@yoginth: today i'm launching http://rag.computer an open source RAG platform built on top of @turbopuffer fast ingestion, fast r…
Summary
bigRAG is an open-source RAG platform built on top of Turbopuffer for fast ingestion and retrieval, supporting multiple document formats and embedding models.
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Cached at: 05/22/26, 07:57 PM
today i’m launching https://t.co/lG6DqJOfZN
an open source RAG platform built on top of @turbopuffer
fast ingestion, fast retrieval, clean developer experience.
one of the few AI infra products that genuinely feels modern.
more soon 👀 https://t.co/nLxNYOnr0F
Turbopuffer-backed RAG for your documents
Source: https://rag.computer/ Open-source · Self-hosted control plane · Turbopuffer search
Upload documents, parse with Docling, embed with your provider, and use Turbopuffer for semantic, keyword, and hybrid retrieval through one API.
Features
Everything you need for RAG
### Any Document Format PDF, DOCX, PPTX, HTML, Markdown, images with OCR, and more — powered by Docling for universal document parsing.### Any Embedding Model OpenAI, Cohere, Voyage, and OpenAI-compatible models with per-collection configuration. Mix providers across collections.### Turbopuffer Search Turbopuffer powers semantic, keyword, and hybrid search from the same chunk store, with Reciprocal Rank Fusion for mixed queries.### Namespace Isolation Each collection maps to a Turbopuffer namespace, keeping vector writes, keyword indexes, exports, and deletes scoped.### Webhooks HMAC-signed webhook payloads with automatic retries for document, collection, connector, and backup events.### TypeScript SDK Zero-dependency TypeScript client for Node.js, browsers, Deno, and Bun. Full type safety and automatic retries.
Simple integration
Upload, embed, and search in minutes
bigRAG handles the entire RAG pipeline. Upload any document format, and it automatically parses, chunks, embeds, and indexes in Turbopuffer for vector and full-text search. Use the TypeScript SDK or REST API.
TypeScript SDK with zero dependencies and full type safety
Turbopuffer stores vectors, chunk text, metadata, and keyword indexes
Full API reference with Swagger docs at /docs
15+
Document formats
PDF, DOCX, images, and more
1
Search backend
Turbopuffer
12+
Embedding models
OpenAI, Cohere, Voyage
5 min
To deploy
Docker Compose
Deploy bigRAG with Turbopuffer search
Run the API, admin UI, Postgres, and Redis yourself, then connect Turbopuffer for managed vector and full-text retrieval.
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