lukawskikacper/qdrant-landing-page-2024-06-21 huggingface dataset:
qdrant-landing/content/blog/cohere-embedding-v3.md

---
draft: false
preview_image: /blog/from_cms/nils-thumbnail.png
sitemapExclude: true
title: "From Content Quality to Compression: The Evolution of Embedding Models
  at Cohere with Nils Reimers"
slug: cohere-embedding-v3
short_description: Nils Reimers head of machine learning at Cohere shares the
  details about their latest embedding model.
description: Nils Reimers head of machine learning at Cohere comes on the recent
  vector space talks to share details about their latest embedding V3 model.
date: 2023-11-19T12:48:36.622Z
author: Demetrios Brinkmann
featured: false
author_link: https://www.linkedin.com/in/dpbrinkm/
tags:
  - Vector Space Talk
  - Cohere
  - Embedding Model
categories:
  - News
  - Vector Space Talk
---

For the second edition of our Vector Space Talks we were joined by none other than Cohere’s Head of Machine Learning Nils Reimers. 

## Key Takeaways

Let's dive right into the five key takeaways from Nils' talk:

1. Content Quality Estimation: Nils explained how embeddings have traditionally focused on measuring topic match, but content quality is just as important. He demonstrated how their model can differentiate between informative and non-informative documents.

2. Compression-Aware Training: He shared how they've tackled the challenge of reducing the memory footprint of embeddings, making it more cost-effective to run vector databases on platforms like [Qdrant](https://cloud.qdrant.io/login).

3. Reinforcement Learning from Human Feedback: Nils revealed how they've borrowed a technique from reinforcement learning and applied it to their embedding models. This allows the model to learn preferences based on human feedback, resulting in highly informative responses.

4. Evaluating Embedding Quality: Nils emphasized the importance of evaluating embedding quality in relative terms rather than looking at individual vectors. It's all about understanding the context and how embeddings relate to each other.

5. New Features in the Pipeline: Lastly, Nils gave us a sneak peek at some exciting features they're developing, including input type support for Langchain and improved compression techniques.

Now, here's a fun fact from the episode: Did you know that the content quality estimation model *can't* differentiate between true and fake statements? It's a challenging task, and the model relies on the information present in its pretraining data.

We loved having Nils as our guest, check out the full talk below. If you or anyone you know would like to come on the Vector Space Talks 

<iframe width="560" height="315" src="https://www.youtube.com/embed/Abh3YCahyqU?si=OB4FXhTivsLLXzQV" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
qdrant-landing/content/blog/cve-2024-2221-response.md

---
title: "Response to CVE-2024-2221: Arbitrary file upload vulnerability"
draft: false
slug: cve-2024-2221-response
short_description:  Qdrant keeps your systems secure
description: Upgrade your deployments to at least v1.9.0. Cloud deployments not materially affected.
preview_image: /blog/cve-2024-2221/cve-2024-2221-response-social-preview.png

# social_preview_image: /blog/Article-Image.png # Optional image used for link previews
# title_preview_image: /blog/Article-Image.png # Optional image used for blog post title
# small_preview_image: /blog/Article-Image.png # Optional image used for small preview in the list of blog posts
date: 2024-04-05T13:00:00-07:00
author: Mike Jang
featured: false
tags:
  - cve
  - security
weight: 0 # Change this weight to change order of posts
# For more guidance, see https://github.com/qdrant/landing_page?tab=readme-ov-file#blog
---

### Summary

A security vulnerability has been discovered in Qdrant affecting all versions
prior to v1.9, described in [CVE-2024-2221](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2024-2221).
The vulnerability allows an attacker to upload arbitrary files to the
filesystem, which can be used to gain remote code execution.

The vulnerability does not materially affect Qdrant cloud deployments, as that
filesystem is read-only and authentication is enabled by default. At worst,
the vulnerability could be used by an authenticated user to crash a cluster,
which is already possible, such as by uploading more vectors than can fit in RAM.

Qdrant has addressed the vulnerability in v1.9.0 and above with code that
restricts file uploads to a folder dedicated to that purpose.

### Action

Check the current version of your Qdrant deployment. Upgrade if your deployment
is not at least v1.9.0.

To confirm the version of your Qdrant deployment in the cloud or on your local
or cloud system, run an API GET call, as described in the [Qdrant Quickstart
guide](/documentation/cloud/quickstart-cloud/#step-2-test-cluster-access).
If your Qdrant deployment is local, you do not need an API key.

Your next step depends on how you installed Qdrant. For details, read the
[Qdrant Installation](/documentation/guides/installation/)
guide.

#### If you use the Qdrant container or binary

Upgrade your deployment. Run the commands in the applicable section of the
[Qdrant Installation](/documentation/guides/installation/)
guide. The default commands automatically pull the latest version of Qdrant.

#### If you use the Qdrant helm chart

If you’ve set up Qdrant on kubernetes using a helm chart, follow the README in
the [qdrant-helm](https://github.com/qdrant/qdrant-helm/tree/main?tab=readme-ov-file#upgrading) repository.
Make sure applicable configuration files point to version v1.9.0 or above.

#### If you use the Qdrant cloud

No action is required. This vulnerability does not materially affect you. However, we suggest that you upgrade your cloud deployment to the latest version.

> Note: This article has been updated on 2024-05-10 to encourage users to upgrade to 1.9.0 to ensure protection from both CVE-2024-2221 and CVE-2024-3829.
qdrant-landing/content/blog/cve-2024-3829-response.md

---
title: "Response to CVE-2024-3829: Arbitrary file upload vulnerability"
draft: false
slug: cve-2024-3829-response
short_description:  Qdrant keeps your systems secure
description: Upgrade your deployments to at least v1.9.0. Cloud deployments not materially affected.
preview_image: /blog/cve-2024-3829-response/cve-2024-3829-response-social-preview.png

# social_preview_image: /blog/Article-Image.png # Optional image used for link previews
# title_preview_image: /blog/Article-Image.png # Optional image used for blog post title
# small_preview_image: /blog/Article-Image.png # Optional image used for small preview in the list of blog posts
date: 2024-06-10T17:00:00Z
author: Mac Chaffee
featured: false
tags:
  - cve
  - security
weight: 0 # Change this weight to change order of posts
# For more guidance, see https://github.com/qdrant/landing_page?tab=readme-ov-file#blog
---

### Summary

A security vulnerability has been discovered in Qdrant affecting all versions
prior to v1.9, described in [CVE-2024-3829](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2024-3829).
The vulnerability allows an attacker to upload arbitrary files to the
filesystem, which can be used to gain remote code execution. This is a different but similar vulnerability to CVE-2024-2221, announced in April 2024.

The vulnerability does not materially affect Qdrant cloud deployments, as that
filesystem is read-only and authentication is enabled by default. At worst,
the vulnerability could be used by an authenticated user to crash a cluster,
which is already possible, such as by uploading more vectors than can fit in RAM.

Qdrant has addressed the vulnerability in v1.9.0 and above with code that
restricts file uploads to a folder dedicated to that purpose.

### Action

Check the current version of your Qdrant deployment. Upgrade if your deployment
is not at least v1.9.0.

To confirm the version of your Qdrant deployment in the cloud or on your local
or cloud system, run an API GET call, as described in the [Qdrant Quickstart
guide](https://qdrant.tech/documentation/cloud/quickstart-cloud/#step-2-test-cluster-access).
If your Qdrant deployment is local, you do not need an API key.

Your next step depends on how you installed Qdrant. For details, read the
[Qdrant Installation](https://qdrant.tech/documentation/guides/installation/)
guide.

#### If you use the Qdrant container or binary

Upgrade your deployment. Run the commands in the applicable section of the
[Qdrant Installation](https://qdrant.tech/documentation/guides/installation/)
guide. The default commands automatically pull the latest version of Qdrant.

#### If you use the Qdrant helm chart

If you’ve set up Qdrant on kubernetes using a helm chart, follow the README in
the [qdrant-helm](https://github.com/qdrant/qdrant-helm/tree/main?tab=readme-ov-file#upgrading) repository.
Make sure applicable configuration files point to version v1.9.0 or above.

#### If you use the Qdrant cloud

No action is required. This vulnerability does not materially affect you. However, we suggest that you upgrade your cloud deployment to the latest version.
qdrant-landing/content/blog/gsoc24-summer-of-code.md

---
draft: false
title: Qdrant Summer of Code 24
slug: qdrant-summer-of-code-24
short_description: Introducing Qdrant Summer of Code 2024 program.
description: "Introducing Qdrant Summer of Code 2024 program. GSoC alternative."
preview_image: /blog/Qdrant-summer-of-code.png
date: 2024-02-21T00:39:53.751Z
author: Andre Zayarni
featured: false
tags:
  - Open Source
  - Vector Database
  - Summer of Code
  - GSoC24
---
Google Summer of Code (#GSoC) is celebrating its 20th anniversary this year with the 2024 program. Over the past 20 years, 19K new contributors were introduced to #opensource through the program under the guidance of thousands of mentors from over 800 open-source organizations in various fields. Qdrant participated successfully in the program last year. Both projects, the UI Dashboard with unstructured data visualization and the advanced Geo Filtering, were completed in time and are now a part of the engine. One of the two young contributors joined the team and continues working on the project. 

We are thrilled to announce that Qdrant was 𝐍𝐎𝐓 𝐚𝐜𝐜𝐞𝐩𝐭𝐞𝐝 into the GSoc 2024 program for unknown reasons, but instead, we are introducing our own 𝐐𝐝𝐫𝐚𝐧𝐭 𝐒𝐮𝐦𝐦𝐞𝐫 𝐨𝐟 𝐂𝐨𝐝𝐞 program with a stipend for contributors! To not reinvent the wheel, we follow all the timelines and rules of the official Google program. 



## Our project ideas. 
We have prepared some excellent project ideas. Take a look and choose if you want to contribute in Rust or a Python-based project.


➡ *WASM-based dimension reduction viz* 📊 

Implement a dimension reduction algorithm in Rust, compile to WASM and integrate the WASM code with Qdrant Web UI.

➡ *Efficient BM25 and Okapi BM25, which uses the BERT Tokenizer* 🥇

BM25 and Okapi BM25 are popular ranking algorithms. Qdrant's FastEmbed supports dense embedding models. We need a fast, efficient, and massively parallel Rust implementation with Python bindings for these. 

➡ *ONNX Cross Encoders in Python* ⚔️ 

Export a cross-encoder ranking models to operate on ONNX runtime and integrate this model with the Qdrant's FastEmbed to support efficient re-ranking

➡ *Ranking Fusion Algorithms implementation in Rust* 🧪

Develop Rust implementations of various ranking fusion algorithms including but not limited to Reciprocal Rank Fusion (RRF). For a complete list, see: https://github.com/AmenRa/ranx
and create Python bindings for the implemented Rust modules.

➡ *Setup Jepsen to test Qdrant’s distributed guarantees* 💣

Design and write Jepsen tests based on implementations for other Databases and create a report or blog with the findings.


See all details on our Notion page: https://www.notion.so/qdrant/GSoC-2024-ideas-1dfcc01070094d87bce104623c4c1110


Contributor application period begins on March 18th. We will accept applications via email. Let's contribute and celebrate together! 

In open-source, we trust! 🦀🤘🚀
qdrant-landing/content/blog/introducing-the-quaterion-a-framework-for-fine-tuning-similarity-learning-models.md

---
draft: true
preview_image: /blog/from_cms/new-cmp-demo.gif
sitemapExclude: true
title: "Introducing the Quaterion: a framework for fine-tuning similarity
  learning models"
slug: quaterion
short_description: Please meet Quaterion—a framework for training and
  fine-tuning similarity learning models.
description: We're happy to share the result of the work we've been into during
  the last months - Quaterion. It is a framework for fine-tuning similarity
  learning models that streamlines the training process to make it significantly
  faster and cost-efficient.
date: 2022-06-28T12:48:36.622Z
author: Andrey Vasnetsov
featured: true
author_link: https://www.linkedin.com/in/andrey-vasnetsov-75268897/
tags:
  - Corporate news
  - Release
  - Quaterion
  - PyTorch
categories:
  - News
  - Release
  - Quaterion
---

We're happy to share the result of the work we've been into during the last months -
[Quaterion](https://quaterion.qdrant.tech/). It is a framework for fine-tuning similarity learning models that streamlines the training process to make it significantly faster and cost-efficient.
To develop Quaterion, we utilized PyTorch Lightning, leveraging a high-performing AI research approach to constructing training loops for ML models.

![quaterion](/blog/from_cms/new-cmp-demo.gif)

This framework empowers vector search [solutions](/solutions/), such as semantic search, anomaly detection, and others, by advanced coaching mechanism, specially designed head layers for pre-trained models, and high flexibility in terms of customization according to large-scale training pipelines and other features.

Here you can read why similarity learning is preferable to the traditional machine learning approach and how Quaterion can help benefit <https://quaterion.qdrant.tech/getting_started/why_quaterion.html#why-quaterion>   

A quick start with Quaterion:<https://quaterion.qdrant.tech/getting_started/quick_start.html>\
\
And try it and give us a star on GitHub :) <https://github.com/qdrant/quaterion>
qdrant-landing/content/blog/new-0-7-update-of-the-qdrant-engine-went-live.md

---
draft: true
title: New 0.7.0 update of the Qdrant engine went live
slug: qdrant-0-7-0-released
short_description: Qdrant v0.7.0 engine has been released
description: Qdrant v0.7.0 engine has been released
preview_image: /blog/from_cms/v0.7.0.png
date: 2022-04-13T08:57:07.604Z
author: Alyona Kavyerina
author_link: https://www.linkedin.com/in/alyona-kavyerina/
featured: true
categories:
  - News
  - Release update
tags:
  - Corporate news
  - Release
sitemapExclude: True
---

We've released the new version of Qdrant neural search engine. 

Let's see what's new in update 0.7.0.

* 0.7 engine now supports JSON as a payload. 
* It redeems a lost API. Alias API in gRPC is available.
* Provides new filtering conditions: refactoring, bool, IsEmpty, and ValuesCount filters are available. 
* It has a lot of improvements regarding geo payload indexing, HNSW performance, and many more.

Read detailed release notes on [GitHub](https://github.com/qdrant/qdrant/releases/tag/v0.7.0).

Stay tuned for new updates.\
If you have any questions or need support, join our [Discord](https://discord.com/invite/tdtYvXjC4h) community.
qdrant-landing/content/blog/qdrant-cloud-on-microsoft-azure.md

---
draft: false
title: Introducing Qdrant Cloud on Microsoft Azure
slug: qdrant-cloud-on-microsoft-azure
short_description: Qdrant Cloud is now available on Microsoft Azure
description: "Learn the benefits of Qdrant Cloud on Azure."
preview_image: /blog/from_cms/qdrant-azure-2-1.png
date: 2024-01-17T08:40:42Z
author: Manuel Meyer
featured: false
tags:
  - Data Science
  - Vector Database
  - Machine Learning
  - Information Retrieval
  - Cloud
  - Azure
---
Great news! We've expanded Qdrant's managed vector database offering — [Qdrant Cloud](https://cloud.qdrant.io/) — to be available on Microsoft Azure. 
You can now effortlessly set up your environment on Azure, which reduces deployment time, so you can hit the ground running.

[Get started](https://cloud.qdrant.io/)

What this means for you:

- **Rapid application development**: Deploy your own cluster through the Qdrant Cloud Console within seconds and scale your resources as needed.
- **Billion vector scale**: Seamlessly grow and handle large-scale datasets with billions of vectors. Leverage Qdrant features like horizontal scaling and binary quantization with Microsoft Azure's scalable infrastructure.

**"With Qdrant, we found the missing piece to develop our own provider independent multimodal generative AI platform at enterprise scale."** -- Jeremy Teichmann (AI Squad Technical Lead & Generative AI Expert), Daly Singh (AI Squad Lead & Product Owner) - Bosch Digital.

Get started by [signing up for a Qdrant Cloud account](https://cloud.qdrant.io). And learn more about Qdrant Cloud in our [docs](/documentation/cloud/).

<video autoplay="true" loop="true" width="100%" controls><source src="/blog/qdrant-cloud-on-azure/azure-cluster-deployment-short.mp4" type="video/mp4"></video>
qdrant-landing/content/blog/qdrant-has-joined-nvidia-inception-program.md

---
draft: false
preview_image: /blog/from_cms/inception.png
sitemapExclude: true
title: Qdrant has joined NVIDIA Inception Program
slug: qdrant-joined-nvidia-inception-program
short_description: Recently Qdrant has become a member of the NVIDIA Inception.
description: Along with the various opportunities it gives, we are the most
  excited about GPU support since it is an essential feature in Qdrant's
  roadmap. Stay tuned for our new updates.
date: 2022-04-04T12:06:36.819Z
author: Alyona Kavyerina
featured: false
author_link: https://www.linkedin.com/in/alyona-kavyerina/
tags:
  - Corporate news
  - NVIDIA
categories:
  - News
---
Recently we've become a member of the NVIDIA Inception. It is a program that helps boost the evolution of technology startups through access to their cutting-edge technology and experts, connects startups with venture capitalists, and provides marketing support.

Along with the various opportunities it gives, we are the most excited about GPU support since it is an essential feature in Qdrant's roadmap.
Stay tuned for our new updates.
qdrant-landing/content/blog/qdrant-v-0-6-0-engine-with-grpc-released.md

---
draft: true
title: Qdrant v0.6.0 engine with gRPC interface has been released
short_description: We’ve released a new engine, version 0.6.0.
description: We’ve released a new engine, version 0.6.0. The main feature of the release in the gRPC interface.
preview_image: /blog/qdrant-v-0-6-0-engine-with-grpc-released/upload_time.png
date: 2022-03-10T01:36:43+03:00
author: Alyona Kavyerina
author_link: https://medium.com/@alyona.kavyerina
featured: true
categories:
- News
tags:
- gRPC
- release
sitemapExclude: True
---
We’ve released a new engine, version 0.6.0.

The main feature of the release in the gRPC interface — it is much faster than the REST API and ensures higher app performance due to the following features:

   - re-use of connection;
   - binarity protocol;
   - separation schema from data.

This results in 3 times faster data uploading on our benchmarks:

![REST API vs gRPC upload time, sec](/blog/qdrant-v-0-6-0-engine-with-grpc-released/upload_time.png)

Read more about the gRPC interface and whether you should use it by this [link](/documentation/quick_start/#grpc).

The release v0.6.0 includes several bug fixes. More information is available in a [changelog](https://github.com/qdrant/qdrant/releases/tag/v0.6.0).

New version was provided in addition to the REST API that the company keeps supporting due to its easy debugging.
qdrant-landing/content/blog/qdrant_and_jina_integration.md

---
draft: false
preview_image: /blog/from_cms/docarray.png
sitemapExclude: true
title: "Qdrant and Jina integration: storage backend support for DocArray"
slug: qdrant-and-jina-integration
short_description: "One more way to use Qdrant: Jina's DocArray is now
  supporting Qdrant as a storage backend."
description: We are happy to announce that Jina.AI integrates Qdrant engine as a
  storage backend to their DocArray solution.
date: 2022-03-15T15:00:00+03:00
author: Alyona Kavyerina
featured: false
author_link: https://medium.com/@alyona.kavyerina
tags:
  - jina integration
  - docarray
categories:
  - News
---
We are happy to announce that [Jina.AI](https://jina.ai/) integrates Qdrant engine as a storage backend to their [DocArray](https://docarray.jina.ai/) solution.

Now you can experience the convenience of Pythonic API and Rust performance in a single workflow.

DocArray library defines a structure for the unstructured data and simplifies processing a collection of documents,
including audio, video, text, and other data types. Qdrant engine empowers scaling of its vector search and storage.

Read more about the integration by this [link](/documentation/install/#docarray)
qdrant-landing/content/blog/qsoc24-interns-announcement.md

---
title: "QSoC 2024: Announcing Our Interns!"
draft: false
slug: qsoc24-interns-announcement # Change this slug to your page slug if needed
short_description:  We are pleased to announce the selection of interns for the inaugural Qdrant Summer of Code (QSoC) program. # Change this
description: We are pleased to announce the selection of interns for the inaugural Qdrant Summer of Code (QSoC) program. # Change this
preview_image: /blog/qsoc24-interns-announcement/qsoc.jpg # Change this

social_preview_image: /blog/qsoc24-interns-announcement/qsoc.jpg # Optional image used for link previews
title_preview_image: /blog/qsoc24-interns-announcement/qsoc.jpg # Optional image used for blog post title
# small_preview_image: /blog/Article-Image.png # Optional image used for small preview in the list of blog posts

date: 2024-05-08T16:44:22-03:00
author: Sabrina Aquino # Change this
featured: false # if true, this post will be featured on the blog page
tags: # Change this, related by tags posts will be shown on the blog page
  - QSoC
  - Qdrant Summer of Code
  - Google Summer of Code
  - vector search

---

We are excited to announce the interns selected for the inaugural Qdrant Summer of Code (QSoC) program! After receiving many impressive applications, we have chosen two talented individuals to work on the following projects:

**[Jishan Bhattacharya](https://www.linkedin.com/in/j16n/): WASM-based Dimension Reduction Visualization**

Jishan will be implementing a dimension reduction algorithm in Rust, compiling it to WebAssembly (WASM), and integrating it with the Qdrant Web UI. This project aims to provide a more efficient and smoother visualization experience, enabling the handling of more data points and higher dimensions efficiently.

**[Celine Hoang](https://www.linkedin.com/in/celine-h-hoang/): ONNX Cross Encoders in Python**

Celine Hoang will focus on porting advanced ranking models—specifically Sentence Transformers, ColBERT, and BGE—to the ONNX (Open Neural Network Exchange) format. This project will enhance Qdrant's model support, making it more versatile and efficient in handling complex ranking tasks that are critical for applications such as recommendation engines and search functionalities.

We look forward to working with Jishan and Celine over the coming months and are excited to see their contributions to the Qdrant project.

Stay tuned for more updates on the QSoC program and the progress of these projects!