Designing vendor-neutral semantic search pipelines using open-source embedding models and FAISS

The rapid growth of semantic search has transformed information retrieval by enabling systems to understand meaning in context instead of simply using keywords as has traditionally been performed. The best available semantic search solutions are often connected to proprietary embedding models and cloud based vector databases, introducing issues around costs, reproducibility, regulations, and vendor lock-in. We detail the architecture and evaluation of a vendor-neutral semantic retrieval pipeline, with all components open-source, fully on-premise, and free of proprietary APIs or managed services. and established open-source embedding models, and the FAISS similarity search library. This proposed architecture emphasizes modularity, scalability and interoperability in a way that maximizes users’ ability to validate and compare different models, thus avoiding closed ecosystems. Using benchmark datasets, we evaluated a series of general open-source embeddings and FAISS index types on retrieval performance and latency, and evaluated retrieval efficiency, finding them satisfactory in retrieval accuracy. Our results suggest that open-source pipelines can achieve comparable retrieval performance to proprietary solutions while offering use-case transparency, flexibility, and low ongoing costs. This research identifies potential advantages with formulating open-source pipelines creating robust semantic search systems that emphasize aspects of reproducibility and sustainable operations.