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import os
import asyncio
from lightrag import LightRAG, QueryParam
from lightrag.utils import EmbeddingFunc
import numpy as np
from dotenv import load_dotenv
import logging
from openai import AzureOpenAI
from lightrag.kg.shared_storage import initialize_pipeline_status
logging.basicConfig(level=logging.INFO)
load_dotenv()
AZURE_OPENAI_API_VERSION = os.getenv("AZURE_OPENAI_API_VERSION")
AZURE_OPENAI_DEPLOYMENT = os.getenv("AZURE_OPENAI_DEPLOYMENT")
AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
AZURE_EMBEDDING_DEPLOYMENT = os.getenv("AZURE_EMBEDDING_DEPLOYMENT")
AZURE_EMBEDDING_API_VERSION = os.getenv("AZURE_EMBEDDING_API_VERSION")
WORKING_DIR = "./dickens"
if os.path.exists(WORKING_DIR):
import shutil
shutil.rmtree(WORKING_DIR)
os.mkdir(WORKING_DIR)
async def llm_model_func(
prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
) -> str:
client = AzureOpenAI(
api_key=AZURE_OPENAI_API_KEY,
api_version=AZURE_OPENAI_API_VERSION,
azure_endpoint=AZURE_OPENAI_ENDPOINT,
)
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
if history_messages:
messages.extend(history_messages)
messages.append({"role": "user", "content": prompt})
chat_completion = client.chat.completions.create(
model=AZURE_OPENAI_DEPLOYMENT, # model = "deployment_name".
messages=messages,
temperature=kwargs.get("temperature", 0),
top_p=kwargs.get("top_p", 1),
n=kwargs.get("n", 1),
)
return chat_completion.choices[0].message.content
async def embedding_func(texts: list[str]) -> np.ndarray:
client = AzureOpenAI(
api_key=AZURE_OPENAI_API_KEY,
api_version=AZURE_EMBEDDING_API_VERSION,
azure_endpoint=AZURE_OPENAI_ENDPOINT,
)
embedding = client.embeddings.create(model=AZURE_EMBEDDING_DEPLOYMENT, input=texts)
embeddings = [item.embedding for item in embedding.data]
return np.array(embeddings)
async def test_funcs():
result = await llm_model_func("How are you?")
print("Resposta do llm_model_func: ", result)
result = await embedding_func(["How are you?"])
print("Resultado do embedding_func: ", result.shape)
print("Dimensão da embedding: ", result.shape[1])
asyncio.run(test_funcs())
embedding_dimension = 3072
async def initialize_rag():
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=embedding_dimension,
max_token_size=8192,
func=embedding_func,
),
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
# Example function demonstrating the new query_with_separate_keyword_extraction usage
async def run_example():
# Initialize RAG instance
rag = await initialize_rag()
book1 = open("./book_1.txt", encoding="utf-8")
book2 = open("./book_2.txt", encoding="utf-8")
rag.insert([book1.read(), book2.read()])
query = "What are the top themes in this story?"
prompt = "Please simplify the response for a young audience."
# Using the new method to ensure the keyword extraction is only applied to the query
response = rag.query_with_separate_keyword_extraction(
query=query,
prompt=prompt,
param=QueryParam(mode="hybrid"), # Adjust QueryParam mode as necessary
)
print("Extracted Response:", response)
# Run the example asynchronously
if __name__ == "__main__":
asyncio.run(run_example())
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