mirror of
https://github.com/HKUDS/LightRAG.git
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111 lines
3.1 KiB
Python
111 lines
3.1 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.utils import EmbeddingFunc
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import numpy as np
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from dotenv import load_dotenv
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import logging
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from openai import AzureOpenAI
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logging.basicConfig(level=logging.INFO)
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load_dotenv()
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AZURE_OPENAI_API_VERSION = os.getenv("AZURE_OPENAI_API_VERSION")
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AZURE_OPENAI_DEPLOYMENT = os.getenv("AZURE_OPENAI_DEPLOYMENT")
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AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
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AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
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AZURE_EMBEDDING_DEPLOYMENT = os.getenv("AZURE_EMBEDDING_DEPLOYMENT")
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AZURE_EMBEDDING_API_VERSION = os.getenv("AZURE_EMBEDDING_API_VERSION")
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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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client = AzureOpenAI(
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api_key=AZURE_OPENAI_API_KEY,
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api_version=AZURE_OPENAI_API_VERSION,
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azure_endpoint=AZURE_OPENAI_ENDPOINT,
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)
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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if history_messages:
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messages.extend(history_messages)
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messages.append({"role": "user", "content": prompt})
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chat_completion = client.chat.completions.create(
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model=AZURE_OPENAI_DEPLOYMENT, # model = "deployment_name".
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messages=messages,
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temperature=kwargs.get("temperature", 0),
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top_p=kwargs.get("top_p", 1),
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n=kwargs.get("n", 1),
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)
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return chat_completion.choices[0].message.content
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async def embedding_func(texts: list[str]) -> np.ndarray:
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client = AzureOpenAI(
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api_key=AZURE_OPENAI_API_KEY,
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api_version=AZURE_EMBEDDING_API_VERSION,
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azure_endpoint=AZURE_OPENAI_ENDPOINT,
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)
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embedding = client.embeddings.create(model=AZURE_EMBEDDING_DEPLOYMENT, input=texts)
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embeddings = [item.embedding for item in embedding.data]
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return np.array(embeddings)
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async def test_funcs():
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result = await llm_model_func("How are you?")
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print("Resposta do llm_model_func: ", result)
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result = await embedding_func(["How are you?"])
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print("Resultado do embedding_func: ", result.shape)
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print("Dimensão da embedding: ", result.shape[1])
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asyncio.run(test_funcs())
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embedding_dimension = 3072
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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=8192,
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func=embedding_func,
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),
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)
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book1 = open("./book_1.txt", encoding="utf-8")
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book2 = open("./book_2.txt", encoding="utf-8")
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rag.insert([book1.read(), book2.read()])
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query_text = "What are the main themes?"
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print("Result (Naive):")
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print(rag.query(query_text, param=QueryParam(mode="naive")))
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print("\nResult (Local):")
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print(rag.query(query_text, param=QueryParam(mode="local")))
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print("\nResult (Global):")
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print(rag.query(query_text, param=QueryParam(mode="global")))
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print("\nResult (Hybrid):")
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print(rag.query(query_text, param=QueryParam(mode="hybrid")))
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