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+---
+title: "Qdrant case study: bloop semantic code search"
+short_description: bloop is a fast code-search engine that combines semantic search, regex search and precise code navigation
+description: bloop is a fast code-search engine that combines semantic search, regex search and precise code navigation
+social_preview_image: /case_studies_data/bloop/social_preview.png
+preview_dir: /case_studies_data/bloop/preview
+date: 2023-02-28T11:48:00.000Z
+---
+
+Founded in early 2021, [bloop](https://bloop.ai/) was one of the first companies to tackle semantic
+search for codebases. A fast, reliable Vector Search Database is a core component of a semantic
+search engine, and bloop surveyed the field of available solutions and even considered building
+their own. They found Qdrant to be the top contender and now use it in production.
+
+This document is intended as a guide for people who intend to introduce semantic search to a novel
+field and want to find out if Qdrant is a good solution for their use case.
+
+## About bloop
+
+
+
+[bloop](https://bloop.ai/) is a fast code-search engine that combines semantic search, regex search
+and precise code navigation into a single lightweight desktop application that can be run locally. It
+helps developers understand and navigate large codebases, enabling them to discover internal libraries,
+reuse code and avoid dependency bloat. bloop’s chat interface explains complex concepts in simple
+language so that engineers can spend less time crawling through code to understand what it does, and
+more time shipping features and fixing bugs.
+
+
+
+bloop’s mission is to make software engineers autonomous and semantic code search is the cornerstone
+of that vision. The project is maintained by a group of Rust and Typescript engineers and ML researchers.
+It leverages many prominent nascent technologies, such as [Tauri](http://tauri.app), [tantivy](https://docs.rs/tantivy),
+[Qdrant](http://qdrant.tech) and [Anthropic](https://www.anthropic.com/).
+
+## About Qdrant
+
+
+
+Qdrant is an open-source Vector Search Database written in Rust . It deploys as an API service providing
+a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders
+can be turned into full-fledged applications for matching, searching, recommending, and many more solutions
+to make the most of unstructured data. It is easy to use, deploy and scale, blazing fast and is accurate
+simultaneously.
+
+Qdrant was founded in 2021 in Berlin by Andre Zayarni and Andrey Vasnestov with the mission to power the
+next generation of AI applications with advanced and high-performant vector similarity search technology.
+Their flagship product is the vector search database which is available as an open source
+https://github.com/qdrant/qdrant or managed cloud solution https://cloud.qdrant.io/.
+
+## The Problem
+
+Firstly, what is semantic search? It’s finding relevant information by comparing meaning, rather than
+simply measuring the textual overlap between queries and documents. We compare meaning by comparing
+*embeddings* - these are vector representations of text that are generated by a neural network. Each document’s
+embedding denotes a position in a *latent* space, so to search you embed the query and find its nearest document
+vectors in that space.
+
+
+
+Why is semantic search so useful for code? As engineers, we often don’t know - or forget - the precise terms
+needed to find what we’re looking for. Semantic search enables us to find things without knowing the exact
+terminology. For example, if an engineer wanted to understand “*What library is used for payment processing?*”
+a semantic code search engine would be able to retrieve results containing “*Stripe*” or “*PayPal*”. A traditional
+lexical search engine would not.
+
+One peculiarity of this problem is that the **usefulness of the solution increases with the size of the code
+base** – if you only have one code file, you’ll be able to search it quickly, but you’ll easily get lost in
+thousands, let alone millions of lines of code. Once a codebase reaches a certain size, it is no longer
+possible for a single engineer to have read every single line, and so navigating large codebases becomes
+extremely cumbersome.
+
+In software engineering, we’re always dealing with complexity. Programming languages, frameworks and tools
+have been developed that allow us to modularize, abstract and compile code into libraries for reuse. Yet we
+still hit limits: Abstractions are still leaky, and while there have been great advances in reducing incidental
+complexity, there is still plenty of intrinsic complexity[^1] in the problems we solve, and with software eating
+the world, the growth of complexity to tackle has outrun our ability to contain it. Semantic code search helps
+us navigate these inevitably complex systems.
+
+But semantic search shouldn’t come at the cost of speed. Search should still feel instantaneous, even when
+searching a codebase as large as Rust (which has over 2.8 million lines of code!). Qdrant gives bloop excellent
+semantic search performance whilst using a reasonable amount of resources, so they can handle concurrent search
+requests.
+
+## The Upshot
+
+[bloop](https://bloop.ai/) are really happy with how Qdrant has slotted into their semantic code search engine:
+it’s performant and reliable, even for large codebases. And it’s written in Rust(!) with an easy to integrate
+qdrant-client crate. In short, Qdrant has helped keep bloop’s code search fast, accurate and reliable.
+
+#### Footnotes:
+
+[^1]: Incidental complexity is the sort of complexity arising from weaknesses in our processes and tools, whereas
+ intrinsic complexity is the sort that we face when trying to describe, let alone solve the problem.
diff --git a/qdrant-landing/content/documentation/payload.md b/qdrant-landing/content/documentation/payload.md
index 635de3228..a6dff6720 100644
--- a/qdrant-landing/content/documentation/payload.md
+++ b/qdrant-landing/content/documentation/payload.md
@@ -279,7 +279,7 @@ client.clear_payload(
## Payload indexing
To search more efficiently with filters, Qdrant allows you to specify payload fields as indexed.
-For marked fields will Qdrant will build an index for the corresponding types of queries.
+For marked fields Qdrant will build an index for the corresponding types of queries.
The indexed fields also affect the vector index. See [Indexing](../indexing) for details.
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+{{- partial "header.html" . -}}
+
+{{- partial "second_header.html" . -}}
+
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+ {{ block "main" . }}{{ end }}
+
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+{{ partial "contact-section" . }}
+{{- partial "footer.html" . -}}
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+{{ define "main" }}
+{{ end }}
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+{{ partial "header" . }}
+
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+
+
+ {{ .Scratch.Set "scope" "single" }}
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+
{{ .Title }}
+ {{ .Content }}
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+
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+
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+ {{ range where site.Pages "Section" "articles" | first 3 }}
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