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Project Lakechain

Titan

Unstable API

0.8.0

@project-lakechain/bedrock-text-processors

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The Titan text processor allows you to leverage Amazon Titan machine-learning models on Amazon Bedrock within your pipelines. Using this construct, you can use prompt engineering techniques to transform text documents, including, text summarization, text translation, information extraction, and more!

📝 Text Generation

Section titled “📝 Text Generation”

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

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

import { S3EventTrigger } from '@project-lakechain/s3-event-trigger';
import { TitanTextProcessor, TitanTextModel } 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');


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


    // Transforms input documents using an Amazon Titan model.
    const titan = new TitanTextProcessor.Builder()
      .withScope(this)
      .withIdentifier('TitanTextProcessor')
      .withCacheStorage(cache)
      .withSource(source)
      .withModel(TitanTextModel.AMAZON_TITAN_TEXT_PREMIER_V1)
      .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({
        maxTokenCount: 3072
      })
      .build();
  }
}

🤖 Model Selection

Section titled “🤖 Model Selection”

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

import { TitanTextProcessor, TitanTextModel } from '@project-lakechain/bedrock-text-processors';


const titan = new TitanTextProcessor.Builder()
  .withScope(this)
  .withIdentifier('TitanTextProcessor')
  .withCacheStorage(cache)
  .withSource(source)
  .withModel(TitanTextModel.AMAZON_TITAN_TEXT_LITE_V1) // 👈 Specify a model
  .withPrompt(prompt)
  .build();

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

Model Name Model identifier
AMAZON_TITAN_TEXT_EXPRESS_V1 amazon.titan-text-express-v1
AMAZON_TITAN_TEXT_LITE_V1 amazon.titan-text-lite-v1
AMAZON_TITAN_TEXT_PREMIER_V1 amazon.titan-text-premier-v1:0

🌐 Region Selection

Section titled “🌐 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 { TitanTextProcessor, TitanTextModel } from '@project-lakechain/bedrock-text-processors';


const titan = new TitanTextProcessor.Builder()
  .withScope(this)
  .withIdentifier('TitanTextProcessor')
  .withCacheStorage(cache)
  .withSource(source)
  .withRegion('eu-central-1') // 👈 Alternate region
  .withModel(TitanTextModel.AMAZON_TITAN_TEXT_EXPRESS_V1)
  .withPrompt(prompt)
  .build();

⚙️ Model Parameters

Section titled “⚙️ Model Parameters”

You can optionally forward specific parameters to the underlying LLM using the .withModelParameters method. Below is a description of the supported parameters.

💁 See the Bedrock Inference Parameters for more information on the parameters supported by the different models.

Parameter Description Min Max Default
temperature Controls the randomness of the generated text. 0 1 0.7
maxTokens The maximum number of tokens to generate. 1 Model dependant 512
topP The cumulative probability of the top tokens to sample from. 0 1 0.9

🏗️ Architecture

Section titled “🏗️ 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

Section titled “🏷️ Properties”

Supported Inputs
Section titled “Supported 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/json+scheduler Used by the Scheduler middleware.
Supported Outputs
Section titled “Supported Outputs”
Mime Type Description
text/plain UTF-8 text documents.
Supported Compute Types
Section titled “Supported Compute Types”
Type Description
CPU This middleware only supports CPU compute.

📖 Examples

Section titled “📖 Examples”