UBS unlocks advanced AI techniques with PostgreSQL on Azure
This blog was authored by Jay Yang, Executive Director, and Orhun Oezbek, GenAI Architect, UBS RiskLab UBS Group AG is a multinational investment bank and world-leading asset manager that manages $5.7 trillion in assets across 15 different markets. We continue to evolve our tools to suit the needs of data scientists and to integrate the use of AI. Our UBS RiskLab data science platform helps over 1,200 UBS data scientists expedite development and deployment of their analytics and AI solutions, which support functions such as risk, compliance, and finance, as well as front-office divisions such as investment banking and wealth management. RiskLab and UBS GOTO (Group Operations and Technology Office) have a long-term AI strategy to provide a scalable and easy-to-use AI platform. This strategy aims to remove friction and pain points for users, such as developers and data scientists, by introducing DevOps automation, centralized governance and AI service simplification. These efforts have significantly democratized AI development for our business users. This blog walks through how we created two RiskLab products using Azure services. We also explain how we’re using Azure Database for PostgreSQL to power advanced Retrieval Augmented-Generation (RAG) techniques—such as new vector search algorithms, parameter tuning, hybrid search, semantic ranking, and a graphRAG approach—to further the work of our financial generative AI use cases. The RiskLab AI Common Ecosystem (AICE) provides fully governed and simplified generative AI platform services, including: Governed production data access for AI development Managed large language model (LLM) endpoints access control Tenanted RAG environments Enhanced document insight AI processing Streamlined AI agent standardization, development, registration, and deployment solutions End-to-end machine learning (ML) model continuous integration, training, deployment, and monitoring processes The AICE Vector Embedding Governance Application (VEGA) is a fully governed and multi-tenant vector store built on top of Azure Database for PostgreSQL that provides self-service vector store lifecycle management and advanced indexing and retrieval techniques for financial RAG use cases. A focus on best practices like AIOps and MLOps As generative AI gained traction in 2023, we noticed the need for a platform that simplified the process for our data scientists to build, test, and deploy generative AI applications. In this age of AI, the focus should be on data science best practices—GenAIOps and MLOps. Most of our data scientists aren’t fully trained on MLOps, GenAIOps, and setting up complex pipelines, so AICE was designed to provide automated, self-serve DevOps provisioning of the Azure resources they need, as well as simplified MLOps and AIOps pipelines libraries. This removes operational complexities from their workflows. The second reason for AICE was to make sure our data scientists were working in fully governed environments that comply with data privacy regulations from the multiple countries in which UBS operates. To meet that need, AICE provides a set of generative AI libraries that fully manages data governance and reduces complexity. Overall, AICE greatly simplifies the work for our data scientists. For instance, the platform provides managed Azure LLM endpoints, MLflow for generative AI evaluation, and AI agent deployment pipelines along with their corresponding Python libraries. Without going into the nitty gritty of setting up a new Azure subscription, managing MLFlow instances, and navigating Azure Kubernetes Service (AKS) deployments, data scientists can just write three lines of code to obtain a fully governed and secure generative AI ecosystem to manage their entire application lifecycle. And, as a governed, secure lab environment, they can also develop and prototype ML models and generative AI applications in the production tier. We found that providing production read-only datasets to build these models significantly expedites our AI development. In fact, the process for developing an ML model, building a pipeline for model training, and putting it into production has dropped from six months to just one month. Azure Database for PostgreSQL and pgvector: The best of both worlds for relational and vector databases Once AICE adoption ramped up, our next step was to develop a comprehensive, flexible vector store that would simplify vector store resource provisioning while supporting hundreds of RAG use cases and tenants across both lab and production environments. Essentially, we needed to create RAG as a Service (RaaS) so our data scientists could build custom AI solutions in a self-service manner. When we started building VEGA and this vector store, we anticipated that effective RAG would require a diverse range of search capabilities covering not only vector searches but also more traditional document searches or even relational queries. Therefore, we needed a database that could pivot easily. We were looking for a really flexible relational database and decided on Azure Database for PostgreSQL. For a while, Azure Database for PostgreSQL has been our go-to database at RiskLab for our structured data use cases because it’s like the Swiss Army Knife of databases. It’s very compact and flexible, and we have all the tools we need in a single package. Azure Database for PostgreSQL offers excellent relational queries and JSONB document search. When used in conjunction with the pgvector extension for vector search, we created some very powerful hybrid search and hierarchical search RAG functionalities for our end users. The relational nature of Azure Database for PostgreSQL also allowed us to build a highly regulated authorization and authentication mechanism that makes it easy and secure for data scientists to share their embeddings. This involved meeting very stringent access control policies so that users’ access to vector stores is on a need-to-know basis. Integrations with the Azure Graph API help us manage those identities and ensure that the environment is fully secure. Using VEGA, data scientists can just click a button to add a user or group and provide access to all their embeddings/documents. It’s very easy, but it’s also governed and highly regulated. Speeding vector store initialization from days to seconds With VEGA, the time it takes to provision a vector store has dropped from days to less than 30 seconds. Instead of waiting days on a request for new instances of Azure Database for PostgreSQL, pgvector, and Azure AI Search, data scientists can now simply write five lines of code to stand up virtual, fully governed, and secure collections. And the same is true for agentic deployment frameworks. This speed is critical for lab work that involves fast iterations and experiments. And because we built on Azure Database for PostgreSQL, a single instance of VEGA can support thousands of vector stores. It’s cost-effective and seamlessly scales. Creating a hybrid search to analyze thousands of documents Since launching VEGA, one of the top hybrid search use cases has been Augmented Indexing Search (AIR Search), allowing data scientists to comb through financial documents and pinpoint the correct sections and text. This search uses LLMs as agents that first filter based on metadata stored in JSONB columns of the Azure Database for PostgreSQL, then apply vector similarity retrieval. Our thousands of well-structured financial documents are built with hierarchical headers that act as metadata, providing a filtering mechanism for agents and allowing them to retrieve sections in our documents to find precisely what they’re looking for. Because these agents are autonomous, they can decide on the best tools to use for the situation—either metadata filtering or vector similarity search. As a hybrid search, this approach also minimizes AI hallucinations because it gives the agents more context to work with. To enable this search, we used ChatGPT and Azure OpenAI. But because most of our financial documents are saved as PDFs, the challenge was retaining hierarchical information from headers that were lost when simply dumping in text from PDFs. We also had to determine how to make sure ChatGPT understood the meaning behind aspects like tables and figures. As a solution, we created PNG images of PDF pages and told ChatGPT to semantically chunk documents by titles and headers. And if it came across a table, we asked it to provide a YAML or JSON representation of it. We also asked ChatGPT to interpret figures to extract information, which is an important step because many of our documents contain financial graphs and charts. We’re now using Azure AI Document Intelligence for layout detection and section detection as the first step, which simplified our document ingestion pipelines significantly. Forecasting economic implications with PostgreSQL Graph Extension Since creating AICE and VEGA using Azure services, we’ve significantly enhanced our data science workflows. We’ve made it faster and easier to develop generative AI applications thanks to the speed and flexibility of Azure Database for PostgreSQL. Making advanced AI features accessible to our data scientists has accelerated innovation in RiskLab and ultimately allowed UBS to deliver exceptional value to our customers. Looking ahead, we plan to use the Apache AGE graph extension in Azure Database for PostgreSQL for macroeconomics knowledge retention capabilities. Specifically, we’re considering Azure tooling such as GraphRAG to equip UBS economist and portfolio managers with advanced RAG capabilities. This will allow them to retrieve more coherent RAG search results for use cases such as economics scenario generation and impact analysis, as well as investment forecasting and decision-making. For instance, a UBS business user will be able to ask an AI agent: if a country’s interest rate increases by a certain percentage, what are the implications to my client’s investment portfolio? The agent can perform a graph search to obtain all other connected economic entity nodes that might be affected by the interest rate entity node in the graph. We anticipate the AI-assisted graph knowledge will gain significant traction in the financial industry. Learn more For a deeper dive on how we created AICE and VEGA, check out this on-demand session from Ignite. We talk through our use of Azure Database for PostgreSQL and pgvector, plus we show a demo of our GraphRAG capabilities. About Azure Database for PostgreSQL Azure Database for PostgreSQL is a fully managed, scalable, and secure relational database service that supports open-source PostgreSQL. It enables organizations to build and manage mission-critical applications with high availability, built-in security, and automated maintenance.New Generative AI Features in Azure Database for PostgreSQL
by: Maxim Lukiyanov, PhD, Principal PM Manager This week at Microsoft Build conference, we're excited to unveil a suite of new Generative AI capabilities in Azure Database for PostgreSQL flexible server. These features unlock a new class of applications powered by an intelligent database layer, expanding the horizons of what application developers can achieve. In this post, we’ll give you a brief overview of these announcements. Data is the fuel of AI. Looking back, the intelligence of Large Language Models (LLMs) can be reframed as intelligence that emerged from the vast data they were trained on. The LLMs just happened to be this technological leap necessary to extract that knowledge, but the knowledge itself was hidden in the data all along. In modern AI applications, the Retrieval-Augmented Generation (RAG) pattern applies this same principle to real-time data. RAG extracts relevant facts from data on the fly to augment an LLM’s knowledge. At Microsoft, we believe this principle will continue to transform technology. Every bit of data will be squeezed dry of every bit of knowledge it holds. And there’s no better place to find the most critical and up-to-date data than in databases. Today, we're excited to announce the next steps on our journey to make databases smarter – so they can help you capture the full potential of your data. Fast and accurate vector search with DiskANN First, we’re announcing the General Availability of DiskANN vector indexing in Azure Database for PostgreSQL. Vector search is at the heart of the RAG pattern, and it continues to be a cornerstone technology for the new generation of AI Agents - giving it contextual awareness and access to fresh knowledge hidden in data. DiskANN brings years of state-of-the-art innovation in vector indexing from Microsoft Research directly to our customers. This release introduces supports for vectors up to 16,000 dimensions — far surpassing the 2,000-dimension limit of the standard pgvector extension in PostgreSQL. This enables the development of highly accurate applications using high-dimensional embeddings. We’ve also accelerated index creation with enhanced memory management, parallel index building, and other optimizations – delivering up to 3x faster index builds while reducing disk I/O. Additionally, we're excited to announce the Public Preview of Product Quantization – a cutting-edge vector compression technique that delivers exceptional compression while maintaining high accuracy. DiskANN Product Quantization enables efficient storage of large vector volumes, making it ideal for production workloads where both performance and cost matter. With Product Quantization enabled, DiskANN offers up to 10x faster performance and 4x cost savings compared to pgvector HNSW. You can learn more about DiskANN in a dedicated blog post. Semantic operators in the database Next, we’re announcing the Public Preview of Semantic Operators in Azure Database for PostgreSQL – bringing a new intelligence layer to relational algebra, integrated directly into the SQL query engine. While vector search is foundational to the Generative AI (GenAI) apps and agents, it only scratches the surface of what’s possible. Semantic relationships between elements of the enterprise data are not visible to the vector search. This knowledge exists within the data but is lost at the lowest level of the stack – vector search – and this loss propagates upward, limiting the agent’s ability to reason about the data. This is where new Semantic Operators come in. Semantic Operators leverage LLMs to add semantic understanding of operational data. Today, we’re introducing four operators: generate() – a versatile generation operator capable of ChatGPT-style responses. is_true() – a semantic filtering operator that evaluates filter conditions and joins in natural language. extract() – a knowledge extraction operator that extracts hidden semantic relationships and other knowledge from your data, bringing a new level of intelligence to your GenAI apps and agents. rank() - a highly accurate semantic ranking operator, offering two types of state-of-the-art re-ranking models: Cohere Rank-v3.5 or OpenAI gpt-4.1 models from Azure AI Foundry Model Catalog. You can learn more about Semantic Operators in a dedicated blog post. Graph database and GraphRAG knowledge graph support Finally, we’re announcing the General Availability of GraphRAG support and the General Availability of the Apache AGE extension in Azure Database for PostgreSQL. Apache AGE extension on Azure Database for PostgreSQL offers a cost-effective, managed graph database service powered by PostgreSQL engine – and serves as the foundation for building GraphRAG applications. The semantic relationships in the data once extracted can be stored in various ways within the database. While relational tables with referential integrity can represent some relationships, this approach is suboptimal for knowledge graphs. Semantic relationships are dynamic; many aren’t known ahead of time and can’t be effectively modeled by a fixed schema. Graph databases provide a much more flexible structure, enabling knowledge graphs to be expressed naturally. Apache AGE supports openCypher, the emerging standard for querying graph data. OpenCypher offers an expressive, intuitive language well-suited for knowledge graph queries. We believe that combining semantic operators with graph support in Azure Database for PostgreSQL creates a compelling data platform for the next generation of AI agents — capable of effectively extracting, storing, and retrieving semantic relationships in your data. You can learn more about graph support in a separate blog post. Resources to help you get started We’re also happy to announce availability of the new resources and tools for application developers: Model Context Protocol (MCP) is an emerging open protocol designed to integrate AI models with external data sources and services. We have integrated MCP server for Azure Database for PostgreSQL into the Azure MCP Server, making it easy to connect your agentic apps not only to Azure Database for PostgreSQL, but to other Azure services as well through one unified interface. To learn more, refer to this blog post. New Solution Accelerator which showcases all of the capabilities we have announced today working together in one solution solving real world problems of ecommerce retail reimagined for agentic era. New PostgreSQL extension for VSCode for application developers and database administrators alike, bringing new generation of query editing and Copilot experiences to the world of PostgreSQL. And read about New enterprise features making Azure Database for PostgreSQL faster and more secure in the accompanying post. Begin your journey Generative AI innovation continues its advancement, bringing new opportunities every month. We’re excited for what is to come and look forward to sharing this journey of discovery with our customers. With today’s announcements - DiskANN vector indexing, Semantic Operators, and GraphRAG - Azure Database for PostgreSQL is ready to help you explore new boundaries of what’s possible. We invite you to begin your Generative AI journey today by exploring our new Solution Accelerator.
