Code Search with Vector Embeddings: A Transformer's Approach

• example-vectorize-codebase

  1 4 3.2 TypeScript

  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

  71 28,637 9.4 C++

  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.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• Milvus

  108 27,510 10.0 Go

  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.

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