Vector Search Is Not All You Want | by Anthony Alcaraz | Sep, 2023
Retrieval Augmented Era (RAG) has revolutionized open-domain query answering, enabling techniques to supply human-like responses to a big selection of queries. On the coronary heart of RAG lies a retrieval module that scans an unlimited corpus to search out related context passages, that are then processed by a neural generative module — typically a pre-trained language mannequin like GPT-3 — to formulate a remaining reply.
Whereas this method has been extremely efficient, it’s not with out its limitations.
One of the important elements, the vector search over embedded passages, has inherent constraints that may hamper the system’s capacity to cause in a nuanced method. That is notably evident when questions require complicated multi-hop reasoning throughout a number of paperwork.
Vector search refers to looking for info utilizing vector representations of information. It includes two key steps:
- Encoding knowledge into vectors
First, the info being searched is encoded into numeric vector representations. For textual content knowledge like passages or paperwork, that is finished utilizing embedding fashions like BERT or RoBERTa. These fashions convert textual content into dense vectors of steady numbers that characterize the semantic which means. Photos, audio, and different codecs will also be encoded into vectors utilizing acceptable deep studying fashions.
2. Looking out utilizing vector similarity
As soon as knowledge is encoded into vectors, looking includes discovering vectors much like the vector illustration of the search question. This depends on distance metrics like cosine similarity to quantify how shut two vectors are and rank outcomes. The vectors with the smallest distance (highest similarity) are returned as probably the most related search hits.
The important thing benefit of vector search is the flexibility to seek for semantic similarity, not simply literal key phrase matches. The vector representations seize conceptual which means, permitting extra related but linguistically distinct outcomes to be recognized. This permits the next high quality of search in comparison with conventional key phrase matching.
Nonetheless, remodeling knowledge into vectors and looking in high-dimensional semantic area additionally comes with limitations. Balancing the tradeoffs of vector search is an lively space of analysis.
On this article, we’ll dissect the constraints of vector search, exploring why it struggles to…
