Semantic search: how to implement it for enterprise search

Semantic search is a powerful tool that leverages the intent behind user queries to provide nuanced and relevant results, contrasting with fuzzy search, which focuses on string similarity to correct minor misspellings. It involves transforming queries into vector representations and using databases to find contextually similar information, often enhanced by large language models (LLMs) for natural language responses. Companies like Guru, Lattice, and Mistral AI use semantic search for enterprise solutions, enabling complex and insightful information retrieval. For effective implementation, best practices include performing full data syncs, avoiding real-time API queries due to delays and limitations, adopting access control lists to protect sensitive data, and leveraging unified API platforms like Merge for seamless integration and data normalization. Semantic search offers benefits like comprehensive query support, product differentiation, and support for agentic workflows, allowing for complex interactions such as PTO requests. It is distinct from retrieval-augmented generation (RAG), which combines retrieved results with user queries via LLMs, and from vector search, as it includes additional steps like query embedding and result sharing.

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