<ul><li>Keyword-based search, such as the BM25 algorithm, ranks documents based on keyword matches and factors like term frequency and inverse document frequency.</li><li>Vector-based search uses embeddings to represent text in high-dimensional spaces, while semantic search focuses on understanding the meaning behind a user's query for better relevance.</li><li>Scalability and cost-effectiveness benefit from a hybrid approach combining traditional and semantic search techniques, allowing for efficient handling of complex queries and diverse data types.</li><li>A hybrid search system balances speed and accuracy by integrating traditional and semantic methods, showcasing the importance of flexibility and context awareness in delivering optimal search results.</li></ul>

How to Build a Hybrid Search and Semantic Retrieval Systems

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