(untitled) (Research, Society and Development, 2025)
The growth in the use of large-scale language models has intensified the demand for vector databases capable of handling high-dimensional semantic representations. This study aimed to comparatively evaluate different combinations of vector databases and multilingual embedding models, considering their applicability to semantic retrieval in the Portuguese language. The research is characterized as experimental and applied, conducted in a local environment, and structured in four stages: database construction, definition of selection criteria, implementation of an experimentation pipeline, and evaluation of relevance, diversity, and efficiency. Classic information retrieval metrics (Recall@k and nDCG) were analyzed, in addition to diversity and balance metrics (α-nDCG and ILD) and computational efficiency indicators (average latency, p95 latency, average CPU usage, RAM usage, and Queries per Second - QPS). The results showed that solutions such as Milvus and Weaviate stand out in scenarios with higher computational demand, while pgvector proved to be more efficient in terms of memory. Alternatives such as Chroma and pgvector demonstrated viability in smaller-scale contexts. Among the embedding models, consistent performance was observed in the multilingual models available on Hugging Face for tasks in Portuguese. As a contribution, this work presents a systematic empirical analysis that highlights the potential and limitations of different vector bank/embedding combinations, offering support for practical decisions in digital curation projects, data observatories, and recommendation systems in Portuguese.