fdb5710491
Chunk 2 gave search a configured set to fan out over. ask and analyze
covered one database still: the RAG capability had no way to be told which
databases a question spanned, and the analysis sandbox mounted one
document tree.
The selection travels as sources on EvidenceState, beside the filter it
scopes with, so both capabilities read it the same way. clients_covering
is the one rule that turns a selection into clients, used by search, the
sandbox mount and the cite fallback, so a question scoped to some
databases cannot search, mount or cite another. Citations carry the
database they came from, and format_for_agent names it, so the model can
attribute evidence while it answers rather than only afterwards.
The sandbox keeps one flat /documents/{id}/ namespace and resolves each id
to the client holding it, which rests on ids being UUID4. A database
copied from another breaks that, so an id held twice is refused rather
than resolved to whichever arrived last.
On the CLI, search, ask and analyze cover the configured set and label
each result with its database. Every other command works on one, named
with --database NAME (a name reaches a database behind a URI, which --db
cannot) or --db PATH, and refuses a set it cannot choose from instead of
silently reading the default database. Cold databases open together, so a
first query costs the slowest open rather than their sum.
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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
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/
- Installation - Provider setup
- Configuration - YAML configuration
- CLI - Command reference
- Python API - Complete API docs