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docs: add contextualai documentation (#30050)
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 <[email protected]>
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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 | ❌ |  |  |\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" | ||
| ] | ||
| } | ||
| ], | ||
| "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 | ||
| } |
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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)" | ||
| ] | ||
| } | ||
| ], | ||
| "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 | ||
| } |
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