41,6 @@ from danswer.connectors.salesforce.connector import SalesforceConnector
 from danswer.connectors.sharepoint.connector import SharepointConnector
 from danswer.connectors.slab.connector import SlabConnector
 from danswer.connectors.slack.connector import SlackPollConnector
-from danswer.connectors.slack.load_connector import SlackLoadConnector
 from danswer.connectors.teams.connector import TeamsConnector
 from danswer.connectors.web.connector import WebConnector
 from danswer.connectors.wikipedia.connector import WikipediaConnector
@@ -63,7 +63,6 @@ def identify_connector_class(
         DocumentSource.WEB: WebConnector,
         DocumentSource.FILE: LocalFileConnector,
         DocumentSource.SLACK: {
-            InputType.LOAD_STATE: SlackLoadConnector,
             InputType.POLL: SlackPollConnector,
             InputType.SLIM_RETRIEVAL: SlackPollConnector,
         },
@@ -103,6 +102,7 @@ def identify_connector_class(
         DocumentSource.XENFORO: XenforoConnector,
         DocumentSource.FRESHDESK: FreshdeskConnector,
         DocumentSource.FIREFLIES: FirefliesConnector,
+        DocumentSource.EGNYTE: EgnyteConnector,
     }
     connector_by_source = connector_map.get(source, {})
 

backend/danswer/connectors/file/connector.py

@@ -17,11 +17,11 @@ from danswer.connectors.models import BasicExpertInfo
 from danswer.connectors.models import Document
 from danswer.connectors.models import Section
 from danswer.db.engine import get_session_with_tenant
-from danswer.file_processing.extract_file_text import check_file_ext_is_valid
 from danswer.file_processing.extract_file_text import detect_encoding
 from danswer.file_processing.extract_file_text import extract_file_text
 from danswer.file_processing.extract_file_text import get_file_ext
 from danswer.file_processing.extract_file_text import is_text_file_extension
+from danswer.file_processing.extract_file_text import is_valid_file_ext
 from danswer.file_processing.extract_file_text import load_files_from_zip
 from danswer.file_processing.extract_file_text import read_pdf_file
 from danswer.file_processing.extract_file_text import read_text_file
@@ -50,7 +50,7 @@ def _read_files_and_metadata(
             file_content, ignore_dirs=True
         ):
             yield os.path.join(directory_path, file_info.filename), file, metadata
-    elif check_file_ext_is_valid(extension):
+    elif is_valid_file_ext(extension):
         yield file_name, file_content, metadata
     else:
         logger.warning(f"Skipping file '{file_name}' with extension '{extension}'")
@@ -63,7 +63,7 @@ def _process_file(
     pdf_pass: str | None = None,
 ) -> list[Document]:
     extension = get_file_ext(file_name)
-    if not check_file_ext_is_valid(extension):
+    if not is_valid_file_ext(extension):
         logger.warning(f"Skipping file '{file_name}' with extension '{extension}'")
         return []
 

backend/danswer/connectors/google_drive/connector.py

@@ -4,17 +4,20 @@ from concurrent.futures import as_completed
 from concurrent.futures import ThreadPoolExecutor
 from functools import partial
 from typing import Any
+from typing import cast
 
 from google.oauth2.credentials import Credentials as OAuthCredentials  # type: ignore
 from google.oauth2.service_account import Credentials as ServiceAccountCredentials  # type: ignore
 
 from danswer.configs.app_configs import INDEX_BATCH_SIZE
+from danswer.configs.app_configs import MAX_FILE_SIZE_BYTES
 from danswer.configs.constants import DocumentSource
 from danswer.connectors.google_drive.doc_conversion import build_slim_document
 from danswer.connectors.google_drive.doc_conversion import (
     convert_drive_item_to_document,
 )
 from danswer.connectors.google_drive.file_retrieval import crawl_folders_for_files
+from danswer.connectors.google_drive.file_retrieval import get_all_files_for_oauth
 from danswer.connectors.google_drive.file_retrieval import get_all_files_in_my_drive
 from danswer.connectors.google_drive.file_retrieval import get_files_in_shared_drive
 from danswer.connectors.google_drive.models import GoogleDriveFileType
@@ -82,12 +85,31 @@ def _process_files_batch(
         yield doc_batch
 
 
+def _clean_requested_drive_ids(
+    requested_drive_ids: set[str],
+    requested_folder_ids: set[str],
+    all_drive_ids_available: set[str],
+) -> tuple[set[str], set[str]]:
+    invalid_requested_drive_ids = requested_drive_ids - all_drive_ids_available
+    filtered_folder_ids = requested_folder_ids - all_drive_ids_available
+    if invalid_requested_drive_ids:
+        logger.warning(
+            f"Some shared drive IDs were not found. IDs: {invalid_requested_drive_ids}"
+        )
+        logger.warning("Checking for folder access instead...")
+        filtered_folder_ids.update(invalid_requested_drive_ids)
+
+    valid_requested_drive_ids = requested_drive_ids - invalid_requested_drive_ids
+    return valid_requested_drive_ids, filtered_folder_ids
+
+
 class GoogleDriveConnector(LoadConnector, PollConnector, SlimConnector):
     def __init__(
         self,
-        include_shared_drives: bool = True,
+        include_shared_drives: bool = False,
+        include_my_drives: bool = False,
+        include_files_shared_with_me: bool = False,
         shared_drive_urls: str | None = None,
-        include_my_drives: bool = True,
         my_drive_emails: str | None = None,
         shared_folder_urls: str | None = None,
         batch_size: int = INDEX_BATCH_SIZE,
@@ -120,22 +142,36 @@ class GoogleDriveConnector(LoadConnector, PollConnector, SlimConnector):
         if (
             not include_shared_drives
             and not include_my_drives
+            and not include_files_shared_with_me
             and not shared_folder_urls
+            and not my_drive_emails
+            and not shared_drive_urls
         ):
             raise ValueError(
-                "At least one of include_shared_drives, include_my_drives,"
-                " or shared_folder_urls must be true"
+                "Nothing to index. Please specify at least one of the following: "
+                "include_shared_drives, include_my_drives, include_files_shared_with_me, "
+                "shared_folder_urls, or my_drive_emails"
             )
 
         self.batch_size = batch_size
 
-        self.include_shared_drives = include_shared_drives
+        specific_requests_made = False
+        if bool(shared_drive_urls) or bool(my_drive_emails) or bool(shared_folder_urls):
+            specific_requests_made = True
+
+        self.include_files_shared_with_me = (
+            False if specific_requests_made else include_files_shared_with_me
+        )
+        self.include_my_drives = False if specific_requests_made else include_my_drives
+        self.include_shared_drives = (
+            False if specific_requests_made else include_shared_drives
+        )
+
         shared_drive_url_list = _extract_str_list_from_comma_str(shared_drive_urls)
         self._requested_shared_drive_ids = set(
             _extract_ids_from_urls(shared_drive_url_list)
         )
 
-        self.include_my_drives = include_my_drives
         self._requested_my_drive_emails = set(
             _extract_str_list_from_comma_str(my_drive_emails)
         )
@@ -225,26 +261,20 @@ class GoogleDriveConnector(LoadConnector, PollConnector, SlimConnector):
             creds=self.creds,
             user_email=self.primary_admin_email,
         )
+        is_service_account = isinstance(self.creds, ServiceAccountCredentials)
         all_drive_ids = set()
-        # We don't want to fail if we're using OAuth because you can
-        # access your my drive as a non admin user in an org still
-        ignore_fetch_failure = isinstance(self.creds, OAuthCredentials)
         for drive in execute_paginated_retrieval(
             retrieval_function=primary_drive_service.drives().list,
             list_key="drives",
-            continue_on_404_or_403=ignore_fetch_failure,
-            useDomainAdminAccess=True,
+            useDomainAdminAccess=is_service_account,
             fields="drives(id)",
         ):
             all_drive_ids.add(drive["id"])
 
         if not all_drive_ids:
             logger.warning(
-                "No drives found. This is likely because oauth user "
-                "is not an admin and cannot view all drive IDs. "
-                "Continuing with only the shared drive IDs specified in the config."
+                "No drives found even though we are indexing shared drives was requested."
             )
-            all_drive_ids = set(self._requested_shared_drive_ids)
 
         return all_drive_ids
 
@@ -261,14 +291,9 @@ class GoogleDriveConnector(LoadConnector, PollConnector, SlimConnector):
 
         # if we are including my drives, try to get the current user's my
         # drive if any of the following are true:
-        # - no specific emails were requested
+        # - include_my_drives is true
         # - the current user's email is in the requested emails
-        # - we are using OAuth (in which case we assume that is the only email we will try)
-        if self.include_my_drives and (
-            not self._requested_my_drive_emails
-            or user_email in self._requested_my_drive_emails
-            or isinstance(self.creds, OAuthCredentials)
-        ):
+        if self.include_my_drives or user_email in self._requested_my_drive_emails:
             yield from get_all_files_in_my_drive(
                 service=drive_service,
                 update_traversed_ids_func=self._update_traversed_parent_ids,
@@ -299,7 +324,7 @@ class GoogleDriveConnector(LoadConnector, PollConnector, SlimConnector):
                 end=end,
             )
 
-    def _fetch_drive_items(
+    def _manage_service_account_retrieval(
         self,
         is_slim: bool,
         start: SecondsSinceUnixEpoch | None = None,
@@ -309,29 +334,16 @@ class GoogleDriveConnector(LoadConnector, PollConnector, SlimConnector):
 
         all_drive_ids: set[str] = self._get_all_drive_ids()
 
-        # remove drive ids from the folder ids because they are queried differently
-        filtered_folder_ids = self._requested_folder_ids - all_drive_ids
-
-        # Remove drive_ids that are not in the all_drive_ids and check them as folders instead
-        invalid_drive_ids = self._requested_shared_drive_ids - all_drive_ids
-        if invalid_drive_ids:
-            logger.warning(
-                f"Some shared drive IDs were not found. IDs: {invalid_drive_ids}"
+        drive_ids_to_retrieve: set[str] = set()
+        folder_ids_to_retrieve: set[str] = set()
+        if self._requested_shared_drive_ids or self._requested_folder_ids:
+            drive_ids_to_retrieve, folder_ids_to_retrieve = _clean_requested_drive_ids(
+                requested_drive_ids=self._requested_shared_drive_ids,
+                requested_folder_ids=self._requested_folder_ids,
+                all_drive_ids_available=all_drive_ids,
             )
-            logger.warning("Checking for folder access instead...")
-            filtered_folder_ids.update(invalid_drive_ids)
-
-        # If including shared drives, use the requested IDs if provided,
-        # otherwise use all drive IDs
-        filtered_drive_ids = set()
-        if self.include_shared_drives:
-            if self._requested_shared_drive_ids:
-                # Remove invalid drive IDs from requested IDs
-                filtered_drive_ids = (
-                    self._requested_shared_drive_ids - invalid_drive_ids
-                )
-            else:
-                filtered_drive_ids = all_drive_ids
+        elif self.include_shared_drives:
+            drive_ids_to_retrieve = all_drive_ids
 
         # Process users in parallel using ThreadPoolExecutor
         with ThreadPoolExecutor(max_workers=10) as executor:
@@ -340,8 +352,8 @@ class GoogleDriveConnector(LoadConnector, PollConnector, SlimConnector):
                     self._impersonate_user_for_retrieval,
                     email,
                     is_slim,
-                    filtered_drive_ids,
-                    filtered_folder_ids,
+                    drive_ids_to_retrieve,
+                    folder_ids_to_retrieve,
                     start,
                     end,
                 ): email
@@ -353,13 +365,103 @@ class GoogleDriveConnector(LoadConnector, PollConnector, SlimConnector):
                 yield from future.result()
 
         remaining_folders = (
-            filtered_drive_ids | filtered_folder_ids
+            drive_ids_to_retrieve | folder_ids_to_retrieve
         ) - self._retrieved_ids
         if remaining_folders:
             logger.warning(
                 f"Some folders/drives were not retrieved. IDs: {remaining_folders}"
             )
 
+    def _manage_oauth_retrieval(
+        self,
+        is_slim: bool,
+        start: SecondsSinceUnixEpoch | None = None,
+        end: SecondsSinceUnixEpoch | None = None,
+    ) -> Iterator[GoogleDriveFileType]:
+        drive_service = get_drive_service(self.creds, self.primary_admin_email)
+
+        if self.include_files_shared_with_me or self.include_my_drives:
+            yield from get_all_files_for_oauth(
+                service=drive_service,
+                include_files_shared_with_me=self.include_files_shared_with_me,
+                include_my_drives=self.include_my_drives,
+                include_shared_drives=self.include_shared_drives,
+                is_slim=is_slim,
+                start=start,
+                end=end,
+            )
+
+        all_requested = (
+            self.include_files_shared_with_me
+            and self.include_my_drives
+            and self.include_shared_drives
+        )
+        if all_requested:
+            # If all 3 are true, we already yielded from get_all_files_for_oauth
+            return
+
+        all_drive_ids = self._get_all_drive_ids()
+        drive_ids_to_retrieve: set[str] = set()
+        folder_ids_to_retrieve: set[str] = set()
+        if self._requested_shared_drive_ids or self._requested_folder_ids:
+            drive_ids_to_retrieve, folder_ids_to_retrieve = _clean_requested_drive_ids(
+                requested_drive_ids=self._requested_shared_drive_ids,
+                requested_folder_ids=self._requested_folder_ids,
+                all_drive_ids_available=all_drive_ids,
+            )
+        elif self.include_shared_drives:
+            drive_ids_to_retrieve = all_drive_ids
+
+        for drive_id in drive_ids_to_retrieve:
+            yield from get_files_in_shared_drive(
+                service=drive_service,
+                drive_id=drive_id,
+                is_slim=is_slim,
+                update_traversed_ids_func=self._update_traversed_parent_ids,
+                start=start,
+                end=end,
+            )
+
+        # Even if no folders were requested, we still check if any drives were requested
+        # that could be folders.
+        remaining_folders = folder_ids_to_retrieve - self._retrieved_ids
+        for folder_id in remaining_folders:
+            yield from crawl_folders_for_files(
+                service=drive_service,
+                parent_id=folder_id,
+                traversed_parent_ids=self._retrieved_ids,
+                update_traversed_ids_func=self._update_traversed_parent_ids,
+                start=start,
+                end=end,
+            )
+
+        remaining_folders = (
+            drive_ids_to_retrieve | folder_ids_to_retrieve
+        ) - self._retrieved_ids
+        if remaining_folders:
+            logger.warning(
+                f"Some folders/drives were not retrieved. IDs: {remaining_folders}"
+            )
+
+    def _fetch_drive_items(
+        self,
+        is_slim: bool,
+        start: SecondsSinceUnixEpoch | None = None,
+        end: SecondsSinceUnixEpoch | None = None,
+    ) -> Iterator[GoogleDriveFileType]:
+        retrieval_method = (
+            self._manage_service_account_retrieval
+            if isinstance(self.creds, ServiceAccountCredentials)
+            else self._manage_oauth_retrieval
+        )
+        drive_files = retrieval_method(
+            is_slim=is_slim,
+            start=start,
+            end=end,
+        )
+
+        return drive_files
+
     def _extract_docs_from_google_drive(
         self,
         start: SecondsSinceUnixEpoch | None = None,
@@ -375,6 +477,15 @@ class GoogleDriveConnector(LoadConnector, PollConnector, SlimConnector):
         files_to_process = []
         # Gather the files into batches to be processed in parallel
         for file in self._fetch_drive_items(is_slim=False, start=start, end=end):
+            if (
+                file.get("size")
+                and int(cast(str, file.get("size"))) > MAX_FILE_SIZE_BYTES
+            ):
+                logger.warning(
+                    f"Skipping file {file.get('name', 'Unknown')} as it is too large: {file.get('size')} bytes"
+                )
+                continue
+
             files_to_process.append(file)
             if len(files_to_process) >= LARGE_BATCH_SIZE:
                 yield from _process_files_batch(

backend/danswer/connectors/google_drive/doc_conversion.py

@@ -2,6 +2,7 @@ import io
 from datetime import datetime
 from datetime import timezone
 
+from googleapiclient.discovery import build  # type: ignore
 from googleapiclient.errors import HttpError  # type: ignore
 
 from danswer.configs.app_configs import CONTINUE_ON_CONNECTOR_FAILURE
@@ -48,6 +49,67 @@ def _extract_sections_basic(
         return [Section(link=link, text=UNSUPPORTED_FILE_TYPE_CONTENT)]
 
     try:
+        if mime_type == GDriveMimeType.SPREADSHEET.value:
+            try:
+                sheets_service = build(
+                    "sheets", "v4", credentials=service._http.credentials
+                )
+                spreadsheet = (
+                    sheets_service.spreadsheets()
+                    .get(spreadsheetId=file["id"])
+                    .execute()
+                )
+
+                sections = []
+                for sheet in spreadsheet["sheets"]:
+                    sheet_name = sheet["properties"]["title"]
+                    sheet_id = sheet["properties"]["sheetId"]
+
+                    # Get sheet dimensions
+                    grid_properties = sheet["properties"].get("gridProperties", {})
+                    row_count = grid_properties.get("rowCount", 1000)
+                    column_count = grid_properties.get("columnCount", 26)
+
+                    # Convert column count to letter (e.g., 26 -> Z, 27 -> AA)
+                    end_column = ""
+                    while column_count:
+                        column_count, remainder = divmod(column_count - 1, 26)
+                        end_column = chr(65 + remainder) + end_column
+
+                    range_name = f"'{sheet_name}'!A1:{end_column}{row_count}"
+
+                    try:
+                        result = (
+                            sheets_service.spreadsheets()
+                            .values()
+                            .get(spreadsheetId=file["id"], range=range_name)
+                            .execute()
+                        )
+                        values = result.get("values", [])
+
+                        if values:
+                            text = f"Sheet: {sheet_name}\n"
+                            for row in values:
+                                text += "\t".join(str(cell) for cell in row) + "\n"
+                            sections.append(
+                                Section(
+                                    link=f"{link}#gid={sheet_id}",
+                                    text=text,
+                                )
+                            )
+                    except HttpError as e:
+                        logger.warning(
+                            f"Error fetching data for sheet '{sheet_name}': {e}"
+                        )
+                        continue
+                return sections
+
+            except Exception as e:
+                logger.warning(
+                    f"Ran into exception '{e}' when pulling data from Google Sheet '{file['name']}'."
+                    " Falling back to basic extraction."
+                )
+
         if mime_type in [
             GDriveMimeType.DOC.value,
             GDriveMimeType.PPT.value,
@@ -65,6 +127,7 @@ def _extract_sections_basic(
                 .decode("utf-8")
             )
             return [Section(link=link, text=text)]
+
         elif mime_type in [
             GDriveMimeType.PLAIN_TEXT.value,
             GDriveMimeType.MARKDOWN.value,

backend/danswer/connectors/google_drive/file_retrieval.py

@@ -16,7 +16,7 @@ logger = setup_logger()
 
 FILE_FIELDS = (
     "nextPageToken, files(mimeType, id, name, permissions, modifiedTime, webViewLink, "
-    "shortcutDetails, owners(emailAddress))"
+    "shortcutDetails, owners(emailAddress), size)"
 )
 SLIM_FILE_FIELDS = (
     "nextPageToken, files(mimeType, id, name, permissions(emailAddress, type), "
@@ -140,8 +140,8 @@ def get_files_in_shared_drive(
 ) -> Iterator[GoogleDriveFileType]:
     # If we know we are going to folder crawl later, we can cache the folders here
     # Get all folders being queried and add them to the traversed set
-    query = f"mimeType = '{DRIVE_FOLDER_TYPE}'"
-    query += " and trashed = false"
+    folder_query = f"mimeType = '{DRIVE_FOLDER_TYPE}'"
+    folder_query += " and trashed = false"
     found_folders = False
     for file in execute_paginated_retrieval(
         retrieval_function=service.files().list,
@@ -152,7 +152,7 @@ def get_files_in_shared_drive(
         supportsAllDrives=True,
         includeItemsFromAllDrives=True,
         fields="nextPageToken, files(id)",
-        q=query,
+        q=folder_query,
     ):
         update_traversed_ids_func(file["id"])
         found_folders = True
@@ -160,9 +160,9 @@ def get_files_in_shared_drive(
         update_traversed_ids_func(drive_id)
 
     # Get all files in the shared drive
-    query = f"mimeType != '{DRIVE_FOLDER_TYPE}'"
-    query += " and trashed = false"
-    query += _generate_time_range_filter(start, end)
+    file_query = f"mimeType != '{DRIVE_FOLDER_TYPE}'"
+    file_query += " and trashed = false"
+    file_query += _generate_time_range_filter(start, end)
     yield from execute_paginated_retrieval(
         retrieval_function=service.files().list,
         list_key="files",
@@ -172,7 +172,7 @@ def get_files_in_shared_drive(
         supportsAllDrives=True,
         includeItemsFromAllDrives=True,
         fields=SLIM_FILE_FIELDS if is_slim else FILE_FIELDS,
-        q=query,
+        q=file_query,
     )
 
 
@@ -185,14 +185,16 @@ def get_all_files_in_my_drive(
 ) -> Iterator[GoogleDriveFileType]:
     # If we know we are going to folder crawl later, we can cache the folders here
     # Get all folders being queried and add them to the traversed set
-    query = "trashed = false and 'me' in owners"
+    folder_query = f"mimeType = '{DRIVE_FOLDER_TYPE}'"
+    folder_query += " and trashed = false"
+    folder_query += " and 'me' in owners"
     found_folders = False
     for file in execute_paginated_retrieval(
         retrieval_function=service.files().list,
         list_key="files",
         corpora="user",
         fields=SLIM_FILE_FIELDS if is_slim else FILE_FIELDS,
-        q=query,
+        q=folder_query,
     ):
         update_traversed_ids_func(file["id"])
         found_folders = True
@@ -200,18 +202,52 @@ def get_all_files_in_my_drive(
         update_traversed_ids_func(get_root_folder_id(service))
 
     # Then get the files
-    query = "trashed = false and 'me' in owners"
-    query += _generate_time_range_filter(start, end)
-    fields = "files(id, name, mimeType, webViewLink, modifiedTime, createdTime)"
-    if not is_slim:
-        fields += ", files(permissions, permissionIds, owners)"
-
+    file_query = f"mimeType != '{DRIVE_FOLDER_TYPE}'"
+    file_query += " and trashed = false"
+    file_query += " and 'me' in owners"
+    file_query += _generate_time_range_filter(start, end)
     yield from execute_paginated_retrieval(
         retrieval_function=service.files().list,
         list_key="files",
         corpora="user",
         fields=SLIM_FILE_FIELDS if is_slim else FILE_FIELDS,
-        q=query,
+        q=file_query,
+    )
+
+
+def get_all_files_for_oauth(
+    service: Any,
+    include_files_shared_with_me: bool,
+    include_my_drives: bool,
+    # One of the above 2 should be true
+    include_shared_drives: bool,
+    is_slim: bool = False,
+    start: SecondsSinceUnixEpoch | None = None,
+    end: SecondsSinceUnixEpoch | None = None,
+) -> Iterator[GoogleDriveFileType]:
+    should_get_all = (
+        include_shared_drives and include_my_drives and include_files_shared_with_me
+    )
+    corpora = "allDrives" if should_get_all else "user"
+
+    file_query = f"mimeType != '{DRIVE_FOLDER_TYPE}'"
+    file_query += " and trashed = false"
+    file_query += _generate_time_range_filter(start, end)
+
+    if not should_get_all:
+        if include_files_shared_with_me and not include_my_drives:
+            file_query += " and not 'me' in owners"
+        if not include_files_shared_with_me and include_my_drives:
+            file_query += " and 'me' in owners"
+
+    yield from execute_paginated_retrieval(
+        retrieval_function=service.files().list,
+        list_key="files",
+        corpora=corpora,
+        includeItemsFromAllDrives=should_get_all,
+        supportsAllDrives=should_get_all,
+        fields=SLIM_FILE_FIELDS if is_slim else FILE_FIELDS,
+        q=file_query,
     )
 
 

backend/danswer/connectors/interfaces.py

@@ -2,6 +2,7 @@ import abc
 from collections.abc import Iterator
 from typing import Any
 
+from danswer.configs.constants import DocumentSource
 from danswer.connectors.models import Document
 from danswer.connectors.models import SlimDocument
 
@@ -64,6 +65,23 @@ class SlimConnector(BaseConnector):
         raise NotImplementedError
 
 
+class OAuthConnector(BaseConnector):
+    @classmethod
+    @abc.abstractmethod
+    def oauth_id(cls) -> DocumentSource:
+        raise NotImplementedError
+
+    @classmethod
+    @abc.abstractmethod
+    def oauth_authorization_url(cls, base_domain: str, state: str) -> str:
+        raise NotImplementedError
+
+    @classmethod
+    @abc.abstractmethod
+    def oauth_code_to_token(cls, code: str) -> dict[str, Any]:
+        raise NotImplementedError
+
+
 # Event driven
 class EventConnector(BaseConnector):
     @abc.abstractmethod

backend/danswer/connectors/linear/connector.py

@@ -132,7 +132,6 @@ class LinearConnector(LoadConnector, PollConnector):
                             branchName
                             customerTicketCount
                             description
-                            descriptionData
                             comments {
                                 nodes {
                                     url
@@ -215,5 +214,6 @@ class LinearConnector(LoadConnector, PollConnector):
 if __name__ == "__main__":
     connector = LinearConnector()
     connector.load_credentials({"linear_api_key": os.environ["LINEAR_API_KEY"]})
+
     document_batches = connector.load_from_state()
     print(next(document_batches))

backend/danswer/connectors/slab/connector.py

@@ -12,12 +12,15 @@ from dateutil import parser
 from danswer.configs.app_configs import INDEX_BATCH_SIZE
 from danswer.configs.constants import DocumentSource
 from danswer.connectors.interfaces import GenerateDocumentsOutput
+from danswer.connectors.interfaces import GenerateSlimDocumentOutput
 from danswer.connectors.interfaces import LoadConnector
 from danswer.connectors.interfaces import PollConnector
 from danswer.connectors.interfaces import SecondsSinceUnixEpoch
+from danswer.connectors.interfaces import SlimConnector
 from danswer.connectors.models import ConnectorMissingCredentialError
 from danswer.connectors.models import Document
 from danswer.connectors.models import Section
+from danswer.connectors.models import SlimDocument
 from danswer.utils.logger import setup_logger
 
 
@@ -28,6 +31,8 @@ logger = setup_logger()
 SLAB_GRAPHQL_MAX_TRIES = 10
 SLAB_API_URL = "https://api.slab.com/v1/graphql"
 
+_SLIM_BATCH_SIZE = 1000
+
 
 def run_graphql_request(
     graphql_query: dict, bot_token: str, max_tries: int = SLAB_GRAPHQL_MAX_TRIES
@@ -158,21 +163,26 @@ def get_slab_url_from_title_id(base_url: str, title: str, page_id: str) -> str:
     return urljoin(urljoin(base_url, "posts/"), url_id)
 
 
-class SlabConnector(LoadConnector, PollConnector):
+class SlabConnector(LoadConnector, PollConnector, SlimConnector):
     def __init__(
         self,
         base_url: str,
         batch_size: int = INDEX_BATCH_SIZE,
-        slab_bot_token: str | None = None,
     ) -> None:
         self.base_url = base_url
         self.batch_size = batch_size
-        self.slab_bot_token = slab_bot_token
+        self._slab_bot_token: str | None = None
 
     def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
-        self.slab_bot_token = credentials["slab_bot_token"]
+        self._slab_bot_token = credentials["slab_bot_token"]
         return None
 
+    @property
+    def slab_bot_token(self) -> str:
+        if self._slab_bot_token is None:
+            raise ConnectorMissingCredentialError("Slab")
+        return self._slab_bot_token
+
     def _iterate_posts(
         self, time_filter: Callable[[datetime], bool] | None = None
     ) -> GenerateDocumentsOutput:
@@ -227,3 +237,21 @@ class SlabConnector(LoadConnector, PollConnector):
         yield from self._iterate_posts(
             time_filter=lambda t: start_time <= t <= end_time
         )
+
+    def retrieve_all_slim_documents(
+        self,
+        start: SecondsSinceUnixEpoch | None = None,
+        end: SecondsSinceUnixEpoch | None = None,
+    ) -> GenerateSlimDocumentOutput:
+        slim_doc_batch: list[SlimDocument] = []
+        for post_id in get_all_post_ids(self.slab_bot_token):
+            slim_doc_batch.append(
+                SlimDocument(
+                    id=post_id,
+                )
+            )
+            if len(slim_doc_batch) >= _SLIM_BATCH_SIZE:
+                yield slim_doc_batch
+                slim_doc_batch = []
+        if slim_doc_batch:
+            yield slim_doc_batch

backend/danswer/connectors/slack/connector.py

@@ -134,7 +134,6 @@ def get_latest_message_time(thread: ThreadType) -> datetime:
 
 
 def thread_to_doc(
-    workspace: str,
     channel: ChannelType,
     thread: ThreadType,
     slack_cleaner: SlackTextCleaner,
@@ -171,15 +170,15 @@ def thread_to_doc(
         else first_message
     )
 
-    doc_sem_id = f"{initial_sender_name} in #{channel['name']}: {snippet}"
+    doc_sem_id = f"{initial_sender_name} in #{channel['name']}: {snippet}".replace(
+        "\n", " "
+    )
 
     return Document(
         id=f"{channel_id}__{thread[0]['ts']}",
         sections=[
             Section(
-                link=get_message_link(
-                    event=m, workspace=workspace, channel_id=channel_id
-                ),
+                link=get_message_link(event=m, client=client, channel_id=channel_id),
                 text=slack_cleaner.index_clean(cast(str, m["text"])),
             )
             for m in thread
@@ -263,7 +262,6 @@ def filter_channels(
 
 def _get_all_docs(
     client: WebClient,
-    workspace: str,
     channels: list[str] | None = None,
     channel_name_regex_enabled: bool = False,
     oldest: str | None = None,
@@ -310,7 +308,6 @@ def _get_all_docs(
                 if filtered_thread:
                     channel_docs += 1
                     yield thread_to_doc(
-                        workspace=workspace,
                         channel=channel,
                         thread=filtered_thread,
                         slack_cleaner=slack_cleaner,
@@ -373,14 +370,12 @@ def _get_all_doc_ids(
 class SlackPollConnector(PollConnector, SlimConnector):
     def __init__(
         self,
-        workspace: str,
         channels: list[str] | None = None,
         # if specified, will treat the specified channel strings as
         # regexes, and will only index channels that fully match the regexes
         channel_regex_enabled: bool = False,
         batch_size: int = INDEX_BATCH_SIZE,
     ) -> None:
-        self.workspace = workspace
         self.channels = channels
         self.channel_regex_enabled = channel_regex_enabled
         self.batch_size = batch_size
@@ -414,7 +409,6 @@ class SlackPollConnector(PollConnector, SlimConnector):
         documents: list[Document] = []
         for document in _get_all_docs(
             client=self.client,
-            workspace=self.workspace,
             channels=self.channels,
             channel_name_regex_enabled=self.channel_regex_enabled,
             # NOTE: need to impute to `None` instead of using 0.0, since Slack will
@@ -438,7 +432,6 @@ if __name__ == "__main__":
 
     slack_channel = os.environ.get("SLACK_CHANNEL")
     connector = SlackPollConnector(
-        workspace=os.environ["SLACK_WORKSPACE"],
         channels=[slack_channel] if slack_channel else None,
     )
     connector.load_credentials({"slack_bot_token": os.environ["SLACK_BOT_TOKEN"]})

backend/danswer/connectors/slack/load_connector.py

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

backend/danswer/connectors/slack/utils.py

@@ -2,6 +2,7 @@ import re
 import time
 from collections.abc import Callable
 from collections.abc import Generator
+from functools import lru_cache
 from functools import wraps
 from typing import Any
 from typing import cast
@@ -21,19 +22,21 @@ basic_retry_wrapper = retry_builder()
 _SLACK_LIMIT = 900
 
 
+@lru_cache()
+def get_base_url(token: str) -> str:
+    """Retrieve and cache the base URL of the Slack workspace based on the client token."""
+    client = WebClient(token=token)
+    return client.auth_test()["url"]
+
+
 def get_message_link(
-    event: dict[str, Any], workspace: str, channel_id: str | None = None
+    event: dict[str, Any], client: WebClient, channel_id: str | None = None
 ) -> str:
-    channel_id = channel_id or cast(
-        str, event["channel"]
-    )  # channel must either be present in the event or passed in
-    message_ts = cast(str, event["ts"])
-    message_ts_without_dot = message_ts.replace(".", "")
-    thread_ts = cast(str | None, event.get("thread_ts"))
-    return (
-        f"https://{workspace}.slack.com/archives/{channel_id}/p{message_ts_without_dot}"
-        + (f"?thread_ts={thread_ts}" if thread_ts else "")
-    )
+    channel_id = channel_id or event["channel"]
+    message_ts = event["ts"]
+    response = client.chat_getPermalink(channel=channel_id, message_ts=message_ts)
+    permalink = response["permalink"]
+    return permalink
 
 
 def _make_slack_api_call_logged(

backend/danswer/connectors/teams/connector.py

@@ -33,7 +33,7 @@ def get_created_datetime(chat_message: ChatMessage) -> datetime:
 
 def _extract_channel_members(channel: Channel) -> list[BasicExpertInfo]:
     channel_members_list: list[BasicExpertInfo] = []
-    members = channel.members.get().execute_query()
+    members = channel.members.get().execute_query_retry()
     for member in members:
         channel_members_list.append(BasicExpertInfo(display_name=member.display_name))
     return channel_members_list
@@ -51,7 +51,7 @@ def _get_threads_from_channel(
         end = end.replace(tzinfo=timezone.utc)
 
     query = channel.messages.get()
-    base_messages: list[ChatMessage] = query.execute_query()
+    base_messages: list[ChatMessage] = query.execute_query_retry()
 
     threads: list[list[ChatMessage]] = []
     for base_message in base_messages:
@@ -65,7 +65,7 @@ def _get_threads_from_channel(
             continue
 
         reply_query = base_message.replies.get_all()
-        replies = reply_query.execute_query()
+        replies = reply_query.execute_query_retry()
 
         # start a list containing the base message and its replies
         thread: list[ChatMessage] = [base_message]
@@ -82,7 +82,7 @@ def _get_channels_from_teams(
     channels_list: list[Channel] = []
     for team in teams:
         query = team.channels.get()
-        channels = query.execute_query()
+        channels = query.execute_query_retry()
         channels_list.extend(channels)
 
     return channels_list
@@ -210,7 +210,7 @@ class TeamsConnector(LoadConnector, PollConnector):
 
         teams_list: list[Team] = []
 
-        teams = self.graph_client.teams.get().execute_query()
+        teams = self.graph_client.teams.get().execute_query_retry()
 
         if len(self.requested_team_list) > 0:
             adjusted_request_strings = [
@@ -234,14 +234,25 @@ class TeamsConnector(LoadConnector, PollConnector):
             raise ConnectorMissingCredentialError("Teams")
 
         teams = self._get_all_teams()
+        logger.debug(f"Found available teams: {[str(t) for t in teams]}")
+        if not teams:
+            msg = "No teams found."
+            logger.error(msg)
+            raise ValueError(msg)
 
         channels = _get_channels_from_teams(
             teams=teams,
         )
+        logger.debug(f"Found available channels: {[c.id for c in channels]}")
+        if not channels:
+            msg = "No channels found."
+            logger.error(msg)
+            raise ValueError(msg)
 
         # goes over channels, converts them into Document objects and then yields them in batches
         doc_batch: list[Document] = []
         for channel in channels:
+            logger.debug(f"Fetching threads from channel: {channel.id}")
             thread_list = _get_threads_from_channel(channel, start=start, end=end)
             for thread in thread_list:
                 converted_doc = _convert_thread_to_document(channel, thread)
@@ -259,8 +270,8 @@ class TeamsConnector(LoadConnector, PollConnector):
     def poll_source(
         self, start: SecondsSinceUnixEpoch, end: SecondsSinceUnixEpoch
     ) -> GenerateDocumentsOutput:
-        start_datetime = datetime.utcfromtimestamp(start)
-        end_datetime = datetime.utcfromtimestamp(end)
+        start_datetime = datetime.fromtimestamp(start, timezone.utc)
+        end_datetime = datetime.fromtimestamp(end, timezone.utc)
         return self._fetch_from_teams(start=start_datetime, end=end_datetime)
 
 

backend/danswer/connectors/zendesk/connector.py

@@ -102,13 +102,21 @@ def _get_tickets(
 
 
 def _fetch_author(client: ZendeskClient, author_id: str) -> BasicExpertInfo | None:
-    author_data = client.make_request(f"users/{author_id}", {})
-    user = author_data.get("user")
-    return (
-        BasicExpertInfo(display_name=user.get("name"), email=user.get("email"))
-        if user and user.get("name") and user.get("email")
-        else None
-    )
+    # Skip fetching if author_id is invalid
+    if not author_id or author_id == "-1":
+        return None
+
+    try:
+        author_data = client.make_request(f"users/{author_id}", {})
+        user = author_data.get("user")
+        return (
+            BasicExpertInfo(display_name=user.get("name"), email=user.get("email"))
+            if user and user.get("name") and user.get("email")
+            else None
+        )
+    except requests.exceptions.HTTPError:
+        # Handle any API errors gracefully
+        return None
 
 
 def _article_to_document(

backend/danswer/context/search/models.py

@@ -8,13 +8,13 @@ from pydantic import field_validator
 
 from danswer.configs.chat_configs import NUM_RETURNED_HITS
 from danswer.configs.constants import DocumentSource
+from danswer.context.search.enums import LLMEvaluationType
+from danswer.context.search.enums import OptionalSearchSetting
+from danswer.context.search.enums import SearchType
 from danswer.db.models import Persona
 from danswer.db.models import SearchSettings
 from danswer.indexing.models import BaseChunk
 from danswer.indexing.models import IndexingSetting
-from danswer.search.enums import LLMEvaluationType
-from danswer.search.enums import OptionalSearchSetting
-from danswer.search.enums import SearchType
 from shared_configs.enums import RerankerProvider
 
 

backend/danswer/context/search/pipeline.py

@@ -5,33 +5,33 @@ from typing import cast
 
 from sqlalchemy.orm import Session
 
+from danswer.chat.models import PromptConfig
 from danswer.chat.models import SectionRelevancePiece
+from danswer.chat.prune_and_merge import _merge_sections
+from danswer.chat.prune_and_merge import ChunkRange
+from danswer.chat.prune_and_merge import merge_chunk_intervals
 from danswer.configs.chat_configs import DISABLE_LLM_DOC_RELEVANCE
+from danswer.context.search.enums import LLMEvaluationType
+from danswer.context.search.enums import QueryFlow
+from danswer.context.search.enums import SearchType
+from danswer.context.search.models import IndexFilters
+from danswer.context.search.models import InferenceChunk
+from danswer.context.search.models import InferenceSection
+from danswer.context.search.models import RerankMetricsContainer
+from danswer.context.search.models import RetrievalMetricsContainer
+from danswer.context.search.models import SearchQuery
+from danswer.context.search.models import SearchRequest
+from danswer.context.search.postprocessing.postprocessing import cleanup_chunks
+from danswer.context.search.postprocessing.postprocessing import search_postprocessing
+from danswer.context.search.preprocessing.preprocessing import retrieval_preprocessing
+from danswer.context.search.retrieval.search_runner import retrieve_chunks
+from danswer.context.search.utils import inference_section_from_chunks
+from danswer.context.search.utils import relevant_sections_to_indices
 from danswer.db.models import User
 from danswer.db.search_settings import get_current_search_settings
 from danswer.document_index.factory import get_default_document_index
 from danswer.document_index.interfaces import VespaChunkRequest
-from danswer.llm.answering.models import PromptConfig
-from danswer.llm.answering.prune_and_merge import _merge_sections
-from danswer.llm.answering.prune_and_merge import ChunkRange
-from danswer.llm.answering.prune_and_merge import merge_chunk_intervals
 from danswer.llm.interfaces import LLM
-from danswer.search.enums import LLMEvaluationType
-from danswer.search.enums import QueryFlow
-from danswer.search.enums import SearchType
-from danswer.search.models import IndexFilters
-from danswer.search.models import InferenceChunk
-from danswer.search.models import InferenceSection
-from danswer.search.models import RerankMetricsContainer
-from danswer.search.models import RetrievalMetricsContainer
-from danswer.search.models import SearchQuery
-from danswer.search.models import SearchRequest
-from danswer.search.postprocessing.postprocessing import cleanup_chunks
-from danswer.search.postprocessing.postprocessing import search_postprocessing
-from danswer.search.preprocessing.preprocessing import retrieval_preprocessing
-from danswer.search.retrieval.search_runner import retrieve_chunks
-from danswer.search.utils import inference_section_from_chunks
-from danswer.search.utils import relevant_sections_to_indices
 from danswer.secondary_llm_flows.agentic_evaluation import evaluate_inference_section
 from danswer.utils.logger import setup_logger
 from danswer.utils.threadpool_concurrency import FunctionCall

backend/danswer/context/search/postprocessing/postprocessing.py

@@ -9,19 +9,19 @@ from danswer.configs.app_configs import BLURB_SIZE
 from danswer.configs.constants import RETURN_SEPARATOR
 from danswer.configs.model_configs import CROSS_ENCODER_RANGE_MAX
 from danswer.configs.model_configs import CROSS_ENCODER_RANGE_MIN
+from danswer.context.search.enums import LLMEvaluationType
+from danswer.context.search.models import ChunkMetric
+from danswer.context.search.models import InferenceChunk
+from danswer.context.search.models import InferenceChunkUncleaned
+from danswer.context.search.models import InferenceSection
+from danswer.context.search.models import MAX_METRICS_CONTENT
+from danswer.context.search.models import RerankMetricsContainer
+from danswer.context.search.models import SearchQuery
 from danswer.document_index.document_index_utils import (
     translate_boost_count_to_multiplier,
 )
 from danswer.llm.interfaces import LLM
 from danswer.natural_language_processing.search_nlp_models import RerankingModel
-from danswer.search.enums import LLMEvaluationType
-from danswer.search.models import ChunkMetric
-from danswer.search.models import InferenceChunk
-from danswer.search.models import InferenceChunkUncleaned
-from danswer.search.models import InferenceSection
-from danswer.search.models import MAX_METRICS_CONTENT
-from danswer.search.models import RerankMetricsContainer
-from danswer.search.models import SearchQuery
 from danswer.secondary_llm_flows.chunk_usefulness import llm_batch_eval_sections
 from danswer.utils.logger import setup_logger
 from danswer.utils.threadpool_concurrency import FunctionCall

backend/danswer/context/search/preprocessing/access_filters.py

@@ -1,8 +1,8 @@
 from sqlalchemy.orm import Session
 
 from danswer.access.access import get_acl_for_user
+from danswer.context.search.models import IndexFilters
 from danswer.db.models import User
-from danswer.search.models import IndexFilters
 
 
 def build_access_filters_for_user(user: User | None, session: Session) -> list[str]:

backend/danswer/context/search/preprocessing/preprocessing.py

@@ -9,21 +9,25 @@ from danswer.configs.chat_configs import HYBRID_ALPHA
 from danswer.configs.chat_configs import HYBRID_ALPHA_KEYWORD
 from danswer.configs.chat_configs import NUM_POSTPROCESSED_RESULTS
 from danswer.configs.chat_configs import NUM_RETURNED_HITS
+from danswer.context.search.enums import LLMEvaluationType
+from danswer.context.search.enums import RecencyBiasSetting
+from danswer.context.search.enums import SearchType
+from danswer.context.search.models import BaseFilters
+from danswer.context.search.models import IndexFilters
+from danswer.context.search.models import RerankingDetails
+from danswer.context.search.models import SearchQuery
+from danswer.context.search.models import SearchRequest
+from danswer.context.search.preprocessing.access_filters import (
+    build_access_filters_for_user,
+)
+from danswer.context.search.retrieval.search_runner import (
+    remove_stop_words_and_punctuation,
+)
 from danswer.db.engine import CURRENT_TENANT_ID_CONTEXTVAR
 from danswer.db.models import User
 from danswer.db.search_settings import get_current_search_settings
 from danswer.llm.interfaces import LLM
 from danswer.natural_language_processing.search_nlp_models import QueryAnalysisModel
-from danswer.search.enums import LLMEvaluationType
-from danswer.search.enums import RecencyBiasSetting
-from danswer.search.enums import SearchType
-from danswer.search.models import BaseFilters
-from danswer.search.models import IndexFilters
-from danswer.search.models import RerankingDetails
-from danswer.search.models import SearchQuery
-from danswer.search.models import SearchRequest
-from danswer.search.preprocessing.access_filters import build_access_filters_for_user
-from danswer.search.retrieval.search_runner import remove_stop_words_and_punctuation
 from danswer.secondary_llm_flows.source_filter import extract_source_filter
 from danswer.secondary_llm_flows.time_filter import extract_time_filter
 from danswer.utils.logger import setup_logger

backend/danswer/context/search/retrieval/search_runner.py

@@ -6,6 +6,16 @@ from nltk.corpus import stopwords  # type:ignore
 from nltk.tokenize import word_tokenize  # type:ignore
 from sqlalchemy.orm import Session
 
+from danswer.context.search.models import ChunkMetric
+from danswer.context.search.models import IndexFilters
+from danswer.context.search.models import InferenceChunk
+from danswer.context.search.models import InferenceChunkUncleaned
+from danswer.context.search.models import InferenceSection
+from danswer.context.search.models import MAX_METRICS_CONTENT
+from danswer.context.search.models import RetrievalMetricsContainer
+from danswer.context.search.models import SearchQuery
+from danswer.context.search.postprocessing.postprocessing import cleanup_chunks
+from danswer.context.search.utils import inference_section_from_chunks
 from danswer.db.search_settings import get_current_search_settings
 from danswer.db.search_settings import get_multilingual_expansion
 from danswer.document_index.interfaces import DocumentIndex
@@ -14,16 +24,6 @@ from danswer.document_index.vespa.shared_utils.utils import (
     replace_invalid_doc_id_characters,
 )
 from danswer.natural_language_processing.search_nlp_models import EmbeddingModel
-from danswer.search.models import ChunkMetric
-from danswer.search.models import IndexFilters
-from danswer.search.models import InferenceChunk
-from danswer.search.models import InferenceChunkUncleaned
-from danswer.search.models import InferenceSection
-from danswer.search.models import MAX_METRICS_CONTENT
-from danswer.search.models import RetrievalMetricsContainer
-from danswer.search.models import SearchQuery
-from danswer.search.postprocessing.postprocessing import cleanup_chunks
-from danswer.search.utils import inference_section_from_chunks
 from danswer.secondary_llm_flows.query_expansion import multilingual_query_expansion
 from danswer.utils.logger import setup_logger
 from danswer.utils.threadpool_concurrency import run_functions_tuples_in_parallel

backend/danswer/context/search/search_settings.py

@@ -1,9 +1,9 @@
 from typing import cast
 
 from danswer.configs.constants import KV_SEARCH_SETTINGS
+from danswer.context.search.models import SavedSearchSettings
 from danswer.key_value_store.factory import get_kv_store
 from danswer.key_value_store.interface import KvKeyNotFoundError
-from danswer.search.models import SavedSearchSettings
 from danswer.utils.logger import setup_logger
 
 logger = setup_logger()

backend/danswer/context/search/utils.py

@@ -2,12 +2,12 @@ from collections.abc import Sequence
 from typing import TypeVar
 
 from danswer.chat.models import SectionRelevancePiece
+from danswer.context.search.models import InferenceChunk
+from danswer.context.search.models import InferenceSection
+from danswer.context.search.models import SavedSearchDoc
+from danswer.context.search.models import SavedSearchDocWithContent
+from danswer.context.search.models import SearchDoc
 from danswer.db.models import SearchDoc as DBSearchDoc
-from danswer.search.models import InferenceChunk
-from danswer.search.models import InferenceSection
-from danswer.search.models import SavedSearchDoc
-from danswer.search.models import SavedSearchDocWithContent
-from danswer.search.models import SearchDoc
 
 
 T = TypeVar(