hybrid search

Articles about hybrid search

1. Article · PostgreSQL

   AlloyDB and Cloud SQL Add Native BM25 Ranking for Full-Text Search

   Learn how native BM25 indexing in AlloyDB and Cloud SQL for PostgreSQL enables relevance-ranked full-text search with the pg_textsearch extension.

   Niharika GuptaOct 122,1851,000
2. Article · Google Cloud

   AlloyDB Native BM25 Ranking for Full-Text Search

   Learn how to use AlloyDB's pg_textsearch extension for native BM25-ranked full-text search in PostgreSQL, including setup, indexing, and hybrid retrieval considerations.

   Niharika GuptaOct 94,3421,000
5. Article · Langchain

   Scaling Intelligence: Architecting a High-Performance RAG System for Millions of Documents

   Build a scalable Graph RAG legal discovery service using LangGraph, ChromaDB, NetworkX, FastAPI, hybrid retrieval, and stateful multi-agent workflows.

   Tuhin PaulOct 34,8181,000
6. Article · Langchain

   Reducing Noise in Vector Search for Enterprise RAG

   Learn how to reduce irrelevant RAG retrieval results with LangGraph using metadata filters, hybrid search, and LLM-based validation in a legal contract discovery workflow.

   Tuhin PaulSep 304,6001,000
7. Article · AI Agents

   Re-Ranking Strategies in Enterprise Multi-Agent RAG Systems

   Learn how to add a cross-encoder re-ranking agent to a LangGraph RAG workflow to filter retrieved documents and improve enterprise support answers.

   Tuhin PaulSep 304,7311,000
8. Article · AI Agents

   Hybrid Search in RAG: Building an Intelligent Multi-Strategy Retrieval System with LangGraph

   Build a LangGraph-based hybrid RAG search system that combines BM25 and vector retrieval, adaptive result fusion, and cross-encoder reranking for better retrieval quality.

   Tuhin PaulSep 304,1921,000
9. 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
   
10. Article · AI Agents

    Intelligent Recharge & Bill Payment Orchestration with Enterprise Multi-Agent LangGraph

    This approach transforms a static payment pipeline into a responsive, policy-aware financial service. For architects and engineers, the key takeaway is the importance of structured state and persistent memory. These elements ensure that as AI becomes more prevalent in fintech, it remains transparent, compliant, and trustworthy. As you prepare for senior roles or train teams, emphasize that in finance, AI must not only be smart but also explainable and auditable.

    Tuhin PaulSep 144,6401,000
    
11. Article · Databases & DBA

    Knowledge Retrieval Architecture Patterns Beyond Vector Databases

    Explore advanced knowledge retrieval patterns beyond vector databases for accurate, trustworthy AI systems. Learn hybrid search, KGs, SQL, multi-source, and agents.

    Niharika GuptaJun 123,1041,000
12. Article · AI

    Beyond RAG: Modern Retrieval Pipeline Patterns for AI Applications

    Explore advanced AI retrieval pipelines beyond RAG: multi-stage, hybrid search, query transformation, knowledge graphs, and agentic workflows.

    Aarav PatelJun 121,5631,000
13. Article · AI

    Vector Search vs Semantic Search: Key Differences for Modern Applications

    Explore Vector Search vs. Semantic Search: understand their core differences, strengths, and when to use each for modern AI applications.

    Ananya DesaiJun 91,5601,000
14. Article · LLMs

    Understanding Hybrid Search Architecture in AI-Powered Applications

    Hybrid search combines keyword and vector search for AI apps, improving accuracy and user experience. Essential for RAG.

    Nidhi SharmaJun 93,0491,000
15. Article · LLMs

    RAG Is Not Enough: Advanced Retrieval Architectures Developers Should Know

    Learn how hybrid search, re-ranking, graph retrieval, memory, and real-time indexing address the limitations of basic RAG in enterprise AI systems.

    Riya PatelMay 291,7101,000
16. Article · n8n

    Types of RAG in n8n (Complete Guide with Real Examples)

    Master Retrieval-Augmented Generation (RAG) in n8n with this practical guide. Learn Naive, Advanced, Adaptive, Multi-Agent, Hybrid, and Self-Reflective RAG with real-world examples. Build powerful AI workflows, improve accuracy, and create scalable automation using vector databases, embeddings, and LLMs

    Jayraj ChhayaApr 13989
17. Article · Generative AI

    Retrieval-Augmented Generation (RAG) in Generative AI

    This article delves into the challenges faced by LLM, the utilization of Retrieval Augmented Generation (RAG) patterns, the anatomy of RAG, various search types, and strategies for overcoming LLM obstacles.

    Janarthanan SJun 5, 20242,8851,000
    
18. 
    Article · SharePoint

    Cloud Hybrid Search In SharePoint

    Cloud hybrid search facilitates the users to search for content, which exists in SharePoint 2013 On-Premise Server along with the content search in Office 365. This is done by crawling the content in SharePoint 2013 Server and indexing it along with the search index in Office 365.

    HShitesh sharmaJul 21, 202028,2651,000