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Yiorgis Gozadinos 72ef18e39d
Reject unknown and out-of-range configuration values 
Every section inherited plain BaseModel, so unknown keys were dropped
silently: providers.docling_serve.timeout was documented for months while
being ignored, and a typo in any setting took the default. Sections now
derive from ConfigModel, which forbids extras, so a stale or misspelled key
fails with its path. This already found search.context_radius in a live app
config and providers.vllm in soliplex's example.

converter, chunker and chunker_type are Literals. Sizes, limits,
dimensions, token budgets, attempt counts and breaker thresholds must be
positive; retention, delays, intervals and cooldowns non-negative;
similarity_threshold within 0-1; port within 0-65535. port 0 keeps its
OS-assigned meaning and worker_count allows 0 for an API-and-reaper-only
process.

get_reranker caught ImportError and returned None, so a configured reranker
whose extra was missing silently disappeared. It now propagates.
raise_missing_extra names the install command and re-raises when the failure
came from inside an installed package, so a broken transitive import is not
reported as a missing one. zeroentropy imported bare and now guards like the
others.

The haiku.rag package declares the jina extra. jina-local already worked
there through cross-encoder's transitive transformers and torch; the
resolved package set is unchanged, but the support is now promised rather
than inherited.

Provider fields stay unconstrained: get_model ends in a pass-through to
pydantic-ai for any provider it supports, so a Literal there would reject
valid configurations.
2026-08-19 15:32:51 +03:00
..
haiku/rag Reject unknown and out-of-range configuration values 2026-08-19 15:32:51 +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/