RECENT POSTS
RAG: Pre-Filtering
In real-world applications, semantic similarity search alone is not always sufficient to meet information retrieval requirements. Beyond semantic relevance, results often need to satisfy specific constraints, such as belonging to a particular category or excluding certain values. When performing similarity search across the entire dataset, the retrieved results may be semantically relevant to the query…
Vector Databases: HNSW Algorithm
To perform a search in a vector database containing millions or even billions of high-dimensional vectors, it would be necessary to compute the similarity between the query vector and every stored vector and then return the top-k nearest vectors. This approach is known as exact search and, although it guarantees exact results, it becomes computationally…
RAG: Hybrid Search
Traditional RAG, which I covered in my previous post, uses dense vector search to retrieve documents that are semantically similar to the user’s query. This approach is especially useful for open-ended or natural language questions, where users describe their intent without necessarily knowing or using the exact terms found in the documents. However, relying solely…
