Move web Dockerfile to single stage since multi-stage has been causing issues w/ github actions

.github/pull_request_template.md

@@ -1,25 +0,0 @@
-## Description
-[Provide a brief description of the changes in this PR]
-
-
-## How Has This Been Tested?
-[Describe the tests you ran to verify your changes]
-
-
-## Accepted Risk
-[Any know risks or failure modes to point out to reviewers]
-
-
-## Related Issue(s)
-[If applicable, link to the issue(s) this PR addresses]
-
-
-## Checklist:
-- [ ] All of the automated tests pass
-- [ ] All PR comments are addressed and marked resolved
-- [ ] If there are migrations, they have been rebased to latest main
-- [ ] If there are new dependencies, they are added to the requirements
-- [ ] If there are new environment variables, they are added to all of the deployment methods
-- [ ] If there are new APIs that don't require auth, they are added to PUBLIC_ENDPOINT_SPECS
-- [ ] Docker images build and basic functionalities work
-- [ ] Author has done a final read through of the PR right before merge

.github/workflows/docker-build-backend-container-on-merge-group.yml

@@ -1,33 +0,0 @@
-name: Build Backend Image on Merge Group
-
-on:
-  merge_group:
-    types: [checks_requested]
-
-env:
-  REGISTRY_IMAGE: danswer/danswer-backend
-
-jobs:
-  build:
-    # TODO: make this a matrix build like the web containers
-    runs-on: 
-      group: amd64-image-builders
-      
-    steps:
-    - name: Checkout code
-      uses: actions/checkout@v2
-
-    - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v3
-
-    - name: Backend Image Docker Build
-      uses: docker/build-push-action@v5
-      with:
-        context: ./backend
-        file: ./backend/Dockerfile
-        platforms: linux/amd64,linux/arm64
-        push: false
-        tags: |
-          ${{ env.REGISTRY_IMAGE }}:latest
-        build-args: |
-          DANSWER_VERSION=v0.0.1

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

@@ -5,38 +5,33 @@ on:
     tags:
       - '*'
 
-env:
-  REGISTRY_IMAGE: danswer/danswer-backend
-
 jobs:
   build-and-push:
-    # TODO: make this a matrix build like the web containers
-    runs-on: 
-      group: amd64-image-builders
+    runs-on: ubuntu-latest
 
     steps:
     - name: Checkout code
       uses: actions/checkout@v2
 
     - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v3
+      uses: docker/setup-buildx-action@v1
 
     - name: Login to Docker Hub
-      uses: docker/login-action@v3
+      uses: docker/login-action@v1
       with:
         username: ${{ secrets.DOCKER_USERNAME }}
         password: ${{ secrets.DOCKER_TOKEN }}
 
     - name: Backend Image Docker Build and Push
-      uses: docker/build-push-action@v5
+      uses: docker/build-push-action@v2
       with:
         context: ./backend
         file: ./backend/Dockerfile
         platforms: linux/amd64,linux/arm64
         push: true
         tags: |
-          ${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
-          ${{ env.REGISTRY_IMAGE }}:latest
+          danswer/danswer-backend:${{ github.ref_name }}
+          danswer/danswer-backend:latest
         build-args: |
           DANSWER_VERSION=${{ github.ref_name }}
 
@@ -44,6 +39,6 @@ jobs:
       uses: aquasecurity/trivy-action@master
       with:
         # To run locally: trivy image --severity HIGH,CRITICAL danswer/danswer-backend
-        image-ref: docker.io/${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
+        image-ref: docker.io/danswer/danswer-backend:${{ github.ref_name }}
         severity: 'CRITICAL,HIGH'
         trivyignores: ./backend/.trivyignore

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

@@ -7,24 +7,23 @@ on:
 
 jobs:
   build-and-push:
-    runs-on: 
-      group: amd64-image-builders
+    runs-on: ubuntu-latest
 
     steps:
     - name: Checkout code
       uses: actions/checkout@v2
 
     - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v3
+      uses: docker/setup-buildx-action@v1
 
     - name: Login to Docker Hub
-      uses: docker/login-action@v3
+      uses: docker/login-action@v1
       with:
         username: ${{ secrets.DOCKER_USERNAME }}
         password: ${{ secrets.DOCKER_TOKEN }}
 
     - name: Model Server Image Docker Build and Push
-      uses: docker/build-push-action@v5
+      uses: docker/build-push-action@v2
       with:
         context: ./backend
         file: ./backend/Dockerfile.model_server

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

@@ -5,115 +5,38 @@ on:
     tags:
       - '*'
 
-env:
-  REGISTRY_IMAGE: danswer/danswer-web-server
-
 jobs:
-  build:
-    runs-on: 
-      group: ${{ matrix.platform == 'linux/amd64' && 'amd64-image-builders' || 'arm64-image-builders' }}
-    strategy:
-      fail-fast: false
-      matrix:
-        platform:
-          - linux/amd64
-          - linux/arm64
-
-    steps:
-      - name: Prepare
-        run: |
-          platform=${{ matrix.platform }}
-          echo "PLATFORM_PAIR=${platform//\//-}" >> $GITHUB_ENV          
-      
-      - name: Checkout
-        uses: actions/checkout@v4
-      
-      - name: Docker meta
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.REGISTRY_IMAGE }}
-          tags: |
-            type=raw,value=${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
-            type=raw,value=${{ env.REGISTRY_IMAGE }}:latest
-      
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-      
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_USERNAME }}
-          password: ${{ secrets.DOCKER_TOKEN }}
-    
-      - name: Build and push by digest
-        id: build
-        uses: docker/build-push-action@v5
-        with:
-          context: ./web
-          file: ./web/Dockerfile
-          platforms: ${{ matrix.platform }}
-          push: true
-          build-args: |
-            DANSWER_VERSION=${{ github.ref_name }}
-          # needed due to weird interactions with the builds for different platforms  
-          no-cache: true
-          labels: ${{ steps.meta.outputs.labels }}
-          outputs: type=image,name=${{ env.REGISTRY_IMAGE }},push-by-digest=true,name-canonical=true,push=true
-      
-      - name: Export digest
-        run: |
-          mkdir -p /tmp/digests
-          digest="${{ steps.build.outputs.digest }}"
-          touch "/tmp/digests/${digest#sha256:}"          
-      
-      - name: Upload digest
-        uses: actions/upload-artifact@v4
-        with:
-          name: digests-${{ env.PLATFORM_PAIR }}
-          path: /tmp/digests/*
-          if-no-files-found: error
-          retention-days: 1
-
-  merge:
+  build-and-push:
     runs-on: ubuntu-latest
-    needs:
-      - build
-    steps:
-      - name: Download digests
-        uses: actions/download-artifact@v4
-        with:
-          path: /tmp/digests
-          pattern: digests-*
-          merge-multiple: true
-      
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-      
-      - name: Docker meta
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.REGISTRY_IMAGE }}
-      
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_USERNAME }}
-          password: ${{ secrets.DOCKER_TOKEN }}
-      
-      - name: Create manifest list and push
-        working-directory: /tmp/digests
-        run: |
-          docker buildx imagetools create $(jq -cr '.tags | map("-t " + .) | join(" ")' <<< "$DOCKER_METADATA_OUTPUT_JSON") \
-            $(printf '${{ env.REGISTRY_IMAGE }}@sha256:%s ' *)          
-      
-      - name: Inspect image
-        run: |
-          docker buildx imagetools inspect ${{ env.REGISTRY_IMAGE }}:${{ steps.meta.outputs.version }}
 
-      - name: Run Trivy vulnerability scanner
-        uses: aquasecurity/trivy-action@master
-        with:
-          image-ref: docker.io/${{ env.REGISTRY_IMAGE }}:${{ github.ref_name }}
-          severity: 'CRITICAL,HIGH'
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v2
+
+    - name: Set up Docker Buildx
+      uses: docker/setup-buildx-action@v1
+
+    - name: Login to Docker Hub
+      uses: docker/login-action@v1
+      with:
+        username: ${{ secrets.DOCKER_USERNAME }}
+        password: ${{ secrets.DOCKER_TOKEN }}
+
+    - name: Web Image Docker Build and Push
+      uses: docker/build-push-action@v2
+      with:
+        context: ./web
+        file: ./web/Dockerfile
+        platforms: linux/amd64,linux/arm64
+        push: true
+        tags: |
+          danswer/danswer-web-server:${{ github.ref_name }}
+          danswer/danswer-web-server:latest
+        build-args: |
+          DANSWER_VERSION=${{ github.ref_name }}
+
+    - name: Run Trivy vulnerability scanner
+      uses: aquasecurity/trivy-action@master
+      with:
+        image-ref: docker.io/danswer/danswer-web-server:${{ github.ref_name }}
+        severity: 'CRITICAL,HIGH'

.github/workflows/docker-build-web-container-on-merge-group.yml

@@ -1,53 +0,0 @@
-name: Build Web Image on Merge Group
-
-on:
-  merge_group:
-    types: [checks_requested]
-
-env:
-  REGISTRY_IMAGE: danswer/danswer-web-server
-
-jobs:
-  build:
-    runs-on: 
-      group: ${{ matrix.platform == 'linux/amd64' && 'amd64-image-builders' || 'arm64-image-builders' }}
-    strategy:
-      fail-fast: false
-      matrix:
-        platform:
-          - linux/amd64
-          - linux/arm64
-
-    steps:
-      - name: Prepare
-        run: |
-          platform=${{ matrix.platform }}
-          echo "PLATFORM_PAIR=${platform//\//-}" >> $GITHUB_ENV          
-      
-      - name: Checkout
-        uses: actions/checkout@v4
-      
-      - name: Docker meta
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.REGISTRY_IMAGE }}
-          tags: |
-            type=raw,value=${{ env.REGISTRY_IMAGE }}:latest
-      
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-    
-      - name: Build by digest
-        id: build
-        uses: docker/build-push-action@v5
-        with:
-          context: ./web
-          file: ./web/Dockerfile
-          platforms: ${{ matrix.platform }}
-          push: false
-          build-args: |
-            DANSWER_VERSION=v0.0.1
-          # needed due to weird interactions with the builds for different platforms  
-          no-cache: true
-          labels: ${{ steps.meta.outputs.labels }}

.gitignore

@@ -5,5 +5,3 @@
 .idea
 /deployment/data/nginx/app.conf
 .vscode/launch.json
-*.sw?
-/backend/tests/regression/answer_quality/search_test_config.yaml

.vscode/env_template.txt

@@ -1,52 +0,0 @@
-# Copy this file to .env at the base of the repo and fill in the <REPLACE THIS> values
-# This will help with development iteration speed and reduce repeat tasks for dev
-# Also check out danswer/backend/scripts/restart_containers.sh for a script to restart the containers which Danswer relies on outside of VSCode/Cursor processes
-
-# For local dev, often user Authentication is not needed
-AUTH_TYPE=disabled
-
-
-# Always keep these on for Dev
-# Logs all model prompts to stdout
-LOG_DANSWER_MODEL_INTERACTIONS=True
-# More verbose logging
-LOG_LEVEL=debug
-
-
-# This passes top N results to LLM an additional time for reranking prior to answer generation
-# This step is quite heavy on token usage so we disable it for dev generally
-DISABLE_LLM_DOC_RELEVANCE=True
-
-
-# Useful if you want to toggle auth on/off (google_oauth/OIDC specifically)
-OAUTH_CLIENT_ID=<REPLACE THIS>
-OAUTH_CLIENT_SECRET=<REPLACE THIS>
-# Generally not useful for dev, we don't generally want to set up an SMTP server for dev
-REQUIRE_EMAIL_VERIFICATION=False
-
-
-# Set these so if you wipe the DB, you don't end up having to go through the UI every time
-GEN_AI_API_KEY=<REPLACE THIS>
-# If answer quality isn't important for dev, use 3.5 turbo due to it being cheaper
-GEN_AI_MODEL_VERSION=gpt-3.5-turbo
-FAST_GEN_AI_MODEL_VERSION=gpt-3.5-turbo
-
-# For Danswer Slack Bot, overrides the UI values so no need to set this up via UI every time
-# Only needed if using DanswerBot
-#DANSWER_BOT_SLACK_APP_TOKEN=<REPLACE THIS>
-#DANSWER_BOT_SLACK_BOT_TOKEN=<REPLACE THIS>
-
-
-# Python stuff
-PYTHONPATH=./backend
-PYTHONUNBUFFERED=1
-
-
-# Internet Search 
-BING_API_KEY=<REPLACE THIS>
-
-
-# Enable the full set of Danswer Enterprise Edition features
-# NOTE: DO NOT ENABLE THIS UNLESS YOU HAVE A PAID ENTERPRISE LICENSE (or if you are using this for local testing/development)
-ENABLE_PAID_ENTERPRISE_EDITION_FEATURES=False
-

.vscode/launch.template.jsonc

@@ -17,7 +17,6 @@
             "request": "launch",
             "cwd": "${workspaceRoot}/web",
             "runtimeExecutable": "npm",
-            "envFile": "${workspaceFolder}/.env",
             "runtimeArgs": [
                 "run", "dev"
             ],
@@ -29,7 +28,6 @@
             "request": "launch",
             "module": "uvicorn",
             "cwd": "${workspaceFolder}/backend",
-            "envFile": "${workspaceFolder}/.env",
             "env": {
                 "LOG_LEVEL": "DEBUG",
                 "PYTHONUNBUFFERED": "1"
@@ -39,8 +37,7 @@
                 "--reload",
                 "--port",
                 "9000"
-            ],
-            "consoleTitle": "Model Server"
+            ]
         },
         {
             "name": "API Server",
@@ -48,9 +45,8 @@
             "request": "launch",
             "module": "uvicorn",
             "cwd": "${workspaceFolder}/backend",
-            "envFile": "${workspaceFolder}/.env",
             "env": {
-                "LOG_DANSWER_MODEL_INTERACTIONS": "True",
+                "LOG_ALL_MODEL_INTERACTIONS": "True",
                 "LOG_LEVEL": "DEBUG",
                 "PYTHONUNBUFFERED": "1"
             },
@@ -59,8 +55,7 @@
                 "--reload",
                 "--port",
                 "8080"
-            ],
-            "consoleTitle": "API Server"
+            ]
         },
         {
             "name": "Indexing",
@@ -68,14 +63,12 @@
             "request": "launch",
             "program": "danswer/background/update.py",
             "cwd": "${workspaceFolder}/backend",
-            "envFile": "${workspaceFolder}/.env",
             "env": {
-                "ENABLE_MULTIPASS_INDEXING": "false",
+                "ENABLE_MINI_CHUNK": "false",
                 "LOG_LEVEL": "DEBUG",
                 "PYTHONUNBUFFERED": "1",
                 "PYTHONPATH": "."
-            },
-            "consoleTitle": "Indexing"
+            }
         },
         // Celery and all async jobs, usually would include indexing as well but this is handled separately above for dev
         {
@@ -84,17 +77,14 @@
             "request": "launch",
             "program": "scripts/dev_run_background_jobs.py",
             "cwd": "${workspaceFolder}/backend",
-            "envFile": "${workspaceFolder}/.env",
             "env": {
-                "LOG_DANSWER_MODEL_INTERACTIONS": "True",
                 "LOG_LEVEL": "DEBUG",
                 "PYTHONUNBUFFERED": "1",
                 "PYTHONPATH": "."
             },
             "args": [
                 "--no-indexing"
-            ],
-            "consoleTitle": "Background Jobs"
+            ]
         },
         // For the listner to access the Slack API,
         // DANSWER_BOT_SLACK_APP_TOKEN & DANSWER_BOT_SLACK_BOT_TOKEN need to be set in .env file located in the root of the project
@@ -110,36 +100,6 @@
                 "PYTHONUNBUFFERED": "1",
                 "PYTHONPATH": "."
             }
-        },
-        {
-            "name": "Pytest",
-            "type": "python",
-            "request": "launch",
-            "module": "pytest",
-            "cwd": "${workspaceFolder}/backend",
-            "envFile": "${workspaceFolder}/.env",
-            "env": {
-                "LOG_LEVEL": "DEBUG",
-                "PYTHONUNBUFFERED": "1",
-                "PYTHONPATH": "."
-            },
-            "args": [
-                "-v"
-                // Specify a sepcific module/test to run or provide nothing to run all tests
-                //"tests/unit/danswer/llm/answering/test_prune_and_merge.py"
-            ]
-        }
-    ],
-    "compounds": [
-        {
-            "name": "Run Danswer",
-            "configurations": [
-                "Web Server",
-                "Model Server",
-                "API Server",
-                "Indexing",
-                "Background Jobs",
-            ]
         }
     ]
-}
+}

CONTRIBUTING.md

@@ -72,10 +72,6 @@ For convenience here's a command for it:
 python -m venv .venv
 source .venv/bin/activate
 ```
-
---> Note that this virtual environment MUST NOT be set up WITHIN the danswer
-directory
-
 _For Windows, activate the virtual environment using Command Prompt:_
 ```bash
 .venv\Scripts\activate

LICENSE

@@ -1,10 +1,6 @@
-Copyright (c) 2023-present DanswerAI, Inc.
+MIT License
 
-Portions of this software are licensed as follows:
-
-* All content that resides under "ee" directories of this repository, if that directory exists, is licensed under the license defined in "backend/ee/LICENSE". Specifically all content under "backend/ee" and "web/src/app/ee" is licensed under the license defined in "backend/ee/LICENSE".
-* All third party components incorporated into the Danswer Software are licensed under the original license provided by the owner of the applicable component.
-* Content outside of the above mentioned directories or restrictions above is available under the "MIT Expat" license as defined below.
+Copyright (c) 2023 Yuhong Sun, Chris Weaver
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -11,7 +11,7 @@
 <a href="https://docs.danswer.dev/" target="_blank">
     <img src="https://img.shields.io/badge/docs-view-blue" alt="Documentation">
 </a>
-<a href="https://join.slack.com/t/danswer/shared_invite/zt-2lcmqw703-071hBuZBfNEOGUsLa5PXvQ" target="_blank">
+<a href="https://join.slack.com/t/danswer/shared_invite/zt-2afut44lv-Rw3kSWu6_OmdAXRpCv80DQ" 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">
@@ -105,25 +105,5 @@ Efficiently pulls the latest changes from:
   * Websites
   * And more ...
 
-## 📚 Editions
-
-There are two editions of Danswer:
-
-  * Danswer Community Edition (CE) is available freely under the MIT Expat license. This version has ALL the core features discussed above. This is the version of Danswer you will get if you follow the Deployment guide above.
-  * Danswer Enterprise Edition (EE) includes extra features that are primarily useful for larger organizations. Specifically, this includes:
-    * Single Sign-On (SSO), with support for both SAML and OIDC
-    * Role-based access control
-    * Document permission inheritance from connected sources
-    * Usage analytics and query history accessible to admins
-    * Whitelabeling
-    * API key authentication
-    * Encryption of secrets
-    * Any many more! Checkout [our website](https://www.danswer.ai/) for the latest.
-
-To try the Danswer 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).
-
 ## 💡 Contributing
 Looking to contribute? Please check out the [Contribution Guide](CONTRIBUTING.md) for more details.

backend/.gitignore

@@ -5,7 +5,7 @@ site_crawls/
 .ipynb_checkpoints/
 api_keys.py
 *ipynb
-.env*
+.env
 vespa-app.zip
 dynamic_config_storage/
 celerybeat-schedule*

backend/Dockerfile

@@ -1,17 +1,15 @@
 FROM python:3.11.7-slim-bookworm
 
 LABEL com.danswer.maintainer="founders@danswer.ai"
-LABEL com.danswer.description="This image is the web/frontend container of Danswer which \
-contains code for both the Community and Enterprise editions of Danswer. If you do not \
-have a contract or agreement with DanswerAI, you are not permitted to use the Enterprise \
-Edition features outside of personal development or testing purposes. Please reach out to \
-founders@danswer.ai for more information. Please visit https://github.com/danswer-ai/danswer"
+LABEL com.danswer.description="This image is for the backend of Danswer. It is MIT Licensed and \
+free for all to use. You can find it at https://hub.docker.com/r/danswer/danswer-backend. For \
+more details, visit https://github.com/danswer-ai/danswer."
 
 # Default DANSWER_VERSION, typically overriden during builds by GitHub Actions.
 ARG DANSWER_VERSION=0.3-dev
 ENV DANSWER_VERSION=${DANSWER_VERSION}
-
 RUN echo "DANSWER_VERSION: ${DANSWER_VERSION}"
+
 # Install system dependencies
 # cmake needed for psycopg (postgres)
 # libpq-dev needed for psycopg (postgres)
@@ -19,32 +17,18 @@ RUN echo "DANSWER_VERSION: ${DANSWER_VERSION}"
 # zip for Vespa step futher down
 # ca-certificates for HTTPS
 RUN apt-get update && \
-    apt-get install -y \
-        cmake \
-        curl \
-        zip \
-        ca-certificates \
-        libgnutls30=3.7.9-2+deb12u3 \
-        libblkid1=2.38.1-5+deb12u1 \
-        libmount1=2.38.1-5+deb12u1 \
-        libsmartcols1=2.38.1-5+deb12u1 \
-        libuuid1=2.38.1-5+deb12u1 \
-        libxmlsec1-dev \
-        pkg-config \
-        gcc && \
+    apt-get install -y cmake curl zip ca-certificates libgnutls30=3.7.9-2+deb12u2 \
+    libblkid1=2.38.1-5+deb12u1 libmount1=2.38.1-5+deb12u1 libsmartcols1=2.38.1-5+deb12u1 \
+    libuuid1=2.38.1-5+deb12u1 && \
     rm -rf /var/lib/apt/lists/* && \
     apt-get clean
 
 # Install Python dependencies
 # Remove py which is pulled in by retry, py is not needed and is a CVE
 COPY ./requirements/default.txt /tmp/requirements.txt
-COPY ./requirements/ee.txt /tmp/ee-requirements.txt
-RUN pip install --no-cache-dir --upgrade \
-        -r /tmp/requirements.txt \
-        -r /tmp/ee-requirements.txt && \
+RUN pip install --no-cache-dir --upgrade -r /tmp/requirements.txt && \
     pip uninstall -y py && \
-    playwright install chromium && \
-    playwright install-deps chromium && \
+    playwright install chromium && playwright install-deps chromium && \
     ln -s /usr/local/bin/supervisord /usr/bin/supervisord
 
 # Cleanup for CVEs and size reduction
@@ -52,25 +36,14 @@ RUN pip install --no-cache-dir --upgrade \
 # xserver-common and xvfb included by playwright installation but not needed after
 # perl-base is part of the base Python Debian image but not needed for Danswer functionality
 # perl-base could only be removed with --allow-remove-essential
-RUN apt-get update && \
-    apt-get remove -y --allow-remove-essential \
-        perl-base \
-        xserver-common \
-        xvfb \
-        cmake \
-        libldap-2.5-0 \
-        libxmlsec1-dev \
-        pkg-config \
-        gcc && \
-    apt-get install -y libxmlsec1-openssl && \
+RUN apt-get remove -y --allow-remove-essential perl-base xserver-common xvfb cmake \
+    libldap-2.5-0 libldap-2.5-0 && \
     apt-get autoremove -y && \
     rm -rf /var/lib/apt/lists/* && \
-    rm -f /usr/local/lib/python3.11/site-packages/tornado/test/test.key
+    rm /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')"
-
+RUN python -c "from transformers import AutoTokenizer; AutoTokenizer.from_pretrained('intfloat/e5-base-v2')"
 
 # Pre-downloading NLTK for setups with limited egress
 RUN python -c "import nltk; \
@@ -80,24 +53,12 @@ nltk.download('punkt', quiet=True);"
 
 # Set up application files
 WORKDIR /app
-
-# Enterprise Version Files
-COPY ./ee /app/ee
-COPY supervisord.conf /etc/supervisor/conf.d/supervisord.conf
-
-# Set up application files
 COPY ./danswer /app/danswer
 COPY ./shared_configs /app/shared_configs
 COPY ./alembic /app/alembic
 COPY ./alembic.ini /app/alembic.ini
 COPY supervisord.conf /usr/etc/supervisord.conf
 
-# Escape hatch
-COPY ./scripts/force_delete_connector_by_id.py /app/scripts/force_delete_connector_by_id.py
-
-# Put logo in assets
-COPY ./assets /app/assets
-
 ENV PYTHONPATH /app
 
 # Default command which does nothing

backend/Dockerfile.model_server

@@ -18,18 +18,14 @@ RUN apt-get remove -y --allow-remove-essential perl-base && \
     apt-get autoremove -y
 
 # Pre-downloading models for setups with limited egress
-# Download tokenizers, distilbert for the Danswer model
-# Download model weights
-# Run Nomic to pull in the custom architecture and have it cached locally
-RUN python -c "from transformers import AutoTokenizer; \
-AutoTokenizer.from_pretrained('distilbert-base-uncased', cache_folder='/root/.cache/temp_huggingface/hub/'); \
-AutoTokenizer.from_pretrained('mixedbread-ai/mxbai-rerank-xsmall-v1', cache_folder='/root/.cache/temp_huggingface/hub/'); \
+RUN python -c "from transformers import AutoModel, AutoTokenizer, TFDistilBertForSequenceClassification; \
 from huggingface_hub import snapshot_download; \
-snapshot_download(repo_id='danswer/hybrid-intent-token-classifier', revision='v1.0.3', cache_dir='/root/.cache/temp_huggingface/hub/'); \
-snapshot_download('nomic-ai/nomic-embed-text-v1', cache_dir='/root/.cache/temp_huggingface/hub/'); \
-snapshot_download('mixedbread-ai/mxbai-rerank-xsmall-v1', cache_dir='/root/.cache/temp_huggingface/hub/'); \
-from sentence_transformers import SentenceTransformer; \
-SentenceTransformer(model_name_or_path='nomic-ai/nomic-embed-text-v1', trust_remote_code=True, cache_folder='/root/.cache/temp_huggingface/hub/');"
+AutoTokenizer.from_pretrained('danswer/intent-model'); \
+AutoTokenizer.from_pretrained('intfloat/e5-base-v2'); \
+AutoTokenizer.from_pretrained('mixedbread-ai/mxbai-rerank-xsmall-v1'); \
+snapshot_download('danswer/intent-model'); \
+snapshot_download('intfloat/e5-base-v2'); \
+snapshot_download('mixedbread-ai/mxbai-rerank-xsmall-v1')"
 
 WORKDIR /app
 

backend/alembic/versions/0568ccf46a6b_add_thread_specific_model_selection.py

@@ -1,27 +0,0 @@
-"""Add thread specific model selection
-
-Revision ID: 0568ccf46a6b
-Revises: e209dc5a8156
-Create Date: 2024-06-19 14:25:36.376046
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "0568ccf46a6b"
-down_revision = "e209dc5a8156"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "chat_session",
-        sa.Column("current_alternate_model", sa.String(), nullable=True),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("chat_session", "current_alternate_model")

backend/alembic/versions/05c07bf07c00_add_search_doc_relevance_details.py

@@ -1,32 +0,0 @@
-"""add search doc relevance details
-
-Revision ID: 05c07bf07c00
-Revises: b896bbd0d5a7
-Create Date: 2024-07-10 17:48:15.886653
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "05c07bf07c00"
-down_revision = "b896bbd0d5a7"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "search_doc",
-        sa.Column("is_relevant", sa.Boolean(), nullable=True),
-    )
-    op.add_column(
-        "search_doc",
-        sa.Column("relevance_explanation", sa.String(), nullable=True),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("search_doc", "relevance_explanation")
-    op.drop_column("search_doc", "is_relevant")

backend/alembic/versions/08a1eda20fe1_add_earliest_indexing_to_connector.py

@@ -1,26 +0,0 @@
-"""add_indexing_start_to_connector
-
-Revision ID: 08a1eda20fe1
-Revises: 8a87bd6ec550
-Create Date: 2024-07-23 11:12:39.462397
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "08a1eda20fe1"
-down_revision = "8a87bd6ec550"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "connector", sa.Column("indexing_start", sa.DateTime(), nullable=True)
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("connector", "indexing_start")

backend/alembic/versions/213fd978c6d8_notifications.py

@@ -1,44 +0,0 @@
-"""notifications
-
-Revision ID: 213fd978c6d8
-Revises: 5fc1f54cc252
-Create Date: 2024-08-10 11:13:36.070790
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "213fd978c6d8"
-down_revision = "5fc1f54cc252"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.create_table(
-        "notification",
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column(
-            "notif_type",
-            sa.String(),
-            nullable=False,
-        ),
-        sa.Column(
-            "user_id",
-            sa.UUID(),
-            nullable=True,
-        ),
-        sa.Column("dismissed", sa.Boolean(), nullable=False),
-        sa.Column("last_shown", sa.DateTime(timezone=True), nullable=False),
-        sa.Column("first_shown", sa.DateTime(timezone=True), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["user_id"],
-            ["user.id"],
-        ),
-        sa.PrimaryKeyConstraint("id"),
-    )
-
-
-def downgrade() -> None:
-    op.drop_table("notification")

backend/alembic/versions/23957775e5f5_remove_feedback_foreignkey_constraint.py

@@ -1,86 +0,0 @@
-"""remove-feedback-foreignkey-constraint
-
-Revision ID: 23957775e5f5
-Revises: bc9771dccadf
-Create Date: 2024-06-27 16:04:51.480437
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "23957775e5f5"
-down_revision = "bc9771dccadf"
-branch_labels = None  # type: ignore
-depends_on = None  # type: ignore
-
-
-def upgrade() -> None:
-    op.drop_constraint(
-        "chat_feedback__chat_message_fk", "chat_feedback", type_="foreignkey"
-    )
-    op.create_foreign_key(
-        "chat_feedback__chat_message_fk",
-        "chat_feedback",
-        "chat_message",
-        ["chat_message_id"],
-        ["id"],
-        ondelete="SET NULL",
-    )
-    op.alter_column(
-        "chat_feedback", "chat_message_id", existing_type=sa.Integer(), nullable=True
-    )
-    op.drop_constraint(
-        "document_retrieval_feedback__chat_message_fk",
-        "document_retrieval_feedback",
-        type_="foreignkey",
-    )
-    op.create_foreign_key(
-        "document_retrieval_feedback__chat_message_fk",
-        "document_retrieval_feedback",
-        "chat_message",
-        ["chat_message_id"],
-        ["id"],
-        ondelete="SET NULL",
-    )
-    op.alter_column(
-        "document_retrieval_feedback",
-        "chat_message_id",
-        existing_type=sa.Integer(),
-        nullable=True,
-    )
-
-
-def downgrade() -> None:
-    op.alter_column(
-        "chat_feedback", "chat_message_id", existing_type=sa.Integer(), nullable=False
-    )
-    op.drop_constraint(
-        "chat_feedback__chat_message_fk", "chat_feedback", type_="foreignkey"
-    )
-    op.create_foreign_key(
-        "chat_feedback__chat_message_fk",
-        "chat_feedback",
-        "chat_message",
-        ["chat_message_id"],
-        ["id"],
-    )
-
-    op.alter_column(
-        "document_retrieval_feedback",
-        "chat_message_id",
-        existing_type=sa.Integer(),
-        nullable=False,
-    )
-    op.drop_constraint(
-        "document_retrieval_feedback__chat_message_fk",
-        "document_retrieval_feedback",
-        type_="foreignkey",
-    )
-    op.create_foreign_key(
-        "document_retrieval_feedback__chat_message_fk",
-        "document_retrieval_feedback",
-        "chat_message",
-        ["chat_message_id"],
-        ["id"],
-    )

backend/alembic/versions/27c6ecc08586_permission_framework.py

@@ -160,28 +160,12 @@ def downgrade() -> None:
             nullable=False,
         ),
     )
-
-    # Check if the constraint exists before dropping
-    conn = op.get_bind()
-    inspector = sa.inspect(conn)
-    constraints = inspector.get_foreign_keys("index_attempt")
-
-    if any(
-        constraint["name"] == "fk_index_attempt_credential_id"
-        for constraint in constraints
-    ):
-        op.drop_constraint(
-            "fk_index_attempt_credential_id", "index_attempt", type_="foreignkey"
-        )
-
-    if any(
-        constraint["name"] == "fk_index_attempt_connector_id"
-        for constraint in constraints
-    ):
-        op.drop_constraint(
-            "fk_index_attempt_connector_id", "index_attempt", type_="foreignkey"
-        )
-
+    op.drop_constraint(
+        "fk_index_attempt_credential_id", "index_attempt", type_="foreignkey"
+    )
+    op.drop_constraint(
+        "fk_index_attempt_connector_id", "index_attempt", type_="foreignkey"
+    )
     op.drop_column("index_attempt", "credential_id")
     op.drop_column("index_attempt", "connector_id")
     op.drop_table("connector_credential_pair")

backend/alembic/versions/325975216eb3_add_icon_color_and_icon_shape_to_persona.py

@@ -1,70 +0,0 @@
-"""Add icon_color and icon_shape to Persona
-
-Revision ID: 325975216eb3
-Revises: 91ffac7e65b3
-Create Date: 2024-07-24 21:29:31.784562
-
-"""
-import random
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.sql import table, column, select
-
-# revision identifiers, used by Alembic.
-revision = "325975216eb3"
-down_revision = "91ffac7e65b3"
-branch_labels: None = None
-depends_on: None = None
-
-
-colorOptions = [
-    "#FF6FBF",
-    "#6FB1FF",
-    "#B76FFF",
-    "#FFB56F",
-    "#6FFF8D",
-    "#FF6F6F",
-    "#6FFFFF",
-]
-
-
-# Function to generate a random shape ensuring at least 3 of the middle 4 squares are filled
-def generate_random_shape() -> int:
-    center_squares = [12, 10, 6, 14, 13, 11, 7, 15]
-    center_fill = random.choice(center_squares)
-    remaining_squares = [i for i in range(16) if not (center_fill & (1 << i))]
-    random.shuffle(remaining_squares)
-    for i in range(10 - bin(center_fill).count("1")):
-        center_fill |= 1 << remaining_squares[i]
-    return center_fill
-
-
-def upgrade() -> None:
-    op.add_column("persona", sa.Column("icon_color", sa.String(), nullable=True))
-    op.add_column("persona", sa.Column("icon_shape", sa.Integer(), nullable=True))
-    op.add_column("persona", sa.Column("uploaded_image_id", sa.String(), nullable=True))
-
-    persona = table(
-        "persona",
-        column("id", sa.Integer),
-        column("icon_color", sa.String),
-        column("icon_shape", sa.Integer),
-    )
-
-    conn = op.get_bind()
-    personas = conn.execute(select(persona.c.id))
-
-    for persona_id in personas:
-        random_color = random.choice(colorOptions)
-        random_shape = generate_random_shape()
-        conn.execute(
-            persona.update()
-            .where(persona.c.id == persona_id[0])
-            .values(icon_color=random_color, icon_shape=random_shape)
-        )
-
-
-def downgrade() -> None:
-    op.drop_column("persona", "icon_shape")
-    op.drop_column("persona", "uploaded_image_id")
-    op.drop_column("persona", "icon_color")

backend/alembic/versions/3879338f8ba1_add_tool_table.py

@@ -11,8 +11,8 @@ import sqlalchemy as sa
 # revision identifiers, used by Alembic.
 revision = "3879338f8ba1"
 down_revision = "f1c6478c3fd8"
-branch_labels: None = None
-depends_on: None = None
+branch_labels = None
+depends_on = None
 
 
 def upgrade() -> None:

backend/alembic/versions/3a7802814195_add_alternate_assistant_to_chat_message.py

@@ -1,35 +0,0 @@
-"""add alternate assistant to chat message
-
-Revision ID: 3a7802814195
-Revises: 23957775e5f5
-Create Date: 2024-06-05 11:18:49.966333
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "3a7802814195"
-down_revision = "23957775e5f5"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "chat_message", sa.Column("alternate_assistant_id", sa.Integer(), nullable=True)
-    )
-    op.create_foreign_key(
-        "fk_chat_message_persona",
-        "chat_message",
-        "persona",
-        ["alternate_assistant_id"],
-        ["id"],
-    )
-
-
-def downgrade() -> None:
-    op.drop_constraint("fk_chat_message_persona", "chat_message", type_="foreignkey")
-    op.drop_column("chat_message", "alternate_assistant_id")

backend/alembic/versions/43cbbb3f5e6a_rename_index_origin_to_index_recursively.py

@@ -1,42 +0,0 @@
-"""Rename index_origin to index_recursively
-
-Revision ID: 1d6ad76d1f37
-Revises: e1392f05e840
-Create Date: 2024-08-01 12:38:54.466081
-
-"""
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision = "1d6ad76d1f37"
-down_revision = "e1392f05e840"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.execute(
-        """
-        UPDATE connector
-        SET connector_specific_config = jsonb_set(
-            connector_specific_config,
-            '{index_recursively}',
-            'true'::jsonb
-        ) - 'index_origin'
-        WHERE connector_specific_config ? 'index_origin'
-    """
-    )
-
-
-def downgrade() -> None:
-    op.execute(
-        """
-        UPDATE connector
-        SET connector_specific_config = jsonb_set(
-            connector_specific_config,
-            '{index_origin}',
-            connector_specific_config->'index_recursively'
-        ) - 'index_recursively'
-        WHERE connector_specific_config ? 'index_recursively'
-    """
-    )

backend/alembic/versions/44f856ae2a4a_add_cloud_embedding_model.py

@@ -1,65 +0,0 @@
-"""add cloud embedding model and update embedding_model
-
-Revision ID: 44f856ae2a4a
-Revises: d716b0791ddd
-Create Date: 2024-06-28 20:01:05.927647
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "44f856ae2a4a"
-down_revision = "d716b0791ddd"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    # Create embedding_provider table
-    op.create_table(
-        "embedding_provider",
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column("name", sa.String(), nullable=False),
-        sa.Column("api_key", sa.LargeBinary(), nullable=True),
-        sa.Column("default_model_id", sa.Integer(), nullable=True),
-        sa.PrimaryKeyConstraint("id"),
-        sa.UniqueConstraint("name"),
-    )
-
-    # Add cloud_provider_id to embedding_model table
-    op.add_column(
-        "embedding_model", sa.Column("cloud_provider_id", sa.Integer(), nullable=True)
-    )
-
-    # Add foreign key constraints
-    op.create_foreign_key(
-        "fk_embedding_model_cloud_provider",
-        "embedding_model",
-        "embedding_provider",
-        ["cloud_provider_id"],
-        ["id"],
-    )
-    op.create_foreign_key(
-        "fk_embedding_provider_default_model",
-        "embedding_provider",
-        "embedding_model",
-        ["default_model_id"],
-        ["id"],
-    )
-
-
-def downgrade() -> None:
-    # Remove foreign key constraints
-    op.drop_constraint(
-        "fk_embedding_model_cloud_provider", "embedding_model", type_="foreignkey"
-    )
-    op.drop_constraint(
-        "fk_embedding_provider_default_model", "embedding_provider", type_="foreignkey"
-    )
-
-    # Remove cloud_provider_id column
-    op.drop_column("embedding_model", "cloud_provider_id")
-
-    # Drop embedding_provider table
-    op.drop_table("embedding_provider")

backend/alembic/versions/4505fd7302e1_added_is_internet_to_dbdoc.py

@@ -1,23 +0,0 @@
-"""added is_internet to DBDoc
-
-Revision ID: 4505fd7302e1
-Revises: c18cdf4b497e
-Create Date: 2024-06-18 20:46:09.095034
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "4505fd7302e1"
-down_revision = "c18cdf4b497e"
-
-
-def upgrade() -> None:
-    op.add_column("search_doc", sa.Column("is_internet", sa.Boolean(), nullable=True))
-    op.add_column("tool", sa.Column("display_name", sa.String(), nullable=True))
-
-
-def downgrade() -> None:
-    op.drop_column("tool", "display_name")
-    op.drop_column("search_doc", "is_internet")

backend/alembic/versions/473a1a7ca408_add_display_model_names_to_llm_provider.py

@@ -1,49 +0,0 @@
-"""Add display_model_names to llm_provider
-
-Revision ID: 473a1a7ca408
-Revises: 325975216eb3
-Create Date: 2024-07-25 14:31:02.002917
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision = "473a1a7ca408"
-down_revision = "325975216eb3"
-branch_labels: None = None
-depends_on: None = None
-
-default_models_by_provider = {
-    "openai": ["gpt-4", "gpt-4o", "gpt-4o-mini"],
-    "bedrock": [
-        "meta.llama3-1-70b-instruct-v1:0",
-        "meta.llama3-1-8b-instruct-v1:0",
-        "anthropic.claude-3-opus-20240229-v1:0",
-        "mistral.mistral-large-2402-v1:0",
-        "anthropic.claude-3-5-sonnet-20240620-v1:0",
-    ],
-    "anthropic": ["claude-3-opus-20240229", "claude-3-5-sonnet-20240620"],
-}
-
-
-def upgrade() -> None:
-    op.add_column(
-        "llm_provider",
-        sa.Column("display_model_names", postgresql.ARRAY(sa.String()), nullable=True),
-    )
-
-    connection = op.get_bind()
-    for provider, models in default_models_by_provider.items():
-        connection.execute(
-            sa.text(
-                "UPDATE llm_provider SET display_model_names = :models WHERE provider = :provider"
-            ),
-            {"models": models, "provider": provider},
-        )
-
-
-def downgrade() -> None:
-    op.drop_column("llm_provider", "display_model_names")

backend/alembic/versions/48d14957fe80_add_support_for_custom_tools.py

@@ -1,61 +0,0 @@
-"""Add support for custom tools
-
-Revision ID: 48d14957fe80
-Revises: b85f02ec1308
-Create Date: 2024-06-09 14:58:19.946509
-
-"""
-from alembic import op
-import fastapi_users_db_sqlalchemy
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision = "48d14957fe80"
-down_revision = "b85f02ec1308"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "tool",
-        sa.Column(
-            "openapi_schema",
-            postgresql.JSONB(astext_type=sa.Text()),
-            nullable=True,
-        ),
-    )
-    op.add_column(
-        "tool",
-        sa.Column(
-            "user_id",
-            fastapi_users_db_sqlalchemy.generics.GUID(),
-            nullable=True,
-        ),
-    )
-    op.create_foreign_key("tool_user_fk", "tool", "user", ["user_id"], ["id"])
-
-    op.create_table(
-        "tool_call",
-        sa.Column("id", sa.Integer(), primary_key=True),
-        sa.Column("tool_id", sa.Integer(), nullable=False),
-        sa.Column("tool_name", sa.String(), nullable=False),
-        sa.Column(
-            "tool_arguments", postgresql.JSONB(astext_type=sa.Text()), nullable=False
-        ),
-        sa.Column(
-            "tool_result", postgresql.JSONB(astext_type=sa.Text()), nullable=False
-        ),
-        sa.Column(
-            "message_id", sa.Integer(), sa.ForeignKey("chat_message.id"), nullable=False
-        ),
-    )
-
-
-def downgrade() -> None:
-    op.drop_table("tool_call")
-
-    op.drop_constraint("tool_user_fk", "tool", type_="foreignkey")
-    op.drop_column("tool", "user_id")
-    op.drop_column("tool", "openapi_schema")

backend/alembic/versions/4a951134c801_moved_status_to_connector_credential_.py

@@ -1,80 +0,0 @@
-"""Moved status to connector credential pair
-
-Revision ID: 4a951134c801
-Revises: 7477a5f5d728
-Create Date: 2024-08-10 19:20:34.527559
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "4a951134c801"
-down_revision = "7477a5f5d728"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "connector_credential_pair",
-        sa.Column(
-            "status",
-            sa.Enum(
-                "ACTIVE",
-                "PAUSED",
-                "DELETING",
-                name="connectorcredentialpairstatus",
-                native_enum=False,
-            ),
-            nullable=True,
-        ),
-    )
-
-    # Update status of connector_credential_pair based on connector's disabled status
-    op.execute(
-        """
-        UPDATE connector_credential_pair
-        SET status = CASE
-            WHEN (
-                SELECT disabled
-                FROM connector
-                WHERE connector.id = connector_credential_pair.connector_id
-            ) = FALSE THEN 'ACTIVE'
-            ELSE 'PAUSED'
-        END
-        """
-    )
-
-    # Make the status column not nullable after setting values
-    op.alter_column("connector_credential_pair", "status", nullable=False)
-
-    op.drop_column("connector", "disabled")
-
-
-def downgrade() -> None:
-    op.add_column(
-        "connector",
-        sa.Column("disabled", sa.BOOLEAN(), autoincrement=False, nullable=True),
-    )
-
-    # Update disabled status of connector based on connector_credential_pair's status
-    op.execute(
-        """
-        UPDATE connector
-        SET disabled = CASE
-            WHEN EXISTS (
-                SELECT 1
-                FROM connector_credential_pair
-                WHERE connector_credential_pair.connector_id = connector.id
-                AND connector_credential_pair.status = 'ACTIVE'
-            ) THEN FALSE
-            ELSE TRUE
-        END
-        """
-    )
-
-    # Make the disabled column not nullable after setting values
-    op.alter_column("connector", "disabled", nullable=False)
-
-    op.drop_column("connector_credential_pair", "status")

backend/alembic/versions/4ea2c93919c1_add_type_to_credentials.py

@@ -1,72 +0,0 @@
-"""Add type to credentials
-
-Revision ID: 4ea2c93919c1
-Revises: 473a1a7ca408
-Create Date: 2024-07-18 13:07:13.655895
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "4ea2c93919c1"
-down_revision = "473a1a7ca408"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    # Add the new 'source' column to the 'credential' table
-    op.add_column(
-        "credential",
-        sa.Column(
-            "source",
-            sa.String(length=100),  # Use String instead of Enum
-            nullable=True,  # Initially allow NULL values
-        ),
-    )
-    op.add_column(
-        "credential",
-        sa.Column(
-            "name",
-            sa.String(),
-            nullable=True,
-        ),
-    )
-
-    # Create a temporary table that maps each credential to a single connector source.
-    # This is needed because a credential can be associated with multiple connectors,
-    # but we want to assign a single source to each credential.
-    # We use DISTINCT ON to ensure we only get one row per credential_id.
-    op.execute(
-        """
-    CREATE TEMPORARY TABLE temp_connector_credential AS
-    SELECT DISTINCT ON (cc.credential_id)
-        cc.credential_id,
-        c.source AS connector_source
-    FROM connector_credential_pair cc
-    JOIN connector c ON cc.connector_id = c.id
-    """
-    )
-
-    # Update the 'source' column in the 'credential' table
-    op.execute(
-        """
-    UPDATE credential cred
-    SET source = COALESCE(
-        (SELECT connector_source
-         FROM temp_connector_credential temp
-         WHERE cred.id = temp.credential_id),
-        'NOT_APPLICABLE'
-    )
-    """
-    )
-    # If no exception was raised, alter the column
-    op.alter_column("credential", "source", nullable=True)  # TODO modify
-    # # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    op.drop_column("credential", "source")
-    op.drop_column("credential", "name")

backend/alembic/versions/5fc1f54cc252_hybrid_enum.py

@@ -1,25 +0,0 @@
-"""hybrid-enum
-
-Revision ID: 5fc1f54cc252
-Revises: 1d6ad76d1f37
-Create Date: 2024-08-06 15:35:40.278485
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "5fc1f54cc252"
-down_revision = "1d6ad76d1f37"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.drop_column("persona", "search_type")
-
-
-def downgrade() -> None:
-    op.add_column("persona", sa.Column("search_type", sa.String(), nullable=True))
-    op.execute("UPDATE persona SET search_type = 'SEMANTIC'")
-    op.alter_column("persona", "search_type", nullable=False)

backend/alembic/versions/70f00c45c0f2_more_descriptive_filestore.py

@@ -1,68 +0,0 @@
-"""More Descriptive Filestore
-
-Revision ID: 70f00c45c0f2
-Revises: 3879338f8ba1
-Create Date: 2024-05-17 17:51:41.926893
-
-"""
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision = "70f00c45c0f2"
-down_revision = "3879338f8ba1"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.add_column("file_store", sa.Column("display_name", sa.String(), nullable=True))
-    op.add_column(
-        "file_store",
-        sa.Column(
-            "file_origin",
-            sa.String(),
-            nullable=False,
-            server_default="connector",  # Default to connector
-        ),
-    )
-    op.add_column(
-        "file_store",
-        sa.Column(
-            "file_type", sa.String(), nullable=False, server_default="text/plain"
-        ),
-    )
-    op.add_column(
-        "file_store",
-        sa.Column(
-            "file_metadata",
-            postgresql.JSONB(astext_type=sa.Text()),
-            nullable=True,
-        ),
-    )
-
-    op.execute(
-        """
-        UPDATE file_store
-        SET file_origin = CASE
-            WHEN file_name LIKE 'chat__%' THEN 'chat_upload'
-            ELSE 'connector'
-        END,
-        file_name = CASE
-            WHEN file_name LIKE 'chat__%' THEN SUBSTR(file_name, 7)
-            ELSE file_name
-        END,
-        file_type = CASE
-            WHEN file_name LIKE 'chat__%' THEN 'image/png'
-            ELSE 'text/plain'
-        END
-    """
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("file_store", "file_metadata")
-    op.drop_column("file_store", "file_type")
-    op.drop_column("file_store", "file_origin")
-    op.drop_column("file_store", "display_name")

backend/alembic/versions/7477a5f5d728_added_model_defaults_for_users.py

@@ -1,24 +0,0 @@
-"""Added model defaults for users
-
-Revision ID: 7477a5f5d728
-Revises: 213fd978c6d8
-Create Date: 2024-08-04 19:00:04.512634
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "7477a5f5d728"
-down_revision = "213fd978c6d8"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.add_column("user", sa.Column("default_model", sa.Text(), nullable=True))
-
-
-def downgrade() -> None:
-    op.drop_column("user", "default_model")

backend/alembic/versions/76b60d407dfb_cc_pair_name_not_unique.py

@@ -28,9 +28,5 @@ def upgrade() -> None:
 
 
 def downgrade() -> None:
-    op.create_unique_constraint(
-        "connector_credential_pair__name__key", "connector_credential_pair", ["name"]
-    )
-    op.alter_column(
-        "connector_credential_pair", "name", existing_type=sa.String(), nullable=True
-    )
+    # This wasn't really required by the code either, no good reason to make it unique again
+    pass

backend/alembic/versions/795b20b85b4b_add_llm_group_permissions_control.py

@@ -1,41 +0,0 @@
-"""add_llm_group_permissions_control
-
-Revision ID: 795b20b85b4b
-Revises: 05c07bf07c00
-Create Date: 2024-07-19 11:54:35.701558
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-
-revision = "795b20b85b4b"
-down_revision = "05c07bf07c00"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.create_table(
-        "llm_provider__user_group",
-        sa.Column("llm_provider_id", sa.Integer(), nullable=False),
-        sa.Column("user_group_id", sa.Integer(), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["llm_provider_id"],
-            ["llm_provider.id"],
-        ),
-        sa.ForeignKeyConstraint(
-            ["user_group_id"],
-            ["user_group.id"],
-        ),
-        sa.PrimaryKeyConstraint("llm_provider_id", "user_group_id"),
-    )
-    op.add_column(
-        "llm_provider",
-        sa.Column("is_public", sa.Boolean(), nullable=False, server_default="true"),
-    )
-
-
-def downgrade() -> None:
-    op.drop_table("llm_provider__user_group")
-    op.drop_column("llm_provider", "is_public")

backend/alembic/versions/7aea705850d5_added_slack_auto_filter.py

@@ -1,35 +0,0 @@
-"""added slack_auto_filter
-
-Revision ID: 7aea705850d5
-Revises: 4505fd7302e1
-Create Date: 2024-07-10 11:01:23.581015
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-revision = "7aea705850d5"
-down_revision = "4505fd7302e1"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "slack_bot_config",
-        sa.Column("enable_auto_filters", sa.Boolean(), nullable=True),
-    )
-    op.execute(
-        "UPDATE slack_bot_config SET enable_auto_filters = FALSE WHERE enable_auto_filters IS NULL"
-    )
-    op.alter_column(
-        "slack_bot_config",
-        "enable_auto_filters",
-        existing_type=sa.Boolean(),
-        nullable=False,
-        server_default=sa.false(),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("slack_bot_config", "enable_auto_filters")

backend/alembic/versions/8a87bd6ec550_associate_index_attempts_with_ccpair.py

@@ -1,103 +0,0 @@
-"""associate index attempts with ccpair
-
-Revision ID: 8a87bd6ec550
-Revises: 4ea2c93919c1
-Create Date: 2024-07-22 15:15:52.558451
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "8a87bd6ec550"
-down_revision = "4ea2c93919c1"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    # Add the new connector_credential_pair_id column
-    op.add_column(
-        "index_attempt",
-        sa.Column("connector_credential_pair_id", sa.Integer(), nullable=True),
-    )
-
-    # Create a foreign key constraint to the connector_credential_pair table
-    op.create_foreign_key(
-        "fk_index_attempt_connector_credential_pair_id",
-        "index_attempt",
-        "connector_credential_pair",
-        ["connector_credential_pair_id"],
-        ["id"],
-    )
-
-    # Populate the new connector_credential_pair_id column using existing connector_id and credential_id
-    op.execute(
-        """
-        UPDATE index_attempt ia
-        SET connector_credential_pair_id =
-            CASE
-                WHEN ia.credential_id IS NULL THEN
-                    (SELECT id FROM connector_credential_pair
-                     WHERE connector_id = ia.connector_id
-                     LIMIT 1)
-                ELSE
-                    (SELECT id FROM connector_credential_pair
-                     WHERE connector_id = ia.connector_id
-                     AND credential_id = ia.credential_id)
-            END
-        WHERE ia.connector_id IS NOT NULL
-        """
-    )
-
-    # Make the new connector_credential_pair_id column non-nullable
-    op.alter_column("index_attempt", "connector_credential_pair_id", nullable=False)
-
-    # Drop the old connector_id and credential_id columns
-    op.drop_column("index_attempt", "connector_id")
-    op.drop_column("index_attempt", "credential_id")
-
-    # Update the index to use connector_credential_pair_id
-    op.create_index(
-        "ix_index_attempt_latest_for_connector_credential_pair",
-        "index_attempt",
-        ["connector_credential_pair_id", "time_created"],
-    )
-
-
-def downgrade() -> None:
-    # Add back the old connector_id and credential_id columns
-    op.add_column(
-        "index_attempt", sa.Column("connector_id", sa.Integer(), nullable=True)
-    )
-    op.add_column(
-        "index_attempt", sa.Column("credential_id", sa.Integer(), nullable=True)
-    )
-
-    # Populate the old connector_id and credential_id columns using the connector_credential_pair_id
-    op.execute(
-        """
-        UPDATE index_attempt ia
-        SET connector_id = ccp.connector_id, credential_id = ccp.credential_id
-        FROM connector_credential_pair ccp
-        WHERE ia.connector_credential_pair_id = ccp.id
-        """
-    )
-
-    # Make the old connector_id and credential_id columns non-nullable
-    op.alter_column("index_attempt", "connector_id", nullable=False)
-    op.alter_column("index_attempt", "credential_id", nullable=False)
-
-    # Drop the new connector_credential_pair_id column
-    op.drop_constraint(
-        "fk_index_attempt_connector_credential_pair_id",
-        "index_attempt",
-        type_="foreignkey",
-    )
-    op.drop_column("index_attempt", "connector_credential_pair_id")
-
-    op.create_index(
-        "ix_index_attempt_latest_for_connector_credential_pair",
-        "index_attempt",
-        ["connector_id", "credential_id", "time_created"],
-    )

backend/alembic/versions/91ffac7e65b3_add_expiry_time.py

@@ -1,26 +0,0 @@
-"""add expiry time
-
-Revision ID: 91ffac7e65b3
-Revises: bc9771dccadf
-Create Date: 2024-06-24 09:39:56.462242
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "91ffac7e65b3"
-down_revision = "795b20b85b4b"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "user", sa.Column("oidc_expiry", sa.DateTime(timezone=True), nullable=True)
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("user", "oidc_expiry")

backend/alembic/versions/a3bfd0d64902_add_chosen_assistants_to_user_table.py

@@ -1,27 +0,0 @@
-"""Add chosen_assistants to User table
-
-Revision ID: a3bfd0d64902
-Revises: ec85f2b3c544
-Create Date: 2024-05-26 17:22:24.834741
-
-"""
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision = "a3bfd0d64902"
-down_revision = "ec85f2b3c544"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "user",
-        sa.Column("chosen_assistants", postgresql.ARRAY(sa.Integer()), nullable=True),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("user", "chosen_assistants")

backend/alembic/versions/b082fec533f0_make_last_attempt_status_nullable.py

@@ -16,6 +16,7 @@ depends_on: None = None
 
 
 def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
     op.alter_column(
         "connector_credential_pair",
         "last_attempt_status",
@@ -28,9 +29,11 @@ def upgrade() -> None:
         ),
         nullable=True,
     )
+    # ### end Alembic commands ###
 
 
 def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
     op.alter_column(
         "connector_credential_pair",
         "last_attempt_status",
@@ -43,3 +46,4 @@ def downgrade() -> None:
         ),
         nullable=False,
     )
+    # ### end Alembic commands ###

backend/alembic/versions/b85f02ec1308_fix_file_type_migration.py

@@ -1,28 +0,0 @@
-"""fix-file-type-migration
-
-Revision ID: b85f02ec1308
-Revises: a3bfd0d64902
-Create Date: 2024-05-31 18:09:26.658164
-
-"""
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision = "b85f02ec1308"
-down_revision = "a3bfd0d64902"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.execute(
-        """
-        UPDATE file_store
-        SET file_origin = UPPER(file_origin)
-    """
-    )
-
-
-def downgrade() -> None:
-    # Let's not break anything on purpose :)
-    pass

backend/alembic/versions/b896bbd0d5a7_backfill_is_internet_data_to_false.py

@@ -1,23 +0,0 @@
-"""backfill is_internet data to False
-
-Revision ID: b896bbd0d5a7
-Revises: 44f856ae2a4a
-Create Date: 2024-07-16 15:21:05.718571
-
-"""
-from alembic import op
-
-
-# revision identifiers, used by Alembic.
-revision = "b896bbd0d5a7"
-down_revision = "44f856ae2a4a"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.execute("UPDATE search_doc SET is_internet = FALSE WHERE is_internet IS NULL")
-
-
-def downgrade() -> None:
-    pass

backend/alembic/versions/bc9771dccadf_create_usage_reports_table.py

@@ -1,51 +0,0 @@
-"""create usage reports table
-
-Revision ID: bc9771dccadf
-Revises: 0568ccf46a6b
-Create Date: 2024-06-18 10:04:26.800282
-
-"""
-from alembic import op
-import sqlalchemy as sa
-import fastapi_users_db_sqlalchemy
-
-# revision identifiers, used by Alembic.
-revision = "bc9771dccadf"
-down_revision = "0568ccf46a6b"
-
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.create_table(
-        "usage_reports",
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column("report_name", sa.String(), nullable=False),
-        sa.Column(
-            "requestor_user_id",
-            fastapi_users_db_sqlalchemy.generics.GUID(),
-            nullable=True,
-        ),
-        sa.Column(
-            "time_created",
-            sa.DateTime(timezone=True),
-            server_default=sa.text("now()"),
-            nullable=False,
-        ),
-        sa.Column("period_from", sa.DateTime(timezone=True), nullable=True),
-        sa.Column("period_to", sa.DateTime(timezone=True), nullable=True),
-        sa.ForeignKeyConstraint(
-            ["report_name"],
-            ["file_store.file_name"],
-        ),
-        sa.ForeignKeyConstraint(
-            ["requestor_user_id"],
-            ["user.id"],
-        ),
-        sa.PrimaryKeyConstraint("id"),
-    )
-
-
-def downgrade() -> None:
-    op.drop_table("usage_reports")

backend/alembic/versions/c18cdf4b497e_add_standard_answer_tables.py

@@ -1,75 +0,0 @@
-"""Add standard_answer tables
-
-Revision ID: c18cdf4b497e
-Revises: 3a7802814195
-Create Date: 2024-06-06 15:15:02.000648
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "c18cdf4b497e"
-down_revision = "3a7802814195"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.create_table(
-        "standard_answer",
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column("keyword", sa.String(), nullable=False),
-        sa.Column("answer", sa.String(), nullable=False),
-        sa.Column("active", sa.Boolean(), nullable=False),
-        sa.PrimaryKeyConstraint("id"),
-        sa.UniqueConstraint("keyword"),
-    )
-    op.create_table(
-        "standard_answer_category",
-        sa.Column("id", sa.Integer(), nullable=False),
-        sa.Column("name", sa.String(), nullable=False),
-        sa.PrimaryKeyConstraint("id"),
-        sa.UniqueConstraint("name"),
-    )
-    op.create_table(
-        "standard_answer__standard_answer_category",
-        sa.Column("standard_answer_id", sa.Integer(), nullable=False),
-        sa.Column("standard_answer_category_id", sa.Integer(), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["standard_answer_category_id"],
-            ["standard_answer_category.id"],
-        ),
-        sa.ForeignKeyConstraint(
-            ["standard_answer_id"],
-            ["standard_answer.id"],
-        ),
-        sa.PrimaryKeyConstraint("standard_answer_id", "standard_answer_category_id"),
-    )
-    op.create_table(
-        "slack_bot_config__standard_answer_category",
-        sa.Column("slack_bot_config_id", sa.Integer(), nullable=False),
-        sa.Column("standard_answer_category_id", sa.Integer(), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["slack_bot_config_id"],
-            ["slack_bot_config.id"],
-        ),
-        sa.ForeignKeyConstraint(
-            ["standard_answer_category_id"],
-            ["standard_answer_category.id"],
-        ),
-        sa.PrimaryKeyConstraint("slack_bot_config_id", "standard_answer_category_id"),
-    )
-
-    op.add_column(
-        "chat_session", sa.Column("slack_thread_id", sa.String(), nullable=True)
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("chat_session", "slack_thread_id")
-
-    op.drop_table("slack_bot_config__standard_answer_category")
-    op.drop_table("standard_answer__standard_answer_category")
-    op.drop_table("standard_answer_category")
-    op.drop_table("standard_answer")

backend/alembic/versions/d5645c915d0e_remove_deletion_attempt_table.py

@@ -19,9 +19,6 @@ depends_on: None = None
 def upgrade() -> None:
     op.drop_table("deletion_attempt")
 
-    # Remove the DeletionStatus enum
-    op.execute("DROP TYPE IF EXISTS deletionstatus;")
-
 
 def downgrade() -> None:
     op.create_table(

backend/alembic/versions/d716b0791ddd_combined_slack_id_fields.py

@@ -1,45 +0,0 @@
-"""combined slack id fields
-
-Revision ID: d716b0791ddd
-Revises: 7aea705850d5
-Create Date: 2024-07-10 17:57:45.630550
-
-"""
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision = "d716b0791ddd"
-down_revision = "7aea705850d5"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.execute(
-        """
-    UPDATE slack_bot_config
-    SET channel_config = jsonb_set(
-        channel_config,
-        '{respond_member_group_list}',
-        coalesce(channel_config->'respond_team_member_list', '[]'::jsonb) ||
-        coalesce(channel_config->'respond_slack_group_list', '[]'::jsonb)
-    ) - 'respond_team_member_list' - 'respond_slack_group_list'
-    """
-    )
-
-
-def downgrade() -> None:
-    op.execute(
-        """
-    UPDATE slack_bot_config
-    SET channel_config = jsonb_set(
-        jsonb_set(
-            channel_config - 'respond_member_group_list',
-            '{respond_team_member_list}',
-            '[]'::jsonb
-        ),
-        '{respond_slack_group_list}',
-        '[]'::jsonb
-    )
-    """
-    )

backend/alembic/versions/dbaa756c2ccf_embedding_models.py

@@ -136,4 +136,4 @@ def downgrade() -> None:
     )
     op.drop_column("index_attempt", "embedding_model_id")
     op.drop_table("embedding_model")
-    op.execute("DROP TYPE IF EXISTS indexmodelstatus;")
+    op.execute("DROP TYPE indexmodelstatus;")

backend/alembic/versions/e1392f05e840_added_input_prompts.py

@@ -1,58 +0,0 @@
-"""Added input prompts
-
-Revision ID: e1392f05e840
-Revises: 08a1eda20fe1
-Create Date: 2024-07-13 19:09:22.556224
-
-"""
-
-import fastapi_users_db_sqlalchemy
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "e1392f05e840"
-down_revision = "08a1eda20fe1"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.create_table(
-        "inputprompt",
-        sa.Column("id", sa.Integer(), autoincrement=True, nullable=False),
-        sa.Column("prompt", sa.String(), nullable=False),
-        sa.Column("content", sa.String(), nullable=False),
-        sa.Column("active", sa.Boolean(), nullable=False),
-        sa.Column("is_public", sa.Boolean(), nullable=False),
-        sa.Column(
-            "user_id",
-            fastapi_users_db_sqlalchemy.generics.GUID(),
-            nullable=True,
-        ),
-        sa.ForeignKeyConstraint(
-            ["user_id"],
-            ["user.id"],
-        ),
-        sa.PrimaryKeyConstraint("id"),
-    )
-    op.create_table(
-        "inputprompt__user",
-        sa.Column("input_prompt_id", sa.Integer(), nullable=False),
-        sa.Column("user_id", sa.Integer(), nullable=False),
-        sa.ForeignKeyConstraint(
-            ["input_prompt_id"],
-            ["inputprompt.id"],
-        ),
-        sa.ForeignKeyConstraint(
-            ["user_id"],
-            ["inputprompt.id"],
-        ),
-        sa.PrimaryKeyConstraint("input_prompt_id", "user_id"),
-    )
-
-
-def downgrade() -> None:
-    op.drop_table("inputprompt__user")
-    op.drop_table("inputprompt")

backend/alembic/versions/e209dc5a8156_added_prune_frequency.py

@@ -1,22 +0,0 @@
-"""added-prune-frequency
-
-Revision ID: e209dc5a8156
-Revises: 48d14957fe80
-Create Date: 2024-06-16 16:02:35.273231
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-revision = "e209dc5a8156"
-down_revision = "48d14957fe80"
-branch_labels = None  # type: ignore
-depends_on = None  # type: ignore
-
-
-def upgrade() -> None:
-    op.add_column("connector", sa.Column("prune_freq", sa.Integer(), nullable=True))
-
-
-def downgrade() -> None:
-    op.drop_column("connector", "prune_freq")

backend/alembic/versions/eb690a089310_migrate_tool_calls.py

@@ -1,59 +0,0 @@
-"""migrate tool calls
-
-Revision ID: eb690a089310
-Revises: ee3f4b47fad5
-Create Date: 2024-08-04 17:07:47.533051
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "eb690a089310"
-down_revision = "ee3f4b47fad5"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Create the new column
-    op.add_column(
-        "chat_message", sa.Column("tool_call_id", sa.Integer(), nullable=True)
-    )
-    op.create_foreign_key(
-        "fk_chat_message_tool_call",
-        "chat_message",
-        "tool_call",
-        ["tool_call_id"],
-        ["id"],
-    )
-
-    # Migrate existing data
-    op.execute(
-        "UPDATE chat_message SET tool_call_id = (SELECT id FROM tool_call WHERE tool_call.message_id = chat_message.id LIMIT 1)"
-    )
-
-    # Drop the old relationship
-    op.drop_constraint("tool_call_message_id_fkey", "tool_call", type_="foreignkey")
-    op.drop_column("tool_call", "message_id")
-
-
-def downgrade() -> None:
-    # Add back the old column
-    op.add_column(
-        "tool_call",
-        sa.Column("message_id", sa.INTEGER(), autoincrement=False, nullable=True),
-    )
-    op.create_foreign_key(
-        "tool_call_message_id_fkey", "tool_call", "chat_message", ["message_id"], ["id"]
-    )
-
-    # Migrate data back
-    op.execute(
-        "UPDATE tool_call SET message_id = (SELECT id FROM chat_message WHERE chat_message.tool_call_id = tool_call.id)"
-    )
-
-    # Drop the new column
-    op.drop_constraint("fk_chat_message_tool_call", "chat_message", type_="foreignkey")
-    op.drop_column("chat_message", "tool_call_id")

backend/alembic/versions/ec85f2b3c544_remove_last_attempt_status_from_cc_pair.py

@@ -1,31 +0,0 @@
-"""Remove Last Attempt Status from CC Pair
-
-Revision ID: ec85f2b3c544
-Revises: 3879338f8ba1
-Create Date: 2024-05-23 21:39:46.126010
-
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers, used by Alembic.
-revision = "ec85f2b3c544"
-down_revision = "70f00c45c0f2"
-branch_labels: None = None
-depends_on: None = None
-
-
-def upgrade() -> None:
-    op.drop_column("connector_credential_pair", "last_attempt_status")
-
-
-def downgrade() -> None:
-    op.add_column(
-        "connector_credential_pair",
-        sa.Column(
-            "last_attempt_status",
-            sa.VARCHAR(),
-            autoincrement=False,
-            nullable=True,
-        ),
-    )

backend/alembic/versions/ee3f4b47fad5_added_alternate_model_to_chat_message.py

@@ -1,28 +0,0 @@
-"""Added alternate model to chat message
-
-Revision ID: ee3f4b47fad5
-Revises: 4a951134c801
-Create Date: 2024-08-12 00:11:50.915845
-
-"""
-
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision = "ee3f4b47fad5"
-down_revision = "4a951134c801"
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    op.add_column(
-        "chat_message",
-        sa.Column("alternate_model", sa.String(length=255), nullable=True),
-    )
-
-
-def downgrade() -> None:
-    op.drop_column("chat_message", "alternate_model")

backend/assets/.gitignore

@@ -1,2 +0,0 @@
-*
-!.gitignore

backend/danswer/access/access.py

@@ -1,23 +1,25 @@
 from sqlalchemy.orm import Session
 
 from danswer.access.models import DocumentAccess
-from danswer.access.utils import prefix_user
 from danswer.configs.constants import PUBLIC_DOC_PAT
 from danswer.db.document import get_acccess_info_for_documents
 from danswer.db.models import User
+from danswer.server.documents.models import ConnectorCredentialPairIdentifier
 from danswer.utils.variable_functionality import fetch_versioned_implementation
 
 
 def _get_access_for_documents(
     document_ids: list[str],
     db_session: Session,
+    cc_pair_to_delete: ConnectorCredentialPairIdentifier | None = None,
 ) -> dict[str, DocumentAccess]:
     document_access_info = get_acccess_info_for_documents(
         db_session=db_session,
         document_ids=document_ids,
+        cc_pair_to_delete=cc_pair_to_delete,
     )
     return {
-        document_id: DocumentAccess.build(user_ids, [], is_public)
+        document_id: DocumentAccess.build(user_ids, is_public)
         for document_id, user_ids, is_public in document_access_info
     }
 
@@ -25,16 +27,23 @@ def _get_access_for_documents(
 def get_access_for_documents(
     document_ids: list[str],
     db_session: Session,
+    cc_pair_to_delete: ConnectorCredentialPairIdentifier | None = None,
 ) -> dict[str, DocumentAccess]:
     """Fetches all access information for the given documents."""
     versioned_get_access_for_documents_fn = fetch_versioned_implementation(
         "danswer.access.access", "_get_access_for_documents"
     )
     return versioned_get_access_for_documents_fn(
-        document_ids, db_session
+        document_ids, db_session, cc_pair_to_delete
     )  # type: ignore
 
 
+def prefix_user(user_id: str) -> str:
+    """Prefixes a user ID to eliminate collision with group names.
+    This assumes that groups are prefixed with a different prefix."""
+    return f"user_id:{user_id}"
+
+
 def _get_acl_for_user(user: User | None, db_session: Session) -> set[str]:
     """Returns a list of ACL entries that the user has access to. This is meant to be
     used downstream to filter out documents that the user does not have access to. The

backend/danswer/access/models.py

@@ -1,30 +1,20 @@
 from dataclasses import dataclass
 from uuid import UUID
 
-from danswer.access.utils import prefix_user
-from danswer.access.utils import prefix_user_group
 from danswer.configs.constants import PUBLIC_DOC_PAT
 
 
 @dataclass(frozen=True)
 class DocumentAccess:
     user_ids: set[str]  # stringified UUIDs
-    user_groups: set[str]  # names of user groups associated with this document
     is_public: bool
 
     def to_acl(self) -> list[str]:
-        return (
-            [prefix_user(user_id) for user_id in self.user_ids]
-            + [prefix_user_group(group_name) for group_name in self.user_groups]
-            + ([PUBLIC_DOC_PAT] if self.is_public else [])
-        )
+        return list(self.user_ids) + ([PUBLIC_DOC_PAT] if self.is_public else [])
 
     @classmethod
-    def build(
-        cls, user_ids: list[UUID | None], user_groups: list[str], is_public: bool
-    ) -> "DocumentAccess":
+    def build(cls, user_ids: list[UUID | None], is_public: bool) -> "DocumentAccess":
         return cls(
             user_ids={str(user_id) for user_id in user_ids if user_id},
-            user_groups=set(user_groups),
             is_public=is_public,
         )

backend/danswer/access/utils.py

@@ -1,10 +0,0 @@
-def prefix_user(user_id: str) -> str:
-    """Prefixes a user ID to eliminate collision with group names.
-    This assumes that groups are prefixed with a different prefix."""
-    return f"user_id:{user_id}"
-
-
-def prefix_user_group(user_group_name: str) -> str:
-    """Prefixes a user group name to eliminate collision with user IDs.
-    This assumes that user ids are prefixed with a different prefix."""
-    return f"group:{user_group_name}"

backend/danswer/auth/invited_users.py

@@ -1,20 +0,0 @@
-from typing import cast
-
-from danswer.configs.constants import KV_USER_STORE_KEY
-from danswer.dynamic_configs.factory import get_dynamic_config_store
-from danswer.dynamic_configs.interface import ConfigNotFoundError
-from danswer.dynamic_configs.interface import JSON_ro
-
-
-def get_invited_users() -> list[str]:
-    try:
-        store = get_dynamic_config_store()
-        return cast(list, store.load(KV_USER_STORE_KEY))
-    except ConfigNotFoundError:
-        return list()
-
-
-def write_invited_users(emails: list[str]) -> int:
-    store = get_dynamic_config_store()
-    store.store(KV_USER_STORE_KEY, cast(JSON_ro, emails))
-    return len(emails)

backend/danswer/auth/noauth_user.py

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

backend/danswer/auth/schemas.py

@@ -9,12 +9,6 @@ class UserRole(str, Enum):
     ADMIN = "admin"
 
 
-class UserStatus(str, Enum):
-    LIVE = "live"
-    INVITED = "invited"
-    DEACTIVATED = "deactivated"
-
-
 class UserRead(schemas.BaseUser[uuid.UUID]):
     role: UserRole
 

backend/danswer/auth/users.py

@@ -1,8 +1,7 @@
+import os
 import smtplib
 import uuid
 from collections.abc import AsyncGenerator
-from datetime import datetime
-from datetime import timezone
 from email.mime.multipart import MIMEMultipart
 from email.mime.text import MIMEText
 from typing import Optional
@@ -28,7 +27,6 @@ from fastapi_users.openapi import OpenAPIResponseType
 from fastapi_users_db_sqlalchemy import SQLAlchemyUserDatabase
 from sqlalchemy.orm import Session
 
-from danswer.auth.invited_users import get_invited_users
 from danswer.auth.schemas import UserCreate
 from danswer.auth.schemas import UserRole
 from danswer.configs.app_configs import AUTH_TYPE
@@ -48,31 +46,21 @@ from danswer.configs.constants import DANSWER_API_KEY_DUMMY_EMAIL_DOMAIN
 from danswer.configs.constants import DANSWER_API_KEY_PREFIX
 from danswer.configs.constants import UNNAMED_KEY_PLACEHOLDER
 from danswer.db.auth import get_access_token_db
-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.engine import get_session
-from danswer.db.engine import get_sqlalchemy_engine
 from danswer.db.models import AccessToken
 from danswer.db.models import User
-from danswer.db.users import get_user_by_email
 from danswer.utils.logger import setup_logger
 from danswer.utils.telemetry import optional_telemetry
 from danswer.utils.telemetry import RecordType
-from danswer.utils.variable_functionality import (
-    fetch_versioned_implementation,
-)
+from danswer.utils.variable_functionality import fetch_versioned_implementation
 
 
 logger = setup_logger()
 
-
-def is_user_admin(user: User | None) -> bool:
-    if AUTH_TYPE == AuthType.DISABLED:
-        return True
-    if user and user.role == UserRole.ADMIN:
-        return True
-    return False
+USER_WHITELIST_FILE = "/home/danswer_whitelist.txt"
+_user_whitelist: list[str] | None = None
 
 
 def verify_auth_setting() -> None:
@@ -104,16 +92,22 @@ def user_needs_to_be_verified() -> bool:
     return AUTH_TYPE != AuthType.BASIC or REQUIRE_EMAIL_VERIFICATION
 
 
-def verify_email_is_invited(email: str) -> None:
-    whitelist = get_invited_users()
-    if (whitelist and email not in whitelist) or not email:
-        raise PermissionError("User not on allowed user whitelist")
+def get_user_whitelist() -> list[str]:
+    global _user_whitelist
+    if _user_whitelist is None:
+        if os.path.exists(USER_WHITELIST_FILE):
+            with open(USER_WHITELIST_FILE, "r") as file:
+                _user_whitelist = [line.strip() for line in file]
+        else:
+            _user_whitelist = []
+
+    return _user_whitelist
 
 
 def verify_email_in_whitelist(email: str) -> None:
-    with Session(get_sqlalchemy_engine()) as db_session:
-        if not get_user_by_email(email, db_session):
-            verify_email_is_invited(email)
+    whitelist = get_user_whitelist()
+    if (whitelist and email not in whitelist) or not email:
+        raise PermissionError("User not on allowed user whitelist")
 
 
 def verify_email_domain(email: str) -> None:
@@ -165,11 +159,11 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
         safe: bool = False,
         request: Optional[Request] = None,
     ) -> models.UP:
-        verify_email_is_invited(user_create.email)
+        verify_email_in_whitelist(user_create.email)
         verify_email_domain(user_create.email)
         if hasattr(user_create, "role"):
             user_count = await get_user_count()
-            if user_count == 0 or user_create.email in get_default_admin_user_emails():
+            if user_count == 0:
                 user_create.role = UserRole.ADMIN
             else:
                 user_create.role = UserRole.BASIC
@@ -191,7 +185,7 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
         verify_email_in_whitelist(account_email)
         verify_email_domain(account_email)
 
-        user = await super().oauth_callback(  # type: ignore
+        return await super().oauth_callback(  # type: ignore
             oauth_name=oauth_name,
             access_token=access_token,
             account_id=account_id,
@@ -203,14 +197,6 @@ class UserManager(UUIDIDMixin, BaseUserManager[User, uuid.UUID]):
             is_verified_by_default=is_verified_by_default,
         )
 
-        # NOTE: google oauth expires after 1hr. We don't want to force the user to
-        # re-authenticate that frequently, so for now we'll just ignore this for
-        # google oauth users
-        if expires_at and AUTH_TYPE != AuthType.GOOGLE_OAUTH:
-            oidc_expiry = datetime.fromtimestamp(expires_at, tz=timezone.utc)
-            await self.user_db.update(user, update_dict={"oidc_expiry": oidc_expiry})
-        return user
-
     async def on_after_register(
         self, user: User, request: Optional[Request] = None
     ) -> None:
@@ -253,12 +239,10 @@ cookie_transport = CookieTransport(
 def get_database_strategy(
     access_token_db: AccessTokenDatabase[AccessToken] = Depends(get_access_token_db),
 ) -> DatabaseStrategy:
-    strategy = DatabaseStrategy(
+    return DatabaseStrategy(
         access_token_db, lifetime_seconds=SESSION_EXPIRE_TIME_SECONDS  # type: ignore
     )
 
-    return strategy
-
 
 auth_backend = AuthenticationBackend(
     name="database",
@@ -355,12 +339,6 @@ async def double_check_user(
             detail="Access denied. User is not verified.",
         )
 
-    if user.oidc_expiry and user.oidc_expiry < datetime.now(timezone.utc):
-        raise HTTPException(
-            status_code=status.HTTP_403_FORBIDDEN,
-            detail="Access denied. User's OIDC token has expired.",
-        )
-
     return user
 
 
@@ -379,5 +357,4 @@ async def current_admin_user(user: User | None = Depends(current_user)) -> User
             status_code=status.HTTP_403_FORBIDDEN,
             detail="Access denied. User is not an admin.",
         )
-
     return user

backend/danswer/background/celery/celery.py

@@ -4,24 +4,13 @@ from typing import cast
 from celery import Celery  # type: ignore
 from sqlalchemy.orm import Session
 
-from danswer.background.celery.celery_utils import extract_ids_from_runnable_connector
-from danswer.background.celery.celery_utils import should_kick_off_deletion_of_cc_pair
-from danswer.background.celery.celery_utils import should_prune_cc_pair
-from danswer.background.celery.celery_utils import should_sync_doc_set
 from danswer.background.connector_deletion import delete_connector_credential_pair
-from danswer.background.connector_deletion import delete_connector_credential_pair_batch
 from danswer.background.task_utils import build_celery_task_wrapper
 from danswer.background.task_utils import name_cc_cleanup_task
-from danswer.background.task_utils import name_cc_prune_task
 from danswer.background.task_utils import name_document_set_sync_task
 from danswer.configs.app_configs import JOB_TIMEOUT
-from danswer.configs.constants import POSTGRES_CELERY_APP_NAME
-from danswer.connectors.factory import instantiate_connector
-from danswer.connectors.models import InputType
 from danswer.db.connector_credential_pair import get_connector_credential_pair
-from danswer.db.connector_credential_pair import get_connector_credential_pairs
 from danswer.db.deletion_attempt import check_deletion_attempt_is_allowed
-from danswer.db.document import get_documents_for_connector_credential_pair
 from danswer.db.document import prepare_to_modify_documents
 from danswer.db.document_set import delete_document_set
 from danswer.db.document_set import fetch_document_sets
@@ -33,6 +22,8 @@ from danswer.db.engine import build_connection_string
 from danswer.db.engine import get_sqlalchemy_engine
 from danswer.db.engine import SYNC_DB_API
 from danswer.db.models import DocumentSet
+from danswer.db.tasks import check_live_task_not_timed_out
+from danswer.db.tasks import get_latest_task
 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 UpdateRequest
@@ -40,9 +31,7 @@ from danswer.utils.logger import setup_logger
 
 logger = setup_logger()
 
-connection_string = build_connection_string(
-    db_api=SYNC_DB_API, app_name=POSTGRES_CELERY_APP_NAME
-)
+connection_string = build_connection_string(db_api=SYNC_DB_API)
 celery_broker_url = f"sqla+{connection_string}"
 celery_backend_url = f"db+{connection_string}"
 celery_app = Celery(__name__, broker=celery_broker_url, backend=celery_backend_url)
@@ -79,9 +68,7 @@ def cleanup_connector_credential_pair_task(
                 f"{connector_id} and Credential ID: {credential_id} does not exist."
             )
 
-        deletion_attempt_disallowed_reason = check_deletion_attempt_is_allowed(
-            connector_credential_pair=cc_pair, db_session=db_session
-        )
+        deletion_attempt_disallowed_reason = check_deletion_attempt_is_allowed(cc_pair)
         if deletion_attempt_disallowed_reason:
             raise ValueError(deletion_attempt_disallowed_reason)
 
@@ -101,74 +88,6 @@ def cleanup_connector_credential_pair_task(
             raise e
 
 
-@build_celery_task_wrapper(name_cc_prune_task)
-@celery_app.task(soft_time_limit=JOB_TIMEOUT)
-def prune_documents_task(connector_id: int, credential_id: int) -> None:
-    """connector pruning task. For a cc pair, this task pulls all document IDs from the source
-    and compares those IDs to locally stored documents and deletes all locally stored IDs missing
-    from the most recently pulled document ID list"""
-    with Session(get_sqlalchemy_engine()) as db_session:
-        try:
-            cc_pair = get_connector_credential_pair(
-                db_session=db_session,
-                connector_id=connector_id,
-                credential_id=credential_id,
-            )
-
-            if not cc_pair:
-                logger.warning(f"ccpair not found for {connector_id} {credential_id}")
-                return
-
-            runnable_connector = instantiate_connector(
-                cc_pair.connector.source,
-                InputType.PRUNE,
-                cc_pair.connector.connector_specific_config,
-                cc_pair.credential,
-                db_session,
-            )
-
-            all_connector_doc_ids: set[str] = extract_ids_from_runnable_connector(
-                runnable_connector
-            )
-
-            all_indexed_document_ids = {
-                doc.id
-                for doc in get_documents_for_connector_credential_pair(
-                    db_session=db_session,
-                    connector_id=connector_id,
-                    credential_id=credential_id,
-                )
-            }
-
-            doc_ids_to_remove = list(all_indexed_document_ids - all_connector_doc_ids)
-
-            curr_ind_name, sec_ind_name = get_both_index_names(db_session)
-            document_index = get_default_document_index(
-                primary_index_name=curr_ind_name, secondary_index_name=sec_ind_name
-            )
-
-            if len(doc_ids_to_remove) == 0:
-                logger.info(
-                    f"No docs to prune from {cc_pair.connector.source} connector"
-                )
-                return
-
-            logger.info(
-                f"pruning {len(doc_ids_to_remove)} doc(s) from {cc_pair.connector.source} connector"
-            )
-            delete_connector_credential_pair_batch(
-                document_ids=doc_ids_to_remove,
-                connector_id=connector_id,
-                credential_id=credential_id,
-                document_index=document_index,
-            )
-        except Exception as e:
-            logger.exception(
-                f"Failed to run pruning for connector id {connector_id} due to {e}"
-            )
-            raise e
-
-
 @build_celery_task_wrapper(name_document_set_sync_task)
 @celery_app.task(soft_time_limit=JOB_TIMEOUT)
 def sync_document_set_task(document_set_id: int) -> None:
@@ -256,68 +175,32 @@ def sync_document_set_task(document_set_id: int) -> None:
     soft_time_limit=JOB_TIMEOUT,
 )
 def check_for_document_sets_sync_task() -> None:
-    """Runs periodically to check if any sync tasks should be run and adds them
-    to the queue"""
+    """Runs periodically to check if any document sets are out of sync
+    Creates a task to sync the set if needed"""
     with Session(get_sqlalchemy_engine()) as db_session:
         # check if any document sets are not synced
         document_set_info = fetch_document_sets(
             user_id=None, db_session=db_session, include_outdated=True
         )
         for document_set, _ in document_set_info:
-            if should_sync_doc_set(document_set, db_session):
-                logger.info(f"Syncing the {document_set.name} document set")
+            if not document_set.is_up_to_date:
+                task_name = name_document_set_sync_task(document_set.id)
+                latest_sync = get_latest_task(task_name, db_session)
+
+                if latest_sync and check_live_task_not_timed_out(
+                    latest_sync, db_session
+                ):
+                    logger.info(
+                        f"Document set '{document_set.id}' is already syncing. Skipping."
+                    )
+                    continue
+
+                logger.info(f"Document set {document_set.id} syncing now!")
                 sync_document_set_task.apply_async(
                     kwargs=dict(document_set_id=document_set.id),
                 )
 
 
-@celery_app.task(
-    name="check_for_cc_pair_deletion_task",
-    soft_time_limit=JOB_TIMEOUT,
-)
-def check_for_cc_pair_deletion_task() -> None:
-    """Runs periodically to check if any deletion tasks should be run"""
-    with Session(get_sqlalchemy_engine()) as db_session:
-        # check if any document sets are not synced
-        cc_pairs = get_connector_credential_pairs(db_session)
-        for cc_pair in cc_pairs:
-            if should_kick_off_deletion_of_cc_pair(cc_pair, db_session):
-                logger.info(f"Deleting the {cc_pair.name} connector credential pair")
-                cleanup_connector_credential_pair_task.apply_async(
-                    kwargs=dict(
-                        connector_id=cc_pair.connector.id,
-                        credential_id=cc_pair.credential.id,
-                    ),
-                )
-
-
-@celery_app.task(
-    name="check_for_prune_task",
-    soft_time_limit=JOB_TIMEOUT,
-)
-def check_for_prune_task() -> None:
-    """Runs periodically to check if any prune tasks should be run and adds them
-    to the queue"""
-
-    with Session(get_sqlalchemy_engine()) as db_session:
-        all_cc_pairs = get_connector_credential_pairs(db_session)
-
-        for cc_pair in all_cc_pairs:
-            if should_prune_cc_pair(
-                connector=cc_pair.connector,
-                credential=cc_pair.credential,
-                db_session=db_session,
-            ):
-                logger.info(f"Pruning the {cc_pair.connector.name} connector")
-
-                prune_documents_task.apply_async(
-                    kwargs=dict(
-                        connector_id=cc_pair.connector.id,
-                        credential_id=cc_pair.credential.id,
-                    )
-                )
-
-
 #####
 # Celery Beat (Periodic Tasks) Settings
 #####
@@ -326,18 +209,4 @@ celery_app.conf.beat_schedule = {
         "task": "check_for_document_sets_sync_task",
         "schedule": timedelta(seconds=5),
     },
-    "check-for-cc-pair-deletion": {
-        "task": "check_for_cc_pair_deletion_task",
-        # don't need to check too often, since we kick off a deletion initially
-        # during the API call that actually marks the CC pair for deletion
-        "schedule": timedelta(minutes=1),
-    },
 }
-celery_app.conf.beat_schedule.update(
-    {
-        "check-for-prune": {
-            "task": "check_for_prune_task",
-            "schedule": timedelta(seconds=5),
-        },
-    }
-)

backend/danswer/background/celery/celery_run.py

@@ -1,9 +0,0 @@
-"""Entry point for running celery worker / celery beat."""
-from danswer.utils.variable_functionality import fetch_versioned_implementation
-from danswer.utils.variable_functionality import set_is_ee_based_on_env_variable
-
-
-set_is_ee_based_on_env_variable()
-celery_app = fetch_versioned_implementation(
-    "danswer.background.celery.celery_app", "celery_app"
-)

backend/danswer/background/celery/celery_utils.py

@@ -1,170 +1,23 @@
-from datetime import datetime
-from datetime import timezone
-
 from sqlalchemy.orm import Session
 
 from danswer.background.task_utils import name_cc_cleanup_task
-from danswer.background.task_utils import name_cc_prune_task
-from danswer.background.task_utils import name_document_set_sync_task
-from danswer.configs.app_configs import ALLOW_SIMULTANEOUS_PRUNING
-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,
-)
-from danswer.connectors.interfaces import BaseConnector
-from danswer.connectors.interfaces import IdConnector
-from danswer.connectors.interfaces import LoadConnector
-from danswer.connectors.interfaces import PollConnector
-from danswer.connectors.models import Document
-from danswer.db.deletion_attempt import check_deletion_attempt_is_allowed
-from danswer.db.engine import get_db_current_time
-from danswer.db.enums import ConnectorCredentialPairStatus
-from danswer.db.models import Connector
-from danswer.db.models import ConnectorCredentialPair
-from danswer.db.models import Credential
-from danswer.db.models import DocumentSet
-from danswer.db.models import TaskQueueState
-from danswer.db.tasks import check_task_is_live_and_not_timed_out
 from danswer.db.tasks import get_latest_task
-from danswer.db.tasks import get_latest_task_by_type
 from danswer.server.documents.models import DeletionAttemptSnapshot
-from danswer.utils.logger import setup_logger
-
-logger = setup_logger()
 
 
 def get_deletion_status(
     connector_id: int, credential_id: int, db_session: Session
-) -> TaskQueueState | None:
+) -> DeletionAttemptSnapshot | None:
     cleanup_task_name = name_cc_cleanup_task(
         connector_id=connector_id, credential_id=credential_id
     )
-    return get_latest_task(task_name=cleanup_task_name, db_session=db_session)
+    task_state = get_latest_task(task_name=cleanup_task_name, db_session=db_session)
 
-
-def get_deletion_attempt_snapshot(
-    connector_id: int, credential_id: int, db_session: Session
-) -> DeletionAttemptSnapshot | None:
-    deletion_task = get_deletion_status(connector_id, credential_id, db_session)
-    if not deletion_task:
+    if not task_state:
         return None
 
     return DeletionAttemptSnapshot(
         connector_id=connector_id,
         credential_id=credential_id,
-        status=deletion_task.status,
+        status=task_state.status,
     )
-
-
-def should_kick_off_deletion_of_cc_pair(
-    cc_pair: ConnectorCredentialPair, db_session: Session
-) -> bool:
-    if cc_pair.status != ConnectorCredentialPairStatus.DELETING:
-        return False
-
-    if check_deletion_attempt_is_allowed(cc_pair, db_session):
-        return False
-
-    deletion_task = get_deletion_status(
-        connector_id=cc_pair.connector_id,
-        credential_id=cc_pair.credential_id,
-        db_session=db_session,
-    )
-    if deletion_task and check_task_is_live_and_not_timed_out(
-        deletion_task,
-        db_session,
-        # 1 hour timeout
-        timeout=60 * 60,
-    ):
-        return False
-
-    return True
-
-
-def should_sync_doc_set(document_set: DocumentSet, db_session: Session) -> bool:
-    if document_set.is_up_to_date:
-        return False
-
-    task_name = name_document_set_sync_task(document_set.id)
-    latest_sync = get_latest_task(task_name, db_session)
-
-    if latest_sync and check_task_is_live_and_not_timed_out(latest_sync, db_session):
-        logger.info(f"Document set '{document_set.id}' is already syncing. Skipping.")
-        return False
-
-    logger.info(f"Document set {document_set.id} syncing now!")
-    return True
-
-
-def should_prune_cc_pair(
-    connector: Connector, credential: Credential, db_session: Session
-) -> bool:
-    if not connector.prune_freq:
-        return False
-
-    pruning_task_name = name_cc_prune_task(
-        connector_id=connector.id, credential_id=credential.id
-    )
-    last_pruning_task = get_latest_task(pruning_task_name, db_session)
-    current_db_time = get_db_current_time(db_session)
-
-    if not last_pruning_task:
-        time_since_initialization = current_db_time - connector.time_created
-        if time_since_initialization.total_seconds() >= connector.prune_freq:
-            return True
-        return False
-
-    if not ALLOW_SIMULTANEOUS_PRUNING:
-        pruning_type_task_name = name_cc_prune_task()
-        last_pruning_type_task = get_latest_task_by_type(
-            pruning_type_task_name, db_session
-        )
-
-        if last_pruning_type_task and check_task_is_live_and_not_timed_out(
-            last_pruning_type_task, db_session
-        ):
-            return False
-
-    if check_task_is_live_and_not_timed_out(last_pruning_task, db_session):
-        return False
-
-    if not last_pruning_task.start_time:
-        return False
-
-    time_since_last_pruning = current_db_time - last_pruning_task.start_time
-    return time_since_last_pruning.total_seconds() >= connector.prune_freq
-
-
-def document_batch_to_ids(doc_batch: list[Document]) -> set[str]:
-    return {doc.id for doc in doc_batch}
-
-
-def extract_ids_from_runnable_connector(runnable_connector: BaseConnector) -> set[str]:
-    """
-    If the PruneConnector hasnt been implemented for the given connector, just pull
-    all docs using the load_from_state and grab out the IDs
-    """
-    all_connector_doc_ids: set[str] = set()
-
-    doc_batch_generator = None
-    if isinstance(runnable_connector, IdConnector):
-        all_connector_doc_ids = runnable_connector.retrieve_all_source_ids()
-    elif isinstance(runnable_connector, LoadConnector):
-        doc_batch_generator = runnable_connector.load_from_state()
-    elif isinstance(runnable_connector, PollConnector):
-        start = datetime(1970, 1, 1, tzinfo=timezone.utc).timestamp()
-        end = datetime.now(timezone.utc).timestamp()
-        doc_batch_generator = runnable_connector.poll_source(start=start, end=end)
-    else:
-        raise RuntimeError("Pruning job could not find a valid runnable_connector.")
-
-    if doc_batch_generator:
-        doc_batch_processing_func = document_batch_to_ids
-        if MAX_PRUNING_DOCUMENT_RETRIEVAL_PER_MINUTE:
-            doc_batch_processing_func = rate_limit_builder(
-                max_calls=MAX_PRUNING_DOCUMENT_RETRIEVAL_PER_MINUTE, period=60
-            )(document_batch_to_ids)
-        for doc_batch in doc_batch_generator:
-            all_connector_doc_ids.update(doc_batch_processing_func(doc_batch))
-
-    return all_connector_doc_ids

backend/danswer/background/connector_deletion.py

@@ -10,6 +10,8 @@ are multiple connector / credential pairs that have indexed it
 connector / credential pair from the access list
 (6) delete all relevant entries from postgres
 """
+import time
+
 from sqlalchemy.orm import Session
 
 from danswer.access.access import get_access_for_documents
@@ -22,8 +24,10 @@ from danswer.db.document import delete_documents_complete__no_commit
 from danswer.db.document import get_document_connector_cnts
 from danswer.db.document import get_documents_for_connector_credential_pair
 from danswer.db.document import prepare_to_modify_documents
-from danswer.db.document_set import delete_document_set_cc_pair_relationship__no_commit
-from danswer.db.document_set import fetch_document_sets_for_documents
+from danswer.db.document_set import get_document_sets_by_ids
+from danswer.db.document_set import (
+    mark_cc_pair__document_set_relationships_to_be_deleted__no_commit,
+)
 from danswer.db.engine import get_sqlalchemy_engine
 from danswer.db.index_attempt import delete_index_attempts
 from danswer.db.models import ConnectorCredentialPair
@@ -31,17 +35,13 @@ from danswer.document_index.interfaces import DocumentIndex
 from danswer.document_index.interfaces import UpdateRequest
 from danswer.server.documents.models import ConnectorCredentialPairIdentifier
 from danswer.utils.logger import setup_logger
-from danswer.utils.variable_functionality import (
-    fetch_versioned_implementation_with_fallback,
-)
-from danswer.utils.variable_functionality import noop_fallback
 
 logger = setup_logger()
 
 _DELETION_BATCH_SIZE = 1000
 
 
-def delete_connector_credential_pair_batch(
+def _delete_connector_credential_pair_batch(
     document_ids: list[str],
     connector_id: int,
     credential_id: int,
@@ -78,37 +78,25 @@ def delete_connector_credential_pair_batch(
             document_ids_to_update = [
                 document_id for document_id, cnt in document_connector_cnts if cnt > 1
             ]
-
-            # maps document id to list of document set names
-            new_doc_sets_for_documents: dict[str, set[str]] = {
-                document_id_and_document_set_names_tuple[0]: set(
-                    document_id_and_document_set_names_tuple[1]
-                )
-                for document_id_and_document_set_names_tuple in fetch_document_sets_for_documents(
-                    db_session=db_session,
-                    document_ids=document_ids_to_update,
-                )
-            }
-
-            # determine future ACLs for documents in batch
             access_for_documents = get_access_for_documents(
                 document_ids=document_ids_to_update,
                 db_session=db_session,
+                cc_pair_to_delete=ConnectorCredentialPairIdentifier(
+                    connector_id=connector_id,
+                    credential_id=credential_id,
+                ),
             )
-
-            # update Vespa
-            logger.debug(f"Updating documents: {document_ids_to_update}")
             update_requests = [
                 UpdateRequest(
                     document_ids=[document_id],
                     access=access,
-                    document_sets=new_doc_sets_for_documents[document_id],
                 )
                 for document_id, access in access_for_documents.items()
             ]
+            logger.debug(f"Updating documents: {document_ids_to_update}")
+
             document_index.update(update_requests=update_requests)
 
-            # clean up Postgres
             delete_document_by_connector_credential_pair__no_commit(
                 db_session=db_session,
                 document_ids=document_ids_to_update,
@@ -120,6 +108,48 @@ def delete_connector_credential_pair_batch(
             db_session.commit()
 
 
+def cleanup_synced_entities(
+    cc_pair: ConnectorCredentialPair, db_session: Session
+) -> None:
+    """Updates the document sets associated with the connector / credential pair,
+    then relies on the document set sync script to kick off Celery jobs which will
+    sync these updates to Vespa.
+
+    Waits until the document sets are synced before returning."""
+    logger.info(f"Cleaning up Document Sets for CC Pair with ID: '{cc_pair.id}'")
+    document_sets_ids_to_sync = list(
+        mark_cc_pair__document_set_relationships_to_be_deleted__no_commit(
+            cc_pair_id=cc_pair.id,
+            db_session=db_session,
+        )
+    )
+    db_session.commit()
+
+    # wait till all document sets are synced before continuing
+    while True:
+        all_synced = True
+        document_sets = get_document_sets_by_ids(
+            db_session=db_session, document_set_ids=document_sets_ids_to_sync
+        )
+        for document_set in document_sets:
+            if not document_set.is_up_to_date:
+                all_synced = False
+
+        if all_synced:
+            break
+
+        # wait for 30 seconds before checking again
+        db_session.commit()  # end transaction
+        logger.info(
+            f"Document sets '{document_sets_ids_to_sync}' not synced yet, waiting 30s"
+        )
+        time.sleep(30)
+
+    logger.info(
+        f"Finished cleaning up Document Sets for CC Pair with ID: '{cc_pair.id}'"
+    )
+
+
 def delete_connector_credential_pair(
     db_session: Session,
     document_index: DocumentIndex,
@@ -139,7 +169,7 @@ def delete_connector_credential_pair(
         if not documents:
             break
 
-        delete_connector_credential_pair_batch(
+        _delete_connector_credential_pair_batch(
             document_ids=[document.id for document in documents],
             connector_id=connector_id,
             credential_id=credential_id,
@@ -147,33 +177,17 @@ def delete_connector_credential_pair(
         )
         num_docs_deleted += len(documents)
 
+    # Clean up document sets / access information from Postgres
+    # and sync these updates to Vespa
+    # TODO: add user group cleanup with `fetch_versioned_implementation`
+    cleanup_synced_entities(cc_pair, db_session)
+
     # clean up the rest of the related Postgres entities
-    # index attempts
     delete_index_attempts(
         db_session=db_session,
         connector_id=connector_id,
         credential_id=credential_id,
     )
-
-    # document sets
-    delete_document_set_cc_pair_relationship__no_commit(
-        db_session=db_session,
-        connector_id=connector_id,
-        credential_id=credential_id,
-    )
-
-    # user groups
-    cleanup_user_groups = fetch_versioned_implementation_with_fallback(
-        "danswer.db.user_group",
-        "delete_user_group_cc_pair_relationship__no_commit",
-        noop_fallback,
-    )
-    cleanup_user_groups(
-        cc_pair_id=cc_pair.id,
-        db_session=db_session,
-    )
-
-    # finally, delete the cc-pair
     delete_connector_credential_pair__no_commit(
         db_session=db_session,
         connector_id=connector_id,

backend/danswer/background/indexing/job_client.py

@@ -41,12 +41,6 @@ def _initializer(
     return func(*args, **kwargs)
 
 
-def _run_in_process(
-    func: Callable, args: list | tuple, kwargs: dict[str, Any] | None = None
-) -> None:
-    _initializer(func, args, kwargs)
-
-
 @dataclass
 class SimpleJob:
     """Drop in replacement for `dask.distributed.Future`"""
@@ -111,15 +105,13 @@ class SimpleJobClient:
         """NOTE: `pure` arg is needed so this can be a drop in replacement for Dask"""
         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}'."
-            )
+            logger.debug("No available workers to run job")
             return None
 
         job_id = self.job_id_counter
         self.job_id_counter += 1
 
-        process = Process(target=_run_in_process, args=(func, args), daemon=True)
+        process = Process(target=_initializer(func=func, args=args), daemon=True)
         job = SimpleJob(id=job_id, process=process)
         process.start()
 

backend/danswer/background/indexing/run_indexing.py

@@ -6,10 +6,11 @@ from datetime import timezone
 
 from sqlalchemy.orm import Session
 
+from danswer.background.connector_deletion import (
+    _delete_connector_credential_pair_batch,
+)
 from danswer.background.indexing.checkpointing import get_time_windows_for_index_attempt
-from danswer.background.indexing.tracer import DanswerTracer
-from danswer.configs.app_configs import INDEXING_SIZE_WARNING_THRESHOLD
-from danswer.configs.app_configs import INDEXING_TRACER_INTERVAL
+from danswer.configs.app_configs import DISABLE_DOCUMENT_CLEANUP
 from danswer.configs.app_configs import POLL_CONNECTOR_OFFSET
 from danswer.connectors.factory import instantiate_connector
 from danswer.connectors.interfaces import GenerateDocumentsOutput
@@ -17,13 +18,15 @@ from danswer.connectors.interfaces import LoadConnector
 from danswer.connectors.interfaces import PollConnector
 from danswer.connectors.models import IndexAttemptMetadata
 from danswer.connectors.models import InputType
+from danswer.db.connector import disable_connector
 from danswer.db.connector_credential_pair import get_last_successful_attempt_time
 from danswer.db.connector_credential_pair import update_connector_credential_pair
+from danswer.db.credentials import backend_update_credential_json
+from danswer.db.document import get_documents_for_connector_credential_pair
 from danswer.db.engine import get_sqlalchemy_engine
-from danswer.db.enums import ConnectorCredentialPairStatus
 from danswer.db.index_attempt import get_index_attempt
 from danswer.db.index_attempt import mark_attempt_failed
-from danswer.db.index_attempt import mark_attempt_in_progress
+from danswer.db.index_attempt import mark_attempt_in_progress__no_commit
 from danswer.db.index_attempt import mark_attempt_succeeded
 from danswer.db.index_attempt import update_docs_indexed
 from danswer.db.models import IndexAttempt
@@ -34,19 +37,16 @@ from danswer.indexing.embedder import DefaultIndexingEmbedder
 from danswer.indexing.indexing_pipeline import build_indexing_pipeline
 from danswer.utils.logger import IndexAttemptSingleton
 from danswer.utils.logger import setup_logger
-from danswer.utils.variable_functionality import global_version
 
 logger = setup_logger()
 
-INDEXING_TRACER_NUM_PRINT_ENTRIES = 5
-
 
 def _get_document_generator(
     db_session: Session,
     attempt: IndexAttempt,
     start_time: datetime,
     end_time: datetime,
-) -> GenerateDocumentsOutput:
+) -> tuple[GenerateDocumentsOutput, bool]:
     """
     NOTE: `start_time` and `end_time` are only used for poll connectors
 
@@ -54,38 +54,31 @@ def _get_document_generator(
     are the complete list of existing documents of the connector. If the task
     of type LOAD_STATE, the list will be considered complete and otherwise incomplete.
     """
-    task = attempt.connector_credential_pair.connector.input_type
+    task = attempt.connector.input_type
 
     try:
-        runnable_connector = instantiate_connector(
-            attempt.connector_credential_pair.connector.source,
+        runnable_connector, new_credential_json = instantiate_connector(
+            attempt.connector.source,
             task,
-            attempt.connector_credential_pair.connector.connector_specific_config,
-            attempt.connector_credential_pair.credential,
-            db_session,
+            attempt.connector.connector_specific_config,
+            attempt.credential.credential_json,
         )
+        if new_credential_json is not None:
+            backend_update_credential_json(
+                attempt.credential, new_credential_json, db_session
+            )
     except Exception as e:
         logger.exception(f"Unable to instantiate connector due to {e}")
-        # since we failed to even instantiate the connector, we pause the CCPair since
-        # it will never succeed
-        update_connector_credential_pair(
-            db_session=db_session,
-            connector_id=attempt.connector_credential_pair.connector.id,
-            credential_id=attempt.connector_credential_pair.credential.id,
-            status=ConnectorCredentialPairStatus.PAUSED,
-        )
+        disable_connector(attempt.connector.id, db_session)
         raise e
 
     if task == InputType.LOAD_STATE:
         assert isinstance(runnable_connector, LoadConnector)
         doc_batch_generator = runnable_connector.load_from_state()
-
+        is_listing_complete = True
     elif task == InputType.POLL:
         assert isinstance(runnable_connector, PollConnector)
-        if (
-            attempt.connector_credential_pair.connector_id is None
-            or attempt.connector_credential_pair.connector_id is None
-        ):
+        if attempt.connector_id is None or attempt.credential_id is None:
             raise ValueError(
                 f"Polling attempt {attempt.id} is missing connector_id or credential_id, "
                 f"can't fetch time range."
@@ -95,12 +88,13 @@ def _get_document_generator(
         doc_batch_generator = runnable_connector.poll_source(
             start=start_time.timestamp(), end=end_time.timestamp()
         )
+        is_listing_complete = False
 
     else:
         # Event types cannot be handled by a background type
         raise RuntimeError(f"Invalid task type: {task}")
 
-    return doc_batch_generator
+    return doc_batch_generator, is_listing_complete
 
 
 def _run_indexing(
@@ -126,8 +120,11 @@ def _run_indexing(
         primary_index_name=index_name, secondary_index_name=None
     )
 
-    embedding_model = DefaultIndexingEmbedder.from_db_embedding_model(
-        db_embedding_model
+    embedding_model = DefaultIndexingEmbedder(
+        model_name=db_embedding_model.model_name,
+        normalize=db_embedding_model.normalize,
+        query_prefix=db_embedding_model.query_prefix,
+        passage_prefix=db_embedding_model.passage_prefix,
     )
 
     indexing_pipeline = build_indexing_pipeline(
@@ -138,31 +135,19 @@ def _run_indexing(
         db_session=db_session,
     )
 
-    db_cc_pair = index_attempt.connector_credential_pair
-    db_connector = index_attempt.connector_credential_pair.connector
-    db_credential = index_attempt.connector_credential_pair.credential
-
+    db_connector = index_attempt.connector
+    db_credential = index_attempt.credential
     last_successful_index_time = (
-        db_connector.indexing_start.timestamp()
-        if index_attempt.from_beginning and db_connector.indexing_start is not None
-        else (
-            0.0
-            if index_attempt.from_beginning
-            else get_last_successful_attempt_time(
-                connector_id=db_connector.id,
-                credential_id=db_credential.id,
-                embedding_model=index_attempt.embedding_model,
-                db_session=db_session,
-            )
+        0.0
+        if index_attempt.from_beginning
+        else get_last_successful_attempt_time(
+            connector_id=db_connector.id,
+            credential_id=db_credential.id,
+            embedding_model=index_attempt.embedding_model,
+            db_session=db_session,
         )
     )
 
-    if INDEXING_TRACER_INTERVAL > 0:
-        logger.info(f"Memory tracer starting: interval={INDEXING_TRACER_INTERVAL}")
-        tracer = DanswerTracer()
-        tracer.start()
-        tracer.snap()
-
     net_doc_change = 0
     document_count = 0
     chunk_count = 0
@@ -175,24 +160,20 @@ def _run_indexing(
             source_type=db_connector.source,
         )
     ):
+        window_start = max(
+            window_start - timedelta(minutes=POLL_CONNECTOR_OFFSET),
+            datetime(1970, 1, 1, tzinfo=timezone.utc),
+        )
+
+        doc_batch_generator, is_listing_complete = _get_document_generator(
+            db_session=db_session,
+            attempt=index_attempt,
+            start_time=window_start,
+            end_time=window_end,
+        )
+
         try:
-            window_start = max(
-                window_start - timedelta(minutes=POLL_CONNECTOR_OFFSET),
-                datetime(1970, 1, 1, tzinfo=timezone.utc),
-            )
-
-            doc_batch_generator = _get_document_generator(
-                db_session=db_session,
-                attempt=index_attempt,
-                start_time=window_start,
-                end_time=window_end,
-            )
-
             all_connector_doc_ids: set[str] = set()
-
-            tracer_counter = 0
-            if INDEXING_TRACER_INTERVAL > 0:
-                tracer.snap()
             for doc_batch in doc_batch_generator:
                 # Check if connector is disabled mid run and stop if so unless it's the secondary
                 # index being built. We want to populate it even for paused connectors
@@ -200,7 +181,7 @@ def _run_indexing(
                 # contents still need to be initially pulled.
                 db_session.refresh(db_connector)
                 if (
-                    db_cc_pair.status == ConnectorCredentialPairStatus.PAUSED
+                    db_connector.disabled
                     and db_embedding_model.status != IndexModelStatus.FUTURE
                 ):
                     # let the `except` block handle this
@@ -211,25 +192,12 @@ def _run_indexing(
                     # Likely due to user manually disabling it or model swap
                     raise RuntimeError("Index Attempt was canceled")
 
-                batch_description = []
-                for doc in doc_batch:
-                    batch_description.append(doc.to_short_descriptor())
-
-                    doc_size = 0
-                    for section in doc.sections:
-                        doc_size += len(section.text)
-
-                    if doc_size > INDEXING_SIZE_WARNING_THRESHOLD:
-                        logger.warning(
-                            f"Document size: doc='{doc.to_short_descriptor()}' "
-                            f"size={doc_size} "
-                            f"threshold={INDEXING_SIZE_WARNING_THRESHOLD}"
-                        )
-
-                logger.debug(f"Indexing batch of documents: {batch_description}")
+                logger.debug(
+                    f"Indexing batch of documents: {[doc.to_short_descriptor() for doc in doc_batch]}"
+                )
 
                 new_docs, total_batch_chunks = indexing_pipeline(
-                    document_batch=doc_batch,
+                    documents=doc_batch,
                     index_attempt_metadata=IndexAttemptMetadata(
                         connector_id=db_connector.id,
                         credential_id=db_credential.id,
@@ -256,16 +224,38 @@ def _run_indexing(
                     docs_removed_from_index=0,
                 )
 
-                tracer_counter += 1
-                if (
-                    INDEXING_TRACER_INTERVAL > 0
-                    and tracer_counter % INDEXING_TRACER_INTERVAL == 0
-                ):
-                    logger.info(
-                        f"Running trace comparison for batch {tracer_counter}. interval={INDEXING_TRACER_INTERVAL}"
+            if is_listing_complete and not DISABLE_DOCUMENT_CLEANUP:
+                # clean up all documents from the index that have not been returned from the connector
+                all_indexed_document_ids = {
+                    d.id
+                    for d in get_documents_for_connector_credential_pair(
+                        db_session=db_session,
+                        connector_id=db_connector.id,
+                        credential_id=db_credential.id,
                     )
-                    tracer.snap()
-                    tracer.log_previous_diff(INDEXING_TRACER_NUM_PRINT_ENTRIES)
+                }
+                doc_ids_to_remove = list(
+                    all_indexed_document_ids - all_connector_doc_ids
+                )
+                logger.debug(
+                    f"Cleaning up {len(doc_ids_to_remove)} documents that are not contained in the newest connector state"
+                )
+
+                # delete docs from cc-pair and receive the number of completely deleted docs in return
+                _delete_connector_credential_pair_batch(
+                    document_ids=doc_ids_to_remove,
+                    connector_id=db_connector.id,
+                    credential_id=db_credential.id,
+                    document_index=document_index,
+                )
+
+                update_docs_indexed(
+                    db_session=db_session,
+                    index_attempt=index_attempt,
+                    total_docs_indexed=document_count,
+                    new_docs_indexed=net_doc_change,
+                    docs_removed_from_index=len(doc_ids_to_remove),
+                )
 
             run_end_dt = window_end
             if is_primary:
@@ -273,6 +263,7 @@ def _run_indexing(
                     db_session=db_session,
                     connector_id=db_connector.id,
                     credential_id=db_credential.id,
+                    attempt_status=IndexingStatus.IN_PROGRESS,
                     net_docs=net_doc_change,
                     run_dt=run_end_dt,
                 )
@@ -289,7 +280,7 @@ def _run_indexing(
             # to give better clarity in the UI, as the next run will never happen.
             if (
                 ind == 0
-                or db_cc_pair.status == ConnectorCredentialPairStatus.PAUSED
+                or db_connector.disabled
                 or index_attempt.status != IndexingStatus.IN_PROGRESS
             ):
                 mark_attempt_failed(
@@ -301,40 +292,32 @@ def _run_indexing(
                 if is_primary:
                     update_connector_credential_pair(
                         db_session=db_session,
-                        connector_id=db_connector.id,
-                        credential_id=db_credential.id,
+                        connector_id=index_attempt.connector.id,
+                        credential_id=index_attempt.credential.id,
+                        attempt_status=IndexingStatus.FAILED,
                         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
             break
 
-    if INDEXING_TRACER_INTERVAL > 0:
-        logger.info(
-            f"Running trace comparison between start and end of indexing. {tracer_counter} batches processed."
-        )
-        tracer.snap()
-        tracer.log_first_diff(INDEXING_TRACER_NUM_PRINT_ENTRIES)
-        tracer.stop()
-        logger.info("Memory tracer stopped.")
-
     mark_attempt_succeeded(index_attempt, db_session)
     if is_primary:
         update_connector_credential_pair(
             db_session=db_session,
             connector_id=db_connector.id,
             credential_id=db_credential.id,
+            attempt_status=IndexingStatus.SUCCESS,
             run_dt=run_end_dt,
         )
 
-    elapsed_time = time.time() - start_time
     logger.info(
-        f"Connector succeeded: docs={document_count} chunks={chunk_count} elapsed={elapsed_time:.2f}s"
+        f"Indexed or refreshed {document_count} total documents for a total of {chunk_count} indexed chunks"
+    )
+    logger.info(
+        f"Connector successfully finished, elapsed time: {time.time() - start_time} seconds"
     )
 
 
@@ -349,7 +332,6 @@ def _prepare_index_attempt(db_session: Session, index_attempt_id: int) -> IndexA
         db_session=db_session,
         index_attempt_id=index_attempt_id,
     )
-
     if attempt is None:
         raise RuntimeError(f"Unable to find IndexAttempt for ID '{index_attempt_id}'")
 
@@ -360,19 +342,26 @@ def _prepare_index_attempt(db_session: Session, index_attempt_id: int) -> IndexA
         )
 
     # only commit once, to make sure this all happens in a single transaction
-    mark_attempt_in_progress(attempt, db_session)
+    mark_attempt_in_progress__no_commit(attempt)
+    is_primary = attempt.embedding_model.status == IndexModelStatus.PRESENT
+    if is_primary:
+        update_connector_credential_pair(
+            db_session=db_session,
+            connector_id=attempt.connector.id,
+            credential_id=attempt.credential.id,
+            attempt_status=IndexingStatus.IN_PROGRESS,
+        )
+    else:
+        db_session.commit()
 
     return attempt
 
 
-def run_indexing_entrypoint(index_attempt_id: int, is_ee: bool = False) -> None:
+def run_indexing_entrypoint(index_attempt_id: int) -> None:
     """Entrypoint for indexing run when using dask distributed.
     Wraps the actual logic in a `try` block so that we can catch any exceptions
     and mark the attempt as failed."""
     try:
-        if is_ee:
-            global_version.set_ee()
-
         # set the indexing attempt ID so that all log messages from this process
         # will have it added as a prefix
         IndexAttemptSingleton.set_index_attempt_id(index_attempt_id)
@@ -383,19 +372,17 @@ def run_indexing_entrypoint(index_attempt_id: int, is_ee: bool = False) -> None:
             attempt = _prepare_index_attempt(db_session, index_attempt_id)
 
             logger.info(
-                f"Indexing starting: "
-                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}'"
+                f"Running indexing attempt for connector: '{attempt.connector.name}', "
+                f"with config: '{attempt.connector.connector_specific_config}', and "
+                f"with credentials: '{attempt.credential_id}'"
             )
 
             _run_indexing(db_session, attempt)
 
             logger.info(
-                f"Indexing finished: "
-                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}'"
+                f"Completed indexing attempt for connector: '{attempt.connector.name}', "
+                f"with config: '{attempt.connector.connector_specific_config}', and "
+                f"with credentials: '{attempt.credential_id}'"
             )
     except Exception as e:
         logger.exception(f"Indexing job with ID '{index_attempt_id}' failed due to {e}")

backend/danswer/background/indexing/tracer.py

@@ -1,77 +0,0 @@
-import tracemalloc
-
-from danswer.utils.logger import setup_logger
-
-logger = setup_logger()
-
-DANSWER_TRACEMALLOC_FRAMES = 10
-
-
-class DanswerTracer:
-    def __init__(self) -> None:
-        self.snapshot_first: tracemalloc.Snapshot | None = None
-        self.snapshot_prev: tracemalloc.Snapshot | None = None
-        self.snapshot: tracemalloc.Snapshot | None = None
-
-    def start(self) -> None:
-        tracemalloc.start(DANSWER_TRACEMALLOC_FRAMES)
-
-    def stop(self) -> None:
-        tracemalloc.stop()
-
-    def snap(self) -> None:
-        snapshot = tracemalloc.take_snapshot()
-        # Filter out irrelevant frames (e.g., from tracemalloc itself or importlib)
-        snapshot = snapshot.filter_traces(
-            (
-                tracemalloc.Filter(False, tracemalloc.__file__),  # Exclude tracemalloc
-                tracemalloc.Filter(
-                    False, "<frozen importlib._bootstrap>"
-                ),  # Exclude importlib
-                tracemalloc.Filter(
-                    False, "<frozen importlib._bootstrap_external>"
-                ),  # Exclude external importlib
-            )
-        )
-
-        if not self.snapshot_first:
-            self.snapshot_first = snapshot
-
-        if self.snapshot:
-            self.snapshot_prev = self.snapshot
-
-        self.snapshot = snapshot
-
-    def log_snapshot(self, numEntries: int) -> None:
-        if not self.snapshot:
-            return
-
-        stats = self.snapshot.statistics("traceback")
-        for s in stats[:numEntries]:
-            logger.info(f"Tracer snap: {s}")
-            for line in s.traceback:
-                logger.info(f"* {line}")
-
-    @staticmethod
-    def log_diff(
-        snap_current: tracemalloc.Snapshot,
-        snap_previous: tracemalloc.Snapshot,
-        numEntries: int,
-    ) -> None:
-        stats = snap_current.compare_to(snap_previous, "traceback")
-        for s in stats[:numEntries]:
-            logger.info(f"Tracer diff: {s}")
-            for line in s.traceback.format():
-                logger.info(f"* {line}")
-
-    def log_previous_diff(self, numEntries: int) -> None:
-        if not self.snapshot or not self.snapshot_prev:
-            return
-
-        DanswerTracer.log_diff(self.snapshot, self.snapshot_prev, numEntries)
-
-    def log_first_diff(self, numEntries: int) -> None:
-        if not self.snapshot or not self.snapshot_first:
-            return
-
-        DanswerTracer.log_diff(self.snapshot, self.snapshot_first, numEntries)

backend/danswer/background/task_utils.py

@@ -22,15 +22,6 @@ def name_document_set_sync_task(document_set_id: int) -> str:
     return f"sync_doc_set_{document_set_id}"
 
 
-def name_cc_prune_task(
-    connector_id: int | None = None, credential_id: int | None = None
-) -> str:
-    task_name = f"prune_connector_credential_pair_{connector_id}_{credential_id}"
-    if not connector_id or not credential_id:
-        task_name = "prune_connector_credential_pair"
-    return task_name
-
-
 T = TypeVar("T", bound=Callable)
 
 

backend/danswer/background/update.py

@@ -16,32 +16,27 @@ from danswer.configs.app_configs import CLEANUP_INDEXING_JOBS_TIMEOUT
 from danswer.configs.app_configs import DASK_JOB_CLIENT_ENABLED
 from danswer.configs.app_configs import DISABLE_INDEX_UPDATE_ON_SWAP
 from danswer.configs.app_configs import NUM_INDEXING_WORKERS
-from danswer.configs.app_configs import NUM_SECONDARY_INDEXING_WORKERS
-from danswer.configs.constants import POSTGRES_INDEXER_APP_NAME
 from danswer.db.connector import fetch_connectors
-from danswer.db.connector_credential_pair import fetch_connector_credential_pairs
+from danswer.db.connector_credential_pair import mark_all_in_progress_cc_pairs_failed
+from danswer.db.connector_credential_pair import update_connector_credential_pair
 from danswer.db.embedding_model import get_current_db_embedding_model
 from danswer.db.embedding_model import get_secondary_db_embedding_model
 from danswer.db.engine import get_db_current_time
 from danswer.db.engine import get_sqlalchemy_engine
-from danswer.db.engine import init_sqlalchemy_engine
-from danswer.db.enums import ConnectorCredentialPairStatus
 from danswer.db.index_attempt import create_index_attempt
 from danswer.db.index_attempt import get_index_attempt
 from danswer.db.index_attempt import get_inprogress_index_attempts
-from danswer.db.index_attempt import get_last_attempt_for_cc_pair
+from danswer.db.index_attempt import get_last_attempt
 from danswer.db.index_attempt import get_not_started_index_attempts
 from danswer.db.index_attempt import mark_attempt_failed
-from danswer.db.models import ConnectorCredentialPair
+from danswer.db.models import Connector
 from danswer.db.models import EmbeddingModel
 from danswer.db.models import IndexAttempt
 from danswer.db.models import IndexingStatus
 from danswer.db.models import IndexModelStatus
 from danswer.db.swap_index import check_index_swap
-from danswer.natural_language_processing.search_nlp_models import warm_up_bi_encoder
+from danswer.search.search_nlp_models import warm_up_encoders
 from danswer.utils.logger import setup_logger
-from danswer.utils.variable_functionality import global_version
-from danswer.utils.variable_functionality import set_is_ee_based_on_env_variable
 from shared_configs.configs import INDEXING_MODEL_SERVER_HOST
 from shared_configs.configs import LOG_LEVEL
 from shared_configs.configs import MODEL_SERVER_PORT
@@ -58,14 +53,12 @@ _UNEXPECTED_STATE_FAILURE_REASON = (
 
 
 def _should_create_new_indexing(
-    cc_pair: ConnectorCredentialPair,
+    connector: Connector,
     last_index: IndexAttempt | None,
     model: EmbeddingModel,
     secondary_index_building: bool,
     db_session: Session,
 ) -> bool:
-    connector = cc_pair.connector
-
     # User can still manually create single indexing attempts via the UI for the
     # currently in use index
     if DISABLE_INDEX_UPDATE_ON_SWAP:
@@ -73,46 +66,28 @@ def _should_create_new_indexing(
             return False
 
     # When switching over models, always index at least once
-    if model.status == IndexModelStatus.FUTURE:
-        if last_index:
-            # No new index if the last index attempt succeeded
-            # Once is enough. The model will never be able to swap otherwise.
-            if last_index.status == IndexingStatus.SUCCESS:
-                return False
-
-            # No new index if the last index attempt is waiting to start
-            if last_index.status == IndexingStatus.NOT_STARTED:
-                return False
-
-            # No new index if the last index attempt is running
-            if last_index.status == IndexingStatus.IN_PROGRESS:
-                return False
-        else:
-            if connector.id == 0:  # Ingestion API
-                return False
+    if model.status == IndexModelStatus.FUTURE and not last_index:
+        if connector.id == 0:  # Ingestion API
+            return False
         return True
 
-    # If the connector is paused or is the ingestion API, don't index
-    # NOTE: during an embedding model switch over, the following logic
-    # is bypassed by the above check for a future model
-    if cc_pair.status == ConnectorCredentialPairStatus.PAUSED or connector.id == 0:
+    # If the connector is disabled, don't index
+    # NOTE: during an embedding model switch over, we ignore this
+    # and index the disabled connectors as well (which is why this if
+    # statement is below the first condition above)
+    if connector.disabled:
         return False
 
-    if not last_index:
-        return True
-
     if connector.refresh_freq is None:
         return False
+    if not last_index:
+        return True
 
-    # Only one scheduled/ongoing job per connector at a time
-    # this prevents cases where
-    # (1) the "latest" index_attempt is scheduled so we show
-    #     that in the UI despite another index_attempt being in-progress
-    # (2) multiple scheduled index_attempts at a time
-    if (
-        last_index.status == IndexingStatus.NOT_STARTED
-        or last_index.status == IndexingStatus.IN_PROGRESS
-    ):
+    # Only one scheduled job per connector at a time
+    # Can schedule another one if the current one is already running however
+    # Because the currently running one will not be until the latest time
+    # Note, this last index is for the given embedding model
+    if last_index.status == IndexingStatus.NOT_STARTED:
         return False
 
     current_db_time = get_db_current_time(db_session)
@@ -136,14 +111,25 @@ def _mark_run_failed(
     """Marks the `index_attempt` row as failed + updates the `
     connector_credential_pair` to reflect that the run failed"""
     logger.warning(
-        f"Marking in-progress attempt 'connector: {index_attempt.connector_credential_pair.connector_id}, "
-        f"credential: {index_attempt.connector_credential_pair.credential_id}' as failed due to {failure_reason}"
+        f"Marking in-progress attempt 'connector: {index_attempt.connector_id}, "
+        f"credential: {index_attempt.credential_id}' as failed due to {failure_reason}"
     )
     mark_attempt_failed(
         index_attempt=index_attempt,
         db_session=db_session,
         failure_reason=failure_reason,
     )
+    if (
+        index_attempt.connector_id is not None
+        and index_attempt.credential_id is not None
+        and index_attempt.embedding_model.status == IndexModelStatus.PRESENT
+    ):
+        update_connector_credential_pair(
+            db_session=db_session,
+            connector_id=index_attempt.connector_id,
+            credential_id=index_attempt.credential_id,
+            attempt_status=IndexingStatus.FAILED,
+        )
 
 
 """Main funcs"""
@@ -156,7 +142,7 @@ def create_indexing_jobs(existing_jobs: dict[int, Future | SimpleJob]) -> None:
     3. There is not already an ongoing indexing attempt for this pair
     """
     with Session(get_sqlalchemy_engine()) as db_session:
-        ongoing: set[tuple[int | None, int]] = set()
+        ongoing: set[tuple[int | None, int | None, int]] = set()
         for attempt_id in existing_jobs:
             attempt = get_index_attempt(
                 db_session=db_session, index_attempt_id=attempt_id
@@ -169,7 +155,8 @@ def create_indexing_jobs(existing_jobs: dict[int, Future | SimpleJob]) -> None:
                 continue
             ongoing.add(
                 (
-                    attempt.connector_credential_pair_id,
+                    attempt.connector_id,
+                    attempt.credential_id,
                     attempt.embedding_model_id,
                 )
             )
@@ -179,26 +166,41 @@ def create_indexing_jobs(existing_jobs: dict[int, Future | SimpleJob]) -> None:
         if secondary_embedding_model is not None:
             embedding_models.append(secondary_embedding_model)
 
-        all_connector_credential_pairs = fetch_connector_credential_pairs(db_session)
-        for cc_pair in all_connector_credential_pairs:
-            for model in embedding_models:
-                # Check if there is an ongoing indexing attempt for this connector credential pair
-                if (cc_pair.id, model.id) in ongoing:
-                    continue
+        all_connectors = fetch_connectors(db_session)
+        for connector in all_connectors:
+            for association in connector.credentials:
+                for model in embedding_models:
+                    credential = association.credential
 
-                last_attempt = get_last_attempt_for_cc_pair(
-                    cc_pair.id, model.id, db_session
-                )
-                if not _should_create_new_indexing(
-                    cc_pair=cc_pair,
-                    last_index=last_attempt,
-                    model=model,
-                    secondary_index_building=len(embedding_models) > 1,
-                    db_session=db_session,
-                ):
-                    continue
+                    # Check if there is an ongoing indexing attempt for this connector + credential pair
+                    if (connector.id, credential.id, model.id) in ongoing:
+                        continue
 
-                create_index_attempt(cc_pair.id, model.id, db_session)
+                    last_attempt = get_last_attempt(
+                        connector.id, credential.id, model.id, db_session
+                    )
+                    if not _should_create_new_indexing(
+                        connector=connector,
+                        last_index=last_attempt,
+                        model=model,
+                        secondary_index_building=len(embedding_models) > 1,
+                        db_session=db_session,
+                    ):
+                        continue
+
+                    create_index_attempt(
+                        connector.id, credential.id, model.id, db_session
+                    )
+
+                    # CC-Pair will have the status that it should for the primary index
+                    # Will be re-sync-ed once the indices are swapped
+                    if model.status == IndexModelStatus.PRESENT:
+                        update_connector_credential_pair(
+                            db_session=db_session,
+                            connector_id=connector.id,
+                            credential_id=credential.id,
+                            attempt_status=IndexingStatus.NOT_STARTED,
+                        )
 
 
 def cleanup_indexing_jobs(
@@ -290,28 +292,24 @@ def kickoff_indexing_jobs(
     # Don't include jobs waiting in the Dask queue that just haven't started running
     # Also (rarely) don't include for jobs that started but haven't updated the indexing tables yet
     with Session(engine) as db_session:
-        # get_not_started_index_attempts orders its returned results from oldest to newest
-        # we must process attempts in a FIFO manner to prevent connector starvation
         new_indexing_attempts = [
             (attempt, attempt.embedding_model)
             for attempt in get_not_started_index_attempts(db_session)
             if attempt.id not in existing_jobs
         ]
 
-    logger.debug(f"Found {len(new_indexing_attempts)} new indexing task(s).")
+    logger.info(f"Found {len(new_indexing_attempts)} new indexing tasks.")
 
     if not new_indexing_attempts:
         return existing_jobs
 
-    indexing_attempt_count = 0
-
     for attempt, embedding_model in new_indexing_attempts:
         use_secondary_index = (
             embedding_model.status == IndexModelStatus.FUTURE
             if embedding_model is not None
             else False
         )
-        if attempt.connector_credential_pair.connector is None:
+        if attempt.connector is None:
             logger.warning(
                 f"Skipping index attempt as Connector has been deleted: {attempt}"
             )
@@ -320,7 +318,7 @@ def kickoff_indexing_jobs(
                     attempt, db_session, failure_reason="Connector is null"
                 )
             continue
-        if attempt.connector_credential_pair.credential is None:
+        if attempt.credential is None:
             logger.warning(
                 f"Skipping index attempt as Credential has been deleted: {attempt}"
             )
@@ -332,67 +330,39 @@ def kickoff_indexing_jobs(
 
         if use_secondary_index:
             run = secondary_client.submit(
-                run_indexing_entrypoint,
-                attempt.id,
-                global_version.get_is_ee_version(),
-                pure=False,
+                run_indexing_entrypoint, attempt.id, pure=False
             )
         else:
-            run = client.submit(
-                run_indexing_entrypoint,
-                attempt.id,
-                global_version.get_is_ee_version(),
-                pure=False,
-            )
+            run = client.submit(run_indexing_entrypoint, attempt.id, pure=False)
 
         if run:
-            if indexing_attempt_count == 0:
-                logger.info(
-                    f"Indexing dispatch starts: pending={len(new_indexing_attempts)}"
-                )
-
-            indexing_attempt_count += 1
-            secondary_str = " (secondary index)" if use_secondary_index else ""
+            secondary_str = "(secondary index) " if use_secondary_index else ""
             logger.info(
-                f"Indexing dispatched{secondary_str}: "
-                f"attempt_id={attempt.id} "
-                f"connector='{attempt.connector_credential_pair.connector.name}' "
-                f"config='{attempt.connector_credential_pair.connector.connector_specific_config}' "
-                f"credentials='{attempt.connector_credential_pair.credential_id}'"
+                f"Kicked off {secondary_str}"
+                f"indexing attempt for connector: '{attempt.connector.name}', "
+                f"with config: '{attempt.connector.connector_specific_config}', and "
+                f"with credentials: '{attempt.credential_id}'"
             )
             existing_jobs_copy[attempt.id] = run
 
-    if indexing_attempt_count > 0:
-        logger.info(
-            f"Indexing dispatch results: "
-            f"initial_pending={len(new_indexing_attempts)} "
-            f"started={indexing_attempt_count} "
-            f"remaining={len(new_indexing_attempts) - indexing_attempt_count}"
-        )
-
     return existing_jobs_copy
 
 
-def update_loop(
-    delay: int = 10,
-    num_workers: int = NUM_INDEXING_WORKERS,
-    num_secondary_workers: int = NUM_SECONDARY_INDEXING_WORKERS,
-) -> None:
+def update_loop(delay: int = 10, num_workers: int = NUM_INDEXING_WORKERS) -> None:
     engine = get_sqlalchemy_engine()
     with Session(engine) as db_session:
         check_index_swap(db_session=db_session)
         db_embedding_model = get_current_db_embedding_model(db_session)
 
-        # So that the first time users aren't surprised by really slow speed of first
-        # batch of documents indexed
-
-        if db_embedding_model.cloud_provider_id is None:
-            logger.debug("Running a first inference to warm up embedding model")
-            warm_up_bi_encoder(
-                embedding_model=db_embedding_model,
-                model_server_host=INDEXING_MODEL_SERVER_HOST,
-                model_server_port=MODEL_SERVER_PORT,
-            )
+    # So that the first time users aren't surprised by really slow speed of first
+    # batch of documents indexed
+    logger.info("Running a first inference to warm up embedding model")
+    warm_up_encoders(
+        model_name=db_embedding_model.model_name,
+        normalize=db_embedding_model.normalize,
+        model_server_host=INDEXING_MODEL_SERVER_HOST,
+        model_server_port=MODEL_SERVER_PORT,
+    )
 
     client_primary: Client | SimpleJobClient
     client_secondary: Client | SimpleJobClient
@@ -407,7 +377,7 @@ def update_loop(
             silence_logs=logging.ERROR,
         )
         cluster_secondary = LocalCluster(
-            n_workers=num_secondary_workers,
+            n_workers=num_workers,
             threads_per_worker=1,
             silence_logs=logging.ERROR,
         )
@@ -417,18 +387,23 @@ def update_loop(
             client_primary.register_worker_plugin(ResourceLogger())
     else:
         client_primary = SimpleJobClient(n_workers=num_workers)
-        client_secondary = SimpleJobClient(n_workers=num_secondary_workers)
+        client_secondary = SimpleJobClient(n_workers=num_workers)
 
     existing_jobs: dict[int, Future | SimpleJob] = {}
 
+    with Session(engine) as db_session:
+        # Previous version did not always clean up cc-pairs well leaving some connectors undeleteable
+        # This ensures that bad states get cleaned up
+        mark_all_in_progress_cc_pairs_failed(db_session)
+
     while True:
         start = time.time()
         start_time_utc = datetime.utcfromtimestamp(start).strftime("%Y-%m-%d %H:%M:%S")
-        logger.debug(f"Running update, current UTC time: {start_time_utc}")
+        logger.info(f"Running update, current UTC time: {start_time_utc}")
 
         if existing_jobs:
             # TODO: make this debug level once the "no jobs are being scheduled" issue is resolved
-            logger.debug(
+            logger.info(
                 "Found existing indexing jobs: "
                 f"{[(attempt_id, job.status) for attempt_id, job in existing_jobs.items()]}"
             )
@@ -451,10 +426,7 @@ def update_loop(
 
 
 def update__main() -> None:
-    set_is_ee_based_on_env_variable()
-    init_sqlalchemy_engine(POSTGRES_INDEXER_APP_NAME)
-
-    logger.info("Starting indexing service")
+    logger.info("Starting Indexing Loop")
     update_loop()
 
 

backend/danswer/chat/chat_utils.py

@@ -1,4 +1,5 @@
 import re
+from collections.abc import Sequence
 from typing import cast
 
 from sqlalchemy.orm import Session
@@ -8,45 +9,53 @@ from danswer.chat.models import LlmDoc
 from danswer.db.chat import get_chat_messages_by_session
 from danswer.db.models import ChatMessage
 from danswer.llm.answering.models import PreviousMessage
+from danswer.search.models import InferenceChunk
 from danswer.search.models import InferenceSection
 from danswer.utils.logger import setup_logger
 
 logger = setup_logger()
 
 
-def llm_doc_from_inference_section(inference_section: InferenceSection) -> LlmDoc:
+def llm_doc_from_inference_section(inf_chunk: InferenceSection) -> LlmDoc:
     return LlmDoc(
-        document_id=inference_section.center_chunk.document_id,
+        document_id=inf_chunk.document_id,
         # This one is using the combined content of all the chunks of the section
         # In default settings, this is the same as just the content of base chunk
-        content=inference_section.combined_content,
-        blurb=inference_section.center_chunk.blurb,
-        semantic_identifier=inference_section.center_chunk.semantic_identifier,
-        source_type=inference_section.center_chunk.source_type,
-        metadata=inference_section.center_chunk.metadata,
-        updated_at=inference_section.center_chunk.updated_at,
-        link=inference_section.center_chunk.source_links[0]
-        if inference_section.center_chunk.source_links
-        else None,
-        source_links=inference_section.center_chunk.source_links,
+        content=inf_chunk.combined_content,
+        blurb=inf_chunk.blurb,
+        semantic_identifier=inf_chunk.semantic_identifier,
+        source_type=inf_chunk.source_type,
+        metadata=inf_chunk.metadata,
+        updated_at=inf_chunk.updated_at,
+        link=inf_chunk.source_links[0] if inf_chunk.source_links else None,
+        source_links=inf_chunk.source_links,
     )
 
 
+def map_document_id_order(
+    chunks: Sequence[InferenceChunk | LlmDoc], one_indexed: bool = True
+) -> dict[str, int]:
+    order_mapping = {}
+    current = 1 if one_indexed else 0
+    for chunk in chunks:
+        if chunk.document_id not in order_mapping:
+            order_mapping[chunk.document_id] = current
+            current += 1
+
+    return order_mapping
+
+
 def create_chat_chain(
     chat_session_id: int,
     db_session: Session,
-    prefetch_tool_calls: bool = True,
-    parent_id: int | None = None,
 ) -> tuple[ChatMessage, list[ChatMessage]]:
     """Build the linear chain of messages without including the root message"""
     mainline_messages: list[ChatMessage] = []
-
     all_chat_messages = get_chat_messages_by_session(
         chat_session_id=chat_session_id,
         user_id=None,
         db_session=db_session,
         skip_permission_check=True,
-        prefetch_tool_calls=prefetch_tool_calls,
     )
     id_to_msg = {msg.id: msg for msg in all_chat_messages}
 
@@ -62,7 +71,7 @@ def create_chat_chain(
     current_message: ChatMessage | None = root_message
     while current_message is not None:
         child_msg = current_message.latest_child_message
-        if not child_msg or (parent_id and current_message.id == parent_id):
+        if not child_msg:
             break
         current_message = id_to_msg.get(child_msg)
 

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/load_yamls.py

@@ -1,20 +1,18 @@
+from typing import cast
+
 import yaml
 from sqlalchemy.orm import Session
 
-from danswer.configs.chat_configs import INPUT_PROMPT_YAML
 from danswer.configs.chat_configs import MAX_CHUNKS_FED_TO_CHAT
 from danswer.configs.chat_configs import PERSONAS_YAML
 from danswer.configs.chat_configs import PROMPTS_YAML
+from danswer.db.chat import get_prompt_by_name
+from danswer.db.chat import upsert_persona
+from danswer.db.chat import upsert_prompt
 from danswer.db.document_set import get_or_create_document_set_by_name
 from danswer.db.engine import get_sqlalchemy_engine
-from danswer.db.input_prompt import insert_input_prompt_if_not_exists
 from danswer.db.models import DocumentSet as DocumentSetDBModel
-from danswer.db.models import Persona
 from danswer.db.models import Prompt as PromptDBModel
-from danswer.db.models import Tool as ToolDBModel
-from danswer.db.persona import get_prompt_by_name
-from danswer.db.persona import upsert_persona
-from danswer.db.persona import upsert_prompt
 from danswer.search.enums import RecencyBiasSetting
 
 
@@ -52,7 +50,7 @@ def load_personas_from_yaml(
     with Session(get_sqlalchemy_engine()) as db_session:
         for persona in all_personas:
             doc_set_names = persona["document_sets"]
-            doc_sets: list[DocumentSetDBModel] = [
+            doc_sets: list[DocumentSetDBModel] | None = [
                 get_or_create_document_set_by_name(db_session, name)
                 for name in doc_set_names
             ]
@@ -60,51 +58,27 @@ def load_personas_from_yaml(
             # Assume if user hasn't set any document sets for the persona, the user may want
             # to later attach document sets to the persona manually, therefore, don't overwrite/reset
             # the document sets for the persona
-            doc_set_ids: list[int] | None = None
-            if doc_sets:
-                doc_set_ids = [doc_set.id for doc_set in doc_sets]
-            else:
-                doc_set_ids = None
+            if not doc_sets:
+                doc_sets = None
 
-            prompt_ids: list[int] | None = None
             prompt_set_names = persona["prompts"]
-            if prompt_set_names:
-                prompts: list[PromptDBModel | None] = [
+            if not prompt_set_names:
+                prompts: list[PromptDBModel | None] | None = None
+            else:
+                prompts = [
                     get_prompt_by_name(prompt_name, user=None, db_session=db_session)
                     for prompt_name in prompt_set_names
                 ]
                 if any([prompt is None for prompt in prompts]):
                     raise ValueError("Invalid Persona configs, not all prompts exist")
 
-                if prompts:
-                    prompt_ids = [prompt.id for prompt in prompts if prompt is not None]
+                if not prompts:
+                    prompts = None
 
             p_id = persona.get("id")
-            tool_ids = []
-            if persona.get("image_generation"):
-                image_gen_tool = (
-                    db_session.query(ToolDBModel)
-                    .filter(ToolDBModel.name == "ImageGenerationTool")
-                    .first()
-                )
-                if image_gen_tool:
-                    tool_ids.append(image_gen_tool.id)
-
-            llm_model_provider_override = persona.get("llm_model_provider_override")
-            llm_model_version_override = persona.get("llm_model_version_override")
-
-            # Set specific overrides for image generation persona
-            if persona.get("image_generation"):
-                llm_model_version_override = "gpt-4o"
-
-            existing_persona = (
-                db_session.query(Persona)
-                .filter(Persona.name == persona["name"])
-                .first()
-            )
-
             upsert_persona(
                 user=None,
+                # Negative to not conflict with existing personas
                 persona_id=(-1 * p_id) if p_id is not None else None,
                 name=persona["name"],
                 description=persona["description"],
@@ -114,52 +88,20 @@ def load_personas_from_yaml(
                 llm_relevance_filter=persona.get("llm_relevance_filter"),
                 starter_messages=persona.get("starter_messages"),
                 llm_filter_extraction=persona.get("llm_filter_extraction"),
-                icon_shape=persona.get("icon_shape"),
-                icon_color=persona.get("icon_color"),
-                llm_model_provider_override=llm_model_provider_override,
-                llm_model_version_override=llm_model_version_override,
+                llm_model_provider_override=None,
+                llm_model_version_override=None,
                 recency_bias=RecencyBiasSetting(persona["recency_bias"]),
-                prompt_ids=prompt_ids,
-                document_set_ids=doc_set_ids,
-                tool_ids=tool_ids,
+                prompts=cast(list[PromptDBModel] | None, prompts),
+                document_sets=doc_sets,
                 default_persona=True,
                 is_public=True,
-                display_priority=existing_persona.display_priority
-                if existing_persona is not None
-                else persona.get("display_priority"),
-                is_visible=existing_persona.is_visible
-                if existing_persona is not None
-                else persona.get("is_visible"),
                 db_session=db_session,
             )
 
 
-def load_input_prompts_from_yaml(input_prompts_yaml: str = INPUT_PROMPT_YAML) -> None:
-    with open(input_prompts_yaml, "r") as file:
-        data = yaml.safe_load(file)
-
-    all_input_prompts = data.get("input_prompts", [])
-    with Session(get_sqlalchemy_engine()) as db_session:
-        for input_prompt in all_input_prompts:
-            # If these prompts are deleted (which is a hard delete in the DB), on server startup
-            # they will be recreated, but the user can always just deactivate them, just a light inconvenience
-            insert_input_prompt_if_not_exists(
-                user=None,
-                input_prompt_id=input_prompt.get("id"),
-                prompt=input_prompt["prompt"],
-                content=input_prompt["content"],
-                is_public=input_prompt["is_public"],
-                active=input_prompt.get("active", True),
-                db_session=db_session,
-                commit=True,
-            )
-
-
 def load_chat_yamls(
     prompt_yaml: str = PROMPTS_YAML,
     personas_yaml: str = PERSONAS_YAML,
-    input_prompts_yaml: str = INPUT_PROMPT_YAML,
 ) -> None:
     load_prompts_from_yaml(prompt_yaml)
     load_personas_from_yaml(personas_yaml)
-    load_input_prompts_from_yaml(input_prompts_yaml)

backend/danswer/chat/models.py

@@ -42,32 +42,11 @@ class QADocsResponse(RetrievalDocs):
         return initial_dict
 
 
+# Second chunk of info for streaming QA
 class LLMRelevanceFilterResponse(BaseModel):
     relevant_chunk_indices: list[int]
 
 
-class RelevanceAnalysis(BaseModel):
-    relevant: bool
-    content: str | None = None
-
-
-class SectionRelevancePiece(RelevanceAnalysis):
-    """LLM analysis mapped to an Inference Section"""
-
-    document_id: str
-    chunk_id: int  # ID of the center chunk for a given inference section
-
-
-class DocumentRelevance(BaseModel):
-    """Contains all relevance information for a given search"""
-
-    relevance_summaries: dict[str, RelevanceAnalysis]
-
-
-class Delimiter(BaseModel):
-    delimiter: bool
-
-
 class DanswerAnswerPiece(BaseModel):
     # A small piece of a complete answer. Used for streaming back answers.
     answer_piece: str | None  # if None, specifies the end of an Answer
@@ -80,14 +59,8 @@ class CitationInfo(BaseModel):
     document_id: str
 
 
-class MessageResponseIDInfo(BaseModel):
-    user_message_id: int | None
-    reserved_assistant_message_id: int
-
-
 class StreamingError(BaseModel):
     error: str
-    stack_trace: str | None = None
 
 
 class DanswerQuote(BaseModel):
@@ -133,20 +106,13 @@ class ImageGenerationDisplay(BaseModel):
     file_ids: list[str]
 
 
-class CustomToolResponse(BaseModel):
-    response: dict
-    tool_name: str
-
-
 AnswerQuestionPossibleReturn = (
     DanswerAnswerPiece
     | DanswerQuotes
     | CitationInfo
     | DanswerContexts
     | ImageGenerationDisplay
-    | CustomToolResponse
     | StreamingError
-    | Delimiter
 )
 
 

backend/danswer/chat/personas.yaml

@@ -5,7 +5,7 @@ personas:
   # this is for DanswerBot to use when tagged in a non-configured channel
   # Careful setting specific IDs, this won't autoincrement the next ID value for postgres
   - id: 0
-    name: "Knowledge"
+    name: "Danswer"
     description: >
       Assistant with access to documents from your Connected Sources.
     # Default Prompt objects attached to the persona, see prompts.yaml
@@ -17,7 +17,7 @@ personas:
     num_chunks: 10
     # Enable/Disable usage of the LLM chunk filter feature whereby each chunk is passed to the LLM to determine
     # if the chunk is useful or not towards the latest user query
-    # This feature can be overriden for all personas via DISABLE_LLM_DOC_RELEVANCE env variable
+    # This feature can be overriden for all personas via DISABLE_LLM_CHUNK_FILTER env variable
     llm_relevance_filter: true
     # Enable/Disable usage of the LLM to extract query time filters including source type and time range filters
     llm_filter_extraction: true
@@ -37,15 +37,12 @@ personas:
     #   - "Engineer Onboarding"
     #   - "Benefits"
     document_sets: []
-    icon_shape: 23013
-    icon_color: "#6FB1FF"
-    display_priority: 1
-    is_visible: true
+
 
   - id: 1
-    name: "General"
+    name: "GPT"
     description: >
-      Assistant with no access to documents. Chat with just the Large Language Model.
+      Assistant with no access to documents. Chat with just the Language Model.
     prompts:
       - "OnlyLLM"
     num_chunks: 0
@@ -53,10 +50,7 @@ personas:
     llm_filter_extraction: true
     recency_bias: "auto"
     document_sets: []
-    icon_shape: 50910
-    icon_color: "#FF6F6F"
-    display_priority: 0
-    is_visible: true
+
 
   - id: 2
     name: "Paraphrase"
@@ -69,25 +63,3 @@ personas:
     llm_filter_extraction: true
     recency_bias: "auto"
     document_sets: []
-    icon_shape: 45519
-    icon_color: "#6FFF8D"
-    display_priority: 2
-    is_visible: false
-
-
-  - id: 3
-    name: "Art"
-    description: >
-      Assistant for generating images based on descriptions.
-    prompts:
-      - "ImageGeneration"
-    num_chunks: 0
-    llm_relevance_filter: false
-    llm_filter_extraction: false
-    recency_bias: "no_decay"
-    document_sets: []
-    icon_shape: 234124 
-    icon_color: "#9B59B6"
-    image_generation: true 
-    display_priority: 3
-    is_visible: true

backend/danswer/chat/process_message.py

@@ -7,21 +7,15 @@ from sqlalchemy.orm import Session
 
 from danswer.chat.chat_utils import create_chat_chain
 from danswer.chat.models import CitationInfo
-from danswer.chat.models import CustomToolResponse
 from danswer.chat.models import DanswerAnswerPiece
-from danswer.chat.models import Delimiter
 from danswer.chat.models import ImageGenerationDisplay
+from danswer.chat.models import LlmDoc
 from danswer.chat.models import LLMRelevanceFilterResponse
-from danswer.chat.models import MessageResponseIDInfo
 from danswer.chat.models import QADocsResponse
 from danswer.chat.models import StreamingError
-from danswer.configs.chat_configs import BING_API_KEY
 from danswer.configs.chat_configs import CHAT_TARGET_CHUNK_PERCENTAGE
-from danswer.configs.chat_configs import DISABLE_LLM_CHOOSE_SEARCH
 from danswer.configs.chat_configs import MAX_CHUNKS_FED_TO_CHAT
 from danswer.configs.constants import MessageType
-from danswer.configs.model_configs import GEN_AI_TEMPERATURE
-from danswer.db.chat import attach_files_to_chat_message
 from danswer.db.chat import create_db_search_doc
 from danswer.db.chat import create_new_chat_message
 from danswer.db.chat import get_chat_message
@@ -29,16 +23,13 @@ from danswer.db.chat import get_chat_session_by_id
 from danswer.db.chat import get_db_search_doc_by_id
 from danswer.db.chat import get_doc_query_identifiers_from_model
 from danswer.db.chat import get_or_create_root_message
-from danswer.db.chat import reserve_message_id
 from danswer.db.chat import translate_db_message_to_chat_message_detail
 from danswer.db.chat import translate_db_search_doc_to_server_search_doc
 from danswer.db.embedding_model import get_current_db_embedding_model
 from danswer.db.engine import get_session_context_manager
 from danswer.db.llm import fetch_existing_llm_providers
 from danswer.db.models import SearchDoc as DbSearchDoc
-from danswer.db.models import ToolCall
 from danswer.db.models import User
-from danswer.db.persona import get_persona_by_id
 from danswer.document_index.factory import get_default_document_index
 from danswer.file_store.models import ChatFileType
 from danswer.file_store.models import FileDescriptor
@@ -51,47 +42,25 @@ from danswer.llm.answering.models import DocumentPruningConfig
 from danswer.llm.answering.models import PreviousMessage
 from danswer.llm.answering.models import PromptConfig
 from danswer.llm.exceptions import GenAIDisabledException
-from danswer.llm.factory import get_llms_for_persona
-from danswer.llm.factory import get_main_llm_from_tuple
-from danswer.llm.interfaces import LLMConfig
-from danswer.llm.utils import litellm_exception_to_error_msg
-from danswer.natural_language_processing.utils import get_tokenizer
-from danswer.search.enums import LLMEvaluationType
+from danswer.llm.factory import get_llm_for_persona
+from danswer.llm.utils import get_default_llm_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.retrieval.search_runner import inference_sections_from_ids
+from danswer.search.retrieval.search_runner import inference_documents_from_ids
 from danswer.search.utils import chunks_or_sections_to_search_docs
-from danswer.search.utils import dedupe_documents
-from danswer.search.utils import drop_llm_indices
-from danswer.search.utils import relevant_sections_to_indices
 from danswer.server.query_and_chat.models import ChatMessageDetail
 from danswer.server.query_and_chat.models import CreateChatMessageRequest
 from danswer.server.utils import get_json_line
-from danswer.tools.built_in_tools import get_built_in_tool_by_id
-from danswer.tools.custom.custom_tool import build_custom_tools_from_openapi_schema
-from danswer.tools.custom.custom_tool import CUSTOM_TOOL_RESPONSE_ID
-from danswer.tools.custom.custom_tool import CustomToolCallSummary
+from danswer.tools.factory import get_tool_cls
 from danswer.tools.force import ForceUseTool
 from danswer.tools.images.image_generation_tool import IMAGE_GENERATION_RESPONSE_ID
 from danswer.tools.images.image_generation_tool import ImageGenerationResponse
 from danswer.tools.images.image_generation_tool import ImageGenerationTool
-from danswer.tools.internet_search.internet_search_tool import (
-    INTERNET_SEARCH_RESPONSE_ID,
-)
-from danswer.tools.internet_search.internet_search_tool import (
-    internet_search_response_to_search_docs,
-)
-from danswer.tools.internet_search.internet_search_tool import InternetSearchResponse
-from danswer.tools.internet_search.internet_search_tool import InternetSearchTool
 from danswer.tools.search.search_tool import SEARCH_RESPONSE_SUMMARY_ID
 from danswer.tools.search.search_tool import SearchResponseSummary
 from danswer.tools.search.search_tool import SearchTool
 from danswer.tools.search.search_tool import SECTION_RELEVANCE_LIST_ID
 from danswer.tools.tool import Tool
 from danswer.tools.tool import ToolResponse
-from danswer.tools.tool_runner import ToolCallMetadata
 from danswer.tools.utils import compute_all_tool_tokens
 from danswer.tools.utils import explicit_tool_calling_supported
 from danswer.utils.logger import setup_logger
@@ -124,21 +93,14 @@ def _handle_search_tool_response_summary(
     packet: ToolResponse,
     db_session: Session,
     selected_search_docs: list[DbSearchDoc] | None,
-    dedupe_docs: bool = False,
-) -> tuple[QADocsResponse, list[DbSearchDoc], list[int] | None]:
+) -> tuple[QADocsResponse, list[DbSearchDoc]]:
     response_sumary = cast(SearchResponseSummary, packet.response)
 
-    dropped_inds = None
     if not selected_search_docs:
         top_docs = chunks_or_sections_to_search_docs(response_sumary.top_sections)
-
-        deduped_docs = top_docs
-        if dedupe_docs:
-            deduped_docs, dropped_inds = dedupe_documents(top_docs)
-
         reference_db_search_docs = [
-            create_db_search_doc(server_search_doc=doc, db_session=db_session)
-            for doc in deduped_docs
+            create_db_search_doc(server_search_doc=top_doc, db_session=db_session)
+            for top_doc in top_docs
         ]
     else:
         reference_db_search_docs = selected_search_docs
@@ -158,81 +120,35 @@ def _handle_search_tool_response_summary(
             recency_bias_multiplier=response_sumary.recency_bias_multiplier,
         ),
         reference_db_search_docs,
-        dropped_inds,
     )
 
 
-def _handle_internet_search_tool_response_summary(
-    packet: ToolResponse,
-    db_session: Session,
-) -> tuple[QADocsResponse, list[DbSearchDoc]]:
-    internet_search_response = cast(InternetSearchResponse, packet.response)
-    server_search_docs = internet_search_response_to_search_docs(
-        internet_search_response
-    )
-
-    reference_db_search_docs = [
-        create_db_search_doc(server_search_doc=doc, db_session=db_session)
-        for doc in server_search_docs
-    ]
-    response_docs = [
-        translate_db_search_doc_to_server_search_doc(db_search_doc)
-        for db_search_doc in reference_db_search_docs
-    ]
-    return (
-        QADocsResponse(
-            rephrased_query=internet_search_response.revised_query,
-            top_documents=response_docs,
-            predicted_flow=QueryFlow.QUESTION_ANSWER,
-            predicted_search=SearchType.SEMANTIC,
-            applied_source_filters=[],
-            applied_time_cutoff=None,
-            recency_bias_multiplier=1.0,
-        ),
-        reference_db_search_docs,
-    )
-
-
-def _get_force_search_settings(
-    new_msg_req: CreateChatMessageRequest, tools: list[Tool]
-) -> ForceUseTool:
-    internet_search_available = any(
-        isinstance(tool, InternetSearchTool) for tool in tools
-    )
-    search_tool_available = any(isinstance(tool, SearchTool) for tool in tools)
-
-    if not internet_search_available and not search_tool_available:
-        # Does not matter much which tool is set here as force is false and neither tool is available
-        return ForceUseTool(force_use=False, tool_name=SearchTool._NAME)
-
-    tool_name = SearchTool._NAME if search_tool_available else InternetSearchTool._NAME
-    # Currently, the internet search tool does not support query override
-    args = (
-        {"query": new_msg_req.query_override}
-        if new_msg_req.query_override and tool_name == SearchTool._NAME
-        else None
-    )
-
-    if new_msg_req.file_descriptors:
-        # If user has uploaded files they're using, don't run any of the search tools
-        return ForceUseTool(force_use=False, tool_name=tool_name)
-
-    should_force_search = any(
-        [
+def _check_should_force_search(
+    new_msg_req: CreateChatMessageRequest,
+) -> ForceUseTool | None:
+    if (
+        new_msg_req.query_override
+        or (
             new_msg_req.retrieval_options
-            and new_msg_req.retrieval_options.run_search
-            == OptionalSearchSetting.ALWAYS,
-            new_msg_req.search_doc_ids,
-            DISABLE_LLM_CHOOSE_SEARCH,
-        ]
-    )
+            and new_msg_req.retrieval_options.run_search == OptionalSearchSetting.ALWAYS
+        )
+        or new_msg_req.search_doc_ids
+    ):
+        args = (
+            {"query": new_msg_req.query_override}
+            if new_msg_req.query_override
+            else None
+        )
+        # if we are using selected docs, just put something here so the Tool doesn't need
+        # to build its own args via an LLM call
+        if new_msg_req.search_doc_ids:
+            args = {"query": new_msg_req.message}
 
-    if should_force_search:
-        # If we are using selected docs, just put something here so the Tool doesn't need to build its own args via an LLM call
-        args = {"query": new_msg_req.message} if new_msg_req.search_doc_ids else args
-        return ForceUseTool(force_use=True, tool_name=tool_name, args=args)
-
-    return ForceUseTool(force_use=False, tool_name=tool_name, args=args)
+        return ForceUseTool(
+            tool_name=SearchTool.name(),
+            args=args,
+        )
+    return None
 
 
 ChatPacket = (
@@ -243,9 +159,6 @@ ChatPacket = (
     | DanswerAnswerPiece
     | CitationInfo
     | ImageGenerationDisplay
-    | CustomToolResponse
-    | MessageResponseIDInfo
-    | Delimiter
 )
 ChatPacketStream = Iterator[ChatPacket]
 
@@ -261,15 +174,15 @@ def stream_chat_message_objects(
     max_document_percentage: float = CHAT_TARGET_CHUNK_PERCENTAGE,
     # if specified, uses the last user message and does not create a new user message based
     # on the `new_msg_req.message`. Currently, requires a state where the last message is a
+    # user message (e.g. this can only be used for the chat-seeding flow).
     use_existing_user_message: bool = False,
-    litellm_additional_headers: dict[str, str] | None = None,
-    is_connected: Callable[[], bool] | None = None,
 ) -> ChatPacketStream:
     """Streams in order:
     1. [conditional] Retrieved documents if a search needs to be run
     2. [conditional] LLM selected chunk indices if LLM chunk filtering is turned on
     3. [always] A set of streamed LLM tokens or an error anywhere along the line if something fails
     4. [always] Details on the final AI response message that is created
+
     """
     try:
         user_id = user.id if user is not None else None
@@ -285,18 +198,7 @@ def stream_chat_message_objects(
         parent_id = new_msg_req.parent_message_id
         reference_doc_ids = new_msg_req.search_doc_ids
         retrieval_options = new_msg_req.retrieval_options
-        alternate_assistant_id = new_msg_req.alternate_assistant_id
-
-        # use alternate persona if alternative assistant id is passed in
-        if alternate_assistant_id is not None:
-            persona = get_persona_by_id(
-                alternate_assistant_id,
-                user=user,
-                db_session=db_session,
-                is_for_edit=False,
-            )
-        else:
-            persona = chat_session.persona
+        persona = chat_session.persona
 
         prompt_id = new_msg_req.prompt_id
         if prompt_id is None and persona.prompts:
@@ -308,21 +210,13 @@ def stream_chat_message_objects(
             )
 
         try:
-            llm, fast_llm = get_llms_for_persona(
-                persona=persona,
-                llm_override=new_msg_req.llm_override or chat_session.llm_override,
-                additional_headers=litellm_additional_headers,
+            llm = get_llm_for_persona(
+                persona, new_msg_req.llm_override or chat_session.llm_override
             )
         except GenAIDisabledException:
             raise RuntimeError("LLM is disabled. Can't use chat flow without LLM.")
 
-        llm_provider = llm.config.model_provider
-        llm_model_name = llm.config.model_name
-
-        llm_tokenizer = get_tokenizer(
-            model_name=llm_model_name,
-            provider_type=llm_provider,
-        )
+        llm_tokenizer = get_default_llm_tokenizer()
         llm_tokenizer_encode_func = cast(
             Callable[[str], list[int]], llm_tokenizer.encode
         )
@@ -346,16 +240,7 @@ def stream_chat_message_objects(
         else:
             parent_message = root_message
 
-        user_message = None
-
-        if new_msg_req.regenerate:
-            final_msg, history_msgs = create_chat_chain(
-                parent_id=parent_id,
-                chat_session_id=chat_session_id,
-                db_session=db_session,
-            )
-
-        elif not use_existing_user_message:
+        if not use_existing_user_message:
             # Create new message at the right place in the tree and update the parent's child pointer
             # Don't commit yet until we verify the chat message chain
             user_message = create_new_chat_message(
@@ -365,7 +250,10 @@ def stream_chat_message_objects(
                 message=message_text,
                 token_count=len(llm_tokenizer_encode_func(message_text)),
                 message_type=MessageType.USER,
-                files=None,  # Need to attach later for optimization to only load files once in parallel
+                files=[
+                    {"id": str(file_id), "type": ChatFileType.IMAGE}
+                    for file_id in new_msg_req.file_ids
+                ],
                 db_session=db_session,
                 commit=False,
             )
@@ -380,8 +268,8 @@ def stream_chat_message_objects(
                     "Be sure to update the chat pointers before calling this."
                 )
 
-            # NOTE: do not commit user message - it will be committed when the
-            # assistant message is successfully generated
+            # Save now to save the latest chat message
+            db_session.commit()
         else:
             # re-create linear history of messages
             final_msg, history_msgs = create_chat_chain(
@@ -394,36 +282,14 @@ def stream_chat_message_objects(
                     "when the last message is not a user message."
                 )
 
-        # Disable Query Rephrasing for the first message
-        # This leads to a better first response since the LLM rephrasing the question
-        # leads to worst search quality
-        if not history_msgs:
-            new_msg_req.query_override = (
-                new_msg_req.query_override or new_msg_req.message
-            )
-
         # load all files needed for this chat chain in memory
-        files = load_all_chat_files(
-            history_msgs, new_msg_req.file_descriptors, db_session
-        )
+        files = load_all_chat_files(history_msgs, new_msg_req.file_ids, db_session)
         latest_query_files = [
-            file
-            for file in files
-            if file.file_id in [f["id"] for f in new_msg_req.file_descriptors]
+            file for file in files if file.file_id in new_msg_req.file_ids
         ]
 
-        if user_message:
-            attach_files_to_chat_message(
-                chat_message=user_message,
-                files=[
-                    new_file.to_file_descriptor() for new_file in latest_query_files
-                ],
-                db_session=db_session,
-                commit=False,
-            )
-
         selected_db_search_docs = None
-        selected_sections: list[InferenceSection] | None = None
+        selected_llm_docs: list[LlmDoc] | None = None
         if reference_doc_ids:
             identifier_tuples = get_doc_query_identifiers_from_model(
                 search_doc_ids=reference_doc_ids,
@@ -433,8 +299,8 @@ def stream_chat_message_objects(
             )
 
             # Generates full documents currently
-            # May extend to use sections instead in the future
-            selected_sections = inference_sections_from_ids(
+            # May extend to include chunk ranges
+            selected_llm_docs = inference_documents_from_ids(
                 doc_identifiers=identifier_tuples,
                 document_index=document_index,
             )
@@ -464,157 +330,90 @@ def stream_chat_message_objects(
                 use_sections=new_msg_req.chunks_above > 0
                 or new_msg_req.chunks_below > 0,
             )
-        reserved_message_id = reserve_message_id(
-            db_session=db_session,
-            chat_session_id=chat_session_id,
-            parent_message=user_message.id
-            if user_message is not None
-            else parent_message.id,
-            message_type=MessageType.ASSISTANT,
-        )
-        yield MessageResponseIDInfo(
-            user_message_id=user_message.id if user_message else None,
-            reserved_assistant_message_id=reserved_message_id,
-        )
 
-        alternate_model = (
-            new_msg_req.llm_override.model_version if new_msg_req.llm_override else None
-        )
         # Cannot determine these without the LLM step or breaking out early
         partial_response = partial(
             create_new_chat_message,
             chat_session_id=chat_session_id,
             parent_message=final_msg,
             prompt_id=prompt_id,
-            alternate_model=alternate_model,
             # message=,
             # rephrased_query=,
             # token_count=,
             message_type=MessageType.ASSISTANT,
-            alternate_assistant_id=new_msg_req.alternate_assistant_id,
             # error=,
             # reference_docs=,
             db_session=db_session,
-            commit=False,
+            commit=True,
         )
 
         if not final_msg.prompt:
             raise RuntimeError("No Prompt found")
 
-        prompt_config = (
-            PromptConfig.from_model(
-                final_msg.prompt,
-                prompt_override=(
-                    new_msg_req.prompt_override or chat_session.prompt_override
-                ),
-            )
-            if not persona
-            else PromptConfig.from_model(persona.prompts[0])
+        prompt_config = PromptConfig.from_model(
+            final_msg.prompt,
+            prompt_override=(
+                new_msg_req.prompt_override or chat_session.prompt_override
+            ),
         )
 
-        # find out what tools to use
-        search_tool: SearchTool | None = None
-        tool_dict: dict[int, list[Tool]] = {}  # tool_id to tool
-        for db_tool_model in persona.tools:
-            # handle in-code tools specially
-            if db_tool_model.in_code_tool_id:
-                tool_cls = get_built_in_tool_by_id(db_tool_model.id, db_session)
-                if tool_cls.__name__ == SearchTool.__name__ and not latest_query_files:
-                    search_tool = SearchTool(
-                        db_session=db_session,
-                        user=user,
-                        persona=persona,
-                        retrieval_options=retrieval_options,
-                        prompt_config=prompt_config,
-                        llm=llm,
-                        fast_llm=fast_llm,
-                        pruning_config=document_pruning_config,
-                        selected_sections=selected_sections,
-                        chunks_above=new_msg_req.chunks_above,
-                        chunks_below=new_msg_req.chunks_below,
-                        full_doc=new_msg_req.full_doc,
-                        evaluation_type=LLMEvaluationType.BASIC
-                        if persona.llm_relevance_filter
-                        else LLMEvaluationType.SKIP,
-                    )
-                    tool_dict[db_tool_model.id] = [search_tool]
-                elif tool_cls.__name__ == ImageGenerationTool.__name__:
-                    img_generation_llm_config: LLMConfig | None = None
-                    if (
-                        llm
-                        and llm.config.api_key
-                        and llm.config.model_provider == "openai"
-                    ):
-                        img_generation_llm_config = llm.config
-                    else:
-                        llm_providers = fetch_existing_llm_providers(db_session)
-                        openai_provider = next(
-                            iter(
-                                [
-                                    llm_provider
-                                    for llm_provider in llm_providers
-                                    if llm_provider.provider == "openai"
-                                ]
-                            ),
-                            None,
-                        )
-                        if not openai_provider or not openai_provider.api_key:
-                            raise ValueError(
-                                "Image generation tool requires an OpenAI API key"
-                            )
-                        img_generation_llm_config = LLMConfig(
-                            model_provider=openai_provider.provider,
-                            model_name=openai_provider.default_model_name,
-                            temperature=GEN_AI_TEMPERATURE,
-                            api_key=openai_provider.api_key,
-                            api_base=openai_provider.api_base,
-                            api_version=openai_provider.api_version,
-                        )
-                    tool_dict[db_tool_model.id] = [
-                        ImageGenerationTool(
-                            api_key=cast(str, img_generation_llm_config.api_key),
-                            api_base=img_generation_llm_config.api_base,
-                            api_version=img_generation_llm_config.api_version,
-                            additional_headers=litellm_additional_headers,
-                        )
-                    ]
-                elif tool_cls.__name__ == InternetSearchTool.__name__:
-                    bing_api_key = BING_API_KEY
-                    if not bing_api_key:
-                        raise ValueError(
-                            "Internet search tool requires a Bing API key, please contact your Danswer admin to get it added!"
-                        )
-                    tool_dict[db_tool_model.id] = [
-                        InternetSearchTool(api_key=bing_api_key)
-                    ]
-
-                continue
-
-            # handle all custom tools
-            if db_tool_model.openapi_schema:
-                tool_dict[db_tool_model.id] = cast(
-                    list[Tool],
-                    build_custom_tools_from_openapi_schema(
-                        db_tool_model.openapi_schema
-                    ),
-                )
-
-        tools: list[Tool] = []
-        for tool_list in tool_dict.values():
-            tools.extend(tool_list)
+        persona_tool_classes = [
+            get_tool_cls(tool, db_session) for tool in persona.tools
+        ]
 
         # factor in tool definition size when pruning
         document_pruning_config.tool_num_tokens = compute_all_tool_tokens(
-            tools, llm_tokenizer
+            persona_tool_classes
         )
         document_pruning_config.using_tool_message = explicit_tool_calling_supported(
-            llm_provider, llm_model_name
+            llm.config.model_provider, llm.config.model_name
         )
-        tool_has_been_called = False  # TODO remove
+
+        # NOTE: for now, only support SearchTool and ImageGenerationTool
+        # in the future, will support arbitrary user-defined tools
+        search_tool: SearchTool | None = None
+        tools: list[Tool] = []
+        for tool_cls in persona_tool_classes:
+            if tool_cls.__name__ == SearchTool.__name__:
+                search_tool = SearchTool(
+                    db_session=db_session,
+                    user=user,
+                    persona=persona,
+                    retrieval_options=retrieval_options,
+                    prompt_config=prompt_config,
+                    llm_config=llm.config,
+                    pruning_config=document_pruning_config,
+                    selected_docs=selected_llm_docs,
+                    chunks_above=new_msg_req.chunks_above,
+                    chunks_below=new_msg_req.chunks_below,
+                    full_doc=new_msg_req.full_doc,
+                )
+                tools.append(search_tool)
+            elif tool_cls.__name__ == ImageGenerationTool.__name__:
+                dalle_key = None
+                if llm and llm.config.api_key and llm.config.model_provider == "openai":
+                    dalle_key = llm.config.api_key
+                else:
+                    llm_providers = fetch_existing_llm_providers(db_session)
+                    openai_provider = next(
+                        iter(
+                            [
+                                llm_provider
+                                for llm_provider in llm_providers
+                                if llm_provider.provider == "openai"
+                            ]
+                        ),
+                        None,
+                    )
+                    if not openai_provider or not openai_provider.api_key:
+                        raise ValueError(
+                            "Image generation tool requires an OpenAI API key"
+                        )
+                    dalle_key = openai_provider.api_key
+                tools.append(ImageGenerationTool(api_key=dalle_key))
 
         # LLM prompt building, response capturing, etc.
         answer = Answer(
-            is_connected=is_connected,
             question=final_msg.message,
             latest_query_files=latest_query_files,
             answer_style_config=AnswerStyleConfig(
@@ -626,71 +425,34 @@ def stream_chat_message_objects(
             prompt_config=prompt_config,
             llm=(
                 llm
-                or get_main_llm_from_tuple(
-                    get_llms_for_persona(
-                        persona=persona,
-                        llm_override=(
-                            new_msg_req.llm_override or chat_session.llm_override
-                        ),
-                        additional_headers=litellm_additional_headers,
-                    )
+                or get_llm_for_persona(
+                    persona, new_msg_req.llm_override or chat_session.llm_override
                 )
             ),
             message_history=[
                 PreviousMessage.from_chat_message(msg, files) for msg in history_msgs
             ],
             tools=tools,
-            force_use_tool=_get_force_search_settings(new_msg_req, tools),
+            force_use_tool=_check_should_force_search(new_msg_req),
         )
 
         reference_db_search_docs = None
         qa_docs_response = None
         ai_message_files = None  # any files to associate with the AI message e.g. dall-e generated images
-        dropped_indices = None
-        tool_result = None
-
         for packet in answer.processed_streamed_output:
             if isinstance(packet, ToolResponse):
-                tool_has_been_called = True
-
                 if packet.id == SEARCH_RESPONSE_SUMMARY_ID:
                     (
                         qa_docs_response,
                         reference_db_search_docs,
-                        dropped_indices,
                     ) = _handle_search_tool_response_summary(
-                        packet=packet,
-                        db_session=db_session,
-                        selected_search_docs=selected_db_search_docs,
-                        # Deduping happens at the last step to avoid harming quality by dropping content early on
-                        dedupe_docs=retrieval_options.dedupe_docs
-                        if retrieval_options
-                        else False,
+                        packet, db_session, selected_db_search_docs
                     )
                     yield qa_docs_response
                 elif packet.id == SECTION_RELEVANCE_LIST_ID:
-                    relevance_sections = packet.response
-
-                    if reference_db_search_docs is not None:
-                        llm_indices = relevant_sections_to_indices(
-                            relevance_sections=relevance_sections,
-                            items=[
-                                translate_db_search_doc_to_server_search_doc(doc)
-                                for doc in reference_db_search_docs
-                            ],
-                        )
-
-                        if dropped_indices:
-                            llm_indices = drop_llm_indices(
-                                llm_indices=llm_indices,
-                                search_docs=reference_db_search_docs,
-                                dropped_indices=dropped_indices,
-                            )
-
-                        yield LLMRelevanceFilterResponse(
-                            relevant_chunk_indices=llm_indices
-                        )
-
+                    yield LLMRelevanceFilterResponse(
+                        relevant_chunk_indices=packet.response
+                    )
                 elif packet.id == IMAGE_GENERATION_RESPONSE_ID:
                     img_generation_response = cast(
                         list[ImageGenerationResponse], packet.response
@@ -706,154 +468,50 @@ def stream_chat_message_objects(
                     yield ImageGenerationDisplay(
                         file_ids=[str(file_id) for file_id in file_ids]
                     )
-                elif packet.id == INTERNET_SEARCH_RESPONSE_ID:
-                    (
-                        qa_docs_response,
-                        reference_db_search_docs,
-                    ) = _handle_internet_search_tool_response_summary(
-                        packet=packet,
-                        db_session=db_session,
-                    )
-                    yield qa_docs_response
-                elif packet.id == CUSTOM_TOOL_RESPONSE_ID:
-                    custom_tool_response = cast(CustomToolCallSummary, packet.response)
-                    yield CustomToolResponse(
-                        response=custom_tool_response.tool_result,
-                        tool_name=custom_tool_response.tool_name,
-                    )
 
             else:
-                if isinstance(packet, Delimiter):
-                    db_citations = None
-
-                    if reference_db_search_docs:
-                        db_citations = translate_citations(
-                            citations_list=answer.citations,
-                            db_docs=reference_db_search_docs,
-                        )
-
-                    # Saving Gen AI answer and responding with message info
-                    tool_name_to_tool_id: dict[str, int] = {}
-                    for tool_id, tool_list in tool_dict.items():
-                        for tool in tool_list:
-                            tool_name_to_tool_id[tool.name] = tool_id
-
-                    if tool_result is None:
-                        tool_call = None
-                    else:
-                        tool_call = ToolCall(
-                            tool_id=tool_name_to_tool_id[tool_result.tool_name],
-                            tool_name=tool_result.tool_name,
-                            tool_arguments=tool_result.tool_args,
-                            tool_result=tool_result.tool_result,
-                        )
-
-                    gen_ai_response_message = partial_response(
-                        message=answer.llm_answer,
-                        rephrased_query=(
-                            qa_docs_response.rephrased_query
-                            if qa_docs_response
-                            else None
-                        ),
-                        reference_docs=reference_db_search_docs,
-                        files=ai_message_files,
-                        token_count=len(llm_tokenizer_encode_func(answer.llm_answer)),
-                        citations=db_citations,
-                        error=None,
-                        tool_call=tool_call,
-                    )
-
-                    db_session.commit()  # actually save user / assistant message
-
-                    msg_detail_response = translate_db_message_to_chat_message_detail(
-                        gen_ai_response_message
-                    )
-
-                    yield msg_detail_response
-                    yield Delimiter(delimiter=True)
-                    partial_response = partial(
-                        create_new_chat_message,
-                        chat_session_id=chat_session_id,
-                        parent_message=gen_ai_response_message,
-                        prompt_id=prompt_id,
-                        # message=,
-                        # rephrased_query=,
-                        # token_count=,
-                        message_type=MessageType.ASSISTANT,
-                        alternate_assistant_id=new_msg_req.alternate_assistant_id,
-                        # error=,
-                        # reference_docs=,
-                        db_session=db_session,
-                        commit=False,
-                    )
-
-                else:
-                    if isinstance(packet, ToolCallMetadata):
-                        tool_result = packet
-                    yield cast(ChatPacket, packet)
-        logger.debug("Reached end of stream")
+                yield cast(ChatPacket, packet)
 
     except Exception as e:
-        error_msg = str(e)
-        logger.exception(f"Failed to process chat message: {error_msg}")
+        logger.exception(e)
 
-        client_error_msg = litellm_exception_to_error_msg(e, llm)
-        if llm.config.api_key and len(llm.config.api_key) > 2:
-            error_msg = error_msg.replace(llm.config.api_key, "[REDACTED_API_KEY]")
-        yield StreamingError(error=client_error_msg, stack_trace=error_msg)
-        db_session.rollback()
+        # Frontend will erase whatever answer and show this instead
+        # This will be the issue 99% of the time
+        yield StreamingError(error="LLM failed to respond, have you set your API key?")
         return
 
-    if not tool_has_been_called:
-        try:
-            db_citations = None
-            if reference_db_search_docs:
-                db_citations = translate_citations(
-                    citations_list=answer.citations,
-                    db_docs=reference_db_search_docs,
-                )
-
-            # Saving Gen AI answer and responding with message info
-            tool_name_to_tool_id = {}
-            for tool_id, tool_list in tool_dict.items():
-                for tool in tool_list:
-                    tool_name_to_tool_id[tool.name] = tool_id
-
-            gen_ai_response_message = partial_response(
-                reserved_message_id=reserved_message_id,
-                message=answer.llm_answer,
-                rephrased_query=(
-                    qa_docs_response.rephrased_query if qa_docs_response else None
-                ),
-                reference_docs=reference_db_search_docs,
-                files=ai_message_files,
-                token_count=len(llm_tokenizer_encode_func(answer.llm_answer)),
-                citations=db_citations,
-                error=None,
-                tool_call=ToolCall(
-                    tool_id=tool_name_to_tool_id[tool_result.tool_name],
-                    tool_name=tool_result.tool_name,
-                    tool_arguments=tool_result.tool_args,
-                    tool_result=tool_result.tool_result,
-                )
-                if tool_result
-                else None,
+    # Post-LLM answer processing
+    try:
+        db_citations = None
+        if reference_db_search_docs:
+            db_citations = translate_citations(
+                citations_list=answer.citations,
+                db_docs=reference_db_search_docs,
             )
 
-            logger.debug("Committing messages")
-            db_session.commit()  # actually save user / assistant message
+        # Saving Gen AI answer and responding with message info
+        gen_ai_response_message = partial_response(
+            message=answer.llm_answer,
+            rephrased_query=(
+                qa_docs_response.rephrased_query if qa_docs_response else None
+            ),
+            reference_docs=reference_db_search_docs,
+            files=ai_message_files,
+            token_count=len(llm_tokenizer_encode_func(answer.llm_answer)),
+            citations=db_citations,
+            error=None,
+        )
 
-            msg_detail_response = translate_db_message_to_chat_message_detail(
-                gen_ai_response_message
-            )
+        msg_detail_response = translate_db_message_to_chat_message_detail(
+            gen_ai_response_message
+        )
 
-            yield msg_detail_response
-        except Exception as e:
-            error_msg = str(e)
-            logger.exception(error_msg)
+        yield msg_detail_response
+    except Exception as e:
+        logger.exception(e)
 
-            # Frontend will erase whatever answer and show this instead
-            yield StreamingError(error="Failed to parse LLM output")
+        # Frontend will erase whatever answer and show this instead
+        yield StreamingError(error="Failed to parse LLM output")
 
 
 @log_generator_function_time()
@@ -861,8 +519,6 @@ def stream_chat_message(
     new_msg_req: CreateChatMessageRequest,
     user: User | None,
     use_existing_user_message: bool = False,
-    litellm_additional_headers: dict[str, str] | None = None,
-    is_connected: Callable[[], bool] | None = None,
 ) -> Iterator[str]:
     with get_session_context_manager() as db_session:
         objects = stream_chat_message_objects(
@@ -870,8 +526,6 @@ def stream_chat_message(
             user=user,
             db_session=db_session,
             use_existing_user_message=use_existing_user_message,
-            litellm_additional_headers=litellm_additional_headers,
-            is_connected=is_connected,
         )
         for obj in objects:
             yield get_json_line(obj.dict())

backend/danswer/chat/prompts.yaml

@@ -8,7 +8,6 @@ prompts:
     # System Prompt (as shown in UI)
     system: >
       You are a question answering system that is constantly learning and improving.
-      The current date is DANSWER_DATETIME_REPLACEMENT.
       
       You can process and comprehend vast amounts of text and utilize this knowledge to provide
       grounded, accurate, and concise answers to diverse queries.
@@ -22,45 +21,18 @@ prompts:
       
       I have not read or seen any of the documents and do not want to read them.
       
-      If there are no relevant documents, refer to the chat history and your internal knowledge.
+      If there are no relevant documents, refer to the chat history and existing knowledge.
     # Inject a statement at the end of system prompt to inform the LLM of the current date/time
-    # If the DANSWER_DATETIME_REPLACEMENT is set, the date/time is inserted there instead
     # Format looks like: "October 16, 2023 14:30"
     datetime_aware: true
     # Prompts the LLM to include citations in the for [1], [2] etc.
     # which get parsed to match the passed in sources
     include_citations: true
-  
-  - name: "ImageGeneration"
-    description: "Generates images based on user prompts!"
-    system: >
-      You are an advanced image generation system capable of creating diverse and detailed images.
-      
-      You can interpret user prompts and generate high-quality, creative images that match their descriptions.
-      
-      You always strive to create safe and appropriate content, avoiding any harmful or offensive imagery.
-    task: >
-      Generate an image based on the user's description.
-      
-      Provide a detailed description of the generated image, including key elements, colors, and composition. 
-      
-      
-      If the request is not possible or appropriate, explain why and suggest alternatives.
-    datetime_aware: true
-    include_citations: false
+
 
   - name: "OnlyLLM"
     description: "Chat directly with the LLM!"
-    system: >
-      You are a helpful AI assistant. The current date is DANSWER_DATETIME_REPLACEMENT
-
-
-      You give concise responses to very simple questions, but provide more thorough responses to
-      more complex and open-ended questions.
-
-
-      You are happy to help with writing, analysis, question answering, math, coding and all sorts
-      of other tasks. You use markdown where reasonable and also for coding.
+    system: "You are a helpful assistant."
     task: ""
     datetime_aware: true
     include_citations: true
@@ -71,11 +43,10 @@ prompts:
     system: >
       You are a text summarizing assistant that highlights the most important knowledge from the
       context provided, prioritizing the information that relates to the user query.
-      The current date is DANSWER_DATETIME_REPLACEMENT.
       
       You ARE NOT creative and always stick to the provided documents.
       If there are no documents, refer to the conversation history.
-
+      
       IMPORTANT: YOU ONLY SUMMARIZE THE IMPORTANT INFORMATION FROM THE PROVIDED DOCUMENTS,
       NEVER USE YOUR OWN KNOWLEDGE.
     task: >
@@ -90,8 +61,7 @@ prompts:
     description: "Recites information from retrieved context! Least creative but most safe!"
     system: >
       Quote and cite relevant information from provided context based on the user query.
-      The current date is DANSWER_DATETIME_REPLACEMENT.
-
+      
       You only provide quotes that are EXACT substrings from provided documents!
       
       If there are no documents provided,

backend/danswer/configs/app_configs.py

@@ -4,7 +4,6 @@ import urllib.parse
 
 from danswer.configs.constants import AuthType
 from danswer.configs.constants import DocumentIndexType
-from danswer.file_processing.enums import HtmlBasedConnectorTransformLinksStrategy
 
 #####
 # App Configs
@@ -46,14 +45,13 @@ DISABLE_AUTH = AUTH_TYPE == AuthType.DISABLED
 # 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
-ENCRYPTION_KEY_SECRET = os.environ.get("ENCRYPTION_KEY_SECRET") or ""
+ENCRYPTION_KEY_SECRET = os.environ.get("ENCRYPTION_KEY_SECRET")
 
 # Turn off mask if admin users should see full credentials for data connectors.
 MASK_CREDENTIAL_PREFIX = (
     os.environ.get("MASK_CREDENTIAL_PREFIX", "True").lower() != "false"
 )
 
-
 SESSION_EXPIRE_TIME_SECONDS = int(
     os.environ.get("SESSION_EXPIRE_TIME_SECONDS") or 86400 * 7
 )  # 7 days
@@ -129,17 +127,6 @@ POSTGRES_HOST = os.environ.get("POSTGRES_HOST") or "localhost"
 POSTGRES_PORT = os.environ.get("POSTGRES_PORT") or "5432"
 POSTGRES_DB = os.environ.get("POSTGRES_DB") or "postgres"
 
-# defaults to False
-POSTGRES_POOL_PRE_PING = os.environ.get("POSTGRES_POOL_PRE_PING", "").lower() == "true"
-
-# recycle timeout in seconds
-POSTGRES_POOL_RECYCLE_DEFAULT = 60 * 20  # 20 minutes
-try:
-    POSTGRES_POOL_RECYCLE = int(
-        os.environ.get("POSTGRES_POOL_RECYCLE", POSTGRES_POOL_RECYCLE_DEFAULT)
-    )
-except ValueError:
-    POSTGRES_POOL_RECYCLE = POSTGRES_POOL_RECYCLE_DEFAULT
 
 #####
 # Connector Configs
@@ -173,11 +160,6 @@ WEB_CONNECTOR_OAUTH_CLIENT_SECRET = os.environ.get("WEB_CONNECTOR_OAUTH_CLIENT_S
 WEB_CONNECTOR_OAUTH_TOKEN_URL = os.environ.get("WEB_CONNECTOR_OAUTH_TOKEN_URL")
 WEB_CONNECTOR_VALIDATE_URLS = os.environ.get("WEB_CONNECTOR_VALIDATE_URLS")
 
-HTML_BASED_CONNECTOR_TRANSFORM_LINKS_STRATEGY = os.environ.get(
-    "HTML_BASED_CONNECTOR_TRANSFORM_LINKS_STRATEGY",
-    HtmlBasedConnectorTransformLinksStrategy.STRIP,
-)
-
 NOTION_CONNECTOR_ENABLE_RECURSIVE_PAGE_LOOKUP = (
     os.environ.get("NOTION_CONNECTOR_ENABLE_RECURSIVE_PAGE_LOOKUP", "").lower()
     == "true"
@@ -196,17 +178,6 @@ CONFLUENCE_CONNECTOR_INDEX_ONLY_ACTIVE_PAGES = (
     os.environ.get("CONFLUENCE_CONNECTOR_INDEX_ONLY_ACTIVE_PAGES", "").lower() == "true"
 )
 
-# Save pages labels as Danswer metadata tags
-# The reason to skip this would be to reduce the number of calls to Confluence due to rate limit concerns
-CONFLUENCE_CONNECTOR_SKIP_LABEL_INDEXING = (
-    os.environ.get("CONFLUENCE_CONNECTOR_SKIP_LABEL_INDEXING", "").lower() == "true"
-)
-
-# Attachments exceeding this size will not be retrieved (in bytes)
-CONFLUENCE_CONNECTOR_ATTACHMENT_SIZE_THRESHOLD = int(
-    os.environ.get("CONFLUENCE_CONNECTOR_ATTACHMENT_SIZE_THRESHOLD", 50 * 1024 * 1024)
-)
-
 JIRA_CONNECTOR_LABELS_TO_SKIP = [
     ignored_tag
     for ignored_tag in os.environ.get("JIRA_CONNECTOR_LABELS_TO_SKIP", "").split(",")
@@ -217,10 +188,6 @@ GONG_CONNECTOR_START_TIME = os.environ.get("GONG_CONNECTOR_START_TIME")
 
 GITHUB_CONNECTOR_BASE_URL = os.environ.get("GITHUB_CONNECTOR_BASE_URL") or None
 
-GITLAB_CONNECTOR_INCLUDE_CODE_FILES = (
-    os.environ.get("GITLAB_CONNECTOR_INCLUDE_CODE_FILES", "").lower() == "true"
-)
-
 DASK_JOB_CLIENT_ENABLED = (
     os.environ.get("DASK_JOB_CLIENT_ENABLED", "").lower() == "true"
 )
@@ -228,23 +195,6 @@ EXPERIMENTAL_CHECKPOINTING_ENABLED = (
     os.environ.get("EXPERIMENTAL_CHECKPOINTING_ENABLED", "").lower() == "true"
 )
 
-PRUNING_DISABLED = -1
-DEFAULT_PRUNING_FREQ = 60 * 60 * 24  # Once a day
-
-ALLOW_SIMULTANEOUS_PRUNING = (
-    os.environ.get("ALLOW_SIMULTANEOUS_PRUNING", "").lower() == "true"
-)
-
-# This is the maxiumum rate at which documents are queried for a pruning job. 0 disables the limitation.
-MAX_PRUNING_DOCUMENT_RETRIEVAL_PER_MINUTE = int(
-    os.environ.get("MAX_PRUNING_DOCUMENT_RETRIEVAL_PER_MINUTE", 0)
-)
-
-# comma delimited list of zendesk article labels to skip indexing for
-ZENDESK_CONNECTOR_SKIP_ARTICLE_LABELS = os.environ.get(
-    "ZENDESK_CONNECTOR_SKIP_ARTICLE_LABELS", ""
-).split(",")
-
 
 #####
 # Indexing Configs
@@ -265,35 +215,20 @@ DISABLE_INDEX_UPDATE_ON_SWAP = (
 # fairly large amount of memory in order to increase substantially, since
 # each worker loads the embedding models into memory.
 NUM_INDEXING_WORKERS = int(os.environ.get("NUM_INDEXING_WORKERS") or 1)
-NUM_SECONDARY_INDEXING_WORKERS = int(
-    os.environ.get("NUM_SECONDARY_INDEXING_WORKERS") or NUM_INDEXING_WORKERS
-)
+CHUNK_OVERLAP = 0
 # More accurate results at the expense of indexing speed and index size (stores additional 4 MINI_CHUNK vectors)
-ENABLE_MULTIPASS_INDEXING = (
-    os.environ.get("ENABLE_MULTIPASS_INDEXING", "").lower() == "true"
-)
+ENABLE_MINI_CHUNK = os.environ.get("ENABLE_MINI_CHUNK", "").lower() == "true"
 # Finer grained chunking for more detail retention
 # Slightly larger since the sentence aware split is a max cutoff so most minichunks will be under MINI_CHUNK_SIZE
 # tokens. But we need it to be at least as big as 1/4th chunk size to avoid having a tiny mini-chunk at the end
 MINI_CHUNK_SIZE = 150
-
-# This is the number of regular chunks per large chunk
-LARGE_CHUNK_RATIO = 4
-
-# Include the document level metadata in each chunk. If the metadata is too long, then it is thrown out
-# 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))
-
-# 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)
+# If set to true, then will not clean up documents that "no longer exist" when running Load connectors
+DISABLE_DOCUMENT_CLEANUP = (
+    os.environ.get("DISABLE_DOCUMENT_CLEANUP", "").lower() == "true"
 )
 
-# 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))
 
 #####
 # Miscellaneous
@@ -307,20 +242,15 @@ JOB_TIMEOUT = 60 * 60 * 6  # 6 hours default
 CURRENT_PROCESS_IS_AN_INDEXING_JOB = (
     os.environ.get("CURRENT_PROCESS_IS_AN_INDEXING_JOB", "").lower() == "true"
 )
-# Sets LiteLLM to verbose logging
+# Logs every model prompt and output, mostly used for development or exploration purposes
 LOG_ALL_MODEL_INTERACTIONS = (
     os.environ.get("LOG_ALL_MODEL_INTERACTIONS", "").lower() == "true"
 )
-# Logs Danswer only model interactions like prompts, responses, messages etc.
-LOG_DANSWER_MODEL_INTERACTIONS = (
-    os.environ.get("LOG_DANSWER_MODEL_INTERACTIONS", "").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 = (
     os.environ.get("LOG_VESPA_TIMING_INFORMATION", "").lower() == "true"
 )
-LOG_ENDPOINT_LATENCY = os.environ.get("LOG_ENDPOINT_LATENCY", "").lower() == "true"
 # Anonymous usage telemetry
 DISABLE_TELEMETRY = os.environ.get("DISABLE_TELEMETRY", "").lower() == "true"
 
@@ -333,15 +263,3 @@ TOKEN_BUDGET_GLOBALLY_ENABLED = (
 CUSTOM_ANSWER_VALIDITY_CONDITIONS = json.loads(
     os.environ.get("CUSTOM_ANSWER_VALIDITY_CONDITIONS", "[]")
 )
-
-
-#####
-# Enterprise Edition Configs
-#####
-# NOTE: this should only be enabled if you have purchased an enterprise license.
-# if you're interested in an enterprise license, please reach out to us at
-# founders@danswer.ai OR message Chris Weaver or Yuhong Sun in the Danswer
-# Slack community (https://join.slack.com/t/danswer/shared_invite/zt-1w76msxmd-HJHLe3KNFIAIzk_0dSOKaQ)
-ENTERPRISE_EDITION_ENABLED = (
-    os.environ.get("ENABLE_PAID_ENTERPRISE_EDITION_FEATURES", "").lower() == "true"
-)

backend/danswer/configs/chat_configs.py

@@ -3,13 +3,9 @@ import os
 
 PROMPTS_YAML = "./danswer/chat/prompts.yaml"
 PERSONAS_YAML = "./danswer/chat/personas.yaml"
-INPUT_PROMPT_YAML = "./danswer/chat/input_prompts.yaml"
 
 NUM_RETURNED_HITS = 50
-# Used for LLM filtering and reranking
-# We want this to be approximately the number of results we want to show on the first page
-# It cannot be too large due to cost and latency implications
-NUM_POSTPROCESSED_RESULTS = 20
+NUM_RERANKED_RESULTS = 15
 
 # May be less depending on model
 MAX_CHUNKS_FED_TO_CHAT = float(os.environ.get("MAX_CHUNKS_FED_TO_CHAT") or 10.0)
@@ -29,10 +25,14 @@ 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
-CONTEXT_CHUNKS_ABOVE = int(os.environ.get("CONTEXT_CHUNKS_ABOVE") or 0)
-CONTEXT_CHUNKS_BELOW = int(os.environ.get("CONTEXT_CHUNKS_BELOW") or 0)
+DISABLE_LLM_FILTER_EXTRACTION = (
+    os.environ.get("DISABLE_LLM_FILTER_EXTRACTION", "").lower() == "true"
+)
+# Whether the LLM should evaluate all of the document chunks passed in for usefulness
+# in relation to the user query
+DISABLE_LLM_CHUNK_FILTER = (
+    os.environ.get("DISABLE_LLM_CHUNK_FILTER", "").lower() == "true"
+)
 # Whether the LLM should be used to decide if a search would help given the chat history
 DISABLE_LLM_CHOOSE_SEARCH = (
     os.environ.get("DISABLE_LLM_CHOOSE_SEARCH", "").lower() == "true"
@@ -43,11 +43,17 @@ DISABLE_LLM_QUERY_REPHRASE = (
 # 1 edit per 20 characters, currently unused due to fuzzy match being too slow
 QUOTE_ALLOWED_ERROR_PERCENT = 0.05
 QA_TIMEOUT = int(os.environ.get("QA_TIMEOUT") or "60")  # 60 seconds
+# Include additional document/chunk metadata in prompt to GenerativeAI
+INCLUDE_METADATA = False
+# Keyword Search Drop Stopwords
+# If user has changed the default model, would most likely be to use a multilingual
+# model, the stopwords are NLTK english stopwords so then we would want to not drop the keywords
+if os.environ.get("EDIT_KEYWORD_QUERY"):
+    EDIT_KEYWORD_QUERY = os.environ.get("EDIT_KEYWORD_QUERY", "").lower() == "true"
+else:
+    EDIT_KEYWORD_QUERY = not os.environ.get("DOCUMENT_ENCODER_MODEL")
 # Weighting factor between Vector and Keyword Search, 1 for completely vector search
 HYBRID_ALPHA = max(0, min(1, float(os.environ.get("HYBRID_ALPHA") or 0.62)))
-HYBRID_ALPHA_KEYWORD = max(
-    0, min(1, float(os.environ.get("HYBRID_ALPHA_KEYWORD") or 0.4))
-)
 # Weighting factor between Title and Content of documents during search, 1 for completely
 # Title based. Default heavily favors Content because Title is also included at the top of
 # Content. This is to avoid cases where the Content is very relevant but it may not be clear
@@ -55,35 +61,12 @@ HYBRID_ALPHA_KEYWORD = max(
 TITLE_CONTENT_RATIO = max(
     0, min(1, float(os.environ.get("TITLE_CONTENT_RATIO") or 0.20))
 )
-
 # A list of languages passed to the LLM to rephase the query
 # For example "English,French,Spanish", be sure to use the "," separator
 MULTILINGUAL_QUERY_EXPANSION = os.environ.get("MULTILINGUAL_QUERY_EXPANSION") or None
-LANGUAGE_HINT = "\n" + (
-    os.environ.get("LANGUAGE_HINT")
-    or "IMPORTANT: Respond in the same language as my query!"
-)
-LANGUAGE_CHAT_NAMING_HINT = (
-    os.environ.get("LANGUAGE_CHAT_NAMING_HINT")
-    or "The name of the conversation must be in the same language as the user query."
-)
-
-# Agentic search takes significantly more tokens and therefore has much higher cost.
-# This configuration allows users to get a search-only experience with instant results
-# and no involvement from the LLM.
-# Additionally, some LLM providers have strict rate limits which may prohibit
-# sending many API requests at once (as is done in agentic search).
-# Whether the LLM should evaluate all of the document chunks passed in for usefulness
-# in relation to the user query
-DISABLE_LLM_DOC_RELEVANCE = (
-    os.environ.get("DISABLE_LLM_DOC_RELEVANCE", "").lower() == "true"
-)
 
 # Stops streaming answers back to the UI if this pattern is seen:
 STOP_STREAM_PAT = os.environ.get("STOP_STREAM_PAT") or None
 
 # The backend logic for this being True isn't fully supported yet
 HARD_DELETE_CHATS = False
-
-# Internet Search
-BING_API_KEY = os.environ.get("BING_API_KEY") or None

backend/danswer/configs/constants.py

@@ -1,6 +1,25 @@
 from enum import Enum
 
+DOCUMENT_ID = "document_id"
+CHUNK_ID = "chunk_id"
+BLURB = "blurb"
+CONTENT = "content"
 SOURCE_TYPE = "source_type"
+SOURCE_LINKS = "source_links"
+SOURCE_LINK = "link"
+SEMANTIC_IDENTIFIER = "semantic_identifier"
+TITLE = "title"
+SKIP_TITLE_EMBEDDING = "skip_title"
+SECTION_CONTINUATION = "section_continuation"
+EMBEDDINGS = "embeddings"
+TITLE_EMBEDDING = "title_embedding"
+ALLOWED_USERS = "allowed_users"
+ACCESS_CONTROL_LIST = "access_control_list"
+DOCUMENT_SETS = "document_sets"
+TIME_FILTER = "time_filter"
+METADATA = "metadata"
+METADATA_LIST = "metadata_list"
+MATCH_HIGHLIGHTS = "match_highlights"
 # stored in the `metadata` of a chunk. Used to signify that this chunk should
 # not be used for QA. For example, Google Drive file types which can't be parsed
 # are still useful as a search result but not for QA.
@@ -8,22 +27,31 @@ IGNORE_FOR_QA = "ignore_for_qa"
 # NOTE: deprecated, only used for porting key from old system
 GEN_AI_API_KEY_STORAGE_KEY = "genai_api_key"
 PUBLIC_DOC_PAT = "PUBLIC"
+PUBLIC_DOCUMENT_SET = "__PUBLIC"
+QUOTE = "quote"
+BOOST = "boost"
+DOC_UPDATED_AT = "doc_updated_at"  # Indexed as seconds since epoch
+PRIMARY_OWNERS = "primary_owners"
+SECONDARY_OWNERS = "secondary_owners"
+RECENCY_BIAS = "recency_bias"
+HIDDEN = "hidden"
+SCORE = "score"
 ID_SEPARATOR = ":;:"
 DEFAULT_BOOST = 0
 SESSION_KEY = "session"
-
-
-# For tool calling
-MAXIMUM_TOOL_CALL_SEQUENCE = 5
+QUERY_EVENT_ID = "query_event_id"
+LLM_CHUNKS = "llm_chunks"
+TOKEN_BUDGET = "token_budget"
+TOKEN_BUDGET_TIME_PERIOD = "token_budget_time_period"
+ENABLE_TOKEN_BUDGET = "enable_token_budget"
+TOKEN_BUDGET_SETTINGS = "token_budget_settings"
 
 # For chunking/processing chunks
-RETURN_SEPARATOR = "\n\r\n"
+TITLE_SEPARATOR = "\n\r\n"
 SECTION_SEPARATOR = "\n\n"
 # For combining attributes, doesn't have to be unique/perfect to work
 INDEX_SEPARATOR = "==="
 
-# For File Connector Metadata override file
-DANSWER_METADATA_FILENAME = ".danswer_metadata.json"
 
 # Messages
 DISABLED_GEN_AI_MSG = (
@@ -32,37 +60,12 @@ DISABLED_GEN_AI_MSG = (
     "You can still use Danswer as a search engine."
 )
 
-# Postgres connection constants for application_name
-POSTGRES_WEB_APP_NAME = "web"
-POSTGRES_INDEXER_APP_NAME = "indexer"
-POSTGRES_CELERY_APP_NAME = "celery"
-POSTGRES_CELERY_BEAT_APP_NAME = "celery_beat"
-POSTGRES_CELERY_WORKER_APP_NAME = "celery_worker"
-POSTGRES_PERMISSIONS_APP_NAME = "permissions"
-POSTGRES_UNKNOWN_APP_NAME = "unknown"
 
 # API Keys
 DANSWER_API_KEY_PREFIX = "API_KEY__"
 DANSWER_API_KEY_DUMMY_EMAIL_DOMAIN = "danswerapikey.ai"
 UNNAMED_KEY_PLACEHOLDER = "Unnamed"
 
-# Key-Value store keys
-KV_REINDEX_KEY = "needs_reindexing"
-KV_SEARCH_SETTINGS = "search_settings"
-KV_USER_STORE_KEY = "INVITED_USERS"
-KV_NO_AUTH_USER_PREFERENCES_KEY = "no_auth_user_preferences"
-KV_CRED_KEY = "credential_id_{}"
-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"
-KV_ENTERPRISE_SETTINGS_KEY = "danswer_enterprise_settings"
-KV_CUSTOM_ANALYTICS_SCRIPT_KEY = "__custom_analytics_script__"
-
 
 class DocumentSource(str, Enum):
     # Special case, document passed in via Danswer APIs without specifying a source type
@@ -90,34 +93,9 @@ class DocumentSource(str, Enum):
     GOOGLE_SITES = "google_sites"
     ZENDESK = "zendesk"
     LOOPIO = "loopio"
-    DROPBOX = "dropbox"
     SHAREPOINT = "sharepoint"
-    TEAMS = "teams"
-    SALESFORCE = "salesforce"
     DISCOURSE = "discourse"
     AXERO = "axero"
-    CLICKUP = "clickup"
-    MEDIAWIKI = "mediawiki"
-    WIKIPEDIA = "wikipedia"
-    S3 = "s3"
-    R2 = "r2"
-    GOOGLE_CLOUD_STORAGE = "google_cloud_storage"
-    OCI_STORAGE = "oci_storage"
-    NOT_APPLICABLE = "not_applicable"
-
-
-class NotificationType(str, Enum):
-    REINDEX = "reindex"
-
-
-class BlobType(str, Enum):
-    R2 = "r2"
-    S3 = "s3"
-    GOOGLE_CLOUD_STORAGE = "google_cloud_storage"
-    OCI_STORAGE = "oci_storage"
-
-    # Special case, for internet search
-    NOT_APPLICABLE = "not_applicable"
 
 
 class DocumentIndexType(str, Enum):
@@ -133,11 +111,6 @@ class AuthType(str, Enum):
     SAML = "saml"
 
 
-class QAFeedbackType(str, Enum):
-    LIKE = "like"  # User likes the answer, used for metrics
-    DISLIKE = "dislike"  # User dislikes the answer, used for metrics
-
-
 class SearchFeedbackType(str, Enum):
     ENDORSE = "endorse"  # boost this document for all future queries
     REJECT = "reject"  # down-boost this document for all future queries
@@ -157,11 +130,3 @@ class TokenRateLimitScope(str, Enum):
     USER = "user"
     USER_GROUP = "user_group"
     GLOBAL = "global"
-
-
-class FileOrigin(str, Enum):
-    CHAT_UPLOAD = "chat_upload"
-    CHAT_IMAGE_GEN = "chat_image_gen"
-    CONNECTOR = "connector"
-    GENERATED_REPORT = "generated_report"
-    OTHER = "other"

backend/danswer/configs/danswerbot_configs.py

@@ -47,6 +47,10 @@ DANSWER_BOT_DISPLAY_ERROR_MSGS = os.environ.get(
 DANSWER_BOT_RESPOND_EVERY_CHANNEL = (
     os.environ.get("DANSWER_BOT_RESPOND_EVERY_CHANNEL", "").lower() == "true"
 )
+# Auto detect query options like time cutoff or heavily favor recently updated docs
+DISABLE_DANSWER_BOT_FILTER_DETECT = (
+    os.environ.get("DISABLE_DANSWER_BOT_FILTER_DETECT", "").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
@@ -69,7 +73,3 @@ DANSWER_BOT_MAX_WAIT_TIME = int(os.environ.get("DANSWER_BOT_MAX_WAIT_TIME") or 1
 DANSWER_BOT_FEEDBACK_REMINDER = int(
     os.environ.get("DANSWER_BOT_FEEDBACK_REMINDER") or 0
 )
-# Set to True to rephrase the Slack users messages
-DANSWER_BOT_REPHRASE_MESSAGE = (
-    os.environ.get("DANSWER_BOT_REPHRASE_MESSAGE", "").lower() == "true"
-)

backend/danswer/configs/model_configs.py

@@ -12,15 +12,13 @@ import os
 # The useable models configured as below must be SentenceTransformer compatible
 # NOTE: DO NOT CHANGE SET THESE UNLESS YOU KNOW WHAT YOU ARE DOING
 # IDEALLY, YOU SHOULD CHANGE EMBEDDING MODELS VIA THE UI
-DEFAULT_DOCUMENT_ENCODER_MODEL = "nomic-ai/nomic-embed-text-v1"
+DEFAULT_DOCUMENT_ENCODER_MODEL = "intfloat/e5-base-v2"
 DOCUMENT_ENCODER_MODEL = (
     os.environ.get("DOCUMENT_ENCODER_MODEL") or DEFAULT_DOCUMENT_ENCODER_MODEL
 )
 # If the below is changed, Vespa deployment must also be changed
 DOC_EMBEDDING_DIM = int(os.environ.get("DOC_EMBEDDING_DIM") or 768)
 # Model should be chosen with 512 context size, ideally don't change this
-# If multipass_indexing is enabled, the max context size would be set to
-# DOC_EMBEDDING_CONTEXT_SIZE * LARGE_CHUNK_RATIO
 DOC_EMBEDDING_CONTEXT_SIZE = 512
 NORMALIZE_EMBEDDINGS = (
     os.environ.get("NORMALIZE_EMBEDDINGS") or "true"
@@ -36,15 +34,16 @@ OLD_DEFAULT_MODEL_NORMALIZE_EMBEDDINGS = False
 SIM_SCORE_RANGE_LOW = float(os.environ.get("SIM_SCORE_RANGE_LOW") or 0.0)
 SIM_SCORE_RANGE_HIGH = float(os.environ.get("SIM_SCORE_RANGE_HIGH") or 1.0)
 # Certain models like e5, BGE, etc use a prefix for asymmetric retrievals (query generally shorter than docs)
-ASYM_QUERY_PREFIX = os.environ.get("ASYM_QUERY_PREFIX", "search_query: ")
-ASYM_PASSAGE_PREFIX = os.environ.get("ASYM_PASSAGE_PREFIX", "search_document: ")
+ASYM_QUERY_PREFIX = os.environ.get("ASYM_QUERY_PREFIX", "query: ")
+ASYM_PASSAGE_PREFIX = os.environ.get("ASYM_PASSAGE_PREFIX", "passage: ")
 # Purely an optimization, memory limitation consideration
 BATCH_SIZE_ENCODE_CHUNKS = 8
-# don't send over too many chunks at once, as sending too many could cause timeouts
-BATCH_SIZE_ENCODE_CHUNKS_FOR_API_EMBEDDING_SERVICES = 512
 # For score display purposes, only way is to know the expected ranges
-CROSS_ENCODER_RANGE_MAX = 1
-CROSS_ENCODER_RANGE_MIN = 0
+CROSS_ENCODER_RANGE_MAX = 12
+CROSS_ENCODER_RANGE_MIN = -12
+
+# Unused currently, can't be used with the current default encoder model due to its output range
+SEARCH_DISTANCE_CUTOFF = 0
 
 
 #####
@@ -101,7 +100,7 @@ DISABLE_LITELLM_STREAMING = (
 ).lower() == "true"
 
 # extra headers to pass to LiteLLM
-LITELLM_EXTRA_HEADERS: dict[str, str] | None = None
+LITELLM_EXTRA_HEADERS = None
 _LITELLM_EXTRA_HEADERS_RAW = os.environ.get("LITELLM_EXTRA_HEADERS")
 if _LITELLM_EXTRA_HEADERS_RAW:
     try:
@@ -114,18 +113,3 @@ if _LITELLM_EXTRA_HEADERS_RAW:
         logger.error(
             "Failed to parse LITELLM_EXTRA_HEADERS, must be a valid JSON object"
         )
-
-# if specified, will pass through request headers to the call to the LLM
-LITELLM_PASS_THROUGH_HEADERS: list[str] | None = None
-_LITELLM_PASS_THROUGH_HEADERS_RAW = os.environ.get("LITELLM_PASS_THROUGH_HEADERS")
-if _LITELLM_PASS_THROUGH_HEADERS_RAW:
-    try:
-        LITELLM_PASS_THROUGH_HEADERS = json.loads(_LITELLM_PASS_THROUGH_HEADERS_RAW)
-    except Exception:
-        # need to import here to avoid circular imports
-        from danswer.utils.logger import setup_logger
-
-        logger = setup_logger()
-        logger.error(
-            "Failed to parse LITELLM_PASS_THROUGH_HEADERS, must be a valid JSON object"
-        )

backend/danswer/connectors/blob/connector.py

@@ -1,277 +0,0 @@
-import os
-from datetime import datetime
-from datetime import timezone
-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
-
-from danswer.configs.app_configs import INDEX_BATCH_SIZE
-from danswer.configs.constants import BlobType
-from danswer.configs.constants import DocumentSource
-from danswer.connectors.interfaces import GenerateDocumentsOutput
-from danswer.connectors.interfaces import LoadConnector
-from danswer.connectors.interfaces import PollConnector
-from danswer.connectors.interfaces import SecondsSinceUnixEpoch
-from danswer.connectors.models import ConnectorMissingCredentialError
-from danswer.connectors.models import Document
-from danswer.connectors.models import Section
-from danswer.file_processing.extract_file_text import extract_file_text
-from danswer.utils.logger import setup_logger
-
-logger = setup_logger()
-
-
-class BlobStorageConnector(LoadConnector, PollConnector):
-    def __init__(
-        self,
-        bucket_type: str,
-        bucket_name: str,
-        prefix: str = "",
-        batch_size: int = INDEX_BATCH_SIZE,
-    ) -> None:
-        self.bucket_type: BlobType = BlobType(bucket_type)
-        self.bucket_name = bucket_name
-        self.prefix = prefix if not prefix or prefix.endswith("/") else prefix + "/"
-        self.batch_size = batch_size
-        self.s3_client: Optional[S3Client] = None
-
-    def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
-        """Checks for boto3 credentials based on the bucket type.
-        (1) R2: Access Key ID, Secret Access Key, Account ID
-        (2) S3: AWS Access Key ID, AWS Secret Access Key
-        (3) GOOGLE_CLOUD_STORAGE: Access Key ID, Secret Access Key, Project ID
-        (4) OCI_STORAGE: Namespace, Region, Access Key ID, Secret Access Key
-
-        For each bucket type, the method initializes the appropriate S3 client:
-        - R2: Uses Cloudflare R2 endpoint with S3v4 signature
-        - S3: Creates a standard boto3 S3 client
-        - GOOGLE_CLOUD_STORAGE: Uses Google Cloud Storage endpoint
-        - OCI_STORAGE: Uses Oracle Cloud Infrastructure Object Storage endpoint
-
-        Raises ConnectorMissingCredentialError if required credentials are missing.
-        Raises ValueError for unsupported bucket types.
-        """
-
-        logger.info(
-            f"Loading credentials for {self.bucket_name} or type {self.bucket_type}"
-        )
-
-        if self.bucket_type == BlobType.R2:
-            if not all(
-                credentials.get(key)
-                for key in ["r2_access_key_id", "r2_secret_access_key", "account_id"]
-            ):
-                raise ConnectorMissingCredentialError("Cloudflare R2")
-            self.s3_client = boto3.client(
-                "s3",
-                endpoint_url=f"https://{credentials['account_id']}.r2.cloudflarestorage.com",
-                aws_access_key_id=credentials["r2_access_key_id"],
-                aws_secret_access_key=credentials["r2_secret_access_key"],
-                region_name="auto",
-                config=Config(signature_version="s3v4"),
-            )
-
-        elif self.bucket_type == BlobType.S3:
-            if not all(
-                credentials.get(key)
-                for key in ["aws_access_key_id", "aws_secret_access_key"]
-            ):
-                raise ConnectorMissingCredentialError("Google Cloud Storage")
-
-            session = boto3.Session(
-                aws_access_key_id=credentials["aws_access_key_id"],
-                aws_secret_access_key=credentials["aws_secret_access_key"],
-            )
-            self.s3_client = session.client("s3")
-
-        elif self.bucket_type == BlobType.GOOGLE_CLOUD_STORAGE:
-            if not all(
-                credentials.get(key) for key in ["access_key_id", "secret_access_key"]
-            ):
-                raise ConnectorMissingCredentialError("Google Cloud Storage")
-
-            self.s3_client = boto3.client(
-                "s3",
-                endpoint_url="https://storage.googleapis.com",
-                aws_access_key_id=credentials["access_key_id"],
-                aws_secret_access_key=credentials["secret_access_key"],
-                region_name="auto",
-            )
-
-        elif self.bucket_type == BlobType.OCI_STORAGE:
-            if not all(
-                credentials.get(key)
-                for key in ["namespace", "region", "access_key_id", "secret_access_key"]
-            ):
-                raise ConnectorMissingCredentialError("Oracle Cloud Infrastructure")
-
-            self.s3_client = boto3.client(
-                "s3",
-                endpoint_url=f"https://{credentials['namespace']}.compat.objectstorage.{credentials['region']}.oraclecloud.com",
-                aws_access_key_id=credentials["access_key_id"],
-                aws_secret_access_key=credentials["secret_access_key"],
-                region_name=credentials["region"],
-            )
-
-        else:
-            raise ValueError(f"Unsupported bucket type: {self.bucket_type}")
-
-        return None
-
-    def _download_object(self, key: str) -> bytes:
-        if self.s3_client is None:
-            raise ConnectorMissingCredentialError("Blob storage")
-        object = self.s3_client.get_object(Bucket=self.bucket_name, Key=key)
-        return object["Body"].read()
-
-    # NOTE: Left in as may be useful for one-off access to documents and sharing across orgs.
-    # def _get_presigned_url(self, key: str) -> str:
-    #     if self.s3_client is None:
-    #         raise ConnectorMissingCredentialError("Blog storage")
-
-    #     url = self.s3_client.generate_presigned_url(
-    #         "get_object",
-    #         Params={"Bucket": self.bucket_name, "Key": key},
-    #         ExpiresIn=self.presign_length,
-    #     )
-    #     return url
-
-    def _get_blob_link(self, key: str) -> str:
-        if self.s3_client is None:
-            raise ConnectorMissingCredentialError("Blob storage")
-
-        if self.bucket_type == BlobType.R2:
-            account_id = self.s3_client.meta.endpoint_url.split("//")[1].split(".")[0]
-            return f"https://{account_id}.r2.cloudflarestorage.com/{self.bucket_name}/{key}"
-
-        elif self.bucket_type == BlobType.S3:
-            region = self.s3_client.meta.region_name
-            return f"https://{self.bucket_name}.s3.{region}.amazonaws.com/{key}"
-
-        elif self.bucket_type == BlobType.GOOGLE_CLOUD_STORAGE:
-            return f"https://storage.cloud.google.com/{self.bucket_name}/{key}"
-
-        elif self.bucket_type == BlobType.OCI_STORAGE:
-            namespace = self.s3_client.meta.endpoint_url.split("//")[1].split(".")[0]
-            region = self.s3_client.meta.region_name
-            return f"https://objectstorage.{region}.oraclecloud.com/n/{namespace}/b/{self.bucket_name}/o/{key}"
-
-        else:
-            raise ValueError(f"Unsupported bucket type: {self.bucket_type}")
-
-    def _yield_blob_objects(
-        self,
-        start: datetime,
-        end: datetime,
-    ) -> GenerateDocumentsOutput:
-        if self.s3_client is None:
-            raise ConnectorMissingCredentialError("Blob storage")
-
-        paginator = self.s3_client.get_paginator("list_objects_v2")
-        pages = paginator.paginate(Bucket=self.bucket_name, Prefix=self.prefix)
-
-        batch: list[Document] = []
-        for page in pages:
-            if "Contents" not in page:
-                continue
-
-            for obj in page["Contents"]:
-                if obj["Key"].endswith("/"):
-                    continue
-
-                last_modified = obj["LastModified"].replace(tzinfo=timezone.utc)
-
-                if not start <= last_modified <= end:
-                    continue
-
-                downloaded_file = self._download_object(obj["Key"])
-                link = self._get_blob_link(obj["Key"])
-                name = os.path.basename(obj["Key"])
-
-                try:
-                    text = extract_file_text(
-                        name,
-                        BytesIO(downloaded_file),
-                        break_on_unprocessable=False,
-                    )
-                    batch.append(
-                        Document(
-                            id=f"{self.bucket_type}:{self.bucket_name}:{obj['Key']}",
-                            sections=[Section(link=link, text=text)],
-                            source=DocumentSource(self.bucket_type.value),
-                            semantic_identifier=name,
-                            doc_updated_at=last_modified,
-                            metadata={},
-                        )
-                    )
-                    if len(batch) == self.batch_size:
-                        yield batch
-                        batch = []
-
-                except Exception as e:
-                    logger.exception(
-                        f"Error decoding object {obj['Key']} as UTF-8: {e}"
-                    )
-        if batch:
-            yield batch
-
-    def load_from_state(self) -> GenerateDocumentsOutput:
-        logger.info("Loading blob objects")
-        return self._yield_blob_objects(
-            start=datetime(1970, 1, 1, tzinfo=timezone.utc),
-            end=datetime.now(timezone.utc),
-        )
-
-    def poll_source(
-        self, start: SecondsSinceUnixEpoch, end: SecondsSinceUnixEpoch
-    ) -> GenerateDocumentsOutput:
-        if self.s3_client is None:
-            raise ConnectorMissingCredentialError("Blob storage")
-
-        start_datetime = datetime.fromtimestamp(start, tz=timezone.utc)
-        end_datetime = datetime.fromtimestamp(end, tz=timezone.utc)
-
-        for batch in self._yield_blob_objects(start_datetime, end_datetime):
-            yield batch
-
-        return None
-
-
-if __name__ == "__main__":
-    credentials_dict = {
-        "aws_access_key_id": os.environ.get("AWS_ACCESS_KEY_ID"),
-        "aws_secret_access_key": os.environ.get("AWS_SECRET_ACCESS_KEY"),
-    }
-
-    # Initialize the connector
-    connector = BlobStorageConnector(
-        bucket_type=os.environ.get("BUCKET_TYPE") or "s3",
-        bucket_name=os.environ.get("BUCKET_NAME") or "test",
-        prefix="",
-    )
-
-    try:
-        connector.load_credentials(credentials_dict)
-        document_batch_generator = connector.load_from_state()
-        for document_batch in document_batch_generator:
-            print("First batch of documents:")
-            for doc in document_batch:
-                print(f"Document ID: {doc.id}")
-                print(f"Semantic Identifier: {doc.semantic_identifier}")
-                print(f"Source: {doc.source}")
-                print(f"Updated At: {doc.doc_updated_at}")
-                print("Sections:")
-                for section in doc.sections:
-                    print(f"  - Link: {section.link}")
-                    print(f"  - Text: {section.text[:100]}...")
-                print("---")
-            break
-
-    except ConnectorMissingCredentialError as e:
-        print(f"Error: {e}")
-    except Exception as e:
-        print(f"An unexpected error occurred: {e}")

backend/danswer/connectors/clickup/connector.py

@@ -1,216 +0,0 @@
-from datetime import datetime
-from datetime import timezone
-from typing import Any
-from typing import Optional
-
-import requests
-
-from danswer.configs.app_configs import INDEX_BATCH_SIZE
-from danswer.configs.constants import DocumentSource
-from danswer.connectors.cross_connector_utils.rate_limit_wrapper import (
-    rate_limit_builder,
-)
-from danswer.connectors.cross_connector_utils.retry_wrapper import retry_builder
-from danswer.connectors.interfaces import GenerateDocumentsOutput
-from danswer.connectors.interfaces import LoadConnector
-from danswer.connectors.interfaces import PollConnector
-from danswer.connectors.interfaces import SecondsSinceUnixEpoch
-from danswer.connectors.models import BasicExpertInfo
-from danswer.connectors.models import ConnectorMissingCredentialError
-from danswer.connectors.models import Document
-from danswer.connectors.models import Section
-
-
-CLICKUP_API_BASE_URL = "https://api.clickup.com/api/v2"
-
-
-class ClickupConnector(LoadConnector, PollConnector):
-    def __init__(
-        self,
-        batch_size: int = INDEX_BATCH_SIZE,
-        api_token: str | None = None,
-        team_id: str | None = None,
-        connector_type: str | None = None,
-        connector_ids: list[str] | None = None,
-        retrieve_task_comments: bool = True,
-    ) -> None:
-        self.batch_size = batch_size
-        self.api_token = api_token
-        self.team_id = team_id
-        self.connector_type = connector_type if connector_type else "workspace"
-        self.connector_ids = connector_ids
-        self.retrieve_task_comments = retrieve_task_comments
-
-    def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
-        self.api_token = credentials["clickup_api_token"]
-        self.team_id = credentials["clickup_team_id"]
-        return None
-
-    @retry_builder()
-    @rate_limit_builder(max_calls=100, period=60)
-    def _make_request(self, endpoint: str, params: Optional[dict] = None) -> Any:
-        if not self.api_token:
-            raise ConnectorMissingCredentialError("Clickup")
-
-        headers = {"Authorization": self.api_token}
-
-        response = requests.get(
-            f"{CLICKUP_API_BASE_URL}/{endpoint}", headers=headers, params=params
-        )
-
-        response.raise_for_status()
-
-        return response.json()
-
-    def _get_task_comments(self, task_id: str) -> list[Section]:
-        url_endpoint = f"/task/{task_id}/comment"
-        response = self._make_request(url_endpoint)
-        comments = [
-            Section(
-                link=f'https://app.clickup.com/t/{task_id}?comment={comment_dict["id"]}',
-                text=comment_dict["comment_text"],
-            )
-            for comment_dict in response["comments"]
-        ]
-
-        return comments
-
-    def _get_all_tasks_filtered(
-        self,
-        start: int | None = None,
-        end: int | None = None,
-    ) -> GenerateDocumentsOutput:
-        doc_batch: list[Document] = []
-        page: int = 0
-        params = {
-            "include_markdown_description": "true",
-            "include_closed": "true",
-            "page": page,
-        }
-
-        if start is not None:
-            params["date_updated_gt"] = start
-        if end is not None:
-            params["date_updated_lt"] = end
-
-        if self.connector_type == "list":
-            params["list_ids[]"] = self.connector_ids
-        elif self.connector_type == "folder":
-            params["project_ids[]"] = self.connector_ids
-        elif self.connector_type == "space":
-            params["space_ids[]"] = self.connector_ids
-
-        url_endpoint = f"/team/{self.team_id}/task"
-
-        while True:
-            response = self._make_request(url_endpoint, params)
-
-            page += 1
-            params["page"] = page
-
-            for task in response["tasks"]:
-                document = Document(
-                    id=task["id"],
-                    source=DocumentSource.CLICKUP,
-                    semantic_identifier=task["name"],
-                    doc_updated_at=(
-                        datetime.fromtimestamp(
-                            round(float(task["date_updated"]) / 1000, 3)
-                        ).replace(tzinfo=timezone.utc)
-                    ),
-                    primary_owners=[
-                        BasicExpertInfo(
-                            display_name=task["creator"]["username"],
-                            email=task["creator"]["email"],
-                        )
-                    ],
-                    secondary_owners=[
-                        BasicExpertInfo(
-                            display_name=assignee["username"],
-                            email=assignee["email"],
-                        )
-                        for assignee in task["assignees"]
-                    ],
-                    title=task["name"],
-                    sections=[
-                        Section(
-                            link=task["url"],
-                            text=(
-                                task["markdown_description"]
-                                if "markdown_description" in task
-                                else task["description"]
-                            ),
-                        )
-                    ],
-                    metadata={
-                        "id": task["id"],
-                        "status": task["status"]["status"],
-                        "list": task["list"]["name"],
-                        "project": task["project"]["name"],
-                        "folder": task["folder"]["name"],
-                        "space_id": task["space"]["id"],
-                        "tags": [tag["name"] for tag in task["tags"]],
-                        "priority": (
-                            task["priority"]["priority"]
-                            if "priority" in task and task["priority"] is not None
-                            else ""
-                        ),
-                    },
-                )
-
-                extra_fields = [
-                    "date_created",
-                    "date_updated",
-                    "date_closed",
-                    "date_done",
-                    "due_date",
-                ]
-                for extra_field in extra_fields:
-                    if extra_field in task and task[extra_field] is not None:
-                        document.metadata[extra_field] = task[extra_field]
-
-                if self.retrieve_task_comments:
-                    document.sections.extend(self._get_task_comments(task["id"]))
-
-                doc_batch.append(document)
-
-                if len(doc_batch) >= self.batch_size:
-                    yield doc_batch
-                    doc_batch = []
-
-            if response.get("last_page") is True or len(response["tasks"]) < 100:
-                break
-
-        if doc_batch:
-            yield doc_batch
-
-    def load_from_state(self) -> GenerateDocumentsOutput:
-        if self.api_token is None:
-            raise ConnectorMissingCredentialError("Clickup")
-
-        return self._get_all_tasks_filtered(None, None)
-
-    def poll_source(
-        self, start: SecondsSinceUnixEpoch, end: SecondsSinceUnixEpoch
-    ) -> GenerateDocumentsOutput:
-        if self.api_token is None:
-            raise ConnectorMissingCredentialError("Clickup")
-
-        return self._get_all_tasks_filtered(int(start * 1000), int(end * 1000))
-
-
-if __name__ == "__main__":
-    import os
-
-    clickup_connector = ClickupConnector()
-
-    clickup_connector.load_credentials(
-        {
-            "clickup_api_token": os.environ["clickup_api_token"],
-            "clickup_team_id": os.environ["clickup_team_id"],
-        }
-    )
-    latest_docs = clickup_connector.load_from_state()
-
-    for doc in latest_docs:
-        print(doc)

backend/danswer/connectors/confluence/connector.py

@@ -1,5 +1,3 @@
-import io
-import os
 from collections.abc import Callable
 from collections.abc import Collection
 from datetime import datetime
@@ -13,10 +11,8 @@ import bs4
 from atlassian import Confluence  # type:ignore
 from requests import HTTPError
 
-from danswer.configs.app_configs import CONFLUENCE_CONNECTOR_ATTACHMENT_SIZE_THRESHOLD
 from danswer.configs.app_configs import CONFLUENCE_CONNECTOR_INDEX_ONLY_ACTIVE_PAGES
 from danswer.configs.app_configs import CONFLUENCE_CONNECTOR_LABELS_TO_SKIP
-from danswer.configs.app_configs import CONFLUENCE_CONNECTOR_SKIP_LABEL_INDEXING
 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
@@ -31,25 +27,22 @@ 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 extract_file_text
 from danswer.file_processing.html_utils import format_document_soup
 from danswer.utils.logger import setup_logger
 
 logger = setup_logger()
 
 # Potential Improvements
-# 1. Include attachments, etc
-# 2. Segment into Sections for more accurate linking, can split by headers but make sure no text/ordering is lost
+# 1. If wiki page instead of space, do a search of all the children of the page instead of index all in the space
+# 2. Include attachments, etc
+# 3. Segment into Sections for more accurate linking, can split by headers but make sure no text/ordering is lost
 
 
-def _extract_confluence_keys_from_cloud_url(wiki_url: str) -> tuple[str, str, str]:
+def _extract_confluence_keys_from_cloud_url(wiki_url: str) -> tuple[str, str]:
     """Sample
-    URL w/ page: https://danswer.atlassian.net/wiki/spaces/1234abcd/pages/5678efgh/overview
-    URL w/o page: https://danswer.atlassian.net/wiki/spaces/ASAM/overview
-
+    https://danswer.atlassian.net/wiki/spaces/1234abcd/overview
     wiki_base is https://danswer.atlassian.net/wiki
     space is 1234abcd
-    page_id is 5678efgh
     """
     parsed_url = urlparse(wiki_url)
     wiki_base = (
@@ -58,25 +51,18 @@ def _extract_confluence_keys_from_cloud_url(wiki_url: str) -> tuple[str, str, st
         + parsed_url.netloc
         + parsed_url.path.split("/spaces")[0]
     )
-
-    path_parts = parsed_url.path.split("/")
-    space = path_parts[3]
-
-    page_id = path_parts[5] if len(path_parts) > 5 else ""
-    return wiki_base, space, page_id
+    space = parsed_url.path.split("/")[3]
+    return wiki_base, space
 
 
-def _extract_confluence_keys_from_datacenter_url(wiki_url: str) -> tuple[str, str, str]:
+def _extract_confluence_keys_from_datacenter_url(wiki_url: str) -> tuple[str, str]:
     """Sample
-    URL w/ page https://danswer.ai/confluence/display/1234abcd/pages/5678efgh/overview
-    URL w/o page https://danswer.ai/confluence/display/1234abcd/overview
+    https://danswer.ai/confluence/display/1234abcd/overview
     wiki_base is https://danswer.ai/confluence
     space is 1234abcd
-    page_id is 5678efgh
     """
-    # /display/ is always right before the space and at the end of the base print()
+    # /display/ is always right before the space and at the end of the base url
     DISPLAY = "/display/"
-    PAGE = "/pages/"
 
     parsed_url = urlparse(wiki_url)
     wiki_base = (
@@ -86,13 +72,10 @@ def _extract_confluence_keys_from_datacenter_url(wiki_url: str) -> tuple[str, st
         + parsed_url.path.split(DISPLAY)[0]
     )
     space = DISPLAY.join(parsed_url.path.split(DISPLAY)[1:]).split("/")[0]
-    page_id = ""
-    if (content := parsed_url.path.split(PAGE)) and len(content) > 1:
-        page_id = content[1]
-    return wiki_base, space, page_id
+    return wiki_base, space
 
 
-def extract_confluence_keys_from_url(wiki_url: str) -> tuple[str, str, str, bool]:
+def extract_confluence_keys_from_url(wiki_url: str) -> tuple[str, str, bool]:
     is_confluence_cloud = (
         ".atlassian.net/wiki/spaces/" in wiki_url
         or ".jira.com/wiki/spaces/" in wiki_url
@@ -100,19 +83,15 @@ def extract_confluence_keys_from_url(wiki_url: str) -> tuple[str, str, str, bool
 
     try:
         if is_confluence_cloud:
-            wiki_base, space, page_id = _extract_confluence_keys_from_cloud_url(
-                wiki_url
-            )
+            wiki_base, space = _extract_confluence_keys_from_cloud_url(wiki_url)
         else:
-            wiki_base, space, page_id = _extract_confluence_keys_from_datacenter_url(
-                wiki_url
-            )
+            wiki_base, space = _extract_confluence_keys_from_datacenter_url(wiki_url)
     except Exception as e:
-        error_msg = f"Not a valid Confluence Wiki Link, unable to extract wiki base, space, and page id. Exception: {e}"
+        error_msg = f"Not a valid Confluence Wiki Link, unable to extract wiki base and space names. Exception: {e}"
         logger.error(error_msg)
         raise ValueError(error_msg)
 
-    return wiki_base, space, page_id, is_confluence_cloud
+    return wiki_base, space, is_confluence_cloud
 
 
 @lru_cache()
@@ -168,24 +147,6 @@ def parse_html_page(text: str, confluence_client: Confluence) -> str:
     return format_document_soup(soup)
 
 
-def get_used_attachments(text: str, confluence_client: Confluence) -> list[str]:
-    """Parse a Confluence html page to generate a list of current
-        attachment in used
-
-    Args:
-        text (str): The page content
-        confluence_client (Confluence): Confluence client
-
-    Returns:
-        list[str]: List of filename currently in used
-    """
-    files_in_used = []
-    soup = bs4.BeautifulSoup(text, "html.parser")
-    for attachment in soup.findAll("ri:attachment"):
-        files_in_used.append(attachment.attrs["ri:filename"])
-    return files_in_used
-
-
 def _comment_dfs(
     comments_str: str,
     comment_pages: Collection[dict[str, Any]],
@@ -213,137 +174,10 @@ def _comment_dfs(
     return comments_str
 
 
-class RecursiveIndexer:
-    def __init__(
-        self,
-        batch_size: int,
-        confluence_client: Confluence,
-        index_recursively: bool,
-        origin_page_id: str,
-    ) -> None:
-        self.batch_size = 1
-        # batch_size
-        self.confluence_client = confluence_client
-        self.index_recursively = index_recursively
-        self.origin_page_id = origin_page_id
-        self.pages = self.recurse_children_pages(0, self.origin_page_id)
-
-    def get_origin_page(self) -> list[dict[str, Any]]:
-        return [self._fetch_origin_page()]
-
-    def get_pages(self, ind: int, size: int) -> list[dict]:
-        if ind * size > len(self.pages):
-            return []
-        return self.pages[ind * size : (ind + 1) * size]
-
-    def _fetch_origin_page(
-        self,
-    ) -> dict[str, Any]:
-        get_page_by_id = make_confluence_call_handle_rate_limit(
-            self.confluence_client.get_page_by_id
-        )
-        try:
-            origin_page = get_page_by_id(
-                self.origin_page_id, expand="body.storage.value,version"
-            )
-            return origin_page
-        except Exception as e:
-            logger.warning(
-                f"Appending orgin page with id {self.origin_page_id} failed: {e}"
-            )
-            return {}
-
-    def recurse_children_pages(
-        self,
-        start_ind: int,
-        page_id: str,
-    ) -> list[dict[str, Any]]:
-        pages: list[dict[str, Any]] = []
-        current_level_pages: list[dict[str, Any]] = []
-        next_level_pages: list[dict[str, Any]] = []
-
-        # Initial fetch of first level children
-        index = start_ind
-        while batch := self._fetch_single_depth_child_pages(
-            index, self.batch_size, page_id
-        ):
-            current_level_pages.extend(batch)
-            index += len(batch)
-
-        pages.extend(current_level_pages)
-
-        # Recursively index children and children's children, etc.
-        while current_level_pages:
-            for child in current_level_pages:
-                child_index = 0
-                while child_batch := self._fetch_single_depth_child_pages(
-                    child_index, self.batch_size, child["id"]
-                ):
-                    next_level_pages.extend(child_batch)
-                    child_index += len(child_batch)
-
-            pages.extend(next_level_pages)
-            current_level_pages = next_level_pages
-            next_level_pages = []
-
-        try:
-            origin_page = self._fetch_origin_page()
-            pages.append(origin_page)
-        except Exception as e:
-            logger.warning(f"Appending origin page with id {page_id} failed: {e}")
-
-        return pages
-
-    def _fetch_single_depth_child_pages(
-        self, start_ind: int, batch_size: int, page_id: str
-    ) -> list[dict[str, Any]]:
-        child_pages: list[dict[str, Any]] = []
-
-        get_page_child_by_type = make_confluence_call_handle_rate_limit(
-            self.confluence_client.get_page_child_by_type
-        )
-
-        try:
-            child_page = get_page_child_by_type(
-                page_id,
-                type="page",
-                start=start_ind,
-                limit=batch_size,
-                expand="body.storage.value,version",
-            )
-
-            child_pages.extend(child_page)
-            return child_pages
-
-        except Exception:
-            logger.warning(
-                f"Batch failed with page {page_id} at offset {start_ind} "
-                f"with size {batch_size}, processing pages individually..."
-            )
-
-            for i in range(batch_size):
-                ind = start_ind + i
-                try:
-                    child_page = get_page_child_by_type(
-                        page_id,
-                        type="page",
-                        start=ind,
-                        limit=1,
-                        expand="body.storage.value,version",
-                    )
-                    child_pages.extend(child_page)
-                except Exception as e:
-                    logger.warning(f"Page {page_id} at offset {ind} failed: {e}")
-                    raise e
-
-            return child_pages
-
-
 class ConfluenceConnector(LoadConnector, PollConnector):
     def __init__(
         self,
         wiki_page_url: str,
-        index_recursively: bool = True,
         batch_size: int = INDEX_BATCH_SIZE,
         continue_on_failure: bool = CONTINUE_ON_CONNECTOR_FAILURE,
         # if a page has one of the labels specified in this list, we will just
@@ -354,26 +188,10 @@ class ConfluenceConnector(LoadConnector, PollConnector):
         self.batch_size = batch_size
         self.continue_on_failure = continue_on_failure
         self.labels_to_skip = set(labels_to_skip)
-        self.recursive_indexer: RecursiveIndexer | None = None
-        self.index_recursively = index_recursively
-        (
-            self.wiki_base,
-            self.space,
-            self.page_id,
-            self.is_cloud,
-        ) = extract_confluence_keys_from_url(wiki_page_url)
-
-        self.space_level_scan = False
-
-        self.confluence_client: Confluence | None = None
-
-        if self.page_id is None or self.page_id == "":
-            self.space_level_scan = True
-
-        logger.info(
-            f"wiki_base: {self.wiki_base}, space: {self.space}, page_id: {self.page_id},"
-            + f" space_level_scan: {self.space_level_scan}, index_recursively: {self.index_recursively}"
+        self.wiki_base, self.space, self.is_cloud = extract_confluence_keys_from_url(
+            wiki_page_url
         )
+        self.confluence_client: Confluence | None = None
 
     def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
         username = credentials["confluence_username"]
@@ -392,8 +210,8 @@ class ConfluenceConnector(LoadConnector, PollConnector):
         self,
         confluence_client: Confluence,
         start_ind: int,
-    ) -> list[dict[str, Any]]:
-        def _fetch_space(start_ind: int, batch_size: int) -> list[dict[str, Any]]:
+    ) -> Collection[dict[str, Any]]:
+        def _fetch(start_ind: int, batch_size: int) -> Collection[dict[str, Any]]:
             get_all_pages_from_space = make_confluence_call_handle_rate_limit(
                 confluence_client.get_all_pages_from_space
             )
@@ -402,11 +220,9 @@ class ConfluenceConnector(LoadConnector, PollConnector):
                     self.space,
                     start=start_ind,
                     limit=batch_size,
-                    status=(
-                        "current"
-                        if CONFLUENCE_CONNECTOR_INDEX_ONLY_ACTIVE_PAGES
-                        else None
-                    ),
+                    status="current"
+                    if CONFLUENCE_CONNECTOR_INDEX_ONLY_ACTIVE_PAGES
+                    else None,
                     expand="body.storage.value,version",
                 )
             except Exception:
@@ -425,11 +241,9 @@ class ConfluenceConnector(LoadConnector, PollConnector):
                                 self.space,
                                 start=start_ind + i,
                                 limit=1,
-                                status=(
-                                    "current"
-                                    if CONFLUENCE_CONNECTOR_INDEX_ONLY_ACTIVE_PAGES
-                                    else None
-                                ),
+                                status="current"
+                                if CONFLUENCE_CONNECTOR_INDEX_ONLY_ACTIVE_PAGES
+                                else None,
                                 expand="body.storage.value,version",
                             )
                         )
@@ -450,44 +264,17 @@ class ConfluenceConnector(LoadConnector, PollConnector):
 
                 return view_pages
 
-        def _fetch_page(start_ind: int, batch_size: int) -> list[dict[str, Any]]:
-            if self.recursive_indexer is None:
-                self.recursive_indexer = RecursiveIndexer(
-                    origin_page_id=self.page_id,
-                    batch_size=self.batch_size,
-                    confluence_client=self.confluence_client,
-                    index_recursively=self.index_recursively,
-                )
-
-            if self.index_recursively:
-                return self.recursive_indexer.get_pages(start_ind, batch_size)
-            else:
-                return self.recursive_indexer.get_origin_page()
-
-        pages: list[dict[str, Any]] = []
-
         try:
-            pages = (
-                _fetch_space(start_ind, self.batch_size)
-                if self.space_level_scan
-                else _fetch_page(start_ind, self.batch_size)
-            )
-            return pages
-
+            return _fetch(start_ind, self.batch_size)
         except Exception as e:
             if not self.continue_on_failure:
                 raise e
 
         # error checking phase, only reachable if `self.continue_on_failure=True`
+        pages: list[dict[str, Any]] = []
         for i in range(self.batch_size):
             try:
-                pages = (
-                    _fetch_space(start_ind, self.batch_size)
-                    if self.space_level_scan
-                    else _fetch_page(start_ind, self.batch_size)
-                )
-                return pages
-
+                pages.extend(_fetch(start_ind + i, 1))
             except Exception:
                 logger.exception(
                     "Ran into exception when fetching pages from Confluence"
@@ -499,7 +286,6 @@ class ConfluenceConnector(LoadConnector, PollConnector):
         get_page_child_by_type = make_confluence_call_handle_rate_limit(
             confluence_client.get_page_child_by_type
         )
-
         try:
             comment_pages = cast(
                 Collection[dict[str, Any]],
@@ -535,61 +321,6 @@ class ConfluenceConnector(LoadConnector, PollConnector):
             logger.exception("Ran into exception when fetching labels from Confluence")
             return []
 
-    def _fetch_attachments(
-        self, confluence_client: Confluence, page_id: str, files_in_used: list[str]
-    ) -> str:
-        get_attachments_from_content = make_confluence_call_handle_rate_limit(
-            confluence_client.get_attachments_from_content
-        )
-        files_attachment_content: list = []
-
-        try:
-            attachments_container = get_attachments_from_content(
-                page_id, start=0, limit=500
-            )
-            for attachment in attachments_container["results"]:
-                if attachment["metadata"]["mediaType"] in [
-                    "image/jpeg",
-                    "image/png",
-                    "image/gif",
-                    "image/svg+xml",
-                    "video/mp4",
-                    "video/quicktime",
-                ]:
-                    continue
-
-                if attachment["title"] not in files_in_used:
-                    continue
-
-                download_link = confluence_client.url + attachment["_links"]["download"]
-
-                attachment_size = attachment["extensions"]["fileSize"]
-                if attachment_size > CONFLUENCE_CONNECTOR_ATTACHMENT_SIZE_THRESHOLD:
-                    logger.warning(
-                        f"Skipping {download_link} due to size. "
-                        f"size={attachment_size} "
-                        f"threshold={CONFLUENCE_CONNECTOR_ATTACHMENT_SIZE_THRESHOLD}"
-                    )
-                    continue
-
-                download_link = confluence_client.url + attachment["_links"]["download"]
-                response = confluence_client._session.get(download_link)
-
-                if response.status_code == 200:
-                    extract = extract_file_text(
-                        attachment["title"], io.BytesIO(response.content), False
-                    )
-                    files_attachment_content.append(extract)
-
-        except Exception as e:
-            if not self.continue_on_failure:
-                raise e
-            logger.exception(
-                f"Ran into exception when fetching attachments from Confluence: {e}"
-            )
-
-        return "\n".join(files_attachment_content)
-
     def _get_doc_batch(
         self, start_ind: int, time_filter: Callable[[datetime], bool] | None = None
     ) -> tuple[list[Document], int]:
@@ -597,8 +328,8 @@ class ConfluenceConnector(LoadConnector, PollConnector):
 
         if self.confluence_client is None:
             raise ConnectorMissingCredentialError("Confluence")
-        batch = self._fetch_pages(self.confluence_client, start_ind)
 
+        batch = self._fetch_pages(self.confluence_client, start_ind)
         for page in batch:
             last_modified_str = page["version"]["when"]
             author = cast(str | None, page["version"].get("by", {}).get("email"))
@@ -614,18 +345,15 @@ class ConfluenceConnector(LoadConnector, PollConnector):
             if time_filter is None or time_filter(last_modified):
                 page_id = page["id"]
 
-                if self.labels_to_skip or not CONFLUENCE_CONNECTOR_SKIP_LABEL_INDEXING:
-                    page_labels = self._fetch_labels(self.confluence_client, page_id)
-
                 # check disallowed labels
                 if self.labels_to_skip:
+                    page_labels = self._fetch_labels(self.confluence_client, page_id)
                     label_intersection = self.labels_to_skip.intersection(page_labels)
                     if label_intersection:
                         logger.info(
                             f"Page with ID '{page_id}' has a label which has been "
                             f"designated as disallowed: {label_intersection}. Skipping."
                         )
-
                         continue
 
                 page_html = (
@@ -638,19 +366,8 @@ class ConfluenceConnector(LoadConnector, PollConnector):
                     logger.debug("Page is empty, skipping: %s", page_url)
                     continue
                 page_text = parse_html_page(page_html, self.confluence_client)
-
-                files_in_used = get_used_attachments(page_html, self.confluence_client)
-                attachment_text = self._fetch_attachments(
-                    self.confluence_client, page_id, files_in_used
-                )
-                page_text += attachment_text
                 comments_text = self._fetch_comments(self.confluence_client, page_id)
                 page_text += comments_text
-                doc_metadata: dict[str, str | list[str]] = {
-                    "Wiki Space Name": self.space
-                }
-                if not CONFLUENCE_CONNECTOR_SKIP_LABEL_INDEXING and page_labels:
-                    doc_metadata["labels"] = page_labels
 
                 doc_batch.append(
                     Document(
@@ -659,10 +376,12 @@ class ConfluenceConnector(LoadConnector, PollConnector):
                         source=DocumentSource.CONFLUENCE,
                         semantic_identifier=page["title"],
                         doc_updated_at=last_modified,
-                        primary_owners=(
-                            [BasicExpertInfo(email=author)] if author else None
-                        ),
-                        metadata=doc_metadata,
+                        primary_owners=[BasicExpertInfo(email=author)]
+                        if author
+                        else None,
+                        metadata={
+                            "Wiki Space Name": self.space,
+                        },
                     )
                 )
         return doc_batch, len(batch)
@@ -704,6 +423,8 @@ class ConfluenceConnector(LoadConnector, PollConnector):
 
 
 if __name__ == "__main__":
+    import os
+
     connector = ConfluenceConnector(os.environ["CONFLUENCE_TEST_SPACE_URL"])
     connector.load_credentials(
         {

backend/danswer/connectors/confluence/rate_limit_handler.py

@@ -1,14 +1,10 @@
-import time
 from collections.abc import Callable
 from typing import Any
 from typing import cast
 from typing import TypeVar
 
 from requests import HTTPError
-
-from danswer.utils.logger import setup_logger
-
-logger = setup_logger()
+from retry import retry
 
 
 F = TypeVar("F", bound=Callable[..., Any])
@@ -22,38 +18,23 @@ class ConfluenceRateLimitError(Exception):
 
 
 def make_confluence_call_handle_rate_limit(confluence_call: F) -> F:
+    @retry(
+        exceptions=ConfluenceRateLimitError,
+        tries=10,
+        delay=1,
+        max_delay=600,  # 10 minutes
+        backoff=2,
+        jitter=1,
+    )
     def wrapped_call(*args: list[Any], **kwargs: Any) -> Any:
-        starting_delay = 5
-        backoff = 2
-        max_delay = 600
-
-        for attempt in range(10):
-            try:
-                return confluence_call(*args, **kwargs)
-            except HTTPError as e:
-                if (
-                    e.response.status_code == 429
-                    or RATE_LIMIT_MESSAGE_LOWERCASE in e.response.text.lower()
-                ):
-                    retry_after = None
-                    try:
-                        retry_after = int(e.response.headers.get("Retry-After"))
-                    except (ValueError, TypeError):
-                        pass
-
-                    if retry_after:
-                        logger.warning(
-                            f"Rate limit hit. Retrying after {retry_after} seconds..."
-                        )
-                        time.sleep(retry_after)
-                    else:
-                        logger.warning(
-                            "Rate limit hit. Retrying with exponential backoff..."
-                        )
-                        delay = min(starting_delay * (backoff**attempt), max_delay)
-                        time.sleep(delay)
-                else:
-                    # re-raise, let caller handle
-                    raise
+        try:
+            return confluence_call(*args, **kwargs)
+        except HTTPError as e:
+            if (
+                e.response.status_code == 429
+                or RATE_LIMIT_MESSAGE_LOWERCASE in e.response.text.lower()
+            ):
+                raise ConfluenceRateLimitError()
+            raise
 
     return cast(F, wrapped_call)

backend/danswer/connectors/cross_connector_utils/miscellaneous_utils.py

@@ -6,7 +6,6 @@ from typing import TypeVar
 
 from dateutil.parser import parse
 
-from danswer.configs.constants import IGNORE_FOR_QA
 from danswer.connectors.models import BasicExpertInfo
 from danswer.utils.text_processing import is_valid_email
 
@@ -58,7 +57,3 @@ def process_in_batches(
 ) -> Iterator[list[U]]:
     for i in range(0, len(objects), batch_size):
         yield [process_function(obj) for obj in objects[i : i + batch_size]]
-
-
-def get_metadata_keys_to_ignore() -> list[str]:
-    return [IGNORE_FOR_QA]

backend/danswer/connectors/danswer_jira/connector.py

@@ -56,16 +56,6 @@ def extract_text_from_content(content: dict) -> str:
     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]:
@@ -127,10 +117,8 @@ def fetch_jira_issues_batch(
             continue
 
         comments = _get_comment_strs(jira, comment_email_blacklist)
-        semantic_rep = (
-            f"{jira.fields.description}\n"
-            if jira.fields.description
-            else "" + "\n".join([f"Comment: {comment}" for comment in comments])
+        semantic_rep = f"{jira.fields.description}\n" + "\n".join(
+            [f"Comment: {comment}" for comment in comments]
         )
 
         page_url = f"{jira_client.client_info()}/browse/{jira.key}"
@@ -159,18 +147,14 @@ def fetch_jira_issues_batch(
             pass
 
         metadata_dict = {}
-        priority = best_effort_get_field_from_issue(jira, "priority")
-        if priority:
-            metadata_dict["priority"] = priority.name
-        status = best_effort_get_field_from_issue(jira, "status")
-        if status:
-            metadata_dict["status"] = status.name
-        resolution = best_effort_get_field_from_issue(jira, "resolution")
-        if resolution:
-            metadata_dict["resolution"] = resolution.name
-        labels = best_effort_get_field_from_issue(jira, "labels")
-        if labels:
-            metadata_dict["label"] = labels
+        if jira.fields.priority:
+            metadata_dict["priority"] = jira.fields.priority.name
+        if jira.fields.status:
+            metadata_dict["status"] = jira.fields.status.name
+        if jira.fields.resolution:
+            metadata_dict["resolution"] = jira.fields.resolution.name
+        if jira.fields.labels:
+            metadata_dict["label"] = jira.fields.labels
 
         doc_batch.append(
             Document(

backend/danswer/connectors/discourse/connector.py

@@ -11,9 +11,6 @@ from requests import Response
 from danswer.configs.app_configs import INDEX_BATCH_SIZE
 from danswer.configs.constants import DocumentSource
 from danswer.connectors.cross_connector_utils.miscellaneous_utils import time_str_to_utc
-from danswer.connectors.cross_connector_utils.rate_limit_wrapper import (
-    rate_limit_builder,
-)
 from danswer.connectors.cross_connector_utils.retry_wrapper import retry_builder
 from danswer.connectors.interfaces import GenerateDocumentsOutput
 from danswer.connectors.interfaces import PollConnector
@@ -61,36 +58,63 @@ class DiscourseConnector(PollConnector):
         self.category_id_map: dict[int, str] = {}
 
         self.batch_size = batch_size
-        self.permissions: DiscoursePerms | None = None
-        self.active_categories: set | None = None
 
-    @rate_limit_builder(max_calls=50, period=60)
-    def _make_request(self, endpoint: str, params: dict | None = None) -> Response:
-        if not self.permissions:
-            raise ConnectorMissingCredentialError("Discourse")
-        return discourse_request(endpoint, self.permissions, params)
+        self.permissions: DiscoursePerms | None = None
 
     def _get_categories_map(
         self,
     ) -> None:
         assert self.permissions is not None
         categories_endpoint = urllib.parse.urljoin(self.base_url, "categories.json")
-        response = self._make_request(
+        response = discourse_request(
             endpoint=categories_endpoint,
+            perms=self.permissions,
             params={"include_subcategories": True},
         )
         categories = response.json()["category_list"]["categories"]
+
         self.category_id_map = {
-            cat["id"]: cat["name"]
-            for cat in categories
-            if not self.categories or cat["name"].lower() in self.categories
+            category["id"]: category["name"]
+            for category in categories
+            if not self.categories or category["name"].lower() in self.categories
         }
-        self.active_categories = set(self.category_id_map)
+
+    def _get_latest_topics(
+        self, start: datetime | None, end: datetime | None
+    ) -> list[int]:
+        assert self.permissions is not None
+        topic_ids = []
+
+        valid_categories = set(self.category_id_map.keys())
+
+        latest_endpoint = urllib.parse.urljoin(self.base_url, "latest.json")
+        response = discourse_request(endpoint=latest_endpoint, perms=self.permissions)
+        topics = response.json()["topic_list"]["topics"]
+        for topic in topics:
+            last_time = topic.get("last_posted_at")
+            if not last_time:
+                continue
+            last_time_dt = time_str_to_utc(last_time)
+
+            if start and start > last_time_dt:
+                continue
+            if end and end < last_time_dt:
+                continue
+
+            if valid_categories and topic.get("category_id") not in valid_categories:
+                continue
+
+            topic_ids.append(topic["id"])
+
+        return topic_ids
 
     def _get_doc_from_topic(self, topic_id: int) -> Document:
         assert self.permissions is not None
         topic_endpoint = urllib.parse.urljoin(self.base_url, f"t/{topic_id}.json")
-        response = self._make_request(endpoint=topic_endpoint)
+        response = discourse_request(
+            endpoint=topic_endpoint,
+            perms=self.permissions,
+        )
         topic = response.json()
 
         topic_url = urllib.parse.urljoin(self.base_url, f"t/{topic['slug']}")
@@ -114,16 +138,10 @@ class DiscourseConnector(PollConnector):
             sections.append(
                 Section(link=topic_url, text=parse_html_page_basic(post["cooked"]))
             )
-        category_name = self.category_id_map.get(topic["category_id"])
-
-        metadata: dict[str, str | list[str]] = (
-            {
-                "category": category_name,
-            }
-            if category_name
-            else {}
-        )
 
+        metadata: dict[str, str | list[str]] = {
+            "category": self.category_id_map[topic["category_id"]],
+        }
         if topic.get("tags"):
             metadata["tags"] = topic["tags"]
 
@@ -139,78 +157,26 @@ class DiscourseConnector(PollConnector):
         )
         return doc
 
-    def _get_latest_topics(
-        self, start: datetime | None, end: datetime | None, page: int
-    ) -> list[int]:
-        assert self.permissions is not None
-        topic_ids = []
-
-        if not self.categories:
-            latest_endpoint = urllib.parse.urljoin(
-                self.base_url, f"latest.json?page={page}"
-            )
-            response = self._make_request(endpoint=latest_endpoint)
-            topics = response.json()["topic_list"]["topics"]
-
-        else:
-            topics = []
-            empty_categories = []
-
-            for category_id in self.category_id_map.keys():
-                category_endpoint = urllib.parse.urljoin(
-                    self.base_url, f"c/{category_id}.json?page={page}&sys=latest"
-                )
-                response = self._make_request(endpoint=category_endpoint)
-                new_topics = response.json()["topic_list"]["topics"]
-
-                if len(new_topics) == 0:
-                    empty_categories.append(category_id)
-                topics.extend(new_topics)
-
-            for empty_category in empty_categories:
-                self.category_id_map.pop(empty_category)
-
-        for topic in topics:
-            last_time = topic.get("last_posted_at")
-            if not last_time:
-                continue
-
-            last_time_dt = time_str_to_utc(last_time)
-            if (start and start > last_time_dt) or (end and end < last_time_dt):
-                continue
-
-            topic_ids.append(topic["id"])
-            if len(topic_ids) >= self.batch_size:
-                break
-
-        return topic_ids
-
     def _yield_discourse_documents(
-        self,
-        start: datetime,
-        end: datetime,
+        self, topic_ids: list[int]
     ) -> GenerateDocumentsOutput:
-        page = 1
-        while topic_ids := self._get_latest_topics(start, end, page):
-            doc_batch: list[Document] = []
-            for topic_id in topic_ids:
-                doc_batch.append(self._get_doc_from_topic(topic_id))
-                if len(doc_batch) >= self.batch_size:
-                    yield doc_batch
-                    doc_batch = []
+        doc_batch: list[Document] = []
+        for topic_id in topic_ids:
+            doc_batch.append(self._get_doc_from_topic(topic_id))
 
-            if doc_batch:
+            if len(doc_batch) >= self.batch_size:
                 yield doc_batch
-            page += 1
+                doc_batch = []
 
-    def load_credentials(
-        self,
-        credentials: dict[str, Any],
-    ) -> dict[str, Any] | None:
+        if doc_batch:
+            yield doc_batch
+
+    def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
         self.permissions = DiscoursePerms(
             api_key=credentials["discourse_api_key"],
             api_username=credentials["discourse_api_username"],
         )
+
         return None
 
     def poll_source(
@@ -218,13 +184,16 @@ class DiscourseConnector(PollConnector):
     ) -> GenerateDocumentsOutput:
         if self.permissions is None:
             raise ConnectorMissingCredentialError("Discourse")
-
         start_datetime = datetime.utcfromtimestamp(start).replace(tzinfo=timezone.utc)
         end_datetime = datetime.utcfromtimestamp(end).replace(tzinfo=timezone.utc)
 
         self._get_categories_map()
 
-        yield from self._yield_discourse_documents(start_datetime, end_datetime)
+        latest_topic_ids = self._get_latest_topics(
+            start=start_datetime, end=end_datetime
+        )
+
+        return self._yield_discourse_documents(latest_topic_ids)
 
 
 if __name__ == "__main__":
@@ -240,5 +209,7 @@ if __name__ == "__main__":
 
     current = time.time()
     one_year_ago = current - 24 * 60 * 60 * 360
+
     latest_docs = connector.poll_source(one_year_ago, current)
+
     print(next(latest_docs))

backend/danswer/connectors/dropbox/connector.py

@@ -1,155 +0,0 @@
-from datetime import timezone
-from io import BytesIO
-from typing import Any
-
-from dropbox import Dropbox  # type: ignore
-from dropbox.exceptions import ApiError  # type:ignore
-from dropbox.files import FileMetadata  # type:ignore
-from dropbox.files import FolderMetadata  # type:ignore
-
-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 PollConnector
-from danswer.connectors.interfaces import SecondsSinceUnixEpoch
-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 extract_file_text
-from danswer.utils.logger import setup_logger
-
-
-logger = setup_logger()
-
-
-class DropboxConnector(LoadConnector, PollConnector):
-    def __init__(self, batch_size: int = INDEX_BATCH_SIZE) -> None:
-        self.batch_size = batch_size
-        self.dropbox_client: Dropbox | None = None
-
-    def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
-        self.dropbox_client = Dropbox(credentials["dropbox_access_token"])
-        return None
-
-    def _download_file(self, path: str) -> bytes:
-        """Download a single file from Dropbox."""
-        if self.dropbox_client is None:
-            raise ConnectorMissingCredentialError("Dropbox")
-        _, resp = self.dropbox_client.files_download(path)
-        return resp.content
-
-    def _get_shared_link(self, path: str) -> str:
-        """Create a shared link for a file in Dropbox."""
-        if self.dropbox_client is None:
-            raise ConnectorMissingCredentialError("Dropbox")
-
-        try:
-            # Check if a shared link already exists
-            shared_links = self.dropbox_client.sharing_list_shared_links(path=path)
-            if shared_links.links:
-                return shared_links.links[0].url
-
-            link_metadata = (
-                self.dropbox_client.sharing_create_shared_link_with_settings(path)
-            )
-            return link_metadata.url
-        except ApiError as err:
-            logger.exception(f"Failed to create a shared link for {path}: {err}")
-            return ""
-
-    def _yield_files_recursive(
-        self,
-        path: str,
-        start: SecondsSinceUnixEpoch | None,
-        end: SecondsSinceUnixEpoch | None,
-    ) -> GenerateDocumentsOutput:
-        """Yield files in batches from a specified Dropbox folder, including subfolders."""
-        if self.dropbox_client is None:
-            raise ConnectorMissingCredentialError("Dropbox")
-
-        result = self.dropbox_client.files_list_folder(
-            path,
-            limit=self.batch_size,
-            recursive=False,
-            include_non_downloadable_files=False,
-        )
-
-        while True:
-            batch: list[Document] = []
-            for entry in result.entries:
-                if isinstance(entry, FileMetadata):
-                    modified_time = entry.client_modified
-                    if modified_time.tzinfo is None:
-                        # If no timezone info, assume it is UTC
-                        modified_time = modified_time.replace(tzinfo=timezone.utc)
-                    else:
-                        # If not in UTC, translate it
-                        modified_time = modified_time.astimezone(timezone.utc)
-
-                    time_as_seconds = int(modified_time.timestamp())
-                    if start and time_as_seconds < start:
-                        continue
-                    if end and time_as_seconds > end:
-                        continue
-
-                    downloaded_file = self._download_file(entry.path_display)
-                    link = self._get_shared_link(entry.path_display)
-                    try:
-                        text = extract_file_text(
-                            entry.name,
-                            BytesIO(downloaded_file),
-                            break_on_unprocessable=False,
-                        )
-                        batch.append(
-                            Document(
-                                id=f"doc:{entry.id}",
-                                sections=[Section(link=link, text=text)],
-                                source=DocumentSource.DROPBOX,
-                                semantic_identifier=entry.name,
-                                doc_updated_at=modified_time,
-                                metadata={"type": "article"},
-                            )
-                        )
-                    except Exception as e:
-                        logger.exception(
-                            f"Error decoding file {entry.path_display} as utf-8 error occurred: {e}"
-                        )
-
-                elif isinstance(entry, FolderMetadata):
-                    yield from self._yield_files_recursive(entry.path_lower, start, end)
-
-            if batch:
-                yield batch
-
-            if not result.has_more:
-                break
-
-            result = self.dropbox_client.files_list_folder_continue(result.cursor)
-
-    def load_from_state(self) -> GenerateDocumentsOutput:
-        return self.poll_source(None, None)
-
-    def poll_source(
-        self, start: SecondsSinceUnixEpoch | None, end: SecondsSinceUnixEpoch | None
-    ) -> GenerateDocumentsOutput:
-        if self.dropbox_client is None:
-            raise ConnectorMissingCredentialError("Dropbox")
-
-        for batch in self._yield_files_recursive("", start, end):
-            yield batch
-
-        return None
-
-
-if __name__ == "__main__":
-    import os
-
-    connector = DropboxConnector()
-    connector.load_credentials(
-        {
-            "dropbox_access_token": os.environ["DROPBOX_ACCESS_TOKEN"],
-        }
-    )
-    document_batches = connector.load_from_state()
-    print(next(document_batches))

backend/danswer/connectors/factory.py

@@ -1,18 +1,13 @@
 from typing import Any
 from typing import Type
 
-from sqlalchemy.orm import Session
-
 from danswer.configs.constants import DocumentSource
 from danswer.connectors.axero.connector import AxeroConnector
-from danswer.connectors.blob.connector import BlobStorageConnector
 from danswer.connectors.bookstack.connector import BookstackConnector
-from danswer.connectors.clickup.connector import ClickupConnector
 from danswer.connectors.confluence.connector import ConfluenceConnector
 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.file.connector import LocalFileConnector
 from danswer.connectors.github.connector import GithubConnector
 from danswer.connectors.gitlab.connector import GitlabConnector
@@ -28,23 +23,17 @@ from danswer.connectors.interfaces import LoadConnector
 from danswer.connectors.interfaces import PollConnector
 from danswer.connectors.linear.connector import LinearConnector
 from danswer.connectors.loopio.connector import LoopioConnector
-from danswer.connectors.mediawiki.wiki import MediaWikiConnector
 from danswer.connectors.models import InputType
 from danswer.connectors.notion.connector import NotionConnector
 from danswer.connectors.productboard.connector import ProductboardConnector
 from danswer.connectors.requesttracker.connector import RequestTrackerConnector
-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
 from danswer.connectors.zendesk.connector import ZendeskConnector
 from danswer.connectors.zulip.connector import ZulipConnector
-from danswer.db.credentials import backend_update_credential_json
-from danswer.db.models import Credential
 
 
 class ConnectorMissingException(Exception):
@@ -82,19 +71,9 @@ def identify_connector_class(
         DocumentSource.GOOGLE_SITES: GoogleSitesConnector,
         DocumentSource.ZENDESK: ZendeskConnector,
         DocumentSource.LOOPIO: LoopioConnector,
-        DocumentSource.DROPBOX: DropboxConnector,
         DocumentSource.SHAREPOINT: SharepointConnector,
-        DocumentSource.TEAMS: TeamsConnector,
-        DocumentSource.SALESFORCE: SalesforceConnector,
         DocumentSource.DISCOURSE: DiscourseConnector,
         DocumentSource.AXERO: AxeroConnector,
-        DocumentSource.CLICKUP: ClickupConnector,
-        DocumentSource.MEDIAWIKI: MediaWikiConnector,
-        DocumentSource.WIKIPEDIA: WikipediaConnector,
-        DocumentSource.S3: BlobStorageConnector,
-        DocumentSource.R2: BlobStorageConnector,
-        DocumentSource.GOOGLE_CLOUD_STORAGE: BlobStorageConnector,
-        DocumentSource.OCI_STORAGE: BlobStorageConnector,
     }
     connector_by_source = connector_map.get(source, {})
 
@@ -120,6 +99,7 @@ def identify_connector_class(
         raise ConnectorMissingException(
             f"Connector for source={source} does not accept input_type={input_type}"
         )
+
     return connector
 
 
@@ -127,14 +107,10 @@ def instantiate_connector(
     source: DocumentSource,
     input_type: InputType,
     connector_specific_config: dict[str, Any],
-    credential: Credential,
-    db_session: Session,
-) -> BaseConnector:
+    credentials: dict[str, Any],
+) -> tuple[BaseConnector, dict[str, Any] | None]:
     connector_class = identify_connector_class(source, input_type)
     connector = connector_class(**connector_specific_config)
-    new_credentials = connector.load_credentials(credential.credential_json)
+    new_credentials = connector.load_credentials(credentials)
 
-    if new_credentials is not None:
-        backend_update_credential_json(credential, new_credentials, db_session)
-
-    return connector
+    return connector, new_credentials

backend/danswer/connectors/file/connector.py

@@ -69,9 +69,7 @@ def _process_file(
 
     if is_text_file_extension(file_name):
         encoding = detect_encoding(file)
-        file_content_raw, file_metadata = read_text_file(
-            file, encoding=encoding, ignore_danswer_metadata=False
-        )
+        file_content_raw, file_metadata = read_text_file(file, encoding=encoding)
 
     # Using the PDF reader function directly to pass in password cleanly
     elif extension == ".pdf":
@@ -85,18 +83,8 @@ def _process_file(
 
     all_metadata = {**metadata, **file_metadata} if metadata else file_metadata
 
-    # add a prefix to avoid conflicts with other connectors
-    doc_id = f"FILE_CONNECTOR__{file_name}"
-    if metadata:
-        doc_id = metadata.get("document_id") or doc_id
-
     # If this is set, we will show this in the UI as the "name" of the file
-    file_display_name = all_metadata.get("file_display_name") or os.path.basename(
-        file_name
-    )
-    title = (
-        all_metadata["title"] or "" if "title" in all_metadata else file_display_name
-    )
+    file_display_name_override = all_metadata.get("file_display_name")
 
     time_updated = all_metadata.get("time_updated", datetime.now(timezone.utc))
     if isinstance(time_updated, str):
@@ -111,7 +99,6 @@ def _process_file(
         for k, v in all_metadata.items()
         if k
         not in [
-            "document_id",
             "time_updated",
             "doc_updated_at",
             "link",
@@ -119,7 +106,6 @@ def _process_file(
             "secondary_owners",
             "filename",
             "file_display_name",
-            "title",
         ]
     }
 
@@ -138,13 +124,13 @@ def _process_file(
 
     return [
         Document(
-            id=doc_id,
+            id=f"FILE_CONNECTOR__{file_name}",  # add a prefix to avoid conflicts with other connectors
             sections=[
                 Section(link=all_metadata.get("link"), text=file_content_raw.strip())
             ],
             source=DocumentSource.FILE,
-            semantic_identifier=file_display_name,
-            title=title,
+            semantic_identifier=file_display_name_override
+            or os.path.basename(file_name),
             doc_updated_at=final_time_updated,
             primary_owners=p_owners,
             secondary_owners=s_owners,

backend/danswer/connectors/gitlab/connector.py

@@ -1,6 +1,4 @@
-import fnmatch
 import itertools
-from collections import deque
 from collections.abc import Iterable
 from collections.abc import Iterator
 from datetime import datetime
@@ -8,10 +6,7 @@ from datetime import timezone
 from typing import Any
 
 import gitlab
-import pytz
-from gitlab.v4.objects import Project
 
-from danswer.configs.app_configs import GITLAB_CONNECTOR_INCLUDE_CODE_FILES
 from danswer.configs.app_configs import INDEX_BATCH_SIZE
 from danswer.configs.constants import DocumentSource
 from danswer.connectors.interfaces import GenerateDocumentsOutput
@@ -24,13 +19,7 @@ from danswer.connectors.models import Document
 from danswer.connectors.models import Section
 from danswer.utils.logger import setup_logger
 
-# List of directories/Files to exclude
-exclude_patterns = [
-    "logs",
-    ".github/",
-    ".gitlab/",
-    ".pre-commit-config.yaml",
-]
+
 logger = setup_logger()
 
 
@@ -83,37 +72,6 @@ def _convert_issue_to_document(issue: Any) -> Document:
     return doc
 
 
-def _convert_code_to_document(
-    project: Project, file: Any, url: str, projectName: str, projectOwner: str
-) -> Document:
-    file_content_obj = project.files.get(
-        file_path=file["path"], ref="master"
-    )  # Replace 'master' with your branch name if needed
-    try:
-        file_content = file_content_obj.decode().decode("utf-8")
-    except UnicodeDecodeError:
-        file_content = file_content_obj.decode().decode("latin-1")
-
-    file_url = f"{url}/{projectOwner}/{projectName}/-/blob/master/{file['path']}"  # Construct the file URL
-    doc = Document(
-        id=file["id"],
-        sections=[Section(link=file_url, text=file_content)],
-        source=DocumentSource.GITLAB,
-        semantic_identifier=file["name"],
-        doc_updated_at=datetime.now().replace(
-            tzinfo=timezone.utc
-        ),  # Use current time as updated_at
-        primary_owners=[],  # Fill this as needed
-        metadata={"type": "CodeFile"},
-    )
-    return doc
-
-
-def _should_exclude(path: str) -> bool:
-    """Check if a path matches any of the exclude patterns."""
-    return any(fnmatch.fnmatch(path, pattern) for pattern in exclude_patterns)
-
-
 class GitlabConnector(LoadConnector, PollConnector):
     def __init__(
         self,
@@ -123,7 +81,6 @@ class GitlabConnector(LoadConnector, PollConnector):
         state_filter: str = "all",
         include_mrs: bool = True,
         include_issues: bool = True,
-        include_code_files: bool = GITLAB_CONNECTOR_INCLUDE_CODE_FILES,
     ) -> None:
         self.project_owner = project_owner
         self.project_name = project_name
@@ -131,7 +88,6 @@ class GitlabConnector(LoadConnector, PollConnector):
         self.state_filter = state_filter
         self.include_mrs = include_mrs
         self.include_issues = include_issues
-        self.include_code_files = include_code_files
         self.gitlab_client: gitlab.Gitlab | None = None
 
     def load_credentials(self, credentials: dict[str, Any]) -> dict[str, Any] | None:
@@ -145,80 +101,45 @@ class GitlabConnector(LoadConnector, PollConnector):
     ) -> GenerateDocumentsOutput:
         if self.gitlab_client is None:
             raise ConnectorMissingCredentialError("Gitlab")
-        project: gitlab.Project = self.gitlab_client.projects.get(
+        project = self.gitlab_client.projects.get(
             f"{self.project_owner}/{self.project_name}"
         )
 
-        # Fetch code files
-        if self.include_code_files:
-            # Fetching using BFS as project.report_tree with recursion causing slow load
-            queue = deque([""])  # Start with the root directory
-            while queue:
-                current_path = queue.popleft()
-                files = project.repository_tree(path=current_path, all=True)
-                for file_batch in _batch_gitlab_objects(files, self.batch_size):
-                    code_doc_batch: list[Document] = []
-                    for file in file_batch:
-                        if _should_exclude(file["path"]):
-                            continue
-
-                        if file["type"] == "blob":
-                            code_doc_batch.append(
-                                _convert_code_to_document(
-                                    project,
-                                    file,
-                                    self.gitlab_client.url,
-                                    self.project_name,
-                                    self.project_owner,
-                                )
-                            )
-                        elif file["type"] == "tree":
-                            queue.append(file["path"])
-
-                    if code_doc_batch:
-                        yield code_doc_batch
-
         if self.include_mrs:
             merge_requests = project.mergerequests.list(
                 state=self.state_filter, order_by="updated_at", sort="desc"
             )
 
             for mr_batch in _batch_gitlab_objects(merge_requests, self.batch_size):
-                mr_doc_batch: list[Document] = []
+                doc_batch: list[Document] = []
                 for mr in mr_batch:
                     mr.updated_at = datetime.strptime(
-                        mr.updated_at, "%Y-%m-%dT%H:%M:%S.%f%z"
+                        mr.updated_at, "%Y-%m-%dT%H:%M:%S.%fZ"
                     )
-                    if start is not None and mr.updated_at < start.replace(
-                        tzinfo=pytz.UTC
-                    ):
-                        yield mr_doc_batch
+                    if start is not None and mr.updated_at < start:
+                        yield doc_batch
                         return
-                    if end is not None and mr.updated_at > end.replace(tzinfo=pytz.UTC):
+                    if end is not None and mr.updated_at > end:
                         continue
-                    mr_doc_batch.append(_convert_merge_request_to_document(mr))
-                yield mr_doc_batch
+                    doc_batch.append(_convert_merge_request_to_document(mr))
+                yield doc_batch
 
         if self.include_issues:
             issues = project.issues.list(state=self.state_filter)
 
             for issue_batch in _batch_gitlab_objects(issues, self.batch_size):
-                issue_doc_batch: list[Document] = []
+                doc_batch = []
                 for issue in issue_batch:
                     issue.updated_at = datetime.strptime(
-                        issue.updated_at, "%Y-%m-%dT%H:%M:%S.%f%z"
+                        issue.updated_at, "%Y-%m-%dT%H:%M:%S.%fZ"
                     )
-                    if start is not None:
-                        start = start.replace(tzinfo=pytz.UTC)
-                        if issue.updated_at < start:
-                            yield issue_doc_batch
-                            return
-                    if end is not None:
-                        end = end.replace(tzinfo=pytz.UTC)
-                        if issue.updated_at > end:
-                            continue
-                    issue_doc_batch.append(_convert_issue_to_document(issue))
-                yield issue_doc_batch
+                    if start is not None and issue.updated_at < start:
+                        yield doc_batch
+                        return
+                    if end is not None and issue.updated_at > end:
+                        continue
+                    doc_batch.append(_convert_issue_to_document(issue))
+                yield doc_batch
 
     def load_from_state(self) -> GenerateDocumentsOutput:
         return self._fetch_from_gitlab()
@@ -242,12 +163,11 @@ if __name__ == "__main__":
         state_filter="all",
         include_mrs=True,
         include_issues=True,
-        include_code_files=GITLAB_CONNECTOR_INCLUDE_CODE_FILES,
     )
 
     connector.load_credentials(
         {
-            "gitlab_access_token": os.environ["GITLAB_ACCESS_TOKEN"],
+            "github_access_token": os.environ["GITLAB_ACCESS_TOKEN"],
             "gitlab_url": os.environ["GITLAB_URL"],
         }
     )

backend/danswer/connectors/gmail/connector.py

@@ -3,8 +3,7 @@ from typing import Any
 from typing import cast
 from typing import Dict
 
-from google.oauth2.credentials import Credentials as OAuthCredentials  # type: ignore
-from google.oauth2.service_account import Credentials as ServiceAccountCredentials  # type: ignore
+from google.auth.credentials import Credentials  # type: ignore
 from googleapiclient import discovery  # type: ignore
 
 from danswer.configs.app_configs import INDEX_BATCH_SIZE
@@ -37,7 +36,7 @@ logger = setup_logger()
 class GmailConnector(LoadConnector, PollConnector):
     def __init__(self, batch_size: int = INDEX_BATCH_SIZE) -> None:
         self.batch_size = batch_size
-        self.creds: OAuthCredentials | ServiceAccountCredentials | None = None
+        self.creds: Credentials | None = None
 
     def load_credentials(self, credentials: dict[str, Any]) -> dict[str, str] | None:
         """Checks for two different types of credentials.
@@ -46,7 +45,7 @@ class GmailConnector(LoadConnector, PollConnector):
         (2) A credential which holds a service account key JSON file, which
         can then be used to impersonate any user in the workspace.
         """
-        creds: OAuthCredentials | ServiceAccountCredentials | None = None
+        creds = None
         new_creds_dict = None
         if DB_CREDENTIALS_DICT_TOKEN_KEY in credentials:
             access_token_json_str = cast(
@@ -75,7 +74,7 @@ class GmailConnector(LoadConnector, PollConnector):
                 str | None, credentials.get(DB_CREDENTIALS_DICT_DELEGATED_USER_KEY)
             )
             if delegated_user_email:
-                creds = creds.with_subject(delegated_user_email) if creds else None  # type: ignore
+                creds = creds.with_subject(delegated_user_email) if creds else None
 
         if creds is None:
             raise PermissionError(

backend/danswer/connectors/gmail/connector_auth.py

@@ -11,17 +11,16 @@ from google_auth_oauthlib.flow import InstalledAppFlow  # type: ignore
 from sqlalchemy.orm import Session
 
 from danswer.configs.app_configs import WEB_DOMAIN
-from danswer.configs.constants import DocumentSource
-from danswer.configs.constants import KV_CRED_KEY
-from danswer.configs.constants import KV_GMAIL_CRED_KEY
-from danswer.configs.constants import KV_GMAIL_SERVICE_ACCOUNT_KEY
+from danswer.connectors.gmail.constants import CRED_KEY
 from danswer.connectors.gmail.constants import (
     DB_CREDENTIALS_DICT_DELEGATED_USER_KEY,
 )
 from danswer.connectors.gmail.constants import DB_CREDENTIALS_DICT_TOKEN_KEY
+from danswer.connectors.gmail.constants import GMAIL_CRED_KEY
 from danswer.connectors.gmail.constants import (
     GMAIL_DB_CREDENTIALS_DICT_SERVICE_ACCOUNT_KEY,
 )
+from danswer.connectors.gmail.constants import GMAIL_SERVICE_ACCOUNT_KEY
 from danswer.connectors.gmail.constants import SCOPES
 from danswer.db.credentials import update_credential_json
 from danswer.db.models import User
@@ -72,7 +71,7 @@ def get_gmail_creds_for_service_account(
 
 
 def verify_csrf(credential_id: int, state: str) -> None:
-    csrf = get_dynamic_config_store().load(KV_CRED_KEY.format(str(credential_id)))
+    csrf = get_dynamic_config_store().load(CRED_KEY.format(str(credential_id)))
     if csrf != state:
         raise PermissionError(
             "State from Gmail Connector callback does not match expected"
@@ -80,7 +79,7 @@ def verify_csrf(credential_id: int, state: str) -> None:
 
 
 def get_gmail_auth_url(credential_id: int) -> str:
-    creds_str = str(get_dynamic_config_store().load(KV_GMAIL_CRED_KEY))
+    creds_str = str(get_dynamic_config_store().load(GMAIL_CRED_KEY))
     credential_json = json.loads(creds_str)
     flow = InstalledAppFlow.from_client_config(
         credential_json,
@@ -92,14 +91,12 @@ def get_gmail_auth_url(credential_id: int) -> str:
     parsed_url = cast(ParseResult, urlparse(auth_url))
     params = parse_qs(parsed_url.query)
 
-    get_dynamic_config_store().store(
-        KV_CRED_KEY.format(credential_id), params.get("state", [None])[0], encrypt=True
-    )  # type: ignore
+    get_dynamic_config_store().store(CRED_KEY.format(credential_id), params.get("state", [None])[0], encrypt=True)  # type: ignore
     return str(auth_url)
 
 
 def get_auth_url(credential_id: int) -> str:
-    creds_str = str(get_dynamic_config_store().load(KV_GMAIL_CRED_KEY))
+    creds_str = str(get_dynamic_config_store().load(GMAIL_CRED_KEY))
     credential_json = json.loads(creds_str)
     flow = InstalledAppFlow.from_client_config(
         credential_json,
@@ -111,9 +108,7 @@ def get_auth_url(credential_id: int) -> str:
     parsed_url = cast(ParseResult, urlparse(auth_url))
     params = parse_qs(parsed_url.query)
 
-    get_dynamic_config_store().store(
-        KV_CRED_KEY.format(credential_id), params.get("state", [None])[0], encrypt=True
-    )  # type: ignore
+    get_dynamic_config_store().store(CRED_KEY.format(credential_id), params.get("state", [None])[0], encrypt=True)  # type: ignore
     return str(auth_url)
 
 
@@ -151,29 +146,28 @@ def build_service_account_creds(
         credential_dict[DB_CREDENTIALS_DICT_DELEGATED_USER_KEY] = delegated_user_email
 
     return CredentialBase(
-        source=DocumentSource.GMAIL,
         credential_json=credential_dict,
         admin_public=True,
     )
 
 
 def get_google_app_gmail_cred() -> GoogleAppCredentials:
-    creds_str = str(get_dynamic_config_store().load(KV_GMAIL_CRED_KEY))
+    creds_str = str(get_dynamic_config_store().load(GMAIL_CRED_KEY))
     return GoogleAppCredentials(**json.loads(creds_str))
 
 
 def upsert_google_app_gmail_cred(app_credentials: GoogleAppCredentials) -> None:
     get_dynamic_config_store().store(
-        KV_GMAIL_CRED_KEY, app_credentials.json(), encrypt=True
+        GMAIL_CRED_KEY, app_credentials.json(), encrypt=True
     )
 
 
 def delete_google_app_gmail_cred() -> None:
-    get_dynamic_config_store().delete(KV_GMAIL_CRED_KEY)
+    get_dynamic_config_store().delete(GMAIL_CRED_KEY)
 
 
 def get_gmail_service_account_key() -> GoogleServiceAccountKey:
-    creds_str = str(get_dynamic_config_store().load(KV_GMAIL_SERVICE_ACCOUNT_KEY))
+    creds_str = str(get_dynamic_config_store().load(GMAIL_SERVICE_ACCOUNT_KEY))
     return GoogleServiceAccountKey(**json.loads(creds_str))
 
 
@@ -181,19 +175,19 @@ def upsert_gmail_service_account_key(
     service_account_key: GoogleServiceAccountKey,
 ) -> None:
     get_dynamic_config_store().store(
-        KV_GMAIL_SERVICE_ACCOUNT_KEY, service_account_key.json(), encrypt=True
+        GMAIL_SERVICE_ACCOUNT_KEY, service_account_key.json(), encrypt=True
     )
 
 
 def upsert_service_account_key(service_account_key: GoogleServiceAccountKey) -> None:
     get_dynamic_config_store().store(
-        KV_GMAIL_SERVICE_ACCOUNT_KEY, service_account_key.json(), encrypt=True
+        GMAIL_SERVICE_ACCOUNT_KEY, service_account_key.json(), encrypt=True
     )
 
 
 def delete_gmail_service_account_key() -> None:
-    get_dynamic_config_store().delete(KV_GMAIL_SERVICE_ACCOUNT_KEY)
+    get_dynamic_config_store().delete(GMAIL_SERVICE_ACCOUNT_KEY)
 
 
 def delete_service_account_key() -> None:
-    get_dynamic_config_store().delete(KV_GMAIL_SERVICE_ACCOUNT_KEY)
+    get_dynamic_config_store().delete(GMAIL_SERVICE_ACCOUNT_KEY)

backend/danswer/connectors/gmail/constants.py

@@ -1,4 +1,7 @@
 DB_CREDENTIALS_DICT_TOKEN_KEY = "gmail_tokens"
 GMAIL_DB_CREDENTIALS_DICT_SERVICE_ACCOUNT_KEY = "gmail_service_account_key"
 DB_CREDENTIALS_DICT_DELEGATED_USER_KEY = "gmail_delegated_user"
+CRED_KEY = "credential_id_{}"
+GMAIL_CRED_KEY = "gmail_app_credential"
+GMAIL_SERVICE_ACCOUNT_KEY = "gmail_service_account_key"
 SCOPES = ["https://www.googleapis.com/auth/gmail.readonly"]

backend/danswer/connectors/gong/connector.py

@@ -198,10 +198,7 @@ class GongConnector(LoadConnector, PollConnector):
                     f"Indexing Gong call from {call_time_str.split('T', 1)[0]}: {call_title}"
                 )
 
-                call_parties = cast(list[dict] | None, call_details.get("parties"))
-                if call_parties is None:
-                    logger.error(f"Couldn't get parties for Call ID: {call_id}")
-                    call_parties = []
+                call_parties = call_details["parties"]
 
                 id_to_name_map = self._parse_parties(call_parties)