mirror of
https://github.com/HKUDS/LightRAG.git
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106 lines
2.6 KiB
Python
106 lines
2.6 KiB
Python
# pip install -q -U google-genai to use gemini as a client
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import os
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import numpy as np
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from google import genai
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from google.genai import types
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from dotenv import load_dotenv
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from lightrag.utils import EmbeddingFunc
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from lightrag import LightRAG, QueryParam
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from sentence_transformers import SentenceTransformer
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from lightrag.kg.shared_storage import initialize_pipeline_status
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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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load_dotenv()
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gemini_api_key = os.getenv("GEMINI_API_KEY")
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WORKING_DIR = "./dickens"
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if os.path.exists(WORKING_DIR):
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import shutil
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shutil.rmtree(WORKING_DIR)
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os.mkdir(WORKING_DIR)
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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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# 1. Initialize the GenAI Client with your Gemini API Key
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client = genai.Client(api_key=gemini_api_key)
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# 2. Combine prompts: system prompt, history, and user prompt
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if history_messages is None:
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history_messages = []
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combined_prompt = ""
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if system_prompt:
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combined_prompt += f"{system_prompt}\n"
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for msg in history_messages:
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# Each msg is expected to be a dict: {"role": "...", "content": "..."}
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combined_prompt += f"{msg['role']}: {msg['content']}\n"
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# Finally, add the new user prompt
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combined_prompt += f"user: {prompt}"
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# 3. Call the Gemini model
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response = client.models.generate_content(
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model="gemini-1.5-flash",
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contents=[combined_prompt],
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config=types.GenerateContentConfig(max_output_tokens=500, temperature=0.1),
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)
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# 4. Return the response text
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return response.text
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async def embedding_func(texts: list[str]) -> np.ndarray:
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model = SentenceTransformer("all-MiniLM-L6-v2")
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embeddings = model.encode(texts, convert_to_numpy=True)
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return embeddings
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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=EmbeddingFunc(
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embedding_dim=384,
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max_token_size=8192,
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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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file_path = "story.txt"
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with open(file_path, "r") as file:
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text = file.read()
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rag.insert(text)
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response = rag.query(
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query="What is the main theme of the story?",
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param=QueryParam(mode="hybrid", top_k=5, response_type="single line"),
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)
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print(response)
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if __name__ == "__main__":
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main()
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