Semantic search and embeddings are both key components in Retrieval-Augmented Generation (RAG) systems, but they serve distinct roles. Semantic search refers to the process of retrieving information based on the meaning of a query, rather than relying solely on keyword matching. Embeddings, on the other hand, are numerical representations of text that capture semantic relationships, enabling algorithms to compare and match content based on similarity. While embeddings are often used within semantic search to power it, they are not the same thing: semantic search is the retrieval strategy, and embeddings are the data format that makes it efficient.
To break this down further, semantic search in RAG aims to find documents or passages that align with the intent and context of a user’s query. For example, if a user asks, “How do I fix a slow computer?” a semantic search system might retrieve results about disk cleanup, malware scans, or hardware upgrades—even if those results don’t explicitly mention “slow computer.” This requires understanding the query’s meaning, which is where embeddings come into play. Embeddings transform text (like the query or a document) into dense vectors—arrays of numbers—that represent semantic features. These vectors allow the system to compute similarity scores (e.g., using cosine similarity) between the query and stored documents, ensuring that conceptually related content is prioritized, even without exact keyword matches.
The relationship between the two is best illustrated through their roles in a RAG pipeline. Embeddings are generated for both the query and the documents in a knowledge base during preprocessing. When a query is made, semantic search uses these precomputed embeddings to efficiently find the most relevant documents. For instance, a document about “optimizing startup programs” might have an embedding close to the query “speed up my PC,” even if the wording differs. Without embeddings, semantic search would lack the ability to map unstructured text into a format that enables fast, meaningful comparisons. However, semantic search could theoretically use other methods (like sparse retrieval combined with contextual reranking), but embeddings are the most common and effective approach in modern RAG systems. In short, embeddings enable semantic search by providing a way to numerically represent and compare meaning, while semantic search defines the retrieval logic that uses those representations to deliver relevant results.