LightRAG/examples/lightrag_azure_openai_demo.py

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


def main():
    rag = asyncio.run(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_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")))


if __name__ == "__main__":
    main()