mirror of
https://github.com/hwchase17/langchain
synced 2024-11-13 19:10:52 +00:00
481d3855dc
- `llm(prompt)` -> `llm.invoke(prompt)`
- `llm(prompt=prompt` -> `llm.invoke(prompt)` (same with `messages=`)
- `llm(prompt, callbacks=callbacks)` -> `llm.invoke(prompt,
config={"callbacks": callbacks})`
- `llm(prompt, **kwargs)` -> `llm.invoke(prompt, **kwargs)`
130 lines
3.9 KiB
Python
130 lines
3.9 KiB
Python
import logging
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from typing import Any, List, Mapping, Optional
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import requests
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.llms import LLM
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from langchain_community.llms.utils import enforce_stop_tokens
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logger = logging.getLogger(__name__)
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class ChatGLM(LLM):
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"""ChatGLM LLM service.
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Example:
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.. code-block:: python
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from langchain_community.llms import ChatGLM
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endpoint_url = (
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"http://127.0.0.1:8000"
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)
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ChatGLM_llm = ChatGLM(
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endpoint_url=endpoint_url
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)
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"""
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endpoint_url: str = "http://127.0.0.1:8000/"
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"""Endpoint URL to use."""
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model_kwargs: Optional[dict] = None
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"""Keyword arguments to pass to the model."""
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max_token: int = 20000
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"""Max token allowed to pass to the model."""
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temperature: float = 0.1
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"""LLM model temperature from 0 to 10."""
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history: List[List] = []
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"""History of the conversation"""
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top_p: float = 0.7
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"""Top P for nucleus sampling from 0 to 1"""
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with_history: bool = False
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"""Whether to use history or not"""
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@property
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def _llm_type(self) -> str:
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return "chat_glm"
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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_model_kwargs = self.model_kwargs or {}
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return {
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**{"endpoint_url": self.endpoint_url},
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**{"model_kwargs": _model_kwargs},
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}
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""Call out to a ChatGLM LLM inference endpoint.
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Args:
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prompt: The prompt to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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The string generated by the model.
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Example:
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.. code-block:: python
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response = chatglm_llm.invoke("Who are you?")
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"""
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_model_kwargs = self.model_kwargs or {}
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# HTTP headers for authorization
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headers = {"Content-Type": "application/json"}
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payload = {
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"prompt": prompt,
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"temperature": self.temperature,
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"history": self.history,
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"max_length": self.max_token,
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"top_p": self.top_p,
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}
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payload.update(_model_kwargs)
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payload.update(kwargs)
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logger.debug(f"ChatGLM payload: {payload}")
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# call api
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try:
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response = requests.post(self.endpoint_url, headers=headers, json=payload)
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except requests.exceptions.RequestException as e:
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raise ValueError(f"Error raised by inference endpoint: {e}")
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logger.debug(f"ChatGLM response: {response}")
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if response.status_code != 200:
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raise ValueError(f"Failed with response: {response}")
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try:
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parsed_response = response.json()
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# Check if response content does exists
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if isinstance(parsed_response, dict):
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content_keys = "response"
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if content_keys in parsed_response:
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text = parsed_response[content_keys]
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else:
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raise ValueError(f"No content in response : {parsed_response}")
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else:
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raise ValueError(f"Unexpected response type: {parsed_response}")
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except requests.exceptions.JSONDecodeError as e:
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raise ValueError(
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f"Error raised during decoding response from inference endpoint: {e}."
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f"\nResponse: {response.text}"
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)
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if stop is not None:
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text = enforce_stop_tokens(text, stop)
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if self.with_history:
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self.history = parsed_response["history"]
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return text
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