docs: Adds pointers from LLM pages to equivalent chat model pages (#22759)

@baskaryan

docs/docs/integrations/llms/anthropic.ipynb

@@ -3,10 +3,15 @@
   {
    "cell_type": "raw",
    "id": "602a52a4",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
    "source": [
     "---\n",
     "sidebar_label: Anthropic\n",
+    "sidebar_class_name: hidden\n",
     "---"
    ]
   },
@@ -17,9 +22,13 @@
    "source": [
     "# AnthropicLLM\n",
     "\n",
-    "This example goes over how to use LangChain to interact with `Anthropic` models.\n",
+    ":::caution\n",
+    "You are currently on a page documenting the use of Anthropic legacy Claude 2 models as [text completion models](/docs/concepts/#llms). The latest and most popular Anthropic models are [chat completion models](/docs/concepts/#chat-models).\n",
     "\n",
-    "NOTE: AnthropicLLM only supports legacy Claude 2 models. To use the newest Claude 3 models, please use [`ChatAnthropic`](/docs/integrations/chat/anthropic) instead.\n",
+    "You are probably looking for [this page instead](/docs/integrations/chat/anthropic/).\n",
+    ":::\n",
+    "\n",
+    "This example goes over how to use LangChain to interact with `Anthropic` models.\n",
     "\n",
     "## Installation"
    ]

docs/docs/integrations/llms/bedrock.ipynb

@@ -11,6 +11,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    ":::caution\n",
+    "You are currently on a page documenting the use of Amazon Bedrock models as [text completion models](/docs/concepts/#llms). Many popular models available on Bedrock are [chat completion models](/docs/concepts/#chat-models).\n",
+    "\n",
+    "You may be looking for [this page instead](/docs/integrations/chat/bedrock/).\n",
+    ":::\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",
     "> `Meta`, `Stability AI`, and `Amazon` via a single API, along with a broad set of capabilities you need to \n",

docs/docs/integrations/llms/cohere.ipynb

@@ -7,6 +7,12 @@
    "source": [
     "# Cohere\n",
     "\n",
+    ":::caution\n",
+    "You are currently on a page documenting the use of Cohere models as [text completion models](/docs/concepts/#llms). Many popular Cohere models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "\n",
+    "You may be looking for [this page instead](/docs/integrations/chat/cohere/).\n",
+    ":::\n",
+    "\n",
     ">[Cohere](https://cohere.ai/about) is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.\n",
     "\n",
     "Head to the [API reference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.cohere.Cohere.html) for detailed documentation of all attributes and methods."

docs/docs/integrations/llms/fireworks.ipynb

@@ -7,6 +7,12 @@
    "source": [
     "# Fireworks\n",
     "\n",
+    ":::caution\n",
+    "You are currently on a page documenting the use of Fireworks models as [text completion models](/docs/concepts/#llms). Many popular Fireworks models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "\n",
+    "You may be looking for [this page instead](/docs/integrations/chat/fireworks/).\n",
+    ":::\n",
+    "\n",
     ">[Fireworks](https://app.fireworks.ai/) accelerates product development on generative AI by creating an innovative AI experiment and production platform. \n",
     "\n",
     "This example goes over how to use LangChain to interact with `Fireworks` models."

docs/docs/integrations/llms/google_ai.ipynb

@@ -25,6 +25,12 @@
    "id": "bead5ede-d9cc-44b9-b062-99c90a10cf40",
    "metadata": {},
    "source": [
+    ":::caution\n",
+    "You are currently on a page documenting the use of Google models as [text completion models](/docs/concepts/#llms). Many popular Google models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "\n",
+    "You may be looking for [this page instead](/docs/integrations/chat/google_generative_ai/).\n",
+    ":::\n",
+    "\n",
     "A guide on using [Google Generative AI](https://developers.generativeai.google/) models with Langchain. Note: It's separate from Google Cloud Vertex AI [integration](/docs/integrations/llms/google_vertex_ai_palm)."
    ]
   },

docs/docs/integrations/llms/google_vertex_ai_palm.ipynb

@@ -15,6 +15,12 @@
    "source": [
     "# Google Cloud Vertex AI\n",
     "\n",
+    ":::caution\n",
+    "You are currently on a page documenting the use of Google Vertex [text completion models](/docs/concepts/#llms). Many Google models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "\n",
+    "You may be looking for [this page instead](/docs/integrations/chat/google_vertex_ai_palm/).\n",
+    ":::\n",
+    "\n",
     "**Note:** This is separate from the `Google Generative AI` integration, it exposes [Vertex AI Generative API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) on `Google Cloud`.\n",
     "\n",
     "VertexAI exposes all foundational models available in google cloud:\n",

docs/docs/integrations/llms/ollama.ipynb

@@ -6,6 +6,12 @@
    "source": [
     "# Ollama\n",
     "\n",
+    ":::caution\n",
+    "You are currently on a page documenting the use of Ollama models as [text completion models](/docs/concepts/#llms). Many popular Ollama models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "\n",
+    "You may be looking for [this page instead](/docs/integrations/chat/ollama/).\n",
+    ":::\n",
+    "\n",
     "[Ollama](https://ollama.ai/) allows you to run open-source large language models, such as Llama 2, locally.\n",
     "\n",
     "Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. \n",

docs/docs/integrations/llms/together.ipynb

@@ -7,6 +7,12 @@
    "source": [
     "# Together AI\n",
     "\n",
+    ":::caution\n",
+    "You are currently on a page documenting the use of Together AI models as [text completion models](/docs/concepts/#llms). Many popular Together AI models are [chat completion models](/docs/concepts/#chat-models).\n",
+    "\n",
+    "You may be looking for [this page instead](/docs/integrations/chat/together/).\n",
+    ":::\n",
+    "\n",
     "[Together AI](https://www.together.ai/) offers an API to query [50+ leading open-source models](https://docs.together.ai/docs/inference-models) in a couple lines of code.\n",
     "\n",
     "This example goes over how to use LangChain to interact with Together AI models."

docs/docs/integrations/platforms/anthropic.mdx

@@ -14,6 +14,18 @@ pip install -U langchain-anthropic
 You need to set the `ANTHROPIC_API_KEY` environment variable.
 You can get an Anthropic API key [here](https://console.anthropic.com/settings/keys)
 
+## Chat Models
+
+### ChatAnthropic
+
+See a [usage example](/docs/integrations/chat/anthropic).
+
+```python
+from langchain_anthropic import ChatAnthropic
+
+model = ChatAnthropic(model='claude-3-opus-20240229')
+```
+
 ## LLMs
 
 ### [Legacy] AnthropicLLM
@@ -28,17 +40,3 @@ from langchain_anthropic import AnthropicLLM
 
 model = AnthropicLLM(model='claude-2.1')
 ```
-
-## Chat Models
-
-### ChatAnthropic
-
-See a [usage example](/docs/integrations/chat/anthropic).
-
-```python
-from langchain_anthropic import ChatAnthropic
-
-model = ChatAnthropic(model='claude-3-opus-20240229')
-```
-
-

docs/docs/integrations/platforms/google.mdx

@@ -2,45 +2,10 @@
 
 All functionality related to [Google Cloud Platform](https://cloud.google.com/) and other `Google` products.
 
-## LLMs
+## Chat models
 
 We recommend individual developers to start with Gemini API (`langchain-google-genai`) and move to Vertex AI (`langchain-google-vertexai`) when they need access to commercial support and higher rate limits. If you’re already Cloud-friendly or Cloud-native, then you can get started in Vertex AI straight away.
-Please, find more information [here](https://ai.google.dev/gemini-api/docs/migrate-to-cloud).
+Please see [here](https://ai.google.dev/gemini-api/docs/migrate-to-cloud) for more information.
-
-### Google Generative AI
-
-Access GoogleAI `Gemini` models such as `gemini-pro` and `gemini-pro-vision` through the `GoogleGenerativeAI` class.
-
-Install python package.
-
-```bash
-pip install langchain-google-genai
-```
-
-See a [usage example](/docs/integrations/llms/google_ai).
-
-```python
-from langchain_google_genai import GoogleGenerativeAI
-```
-
-### Vertex AI Model Garden
-
-Access `PaLM` and hundreds of OSS models via `Vertex AI Model Garden` service.
-
-We need to install `langchain-google-vertexai` python package.
-
-```bash
-pip install langchain-google-vertexai
-```
-
-See a [usage example](/docs/integrations/llms/google_vertex_ai_palm#vertex-model-garden).
-
-```python
-from langchain_google_vertexai import VertexAIModelGarden
-```
-
-
-## Chat models
 
 ### Google Generative AI
 
@@ -107,6 +72,40 @@ See a [usage example](/docs/integrations/chat/google_vertex_ai_palm).
 from langchain_google_vertexai import ChatVertexAI
 ```
 
+## LLMs
+
+### Google Generative AI
+
+Access GoogleAI `Gemini` models such as `gemini-pro` and `gemini-pro-vision` through the `GoogleGenerativeAI` class.
+
+Install python package.
+
+```bash
+pip install langchain-google-genai
+```
+
+See a [usage example](/docs/integrations/llms/google_ai).
+
+```python
+from langchain_google_genai import GoogleGenerativeAI
+```
+
+### Vertex AI Model Garden
+
+Access `PaLM` and hundreds of OSS models via `Vertex AI Model Garden` service.
+
+We need to install `langchain-google-vertexai` python package.
+
+```bash
+pip install langchain-google-vertexai
+```
+
+See a [usage example](/docs/integrations/llms/google_vertex_ai_palm#vertex-model-garden).
+
+```python
+from langchain_google_vertexai import VertexAIModelGarden
+```
+
 ## Embedding models
 
 ### Google Generative AI Embeddings