# pgvector Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/pgvector
> Markdown URL: https://aitinkerers.org/technologies/pgvector.md
> Technology record last updated: 2026-09-18T15:13:46Z
> Generated: 2026-09-20T15:43:41Z

pgvector is the open-source PostgreSQL extension that enables efficient storage and vector similarity search of high-dimensional embeddings.

pgvector transforms PostgreSQL into a powerful vector database, allowing you to store and query machine learning embeddings directly alongside your relational data . It introduces the `vector` data type and supports critical operations like k-nearest neighbor (k-NN) search using standard SQL . For performance, it offers approximate nearest neighbor indexing with HNSW and IVFFlat algorithms . You can leverage multiple distance metrics (L2 distance, inner product, cosine distance) to power modern AI applications like semantic search and recommendation systems, all within the robust Postgres environment .

- Official technology site: https://github.com/pgvector/pgvector
- Public AI Tinkerers demos and talks: 25
- Result page: 1 of 2

## Recent Public Talks and Demos

### [Bias-Free RAG: Simulating Political Candidates with AI](https://saopaulo.aitinkerers.org/talks/rsvp_uTVDpnRMXT4)

Café com o Candidato is an open-source site where users pick a 2026 Brazilian presidential candidate and chat with an AI simulation powered by RAG over public data, replying with the candidate's speech patterns and mannerisms — always with a clear disclaimer that it's a simulation with no affiliation. In the live demo I'll show the chat working end-to-end and, behind it, the RAG pipeline: how a user's question pulls the most relevant chunks from public sources via pgvector before reaching the model.

- Event context: AI Tinkerers SP e Oracle - Meetup Agosto — 2026-08-27 — São Paulo
- Public talk page: https://saopaulo.aitinkerers.org/talks/rsvp_uTVDpnRMXT4

### [Analyser des milliers d'avis avec une touche d'IA](https://paris.aitinkerers.org/talks/rsvp_VFjJTra-82Y)

Un pipeline d'analyse thématique qui transforme des milliers de contributions citoyennes en texte libre, issues de consultations publiques, en une poignée de thèmes nommés et lisibles par un humain. En 5 minutes, je déroule l'architecture de bout en bout : préparation et découpage du texte, vectorisation par embeddings, regroupement non supervisé, puis génération automatique du nom de chaque thème. Je montre où chaque étape vit dans le code Ruby, l'enchaînement des services, et surtout le point unique où un LLM intervient réellement.

- Event context: AI on Rails: Level Up the Way You Code with AI w/ Paris.rb — 2026-08-04 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_VFjJTra-82Y

### [Scrape, sense, snipe: local LLMs reading Twitter to trade Polymarket](https://zurich.aitinkerers.org/talks/rsvp_jO-ELvIgcaQ)

A self-hosted pipeline that scrapes Twitter, runs local LLMs to score sentiment, cross-references on-chain Polymarket activity, and surfaces ranked trade suggestions on a dashboard. I review the suggestions and place the trade myself.

- Event context: AI Tinkerers Zürich: July Build Night with Swisscom Ventures — 2026-07-01 — Zürich
- Public talk page: https://zurich.aitinkerers.org/talks/rsvp_jO-ELvIgcaQ

### [Orquestração multi-agente em n8n: sub-workflows virando tools dinâmicas](https://curitiba.aitinkerers.org/talks/rsvp_F61Msr-AenQ)

Construí um sistema multi-agente que opera o assistente de WhatsApp de uma marca brasileira de suplementos "clean label" com catálogo de 20+ SKUs em 8 categorias (sono, estética, emagrecimento, imunidade, saúde feminina 40+, entre outros) e dois canais ativos de WhatsApp para quiosque físico e delivery. O problema real: volume de perguntas simultâneas sobre produtos específicos (composição, indicação, contraindicação, disponibilidade), mix de intenções radicalmente diferentes: consumidora final comprando pela primeira vez, cliente em recompra, nutricionista prescrevendo e a necessidade de escalar atendimento sem perder o tom consultivo que a marca construiu. Em vez de LangGraph ou CrewAI, usei n8n como orquestrador e Claude como motor de decisão. Mensagens chegam via Evolution API num webhook do n8n; o Claude classifica a intenção (tool use) e roteia para a tool certa; mas cada "tool" é um sub-workflow n8n separado, chamado via Execute Workflow. Cada sub-workflow tem seu próprio prompt, seu próprio acesso ao Supabase (RAG sobre o catálogo completo com pgvector, histórico de pedidos, perfil do cliente) e seu próprio guard-rail. Quando a confiança do Claude cai especialmente em perguntas clínicas sobre contraindicações, o agente passa para humano via Chatwoot. Tudo self-hosted na Hetzner (Docker Swarm + Traefik). Na demo ao vivo mostro: uma mensagem real chegando no WhatsApp, o painel do n8n executando nó a nó, o sub-workflow correto ativando como tool, e os logs no Supabase. Compartilho o padrão "sub-workflow-as-tool" e o JSON exportado.

- Event context: AI Tinkerers Curitiba: Evento Inaugural — 2026-06-10 — Curitiba
- Public talk page: https://curitiba.aitinkerers.org/talks/rsvp_F61Msr-AenQ

### [Building Document Consciousness: How I Taught Gemini to Think in 6 Dimensions](https://san-diego.aitinkerers.org/talks/rsvp_CuPjQckMXvM)

I'll demo Clasio, a document intelligence platform I built solo on a 100% Google Cloud stack, showing how I use Gemini 2.5 Flash (extensible to Gemini 3 in a few keystrokes) to extract what I call "6D Document Consciousness" - analyzing every uploaded document across What, Who, When, Where, Why, and How dimensions simultaneously. I received $25K in credits from Google Cloud for Startups for Clasio. The technical meat of the talk: - How I built an async AI queue that processes 25 documents in 75 seconds using 30 parallel Gemini workers on Cloud Run - The structured extraction prompt engineering that gets Gemini to reliably output 6D consciousness JSON (and what failed before it worked) - A 6-tier search waterfall that goes from exact consciousness match down to fuzzy vector similarity using pgvector on Cloud SQL - returning direct answers, not document lists - How I handle connection pool management when you have 30 concurrent Gemini API calls each taking 2-10 seconds (spoiler: release the DB connection before the API call, not after) - Live demo: upload a stack of immigration documents and watch the system classify, extract entities, detect deadlines, and answer natural language questions in real time This is a solo founder build - no team, no VC money, just a product person who learned TypeScript and shipped to production on GCP.

- Event context: AI Tinkerers San Diego: February Meetup at Google — 2026-02-27 — San Diego
- Public talk page: https://san-diego.aitinkerers.org/talks/rsvp_CuPjQckMXvM

### [AI-driven construction contract management tool](https://nashville.aitinkerers.org/talks/rsvp_wDbnTDTr_F0)

Mori Solution is an AI-driven contract analytics tool focused on interpreting complex construction contracts and technical specifications. This presentation demonstrates a working Retrieval-Augmented Generation (RAG) pipeline that enables semantic search and question answering over large, unstructured PDF documents. I will cover how construction contracts are parsed, chunked, embedded, and retrieved to support accurate queries related to safety requirements, bonds, and payment terms. The demo includes a live code walkthrough of the ingestion and retrieval pipeline, along with a speech-recognition interface that allows users to ask natural language questions and receive grounded, source-cited responses from the underlying contract data.

- Event context: AI Tinkerers Nashville – January 29th, 2026: Live Demos, Code, and Architecture — 2026-01-29 — Nashville
- Public talk page: https://nashville.aitinkerers.org/talks/rsvp_wDbnTDTr_F0

### [Link Library](https://atlanta.aitinkerers.org/talks/rsvp_hO_OO0krZGI)

LinkLibrary started as a personal frustration: saving links everywhere (Notion, Notes, Slack, browser bookmarks) and never finding them again. In this talk, I’ll walk through how LinkLibrary was actually built, from early scrappy experiments to a more AI-native system. I’ll show: • How we designed the core data model for links, collections, and social signals • How we approached semantic chunking, embeddings, and retrieval (what worked and what didn’t) • Early attempts at AI agents for tagging, summarization, and discovery • Technical tradeoffs across FastAPI, Supabase/Postgres (pgvector), and React • Lessons learned from shipping v1, demoing to real users, and iterating in public This is a behind-the-scenes, technical walkthrough focused on architecture, decisions etc

- Event context: AI Tinkerers Atlanta: Code-First Demos & Technical Deep Dives | January 27, 2026 — 2026-01-27 — Atlanta
- Public talk page: https://atlanta.aitinkerers.org/talks/rsvp_hO_OO0krZGI

### [Multi-Modal Video Understanding for Hyperlocal Discovery: Building AI That Sees, Hears, and Understands NYC](https://nyc.aitinkerers.org/talks/rsvp_r8SCSp4qwsE)

I built CityPulse, a hyperlocal video discovery platform for NYC that uses multi-modal AI to understand what's actually happening in videos - not just what users say in titles or hashtags. The Core Technical Innovation: Traditional video platforms rely on user-provided metadata. We extract semantic meaning from the content itself by combining three AI modalities: Vision (LLaVA 13b) - Frame-by-frame analysis detecting venues, atmospheres, activities, and reading signs/text Audio (Whisper) - Transcribing what people say, detecting music vs speech, extracting venue names from conversation Text (Llama 3.2) - Generating context-aware titles and summaries from combined visual + audio understanding These combine into 384-dimensional embeddings stored in PostgreSQL with pgvector, enabling semantic search like: "Show me comedy clubs in Brooklyn" - which returns results by detecting microphones, stages, and audience setups in frames, even when "comedy" appears nowhere in the title. Technical Deep Dive: Multi-modal RAG pipeline - How we combine vision, audio, and text into searchable embeddings Progressive enhancement architecture - Videos visible in 5 seconds, fully AI-enhanced in 90 seconds through 3-tier incremental processing Semantic search with pgvector - Cosine similarity search returning results based on understanding not keyword matching Local AI stack - Running Whisper, LLaVA, and Llama models for privacy and customization (no external APIs) Parallel processing patterns - SQLAlchemy async session management for concurrent vision + audio analysis The "Wow" Moment: Search "street art in Brooklyn" and get videos titled "Morning Walk" - because our vision model detected graffiti and murals in the frames. Ask "What's happening in Williamsburg tonight?" and get AI-generated summaries from videos uploaded in the last 24 hours, understanding context across multiple sources. This is a code walkthrough of building multi-modal understanding for real-world video content, handling the messy reality of street-level footage where the interesting context isn't in metadata - it's in what the AI sees and hears.

- Event context: November Demo Day ft. Google Cloud and CopilotKit — 2025-11-17 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_r8SCSp4qwsE

### [From Crawl to Chat: Building AI Assistants with Web Crawlers, RAG, and LLMs](https://dubai.aitinkerers.org/talks/rsvp_A-VRYf-mM60)

This talk explores how to build domain-specific AI assistants using web crawling, vector databases (pgvector), and Retrieval-Augmented Generation powered by OpenAI. It walks through an end-to-end system—data ingestion, vector storage, real-time response generation—and includes lessons learned in optimizing latency, retraining pipelines, and orchestrating services with RabbitMQ. The talk also looks ahead: exploring how this system could integrate with recent developments like OpenAI’s ChatGPT Agents and Google’s A2A protocol, transforming the chatbot into a multi-agent AI system. Ideal for ML engineers and developers, the session offers not just code, but vision—bridging today's RAG pipelines with tomorrow’s autonomous AI workflows.

- Event context: AI Tinkerers Dubai Demo Day - November 15, 2025 — 2025-11-15 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp_A-VRYf-mM60

### [Intelligent and Scalable Legislative Monitoring with Gen AI](https://austin.aitinkerers.org/talks/rsvp_bWSgMYVrXUQ)

I will be presenting a project called GovPal. It is a realtime state and federal legislative monitoring and analysis platform powered by Gen AI. It uses a React front end and FastAPI backend. The frontend allows you to run semantic search over all federal and Texas legislation as well as chat with individual bills. It also allows you to create custom email alerts such as “policy changes related to I-485 applications” which will then email you when a new bill is introduced that is related to your text description.

- Event context: AI Tinkerers Austin Demo Night — November 10, 2025 — 2025-11-10 — Austin
- Public talk page: https://austin.aitinkerers.org/talks/rsvp_bWSgMYVrXUQ

### [This Is So You! – An Event Recommendation Newsletter](https://toronto.aitinkerers.org/talks/rsvp_5C0wWkaQrcI)

This is an ongoing team capstone project between HFF and the University of Toronto that explores how personalized recommendation systems can be built into real products. The project focuses on designing and developing a digital newsletter that presents curated local events based on individual users’ preferences. By combining data scraping, natural language embeddings, and personalized filtering, This Is So You! automatically selects and delivers relevant events in a simple, engaging newsletter format. The goal is to make event discovery effortless while demonstrating how recommendation systems can be implemented end-to-end within a production-ready application.

- Event context: AI Tinkerers Toronto - November 2025 Meetup at Shopify! — 2025-11-10 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_5C0wWkaQrcI

### [This Is So You! – An Event Recommendation Newsletter](https://toronto.aitinkerers.org/talks/rsvp_tWpI94uPzkk)

This is an ongoing team capstone project between HFF and the University of Toronto that explores how personalized recommendation systems can be built into real products. The project focuses on designing and developing a digital newsletter that presents curated local events based on individual users’ preferences. By combining data scraping, natural language embeddings, and personalized filtering, This Is So You! automatically selects and delivers relevant events in a simple, engaging newsletter format. The goal is to make event discovery effortless while demonstrating how recommendation systems can be implemented end-to-end within a production-ready application.

- Event context: AI Tinkerers Toronto - November 2025 Meetup at Shopify! — 2025-11-10 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_tWpI94uPzkk

### [This Is So You! – An Event Recommendation Newsletter](https://toronto.aitinkerers.org/talks/rsvp_Dx373lfr4mM)

This is an ongoing team capstone project between HFF and the University of Toronto that explores how personalized recommendation systems can be built into real products. The project focuses on designing and developing a digital newsletter that presents curated local events based on individual users’ preferences. By combining data scraping, natural language embeddings, and personalized filtering, This Is So You! automatically selects and delivers relevant events in a simple, engaging newsletter format. The goal is to make event discovery effortless while demonstrating how recommendation systems can be implemented end-to-end within a production-ready application.

- Event context: AI Tinkerers Toronto - November 2025 Meetup at Shopify! — 2025-11-10 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_Dx373lfr4mM

### [Building CityPulseNYC : Multi-Modal RAG for Hyperlocal NYC Video Discovery](https://nyc.aitinkerers.org/talks/rsvp_cm_jM1WdXsM)

I'll demonstarate CityPulseNYC, a hyperlocal video platform for NYC residents and tourists that lests users discover whats happening in their borough through semantic search and video feeds. Technical Walkthrough will show: THE SOFTWARE: Borough based video feed(Manhattan, Brooklyn, Queens, Bronx, Staten Island) with activity based ranking "Ask NYC" a natural language search across all boroughs and all kinds of video content("What festivals are happening in Brooklyn this week?") 3 Tier processing making videos playable in 5 seconds or less, fully searchable in 90 seconds THE IMPLEMENTATION: Multimodal RAG pipeline: Whisper transcription+ LLaVa Vision analysis -> 384 dim embeddings -> PG Vector Search Two stage result retrieval: Strict semantic search falling back to expanded time window Activity Scoring Algorithm prioritizing results related to user search ARCHITECTURE: DB schema, Software Patterns used Live Demo will include actual NYC video content, SQL Queries showing vector operations

- Event context: Auth0 x AI Tinkerers: Fall Social Demo Night 🍁 ft. Hex — 2025-11-06 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_cm_jM1WdXsM

### [Semantic Search](https://minneapolis-saint-paul.aitinkerers.org/talks/rsvp_HXVo0I40FM8)

I will be presenting the semantic search system I built out that powers searching the kigo offers platform with natural language. I will show at a high level how I built the system and how it works.

- Event context: AI Tinkerers September Meetup - Minneapolis-Saint Paul — 2025-09-10 — Minneapolis Saint Paul
- Public talk page: https://minneapolis-saint-paul.aitinkerers.org/talks/rsvp_HXVo0I40FM8

### [Cognitive Architecture + Conversational Agents](https://seattle.aitinkerers.org/talks/rsvp_WBCt9jEiih4)

I'll demo how I've applied cognitive architecture in the form of utterance classification and modular memory to create a highly configurable deterministic layer for AI based chat interactions

- Event context: AI Tinkerers Seattle – June Meetup — 2025-06-28 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_WBCt9jEiih4

### [Making easy to the final user](https://saopaulo.aitinkerers.org/talks/rsvp_ao0j35L42iU)

Attia is a platform that provides virtual AI assistants for small businesses in the fields of medical and dental offices, aesthetic clinics, and independent professionals such as nutritionists and physiotherapists. The agents handle customer service via WhatsApp, answering questions about the business, its products/services, and scheduling appointments or procedures.

- Event context: AI Tinkerers São Paulo - Junho 2025 — 2025-06-26 — São Paulo
- Public talk page: https://saopaulo.aitinkerers.org/talks/rsvp_ao0j35L42iU

### [Automated Sales Assistant That's a Helpful "know-it-all"](https://nairobi.aitinkerers.org/talks/rsvp_1T3MP7AVspk)

The demo will show how this "know-it-all" sales assistant transforms casual browsers into informed buyers while reducing support costs and increasing customer satisfaction through expert-level product guidance that scales.

- Event context: AI Tinkerers - Nairobi #2 - June 2025 — 2025-06-11 — Nairobi
- Public talk page: https://nairobi.aitinkerers.org/talks/rsvp_1T3MP7AVspk

### [Ho Jiak Bo: a food recommender app based on top food blogs](https://singapore.aitinkerers.org/talks/rsvp_agtw4_hzm6k)

Ho Jiak Bo is an end-to-end, AI-powered recommender that ingests and unifies Singapore’s top food-blog reviews into a single, searchable interface. In my talk, I’ll walk through: 1. Data ingestion pipeline – using Python + asyncio and Playwright to crawl and render JavaScript-heavy blogs (Daniel Food Diary, EatBook.sg, Miss Tam Chiak). 2. Content structuring – how we leverage DeepSeek/OpenAI to extract clean JSON records, enrich them with Google Maps metadata, and generate embeddings. 3. Hybrid search engine – combining PostgreSQL full-text search and OpenAI embeddings (text-embedding-3-small) with distance-decay scoring (via PostGIS) to deliver top-5, location-aware hawker stall recommendations.

- Event context: AI Tinkerers Singapore: May Meetup - May 21st, 2025 — 2025-05-21 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_agtw4_hzm6k

### [DoppelGoner: Building a Vector-Based Entity Clustering System for Federated Data](https://seattle.aitinkerers.org/talks/rsvp_eeZXANggQPM)

I'll demonstrate how I built DoppelGoner, an open-source Rust implementation that uses transformer embeddings and graph-based clustering to solve entity reconciliation across federated databases. The demo will include: Live code walkthrough of the vector similarity pipeline using BGE-small embeddings Technical deep-dive into the incremental processing architecture that enables efficient repeated runs Demonstration of the graph-based cluster consolidation using petgraph for transitive relationship discovery Performance optimization techniques for pgvector operations and parallel embedding generation Live demo of semantic service matching where I'll show how the system identifies semantically similar services even with different terminology I'll run everything live on my MacBook showing that sophisticated AI tooling can be deployed efficiently without massive compute resources.

- Event context: AI Tinkerers Seattle - April Meetup — 2025-04-25 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_eeZXANggQPM

### [Building an AI Product Manager](https://poland.aitinkerers.org/talks/rsvp_PEiTstd5jXk)

A fun quick demo of the PostHog AI Product Manager… followed by a dive into the cases where things truly break down. Technical challenges at the edges of LLM-driven data analysis.

- Event context: AI Tinkerers Poland #3 - Meetup in Warsaw (March) — 2025-03-20 — Poland
- Public talk page: https://poland.aitinkerers.org/talks/rsvp_PEiTstd5jXk

### [Categorize and search Hacker News monthy job posts using Ollama and PostgreSQL](https://chicago.aitinkerers.org/talks/rsvp_e0TaBrzOJAw)

Using Ollama and open-source software, this talk demonstrates how to combine LLMs and PostgreSQL to create a powerful job search engine. This talk will guide the audience through the base infrastructure, the ETL pipeline used for scraping, tagging and categorizing jobs as well the the LLM prompts used at each step of the way.

- Event context: AI Tinkerers Chicago January Meetup — 2025-01-14 — Chicago
- Public talk page: https://chicago.aitinkerers.org/talks/rsvp_e0TaBrzOJAw

### [Q&A Chatbot (Langfuse Docs)](https://berlin.aitinkerers.org/talks/rsvp_ziwzF7Fao8I)

Q&A chatbot that I've built for the Langfuse docs using pgvector, edge functions, GH actions embedding pipeline for mdx documentation, Openai streaming, the Vercel AI SDK, shadcn frontend components, and Langfuse (open-source analytics to understand what does not work and how to improve it).

- Event context: AI Tinkerers Berlin - November 24 — 2023-11-24 — Berlin
- Public talk page: https://berlin.aitinkerers.org/talks/rsvp_ziwzF7Fao8I

### [Scale your AI apps to millions of rows with pg_embedding](https://seattle.aitinkerers.org/talks/rsvp__Dhv_fahqR4)

Scaling databases is hard. This remains true even for vector databases. Nearly 50% of professional developers use Postgres, and many of them are building LLMs apps. The issue is that it is hard to scale with pgvector because of limitations around IVF index. As a response, we built and open sourced pg_embedding, which implements HNSW index in Postgres and performs 20x better than pgvector, to help developers scale their AI apps to millions of rows. In this talk, we will see how pg_embedding and its serverless driver perform against some well-known vector databases.

- Event context: AI Tinkerers Seattle - July Meetup — 2023-07-27 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp__Dhv_fahqR4

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## More Results

- Next: https://aitinkerers.org/technologies/pgvector.md?page=2