A dense retriever is the ideal component for a Retrieval-Augmented Generation (RAG) system focused on semantic similarity. This method employs deep learning models to transform both the query and the documents into dense numerical vectors, or "embeddings," within a shared high-dimensional vector space. The retrieval process then involves identifying documents whose vectors are closest to the query vector, using metrics like cosine similarity or dot product. This approach effectively captures the underlying meaning and context of the text, allowing the system to retrieve relevant documents even if they do not share exact keywords with the query.

A. A Boolean retriever operates on keyword presence and logical operators, failing to capture the semantic meaning or contextual nuances required for advanced RAG systems.

B. A hierarchical retriever is a multi-stage process. While it can be effective, its primary mechanism is not solely based on semantic similarity in a vector space from the outset.

D. An exact match retriever is too restrictive, as it only finds documents containing the precise query string, completely ignoring synonyms, related concepts, and overall semantic context.

1. Academic Publication: Lewis

P.

Perez

E.

Piktus

A.

Petroni

F.

Karpukhin

V.

Nogueira

R.

... & Kiela

D. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." Advances in Neural Information Processing Systems

33

9459-9474. The paper introduces RAG and explicitly describes using a Dense Passage Retriever (DPR)

a prime example of a dense retriever

which "embeds questions and passages into a low-dimensional continuous space" (Section 2.1

Retriever).

2. Official Vendor Documentation: IBM. (2024). watsonx.ai Documentation: Retrieval-augmented generation. IBM Cloud. In the "Retrieval-augmented generation (RAG)" section

the architecture is described: "The user query is converted into a vector format... Then

a similarity search is used to find vector embeddings from the knowledge base that are relevant to the query." This process of converting to vectors and using similarity search is the core function of a dense retriever.

3. University Courseware: Stanford University. (2023). CS224N: Natural Language Processing with Deep Learning

Winter 2023. Lecture 15: "Retrieval and Question Answering". The lecture details dense retrieval methods

explaining that they "learn a function... to embed both query and document into the same d-dimensional space" and retrieve based on vector similarity

contrasting them with sparse methods like TF-IDF.