A selection outside the page stayed applied while its checkbox was gone, so there was no way to remove it. Appending those documents to the page instead loses the bound the page exists to keep, since selections accumulate across searches. Both listings page at `DOCUMENT_PAGE`, and `Selected` switches between them, so the mounted widgets stay bounded whichever is showing and every selection is a page away rather than unreachable. The count reads the checkboxes on screen, so narrowing as the user types reports what is visible. A listing with nothing in it says so, instead of leaving an empty box that reads as still loading. |
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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