Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/build-rag-and-agent-based-generative-ai-applications-with-new-amazon-titan-text-premier-model-available-in-amazon-bedrock/

Today, we’re happy to welcome a new member of the Amazon Titan family of models: Amazon Titan Text Premier, now available in Amazon Bedrock.

Following Amazon Titan Text Lite and Titan Text Express, Titan Text Premier is the latest large language model (LLM) in the Amazon Titan family of models, further increasing your model choice within Amazon Bedrock. You can now choose between the following Titan Text models in Bedrock:

• Titan Text Premier is the most advanced Titan LLM for text-based enterprise applications. With a maximum context length of 32K tokens, it has been specifically optimized for enterprise use cases, such as building Retrieval Augmented Generation (RAG) and agent-based applications with Knowledge Bases and Agents for Amazon Bedrock. As with all Titan LLMs, Titan Text Premier has been pre-trained on multilingual text data but is best suited for English-language tasks. You can further custom fine-tune (preview) Titan Text Premier with your own data in Amazon Bedrock to build applications that are specific to your domain, organization, brand style, and use case. I’ll dive deeper into model highlights and performance in the following sections of this post.
• Titan Text Express is ideal for a wide range of tasks, such as open-ended text generation and conversational chat. The model has a maximum context length of 8K tokens.
• Titan Text Lite is optimized for speed, is highly customizable, and is ideal to be fine-tuned for tasks such as article summarization and copywriting. The model has a maximum context length of 4K tokens.

Now, let’s discuss Titan Text Premier in more detail.

Amazon Titan Text Premier model highlights
Titan Text Premier has been optimized for high-quality RAG and agent-based applications and customization through fine-tuning while incorporating responsible artificial intelligence (AI) practices.

Optimized for RAG and agent-based applications – Titan Text Premier has been specifically optimized for RAG and agent-based applications in response to customer feedback, where respondents named RAG as one of their key components in building generative AI applications. The model training data includes examples for tasks like summarization, Q&A, and conversational chat and has been optimized for integration with Knowledge Bases and Agents for Amazon Bedrock. The optimization includes training the model to handle the nuances of these features, such as their specific prompt formats.

• High-quality RAG through integration with Knowledge Bases for Amazon Bedrock – With a knowledge base, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for RAG. You can now choose Titan Text Premier with Knowledge Bases to implement question-answering and summarization tasks over your company’s proprietary data.
  
• Automating tasks through integration with Agents for Amazon Bedrock – You can also create custom agents that can perform multistep tasks across different company systems and data sources using Titan Text Premier with Agents for Amazon Bedrock. Using agents, you can automate tasks for your internal or external customers, such as managing retail orders or processing insurance claims.
  

We already see customers exploring Titan Text Premier to implement interactive AI assistants that create summaries from unstructured data such as emails. They’re also exploring the model to extract relevant information across company systems and data sources to create more meaningful product summaries.

Here’s a demo video created by my colleague Brooke Jamieson that shows an example of how you can put Titan Text Premier to work for your business.

Custom fine-tuning of Amazon Titan Text Premier (preview) – You can fine-tune Titan Text Premier with your own data in Amazon Bedrock to increase model accuracy by providing your own task-specific labeled training dataset. Customizing Titan Text Premier helps to further specialize your model and create unique user experiences that reflect your company’s brand, style, voice, and services.

Built responsibly – Amazon Titan Text Premier incorporates safe, secure, and trustworthy practices. The AWS AI Service Card for Amazon Titan Text Premier documents the model’s performance across key responsible AI benchmarks from safety and fairness to veracity and robustness. The model also integrates with Guardrails for Amazon Bedrock so you can implement additional safeguards customized to your application requirements and responsible AI policies. Amazon indemnifies customers who responsibly use Amazon Titan models against claims that generally available Amazon Titan models or their outputs infringe on third-party copyrights.

Amazon Titan Text Premier model performance
Titan Text Premier has been built to deliver broad intelligence and utility relevant for enterprises. The following table shows evaluation results on public benchmarks that assess critical capabilities, such as instruction following, reading comprehension, and multistep reasoning against price-comparable models. The strong performance across these diverse and challenging benchmarks highlights that Titan Text Premier is built to handle a wide range of use cases in enterprise applications, offering great price performance. For all benchmarks listed below, a higher score is a better score.

Note: Benchmarks evaluate model performance using a variation of few-shot and zero-shot prompting. With few-shot prompting, you provide the model with a number of concrete examples (three for 3-shot, five for 5-shot, etc.) of how to solve a specific task. This demonstrates the model’s ability to learn from example, called in-context learning. With zero-shot prompting on the other hand, you evaluate a model’s ability to perform tasks by relying only on its preexisting knowledge and general language understanding without providing any examples.

Get started with Amazon Titan Text Premier
To enable access to Amazon Titan Text Premier, navigate to the Amazon Bedrock console and choose Model access on the bottom left pane. On the Model access overview page, choose the Manage model access button in the upper right corner and enable access to Amazon Titan Text Premier.

To use Amazon Titan Text Premier in the Bedrock console, choose Text or Chat under Playgrounds in the left menu pane. Then choose Select model and select Amazon as the category and Titan Text Premier as the model. To explore the model, you can load examples. The following screenshot shows one of those examples that demonstrates the model’s chain of thought (CoT) and reasoning capabilities.

By choosing View API request, you can get a code example of how to invoke the model using the AWS Command Line Interface (AWS CLI) with the current example prompt. You can also access Amazon Bedrock and available models using the AWS SDKs. In the following example, I will use the AWS SDK for Python (Boto3).

Amazon Titan Text Premier in action
For this demo, I ask Amazon Titan Text Premier to summarize one of my previous AWS News Blog posts that announced the availability of Amazon Titan Image Generator and the watermark detection feature.

For summarization tasks, a recommended prompt template looks like this:

The following is text from a {{Text Category}}:
{{Text}}
Summarize the {{Text Category}} in {{length of summary}}

For more prompting best practices, check out the Amazon Titan Text Prompt Engineering Guidelines.

I adapt this template to my example and define the prompt. In preparation, I saved my News Blog post as a text file and read it into the post string variable.

prompt = """
The following is text from a AWS News Blog post:

<text>
%s
</text>

Summarize the above AWS News Blog post in a short paragraph.
""" % post

Similar to previous Amazon Titan Text models, Amazon Titan Text Premier supports temperature and topP inference parameters to control the randomness and diversity of the response, as well as maxTokenCount and stopSequences to control the length of the response.

import boto3
import json

bedrock_runtime = boto3.client(service_name="bedrock-runtime")

body = json.dumps({
    "inputText": prompt, 
    "textGenerationConfig":{  
        "maxTokenCount":256,
        "stopSequences":[],
        "temperature":0,
        "topP":0.9
    }
})

Then, I use the InvokeModel API to send the inference request.

response = bedrock_runtime.invoke_model(
    body=body,
	modelId="amazon.titan-text-premier-v1:0",
    accept="application/json", 
    contentType="application/json"
)

response_body = json.loads(response.get('body').read())
print(response_body.get('results')[0].get('outputText'))

And here’s the response:

Amazon Titan Image Generator is now generally available in Amazon Bedrock, giving you an easy way to build and scale generative AI applications with new image generation and image editing capabilities, including instant customization of images. Watermark detection for Titan Image Generator is now generally available in the Amazon Bedrock console. Today, we’re also introducing a new DetectGeneratedContent API (preview) in Amazon Bedrock that checks for the existence of this watermark and helps you confirm whether an image was generated by Titan Image Generator.

For more examples in different programming languages, check out the code examples section in the Amazon Bedrock User Guide.

More resources
Here are some additional resources that you might find helpful:

Intended use cases and more — Check out the AWS AI Service Card for Amazon Titan Text Premier to learn more about the models’ intended use cases, design, and deployment, as well as performance optimization best practices.

AWS Generative AI CDK Constructs — Amazon Titan Text Premier is supported by the AWS Generative AI CDK Constructs, an open source extension of the AWS Cloud Development Kit (AWS CDK), providing sample implementations of AWS CDK for common generative AI patterns.

Amazon Titan models — If you’re curious to learn more about Amazon Titan models in general, check out the following video. Dr. Sherry Marcus, Director of Applied Science for Amazon Bedrock, shares how the Amazon Titan family of models incorporates the 25 years of experience Amazon has innovating with AI and machine learning (ML) across its business.

Now available
Amazon Titan Text Premier is available today in the AWS US East (N. Virginia) Region. Custom fine-tuning for Amazon Titan Text Premier is available today in preview in the AWS US East (N. Virginia) Region. Check the full Region list for future updates. To learn more about the Amazon Titan family of models, visit the Amazon Titan product page. For pricing details, review the Amazon Bedrock pricing page.

Give Amazon Titan Text Premier a try in the Amazon Bedrock console today, send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS contacts, and engage with the generative AI builder community at community.aws.

— Antje

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/amazon-titan-image-generator-and-watermark-detection-api-are-now-available-in-amazon-bedrock/

During AWS re:Invent 2023, we announced the preview of Amazon Titan Image Generator, a generative artificial intelligence (generative AI) foundation model (FM) that you can use to quickly create and refine realistic, studio-quality images using English natural language prompts.

I’m happy to share that Amazon Titan Image Generator is now generally available in Amazon Bedrock, giving you an easy way to build and scale generative AI applications with new image generation and image editing capabilities, including instant customization of images.

In my previous post, I also mentioned that all images generated by Titan Image Generator contain an invisible watermark, by default, which is designed to help reduce the spread of misinformation by providing a mechanism to identify AI-generated images.

I’m excited to announce that watermark detection for Titan Image Generator is now generally available in the Amazon Bedrock console. Today, we’re also introducing a new DetectGeneratedContent API (preview) in Amazon Bedrock that checks for the existence of this watermark and helps you confirm whether an image was generated by Titan Image Generator.

Let me show you how to get started with these new capabilities.

Instant image customization using Amazon Titan Image Generator
You can now generate new images of a subject by providing up to five reference images. You can create the subject in different scenes while preserving its key features, transfer the style from the reference images to new images, or mix styles from multiple reference images. All this can be done without additional prompt engineering or fine-tuning of the model.

For this demo, I prompt Titan Image Generator to create an image of a “parrot eating a banana.” In the first attempt, I use Titan Image Generator to create this new image without providing a reference image.

Note: In the following code examples, I’ll use the AWS SDK for Python (Boto3) to interact with Amazon Bedrock. You can find additional code examples for C#/.NET, Go, Java, and PHP in the Bedrock User Guide.

import boto3
import json

bedrock_runtime = boto3.client(service_name="bedrock-runtime")

body = json.dumps(
    {
        "taskType": "TEXT_IMAGE",
        "textToImageParams": {
            "text": "parrot eating a banana",   
        },
        "imageGenerationConfig": {
            "numberOfImages": 1,   
            "quality": "premium", 
            "height": 768,
            "width": 1280,
            "cfgScale": 10, 
            "seed": 42
        }
    }
)
response = bedrock_runtime.invoke_model(
    body=body, 
    modelId="amazon.titan-image-generator-v1",
    accept="application/json", 
    contentType="application/json"
)

You can display the generated image using the following code.

import io
import base64
from PIL import Image

response_body = json.loads(response.get("body").read())

images = [
    Image.open(io.BytesIO(base64.b64decode(base64_image)))
    for base64_image in response_body.get("images")
]

for img in images:
    display(img)

Here’s the generated image:

Then, I use the new instant image customization capability with the same prompt, but now also providing the following two reference images. For easier comparison, I’ve resized the images, added a caption, and plotted them side by side.

Here’s the code. The new instant customization is available through the IMAGE_VARIATION task:

# Import reference images
image_path_1 = "parrot-cartoon.png"
image_path_2 = "bird-sketch.png"

with open(image_path_1, "rb") as image_file:
    input_image_1 = base64.b64encode(image_file.read()).decode("utf8")

with open(image_path_2, "rb") as image_file:
    input_image_2 = base64.b64encode(image_file.read()).decode("utf8")

# ImageVariationParams options:
#   text: Prompt to guide the model on how to generate variations
#   images: Base64 string representation of a reference image, up to 5 images are supported
#   similarityStrength: Parameter you can tune to control similarity with reference image(s)

body = json.dumps(
    {
        "taskType": "IMAGE_VARIATION",
        "imageVariationParams": {
            "text": "parrot eating a banana",  # Required
            "images": [input_image_1, input_image_2],  # Required 1 to 5 images
            "similarityStrength": 0.7,  # Range: 0.2 to 1.0
        },
        "imageGenerationConfig": {
            "numberOfImages": 1,
            "quality": "premium",
            "height": 768,
            "width": 1280,
            "cfgScale": 10,
            "seed": 42
        }
    }
)

response = bedrock_runtime.invoke_model(
    body=body, 
    modelId="amazon.titan-image-generator-v1",
    accept="application/json", 
    contentType="application/json"
)

Once again, I’ve resized the generated image, added a caption, and plotted it side by side with the originally generated image.

You can see how the parrot in the second image that has been generated using the instant image customization capability resembles in style the combination of the provided reference images.

Watermark detection for Amazon Titan Image Generator
All Amazon Titan FMs are built with responsible AI in mind. They detect and remove harmful content from data, reject inappropriate user inputs, and filter model outputs. As content creators create realistic-looking images with AI, it’s important to promote responsible development of this technology and reduce the spread of misinformation. That’s why all images generated by Titan Image Generator contain an invisible watermark, by default. Watermark detection is an innovative technology, and Amazon Web Services (AWS) is among the first major cloud providers to widely release built-in watermarks for AI image outputs.

Titan Image Generator’s new watermark detection feature is a mechanism that allows you to identify images generated by Amazon Titan. These watermarks are designed to be tamper-resistant, helping increase transparency around AI-generated content as these capabilities continue to advance.

Watermark detection using the console
Watermark detection is generally available in the Amazon Bedrock console. You can upload an image to detect watermarks embedded in images created by Titan Image Generator, including those generated by the base model and any customized versions. If you upload an image that was not created by Titan Image Generator, then the model will indicate that a watermark has not been detected.

The watermark detection feature also comes with a confidence score. The confidence score represents the confidence level in watermark detection. In some cases, the detection confidence may be low if the original image has been modified. This new capability enables content creators, news organizations, risk analysts, fraud detection teams, and others to better identify and mitigate misleading AI-generated content, promoting transparency and responsible AI deployment across organizations.

Watermark detection using the API (preview)
In addition to watermark detection using the console, we’re introducing a new DetectGeneratedContent API (preview) in Amazon Bedrock that checks for the existence of this watermark and helps you confirm whether an image was generated by Titan Image Generator. Let’s see how this works.

For this demo, let’s check if the image of the green iguana I showed in the Titan Image Generator preview post was indeed generated by the model.

I define the imports, set up the Amazon Bedrock boto3 runtime client, and base64-encode the image. Then, I call the DetectGeneratedContent API by specifying the foundation model and providing the encoded image.

import boto3
import json
import base64

bedrock_runtime = boto3.client(service_name="bedrock-runtime")

image_path = "green-iguana.png"

with open(image_path, "rb") as image_file:
    input_image_iguana = image_file.read()

response = bedrock_runtime.detect_generated_content(
    foundationModelId = "amazon.titan-image-generator-v1",
    content = {
        "imageContent": { "bytes": input_image_iguana }
    }
)

Let’s check the response.

response.get("detectionResult")
'GENERATED'
response.get("confidenceLevel")
'HIGH'

The response GENERATED with the confidence level HIGH confirms that Amazon Bedrock detected a watermark generated by Titan Image Generator.

Now, let’s check another image I generated using Stable Diffusion XL 1.0 on Amazon Bedrock. In this case, a “meerkat facing the sunset.”

I call the API again, this time with the image of the meerkat.

image_path = "meerkat.png"

with open(image_path, "rb") as image_file:
    input_image_meerkat = image_file.read()

response = bedrock_runtime.detect_generated_content(
    foundationModelId = "amazon.titan-image-generator-v1",
    content = {
        "imageContent": { "bytes": input_image_meerkat }
    }
)

response.get("detectionResult")
'NOT_GENERATED'

And indeed, the response NOT_GENERATED tells me that there was no watermark by Titan Image Generator detected, and therefore, the image most likely wasn’t generated by the model.

Using Amazon Titan Image Generator and watermark detection in the console
Here’s a short demo of how to get started with Titan Image Generator and the new watermark detection feature in the Amazon Bedrock console, put together by my colleague Nirbhay Agarwal.

Availability
Amazon Titan Image Generator, the new instant customization capabilities, and watermark detection in the Amazon Bedrock console are available today in the AWS Regions US East (N. Virginia) and US West (Oregon). Check the full Region list for future updates. The new DetectGeneratedContent API in Amazon Bedrock is available today in public preview in the AWS Regions US East (N. Virginia) and US West (Oregon).

Amazon Titan Image Generator, now also available in PartyRock
Titan Image Generator is now also available in PartyRock, an Amazon Bedrock playground. PartyRock gives you a no-code, AI-powered app-building experience that doesn’t require a credit card. You can use PartyRock to create apps that generate images in seconds by selecting from your choice of image generation models from Stability AI and Amazon.

More resources
To learn more about the Amazon Titan family of models, visit the Amazon Titan product page. For pricing details, check Amazon Bedrock Pricing.

Give Amazon Titan Image Generator a try in PartyRock or explore the model’s advanced image generation and editing capabilities in the Amazon Bedrock console. Send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS contacts.

For more deep-dive technical content and to engage with the generative AI Builder community, visit our generative AI space at community.aws.

— Antje