How to Build an Efficient Knowledge Base for AI Models

Building an efficient knowledge base for AI models is an iterative process that prioritizes high-value, relevant data over sheer volume. After collection, the data is cleaned to remove duplicates and irrelevant details, then segmented into logical chunks, ideally based on potential user queries. These chunks are converted into numerical vectors using an embedding model, enriched with metadata, and stored in a vector database. To optimize performance, vectors are normalized and quantized to reduce storage and speed up retrieval. Orchestration frameworks like LlamaIndex and LangChain are then used to search the vector database and process the retrieved information to respond to user prompts.