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overview.md repeated the landing page: the same install-and-ask block and five of six identical links. It was positioning prose, where the docs had no page describing how the system works. Rewrite it as Architecture, following the data through: source adapter, converter, chunker, embedder, transaction; then storage and its versioning; then retrieval, with the 10x rerank fetch and section-bounded expansion; then the two capabilities; then laptop versus ingester. Retitled in the nav and on the landing page, filename kept so existing links resolve. Extras were listed in three places and none was complete. docs/installation.md now carries a table of all fifteen slim extras, what each provides, and which the full package already includes. haiku_rag_slim/README.md names them and links there. The claim that other providers need their own pydantic-ai extra was wrong: haiku.rag-slim defines anthropic, google, groq, mistral, bedrock and vertexai itself. configuration/storage.md opens with the four operational constraints, which were either buried in an S3 section or undocumented: one writer per URI, reader lag by read_consistency_interval_seconds, migrate after a schema-changing upgrade, and the fixed embedding dimension with what ConfigMismatchError means and which rebuild mode resolves it. The one-writer rule is stated as a haiku.rag constraint, which is what it is: the multi-table lock, version snapshot and rollback are process-local, so a second writer can commit inside another's transaction and be reverted by its rollback. storage.md and ingester.md both claimed it was a LanceDB property that corrupts manifests. The S3 deployment section now links to the constraint instead of restating it. Get started reads index, Quickstart, Installation, Architecture. The landing page's list was missing Installation.
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| title | description |
|---|---|
| haiku.rag | Local-first agentic RAG. Index PDFs, web pages, and whole directories, then ask questions and get answers cited to page numbers and section headings. Hybrid search, reranking, and multimodal retrieval on embedded LanceDB. |
haiku.rag indexes PDFs, web pages, and whole directories, retrieves with hybrid search, and answers with citations down to the page number and section heading. It runs on an embedded database with open models, so your documents stay on your machine and there is no server to operate.
uv pip install haiku.rag
haiku-rag init
haiku-rag add-src ~/Documents/some-paper.pdf
haiku-rag ask "what does it conclude?"
Quickstart covers provider setup and the first ingestion.
Why haiku.rag
Answers you can check. Every answer carries citations with page numbers and section headings. Visual grounding shows the cited chunk highlighted on the original page image. Optional capabilities require an answer to declare what grounds it, including declaring that nothing does.
Local-first, no server. Embedded LanceDB and open models through Ollama by default. No database to run and no API keys required. The same code runs against S3, GCS, Azure, LanceDB Cloud, or any provider Pydantic AI supports.
Built for agents. Native Pydantic AI capabilities compose into your own agents. An MCP server exposes the same database to Claude Desktop and other assistants. The analysis capability runs sandboxed Python across documents for questions that need computation rather than retrieval.
Measured, not asserted. Retrieval and answer quality are tracked against public benchmarks with runnable configs. See Benchmarks.
Start here
- Quickstart: install, index, chat.
- Installation: packages and extras.
- Architecture: how a document becomes a cited answer.
- Capabilities: native RAG and analysis capabilities for Pydantic AI agents.
- Python API: use haiku.rag from code.
- MCP server: expose haiku.rag to Claude Desktop or other AI assistants.
- Configuration: every setting.
MIT licensed. Source on GitHub.