hkuds--lightrag
wehub-resource-sync
123 行
3.3 KiB
Python
123 行
3.3 KiB
Python
"""
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LightRAG Demo with Google Gemini Models
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This example demonstrates how to use LightRAG with Google's Gemini 2.0 Flash model
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for text generation and the text-embedding-004 model for embeddings.
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Prerequisites:
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1. Set GEMINI_API_KEY environment variable:
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export GEMINI_API_KEY='your-actual-api-key'
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2. Prepare a text file named 'book.txt' in the current directory
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(or modify BOOK_FILE constant to point to your text file)
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Usage:
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python examples/lightrag_gemini_demo.py
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"""
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import os
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import asyncio
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import nest_asyncio
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import numpy as np
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.gemini import gemini_model_complete, gemini_embed
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from lightrag.utils import wrap_embedding_func_with_attrs
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nest_asyncio.apply()
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WORKING_DIR = "./rag_storage"
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BOOK_FILE = "./book.txt"
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# Validate API key
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GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
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if not GEMINI_API_KEY:
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raise ValueError(
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"GEMINI_API_KEY environment variable is not set. "
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"Please set it with: export GEMINI_API_KEY='your-api-key'"
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)
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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# --------------------------------------------------
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# LLM function
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# --------------------------------------------------
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async def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs):
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return await gemini_model_complete(
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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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api_key=GEMINI_API_KEY,
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model_name="gemini-2.0-flash",
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**kwargs,
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)
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# --------------------------------------------------
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# Embedding function
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# --------------------------------------------------
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@wrap_embedding_func_with_attrs(
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embedding_dim=768,
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send_dimensions=True,
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max_token_size=2048,
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model_name="models/text-embedding-004",
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)
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async def embedding_func(texts: list[str]) -> np.ndarray:
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return await gemini_embed.func(
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texts, api_key=GEMINI_API_KEY, model="models/text-embedding-004"
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)
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# --------------------------------------------------
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# Initialize RAG
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# --------------------------------------------------
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async def initialize_rag():
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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=embedding_func,
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llm_model_name="gemini-2.0-flash",
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)
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# 🔑 REQUIRED
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await rag.initialize_storages()
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return rag
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# --------------------------------------------------
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# Main
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# --------------------------------------------------
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def main():
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# Validate book file exists
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if not os.path.exists(BOOK_FILE):
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raise FileNotFoundError(
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f"'{BOOK_FILE}' not found. "
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"Please provide a text file to index in the current directory."
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)
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rag = asyncio.run(initialize_rag())
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# Insert text
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with open(BOOK_FILE, "r", encoding="utf-8") as f:
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rag.insert(f.read())
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query = "What are the top themes?"
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print("\nNaive Search:")
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print(rag.query(query, param=QueryParam(mode="naive")))
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print("\nLocal Search:")
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print(rag.query(query, param=QueryParam(mode="local")))
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print("\nGlobal Search:")
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print(rag.query(query, param=QueryParam(mode="global")))
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print("\nHybrid Search:")
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print(rag.query(query, param=QueryParam(mode="hybrid")))
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if __name__ == "__main__":
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main()
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