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haiku.rag/haiku_rag_slim
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Yiorgis Gozadinos d1691d3942
Share document preparation and HTTP acquisition 
Five call sites repeated the same post-conversion preparation: store the
Docling representation and resolve a title when none was supplied.
_prepare_and_title now owns that sequence. update_document continues to
call _prepare_document_from_docling directly because an explicit update
must preserve an existing empty title.

create_document, both content-replacement branches of update_document,
and source ingestion embedded eagerly before passing chunks to a
persistence funnel that checked them again. The funnels now own
embedding, including the checks required by import_document and
import_documents for caller-supplied chunks.

Move the document.embed span into ensure_chunks_embedded after its early
return. Every path that performs embedding is now instrumented, while
operations whose chunks are already embedded emit no span.

convert() previously used its own HTTP client and temporary-file path.
Route URL conversion through HTTPSource, matching source ingestion, and
move _write_fetch_body to processing.py so both paths share temporary
file handling without an import cycle.

Add walk_files for filesystem enumeration and use it from both
FSSource.discover and one-shot directory ingestion. Symlink escape
filtering now has one implementation.
2026-08-20 10:50:33 +03:00
..
haiku/rag Share document preparation and HTTP acquisition 2026-08-20 10:50:33 +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/