graphrag-lite 0.1.3

A minimal GraphRAG implementation in ~600 lines of Python code

Lightweight GraphRAG implementation with sync/async APIs and knowledge traceability

中文文档

GraphRAG-Lite is a lightweight, educational implementation of GraphRAG (Graph-based Retrieval-Augmented Generation). Perfect for learning the core principles of knowledge graph enhanced RAG systems.

Why GraphRAG-Lite?

• Learn by Reading: Clean, well-documented code you can understand in an afternoon
• Production Patterns: Real-world optimizations like batch embeddings and LLM caching
• Sync/Async APIs: Both synchronous and asynchronous methods for different use cases
• Knowledge Traceability: Answers include citations to knowledge graph sources
• Minimal Dependencies: Just openai, numpy, tiktoken, loguru, and tqdm

Features

Feature	Description
4 Query Modes	local, global, mix, naive - choose the right strategy
Sync/Async APIs	insert/ainsert, query/aquery dual-mode support
Knowledge Traceability	Answers with [Entities (X); Relationships (Y)] citations
Batch Embeddings	Reduce API calls with intelligent batching
Streaming Output	Real-time response streaming (sync and async)
Persistent Storage	JSON-based storage, no external database needed

Installation

pip install graphrag-lite

Or install from source:

git clone https://github.com/shibing624/graphrag-lite.git
cd graphrag-lite
pip install -e .

Quick Start

Synchronous Mode

import os
from graphrag_lite import GraphRAGLite

# Initialize
graph = GraphRAGLite(
    storage_path="./my_graph",
    api_key=os.getenv("OPENAI_API_KEY"),
    base_url=os.getenv("OPENAI_BASE_URL"),  # Optional: for compatible APIs
)

# Insert documents
graph.insert("""
Charles Dickens wrote "A Christmas Carol" in 1843.
The story features Ebenezer Scrooge, a miserly old man,
and the ghost of his former business partner Jacob Marley.
""")

# Query with knowledge graph context
answer = graph.query("What is the relationship between Scrooge and Marley?")
print(answer)

Asynchronous Mode (Recommended for Large Documents)

import asyncio
from graphrag_lite import GraphRAGLite

async def main():
    graph = GraphRAGLite(storage_path="./my_graph")
    
    # Async insert (with progress bar)
    await graph.ainsert(long_document, show_progress=True)
    
    # Async query
    answer = await graph.aquery("What is the question?")
    print(answer)
    
    # Async streaming
    stream = await graph.aquery("What is the question?", stream=True)
    async for chunk in stream:
        print(chunk, end="", flush=True)

asyncio.run(main())

Query Modes

Mode	Strategy	Best For
local	Entity → Related relations	"Who is X?" questions
global	Relation → Related entities	"How are X and Y related?"
mix	Entity + Relation + Chunks	General purpose (recommended)
naive	Text chunks only	Baseline comparison

# Choose the right mode for your question
answer = graph.query("Who is Scrooge?", mode="local")
answer = graph.query("How are Scrooge and Marley connected?", mode="global")
answer = graph.query("Tell me about the story", mode="mix")      # Recommended
answer = graph.query("What happened?", mode="naive")

Knowledge Traceability

Answers automatically include citations to knowledge graph sources for credibility:

Ebenezer Scrooge is the main character of "A Christmas Carol" [Entities (0)].
He was the business partner of Jacob Marley [Relationships (1, 2)].

Streaming Output

# Sync streaming
for chunk in graph.query("Who is Scrooge?", stream=True):
    print(chunk, end="", flush=True)

# Async streaming
stream = await graph.aquery("Who is Scrooge?", stream=True)
async for chunk in stream:
    print(chunk, end="", flush=True)

API Reference

GraphRAGLite

GraphRAGLite(
    storage_path: str = "./graphrag_data",  # Data storage directory
    api_key: str = None,                     # OpenAI API key
    base_url: str = None,                    # OpenAI-compatible API base URL
    model: str = "gpt-4o-mini",              # LLM model
    embedding_model: str = "text-embedding-3-small",  # Embedding model
    enable_cache: bool = True,               # Enable LLM response caching
)

Methods

Method	Description
insert(text, doc_id=None)	Sync insert document
ainsert(text, doc_id=None, show_progress=True)	Async insert document (with progress bar)
query(question, mode="mix", top_k=10, stream=False)	Sync query
aquery(question, mode="mix", top_k=10, stream=False)	Async query
local_search(query, top_k)	Search from entities → related relations
global_search(query, top_k)	Search from relations → related entities
mix_search(query, top_k)	Search entities + relations + text chunks
naive_search(query, top_k)	Search text chunks only
has_data()	Check if graph has data
get_stats()	Get graph statistics
list_entities()	List all entities
list_relations()	List all relations
clear()	Clear all data

How It Works

Insert Pipeline:

Document → Chunking → LLM Entity Extraction → Batch Embedding → Storage

Query Pipeline:

Question → Vector Search → Context Building → LLM Generation (with citations) → Answer

Use Cases

• Learning GraphRAG: Understand how knowledge graphs enhance RAG
• Prototyping: Quickly validate GraphRAG for your domain
• Research: Baseline for comparing retrieval strategies
• Education: Teaching material for RAG concepts

Community & Support

• GitHub Issues: Submit an issue
• WeChat: Add xuming624 with note "llm" to join the LLM tech wechat group

License

Apache License 2.0

Citation

@software{graphrag-lite,
  author = {Xu Ming},
  title = {GraphRAG-Lite: Lightweight GraphRAG Implementation},
  year = {2026},
  url = {https://github.com/shibing624/graphrag-lite}
}