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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 aiohttp
import logging
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=[], **kwargs
) -> str:
headers = {
"Content-Type": "application/json",
"api-key": AZURE_OPENAI_API_KEY,
}
endpoint = f"{AZURE_OPENAI_ENDPOINT}openai/deployments/{AZURE_OPENAI_DEPLOYMENT}/chat/completions?api-version={AZURE_OPENAI_API_VERSION}"
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
if history_messages:
messages.extend(history_messages)
messages.append({"role": "user", "content": prompt})
payload = {
"messages": messages,
"temperature": kwargs.get("temperature", 0),
"top_p": kwargs.get("top_p", 1),
"n": kwargs.get("n", 1),
}
async with aiohttp.ClientSession() as session:
async with session.post(endpoint, headers=headers, json=payload) as response:
if response.status != 200:
raise ValueError(
f"Request failed with status {response.status}: {await response.text()}"
)
result = await response.json()
return result["choices"][0]["message"]["content"]
async def embedding_func(texts: list[str]) -> np.ndarray:
headers = {
"Content-Type": "application/json",
"api-key": AZURE_OPENAI_API_KEY,
}
endpoint = f"{AZURE_OPENAI_ENDPOINT}openai/deployments/{AZURE_EMBEDDING_DEPLOYMENT}/embeddings?api-version={AZURE_EMBEDDING_API_VERSION}"
payload = {"input": texts}
async with aiohttp.ClientSession() as session:
async with session.post(endpoint, headers=headers, json=payload) as response:
if response.status != 200:
raise ValueError(
f"Request failed with status {response.status}: {await response.text()}"
)
result = await response.json()
embeddings = [item["embedding"] for item in result["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
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,
),
)
book1 = open("./book_1.txt", encoding="utf-8")
book2 = open("./book_2.txt", encoding="utf-8")
rag.insert([book1.read(), book2.read()])
query_text = "What are the main themes?"
print("Result (Naive):")
print(rag.query(query_text, param=QueryParam(mode="naive")))
print("\nResult (Local):")
print(rag.query(query_text, param=QueryParam(mode="local")))
print("\nResult (Global):")
print(rag.query(query_text, param=QueryParam(mode="global")))
print("\nResult (Hybrid):")
print(rag.query(query_text, param=QueryParam(mode="hybrid")))
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