community: Add ChatGLM3 (#15265)

Add [ChatGLM3](https://github.com/THUDM/ChatGLM3) and updated
[chatglm.ipynb](https://python.langchain.com/docs/integrations/llms/chatglm)

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

libs/community/langchain_community/llms/chatglm3.py

@@ -0,0 +1,151 @@
+import json
+import logging
+from typing import Any, List, Optional, Union
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models.llms import LLM
+from langchain_core.messages import (
+    AIMessage,
+    BaseMessage,
+    FunctionMessage,
+    HumanMessage,
+    SystemMessage,
+)
+from langchain_core.pydantic_v1 import Field
+
+from langchain_community.llms.utils import enforce_stop_tokens
+
+logger = logging.getLogger(__name__)
+HEADERS = {"Content-Type": "application/json"}
+DEFAULT_TIMEOUT = 30
+
+
+def _convert_message_to_dict(message: BaseMessage) -> dict:
+    if isinstance(message, HumanMessage):
+        message_dict = {"role": "user", "content": message.content}
+    elif isinstance(message, AIMessage):
+        message_dict = {"role": "assistant", "content": message.content}
+    elif isinstance(message, SystemMessage):
+        message_dict = {"role": "system", "content": message.content}
+    elif isinstance(message, FunctionMessage):
+        message_dict = {"role": "function", "content": message.content}
+    else:
+        raise ValueError(f"Got unknown type {message}")
+    return message_dict
+
+
+class ChatGLM3(LLM):
+    """ChatGLM3 LLM service."""
+
+    model_name: str = Field(default="chatglm3-6b", alias="model")
+    endpoint_url: str = "http://127.0.0.1:8000/v1/chat/completions"
+    """Endpoint URL to use."""
+    model_kwargs: Optional[dict] = None
+    """Keyword arguments to pass to the model."""
+    max_tokens: int = 20000
+    """Max token allowed to pass to the model."""
+    temperature: float = 0.1
+    """LLM model temperature from 0 to 10."""
+    top_p: float = 0.7
+    """Top P for nucleus sampling from 0 to 1"""
+    prefix_messages: List[BaseMessage] = Field(default_factory=list)
+    """Series of messages for Chat input."""
+    streaming: bool = False
+    """Whether to stream the results or not."""
+    http_client: Union[Any, None] = None
+    timeout: int = DEFAULT_TIMEOUT
+
+    @property
+    def _llm_type(self) -> str:
+        return "chat_glm_3"
+
+    @property
+    def _invocation_params(self) -> dict:
+        """Get the parameters used to invoke the model."""
+        params = {
+            "model": self.model_name,
+            "temperature": self.temperature,
+            "max_tokens": self.max_tokens,
+            "top_p": self.top_p,
+            "stream": self.streaming,
+        }
+        return {**params, **(self.model_kwargs or {})}
+
+    @property
+    def client(self) -> Any:
+        import httpx
+
+        return self.http_client or httpx.Client(timeout=self.timeout)
+
+    def _get_payload(self, prompt: str) -> dict:
+        params = self._invocation_params
+        messages = self.prefix_messages + [HumanMessage(content=prompt)]
+        params.update(
+            {
+                "messages": [_convert_message_to_dict(m) for m in messages],
+            }
+        )
+        return params
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call out to a ChatGLM3 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("Who are you?")
+        """
+        import httpx
+
+        payload = self._get_payload(prompt)
+        logger.debug(f"ChatGLM3 payload: {payload}")
+
+        try:
+            response = self.client.post(
+                self.endpoint_url, headers=HEADERS, json=payload
+            )
+        except httpx.NetworkError as e:
+            raise ValueError(f"Error raised by inference endpoint: {e}")
+
+        logger.debug(f"ChatGLM3 response: {response}")
+
+        if response.status_code != 200:
+            raise ValueError(f"Failed with response: {response}")
+
+        try:
+            parsed_response = response.json()
+
+            if isinstance(parsed_response, dict):
+                content_keys = "choices"
+                if content_keys in parsed_response:
+                    choices = parsed_response[content_keys]
+                    if len(choices):
+                        text = choices[0]["message"]["content"]
+                else:
+                    raise ValueError(f"No content in response : {parsed_response}")
+            else:
+                raise ValueError(f"Unexpected response type: {parsed_response}")
+
+        except json.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)
+
+        return text