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Yiorgis Gozadinos 46f7ab8d97
Name the search result each page image belongs to 
ToolReturn.content reaches the model as a user-role message and the pictures
arrive bare, so nothing connects a figure to the chunk it came from:
BinaryContent.identifier does not survive serialization to the vision API, and
the captions in the result text correlate only by position.

Precede each picture with its position, source chunk id and self_ref.
build_binary_parts_from_results becomes build_image_content_from_results and
returns the labels interleaved with the pictures, so both attachment sites emit
them the same way.

This does not stop a model narrating retrieved pictures as user-supplied.
Measured on gemma4-26b with a single note ahead of the batch, and again with
per-image labels: it quotes the label and still says the user provided them.
The message role wins over its text.
2026-08-13 13:00:01 +03:00
..
haiku/rag Name the search result each page image belongs to 2026-08-13 13:00:01 +03:00
LICENSE Restructure into uv workspace to support minimal and full installations 2025-11-04 17:59:12 +02:00
pyproject.toml vb 2026-08-06 13:55:57 +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/