manasrag 0.1.1

Hierarchical Retrieval-Augmented Generation with Haystack

ManasRAG

Hierarchical Retrieval-Augmented Generation with Haystack

This project implements HiRAG using the Haystack framework. ManasRAG is a hierarchical knowledge retrieval approach that combines knowledge graphs with community-based summarization for improved RAG systems.

Features

• Hierarchical Knowledge Structure: Uses Leiden clustering to build multi-level community hierarchies
• Multiple Retrieval Modes:
  • naive: Basic RAG with document chunks
  • local: Local entity and relationship knowledge
  • global: Global community report knowledge
  • bridge: Cross-community reasoning paths
  • nobridge: Local + global combined (no paths)
  • hi: Full hierarchical retrieval combining all modes
• Flexible Storage: Supports NetworkX (in-memory) and Neo4j graph databases
• Haystack Integration: Built on Haystack's component and pipeline architecture

Installation

# Basic installation
pip install -e .

# With OpenAI support
pip install -e ".[openai]"

# With Neo4j support
pip install -e ".[neo4j]"

# All optional dependencies
pip install -e ".[all]"

Configuration

Environment Variables

The project supports loading environment variables from a .env file. Copy the example file and configure it:

cp .env.example .env

Edit .env and add your API key:

OPENAI_API_KEY=your-openai-api-key-here

# Optional: Custom API base URL
# OPENAI_BASE_URL=https://api.openai.com/v1

The examples will automatically load environment variables from the .env file.

Quick Start

from manasrag import ManasRAG
from haystack.components.generators import OpenAIGenerator
import os

# Initialize with OpenAI
manas = ManasRAG(
    working_dir="./manas_data",
    generator=OpenAIGenerator(
        model="gpt-4o-mini",
        api_key=os.getenv("OPENAI_API_KEY")
    ),
)

# Index documents
documents = """
# Machine Learning

Machine Learning is a subset of Artificial Intelligence focused on
algorithms that can learn from data...

# Neural Networks

Neural networks are computing systems inspired by biological neurons...
"""

manas.index(documents)

# Query with different modes
result = manas.query(
    "How are neural networks related to machine learning?",
    mode="hi"  # Full hierarchical retrieval
)

print(result["answer"])

Retrieval Modes

Mode	Description	Components
naive	Basic RAG	Document chunks only
local	Local knowledge	Entities + Relations + Chunks
global	Global knowledge	Community reports + Chunks
bridge	Bridge knowledge	Cross-community reasoning paths
nobridge	No-bridge	Local + Global (no paths)
hi	Full hierarchical	All components combined

Advanced Usage

Custom Query Parameters

from manasrag import QueryParam

param = QueryParam(
    mode="hi",
    top_k=20,           # Number of entities to retrieve
    top_m=10,           # Key entities per community
    max_token_for_text_unit=20000,
    response_type="Multiple Paragraphs",
)

result = manas.query("Your query here", param=param)

Using Custom LLM

from haystack.components.generators import HuggingFaceLocalGenerator

generator = HuggingFaceLocalGenerator(
    model="HuggingFaceH4/zephyr-7b-beta"
)

manas = ManasRAG(generator=generator)

Accessing Communities

# After indexing, access detected communities
for comm_id, community in manas.communities.items():
    print(f"Community: {community.title}")
    print(f"Entities: {len(community.nodes)}")
    print(f"Report: {community.report_string[:200]}...")

Architecture

┌─────────────────────────────────────────────────────────────┐
│                      Indexing Pipeline                        │
├─────────────────────────────────────────────────────────────┤
│  Documents → Splitter → EntityExtractor → GraphDocumentStore │
│                                    ↓                          │
│                          CommunityDetector                     │
│                                    ↓                          │
│                       CommunityReportGenerator                │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│                       Query Pipeline                          │
├─────────────────────────────────────────────────────────────┤
│  Query → EntityRetriever → HierarchicalRetriever            │
│                            ↓                                 │
│                      ContextBuilder                          │
│                            ↓                                 │
│                       PromptBuilder                          │
│                            ↓                                 │
│                       ChatGenerator → Answer                  │
└─────────────────────────────────────────────────────────────┘

Project Structure

manasrag/
├── core/           # Core data structures
├── stores/         # Graph storage backends
├── components/     # Haystack components
├── pipelines/      # Indexing and query pipelines
└── __init__.py     # High-level API

References

• HiRAG Paper
• HiRAG GitHub
• Haystack Documentation

License

MIT

Acknowledgments

Based on HiRAG by Haoyu Huang et al.