Tag Archives: AWS re:Invent

AWS Clean Rooms Differential Privacy enhances privacy protection of your users data (preview)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/aws-clean-rooms-differential-privacy-enhances-privacy-protection-of-your-users-data-preview/

Starting today, you can use AWS Clean Rooms Differential Privacy (preview) to help protect the privacy of your users with mathematically backed and intuitive controls in a few steps. As a fully managed capability of AWS Clean Rooms, no prior differential privacy experience is needed to help you prevent the reidentification of your users.

AWS Clean Rooms Differential Privacy obfuscates the contribution of any individual’s data in generating aggregate insights in collaborations so that you can run a broad range of SQL queries to generate insights about advertising campaigns, investment decisions, clinical research, and more.

Quick overview on differential privacy
Differential privacy is not new. It is a strong, mathematical definition of privacy compatible with statistical and machine learning based analysis, and has been used by the United States Census Bureau as well as companies with vast amounts of data.

Differential privacy helps with a wide variety of use cases involving large datasets, where adding or removing a few individuals has a small impact on the overall result, such as population analyses using count queries, histograms, benchmarking, A/B testing, and machine learning.

The following illustration shows how differential privacy works when it is applied to SQL queries.

When an analyst runs a query, differential privacy adds a carefully calibrated amount of error (also referred to as noise) to query results at run-time, masking the contribution of individuals while still keeping the query results accurate enough to provide meaningful insights. The noise is carefully fine-tuned to mask the presence or absence of any possible individual in the dataset.

Differential privacy also has another component called privacy budget. The privacy budget is a finite resource consumed each time a query is run and thus controls the number of queries that can be run on your datasets, helping ensure that the noise cannot be averaged out to reveal any private information about an individual. When the privacy budget is fully exhausted, no more queries can be run on your tables until it is increased or refreshed.

However, differential privacy is not easy to implement because this technique requires an in-depth understanding of mathematically rigorous formulas and theories to apply it effectively. Configuring differential privacy is also a complex task because customers need to calculate the right level of noise in order to preserve the privacy of their users without negatively impacting the utility of query results.

Customers also want to enable their partners to conduct a wide variety of analyses including highly complex and customized queries on their data. This requirement is hard to support with differential privacy because of the intricate nature of the calculations involved in calibrating the noise while processing various query components such as aggregations, joins, and transformations.

We created AWS Clean Rooms Differential Privacy to help you protect the privacy of your users with mathematically backed controls in a few clicks.

How differential privacy works in AWS Clean Rooms
While differential privacy is quite a sophisticated technique, AWS Clean Rooms Differential Privacy makes it easy for you to apply it and protect the privacy of your users with mathematically backed, flexible, and intuitive controls. You can begin using it with just a few steps after starting or joining an AWS Clean Rooms collaboration as a member with abilities to contribute data.

You create a configured table, which is a reference to your table in the AWS Glue Data Catalog, and choose to turn on differential privacy while adding a custom analysis rule to the configured table.

Next, you associate the configured table to your AWS Clean Rooms collaboration and configure a differential privacy policy in the collaboration to make your table available for querying. You can use a default policy to quickly complete the setup or customize it to meet your specific requirements. As part of this step, you will configure the following:

Privacy budget
Quantified as a value that we call epsilon, the privacy budget controls the level of privacy protection. It is a common, finite resource that is applied for all of your tables protected with differential privacy in the collaboration because the goal is to preserve the privacy of your users whose information can be present in multiple tables. The privacy budget is consumed every time a query is run on your tables. You have the flexibility to increase the privacy budget value any time during the collaboration and automatically refresh it each calendar month.

Noise added per query
Measured in terms of the number of users whose contributions you want to obscure, this input parameter governs the rate at which the privacy budget is depleted.

In general, you need to balance your privacy needs against the number of queries you want to permit and the accuracy of those queries. AWS Clean Rooms makes it easy for you to complete this step by helping you understand the resulting utility you are providing to your collaboration partner. You can also use the interactive examples to understand how your chosen settings would impact the results for different types of SQL queries.

Now that you have successfully enabled differential privacy protection for your data, let’s see AWS Clean Rooms Differential Privacy in action. For this demo, let’s assume I am your partner in the AWS Clean Rooms collaboration.

Here, I’m running a query to count the number of overlapping customers and the result shows there are 3,227,643 values for tv.customer_id.

Now, if I run the same query again after removing records about an individual from coffee_customers table, it shows a different result, 3,227,604 tv.customer_id. This variability in the query results prevents me from identifying the individuals from observing the difference in query results.

I can also see the impact of differential privacy, including the remaining queries I can run.

Available for preview
Join this preview and start protecting the privacy of your users with AWS Clean Rooms Differential Privacy. During this preview period, you can use AWS Clean Rooms Differential Privacy wherever AWS Clean Rooms is available. To learn more on how to get started, visit the AWS Clean Rooms Differential Privacy page.

Happy collaborating!
— Donnie

AWS Clean Rooms ML helps customers and partners apply ML models without sharing raw data (preview)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/aws-clean-rooms-ml-helps-customers-and-partners-apply-ml-models-without-sharing-raw-data-preview/

Today, we’re introducing AWS Clean Rooms ML (preview), a new capability of AWS Clean Rooms that helps you and your partners apply machine learning (ML) models on your collective data without copying or sharing raw data with each other. With this new capability, you can generate predictive insights using ML models while continuing to protect your sensitive data.

During this preview, AWS Clean Rooms ML introduces its first model specialized to help companies create lookalike segments for marketing use cases. With AWS Clean Rooms ML lookalike, you can train your own custom model, and you can invite partners to bring a small sample of their records to collaborate and generate an expanded set of similar records while protecting everyone’s underlying data.

In the coming months, AWS Clean Rooms ML will release a healthcare model. This will be the first of many models that AWS Clean Rooms ML will support next year.

AWS Clean Rooms ML helps you to unlock various opportunities for you to generate insights. For example:

  • Airlines can take signals about loyal customers, collaborate with online booking services, and offer promotions to users with similar characteristics.
  • Auto lenders and car insurers can identify prospective auto insurance customers who share characteristics with a set of existing lease owners.
  • Brands and publishers can model lookalike segments of in-market customers and deliver highly relevant advertising experiences.
  • Research institutions and hospital networks can find candidates similar to existing clinical trial participants to accelerate clinical studies (coming soon).

AWS Clean Rooms ML lookalike modeling helps you apply an AWS managed, ready-to-use model that is trained in each collaboration to generate lookalike datasets in a few clicks, saving months of development work to build, train, tune, and deploy your own model.

How to use AWS Clean Rooms ML to generate predictive insights
Today I will show you how to use lookalike modeling in AWS Clean Rooms ML and assume you have already set up a data collaboration with your partner. If you want to learn how to do that, check out the AWS Clean Rooms Now Generally Available — Collaborate with Your Partners without Sharing Raw Data post.

With your collective data in the AWS Clean Rooms collaboration, you can work with your partners to apply ML lookalike modeling to generate a lookalike segment. It works by taking a small sample of representative records from your data, creating a machine learning (ML) model, then applying the particular model to identify an expanded set of similar records from your business partner’s data.

The following screenshot shows the overall workflow for using AWS Clean Rooms ML.

By using AWS Clean Rooms ML, you don’t need to build complex and time-consuming ML models on your own. AWS Clean Rooms ML trains a custom, private ML model, which saves months of your time while still protecting your data.

Eliminating the need to share data
As ML models are natively built within the service, AWS Clean Rooms ML helps you protect your dataset and customer’s information because you don’t need to share your data to build your ML model.

You can specify the training dataset using the AWS Glue Data Catalog table, which contains user-item interactions.

Under Additional columns to train, you can define numerical and categorical data. This is useful if you need to add more features to your dataset, such as the number of seconds spent watching a video, the topic of an article, or the product category of an e-commerce item.

Applying custom-trained AWS-built models
Once you have defined your training dataset, you can now create a lookalike model. A lookalike model is a machine learning model used to find similar profiles in your partner’s dataset without either party having to share their underlying data with each other.

When creating a lookalike model, you need to specify the training dataset. From a single training dataset, you can create many lookalike models. You also have the flexibility to define the date window in your training dataset using Relative range or Absolute range. This is useful when you have data that is constantly updated within AWS Glue, such as articles read by users.

Easy-to-tune ML models
After you create a lookalike model, you need to configure it to use in AWS Clean Rooms collaboration. AWS Clean Rooms ML provides flexible controls that enable you and your partners to tune the results of the applied ML model to garner predictive insights.

On the Configure lookalike model page, you can choose which Lookalike model you want to use and define the Minimum matching seed size you need. This seed size defines the minimum number of profiles in your seed data that overlap with profiles in the training data.

You also have the flexibility to choose whether the partner in your collaboration receives metrics in Metrics to share with other members.

With your lookalike models properly configured, you can now make the ML models available for your partners by associating the configured lookalike model with a collaboration.

Creating lookalike segments
Once the lookalike models have been associated, your partners can now start generating insights by selecting Create lookalike segment and choosing the associated lookalike model for your collaboration.

Here on the Create lookalike segment page, your partners need to provide the Seed profiles. Examples of seed profiles include your top customers or all customers who purchased a specific product. The resulting lookalike segment will contain profiles from the training data that are most similar to the profiles from the seed.

Lastly, your partner will get the Relevance metrics as the result of the lookalike segment using the ML models. At this stage, you can use the Score to make a decision.

Export data and use programmatic API
You also have the option to export the lookalike segment data. Once it’s exported, the data is available in JSON format and you can process this output by integrating with AWS Clean Rooms API and your applications.

Join the preview
AWS Clean Rooms ML is now in preview and available via AWS Clean Rooms in US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Seoul, Singapore, Sydney, Tokyo), and Europe (Frankfurt, Ireland, London). Support for additional models is in the works.

Learn how to apply machine learning with your partners without sharing underlying data on the AWS Clean Rooms ML page.

Happy collaborating!
— Donnie

Announcing Amazon OpenSearch Service zero-ETL integration with Amazon S3 (preview)

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/amazon-opensearch-service-zero-etl-integration-with-amazon-s3-preview/

Today we are announcing a preview of Amazon OpenSearch Service zero-ETL integration with Amazon S3, a new way to query operational logs in Amazon S3 and S3-based data lakes without needing to switch between services. You can now analyze infrequently queried data in cloud object stores and simultaneously use the operational analytics and visualization capabilities of OpenSearch Service.

Amazon OpenSearch Service direct queries with Amazon S3 provides a zero-ETL integration to reduce the operational complexity of duplicating data or managing multiple analytics tools by enabling customers to directly query their operational data, reducing costs and time to action. This zero-ETL integration will be configurable within OpenSearch Service, where you can take advantage of various log type templates, including predefined dashboards, and configure data accelerations tailored to that log type. Templates include VPC Flow Logs, Elastic Load Balancing logs, and NGINX logs, and accelerations include skipping indexes, materialized views, and covered indexes.

With direct queries with Amazon S3, you can perform complex queries critical to security forensic and threat analysis that correlate data across multiple data sources, which aids teams in investigating service downtime and security events. After creating an integration, you can start querying their data directly from the OpenSearch Dashboards or OpenSearch API. You can easily audit connections to ensure that they are set up in a scalable, cost-efficient, and secure way.

Getting started with direct queries with Amazon S3
You can easily get started by creating a new Amazon S3 direct query data source for OpenSearch Service through the AWS Management Console or the API. Each new data source uses AWS Glue Data Catalog to manage tables that represent S3 buckets. Once you create a data source, you can configure Amazon S3 tables and data indexing and query data in OpenSearch Dashboards.

1. Create a data source in OpenSearch Service
Before you create a data source, you should have an OpenSearch Service domain with version 2.11 or later and a target Amazon S3 table in AWS Glue Data Catalog with the appropriate IAM permissions. IAM will need access to the desired S3 bucket(s) and read and write access to AWS Glue Data Catalog. To learn more about IAM prerequisites, see Creating a data source in the AWS documentation.

Go to the OpenSearch Service console and choose the domain you want to set up a new data source for. In the domain details page, choose the Connections tab below the general information and see the Direct Query section.

To create a new data source, choose Create, input the name of your new data source, select the data source type as Amazon S3 with AWS Glue Data Catalog, and choose the IAM role for your data source.

Once you create a data source, you can go to the OpenSearch Dashboards of the domain, which you use to configure access control, define tables, set up log type–based dashboards for popular log types, and query your data.

2. Configuring your data source in OpenSearch Dashboards
To configure data source in OpenSearch Dashboards, choose Configure in the console and go to OpenSearch Dashboards. In the left-hand navigation of OpenSearch Dashboards, under Management, choose Data sources. Under Manage data sources, choose the name of the data source you created in the console.

Direct queries from OpenSearch Service to Amazon S3 use Spark tables within AWS Glue Data Catalog. To create a new table you want to direct query, go to the Query Workbench in the Open Search Plugins menu.

Now run as in the following SQL statement to create http_logs table and run MSCK REPAIR TABLE mys3.default.http_logs command to update the metadata in the catalog

CREATE EXTERNAL TABLE IF NOT EXISTS mys3.default.http_logs (
   `@timestamp` TIMESTAMP,
    clientip STRING,
    request STRING, 
    status INT, 
    size INT, 
    year INT, 
    month INT, 
    day INT) 
USING json PARTITIONED BY(year, month, day) OPTIONS (path 's3://mys3/data/http_log/http_logs_partitioned_json_bz2/', compression 'bzip2')

To ensure a fast experience with your data in Amazon S3, you can set up any of three different types of accelerations to index data into OpenSearch Service, such as skipping indexes, materialized views, and covering indexes. To create OpenSearch indexes from external data connections for better performance, choose the Accelerate Table.

  • Skipping indexes allow you to index only the metadata of the data stored in Amazon S3. Skipping indexes help quickly identify data stored by narrowing down a specific location of where the data is stored.
  • Materialized views enable you to use complex queries such as aggregations, which can be used for querying or powering dashboard visualizations. Materialized views ingest data into OpenSearch Service for anomaly detection or geospatial capabilities.
  • Covering indexes will ingest all the data from the specified table column. Covering indexes are the most performant of the three indexing types.

3. Query your data source in OpenSearch Dashboards
After you set up your tables, you can query your data using Discover. You can run a sample SQL query for the http_logs table you created in AWS Glue Data Catalog tables.

To learn more, see Working with Amazon OpenSearch Service direct queries with Amazon S3 in the AWS documentation.

Join the preview
Amazon OpenSearch Service zero-ETL integration with Amazon S3 is now previewed in the AWS US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), Europe (Frankfurt), and Europe (Ireland) Regions.

OpenSearch Service separately charges for only the compute needed as OpenSearch Compute Units to query your external data as well as maintain indexes in OpenSearch Service. For more information, see Amazon OpenSearch Service Pricing.

Give it a try and send feedback to the AWS re:Post for Amazon OpenSearch Service or through your usual AWS Support contacts.

— Channy

Analyze large amounts of graph data to get insights and find trends with Amazon Neptune Analytics

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/introducing-amazon-neptune-analytics-a-high-performance-graph-analytics/

I am happy to announce the general availability of Amazon Neptune Analytics, a new analytics database engine that makes it faster for data scientists and application developers to quickly analyze large amounts of graph data. With Neptune Analytics, you can now quickly load your dataset from Amazon Neptune or your data lake on Amazon Simple Storage Service (Amazon S3), run your analysis tasks in near real time, and optionally terminate your graph afterward.

Graph data enables the representation and analysis of intricate relationships and connections within diverse data domains. Common applications include social networks, where it aids in identifying communities, recommending connections, and analyzing information diffusion. In supply chain management, graphs facilitate efficient route optimization and bottleneck identification. In cybersecurity, they reveal network vulnerabilities and identify patterns of malicious activity. Graph data finds application in knowledge management, financial services, digital advertising, and network security, performing tasks such as identifying money laundering networks in banking transactions and predicting network vulnerabilities.

Since the launch of Neptune in May 2018, thousands of customers have embraced the service for storing their graph data and performing updates and deletion on specific subsets of the graph. However, analyzing data for insights often involves loading the entire graph into memory. For instance, a financial services company aiming to detect fraud may need to load and correlate all historical account transactions.

Performing analyses on extensive graph datasets, such as running common graph algorithms, requires specialized tools. Utilizing separate analytics solutions demands the creation of intricate pipelines to transfer data for processing, which is challenging to operate, time-consuming, and prone to errors. Furthermore, loading large datasets from existing databases or data lakes to a graph analytic solution can take hours or even days.

Neptune Analytics offers a fully managed graph analytics experience. It takes care of the infrastructure heavy lifting, enabling you to concentrate on problem-solving through queries and workflows. Neptune Analytics automatically allocates compute resources according to the graph’s size and quickly loads all the data in memory to run your queries in seconds. Our initial benchmarking shows that Neptune Analytics loads data from Amazon S3 up to 80x faster than existing AWS solutions.

Neptune Analytics supports 5 families of algorithms covering 15 different algorithms, each with multiple variants. For example, we provide algorithms for path-finding, detecting communities (clustering), identifying important data (centrality), and quantifying similarity. Path-finding algorithms are used for use cases such as route planning for supply chain optimization. Centrality algorithms like page rank identify the most influential sellers in a graph. Algorithms like connected components, clustering, and similarity algorithms can be used for fraud-detection use cases to determine whether the connected network is a group of friends or a fraud ring formed by a set of coordinated fraudsters.

Neptune Analytics facilitates the creation of graph applications using openCypher, presently one of the widely adopted graph query languages. Developers, business analysts, and data scientists appreciate openCypher’s SQL-inspired syntax, finding it familiar and structured for composing graph queries.

Let’s see it at work
As we usually do on the AWS News blog, let’s show how it works. For this demo, I first navigate to Neptune in the AWS Management Console. There is a new Analytics section on the left navigation pane. I select Graphs and then Create graph.

Neptune Analytics - create graph 1

On the Create graph page, I enter the details of my graph analytics database engine. I won’t detail each parameter here; their names are self-explanatory.

Neptune Analytics - Create graph 1

Pay attention to Allow from public because, the vast majority of the time, you want to keep your graph only available from the boundaries of your VPC. I also create a Private endpoint to allow private access from machines and services inside my account VPC network.

Neptune Analytics - Create graph 2

In addition to network access control, users will need proper IAM permissions to access the graph.

Finally, I enable Vector search to perform similarity search using embeddings in the dataset. The dimension of the vector depends on the large language model (LLM) that you use to generate the embedding.

Neptune Analytics - Create graph 3

When I am ready, I select Create graph (not shown here).

After a few minutes, my graph is available. Under Connectivity & security, I take note of the Endpoint. This is the DNS name I will use later to access my graph from my applications.

I can also create Replicas. A replica is a warm standby copy of the graph in another Availability Zone. You might decide to create one or more replicas for high availability. By default, we create one replica, and depending on your availability requirements, you can choose not to create replicas.

Neptune Analytics - create graph 3

Business queries on graph data
Now that the Neptune Analytics graph is available, let’s load and analyze data. For the rest of this demo, imagine I’m working in the finance industry.

I have a dataset obtained from the US Securities and Exchange Commission (SEC). This dataset contains the list of positions held by investors that have more than $100 million in assets. Here is a diagram to illustrate the structure of the dataset I use in this demo.

Nuptune graph analytics - dataset structure

I want to get a better understanding of the positions held by one investment firm (let’s name it “Seb’s Investments LLC”). I wonder what its top five holdings are and who else holds more than $1 billion in the same companies. I am also curious to know what are other investment companies that have a similar portfolio as Seb’s Investments LLC.

To start my analysis, I create a Jupyter notebook in the Neptune section of the AWS Management Console. In the notebook, I first define my analytics endpoint and load the data set from an S3 bucket. It takes only 18 seconds to load 17 million records.

Neptune Analytics - load data

Then, I start to explore the dataset using openCypher queries. I start by defining my parameters:

params = {'name': "Seb's Investments LLC", 'quarter': '2023Q4'}

First, I want to know what the top five holdings are for Seb’s Investments LLC in this quarter and who else holds more than $1 billion in the same companies. In openCypher, it translates to the query hereafter. The $name parameter’s value is “Seb’s Investment LLC” and the $quarter parameter’s value is 2023Q4.

MATCH p=(h:Holder)-->(hq1)-[o:owns]->(holding)
WHERE h.name = $name AND hq1.name = $quarter
WITH DISTINCT holding as holding, o ORDER BY o.value DESC LIMIT 5
MATCH (holding)<-[o2:owns]-(hq2)<--(coholder:Holder)
WHERE hq2.name = '2023Q4'
WITH sum(o2.value) AS totalValue, coholder, holding
WHERE totalValue > 1000000000
RETURN coholder.name, collect(holding.name)

Neptune Analytics - query 1

Then, I want to know what the other top five companies are that have similar holdings as “Seb’s Investments LLC.” I use the topKByNode() function to perform a vector search.

MATCH (n:Holder)
WHERE n.name = $name
CALL neptune.algo.vectors.topKByNode(n)
YIELD node, score
WHERE score >0
RETURN node.name LIMIT 5

This query identifies a specific Holder node with the name “Seb’s Investments LLC.” Then, it utilizes the Neptune Analytics custom vector similarity search algorithm on the embedding property of the Holder node to find other nodes in the graph that are similar. The results are filtered to include only those with a positive similarity score, and the query finally returns the names of up to five related nodes.

Neptune Analytics - query 2

Pricing and availability
Neptune Analytics is available today in seven AWS Regions: US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Singapore, Tokyo), and Europe (Frankfurt, Ireland).

AWS charges for the usage on a pay-as-you-go basis, with no recurring subscriptions or one-time setup fees.

Pricing is based on configurations of memory-optimized Neptune capacity units (m-NCU). Each m-NCU corresponds to one hour of compute and networking capacity and 1 GiB of memory. You can choose configurations starting with 128 m-NCUs and up to 4096 m-NCUs. In addition to m-NCU, storage charges apply for graph snapshots.

I invite you to read the Neptune pricing page for more details

Neptune Analytics is a new analytics database engine to analyze large graph datasets. It helps you discover insights faster for use cases such as fraud detection and prevention, digital advertising, cybersecurity, transportation logistics, and bioinformatics.

Get started
 Log in to the AWS Management Console to give Neptune Analytics a try.

— seb

Vector search for Amazon DocumentDB (with MongoDB compatibility) is now generally available

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/vector-search-for-amazon-documentdb-with-mongodb-compatibility-is-now-generally-available/

Today, we are announcing the general availability of vector search for Amazon DocumentDB (with MongoDB compatibility), a new built-in capability that lets you store, index, and search millions of vectors with millisecond response times within your document database.

Vector search is an emerging technique used in machine learning (ML) to find similar data points to given data by comparing their vector representations using distance or similarity metrics. Vectors are numerical representation of unstructured data created from large language models (LLM) hosted in Amazon Bedrock, Amazon SageMaker, and other open source or proprietary ML services. This approach is useful in creating generative artificial intelligence (AI) applications, such as intuitive search, product recommendation, personalization, and chatbots using Retrieval Augmented Generation (RAG) model approach. For example, if your data set contained individual documents for movies, you could semantically search for movies similar to Titanic based on shared context such as “boats”, “tragedy”, or “movies based on true stories” instead of simply matching keywords.

With vector search for Amazon DocumentDB, you can effectively search the database based on nuanced meaning and context without spending time and cost to manage a separate vector database infrastructure. You also benefit from the fully managed, scalable, secure, and highly available JSON-based document database that Amazon DocumentDB provides.

Getting started with vector search on Amazon DocumentDB
The vector search feature is available on your Amazon DocumentDB 5.0 instance-based clusters. To implement a vector search application, you generate vectors using embedding models for fields inside your document and store vectors side by side your source data inside Amazon DocumentDB.

Next, you create a vector index on a vector field that will help retrieve similar vectors and can search the Amazon DocumentDB database using semantic search. Finally, user-submitted queries are converted to vectors using the same embedding model to get semantically similar documents and return them to the client.

Let’s look at how to implement a simple semantic search application using vector search on Amazon DocumentDB.

Step 1. Create vector embeddings using the Amazon Titan Embeddings model
Let’s use the Amazon Titan Embeddings model to create an embedding vector. Amazon Titan Embeddings model is available in Amazon Bedrock, a serverless generative AI service. You can easily access it using a single API and without managing any infrastructure.

prompt = "I love dog and cat."
response = bedrock_runtime.invoke_model(
    body= json.dumps({"inputText": prompt}), 
    modelId='amazon.titan-embed-text-v1', 
    accept='application/json', 
    contentType='application/json'
)
response_body = json.loads(response['body'].read())
embedding = response_body.get('embedding')

The returned vector embedding will look similar to this:

[0.82421875, -0.6953125, -0.115722656, 0.87890625, 0.05883789, -0.020385742, 0.32421875, -0.00078201294, -0.40234375, 0.44140625, ...]

Step 2. Insert vector embeddings and create a vector index
You can add generated vector embeddings using the insertMany( [{},...,{}] ) operation with a list of the documents that you want added to your collection in Amazon DocumentDB.

db.collection.insertMany([
    {sentence: "I love a dog and cat.", vectorField: [0.82421875, -0.6953125,...]},
    {sentence: "My dog is very cute.", vectorField: [0.05883789, -0.020385742,...]},
    {sentence: "I write with a pen.", vectorField: [-0.020385742, 0.32421875,...]},
  ...
]);

You can create a vector index using the createIndex command. Amazon DocumentDB performs an approximate nearest neighbor (ANN) search using the inverted file with flat compression (IVFFLAT) vector index. The feature supports three distance metrics: euclidean, cosine, and inner product. We will use the euclidean distance, a measure of the straight-line distance between two points in space. The smaller the euclidean distance, the closer the vectors are to each other.

db.collection.createIndex (
   { vectorField: "vector" },
   { "name": "index name",
     "vectorOptions": {
        "dimensions": 100, // the number of vector data dimensions
        "similarity": "euclidean", // Or cosine and dotProduct
        "lists": 100 
      }
   }
);

Step 3.  Search vector embeddings from Amazon DocumentDB
You can now search for similar vectors within your documents using a new aggregation pipeline operator within $search. The example code to search “I like pets” is as follows:

db.collection.aggregate ({
  $search: {
    "vectorSearch": {
      "vector": [0.82421875, -0.6953125,...], // Search for ‘I like pets’
      "path": vectorField,
      "k": 5,
      "similarity": "euclidean", // Or cosine and dotProduct
      "probes": 1 // the number of clusters for vector search
      }
     }
   });

This returns search results such as “I love a dog and cat.” which is semantically similar.

To learn more, see Amazon DocumentDB documentation. To see a more practical example—a semantic movie search with Amazon DocumentDB—find the Python source codes and data-sets in the GitHub repository.

Now available
Vector search for Amazon DocumentDB is now available at no additional cost to all customers using Amazon DocumentDB 5.0 instance-based clusters in all AWS Regions where Amazon DocumentDB is available. Standard compute, I/O, storage, and backup charges will apply as you store, index, and search vector embeddings on Amazon DocumentDB.

To learn more, see the Amazon DocumentDB documentation and send feedback to AWS re:Post for Amazon DocumentDB or through your usual AWS Support contacts.

— Channy

Vector engine for Amazon OpenSearch Serverless is now available

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/vector-engine-for-amazon-opensearch-serverless-is-now-generally-available/

Today we are announcing the general availability of the vector engine for Amazon OpenSearch Serverless with new features. In July 2023, we introduced the preview release of the vector engine for Amazon OpenSearch Serverless, a simple, scalable, and high-performing similarity search capability. The vector engine makes it easy for you to build modern machine learning (ML) augmented search experiences and generative artificial intelligence (generative AI) applications without needing to manage the underlying vector database infrastructure.

You can now store, update, and search billions of vector embeddings with thousands of dimensions in milliseconds. The highly performant similarity search capability of vector engine enables generative AI-powered applications to deliver accurate and reliable results with consistent milliseconds-scale response times.

The vector engine also enables you to optimize and tune results with hybrid search by combining vector search and full-text search in the same query, removing the need to manage and maintain separate data stores or a complex application stack. The vector engine provides a secure, reliable, scalable, and enterprise-ready platform to cost effectively build a prototyping application and then seamlessly scale to production.

You can now get started in minutes with the vector engine by creating a specialized vector engine–based collection, which is a logical grouping of embeddings that works together to support a workload.

The vector engine uses OpenSearch Compute Units (OCUs), compute capacity unit, to ingest and run similarity search queries. One OCU can handle up to 2 million vectors for 128 dimensions or 500,000 for 768 dimensions at 99 percent recall rate.

The vector engine built on OpenSearch Serverless is a highly available service by default. It requires a minimum of four OCUs (2 OCUs for the ingest, including primary and standby, and 2 OCUs for the search with two active replicas across Availability Zones) for the first collection in an account. All subsequent collections using the same AWS Key Management Service (AWS KMS) key can share those OCUs.

What’s new at GA?
Since the preview, the vector engine for Amazon OpenSearch Serverless became one of the vector database options in the knowledge base of Amazon Bedrock to build generative AI applications using a Retrieval Augmented Generation (RAG) concept.

Here are some new or improved features for this GA release:

Disable redundant replica (development and test focused) option
As we announced in our preview blog post, this feature eliminates the need to have redundant OCUs in another Availability Zone solely for availability purposes. A collection can be deployed with two OCUs – one for indexing and one for search. This cuts the costs in half compared to default deployment with redundant replicas. The reduced cost makes this configuration suitable and economical for development and testing workloads.

With this option, we will still provide durability guarantees since the vector engine persists all the data in Amazon S3, but single-AZ failures would impact your availability.

If you want to disable a redundant replica, uncheck Enable redundancy when creating a new vector search
collection.

Fractional OCU for the development and test focused option
Support for fractional OCU billing for development and test focused workloads (that is, no redundant replica option) reduces the floor price for vector search collection. The vector engine will initially deploy smaller 0.5 OCUs while providing the same capabilities at lower scale and will scale up to a full OCU and beyond to meet your workload demand. This option will further reduce the monthly costs when experimenting with using the vector engine.

Automatic scaling for a billion scale
With vector engine’s seamless auto-scaling, you no longer have to reindex for scaling purposes. At preview, we were supporting about 20 million vector embeddings. With the general availability of vector engine, we have raised the limits to support a billion vector scale.

Now available
The vector engine for Amazon OpenSearch Serverless is now available in all AWS Regions where Amazon OpenSearch Serverless is available.

To get started, you can refer to the following resources:

Give it a try and send feedback to AWS re:Post for Amazon OpenSearch Service or through your usual AWS support contacts.

— Channy

Introducing Amazon SageMaker HyperPod, a purpose-built infrastructure for distributed training at scale

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/introducing-amazon-sagemaker-hyperpod-a-purpose-built-infrastructure-for-distributed-training-at-scale/

Today, we are introducing Amazon SageMaker HyperPod, which helps reducing time to train foundation models (FMs) by providing a purpose-built infrastructure for distributed training at scale. You can now use SageMaker HyperPod to train FMs for weeks or even months while SageMaker actively monitors the cluster health and provides automated node and job resiliency by replacing faulty nodes and resuming model training from a checkpoint.

The clusters come preconfigured with SageMaker’s distributed training libraries that help you split your training data and model across all the nodes to process them in parallel and fully utilize the cluster’s compute and network infrastructure. You can further customize your training environment by installing additional frameworks, debugging tools, and optimization libraries.

Let me show you how to get started with SageMaker HyperPod. In the following demo, I create a SageMaker HyperPod and show you how to train a Llama 2 7B model using the example shared in the AWS ML Training Reference Architectures GitHub repository.

Create and manage clusters
As the SageMaker HyperPod admin, you can create and manage clusters using the AWS Management Console or AWS Command Line Interface (AWS CLI). In the console, navigate to Amazon SageMaker, select Cluster management under HyperPod Clusters in the left menu, then choose Create a cluster.

Amazon SageMaker HyperPod Clusters

In the setup that follows, provide a cluster name and configure instance groups with your instance types of choice and the number of instances to allocate to each instance group.

Amazon SageMaker HyperPod

You also need to prepare and upload one or more lifecycle scripts to your Amazon Simple Storage Service (Amazon S3) bucket to run in each instance group during cluster creation. With lifecycle scripts, you can customize your cluster environment and install required libraries and packages. You can find example lifecycle scripts for SageMaker HyperPod in the GitHub repo.

Using the AWS CLI
You can also use the AWS CLI to create and manage clusters. For my demo, I specify my cluster configuration in a JSON file. I choose to create two instance groups, one for the cluster controller node(s) that I call “controller-group,” and one for the cluster worker nodes that I call “worker-group.” For the worker nodes that will perform model training, I specify Amazon EC2 Trn1 instances powered by AWS Trainium chips.

// demo-cluster.json
{
   "InstanceGroups": [
        {
            "InstanceGroupName": "controller-group",
            "InstanceType": "ml.m5.xlarge",
            "InstanceCount": 1,
            "lifecycleConfig": {
                "SourceS3Uri": "s3://<your-s3-bucket>/<lifecycle-script-directory>/",
                "OnCreate": "on_create.sh"
            },
            "ExecutionRole": "arn:aws:iam::111122223333:role/my-role-for-cluster",
            "ThreadsPerCore": 1
        },
        {
            "InstanceGroupName": "worker-group",
            "InstanceType": "trn1.32xlarge",
            "InstanceCount": 4,
            "lifecycleConfig": {
                "SourceS3Uri": "s3://<your-s3-bucket>/<lifecycle-script-directory>/",
                "OnCreate": "on_create.sh"
            },
            "ExecutionRole": "arn:aws:iam::111122223333:role/my-role-for-cluster",
            "ThreadsPerCore": 1
        }
    ]
}

To create the cluster, I run the following AWS CLI command:

aws sagemaker create-cluster \
--cluster-name antje-demo-cluster \
--instance-groups file://demo-cluster.json

Upon creation, you can use aws sagemaker describe-cluster and aws sagemaker list-cluster-nodes to view your cluster and node details. Note down the cluster ID and instance ID of your controller node. You need that information to connect to your cluster.

You also have the option to attach a shared file system, such as Amazon FSx for Lustre. To use FSx for Lustre, you need to set up your cluster with an Amazon Virtual Private Cloud (Amazon VPC) configuration. Here’s an AWS CloudFormation template that shows how to create a SageMaker VPC and how to deploy FSx for Lustre.

Connect to your cluster
As a cluster user, you need to have access to the cluster provisioned by your cluster admin. With access permissions in place, you can connect to the cluster using SSH to schedule and run jobs. You can use the preinstalled AWS CLI plugin for AWS Systems Manager to connect to the controller node of your cluster.

For my demo, I run the following command specifying my cluster ID and instance ID of the control node as the target.

aws ssm start-session \
--target sagemaker-cluster:ntg44z9os8pn_i-05a854e0d4358b59c \
--region us-west-2

Schedule and run jobs on the cluster using Slurm
At launch, SageMaker HyperPod supports Slurm for workload orchestration. Slurm is a popular an open source cluster management and job scheduling system. You can install and set up Slurm through lifecycle scripts as part of the cluster creation. The example lifecycle scripts show how. Then, you can use the standard Slurm commands to schedule and launch jobs. Check out the Slurm Quick Start User Guide for architecture details and helpful commands.

For this demo, I’m using this example from the AWS ML Training Reference Architectures GitHub repo that shows how to train Llama 2 7B on Slurm with Trn1 instances. My cluster is already setup with Slurm, and I have an FSx for Lustre filesystem mounted.

Note
The Llama 2 model is governed by Meta. You can request access through the Meta request access page.

Set up the cluster environment
SageMaker HyperPod supports training in a range of environments, including Conda, venv, Docker, and enroot. Following the instructions in the README, I build my virtual environment aws_neuron_venv_pytorch and set up the torch_neuronx and neuronx-nemo-megatron libraries for training models on Trn1 instances.

Prepare model, tokenizer, and dataset
I follow the instructions to download the Llama 2 model and tokenizer and convert the model into the Hugging Face format. Then, I download and tokenize the RedPajama dataset. As a final preparation step, I pre-compile the Llama 2 model using ahead-of-time (AOT) compilation to speed up model training.

Launch jobs on the cluster
Now, I’m ready to start my model training job using the sbatch command.

sbatch --nodes 4 --auto-resume=1 run.slurm ./llama_7b.sh

You can use the squeue command to view the job queue. Once the training job is running, the SageMaker HyperPod resiliency features are automatically enabled. SageMaker HyperPod will automatically detect hardware failures, replace nodes as needed, and resume training from checkpoints if the auto-resume parameter is set, as shown in the preceding command.

You can view the output of the model training job in the following file:

tail -f slurm-run.slurm-<JOB_ID>.out

A sample output indicating that model training has started will look like this:

Epoch 0:  22%|██▏       | 4499/20101 [22:26:14<77:48:37, 17.95s/it, loss=2.43, v_num=5563, reduced_train_loss=2.470, gradient_norm=0.121, parameter_norm=1864.0, global_step=4512.0, consumed_samples=1.16e+6, iteration_time=16.40]
Epoch 0:  22%|██▏       | 4500/20101 [22:26:32<77:48:18, 17.95s/it, loss=2.43, v_num=5563, reduced_train_loss=2.470, gradient_norm=0.121, parameter_norm=1864.0, global_step=4512.0, consumed_samples=1.16e+6, iteration_time=16.40]
Epoch 0:  22%|██▏       | 4500/20101 [22:26:32<77:48:18, 17.95s/it, loss=2.44, v_num=5563, reduced_train_loss=2.450, gradient_norm=0.120, parameter_norm=1864.0, global_step=4512.0, consumed_samples=1.16e+6, iteration_time=16.50]

To further monitor and profile your model training jobs, you can use SageMaker hosted TensorBoard or any other tool of your choice.

Now available
SageMaker HyperPod is available today in AWS Regions US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm).

Learn more:

— Antje

PS: Writing a blog post at AWS is always a team effort, even when you see only one name under the post title. In this case, I want to thank Brad Doran, Justin Pirtle, Ben Snyder, Pierre-Yves Aquilanti, Keita Watanabe, and Verdi March for their generous help with example code and sharing their expertise in managing large-scale model training infrastructures, Slurm, and SageMaker HyperPod.

Amazon Titan Image Generator, Multimodal Embeddings, and Text models are now available in Amazon Bedrock

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/amazon-titan-image-generator-multimodal-embeddings-and-text-models-are-now-available-in-amazon-bedrock/

Today, we’re introducing two new Amazon Titan multimodal foundation models (FMs): Amazon Titan Image Generator (preview) and Amazon Titan Multimodal Embeddings. I’m also happy to share that Amazon Titan Text Lite and Amazon Titan Text Express are now generally available in Amazon Bedrock. You can now choose from three available Amazon Titan Text FMs, including Amazon Titan Text Embeddings.

Amazon Titan models incorporate 25 years of artificial intelligence (AI) and machine learning (ML) innovation at Amazon and offer a range of high-performing image, multimodal, and text model options through a fully managed API. AWS pre-trained these models on large datasets, making them powerful, general-purpose models built to support a variety of use cases while also supporting the responsible use of AI.

You can use the base models as is, or you can privately customize them with your own data. To enable access to Amazon Titan FMs, navigate to the Amazon Bedrock console and select Model access on the bottom left menu. On the model access overview page, choose Manage model access and enable access to the Amazon Titan FMs.

Amazon Titan Models

Let me give you a quick tour of the new models.

Amazon Titan Image Generator (preview)
As a content creator, you can now use Amazon Titan Image Generator to quickly create and refine images using English natural language prompts. This helps companies in advertising, e-commerce, and media and entertainment to create studio-quality, realistic images in large volumes and at low cost. The model makes it easy to iterate on image concepts by generating multiple image options based on the text descriptions. The model can understand complex prompts with multiple objects and generates relevant images. It is trained on high-quality, diverse data to create more accurate outputs, such as realistic images with inclusive attributes and limited distortions.

Titan Image Generator’s image editing features include the ability to automatically edit an image with a text prompt using a built-in segmentation model. The model supports inpainting with an image mask and outpainting to extend or change the background of an image. You can also configure image dimensions and specify the number of image variations you want the model to generate.

In addition, you can customize the model with proprietary data to generate images consistent with your brand guidelines or to generate images in a specific style, for example, by fine-tuning the model with images from a previous marketing campaign. Titan Image Generator also mitigates harmful content generation to support the responsible use of AI. All images generated by Amazon Titan contain an invisible watermark, by default, designed to help reduce the spread of misinformation by providing a discreet mechanism to identify AI-generated images.

Amazon Titan Image Generator in action
You can start using the model in the Amazon Bedrock console by submitting either an English natural language prompt to generate images or by uploading an image for editing. In the following example, I show you how to generate an image with Amazon Titan Image Generator using the AWS SDK for Python (Boto3).

First, let’s have a look at the configuration options for image generation that you can specify in the body of the inference request. For task type, I choose TEXT_IMAGE to create an image from a natural language prompt.

import boto3
import json

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

# ImageGenerationConfig Options:
#   numberOfImages: Number of images to be generated
#   quality: Quality of generated images, can be standard or premium
#   height: Height of output image(s)
#   width: Width of output image(s)
#   cfgScale: Scale for classifier-free guidance
#   seed: The seed to use for reproducibility  

body = json.dumps(
    {
        "taskType": "TEXT_IMAGE",
        "textToImageParams": {
            "text": "green iguana",   # Required
#           "negativeText": "<text>"  # Optional
        },
        "imageGenerationConfig": {
            "numberOfImages": 1,   # Range: 1 to 5 
            "quality": "premium",  # Options: standard or premium
            "height": 768,         # Supported height list in the docs 
            "width": 1280,         # Supported width list in the docs
            "cfgScale": 7.5,       # Range: 1.0 (exclusive) to 10.0
            "seed": 42             # Range: 0 to 214783647
        }
    }
)

Next, specify the model ID for Amazon Titan Image Generator and use the InvokeModel API to send the inference request.

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

Then, parse the response and decode the base64-encoded image.

import base64
from PIL import Image
from io import BytesIO

response_body = json.loads(response.get("body").read())
images = [Image.open(BytesIO(base64.b64decode(base64_image))) for base64_image in response_body.get("images")]

for img in images:
    display(img)

Et voilà, here’s the green iguana (one of my favorite animals, actually):

Green iguana generated by Amazon Titan Image Generator

To learn more about all the Amazon Titan Image Generator features, visit the Amazon Titan product page. (You’ll see more of the iguana over there.)

Next, let’s use this image with the new Amazon Titan Multimodal Embeddings model.

Amazon Titan Multimodal Embeddings
Amazon Titan Multimodal Embeddings helps you build more accurate and contextually relevant multimodal search and recommendation experiences for end users. Multimodal refers to a system’s ability to process and generate information using distinct types of data (modalities). With Titan Multimodal Embeddings, you can submit text, image, or a combination of the two as input.

The model converts images and short English text up to 128 tokens into embeddings, which capture semantic meaning and relationships between your data. You can also fine-tune the model on image-caption pairs. For example, you can combine text and images to describe company-specific manufacturing parts to understand and identify parts more effectively.

By default, Titan Multimodal Embeddings generates vectors of 1,024 dimensions, which you can use to build search experiences that offer a high degree of accuracy and speed. You can also configure smaller vector dimensions to optimize for speed and price performance. The model provides an asynchronous batch API, and the Amazon OpenSearch Service will soon offer a connector that adds Titan Multimodal Embeddings support for neural search.

Amazon Titan Multimodal Embeddings in action
For this demo, I create a combined image and text embedding. First, I base64-encode my image, and then I specify either inputText, inputImage, or both in the body of the inference request.

# Maximum image size supported is 2048 x 2048 pixels
with open("iguana.png", "rb") as image_file:
    input_image = base64.b64encode(image_file.read()).decode('utf8')

# You can specify either text or image or both
body = json.dumps(
    {
        "inputText": "Green iguana on tree branch",
        "inputImage": input_image
    }
)

Next, specify the model ID for Amazon Titan Multimodal Embeddings and use the InvokeModel API to send the inference request.

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

Let’s see the response.

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

[0.005087942, -0.004392853, -0.04764151, -0.024312444, 0.049922388, 0.0132532045, 0.014374298, 0.005523709, -0.015199458, 0.02182385, ...]

I redacted the output for brevity. The distance between multimodal embedding vectors, measured with metrics like cosine similarity or euclidean distance, shows how similar or different the represented information is across modalities. Smaller distances mean more similarity, while larger distances mean more dissimilarity.

As a next step, you could build an image database by storing and indexing the multimodal embeddings in a vector store or vector database. To implement text-to-image search, query the database with inputText. For image-to-image search, query the database with inputImage. For image+text-to-image search, query the database with both inputImage and inputText.

Amazon Titan Text
Amazon Titan Text Lite and Amazon Titan Text Express are large language models (LLMs) that support a wide range of text-related tasks, including summarization, translation, and conversational chatbot systems. They can also generate code and are optimized to support popular programming languages and text formats like JSON and CSV.

Titan Text Express – Titan Text Express has a maximum context length of 8,192 tokens and is ideal for a wide range of tasks, such as open-ended text generation and conversational chat, and support within Retrieval Augmented Generation (RAG) workflows.

Titan Text Lite – Titan Text Lite has a maximum context length of 4,096 tokens and is a price-performant version that is ideal for English-language tasks. The model is highly customizable and can be fine-tuned for tasks such as article summarization and copywriting.

Amazon Titan Text in action
For this demo, I ask Titan Text to write an email to my team members suggesting they organize a live stream: “Compose a short email from Antje, Principal Developer Advocate, encouraging colleagues in the developer relations team to organize a live stream to demo our new Amazon Titan V1 models.”

body = json.dumps({
    "inputText": prompt, 
    "textGenerationConfig":{  
        "maxTokenCount":512,
        "stopSequences":[],
        "temperature":0,
        "topP":0.9
    }
})

Titan Text FMs support 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.

Next, choose the model ID for one of the Titan Text models and use the InvokeModel API to send the inference request.

response = bedrock_runtime.invoke_model(
    body=body,
	# Choose modelID
	# Titan Text Express: "amazon.titan-text-express-v1"
	# Titan Text Lite: "amazon.titan-text-lite-v1"
	modelID="amazon.titan-text-express-v1"
    accept="application/json", 
    contentType="application/json"
)

Let’s have a look at the response.

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

text = outputText[outputText.index('\n')+1:]
email = text.strip()
print(email)

Subject: Demo our new Amazon Titan V1 models live!

Dear colleagues,

I hope this email finds you well. I am excited to announce that we have recently launched our new Amazon Titan V1 models, and I believe it would be a great opportunity for us to showcase their capabilities to the wider developer community.

I suggest that we organize a live stream to demo these models and discuss their features, benefits, and how they can help developers build innovative applications. This live stream could be hosted on our YouTube channel, Twitch, or any other platform that is suitable for our audience.

I believe that showcasing our new models will not only increase our visibility but also help us build stronger relationships with developers. It will also provide an opportunity for us to receive feedback and improve our products based on the developer’s needs.

If you are interested in organizing this live stream, please let me know. I am happy to provide any support or guidance you may need. Together, let’s make this live stream a success and showcase the power of Amazon Titan V1 models to the world!

Best regards,
Antje
Principal Developer Advocate

Nice. I could send this email right away!

Availability and pricing
Amazon Titan Text FMs are available today in AWS Regions US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore, Tokyo), and Europe (Frankfurt). Amazon Titan Multimodal Embeddings is available today in the AWS Regions US East (N. Virginia) and US West (Oregon). Amazon Titan Image Generator is available in public preview in the AWS Regions US East (N. Virginia) and US West (Oregon). For pricing details, see the Amazon Bedrock Pricing page.

Learn more

Go to the AWS Management Console to start building generative AI applications with Amazon Titan FMs on Amazon Bedrock today!

— Antje

Amazon Bedrock now provides access to Anthropic’s latest model, Claude 2.1

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/amazon-bedrock-now-provides-access-to-anthropics-latest-model-claude-2-1/

Today, we’re announcing the availability of Anthropic’s Claude 2.1 foundation model (FM) in Amazon Bedrock. Last week, Anthropic introduced its latest model, Claude 2.1, delivering key capabilities for enterprises such as an industry-leading 200,000 token context window (2x the context of Claude 2.0), reduced rates of hallucination, improved accuracy over long documents, system prompts, and a beta tool use feature for function calling and workflow orchestration.

With Claude 2.1’s availability in Amazon Bedrock, you can build enterprise-ready generative artificial intelligence (AI) applications using more honest and reliable AI systems from Anthropic. You can now use the Claude 2.1 model provided by Anthropic in the Amazon Bedrock console.

Here are some key highlights about the new Claude 2.1 model in Amazon Bedrock:

200,000 token context window – Enterprise applications demand larger context windows and more accurate outputs when working with long documents such as product guides, technical documentation, or financial or legal statements. Claude 2.1 supports 200,000 tokens, the equivalent of roughly 150,000 words or over 500 pages of documents. When uploading extensive information to Claude, you can summarize, perform Q&A, forecast trends, and compare and contrast multiple documents for drafting business plans and analyzing complex contracts.

Strong accuracy upgrades – Claude 2.1 has also made significant gains in honesty, with a 2x decrease in hallucination rates, 50 percent fewer hallucinations in open-ended conversation and document Q&A, a 30 percent reduction in incorrect answers, and a 3–4 times lower rate of mistakenly concluding that a document supports a particular claim compared to Claude 2.0. Claude increasingly knows what it doesn’t know and will more likely demur rather than hallucinate. With this improved accuracy, you can build more reliable, mission-critical applications for your customers and employees.

System prompts – Claude 2.1 now supports system prompts, a new feature that can improve Claude’s performance in a variety of ways, including greater character depth and role adherence in role-playing scenarios, particularly over longer conversations, as well as stricter adherence to guidelines, rules, and instructions. This represents a structural change, but not a content change from former ways of prompting Claude.

Tool use for function calling and workflow orchestration – Available as a beta feature, Claude 2.1 can now integrate with your existing internal processes, products, and APIs to build generative AI applications. Claude 2.1 accurately retrieves and processes data from additional knowledge sources as well as invokes functions for a given task.  Claude 2.1 can answer questions by searching databases using private APIs and a web search API, translate natural language requests into structured API calls, or connect to product datasets to make recommendations and help customers complete purchases. Access to this feature is currently limited to select early access partners, with plans for open access in the near future. If you are interested in gaining early access, please contact your AWS account team.

To learn more about Claude 2.1’s features and capabilities, visit Anthropic Claude on Amazon Bedrock and the Amazon Bedrock documentation.

Claude 2.1 in action
To get started with Claude 2.1 in Amazon Bedrock, go to the Amazon Bedrock console. Choose Model access on the bottom left pane, then choose Manage model access on the top right side, submit your use case, and request model access to the Anthropic Claude model. It may take several minutes to get access to models. If you already have access to the Claude model, you don’t need to request access separately for Claude 2.1.

To test Claude 2.1 in chat mode, choose Text or Chat under Playgrounds in the left menu pane. Then select Anthropic and then Claude v2.1.

By choosing View API request, you can also access the model via code examples in the AWS Command Line Interface (AWS CLI) and AWS SDKs. Here is a sample of the AWS CLI command:

$ aws bedrock-runtime invoke-model \
      --model-id anthropic.claude-v2:1 \
      --body "{\"prompt\":\"Human: \\n\\nHuman: Tell me funny joke about outer space!\n\nAssistant:", "max_tokens_to_sample": 50}' \
      --cli-binary-format raw-in-base64-out \
      invoke-model-output.txt

You can use system prompt engineering techniques provided by the Claude 2.1 model, where you place your inputs and documents before any questions that reference or utilize that content. Inputs can be natural language text, structured documents, or code snippets using <document>, <papers>, <books>, or <code> tags, and so on. You can also use conversational text, such as chat history, and Retrieval Augmented Generation (RAG) results, such as chunked documents.

Here is a system prompt example for support agents to respond to customer questions based on corporate documents.

Here are some documents for you to reference for your task:
<documents>
 <document index="1">
  <document_content>
  (the text content of the document - could be a passage, web page, article, etc)
   </document_content>
<document index="2">
  <source>https://mycompany.repository/userguide/what-is-it.html</source>
</document>
<document index="3">
  <source>https://mycompany.repository/docs/techspec.pdf</source>
 </document>
...
</documents>

You are Larry, and you are a customer advisor with deep knowledge of your company's products. Larry has a great deal of patience with his customers, even when they say nonsense or are sarcastic. Larry's answers are polite but sometimes funny. However, he only answers questions about the company's products and doesn't know much about other questions. Use the provided documentation to answer user questions.

Human: Your product is making a weird stuttering sound when I operate. What might be the problem?

To learn more about prompt engineering on Amazon Bedrock, see the Prompt engineering guidelines included in the Amazon Bedrock documentation. You can learn general prompt techniques, templates, and examples for Amazon Bedrock text models, including Claude.

Now available
Claude 2.1 is available today in the US East (N. Virginia) and US West (Oregon) Regions.

You only pay for what you use, with no time-based term commitments for on-demand mode. For text generation models, you are charged for every input token processed and every output token generated. Or you can choose the provisioned throughput mode to meet your application’s performance requirements in exchange for a time-based term commitment. To learn more, see Amazon Bedrock Pricing.

Give Anthropic Claude 2.1 a try in Amazon Bedrock console today and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

— Channy

New generative AI capabilities for Amazon DataZone to further simplify data cataloging and discovery (preview)

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/new-generative-ai-capabilities-for-amazon-datazone-to-further-simplify-data-cataloging-and-discovery-preview/

Today, we are announcing a preview of an automation feature backed by generative artificial intelligence (AI) for Amazon DataZone that will dramatically decrease the amount of time needed to provide context for organizational data. The new feature can automate the traditionally labor-intensive process of data cataloging. Powered by the large language models (LLMs) of Amazon Bedrock, it generates detailed descriptions of data assets and their schemas, and suggests analytical use cases. You can generate a comprehensive business context with a single click.

We heard from customers that data consumers such as data analysts, scientists, and engineers in organizations struggle to understand the data’s relevance with little metadata. As a result, they either spend more time interpreting the data, or they return to data producers with continued questions. So, data producers such as data owners, engineers, and analysts who own the data and make it available for consumers need to manually enter detailed context for higher-priority data to make data shareable and discoverable. This is time-consuming and the number one problem customers have when trying to collate their data in a system for self-service by consumers.

When we launched the general availability of Amazon DataZone in October 2023, we introduced the first feature that brings generative AI capabilities to automate the generation of the table name and column names of a business catalog asset. In the data portal of Amazon DataZone, the green brain icon indicates automatically generated metadata suggestions. You could accept, edit, or reject each suggestion recommended by Amazon DataZone.

What’s new with today’s preview announcement?
Now, in addition to column and table names, you can automatically generate more detailed descriptions of the table and schema, as well as suggested uses.

In the Business Metadata tab in the data portal, when you choose Generate summary, new content will be generated to explain the table and its metadata.

You can also accept, edit, and reject this recommendation.

When you choose the Schema tab, you can also see new Description recommendations as well as the Name. You can review generated metadata and choose to accept, edit, or reject the recommendation.

This new feature will enhance data discoverability and reduce on back-and-forth communications between data consumers and producers. You will have a richer search experience based on extensive data insights in the future.

Join the preview
The new metadata generation ability is now previewed in the AWS US East (N. Virginia) and US West (Oregon) Regions. With this new generative AI capability, you can reduce time-to-insight by accelerating data cataloging and boosting data discovery. To learn more, visit the Amazon DataZone: Automate Data Discovery.

Give it a try and send feedback to AWS re:Post for Amazon DataZone or through your usual AWS Support contacts.

— Channy

Amazon DynamoDB zero-ETL integration with Amazon OpenSearch Service is now available

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/amazon-dynamodb-zero-etl-integration-with-amazon-opensearch-service-is-now-generally-available/

Today, we are announcing the general availability of Amazon DynamoDB zero-ETL integration with Amazon OpenSearch Service, which lets you perform a search on your DynamoDB data by automatically replicating and transforming it without custom code or infrastructure. This zero-ETL integration reduces the operational burden and cost involved in writing code for a data pipeline architecture, keeping the data in sync, and updating code with frequent application changes, enabling you to focus on your application.

With this zero-ETL integration, Amazon DynamoDB customers can now use the powerful search features of Amazon OpenSearch Service, such as full-text search, fuzzy search, auto-complete, and vector search for machine learning (ML) capabilities to offer new experiences that boost user engagement and improve satisfaction with their applications.

This zero-ETL integration uses Amazon OpenSearch Ingestion to synchronize the data between Amazon DynamoDB and Amazon OpenSearch Service. You choose the DynamoDB table whose data needs to be synchronized and Amazon OpenSearch Ingestion synchronizes the data to an Amazon OpenSearch managed cluster or serverless collection within seconds of it being available.

You can also specify index mapping templates to ensure that your Amazon DynamoDB fields are mapped to the correct fields in your Amazon OpenSearch Service indexes. Also, you can synchronize data from multiple DynamoDB tables into one Amazon OpenSearch Service managed cluster or serverless collection to offer holistic insights across several applications.

Getting started with this zero-ETL integration
With a few clicks, you can synchronize data from DynamoDB to OpenSearch Service. To create an integration between DynamoDB and OpenSearch Service, choose the Integrations menu in the left pane of the DynamoDB console and the DynamoDB table whose data you want to synchronize.

You must turn on point-in-time recovery (PITR) and the DynamoDB Streams feature. This feature allows you to capture item-level changes in your table and push the changes to a stream. Choose Turn on for PITR and enable DynamoDB Streams in the Exports and streams tab.

After turning on PITR and DynamoDB Stream, choose Create to set up an OpenSearch Ingestion pipeline in your account that replicates the data to an OpenSearch Service managed domain.

In the first step, enter a unique pipeline name and set up pipeline capacity and compute resources to automatically scale your pipeline based on the current ingestion workload.

Now you can configure the pre-defined pipeline configuration in YAML file format. You can browse resources to look up and paste information to build the pipeline configuration. This pipeline is a combination of a source part from DyanmoDB settings and a sink part for OpenSearch Service.

You must set multiple IAM roles (sts_role_arn) with the necessary permissions to read data from the DynamoDB table and write to an OpenSearch domain. This role is then assumed by OpenSearch Ingestion pipelines to ensure that the right security posture is always maintained when moving the data from source to destination. To learn more, see Setting up roles and users in Amazon OpenSearch Ingestion in the AWS documentation.

After entering all required values, you can validate the pipeline configuration to ensure that your configuration is valid. To learn more, see Creating Amazon OpenSearch Ingestion pipelines in the AWS documentation.

Take a few minutes to set up the OpenSearch Ingestion pipeline, and you can see your integration is completed in the DynamoDB table.

Now you can search synchronized items in the OpenSearch Dashboards.

Things to know
Here are a couple of things that you should know about this feature:

  • Custom schema – You can specify your custom data schema along with the index mappings used by OpenSearch Ingestion when writing data from Amazon DynamoDB to OpenSearch Service. This experience is added to the console within Amazon DynamoDB so that you have full control over the format of indices that are created on OpenSearch Service.
  • Pricing – There will be no additional cost to use this feature apart from the cost of the existing underlying components. Note that Amazon OpenSearch Ingestion charges OpenSearch Compute Units (OCUs) which will be used to replicate data between Amazon DynamoDB and Amazon OpenSearch Service. Furthermore, this feature uses Amazon DynamoDB streams for the change data capture (CDC) and you will incur the standard costs for Amazon DynamoDB Streams.
  • Monitoring – You can monitor the state of the pipelines by checking the status of the integration on the DynamoDB console or using the OpenSearch Ingestion dashboard. Additionally, you can use Amazon CloudWatch to provide real-time metrics and logs, which lets you to set up alerts in case of a breach of user-defined thresholds.

Now available
Amazon DynamoDB zero-ETL integration with Amazon OpenSearch Service is now generally available in all AWS Regions where OpenSearch Ingestion is available today.

— Channy

New generative AI features in Amazon Connect, including Amazon Q, facilitate improved contact center service

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/new-generative-ai-features-in-amazon-connect-including-amazon-q-facilitate-improved-contact-center-service/

If you manage a contact center, then you know the critical role that agents play in helping your organization build customer trust and loyalty. Those of us who’ve reached out to a contact center know how important agents are with guiding through complex decisions and providing fast and accurate solutions where needed. This can take time, and if not done correctly, then it may lead to frustration.

Generative AI capabilities in Amazon Connect
Today, we’re announcing that the existing artificial intelligence (AI) features of Amazon Connect now have generative AI capabilities that are powered by large language models (LLMs) available through Amazon Bedrock to transform how contact centers provide service to customers. LLMs are pre-trained on vast amounts of data, commonly known as foundation models (FMs), and they can understand and learn, generate text, engage in interactive conversations, answer questions, summarize dialogs and documents, and provide recommendations.

Amazon Q in Connect: recommended responses and actions for faster customer support
Organizations are in a state of constant change. To maintain a high level of performance that keeps up with these organizational changes, contact centers continuously onboard, train, and coach agents. Even with training and coaching, agents must often search through different sources of information, such as product guides and organization policies, to provide exceptional service to customers. This can increase customer wait times, lowering customer satisfaction and increasing contact center costs.

Amazon Q in Connect, a generative AI-powered agent assistant that includes functionality formerly available as Amazon Connect Wisdom, understands customer intents and uses relevant sources of information to deliver accurate responses and actions for the agent to communicate and resolve unique customer needs, all in real-time. Try Amazon Q in Connect for no charge until March 1, 2024. The feature is easy to enable, and you can get started in the Amazon Connect console.

Amazon Connect Contact Lens: generative post-contact summarization for increased productivity
To improve customer interactions and make sure details are available for future reference, contact center managers rely on the notes that agents manually create after every customer interaction. These notes include details on how a customer issue was addressed, key moments of the conversation, and any pending follow-up items.

Amazon Connect Contact Lens now provides generative AI-powered post-contact summarization, and enables contact center managers to more efficiently monitor and help improve contact quality and agent performance. For example, you can use summaries to track commitments made to customers and make sure of the prompt completion of follow-up actions. Moments after a customer interaction, Contact Lens now condenses the conversation into a concise and coherent summary.

Amazon Lex in Amazon Connect: assisted slot resolution
Using Amazon Lex, you can already build chatbots, virtual agents, and interactive voice response (IVR) which lets your customers schedule an appointment without speaking to a human agent. For example, “I need to change my travel reservation for myself and my two children,” might be difficult for a traditional bot to resolve to a numeric value (how many people are on the travel reservation?).

With the new assisted slot resolution feature, Amazon Lex can now resolve slot values in user utterances with great accuracy (for example, providing an answer to the previous question by providing a correct numeric value of three). This is powered by the advanced reasoning capabilities of LLMs which improve accuracy and provide a better customer experience. Learn about all the features of Amazon Lex, including the new generative AI-powered capabilities to help you build better self-service experiences.

Amazon Connect Customer Profiles: quicker creation of unified customer profiles for personalized customer experiences
Customers expect personalized customer service experiences. To provide this, contact centers need a comprehensive understanding of customers’ preferences, purchases, and interactions. To achieve that, contact center administrators create unified customer profiles by merging customer data from a number of applications. These applications each have different types of customer data stored in varied formats across a range of data stores. Stitching together data from these various data stores needs contact center administrators to understand their data and figure out how to organize and combine it into a unified format. To accomplish this, they spend weeks compiling unified customer profiles.

Starting today, Amazon Connect Customer Profiles uses LLMs to shorten the time needed to create unified customer profiles. When contact center administrators add data sources such as Amazon Simple Storage Service (Amazon S3), Adobe Analytics, Salesforce, ServiceNow, and Zendesk, Customer Profiles analyze the data to understand what the data format and content represents and how the data relates to customers’ profiles. Then, Customer Profiles then automatically determines how to organize and combine data from different sources into complete, accurate profiles. With just a few steps, managers can review, make any necessary edits, and complete the setup of customer profiles.

Review summary mapping

In-app, web, and video capabilities in Amazon Connect
As an organization, you want to provide great, easy-to-use, and convenient customer service. Earlier in this post I talked about self-service chatbots and how they help you with this. At times customers want to move beyond the chatbot, and beyond an audio conversation with the agent.

Amazon Connect now has in-app, web, and video capabilities to help you deliver rich, personalized customer experiences (see Amazon Lex features for details). Using the fully-managed communication widget, and with a few lines of code, you can implement these capabilities on your web and mobile applications. This allows your customers to get support from a web or mobile application without ever having to leave the page. Video can be enabled by either the agent only, by the customer only, or by both agent and customer.

Video calling

Amazon Connect SMS: two-way SMS capabilities
Almost everyone owns a mobile device and we love the flexibility of receiving text-based support on-the-go. Contact center leaders know this, and in the past have relied on disconnected, third-party solutions to provide two-way SMS to customers.

Amazon Connect now has two-way SMS capabilities to enable contact center leaders to provide this flexibility (see Amazon Lex features for details). This improves customer satisfaction and increases agent productivity without costly integration with third-party solutions. SMS chat can be enabled using the same configuration, Amazon Connect agent workspace, and analytics as calls and chats.

Learn more

Send feedback

– Veliswa

New Amazon Q in QuickSight uses generative AI assistance for quicker, easier data insights (preview)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/new-amazon-q-in-quicksight-uses-generative-ai-assistance-for-quicker-easier-data-insights-preview/

Today, I’m happy to share that Amazon Q in QuickSight is available for preview. Now you can experience the Generative BI capabilities in Amazon QuickSight announced on July 26, as well as two additional capabilities for business users.

Turning insights into impact faster with Amazon Q in QuickSight
With this announcement, business users can now generate compelling sharable stories examining their data, see executive summaries of dashboards surfacing key insights from data in seconds, and confidently answer questions of data not answered by dashboards and reports with a reimagined Q&A experience.

Before we go deeper into each capability, here’s a quick summary:

  • Stories — This is a new and visually compelling way to present and share insights. Stories can automatically generated in minutes using natural language prompts, customized using point-and-click options, and shared securely with others.
  • Executive summaries — With this new capability, Amazon Q helps you to understand key highlights in your dashboard.
  • Data Q&A — This capability provides a new and easy-to-use natural-language Q&A experience to help you get answers for questions beyond what is available in existing dashboards and reports.​​

To get started, you need to enable Preview Q Generative Capabilities in Preview manager.

Once enabled, you’re ready to experience what Amazon Q in QuickSight brings for business users and business analysts building dashboards.

Stories automatically builds formatted narratives
Business users often need to share their findings of data with others to inform team decisions; this has historically involved taking data out of the business intelligence (BI) system. Stories are a new feature enabling business users to create beautifully formatted narratives that describe data, and include visuals, images, and text in document or slide format directly that can easily be shared with others within QuickSight.

Now, business users can use natural language to ask Amazon Q to build a story about their data by starting from the Amazon Q Build menu on an Amazon QuickSight dashboard. Amazon Q extracts data insights and statistics from selected visuals, then uses large language models (LLMs) to build a story in multiple parts, examining what the data may mean to the business and suggesting ideas to achieve specific goals.

For example, a sales manager can ask, “Build me a story about overall sales performance trends. Break down data by product and region. Suggest some strategies for improving sales.” Or, “Write a marketing strategy that uses regional sales trends to uncover opportunities that increase revenue.” Amazon Q will build a story exploring specific data insights, including strategies to grow sales.

Once built, business users get point-and-click tools augmented with artificial intelligence- (AI) driven rewriting capabilities to customize stories using a rich text editor to refine the message, add ideas, and highlight important details.

Stories can also be easily and securely shared with other QuickSight users by email.

Executive summaries deliver a quick snapshot of important information
Executive summaries are now available with a single click using the Amazon Q Build menu in Amazon QuickSight. Amazon QuickSight automatically determines interesting facts and statistics, then use LLMs to write about interesting trends.

This new capability saves time in examining detailed dashboards by providing an at-a-glance view of key insights described using natural language.

The executive summaries feature provides two advantages. First, it helps business users generate all the key insights without the need to browse through tens of visuals on the dashboard and understand changes from each. Secondly, it enables readers to find key insights based on information in the context of dashboards and reports with minimum effort.

New data Q&A experience
Once an interesting insight is discovered, business users frequently need to dig in to understand data more deeply than they can from existing dashboards and reports. Natural language query (NLQ) solutions designed to solve this problem frequently expect that users already know what fields may exist or how they should be combined to answer business questions. However, business users aren’t always experts in underlying data schemas, and their questions frequently come in more general terms, like “How were sales last week in NY?” Or, “What’s our top campaign?”

The new Q&A experience accessed within the dashboards and reports helps business users confidently answer questions about data. It includes AI-suggested questions and a profile of what data can be asked about and automatically generated multi-visual answers with narrative summaries explaining data context.

Furthermore, Amazon Q brings the ability to answer vague questions and offer alternatives for specific data. For example, customers can ask a vague question, such as “Top products,” and Amazon Q will provide an answer that breaks down products by sales and offers alternatives for products by customer count and products by profit. Amazon Q explains answer context in a narrative summarizing total sales, number of products, and picking out the sales for the top product.

Customers can search for specific data values and even a single word such as, for example, the product name “contactmatcher.” Amazon Q returns a complete set of data related to that product and provides a natural language breakdown explaining important insights like total units sold. Specific visuals from the answers can also be added to a pinboard for easy future access.

Watch the demo
To see these new capabilities in action, have a look at the demo.

Things to Know
Here are a few additional things that you need to know:

Join the preview
Amazon Q in QuickSight product page

Happy building!
— Donnie

Introducing Amazon Q, a new generative AI-powered assistant (preview)

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/introducing-amazon-q-a-new-generative-ai-powered-assistant-preview/

Today, we are announcing Amazon Q, a new generative artificial intelligence- (AI)-powered assistant designed for work that can be tailored to your business. You can use Amazon Q to have conversations, solve problems, generate content, gain insights, and take action by connecting to your company’s information repositories, code, data, and enterprise systems. Amazon Q provides immediate, relevant information and advice to employees to streamline tasks, accelerate decision-making and problem-solving, and help spark creativity and innovation at work.

Amazon Q offers user-based plans, so you get features, pricing, and options tailored to how you use the product. Amazon Q can adapt its interactions to each individual user based on the existing identities, roles, and permissions of your business. AWS never uses customers’ content from Amazon Q to train the underlying models. In other words, your company information remains secure and private.

In this post, I’ll give you a quick tour of how you can use Amazon Q for general business use. 

Amazon Q is your business expert
Let’s look at a few examples of how Amazon Q can help business users complete tasks using simple natural language prompts. As a marketing manager, you could ask Amazon Q to transform a press release into a blog post, create a summary of the press release, or create an email draft based on the provided release. Amazon Q searches through your company content, which can include internal style guides, for example, to provide a response appropriate to your company’s brand standards. Then, you could ask Amazon Q to generate tailored social media prompts to promote your story through each of your social media channels. Later, you can ask Amazon Q to analyze the results of your campaign and summarize them for leadership reviews.

Amazon Q

In the following example, I deployed Amazon Q with access to my AWS News Blog posts from 2023 and called the assistant “AWS Blog Expert.”

Amazon Q

Coming back to my previous example, let’s assume I’m a marketing manager and want Amazon Q to help me create social media posts for recent company blog posts.

I enter the following prompt: “Summarize the key insights from Antje’s recent AWS Weekly Roundup posts and craft a compelling social media post that not only highlights the most important points but also encourages engagement. Consider our target audience and aim for a tone that aligns with our brand identity. The social media post should be concise, informative, and enticing to encourage readers to click through and read the full articles. Please ensure the content is shareable and includes relevant hashtags for maximum visibility.”

Amazon Q

Behind the scenes, Amazon Q searches the documents in connected data sources and creates a relevant and detailed suggestion for a social media post based on my blog posts. Amazon Q also tells me which document was used to generate the answer. In this case, it is PDF file of the blog posts in question.

As an administrator, you can define the context for responses, restrict irrelevant topics, and configure whether to respond only using trusted company information or complement responses with knowledge from the underlying model. Restricting responses to trusted company information helps mitigate hallucinations, a common phenomenon where the underlying model generates responses that sound plausible but are based on misinterpreted or nonexistent data.

Amazon Q provides fine-grained access controls that restrict responses to only using data or acting based on the employee’s level of access and provides citations and references to the original sources for fact-checking and traceability. You can choose among 40+ built-in connectors for popular data sources and enterprise systems, including Amazon S3, Google Drive, Microsoft SharePoint, Salesforce, ServiceNow, and Slack.

How to tailor Amazon Q to your business
To tailor Amazon Q to your business, navigate to Amazon Q in the console, select Applications in the left menu, and choose Create application.

Amazon Q

This starts the following workflow.

Step 1. Create application. Provide an application name and create a new or select an existing AWS Identity and Access Management (IAM) service role that Amazon Q is allowed to assume. I call my application AWS-Blog-Expert. Then, choose Create.

Amazon Q

Step 2. Select retriever. A retriever pulls data from the index in real time during a conversation. You can choose between two options: use the Amazon Q native retriever or use an existing Amazon Kendra retriever. The native retriever can connect to the Amazon Q supported data sources. If you already use Amazon Kendra, you can select the existing Amazon Kendra retriever to connect the associated data sources to your Amazon Q application. I select the native retriever option. Then, choose Next.

Amazon Q

Step 3. Connect data sources. Amazon Q comes with built-in connectors for popular data sources and enterprise systems. For this demo, I choose Amazon S3 and configure the data source by pointing to my S3 bucket with the PDFs of my blog posts.

Amazon Q
Once the data source sync is successfully complete and the retriever shows the accurate document count, you can preview the web experience and start a conversation. Note that the data source sync can take from a few minutes to a few hours, depending on the amount and size of data to index.

You can also connect plugins that manage access to enterprise systems, including ServiceNow, Jira, Salesforce, and Zendesk. Plugins enable Amazon Q to perform user-requested tasks, such as creating support tickets or analyzing sales forecasts.

Amazon Q

Preview and deploy web experience
In the application overview, choose Preview web experience. This opens the web experience with the conversational interface to chat with the tailored Amazon Q AWS Blog Expert. In the final step, you deploy the Amazon Q web experience. You can integrate your SAML 2.0–compliant external identity provider (IdP) using IAM. Amazon Q can work with any IdP that’s compliant with SAML 2.0. Amazon Q uses service-initiated single sign-on (SSO) to authenticate users.

Join the preview
Amazon Q is available today in preview in AWS Regions US East (N. Virginia) and US West (Oregon). Visit the product page to learn how Amazon Q can become your expert in your business.

Also, check out the Amazon Q Slack Gateway GitHub repository that shows how to make Amazon Q available to users as a Slack Bot application.Amazon Q Slack Bot

Learn more

— Antje

Improve developer productivity with generative-AI powered Amazon Q in Amazon CodeCatalyst (preview)

Post Syndicated from Irshad Buchh original https://aws.amazon.com/blogs/aws/improve-developer-productivity-with-generative-ai-powered-amazon-q-in-amazon-codecatalyst-preview/

Today, I’m excited to introduce the preview of new generative artificial intelligence (AI) capabilities within Amazon CodeCatalyst that accelerate software delivery using Amazon Q.

Accelerate feature development – The feature development capability in Amazon Q can help you accelerate the implementation of software development tasks such as adding comments and READMEs, refining issue descriptions, generating small classes and unit tests, and updating CodeCatalyst workflows — tedious and undifferentiated tasks that take up developers’ time. Developers can go from an idea in an issue to fully tested, merge-ready, running code with only natural language inputs, in just a few clicks. AI does the heavy lifting of converting the human prompt to an actionable plan, summarizing source code repositories, generating code, unit tests, and workflows, and summarizing any changes in a pull request which is assigned back to the developer. You can also provide feedback to Amazon Q directly on the published pull request and ask it to generate a new revision. If the code change falls short of expectations, you can create a development environment directly from the pull request, make any necessary adjustments manually, publish a new revision, and proceed with the merge upon approval.

Example: make an API change in an existing application
In the navigation pane, I choose Issues and then I choose Create issue. I give the issue the title, Change the get_all_mysfits() API to return mysfits sorted by the Age attribute. I then assign this issue to Amazon Q and choose Create issue.

Create-issue

Amazon Q will automatically move the issue into the In progress state while it analyzes the issue title and description to formulate a potential solution approach. If there is already some discussion on the issue, it should be summarized in the description to help Q understand what needs to be done. As it works, Amazon Q will report on its progress by leaving comments on the issue at every stage. It will attempt to create a solution based on its understanding of the code already present in the repository and the approach it formulated. If Amazon Q is able to successfully generate a potential solution, it will create a branch and commit code to that branch. It will then create a pull request that will merge the changes into the default branch once approved. Once the pull request is published, Amazon Q will change the issue status to In Review so that you and your team know that the code is now ready for you to review.

pull-request

Summarize a change – Pull request authors can save time by asking Amazon Q to summarize the change they are publishing for review. Today pull request authors have to write the description manually or they may choose not to write it at all. If the author does not provide a description, it makes it harder for reviewers to understand what changes are being made and why, delaying the review process and slowing down software delivery.

Pull request authors and reviewers can also save time by asking Amazon Q to summarize the comments left on the pull request. The summary is useful for the author because they can easily see common feedback themes. For the reviewers it is useful because they can quickly catch up on the conversation and feedback from themselves and other team members. The overall benefits are streamlined collaboration, accelerated review process, and faster software delivery.

Join the preview
Amazon Q is available in Amazon CodeCatalyst today for spaces in AWS Region US West (Oregon).

Learn more

— Irshad

Amazon Q brings generative AI-powered assistance to IT pros and developers (preview)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/amazon-q-brings-generative-ai-powered-assistance-to-it-pros-and-developers-preview/

Today, we are announcing the preview of Amazon Q, a new type of generative artificial intelligence (AI) powered assistant that is specifically for work and can be tailored to a customer’s business.

Amazon Q brings a set of capabilities to support developers and IT professionals. Now you can use Amazon Q to get started building applications on AWS, research best practices, resolve errors, and get assistance in coding new features for your applications. For example, Amazon Q Code Transformation can perform Java application upgrades now, from version 8 and 11 to version 17.

Amazon Q is available in multiple areas of AWS to provide quick access to answers and ideas wherever you work. Here’s a quick look at Amazon Q, including in integrated development environment (IDE):

Building applications together with Amazon Q
Application development is a journey. It involves a continuous cycle of researching, developing, deploying, optimizing, and maintaining. At each stage, there are many questions—from figuring out the right AWS services to use, to troubleshooting issues in the application code.

Trained on 17 years of AWS knowledge and best practices, Amazon Q is designed to help you at each stage of development with a new experience for building applications on AWS. With Amazon Q, you minimize the time and effort you need to gain the knowledge required to answer AWS questions, explore new AWS capabilities, learn unfamiliar technologies, and architect solutions that fuel innovation.

Let us show you some capabilities of Amazon Q.

1. Conversational Q&A capability
You can interact with the Amazon Q conversational Q&A capability to get started, learn new things, research best practices, and iterate on how to build applications on AWS without needing to shift focus away from the AWS console.

To start using this feature, you can select the Amazon Q icon on the right-hand side of the AWS Management Console.

For example, you can ask, “What are AWS serverless services to build serverless APIs?” Amazon Q provides concise explanations along with references you can use to follow up on your questions and validate the guidance. You can also use Amazon Q to follow up on and iterate your questions. Amazon Q will show more deep-dive answers for you with references.

There are times when we have questions for a use case with fairly specific requirements. With Amazon Q, you can elaborate on your use cases in more detail to provide context.

For example, you can ask Amazon Q, “I’m planning to create serverless APIs with 100k requests/day. Each request needs to lookup into the database. What are the best services for this workload?” Amazon Q responds with a list of AWS services you can use and tries to limit the answer results to those that are accurately referenceable and verified with best practices.

Here is some additional information that you might want to note:

2. Optimize Amazon EC2 instance selection
Choosing the right Amazon Elastic Compute Cloud (Amazon EC2) instance type for your workload can be challenging with all the options available. Amazon Q aims to make this easier by providing personalized recommendations.

To use this feature, you can ask Amazon Q, “Which instance families should I use to deploy a Web App Server for hosting an application?” This feature is also available when you choose to launch an instance in the Amazon EC2 console. In Instance type, you can select Get advice on instance type selection. This will show a dialog to define your requirements.

Your requirements are automatically translated into a prompt on the Amazon Q chat panel. Amazon Q returns with a list of suggestions of EC2 instances that are suitable for your use cases. This capability helps you pick the right instance type and settings so your workloads will run smoothly and more cost-efficiently.

This capability to provide EC2 instance type recommendations based on your use case is available in preview in all commercial AWS Regions.

3. Troubleshoot and solve errors directly in the console
Amazon Q can also help you to solve errors for various AWS services directly in the console. With Amazon Q proposed solutions, you can avoid slow manual log checks or research.

Let’s say that you have an AWS Lambda function that tries to interact with an Amazon DynamoDB table. But, for an unknown reason (yet), it fails to run. Now, with Amazon Q, you can troubleshoot and resolve this issue faster by selecting Troubleshoot with Amazon Q.

Amazon Q provides concise analysis of the error which helps you to understand the root cause of the problem and the proposed resolution. With this information, you can follow the steps described by Amazon Q to fix the issue.

In just a few minutes, you will have the solution to solve your issues, saving significant time without disrupting your development workflow. The Amazon Q capability to help you troubleshoot errors in the console is available in preview in the US West (Oregon) for Amazon Elastic Compute Cloud (Amazon EC2), Amazon Simple Storage Service (Amazon S3), Amazon ECS, and AWS Lambda.

4. Network troubleshooting assistance
You can also ask Amazon Q to assist you in troubleshooting network connectivity issues caused by network misconfiguration in your current AWS account. For this capability, Amazon Q works with Amazon VPC Reachability Analyzer to check your connections and inspect your network configuration to identify potential issues.

This makes it easy to diagnose and resolve AWS networking problems, such as “Why can’t I SSH to my EC2 instance?” or “Why can’t I reach my web server from the Internet?” which you can ask Amazon Q.

Then, on the response text, you can select preview experience here, which will provide explanations to help you to troubleshoot network connectivity-related issues.

Here are a few things you need to know:

5. Integration and conversational capabilities within your IDEs
As we mentioned, Amazon Q is also available in supported IDEs. This allows you to ask questions and get help within your IDE by chatting with Amazon Q or invoking actions by typing / in the chat box.

To get started, you need to install or update the latest AWS Toolkit and sign in to Amazon CodeWhisperer. Once you’re signed in to Amazon CodeWhisperer, it will automatically activate the Amazon Q conversational capability in the IDE. With Amazon Q enabled, you can now start chatting to get coding assistance.

You can ask Amazon Q to describe your source code file.

From here, you can improve your application, for example, by integrating it with Amazon DynamoDB. You can ask Amazon Q, “Generate code to save data into DynamoDB table called save_data() accepting data parameter and return boolean status if the operation successfully runs.”

Once you’ve reviewed the generated code, you can do a manual copy and paste into the editor. You can also select Insert at cursor to place the generated code into the source code directly.

This feature makes it really easy to help you focus on building applications because you don’t have to leave your IDE to get answers and context-specific coding guidance. You can try the preview of this feature in Visual Studio Code and JetBrains IDEs.

6. Feature development capability
Another exciting feature that Amazon Q provides is guiding you interactively from idea to building new features within your IDE and Amazon CodeCatalyst. You can go from a natural language prompt to application features in minutes, with interactive step-by-step instructions and best practices, right from your IDE. With a prompt, Amazon Q will attempt to understand your application structure and break down your prompt into logical, atomic implementation steps.

To use this capability, you can start by invoking an action command /dev in Amazon Q and describe the task you need Amazon Q to process.

Then, from here, you can review, collaborate and guide Amazon Q in the chat for specific areas that need to be implemented.

Additional capabilities to help you ship features faster with complete pull requests are available if you’re using Amazon CodeCatalyst. In Amazon CodeCatalyst, you can assign a new or an existing issue to Amazon Q, and it will process an end-to-end development workflow for you. Amazon Q will review the existing code, propose a solution approach, seek feedback from you on the approach, generate merge-ready code, and publish a pull request for review. All you need to do after is to review the proposed solutions from Amazon Q.

The following screenshots show a pull request created by Amazon Q in Amazon CodeCatalyst.

Here are a couple of things that you should know:

  • Amazon Q feature development capability is currently in preview in Visual Studio Code and Amazon CodeCatalyst
  • To use this capability in IDE, you need to have the Amazon CodeWhisperer Professional tier. Learn more on the Amazon CodeWhisperer pricing page.

7. Upgrade applications with Amazon Q Code Transformation
With Amazon Q, you can now upgrade an entire application within a few hours by starting a guided code transformation. This capability, called Amazon Q Code Transformation, simplifies maintaining, migrating, and upgrading your existing applications.

To start, navigate to the CodeWhisperer section and then select Transform. Amazon Q Code Transformation automatically analyzes your existing codebase, generates a transformation plan, and completes the key transformation tasks suggested by the plan.

Some additional information about this feature:

  • Amazon Q Code Transformation is available in preview today in the AWS Toolkit for IntelliJ IDEA and the AWS Toolkit for Visual Studio Code.
  • To use this capability, you need to have the Amazon CodeWhisperer Professional tier during the preview.
  • During preview, you can can upgrade Java 8 and 11 applications to version 17, a Java Long-Term Support (LTS) release.

Get started with Amazon Q today
With Amazon Q, you have an AI expert by your side to answer questions, write code faster, troubleshoot issues, optimize workloads, and even help you code new features. These capabilities simplify every phase of building applications on AWS.

Amazon Q lets you engage with AWS Support agents directly from the Q interface if additional assistance is required, eliminating any dead ends in the customer’s self-service experience. The integration with AWS Support is available in the console and will honor the entitlements of your AWS Support plan.

Learn more

— Donnie & Channy

Guardrails for Amazon Bedrock helps implement safeguards customized to your use cases and responsible AI policies (preview)

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/guardrails-for-amazon-bedrock-helps-implement-safeguards-customized-to-your-use-cases-and-responsible-ai-policies-preview/

As part of your responsible artificial intelligence (AI) strategy, you can now use Guardrails for Amazon Bedrock (preview) to promote safe interactions between users and your generative AI applications by implementing safeguards customized to your use cases and responsible AI policies.

AWS is committed to developing generative AI in a responsible, people-centric way by focusing on education and science and helping developers to integrate responsible AI across the AI lifecycle. With Guardrails for Amazon Bedrock, you can consistently implement safeguards to deliver relevant and safe user experiences aligned with your company policies and principles. Guardrails help you define denied topics and content filters to remove undesirable and harmful content from interactions between users and your applications. This provides an additional level of control on top of any protections built into foundation models (FMs).

You can apply guardrails to all large language models (LLMs) in Amazon Bedrock, including fine-tuned models, and Agents for Amazon Bedrock. This drives consistency in how you deploy your preferences across applications so you can innovate safely while closely managing user experiences based on your requirements. By standardizing safety and privacy controls, Guardrails for Amazon Bedrock helps you build generative AI applications that align with your responsible AI goals.

Guardrails for Amazon Bedrock

Let me give you a quick tour of the key controls available in Guardrails for Amazon Bedrock.

Key controls
Using Guardrails for Amazon Bedrock, you can define the following set of policies to create safeguards in your applications.

Denied topics – You can define a set of topics that are undesirable in the context of your application using a short natural language description. For example, as a developer at a bank, you might want to set up an assistant for your online banking application to avoid providing investment advice.

I specify a denied topic with the name “Investment advice” and provide a natural language description, such as “Investment advice refers to inquiries, guidance, or recommendations regarding the management or allocation of funds or assets with the goal of generating returns or achieving specific financial objectives.”

Guardrails for Amazon Bedrock

Guardrails for Amazon Bedrock

Content filters – You can configure thresholds to filter harmful content across hate, insults, sexual, and violence categories. While many FMs already provide built-in protections to prevent the generation of undesirable and harmful responses, guardrails give you additional controls to filter such interactions to desired degrees based on your use cases and responsible AI policies. A higher filter strength corresponds to stricter filtering.

Guardrails for Amazon Bedrock

PII redaction (in the works) – You will be able to select a set of personally identifiable information (PII) such as name, e-mail address, and phone number, that can be redacted in FM-generated responses or block a user input if it contains PII.

Guardrails for Amazon Bedrock integrates with Amazon CloudWatch, so you can monitor and analyze user inputs and FM responses that violate policies defined in the guardrails.

Join the preview
Guardrails for Amazon Bedrock is available today in limited preview. Reach out through your usual AWS Support contacts if you’d like access to Guardrails for Amazon Bedrock.

During preview, guardrails can be applied to all large language models (LLMs) available in Amazon Bedrock, including Amazon Titan Text, Anthropic Claude, Meta Llama 2, AI21 Jurassic, and Cohere Command. You can also use guardrails with custom models as well as Agents for Amazon Bedrock.

To learn more, visit the Guardrails for Amazon Bedrock web page.

— Antje

Agents for Amazon Bedrock is now available with improved control of orchestration and visibility into reasoning

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/agents-for-amazon-bedrock-is-now-available-with-improved-control-of-orchestration-and-visibility-into-reasoning/

Back in July, we introduced Agents for Amazon Bedrock in preview. Today, Agents for Amazon Bedrock is generally available.

Agents for Amazon Bedrock helps you accelerate generative artificial intelligence (AI) application development by orchestrating multistep tasks. Agents uses the reasoning capability of foundation models (FMs) to break down user-requested tasks into multiple steps. They use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to provide a final response to the end user. If you’re curious how this works, check out my previous posts on agents that include a primer on advanced reasoning and a primer on RAG.

Starting today, Agents for Amazon Bedrock also comes with enhanced capabilities that include improved control of the orchestration and better visibility into the chain of thought reasoning.

Behind the scenes, Agents for Amazon Bedrock automates the prompt engineering and orchestration of user-requested tasks, such as managing retail orders or processing insurance claims. An agent automatically builds the orchestration prompt and, if connected to knowledge bases, augments it with your company-specific information and invokes APIs to provide responses to the user in natural language.

As a developer, you can use the new trace capability to follow the reasoning that’s used as the plan is carried out. You can view the intermediate steps in the orchestration process and use this information to troubleshoot issues.

You can also access and modify the prompt that the agent automatically creates so you can further enhance the end-user experience. You can update this automatically created prompt (or prompt template) to help the FM enhance the orchestration and responses, giving you more control over the orchestration.

Let me show you how to view the reasoning steps and how to modify the prompt.

View reasoning steps
Traces gives you visibility into the agent’s reasoning, known as the chain of thought (CoT). You can use the CoT trace to see how the agent performs tasks step by step. The CoT prompt is based on a reasoning technique called ReAct (synergizing reasoning and acting). Check out the primer on advanced reasoning in my previous blog post to learn more about ReAct and the specific prompt structure.

To get started, navigate to the Amazon Bedrock console and select the working draft of an existing agent. Then, select the Test button and enter a sample user request. In the agent’s response, select Show trace.

Agents for Amazon Bedrock

The CoT trace shows the agent’s reasoning step-by-step. Open each step to see the CoT details.

Agents for Amazon Bedrock

The enhanced visibility helps you understand the rationale used by the agent to complete the task. As a developer, you can use this information to refine the prompts, instructions, and action descriptions to adjust the agent’s actions and responses when iteratively testing and improving the user experience.

Modify agent-created prompts
The agent automatically creates a prompt template from the provided instructions. You can update the preprocessing of user inputs, the orchestration plan, and the postprocessing of the FM response.

To get started, navigate to the Amazon Bedrock console and select the working draft of an existing agent. Then, select the Edit button next to Advanced prompts.

Agents for Amazon Bedrock

Here, you have access to four different types of templates. Preprocessing templates define how an agent
contextualizes and categorizes user inputs. The orchestration template equips an agent with short-term memory, a list of available actions and knowledge bases along with their descriptions, as well as few-shot examples of how to break down the problem and use these actions and knowledge in different sequences or combinations. Knowledge base response generation templates define how knowledge bases will be used and summarized in the response. Postprocessing templates define how an agent will format and present a final response to the end user. You can either keep using the template defaults or edit and override the template defaults.

Things to know
Here are a few best practices and important things to know when you’re working with Agents for Amazon Bedrock.

Agents perform best when you allow them to focus on a specific task. The clearer the objective (instructions) and the more focused the available set of actions (APIs), the easier it will be for the FM to reason and identify the right steps. If you need agents to cover various tasks, consider creating separate, individual agents.

Here are a few additional guidelines:

  • Number of APIs – Use three to five APIs with a couple of input parameters in your agents.
  • API design – Follow general best practices for designing APIs, such as ensuring idempotency.
  • API call validations – Follow best practices of API design by employing exhaustive validation for all API calls. This is particularly important because large language models (LLMs) may generate hallucinated inputs and outputs, and these validations prove helpful during such occurrences.

Availability and pricing
Agents for Amazon Bedrock are available today in AWS Regions US East (N. Virginia) and US West (Oregon). You will be charged for the inference calls (InvokeModel API) made by agents. The InvokeAgent API is not charged separately. Amazon Bedrock Pricing has all the details.

Learn more

— Antje

Customize models in Amazon Bedrock with your own data using fine-tuning and continued pre-training

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/customize-models-in-amazon-bedrock-with-your-own-data-using-fine-tuning-and-continued-pre-training/

Today, I’m excited to share that you can now privately and securely customize foundation models (FMs) with your own data in Amazon Bedrock to build applications that are specific to your domain, organization, and use case. With custom models, you can create unique user experiences that reflect your company’s style, voice, and services.

With fine-tuning, you can increase model accuracy by providing your own task-specific labeled training dataset and further specialize your FMs. With continued pre-training, you can train models using your own unlabeled data in a secure and managed environment with customer managed keys. Continued pre-training helps models become more domain-specific by accumulating more robust knowledge and adaptability—beyond their original training.

Let me give you a quick tour of both model customization options. You can create fine-tuning and continued pre-training jobs using the Amazon Bedrock console or APIs. In the console, navigate to Amazon Bedrock, then select Custom models.

Amazon Bedrock - Custom Models

Fine-tune Meta Llama 2, Cohere Command Light, and Amazon Titan FMs
Amazon Bedrock now supports fine-tuning for Meta Llama 2, Cohere Command Light, as well as Amazon Titan models. To create a fine-tuning job in the console, choose Customize model, then choose Create Fine-tuning job.

Amazon Bedrock - Custom Models

Here’s a quick demo using the AWS SDK for Python (Boto3). Let’s fine-tune Cohere Command Light to summarize dialogs. For demo purposes, I’m using the public dialogsum dataset, but this could be your own company-specific data.

To prepare for fine-tuning on Amazon Bedrock, I converted the dataset into JSON Lines format and uploaded it to Amazon S3. Each JSON line needs to have both a prompt and a completion field. You can specify up to 10,000 training data records, but you may already see model performance improvements with a few hundred examples.

{"completion": "Mr. Smith's getting a check-up, and Doctor Haw...", "prompt": Summarize the following conversation.\n\n#Pers..."}
{"completion": "Mrs Parker takes Ricky for his vaccines. Dr. P...", "prompt": "Summarize the following conversation.\n\n#Pers..."}
{"completion": "#Person1#'s looking for a set of keys and asks...", "prompt": "Summarize the following conversation.\n\n#Pers..."}

I redacted the prompt and completion fields for brevity.

You can list available foundation models that support fine-tuning with the following command:

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

for model in bedrock.list_foundation_models(
    byCustomizationType="FINE_TUNING")["modelSummaries"]:
    for key, value in model.items():
        print(key, ":", value)
    print("-----\n")

Next, I create a model customization job. I specify the Cohere Command Light model ID that supports fine-tuning, set customization type to FINE_TUNING, and point to the Amazon S3 location of the training data. If needed, you can also adjust the hyperparameters for fine-tuning.

# Select the foundation model you want to customize
base_model_id = "cohere.command-light-text-v14:7:4k"

bedrock.create_model_customization_job(
    customizationType="FINE_TUNING",
    jobName=job_name,
    customModelName=model_name,
    roleArn=role,
    baseModelIdentifier=base_model_id,
    hyperParameters = {
        "epochCount": "1",
        "batchSize": "8",
        "learningRate": "0.00001",
    },
    trainingDataConfig={"s3Uri": "s3://path/to/train-summarization.jsonl"},
    outputDataConfig={"s3Uri": "s3://path/to/output"},
)

# Check for the job status
status = bedrock.get_model_customization_job(jobIdentifier=job_name)["status"]

Once the job is complete, you receive a unique model ID for your custom model. Your fine-tuned model is stored securely by Amazon Bedrock. To test and deploy your model, you need to purchase Provisioned Throughput.

Let’s see the results. I select one example from the dataset and ask the base model before fine-tuning, as well as the custom model after fine-tuning, to summarize the following dialog:

prompt = """Summarize the following conversation.\\n\\n
#Person1#: Hello. My name is John Sandals, and I've got a reservation.\\n
#Person2#: May I see some identification, sir, please?\\n
#Person1#: Sure. Here you are.\\n
#Person2#: Thank you so much. Have you got a credit card, Mr. Sandals?\\n
#Person1#: I sure do. How about American Express?\\n
#Person2#: Unfortunately, at the present time we take only MasterCard or VISA.\\n
#Person1#: No American Express? Okay, here's my VISA.\\n
#Person2#: Thank you, sir. You'll be in room 507, nonsmoking, with a queen-size bed. Do you approve, sir?\\n
#Person1#: Yeah, that'll be fine.\\n
#Person2#: That's great. This is your key, sir. If you need anything at all, anytime, just dial zero.\\n\\n
Summary: """

Use the Amazon Bedrock InvokeModel API to query the models.

body = {
    "prompt": prompt,
    "temperature": 0.5,
    "p": 0.9,
    "max_tokens": 512,
}

response = bedrock_runtime.invoke_model(
	# Use on-demand inference model ID for response before fine-tuning
    # modelId="cohere.command-light-text-v14",
	# Use ARN of your deployed custom model for response after fine-tuning
	modelId=provisioned_custom_model_arn,
    modelId=base_model_id, 
    body=json.dumps(body)
)

Here’s the base model response before fine-tuning:

#Person2# helps John Sandals with his reservation. John gives his credit card information and #Person2# confirms that they take only MasterCard and VISA. John will be in room 507 and #Person2# will be his host if he needs anything.

Here’s the response after fine-tuning, shorter and more to the point:

John Sandals has a reservation and checks in at a hotel. #Person2# takes his credit card and gives him a key.

Continued pre-training for Amazon Titan Text (preview)
Continued pre-training on Amazon Bedrock is available today in public preview for Amazon Titan Text models, including Titan Text Express and Titan Text Lite. To create a continued pre-training job in the console, choose Customize model, then choose Create Continued Pre-training job.

Amazon Bedrock - Custom Models

Here’s a quick demo again using boto3. Let’s assume you work at an investment company and want to continue pre-training the model with financial and analyst reports to make it more knowledgeable about financial industry terminology. For demo purposes, I selected a collection of Amazon shareholder letters as my training data.

To prepare for continued pre-training, I converted the dataset into JSON Lines format again and uploaded it to Amazon S3. Because I’m working with unlabeled data, each JSON line only needs to have the prompt field. You can specify up to 100,000 training data records and usually see positive effects after providing at least 1 billion tokens.

{"input": "Dear shareholders: As I sit down to..."}
{"input": "Over the last several months, we to..."}
{"input": "work came from optimizing the conne..."}
{"input": "of the Amazon shopping experience f..."}

I redacted the input fields for brevity.

Then, create a model customization job with customization type CONTINUED_PRE_TRAINING that points to the data. If needed, you can also adjust the hyperparameters for continued pre-training.

# Select the foundation model you want to customize
base_model_id = "amazon.titan-text-express-v1"

bedrock.create_model_customization_job(
    customizationType="CONTINUED_PRE_TRAINING",
    jobName=job_name,
    customModelName=model_name,
    roleArn=role,
    baseModelIdentifier=base_model_id,
    hyperParameters = {
        "epochCount": "10",
        "batchSize": "8",
        "learningRate": "0.00001",
    },
    trainingDataConfig={"s3Uri": "s3://path/to/train-continued-pretraining.jsonl"},
    outputDataConfig={"s3Uri": "s3://path/to/output"},
)

Once the job is complete, you receive another unique model ID. Your customized model is securely stored again by Amazon Bedrock. As with fine-tuning, you need to purchase Provisioned Throughput to test and deploy your model.

Things to know
Here are a couple of important things to know:

Data privacy and network security – With Amazon Bedrock, you are in control of your data, and all your inputs and customizations remain private to your AWS account. Your data, such as prompts, completions, custom models, and data used for fine-tuning or continued pre-training, is not used for service improvement and is never shared with third-party model providers. Your data remains in the AWS Region where the API call is processed. All data is encrypted in transit and at rest. You can use AWS PrivateLink to create a private connection between your VPC and Amazon Bedrock.

Billing – Amazon Bedrock charges for model customization, storage, and inference. Model customization is charged per tokens processed. This is the number of tokens in the training dataset multiplied by the number of training epochs. An epoch is one full pass through the training data during customization. Model storage is charged per month, per model. Inference is charged hourly per model unit using provisioned throughput. For detailed pricing information, see Amazon Bedrock Pricing.

Custom models and provisioned throughput – Amazon Bedrock allows you to run inference on custom models by purchasing provisioned throughput. This guarantees a consistent level of throughput in exchange for a term commitment. You specify the number of model units needed to meet your application’s performance needs. For evaluating custom models initially, you can purchase provisioned throughput hourly with no long-term commitment. With no commitment, a quota of one model unit is available per provisioned throughput. You can create up to two provisioned throughputs per account.

Availability
Fine-tuning support on Meta Llama 2, Cohere Command Light, and Amazon Titan Text FMs is available today in AWS Regions US East (N. Virginia) and US West (Oregon). Continued pre-training is available today in public preview in AWS Regions US East (N. Virginia) and US West (Oregon). To learn more, visit the Amazon Bedrock Developer Experience web page and check out the User Guide.

Customize FMs with Amazon Bedrock today!

— Antje

Knowledge Bases now delivers fully managed RAG experience in Amazon Bedrock

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/knowledge-bases-now-delivers-fully-managed-rag-experience-in-amazon-bedrock/

Back in September, we introduced Knowledge Bases for Amazon Bedrock in preview. Starting today, Knowledge Bases for Amazon Bedrock is generally available.

With a knowledge base, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for Retrieval Augmented Generation (RAG). Access to additional data helps the model generate more relevant, context-specific, and accurate responses without continuously retraining the FM. All information retrieved from knowledge bases comes with source attribution to improve transparency and minimize hallucinations. If you’re curious how this works, check out my previous post that includes a primer on RAG.

With today’s launch, Knowledge Bases gives you a fully managed RAG experience and the easiest way to get started with RAG in Amazon Bedrock. Knowledge Bases now manages the initial vector store setup, handles the embedding and querying, and provides source attribution and short-term memory needed for production RAG applications. If needed, you can also customize the RAG workflows to meet specific use case requirements or integrate RAG with other generative artificial intelligence (AI) tools and applications.

Fully managed RAG experience
Knowledge Bases for Amazon Bedrock manages the end-to-end RAG workflow for you. You specify the location of your data, select an embedding model to convert the data into vector embeddings, and have Amazon Bedrock create a vector store in your account to store the vector data. When you select this option (available only in the console), Amazon Bedrock creates a vector index in Amazon OpenSearch Serverless in your account, removing the need to manage anything yourself.

Knowledge bases for Amazon Bedrock

Vector embeddings include the numeric representations of text data within your documents. Each embedding aims to capture the semantic or contextual meaning of the data. Amazon Bedrock takes care of creating, storing, managing, and updating your embeddings in the vector store, and it ensures your data is always in sync with your vector store.

Amazon Bedrock now also supports two new APIs for RAG that handle the embedding and querying and provide the source attribution and short-term memory needed for production RAG applications.

With the new RetrieveAndGenerate API, you can directly retrieve relevant information from your knowledge bases and have Amazon Bedrock generate a response from the results by specifying a FM in your API call. Let me show you how this works.

Use the RetrieveAndGenerate API
To give it a try, navigate to the Amazon Bedrock console, create and select a knowledge base, then select Test knowledge base. For this demo, I created a knowledge base that has access to a PDF of Generative AI on AWS. I choose Select Model to specify a FM.

Knowledge Bases for Amazon Bedrock

Then, I ask, “What is Amazon Bedrock?”

Knowledge Bases for Amazon Bedrock

Behind the scenes, Amazon Bedrock converts the queries into embeddings, queries the knowledge base, and then augments the FM prompt with the search results as context information and returns the FM-generated response to my question. For multi-turn conversations, Knowledge Bases manages the short-term memory of the conversation to provide more contextual results.

Here’s a quick demo of how to use the APIs with the AWS SDK for Python (Boto3).

def retrieveAndGenerate(input, kbId):
    return bedrock_agent_runtime.retrieve_and_generate(
        input={
            'text': input
        },
        retrieveAndGenerateConfiguration={
            'type': 'KNOWLEDGE_BASE',
            'knowledgeBaseConfiguration': {
                'knowledgeBaseId': kbId,
                'modelArn': 'arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-instant-v1'
                }
            }
        )

response = retrieveAndGenerate("What is Amazon Bedrock?", "AES9P3MT9T")["output"]["text"]

The output of the RetrieveAndGenerate API includes the generated response, the source attribution, and the retrieved text chunks. In my demo, the API response looks like this (with some of the output redacted for brevity):


{ ... 
    'output': {'text': 'Amazon Bedrock is a managed service from AWS that ...'}, 
    'citations': 
        [{'generatedResponsePart': 
             {'textResponsePart': 
                 {'text': 'Amazon Bedrock is ...', 'span': {'start': 0, 'end': 241}}
             }, 
	      'retrievedReferences': 
			[{'content':
                 {'text': 'All AWS-managed service API activity...'}, 
				 'location': {'type': 'S3', 's3Location': {'uri': 's3://data-generative-ai-on-aws/gaia.pdf'}}}, 
		     {'content': 
			      {'text': 'Changing a portion of the image using ...'}, 
				  'location': {'type': 'S3', 's3Location': {'uri': 's3://data-generative-ai-on-aws/gaia.pdf'}}}, ...]
        ...}]
}

The generated response looks like this:

Amazon Bedrock is a managed service that offers a serverless experience for generative AI through a simple API. It provides access to foundation models from Amazon and third parties for tasks like text generation, image generation, and building conversational agents. Data processed through Amazon Bedrock remains private and encrypted.

Customize RAG workflows
If you want to process the retrieved text chunks further, see the relevance scores of the retrievals, or develop your own orchestration for text generation, you can use the new Retrieve API. This API converts user queries into embeddings, searches the knowledge base, and returns the relevant results, giving you more control to build custom workflows on top of the semantic search results.

Use the Retrieve API
In the Amazon Bedrock console, I toggle the switch to disable Generate responses.

Knowledge Bases for Amazon Bedrock

Then, I ask again, “What is Amazon Bedrock?” This time, the output shows me the retrieval results with links to the source documents where the text chunks came from.

Knowledge Bases for Amazon Bedrock

Here’s how to use the Retrieve API with boto3.

import boto3

bedrock_agent_runtime = boto3.client(
    service_name = "bedrock-agent-runtime"
)

def retrieve(query, kbId, numberOfResults=5):
    return bedrock_agent_runtime.retrieve(
        retrievalQuery= {
            'text': query
        },
        knowledgeBaseId=kbId,
        retrievalConfiguration= {
            'vectorSearchConfiguration': {
                'numberOfResults': numberOfResults
            }
        }
    )

response = retrieve("What is Amazon Bedrock?", "AES9P3MT9T")["retrievalResults"]

The output of the Retrieve API includes the retrieved text chunks, the location type and URI of the source data, and the scores of the retrievals. The score helps to determine chunks that match more closely with the query.

In my demo, the API response looks like this (with some of the output redacted for brevity):

[{'content': {'text': 'Changing a portion of the image using ...'},
  'location': {'type': 'S3',
   's3Location': {'uri': 's3://data-generative-ai-on-aws/gaia.pdf'}},
  'score': 0.7329834},
 {'content': {'text': 'back to the user in natural language. For ...'},
  'location': {'type': 'S3',
   's3Location': {'uri': 's3://data-generative-ai-on-aws/gaia.pdf'}},
  'score': 0.7331088},
...]

To further customize your RAG workflows, you can define a custom chunking strategy and select a custom vector store.

Custom chunking strategy – To enable effective retrieval from your data, a common practice is to first split the documents into manageable chunks. This enhances the model’s capacity to comprehend and process information more effectively, leading to improved relevant retrievals and generation of coherent responses. Knowledge Bases for Amazon Bedrock manages the chunking of your documents.

When you configure the data source for your knowledge base, you can now define a chunking strategy. Default chunking splits data into chunks of up to 200 tokens and is optimized for question-answer tasks. Use default chunking when you are not sure of the optimal chunk size for your data.

You also have the option to specify a custom chunk size and overlap with fixed-size chunking. Use fixed-size chunking if you know the optimal chunk size and overlap for your data (based on file attributes, accuracy testing, and so on). An overlap between chunks in the recommended range of 0–20 percent can help improve accuracy. Higher overlap can lead to decreased relevancy scores.

If you select to create one embedding per document, Knowledge Bases keeps each file as a single chunk. Use this option if you don’t want Amazon Bedrock to chunk your data, for example, if you want to chunk your data offline using an algorithm that is specific to your use case. Common use cases include code documentation.

Custom vector store – You can also select a custom vector store. The available vector database options include vector engine for Amazon OpenSearch Serverless, Pinecone, and Redis Enterprise Cloud. To use a custom vector store, you must create a new, empty vector database from the list of supported options and provide the vector database index name as well as index field and metadata field mappings. This vector database will need to be for exclusive use with Amazon Bedrock.

Knowledge Bases for Amazon Bedrock

Integrate RAG with other generative AI tools and applications
If you want to build an AI assistant that can perform multistep tasks and access company data sources to generate more relevant and context-aware responses, you can integrate Knowledge Bases with Agents for Amazon Bedrock. You can also use the Knowledge Bases retrieval plugin for LangChain to integrate RAG workflows into your generative AI applications.

Availability
Knowledge bases for Amazon Bedrock is available today in AWS Regions US East (N. Virginia) and US West (Oregon).

Learn more

— Antje