Both optional capabilities read what earlier questions retrieved and cited from the capability's state, so a host that carries only the message history hands every run an empty record. Compaction then replaced the earlier evidence with receipts and retained nothing, and the loss was invisible: the citations the host already displayed were still there. It now refuses when it finds evidence from an earlier question and no record of what that question cited. `state_carried` reaches the optional capabilities through discovery, so the refusal distinguishes a host that never carries state from a question that simply cited nothing. The documentation taught the pattern that breaks: the compose example is now stateful and the requirement is stated where each capability is introduced. The app's browser storage was doing exactly this, keeping only the fields the UI reads. It now persists the whole namespace map, so the citation policy's violations survive a reload as well as the evidence record. |
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| haiku/rag | ||
| LICENSE | ||
| pyproject.toml | ||
| 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-transformerscohere- Coherezeroentropy- Zero Entropy
Model Providers:
- OpenAI/Ollama - included in core (OpenAI-compatible APIs)
anthropic- Anthropic Claudegroq- Groqgoogle- Google Geminimistral- Mistral AIbedrock- AWS Bedrockvertexai- 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/
- Installation - Provider setup
- Configuration - YAML configuration
- CLI - Command reference
- Python API - Complete API docs