docs: add modelscope endpoint

docs/docs/integrations/chat/modelscope_chat_endpoint.ipynb

@@ -0,0 +1,247 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: ModelScope\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ModelScopeChatEndpoint\n",
+    "\n",
+    "\n",
+    "ModelScope ([Home](https://www.modelscope.cn/) | [GitHub](https://github.com/modelscope/modelscope)) is built upon the notion of “Model-as-a-Service” (MaaS). It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. The core ModelScope library open-sourced in this repository provides the interfaces and implementations that allow developers to perform model inference, training and evaluation. \n",
+    "\n",
+    "This will help you getting started with ModelScope Chat Endpoint.\n",
+    "\n",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "|Provider| Class | Package | Local | Serializable | Package downloads | Package latest |\n",
+    "|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n",
+    "|[ModelScope](/docs/integrations/providers/modelscope/)| ModelScopeChatEndpoint | [langchain-modelscope-integration](https://pypi.org/project/langchain-modelscope-integration/) | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-modelscope-integration?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-modelscope-integration?style=flat-square&label=%20) |\n",
+    "\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access ModelScope chat endpoint you'll need to create a ModelScope account, get an SDK token, and install the `langchain-modelscope-integration` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [ModelScope](https://modelscope.cn/) to sign up to ModelScope and generate an [SDK token](https://modelscope.cn/my/myaccesstoken). Once you've done this set the `MODELSCOPE_SDK_TOKEN` environment variable:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"MODELSCOPE_SDK_TOKEN\"):\n",
+    "    os.environ[\"MODELSCOPE_SDK_TOKEN\"] = getpass.getpass(\n",
+    "        \"Enter your ModelScope SDK token: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain ModelScope integration lives in the `langchain-modelscope-integration` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-modelscope-integration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a38cde65-254d-4219-a441-068766c0d4b5",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_modelscope import ModelScopeChatEndpoint\n",
+    "\n",
+    "llm = ModelScopeChatEndpoint(\n",
+    "    model=\"Qwen/Qwen2.5-Coder-32B-Instruct\",\n",
+    "    temperature=0,\n",
+    "    max_tokens=1024,\n",
+    "    timeout=60,\n",
+    "    max_retries=2,\n",
+    "    # other params...\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='我喜欢编程。', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 3, 'prompt_tokens': 33, 'total_tokens': 36, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'qwen2.5-coder-32b-instruct', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-60bb3461-60ae-4c0b-8997-ab55ef77fcd6-0', usage_metadata={'input_tokens': 33, 'output_tokens': 3, 'total_tokens': 36, 'input_token_details': {}, 'output_token_details': {}})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    (\n",
+    "        \"system\",\n",
+    "        \"You are a helpful assistant that translates English to Chinese. Translate the user sentence.\",\n",
+    "    ),\n",
+    "    (\"human\", \"I love programming.\"),\n",
+    "]\n",
+    "ai_msg = llm.invoke(messages)\n",
+    "ai_msg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "我喜欢编程。\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='我喜欢编程。', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 3, 'prompt_tokens': 28, 'total_tokens': 31, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'qwen2.5-coder-32b-instruct', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-9f011a3a-9a11-4759-8d16-5b1843a78862-0', usage_metadata={'input_tokens': 28, 'output_tokens': 3, 'total_tokens': 31, 'input_token_details': {}, 'output_token_details': {}})"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
+    "        ),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | llm\n",
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"Chinese\",\n",
+    "        \"input\": \"I love programming.\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ModelScopeChatEndpoint features and configurations head to the reference: https://modelscope.cn/docs/model-service/API-Inference/intro\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}