Welcome to this repository where we explore the intriguing world of vector search within Snowflake, a popular cloud-based data warehousing service. This guide provides a step-by-step walkthrough, complete with code snippets and explanations, to demonstrate how Snowflake can be utilized as a vector database, extending its capabilities beyond traditional data warehousing.

Here is blog entry on medium : https://dhnanjay.medium.com/semantic-search-in-snowflake-a-journey-from-sql-to-vectors-5c5660a8c8b8

Before diving into the code and explanations, please ensure you have the following prerequisites covered:

• A Snowflake account with necessary permissions to create tables, insert data, and execute queries.

• Python 3.8 or higher installed on your local machine.

• Required Python libraries installed. You can install these libraries using the provided requirements.txt file:

• pip install -r requirements.txt

This repository is structured as follows:

• vector_search_snowflake.ipynb: Jupyter notebook containing the code and detailed explanations for each step.
• requirements.txt: A text file listing all the required Python libraries.
• data/: Directory containing sample data files used in this demonstration.
• README.md: This file, providing an overview and instructions for using this repository.

1. Clone the Repository: Clone this repository to your local machine to get started.

• git clone https://github.com/dhnanjay/Vector_Search_in_Snowflake.git

• cd vector-search-snowflake

• Install Dependencies: Install the required Python libraries using the requirements.txt file.

1. pip install -r requirements.txt

2. Configure Snowflake and Run the Notebook: Follow the instructions in the vector_search_snowflake.ipynb notebook to configure Snowflake, insert your data, and run the code.

This project is open source and available under the MIT License.

Happy coding and exploring! 🚀