langchain-aws[major]: LangChain AWS package (#5907)

* langchain-aws[major]: LangChain AWS package

* cr

* implemented code and tests

* refactor utils/types

* implemented streaming

* implemented bindTools

* implemented streaming tool calls

* tool_choice test

* getLsParams and standard tests

* final

* add guardrails

* docs

* chore: lint files

* cr

* export types file

* cr

docs/core_docs/docs/integrations/chat/bedrock.mdx

@@ -9,6 +9,10 @@ sidebar_label: Bedrock
 
 ## Setup
 
+:::tip
+The [`ChatBedrockConverse` chat model is now available via `@langchain/aws`](/docs/integrations/chat/bedrock_converse). Access tool calling with more models with this package.
+:::
+
 You'll need to install the `@langchain/community` package:
 
 import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";

docs/core_docs/docs/integrations/chat/bedrock_converse.mdx

@@ -0,0 +1,77 @@
+---
+sidebar_label: Bedrock Converse
+---
+
+# ChatBedrockConverse
+
+> [Amazon Bedrock Converse](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html) is a fully managed service that makes Foundation Models (FMs)
+> 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.
+
+## Setup
+
+You'll need to install the `@langchain/aws` package:
+
+import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
+
+<IntegrationInstallTooltip></IntegrationInstallTooltip>
+
+```bash npm2yarn
+npm install @langchain/aws
+```
+
+## Usage
+
+import UnifiedModelParamsTooltip from "@mdx_components/unified_model_params_tooltip.mdx";
+
+<UnifiedModelParamsTooltip></UnifiedModelParamsTooltip>
+
+import CodeBlock from "@theme/CodeBlock";
+import BedrockConverseExample from "@examples/models/chat/integration_bedrock_converse.ts";
+
+<CodeBlock language="typescript">{BedrockConverseExample}</CodeBlock>
+
+:::tip
+See the LangSmith traces for the above example [here](https://smith.langchain.com/public/7aeb0c56-9afa-441d-8659-4d52c007eae0/r), and [here for steaming](https://smith.langchain.com/public/74606291-45cd-478c-a874-568b2905427f/r).
+:::
+
+## Multimodal inputs
+
+:::tip
+Multimodal inputs are currently only supported by Anthropic Claude-3 models.
+:::
+
+Anthropic Claude-3 models hosted on Bedrock have multimodal capabilities and can reason about images. Here's an example:
+
+import BedrockMultimodalExample from "@examples/models/chat/integration_bedrock_multimodal_converse.ts";
+
+<CodeBlock language="typescript">{BedrockMultimodalExample}</CodeBlock>
+
+:::tip
+See the LangSmith trace [here](https://smith.langchain.com/public/c40f8d09-123a-4b3b-934a-625d5ee0f57a/r).
+:::
+
+## Tool calling
+
+The examples below demonstrate how to use tool calling, along with the `withStructuredOutput` method to easily compose structured output LLM calls.
+
+import ToolCalling from "@examples/models/chat/integration_bedrock_tools_converse.ts";
+
+<CodeBlock language="typescript">{ToolCalling}</CodeBlock>
+
+Check out the output of this tool call! We can see here it's using chain-of-thought before calling the tool, where it describes what it's going to do in plain text before calling the tool: `Okay, let's get the weather for New York City.`.
+
+:::tip
+See the LangSmith trace [here](https://smith.langchain.com/public/d34e378d-5044-4b5b-9ed7-3d2486fe5d47/r)
+:::
+
+### `.withStructuredOutput({ ... })`
+
+Using the `.withStructuredOutput` method, you can easily make the LLM return structured output, given only a Zod or JSON schema:
+
+import WSOExample from "@examples/models/chat/integration_bedrock_wso_converse.ts";
+
+<CodeBlock language="typescript">{WSOExample}</CodeBlock>
+
+:::tip
+See the LangSmith trace [here](https://smith.langchain.com/public/982940c4-5f96-4168-80c9-99102c3e073a/r)
+:::

examples/package.json

@@ -34,6 +34,7 @@
     "@gomomento/sdk": "^1.51.1",
     "@google/generative-ai": "^0.7.0",
     "@langchain/anthropic": "workspace:*",
+    "@langchain/aws": "workspace:*",
     "@langchain/azure-dynamic-sessions": "workspace:^",
     "@langchain/azure-openai": "workspace:*",
     "@langchain/cloudflare": "workspace:*",

examples/src/models/chat/integration_bedrock_converse.ts

@@ -0,0 +1,65 @@
+import { ChatBedrockConverse } from "@langchain/aws";
+import { HumanMessage } from "@langchain/core/messages";
+
+const model = new ChatBedrockConverse({
+  model: "anthropic.claude-3-sonnet-20240229-v1:0",
+  region: "us-east-1",
+  credentials: {
+    accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!,
+    secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!,
+  },
+});
+
+const res = await model.invoke([
+  new HumanMessage({ content: "Tell me a joke" }),
+]);
+console.log(res);
+
+/*
+AIMessage {
+  content: "Here's a joke for you:\n" +
+    '\n' +
+    "Why can't a bicycle stand up by itself? Because it's two-tired!",
+  response_metadata: { ... },
+  id: '08afa4fb-c212-4c1e-853a-d854972bec78',
+  usage_metadata: { input_tokens: 11, output_tokens: 28, total_tokens: 39 }
+}
+*/
+
+const stream = await model.stream([
+  new HumanMessage({ content: "Tell me a joke" }),
+]);
+
+for await (const chunk of stream) {
+  console.log(chunk.content);
+}
+
+/*
+Here
+'s
+ a
+ silly
+ joke
+ for
+ you
+:
+
+
+Why
+ di
+d the
+ tom
+ato
+ turn
+ re
+d?
+ Because
+ it
+ saw
+ the
+ sal
+a
+d
+dressing
+!
+*/

examples/src/models/chat/integration_bedrock_multimodal_converse.ts

@@ -0,0 +1,42 @@
+import * as fs from "node:fs/promises";
+
+import { ChatBedrockConverse } from "@langchain/aws";
+import { HumanMessage } from "@langchain/core/messages";
+
+const model = new ChatBedrockConverse({
+  model: "anthropic.claude-3-sonnet-20240229-v1:0",
+  region: "us-east-1",
+  credentials: {
+    accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!,
+    secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!,
+  },
+});
+
+const imageData = await fs.readFile("./hotdog.jpg");
+
+const res = await model.invoke([
+  new HumanMessage({
+    content: [
+      {
+        type: "text",
+        text: "What's in this image?",
+      },
+      {
+        type: "image_url",
+        image_url: {
+          url: `data:image/jpeg;base64,${imageData.toString("base64")}`,
+        },
+      },
+    ],
+  }),
+]);
+console.log(res);
+
+/*
+AIMessage {
+  content: 'The image shows a hot dog or frankfurter. It has a reddish-pink sausage inside a light tan-colored bread bun. The hot dog bun is split open, allowing the sausage filling to be visible. The image appears to be focused solely on depicting this classic American fast food item against a plain white background.',
+  response_metadata: { ... },
+  id: '1608d043-575a-450e-8eac-2fef6297cfe2',
+  usage_metadata: { input_tokens: 276, output_tokens: 75, total_tokens: 351 }
+}
+*/

examples/src/models/chat/integration_bedrock_tools_converse.ts

@@ -0,0 +1,55 @@
+import { ChatBedrockConverse } from "@langchain/aws";
+import { tool } from "@langchain/core/tools";
+import { z } from "zod";
+
+const model = new ChatBedrockConverse({
+  model: "anthropic.claude-3-sonnet-20240229-v1:0",
+  region: "us-east-1",
+  credentials: {
+    accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!,
+    secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!,
+  },
+});
+
+const weatherTool = tool(
+  ({ city, state }) => `The weather in ${city}, ${state} is 72°F and sunny`,
+  {
+    name: "weather_tool",
+    description: "Get the weather for a city",
+    schema: z.object({
+      city: z.string().describe("The city to get the weather for"),
+      state: z.string().describe("The state to get the weather for").optional(),
+    }),
+  }
+);
+
+const modelWithTools = model.bindTools([weatherTool]);
+// Optionally, you can bind tools via the `.bind` method:
+// const modelWithTools = model.bind({
+//   tools: [weatherTool]
+// });
+
+const res = await modelWithTools.invoke("What's the weather in New York?");
+console.log(res);
+
+/*
+AIMessage {
+  content: [
+    {
+      type: 'text',
+      text: "Okay, let's get the weather for New York City."
+    }
+  ],
+  response_metadata: { ... },
+  id: '49a97da0-e971-4d7f-9f04-2495e068c15e',
+  tool_calls: [
+    {
+      id: 'tooluse_O6Q1Ghm7SmKA9mn2ZKmBzg',
+      name: 'weather_tool',
+      args: {
+        'city': 'New York',
+      },
+  ],
+  usage_metadata: { input_tokens: 289, output_tokens: 68, total_tokens: 357 }
+}
+*/

examples/src/models/chat/integration_bedrock_wso_converse.ts

@@ -0,0 +1,31 @@
+import { ChatBedrockConverse } from "@langchain/aws";
+import { z } from "zod";
+
+const model = new ChatBedrockConverse({
+  model: "anthropic.claude-3-sonnet-20240229-v1:0",
+  region: "us-east-1",
+  credentials: {
+    accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID!,
+    secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY!,
+  },
+});
+
+const weatherSchema = z
+  .object({
+    city: z.string().describe("The city to get the weather for"),
+    state: z.string().describe("The state to get the weather for").optional(),
+  })
+  .describe("Get the weather for a city");
+
+const modelWithStructuredOutput = model.withStructuredOutput(weatherSchema, {
+  name: "weather_tool", // Optional, defaults to 'extract'
+});
+
+const res = await modelWithStructuredOutput.invoke(
+  "What's the weather in New York?"
+);
+console.log(res);
+
+/*
+{ city: 'New York', state: 'NY' }
+*/

libs/langchain-aws/.eslintrc.cjs

@@ -0,0 +1,74 @@
+module.exports = {
+  extends: [
+    "airbnb-base",
+    "eslint:recommended",
+    "prettier",
+    "plugin:@typescript-eslint/recommended",
+  ],
+  parserOptions: {
+    ecmaVersion: 12,
+    parser: "@typescript-eslint/parser",
+    project: "./tsconfig.json",
+    sourceType: "module",
+  },
+  plugins: ["@typescript-eslint", "no-instanceof"],
+  ignorePatterns: [
+    ".eslintrc.cjs",
+    "scripts",
+    "node_modules",
+    "dist",
+    "dist-cjs",
+    "*.js",
+    "*.cjs",
+    "*.d.ts",
+  ],
+  rules: {
+    "no-process-env": 2,
+    "no-instanceof/no-instanceof": 2,
+    "@typescript-eslint/explicit-module-boundary-types": 0,
+    "@typescript-eslint/no-empty-function": 0,
+    "@typescript-eslint/no-shadow": 0,
+    "@typescript-eslint/no-empty-interface": 0,
+    "@typescript-eslint/no-use-before-define": ["error", "nofunc"],
+    "@typescript-eslint/no-unused-vars": ["warn", { args: "none" }],
+    "@typescript-eslint/no-floating-promises": "error",
+    "@typescript-eslint/no-misused-promises": "error",
+    camelcase: 0,
+    "class-methods-use-this": 0,
+    "import/extensions": [2, "ignorePackages"],
+    "import/no-extraneous-dependencies": [
+      "error",
+      { devDependencies: ["**/*.test.ts"] },
+    ],
+    "import/no-unresolved": 0,
+    "import/prefer-default-export": 0,
+    "keyword-spacing": "error",
+    "max-classes-per-file": 0,
+    "max-len": 0,
+    "no-await-in-loop": 0,
+    "no-bitwise": 0,
+    "no-console": 0,
+    "no-restricted-syntax": 0,
+    "no-shadow": 0,
+    "no-continue": 0,
+    "no-void": 0,
+    "no-underscore-dangle": 0,
+    "no-use-before-define": 0,
+    "no-useless-constructor": 0,
+    "no-return-await": 0,
+    "consistent-return": 0,
+    "no-else-return": 0,
+    "func-names": 0,
+    "no-lonely-if": 0,
+    "prefer-rest-params": 0,
+    "new-cap": ["error", { properties: false, capIsNew: false }],
+  },
+  overrides: [
+    {
+      files: ["**/*.test.ts"],
+      rules: {
+        "@typescript-eslint/no-unused-vars": "off",
+      },
+    },
+  ],
+};

libs/langchain-aws/.gitignore

@@ -0,0 +1,7 @@
+index.cjs
+index.js
+index.d.ts
+index.d.cts
+node_modules
+dist
+.yarn

libs/langchain-aws/.prettierrc

@@ -0,0 +1,19 @@
+{
+  "$schema": "https://json.schemastore.org/prettierrc",
+  "printWidth": 80,
+  "tabWidth": 2,
+  "useTabs": false,
+  "semi": true,
+  "singleQuote": false,
+  "quoteProps": "as-needed",
+  "jsxSingleQuote": false,
+  "trailingComma": "es5",
+  "bracketSpacing": true,
+  "arrowParens": "always",
+  "requirePragma": false,
+  "insertPragma": false,
+  "proseWrap": "preserve",
+  "htmlWhitespaceSensitivity": "css",
+  "vueIndentScriptAndStyle": false,
+  "endOfLine": "lf"
+}