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docs[minor]: Update bedrock llm integration doc (#6331)
* docs[minor]: Update bedrock llm integration doc * getEnvVar util func instead of Deno.env.get(...) * cr * cr
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‎deno.json

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"youtube-transcript": "npm:/youtube-transcript",
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"neo4j-driver": "npm:/neo4j-driver",
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"axios": "npm:/axios",
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"@mendable/firecrawl-js": "npm:/@mendable/firecrawl-js"
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"@mendable/firecrawl-js": "npm:/@mendable/firecrawl-js",
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"@aws-crypto/sha256-js": "npm:/@aws-crypto/sha256-js",
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"@aws-sdk/credential-provider-node": "npm:/@aws-sdk/credential-provider-node",
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"@smithy/protocol-http": "npm:/@smithy/protocol-http",
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"@smithy/signature-v4": "npm:/@smithy/signature-v4",
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"@smithy/eventstream-codec": "npm:/@smithy/eventstream-codec",
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"@smithy/util-utf8": "npm:/@smithy/util-utf8",
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"@aws-sdk/types": "npm:/@aws-sdk/types"
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}
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}

‎docs/core_docs/docs/integrations/llms/bedrock.ipynb

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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "67db2992",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Bedrock\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9597802c",
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"metadata": {},
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"source": [
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"# Bedrock\n",
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"\n",
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"```{=mdx}\n",
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"\n",
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":::caution\n",
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"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",
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"\n",
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"You may be looking for [this page instead](/docs/integrations/chat/bedrock/).\n",
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":::\n",
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"\n",
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"```\n",
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"\n",
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"> [Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes Foundation Models (FMs)\n",
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"> from leading AI startups and Amazon available via an API. You can choose from a wide range of FMs to find the model that is best suited for your use case.\n",
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"\n",
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"This will help you get started with Bedrock completion models (LLMs) using LangChain. For detailed documentation on `Bedrock` features and configuration options, please refer to the [API reference](https://api.js.langchain.com/classes/langchain_community_llms_bedrock.Bedrock.html).\n",
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"\n",
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"## Overview\n",
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"### Integration details\n",
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"\n",
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"- TODO: Fill in table features.\n",
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"- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
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"- TODO: Make sure API reference links are correct.\n",
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"\n",
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"| Class | Package | Local | Serializable | [PY support](https://python.langchain.com/docs/integrations/llms/bedrock) | Package downloads | Package latest |\n",
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"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
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"| [Bedrock](https://api.js.langchain.com/classes/langchain_community_llms_bedrock.Bedrock.html) | [@langchain/community](https://api.js.langchain.com/modules/langchain_community_llms_bedrock.html) | ❌ | ✅ | ✅ | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/community?style=flat-square&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/community?style=flat-square&label=%20&) |\n",
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"\n",
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"## Setup\n",
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"\n",
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"To access Bedrock models you'll need to create an AWS account, get an API key, and install the `@langchain/community` integration, along with a few peer dependencies.\n",
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"\n",
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"### Credentials\n",
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"\n",
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"Head to [aws.amazon.com](https://aws.amazon.com) to sign up to AWS Bedrock and generate an API key. Once you've done this set the environment variables:\n",
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"\n",
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"```bash\n",
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"export BEDROCK_AWS_REGION=\"your-region-url\"\n",
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"export BEDROCK_AWS_ACCESS_KEY_ID=\"your-access-key-id\"\n",
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"export BEDROCK_AWS_SECRET_ACCESS_KEY=\"your-secret-access-key\"\n",
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"```\n",
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"\n",
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"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:\n",
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"\n",
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"```bash\n",
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"# export LANGCHAIN_TRACING_V2=\"true\"\n",
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"# export LANGCHAIN_API_KEY=\"your-api-key\"\n",
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"```\n",
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"\n",
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"### Installation\n",
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"\n",
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"The LangChain Bedrock integration lives in the `@langchain/community` package:\n",
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"\n",
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"```{=mdx}\n",
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"import IntegrationInstallTooltip from \"@mdx_components/integration_install_tooltip.mdx\";\n",
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"import Npm2Yarn from \"@theme/Npm2Yarn\";\n",
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"\n",
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"<IntegrationInstallTooltip></IntegrationInstallTooltip>\n",
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"\n",
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"<Npm2Yarn>\n",
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" @langchain/community\n",
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"</Npm2Yarn>\n",
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"\n",
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"And install the peer dependencies:\n",
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"\n",
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"<Npm2Yarn>\n",
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" @aws-crypto/sha256-js @aws-sdk/credential-provider-node @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types\n",
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"</Npm2Yarn>\n",
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"\n",
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"You can also use Bedrock in web environments such as Edge functions or Cloudflare Workers by omitting the `@aws-sdk/credential-provider-node` dependency\n",
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"and using the `web` entrypoint:\n",
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"\n",
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"<Npm2Yarn>\n",
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" @aws-crypto/sha256-js @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types\n",
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"</Npm2Yarn>\n",
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"\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0a760037",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"Now we can instantiate our model object and generate chat completions:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "093ae37f",
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"metadata": {},
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"outputs": [],
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"source": [
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"// @lc-docs-hide-cell\n",
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"// Deno requires these imports, and way of loading env vars.\n",
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"// we don't want to expose in the docs.\n",
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"// Below this cell we have a typescript markdown codeblock with\n",
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"// the node code.\n",
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"import \"@aws-sdk/credential-provider-node\";\n",
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"import \"@smithy/protocol-http\";\n",
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"import \"@aws-crypto/sha256-js\";\n",
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"import \"@smithy/protocol-http\";\n",
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"import \"@smithy/signature-v4\";\n",
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"import \"@smithy/eventstream-codec\";\n",
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"import \"@smithy/util-utf8\";\n",
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"import \"@aws-sdk/types\";\n",
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"import { Bedrock } from \"@langchain/community/llms/bedrock\"\n",
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"import { getEnvironmentVariable } from \"@langchain/core/utils/env\";\n",
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"\n",
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"const llm = new Bedrock({\n",
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" model: \"anthropic.claude-v2\",\n",
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" region: \"us-east-1\",\n",
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" // endpointUrl: \"custom.amazonaws.com\",\n",
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" credentials: {\n",
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" accessKeyId: getEnvironmentVariable(\"BEDROCK_AWS_ACCESS_KEY_ID\"),\n",
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" secretAccessKey: getEnvironmentVariable(\"BEDROCK_AWS_SECRET_ACCESS_KEY\"),\n",
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" },\n",
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" temperature: 0,\n",
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" maxTokens: undefined,\n",
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" maxRetries: 2,\n",
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" // other params...\n",
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"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a0562a13",
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"metadata": {},
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"source": [
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"```typescript\n",
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"import { Bedrock } from \"@langchain/community/llms/bedrock\"\n",
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"\n",
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"const llm = new Bedrock({\n",
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" model: \"anthropic.claude-v2\",\n",
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" region: process.env.BEDROCK_AWS_REGION ?? \"us-east-1\",\n",
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" // endpointUrl: \"custom.amazonaws.com\",\n",
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" credentials: {\n",
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" accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID,\n",
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" secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY,\n",
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" },\n",
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" temperature: 0,\n",
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" maxTokens: undefined,\n",
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" maxRetries: 2,\n",
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" // other params...\n",
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"})\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0ee90032",
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"metadata": {},
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"source": [
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"## Invocation\n",
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"\n",
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"Note that some models require specific prompting techniques. For example, Anthropic's Claude-v2 model will throw an error if\n",
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"the prompt does not start with `Human: `."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "035dea0f",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\u001b[32m\" Here are a few key points about Bedrock AI:\\n\"\u001b[39m +\n",
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" \u001b[32m\"\\n\"\u001b[39m +\n",
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" \u001b[32m\"- Bedrock was founded in 2021 and is based in San Fran\"\u001b[39m... 116 more characters"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"const inputText = \"Human: Bedrock is an AI company that\\nAssistant: \"\n",
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"\n",
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"const completion = await llm.invoke(inputText)\n",
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"completion"
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]
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},
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{
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"cell_type": "markdown",
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"id": "add38532",
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"metadata": {},
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"source": [
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"## Chaining\n",
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"\n",
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"We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "078e9db2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"\u001b[32m' Here is how to say \"I love programming\" in German:\\n'\u001b[39m +\n",
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" \u001b[32m\"\\n\"\u001b[39m +\n",
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" \u001b[32m\"Ich liebe das Programmieren.\"\u001b[39m"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import { PromptTemplate } from \"@langchain/core/prompts\"\n",
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"\n",
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"const prompt = PromptTemplate.fromTemplate(\"Human: How to say {input} in {output_language}:\\nAssistant:\")\n",
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"\n",
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"const chain = prompt.pipe(llm);\n",
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"await chain.invoke(\n",
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" {\n",
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" output_language: \"German\",\n",
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" input: \"I love programming.\",\n",
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" }\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e9bdfcef",
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"metadata": {},
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"source": [
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"## API reference\n",
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"\n",
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"For detailed documentation of all Bedrock features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_community_llms_bedrock.Bedrock.html"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Deno",
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"language": "typescript",
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"name": "deno"
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},
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"language_info": {
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"file_extension": ".ts",
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"mimetype": "text/x.typescript",
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"name": "typescript",
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"nb_converter": "script",
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"pygments_lexer": "typescript",
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"version": "5.3.3"
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},
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"vscode": {
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"interpreter": {
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"hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎docs/core_docs/docs/integrations/llms/bedrock.mdx

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‎libs/langchain-scripts/src/cli/docs/templates/llms.ipynb

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"import { PromptTemplate } from \"@langchain/core/prompts\"\n",
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"\n",
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"const prompt = new PromptTemplate({\n",
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" template: \"How to say {input} in {output_language}:\\n\",\n",
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"})\n",
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"const prompt = PromptTemplate.fromTemplate(\"How to say {input} in {output_language}:\\n\")\n",
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"\n",
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"const chain = prompt.pipe(llm);\n",
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"await chain.invoke(\n",

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