Semantic search with meaning through academic paper corpus
Navigating through academic articles presents a significant challenge due to the uncertainty surrounding the precise search terms. It becomes imperative to facilitate searches utilizing broad key phrases. The efficacy of a refined search serves as a compelling draw for the intended scientific audience. In response, we have programmed a semantic search engine that empowers our customers to access more relevant content.
Large Language Model
Typically, search relies on exact matching and content indexing, constituting the term-based approach. This entails scanning the content for precise keyword matches within the text.
In contrast, semantic-based approaches involve generating dense representations for both queries and documents. This facilitates the discovery of documents even without an exact keyword match in the query. Crafting a semantic search holds greater allure for customers, as it enables the retrieval of documents indirectly connected to the search query.
To address this challenge, we employed a pre-trained large language model to compute dense representations for each document (its embeddings). These representations were stored within an open-source vector database. Subsequently, when a customer conducts a search using a term or phrase, we identify the document closest in meaning to the query. This project encapsulates not only an AI architectural challenge but also a substantial endeavor in implementing the requisite MLOps infrastructure to ensure the dependable and swift operation of this solution.