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:
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A Snowflake account with necessary permissions to create tables, insert data, and execute queries.
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Python 3.8 or higher installed on your local machine.
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Required Python libraries installed. You can install these libraries using the provided
requirements.txtfile: -
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.
- Clone the Repository: Clone this repository to your local machine to get started.
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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.txtfile.
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pip install -r requirements.txt -
Configure Snowflake and Run the Notebook: Follow the instructions in the
vector_search_snowflake.ipynbnotebook 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! 🚀