Alex 12dd7c7eab fix: stop the re-embed migration from destroying the index it rebuilds
Five defects from a review of the embeddings work, four of them silent.

- Write local files atomically. `LocalStorage.save_file` streamed straight onto
  the destination, so an interrupted write left a truncated file. `reembed`
  rewrites every index it touches, and a half-written `index.faiss` loads at
  neither the old width nor the new one -- the source was unrecoverable, with
  no backup and no temp file left behind. Bytes now land beside the destination
  and move into place with `os.replace`. S3 was already safe (single PUT).

- Read pgvector chunks a page at a time. `reembed_pgvector` materialised every
  `(id, text)` row for a source before embedding -- ~1.6 GB at 200k chunks and
  several times that for non-Latin scripts, with the `PGresult` held alongside
  until the cursor closed. Inside the shipped 4Gi limit, while also holding the
  model, that is an OOMKill -- which is exactly the SIGKILL the point above
  turned into a destroyed index. It now walks the source by keyset.

- Bound the first wave of delegated embeds. The failure cooldown is only latched
  once the first `get()` returns, so every request already in flight paid the
  full EMBEDDINGS_DELEGATE_TIMEOUT: measured 64 threads all timing out together,
  and at the shipped 60s across a 96-thread WSGI pool that is an API serving
  nothing at all, health checks included. One caller now probes while the rest
  fail fast; after a single success the gate leaves the path entirely.

- Ship EMBEDDINGS_NAME commented in .env-template. The comment directly above it
  says to leave it commented when upgrading, and the line shipped set. Any value
  reaching `.env` lands in `model_fields_set`, which makes `resolve_embeddings_pin`
  bail -- so a template-derived `.env` disabled the legacy pin outright and
  repointed a populated index at a different 768-dim model, where no width check
  fires. The pin already picks granite for a fresh install and mpnet for an
  existing one, so nothing needs to be set by hand.

- Stamp `sources.model` on wiki sources. They were created with the column NULL
  and then embedded like any other source, and the boot check reads NULL as
  "pre-dates the column, therefore the legacy model" -- reporting a correctly
  embedded source as stale on every startup of every process. Stamped at
  creation, and again on each page re-embed so existing rows heal.

The two docs that promised the FAISS index survives a failed run said so of the
embed only; both now describe the write, and upgrading.mdx says to stop ingest
for the duration.
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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.


video-example-of-docs-gpt

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.

Get a Demo 👋⁠

Send Email 📧

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.

Learn More & Apply →

QuickStart

Note

Make sure you have Docker installed

A more detailed Quickstart is available in our documentation

  1. Clone the repository:

    git clone https://github.com/arc53/DocsGPT.git
    cd DocsGPT
    

For macOS and Linux:

  1. Run the setup script:

    ./setup.sh
    

For Windows:

  1. 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

Architecture chart

Project Structure

  • Application - Backend Flask application.

  • Extensions - Integrations and widgets (e.g., Chatwoot, React widget).

  • Frontend - Web UI built with Vite and React.

  • 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⚡

Contributors

License

The source code license is MIT, as described in the LICENSE file.

This project is supported by:

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Description
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
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