docs: add contextualai documentation

docs/docs/integrations/chat/contextual.ipynb

@@ -0,0 +1,253 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "---\n",
+    "sidebar_label: ContextualAI\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatContextual\n",
+    "\n",
+    "This will help you getting started with Contextual AI's Grounded Language Model [chat models](/docs/concepts/chat_models/).\n",
+    "\n",
+    "To learn more about Contextual AI, please visit our [documentation](https://docs.contextual.ai/).\n",
+    "\n",
+    "This integration requires the `contextual-client` Python SDK. Learn more about it [here](https://github.com/ContextualAI/contextual-client-python).\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This integration invokes Contextual AI's Grounded Language Model.\n",
+    "\n",
+    "### Integration details\n",
+    "\n",
+    "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [ChatContextual](https://python.langchain.com/api_reference/en/latest/chat_models/langchain_contextual.chat_models.ChatContextual.html) | [langchain-contextual](https://python.langchain.com/api_reference/en/latest/contextual_api_reference.html) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-contextual?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-contextual?style=flat-square&label=%20) |\n",
+    "\n",
+    "### Model features\n",
+    "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
+    "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: | :---: |\n",
+    "| ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | \n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access Contextual models you'll need to create a/an Contextual AI account, get an API key, and install the `langchain-contextual` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [app.contextual.ai](https://app.contextual.ai) to sign up to Contextual and generate an API key. Once you've done this set the CONTEXTUAL_AI_API_KEY environment variable:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"CONTEXTUAL_AI_API_KEY\"):\n",
+    "    os.environ[\"CONTEXTUAL_AI_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your Contextual API key: \"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ee0c4b-9764-423a-9dbf-95129e185210",
+   "metadata": {},
+   "source": [
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0730d6a1-c893-4840-9817-5e5251676d5d",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
+    "\n",
+    "The LangChain Contextual integration lives in the `langchain-contextual` package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "652d6238-1f87-422a-b135-f5abbb8652fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-contextual"
+   ]
+  },
+  {
+   "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",
+    "\n",
+    "The chat client can be instantiated with these following additional settings:\n",
+    "\n",
+    "| Parameter | Type | Description | Default |\n",
+    "|-----------|------|-------------|---------|\n",
+    "| temperature | Optional[float] | The sampling temperature, which affects the randomness in the response. Note that higher temperature values can reduce groundedness. | 0 |\n",
+    "| top_p | Optional[float] | A parameter for nucleus sampling, an alternative to temperature which also affects the randomness of the response. Note that higher top_p values can reduce groundedness. | 0.9 |\n",
+    "| max_new_tokens | Optional[int] | The maximum number of tokens that the model can generate in the response. Minimum is 1 and maximum is 2048. | 1024 |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_contextual import ChatContextual\n",
+    "\n",
+    "llm = ChatContextual(\n",
+    "    model=\"v1\",  # defaults to `v1`\n",
+    "    api_key=\"\",\n",
+    "    temperature=0,  # defaults to 0\n",
+    "    top_p=0.9,  # defaults to 0.9\n",
+    "    max_new_tokens=1024,  # defaults to 1024\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "The Contextual Grounded Language Model accepts additional `kwargs` when calling the `ChatContextual.invoke` method.\n",
+    "\n",
+    "These additional inputs are:\n",
+    "\n",
+    "| Parameter | Type | Description |\n",
+    "|-----------|------|-------------|\n",
+    "| knowledge | list[str] | Required: A list of strings of knowledge sources the grounded language model can use when generating a response. |\n",
+    "| system_prompt | Optional[str] | Optional: Instructions the model should follow when generating responses. Note that we do not guarantee that the model follows these instructions exactly. |\n",
+    "| avoid_commentary | Optional[bool] | Optional (Defaults to `False`): Flag to indicate whether the model should avoid providing additional commentary in responses. Commentary is conversational in nature and does not contain verifiable claims; therefore, commentary is not strictly grounded in available context. However, commentary may provide useful context which improves the helpfulness of responses. |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# include a system prompt (optional)\n",
+    "system_prompt = \"You are a helpful assistant that uses all of the provided knowledge to answer the user's query to the best of your ability.\"\n",
+    "\n",
+    "# provide your own knowledge from your knowledge-base here in an array of string\n",
+    "knowledge = [\n",
+    "    \"There are 2 types of dogs in the world: good dogs and best dogs.\",\n",
+    "    \"There are 2 types of cats in the world: good cats and best cats.\",\n",
+    "]\n",
+    "\n",
+    "# create your message\n",
+    "messages = [\n",
+    "    (\"human\", \"What type of cats are there in the world and what are the types?\"),\n",
+    "]\n",
+    "\n",
+    "# invoke the GLM by providing the knowledge strings, optional system prompt\n",
+    "# if you want to turn off the GLM's commentary, pass True to the `avoid_commentary` argument\n",
+    "ai_msg = llm.invoke(\n",
+    "    messages, knowledge=knowledge, system_prompt=system_prompt, avoid_commentary=True\n",
+    ")\n",
+    "\n",
+    "print(ai_msg.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c35a9e0",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can chain the Contextual Model with output parsers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "545e1e16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "chain = llm | StrOutputParser\n",
+    "\n",
+    "chain.invoke(\n",
+    "    messages, knowledge=knowledge, systemp_prompt=system_prompt, avoid_commentary=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "For detailed documentation of all ChatContextual features and configurations head to the API reference: https://python.langchain.com/api_reference/en/latest/chat_models/langchain_contextual.chat_models.ChatContextual.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/providers/contextual.ipynb

@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Contextual AI\n",
+    "\n",
+    "Contextual AI is a platform that offers state-of-the-art Retrieval-Augmented Generation (RAG) technology for enterprise applications. Our platformand models helps innovative teams build production-ready AI applications that can process millions of pages of documents with exceptional accuracy.\n",
+    "\n",
+    "## Grounded Language Model (GLM)\n",
+    "\n",
+    "The Grounded Language Model (GLM), specifically engineered to minimize hallucinations in RAG and agentic applications. The GLM achieves:\n",
+    "\n",
+    "- State-of-the-art performance on the FACTS benchmark\n",
+    "- Responses strictly grounded in provided knowledge sources\n",
+    "\n",
+    "## Using Contextual AI with LangChain\n",
+    "\n",
+    "See details [here](/docs/integrations/chat/contextual).\n",
+    "\n",
+    "This integration allows you to easily incorporate Contextual AI's GLM into your LangChain workflows. Whether you're building applications for regulated industries or security-conscious environments, Contextual AI provides the grounded and reliable responses your use cases demand.\n",
+    "\n",
+    "Get started with a free trial today and experience the most grounded language model for enterprise AI applications."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "y8ku6X96sebl"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "According to the information available, there are two types of cats in the world:\n",
+      "\n",
+      "1. Good cats\n",
+      "2. Best cats\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "from langchain_contextual import ChatContextual\n",
+    "\n",
+    "# Set credentials\n",
+    "if not os.getenv(\"CONTEXTUAL_AI_API_KEY\"):\n",
+    "    os.environ[\"CONTEXTUAL_AI_API_KEY\"] = getpass.getpass(\n",
+    "        \"Enter your Contextual API key: \"\n",
+    "    )\n",
+    "\n",
+    "# intialize Contextual llm\n",
+    "llm = ChatContextual(\n",
+    "    model=\"v1\",\n",
+    "    api_key=\"\",\n",
+    ")\n",
+    "# include a system prompt (optional)\n",
+    "system_prompt = \"You are a helpful assistant that uses all of the provided knowledge to answer the user's query to the best of your ability.\"\n",
+    "\n",
+    "# provide your own knowledge from your knowledge-base here in an array of string\n",
+    "knowledge = [\n",
+    "    \"There are 2 types of dogs in the world: good dogs and best dogs.\",\n",
+    "    \"There are 2 types of cats in the world: good cats and best cats.\",\n",
+    "]\n",
+    "\n",
+    "# create your message\n",
+    "messages = [\n",
+    "    (\"human\", \"What type of cats are there in the world and what are the types?\"),\n",
+    "]\n",
+    "\n",
+    "# invoke the GLM by providing the knowledge strings, optional system prompt\n",
+    "# if you want to turn off the GLM's commentary, pass True to the `avoid_commentary` argument\n",
+    "ai_msg = llm.invoke(\n",
+    "    messages, knowledge=knowledge, system_prompt=system_prompt, avoid_commentary=True\n",
+    ")\n",
+    "\n",
+    "print(ai_msg.content)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

libs/packages.yml

@@ -495,7 +495,14 @@ packages:
   downloads_updated_at: '2025-03-09T00:14:26.697616+00:00'
 - name: ads4gpts-langchain
   name_title: ADS4GPTs
+  provider_page: ads4gpts
   path: libs/python-sdk/ads4gpts-langchain
   repo: ADS4GPTs/ads4gpts
   downloads: 733
   downloads_updated_at: '2025-03-09T00:15:16.651181+00:00'
+- name: langchain-contextual
+  name_title: Contextual AI
+  path: langchain-contextual
+  repo: ContextualAI//langchain-contextual
+  downloads: 432
+  downloads_updated_at: '2025-03-09T01:40:49.430540+00:00'