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Yiorgis Gozadinos 0aa6d79f88
Build one capsule of cited evidence from the records 
`EvidenceCompactionCapability` reads what the evidence capabilities recorded out of
the run registry, and `build_capsule` renders it: every cited item, grouped by the
question that last cited it, newest group first, each rendered once, with the
pictures of cited evidence and the labels that must accompany them. Discovery runs
one way and reads only, so no capability holds a reference to another and a host
running one, both or neither needs no wiring change.

Everything cited is kept whole and everything else is dropped. There is no character
budget, no picture cap and nothing to configure: a cap would only half-rescue models
that fail on long conversations regardless, and a host that needs earlier evidence
pruned can compact its own requests further.

A capability reports which of its tools produce evidence, so a cite acknowledgement
is never mistaken for one. Pictures are identified by owner, document and reference,
so one figure cited through overlapping chunks is attached once while the same
reference in another document stays a different picture.

The builder does no I/O and never sees the message history, so a picture travels with
its label and the caller fetches the bytes. Nothing reaches the wire yet.
2026-08-13 13:00:02 +03:00
..
haiku/rag Build one capsule of cited evidence from the records 2026-08-13 13:00:02 +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/