mirror of
https://github.com/HKUDS/LightRAG.git
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102 lines
2.7 KiB
Python
102 lines
2.7 KiB
Python
import os
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.openai import openai_complete_if_cache, openai_embed
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from lightrag.utils import EmbeddingFunc
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import numpy as np
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import asyncio
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import nest_asyncio
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# Apply nest_asyncio to solve event loop issues
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nest_asyncio.apply()
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DEFAULT_RAG_DIR = "index_default"
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# Configure working directory
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WORKING_DIR = os.environ.get("RAG_DIR", f"{DEFAULT_RAG_DIR}")
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print(f"WORKING_DIR: {WORKING_DIR}")
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LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4o-mini")
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print(f"LLM_MODEL: {LLM_MODEL}")
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EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-small")
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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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BASE_URL = os.environ.get("BASE_URL", "https://api.openai.com/v1")
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print(f"BASE_URL: {BASE_URL}")
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API_KEY = os.environ.get("API_KEY", "xxxxxxxx")
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print(f"API_KEY: {API_KEY}")
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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# LLM model function
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async def llm_model_func(
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prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
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) -> str:
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return await openai_complete_if_cache(
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model=LLM_MODEL,
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prompt=prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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base_url=BASE_URL,
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api_key=API_KEY,
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**kwargs,
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)
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# Embedding function
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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=texts,
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model=EMBEDDING_MODEL,
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base_url=BASE_URL,
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api_key=API_KEY,
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)
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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=}")
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return embedding_dim
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# Initialize RAG instance
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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=asyncio.run(get_embedding_dim()),
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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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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("What are the top themes in this story?", param=QueryParam(mode="naive"))
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)
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# Perform local search
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print(
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rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
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)
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# Perform global search
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print(
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rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
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)
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# Perform hybrid search
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print(
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rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
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)
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