pgvector

Articles about pgvector

1. Article · AI

   Updating Vector Embeddings When Your Source Documents Change

   Learn how to detect source document changes, regenerate affected embeddings, and synchronize vector databases to keep RAG search results accurate.

   Riya Patel4d3,1201,000
3. Article · AI Agents

   Streaming RAG Architecture in Enterprise Multi-Agent LangGraph

   Build a streaming multi-agent RAG system with LangGraph, FastAPI, pgvector, Azure OpenAI, and React, delivering retrieval progress, citations, and token-by-token responses.

   Tuhin PaulOct 24,7161,000
4. Article · Databases & DBA

   MySQL vs PostgreSQL for AI: Which Is More Intelligent?

   Compare MySQL and PostgreSQL for AI, vector search, RAG, machine learning, and automation. See when PostgreSQL, MySQL AI, or HeatWave fits best.

   Mahesh ChandSep 294,4921,000
   
5. 
   Article · .NET Core

   Optimizing Retrieval in Manufacturing: A Multi-Agent Graph RAG Approach with Hybrid Search and Reranking

   Our system combines vector similarity, keyword matching, and knowledge graph relationships to provide contextually accurate answers while maintaining conversational memory across multiple interactions.

   Tuhin PaulSep 284,7951,000
6. Article · AI Agents

   When Prompt Engineering Stops Helping: Model and Retrieval Changes in Enterprise Tax Management AI

   Prompt engineering is a powerful tool, but it is not a panacea. In enterprise tax management, the cost of error is too high to rely on prompts alone. By building a multi-agent system with LangGraph, we can isolate failures: if retrieval is poor, we fix the vector store; if reasoning is weak, we upgrade the model; if compliance is risky, we add guardrails. This architectural approach ensures that our AI copilot is not just smart, but also safe, accurate, and auditable. The next step is to integrate real-time regulatory feeds and rigorous evaluation pipelines to evolve this POC into a production-grade platform.

   Tuhin PaulSep 184,2221,000
   
7. Article · AI Agents

   The Silent Killer of AI: Detecting and Preventing Target Leakage in Enterprise RAG

   Target leakage is the difference between a model that works in a notebook and one that works in the world. By implementing a Multi-Agent Leakage Prevention Pipeline in LangGraph, you ensure that your Enterprise RAG system is not just smart, but also honest. For FinBank, this means their loan default predictions are based on what they actually know about the applicant today, not on what happened to them tomorrow. This rigor is what separates experimental AI from enterprise-grade reliability.

   Tuhin PaulSep 21291,000
   
8. Article · AI Agents

   Implementing Human-in-the-Loop (HITL) in Enterprise LangGraph: The Ultimate Safety Net

   Human-in-the-Loop is not a bottleneck; it is the ultimate safety valve for enterprise AI. By leveraging LangGraph’s native interrupt() and Checkpointer mechanisms, you can build systems that combine the speed and analytical power of LLM-driven RAG with the judgment, accountability, and regulatory compliance of human experts.

   Tuhin PaulAug 262701,000
   
9. Article · AI Agents

   Handling State Divergence in Distributed LangGraph: A Guide for Enterprise RAG

   Handling state divergence in a distributed LangGraph setup isn't about avoiding conflicts—it's about managing them through strict versioning and persistent storage.

   Tuhin PaulAug 252461,000
   
10. Article · Databases & DBA

    Vector Databases with .NET: PostgreSQL pgvector vs Azure AI Search vs Qdrant

    Compare PostgreSQL pgvector, Azure AI Search, and Qdrant for .NET AI apps. Choose the best vector database for RAG, semantic search, and more.

    Ananya DesaiJul 284821,000
12. Article · AI Agents

    🚀 Building a Local Document RAG System Using Node.js, Supabase, and OpenAI

    Build a local RAG system with Node.js, Supabase, and OpenAI! Index PDFs, ask questions in natural language, and get accurate answers. Secure & scalable!

    Nitin PanditNov 25, 20253,4111,000