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Yiorgis Gozadinos 5d76461b9f
Trim earlier-question evidence off the wire, keeping the images 
_compact_old_tool_returns ran in before_model_request, whose result core
assigns back onto ctx.state.message_history, so the trim reached
all_messages() and every host that persists a thread. It now runs in
wrap_model_request, which operates on a detached list: the model sees the
trimmed history, the host keeps what it retrieved.

Content attached to a ToolReturn arrives as its own UserPromptPart in the
same ModelRequest as the ToolReturnPart, so the turn-boundary scan read an
image-bearing search result as a new user turn and discarded evidence
retrieved earlier in the same turn. _is_user_turn() now requires a request
with no tool returns.

Page images on a replaced return stay. Dropping them with their text bounds
context growth, but a follow-up about a figure already shown ("what colour
is that box?") carries no terms that could retrieve it again: measured on
gemma4-26b against two ORB figures, removing the image turned both answers
into "I cannot find enough information", and keeping it answers correctly.
Bounding that growth needs to preserve cited figures, which is a separate
change.

The replacement notice no longer claims citations remain in state;
_clear_invocation_state has cleared them by then.
2026-08-13 12:59:22 +03:00
..
haiku/rag Trim earlier-question evidence off the wire, keeping the images 2026-08-13 12:59:22 +03:00
LICENSE
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/