ChatGLM 集成LangChain工具详解

from langchain.llms.base import LLM
from typing import Optional, List, Mapping, Any
class CustomLLM(LLM):
  n:int
  @property
  def _llm_type(self) -> str:
    return "custom"
  def _call(self,prompt:str,stop:Optional[List[str]]=None) -> str:
    if stop is not None:
      raise ValueError("stop kwargs are not permitted")
    return prompt[:self.n]
  @property
  def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        return {"n": self.n}

curl -X POST "http://{your_host}:8000" \
     -H 'Content-Type: application/json' \
     -d '{"prompt": "你好", "history": []}'

{
  "response":"你好👋！我是人工智能助手 ChatGLM-6B，很高兴见到你，欢迎问我任何问题。",
  "history":[["你好","你好👋！我是人工智能助手 ChatGLM-6B，很高兴见到你，欢迎问我任何问题。"]],
  "status":200,
  "time":"2023-03-23 21:38:40"
}

from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from typing import Dict, List, Optional, Tuple, Union
import requests
import json
class ChatGLM(LLM):
    max_token: int = 10000
    temperature: float = 0.1
    top_p = 0.9
    history = []
    def __init__(self):
        super().__init__()
    @property
    def _llm_type(self) -> str:
        return "ChatGLM"
    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
        # headers中添加上content-type这个参数，指定为json格式
        headers = {'Content-Type': 'application/json'}
        data=json.dumps({
          'prompt':prompt,
          'temperature':self.temperature,
          'history':self.history,
          'max_length':self.max_token
        })
        # print("ChatGLM prompt:",prompt)
        # 调用api
        response = requests.post("{your_host}/api",headers=headers,data=data)
		# print("ChatGLM resp:",response)
        if response.status_code!=200:
          return "查询结果错误"
        resp = response.json()
        if stop is not None:
            response = enforce_stop_tokens(response, stop)
        self.history = self.history+[[None, resp['response']]]
        return resp['response']

llm = ChatGLM()
print(llm("你会做什么"))