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Yiorgis Gozadinos 85594a1fd1
Record what each evidence capability retrieved and cited 
`CapabilityEvidenceRecord` holds the relationships a transcript cannot express:
which chunks a capability retrieved, which it cited, in which questions, and at
which point in the conversation. RAG and analysis each own one in their own state
namespace. Nothing is co-written: the host's state is JSON storage, so a shared
record would be overwritten by whichever capability synced last, and merging
happens in transient per-request views instead.

Both clocks are derived from the conversation rather than counted locally, so
every participant computes the same values without sharing a counter. Question
identity is the message count when the question arrived; epoch is the message
count at an outcome. Epochs are therefore globally comparable, which is what
lets `citation_status` require a declaration to follow the newest evidence of
every capability, and what makes equal epochs mean one request.

A declaration is written only after `resolve_citations` succeeds, so a call
naming only unresolvable ids is not a citation. Status is derived, never stored,
so refs and status cannot contradict.

Resuming a question requires the host to carry the capability state from the run
being resumed. Without it the identity of the question in progress is unknowable,
and adopting the current message count would relabel that question as a new one
and judge every declaration in it against the wrong identity.

Nothing reads the records yet and no wire behaviour changes.
2026-08-13 13:00:01 +03:00
..
haiku/rag Record what each evidence capability retrieved and cited 2026-08-13 13:00:01 +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/