配置线程

import os
import json
from dotenv import load_dotenv

# 从 .env 文件加载环境变量
load_dotenv()

import openai
from langsmith import traceable, Client, uuid7
from langsmith.wrappers import wrap_openai

# 初始化客户端
langsmith_client = Client()
client = wrap_openai(openai.Client())

# 配置
THREAD_ID = str(uuid7())

# 使用本地目录存储线程历史。生产环境中，请使用持久化存储解决方案。
THREADS_DIR = os.path.join(os.path.dirname(__file__), "threads")

# 获取线程中所有大语言模型调用的历史以构建对话历史
def get_thread_history(thread_id: str) -> list:
    path = os.path.join(THREADS_DIR, f"{thread_id}.json")
    if not os.path.exists(path):
        return []
    with open(path, "r") as f:
        return json.load(f)

def save_thread_history(thread_id: str, messages: list):
    os.makedirs(THREADS_DIR, exist_ok=True)
    with open(os.path.join(THREADS_DIR, f"{thread_id}.json"), "w") as f:
        json.dump(messages, f, indent=2, default=str)


@traceable(name="Chat Bot", metadata={"thread_id": THREAD_ID})
def chat_pipeline(messages: list, get_chat_history: bool = False):
    # 是继续现有线程还是开始新线程
    if get_chat_history:
        history_messages = get_thread_history(THREAD_ID)
        # 获取现有对话历史并追加新消息
        all_messages = history_messages + messages
    else:
        all_messages = messages

    # 调用模型
    chat_completion = client.chat.completions.create(
        model="gpt-5.4-mini", messages=all_messages
    )

    response_message = chat_completion.choices[0].message
    print("模型响应:", response_message)

    full_conversation = all_messages + [{"role": response_message.role, "content": response_message.content}]
    save_thread_history(THREAD_ID, full_conversation)

    return {"messages": full_conversation}


# 格式化消息
messages = [
    {
        "content": "你好，我叫萨莉",
        "role": "user"
    }
]

# 调用聊天管道
result = chat_pipeline(messages, get_chat_history=False)