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102 lines
6.0 KiB
Markdown
102 lines
6.0 KiB
Markdown
---
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draft: false
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title: Powering Bloop semantic code search
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slug: case-study-bloop
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short_description: Bloop is a fast code-search engine that combines semantic
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search, regex search and precise code navigation
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description: Bloop is a fast code-search engine that combines semantic search,
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regex search and precise code navigation
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preview_image: /case-studies/bloop/social_preview.png
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date: 2023-02-28T09:48:00.000Z
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author: Qdrant Team
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featured: false
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aliases:
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- /case-studies/bloop/
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---
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Founded in early 2021, [bloop](https://bloop.ai/) was one of the first companies to tackle semantic
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search for codebases. A fast, reliable Vector Search Database is a core component of a semantic
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search engine, and bloop surveyed the field of available solutions and even considered building
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their own. They found Qdrant to be the top contender and now use it in production.
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This document is intended as a guide for people who intend to introduce semantic search to a novel
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field and want to find out if Qdrant is a good solution for their use case.
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## About bloop
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[bloop](https://bloop.ai/) is a fast code-search engine that combines semantic search, regex search
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and precise code navigation into a single lightweight desktop application that can be run locally. It
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helps developers understand and navigate large codebases, enabling them to discover internal libraries,
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reuse code and avoid dependency bloat. bloop’s chat interface explains complex concepts in simple
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language so that engineers can spend less time crawling through code to understand what it does, and
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more time shipping features and fixing bugs.
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bloop’s mission is to make software engineers autonomous and semantic code search is the cornerstone
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of that vision. The project is maintained by a group of Rust and Typescript engineers and ML researchers.
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It leverages many prominent nascent technologies, such as [Tauri](http://tauri.app), [tantivy](https://docs.rs/tantivy),
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[Qdrant](https://github.com/qdrant/qdrant) and [Anthropic](https://www.anthropic.com/).
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## About Qdrant
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Qdrant is an open-source Vector Search Database written in Rust . It deploys as an API service providing
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a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders
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can be turned into full-fledged applications for matching, searching, recommending, and many more solutions
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to make the most of unstructured data. It is easy to use, deploy and scale, blazing fast and is accurate
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simultaneously.
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Qdrant was founded in 2021 in Berlin by Andre Zayarni and Andrey Vasnetsov with the mission to power the
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next generation of AI applications with advanced and high-performant [vector similarity](https://qdrant.tech/articles/vector-similarity-beyond-search/) search technology.
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Their flagship product is the vector search database which is available as an open source
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https://github.com/qdrant/qdrant or managed cloud solution https://cloud.qdrant.io/.
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## The Problem
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Firstly, what is semantic search? It’s finding relevant information by comparing meaning, rather than
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simply measuring the textual overlap between queries and documents. We compare meaning by comparing
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*embeddings* - these are vector representations of text that are generated by a neural network. Each document’s
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embedding denotes a position in a *latent* space, so to search you embed the query and find its nearest document
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vectors in that space.
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Why is semantic search so useful for code? As engineers, we often don’t know - or forget - the precise terms
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needed to find what we’re looking for. Semantic search enables us to find things without knowing the exact
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terminology. For example, if an engineer wanted to understand “*What library is used for payment processing?*”
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a semantic code search engine would be able to retrieve results containing “*Stripe*” or “*PayPal*”. A traditional
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lexical search engine would not.
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One peculiarity of this problem is that the **usefulness of the solution increases with the size of the code
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base** – if you only have one code file, you’ll be able to search it quickly, but you’ll easily get lost in
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thousands, let alone millions of lines of code. Once a codebase reaches a certain size, it is no longer
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possible for a single engineer to have read every single line, and so navigating large codebases becomes
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extremely cumbersome.
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In software engineering, we’re always dealing with complexity. Programming languages, frameworks and tools
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have been developed that allow us to modularize, abstract and compile code into libraries for reuse. Yet we
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still hit limits: Abstractions are still leaky, and while there have been great advances in reducing incidental
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complexity, there is still plenty of intrinsic complexity[^1] in the problems we solve, and with software eating
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the world, the growth of complexity to tackle has outrun our ability to contain it. Semantic code search helps
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us navigate these inevitably complex systems.
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But semantic search shouldn’t come at the cost of speed. Search should still feel instantaneous, even when
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searching a codebase as large as Rust (which has over 2.8 million lines of code!). Qdrant gives bloop excellent
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semantic search performance whilst using a reasonable amount of resources, so they can handle concurrent search
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requests.
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## The Upshot
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[bloop](https://bloop.ai/) are really happy with how Qdrant has slotted into their semantic code search engine:
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it’s performant and reliable, even for large codebases. And it’s written in Rust(!) with an easy to integrate
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qdrant-client crate. In short, Qdrant has helped keep bloop’s code search fast, accurate and reliable.
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#### Footnotes:
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[^1]: Incidental complexity is the sort of complexity arising from weaknesses in our processes and tools, whereas
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intrinsic complexity is the sort that we face when trying to describe, let alone solve the problem.
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