Alex
47e53a71c4
The API embeds every query it serves, so it held its own copy of the model: ~890 MB it never needed. EMBEDDINGS_DELEGATE_TO_WORKER (on by default) sends the text to the Celery worker instead and gets the vector back, taking an API process from 1176 MB to 285 MB with no ONNX Runtime imported at all. The client embeds locally when it finds itself inside a worker task, so the worker never dispatches to itself -- the same self-deadlock DOCUMENT_PARSE_QUEUE avoids on the parsing side. EMBEDDINGS_BASE_URL still wins over it, and remains the right answer for production. ensure_vector_schema was constructing the embeddings instance purely to read .dimension off it, loading several hundred MB of ONNX into every API and worker process at import. For a model the registry describes that is a lookup; only an unregistered name now falls back to loading. EMBEDDINGS_BATCH_SIZE was sizing two unrelated things: chunks per store transaction (and per remote embed request) and documents per ONNX forward pass. Each pass pads every input up to its longest, and that waste grows with the square of chunk length, so at the 1250-token default a batch of 32 peaked at 6.6 GB and took 326s where a batch of 1 peaked at 2.9 GB and took 90s. The forward pass is now sized by EMBEDDINGS_MODEL_BATCH_SIZE, defaulting to 1; storage and remote batching are unchanged at 32. reembed embeds in-process: a batch job that walks the whole index should not round-trip every chunk through a broker, and loading the model there reports a real failure instead of timing out against an empty queue. Also drops the mpnet zip download from the docs and the devcontainer, which pointed at a SentenceTransformers export with no ONNX graph and had been inert since the FastEmbed swap; corrects the claim that any sentence-transformers model works; and settles the Configuring/Settings pages on what the registry and the repository metadata actually decide.
DocsGPT 🦖
Private AI for agents, assistants and enterprise search
DocsGPT is an open-source AI platform for building intelligent agents and assistants. Features Agent Builder, deep research tools, document analysis (PDF, Office, web content, and audio), Multi-model support (choose your provider or run locally), and rich API connectivity for agents with actionable tools and integrations. Deploy anywhere with complete privacy control.
Key Features:
- 🗂️ Wide Format Support: Reads PDF, DOCX, CSV, XLSX, EPUB, MD, RST, HTML, MDX, JSON, PPTX, images, and audio files such as MP3, WAV, M4A, OGG, and WebM.
- 🎙️ Speech Workflows: Record voice input into chat, transcribe audio on the backend, and ingest meeting recordings or voice notes as searchable knowledge.
- 🌐 Web & Data Integration: Ingests from URLs, sitemaps, Reddit, GitHub and web crawlers.
- ✅ Reliable Answers: Get accurate, hallucination-free responses with source citations viewable in a clean UI.
- 🔑 Streamlined API Keys: Generate keys linked to your settings, documents, and models, simplifying chatbot and integration setup.
- 🔗 Actionable Tooling: Connect to APIs, tools, and other services to enable LLM actions.
- 🧩 Pre-built Integrations: Use readily available HTML/React chat widgets, search tools, Discord/Telegram bots, and more.
- 🔌 Flexible Deployment: Works with major LLMs (OpenAI, Google, Anthropic) and local models (Ollama, llama_cpp).
- 🏢 Secure & Scalable: Run privately and securely with Kubernetes support, designed for enterprise-grade reliability.
Roadmap
- Agent Workflow Builder with conditional nodes ( February 2026 )
- Research mode ( March 2026 )
- SharePoint & Confluence connectors ( March – April 2026 )
- Postgres migration for user data ( April 2026 )
- OpenTelemetry observability ( April 2026 )
- Bring Your Own Model (BYOM) ( April 2026 )
- Agent scheduling (RedBeat-backed) ( April 2026 )
- Notifications & conversation search ( May 2026 )
- Analytics & logs revamp with per-agent attribution ( June 2026 )
- OIDC / SSO login with SCIM provisioning & groups ( June 2026 )
- Admin dashboard & role-based access control (RBAC) ( June 2026 )
- Agent import / export ( June 2026 )
- Teams with team-scoped sharing & roles ( June 2026 )
You can find our full roadmap here. Please don't hesitate to contribute or create issues, it helps us improve DocsGPT!
Production Support / Help for Companies:
We're eager to provide personalized assistance when deploying your DocsGPT to a live environment.
Join the Lighthouse Program 🌟
Calling all developers and GenAI innovators! The DocsGPT Lighthouse Program connects technical leaders actively deploying or extending DocsGPT in real-world scenarios. Collaborate directly with our team to shape the roadmap, access priority support, and build enterprise-ready solutions with exclusive community insights.
QuickStart
Note
Make sure you have Docker installed
A more detailed Quickstart is available in our documentation
-
Clone the repository:
git clone https://github.com/arc53/DocsGPT.git cd DocsGPT
For macOS and Linux:
-
Run the setup script:
./setup.sh
For Windows:
-
Run the PowerShell setup script:
PowerShell -ExecutionPolicy Bypass -File .\setup.ps1
Either script will guide you through setting up DocsGPT. Five options are available: using the public API, running locally, connecting to a local inference engine, using a cloud API provider, or building the docker image locally. The scripts will automatically configure your .env file and handle necessary downloads and installations based on your chosen option.
Navigate to http://localhost:5173/
To stop DocsGPT, open a terminal in the DocsGPT directory and run:
docker compose -f deployment/docker-compose.yaml down
(or use the specific docker compose down command shown after running the setup script).
Note
For development environment setup instructions, please refer to the Development Environment Guide.
Contributing
Please refer to the CONTRIBUTING.md file for information about how to get involved. We welcome issues, questions, and pull requests.
Architecture
Project Structure
-
Application - Backend Flask application.
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Extensions - Integrations and widgets (e.g., Chatwoot, React widget).
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Scripts - Miscellaneous utility scripts.
Code Of Conduct
We as members, contributors, and leaders, pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. Please refer to the CODE_OF_CONDUCT.md file for more information about contributing.
Many Thanks To Our Contributors⚡
License
The source code license is MIT, as described in the LICENSE file.
This project is supported by:
