LightRAG is a knowledge graph-based retrieval-augmented generation framework that integrates graph structures with vector representations to enable efficient, dual-level information retrieval from document collections. With over 30,000 GitHub stars and an associated EMNLP 2025 paper, it represents a significant evolution beyond classical RAG approaches.

• Knowledge Graph Construction – Automatically extracts entities and relationships from documents using LLMs, building a comprehensive knowledge graph
• Dual-Level Retrieval – Low-level retrieval for precise entity/edge queries and high-level retrieval for broad topic aggregation
• Five Query Modes – Naive, Local, Global, Hybrid, and Mix retrieval strategies
• Incremental Updates – Seamlessly integrates new documents by merging graph data without rebuilding the entire index
• Web UI – Built-in web server with knowledge graph visualization and API support
• Multi-Hop Reasoning – Graph structures enable extraction of global information from multi-hop subgraphs

LightRAG's architecture consists of three primary components that work together to provide graph-enhanced retrieval:

1. Graph-Based Text Indexing: Documents are segmented into chunks, and LLMs extract entities (names, dates, locations, events) and their relationships. This constructs a comprehensive knowledge graph with key-value data structures for optimized retrieval.

2. Dual-Level Retrieval Paradigm: The system generates query keys at both detailed and abstract levels to accommodate diverse query types.

3. Retrieval-Augmented Answer Generation: A general-purpose LLM generates answers by processing concatenated values from relevant entities, relations, names, descriptions, and text excerpts.

The retrieval paradigm operates at two distinct levels:

• Low-Level Retrieval – Detail-oriented queries that extract precise information about specific nodes or edges within the graph. Example: “Who wrote Pride and Prejudice?” targets a specific entity-relationship pair.
• High-Level Retrieval – Broader topic queries that aggregate information across multiple related entities and relationships, providing insights into higher-level concepts and summaries.

By generating query keys at both levels, LightRAG ensures comprehensive, contextually relevant responses regardless of query complexity.

LightRAG supports five distinct query modes:

• Naive – Simple retrieval without graph traversal (baseline)
• Local – Uses local subgraph around query entities for focused retrieval
• Global – Considers the entire knowledge graph for comprehensive answers
• Hybrid – Combines local and global approaches
• Mix – Advanced combination strategy for optimal results

from lightrag import LightRAG, QueryParam
from lightrag.llm import openai_complete, openai_embedding
import os
 
os.environ["OPENAI_API_KEY"] = "your-api-key"
 
# Initialize LightRAG with working directory
rag = LightRAG(
    working_dir="./lightrag_data",
    llm_model_func=openai_complete,
    llm_model_name="gpt-4o",
    embedding_func=openai_embedding,
    embedding_model_name="text-embedding-3-small"
)
 
# Index documents (builds knowledge graph automatically)
with open("research_paper.txt", "r") as f:
    rag.insert(f.read())
 
# Query with different modes
result_naive = rag.query("What are the main findings?",
                          param=QueryParam(mode="naive"))
result_local = rag.query("What did the authors conclude about X?",
                          param=QueryParam(mode="local"))
result_hybrid = rag.query("How does this relate to the broader field?",
                           param=QueryParam(mode="hybrid"))
 
print(result_hybrid)

• LightRAG GitHub Repository
• LightRAG Paper (arXiv)
• LightRAG Project Page

• RAGFlow – RAG engine with deep document understanding
• Dify – Agentic workflow platform with RAG
• Milvus – Vector database for embedding storage
• ChromaDB – AI-native embedding database
• Qdrant – High-performance vector database