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import os
from lightrag import LightRAG, QueryParam
from lightrag.llm.ollama import ollama_model_complete, ollama_embed
from lightrag.utils import EmbeddingFunc
import asyncio
import nest_asyncio
nest_asyncio.apply()
from lightrag.kg.shared_storage import initialize_pipeline_status
# WorkingDir
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
WORKING_DIR = os.path.join(ROOT_DIR, "myKG")
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
print(f"WorkingDir: {WORKING_DIR}")
# mongo
os.environ["MONGO_URI"] = "mongodb://root:root@localhost:27017/"
os.environ["MONGO_DATABASE"] = "LightRAG"
# neo4j
BATCH_SIZE_NODES = 500
BATCH_SIZE_EDGES = 100
os.environ["NEO4J_URI"] = "bolt://localhost:7687"
os.environ["NEO4J_USERNAME"] = "neo4j"
os.environ["NEO4J_PASSWORD"] = "neo4j"
# milvus
os.environ["MILVUS_URI"] = "http://localhost:19530"
os.environ["MILVUS_USER"] = "root"
os.environ["MILVUS_PASSWORD"] = "root"
os.environ["MILVUS_DB_NAME"] = "lightrag"
async def initialize_rag():
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=ollama_model_complete,
llm_model_name="qwen2.5:14b",
llm_model_max_async=4,
llm_model_max_token_size=32768,
llm_model_kwargs={
"host": "http://127.0.0.1:11434",
"options": {"num_ctx": 32768},
},
embedding_func=EmbeddingFunc(
embedding_dim=1024,
max_token_size=8192,
func=lambda texts: ollama_embed(
texts=texts, embed_model="bge-m3:latest", host="http://127.0.0.1:11434"
),
),
kv_storage="MongoKVStorage",
graph_storage="Neo4JStorage",
vector_storage="MilvusVectorDBStorage",
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
def main():
# Initialize RAG instance
rag = asyncio.run(initialize_rag())
# Insert example text
with open("./book.txt", "r", encoding="utf-8") as f:
rag.insert(f.read())
# Test different query modes
print("\nNaive Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="naive")
)
)
print("\nLocal Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="local")
)
)
print("\nGlobal Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="global")
)
)
print("\nHybrid Search:")
print(
rag.query(
"What are the top themes in this story?", param=QueryParam(mode="hybrid")
)
)
if __name__ == "__main__":
main()
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