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This project uses Python, Hugging Face (sentence-transformers), Milvus + Docker (container running Vector DB) to create a vector database, populate it with details of many people (names, ages, salaries, addresses and their introductions) and enable searching and querying on the database contents using Cosine-Similarity distances on IVF Flat index.

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adityapathakk/Milvus-Querying

 
 

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Milvus-Querying

In this project, a vector database is created and populated with details (names, ages, salaries, addresses and introductions) of many people (50 examples present in the code). Searching and querying on the database contents is also functional.

Technologies Used

  • Developed using VS Code, database running via Docker container
  • Python and it's libraries - pymilvus and sentence-transformers

Methodology

Acknowledgements

Thanks to Akshay sir, Abhishek sir, Roshan sir and Himanshu sir for the constant guidance and support.
Thanks to Cubastion Consulting Pvt. Ltd. for a productive and supportive environment that fosters learning.

About

This project uses Python, Hugging Face (sentence-transformers), Milvus + Docker (container running Vector DB) to create a vector database, populate it with details of many people (names, ages, salaries, addresses and their introductions) and enable searching and querying on the database contents using Cosine-Similarity distances on IVF Flat index.

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  • Python 100.0%