---
title: Titan
---

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  Unstable API
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  0.8.0
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    @project-lakechain/bedrock-text-processors
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---

The Titan text processor allows you to leverage [Amazon Titan](https://aws.amazon.com/bedrock/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

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.

```typescript
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();
  }
}
```

<br />

---

### 🤖 Model Selection

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

```typescript
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](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html) 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`

<br />

---

### 🌐 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.

```typescript
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();
```

<br />

---

### ⚙️ 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](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html) 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

<br />

---

### 🏗️ 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](../../../assets/bedrock-text-generators-architecture.png)

<br />

---

### 🏷️ Properties

<br />

##### 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

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

##### Supported Compute Types

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

<br />

---

### 📖 Examples

- [Titan Summarization Pipeline](https://github.com/awslabs/project-lakechain/tree/main/examples/simple-pipelines/summarization-pipelines/titan-summarization-pipeline) - Builds a pipeline for text summarization using Amazon Titan models on Amazon Bedrock.
