Embedding Engines Explained: Fuel Semantic Search in AI

Discover how embedding engines convert text into vectors for similarity searches and context retrieval in AI systems.

The Embedding Engine is a vital part of our AI assistant project, responsible for transforming text into numerical vectors that capture the semantic meaning of words, sentences, or documents. These embeddings are then used for tasks like similarity search, semantic search, and retrieval-augmented generation (RAG). The embedding engine essentially allows the AI to understand the deeper relationships between different pieces of text.

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Here’s how it works: when a user inputs a query, the embedding engine uses a model (like Sentence Transformers, OpenAI’s embedding models, or Ollama’s custom embeddings) to convert the text into a high-dimensional vector. Each number in the vector represents a feature of the text’s meaning. When we compare vectors using techniques like cosine similarity, we can identify texts that are semantically close. This process is key to finding relevant documents or snippets that match a query, even if they don’t use the exact same words.

Disclaimer: This post is part of our comprehensive guide “Building an AI Assistant: Essential…