mirror of
https://github.com/HKUDS/LightRAG.git
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108 lines
2.8 KiB
Python
108 lines
2.8 KiB
Python
import os
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import asyncio
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.openai import gpt_4o_mini_complete, openai_embed
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from lightrag.utils import EmbeddingFunc
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import numpy as np
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from lightrag.kg.shared_storage import initialize_pipeline_status
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#########
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# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
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# import nest_asyncio
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# nest_asyncio.apply()
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#########
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WORKING_DIR = "./mongodb_test_dir"
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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os.environ["OPENAI_API_KEY"] = "sk-"
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os.environ["MONGO_URI"] = "mongodb://0.0.0.0:27017/?directConnection=true"
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os.environ["MONGO_DATABASE"] = "LightRAG"
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os.environ["MONGO_KG_COLLECTION"] = "MDB_KG"
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# Embedding Configuration and Functions
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EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large")
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EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
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async def embedding_func(texts: list[str]) -> np.ndarray:
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return await openai_embed(
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texts,
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model=EMBEDDING_MODEL,
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)
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async def get_embedding_dimension():
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test_text = ["This is a test sentence."]
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embedding = await embedding_func(test_text)
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return embedding.shape[1]
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async def create_embedding_function_instance():
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# Get embedding dimension
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embedding_dimension = await get_embedding_dimension()
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# Create embedding function instance
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return EmbeddingFunc(
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embedding_dim=embedding_dimension,
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max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
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func=embedding_func,
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)
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async def initialize_rag():
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embedding_func_instance = await create_embedding_function_instance()
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=gpt_4o_mini_complete,
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embedding_func=embedding_func_instance,
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graph_storage="MongoGraphStorage",
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log_level="DEBUG",
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)
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await rag.initialize_storages()
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await initialize_pipeline_status()
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return rag
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def main():
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# Initialize RAG instance
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rag = asyncio.run(initialize_rag())
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with open("./book.txt", "r", encoding="utf-8") as f:
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rag.insert(f.read())
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# Perform naive search
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="naive")
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)
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)
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# Perform local search
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="local")
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)
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)
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# Perform global search
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="global")
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)
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)
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# Perform hybrid search
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="hybrid")
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)
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)
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if __name__ == "__main__":
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main()
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