agent.py

import os
import uvicorn
from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
import json
import datetime
from langchain_milvus import Milvus, BM25BuiltInFunction
from model import OpenAIEmbeddings
from dotenv import load_dotenv

# 加载 .env 文件中的环境变量, 隐藏 API Keys
load_dotenv()

os.environ["TOKENIZERS_PARALLELISM"] = "false"

# ====== 开关: True=本地vLLM, False=ChatGPT ======
USE_LOCAL_LLM = False

if USE_LOCAL_LLM:
    from model import create_local_llm_client as create_client
    from model import generate_local_answer as generate_answer
else:
    from model import create_chatgpt_client as create_client
    from model import generate_chatgpt_answer as generate_answer

client_llm = create_client()
print(f"创建 {'本地 vLLM' if USE_LOCAL_LLM else 'ChatGPT'} 客户端成功......")

app = FastAPI()

# 允许所有域的请求
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# 创建 Embedding 模型
embedding_model = OpenAIEmbeddings()
print("创建 Embedding 模型成功......")

# 设置默认的 Milvus 数据库文件路径
URI = "./milvus_agent.db"

# 创建 Milvus 连接
milvus_vectorstore = Milvus(
    embedding_function=embedding_model,
    builtin_function=BM25BuiltInFunction(),
    vector_field=["dense", "sparse"],
    index_params=[
        {
            "metric_type": "IP",
            "index_type": "IVF_FLAT",
        },
        {
            "metric_type": "BM25",
            "index_type": "SPARSE_INVERTED_INDEX"
        }
    ],
    connection_args={"uri": URI},
)

retriever = milvus_vectorstore.as_retriever()
print("创建 Milvus 连接成功......")


def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


@app.post("/")
async def chatbot(request: Request):
    global milvus_vectorstore, retriever

    json_post_raw = await request.json()
    json_post = json.dumps(json_post_raw)
    json_post_list = json.loads(json_post)

    query = json_post_list.get('question')

    # 召回 & 排序
    # 在集合中搜索问题并检索语义 top-10 匹配项, 而且已经配置了 reranker 的处理, 采用RRF算法
    recall_rerank_milvus = milvus_vectorstore.similarity_search(
        query,
        k=10,
        ranker_type="rrf",
        ranker_params={"k": 100}
    )

    if recall_rerank_milvus:
        # 检索结果存放在列表中
        context = format_docs(recall_rerank_milvus)
    else:
        context = []

    # 为LLM定义系统和用户提示, 这个提示是由从Milvus检索到的文档组装而成的.
    SYSTEM_PROMPT = """
    System: 你是一个非常得力的医学助手, 你可以通过从数据库中检索出的信息找到问题的答案.
    """

    USER_PROMPT = f"""
    User: 利用介于<context>和</context>之间的从数据库中检索出的信息来回答问题, 具体的问题介于<question>和</question>之间. 如果提供的信息为空, 则按照你的经验知识来给出尽可能严谨准确的回答, 不知道的时候坦诚的承认不了解, 不要编造不真实的信息.
    <context>
    {context}
    </context>

    <question>
    {query}
    </question>
    """

    # 使用 LLM 模型, 根据提示生成回复 (根据 USE_LOCAL_LLM 开关自动选择 vLLM 或 ChatGPT)
    response = generate_answer(client_llm, SYSTEM_PROMPT + USER_PROMPT)

    now = datetime.datetime.now()
    time = now.strftime("%Y-%m-%d %H:%M:%S")

    answer = {
        "response": response,
        "status": 200,
        "time": time
    }

    return answer


if __name__ == '__main__':
    # 主函数中直接启动fastapi服务
    uvicorn.run(app, host='0.0.0.0', port=8103, workers=1)