RECENT POSTS
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…
RAG: Getting Started
Although an LLM is trained on a vast amount of data and is capable of generating high-quality text, it has inherent limitations, such as being restricted to the knowledge available up to its training cutoff and lacking access to domain-specific or private knowledge bases. Retrieval-Augmented Generation (RAG) is widely used to overcome these limitations by…
