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Yiorgis Gozadinos 97a29d168f
Confine the ledger's epochs to the question that recorded them 
A question's evidence epoch and declaration describe that question and end with
it, so `begin_question` clears them. Held past it, they let the last question's
citations ground the next one and hold a horizon its own declarations cannot
pass, and they force every question's message count to be comparable to counts
taken over a history that a host may since have rebuilt: a thread reconstructed
without a run that never finished shifts every later position, and the next
question was refused where answering it is correct.

Identities still separate one question from the next. Occurrences outlive the
question that recorded them and carry the identities that cited them, which the
capsule is grouped and ordered by, so reuse merges two questions into one group
and a lower identity renders later evidence as earlier.
2026-08-13 13:00:02 +03:00
..
haiku/rag Confine the ledger's epochs to the question that recorded them 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/