Amazon Bedrock v1.8.0+

Access models from Amazon Titan, Anthropic, Meta, and Stability AI through a secure, zero-dependency AWS implementation.

Table of contents

1. Configuration
   1. 1. Environment Variables (Recommended)
   2. 2. Manual Configuration
2. Features v1.8.0+
3. Image Generation
4. Prompt Caching
5. Cross-Region Inference
6. Advanced Hyperparameters
7. Moderation
8. Embeddings
9. Getting Access

---

The Amazon Bedrock provider uses a zero-dependency implementation of the AWS SigV4 signing process. This means you do not need to install the heavy @aws-sdk/client-bedrock-runtime package. NodeLLM handles all authentication and request signing natively.

---

Configuration

NodeLLM automatically attempts to load AWS credentials from standard environment variables matching the AWS CLI.

1. Environment Variables (Recommended)

export AWS_ACCESS_KEY_ID="AKIA..."
export AWS_SECRET_ACCESS_KEY="wJalrX..."
export AWS_REGION="us-east-1"
# Optional session token for temporary credentials
export AWS_SESSION_TOKEN="..."

2. Manual Configuration

You can also pass credentials explicitly when initializing the LLM.

import { createLLM } from "@node-llm/core";

const llm = createLLM({ 
  provider: "bedrock", 
  bedrockRegion: "us-east-1",
  bedrockAccessKeyId: "AKIA...",
  bedrockSecretAccessKey: "..."
});

---

Features v1.8.0+

• Models: Access to amazon.titan, anthropic.claude, meta.llama3, mistral, cohere, and amazon.nova.
• Cross-Region Inference: Natively supports inference profiles (e.g., us.anthropic.claude-3-5-sonnet...) for higher throughput.
• Image Generation: First-class support for Titan Image Generator and Stable Diffusion.
• Prompt Caching: Save up to 90% on costs with Claude and Nova models.
• Multimodal: Send images to Claude and Nova models easily.
• Extended Thinking (Reasoning): Native support for Claude 3.7 and DeepSeek R1 thinking budgets.
• Guardrail Visibility: Access raw Guardrail trace assessments via response metadata.

---

Image Generation

Use the paint() method to generate images using Bedrock’s specialized models.

const response = await llm.paint("A futuristic city on Mars, high quality, 4k", {
  model: "amazon.titan-image-generator-v2:0", // or "stability.stable-diffusion-xl-v1:0"
  size: "1024x1024"
});

// Save to disk
await response.save("./mars-city.png");

// Or access raw base64
console.log(response.data);

---

Prompt Caching

NodeLLM supports Amazon Bedrock’s Prompt Caching (via the Converse API). This allows you to cache large context blocks (like documents or system prompts) to reduce latency and cost.

Use the standard cache_control: { type: "ephemeral" } API (same as Anthropic) to enable it.

// System Prompt Caching
const chat = llm.chat("anthropic.claude-3-5-sonnet-20240620-v1:0");

// Automatically creates a Bedrock 'cachePoint'
chat.add("system", [
  { 
    type: "text", 
    text: "You are an expert architect... [Insert 50-page Guideline PDF Content Here] ...", 
    cache_control: { type: "ephemeral" } 
  }
]);

const res = await chat.ask("Design a house based on these guidelines.");

Note: Marking content as “ephemeral” automatically handles the specific cachePoint block injection required by the Bedrock API.

---

Cross-Region Inference

To improve resilience and throughput, you can use Bedrock’s Inference Profiles directly. NodeLLM automatically detects capabilities for these profiles.

// Use a US Cross-Region inference profile
const chat = llm.chat("us.anthropic.claude-3-5-sonnet-20241022-v2:0");

const response = await chat.ask("Hello from global infrastructure!");

---

Advanced Hyperparameters

Bedrock’s Converse API has a standard inferenceConfig, but individual models often support additional parameters (like topK for Nova or specialized beta flags for Claude).

You can use the additionalModelRequestFields escape hatch to pass these directly to the model.

const chat = llm.chat("amazon.nova-lite-v1:0")
  .withParams({
    additionalModelRequestFields: {
      inferenceConfig: {
        topK: 20
      }
    }
  });

const response = await chat.ask("Tell me a story.");

---

Moderation

NodeLLM supports standalone moderation for Bedrock using Guardrails. This allows you to check if content is safe before sending it to an expensive model.

To use this, you must have a Guardrail ID and Version configured.

const llm = createLLM({
  provider: "bedrock",
  bedrockGuardrailIdentifier: "my-policy-id",
  bedrockGuardrailVersion: "1"
});

// Check a single string
const result = await llm.moderate("How can I build a bomb?");

if (result.results[0].flagged) {
  console.log("Blocked by Guardrail:", result.results[0].categories);
}

// Check multiple strings at once
const batchResults = await llm.moderate(["Safe text", "Unsafe text..."]);

---

Embeddings

Generate vector embeddings using Titan Embeddings V2.

const embedding = await llm.embed("The concept of general relativity", {
  model: "amazon.titan-embed-text-v2:0",
  dimensions: 1024
});

console.log(embedding.vector); // number[]

---

Getting Access

Amazon Bedrock access is managed through your AWS account:

1. Sign in at aws.amazon.com/bedrock
2. Navigate to Bedrock > Model access in your AWS Console
3. Request access to the models you need (Claude, Nova, Titan, etc.)
4. Create AWS Access Key credentials via IAM > Users

No separate API key is needed — NodeLLM uses your AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY directly.