Supercharge Your AI Agents With Postgres: An Experiment With OpenAI's GPT-4

Supercharge Your AI Agents With Postgres: An Experiment With OpenAI's GPT-4

Hello developers, AI enthusiasts, and everyone eager to push the boundaries of what's possible with technology! Today, we're exploring AI agents as intermediaries in a fascinating intersection of fields: Artificial Intelligence and databases.

The Dawn of AI Agents

AI agents are at the heart of the tech industry's ongoing revolution. As programs capable of autonomous actions in their environment, AI agents analyze, make decisions, and execute actions that drive a myriad of applications. From autonomous vehicles and voice assistants to recommendation systems and customer service bots, AI agents are changing the way we interact with technology.

But what if we could take it a step further? What if we could use AI to simplify how we interact with databases? Could AI agents act as intermediaries, interpreting human language and converting it into structured database queries?

A Ruby Experiment With GPT-4

That's exactly what we tried to achieve in a recent experiment. Leveraging OpenAI's GPT-4, a powerful language model, we conducted an experiment to see how we could use AI to interact with our databases using everyday language.

The experiment was built using Ruby, and you can find the detailed explanation and code here. The results were fascinating, revealing the potential power of using AI as a “middle-man” (Middle-tech? Middle-bot?) between humans and databases.

Check out the videos throughout this blog post to see it in action:

Why Store Data for AI Agents?

Data storage is crucial for the successful application of AI, particularly for training and fine-tuning models. By storing interactions, results, and other relevant data, we can improve the performance and accuracy of our AI agents over time.

But data storage is not just about improving our AI; it's also about cost-effectiveness. With the OpenAI API, you pay per token, which can add up when dealing with large amounts of data. By using PostgreSQL as long-term memory for your AI agent, you can reduce the number of tokens you send to the OpenAI API, saving computational resources and money.

PostgreSQL: Flexible and Robust

PostgreSQL is a powerful, open-source relational database system. With a reputation for reliability, robustness, and performance, it's a fantastic choice for your AI's long-term memory. PostgreSQL also offers flexibility and scalability, making it suitable for projects of all sizes.

Whether you're conducting experiments or deploying production-ready applications, PostgreSQL's flexibility and robust nature make it an excellent companion for your AI.

Needless to say, we’re huge PostgreSQL enthusiasts here at Timescale—so much so that we built Timescale on PostgreSQL. Timescale works just like PostgreSQL under the hood, offering the same 100 percent SQL support (not SQL-like) and a rich ecosystem of connectors and tools but supercharging PostgreSQL for analytics, events, and time series (and time-series-like workloads).

With additional features like compression and automatically updated incremental materialized views—we call them continuous aggregates—Timescale allows you to scale PostgreSQL further for optimal performance while enjoying the best developer experience and cost-effectiveness.

But why all this talk about Timescale? As the conversation between human and machine is happening on point in time, I realize I’m dealing with time-series data. Cue in TimescaleDB for the rescue!

Join the Timescale Community

We're just scratching the surface of what's possible when combining AI with databases like PostgreSQL, and we'd love for you to join us on this journey.

Got a cool idea? A question? Or just want to share your thoughts on this topic? Join the Timescale Community on Slack and head over to the #ai-llm-discussion channel. Let's push the boundaries together and shape the future of AI!

Check this page to learn how to power agents, chatbots, and other large language models AI applications with PostgreSQL. To see what my fellow Timescalers Avthar, Mat, and Sam are already building, read their post on PostgreSQL as a Vector Database: Create, Store, and Query OpenAI Embeddings With pgvector.

Remember, technology grows exponentially when great minds come together. See you there!

Ingest and query in milliseconds, even at terabyte scale.
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