keyword search
keyword search at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Why Simple Keyword Search Still Beats AI Models for Agent Tool Selection”, “Explore how hybrid search transforms discovery on Papers with Code”, and “Build and benchmark production RAG with fully open models”. On 200 ToolRet questions with up to 3,794 tools, simple keyword search, BM25 on a CPU, ran roughly 40 times faster than BGE-large and matched an 8B embedding model at picking agent tools. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every keyword search story on Beyond Market Intelligence, newest first.
Why Simple Keyword Search Still Beats AI Models for Agent Tool Selection
On 200 ToolRet questions with up to 3,794 tools, simple keyword search, BM25 on a CPU, ran roughly 40 times faster than BGE-large and matched an 8B embedding model at picking agent tools. The best setup just used full tool documentation. That speed advantage matters when every millisecond counts in an agent loop. For deeper context on why simpler approaches often outperform, see our coverage of "When Search Context Swells, Retrieval Accuracy Drops 40 Points."
Explore how hybrid search transforms discovery on Papers with Code
Building a better search engine for research papers rarely comes down to a single clever trick. On Papers with Code, Niels Rogge shows how combining PostgreSQL's pgvector with Qwen3 embeddings creates a hybrid system that outperforms keyword or semantic search alone. What stands out is the pragmatic stack: batch embeddings via Hugging Face Jobs, storage in Buckets, and a live model on Inference Endpoints. That same infrastructure drives related-paper recommendations. It's a thoughtful, reproducible approach.
Build and benchmark production RAG with fully open models
Building a RAG system that actually works in production takes more than stitching together a vector database. This workshop on August 29, led by Ben Auffarth of Chelsea AI Ventures, tackles the messy parts head-on: hybrid retrieval, reranking, and guardrails built in from the start. It's refreshing to see evaluation with RAGAS and real cost benchmarking treated as essentials, not afterthoughts. If you're tired of demo-grade AI, this hands-on session is worth exploring.