For an AI to understand and process human language, it first needs to convert that language into a format it can work with – numbers. This is where vectorizing text and embeddings come into play, forming the foundational layer for Query Mate's intelligent search capabilities.
Vectorizing text is the process of transforming words, phrases, and even entire documents into numerical representations called vectors. These vectors are essentially lists of numbers that capture the semantic meaning of the text. Think of it like assigning a unique "coordinate" in a multi-dimensional space to every piece of text.
These numerical vectors are often referred to as embeddings. The remarkable property of good embeddings is that texts with similar meanings will have vectors that are numerically "close" to each other in this multi-dimensional space. For example, the embedding for "King" might be very close to "Queen," but further away from "Table."
Query Mate leverages state-of-the-art embedding models to generate these numerical representations for all your company's data. This includes:
When you submit a query to Query Mate, your question is also converted into an embedding. The system then rapidly compares the embedding of your query with the embeddings of all the vectorized content in your private knowledge base. Because similar meanings result in similar embeddings, Query Mate can quickly identify and retrieve the most relevant "chunks" of information that semantically match your question.
This highly efficient and accurate process is powered by our use of open-source PostgreSQL database with pgvector, ensuring robust and scalable storage and retrieval of your vectorized data, all while maintaining strict privacy standards.