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Yiorgis Gozadinos c184a25d68
Index every hot lookup key from one shared definition 
`_init_tables` left `chunks.id`, `chunks.document_id` and `documents.id`
unindexed, so those lookups scanned the column. On object storage that is
network I/O per query, on paths that run per document: citation lookup,
delete-by-document, the re-ingest merge, and every dedup probe.

Declare the index set per table in `index_specs()` and apply it through
`ensure_indexes()`, which skips a column only when it is already indexed with
the declared type. Both halves of that are load-bearing. Skipping is required
because `create_index(replace=True)` rebuilds an identical index, writing a new
index and a new table version and orphaning the old files until the next vacuum.
Comparing the type is required because column coverage alone would let a
wrong-typed index stand, and a BTree on `label` silently loses the
low-cardinality equality lookup the Bitmap is there for.

Columns not declared for a table are left alone, so an externally created index
such as a vector index on `chunks` survives.

`_init_tables`, `recreate_embeddings_table`, `ChunkRepository.delete_all` and
`DocumentRepository.delete_all` now all route through it instead of repeating
their own subsets.

Also recreate `document_items` from `get_document_items_arrow_schema()` in
`DocumentRepository.delete_all`, which was using the LanceModel and so returned
`picture_data` as 32-bit `binary`.

Existing databases are unchanged; the migration follows separately.
2026-08-17 15:05:37 +03:00
..
haiku/rag Index every hot lookup key from one shared definition 2026-08-17 15:05:37 +03:00
LICENSE Restructure into uv workspace to support minimal and full installations 2025-11-04 17:59:12 +02:00
pyproject.toml vb 2026-08-13 16:16:35 +03:00
README.md Remove the mxbai reranking provider 2026-07-14 11:09:55 +03:00

README.md

haiku.rag-slim

Opinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling - Core package with minimal dependencies.

haiku.rag-slim is the core package for users who want to install only the dependencies they need. Document processing (docling), and reranker support are all optional extras.

For most users, we recommend installing haiku.rag instead, which includes all features out of the box.

Installation

Python 3.12 or newer required

Minimal Installation

uv pip install haiku.rag-slim

Core functionality with OpenAI/Ollama support, MCP server, and Logfire observability. Document processing (docling) is optional.

With Document Processing

uv pip install haiku.rag-slim[docling]

Adds support for 40+ file formats including PDF, DOCX, HTML, and more.

Available Extras

Document Processing:

  • docling - PDF, DOCX, HTML, and 40+ file formats

Embedding Providers:

  • voyageai - VoyageAI embeddings

Rerankers:

  • cross-encoder - Local reranking via sentence-transformers
  • cohere - Cohere
  • zeroentropy - Zero Entropy

Model Providers:

  • OpenAI/Ollama - included in core (OpenAI-compatible APIs)
  • anthropic - Anthropic Claude
  • groq - Groq
  • google - Google Gemini
  • mistral - Mistral AI
  • bedrock - AWS Bedrock
  • vertexai - Google Vertex AI
# Common combinations
uv pip install haiku.rag-slim[docling,anthropic,cross-encoder]
uv pip install haiku.rag-slim[docling,groq]

Usage

See the main haiku.rag repository for:

  • Quick start guide
  • CLI examples
  • Python API usage
  • MCP server setup

Documentation

Full documentation: https://ggozad.github.io/haiku.rag/