Project name: SVOP semantic search.
The goal of the project was to improve full-text search to return more accurate results. Traditional word-based search approaches were to be replaced by semantic search, a method that compares text based on meaning rather than exact word matching, enabling more relevant results to be returned in response to user queries.
We implemented a semantic search system built around the Milvus vector database. The solution operated as:
“One of the most rewarding aspects of our work as a research institute is seeing research move beyond the research “lab” and into practice. This project is a great example of how expertise in AI models and semantic technologies can be transformed into a solution that measurably improves the performance of an operational system.”
“The project was interesting because it was a prototype of semantic search, which is an approach based on the meaning of the text. The technology used, namely semantic search using vector databases, is a very powerful tool for finding duplicates or significantly similar texts, and the quality of this technology will only grow with the improvement of embedding models and vector databases. Since the client was technically savvy and communicative, the project developed smoothly and helped the client improve their product.”
The partner used our solution as a reference to implement their own semantic search system, built on the ElasticSearch database.
“Our collaboration with KInIT within the Hopero project brought us many new insights and showed us new directions that we had not dared to consider before. The consultations, combined with practical demonstrations, significantly accelerated our ongoing development and enabled its expansion in multiple directions.”
https://kinit.sk/svop-improving-document-retrieval-accuracy-through-semantic-search