import asyncio
import logging
import os
import time
from dotenv import load_dotenv

from lightrag import LightRAG, QueryParam
from lightrag.utils import EmbeddingFunc
from lightrag.kg.shared_storage import initialize_pipeline_status

from Config.Config import EMBED_DIM, EMBED_MAX_TOKEN_SIZE, LLM_MODEL_NAME
from Util.LightRagUtil import embedding_func, llm_model_func

load_dotenv()

# 在程序开始时添加以下配置
logging.basicConfig(
    level=logging.INFO,  # 设置日志级别为INFO
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# 或者如果你想更详细地控制日志输出
logger = logging.getLogger('lightrag')
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
logger.addHandler(handler)

ROOT_DIR = '.'
WORKING_DIR = f"{ROOT_DIR}/dickens-pg"

logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)

if not os.path.exists(WORKING_DIR):
    os.mkdir(WORKING_DIR)

# AGE
os.environ["AGE_GRAPH_NAME"] = "dickens"

os.environ["POSTGRES_HOST"] = "10.10.14.208"
os.environ["POSTGRES_PORT"] = "5432"
os.environ["POSTGRES_USER"] = "postgres"
os.environ["POSTGRES_PASSWORD"] = "postgres"
os.environ["POSTGRES_DATABASE"] = "rag"


async def initialize_rag():
    rag = LightRAG(
        working_dir=WORKING_DIR,
        llm_model_func=llm_model_func,
        llm_model_name=LLM_MODEL_NAME,
        llm_model_max_async=4,
        llm_model_max_token_size=32768,
        enable_llm_cache_for_entity_extract=True,
        embedding_func=EmbeddingFunc(
            embedding_dim=EMBED_DIM,
            max_token_size=EMBED_MAX_TOKEN_SIZE,
            func=embedding_func
        ),
        kv_storage="PGKVStorage",
        doc_status_storage="PGDocStatusStorage",
        graph_storage="PGGraphStorage",
        vector_storage="PGVectorStorage",
        auto_manage_storages_states=False,
    )

    await rag.initialize_storages()
    await initialize_pipeline_status()

    return rag


async def main():
    # Initialize RAG instance
    rag = await initialize_rag()

    # add embedding_func for graph database, it's deleted in commit 5661d76860436f7bf5aef2e50d9ee4a59660146c
    rag.chunk_entity_relation_graph.embedding_func = rag.embedding_func

    with open(f"{ROOT_DIR}/book.txt", "r", encoding="utf-8") as f:
        await rag.ainsert(f.read())

    print("==== Trying to test the rag queries ====")
    print("**** Start Naive Query ****")
    start_time = time.time()
    # Perform naive search
    print(
        await rag.aquery(
            "What are the top themes in this story?", param=QueryParam(mode="naive")
        )
    )
    print(f"Naive Query Time: {time.time() - start_time} seconds")
    # Perform local search
    print("**** Start Local Query ****")
    start_time = time.time()
    print(
        await rag.aquery(
            "What are the top themes in this story?", param=QueryParam(mode="local")
        )
    )
    print(f"Local Query Time: {time.time() - start_time} seconds")
    # Perform global search
    print("**** Start Global Query ****")
    start_time = time.time()
    print(
        await rag.aquery(
            "What are the top themes in this story?", param=QueryParam(mode="global")
        )
    )
    print(f"Global Query Time: {time.time() - start_time}")
    # Perform hybrid search
    print("**** Start Hybrid Query ****")
    print(
        await rag.aquery(
            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
        )
    )
    print(f"Hybrid Query Time: {time.time() - start_time} seconds")


if __name__ == "__main__":
    asyncio.run(main())