removed case studies section, added redirects for old links (#509)

2026-09-26 22:48:30 +02:00 · 2024-01-08 18:34:17 +00:00
parent 1bf4caebdf
commit 2a0364ceb5
6 changed files with 4 additions and 148 deletions
@@ -147,13 +147,6 @@ keywords = "search engine, vector database, neural network, matching, filter, Sa
    weight = 1
    url = "/documentation/"
  [[menu.main]]
    identifier = "case-studies"
    name = "Case Studies"
    weight = 2
    parent = "resources"
    url = "/case-studies/"
  [[menu.main]]
    identifier = "articles"
    name = "Articles"
@@ -5,6 +5,8 @@ description: Bloop is a fast code-search engine that combines semantic search, r
 preview_image: /case-studies/bloop/social_preview.png
 author: Qdrant Team
 date: 2023-02-28T09:48:00.000Z
 aliases:
 - /case-studies/bloop/
 ---
 Founded in early 2021, [bloop](https://bloop.ai/) was one of the first companies to tackle semantic 
@@ -5,6 +5,8 @@ description: Why Pienso chose Qdrant as a cornerstone for building domain-specif
 preview_image: /case-studies/pienso/social_preview.png
 author: Qdrant Team
 date: 2023-02-28T09:48:00.000Z
 aliases:
 - /case-studies/pienso/
 ---
 The partnership between Pienso and Qdrant is set to revolutionize interactive deep learning, making it practical, efficient, and scalable for global customers. Pienso's low-code platform provides a streamlined and user-friendly process for deep learning tasks. This exceptional level of convenience is augmented by Qdrant’s scalable and cost-efficient high vector computation capabilities, which enable reliable retrieval of similar vectors from high-dimensional spaces.
@@ -1,3 +0,0 @@
 ---
 title: Case Studies
 ---
@@ -1,95 +0,0 @@
 ---
 title: "Powering 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/bloop/social_preview.png
 preview_dir: /case-studies/bloop/preview
 weight: 2
 date: 2023-02-28T09:48:00.000Z
 ---
 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
 ![](/case-studies/bloop/screenshot.png)
 [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.
 ![](/case-studies/bloop/bloop-logo.png)
 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
 ![](/case-studies/bloop/qdrant-logo.png)
 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.
 ![](/case-studies/bloop/vector-space.png)
 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.
@@ -1,43 +0,0 @@
 ---
 title: "Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers"
 short_description: Why Pienso chose Qdrant as a cornerstone for building domain-specific foundation models.
 description: Why Pienso chose Qdrant as a cornerstone for building domain-specific foundation models.
 social_preview_image: /case-studies/pienso/social_preview.png
 preview_dir: /case-studies/pienso/preview
 weight: 1
 ---
 The partnership between Pienso and Qdrant is set to revolutionize interactive deep learning, making it practical, efficient, and scalable for global customers. Pienso's low-code platform provides a streamlined and user-friendly process for deep learning tasks. This exceptional level of convenience is augmented by Qdrant’s scalable and cost-efficient high vector computation capabilities, which enable reliable retrieval of similar vectors from high-dimensional spaces.
 Together, Pienso and Qdrant will empower enterprises to harness the full potential of generative AI on a large scale. By combining the technologies of both companies, organizations will be able to train their own large language models and leverage them for downstream tasks that demand data sovereignty and model autonomy. This collaboration will help customers unlock new possibilities and achieve advanced AI-driven solutions.
 Strengthening LLM Performance
 Qdrant enhances the accuracy of large language models (LLMs) by offering an alternative to relying solely on patterns identified during the training phase. By integrating with Qdrant, Pienso will empower customer LLMs with dynamic long-term storage, which will ultimately enable them to generate concrete and factual responses. Qdrant effectively preserves the extensive context windows managed by advanced LLMs, allowing for a broader analysis of the conversation or document at hand. By leveraging this extended context, LLMs can achieve a more comprehensive understanding and produce contextually relevant outputs.
 ## Joint Dedication to Scalability, Efficiency and Reliability
 > “Every commercial generative AI use case we encounter benefits from faster training and inference, whether mining customer interactions for next best actions or sifting clinical data to speed a therapeutic through trial and patent processes.” - Birago Jones, CEO, Pienso
 Pienso chose Qdrant for its exceptional LLM interoperability, recognizing the potential it offers in maximizing the power of large language models and interactive deep learning for large enterprises. Qdrant excels in efficient nearest neighbor search, which is an expensive and computationally demanding task. Our ability to store and search high-dimensional vectors with remarkable performance and precision will offer a significant peace of mind to Pienso’s customers. Through intelligent indexing and partitioning techniques, Qdrant will significantly boost the speed of these searches, accelerating both training and inference processes for users. 
 ### Scalability: Preparing for Sustained Growth in Data Volumes
 Qdrant's distributed deployment mode plays a vital role in empowering large enterprises dealing with massive data volumes. It ensures that increasing data volumes do not hinder performance but rather enrich the model's capabilities, making scalability a seamless process. Moreover, Qdrant is well-suited for Pienso’s enterprise customers as it operates best on bare metal infrastructure, enabling them to maintain complete control over their data sovereignty and autonomous LLM regimes. This ensures that enterprises can maintain their full span of control while leveraging the scalability and performance benefits of Qdrant's solution.
 ### Efficiency: Maximizing the Customer Value Proposition
 Qdrant's storage efficiency delivers cost savings on hardware while ensuring a responsive system even with extensive data sets. In an independent benchmark stress test, Pienso discovered that Qdrant could efficiently store 128 million documents, consuming a mere 20.4GB of storage and only 1.25GB of memory. This storage efficiency not only minimizes hardware expenses for Pienso’s customers, but also ensures optimal performance, making Qdrant an ideal solution for managing large-scale data with ease and efficiency.
 ### Reliability: Fast Performance in a Secure Environment
 Qdrant's utilization of Rust, coupled with its memmap storage and write-ahead logging, offers users a powerful combination of high-performance operations, robust data protection, and enhanced data safety measures. Our memmap storage feature offers Pienso fast performance comparable to in-memory storage. In the context of machine learning, where rapid data access and retrieval are crucial for training and inference tasks, this capability proves invaluable. Furthermore, our write-ahead logging (WAL), is critical to ensuring changes are logged before being applied to the database. This approach adds additional layers of data safety, further safeguarding the integrity of the stored information.
 > “We chose Qdrant because it's fast to query, has a small memory footprint and allows for instantaneous setup of a new vector collection that is going to be queried. Other solutions we evaluated had long bootstrap times and also long collection initialization times {..} This partnership comes at a great time, because it allows Pienso to use Qdrant to its maximum potential, giving our customers a seamless experience while they explore and get meaningful insights about their data.” - Felipe Balduino Cassar, Senior Software Engineer, Pienso
 ## What's Next?
 Pienso and Qdrant are dedicated to jointly develop the most reliable customer offering for the long term. Our partnership will deliver a combination of no-code/low-code interactive deep learning with efficient vector computation engineered for open source models and libraries. 
 ### To learn more about how we plan on achieving this, join the founders for a [technical fireside chat at 09:30 PST Thursday, 20th July on Discord](https://discord.gg/Vnvg3fHE?event=1128331722270969909). 
 ![founders chat](/case-studies/pienso/founderschat.png)