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---
title: Setup Hybrid Cloud
weight: 1
---
# Creating a Hybrid Cloud Environment
The following instruction set will show you how to properly set up a **Qdrant cluster** in your **Hybrid Cloud Environment**.
To learn how Hybrid Cloud works, [read the overview document](/documentation/hybrid-cloud/).
##... | documentation/hybrid-cloud/hybrid-cloud-setup.md |
---
title: Configure the Qdrant Operator
weight: 3
---
# Configuring Qdrant Operator: Advanced Options
The Qdrant Operator has several configuration options, which can be configured in the advanced section of your Hybrid Cloud Environment.
The following YAML shows all configuration options with their defau... | documentation/hybrid-cloud/operator-configuration.md |
---
title: Networking, Logging & Monitoring
weight: 4
---
# Configuring Networking, Logging & Monitoring in Qdrant Hybrid Cloud
## Configure network policies
For security reasons, each database cluster is secured with network policies. By default, database pods only allow egress traffic between each and allow... | documentation/hybrid-cloud/networking-logging-monitoring.md |
---
title: Deployment Platforms
weight: 5
---
# Qdrant Hybrid Cloud: Hosting Platforms & Deployment Options
This page provides an overview of how to deploy Qdrant Hybrid Cloud on various managed Kubernetes platforms.
For a general list of prerequisites and installation steps, see our [Hybrid Cloud setup gui... | documentation/hybrid-cloud/platform-deployment-options.md |
---
title: Create a Cluster
weight: 2
---
# Creating a Qdrant Cluster in Hybrid Cloud
Once you have created a Hybrid Cloud Environment, you can create a Qdrant cluster in that enviroment. Use the same process to [Create a cluster](/documentation/cloud/create-cluster/). Make sure to select your Hybrid Cloud Env... | documentation/hybrid-cloud/hybrid-cloud-cluster-creation.md |
---
title: Hybrid Cloud
weight: 9
---
# Qdrant Hybrid Cloud
Seamlessly deploy and manage your vector database across diverse environments, ensuring performance, security, and cost efficiency for AI-driven applications.
[Qdrant Hybrid Cloud](/hybrid-cloud/) integrates Kubernetes clusters from any setting - c... | documentation/hybrid-cloud/_index.md |
---
title: Multitenancy
weight: 12
aliases:
- ../tutorials/multiple-partitions
- /tutorials/multiple-partitions/
---
# Configure Multitenancy
**How many collections should you create?** In most cases, you should only use a single collection with payload-based partitioning. This approach is called multiten... | documentation/guides/multiple-partitions.md |
---
title: Administration
weight: 10
aliases:
- ../administration
---
# Administration
Qdrant exposes administration tools which enable to modify at runtime the behavior of a qdrant instance without changing its configuration manually.
## Locking
A locking API enables users to restrict the possible o... | documentation/guides/administration.md |
---
title: Troubleshooting
weight: 170
aliases:
- ../tutorials/common-errors
- /documentation/troubleshooting/
---
# Solving common errors
## Too many files open (OS error 24)
Each collection segment needs some files to be open. At some point you may encounter the following errors in your server log:... | documentation/guides/common-errors.md |
---
title: Configuration
weight: 160
aliases:
- ../configuration
- /guides/configuration/
---
# Configuration
To change or correct Qdrant's behavior, default collection settings, and network interface parameters, you can use configuration files.
The default configuration file is located at [config/co... | documentation/guides/configuration.md |
---
title: Optimize Resources
weight: 11
aliases:
- ../tutorials/optimize
---
# Optimize Qdrant
Different use cases have different requirements for balancing between memory, speed, and precision.
Qdrant is designed to be flexible and customizable so you can tune it to your needs.
 at any time, and any data that has already been collected can be [deleted on request](#request-informa... | documentation/guides/telemetry.md |
---
title: Distributed Deployment
weight: 100
aliases:
- ../distributed_deployment
- /guides/distributed_deployment
---
# Distributed deployment
Since version v0.8.0 Qdrant supports a distributed deployment mode.
In this mode, multiple Qdrant services communicate with each other to distribute the data ... | documentation/guides/distributed_deployment.md |
---
title: Installation
weight: 5
aliases:
- ../install
- ../installation
---
## Installation requirements
The following sections describe the requirements for deploying Qdrant.
### CPU and memory
The CPU and RAM that you need depends on:
- Number of vectors
- Vector dimensions
- [Payloads](/... | documentation/guides/installation.md |
---
title: Quantization
weight: 120
aliases:
- ../quantization
- /articles/dedicated-service/documentation/guides/quantization/
- /guides/quantization/
---
# Quantization
Quantization is an optional feature in Qdrant that enables efficient storage and search of high-dimensional vectors.
By transform... | documentation/guides/quantization.md |
---
title: Monitoring
weight: 155
aliases:
- ../monitoring
---
# Monitoring
Qdrant exposes its metrics in [Prometheus](https://prometheus.io/docs/instrumenting/exposition_formats/#text-based-format)/[OpenMetrics](https://github.com/OpenObservability/OpenMetrics) format, so you can integrate them easily
wi... | documentation/guides/monitoring.md |
---
title: Guides
weight: 12
# If the index.md file is empty, the link to the section will be hidden from the sidebar
is_empty: true
--- | documentation/guides/_index.md |
---
title: Security
weight: 165
aliases:
- ../security
---
# Security
Please read this page carefully. Although there are various ways to secure your Qdrant instances, **they are unsecured by default**.
You need to enable security measures before production use. Otherwise, they are completely open to anyo... | documentation/guides/security.md |
---
title: Private RAG Information Extraction Engine
weight: 32
social_preview_image: /blog/hybrid-cloud-vultr/hybrid-cloud-vultr-tutorial.png
aliases:
- /documentation/tutorials/rag-chatbot-vultr-dspy-ollama/
---
# Private RAG Information Extraction Engine
| Time: 90 min | Level: Advanced | | |
|---... | documentation/examples/rag-chatbot-vultr-dspy-ollama.md |
---
title: "Inference with Mighty"
short_description: "Mighty offers a speedy scalable embedding, a perfect fit for the speedy scalable Qdrant search. Let's combine them!"
description: "We combine Mighty and Qdrant to create a semantic search service in Rust with just a few lines of code."
weight: 17
author: Andre... | documentation/examples/mighty.md |
---
title: Question-Answering System for AI Customer Support
weight: 26
social_preview_image: /blog/hybrid-cloud-airbyte/hybrid-cloud-airbyte-tutorial.png
aliases:
- /documentation/tutorials/rag-customer-support-cohere-airbyte-aws/
---
# Question-Answering System for AI Customer Support
| Time: 120 min | ... | documentation/examples/rag-customer-support-cohere-airbyte-aws.md |
---
title: Movie Recommendation System
weight: 34
social_preview_image: /blog/hybrid-cloud-ovhcloud/hybrid-cloud-ovhcloud-tutorial.png
aliases:
- /documentation/tutorials/recommendation-system-ovhcloud/
---
# Movie Recommendation System
| Time: 120 min | Level: Advanced | Output: [GitHub](https://github... | documentation/examples/recommendation-system-ovhcloud.md |
---
title: Chat With Product PDF Manuals Using Hybrid Search
weight: 27
social_preview_image: /blog/hybrid-cloud-llamaindex/hybrid-cloud-llamaindex-tutorial.png
aliases:
- /documentation/tutorials/hybrid-search-llamaindex-jinaai/
---
# Chat With Product PDF Manuals Using Hybrid Search
| Time: 120 min | ... | documentation/examples/hybrid-search-llamaindex-jinaai.md |
---
title: Region-Specific Contract Management System
weight: 28
social_preview_image: /blog/hybrid-cloud-aleph-alpha/hybrid-cloud-aleph-alpha-tutorial.png
aliases:
- /documentation/tutorials/rag-contract-management-stackit-aleph-alpha/
---
# Region-Specific Contract Management System
| Time: 90 min | Lev... | documentation/examples/rag-contract-management-stackit-aleph-alpha.md |
---
title: Implement Cohere RAG connector
weight: 24
aliases:
- /documentation/tutorials/cohere-rag-connector/
---
# Implement custom connector for Cohere RAG
| Time: 45 min | Level: Intermediate | | |
|--------------|---------------------|-|----|
The usual approach to implementing Retrieval Augment... | documentation/examples/cohere-rag-connector.md |
---
title: Aleph Alpha Search
weight: 16
draft: true
---
# Multimodal Semantic Search with Aleph Alpha
| Time: 30 min | Level: Beginner | | |
| --- | ----------- | ----------- |----------- |
This tutorial shows you how to run a proper multimodal semantic search system with a few lines of code, without... | documentation/examples/aleph-alpha-search.md |
---
title: Private Chatbot for Interactive Learning
weight: 23
social_preview_image: /blog/hybrid-cloud-red-hat-openshift/hybrid-cloud-red-hat-openshift-tutorial.png
aliases:
- /documentation/tutorials/rag-chatbot-red-hat-openshift-haystack/
---
# Private Chatbot for Interactive Learning
| Time: 120 min ... | documentation/examples/rag-chatbot-red-hat-openshift-haystack.md |
---
title: Blog-Reading Chatbot with GPT-4o
weight: 35
social_preview_image: /blog/hybrid-cloud-scaleway/hybrid-cloud-scaleway-tutorial.png
aliases:
- /documentation/tutorials/rag-chatbot-scaleway/
---
# Blog-Reading Chatbot with GPT-4o
| Time: 90 min | Level: Advanced |[GitHub](https://github.com/qdrant... | documentation/examples/rag-chatbot-scaleway.md |
---
title: Multitenancy with LlamaIndex
weight: 18
aliases:
- /documentation/tutorials/llama-index-multitenancy/
---
# Multitenancy with LlamaIndex
If you are building a service that serves vectors for many independent users, and you want to isolate their
data, the best practice is to use a single collect... | documentation/examples/llama-index-multitenancy.md |
---
title: Build Prototypes
weight: 19
---
# Examples
| End-to-End Code Samples | Description | Stack |
|----------------------------------------------... | documentation/examples/_index.md |
---
title: RAG System for Employee Onboarding
weight: 30
social_preview_image: /blog/hybrid-cloud-oracle-cloud-infrastructure/hybrid-cloud-oracle-cloud-infrastructure-tutorial.png
aliases:
- /documentation/tutorials/natural-language-search-oracle-cloud-infrastructure-cohere-langchain/
---
# RAG System for Em... | documentation/examples/natural-language-search-oracle-cloud-infrastructure-cohere-langchain.md |
---
title: Authentication
weight: 30
---
# Authenticating to Qdrant Cloud
This page shows you how to use the Qdrant Cloud Console to create a custom API key for a cluster. You will learn how to connect to your cluster using the new API key.
## Create API keys
The API key is only shown once after creation... | documentation/cloud/authentication.md |
---
title: Account Setup
weight: 10
aliases:
---
# Setting up a Qdrant Cloud Account
## Registration
There are different ways to register for a Qdrant Cloud account:
* With an email address and passwordless login via email
* With a Google account
* With a GitHub account
* By connection an enterprise ... | documentation/cloud/qdrant-cloud-setup.md |
---
title: Create a Cluster
weight: 20
---
# Creating a Qdrant Cloud Cluster
Qdrant Cloud offers two types of clusters: **Free** and **Standard**.
## Free Clusters
Free tier clusters are perfect for prototyping and testing. You don't need a credit card to join.
A free tier cluster only includes 1 sing... | documentation/cloud/create-cluster.md |
---
title: Cloud Support
weight: 99
aliases:
---
# Qdrant Cloud Support and Troubleshooting
All Qdrant Cloud users are welcome to join our [Discord community](https://qdrant.to/discord/). Our Support Engineers are available to help you anytime.
Paid customers... | documentation/cloud/support.md |
---
title: Backup Clusters
weight: 61
---
# Backing up Qdrant Cloud Clusters
Qdrant organizes cloud instances as clusters. On occasion, you may need to
restore your cluster because of application or system failure.
You may already have a source of truth for your data in a regular database. If you
have a p... | documentation/cloud/backups.md |
---
title: Configure Size & Capacity
weight: 40
aliases:
- capacity
---
# Configuring Qdrant Cloud Cluster Capacity and Size
We have been asked a lot about the optimal cluster configuration to serve a number of vectors.
The only right answer is “It depends”.
It depends on a number of factors and option... | documentation/cloud/capacity-sizing.md |
---
title: Scale Clusters
weight: 50
---
# Scaling Qdrant Cloud Clusters
The amount of data is always growing and at some point you might need to upgrade or downgrade the capacity of your cluster.
There are different options for how it can be d... | documentation/cloud/cluster-scaling.md |
---
title: Monitor Clusters
weight: 55
---
# Monitoring Qdrant Cloud Clusters
## Telemetry
Qdrant Cloud provides you with a set of metrics to monitor the health of your database cluster. You can access these metrics in the Qdrant Cloud Console in the **Metrics** and **Request** sections of the cluster detai... | documentation/cloud/cluster-monitoring.md |
---
title: Billing & Payments
weight: 65
aliases:
- aws-marketplace
- gcp-marketplace
- azure-marketplace
---
# Qdrant Cloud Billing & Payments
Qdrant database clusters in Qdrant Cloud are priced based on CPU, memory, and disk storage usage. To get a clearer idea for the pricing structure, based on t... | documentation/cloud/pricing-payments.md |
---
title: Upgrade Clusters
weight: 55
---
# Upgrading Qdrant Cloud Clusters
As soon as a new Qdrant version is available. Qdrant Cloud will show you an upgrade notification in the Cluster list and on the Cluster details page.
To upgrade to a new version, go to the Cluster details page, choose the new versi... | documentation/cloud/cluster-upgrades.md |
---
title: Managed Cloud
weight: 8
aliases:
- /documentation/overview/qdrant-alternatives/documentation/cloud/
---
# About Qdrant Managed Cloud
Qdrant Managed Cloud is our SaaS (software-as-a-service) solution, providing managed Qdrant database clusters on the cloud. We provide you the same fast and reliab... | documentation/cloud/_index.md |
---
title: Storage
weight: 80
aliases:
- ../storage
---
# Storage
All data within one collection is divided into segments.
Each segment has its independent vector and payload storage as well as indexes.
Data stored in segments usually do not overlap.
However, storing the same point in different segmen... | documentation/concepts/storage.md |
---
title: Explore
weight: 55
aliases:
- ../explore
---
# Explore the data
After mastering the concepts in [search](../search/), you can start exploring your data in other ways. Qdrant provides a stack of APIs that allow you to find similar vectors in a different fashion, as well as to find the most dissim... | documentation/concepts/explore.md |
---
title: Optimizer
weight: 70
aliases:
- ../optimizer
---
# Optimizer
It is much more efficient to apply changes in batches than perform each change individually, as many other databases do. Qdrant here is no exception. Since Qdrant operates with data structures that are not always easy to change, it is ... | documentation/concepts/optimizer.md |
---
title: Search
weight: 50
aliases:
- ../search
---
# Similarity search
Searching for the nearest vectors is at the core of many representational learning applications.
Modern neural networks are trained to transform objects into vectors so that objects close in the real world appear close in vector spa... | documentation/concepts/search.md |
---
title: Payload
weight: 45
aliases:
- ../payload
---
# Payload
One of the significant features of Qdrant is the ability to store additional information along with vectors.
This information is called `payload` in Qdrant terminology.
Qdrant allows you to store any information that can be represented u... | documentation/concepts/payload.md |
---
title: Collections
weight: 30
aliases:
- ../collections
- /concepts/collections/
- /documentation/frameworks/fondant/documentation/concepts/collections/
---
# Collections
A collection is a named set of points (vectors with a payload) among which you can search. The vector of each point within the... | documentation/concepts/collections.md |
---
title: Indexing
weight: 90
aliases:
- ../indexing
---
# Indexing
A key feature of Qdrant is the effective combination of vector and traditional indexes. It is essential to have this because for vector search to work effectively with filters, having vector index only is not enough. In simpler terms, a v... | documentation/concepts/indexing.md |
---
title: Points
weight: 40
aliases:
- ../points
---
# Points
The points are the central entity that Qdrant operates with.
A point is a record consisting of a [vector](../vectors/) and an optional [payload](../payload/).
It looks like this:
```json
// This is a simple point
{
"id": 129,
... | documentation/concepts/points.md |
---
title: Vectors
weight: 41
aliases:
- /vectors
---
# Vectors
Vectors (or embeddings) are the core concept of the Qdrant Vector Search engine.
Vectors define the similarity between objects in the vector space.
If a pair of vectors are similar in vector space, it means that the objects they represe... | documentation/concepts/vectors.md |
---
title: Snapshots
weight: 110
aliases:
- ../snapshots
---
# Snapshots
*Available as of v0.8.4*
Snapshots are `tar` archive files that contain data and configuration of a specific collection on a specific node at a specific time. In a distributed setup, when you have multiple nodes in your cluster, yo... | documentation/concepts/snapshots.md |
---
title: Hybrid Queries #required
weight: 57 # This is the order of the page in the sidebar. The lower the number, the higher the page will be in the sidebar.
aliases:
- ../hybrid-queries
hideInSidebar: false # Optional. If true, the page will not be shown in the sidebar. It can be used in regular documentati... | documentation/concepts/hybrid-queries.md |
---
title: Filtering
weight: 60
aliases:
- ../filtering
---
# Filtering
With Qdrant, you can set conditions when searching or retrieving points.
For example, you can impose conditions on both the [payload](../payload/) and the `id` of the point.
Setting additional conditions is important when it is imp... | documentation/concepts/filtering.md |
---
title: Concepts
weight: 11
# If the index.md file is empty, the link to the section will be hidden from the sidebar
---
# Concepts
Think of these concepts as a glossary. Each of these concepts include a link to
detailed information, usually with examples. If you're new to AI, these concepts
can help you... | documentation/concepts/_index.md |
---
title: Bulk Upload Vectors
weight: 13
---
# Bulk upload a large number of vectors
Uploading a large-scale dataset fast might be a challenge, but Qdrant has a few tricks to help you with that.
The first important detail about data uploading is that the bottleneck is usually located on the client side, no... | documentation/tutorials/bulk-upload.md |
---
title: Semantic code search
weight: 22
---
# Use semantic search to navigate your codebase
| Time: 45 min | Level: Intermediate | [](https://colab.research.google.com/github/qdrant/examples/blob/master/code-search/code-search.ipynb) ... | documentation/tutorials/code-search.md |
---
title: Measure retrieval quality
weight: 21
---
# Measure retrieval quality
| Time: 30 min | Level: Intermediate | | |
|--------------|---------------------|--|----|
Semantic search pipelines are as good as the embeddings they use. If your model cannot properly represent input data, similar objects... | documentation/tutorials/retrieval-quality.md |
---
title: Neural Search Service
weight: 1
---
# Create a Simple Neural Search Service
| Time: 30 min | Level: Beginner | Output: [GitHub](https://github.com/qdrant/qdrant_demo/tree/sentense-transformers) | [](https://colab.research.goog... | documentation/tutorials/neural-search.md |
---
title: Semantic Search 101
weight: -100
aliases:
- /documentation/tutorials/mighty.md/
---
# Semantic Search for Beginners
| Time: 5 - 15 min | Level: Beginner | | |
| --- | ----------- | ----------- |----------- |
<p align="center"><iframe width="560" height="315" src="https://www.youtube.com/e... | documentation/tutorials/search-beginners.md |
---
title: Multimodal Search
weight: 4
---
# Multimodal Search with Qdrant and FastEmbed
| Time: 15 min | Level: Beginner |Output: [GitHub](https://github.com/qdrant/examples/blob/master/multimodal-search/Multimodal_Search_with_FastEmbed.ipynb)|[ provides a platform for sharing and using ML models and
datasets. [Qdrant](https://huggingface.co/Qdrant) also publishes datasets along with the
embeddings that you can use to... | documentation/tutorials/huggingface-datasets.md |
---
title: Hybrid Search with Fastembed
weight: 2
aliases:
- /documentation/tutorials/neural-search-fastembed/
---
# Create a Hybrid Search Service with Fastembed
| Time: 20 min | Level: Beginner | Output: [GitHub](https://github.com/qdrant/qdrant_demo/) |
| --- | ----------- | ----------- |----------- ... | documentation/tutorials/hybrid-search-fastembed.md |
---
title: Asynchronous API
weight: 14
---
# Using Qdrant asynchronously
Asynchronous programming is being broadly adopted in the Python ecosystem. Tools such as FastAPI [have embraced this new
paradigm](https://fastapi.tiangolo.com/async/), but it is also becoming a standard for ML models served as SaaS. Fo... | documentation/tutorials/async-api.md |
---
title: Create and restore from snapshot
weight: 14
---
# Create and restore collections from snapshot
| Time: 20 min | Level: Beginner | | |
|--------------|-----------------|--|----|
A collection is a basic unit of data storage in Qdrant. It contains vectors, their IDs, and payloads. However, keep... | documentation/tutorials/create-snapshot.md |
---
title: Collaborative filtering
short_description: "Build an effective movie recommendation system using collaborative filtering and Qdrant's similarity search."
description: "Build an effective movie recommendation system using collaborative filtering and Qdrant's similarity search."
preview_image: /blog/colla... | documentation/tutorials/collaborative-filtering.md |
---
title: Tutorials
weight: 13
# If the index.md file is empty, the link to the section will be hidden from the sidebar
is_empty: false
aliases:
- how-to
- tutorials
---
# Tutorials
These tutorials demonstrate different ways you can build vector search into your applications.
| Essential How-Tos... | documentation/tutorials/_index.md |
---
title: Semantic-Router
---
# Semantic-Router
[Semantic-Router](https://www.aurelio.ai/semantic-router/) is a library to build decision-making layers for your LLMs and agents. It uses vector embeddings to make tool-use decisions rather than LLM generations, routing our requests using semantic meaning.
Qdr... | documentation/frameworks/semantic-router.md |
---
title: Testcontainers
---
# Testcontainers
Qdrant is available as a [Testcontainers module](https://testcontainers.com/modules/qdrant/) in multiple languages. It facilitates the spawning of a Qdrant instance for end-to-end testing.
As noted by [Testcontainers](https://testcontainers.com/), it "is an ope... | documentation/frameworks/testcontainers.md |
---
title: Stanford DSPy
aliases: [ ../integrations/dspy/ ]
---
# Stanford DSPy
[DSPy](https://github.com/stanfordnlp/dspy) is the framework for solving advanced tasks with language models (LMs) and retrieval models (RMs). It unifies techniques for prompting and fine-tuning LMs — and approaches for reasoning, ... | documentation/frameworks/dspy.md |
---
title: FiftyOne
aliases: [ ../integrations/fifty-one ]
---
# FiftyOne
[FiftyOne](https://voxel51.com/) is an open-source toolkit designed to enhance computer vision workflows by optimizing dataset quality
and providing valuable insights about your models. FiftyOne 0.20, which includes a native integratio... | documentation/frameworks/fifty-one.md |
---
title: Pinecone Canopy
---
# Pinecone Canopy
[Canopy](https://github.com/pinecone-io/canopy) is an open-source framework and context engine to build chat assistants at scale.
Qdrant is supported as a knowledge base within Canopy for context retrieval and augmented generation.
## Usage
Install the S... | documentation/frameworks/canopy.md |
---
title: Langchain Go
---
# Langchain Go
[Langchain Go](https://tmc.github.io/langchaingo/docs/) is a framework for developing data-aware applications powered by language models in Go.
You can use Qdrant as a vector store in Langchain Go.
## Setup
Install the `langchain-go` project dependency
```b... | documentation/frameworks/langchain-go.md |
---
title: Firebase Genkit
---
# Firebase Genkit
[Genkit](https://firebase.google.com/products/genkit) is a framework to build, deploy, and monitor production-ready AI-powered apps.
You can build apps that generate custom content, use semantic search, handle unstructured inputs, answer questions with your bu... | documentation/frameworks/genkit.md |
---
title: Langchain4J
---
# LangChain for Java
LangChain for Java, also known as [Langchain4J](https://github.com/langchain4j/langchain4j), is a community port of [Langchain](https://www.langchain.com/) for building context-aware AI applications in Java
You can use Qdrant as a vector store in Langchain4J th... | documentation/frameworks/langchain4j.md |
---
title: Langchain
aliases:
- ../integrations/langchain/
- /documentation/overview/integrations/langchain/
---
# Langchain
Langchain is a library that makes developing Large Language Model-based applications much easier. It unifies the interfaces
to different libraries, including major embedding provi... | documentation/frameworks/langchain.md |
---
title: LlamaIndex
aliases:
- ../integrations/llama-index/
- /documentation/overview/integrations/llama-index/
---
# LlamaIndex
Llama Index acts as an interface between your external data and Large Language Models. So you can bring your
private data and augment LLMs with it. LlamaIndex simplifies da... | documentation/frameworks/llama-index.md |
---
title: DocArray
aliases: [ ../integrations/docarray/ ]
---
# DocArray
You can use Qdrant natively in DocArray, where Qdrant serves as a high-performance document store to enable scalable vector search.
DocArray is a library from Jina AI for nested, unstructured data in transit, including text, image, au... | documentation/frameworks/docarray.md |
---
title: Pandas-AI
---
# Pandas-AI
Pandas-AI is a Python library that uses a generative AI model to interpret natural language queries and translate them into Python code to interact with pandas data frames and return the final results to the user.
## Installation
```console
pip install pandasai[qdrant... | documentation/frameworks/pandas-ai.md |
---
title: MemGPT
---
# MemGPT
[MemGPT](https://memgpt.ai/) is a system that enables LLMs to manage their own memory and overcome limited context windows to
- Create perpetual chatbots that learn about you and change their personalities over time.
- Create perpetual chatbots that can interface with large da... | documentation/frameworks/memgpt.md |
---
title: Vanna.AI
---
# Vanna.AI
[Vanna](https://vanna.ai/) is a Python package that uses retrieval augmentation to help you generate accurate SQL queries for your database using LLMs.
Vanna works in two easy steps - train a RAG "model" on your data, and then ask questions which will return SQL queries tha... | documentation/frameworks/vanna-ai.md |
---
title: Spring AI
---
# Spring AI
[Spring AI](https://docs.spring.io/spring-ai/reference/) is a Java framework that provides a [Spring-friendly](https://spring.io/) API and abstractions for developing AI applications.
Qdrant is available as supported vector database for use within your Spring AI projects.... | documentation/frameworks/spring-ai.md |
---
title: Autogen
aliases: [ ../integrations/autogen/ ]
---
# Microsoft Autogen
[AutoGen](https://github.com/microsoft/autogen) is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, ... | documentation/frameworks/autogen.md |
---
title: txtai
aliases: [ ../integrations/txtai/ ]
---
# txtai
Qdrant might be also used as an embedding backend in [txtai](https://neuml.github.io/txtai/) semantic applications.
txtai simplifies building AI-powered semantic search applications using Transformers. It leverages the neural embeddings and th... | documentation/frameworks/txtai.md |
---
title: Frameworks
weight: 15
---
## Framework Integrations
| Framework | Description |
| ------------------------------------- | -----------------------------------------------------------... | documentation/frameworks/_index.md |
---
title: Haystack
aliases:
- ../integrations/haystack/
- /documentation/overview/integrations/haystack/
---
# Haystack
[Haystack](https://haystack.deepset.ai/) serves as a comprehensive NLP framework, offering a modular methodology for constructing
cutting-edge generative AI, QA, and semantic knowledg... | documentation/frameworks/haystack.md |
---
title: Cheshire Cat
aliases: [ ../integrations/cheshire-cat/ ]
---
# Cheshire Cat
[Cheshire Cat](https://cheshirecat.ai/) is an open-source framework that allows you to develop intelligent agents on top of many Large Language Models (LLM). You can develop your custom AI architecture to assist you in a wide... | documentation/frameworks/cheshire-cat.md |
---
title: Understanding Vector Search in Qdrant
weight: 1
social_preview_image: /docs/gettingstarted/vector-social.png
---
# How Does Vector Search Work in Qdrant?
<p align="center"><iframe width="560" height="315" src="https://www.youtube.com/embed/mXNrhyw4q84?si=wruP9wWSa8JW4t78" title="YouTube video playe... | documentation/overview/vector-search.md |
---
title: What is Qdrant?
weight: 3
aliases:
- overview
---
# Introduction
Vector databases are a relatively new way for interacting with abstract data representations
derived from opaque machine learning models such as deep learning architectures. These
representations are often called vectors or emb... | documentation/overview/_index.md |
---
title: Qdrant Web UI
weight: 2
aliases:
- /documentation/web-ui/
---
# Qdrant Web UI
You can manage both local and cloud Qdrant deployments through the Web UI.
If you've set up a deployment locally with the Qdrant [Quickstart](/documentation/quick-start/),
navigate to http://localhost:6333/dashboar... | documentation/interfaces/web-ui.md |
---
title: API & SDKs
weight: 6
aliases:
- /documentation/interfaces/
---
# Interfaces
Qdrant supports these "official" clients.
> **Note:** If you are using a language that is not listed here, you can use the REST API directly or generate a client for your language
using [OpenAPI](https://github.com... | documentation/interfaces/_index.md |
---
title: API Reference
weight: 1
type: external-link
external_url: https://api.qdrant.tech/api-reference
sitemapExclude: True
--- | documentation/interfaces/api-reference.md |
---
title: About Us
--- | about-us/_index.md |
---
title: Retrieval Augmented Generation (RAG)
description: Unlock the full potential of your AI with RAG powered by Qdrant. Dive into a new era of intelligent applications that understand and interact with unprecedented accuracy and depth.
startFree:
text: Get Started
url: https://cloud.qdrant.io/
learnMore... | retrieval-augmented-generation/retrieval-augmented-generation-hero.md |
---
title: RAG with Qdrant
description: RAG, powered by Qdrant's efficient data retrieval, elevates AI's capacity to generate rich, context-aware content across text, code, and multimedia, enhancing relevance and precision on a scalable platform. Discover why Qdrant is the perfect choice for your RAG project.
featur... | retrieval-augmented-generation/retrieval-augmented-generation-features.md |
---
title: Learn how to get started with Qdrant for your RAG use case
features:
- id: 0
image:
src: /img/retrieval-augmented-generation-use-cases/case1.svg
srcMobile: /img/retrieval-augmented-generation-use-cases/case1-mobile.svg
alt: Music recommendation
title: Question and Answer System with L... | retrieval-augmented-generation/retrieval-augmented-generation-use-cases.md |
---
title: RAG Evaluation
descriptionFirstPart: Retrieval Augmented Generation (RAG) harnesses large language models to enhance content generation by effectively leveraging existing information. By amalgamating specific details from various sources, RAG facilitates accurate and relevant query results, making it inval... | retrieval-augmented-generation/retrieval-augmented-generation-evaluation.md |
---
title: Qdrant integrates with all leading LLM providers and frameworks
integrations:
- id: 0
icon:
src: /img/integrations/integration-cohere.svg
alt: Cohere logo
title: Cohere
description: Integrate Qdrant with Cohere's co.embed API and Python SDK.
- id: 1
icon:
src: /img/integrations... | retrieval-augmented-generation/retrieval-augmented-generation-integrations.md |
---
title: "RAG Use Case: Advanced Vector Search for AI Applications"
description: "Learn how Qdrant's advanced vector search enhances Retrieval-Augmented Generation (RAG) AI applications, offering scalable and efficient solutions."
url: rag
build:
render: always
cascade:
- build:
list: local
publish... | retrieval-augmented-generation/_index.md |
---
title: Qdrant Hybrid Cloud
salesTitle: Hybrid Cloud
description: Bring your own Kubernetes clusters from any cloud provider, on-premise infrastructure, or edge locations and connect them to the Managed Cloud.
cards:
- id: 0
icon: /icons/outline/separate-blue.svg
title: Deployment Flexibility
descripti... | contact-hybrid-cloud/_index.md |
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