Source code for langchain_community.llms.chatglm

import logging
from typing import Any, List, Mapping, Optional

import requests
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM

from langchain_community.llms.utils import enforce_stop_tokens

logger = logging.getLogger(__name__)

[docs]class ChatGLM(LLM): """ChatGLM LLM service. Example: .. code-block:: python from langchain_community.llms import ChatGLM endpoint_url = ( "" ) ChatGLM_llm = ChatGLM( endpoint_url=endpoint_url ) """ endpoint_url: str = "" """Endpoint URL to use.""" model_kwargs: Optional[dict] = None """Keyword arguments to pass to the model.""" max_token: int = 20000 """Max token allowed to pass to the model.""" temperature: float = 0.1 """LLM model temperature from 0 to 10.""" history: List[List] = [] """History of the conversation""" top_p: float = 0.7 """Top P for nucleus sampling from 0 to 1""" with_history: bool = False """Whether to use history or not""" @property def _llm_type(self) -> str: return "chat_glm" @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" _model_kwargs = self.model_kwargs or {} return { **{"endpoint_url": self.endpoint_url}, **{"model_kwargs": _model_kwargs}, } def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to a ChatGLM LLM inference endpoint. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = chatglm_llm.invoke("Who are you?") """ _model_kwargs = self.model_kwargs or {} # HTTP headers for authorization headers = {"Content-Type": "application/json"} payload = { "prompt": prompt, "temperature": self.temperature, "history": self.history, "max_length": self.max_token, "top_p": self.top_p, } payload.update(_model_kwargs) payload.update(kwargs) logger.debug(f"ChatGLM payload: {payload}") # call api try: response =, headers=headers, json=payload) except requests.exceptions.RequestException as e: raise ValueError(f"Error raised by inference endpoint: {e}") logger.debug(f"ChatGLM response: {response}") if response.status_code != 200: raise ValueError(f"Failed with response: {response}") try: parsed_response = response.json() # Check if response content does exists if isinstance(parsed_response, dict): content_keys = "response" if content_keys in parsed_response: text = parsed_response[content_keys] else: raise ValueError(f"No content in response : {parsed_response}") else: raise ValueError(f"Unexpected response type: {parsed_response}") except requests.exceptions.JSONDecodeError as e: raise ValueError( f"Error raised during decoding response from inference endpoint: {e}." f"\nResponse: {response.text}" ) if stop is not None: text = enforce_stop_tokens(text, stop) if self.with_history: self.history = parsed_response["history"] return text