BM25

Articles about BM25

1. 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
2. Article · AI Agents

   Dense vs Sparse Retrieval: Building an Intelligent Hybrid Search System with Multi-Agent LangGraph

   Build a LangGraph-based hybrid search system that combines BM25 sparse retrieval and dense vector search, then uses Reciprocal Rank Fusion and reranking to improve results.

   Tuhin PaulSep 304,3461,000
3. 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
   
4. Article · Langchain

   Hybrid Retrieval (BM25 + Vector + Reranking) with LangGraph

   Build a Hybrid RAG pipeline with LangGraph: BM25 + Vector Search + Reranking for efficient, parallel retrieval.

   Tuhin PaulJun 223801,000