<p>This application allows for fast and scalable searching across collections of documents, making it suitable for real-time applications.Vector embeddings, especially those obtained through deep learning models, often have reduced dimensionality compared to the original documents. This reduction helps speed up search operations and reduces storage requirements.By using embeddings that capture semantic information, vector stores can perform semantic search. This means that even for queries with different phrasing or wording, the search can find similar documents based on their semantic similarity, making the search results more relevant.</p><p>Overall, using vector stores for document search enables efficient and powerful similarity-based querying, making it a valuable tool in various applications, including information retrieval, recommender systems, content-based search, and more.</p>

Analyze Tabular Data in the Document (OCR and Tabular Data enabled)

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This application helps to chat and analyze your documents for the context enquired and present the context related to the query along with the page number for reference.

Analyze Tabular Data in the Document (OCR and Tabu...

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