agent2.py

import os
import uvicorn
from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
import json
import datetime
from openai import OpenAI
from langchain_milvus import Milvus, BM25BuiltInFunction
from vector import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.stores import InMemoryStore
from langchain_classic.retrievers.parent_document_retriever import ParentDocumentRetriever
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"
app = FastAPI()


# ============================================================
# OpenAI LLM 客户端封装 (替代讲义中的 DeepSeek)
# ============================================================

def create_openai_client():
    """创建 OpenAI 客户端"""
    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    return client


def generate_openai_answer(client, prompt):
    """使用 OpenAI 生成回复"""
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "user", "content": prompt}
        ],
        temperature=0.7,
    )
    return response.choices[0].message.content


# 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"
URI1 = "./pdf_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......")


docstore = InMemoryStore()

# 文本分割器
child_splitter = RecursiveCharacterTextSplitter(
                    chunk_size=200,
                    chunk_overlap=50,
                    length_function=len,
                    separators=["\n\n", "\n", "。", "！", "？", "；", "，", " ", ""]
                )

parent_splitter = RecursiveCharacterTextSplitter(
                    chunk_size=1000,
                    chunk_overlap=200
                )

pdf_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": URI1},
    consistency_level="Bounded",
    drop_old=False,
)

# 设置父子文档检索器
parent_retriever = ParentDocumentRetriever(
    vectorstore=pdf_vectorstore,
    docstore=docstore,
    child_splitter=child_splitter,
    parent_splitter=parent_splitter,
)

print("创建 Parent Milvus 连接成功......")

# 创建大语言模型, 采用 OpenAI 
client_llm = create_openai_client()
print("创建 OpenAI LLM 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')

    # 1: Milvus 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 = [r.page_content for r in recall_rerank_milvus]
        context = format_docs(recall_rerank_milvus)
    else:
        context = ""

    # 2: PDF 文档的 Milvus 召回
    # 父文档检索器按照query进行召回
    res = ""
    retrieved_docs = parent_retriever.invoke(query)

    if retrieved_docs is not None and len(retrieved_docs) >= 1:
        res = retrieved_docs[0].page_content
        print("PDF res: ", res)

    context = context + "\n" + res

    # 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>
        """

    # 3. 使用 OpenAI 最新版本模型, 根据提示生成回复
    response = generate_openai_answer(client_llm, SYSTEM_PROMPT + USER_PROMPT.format(context, query))

    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)