docs[minor]: Update bedrock llm integration doc

deno.json

@@ -31,6 +31,13 @@
     "youtube-transcript": "npm:/youtube-transcript",
     "neo4j-driver": "npm:/neo4j-driver",
     "axios": "npm:/axios",
-    "@mendable/firecrawl-js": "npm:/@mendable/firecrawl-js"
+    "@mendable/firecrawl-js": "npm:/@mendable/firecrawl-js",
+    "@aws-crypto/sha256-js": "npm:/@aws-crypto/sha256-js",
+    "@aws-sdk/credential-provider-node": "npm:/@aws-sdk/credential-provider-node",
+    "@smithy/protocol-http": "npm:/@smithy/protocol-http",
+    "@smithy/signature-v4": "npm:/@smithy/signature-v4",
+    "@smithy/eventstream-codec": "npm:/@smithy/eventstream-codec",
+    "@smithy/util-utf8": "npm:/@smithy/util-utf8",
+    "@aws-sdk/types": "npm:/@aws-sdk/types"
   }
 }

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

@@ -0,0 +1,284 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "67db2992",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Bedrock\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9597802c",
+   "metadata": {},
+   "source": [
+    "# Bedrock\n",
+    "\n",
+    "```{=mdx}\n",
+    "\n",
+    ":::caution\n",
+    "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",
+    "\n",
+    "You may be looking for [this page instead](/docs/integrations/chat/bedrock/).\n",
+    ":::\n",
+    "\n",
+    "```\n",
+    "\n",
+    "> [Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes Foundation Models (FMs)\n",
+    "> 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",
+    "\n",
+    "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",
+    "\n",
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "- TODO: Fill in table features.\n",
+    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
+    "- TODO: Make sure API reference links are correct.\n",
+    "\n",
+    "| Class | Package | Local | Serializable | [PY support](https://python.langchain.com/docs/integrations/llms/bedrock) | Package downloads | Package latest |\n",
+    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
+    "| [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",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "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",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "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",
+    "\n",
+    "```bash\n",
+    "export BEDROCK_AWS_REGION=\"your-region-url\"\n",
+    "export BEDROCK_AWS_ACCESS_KEY_ID=\"your-access-key-id\"\n",
+    "export BEDROCK_AWS_SECRET_ACCESS_KEY=\"your-secret-access-key\"\n",
+    "```\n",
+    "\n",
+    "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",
+    "\n",
+    "```bash\n",
+    "# export LANGCHAIN_TRACING_V2=\"true\"\n",
+    "# export LANGCHAIN_API_KEY=\"your-api-key\"\n",
+    "```\n",
+    "\n",
+    "### Installation\n",
+    "\n",
+    "The LangChain Bedrock integration lives in the `@langchain/community` 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/community\n",
+    "</Npm2Yarn>\n",
+    "\n",
+    "And install the peer dependencies:\n",
+    "\n",
+    "<Npm2Yarn>\n",
+    "  @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",
+    "</Npm2Yarn>\n",
+    "\n",
+    "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",
+    "and using the `web` entrypoint:\n",
+    "\n",
+    "<Npm2Yarn>\n",
+    "  @aws-crypto/sha256-js @smithy/protocol-http @smithy/signature-v4 @smithy/eventstream-codec @smithy/util-utf8 @aws-sdk/types\n",
+    "</Npm2Yarn>\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a760037",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "093ae37f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "// @lc-docs-hide-cell\n",
+    "// Deno requires these imports, and way of loading env vars.\n",
+    "// we don't want to expose in the docs.\n",
+    "// Below this cell we have a typescript markdown codeblock with\n",
+    "// the node code.\n",
+    "import \"@aws-sdk/credential-provider-node\";\n",
+    "import \"@smithy/protocol-http\";\n",
+    "import \"@aws-crypto/sha256-js\";\n",
+    "import \"@smithy/protocol-http\";\n",
+    "import \"@smithy/signature-v4\";\n",
+    "import \"@smithy/eventstream-codec\";\n",
+    "import \"@smithy/util-utf8\";\n",
+    "import \"@aws-sdk/types\";\n",
+    "import { Bedrock } from \"@langchain/community/llms/bedrock\"\n",
+    "import { getEnvironmentVariable } from \"@langchain/core/utils/env\";\n",
+    "\n",
+    "const llm = new Bedrock({\n",
+    "  model: \"anthropic.claude-v2\",\n",
+    "  region: \"us-east-1\",\n",
+    "  // endpointUrl: \"custom.amazonaws.com\",\n",
+    "  credentials: {\n",
+    "    accessKeyId: getEnvironmentVariable(\"BEDROCK_AWS_ACCESS_KEY_ID\"),\n",
+    "    secretAccessKey: getEnvironmentVariable(\"BEDROCK_AWS_SECRET_ACCESS_KEY\"),\n",
+    "  },\n",
+    "  temperature: 0,\n",
+    "  maxTokens: undefined,\n",
+    "  maxRetries: 2,\n",
+    "  // other params...\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0562a13",
+   "metadata": {},
+   "source": [
+    "```typescript\n",
+    "import { Bedrock } from \"@langchain/community/llms/bedrock\"\n",
+    "\n",
+    "const llm = new Bedrock({\n",
+    "  model: \"anthropic.claude-v2\",\n",
+    "  region: process.env.BEDROCK_AWS_REGION ?? \"us-east-1\",\n",
+    "  // endpointUrl: \"custom.amazonaws.com\",\n",
+    "  credentials: {\n",
+    "    accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID,\n",
+    "    secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY,\n",
+    "  },\n",
+    "  temperature: 0,\n",
+    "  maxTokens: undefined,\n",
+    "  maxRetries: 2,\n",
+    "  // other params...\n",
+    "})\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ee90032",
+   "metadata": {},
+   "source": [
+    "## Invocation\n",
+    "\n",
+    "Note that some models require specific prompting techniques. For example, Anthropic's Claude-v2 model will throw an error if\n",
+    "the prompt does not start with `Human: `."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "035dea0f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[32m\" Here are a few key points about Bedrock AI:\\n\"\u001b[39m +\n",
+       "  \u001b[32m\"\\n\"\u001b[39m +\n",
+       "  \u001b[32m\"- Bedrock was founded in 2021 and is based in San Fran\"\u001b[39m... 116 more characters"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "const inputText = \"Human: Bedrock is an AI company that\\nAssistant: \"\n",
+    "\n",
+    "const completion = await llm.invoke(inputText)\n",
+    "completion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "add38532",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "078e9db2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[32m' Here is how to say \"I love programming\" in German:\\n'\u001b[39m +\n",
+       "  \u001b[32m\"\\n\"\u001b[39m +\n",
+       "  \u001b[32m\"Ich liebe das Programmieren.\"\u001b[39m"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import { PromptTemplate } from \"@langchain/core/prompts\"\n",
+    "\n",
+    "const prompt = PromptTemplate.fromTemplate(\"Human: How to say {input} in {output_language}:\\nAssistant:\")\n",
+    "\n",
+    "const chain = prompt.pipe(llm);\n",
+    "await chain.invoke(\n",
+    "  {\n",
+    "    output_language: \"German\",\n",
+    "    input: \"I love programming.\",\n",
+    "  }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9bdfcef",
+   "metadata": {},
+   "source": [
+    "## API reference\n",
+    "\n",
+    "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"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Deno",
+   "language": "typescript",
+   "name": "deno"
+  },
+  "language_info": {
+   "file_extension": ".ts",
+   "mimetype": "text/x.typescript",
+   "name": "typescript",
+   "nb_converter": "script",
+   "pygments_lexer": "typescript",
+   "version": "5.3.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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

@@ -160,9 +160,7 @@
    "source": [
     "import { PromptTemplate } from \"@langchain/core/prompts\"\n",
     "\n",
-    "const prompt = new PromptTemplate({\n",
-    "  template: \"How to say {input} in {output_language}:\\n\",\n",
-    "})\n",
+    "const prompt = PromptTemplate.fromTemplate(\"How to say {input} in {output_language}:\\n\")\n",
     "\n",
     "const chain = prompt.pipe(llm);\n",
     "await chain.invoke(\n",