Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-amazon-bedrock-capabilities-enhance-data-processing-and-retrieval/

Today, Amazon Bedrock introduces four enhancements that streamline how you can analyze data with generative AI:

Amazon Bedrock Data Automation (preview) – A fully managed capability of Amazon Bedrock that streamlines the generation of valuable insights from unstructured, multimodal content such as documents, images, audio, and videos. With Amazon Bedrock Data Automation, you can build automated intelligent document processing (IDP), media analysis, and Retrieval-Augmented Generation (RAG) workflows quickly and cost-effectively. Insights include video summaries of key moments, detection of inappropriate image content, automated analysis of complex documents, and much more. You can customize outputs to tailor insights into your specific business needs. Amazon Bedrock Data Automation can be used as a standalone feature or as a parser when setting up a knowledge base for RAG workflows.

Amazon Bedrock Knowledge Bases now processes multimodal data –To help build applications that process both text and visual elements in documents and images, you can configure a knowledge base to parse documents using either Amazon Bedrock Data Automation or use a foundation model (FM) as the parser. Multimodal data processing can improve the accuracy and relevancy of the responses you get from a knowledge base which includes information embedded in both images and text.

Amazon Bedrock Knowledge Bases now supports GraphRAG (preview) – We now offer one of the first fully-managed GraphRAG capabilities. GraphRAG enhances generative AI applications by providing more accurate and comprehensive responses to end users by using RAG techniques combined with graphs.

Amazon Bedrock Knowledge Bases now supports structured data retrieval – This capability extends a knowledge base to support natural language querying of data warehouses and data lakes so that applications can access business intelligence (BI) through conversational interfaces and improve the accuracy of the responses by including critical enterprise data. Amazon Bedrock Knowledge Bases provides one of the first fully-managed out-of-the-box RAG solutions that can natively query structured data from where it resides. This capability helps break data silos across data sources and accelerates building generative AI applications from over a month to just a few days.

These new capabilities make it easier to build comprehensive AI applications that can process, understand, and retrieve information from structured and unstructured data sources. For example, a car insurance company can use Amazon Bedrock Data Automation to automate their claims adjudication workflow to reduce the time taken to process automobile claims, improving the productivity of their claims department.

Similarly, a media company can analyze TV shows and extract insights needed for smart advertisement placement such as scene summaries, industry standard advertising taxonomies (IAB), and company logos. A media production company can generate scene-by-scene summaries and capture key moments in their video assets. A financial services company can process complex financial documents containing charts and tables and use GraphRAG to understand relationships between different financial entities. All these companies can use structured data retrieval to query their data warehouse while retrieving information from their knowledge base.

Let’s take a closer look at these features.

Introducing Amazon Bedrock Data Automation
Amazon Bedrock Data Automation is a capability of Amazon Bedrock that simplifies the process of extracting valuable insights from multimodal, unstructured content, such as documents, images, videos, and audio files.

Amazon Bedrock Data Automation provides a unified, API-driven experience that developers can use to process multimodal content through a single interface, eliminating the need to manage and orchestrate multiple AI models and services. With built-in safeguards, such as visual grounding and confidence scores, Amazon Bedrock Data Automation helps promote the accuracy and trustworthiness of the extracted insights, making it easier to integrate into enterprise workflows.

Amazon Bedrock Data Automation supports 4 modalities (documents, images, video, and audio). When used in an application, all modalities use the same asynchronous inference API, and results are written to an Amazon Simple Storage Service (Amazon S3) bucket.

For each modality, you can configure the output based on your processing needs and generate two types of outputs:

Standard output – With standard output, you get predefined default insights that are relevant to the input data type. Examples include semantic representation of documents, summaries of videos by scene, audio transcripts and more. You can configure which insights you want to extract with just a few steps.

Custom output – With custom output, you have the flexibility to define and specify your extraction needs using artifacts called “blueprints” to generate insights tailored to your business needs. You can also transform the generated output into a specific format or schema that is compatible with your downstream systems such as databases or other applications.

Standard output can be used with all formats (audio, documents, images, and videos). During the preview, custom output can only be used with documents and images.

Both standard and custom output configurations can be saved in a project to reference in the Amazon Bedrock Data Automation inference API. A project can be configured to generate both standard output and custom output for each processed file.

Let’s look at an example of processing a document for both standard and custom outputs.

Using Amazon Bedrock Data Automation
On the Amazon Bedrock console, I choose Data Automation in the navigation pane. Here, I can review how this capability works with a few sample use cases.

Then, I choose Demo in the Data Automation section of the navigation pane. I can try this capability using one of the provided sample documents or by uploading my own. For example, let’s say I am working on an application that needs to process birth certificates.

I start by uploading a birth certificate to see the standard output results. The first time I upload a document, I’m asked to confirm to create an S3 bucket to store the assets. When I look at the standard output, I can tailor the result with a few quick settings.

I choose the Custom output tab. The document is recognized by one of the sample blueprints and information is extracted across multiple fields.

Most of the data for my application is there but I need a few customizations. For example, the date the birth certificate was issued (JUNE 10, 2022) is in a different format than the other dates in the document. I also need the state that issued the certificate and a couple of flags that tell me if the child last name matches the one from the mother or the father.

Most of the fields in the previous blueprint use the Explicit extraction type. That means they’re extracted as they are from the document.

If I want a date in a specific format, I can create a new field using the Inferred extraction type and add instructions on how to format the result starting from the content of the document. Inferred extractions can be used to perform transformations, such as date or Social Security number (SSN) format, or validations, for example, to check if a person is over 21 based on today’s date.

Sample blueprints cannot be edited. I choose Duplicate blueprint to create a new blueprint that I can edit and then Add field from the Fields drop down.

I add four fields with extraction type Inferred and these instructions:

1. The date the birth certificate was issued in MM/DD/YYYY format
2. The state that issued the birth certificate 
3. Is ChildLastName equal to FatherLastName
4. Is ChildLastName equal to MotherLastName

The first two fields are strings and the last two booleans.

After I create the new fields, I can apply the new blueprint to the document I previously uploaded.

I choose Get result and look for the new fields in the results. I see the date formatted as I need, the two flags, and the state.

Now that I have created this custom blueprint tailored to the needs of my application, I can add it to a project. I can associate multiple blueprints with a project for the different document types I want to process, such as a blueprint for passports, a blueprint for birth certificates, a blueprint for invoices, and so on. When processing documents, Amazon Bedrock Data Automation matches each document to a blueprints within the project to extract relevant information.

I can also create a new blueprint form scratch. In that case, I can start with a prompt where I declare any fields I expect to find in the uploaded document and perform normalizations or validations.

Amazon Bedrock Data Automation can also process audio and video files. For example, here’s the standard output when uploading a video from a keynote presentation by Swami Sivasubramanian VP, AI and Data at AWS.

It takes a few minutes to get the output. The results include a summarization of the overall video, a summary scene by scene, and the text that appears during the video. From here, I can toggle the options to have a full audio transcript, content moderation, or Interactive Advertising Bureau (IAB) taxonomy.

I can also use Amazon Bedrock Data Automation as a parser when creating a knowledge base to extract insights from visually rich documents and images, for retrieval and response generation. Let’s see that in the next section.

Using multimodal data processing in Amazon Bedrock Knowledge Bases
Multimodal data processing support enables applications to understand both text and visual elements in documents.

With multimodal data processing, applications can use a knowledge base to:

• Retrieve answers from visual elements in addition to existing support of text.
• Generate responses based on the context that includes both text and visual data.
• Provide source attribution that references visual elements from the original documents.

When creating a knowledge base in the Amazon Bedrock console, I now have the option to select Amazon Bedrock Data Automation as Parsing strategy.

When I select Amazon Bedrock Data Automation as parser, Amazon Bedrock Data Automation handles the extraction, transformation, and generation of insights from visually rich content, while Amazon Bedrock Knowledge Bases manages ingestion, retrieval, model response generation, and source attribution.

Alternatively, I can use the existing Foundation models as a parser option. With this option, there’s now support for Anthropic’s Claude 3.5 Sonnet as parser, and I can use the default prompt or modify it to suit a specific use case.

In the next step, I specify the Multimodal storage destination on Amazon S3 that will be used by Amazon Bedrock Knowledge Bases to store images extracted from my documents in the knowledge base data source. These images can be retrieved based on a user query, used to generate the response, and cited in the response.

When using the knowledge base, the information extracted by Amazon Bedrock Data Automation or FMs as parser is used to retrieve information about visual elements, understand charts and diagrams, and provide responses that reference both textual and visual content.

Using GraphRAG in Amazon Bedrock Knowledge Bases
Extracting insights from scattered data sources presents significant challenges for RAG applications, requiring multi-step reasoning across these data sources to generate relevant responses. For example, a customer might ask a generative AI-powered travel application to identify family-friendly beach destinations with direct flights from their home location that also offer good seafood restaurants. This requires a connected workflow to identify suitable beaches that other families have enjoyed, match these to flight routes, and select highly-rated local restaurants. A traditional RAG system may struggle to synthesize all these pieces into a cohesive recommendation because the information lives in disparate sources and is not interlinked.

Knowledge graphs can address this challenge by modeling complex relationships between entities in a structured way. However, building and integrating graphs into an application requires significant expertise and effort.

Amazon Bedrock Knowledge Bases now offers one of the first fully managed GraphRAG capabilities that enhances generative AI applications by providing more accurate and comprehensive responses to end users by using RAG techniques combined with graphs.

When creating a knowledge base, I can now enable GraphRAG in just a few steps by choosing Amazon Neptune Analytics as database, automatically generating vector and graph representations of the underlying data, entities and their relationships, and reducing development effort from several weeks to just a few hours.

I start the creation of new knowledge base. In the Vector database section, when creating a new vector store, I select Amazon Neptune Analytics (GraphRAG). If I don’t want to create a new graph, I can provide an existing vector store and select a Neptune Analytics graph from the list. GraphRAG uses Anthropic’s Claude 3 Haiku to automatically build graphs for a knowledge base.

After I complete the creation of the knowledge base, Amazon Bedrock automatically builds a graph, linking related concepts and documents. When retrieving information from the knowledge base, GraphRAG traverses these relationships to provide more comprehensive and accurate responses.

Using structured data retrieval in Amazon Bedrock Knowledge Bases
Structured data retrieval allows natural language querying of databases and data warehouses. For example, a business analyst might ask, “What were our top-selling products last quarter?” and the system automatically generates and runs the appropriate SQL query for a data warehouse stored in an Amazon Redshift database.

When creating a knowledge base, I now have the option to use a structured data store.

I enter a name and description for the knowledge base. In Data source details, I use Amazon Redshift as Query engine. I create a new AWS Identity and Access Management (IAM) service role to manage the knowledge base resources and choose Next.

I choose Redshift serverless in Connection options and the Workgroup to use. Amazon Redshift provisioned clusters are also supported. I use the previously created IAM role for Authentication. Storage metadata can be managed with AWS Glue Data Catalog or directly within an Amazon Redshift database. I select a database from the list.

In the configuration of the knowledge base, I can define the maximum duration for a query and include or exclude access to tables or columns. To improve the accuracy of query generation from natural language, I can optionally add a description for tables and columns and a list of curated queries that provides practical examples of how to translate a question into a SQL query for my database. I choose Next, review the settings, and complete the creation of the knowledge base

After a few minutes, the knowledge base is ready. Once synced, Amazon Bedrock Knowledge Bases handles generating, running, and formatting the result of the query, making it easy to build natural language interfaces to structured data. When invoking a knowledge base using structured data, I can ask to only generate SQL, retrieve data, or summarize the data in natural language.

Things to know
These new capabilities are available today in the following AWS Regions:

• Amazon Bedrock Data Automation is available in preview in US West (Oregon).
• Multimodal data processing support in Amazon Bedrock Knowledge Bases using Amazon Bedrock Data Automation as parser is available in preview in US West (Oregon). FM as a parser is available in all Regions where Amazon Bedrock Knowledge Bases is offered.
• GraphRAG in Amazon Bedrock Knowledge Bases is available in preview in all commercial Regions where Amazon Bedrock Knowledge Bases and Amazon Neptune Analytics are offered.
• Structured data retrieval is available in Amazon Bedrock Knowledge Bases in all commercial Regions where Amazon Bedrock Knowledge Bases is offered.

As usual with Amazon Bedrock, pricing is based on usage:

• Amazon Bedrock Data Automation charges per images, per page for documents, and per minute for audio or video.
• Multimodal data processing in Amazon Bedrock Knowledge Bases is charged based on the use of either Amazon Bedrock Data Automation or the FM as parser.
• There is no additional cost for using GraphRAG in Amazon Bedrock Knowledge Bases but you pay for using Amazon Neptune Analytics as the vector store. For more information, visit Amazon Neptune pricing.
• There is an additional cost when using structured data retrieval in Amazon Bedrock Knowledge Bases.

For detailed pricing information, see Amazon Bedrock pricing.

Each capability can be used independently or in combination. Together, they make it easier and faster to build applications that use AI to process data. To get started, visit the Amazon Bedrock console. To learn more, you can access the Amazon Bedrock documentation and send feedback to AWS re:Post for Amazon Bedrock. You can find deep-dive technical content and discover how our Builder communities are using Amazon Bedrock at community.aws. Let us know what you build with these new capabilities!

— Danilo

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/reduce-costs-and-latency-with-amazon-bedrock-intelligent-prompt-routing-and-prompt-caching-preview/

Today, Amazon Bedrock has introduced in preview two capabilities that help reduce costs and latency for generative AI applications:

Amazon Bedrock Intelligent Prompt Routing – When invoking a model, you can now use a combination of foundation models (FMs) from the same model family to help optimize for quality and cost. For example, with the Anthropic’s Claude model family, Amazon Bedrock can intelligently route requests between Claude 3.5 Sonnet and Claude 3 Haiku depending on the complexity of the prompt. Similarly, Amazon Bedrock can route requests between Meta Llama 3.1 70B and 8B. The prompt router predicts which model will provide the best performance for each request while optimizing the quality of response and cost. This is particularly useful for applications such as customer service assistants, where uncomplicated queries can be handled by smaller, faster, and more cost-effective models, and complex queries are routed to more capable models. Intelligent Prompt Routing can reduce costs by up to 30 percent without compromising on accuracy.

Amazon Bedrock now supports prompt caching – You can now cache frequently used context in prompts across multiple model invocations. This is especially valuable for applications that repeatedly use the same context, such as document Q&A systems where users ask multiple questions about the same document or coding assistants that need to maintain context about code files. The cached context remains available for up to 5 minutes after each access. Prompt caching in Amazon Bedrock can reduce costs by up to 90% and latency by up to 85% for supported models.

These features make it easier to reduce latency and balance performance with cost efficiency. Let’s look at how you can use them in your applications.

Using Amazon Bedrock Intelligent Prompt Routing in the console
Amazon Bedrock Intelligent Prompt Routing uses advanced prompt matching and model understanding techniques to predict the performance of each model for every request, optimizing for quality of responses and cost. During the preview, you can use the default prompt routers for Anthropic’s Claude and Meta Llama model families.

Intelligent prompt routing can be accessed through the AWS Management Console, the AWS Command Line Interface (AWS CLI), and the AWS SDKs. In the Amazon Bedrock console, I choose Prompt routers in the Foundation models section of the navigation pane.

I choose the Anthropic Prompt Router default router to get more information.

From the configuration of the prompt router, I see that it’s routing requests between Claude 3.5 Sonnet and Claude 3 Haiku using cross-Region inference profiles. The routing criteria defines the quality difference between the response of the largest model and the smallest model for each prompt as predicted by the router internal model at runtime. The fallback model, used when none of the chosen models meet the desired performance criteria, is Anthropic’s Claude 3.5 Sonnet.

I choose Open in Playground to chat using the prompt router and enter this prompt:

Alice has N brothers and she also has M sisters. How many sisters does Alice’s brothers have?

The result is quickly provided. I choose the new Router metrics icon on the right to see which model was selected by the prompt router. In this case, because the question is rather complex, Anthropic’s Claude 3.5 Sonnet was used.

Now I ask a straightforward question to the same prompt router:

Describe the purpose of a 'hello world' program in one line.

This time, Anthropic’s Claude 3 Haiku has been selected by the prompt router.

I select the Meta Prompt Router to check its configuration. It’s using the cross-Region inference profiles for Llama 3.1 70B and 8B with the 70B model as fallback.

Prompt routers are integrated with other Amazon Bedrock capabilities, such as Amazon Bedrock Knowledge Bases and Amazon Bedrock Agents, or when performing evaluations. For example, here I create a model evaluation to help me compare, for my use case, a prompt router to another model or prompt router.

To use a prompt router in an application, I need to set the prompt router Amazon Resource Name (ARN) as model ID in the Amazon Bedrock API. Let’s see how this works with the AWS CLI and an AWS SDK.

Using Amazon Bedrock Intelligent Prompt Routing with the AWS CLI
The Amazon Bedrock API has been extended to handle prompt routers. For example, I can list the existing prompt routes in an AWS Region using ListPromptRouters:

aws bedrock list-prompt-routers

In output, I receive a summary of the existing prompt routers, similar to what I saw in the console.

Here’s the full output of the previous command:

{
    "promptRouterSummaries": [
        {
            "promptRouterName": "Anthropic Prompt Router",
            "routingCriteria": {
                "responseQualityDifference": 0.26
            },
            "description": "Routes requests among models in the Claude family",
            "createdAt": "2024-11-20T00:00:00+00:00",
            "updatedAt": "2024-11-20T00:00:00+00:00",
            "promptRouterArn": "arn:aws:bedrock:us-east-1:123412341234:default-prompt-router/anthropic.claude:1",
            "models": [
                {
                    "modelArn": "arn:aws:bedrock:us-east-1:123412341234:inference-profile/us.anthropic.claude-3-haiku-20240307-v1:0"
                },
                {
                    "modelArn": "arn:aws:bedrock:us-east-1:123412341234:inference-profile/us.anthropic.claude-3-5-sonnet-20240620-v1:0"
                }
            ],
            "fallbackModel": {
                "modelArn": "arn:aws:bedrock:us-east-1:123412341234:inference-profile/us.anthropic.claude-3-5-sonnet-20240620-v1:0"
            },
            "status": "AVAILABLE",
            "type": "default"
        },
        {
            "promptRouterName": "Meta Prompt Router",
            "routingCriteria": {
                "responseQualityDifference": 0.0
            },
            "description": "Routes requests among models in the LLaMA family",
            "createdAt": "2024-11-20T00:00:00+00:00",
            "updatedAt": "2024-11-20T00:00:00+00:00",
            "promptRouterArn": "arn:aws:bedrock:us-east-1:123412341234:default-prompt-router/meta.llama:1",
            "models": [
                {
                    "modelArn": "arn:aws:bedrock:us-east-1:123412341234:inference-profile/us.meta.llama3-1-8b-instruct-v1:0"
                },
                {
                    "modelArn": "arn:aws:bedrock:us-east-1:123412341234:inference-profile/us.meta.llama3-1-70b-instruct-v1:0"
                }
            ],
            "fallbackModel": {
                "modelArn": "arn:aws:bedrock:us-east-1:123412341234:inference-profile/us.meta.llama3-1-70b-instruct-v1:0"
            },
            "status": "AVAILABLE",
            "type": "default"
        }
    ]
}

I can get information about a specific prompt router using GetPromptRouter with a prompt router ARN. For example, for the Meta Llama model family:

aws bedrock get-prompt-router --prompt-router-arn arn:aws:bedrock:us-east-1:123412341234:default-prompt-router/meta.llama:1

{
    "promptRouterName": "Meta Prompt Router",
    "routingCriteria": {
        "responseQualityDifference": 0.0
    },
    "description": "Routes requests among models in the LLaMA family",
    "createdAt": "2024-11-20T00:00:00+00:00",
    "updatedAt": "2024-11-20T00:00:00+00:00",
    "promptRouterArn": "arn:aws:bedrock:us-east-1:123412341234:default-prompt-router/meta.llama:1",
    "models": [
        {
            "modelArn": "arn:aws:bedrock:us-east-1:123412341234:inference-profile/us.meta.llama3-1-8b-instruct-v1:0"
        },
        {
            "modelArn": "arn:aws:bedrock:us-east-1:123412341234:inference-profile/us.meta.llama3-1-70b-instruct-v1:0"
        }
    ],
    "fallbackModel": {
        "modelArn": "arn:aws:bedrock:us-east-1:123412341234:inference-profile/us.meta.llama3-1-70b-instruct-v1:0"
    },
    "status": "AVAILABLE",
    "type": "default"
}

To use a prompt router with Amazon Bedrock, I set the prompt router ARN as model ID when making API calls. For example, here I use the Anthropic Prompt Router with the AWS CLI and the Amazon Bedrock Converse API:

aws bedrock-runtime converse \
    --model-id arn:aws:bedrock:us-east-1:123412341234:default-prompt-router/anthropic.claude:1 \
    --messages '[{ "role": "user", "content": [ { "text": "Alice has N brothers and she also has M sisters. How many sisters does Alice’s brothers have?" } ] }]' \

In output, invocations using a prompt router include a new trace section that tells which model was actually used. In this case, it’s Anthropic’s Claude 3.5 Sonnet:

{
    "output": {
        "message": {
            "role": "assistant",
            "content": [
                {
                    "text": "To solve this problem, let's think it through step-by-step:\n\n1) First, we need to understand the relationships:\n   - Alice has N brothers\n   - Alice has M sisters\n\n2) Now, we need to consider who Alice's brothers' sisters are:\n   - Alice herself is a sister to all her brothers\n   - All of Alice's sisters are also sisters to Alice's brothers\n\n3) So, the total number of sisters that Alice's brothers have is:\n   - The number of Alice's sisters (M)\n   - Plus Alice herself (+1)\n\n4) Therefore, the answer can be expressed as: M + 1\n\nThus, Alice's brothers have M + 1 sisters."
                }
            ]
        }
    },
    . . .
    "trace": {
        "promptRouter": {
            "invokedModelId": "arn:aws:bedrock:us-east-1:123412341234:inference-profile/us.anthropic.claude-3-5-sonnet-20240620-v1:0"
        }
    }
}

Using Amazon Bedrock Intelligent Prompt Routing with an AWS SDK
Using an AWS SDK with a prompt router is similar to the previous command line experience. When invoking a model, I set the model ID to the prompt model ARN. For example, in this Python code I’m using the Meta Llama router with the ConverseStream API:

import json
import boto3

bedrock_runtime = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1",
)

MODEL_ID = "arn:aws:bedrock:us-east-1:123412341234:default-prompt-router/meta.llama:1"

user_message = "Describe the purpose of a 'hello world' program in one line."
messages = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

streaming_response = bedrock_runtime.converse_stream(
    modelId=MODEL_ID,
    messages=messages,
)

for chunk in streaming_response["stream"]:
    if "contentBlockDelta" in chunk:
        text = chunk["contentBlockDelta"]["delta"]["text"]
        print(text, end="")
    if "messageStop" in chunk:
        print()
    if "metadata" in chunk:
        if "trace" in chunk["metadata"]:
            print(json.dumps(chunk['metadata']['trace'], indent=2))

This script prints the response text and the content of the trace in response metadata. For this uncomplicated request, the faster and more affordable model has been selected by the prompt router:

A "Hello World" program is a simple, introductory program that serves as a basic example to demonstrate the fundamental syntax and functionality of a programming language, typically used to verify that a development environment is set up correctly.
{
  "promptRouter": {
    "invokedModelId": "arn:aws:bedrock:us-east-1:123412341234:inference-profile/us.meta.llama3-1-8b-instruct-v1:0"
  }
}

Using prompt caching with an AWS SDK
You can use prompt caching with the Amazon Bedrock Converse API. When you tag content for caching and send it to the model for the first time, the model processes the input and saves the intermediate results in a cache. For subsequent requests containing the same content, the model loads the preprocessed results from the cache, significantly reducing both costs and latency.

You can implement prompt caching in your applications with a few steps:

1. Identify the portions of your prompts that are frequently reused.
2. Tag these sections for caching in the list of messages using the new cachePoint block.
3. Monitor cache usage and latency improvements in the response metadata usage section.

Here’s an example of implementing prompt caching when working with documents.

First, I download three decision guides in PDF format from the AWS website. These guides help choose the AWS services that fit your use case.

Then, I use a Python script to ask three questions about the documents. In the code, I create a converse() function to handle the conversation with the model. The first time I call the function, I include a list of documents and a flag to add a cachePoint block.

import json

import boto3

MODEL_ID = "us.anthropic.claude-3-5-sonnet-20241022-v2:0"
AWS_REGION = "us-west-2"

bedrock_runtime = boto3.client(
    "bedrock-runtime",
    region_name=AWS_REGION,
)

DOCS = [
    "bedrock-or-sagemaker.pdf",
    "generative-ai-on-aws-how-to-choose.pdf",
    "machine-learning-on-aws-how-to-choose.pdf",
]

messages = []


def converse(new_message, docs=[], cache=False):

    if len(messages) == 0 or messages[-1]["role"] != "user":
        messages.append({"role": "user", "content": []})

    for doc in docs:
        print(f"Adding document: {doc}")
        name, format = doc.rsplit('.', maxsplit=1)
        with open(doc, "rb") as f:
            bytes = f.read()
        messages[-1]["content"].append({
            "document": {
                "name": name,
                "format": format,
                "source": {"bytes": bytes},
            }
        })

    messages[-1]["content"].append({"text": new_message})

    if cache:
        messages[-1]["content"].append({"cachePoint": {"type": "default"}})

    response = bedrock_runtime.converse(
        modelId=MODEL_ID,
        messages=messages,
    )

    output_message = response["output"]["message"]
    response_text = output_message["content"][0]["text"]

    print("Response text:")
    print(response_text)

    print("Usage:")
    print(json.dumps(response["usage"], indent=2))

    messages.append(output_message)


converse("Compare AWS Trainium and AWS Inferentia in 20 words or less.", docs=DOCS, cache=True)
converse("Compare Amazon Textract and Amazon Transcribe in 20 words or less.")
converse("Compare Amazon Q Business and Amazon Q Developer in 20 words or less.")

For each invocation, the script prints the response and the usage counters.

Adding document: bedrock-or-sagemaker.pdf
Adding document: generative-ai-on-aws-how-to-choose.pdf
Adding document: machine-learning-on-aws-how-to-choose.pdf
Response text:
AWS Trainium is optimized for machine learning training, while AWS Inferentia is designed for low-cost, high-performance machine learning inference.
Usage:
{
  "inputTokens": 4,
  "outputTokens": 34,
  "totalTokens": 29879,
  "cacheReadInputTokenCount": 0,
  "cacheWriteInputTokenCount": 29841
}
Response text:
Amazon Textract extracts text and data from documents, while Amazon Transcribe converts speech to text from audio or video files.
Usage:
{
  "inputTokens": 59,
  "outputTokens": 30,
  "totalTokens": 29930,
  "cacheReadInputTokenCount": 29841,
  "cacheWriteInputTokenCount": 0
}
Response text:
Amazon Q Business answers questions using enterprise data, while Amazon Q Developer assists with building and operating AWS applications and services.
Usage:
{
  "inputTokens": 108,
  "outputTokens": 26,
  "totalTokens": 29975,
  "cacheReadInputTokenCount": 29841,
  "cacheWriteInputTokenCount": 0
}

The usage section of the response contains two new counters: cacheReadInputTokenCount and cacheWriteInputTokenCount. The total number of tokens for an invocation is the sum of the input and output tokens plus the tokens read and written into the cache.

Each invocation processes a list of messages. The messages in the first invocation contain the documents, the first question, and the cache point. Because the messages preceding the cache point aren’t currently in the cache, they’re written to cache. According to the usage counters, 29,841 tokens have been written into the cache.

"cacheWriteInputTokenCount": 29841

For the next invocations, the previous response and the new question are appended to the list of messages. The messages before the cachePoint are not changed and found in the cache.

As expected, we can tell from the usage counters that the same number of tokens previously written is now read from the cache.

"cacheReadInputTokenCount": 29841

In my tests, the next invocations take 55 percent less time to complete compared to the first one. Depending on your use case (for example, with more cached content), prompt caching can improve latency up to 85 percent.

Depending on the model, you can set more than one cache point in a list of messages. To find the right cache points for your use case, try different configurations and look at the effect on the reported usage.

Things to know
Amazon Bedrock Intelligent Prompt Routing is available in preview today in US East (N. Virginia) and US West (Oregon) AWS Regions. During the preview, you can use the default prompt routers, and there is no additional cost for using a prompt router. You pay the cost of the selected model. You can use prompt routers with other Amazon Bedrock capabilities such as performing evaluations, using knowledge bases, and configuring agents.

Because the internal model used by the prompt routers needs to understand the complexity of a prompt, intelligent prompt routing currently only supports English language prompts.

Amazon Bedrock support for prompt caching is available in preview in US West (Oregon) for Anthropic’s Claude 3.5 Sonnet V2 and Claude 3.5 Haiku. Prompt caching is also available in US East (N. Virginia) for Amazon Nova Micro, Amazon Nova Lite, and Amazon Nova Pro.

With prompt caching, cache reads receive a 90 percent discount compared to noncached input tokens. There are no additional infrastructure charges for cache storage. When using Anthropic models, you pay an additional cost for tokens written in the cache. There are no additional costs for cache writes with Amazon Nova models. For more information, see Amazon Bedrock pricing.

When using prompt caching, content is cached for up to 5 minutes, with each cache hit resetting this countdown. Prompt caching has been implemented to transparently support cross-Region inference. In this way, your applications can get the cost optimization and latency benefit of prompt caching with the flexibility of cross-Region inference.

These new capabilities make it easier to build cost-effective and high-performing generative AI applications. By intelligently routing requests and caching frequently used content, you can significantly reduce your costs while maintaining and even improving application performance.

To learn more and start using these new capabilities today, visit the Amazon Bedrock documentation and send feedback to AWS re:Post for Amazon Bedrock. You can find deep-dive technical content and discover how our Builder communities are using Amazon Bedrock at community.aws.

— Danilo