A developer built a reusable tool server using the Model Context Protocol (MCP) to expose pgvector search functions as a standalone server that any LLM client can connect to. The implementation uses F…
A developer built a RAG system from scratch using pgvector and Gemini embeddings, explaining design decisions such as choosing pgvector over dedicated vector databases, using 768-dimensional embedding…
A developer built a RAG (Retrieval-Augmented Generation) system from scratch using pgvector and Google Gemini, implementing a full pipeline for embedding, vector storage, and semantic search. The proj…
A developer demonstrates how to integrate AI capabilities directly into PostgreSQL using the pgvector extension, enabling semantic search, natural language queries, and vector storage without a separa…
A developer built FailureDNA, a persistent memory system for incident-response agents that prevents them from repeating known failures or blindly reusing stale fixes. The system uses a deterministic v…
A developer built ChatScroll for the AWS H0 Hackathon, an app that uses Amazon Aurora PostgreSQL with pgvector to create a personal AI knowledge base. The app saves AI answers as searchable 'Scrolls' …
A developer completed Module 2 of the LLM Zoomcamp 2026 and built a hybrid search engine from scratch, combining keyword and vector search. The project implemented vector search using ONNX runtime emb…
A developer warns that diagnostic error messages containing API keys can poison vector databases used by AI agents, leading to credential leaks via prompt injection. The developer proposes an active r…
A developer optimized pgvector performance for the vibe-memory semantic memory service, reducing query time from 800ms to 15ms. Switching from the default IVFFlat index to HNSW provided a 5x improveme…
A developer built Vibe-Memory, an AI-powered semantic memory system that addresses ChatGPT's lack of long-term memory. The system uses pgvector on PostgreSQL and OpenAI embeddings to store and retriev…
Jarvis AI Platform implemented semantic memory retrieval using pgvector and Ollama's nomic-embed-text model. The system converts user queries and stored memories into 768-dimensional embeddings, enabl…
A developer building a RAG application from scratch encountered two distinct failures that appeared identical externally: a chunking bug and a 3B model running out of capacity. The project, called Ken…
A developer compared five vector databases—pgvector, Pinecone, Qdrant, Weaviate, and Milvus—for RAG applications, finding that the best choice depends on existing infrastructure, operational preferenc…
A developer advocates using PostgreSQL with the pgvector extension for semantic search instead of dedicated vector databases like Pinecone or Weaviate. The approach reduces infrastructure complexity b…
MemoryOps AI launched an open-source governed memory infrastructure for AI assistants, implementing a ChatGPT-style memory lifecycle with policy enforcement, typed storage, hybrid retrieval, and audit…
Twio, an AI SaaS for loan brokers, migrated its production RAG system from pgvector to Vertex AI Search after scaling challenges revealed that vector storage alone was insufficient for handling messy …
A developer built a high-performance semantic caching system for LLM calls using Spring AI and pgvector, intercepting prompts with a CallAroundAdvisor and a local embedding model to generate query emb…
A developer compares four vector databases—pgvector, Qdrant, Pinecone, and Weaviate—for RAG workloads, explaining that the choice depends on specific workload metrics like vector count, filtering need…
A developer published a guide to building production RAG systems in Node.js, covering setup, database schema with pgvector, and multi-tenant architecture. The tutorial includes code examples for Postg…
A developer is seeking advice on building a local, offline document retrieval and LLM pipeline for RAG systems, focusing on storage, ingestion, querying, and highlighting. The system aims to support P…