docs[patch]: chat model page names (#14264)

10 months ago · 956d55de2b
parent b49104c2c9
commit 956d55de2b
27 changed files with 350 additions and 80 deletions
--- a/docs/docs/integrations/chat/anthropic.ipynb
+++ b/docs/docs/integrations/chat/anthropic.ipynb
@ -1,11 +1,21 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "a016701c",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Anthropic\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "bf733a38-db84-4363-89e2-de6735c37230",
   "metadata": {},
   "source": [
-    "# Anthropic\n",
+    "# ChatAnthropic\n",
    "\n",
    "This notebook covers how to get started with Anthropic chat models."
   ]
--- a/docs/docs/integrations/chat/anyscale.ipynb
+++ b/docs/docs/integrations/chat/anyscale.ipynb
@ -1,12 +1,22 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "31895fc4",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Anyscale\n",
+    "---"
+   ]
+  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "642fd21c-600a-47a1-be96-6e1438b421a9",
   "metadata": {},
   "source": [
-    "# Anyscale\n",
+    "# ChatAnyscale\n",
    "\n",
    "This notebook demonstrates the use of `langchain.chat_models.ChatAnyscale` for [Anyscale Endpoints](https://endpoints.anyscale.com/).\n",
    "\n",
@ -33,7 +43,7 @@
   "metadata": {},
   "outputs": [
    {
-     "name": "stdin",
+     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ········\n"
--- a/docs/docs/integrations/chat/azure_chat_openai.ipynb
+++ b/docs/docs/integrations/chat/azure_chat_openai.ipynb
@ -1,11 +1,21 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "641f8cb0",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Azure OpenAI\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "38f26d7a",
   "metadata": {},
   "source": [
-    "# Azure OpenAI\n",
+    "# AzureChatOpenAI\n",
    "\n",
    ">[Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview) provides REST API access to OpenAI's powerful language models including the GPT-4, GPT-3.5-Turbo, and Embeddings model series. These models can be easily adapted to your specific task including but not limited to content generation, summarization, semantic search, and natural language to code translation. Users can access the service through REST APIs, Python SDK, or a web-based interface in the Azure OpenAI Studio.\n",
    "\n",
--- a/docs/docs/integrations/chat/azureml_chat_endpoint.ipynb
+++ b/docs/docs/integrations/chat/azureml_chat_endpoint.ipynb
@ -1,10 +1,19 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Azure ML Endpoint\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Azure ML Endpoint\n",
+    "# AzureMLChatOnlineEndpoint\n",
    "\n",
    ">[Azure Machine Learning](https://azure.microsoft.com/en-us/products/machine-learning/) is a platform used to build, train, and deploy machine learning models. Users can explore the types of models to deploy in the Model Catalog, which provides Azure Foundation Models and OpenAI Models. `Azure Foundation Models` include various open-source models and popular Hugging Face models. Users can also import models of their liking into AzureML.\n",
    ">\n",
--- a/docs/docs/integrations/chat/baichuan.ipynb
+++ b/docs/docs/integrations/chat/baichuan.ipynb
@ -1,10 +1,19 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Baichuan Chat\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Baichuan Chat\n",
+    "# ChatBaichuan\n",
    "\n",
    "Baichuan chat models API by Baichuan Intelligent Technology. For more information, see [https://platform.baichuan-ai.com/docs/api](https://platform.baichuan-ai.com/docs/api)"
   ]
@ -63,7 +72,9 @@
   "outputs": [
    {
     "data": {
-      "text/plain": "AIMessage(content='首先，我们需要确定闰年的二月有多少天。闰年的二月有29天。\\n\\n然后，我们可以计算你的月薪：\\n\\n日薪 = 月薪 / (当月天数)\\n\\n所以，你的月薪 = 日薪 * 当月天数\\n\\n将数值代入公式：\\n\\n月薪 = 8元/天 * 29天 = 232元\\n\\n因此，你在闰年的二月的月薪是232元。')"
+      "text/plain": [
+       "AIMessage(content='首先，我们需要确定闰年的二月有多少天。闰年的二月有29天。\\n\\n然后，我们可以计算你的月薪：\\n\\n日薪 = 月薪 / (当月天数)\\n\\n所以，你的月薪 = 日薪 * 当月天数\\n\\n将数值代入公式：\\n\\n月薪 = 8元/天 * 29天 = 232元\\n\\n因此，你在闰年的二月的月薪是232元。')"
+      ]
     },
     "execution_count": 3,
     "metadata": {},
@ -76,16 +87,23 @@
  },
  {
   "cell_type": "markdown",
-   "source": [
-    "## For ChatBaichuan with Streaming"
-   ],
   "metadata": {
    "collapsed": false
-   }
+   },
+   "source": [
+    "## For ChatBaichuan with Streaming"
+   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-17T15:14:25.870044Z",
+     "start_time": "2023-10-17T15:14:25.863381Z"
+    },
+    "collapsed": false
+   },
   "outputs": [],
   "source": [
    "chat = ChatBaichuan(\n",
@ -93,22 +111,24 @@
    "    baichuan_secret_key=\"YOUR_SECRET_KEY\",\n",
    "    streaming=True,\n",
    ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-10-17T15:14:25.870044Z",
-     "start_time": "2023-10-17T15:14:25.863381Z"
-    }
-   }
+   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-17T15:14:27.153546Z",
+     "start_time": "2023-10-17T15:14:25.868470Z"
+    },
+    "collapsed": false
+   },
   "outputs": [
    {
     "data": {
-      "text/plain": "AIMessageChunk(content='首先，我们需要确定闰年的二月有多少天。闰年的二月有29天。\\n\\n然后，我们可以计算你的月薪：\\n\\n日薪 = 月薪 / (当月天数)\\n\\n所以，你的月薪 = 日薪 * 当月天数\\n\\n将数值代入公式：\\n\\n月薪 = 8元/天 * 29天 = 232元\\n\\n因此，你在闰年的二月的月薪是232元。')"
+      "text/plain": [
+       "AIMessageChunk(content='首先，我们需要确定闰年的二月有多少天。闰年的二月有29天。\\n\\n然后，我们可以计算你的月薪：\\n\\n日薪 = 月薪 / (当月天数)\\n\\n所以，你的月薪 = 日薪 * 当月天数\\n\\n将数值代入公式：\\n\\n月薪 = 8元/天 * 29天 = 232元\\n\\n因此，你在闰年的二月的月薪是232元。')"
+      ]
     },
     "execution_count": 6,
     "metadata": {},
@ -117,14 +137,7 @@
   ],
   "source": [
    "chat([HumanMessage(content=\"我日薪8块钱，请问在闰年的二月，我月薪多少\")])"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-10-17T15:14:27.153546Z",
-     "start_time": "2023-10-17T15:14:25.868470Z"
-    }
-   }
+   ]
  }
 ],
 "metadata": {
--- a/docs/docs/integrations/chat/baidu_qianfan_endpoint.ipynb
+++ b/docs/docs/integrations/chat/baidu_qianfan_endpoint.ipynb
@ -1,11 +1,20 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Baidu Qianfan\n",
+    "---"
+   ]
+  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Baidu Qianfan\n",
+    "# QianfanChatEndpoint\n",
    "\n",
    "Baidu AI Cloud Qianfan Platform is a one-stop large model development and service operation platform for enterprise developers. Qianfan not only provides including the model of Wenxin Yiyan (ERNIE-Bot) and the third-party open-source models, but also provides various AI development tools and the whole set of development environment, which facilitates customers to use and develop large model applications easily.\n",
    "\n",
--- a/docs/docs/integrations/chat/bedrock.ipynb
+++ b/docs/docs/integrations/chat/bedrock.ipynb
@ -1,11 +1,21 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "fbc66410",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Bedrock Chat\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "bf733a38-db84-4363-89e2-de6735c37230",
   "metadata": {},
   "source": [
-    "# Bedrock Chat\n",
+    "# BedrockChat\n",
    "\n",
    ">[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of \n",
    "> high-performing foundation models (FMs) from leading AI companies like `AI21 Labs`, `Anthropic`, `Cohere`, \n",
--- a/docs/docs/integrations/chat/cohere.ipynb
+++ b/docs/docs/integrations/chat/cohere.ipynb
@ -1,11 +1,21 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "53fbf15f",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Cohere\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "bf733a38-db84-4363-89e2-de6735c37230",
   "metadata": {},
   "source": [
-    "# Cohere\n",
+    "# ChatCohere\n",
    "\n",
    "This notebook covers how to get started with Cohere chat models."
   ]
--- a/docs/docs/integrations/chat/ernie.ipynb
+++ b/docs/docs/integrations/chat/ernie.ipynb
@ -1,10 +1,19 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Ernie Bot Chat\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# ERNIE-Bot Chat\n",
+    "# ErnieBotChat\n",
    "\n",
    "[ERNIE-Bot](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/jlil56u11) is a large language model developed by Baidu, covering a huge amount of Chinese data.\n",
    "This notebook covers how to get started with ErnieBot chat models.\n",
--- a/docs/docs/integrations/chat/everlyai.ipynb
+++ b/docs/docs/integrations/chat/everlyai.ipynb
@ -1,11 +1,21 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "5e45f35c",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: EverlyAI\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "642fd21c-600a-47a1-be96-6e1438b421a9",
   "metadata": {},
   "source": [
-    "# EverlyAI\n",
+    "# ChatEverlyAI\n",
    "\n",
    ">[EverlyAI](https://everlyai.xyz) allows you to run your ML models at scale in the cloud. It also provides API access to [several LLM models](https://everlyai.xyz).\n",
    "\n",
--- a/docs/docs/integrations/chat/fireworks.ipynb
+++ b/docs/docs/integrations/chat/fireworks.ipynb
@ -1,12 +1,22 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "529aeba9",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Fireworks\n",
+    "---"
+   ]
+  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "642fd21c-600a-47a1-be96-6e1438b421a9",
   "metadata": {},
   "source": [
-    "# Fireworks\n",
+    "# ChatFireworks\n",
    "\n",
    ">[Fireworks](https://app.fireworks.ai/) accelerates product development on generative AI by creating an innovative AI experiment and production platform. \n",
    "\n",
--- a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb
+++ b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb
@ -1,11 +1,20 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Google Cloud Vertex AI\n",
+    "---"
+   ]
+  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Google Cloud Vertex AI \n",
+    "# ChatVertexAI\n",
    "\n",
    "Note: This is separate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
    "\n",
--- a/docs/docs/integrations/chat/hunyuan.ipynb
+++ b/docs/docs/integrations/chat/hunyuan.ipynb
@ -1,10 +1,19 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Tencent Hunyuan\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Tencent Hunyuan\n",
+    "# ChatHunyuan\n",
    "\n",
    "Hunyuan chat model API by Tencent. For more information, see [https://cloud.tencent.com/document/product/1729](https://cloud.tencent.com/document/product/1729)"
   ]
@ -54,7 +63,9 @@
   "outputs": [
    {
     "data": {
-      "text/plain": "AIMessage(content=\"J'aime programmer.\")"
+      "text/plain": [
+       "AIMessage(content=\"J'aime programmer.\")"
+      ]
     },
     "execution_count": 3,
     "metadata": {},
@ -73,16 +84,23 @@
  },
  {
   "cell_type": "markdown",
-   "source": [
-    "## For ChatHunyuan with Streaming"
-   ],
   "metadata": {
    "collapsed": false
-   }
+   },
+   "source": [
+    "## For ChatHunyuan with Streaming"
+   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-19T10:20:41.507720Z",
+     "start_time": "2023-10-19T10:20:41.496456Z"
+    },
+    "collapsed": false
+   },
   "outputs": [],
   "source": [
    "chat = ChatHunyuan(\n",
@ -91,22 +109,24 @@
    "    hunyuan_secret_key=\"YOUR_SECRET_KEY\",\n",
    "    streaming=True,\n",
    ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-10-19T10:20:41.507720Z",
-     "start_time": "2023-10-19T10:20:41.496456Z"
-    }
-   }
+   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-19T10:20:46.275673Z",
+     "start_time": "2023-10-19T10:20:44.241097Z"
+    },
+    "collapsed": false
+   },
   "outputs": [
    {
     "data": {
-      "text/plain": "AIMessageChunk(content=\"J'aime programmer.\")"
+      "text/plain": [
+       "AIMessageChunk(content=\"J'aime programmer.\")"
+      ]
     },
     "execution_count": 3,
     "metadata": {},
@ -121,26 +141,19 @@
    "        )\n",
    "    ]\n",
    ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-10-19T10:20:46.275673Z",
-     "start_time": "2023-10-19T10:20:44.241097Z"
-    }
-   }
+   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
-   "outputs": [],
-   "source": [],
   "metadata": {
-    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-10-19T10:19:56.233477Z"
-    }
-   }
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
  }
 ],
 "metadata": {
--- a/docs/docs/integrations/chat/konko.ipynb
+++ b/docs/docs/integrations/chat/konko.ipynb
@ -1,10 +1,19 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Konko\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Konko\n",
+    "# ChatKonko\n",
    "\n",
    ">[Konko](https://www.konko.ai/) API is a fully managed Web API designed to help application developers:\n",
    "\n",
--- a/docs/docs/integrations/chat/litellm.ipynb
+++ b/docs/docs/integrations/chat/litellm.ipynb
@ -1,12 +1,22 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "59148044",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: LiteLLM\n",
+    "---"
+   ]
+  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bf733a38-db84-4363-89e2-de6735c37230",
   "metadata": {},
   "source": [
-    "# 🚅 LiteLLM\n",
+    "# ChatLiteLLM\n",
    "\n",
    "[LiteLLM](https://github.com/BerriAI/litellm) is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc. \n",
    "\n",
--- a/docs/docs/integrations/chat/llama2_chat.ipynb
+++ b/docs/docs/integrations/chat/llama2_chat.ipynb
@ -1,11 +1,21 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "7320f16b",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Llama 2 Chat\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "90a1faf2",
   "metadata": {},
   "source": [
-    "# Llama-2 Chat\n",
+    "# Llama2Chat\n",
    "\n",
    "This notebook shows how to augment Llama-2 `LLM`s with the `Llama2Chat` wrapper to support the [Llama-2 chat prompt format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). Several `LLM` implementations in LangChain can be used as interface to Llama-2 chat models. These include [HuggingFaceTextGenInference](https://python.langchain.com/docs/integrations/llms/huggingface_textgen_inference), [LlamaCpp](https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa), [GPT4All](https://python.langchain.com/docs/integrations/llms/gpt4all), ..., to mention a few examples. \n",
    "\n",
@ -721,7 +731,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.11.4"
  }
 },
 "nbformat": 4,
--- a/docs/docs/integrations/chat/llama_api.ipynb
+++ b/docs/docs/integrations/chat/llama_api.ipynb
@ -1,11 +1,21 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "71b5cfca",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Llama API\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "90a1faf2",
   "metadata": {},
   "source": [
-    "# Llama API\n",
+    "# ChatLlamaAPI\n",
    "\n",
    "This notebook shows how to use LangChain with [LlamaAPI](https://llama-api.com/) - a hosted version of Llama2 that adds in support for function calling."
   ]
--- a/docs/docs/integrations/chat/minimax.ipynb
+++ b/docs/docs/integrations/chat/minimax.ipynb
@ -1,11 +1,20 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: MiniMax\n",
+    "---"
+   ]
+  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# MiniMax\n",
+    "# MiniMaxChat\n",
    "\n",
    "[Minimax](https://api.minimax.chat) is a Chinese startup that provides LLM service for companies and individuals.\n",
    "\n",
--- a/docs/docs/integrations/chat/ollama.ipynb
+++ b/docs/docs/integrations/chat/ollama.ipynb
@ -1,10 +1,19 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Ollama\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Ollama\n",
+    "# ChatOllama\n",
    "\n",
    "[Ollama](https://ollama.ai/) allows you to run open-source large language models, such as LLaMA2, locally.\n",
    "\n",
--- a/docs/docs/integrations/chat/ollama_functions.ipynb
+++ b/docs/docs/integrations/chat/ollama_functions.ipynb
@ -1,10 +1,19 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Ollama Functions\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Ollama Functions\n",
+    "# OllamaFunctions\n",
    "\n",
    "This notebook shows how to use an experimental wrapper around Ollama that gives it the same API as OpenAI Functions.\n",
    "\n",
--- a/docs/docs/integrations/chat/openai.ipynb
+++ b/docs/docs/integrations/chat/openai.ipynb
@ -1,11 +1,21 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: OpenAI\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "e49f1e0d",
   "metadata": {},
   "source": [
-    "# OpenAI\n",
+    "# ChatOpenAI\n",
    "\n",
    "This notebook covers how to get started with OpenAI chat models."
   ]
--- a/docs/docs/integrations/chat/pai_eas_chat_endpoint.ipynb
+++ b/docs/docs/integrations/chat/pai_eas_chat_endpoint.ipynb
@ -1,10 +1,19 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: AliCloud PAI EAS\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# AliCloud PAI EAS\n",
+    "# PaiEasChatEndpoint\n",
    "Machine Learning Platform for AI of Alibaba Cloud is a machine learning or deep learning engineering platform intended for enterprises and developers. It provides easy-to-use, cost-effective, high-performance, and easy-to-scale plug-ins that can be applied to various industry scenarios. With over 140 built-in optimization algorithms, Machine Learning Platform for AI provides whole-process AI engineering capabilities including data labeling (PAI-iTAG), model building (PAI-Designer and PAI-DSW), model training (PAI-DLC), compilation optimization, and inference deployment (PAI-EAS). PAI-EAS supports different types of hardware resources, including CPUs and GPUs, and features high throughput and low latency. It allows you to deploy large-scale complex models with a few clicks and perform elastic scale-ins and scale-outs in real time. It also provides a comprehensive O&M and monitoring system."
   ]
  },
--- a/docs/docs/integrations/chat/promptlayer_chatopenai.ipynb
+++ b/docs/docs/integrations/chat/promptlayer_chatopenai.ipynb
@ -1,12 +1,22 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "ce3672d3",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: PromptLayer ChatOpenAI\n",
+    "---"
+   ]
+  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "959300d4",
   "metadata": {},
   "source": [
-    "# PromptLayer ChatOpenAI\n",
+    "# PromptLayerChatOpenAI\n",
    "\n",
    "This example showcases how to connect to [PromptLayer](https://www.promptlayer.com) to start recording your ChatOpenAI requests."
   ]
--- a/docs/docs/integrations/chat/tongyi.ipynb
+++ b/docs/docs/integrations/chat/tongyi.ipynb
@ -1,5 +1,14 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Tongyi Qwen\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "metadata": {
@ -9,7 +18,7 @@
    }
   },
   "source": [
-    "# Tongyi Qwen\n",
+    "# ChatTongyi\n",
    "Tongyi Qwen is a large language model developed by Alibaba's Damo Academy. It is capable of understanding user intent through natural language understanding and semantic analysis, based on user input in natural language. It provides services and assistance to users in different domains and tasks. By providing clear and detailed instructions, you can obtain results that better align with your expectations.\n",
    "In this notebook, we will introduce how to use langchain with [Tongyi](https://www.aliyun.com/product/dashscope) mainly in `Chat` corresponding\n",
    " to the package `langchain/chat_models` in langchain"
@ -41,7 +50,7 @@
   },
   "outputs": [
    {
-     "name": "stdin",
+     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ········\n"
--- a/docs/docs/integrations/chat/vllm.ipynb
+++ b/docs/docs/integrations/chat/vllm.ipynb
@ -1,5 +1,15 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "eb65deaa",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: vLLM Chat\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "eb7e5679-aa06-47e4-a1a3-b6b70e604017",
--- a/docs/docs/integrations/chat/volcengine_maas.ipynb
+++ b/docs/docs/integrations/chat/volcengine_maas.ipynb
@ -1,5 +1,15 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "66107bdd",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Volc Enging Maas\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "404758628c7b20f6",
@ -7,7 +17,7 @@
    "collapsed": false
   },
   "source": [
-    "# Volc Engine Maas\n",
+    "# VolcEngineMaasChat\n",
    "\n",
    "This notebook provides you with a guide on how to get started with volc engine maas chat models."
   ]
@ -86,7 +96,9 @@
   "outputs": [
    {
     "data": {
-      "text/plain": "AIMessage(content='好的，这是一个笑话：\\n\\n为什么鸟儿不会玩电脑游戏？\\n\\n因为它们没有翅膀！')"
+      "text/plain": [
+       "AIMessage(content='好的，这是一个笑话：\\n\\n为什么鸟儿不会玩电脑游戏？\\n\\n因为它们没有翅膀！')"
+      ]
     },
     "execution_count": 26,
     "metadata": {},
@ -141,7 +153,9 @@
   "outputs": [
    {
     "data": {
-      "text/plain": "AIMessage(content='好的，这是一个笑话：\\n\\n三岁的女儿说她会造句了，妈妈让她用“年轻”造句，女儿说：“妈妈减肥，一年轻了好几斤”。')"
+      "text/plain": [
+       "AIMessage(content='好的，这是一个笑话：\\n\\n三岁的女儿说她会造句了，妈妈让她用“年轻”造句，女儿说：“妈妈减肥，一年轻了好几斤”。')"
+      ]
     },
     "execution_count": 28,
     "metadata": {},
--- a/docs/docs/integrations/chat/yandex.ipynb
+++ b/docs/docs/integrations/chat/yandex.ipynb
@ -1,11 +1,21 @@
 {
 "cells": [
+  {
+   "cell_type": "raw",
+   "id": "b4154fbe",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: YandexGPT\n",
+    "---"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "af63c9db-e4bd-4d3b-a4d7-7927f5541734",
   "metadata": {},
   "source": [
-    "# YandexGPT\n",
+    "# ChatYandexGPT\n",
    "\n",
    "This notebook goes over how to use Langchain with [YandexGPT](https://cloud.yandex.com/en/services/yandexgpt) chat model.\n",
    "\n",