mirror of
https://github.com/HKUDS/LightRAG.git
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146 lines
3.9 KiB
Python
146 lines
3.9 KiB
Python
import os
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.llama_index_impl import (
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llama_index_complete_if_cache,
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llama_index_embed,
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)
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from lightrag.utils import EmbeddingFunc
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from llama_index.llms.litellm import LiteLLM
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from llama_index.embeddings.litellm import LiteLLMEmbedding
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import asyncio
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import nest_asyncio
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nest_asyncio.apply()
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from lightrag.kg.shared_storage import initialize_pipeline_status
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# Configure working directory
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WORKING_DIR = "./index_default"
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print(f"WORKING_DIR: {WORKING_DIR}")
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# Model configuration
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LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4")
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print(f"LLM_MODEL: {LLM_MODEL}")
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EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large")
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print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}")
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EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
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print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}")
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# LiteLLM configuration
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LITELLM_URL = os.environ.get("LITELLM_URL", "http://localhost:4000")
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print(f"LITELLM_URL: {LITELLM_URL}")
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LITELLM_KEY = os.environ.get("LITELLM_KEY", "sk-1234")
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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# Initialize LLM function
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async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs):
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try:
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# Initialize LiteLLM if not in kwargs
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if "llm_instance" not in kwargs:
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llm_instance = LiteLLM(
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model=f"openai/{LLM_MODEL}", # Format: "provider/model_name"
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api_base=LITELLM_URL,
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api_key=LITELLM_KEY,
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temperature=0.7,
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)
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kwargs["llm_instance"] = llm_instance
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response = await llama_index_complete_if_cache(
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kwargs["llm_instance"],
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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**kwargs,
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)
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return response
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except Exception as e:
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print(f"LLM request failed: {str(e)}")
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raise
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# Initialize embedding function
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async def embedding_func(texts):
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try:
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embed_model = LiteLLMEmbedding(
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model_name=f"openai/{EMBEDDING_MODEL}",
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api_base=LITELLM_URL,
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api_key=LITELLM_KEY,
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)
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return await llama_index_embed(texts, embed_model=embed_model)
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except Exception as e:
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print(f"Embedding failed: {str(e)}")
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raise
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# Get embedding dimension
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async def get_embedding_dim():
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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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embedding_dim = embedding.shape[1]
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print(f"embedding_dim={embedding_dim}")
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return embedding_dim
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async def initialize_rag():
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embedding_dimension = await get_embedding_dim()
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=llm_model_func,
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embedding_func=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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)
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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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# Insert example text
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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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# Test different query modes
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print("\nNaive 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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print("\nLocal 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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print("\nGlobal 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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print("\nHybrid 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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