|
|
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 |
|
|
|
|
|
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, |
|
|
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 |
|
|
|
|
|
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"))) |
|
|
|
