Langchain如何调用Siliconflow的模型

硅基流动之前宣布了部分模型免费，那我们就可以调用API来搞些事情。
官网（Chat Completions (siliconflow.cn)）上给出了详细的调用方式，我们可以方便的ctrl C 和 ctrl V

而Langchain支持自定义封装LLM（Custom LLM | 🦜️🔗 Langchain）

那我们就可以结合搞点事情，封装好的代码在这，需要自取：

from langchain.llms.base import LLM
from langchain_community.llms.utils import enforce_stop_tokens
import requests
import os

# 设置API密钥和基础URL环境变量
API_KEY = os.getenv("CUSTOM_API_KEY", "<Your Key>")
BASE_URL = "https://api.siliconflow.cn/v1/chat/completions"

class SiliconFlow(LLM):
    def __init__(self):
        super().__init__()

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

    def siliconflow_completions(self, model: str, prompt: str) -> str:
        payload = {
            "model": model,
            "messages": [{"role": "user", "content": prompt}],
            "stream": False
        }
        headers = {
            "accept": "application/json",
            "content-type": "application/json",
            "authorization": f"Bearer {API_KEY}"
        }

        response = requests.post(BASE_URL, json=payload, headers=headers)
        response.raise_for_status()
        return response.json()["choices"][0]["message"]["content"]

    def _call(self, prompt: str, stop: list = None, model: str = "default-model") -> str:
        response = self.siliconflow_completions(model=model, prompt=prompt)
        if stop is not None:
            response = enforce_stop_tokens(response, stop)
        return response

if __name__ == "__main__":
    llm = SiliconFlow()
    response = llm._call(prompt="<Prompt>", model="<Model>")
    print(response)

作者：LJY_LU