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docs[patch]: Update AWS Kendra docs (#6380)
* Update AWS Kendra docs * Update kendra-retriever.ipynb
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docs/core_docs/docs/integrations/retrievers/kendra-retriever.ipynb
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "raw", | ||
| "id": "afaf8039", | ||
| "metadata": { | ||
| "vscode": { | ||
| "languageId": "raw" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "---\n", | ||
| "sidebar_label: Amazon Kendra Retriever\n", | ||
| "---" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "e49f1e0d", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# AWSKendraRetriever\n", | ||
| "\n", | ||
| "## Overview\n", | ||
| "\n", | ||
| "[Amazon Kendra](https://aws.amazon.com/kendra/) is an intelligent search service provided by Amazon Web Services (AWS).\n", | ||
| "It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization.\n", | ||
| "Kendra is designed to help users find the information they need quickly and accurately, improving productivity and decision-making.\n", | ||
| "\n", | ||
| "With Kendra, users can search across a wide range of content types, including documents, FAQs, knowledge bases, manuals, and websites.\n", | ||
| "It supports multiple languages and can understand complex queries, synonyms, and contextual meanings to provide highly relevant search results.\n", | ||
| "\n", | ||
| "This will help you getting started with the Amazon Kendra [`retriever`](/docs/concepts/#retrievers). For detailed documentation of all `AWSKendraRetriever` features and configurations head to the [API reference](https://api.js.langchain.com/classes/langchain_aws.AmazonKendraRetriever.html).\n", | ||
| "\n", | ||
| "### Integration details\n", | ||
| "\n", | ||
| "| Retriever | Source | Package |\n", | ||
| "| :--- | :--- | :---: |\n", | ||
| "[AWSKendraRetriever](https://api.js.langchain.com/classes/langchain_aws.AmazonKendraRetriever.html) | Various AWS resources | [`@langchain/aws`](https://www.npmjs.com/package/@langchain/aws) |\n", | ||
| "\n", | ||
| "## Setup\n", | ||
| "\n", | ||
| "You'll need an AWS account and an Amazon Kendra instance to get started. See this [tutorial](https://docs.aws.amazon.com/kendra/latest/dg/getting-started.html) from AWS for more information.\n", | ||
| "\n", | ||
| "If you want to get automated tracing from individual queries, you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:\n", | ||
| "\n", | ||
| "```typescript\n", | ||
| "// process.env.LANGSMITH_API_KEY = \"<YOUR API KEY HERE>\";\n", | ||
| "// process.env.LANGSMITH_TRACING = \"true\";\n", | ||
| "```\n", | ||
| "\n", | ||
| "### Installation\n", | ||
| "\n", | ||
| "This retriever lives in the `@langchain/aws` package:\n", | ||
| "\n", | ||
| "```{=mdx}\n", | ||
| "import IntegrationInstallTooltip from \"@mdx_components/integration_install_tooltip.mdx\";\n", | ||
| "import Npm2Yarn from \"@theme/Npm2Yarn\";\n", | ||
| "\n", | ||
| "<IntegrationInstallTooltip></IntegrationInstallTooltip>\n", | ||
| "\n", | ||
| "<Npm2Yarn>\n", | ||
| " @langchain/aws\n", | ||
| "</Npm2Yarn>\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "a38cde65-254d-4219-a441-068766c0d4b5", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Instantiation\n", | ||
| "\n", | ||
| "Now we can instantiate our retriever:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "70cc8e65-2a02-408a-bbc6-8ef649057d82", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import { AmazonKendraRetriever } from \"@langchain/aws\";\n", | ||
| "\n", | ||
| "const retriever = new AmazonKendraRetriever({\n", | ||
| " topK: 10,\n", | ||
| " indexId: \"YOUR_INDEX_ID\",\n", | ||
| " region: \"us-east-2\", // Your region\n", | ||
| " clientOptions: {\n", | ||
| " credentials: {\n", | ||
| " accessKeyId: \"YOUR_ACCESS_KEY_ID\",\n", | ||
| " secretAccessKey: \"YOUR_SECRET_ACCESS_KEY\",\n", | ||
| " },\n", | ||
| " },\n", | ||
| "});" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "5c5f2839-4020-424e-9fc9-07777eede442", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Usage" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "51a60dbe-9f2e-4e04-bb62-23968f17164a", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "const query = \"...\"\n", | ||
| "\n", | ||
| "await retriever.invoke(query);" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "dfe8aad4-8626-4330-98a9-7ea1ca5d2e0e", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Use within a chain\n", | ||
| "\n", | ||
| "Like other retrievers, __module_name__ can be incorporated into LLM applications via [chains](/docs/how_to/sequence/).\n", | ||
| "\n", | ||
| "We will need a LLM or chat model:\n", | ||
| "\n", | ||
| "```{=mdx}\n", | ||
| "import ChatModelTabs from \"@theme/ChatModelTabs\";\n", | ||
| "\n", | ||
| "<ChatModelTabs customVarName=\"llm\" />\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "25b647a3-f8f2-4541-a289-7a241e43f9df", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "// @ls-docs-hide-cell\n", | ||
| "\n", | ||
| "import { ChatOpenAI } from \"@langchain/openai\";\n", | ||
| "\n", | ||
| "const llm = new ChatOpenAI({\n", | ||
| " model: \"gpt-4o-mini\",\n", | ||
| " temperature: 0,\n", | ||
| "});" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "23e11cc9-abd6-4855-a7eb-799f45ca01ae", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import { ChatPromptTemplate } from \"@langchain/core/prompts\";\n", | ||
| "import { RunnablePassthrough, RunnableSequence } from \"@langchain/core/runnables\";\n", | ||
| "import { StringOutputParser } from \"@langchain/core/output_parsers\";\n", | ||
| "\n", | ||
| "import type { Document } from \"@langchain/core/documents\";\n", | ||
| "\n", | ||
| "const prompt = ChatPromptTemplate.fromTemplate(`\n", | ||
| "Answer the question based only on the context provided.\n", | ||
| "\n", | ||
| "Context: {context}\n", | ||
| "\n", | ||
| "Question: {question}`);\n", | ||
| "\n", | ||
| "const formatDocs = (docs: Document[]) => {\n", | ||
| " return docs.map((doc) => doc.pageContent).join(\"\\n\\n\");\n", | ||
| "}\n", | ||
| "\n", | ||
| "// See https://js.langchain.com/v0.2/docs/tutorials/rag\n", | ||
| "const ragChain = RunnableSequence.from([\n", | ||
| " {\n", | ||
| " context: retriever.pipe(formatDocs),\n", | ||
| " question: new RunnablePassthrough(),\n", | ||
| " },\n", | ||
| " prompt,\n", | ||
| " llm,\n", | ||
| " new StringOutputParser(),\n", | ||
| "]);" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "d47c37dd-5c11-416c-a3b6-bec413cd70e8", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "await ragChain.invoke(query);" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## API reference\n", | ||
| "\n", | ||
| "For detailed documentation of all `AmazonKendraRetriever` features and configurations head to the [API reference](https://api.js.langchain.com/classes/langchain_aws.AmazonKendraRetriever.html)." | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "TypeScript", | ||
| "language": "typescript", | ||
| "name": "tslab" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "typescript", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.10.4" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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docs/core_docs/docs/integrations/retrievers/kendra-retriever.mdx
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