Add LLM for ChatGLM(2)-6B API (#7774)

Description:
Add LLM for ChatGLM-6B & ChatGLM2-6B API

Related Issue: 
Will the langchain support ChatGLM? #4766
Add support for selfhost models like ChatGLM or transformer models #1780

Dependencies: 
No extra library install required. 
It wraps api call to a ChatGLM(2)-6B server(start with api.py), so api
endpoint is required to run.

Tag maintainer:  @mlot 

Any comments on this PR would be appreciated.
---------

Co-authored-by: mlot <limpo2000@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>

docs/extras/modules/model_io/models/llms/integrations/chatglm.ipynb

@@ -0,0 +1,121 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ChatGLM\n",
+    "\n",
+    "[ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) is an open bilingual language model based on General Language Model (GLM) framework, with 6.2 billion parameters. With the quantization technique, users can deploy locally on consumer-grade graphics cards (only 6GB of GPU memory is required at the INT4 quantization level). \n",
+    "\n",
+    "[ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B) is the second-generation version of the open-source bilingual (Chinese-English) chat model ChatGLM-6B. It retains the smooth conversation flow and low deployment threshold of the first-generation model, while introducing the new features like better performance, longer context and more efficient inference.\n",
+    "\n",
+    "This example goes over how to use LangChain to interact with ChatGLM2-6B Inference for text completion.\n",
+    "ChatGLM-6B and ChatGLM2-6B has the same api specs, so this example should work with both."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import ChatGLM\n",
+    "from langchain import PromptTemplate, LLMChain\n",
+    "\n",
+    "# import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = \"\"\"{question}\"\"\"\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# default endpoint_url for a local deployed ChatGLM api server\n",
+    "endpoint_url = \"http://127.0.0.1:8000\"\n",
+    "\n",
+    "# direct access endpoint in a proxied environment\n",
+    "# os.environ['NO_PROXY'] = '127.0.0.1'\n",
+    "\n",
+    "llm = ChatGLM(\n",
+    "    endpoint_url=endpoint_url,\n",
+    "    max_token=80000,\n",
+    "    history=[[\"我将从美国到中国来旅游，出行前希望了解中国的城市\", \"欢迎问我任何问题。\"]],\n",
+    "    top_p=0.9,\n",
+    "    model_kwargs={\"sample_model_args\": False},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChatGLM payload: {'prompt': '北京和上海两座城市有什么不同？', 'temperature': 0.1, 'history': [['我将从美国到中国来旅游，出行前希望了解中国的城市', '欢迎问我任何问题。']], 'max_length': 80000, 'top_p': 0.9, 'sample_model_args': False}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'北京和上海是中国的两个首都，它们在许多方面都有所不同。\\n\\n北京是中国的政治和文化中心，拥有悠久的历史和灿烂的文化。它是中国最重要的古都之一，也是中国历史上最后一个封建王朝的都城。北京有许多著名的古迹和景点，例如紫禁城、天安门广场和长城等。\\n\\n上海是中国最现代化的城市之一，也是中国商业和金融中心。上海拥有许多国际知名的企业和金融机构，同时也有许多著名的景点和美食。上海的外滩是一个历史悠久的商业区，拥有许多欧式建筑和餐馆。\\n\\n除此之外，北京和上海在交通和人口方面也有很大差异。北京是中国的首都，人口众多，交通拥堵问题较为严重。而上海是中国的商业和金融中心，人口密度较低，交通相对较为便利。\\n\\n总的来说，北京和上海是两个拥有独特魅力和特点的城市，可以根据自己的兴趣和时间来选择前往其中一座城市旅游。'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "question = \"北京和上海两座城市有什么不同？\"\n",
+    "\n",
+    "llm_chain.run(question)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "langchain-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

langchain/llms/__init__.py

@@ -14,6 +14,7 @@ from langchain.llms.baseten import Baseten
 from langchain.llms.beam import Beam
 from langchain.llms.bedrock import Bedrock
 from langchain.llms.cerebriumai import CerebriumAI
+from langchain.llms.chatglm import ChatGLM
 from langchain.llms.clarifai import Clarifai
 from langchain.llms.cohere import Cohere
 from langchain.llms.ctransformers import CTransformers
@@ -69,6 +70,7 @@ __all__ = [
     "Bedrock",
     "CTransformers",
     "CerebriumAI",
+    "ChatGLM",
     "Clarifai",
     "Cohere",
     "Databricks",
@@ -125,6 +127,7 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
     "baseten": Baseten,
     "beam": Beam,
     "cerebriumai": CerebriumAI,
+    "chat_glm": ChatGLM,
     "clarifai": Clarifai,
     "cohere": Cohere,
     "ctransformers": CTransformers,

langchain/llms/chatglm.py

@@ -0,0 +1,123 @@
+from typing import Any, List, Mapping, Optional
+
+import requests
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms.base import LLM
+from langchain.llms.utils import enforce_stop_tokens
+
+
+class ChatGLM(LLM):
+    """Wrapper around ChatGLM's LLM inference service.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.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
+    """Key word 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"""
+
+    @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("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)
+
+        # print("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}")
+
+        # print("ChatGLM resp:", 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)
+        self.history = self.history + [[None, parsed_response["response"]]]
+        return text

tests/integration_tests/llms/test_chatglm.py

@@ -0,0 +1,18 @@
+"""Test ChatGLM API wrapper."""
+from langchain.llms.chatglm import ChatGLM
+from langchain.schema import LLMResult
+
+
+def test_chatglm_call() -> None:
+    """Test valid call to chatglm."""
+    llm = ChatGLM()
+    output = llm("北京和上海这两座城市有什么不同？")
+    assert isinstance(output, str)
+
+
+def test_chatglm_generate() -> None:
+    """Test valid call to chatglm."""
+    llm = ChatGLM()
+    output = llm.generate(["who are you"])
+    assert isinstance(output, LLMResult)
+    assert isinstance(output.generations, list)