RECENT POSTS
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…
Vector Databases: Getting Started
A vector database is a system designed to store and retrieve high-dimensional vectors through similarity search. These vectors are numerical representations of different types of data, such as text, images, audio, and video. In practice, vector databases are primarily used to store embeddings, which are numerical representations generated by machine learning models that preserve the…
Edvaldo Melo 