Semantic search with meaning through academic paper corpus
Industrialized AI solution for fast semantic search
Chunking, Embedding Indexing, and Vector Database Storage to achieve Better Search Results
Engagement model
Technology Partner
Academic publishing
AI Architects 1
ML engineer 1
MLOps 1
Company name
Business activity
Academic publishing

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.

Case highlights

Large Language Model
Vector Databases
Semantic Search


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.
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