Operationalizing AI with Postgres: Vector Databases and More

September 05, 2024

Learn from experts how Postgres helps power AI innovations

AI continues to make headlines, largely due to the underlying vector databases and data that empower its capabilities and potential. This is why our experts regularly host presentations and write about AI and AI databases. We are dedicated to helping our customers unlock the full potential of this revolutionary technology, and we are passionate about sharing our AI knowledge and expertise with the vibrant open source database community.

In our recent webinar, EDB's Chief Architect for Analytics and AI, Torsten Steinbach, discussed how AI and databases collaborate to drive innovation and outlined how you can begin leveraging Postgres for your AI workloads and initiatives.

Ingredients for Successful AI Solutions

Torsten kicked off the webinar by discussing the parallels between AI and human intelligence. Similar to how human intelligence operates, AI needs three essential components: a brain to generate ideas and make decisions, consciousness to set and achieve goals, and memory, which encompasses knowledge and experience. All these elements are vital for building a successful AI solution.

Chatbots serve as a prime example of these principles in action. Often considered the face of generative AI, chatbots are AI applications that many users interact with daily. When you engage with a chatbot, you are essentially communicating with AI.

To function effectively, chatbots depend on stored data, and this is where vector databases come into play. They store numeric representations (vectors) of documents, images, and other data types. These vectors are generated by encoding large language models (LLMs), which convert raw data into a format that is suitable for AI processing. Although the process of vectorization can be time-consuming, especially for large datasets, it is a critical step in preparing data for AI applications.

RAG: Boosting Chatbot Intelligence with Data-Driven Insights

Retrieval Augmented Generation (RAG) is a crucial process for AI chatbots that enhances their communication capabilities. This generative AI framework improves large language models (LLMs) by integrating relevant internal data, enabling chatbots to respond more effectively. Here’s a simple breakdown of how RAG operates:

  1. The user submits a chat message.
  2. An encoder LLM transforms the message into a vector.
  3. This vector is then used to search a vector database for relevant data.
  4. The system retrieves the most similar data from the database.
  5. The original chat message is augmented with this relevant context.
  6. This augmented prompt is forwarded to a decoder LLM (such as GPT models).
  7. The decoder generates a targeted response using both the original question and contextual data.

RAG plays a vital role in preventing inaccuracies, commonly referred to as hallucinations, by supplying domain-specific knowledge that the model may not have encountered during training. Without this added context, popular public models like ChatGPT may produce misleading answers when faced with unfamiliar questions. (Yes, it’s actually called AI hallucinations.)

Watch Video on RAG

What Happens If Your Chatbot Is Successful?

If you’ve built a successful chatbot using RAG, and it’s delivering meaningful responses while attracting more users who submit data, it may significantly reduce customer support traffic. As a result, the chatbot can become essential for both your users and your organization. However, this reliance can create challenges if your database or chatbot encounters problems or goes offline. Therefore, it’s crucial to operationalize your chatbot solution to ensure it remains reliable and effective.

What’s Needed for an Operational Chatbot?

For your AI system and chatbot to succeed in the long term, several key requirements must be met:

  • Always On: Your AI solution should operate 24/7, with no planned downtime.
  • Resilient: High availability and disaster recovery plans are essential to ensure business continuity.
  • Secure: Only the necessary data for a specific user should be accessible. All data must be encrypted, protected, and governed.
  • Responsive: Fast interactive response times and automatic data indexing are critical for retaining users.
  • Scalable: The system should be capable of scaling to accommodate a growing user base.
  • Enterprise Support: Reliable vendor support is vital for ongoing maintenance and troubleshooting.
  • Ecosystem: The technology should be well-established, with available tools and skilled talent.
  • Business Data: Your chatbot must seamlessly connect to existing support tickets and other company data.
  • Business Events: The system must automatically incorporate and retrieve new knowledge as it becomes available.

These requirements are often overlooked when considering the development of a chatbot, yet they are crucial for ensuring effectiveness and reliability.

It’s All About the Data

Developing a chatbot isn’t as complicated as it used to be, thanks to a range of AI solution frameworks like LangChain and LlamaIndex that facilitate the AI flow.

While these frameworks simplify the process of building AI applications, they primarily automate the app's workflow. However, they don't operationalize your solution or manage your data effectively, nor do they address the stateful aspects, which include your data and models. This is why organizations need to create their own strategies for managing and storing data.

Enhancing Postgres for AI Workloads

Postgres is well-established for transactional data workloads. This open source database meets all the necessary service qualities for operational systems and has extensive support, frameworks, and libraries. That’s why EDB is built around Postgres.

However, using Postgres for AI requires handling new types of data, such as open table formats and binary or unstructured data, including text, images, voice recordings, and video. Traditionally, Postgres cannot process these data types out of the box, which is why we have enhanced it to effectively comprehend, store, and manage this new data. Our enhancements allow for data processing across various scales, linking it with other data, and converting it into open table formats suitable for modern data analysis.

We’ve also introduced new processing capabilities like columnar processing, which enables efficient data operations in open table formats (see EDB Postgres Lakehouse). Additionally, we support vector and hybrid search, as well as native AI model integration. To accommodate larger data volumes cost-effectively, we are incorporating new infrastructure like optical storage capacity and support for GPUs. These enhancements enable new workloads to run against Postgres, including BI analytics, data warehousing, hybrid transactional analytical processing, and generative AI applications.

Building an AI Solution: From Vector Database to AI Platform

Building an AI solution with Postgres begins with a vector database. Vector databases are designed to accommodate vector data and perform vector searches. However, they do have limitations; they typically do not store AI data on object storage or compute vectors themselves. Effectively capturing, maintaining, preparing, and storing vector data in the database often requires specialized knowledge from data engineers or data scientists.

When transforming Postgres from a basic vector database into a comprehensive AI database, it serves as the memory that empowers AI systems. This enables efficient and effective data storage, management, and retrieval.

Furthermore, advanced features for fine-tuning models or implementing augmented generation go beyond what a standard AI database can offer. These capabilities elevate an AI database to an AI data platform, which is the ultimate goal and guiding vision for EDB.

As we advance in our efforts, we invite you to preview our AI technology for free. You can also start using a EDB Postgres Lakehouse here.

Together, we can shape Postgres into the AI data platform of the future and empower Postgres developers to become the most skilled and effective AI builders on the planet.

For those interested in advancing AI in Postgres, don't miss the opportunity to watch the full webinar: Operationalizing AI for Postgres. You can also gain strategic approaches to leveraging proprietary datasets, featuring Torsten Steinbach and other AI thought leaders, at Ai4’s AI conference during the Generative AI Data Strategy Panel Discussion.

Share this
What is the main focus of operationalizing AI with Postgres? chevron_right

Operationalizing AI with Postgres focuses on integrating AI workloads using vector databases and open source data warehousing strategies to enhance performance and capabilities.

How do vector databases contribute to AI workloads? chevron_right

Vector databases store numeric representations of data, enabling AI applications to process and retrieve relevant data efficiently, which is crucial for workloads like chatbots that depend on stored data.

What is Retrieval Augmented Generation (RAG) and how does it work? chevron_right

RAG is a process that enhances AI chatbots' capabilities by integrating internal data into large language models, allowing them to generate more accurate responses by using contextual information from a vector database.

Why is it important to operationalize chatbots? chevron_right

Operationalizing chatbots ensures continuous, reliable performance and availability while minimizing downtime and security risks, crucial for maintaining user trust and system efficiency.

What are the key requirements for a successful AI system and chatbot? chevron_right

Key requirements include being always available, resilient, secure, responsive, scalable, and supported by enterprise-level infrastructure to handle growing data and user demands.

How does Postgres support AI workloads? chevron_right

Postgres supports AI workloads by offering enhancements for handling new data types, columnar processing, vector and hybrid search, and integration with AI models, making it suitable for complex AI applications.

What role do vector databases play in building an AI solution with Postgres? chevron_right

Vector databases serve as the foundational memory for AI systems, enabling efficient storage, management, and retrieval of vector data needed for AI operations.

What are common misconceptions about AI hallucinations, and how does RAG address them? chevron_right

AI hallucinations occur when models generate misleading responses due to a lack of domain-specific knowledge. RAG mitigates this by providing relevant context and data from vector databases.

What are the benefits of using Postgres for AI solutions? chevron_right

Benefits include robust data management, scalability, open source flexibility, and the ability to integrate with new AI technologies, making it a versatile choice for AI applications.

What infrastructure enhancements are necessary for AI applications on Postgres? chevron_right

Infrastructure enhancements include optical storage, GPU support, and advanced processing capabilities to manage large data volumes and complex AI tasks efficiently.

Why is scalability important for AI applications? chevron_right

Scalability is crucial to accommodate growing user bases and data volumes, ensuring the system can handle increased demands without compromising performance.

How can businesses leverage Postgres for AI data platforms? chevron_right

Businesses can transform Postgres from a basic vector database into a comprehensive AI data platform by implementing advanced features for data management and AI model integration, enhancing their AI capabilities.

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