As the need for customers to build copilots over their data grows, Vector Databases are becoming crucial in the architecture of production-grade copilot applications. This repository is a collection of samples that demonstrates how to use different vector database tools in Azure to store and query embeddings from text, documents and images.
The samples focus on -
- Working with text, documents and images
- Ingesting embeddings and constructing complex queries
- IaC scripts to spin up vector storage in Azure
- Common best practices
What this repository is not - This repository doesn't offer any guidance on how to build LLM apps (for example RAG pattern). Please check the following repositories for LLM app development guidance.
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To run the code locally, install the Jupyter extension in Visual Studio Code. Please check Jupyter Notebooks in VS Code to understand how to use this extension.
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The samples uses conda to manage python dependencies. Each sample comes with a conda environment (yml) file. Use the following command to create the conda environment.
conda env create -f environment.ymlPlease check Python environments in VS Code how to use conda with VS Code.