import time
from langchain.chat_models import init_chat_model
from model.multimodal_embedding import Embedding
from conf.settings import model_settings, ragflow_settings, tag_search_settings
from utils.infinity import get_client
from model.jina_rerank import JinaRerank
from langchain_core.documents import Document
from utils.infinity.result_util import convert_to_langchain_docs

# 初始化多模态嵌入模型
embedding_model = Embedding(
    model_name=model_settings.embedding_model_name,
    api_key=model_settings.api_key
)

llm = init_chat_model(
    model_provider=model_settings.model_provider,
    model=model_settings.model_name,
    base_url=model_settings.base_url,
    api_key=model_settings.api_key,
    temperature=0.3
)

compressor = JinaRerank(
    base_url=model_settings.base_url,
    jina_api_key=model_settings.api_key,
    model=model_settings.rank_model_name, # 建议先确认硅基后台此模型 ID 是否正确
    top_n=5
)

output_fields = [
                    "important_keywords",
                    "content",
                    "kb_id",
                    "doc_id"
                ]

search_query = {
    "matching_text": """
     莉莉兰中都有哪些小虫虫
    """,
    "query_vector": [],
    "vector_field": "q_1024_vec",
    "match_field": "content",
    "topn": 5
}

def main():
   
    
    infinity_client = get_client(database="ragflow_db")
    # 问题向量化
     # 记录开始时间
    start_time = time.time()
    embedding = embedding_model.get_text_embedding(search_query["matching_text"])
    embedding_time = time.time() - start_time
    print(f"向量化耗时: {embedding_time:.4f} 秒")
    search_query["query_vector"] = embedding
    # TAG_TABLE_NAME="ragflow_92162247e93e11f084830242ac1d0002_52275b36f03611f0a5340242c0a85002"
    TAG_TABLE_NAME="ragflow_92162247e93e11f084830242ac1d0002_6d2e0990f28b11f0b5200242c0a85002"
    results = infinity_client.vector_search(TAG_TABLE_NAME, output_fields, search_query).to_result()
    candidate_docs = convert_to_langchain_docs(results)
    # print(candidate_docs)
    
    # # 4. 直接调用重排序逻辑
    reranked_docs = compressor.compress_documents(
        documents=candidate_docs, 
        query=search_query["matching_text"],
        top_n=3
    )
    # # print(reranked_docs)
    # # 5. 查看结果
    for i, doc in enumerate(reranked_docs):
        print(f"排名 {i+1}: 分数 {doc.metadata['relevance_score']:.4f}")
        print(f"内容: {doc.page_content}")
        print(f"标签: {doc.metadata['important_keywords']}\n")
    
    # 记录结束时间并计算执行时间
    end_time = time.time()
    execution_time = end_time - start_time
    print(f"执行时间: {execution_time:.4f} 秒")

# from utils.asymmetric_encryption import AsymmetricEncryption

# def main2():
#     # passwd = "zhangqi@lelequ.net"
#     # loaded_public_pem = AsymmetricEncryption.load_key_from_file(r"D:\project\work\graph_rag_server\public_key.pem")
#     loaded_private_pem = AsymmetricEncryption.load_key_from_file(r"D:\project\work\graph_rag_server\private_key.pem")
#     # encrypted = AsymmetricEncryption.encrypt(passwd, loaded_public_pem)
#     # print(encrypted)
#     decrypted = AsymmetricEncryption.decrypt(ragflow_settings.ragflow_user_name, loaded_private_pem)
#     print(decrypted)
#     # assert decrypted2 == test_message, "使用加载的密钥解密失败！"
#     # print("✓ 使用加载的密钥加密解密测试通过！")

if __name__ == "__main__":
    main()