远程调用大模型

1. OpenAI（GPT-3.5/4）封装代码

from langchain.llms.base import LLM
from openai import OpenAI
from langchain_core.messages.ai import AIMessage
from typing import Any, List, Dict

# 替换为你的OpenAI API Key
openai_api_key = "你的OpenAI API密钥"
# 国内用户需配置代理（可选）
# import os
# os.environ["HTTP_PROXY"] = "http://127.0.0.1:7890"
# os.environ["HTTPS_PROXY"] = "http://127.0.0.1:7890"

class ChatOpenAI(LLM):
    history: List[Dict[str, str]] = []
    client: Any = None

    def __init__(self):
        super().__init__()
        self.client = OpenAI(api_key=openai_api_key)

    @property
    def _llm_type(self) -> str:
        return "ChatOpenAI"

    def invoke(self, prompt: str, history: List[Dict[str, str]] = None) -> AIMessage:
        if history is None:
            history = []
        history.append({"role": "user", "content": prompt})
        
        response = self.client.chat.completions.create(
            model="gpt-3.5-turbo",  # 可选gpt-4/gpt-4o
            messages=history,
            temperature=0.7
        )
        
        result = response.choices[0].message.content
        history.append({"role": "assistant", "content": result})
        return AIMessage(content=result)

    def _call(self, prompt: str, history: List[Dict[str, str]] = None) -> str:
        ai_message = self.invoke(prompt, history)
        return ai_message.content

    def stream(self, prompt: str, history: List[Dict[str, str]] = None):
        if history is None:
            history = []
        history.append({"role": "user", "content": prompt})
        
        response = self.client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=history,
            stream=True,
            temperature=0.7
        )
        
        full_content = ""
        for chunk in response:
            if chunk.choices and chunk.choices[0].delta.content:
                content = chunk.choices[0].delta.content
                full_content += content
                yield content
        
        history.append({"role": "assistant", "content": full_content})

# 测试
if __name__ == "__main__":
    llm = ChatOpenAI()
    result = llm.invoke("你好，介绍下自己")
    print(result.content)

2. 百度文心一言（ERNIE）封装代码

from langchain.llms.base import LLM
from erniebot import ChatCompletion
import os
from langchain_core.messages.ai import AIMessage
from typing import Any, List, Dict

# 配置百度文心一言密钥
os.environ["EB_API_KEY"] = "你的文心一言API Key"
os.environ["EB_SECRET_KEY"] = "你的文心一言Secret Key"

class ChatERNIE(LLM):
    history: List[Dict[str, str]] = []

    @property
    def _llm_type(self) -> str:
        return "ChatERNIE"

    def invoke(self, prompt: str, history: List[Dict[str, str]] = None) -> AIMessage:
        if history is None:
            history = []
        # 转换历史记录格式适配文心API
        messages = [{"role": msg["role"], "content": msg["content"]} for msg in history]
        messages.append({"role": "user", "content": prompt})
        
        response = ChatCompletion.create(
            model="ernie-4.0",  # 可选ernie-3.5/ernie-4.0-turbo
            messages=messages,
            temperature=0.7
        )
        
        result = response.result
        history.append({"role": "user", "content": prompt})
        history.append({"role": "assistant", "content": result})
        return AIMessage(content=result)

    def _call(self, prompt: str, history: List[Dict[str, str]] = None) -> str:
        ai_message = self.invoke(prompt, history)
        return ai_message.content

    def stream(self, prompt: str, history: List[Dict[str, str]] = None):
        if history is None:
            history = []
        messages = [{"role": msg["role"], "content": msg["content"]} for msg in history]
        messages.append({"role": "user", "content": prompt})
        
        response = ChatCompletion.create(
            model="ernie-4.0",
            messages=messages,
            stream=True,
            temperature=0.7
        )
        
        full_content = ""
        for chunk in response:
            if chunk.result:
                content = chunk.result
                full_content += content
                yield content
        
        history.append({"role": "user", "content": prompt})
        history.append({"role": "assistant", "content": full_content})

# 测试
if __name__ == "__main__":
    llm = ChatERNIE()
    result = llm.invoke("你好，介绍下自己")
    print(result.content)

3. 阿里通义千问（Qwen）封装代码

from langchain.llms.base import LLM
from openai import OpenAI
from langchain_core.messages.ai import AIMessage
from typing import Any, List, Dict

# 替换为你的通义千问API Key
qwen_api_key = "你的通义千问API密钥"

class ChatQwen(LLM):
    history: List[Dict[str, str]] = []
    client: Any = None

    def __init__(self):
        super().__init__()
        # 通义千问兼容OpenAI接口规范
        self.client = OpenAI(
            api_key=qwen_api_key,
            base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
        )

    @property
    def _llm_type(self) -> str:
        return "ChatQwen"

    def invoke(self, prompt: str, history: List[Dict[str, str]] = None) -> AIMessage:
        if history is None:
            history = []
        history.append({"role": "user", "content": prompt})
        
        response = self.client.chat.completions.create(
            model="qwen-turbo",  # 可选qwen-plus/qwen-max
            messages=history,
            temperature=0.7
        )
        
        result = response.choices[0].message.content
        history.append({"role": "assistant", "content": result})
        return AIMessage(content=result)

    def _call(self, prompt: str, history: List[Dict[str, str]] = None) -> str:
        ai_message = self.invoke(prompt, history)
        return ai_message.content

    def stream(self, prompt: str, history: List[Dict[str, str]] = None):
        if history is None:
            history = []
        history.append({"role": "user", "content": prompt})
        
        response = self.client.chat.completions.create(
            model="qwen-turbo",
            messages=history,
            stream=True,
            temperature=0.7
        )
        
        full_content = ""
        for chunk in response:
            if chunk.choices and chunk.choices[0].delta.content:
                content = chunk.choices[0].delta.content
                full_content += content
                yield content
        
        history.append({"role": "assistant", "content": full_content})

# 测试
if __name__ == "__main__":
    llm = ChatQwen()
    result = llm.invoke("你好，介绍下自己")
    print(result.content)