Smart-Seek

Smart Seek leverages advanced AI models and TiDB’s vector database to enable accurate file retrieval through descriptive queries via Natural Language Description.

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Project Report: Smart Seek - Advanced File Retrieval System Using TiDB Vector Search and Serverless Features

1. Introduction

In today’s digital age, managing and retrieving files efficiently from vast repositories has become a significant challenge. Traditional file search mechanisms often fall short in handling complex queries or providing relevant results due to their reliance on simple keyword-based indexing. To address this challenge, we have developed Smart Seek, an advanced application designed to leverage artificial intelligence and modern database technologies for enhanced file retrieval.

2. Concept and Inspiration

2.1. Problem Statement

As the volume of digital content grows, traditional file retrieval systems become increasingly inadequate. Users often struggle to find specific files amidst a sea of documents due to limitations in search functionality, which typically relies on keyword matching rather than understanding the content’s context.

2.2. Inspiration

The idea for Smart Seek emerged from the need for a more intelligent file retrieval system that can understand and interpret user queries in natural language. We were inspired by the advancements in AI and vector databases that allow for semantic understanding and contextual searches. Observations of the limitations in existing solutions highlighted the need for a system that could provide more accurate and relevant search results based on the content of the files rather than just metadata or keywords.

3. Solution Overview

Smart Seek integrates advanced AI models with TiDB’s vector database to provide a seamless and efficient file search experience. The core components of the solution include:

3.1. File Indexing

  • Selection: Users select folders for indexing. This flexibility allows users to target specific sets of files based on their needs.
  • Processing: The files within the selected folders are processed using advanced techniques:
    • Captioning: Extracts meaningful text or descriptions from files where possible.
    • Embedding: Converts the textual content into vector embeddings that capture the semantic meaning.
    • OCR (Optical Character Recognition): Extracts text from images or scanned documents, enabling the search of text within non-digital documents.

3.2. Vector Database

  • TiDB Vector Database: We chose TiDB’s vector database for its scalability, flexibility, and advanced search capabilities. TiDB’s support for vector data allows us to store and manage embeddings efficiently and perform high-speed, scalable searches.
    • Scalability: TiDB’s architecture supports horizontal scaling, which is essential for handling large datasets and growing file repositories.
    • Performance: With TiDB’s optimized vector search features, we can deliver quick and accurate search results even with complex queries.

3.3. Search Capability

  • Natural Language Queries: Users can perform searches using natural language descriptions. The AI models translate these queries into semantic vectors, which are then matched against the indexed file embeddings.
  • Ranking: Results are ranked based on semantic similarity, ensuring that the most relevant files are presented at the top of the search results.

4. Why TiDB?

4.1. Vector Search and Database Requirements

TiDB was chosen due to its robust support for vector data and scalable architecture. Unlike traditional relational databases, TiDB's vector database capabilities allow us to handle high-dimensional vector data efficiently. This is crucial for implementing the embedding and search functionalities that Smart Seek relies on.

4.2. Scalability and Performance

TiDB's distributed architecture supports horizontal scaling, which is essential for managing large volumes of files and queries. The database’s performance in handling complex vector searches ensures that Smart Seek can deliver fast and accurate results even as the dataset grows.

5. Future Scope

5.1. Integration with Cloud Storage Providers

To expand Smart Seek’s usability, we plan to integrate it with major cloud storage providers. This would allow users to index and search files stored in cloud environments seamlessly.

5.2. Enhanced Search Capabilities

Future developments will include enhancements using a large language model (LLM) interface. This will further improve the system’s ability to understand and interpret complex queries, providing even more accurate and contextually relevant search results.

6. Conclusion

Smart Seek represents a significant advancement in file retrieval systems, combining AI-driven semantic search with TiDB’s powerful vector database. By addressing the limitations of traditional search methods and leveraging cutting-edge technology, Smart Seek offers a more intuitive and effective way to find files based on natural language queries. As we continue to evolve the application, the integration with cloud storage and enhanced search capabilities will further enhance its value and functionality.

Prepared by: Rakshit and Shreyansh
Date: 25/08/2024

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