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Yiorgis Gozadinos 0592206f16
Make run_db_checks an orchestration list 
At 327 lines it interleaved reading the tables, deriving the lookups every
check needs, and the bodies of ten checks. Six checks were already
functions; the rest were inline, so none of them could be read or tested
without the others around them.

Each one is now a function taking exactly what it needs:
_check_document_meta_parity, _check_orphaned_chunks, _check_orphaned_items,
_check_documents_without_items, _check_dangling_item_refs,
_check_vector_dimension, _check_unembedded_chunks, _check_picture_data,
_check_settings_row and _check_pending_migrations. run_db_checks reads the
tables, then appends results.

_document_centroids takes the vector reduction. Passing the matrix as a
parameter keeps it a local of run_db_checks, so the del before clustering
still drops the last reference — measured at 6.2 MB allocated to reduce a
102 MB matrix, no second copy. There is no snapshot object: one holding
vectors would keep the largest allocation alive past the del.

The reduction also rebound doc_ids from the document-id set to the centroid
id list halfway through the function. The centroid ids have their own name
now.

No test changes: the 80 doctor tests cover these through run_db_checks and
pass unchanged.
2026-08-20 14:22:29 +03:00
..
haiku/rag Make run_db_checks an orchestration list 2026-08-20 14:22:29 +03:00
LICENSE Restructure into uv workspace to support minimal and full installations 2025-11-04 17:59:12 +02:00
pyproject.toml Fix the MCP registry entry and fill in package and docs metadata 2026-08-18 14:37:05 +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/