initial commit

venv/Lib/site-packages/langchain_community/llms/chatglm.py

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+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__)
+
+
+class ChatGLM(LLM):
+    """ChatGLM LLM service.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.llms import ChatGLM
+            endpoint_url = (
+                "http://127.0.0.1:8000"
+            )
+            ChatGLM_llm = ChatGLM(
+                endpoint_url=endpoint_url
+            )
+    """
+
+    endpoint_url: str = "http://127.0.0.1:8000/"
+    """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 = requests.post(self.endpoint_url, 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