Semantic Search is a transformative technology that moves beyond traditional keyword-based search to focus on capturing the essence of meaning and semantic relationships. By converting content into vectors using Large Language Models (LLMs), Semantic Search calculates the distance between user input and document embeddings to provide more relevant search results. This approach offers a nuanced understanding of user intent and contextual relevance, enhancing the search experience.
To implement Semantic Search effectively, considerations include data security, choosing the right LLM for the use case, optimizing chunk sizes, and selecting the appropriate vector database. Hybrid search, which combines keyword and semantic search, can address user habits of using short keywords and ensure results are always returned. Query expansion and entity recognition can also enhance search results by adding extra context to user queries.
The power of Semantic Search lies in its ability to uncover meaning within user queries, providing a refined and sophisticated search experience. By delving into the intricacies of language and meaning, Semantic Search offers a more precise and nuanced search encounter. Further exploration into Smart Search technologies can enhance the depth of search capabilities and understanding, making search experiences more meaningful and accurate.
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Source link: https://medium.com/ordina-data/semantic-search-stop-looking-for-words-search-for-meaning-47848a5c5e87?source=rss——llm-5