bigint / rag.computer
Self-hostable RAG platform - document ingestion, embedding, and vector search behind a simple REST API
Why this job: says: rag, vector search; topics: rag, vector-database, embeddings
What it does
- Document ingestion. PDF, DOCX, PPTX, HTML, Markdown, images, and more via Docling
- Embedding providers. OpenAI, OpenAI-compatible gateways, Cohere, and Voyage
- Embedding presets. save named provider/model configs once, reuse across collections
- Turbopuffer search. vectors, chunk text, metadata filters, BM25 keyword search, and hybrid retrieval via Turbopuffer
- Namespace isolation. each collection maps to a Turbopuffer namespace for scoped writes, exports, truncation, and deletion
Get started
docker compose up -d- Maintenance
- Active
- Last commit 2 months ago
- Stars
- 100
- On GitHub
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- Python
- 53% of the code
Is it looked after?
- Contributors
- 1
- Last 90 days
- Issues
- 87% closed
- 2 still open
- Pull requests
- 40 merged
- 15 waiting
- Releases
- None
- Never published
- Contributing guide (opens in new tab)
- Security policy (opens in new tab)
- No code of conduct
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More: languages, topics and project record
Languages
- Python 53%
- TypeScript 34%
- MDX 12%
- Shell <1%
Topics
- ai
- embeddings
- rag
- rag-pipeline
- vector-database
Project record
- JobRAG and search
- StatusListed
- Commits1,145
- Forks11
- Watching0
- Stars in 28 days0
- CreatedMar 28, 2026
- First seen by usNov 7, 2026
- CheckedNov 7, 2026