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README.md
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# Haiku RAG # Haiku RAG
mcp-name: io.github.ggozad/haiku-rag
Retrieval-Augmented Generation (RAG) library built on LanceDB. Retrieval-Augmented Generation (RAG) library built on LanceDB.
`haiku.rag` is a Retrieval-Augmented Generation (RAG) library built to work with LanceDB as a local vector database. It uses LanceDB for storing embeddings and performs semantic (vector) search as well as full-text search combined through native hybrid search with Reciprocal Rank Fusion. Both open-source (Ollama) as well as commercial (OpenAI, VoyageAI) embedding providers are supported. `haiku.rag` is a Retrieval-Augmented Generation (RAG) library built to work with LanceDB as a local vector database. It uses LanceDB for storing embeddings and performs semantic (vector) search as well as full-text search combined through native hybrid search with Reciprocal Rank Fusion. Both open-source (Ollama) as well as commercial (OpenAI, VoyageAI) embedding providers are supported.
@ -203,3 +201,5 @@ Full documentation at: https://ggozad.github.io/haiku.rag/
- [MCP Server](https://ggozad.github.io/haiku.rag/mcp/) - Model Context Protocol integration - [MCP Server](https://ggozad.github.io/haiku.rag/mcp/) - Model Context Protocol integration
- [A2A Agent](https://ggozad.github.io/haiku.rag/a2a/) - Agent-to-Agent protocol support - [A2A Agent](https://ggozad.github.io/haiku.rag/a2a/) - Agent-to-Agent protocol support
- [Benchmarks](https://ggozad.github.io/haiku.rag/benchmarks/) - Performance Benchmarks - [Benchmarks](https://ggozad.github.io/haiku.rag/benchmarks/) - Performance Benchmarks
mcp-name: io.github.ggozad/haiku-rag

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evaluations/README.md
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Contains evaluation scripts for benchmarking RAG performance using datasets like: Contains evaluation scripts for benchmarking RAG performance using datasets like:
- RepliQA - RepliQA
- WiX - WiX
Uses `pydantic-evals` for evaluation framework and `logfire` for observability.

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haiku_rag_slim/README.md
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# Haiku RAG - Slim # haiku.rag-slim
mcp-name: io.github.ggozad/haiku-rag Retrieval-Augmented Generation (RAG) library built on LanceDB - Core package with minimal dependencies.
Retrieval-Augmented Generation (RAG) library built on LanceDB - Minimal dependencies. `haiku.rag-slim` is the core package for users who want to install only the dependencies they need. Document processing (docling), rerankers, and A2A support are all optional extras.
`haiku.rag-slim` is the core package with minimal dependencies. Document processing via docling is optional and can be installed separately. **For most users, we recommend installing [`haiku.rag`](https://pypi.org/project/haiku.rag/) instead**, which includes all features out of the box.
For a batteries-included experience with all extras, see [`haiku.rag`](https://pypi.org/project/haiku.rag/).
## Installation ## Installation
**Python 3.12 or newer required**
### Minimal Installation
```bash ```bash
# Minimal installation (no docling)
uv pip install haiku.rag-slim uv pip install haiku.rag-slim
```
# With docling support for document processing Basic functionality without document processing (docling). You can still use text input and URLs.
### With Document Processing
```bash
uv pip install haiku.rag-slim[docling] uv pip install haiku.rag-slim[docling]
```
# With specific extras Adds support for 40+ file formats including PDF, DOCX, HTML, and more.
### Available Extras
- `docling` - Document processing for PDFs, DOCX, HTML, etc.
- `voyageai` - VoyageAI embedding provider
- `mxbai` - MixedBread AI reranker
- `cohere` - Cohere reranker
- `zeroentropy` - Zero Entropy reranker
- `a2a` - Agent-to-Agent protocol support
```bash
# Multiple extras
uv pip install haiku.rag-slim[docling,voyageai,mxbai] uv pip install haiku.rag-slim[docling,voyageai,mxbai]
``` ```
> **Note**: Configuration now uses YAML files instead of environment variables. If you're upgrading from an older version, run `haiku-rag init-config --from-env` to migrate your `.env` file to `haiku.rag.yaml`. See [Configuration](https://ggozad.github.io/haiku.rag/configuration/) for details. ## Usage
## Features See the main [`haiku.rag`](https://github.com/ggozad/haiku.rag) repository for:
- Quick start guide
- **Local LanceDB**: No external servers required, supports also LanceDB cloud storage, S3, Google Cloud & Azure - CLI examples
- **Multiple embedding providers**: Ollama, VoyageAI, OpenAI, vLLM - Python API usage
- **Multiple QA providers**: Any provider/model supported by Pydantic AI - MCP server setup
- **Research graph (multi‑agent)**: Plan → Search → Evaluate → Synthesize with agentic AI - A2A agent configuration
- **Native hybrid search**: Vector + full-text search with native LanceDB RRF reranking
- **Reranking**: Default search result reranking with MixedBread AI, Cohere, Zero Entropy, or vLLM
- **Question answering**: Built-in QA agents on your documents
- **File monitoring**: Auto-index files when run as server
- **40+ file formats**: PDF, DOCX, HTML, Markdown, code files, URLs
- **MCP server**: Expose as tools for AI assistants
- **A2A agent**: Conversational agent with context and multi-turn dialogue
- **CLI & Python API**: Use from command line or Python
## Quick Start
```bash
# Install
# Python 3.12 or newer required
uv pip install haiku.rag-slim[docling]
# Add documents
haiku-rag add "Your content here"
haiku-rag add "Your content here" --meta author=alice --meta topic=notes
haiku-rag add-src document.pdf --meta source=manual
# Search
haiku-rag search "query"
# Search with filters
haiku-rag search "query" --filter "uri LIKE '%.pdf' AND title LIKE '%paper%'"
# Ask questions
haiku-rag ask "Who is the author of haiku.rag?"
# Ask questions with citations
haiku-rag ask "Who is the author of haiku.rag?" --cite
# Deep QA (multi-agent question decomposition)
haiku-rag ask "Who is the author of haiku.rag?" --deep --cite
# Deep QA with verbose output
haiku-rag ask "Who is the author of haiku.rag?" --deep --verbose
# Multi‑agent research (iterative plan/search/evaluate)
haiku-rag research \
"What are the main drivers and trends of global temperature anomalies since 1990?" \
--max-iterations 2 \
--confidence-threshold 0.8 \
--max-concurrency 3 \
--verbose
# Rebuild database (re-chunk and re-embed all documents)
haiku-rag rebuild
# Start server with file monitoring
haiku-rag serve --monitor
```
To customize settings, create a `haiku.rag.yaml` config file (see [Configuration](https://ggozad.github.io/haiku.rag/configuration/)).
## Python Usage
```python
from haiku.rag.client import HaikuRAG
from haiku.rag.research import (
PlanNode,
ResearchContext,
ResearchDeps,
ResearchState,
build_research_graph,
stream_research_graph,
)
async with HaikuRAG("database.lancedb") as client:
# Add document
doc = await client.create_document("Your content")
# Search (reranking enabled by default)
results = await client.search("query")
for chunk, score in results:
print(f"{score:.3f}: {chunk.content}")
# Ask questions
answer = await client.ask("Who is the author of haiku.rag?")
print(answer)
# Ask questions with citations
answer = await client.ask("Who is the author of haiku.rag?", cite=True)
print(answer)
# Multi‑agent research pipeline (Plan → Search → Evaluate → Synthesize)
graph = build_research_graph()
question = (
"What are the main drivers and trends of global temperature "
"anomalies since 1990?"
)
state = ResearchState(
context=ResearchContext(original_question=question),
max_iterations=2,
confidence_threshold=0.8,
max_concurrency=2,
)
deps = ResearchDeps(client=client)
# Blocking run (final result only)
result = await graph.run(
PlanNode(provider="openai", model="gpt-4o-mini"),
state=state,
deps=deps,
)
print(result.output.title)
# Streaming progress (log/report/error events)
async for event in stream_research_graph(
graph,
PlanNode(provider="openai", model="gpt-4o-mini"),
state,
deps,
):
if event.type == "log":
iteration = event.state.iterations if event.state else state.iterations
print(f"[{iteration}] {event.message}")
elif event.type == "report":
print("\nResearch complete!\n")
print(event.report.title)
print(event.report.executive_summary)
```
## MCP Server
Use with AI assistants like Claude Desktop:
```bash
haiku-rag serve --stdio
```
Provides tools for document management and search directly in your AI assistant.
## A2A Agent
Run as a conversational agent with the Agent-to-Agent protocol:
```bash
# Start the A2A server
haiku-rag serve --a2a
# Connect with the interactive client (in another terminal)
haiku-rag a2aclient
```
The A2A agent provides:
- Multi-turn dialogue with context
- Intelligent multi-search for complex questions
- Source citations with titles and URIs
- Full document retrieval on request
## Examples
See the [examples directory](examples/) for working examples:
- **[Interactive Research Assistant](examples/ag-ui-research/)** - Full-stack research assistant with Pydantic AI and AG-UI featuring human-in-the-loop approval and real-time state synchronization
- **[Docker Setup](examples/docker/)** - Complete Docker deployment with file monitoring, MCP server, and A2A agent
- **[A2A Security](examples/a2a-security/)** - Authentication examples (API key, OAuth2, GitHub)
## Documentation ## Documentation
Full documentation at: https://ggozad.github.io/haiku.rag/ Full documentation: https://ggozad.github.io/haiku.rag/
- [Installation](https://ggozad.github.io/haiku.rag/installation/) - Provider setup - [Installation](https://ggozad.github.io/haiku.rag/installation/) - Provider setup
- [Configuration](https://ggozad.github.io/haiku.rag/configuration/) - YAML configuration - [Configuration](https://ggozad.github.io/haiku.rag/configuration/) - YAML configuration
- [CLI](https://ggozad.github.io/haiku.rag/cli/) - Command reference - [CLI](https://ggozad.github.io/haiku.rag/cli/) - Command reference
- [Python API](https://ggozad.github.io/haiku.rag/python/) - Complete API docs - [Python API](https://ggozad.github.io/haiku.rag/python/) - Complete API docs
- [Agents](https://ggozad.github.io/haiku.rag/agents/) - QA agent and multi-agent research
- [MCP Server](https://ggozad.github.io/haiku.rag/mcp/) - Model Context Protocol integration
- [A2A Agent](https://ggozad.github.io/haiku.rag/a2a/) - Agent-to-Agent protocol support
- [Benchmarks](https://ggozad.github.io/haiku.rag/benchmarks/) - Performance Benchmarks