Opening a database ran `list_tables` three times, opened the settings table
twice, and read and parsed the same settings row three times: once for the stored
vector dimension, once for the version behind the migration check, and once for
config validation. On object storage each of those is a round trip.
`_initialize` now reads both once and threads them down. `_init_tables` and
`_check_migrations` take what it read instead of fetching their own copy, and
`validate_config_compatibility` accepts the settings it should compare against,
still reading for itself when called directly.
Passing the pre-init read to validation is equivalent: nothing between the read
and the validation rewrites `embeddings`, which is all it compares.
The settings read no longer swallows every exception. It did before, when the
only consequence was falling back to the configured vector dimension; now the
same empty result feeds the migration check, where it would read as version
0.0.0 and declare every migration pending. Only decode failures are tolerated,
and a decoded non-object normalizes to {} rather than reaching callers that
expect a mapping.
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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