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Join us for the Open Source AI Challenge with pgai and Ollama: $3,000 in Prizes!

We are thrilled to team up with Timescale to bring the community our newest challenge. We think you'll like this one.

Running through November 10, the Open Source AI Challenge with pgai and Ollama provides an opportunity to build with AI and experience the power of Postgres – all within the open source ecosystem!

Whether you’re new to coding or have been in the industry for years, this will be a fun way to learn something new and stretch your creativity. There is one prompt for this challenge but four ways to win.

We hope you give it a try!

Our Prompt

Your mandate is to build an AI application using open-source tools and the open-source database PostgreSQL as your vector database, using two or more of the following related tools: pgvector, pgvectorscale, pgai, and pgai Vectorizer.

PostgreSQL offers a number of extensions for AI (pgvector, pgvectorscale, and pgai) making it possible to power AI applications like search, RAG, and AI Agents without using a separate vector database.

Additional Prize Categories

In addition to being able to win the overall prompt, we have three additional prize categories you can work towards!

  • Open-source Models from Ollama: Awarded to a project that utilizes open-source LLMs via Ollama for embedding and/or generation models.
  • Vectorizer Vibe: Awarded to a project that leverages the pgai Vectorizer tool for embedding creation in their RAG application.
  • All the Extensions!: Awarded to a project that leverages all three of the PostgreSQL extensions for AI: pgvector, pgvectorscale, and pgai.

Be sure to indicate all the categories your project may qualify for as part of your submission.

How To Participate

In order to participate, you will need to publish a post using the submission template below. Your application can use a free PostgreSQL database on Timescale, for which no credit card is required, or you may use a self-hosted PostgreSQL database via the pgvectorscale and/or pgai Github repositories for free using Docker.

You are welcome to use any AI models (OpenAI, Cohere, Anthropic, etc), but as noted, there is a special prize for using open-source models via Ollama.

Challenge Submission Template

Please review our full rules, guidelines, and FAQ page before submitting so you understand our participation guidelines and official contests rules such eligibility requirements.

Judging Criteria and Prizes

All three prompts will be judged on the following:

  • Use of underlying technology
  • Usability and User Experience
  • Accessibility
  • Creativity
  • If applicable, additional prize category requirements

Overall Prompt Winner (1) will receive:

Prize Category Winners (3) will receive:

All Participants with a valid submission will receive a completion badge on their DEV profile.

Additional Resources

We encourage everyone participating in the challenge to join the pgai Discord community to connect with fellow builders.

GitHub logo timescale / pgvectorscale

A complement to pgvector for high performance, cost efficient vector search on large workloads.

pgvectorscale

pgvectorscale builds on pgvector with higher performance embedding search and cost-efficient storage for AI applications.

Discord Try Timescale for free

pgvectorscale complements pgvector, the open-source vector data extension for PostgreSQL, and introduces the following key innovations for pgvector data:

  • A new index type called StreamingDiskANN, inspired by the DiskANN algorithm, based on research from Microsoft.
  • Statistical Binary Quantization: developed by Timescale researchers, This compression method improves on standard Binary Quantization.

On a benchmark dataset of 50 million Cohere embeddings with 768 dimensions each, PostgreSQL with pgvector and pgvectorscale achieves 28x lower p95 latency and 16x higher query throughput compared to Pinecone's storage optimized (s1) index for approximate nearest neighbor queries at 99% recall all at 75% less cost when self-hosted on AWS EC2.

Benchmarks

To learn more about the performance impact of pgvectorscale, and details about benchmark methodology and results, see the pgvector vs Pinecone comparison blog post.

In contrast to pgvector, which…


GitHub logo timescale / pgai

A suite of tools to develop RAG, semantic search, and other AI applications more easily with PostgreSQL

pgai

pgai allows you to develop RAG, semantic search, and other AI applications directly in PostgreSQL

Discord Try Timescale for free

pgai simplifies the process of building search Retrieval Augmented Generation (RAG), and other AI applications with PostgreSQL. It complements popular extensions for vector serch in PostgreSQL like pgvector and pgvectorscale, building on top of their capabilities.

Overview

The goal of pgai is to make working with AI easier and more accessible to developers. Because data is the foundation of most AI applications, pgai makes it easier to leverage your data in AI workflows. In particular, pgai supports:

Working with embeddings generated from your data:

  • Automatically create and sync vector embeddings for your data (learn more)
  • Search your data using vector and semantic search (learn more)
  • Implement Retrieval Augmented Generation inside a single SQL statement (learn more)
  • Perform high-performance, cost-efficient ANN search on large vector workloads with pgvectorscale

Important Dates

  • October 30: Open Source AI Challenge with pgai and Ollama begins!
  • November 10: Submissions due at 11:59 PM PDT
  • November 12: Winners Announced

We can’t wait to see what you build with PostgreSQL and open-source AI tools! Questions about the challenge? Ask them below.

Good luck and happy coding!

Top comments (7)

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jess profile image
Jess Lee

Good luck everyone!

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andypiper profile image
Andy Piper

Is this compatible with the Open Source AI Definition just ratified by the OSI?

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jonatas profile image
Jônatas Davi Paganini

This looks awesome! I love hackathons!

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programordie profile image
programORdie

I don't think my 5 years old laptop with 512mb of GPU ram will be able to run a 7B LLM 😂

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douglasfugazi profile image
Douglas Fugazi

YAY!!!

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delaaja profile image
Dela

If we want to utilize Ollama model, does that mean that we have to host the model ourselves and open the access to public?

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raj_acharya_00 profile image
Raj Acharya

hell yeah