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Yiorgis Gozadinos 61a756da7f
Support Pydantic AI agent specs via from_spec 
`Agent.from_spec` raised `TypeError` on `RAGCapability` and `AnalysisCapability`,
whose constructors take a state class, packaged instruction text and a tool-name
set, and silently omitted both from the generated spec schema. The two
zero-configuration capabilities constructed but with `id=None`, so pydantic-ai's
duplicate-id rejection no longer held and a spec could register two citation
policies, defeating the single-decision-maker invariant.

Override `from_spec` on all four, delegating to `create_capability()` so ids and
instructions come from one place. The spec surface is `db_path`, `config`,
`defer_loading`, `request_limit` and `vision`; a live `HaikuRAG` client stays out
of it, and a `config` mapping is validated through `AppConfig`.
2026-08-21 13:04:09 +03:00
..
haiku/rag Support Pydantic AI agent specs via from_spec 2026-08-21 13:04:09 +03:00
LICENSE Restructure into uv workspace to support minimal and full installations 2025-11-04 17:59:12 +02:00
pyproject.toml vb 2026-08-20 15:48:23 +03:00
README.md Give the docs an architecture page and one extras list 2026-08-20 15:07:06 +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

docling, tui, voyageai, cohere, zeroentropy, cross-encoder, jina, s3, ingester, and one per model provider: anthropic, google, groq, mistral, bedrock, vertexai. Ollama and any OpenAI-compatible endpoint need no extra.

What each provides, and which ones the full haiku.rag package already includes: Installation.

# 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/