All posts by Richy George

Posted by Richy George on 2 May, 2024

MongoDB has made Atlas Stream Processing, a new capability it trailed last June, generally available, it announced at its MongoDB.local event in New York City.

It added  Atlas Stream processing to its NoSQL Atlas database-as-a-service (DBaaS) in order to help enterprises manage real-time streaming data from multiple sources in a single interface.

The new interface that can process any kind of data and has a flexible data model, bypassing the need for developers to use multiple specialized programming languages, libraries, application programming interfaces (APIs), and drivers, while avoiding the complexity of using these multiple tools, the company said, adding that it can work with both streaming and historical data using the document model.

Atlas Search Nodes is also generally available on AWS and Google Cloud, although the capability is still in preview on Microsoft Azure. This too was showcased last year: It’s a new capability inside the Atlas database that isolates search workloads from database workloads in order to maintain database and search performance.

Users will have to wait for one new capability: Atlas Edge Server. This feature, now in preview, gives developers the capability to deploy and operate distributed applications in the cloud and at the edge, the company said. It provides a local instance of MongoDB with a synchronization server that runs on local or remote infrastructure and significantly reduces the complexity and risk involved in managing applications in edge environments, allowing applications to access operational data even with intermittent connections to the cloud.

One other MongoDB feature also entered general availability: its Vector Search integration with AWS’ generative AI service, Amazon Bedrock. This means that enterprises can use the integration to customize foundation large language models with real-time operational data by converting it into vector embeddings.

Further, enterprises can also use Agents for Amazon Bedrock for retrieval-augmented generation (RAG), the company said.

Posted by Richy George on 2 May, 2024

Oracle is making the latest long-term support release version of its database offering — Database 23c — generally available for enterprises under the name Oracle Database 23ai.

The change in nomenclature can be attributed to the addition of new features to the database that are expected to help with AI-based application development among other tasks, the company said.

Database 23c, showcased for the first time at the company’s annual event in 2022, was released to developers in early 2023 before being released to enterprises, marking a shift in the company’s tradition for the first time.

Stiff competition from database rivals forced Oracle to shift its strategy for its databases business in favor of developers, who could offer the company a much-needed impetus for growth.

In September last year, Oracle said it was working on adding vector search capabilities to Database 23c at its annual CloudWorld conference.

These capabilities, dubbed AI Vector Search, included a new vector data type, vector indexes, and vector search SQL operators that enable the Oracle Database to store the semantic content of documents, images, and other unstructured data as vectors, and use these to run fast similarity queries, the company said.

AI Vector Search in Database 23c that has been passed onto 23ai along with other features, according to the company, also supports retrieval-augmented generation (RAG), a generative AI technique, that combines large language models (LLMs) and private business data to deliver responses to natural language questions.

Other notable features of Database 23c that have been passed onto 23ai include JSON Relational Duality, which unifies the relational and document data models, SQL support for Graph queries directly on OLTP data, and stored procedures in JavaScript, allowing developers to build applications in either relational or JSON paradigms.

Database 23ai, according to Oracle, will be available as a cloud service as well as on-premises through a variety of offerings, including Oracle Exadata Database Service, Oracle Exadata Cloud@Customer, and Oracle Base Database Service, as well as on Oracle Database@Azure.

While Oracle did not release Database 23ai’s pricing, the developer version of Database 23c continues to be free since its release.

The reason to offer Database 23c for free can be attributed to the company’s strategy to lower the barriers to the adoption of its database as rival database providers also add newer features, such as vector search, to support AI workloads.

Several database vendors, such as MongoDB, AWS, Google Cloud, Microsoft, Zilliz, DataStax, Pinecone, Couchbase, Snowflake, and SingleStore, have all added capabilities to support AI-based tasks.

Vector databases and vector search are two technologies that developers use to convert unstructured information into vectors, now more commonly called embeddings.

These embeddings, in turn, make storing, searching, and comparing the information easier, faster, and significantly more scalable for large datasets. 

Posted by Richy George on 30 April, 2024

Data has the potential to provide transformative business insights across various industries, yet harnessing that data presents significant challenges. Many businesses struggle with data overload, with vast amounts of data that are siloed and underutilized. How can organizations deal with large and growing volumes of data without sacrificing performance and operational efficiency? Another challenge is extracting insights from complex data. Traditionally, this work has required significant technical expertise, restricting access to specialized data scientists and analysts. 

Recent AI breakthroughs in natural language processing are democratizing data access, enabling a wider range of users to query and interpret complex data sets. This broadened access helps organizations make informed decisions swiftly, capitalizing on the capability of AI copilots to process and analyze large-scale data in real time. AI copilots can also curb the high costs associated with managing large data sets by automating complex data processes and empowering less technical staff to undertake sophisticated data analysis, thus optimizing overall resource allocation.

Generative AI and large language models (LLMs) are not without their shortcomings, however. Most LLMs are built on general purpose, public knowledge. They won’t know the specific and sometimes confidential data of a particular organization. It’s also very challenging to keep LLMs up-to-date with ever-changing information. The most serious problem, however, is hallucinations—when the statistical processes in a generative model generate statements that simply aren’t true.

There’s an urgent need for AI that is more contextually relevant and less error-prone. This is particularly vital in predictive analytics and machine learning, where the quality of data can directly impact business outcomes.

Introducing TigerGraph CoPilot

TigerGraph CoPilot is an AI assistant that combines the powers of graph databases and generative AI to enhance productivity across various business functions, including analytics, development, and administration tasks. TigerGraph CoPilot allows business analysts, data scientists, and developers to use natural language to execute real-time queries against up-to-date data at scale. The insights can then be presented and analyzed through natural language, graph visualizations, and other perspectives. 

TigerGraph CoPilot adds value to generative AI applications by increasing accuracy and reducing hallucinations. With CoPilot, organizations can tap the full potential of their data and drive informed decision-making across a spectrum of domains, including customer service, marketing, sales, data science, devops, and engineering.

TigerGraph CoPilot key features and benefits

• Graph-augmented natural language inquiry
• Graph-augmented generative AI
• Reliable and responsible AI
• High scalability and performance

Graph-augmented natural language inquiry

TigerGraph CoPilot allows non-technical users to use their everyday speech to query and analyze their data, freeing them to focus on mining insights rather than having to learn a new technology or computer language. For each question, CoPilot employs a novel three-phase interaction with both the TigerGraph database and a LLM of the user’s choice, to obtain accurate and relevant responses.

The first phase aligns the question with the particular data available in the database. TigerGraph CoPilot uses the LLM to compare the question with the graph’s schema and replace entities in the question by graph elements. For example, if there is a vertex type of BareMetalNode and the user asks “How many servers are there?,” then the question will be translated to “How many BareMetalNode vertices are there?”

In the second phase, TigerGraph CoPilot uses the LLM to compare the transformed question with a set of curated database queries and functions in order to select the best match. Using pre-approved queries provides multiple benefits. First and foremost, it reduces the likelihood of hallucinations, because the meaning and behavior of each query has been validated. Second, the system has the potential of predicting the execution resources needed to answer the question.

In the third phase, TigerGraph CoPilot executes the identified query and returns the result in natural language along with the reasoning behind the actions. CoPilot’s graph-augmented natural language inquiry provides strong guardrails, mitigating the risk of model hallucinations, clarifying the meaning of each query, and offering an understanding of the consequences. 

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Graph-augmented generative AI

TigerGraph CoPilot also can create chatbots with graph-augmented AI on a user’s own documents. There’s no need to have an existing graph database. In this mode of operation, TigerGraph CoPilot builds a knowledge graph from source material and applies its unique variant of retrieval-augmented generation (RAG) to improve the contextual relevance and accuracy of answers to natural language questions.

First, when loading users’ documents, TigerGraph CoPilot extracts entities and relationships from document chunks and constructs a knowledge graph from the documents. Knowledge graphs organize information in a structured format, connecting data points through relationships. CoPilot will also identify concepts and build an ontology, adding semantics and reasoning to the knowledge graph, or users can provide their own concept ontology. Then, using this comprehensive knowledge graph, CoPilot performs hybrid retrievals, combining traditional vector search and graph traversals, to collect more relevant information and richer context to answer users’ questions.

Organizing the data as a knowledge graph allows a chatbot to access accurate, fact-based information quickly and efficiently, thereby reducing the reliance on generating responses from patterns learned during training, which can sometimes be incorrect or outdated.

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Reliable and responsible AI

TigerGraph CoPilot mitigates hallucinations by allowing LLMs to access the graph database via curated queries. It also adheres to the same role-based access control and security measures (already part of the TigerGraph database) to assure responsible AI. TigerGraph CoPilot also supports openness and transparency by open-sourcing its major components and allowing users to choose their LLM service.

High scalability and performance

By leveraging the TigerGraph database, TigerGraph CoPilot brings high performance to graph analytics. As a graph-RAG solution, it supports large-scale knowledge bases for knowledge graph-powered Q&A solutions.

TigerGraph CoPilot key use cases 

• Natural language to data insights
• Context-rich Q&A

Natural language to data insights

Whether you are a business analyst, specialist, or investigator, TigerGraph CoPilot enables you to get information and insights quickly from your data. For example, CoPilot can generate reports for fraud investigators by answering questions like “Show me the list of recent fraud cases that were false positives.” CoPilot also facilitates more accurate investigations like “Who had transactions with account 123 in the past month with amounts larger than $1000?”

TigerGraph CoPilot can even answer “What if” questions by traversing your graph along dependencies. For example, you can easily find out “What suppliers can cover the shortage of part 123?” from your supply chain graph, or “What services would be affected by an upgrade to server 321” from your digital infrastructure graph.

Context-rich Q&A

TigerGraph CoPilot provides a complete solution for building Q&A chatbot on your own data and documents. Its knowledge graph-based RAG approach enables contextually accurate information retrieval that facilitates better answers and more informed decisions. CoPilot’s context-rich Q&A directly improves productivity and reduces costs in typical Q&A applications such as call centers, customer services, and knowledge search.

Furthermore, by merging a document knowledge graph and an existing business graph (e.g., product graph) into one intelligence graph, TigerGraph CoPilot can tackle problems that cannot be addressed by other RAG solutions. For example, by combining customers’ purchase history with product graphs, CoPilot can make more accurate personalized recommendations when customers type in their search queries or ask for recommendations. By combining patients’ medical history with healthcare graphs, doctors or health specialists can get more useful information about the patients to provide better diagnoses or treatments.  

Graph meets generative AI

TigerGraph CoPilot addresses both the complex challenges associated with data management and analysis and the serious shortcomings of LLMs for business applications. By leveraging the power of natural language processing and advanced algorithms, organizations can unlock transformative business insights while navigating data overload and accessibility barriers. By tapping graph-based RAG, they can ensure the accuracy and relevance of LLM output.

CoPilot allows a wider range of users to leverage data effectively, driving informed decision-making and optimizing resource allocation across organizations. We believe it is a significant step forward in democratizing data access and empowering organizations to harness the full potential of their data assets.

Hamid Azzawe is CEO of TigerGraph.

—

Generative AI Insights provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss the challenges and opportunities of generative artificial intelligence. The selection is wide-ranging, from technology deep dives to case studies to expert opinion, but also subjective, based on our judgment of which topics and treatments will best serve InfoWorld’s technically sophisticated audience. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Contact doug_dineley@foundryco.com.

Posted by Richy George on 29 April, 2024

At the end of March 2024, Mike Stonebraker announced in a blog post the release of DBOS Cloud, “a transactional serverless computing platform, made possible by a revolutionary new operating system, DBOS, that implements OS services on top of a distributed database.” That sounds odd, to put it mildly, but it makes more sense when you read the origin story:

The idea for DBOS (DataBase oriented Operating System) originated 3 years ago with my realization that the state an operating system must maintain (files, processes, threads, messages, etc.) has increased in size by about 6 orders of magnitude since I began using Unix on a PDP-11/40 in 1973. As such, storing OS state is a database problem. Also, Linux is legacy code at the present time and is having difficulty making forward progress. For example there is no multi-node version of Linux, requiring people to run an orchestrator such as Kubernetes. When I heard a talk by Matei Zaharia in which he said Databricks could not use traditional OS scheduling technology at the scale they were running and had turned to a DBMS solution instead, it was clear that it was time to move the DBMS into the kernel and build a new operating system.”

If you don’t know Stonebraker, he’s been a database-focused computer scientist (and professor) since the early 1970s, when he and his UC Berkeley colleagues Eugene Wong and Larry Rowe founded Ingres. Ingres later inspired Sybase, which was eventually the basis for Microsoft SQL Server. After selling Ingres to Computer Associates, Stonebraker and Rowe started researching Postgres, which later became PostgreSQL and also evolved into Illustra, which was purchased by Informix.

I heard Stonebraker talk about Postgres at a DBMS conference in 1980. What I got out of that talk, aside from an image of “jungle drums” calling for SQL, was the idea that you could add support for complex data types to the database by implementing new index types, extending the query language, and adding support for that to the query parser and optimizer. The example he used was geospatial information, and he explained one kind of index structure that would make 2D geometric database queries go very fast. (This facility eventually became PostGIS. The R-tree currently used by default in PostGIS GiST indexes wasn’t invented until 1984, so Mike was probably talking about the older quadtree index.)

Skipping ahead 44 years, it should surprise precisely nobody in the database field that DBOS uses a distributed version of PostgreSQL as its kernel database layer.

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DBOS features

DBOS Transact, an open-source TypeScript framework, supports Postgres-compatible transactions, reliable workflow orchestration, HTTP serving using GET and POST, communication with external services and third-party APIs, idempotent requests using UUID keys, authentication and authorization, Kafka integration with exactly-once semantics, unit testing, and self-hosting. DBOS Cloud, a transactional serverless platform for deploying DBOS Transact applications, supports serverless app deployment, time-travel debugging, cloud database management, and observability.

Let’s highlight some major areas of interest.

DBOS Transact

The code shown in the screenshot below demonstrates transactions, as well as HTTP serving using GET. It’s worthwhile to read the code closely. It’s only 18 lines, not counting blank lines.

The first import (line 1) brings in the DBOS SDK classes that we’ll need. The second import (line 2) brings in the Knex.js SQL query builder, which handles sending the parameterized query to the Postgres database and returning the resulting rows. The database table schema is defined in lines 4 through 8; the only columns are a name string and a greet_count integer.

There is only one method in the Hello class, helloTransaction. It is wrapped in @GetApi and @Transaction decorators, which respectively cause the method to be served in response to an HTTP GET request on the path /greeting/ followed by the username parameter you want to pass in and wrap the database call in a transaction, so that two instances can’t update the database simultaneously.

The database query string (line 16) uses PostgreSQL syntax to try to insert a row into the database for the supplied name and an initial count of 1. If the row already exists, then the ON CONFLICT trigger runs an update operation that increments the count in the database.

Line 17 uses Knex.js to send the SQL query to the DBOS system database and retrieves the result. Line 18 pulls the count out of the first row of results and returns the greeting string to the calling program.

The use of SQL and a database for what feels like should be a core in-memory system API, such as a Linux atomic counter or a Windows interlocked variable, seems deeply weird. Nevertheless, it works.

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DBOS Time Travel Debugger

When you run an application in DBOS Cloud it records every step and change it makes (the workflow) in the database. You can debug that using Visual Studio Code and the DBOS Time Travel Debugger extension. The time-travel debugger allows you to debug your DBOS application against the database as it existed at the time the selected workflow originally executed.

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DBOS Quickstart

The DBOS Quickstart tutorial requires Node.js 20 or later and a PostgreSQL database you can connect to, either locally, in a Docker container, or remotely. I already had Node.js v20.9.0 installed on my M1 MacBook, but I upgraded it to v20.12.1 from the Node.js website.

I didn’t have PostgreSQL installed, so I downloaded and ran the interactive installer for v16.2 from EnterpriseDB. This installer creates a full-blown macOS server and applications. If I had used Homebrew instead, it would have created command-line applications, and if I had used Postgres.app, I would have gotten a menu-bar app.

The Quickstart proper starts by creating a DBOS app directory using Node.js.

martinheller@Martins-M1-MBP ~ % npx -y @dbos-inc/create@latest -n myapp
Merged .gitignore files saved to myapp/.gitignore
added 590 packages, and audited 591 packages in 25s
found 0 vulnerabilities
added 1 package, and audited 592 packages in 1s
found 0 vulnerabilities
added 129 packages, and audited 721 packages in 5s
found 0 vulnerabilities
Application initialized successfully!

Then you configure the app to use your Postgres server and export your Postgres password into an enviroment variable.

martinheller@Martins-M1-MBP ~ % cd myapp
martinheller@Martins-M1-MBP myapp % npx dbos configure
? What is the hostname of your Postgres server? localhost
? What is the port of your Postgres server? 5432
? What is your Postgres username? postgres
martinheller@Martins-M1-MBP myapp % export PGPASSWORD=*********

After that, you create a “Hello” database using Node.js and Knex.js.

martinheller@Martins-M1-MBP myapp % npx dbos migrate
2024-04-09 15:01:42 [info]: Starting migration: creating database hello if it does not exist
2024-04-09 15:01:42 [info]: Database hello does not exist, creating...
2024-04-09 15:01:42 [info]: Executing migration command: npx knex migrate:latest
2024-04-09 15:01:43 [info]: Batch 1 run: 1 migrations
2024-04-09 15:01:43 [info]: Creating DBOS tables and system database.
2024-04-09 15:01:43 [info]: Migration successful!

With that complete, you build and run the DBOS app locally.

martinheller@Martins-M1-MBP myapp % npm run build
npx dbos start

> myapp@0.0.1 build
> tsc

2024-04-09 15:02:30 [info]: Workflow executor initialized
2024-04-09 15:02:30 [info]: HTTP endpoints supported:
2024-04-09 15:02:30 [info]:     GET   :  /greeting/:user
2024-04-09 15:02:30 [info]: DBOS Server is running at http://localhost:3000
2024-04-09 15:02:30 [info]: DBOS Admin Server is running at http://localhost:3001
^C

At this point, you can browse to http://localhost:3000 to test the application. That done, you register for the DBOS Cloud and provision your own database there.

martinheller@Martins-M1-MBP myapp % npx dbos-cloud register -u meheller
2024-04-09 15:11:35 [info]: Welcome to DBOS Cloud!
2024-04-09 15:11:35 [info]: Before creating an account, please tell us a bit about yourself!
Enter First/Given Name: Martin
Enter Last/Family Name: Heller
Enter Company: self
2024-04-09 15:12:06 [info]: Please authenticate with DBOS Cloud!
Login URL: https://login.dbos.dev/activate?user_code=QWKW-TXTB
2024-04-09 15:12:12 [info]: Waiting for login...
2024-04-09 15:12:17 [info]: Waiting for login...
2024-04-09 15:12:22 [info]: Waiting for login...
2024-04-09 15:12:27 [info]: Waiting for login...
2024-04-09 15:12:32 [info]: Waiting for login...
2024-04-09 15:12:38 [info]: Waiting for login...
2024-04-09 15:12:44 [info]: meheller successfully registered!
martinheller@Martins-M1-MBP myapp % npx dbos-cloud db provision iw_db -U meheller
Database Password: ********
2024-04-09 15:19:22 [info]: Successfully started provisioning database: iw_db
2024-04-09 15:19:28 [info]: {"PostgresInstanceName":"iw_db","HostName":"userdb-51fcc211-6ed3-4450-a90e-0f864fc1066c.cvc4gmaa6qm9.us-east-1.rds.amazonaws.com","Status":"available","Port":5432,"DatabaseUsername":"meheller","AdminUsername":"meheller"}
2024-04-09 15:19:28 [info]: Database successfully provisioned!

Finally, you can register and deploy your app in the DBOS Cloud.

martinheller@Martins-M1-MBP myapp % npx dbos-cloud app register -d iw_db
2024-04-09 15:20:09 [info]: Loaded application name from package.json: myapp
2024-04-09 15:20:09 [info]: Registering application: myapp
2024-04-09 15:20:11 [info]: myapp ID: d8806829-c5b8-4df0-8b5a-2d1bf87c3322
2024-04-09 15:20:11 [info]: Successfully registered myapp!
martinheller@Martins-M1-MBP myapp % npx dbos-cloud app deploy
2024-04-09 15:20:35 [info]: Loaded application name from package.json: myapp
2024-04-09 15:20:35 [info]: Submitting deploy request for myapp
2024-04-09 15:21:09 [info]: Submitted deploy request for myapp. Assigned version: 1712676035
2024-04-09 15:21:13 [info]: Waiting for myapp with version 1712676035 to be available
2024-04-09 15:21:21 [info]: Successfully deployed myapp!
2024-04-09 15:21:21 [info]: Access your application at https://meheller-myapp.cloud.dbos.dev/

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DBOS applications

The “Hello” application does illustrate some of the core features of DBOS Transact and the DBOS Cloud, but it’s so basic that it’s barely a toy. The Programming Quickstart adds a few more details, and it’s worth your time to go through it. You’ll learn how to use communicator functions to access third-party services (email, in this example) as well as how to compose reliable workflows. You’ll literally interrupt the workflow and restart it without re-sending the email: DBOS workflows always run to completion and each of their operations executes once and only once. That’s possible because DBOS persists the output of each step in your database.

Once you’ve understood the programming Quickstart, you’ll be ready to try out the two DBOS demo applications, which do rise to the level of being toys. Both demos use Next.js for their front ends, and both use DBOS workflows, transactions, and communicators.

The first demo, E-Commerce, is a web shopping and payment processing system. It’s worthwhile reading the Under the Covers section of the README in the demo’s repository to understand how it works and how you might want to upgrade it to, for example, use a real-world payment provider.

The second demo, YKY Social, simulates a simple social network, and uses TypeORM rather than Knex.js for its database code. It also uses Amazon S3 for profile photos. If you’re serious about using DBOS yourself, you should work though both demo applications.

A tantalizing glimpse

I have to say that DBOS and DBOS Cloud look very interesting. Reliable execution and time-travel debugging, for example, are quite desirable. On the other hand, I wouldn’t want to build a real application on DBOS or DBOS Cloud at this point. I have lots of questions, starting with “How does it scale in practice?” and probably ending with “How much will it cost at X scale?”

I mentioned earlier that DBOS code looks weird but works. I would imagine that any programming shop considering writing an application on it would be discouraged or even repelled by the “it looks weird” part, as developers tend to be set in their ways until what they are doing no longer works.

I also have to point out that the current implementation of DBOS is very far from the system diagram you saw near the beginning of this review. Where’s the minimal kernel? DBOS currently runs on macOS, Linux, and Windows. None of those are minimal kernels. DBOS Cloud currently runs on AWS. Again, not a minimal kernel.

So, overall, DBOS is a tantalizing glimpse of something that may eventually turn out to be cool. It’s new and shiny, and it comes from smart people, but it will be awhile before it could possibly become a mainstream system.

—

Cost: Free with usage limits; paid plans require you to contact sales.

Platform: macOS, Linux, Windows, AWS.

Posted by Richy George on 29 April, 2024

At the end of March 2024, Mike Stonebraker announced in a blog post the release of DBOS Cloud, “a transactional serverless computing platform, made possible by a revolutionary new operating system, DBOS, that implements OS services on top of a distributed database.” That sounds odd, to put it mildly, but it makes more sense when you read the origin story:

The idea for DBOS (DataBase oriented Operating System) originated 3 years ago with my realization that the state an operating system must maintain (files, processes, threads, messages, etc.) has increased in size by about 6 orders of magnitude since I began using Unix on a PDP-11/40 in 1973. As such, storing OS state is a database problem. Also, Linux is legacy code at the present time and is having difficulty making forward progress. For example there is no multi-node version of Linux, requiring people to run an orchestrator such as Kubernetes. When I heard a talk by Matei Zaharia in which he said Databricks could not use traditional OS scheduling technology at the scale they were running and had turned to a DBMS solution instead, it was clear that it was time to move the DBMS into the kernel and build a new operating system.”

If you don’t know Stonebraker, he’s been a database-focused computer scientist (and professor) since the early 1970s, when he and his UC Berkeley colleagues Eugene Wong and Larry Rowe founded Ingres. Ingres later inspired Sybase, which was eventually the basis for Microsoft SQL Server. After selling Ingres to Computer Associates, Stonebraker and Rowe started researching Postgres, which later became PostgreSQL and also evolved into Illustra, which was purchased by Informix.

I heard Stonebraker talk about Postgres at a DBMS conference in 1980. What I got out of that talk, aside from an image of “jungle drums” calling for SQL, was the idea that you could add support for complex data types to the database by implementing new index types, extending the query language, and adding support for that to the query parser and optimizer. The example he used was geospatial information, and he explained one kind of index structure that would make 2D geometric database queries go very fast. (This facility eventually became PostGIS. The R-tree currently used by default in PostGIS GiST indexes wasn’t invented until 1984, so Mike was probably talking about the older quadtree index.)

Skipping ahead 44 years, it should surprise precisely nobody in the database field that DBOS uses a distributed version of PostgreSQL as its kernel database layer.

IDG

DBOS features

DBOS Transact, an open-source TypeScript framework, supports Postgres-compatible transactions, reliable workflow orchestration, HTTP serving using GET and POST, communication with external services and third-party APIs, idempotent requests using UUID keys, authentication and authorization, Kafka integration with exactly-once semantics, unit testing, and self-hosting. DBOS Cloud, a transactional serverless platform for deploying DBOS Transact applications, supports serverless app deployment, time-travel debugging, cloud database management, and observability.

Let’s highlight some major areas of interest.

DBOS Transact

The code shown in the screenshot below demonstrates transactions, as well as HTTP serving using GET. It’s worthwhile to read the code closely. It’s only 18 lines, not counting blank lines.

The first import (line 1) brings in the DBOS SDK classes that we’ll need. The second import (line 2) brings in the Knex.js SQL query builder, which handles sending the parameterized query to the Postgres database and returning the resulting rows. The database table schema is defined in lines 4 through 8; the only columns are a name string and a greet_count integer.

There is only one method in the Hello class, helloTransaction. It is wrapped in @GetApi and @Transaction decorators, which respectively cause the method to be served in response to an HTTP GET request on the path /greeting/ followed by the username parameter you want to pass in and wrap the database call in a transaction, so that two instances can’t update the database simultaneously.

The database query string (line 16) uses PostgreSQL syntax to try to insert a row into the database for the supplied name and an initial count of 1. If the row already exists, then the ON CONFLICT trigger runs an update operation that increments the count in the database.

Line 17 uses Knex.js to send the SQL query to the DBOS system database and retrieves the result. Line 18 pulls the count out of the first row of results and returns the greeting string to the calling program.

The use of SQL and a database for what feels like should be a core in-memory system API, such as a Linux atomic counter or a Windows interlocked variable, seems deeply weird. Nevertheless, it works.

IDG

DBOS Time Travel Debugger

When you run an application in DBOS Cloud it records every step and change it makes (the workflow) in the database. You can debug that using Visual Studio Code and the DBOS Time Travel Debugger extension. The time-travel debugger allows you to debug your DBOS application against the database as it existed at the time the selected workflow originally executed.

IDG

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DBOS Quickstart

The DBOS Quickstart tutorial requires Node.js 20 or later and a PostgreSQL database you can connect to, either locally, in a Docker container, or remotely. I already had Node.js v20.9.0 installed on my M1 MacBook, but I upgraded it to v20.12.1 from the Node.js website.

I didn’t have PostgreSQL installed, so I downloaded and ran the interactive installer for v16.2 from EnterpriseDB. This installer creates a full-blown macOS server and applications. If I had used Homebrew instead, it would have created command-line applications, and if I had used Postgres.app, I would have gotten a menu-bar app.

The Quickstart proper starts by creating a DBOS app directory using Node.js.

martinheller@Martins-M1-MBP ~ % npx -y @dbos-inc/create@latest -n myapp
Merged .gitignore files saved to myapp/.gitignore
added 590 packages, and audited 591 packages in 25s
found 0 vulnerabilities
added 1 package, and audited 592 packages in 1s
found 0 vulnerabilities
added 129 packages, and audited 721 packages in 5s
found 0 vulnerabilities
Application initialized successfully!

Then you configure the app to use your Postgres server and export your Postgres password into an enviroment variable.

martinheller@Martins-M1-MBP ~ % cd myapp
martinheller@Martins-M1-MBP myapp % npx dbos configure
? What is the hostname of your Postgres server? localhost
? What is the port of your Postgres server? 5432
? What is your Postgres username? postgres
martinheller@Martins-M1-MBP myapp % export PGPASSWORD=*********

After that, you create a “Hello” database using Node.js and Knex.js.

martinheller@Martins-M1-MBP myapp % npx dbos migrate
2024-04-09 15:01:42 [info]: Starting migration: creating database hello if it does not exist
2024-04-09 15:01:42 [info]: Database hello does not exist, creating...
2024-04-09 15:01:42 [info]: Executing migration command: npx knex migrate:latest
2024-04-09 15:01:43 [info]: Batch 1 run: 1 migrations
2024-04-09 15:01:43 [info]: Creating DBOS tables and system database.
2024-04-09 15:01:43 [info]: Migration successful!

With that complete, you build and run the DBOS app locally.

martinheller@Martins-M1-MBP myapp % npm run build
npx dbos start

> myapp@0.0.1 build
> tsc

2024-04-09 15:02:30 [info]: Workflow executor initialized
2024-04-09 15:02:30 [info]: HTTP endpoints supported:
2024-04-09 15:02:30 [info]:     GET   :  /greeting/:user
2024-04-09 15:02:30 [info]: DBOS Server is running at http://localhost:3000
2024-04-09 15:02:30 [info]: DBOS Admin Server is running at http://localhost:3001
^C

At this point, you can browse to http://localhost:3000 to test the application. That done, you register for the DBOS Cloud and provision your own database there.

martinheller@Martins-M1-MBP myapp % npx dbos-cloud register -u meheller
2024-04-09 15:11:35 [info]: Welcome to DBOS Cloud!
2024-04-09 15:11:35 [info]: Before creating an account, please tell us a bit about yourself!
Enter First/Given Name: Martin
Enter Last/Family Name: Heller
Enter Company: self
2024-04-09 15:12:06 [info]: Please authenticate with DBOS Cloud!
Login URL: https://login.dbos.dev/activate?user_code=QWKW-TXTB
2024-04-09 15:12:12 [info]: Waiting for login...
2024-04-09 15:12:17 [info]: Waiting for login...
2024-04-09 15:12:22 [info]: Waiting for login...
2024-04-09 15:12:27 [info]: Waiting for login...
2024-04-09 15:12:32 [info]: Waiting for login...
2024-04-09 15:12:38 [info]: Waiting for login...
2024-04-09 15:12:44 [info]: meheller successfully registered!
martinheller@Martins-M1-MBP myapp % npx dbos-cloud db provision iw_db -U meheller
Database Password: ********
2024-04-09 15:19:22 [info]: Successfully started provisioning database: iw_db
2024-04-09 15:19:28 [info]: {"PostgresInstanceName":"iw_db","HostName":"userdb-51fcc211-6ed3-4450-a90e-0f864fc1066c.cvc4gmaa6qm9.us-east-1.rds.amazonaws.com","Status":"available","Port":5432,"DatabaseUsername":"meheller","AdminUsername":"meheller"}
2024-04-09 15:19:28 [info]: Database successfully provisioned!

Finally, you can register and deploy your app in the DBOS Cloud.

martinheller@Martins-M1-MBP myapp % npx dbos-cloud app register -d iw_db
2024-04-09 15:20:09 [info]: Loaded application name from package.json: myapp
2024-04-09 15:20:09 [info]: Registering application: myapp
2024-04-09 15:20:11 [info]: myapp ID: d8806829-c5b8-4df0-8b5a-2d1bf87c3322
2024-04-09 15:20:11 [info]: Successfully registered myapp!
martinheller@Martins-M1-MBP myapp % npx dbos-cloud app deploy
2024-04-09 15:20:35 [info]: Loaded application name from package.json: myapp
2024-04-09 15:20:35 [info]: Submitting deploy request for myapp
2024-04-09 15:21:09 [info]: Submitted deploy request for myapp. Assigned version: 1712676035
2024-04-09 15:21:13 [info]: Waiting for myapp with version 1712676035 to be available
2024-04-09 15:21:21 [info]: Successfully deployed myapp!
2024-04-09 15:21:21 [info]: Access your application at https://meheller-myapp.cloud.dbos.dev/

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DBOS applications

The “Hello” application does illustrate some of the core features of DBOS Transact and the DBOS Cloud, but it’s so basic that it’s barely a toy. The Programming Quickstart adds a few more details, and it’s worth your time to go through it. You’ll learn how to use communicator functions to access third-party services (email, in this example) as well as how to compose reliable workflows. You’ll literally interrupt the workflow and restart it without re-sending the email: DBOS workflows always run to completion and each of their operations executes once and only once. That’s possible because DBOS persists the output of each step in your database.

Once you’ve understood the programming Quickstart, you’ll be ready to try out the two DBOS demo applications, which do rise to the level of being toys. Both demos use Next.js for their front ends, and both use DBOS workflows, transactions, and communicators.

The first demo, E-Commerce, is a web shopping and payment processing system. It’s worthwhile reading the Under the Covers section of the README in the demo’s repository to understand how it works and how you might want to upgrade it to, for example, use a real-world payment provider.

The second demo, YKY Social, simulates a simple social network, and uses TypeORM rather than Knex.js for its database code. It also uses Amazon S3 for profile photos. If you’re serious about using DBOS yourself, you should work though both demo applications.

A tantalizing glimpse

I have to say that DBOS and DBOS Cloud look very interesting. Reliable execution and time-travel debugging, for example, are quite desirable. On the other hand, I wouldn’t want to build a real application on DBOS or DBOS Cloud at this point. I have lots of questions, starting with “How does it scale in practice?” and probably ending with “How much will it cost at X scale?”

I mentioned earlier that DBOS code looks weird but works. I would imagine that any programming shop considering writing an application on it would be discouraged or even repelled by the “it looks weird” part, as developers tend to be set in their ways until what they are doing no longer works.

I also have to point out that the current implementation of DBOS is very far from the system diagram you saw near the beginning of this review. Where’s the minimal kernel? DBOS currently runs on macOS, Linux, and Windows. None of those are minimal kernels. DBOS Cloud currently runs on AWS. Again, not a minimal kernel.

So, overall, DBOS is a tantalizing glimpse of something that may eventually turn out to be cool. It’s new and shiny, and it comes from smart people, but it will be awhile before it could possibly become a mainstream system.

—

Cost: Free with usage limits; paid plans require you to contact sales.

Platform: macOS, Linux, Windows, AWS.

Posted by Richy George on 16 April, 2024

Open-source database provider Qdrant has made available Qdrant Hybrid Cloud, a dedicated vector database to be offered in a managed hybrid cloud model.

Qdrant, the open-source foundation of both Qdrant Cloud and Qdrant Hybrid Cloud, is a vector similarity search engine and vector database written in Rust. Qdrant offers a set of features for performance optimization and can handle billions of vectors with scale and memory safety, the company said. 

Qdrant said it lets businesses deploy vector databases across any cloud provider, on-prem data center, or edge location, thus ensuring performance, security, and cost efficiency for AI-driven applications. Vector databases have emerged as a critical component for building generative AI applications.

Qdrant Hybrid Cloud lets customers deploy a vector database in their chosen environment without sacrificing the benefits of a managed cloud service. In addition to running on Google Cloud Platform and Microsoft Azure, Qdrant Hybrid Cloud can run in Oracle Cloud Infrastructure, Red Hat OpenShift, Vultur, DigitalOcean, OVHcloud, Scaleway, Stackit, Civo, or any other private or public infrastructure with Kubernetes support.

Posted by Richy George on 9 April, 2024

Google Cloud is adding capabilities driven by its proprietary large language model, Gemini to its database offerings, which include Bigtable, Spanner, Memorystore for Redis, Firestore, CloudSQL for MySQL, and AlloyDB for PostgreSQL, the company announced at its annual Next conference.

The Gemini-driven capabilities, which are currently in public preview, include SQL generation, and AI assistance in managing and migrating databases.

Last year, the company added Duet AI, now rebranded to Gemini, in Spanner and its Database Migration Service.

The SQL generation capability can be accessed via the company’s SQL editor named Database Studio to be found inside Google’s Cloud Console.

As the name suggests, this capability allows developers to easily generate, summarize, and fix SQL code with intelligent code assistance, code completion, and guidance directly inside Database Studio, which in turn improves productivity, the company said, adding that Database Studio supports both MySQL and PostgreSQL dialects.

In addition, Database Studio comes with a context-aware chat interface that can take input in natural language to help build database applications faster, according to the company.

Google is not the only database provider that has added SQL code generation to its list of capabilities, analysts said.

“SQL code generation with assistance from generative AI has become one of the low-hanging fruits for generative AI over the past year,” said Tony Baer, principal analyst at dbInsight.

“The new breed of generative AI database code assistants should eventually have a key advantage over those assistants that cater to general-purpose languages, which is that they are database-specific and can therefore read the metadata of databases to not just form, but also optimize SQL code,” Baer explained.

Managing and migrating databases with Gemini

In order to help manage databases better, the cloud service provider is adding a new feature called the Database Center, which will allow operators to manage an entire fleet of databases from a single pane.

Database Center also provides intelligent dashboards to proactively assess availability, data protection, security, and compliance posture, the company said.

Further, the company is infusing Gemini into the Database Center via a natural language-based chat window that will allow enterprise teams to interact with the databases and find more insights about them.

The chat window also can be used to generate troubleshooting tips for database-related issues, the company said.

Google’s idea to have a single pane to manage multiple databases takes inspiration from Oracle, according to Baer.

While Oracle provides the capability for multiple instances of the same databases, which is multimodal, Google extends the capability to a heterogenous collection of databases, Baer said.

“Having central control means that enterprises can be consistent with their policies for security, data access, and service level agreements (SLAs). That’s a major step toward the simplification that we expect from the cloud,” the principal analyst explained.

Google has also extended Gemini to its Database Migration Service, which earlier had support for Duet AI.

Gemini’s improved features will make the service better, the company said, adding that Gemini can help convert database-resident code, such as stored procedures, functions to PostgreSQL dialect.

Additionally, Gemini-powered database migration also focuses on explaining the translation of the code with a side-by-side comparison of dialects, along with detailed explanations of the code and recommendations.

The focus on explaining the code has been planned to help upskill and retrain SQL developers, the company said.

AlloyDB AI gets new features

In addition to powering databases with Gemini, Google has added new features to AlloyDB AI.

AlloyDB AI, which was introduced last year as part of its AlloyDB for PostgreSQL database service, is a suite of integrated capabilities targeted at helping developers build generative AI-based applications using real-time data.  

The new features include allowing generative AI-based applications to query data with natural language and a new type of database view.

The enablement of querying data with natural language will allow AI-based applications to respond to more sets of questions from enterprise teams, the company said.

On the other hand, the new type of database view — parameterized secure view — allows enterprise teams to secure data based on the end-users’ context.

AlloyDB AI can be downloaded using AlloyDB Omni, which has been made generally available. AlloyDB Omni is a downloadable version of Google Cloud’s PostgreSQL-compatible database service.

Other updates include the addition of Bigtable Data Boost, similar to Spanner Data Boost released last year, and performance enhancements to Memorystore for Redis.

Posted by Richy George on 4 April, 2024

Once you get past the chatbot hype, it’s clear that generative AI is a useful tool, providing a way of navigating applications and services using natural language. By tying our large language models (LLMs) to specific data sources, we can avoid the risks that come with using nothing but training data.

While it is possible to fine-tune an LLM on specific data, that can be expensive and time-consuming, and it can also lock you into a specific time frame. If you want accurate, timely responses, you need to use retrieval-augmented generation (RAG) to work with your data.

RAG: the heart of Microsoft’s Copilots

The neural networks that power LLMs are, at heart, sophisticated vector search engines that extrapolate the paths of semantic vectors in an n-dimensional space, where the higher the dimensionality, the more complex the model. So, if you’re going to use RAG, you need to have a vector representation of your data that can both build prompts and seed the vectors used to generate output from an LLM. That’s why it’s one of the techniques that powers Microsoft’s various Copilots.

I’ve talked about these approaches before, looking at Azure AI Studio’s Prompt Flow, Microsoft’s intelligent agent framework Semantic Kernel, the Power Platform’s Open AI-powered boost in its re-engineered Q and A Maker Copilot Studio, and more. In all those approaches, there’s one key tool you need to bring to your applications: a vector database. This allows you to use the embedding tools used by an LLM to generate text vectors for your content, speeding up search and providing the necessary seeds to drive a RAG workflow. At the same time, RAG and similar approaches ensure that your enterprise data stays in your servers and isn’t exposed to the wider world beyond queries that are protected using role-based access controls.

While Microsoft has been adding vector search and vector index capabilities to its own databases, as well as supporting third-party vector stores in Azure, one key database technology has been missing from the RAG story. These missing databases are graph databases, a NoSQL approach that provides an easy route to a vector representation of your data with the added bonus of encoding relationships in the vertices that link the graph nodes that store your data.

Adding graphs to Azure AI with Neo4j

Graph databases like this shouldn’t be confused with the Microsoft Graph. It uses a node model for queries, but it doesn’t use it to infer relationships between nodes. Graph databases are a more complex tool, and although they can be queried using GraphQL, they have a much more complex query process, using tools such as the Gremlin query engine.

One of the best-known graph databases is Neo4j, which recently announced support for the enterprise version of its cloud-hosted service, Aura, on Azure. Available in the Azure Marketplace, it’s a SaaS version of the familiar on-premises tool, allowing you to get started with data without having to spend time configuring your install. Two versions are available, with different memory options built on reserved capacity so you don’t need to worry about instances not being available when you need them. It’s not cheap, but it does simplify working with large amounts of data, saving a lot of time when working with large-scale data lakes in Fabric.

Building knowledge graphs from your data

One key feature of Neo4J is the concept of the knowledge graph, linking unstructured information in nodes into a structured graph. This way you can quickly see relationships between, say, a product manual and the whole bill of materials that goes into the product. Instead of pointing out a single part that needs to be replaced for a fix, you have a complete dependency graph that shows what it affects and what’s necessary to make the fix.

A tool like Neo4j that can sit on top of a large-scale data lake like Microsoft’s Fabric gives you another useful way to build out the information sources for a RAG application. Here, you can use the graph visualization tool that comes as part of Neo4j to explore the complexities of your lakehouses, generating the underlying links between your data and giving you a more flexible and understandable view of your data.

One important aspect of a knowledge graph is that you don’t need to use it all. You can use the graph relationships to quickly filter out information you don’t need for your application. This reduces complexity and speeds up searches. By ensuring that the resulting vectors and prompts are confined to a strict set of relationships, it reduces the risks of erroneous outputs from your LLM.

There’s even the prospect of using LLMs to help generate those knowledge graphs. The summarization tools identify specific entities within the graph database and then provide the links needed to define relationships. This approach lets you quickly extend existing data models into graphs, making them more useful as part of an AI-powered application. At the same time, you can use the Azure Open AI APIs to add a set of embeddings to your data in order to use vector search to explore your data as part of an agent-style workflow using LangChain or Semantic Kernel.

Using graphs in AI: GraphRAG

The real benefit of using a graph database with a large language model comes with a variation on the familiar RAG approach, GraphRAG. Developed by Microsoft Research, GraphRAG uses knowledge graphs to improve grounding in private data, going beyond the capabilities of a standard RAG approach to use the knowledge graph to link related pieces of information and generate complex answers.

One point to understand when working with large amounts of private data using an LLM is the size of the context window. In practice, it’s too computationally expensive to use the number of tokens needed to deliver a lot of data as part of a prompt. You need a RAG approach to get around this limitation, and GraphRAG goes further, letting you deliver a lot more context around your query.

The original GraphRAG research uses a database of news stories, which a traditional RAG fails to parse effectively. However, with a knowledge graph, entities and relationships are relatively simple to extract from the sources, allowing the application to select and summarize news stories that contain the search terms, by providing the LLM with much more context. This is because the graph database structure naturally clusters similar semantic entities, while providing deeper context in the relationships encoded in the vertices between those nodes.

Instead of searching for like terms, much like a traditional search engine, GraphRAG allows you to extract information from the entire dataset you’re using, whether transcripts of support calls or all the documents associated with a specific project.

Although the initial research uses automation to build and cluster the knowledge graph, there is the opportunity to use Neo4j to work with massive data lakes in the Microsoft Fabric, providing a way to visualize that data so that data scientists and business analysts can create their own clusters, which can help produce GraphRAG applications that are driven by what matters to your business as much as by the underlying patterns in the data.

Having a graph database like Neo4j in the Azure Marketplace gives you a tool that helps you understand and visualize the relationships in your data in a way that supports both humans and machines. Integrating it with Fabric should help build large-scale, context-aware, LLM-powered applications, letting you get grounded results from your data in a way that standard RAG approaches can miss. It’ll be interesting to see if Microsoft starts implementing GraphRAG in its own Prompt Flow LLM tool.

Posted by Richy George on 27 March, 2024

Microsoft has announced a private preview of Copilot in Azure SQL Database, an AI assistant that improves productivity in the Azure portal by offering natural language to SQL conversion, along with self-help for database administration.

Microsoft announced the preview on March 21. To sign up for the preview, users can request access.

Copilot in Azure SQL Database enables the Azure portal query editor to translate natural language queries to SQL, making database interactions more intuitive, Microsoft said. Plus, Azure Copilot integration adds Azure SQL Database skills into Microsoft Copilot for Azure, providing self-guided assistance that enables users to manage databases and solve issues independently.

Copilot in Azure SQL Database integrates data and formulates responses using public documentation, database schema, dynamic management views, catalog views, and Azure supportability diagnostics, Microsoft said.

Within the Azure portal query editor, Copilot for Azure SQL Database uses table and view names, column names, primary key, and foreign key metadata to generate T-SQL code. Users then can review and execute the code suggestion. The Copilot also can answer questions with prompts like “Which real estate agent has listed more than two properties for sale?” or “Show me a pivot summary table that displays the number of properties sold in each year from 2020 to 2023,” Microsoft said.

Posted by Richy George on 26 March, 2024

Vertex AI Studio is an online environment for building AI apps, featuring Gemini, Google’s own multimodal generative AI model that can work with text, code, audio, images, and video. In addition to Gemini, Vertex AI provides access to more than 40 proprietary models and more than 60 open source models in its Model Garden, for example the proprietary PaLM 2, Imagen, and Codey models from Google Research, open source models like Llama 2 from Meta, and Claude 2 and Claude 3 from Anthropic. Vertex AI also offers pre-trained APIs for speech, natural language, translation, and vision.

Vertex AI supports prompt engineering, hyper-parameter tuning, retrieval-augmented generation (RAG), and model tuning. You can tune foundation models with your own data, using tuning options such as adapter tuning and reinforcement learning from human feedback (RLHF), or perform style and subject tuning for image generation.

Vertex AI Extensions connect models to real-world data and real-time actions. Vertex AI allows you to work with models both in the Google Cloud console and via APIs in Python, Node.js, Java, and Go.

Competitive products include Amazon Bedrock, Azure AI Studio, LangChain/LangSmith, LlamaIndex, Poe, and the ChatGPT GPT Builder. The technical levels, scope, and programming language support of these products vary.

Vertex AI Studio

Vertex AI Studio is a Google Cloud console tool for building and testing generative AI models. It allows you to design and test prompts and customize foundation models to meet your application’s needs.

Foundation models are another term for the generative AI models found in Vertex AI. Calling them foundation models emphasizes the fact that they can be customized with your data for the specialized purposes of your application. They can generate text, chat, image, code, video, multimodal data, and embeddings.

Embeddings are vector representations of other data, for example text. Search engines often use vector embeddings, a cosine metric, and a nearest-neighbor algorithm to find text that is relevant (similar) to a query string.

The proprietary Google generative AI models available in Vertex AI include:

• Gemini API: Advanced reasoning, multi-turn chat, code generation, and multimodal prompts.
• PaLM API: Natural language tasks, text embeddings, and multi-turn chat.
• Codey APIs: Code generation, code completion, and code chat.
• Imagen API: Image generation, image editing, and visual captioning.
• MedLM: Medical question answering and summarization (private GA).

Vertex AI Studio allows you to test models using prompt samples. The prompt galleries are organized by the type of model (multimodal, text, vision, or speech) and the task being demonstrated, for example “summarize key insights from a financial report table” (text) or “read the text from this handwritten note image” (multimodal).

Vertex AI also helps you to design and save your own prompts. The types of prompt are broken down by purpose, for example text generation versus code generation and single-shot versus chat. Iterating on your prompts is a surprisingly powerful way of customizing a model to produce the output you want, as we’ll discuss below.

When prompt engineering isn’t enough to coax a model into producing the desired output, and you have a training data set in a suitable format, you can take the next step and tune a foundation model in one of several ways: supervised tuning, RLHF tuning, or distillation. Again, we’ll discuss this in more detail later on in this review.

The Vertex AI Studio speech tool can convert speech to text and text to speech. For text to speech you can choose your preferred voice and control its speed. For speech to text, Vertex AI Studio uses the Chirp model, but has length and file format limits. You can circumvent those by using the Cloud Speech-to-Text Console instead.

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Generative AI workflow

As you can see in the diagram below, Google Vertex AI’s generative AI workflow is a bit more complicated than simply throwing a prompt over the wall and getting a response back. Google’s responsible AI and safety filter applies both to the input and output, shielding the model from malicious prompts and the user from malicious responses.

The foundation model that processes the query can be pre-trained or tuned. Model tuning, if desired, can be performed using several methods, all of which are out-of-band for the query/response workflow and quite time-consuming.

If grounding is required, it’s applied here. The diagram shows the grounding service after the model in the flow; that’s not exactly how RAG works, as I explained in January. Out-of-band, you build your vector database. In-band, you generate an embedding vector for the query, use it to perform a similarity search against the vector database, and finally you include what you’ve retrieved from the vector database as an augmentation to the original query and pass it to the model.

At this point, the model generates answers, possibly based on multiple documents. The workflow allows for the inclusion of citations before sending the response back to the user through the safety filter.

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Grounding and Vertex AI Search

As you might expect from the way RAG works, Vertex AI requires you to take a few steps to enable RAG. First, you need to “onboard to Vertex AI Search and Conversation,” a matter of a few clicks and a few minutes of waiting. Then you need to create an AI Search data store, which can be accomplished by crawling websites, importing data from a BigQuery table, importing data from a Cloud Storage bucket (PDF, HTML, TXT, JSONL, CSV, DOCX, or PPTX formats), or by calling an API.

Finally, you need to set up a prompt with a model that supports RAG (currently only text-bison and chat-bison, both PaLM 2 language models) and configure it to use your AI Search and Conversation data store. If you are using the Vertex AI console, this setup is in the advanced section of the prompt parameters, as shown in the first screenshot below. If you are using the Vertex AI API, this setup is in the groundingConfig section of the parameters:

{
  "instances": [
    { "prompt": "PROMPT"}
  ],
  "parameters": {
    "temperature": TEMPERATURE,
    "maxOutputTokens": MAX_OUTPUT_TOKENS,
    "topP": TOP_P,
    "topK": TOP_K,
    "groundingConfig": {
      "sources": [
          {
              "type": "VERTEX_AI_SEARCH",
              "vertexAiSearchDatastore": "VERTEX_AI_SEARCH_DATA_STORE"
          }
      ]
    }
  }
}

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Note that grounding or RAG may or may not be needed, depending on how and when the model was trained.

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Gemini, Imagen, Chirp, Codey, and PaLM 2

Google’s proprietary models offer some of the added value of the Vertex AI site. Gemini was unique in being a multimodal model (as well as a text and code generation model) as recently as a few weeks before I wrote this. Then OpenAI GPT-4 incorporated DALL-E, which allowed it to generate text or images. Currently, Gemini can generate text from images and videos, but GPT-4/DALL-E can’t.

Gemini versions currently offered on Vertex AI include Gemini Pro, a language model with “the best performing Gemini model with features for a wide range of tasks;” Gemini Pro Vision, a multimodal model “created from the ground up to be multimodal (text, images, videos) and to scale across a wide range of tasks;” and Gemma, “open checkpoint variants of Google DeepMind’s Gemini model suited for a variety of text generation tasks.”

Additional Gemini versions have been announced: Gemini 1.0 Ultra, Gemini Nano (to run on devices), and Gemini 1.5 Pro, a mixture-of-experts (MoE) mid-size multimodal model, optimized for scaling across a wide range of tasks, that performs at a similar level to Gemini 1.0 Ultra. According to Demis Hassabis, CEO and co-founder of Google DeepMind, Gemini 1.5 Pro comes with a standard 128,000 token context window, but a limited group of customers can try it with a context window of up to 1 million tokens via Vertex AI in private preview.

Imagen 2 is a text-to-image diffusion model from Google Brain Research that Google says has “an unprecedented degree of photorealism and a deep level of language understanding.” It’s competitive with DALL-E 3, Midjourney 6, and Adobe Firefly 2, among others.

Chirp is a version of a Universal Speech Model that has over 2B parameters and can transcribe in over 100 languages in a single model. It can turn audio speech to formatted text, caption videos for subtitles, and transcribe audio content for entity extraction and content classification.

Codey exists in versions for code completion (code-gecko), code generation (̉code-bison), and code chat (codechat-bison). The Codey APIs support the Go, GoogleSQL, Java, JavaScript, Python, and TypeScript languages, and Google Cloud CLI, Kubernetes Resource Model (KRM), and Terraform infrastructure as code. Codey competes with GitHub Copilot, StarCoder 2, CodeLlama, LocalLlama, DeepSeekCoder, CodeT5+, CodeBERT, CodeWhisperer, Bard, and various other LLMs that have been fine-tuned on code such as OpenAI Codex, Tabnine, and ChatGPTCoding.

PaLM 2 exists in versions for text (text-bison and text-unicorn), chat (̉chat-bison), and security-specific tasks (sec-palm, currently only available by invitation). PaLM 2 text-bison is good for summarization, question answering, classification, sentiment analysis, and entity extraction. PaLM 2 chat-bison is fine-tuned to conduct natural conversation, for example to perform customer service and technical support or serve as a conversational assistant for websites. PaLM 2 text-unicorn, the largest model in the PaLM family, excels at complex tasks such as coding and chain-of-thought (CoT).

Google also provides embedding models for text (textembedding-gecko and textembedding-gecko-multilingual) and multimodal (multimodalembedding). Embeddings plus a vector database (Vertex AI Search) allow you to implement semantic or similarity search and RAG, as described above.

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Vertex AI Model Garden

In addition to Google’s proprietary models, the Model Garden (documentation) currently offers roughly 90 open-source models and 38 task-specific solutions. In general, the models have model cards. The Google models are available through Vertex AI APIs and Google Colab as well as in the Vertex AI console. The APIs are billed on a usage basis.

The other models are typically available in Colab Enterprise and can be deployed as an endpoint. Note that endpoints are deployed on serious instances with accelerators (for example 96 CPUs and 8 GPUs), and therefore accrue significant charges as long as they are deployed.

Foundation models offered include Claude 3 Opus (coming soon), Claude 3 Sonnet (preview), Claude 3 Haiku (coming soon), Llama 2, and Stable Diffusion v1-5. Fine-tunable models include PyTorch-ZipNeRF for 3D reconstruction, AutoGluon for tabular data, Stable Diffusion LoRA (MediaPipe) for text to image generation, and ̉̉MoViNet Video Action Recognition.

Generative AI prompt design

The Google AI prompt design strategies page does a decent and generally vendor-neutral job of explaining how to design prompts for generative AI. It emphasizes clarity, specificity, including examples (few-shot learning), adding contextual information, using prefixes for clarity, letting models complete partial inputs, breaking down complex prompts into simpler components, and experimenting with different parameter values to optimize results.

Let’s look at three examples, one each for multimodal, text, and vision. The multimodal example is interesting because it uses two images and a text question to get an answer.

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Vertex AI model tuning

It’s almost always worthwhile to try prompt engineering first, but if that fails, the next step to customize a base model for your own purposes is model tuning. The Google AI guidance on model tuning and model tuning with AI Studio is very good, and to the point.

Currently Vertex AI only supports supervised fine-tuning of two text foundation models, Gemini 1.0 Pro and text-bison-001. While you can sometimes get by with as few as 20 examples when you’re doing supervised learning for fine-tuning, the usual recommendations from Google are listed in the table below.

Task	No. of examples in data set
Classification	100+
Summarization	100-500+
Document search	100+

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If you’d like to try out fine-tuning for free, you can run it in Colab using this Python Quickstart.

There are two supported methods for fine-tuning text models in AI Studio, supervised tuning and RLHF tuning. Google recommends supervised tuning for classification, sentiment analysis, entity extraction, summarization of content that’s not complex, and domain-specific queries. Google recommends RLHF tuning for question answering, summarization of complex content, and content creation. However, supervised tuning is the only option for code models. 

You can also tune embedding models and create distilled text models. Distilled text models use a large, capable teacher model and a labeled or an unlabeled training data set to train a smaller but more accurate student model.

If all of the above fails, your next step might be continued pre-training. The good news about that is that it uses unlabeled data. The bad news is that it requires lots of exemplars, takes days to run, and can be quite expensive.

Vertex AI Studio vs. the competition

Overall, Vertex AI Studio is a promising product that could potentially compete strongly with Amazon Bedrock and Azure AI Studio. On the other hand, Google has been busy shooting itself in the foot. The company mismanaged its Gemini Image rollout to the point where co-founder Sergey Brin returned from wherever he has been hiding to say “We definitely messed up on the image generation.” I won’t repeat my rant about Google’s history of eating its young from my review of Project IDX, but you can read it at the end of that article.

There are lots of good things about Vertex AI Studio, including its use of Google’s own models, its rapid adoption and deployment of new models from other vendors, and its straightforward support for RAG and model tuning. Meanwhile, why are the Generative AI Extensions—potentially the most useful facility of Vertex AI Studio, and the piece that competes with OpenAI GPTs—buried in a private preview?

As far as which AI app building platform you should choose, if you’re already heavily invested in the Google Cloud, then using Google Vertex AI Studio for building AI apps is probably a no-brainer, as long as you can get access to all the models and capabilities that you need, rather than be told that you can’t have them because they are in private preview.

Given Google’s investment in its cloud platform, I don’t seriously expect Vertex AI Studio to be killed outright in the next couple of years, but I wouldn’t be at all surprised by yet another rebranding.

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Cost: Cost is based on usage. See https://cloud.google.com/vertex-ai/pricing#generative_ai_models.

Platform: Google Cloud Platform.

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