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docs: add contextualai documentation (#30050)

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Thank you for contributing to LangChain!
 
**Description:** adds ContextualAI's `langchain-contextual` package's
documentation

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>
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docs/docs/integrations/chat/contextual.ipynb Normal file
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{
"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://github.com/ContextualAI//langchain-contextual) | [langchain-contextual](https://pypi.org/project/langchain-contextual/) | ❌ | 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 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 Github page: https://github.com/ContextualAI//langchain-contextual"
]
}
],
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"display_name": ".venv",
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}

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{
"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 platformant 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) is 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)"
]
}
],
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7
libs/packages.yml
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@ -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'