🔗 Beyond vector databases: RAG architectures without embeddings

Retrieval-Augmented Generation is the de facto method for providing grounding information to large language models. The standard RAG pipeline is based on embeddings (numeric vector representations of text) and a vector database for semantic search.

Documents are split into chunks, embedded as high-dimensional vectors, stored in a vector database, and queried via nearest-neighbor search to retrieve relevant context for the LLM. Models then search for information by semantic meaning.

However, the ‘vector DB + embeddings’ method is associated with significant overhead in cost, complexity, and performance. With these challenges in mind, there has been increasing interest in exploring alternatives to embedding-based RAG. Researchers have begun to develop RAG without embedding methods and systems, avoiding vector search. In this article, we define what embedding-free RAG means, explore the reasons for its current emergence, and compare it to traditional vector database approaches.

Key Takeaways:

• Traditional RAG systems rely on embeddings and vector databases. The documents are chunked, embedded into high-dimensional vectors, and indexed in a vector database for nearest-neighbor search to provide semantic context for LLMs.
• Vector search has limitations such as semantic gaps, reduced retrieval accuracy, and a lack of interpretability. There are also challenges in precision-sensitive domains where embeddings might retrieve topically similar but non-answer-bearing passages.
• Embedding-based RAG faces infrastructure complexity and high costs. Generating embeddings, maintaining a vector database, and re-indexing updated data demand significant compute and storage resources.
• RAG without embeddings can use alternatives to embedding and vector search. This includes keyword-based search (BM25), LLM-driven iterative retrieval (ELITE), knowledge-graph-based approaches (GraphRAG), and prompt-based retrieval (Prompt-RAG) to address semantic and operational limitations.
• Embedding-free RAG offers interpretability, lower latency, reduced storage, and domain adaptability. This makes it valuable in specialized domains (healthcare, law, finance) and use cases requiring transparency or reasoning across documents.