text stringlengths 25 143k | source stringlengths 12 112 |
|---|---|
---
logos:
- /img/customers-logo/discord.svg
- /img/customers-logo/johnson-and-johnson.svg
- /img/customers-logo/perplexity.svg
- /img/customers-logo/mozilla.svg
- /img/customers-logo/voiceflow.svg
- /img/customers-logo/bosch-digital.svg
sitemapExclude: true
--- | customers/logo-cards-1.md |
---
review: “We looked at all the big options out there right now for vector databases, with our focus on ease of use, performance, pricing, and communication. <strong>Qdrant came out on top in each category...</strong> ultimately, it wasn't much of a contest.”
names: Alex Webb
positions: Director of Engineering, CB... | customers/customers-testimonial1.md |
---
title: Customers
description: Learn how Qdrant powers thousands of top AI solutions that require vector search with unparalleled efficiency, performance and massive-scale data processing.
caseStudy:
logo:
src: /img/customers-case-studies/customer-logo.svg
alt: Logo
title: Recommendation Engine wi... | customers/customers-case-studies.md |
---
review: “We LOVE Qdrant! The exceptional engineering, strong business value, and outstanding team behind the product drove our choice. Thank you for your great contribution to the technology community!”
names: Kyle Tobin
positions: Principal, Cognizant
avatar:
src: /img/customers/kyle-tobin.png
alt: Kyle ... | customers/customers-testimonial2.md |
---
logos:
- /img/customers-logo/gitbook.svg
- /img/customers-logo/deloitte.svg
- /img/customers-logo/disney.svg
sitemapExclude: true
--- | customers/logo-cards-3.md |
---
title: Vector Space Wall
link:
url: https://testimonial.to/qdrant/all
text: Submit Your Testimonial
testimonials:
- id: 0
name: Jonathan Eisenzopf
position: Chief Strategy and Research Officer at Talkmap
avatar:
src: /img/customers/jonathan-eisenzopf.svg
alt: Avatar
text: “With Qdran... | customers/customers-vector-space-wall.md |
---
title: Customers
description: Learn how Qdrant powers thousands of top AI solutions that require vector search with unparalleled efficiency, performance and massive-scale data processing.
sitemapExclude: true
---
| customers/customers-hero.md |
---
title: Customers
description: Customers
build:
render: always
cascade:
- build:
list: local
publishResources: false
render: never
---
| customers/_index.md |
---
logos:
- /img/customers-logo/flipkart.svg
- /img/customers-logo/x.svg
- /img/customers-logo/quora.svg
sitemapExclude: true
--- | customers/logo-cards-2.md |
---
title: Qdrant Demos and Tutorials
description: Experience firsthand how Qdrant powers intelligent search, anomaly detection, and personalized recommendations, showcasing the full capabilities of vector search to revolutionize data exploration and insights.
cards:
- id: 0
title: Semantic Search Demo - Sta... | demo/_index.md |
---
content: Learn more about all features that are supported on Qdrant Cloud.
link:
text: Qdrant Features
url: /qdrant-vector-database/
sitemapExclude: true
---
| qdrant-cloud/qdrant-cloud-features-link.md |
---
title: Qdrant Cloud
description: Qdrant Cloud provides optimal flexibility and offers a suite of features focused on efficient and scalable vector search - fully managed. Available on AWS, Google Cloud, and Azure.
startFree:
text: Start Free
url: https://cloud.qdrant.io/
contactUs:
text: Contact us
... | qdrant-cloud/qdrant-cloud-hero.md |
---
items:
- id: 0
title: Run Anywhere
description: Available on <b>AWS</b>, <b>Google Cloud</b>, and <b>Azure</b> regions globally for deployment flexibility and quick data access.
image:
src: /img/qdrant-cloud-bento-cards/run-anywhere-graphic.png
alt: Run anywhere graphic
- id: 1
title: Simpl... | qdrant-cloud/qdrant-cloud-bento-cards.md |
---
title: "Qdrant Cloud: Scalable Managed Cloud Services"
url: cloud
description: "Discover Qdrant Cloud, the cutting-edge managed cloud for scalable, high-performance AI applications. Manage and deploy your vector data with ease today."
build:
render: always
cascade:
- build:
list: local
publishRes... | qdrant-cloud/_index.md |
---
logo:
title: Our Logo
description: "The Qdrant logo represents a paramount expression of our core brand identity. With consistent placement, sizing, clear space, and color usage, our logo affirms its recognition across all platforms."
logoCards:
- id: 0
logo:
src: /img/brand-resources-logos... | brand-resources/brand-resources-content.md |
---
title: Qdrant Brand Resources
buttons:
- id: 0
url: "#logo"
text: Logo
- id: 1
url: "#colors"
text: Colors
- id: 2
url: "#typography"
text: Typography
- id: 3
url: "#trademarks"
text: Trademarks
sitemapExclude: true
---
| brand-resources/brand-resources-hero.md |
---
title: brand-resources
description: brand-resources
build:
render: always
cascade:
- build:
list: local
publishResources: false
render: never
---
| brand-resources/_index.md |
---
title: Cloud Quickstart
weight: 4
aliases:
- quickstart-cloud
- ../cloud-quick-start
- cloud-quick-start
- cloud-quickstart
- cloud/quickstart-cloud/
---
# How to Get Started With Qdrant Cloud
<p align="center"><iframe width="560" height="315" src="https://www.youtube.com/embed/g6uJhjAoNMg?si... | documentation/quickstart-cloud.md |
---
title: Release Notes
weight: 24
type: external-link
external_url: https://github.com/qdrant/qdrant/releases
sitemapExclude: True
---
| documentation/release-notes.md |
---
title: Benchmarks
weight: 33
draft: true
---
| documentation/benchmarks.md |
---
title: Community links
weight: 42
draft: true
---
# Community Contributions
Though we do not officially maintain this content, we still feel that is is valuable and thank our dedicated contributors.
| Link | Description | Stack |
|------|------------------------------|--------|
... | documentation/community-links.md |
---
title: Local Quickstart
weight: 5
aliases:
- quick_start
- quick-start
- quickstart
---
# How to Get Started with Qdrant Locally
In this short example, you will use the Python Client to create a Collection, load data into it and run a basic search query.
<aside role="status">Before you start, pl... | documentation/quickstart.md |
---
title: Qdrant Cloud API
weight: 10
---
# Qdrant Cloud API
The Qdrant Cloud API lets you manage Cloud accounts and their respective Qdrant clusters. You can use this API to manage your clusters, authentication methods, and cloud configurations.
| REST API | Documentation ... | documentation/qdrant-cloud-api.md |
---
#Delimiter files are used to separate the list of documentation pages into sections.
title: "Getting Started"
type: delimiter
weight: 1 # Change this weight to change order of sections
sitemapExclude: True
_build:
publishResources: false
render: never
--- | documentation/0-dl.md |
---
#Delimiter files are used to separate the list of documentation pages into sections.
title: "Integrations"
type: delimiter
weight: 14 # Change this weight to change order of sections
sitemapExclude: True
_build:
publishResources: false
render: never
--- | documentation/2-dl.md |
---
title: Roadmap
weight: 32
draft: true
---
# Qdrant 2023 Roadmap
Goals of the release:
* **Maintain easy upgrades** - we plan to keep backward compatibility for at least one major version back.
* That means that you can upgrade Qdrant without any downtime and without any changes in your client code ... | documentation/roadmap.md |
---
#Delimiter files are used to separate the list of documentation pages into sections.
title: "Managed Services"
type: delimiter
weight: 7 # Change this weight to change order of sections
sitemapExclude: True
_build:
publishResources: false
render: never
--- | documentation/4-dl.md |
---
#Delimiter files are used to separate the list of documentation pages into sections.
title: "Examples"
type: delimiter
weight: 17 # Change this weight to change order of sections
sitemapExclude: True
_build:
publishResources: false
render: never
--- | documentation/3-dl.md |
---
title: Practice Datasets
weight: 23
---
# Common Datasets in Snapshot Format
You may find that creating embeddings from datasets is a very resource-intensive task.
If you need a practice dataset, feel free to pick one of the ready-made snapshots on this page.
These snapshots contain pre-computed vectors... | documentation/datasets.md |
---
#Delimiter files are used to separate the list of documentation pages into sections.
title: "User Manual"
type: delimiter
weight: 10 # Change this weight to change order of sections
sitemapExclude: True
_build:
publishResources: false
render: never
--- | documentation/1-dl.md |
---
#Delimiter files are used to separate the list of documentation pages into sections.
title: "Support"
type: delimiter
weight: 21 # Change this weight to change order of sections
sitemapExclude: True
_build:
publishResources: false
render: never
--- | documentation/5-dl.md |
---
title: Home
weight: 2
hideTOC: true
---
# Documentation
Qdrant is an AI-native vector dabatase and a semantic search engine. You can use it to extract meaningful information from unstructured data. Want to see how it works? [Clone this repo now](https://github.com/qdrant/qdrant_demo/) and build a search eng... | documentation/_index.md |
---
title: Contribution Guidelines
weight: 35
draft: true
---
# How to contribute
If you are a Qdrant user - Data Scientist, ML Engineer, or MLOps, the best contribution would be the feedback on your experience with Qdrant.
Let us know whenever you have a problem, face an unexpected behavior, or see a lack o... | documentation/contribution-guidelines.md |
---
title: Bubble
aliases: [ ../frameworks/bubble/ ]
---
# Bubble
[Bubble](https://bubble.io/) is a software development platform that enables anyone to build and launch fully functional web applications without writing code.
You can use the [Qdrant Bubble plugin](https://bubble.io/plugin/qdrant-17168043741... | documentation/platforms/bubble.md |
---
title: Make.com
aliases: [ ../frameworks/make/ ]
---
# Make.com
[Make](https://www.make.com/) is a platform for anyone to design, build, and automate anything—from tasks and workflows to apps and systems without code.
Find the comprehensive list of available Make apps [here](https://www.make.com/en/inte... | documentation/platforms/make.md |
---
title: Portable.io
aliases: [ ../frameworks/portable/ ]
---
# Portable
[Portable](https://portable.io/) is an ELT platform that builds connectors on-demand for data teams. It enables connecting applications to your data warehouse with no code.
You can avail the [Qdrant connector](https://portable.io/con... | documentation/platforms/portable.md |
---
title: BuildShip
aliases: [ ../frameworks/buildship/ ]
---
# BuildShip
[BuildShip](https://buildship.com/) is a low-code visual builder to create APIs, scheduled jobs, and backend workflows with AI assitance.
You can use the [Qdrant integration](https://buildship.com/integrations/qdrant) to developmen... | documentation/platforms/buildship.md |
---
title: Apify
aliases: [ ../frameworks/apify/ ]
---
# Apify
[Apify](https://apify.com/) is a web scraping and browser automation platform featuring an [app store](https://apify.com/store) with over 1,500 pre-built micro-apps known as Actors. These serverless cloud programs, which are essentially dockers un... | documentation/platforms/apify.md |
---
title: PrivateGPT
aliases: [ ../integrations/privategpt/, ../frameworks/privategpt/ ]
---
# PrivateGPT
[PrivateGPT](https://docs.privategpt.dev/) is a production-ready AI project that allows you to inquire about your documents using Large Language Models (LLMs) with offline support.
PrivateGPT uses Qdra... | documentation/platforms/privategpt.md |
---
title: Pipedream
aliases: [ ../frameworks/pipedream/ ]
---
# Pipedream
[Pipedream](https://pipedream.com/) is a development platform that allows developers to connect many different applications, data sources, and APIs in order to build automated cross-platform workflows. It also offers code-level control ... | documentation/platforms/pipedream.md |
---
title: Ironclad Rivet
aliases: [ ../frameworks/rivet/ ]
---
# Ironclad Rivet
[Rivet](https://rivet.ironcladapp.com/) is an Integrated Development Environment (IDE) and library designed for creating AI agents using a visual, graph-based interface.
Qdrant is available as a [plugin](https://github.com/qdra... | documentation/platforms/rivet.md |
---
title: DocsGPT
aliases: [ ../frameworks/docsgpt/ ]
---
# DocsGPT
[DocsGPT](https://docsgpt.arc53.com/) is an open-source documentation assistant that enables you to build conversational user experiences on top of your data.
Qdrant is supported as a vectorstore in DocsGPT to ingest and semantically retri... | documentation/platforms/docsgpt.md |
---
title: Platforms
weight: 15
---
## Platform Integrations
| Platform | Description |
| ------------------------------------- | --------------------------------------------------------------... | documentation/platforms/_index.md |
---
title: N8N
aliases: [ ../frameworks/n8n/ ]
---
# N8N
[N8N](https://n8n.io/) is an automation platform that allows you to build flexible workflows focused on deep data integration.
Qdrant is available as a vectorstore node in N8N for building AI-powered functionality within your workflows.
## Prerequi... | documentation/platforms/n8n.md |
---
title: Semantic Querying with Airflow and Astronomer
weight: 36
aliases:
- /documentation/examples/qdrant-airflow-astronomer/
---
# Semantic Querying with Airflow and Astronomer
| Time: 45 min | Level: Intermediate | | |
| ------------ | ------------------- | --- | --- |
In this tutorial, y... | documentation/send-data/qdrant-airflow-astronomer.md |
---
title: Qdrant on Databricks
weight: 36
aliases:
- /documentation/examples/databricks/
---
# Qdrant on Databricks
| Time: 30 min | Level: Intermediate | [Complete Notebook](https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/4750876096379825/93425612168199/694... | documentation/send-data/databricks.md |
---
title: How to Setup Seamless Data Streaming with Kafka and Qdrant
weight: 49
aliases:
- /examples/data-streaming-kafka-qdrant/
---
# Setup Data Streaming with Kafka via Confluent
**Author:** [M K Pavan Kumar](https://www.linkedin.com/in/kameshwara-pavan-kumar-mantha-91678b21/) , research scholar at [II... | documentation/send-data/data-streaming-kafka-qdrant.md |
---
title: Send Data to Qdrant
weight: 18
---
## How to Send Your Data to a Qdrant Cluster
| Example | Description | Stack |
|-----------------------... | documentation/send-data/_index.md |
---
title: Snowflake Models
weight: 2900
---
# Snowflake
Qdrant supports working with [Snowflake](https://www.snowflake.com/blog/introducing-snowflake-arctic-embed-snowflakes-state-of-the-art-text-embedding-family-of-models/) text embedding models. You can find all the available models on [HuggingFace](https:/... | documentation/embeddings/snowflake.md |
---
title: Watsonx
weight: 3000
aliases:
- /documentation/examples/watsonx-search/
- /documentation/tutorials/watsonx-search/
- /documentation/integrations/watsonx/
---
# Using Watsonx with Qdrant
Watsonx is IBM's platform for AI embeddings, focusing on enterprise-level text and data analytics. T... | documentation/embeddings/watsonx.md |
---
title: Instruct
weight: 1800
---
# Using Instruct with Qdrant
Instruct is a specialized provider offering detailed embeddings for instructional content, which can be effectively used with Qdrant. With Instruct every text input is embedded together with instructions explaining the use case (e.g., task and... | documentation/embeddings/instruct.md |
---
title: GPT4All
weight: 1700
---
# Using GPT4All with Qdrant
GPT4All offers a range of large language models that can be fine-tuned for various applications. GPT4All runs large language models (LLMs) privately on everyday desktops & laptops.
No API calls or GPUs required - you can just download the appl... | documentation/embeddings/gpt4all.md |
---
title: Voyage AI
weight: 3200
---
# Voyage AI
Qdrant supports working with [Voyage AI](https://voyageai.com/) embeddings. The supported models' list can be found [here](https://docs.voyageai.com/docs/embeddings).
You can generate an API key from the [Voyage AI dashboard](<https://dash.voyageai.com/>) to... | documentation/embeddings/voyage.md |
---
title: Together AI
weight: 3000
---
# Using Together AI with Qdrant
Together AI focuses on collaborative AI embeddings that enhance multi-user search scenarios when integrated with Qdrant.
## Installation
You can install the required package using the following pip command:
```bash
pip install t... | documentation/embeddings/togetherai.md |
---
title: OpenAI
weight: 2700
aliases: [ ../integrations/openai/ ]
---
# OpenAI
Qdrant supports working with [OpenAI embeddings](https://platform.openai.com/docs/guides/embeddings/embeddings).
There is an official OpenAI Python package that simplifies obtaining them, and it can be installed with pip:
... | documentation/embeddings/openai.md |
---
title: AWS Bedrock
weight: 1000
---
# Bedrock Embeddings
You can use [AWS Bedrock](https://aws.amazon.com/bedrock/) with Qdrant. AWS Bedrock supports multiple [embedding model providers](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html).
You'll need the following information fr... | documentation/embeddings/bedrock.md |
---
title: Aleph Alpha
weight: 900
aliases:
- /documentation/examples/aleph-alpha-search/
- /documentation/tutorials/aleph-alpha-search/
- /documentation/integrations/aleph-alpha/
---
# Using Aleph Alpha Embeddings with Qdrant
Aleph Alpha is a multimodal and multilingual embeddings' provider. Their... | documentation/embeddings/aleph-alpha.md |
---
title: Ollama
weight: 2600
---
# Using Ollama with Qdrant
Ollama provides specialized embeddings for niche applications. Ollama supports a variety of embedding models, making it possible to build retrieval augmented generation (RAG) applications that combine text prompts with existing documents or other d... | documentation/embeddings/ollama.md |
---
title: OpenCLIP
weight: 2750
---
# Using OpenCLIP with Qdrant
OpenCLIP is an open-source implementation of the CLIP model, allowing for open source generation of multimodal embeddings that link text and images.
```python
import qdrant_client
from qdrant_client.models import Batch
import open_clip
... | documentation/embeddings/openclip.md |
---
title: Databricks Embeddings
weight: 1500
---
# Using Databricks Embeddings with Qdrant
Databricks offers an advanced platform for generating embeddings, especially within large-scale data environments. You can use the following Python code to integrate Databricks-generated embeddings with Qdrant.
```py... | documentation/embeddings/databricks.md |
---
title: Cohere
weight: 1400
aliases: [ ../integrations/cohere/ ]
---
# Cohere
Qdrant is compatible with Cohere [co.embed API](https://docs.cohere.ai/reference/embed) and its official Python SDK that
might be installed as any other package:
```bash
pip install cohere
```
The embeddings returned by ... | documentation/embeddings/cohere.md |
---
title: Clip
weight: 1300
---
# Using Clip with Qdrant
CLIP (Contrastive Language-Image Pre-Training) provides advanced AI capabilities including natural language processing and computer vision. CLIP is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language t... | documentation/embeddings/clip.md |
---
title: Clarifai
weight: 1200
---
# Using Clarifai Embeddings with Qdrant
Clarifai is a leading provider of visual embeddings, which are particularly strong in image and video analysis. Clarifai offers an API that allows you to create embeddings for various media types, which can be integrated into Qdrant ... | documentation/embeddings/clarifai.md |
---
title: Mistral
weight: 2100
---
| Time: 10 min | Level: Beginner | [](https://githubtocolab.com/qdrant/examples/blob/mistral-getting-started/mistral-embed-getting-started/mistral_qdrant_getting_started.ipynb) |
| --- | ----------- |... | documentation/embeddings/mistral.md |
---
title: "Nomic"
weight: 2300
---
# Nomic
The `nomic-embed-text-v1` model is an open source [8192 context length](https://github.com/nomic-ai/contrastors) text encoder.
While you can find it on the [Hugging Face Hub](https://huggingface.co/nomic-ai/nomic-embed-text-v1),
you may find it easier to obtain th... | documentation/embeddings/nomic.md |
---
title: Nvidia
weight: 2400
---
# Nvidia
Qdrant supports working with [Nvidia embeddings](https://build.nvidia.com/explore/retrieval).
You can generate an API key to authenticate the requests from the [Nvidia Playground](<https://build.nvidia.com/nvidia/embed-qa-4>).
### Setting up the Qdrant client a... | documentation/embeddings/nvidia.md |
---
title: Prem AI
weight: 2800
---
# Prem AI
[PremAI](https://premai.io/) is a unified generative AI development platform for fine-tuning deploying, and monitoring AI models.
Qdrant is compatible with PremAI APIs.
### Installing the SDKs
```bash
pip install premai qdrant-client
```
To install th... | documentation/embeddings/premai.md |
---
title: GradientAI
weight: 1750
---
# Using GradientAI with Qdrant
GradientAI provides state-of-the-art models for generating embeddings, which are highly effective for vector search tasks in Qdrant.
## Installation
You can install the required packages using the following pip command:
```bash
pi... | documentation/embeddings/gradientai.md |
---
title: Gemini
weight: 1600
---
| Time: 10 min | Level: Beginner | [](https://githubtocolab.com/qdrant/examples/blob/gemini-getting-started/gemini-getting-started/gemini-getting-started.ipynb) |
| --- | ----------- | ----------- |
... | documentation/embeddings/gemini.md |
---
title: OCI (Oracle Cloud Infrastructure)
weight: 2500
---
# Using OCI (Oracle Cloud Infrastructure) with Qdrant
OCI provides robust cloud-based embeddings for various media types. The Generative AI Embedding Models convert textual input - ranging from phrases and sentences to entire paragraphs - into a st... | documentation/embeddings/oci.md |
---
title: Jina Embeddings
weight: 1900
aliases:
- /documentation/embeddings/jina-emebddngs/
- ../integrations/jina-embeddings/
---
# Jina Embeddings
Qdrant can also easily work with [Jina embeddings](https://jina.ai/embeddings/) which allow for model input lengths of up to 8192 tokens.
To call thei... | documentation/embeddings/jina-embeddings.md |
---
title: Upstage
weight: 3100
---
# Upstage
Qdrant supports working with the Solar Embeddings API from [Upstage](https://upstage.ai/).
[Solar Embeddings](https://developers.upstage.ai/docs/apis/embeddings) API features dual models for user queries and document embedding, within a unified vector space, des... | documentation/embeddings/upstage.md |
---
title: John Snow Labs
weight: 2000
---
# Using John Snow Labs with Qdrant
John Snow Labs offers a variety of models, particularly in the healthcare domain. They have pre-trained models that can generate embeddings for medical text data.
## Installation
You can install the required package using the ... | documentation/embeddings/johnsnow.md |
---
title: Embeddings
weight: 15
---
# Supported Embedding Providers & Models
Qdrant supports all available text and multimodal dense vector embedding models as well as vector embedding services without any limitations.
## Some of the Embeddings you can use with Qdrant:
SentenceTransformers, BERT, SBERT... | documentation/embeddings/_index.md |
---
title: MixedBread
weight: 2200
---
# Using MixedBread with Qdrant
MixedBread is a unique provider offering embeddings across multiple domains. Their models are versatile for various search tasks when integrated with Qdrant. MixedBread is creating state-of-the-art models and tools that make search smarter,... | documentation/embeddings/mixedbread.md |
---
title: Azure OpenAI
weight: 950
---
# Using Azure OpenAI with Qdrant
Azure OpenAI is Microsoft's platform for AI embeddings, focusing on powerful text and data analytics. These embeddings are suitable for high-precision vector searches in Qdrant.
## Installation
You can install the required packages... | documentation/embeddings/azure.md |
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