examples/query_keyword_separation_example.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

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

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()])


# Example function demonstrating the new query_with_separate_keyword_extraction usage
async def run_example():
    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())