// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Dense Retrieval
Dense retrieval is a search method that uses AI models to understand the meaning of queries and documents, matching them based on their semantic similarity rather than just exact keywords. It relies on vector embeddings.
TECHNICAL DEFINITION
A retrieval technique that employs neural networks to encode queries and documents into dense, fixed-size vector embeddings, enabling similarity-based search in a high-dimensional semantic space for conceptual matching.
BACKGROUND
Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information from external data sources. With RAG, LLMs first refer to a specified set of documents, then respond to user queries. These documents supplement information from the LLM's pre-existing training data. This allows LLMs to use domain-specific and/or updated information that is not available in the training data. For example, this enables LLM-based chatbots to access internal company data or generate responses based on authoritative sources.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Vector search
- Semantic search
- Embedding search
- Neural retrieval
- Vector similarity search
- Embedding-based retrieval
USAGE NOTE
Dense retrieval is a cornerstone of modern RAG architectures, allowing for conceptual matching beyond exact keyword overlaps.
DEVELOPERS
Organizations developing technology related to Dense Retrieval.
Google Research and Google AI are at the forefront of developing deep learning models and retrieval techniques, including dense retrieval, which power many of Google's products and are published as influential research papers.
Meta AI has made significant contributions to dense retrieval research, notably through models like Dense Passage Retrieval (DPR) and its extensions, which are widely used in academic and industry applications.
Microsoft Research actively develops and applies dense retrieval techniques for various products, including search engines (Bing) and enterprise AI solutions, leveraging large-scale language models.
Hugging Face provides extensive open-source libraries (e.g., Transformers, sentence-transformers) and a model hub that enables the development, deployment, and use of dense retrieval models for a wide range of applications.
Cohere focuses on enterprise-grade large language models and powerful embedding models, which are directly applied to dense retrieval tasks for semantic search, recommendation, and RAG architectures.
Pinecone develops a specialized vector database designed for efficient storage and querying of high-dimensional vectors, making it a crucial infrastructure provider for scalable dense retrieval systems.
Weaviate is an open-source vector database that enables semantic search and dense retrieval by storing data objects and their vectors, allowing for advanced AI applications built on top of embedding models.
Elastic has integrated advanced vector search capabilities, including support for dense retrieval, directly into Elasticsearch, allowing users to build semantic search and RAG applications on their data.