Anthropic

Unstable API

0.7.0

@project-lakechain/bedrock-text-processors

Bedrock text processors expose different constructs that leverage the Generative AI text models powered by Amazon Bedrock. Using these constructs, you can use prompt engineering techniques to transform text documents, including, text summarization, text translation, information extraction, and more!

The Anthropic text processor is part of the Bedrock text processors and allows you to leverage large-language models provided by Anthropic within your pipelines.

📝 Text Generation

To start using Anthropic models in your pipelines, you import the AnthropicTextProcessor construct in your CDK stack, and specify the specific text model you want to use.

💁 The below example demonstrates how to use the Anthropic text processor to summarize input documents uploaded to an S3 bucket.

import { S3EventTrigger } from '@project-lakechain/s3-event-trigger';
import { AnthropicTextProcessor, AnthropicTextModel } from '@project-lakechain/bedrock-text-processors';
import { CacheStorage } from '@project-lakechain/core';

class Stack extends cdk.Stack {
  constructor(scope: cdk.Construct, id: string) {
    const cache = new CacheStorage(this, 'Cache');

    // Monitor the S3 bucket for new documents.
    const trigger = new S3EventTrigger.Builder()
      .withScope(this)
      .withIdentifier('Trigger')
      .withCacheStorage(cache)
      .withBucket(bucket)
      .build();

    // Transforms input documents using an Anthropic model.
    const anthropic = new AnthropicTextProcessor.Builder()
      .withScope(this)
      .withIdentifier('AnthropicTextProcessor')
      .withCacheStorage(cache)
      .withSource(trigger)
      .withModel(AnthropicTextModel.ANTHROPIC_CLAUDE_V3_HAIKU)
      .withPrompt(`
        Give a detailed summary of the text with the following constraints:
        - Write the summary in the same language as the original text.
        - Keep the original meaning, style, and tone of the text in the summary.
      `)
      .withModelParameters({
        temperature: 0.5,
        max_tokens: 4096
      })
      .build();
  }
}

ℹ️ Tip - Note that the Claude v3 family of models is multi-modal, and supports both text and image documents as an input.

🤖 Model Selection

You can select the specific Anthropic model to use with this middleware using the .withModel API.

import { AnthropicTextProcessor, AnthropicTextModel } from '@project-lakechain/bedrock-text-processors';

const anthropic = new AnthropicTextProcessor.Builder()
  .withScope(this)
  .withIdentifier('AnthropicTextProcessor')
  .withCacheStorage(cache)
  .withSource(source)
  .withModel(AnthropicTextModel.ANTHROPIC_CLAUDE_V3_SONNET) // 👈 Model selection
  .withPrompt(prompt)
  .build();

💁 You can choose amongst the following models — see the Bedrock documentation for more information.

Model Name	Model identifier
ANTHROPIC_CLAUDE_INSTANT_V1	`anthropic.claude-instant-v1`
ANTHROPIC_CLAUDE_V2	`anthropic.claude-v2`
ANTHROPIC_CLAUDE_V2_1	`anthropic.claude-v2:1`
ANTHROPIC_CLAUDE_V3_HAIKU	`anthropic.claude-3-haiku-20240307-v1:0`
ANTHROPIC_CLAUDE_V3_SONNET	`anthropic.claude-3-sonnet-20240229-v1:0`
ANTHROPIC_CLAUDE_V3_5_SONNET	`anthropic.claude-3-5-sonnet-20240620-v1:0`
ANTHROPIC_CLAUDE_V3_OPUS	`anthropic.claude-3-opus-20240229-v1:0`

🌐 Region Selection

You can specify the AWS region in which you want to invoke Amazon Bedrock using the .withRegion API. This can be helpful if Amazon Bedrock is not yet available in your deployment region.

💁 By default, the middleware will use the current region in which it is deployed.

import { AnthropicTextProcessor, AnthropicTextModel } from '@project-lakechain/bedrock-text-processors';

const anthropic = new AnthropicTextProcessor.Builder()
  .withScope(this)
  .withIdentifier('AnthropicTextProcessor')
  .withCacheStorage(cache)
  .withSource(source)
  .withRegion('eu-central-1') // 👈 Alternate region
  .withModel(AnthropicTextModel.ANTHROPIC_CLAUDE_V3_HAIKU)
  .withPrompt(prompt)
  .build();

⚙️ Model Parameters

You can forward specific parameters to the text models using the .withModelParameters method. Below is a description of the supported parameters.

Parameter	Description	Min	Max	Default
temperature	Controls the randomness of the generated text.	0	1	N/A
max_tokens	The maximum number of tokens to generate.	1	4096	4096
top_p	The cumulative probability of the top tokens to sample from.	0	1	N/A
top_k	The number of top tokens to sample from.	1	100000000	N/A

💬 Prompts

The Anthropic text processor exposes an interface allowing users to specify prompts to the underlying model. A prompt is a piece of text that guides the model on how to generate the output. Using this middleware you can define 3 types of prompts to the Anthropic model.

Type	Method	Optional	Description
User prompt	.withPrompt	No	The user prompt is text that provides instructions to the model.
System prompt	.withSystemPrompt	Yes	The system prompt is text that provides context to the model.
Assistant Prefill	.withAssistantPrefill	Yes	The assistant prefill is text that directly guides the model on how to further complete its output.

💁 The below example demonstrates how to use both a user prompt and an assistant prefill to guide the model into outputting valid JSON.

import { AnthropicTextProcessor, AnthropicTextModel } from '@project-lakechain/bedrock-text-processors';

const anthropic = new AnthropicTextProcessor.Builder()
  .withScope(this)
  .withIdentifier('AnthropicTextProcessor')
  .withCacheStorage(cache)
  .withSource(source)
  .withModel(AnthropicTextModel.ANTHROPIC_CLAUDE_V3_HAIKU)
  .withPrompt('Extract metadata from the document as a JSON document.')
  .withAssistantPrefill('{')
  .build();

🧩 Composite Events

In addition to handling single documents, the Anthropic text processor also supports composite events as an input. This means that it can take multiple text and image documents and compile them into a single input for the model.

This can come in handy in map-reduce pipelines where you use the Reducer to combine multiple documents into a single input having a similar semantic, for example, multiple pages of a PDF document that you would like the model to summarize as a whole, while keeping the context between the pages.

🏗️ Architecture

This middleware is based on a Lambda compute running on an ARM64 architecture, and integrate with Amazon Bedrock to generate text based on the given prompt and input documents.

Architecture

🏷️ Properties

Supported Inputs

The supported inputs depend on the selected model as the Claude v3 models are multi-modal and support text and images, while the Claude v2 model only support text. The following table lists the supported inputs for each model.

Model	Supported Inputs
`ANTHROPIC_CLAUDE_INSTANT_V1`	Text
`ANTHROPIC_CLAUDE_V2`	Text
`ANTHROPIC_CLAUDE_V2_1`	Text
`ANTHROPIC_CLAUDE_V3_HAIKU`	Text, Image
`ANTHROPIC_CLAUDE_V3_SONNET`	Text, Image
`ANTHROPIC_CLAUDE_V3_5_SONNET`	Text, Image
`ANTHROPIC_CLAUDE_V3_OPUS`	Text, Image

Text Inputs

Below is a list of supported text inputs.

Mime Type	Description
`text/plain`	UTF-8 text documents.
`text/markdown`	Markdown documents.
`text/csv`	CSV documents.
`text/html`	HTML documents.
`application/x-subrip`	SubRip subtitles.
`text/vtt`	Web Video Text Tracks (WebVTT) subtitles.
`application/json`	JSON documents.
`application/xml`	XML documents.

Image Inputs

Below is a list of supported image inputs.

Mime Type	Description
`image/jpeg`	JPEG images.
`image/png`	PNG images.
`image/gif`	GIF images.
`image/webp`	WebP images.

Composite Inputs

The middleware also supports composite events as an input, which can be used to combine multiple text and image documents into a single input for the model.

Mime Type	Description
`application/cloudevents+json`	Composite events emitted by the `Reducer`.

Supported Outputs

Mime Type	Description
`text/plain`	UTF-8 text documents.

Supported Compute Types

Type	Description
`CPU`	This middleware only supports CPU compute.

📖 Examples

Bedrock Summarization Pipeline - Builds a pipeline for text summarization using Amazon Bedrock.
Audio Recording Summarization Pipeline - Builds a pipeline for summarizing audio recordings using Amazon Transcribe and Amazon Bedrock.
Video Summarization Pipeline - Builds a pipeline for video summarization.