Smarter Search for AI with Semantic Understanding

Move Beyond Keywords to Retrieve Meaningful Results

Semantic Search is a method of searching that focuses on the meaning of text rather than exact word matching. In our AI assistant project, semantic search is the engine that allows us to retrieve relevant documents, knowledge, or responses based on the user’s intent, even if their query doesn’t contain the same keywords as the stored content.

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Here’s how it works: when a user inputs a query, the system converts the query into an embedding vector using the embedding engine. This vector captures the semantic meaning of the query. Then, the system searches a vector database (like PGVector) to find stored embeddings that are closest to the query vector in terms of meaning. Once relevant content is identified, it’s passed into the AI model (via RAG) to generate a precise and context-aware response. Unlike traditional keyword-based search, semantic search focuses on concepts and context, providing more accurate and meaningful results.

Disclaimer: This post is part of our comprehensive guide “Building an AI Assistant: Essential Tools and Concepts”. Each topic, including this one, is significant enough to warrant its own space to ensure clarity and depth. This detailed…