Implement semantic search using text embeddings for more relevant results than keyword matching.
Read more →Tag: Semantic Search
ETL for Vector Embeddings: Preparing Data for RAG
Preparing data for RAG requires specialized ETL pipelines. After building pipelines for 50+ RAG systems, I’ve learned what works. Here’s the complete guide to ETL for vector embeddings.
Read more →Hybrid Search Strategies: Combining Keyword and Semantic Search for Superior Retrieval
Introduction: Neither keyword search nor semantic search is perfect alone. Keyword search excels at exact matches and specific terms but misses semantic relationships. Semantic search understands meaning but can miss exact phrases and rare terms. Hybrid search combines both approaches, leveraging the strengths of each to deliver superior retrieval quality. This guide covers practical hybrid […]
Read more →Semantic Search Optimization: Building High-Quality Retrieval Systems
Introduction: Semantic search goes beyond keyword matching to understand the meaning and intent behind queries. By converting text to dense vector embeddings, semantic search finds conceptually similar content even when exact words don’t match. However, naive implementations often underperform—poor embedding choices, suboptimal indexing, and lack of reranking lead to irrelevant results. This guide covers practical […]
Read more →Vector Database Optimization: Scaling Semantic Search to Millions of Embeddings
Introduction: Vector databases are the backbone of modern AI applications—powering semantic search, RAG systems, and recommendation engines. But as your vector collection grows from thousands to millions of embeddings, naive approaches break down. Query latency spikes, memory costs explode, and recall accuracy degrades. This guide covers practical optimization strategies: choosing the right index type for […]
Read more →Production RAG Architecture: Building Scalable Vector Search Systems
Three months into production, our RAG system started failing at 2AM. Not gracefully—complete outages. The problem wasn’t the models or the embeddings. It was the architecture. After rebuilding it twice, here’s what I learned about building RAG systems that actually work in production. Figure 1: Production RAG Architecture Overview The Night Everything Broke It was […]
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