Code Search with Vector Embeddings: A Transformer's Approach

This page summarizes the projects mentioned and recommended in the original post on dev.to

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  • example-vectorize-codebase

    A minimal example of how to vectorize a small project to perform natural language queries on

  • For a more detailed walkthrough, including potential customizations and optimizations, check out the companion GitHub repository.

  • faiss

    A library for efficient similarity search and clustering of dense vectors.

  • As the size of the codebase grows, storing and searching through embeddings in memory becomes inefficient. This is where vector databases come into play. Tools like Milvus, Faiss, and others are designed to handle large-scale vector data and provide efficient similarity search capabilities. I've wrtten about how to also use sqlite to store vector embeddings. By integrating a vector database, you can scale your code search tool to handle much larger codebases without compromising on search speed.

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  • Milvus

    A cloud-native vector database, storage for next generation AI applications

  • As the size of the codebase grows, storing and searching through embeddings in memory becomes inefficient. This is where vector databases come into play. Tools like Milvus, Faiss, and others are designed to handle large-scale vector data and provide efficient similarity search capabilities. I've wrtten about how to also use sqlite to store vector embeddings. By integrating a vector database, you can scale your code search tool to handle much larger codebases without compromising on search speed.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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