simple-graph-retriever 1.0.0rc10

A simple graph-based retriever using Neo4j and Qdrant

Simple Graph Retriever

A Python SDK for indexing a Neo4j graph into a vector database (Qdrant) and performing retrieval-augmented generation (RAG) tasks against it.

This SDK handles the heavy lifting of graph processing, including:

• Community Detection: Uses the Leiden algorithm to identify communities within your graph structure.
• Chunking: Breaks down nodes and their local context into "chunks" suitable for embedding.
• Vector Indexing: Embeds and indexes both community summaries and individual chunks into Qdrant.
• Graph Retrieval: Retrieves relevant subgraphs based on a natural language query, with fine-grained control over the expansion process.

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Table of Contents

• Simple Graph Retriever
  • Table of Contents
  • 1. Installation
  • 2. Configuration
  • 3. Usage
    • Initializing the Client
    • Indexing the Graph
    • Retrieving a Subgraph
      • Basic Retrieval
      • Advanced Retrieval with Graph Expansion Control
  • 4. Project Structure
  • 5. Contributing
  • 6. License

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1. Installation

To install the SDK, clone this repository and use pip to install it in editable mode, which is recommended for development.

pip install simple_graph_retriever==1.0.0-rc.10

2. Configuration

The SDK is configured via environment variables, which are loaded from a .env file in your project's root directory.

Variable	Default Value	Description
NEO4J_URI	bolt://localhost:7687	The URI for your Neo4j database.
NEO4J_USER	neo4j	The username for your Neo4j database.
NEO4J_PASSWORD	password	The password for your Neo4j database.
QDRANT_URL	http://localhost:6333	The URL for your Qdrant vector database instance.
QDRANT_API_KEY	None	Optional: The API key for authenticating with your Qdrant instance.
QDRANT_CHUNKS_COLLECTION	chunks	The name of the collection for storing graph chunks.
QDRANT_COMMUNITIES_COLLECTION	communities	The name of the collection for storing community summaries.
EMBEDDER_URL	http://localhost:8080	The URL of the text embedding service. It must accept a POST request with {"inputs": ["text"]}.
VECTOR_SIZE	384	The dimension of the vectors produced by your embedding model.
LOGLEVEL	INFO	The logging level for the SDK (DEBUG, INFO, WARNING, ERROR).

Example .env file:

# Neo4j Database Credentials
NEO4J_URI="bolt://localhost:7687"
NEO4J_USER="neo4j"
NEO4J_PASSWORD="your_secure_password"

# Qdrant Vector Database URL
QDRANT_URL="http://localhost:6333"
QDRANT_API_KEY="your_qdrant_api_key" # Optional
QDRANT_CHUNKS_COLLECTION="chunks"
QDRANT_COMMUNITIES_COLLECTION="communities"

# Text Embedding Model URL
EMBEDDER_URL="http://localhost:8080/embed"

3. Usage

Initializing the Client

The main entry point is the GraphRetrievalClient. It automatically loads settings from your .env file.

from graph_retrieval_sdk.client import GraphRetrievalClient

# Initialize the client
client = GraphRetrievalClient()

Indexing the Graph

The index() method runs the full, idempotent pipeline to populate Qdrant with your graph data.

# This runs the full pipeline:
# 1. Community detection
# 2. Chunk creation
# 3. Chunk and Community indexing
client.index()

print("Indexing complete!")

To completely reset your vector index, use the clear_index() method. Warning: This is a destructive operation that deletes and recreates the Qdrant collections.

client.clear_index()

Retrieving a Subgraph

Use the retrieve_graph() method to query your indexed graph. The RetrievalConfig model allows for fine-grained control over the process.

Basic Retrieval

from graph_retrieval_sdk.models import RetrievalConfig
import json

query = "Who was the emperor of Rome?"
config = RetrievalConfig()

subgraph = client.retriever.retrieve_graph(query, config)

if subgraph:
    print(f"Retrieved {len(subgraph[0]['nodes'])} nodes and {len(subgraph[0]['relationships'])} relationships.")

Advanced Retrieval with Graph Expansion Control

Customize the RetrievalConfig to tune the retrieval and expansion behavior.

from graph_retrieval_sdk.models import RetrievalConfig

query = "Tell me about the Roman emperors and their families, but exclude servants."

advanced_config = RetrievalConfig(
    # --- Retrieval Settings ---
    max_communities=5,
    max_chunks=15,

    # --- Graph Expansion Settings ---
    max_hops=2,
    community_expansion_limit=5,

    # --- Relationship Filtering ---
    allowed_rel_types=["HAS_SON", "MARRIED_TO", "SUCCESSOR_OF"],
    denied_rel_types=["HAS_SERVANT"]
)

subgraph = client.retriever.retrieve_graph(query, advanced_config)

if subgraph:
    print(f"Retrieved {len(subgraph[0]['nodes'])} nodes with advanced configuration.")

# Close the client connection when done
client.close()

4. Project Structure

The project is organized as a standard Python package:

├── graph_retrieval_sdk/
│   ├── __init__.py
│   ├── client.py         # Main client class
│   ├── config.py         # Configuration and logging setup
│   ├── indexer.py        # Logic for indexing the graph
│   ├── retriever.py      # Logic for retrieving subgraphs
│   └── models.py         # Pydantic models (e.g., RetrievalConfig)
├── examples/
│   ├── retrieve_graph.py
│   └── run_indexing.py
├── pyproject.toml        # Project metadata and dependencies
└── README.md             # This file

5. Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue.

6. License

This project is licensed under the MIT License.