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What is Qdrant? The Ultimate Guide to Understanding This Vector Search Engine

What is Qdrant? The Ultimate Guide to Understanding This Vector Search Engine

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You've heard the buzz about vector search engines, but have you ever wondered what makes one stand out from the rest? Enter Qdrant, a game-changer in the realm of high-dimensional data search. This article aims to demystify what Qdrant is, how it works, and why you should care.

Whether you're a data scientist, a developer, or someone just interested in the latest advancements in search technology, this guide is for you. We'll dive deep into the technical aspects, compare it with other engines like Faiss, and even guide you through installation and usage. So, let's get started!

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What is Qdrant?

What is Qdrant in Technical Terms?

Qdrant is an open-source vector search engine designed to handle high-dimensional data. It employs advanced algorithms like Hierarchical Navigable Small World (HNSW) graphs and Product Quantization. These algorithms make it incredibly efficient at indexing and searching vectors, even when dealing with massive datasets.

  • HNSW Graphs: These are used for efficient indexing. They allow Qdrant to sift through high-dimensional data quickly, reducing the time it takes to find relevant results.

  • Product Quantization: This is a technique used to compress vectors. It ensures that the engine is not just fast but also memory-efficient.

What is Qdrant in Plain English?

Think of Qdrant as a super-smart organizer for your digital content. Let's say you have a massive collection of photos, articles, or even songs. Finding something specific in this pile can be like looking for a needle in a haystack. Qdrant uses its "smartness" to quickly find what you're looking for. It's like having a personal assistant that knows your collection as well as you do, maybe even better!

What Makes Qdrant Unique

  • HNSW Graphs: These graphs are a form of data structure that allows Qdrant to index high-dimensional data efficiently. They reduce the computational complexity, making the search process faster.

  • Product Quantization: This technique compresses the vectors in the database. It's like zipping a file; the content is the same, but it takes up less space. This is crucial for handling large datasets without compromising on speed.

  • Semantic Search: This is the ability to understand the context and nuances of a query.

Traditional search engines are limited to keyword-based searches. Qdrant, however, employs semantic search. This means it understands the meaning behind your query, not just the words you use.

For example, if you search for "Apple," a keyword-based search might bring up results related to the fruit and the tech company. A semantic search would understand the context and provide more relevant results.

Qdrant sets itself apart in several ways:

  • Open-Source: Being open-source means anyone can contribute to its development. This creates a community-driven environment that fosters innovation and transparency.

  • Efficiency: Qdrant is designed to provide fast and accurate search results. Its use of advanced algorithms ensures that it stands out in terms of speed and reliability.

Qdrant vs Faiss: Benchmark Comparison

In the vector search engine space, Qdrant and Faiss are often evaluated side by side. However, the absence of a unified benchmark has made it challenging to draw clear comparisons. This analysis aims to provide a technical and data-driven perspective on how Qdrant stacks up against Faiss and other competitors.

The following table presents key performance metrics from a benchmark test. The test was conducted on the deep-image-96-angular dataset, with each engine configured differently.

EngineSetup NameDatasetUpload Time (s)Upload + Index Time (s)P95 (s)RPSParallelP99 (s)Latency (s)PrecisionEngine Params

Parameters Explained:

  1. Upload Time: Qdrant has a moderate upload time of 845.78 seconds, which is faster than Weaviate but slower than Milvus.

  2. Indexing Time: The total time for upload and indexing in Qdrant is 8959.44 seconds, which is the highest among the engines tested.

  3. Latency: Qdrant excels in latency with just 0.024 seconds, significantly outperforming all other engines.

  4. Requests Per Second (RPS): Qdrant leads with an RPS of 1541.86, indicating higher throughput.

  5. Precision: Qdrant and Milvus both have high precision scores, with Qdrant at 0.96 and Milvus at 0.97.

  6. P95 and P99 Latencies: Qdrant has the lowest P95 and P99 latencies, indicating better performance consistency.

Technical Insights fromt the data:

  • Qdrant uses an HNSW (Hierarchical Navigable Small World) graph with an hnsw_ef parameter set to 64, optimizing search performance.

  • Weaviate uses a different ef parameter set to 256, which might explain its higher latency and lower RPS.

  • Milvus also uses an ef parameter set to 128 but manages to achieve a higher precision score of 0.97.

Based on the data, Qdrant shows a strong performance in terms of latency and throughput (RPS), although it takes more time for upload and indexing. Its high precision score also makes it a reliable choice for similarity search tasks.

How to Install Qdrant: A Comprehensive Guide

Installing Qdrant Using Docker

Docker is a popular platform for containerization, and it's one of the easiest ways to get Qdrant up and running. Here's a step-by-step guide:

  1. Install Docker: If Docker isn't already installed on your machine, download and install it from the official website.

  2. Pull Qdrant Image: Open your terminal and execute the following command to pull the latest Qdrant image from Docker Hub.

    docker pull qdrant/qdrant
  3. Run Qdrant Container: To start a new container based on the pulled image, run:

    docker run -p 6333:6333 qdrant/qdrant
  4. Verify Installation: To ensure Qdrant is running, open a new terminal and execute:

    curl http://localhost:6333

    If you get a JSON response, your Qdrant installation is successful.

Installing Qdrant on the Cloud

Qdrant Cloud offers a managed service for those who prefer not to handle the infrastructure. Here's how to get started:

  1. Sign Up: Visit the Qdrant Cloud website and create an account.

  2. Create an Instance: Follow the on-screen instructions to set up a new Qdrant instance.

  3. API Keys: After the instance is created, you'll be provided with API keys and endpoints. Store these securely.

  4. Test the Instance: Use the API keys to make a test query. A successful response means your cloud instance is operational.

Installing Qdrant Using Python

For Python developers, Qdrant offers a Python client library. Here's how to install it:

  1. Install Python and pip: If not already installed, download and install Python and pip.

  2. Install Qdrant Client: Open a terminal and run:

    pip install qdrant-client
  3. Python Script: To interact with Qdrant, you can use the following sample code:

    from qdrant_client import QdrantClient
    client = QdrantClient(host='localhost', port=6333)

Qdrant Tutorial: Build Question and Answer system with Qdrant

Step 1: Initialize Variables and Import Libraries

Before diving into the code, initialize the variables and import the necessary libraries.

import openai
from qdrant_client.http.models import PointStruct
points = []
i = 1

Step 2: Loop Through Text Chunks

Iterate through each text chunk to generate embeddings. The text chunks should have been previously created and stored in a list called chunks.

for chunk in chunks:
    i += 1

Step 3: Generate Embeddings

Within the loop, call OpenAI's ada002 model to create embeddings for each text chunk.

    response = openai.Embedding.create(
    embeddings = response['data'][0]['embedding']

Step 4: Store Embeddings

After generating the embeddings, store them in a list along with an ID and the original text.

    points.append(PointStruct(id=i, vector=embeddings, payload={"text": chunk}))

Why Use ada002?

The ada002 model is designed to capture the semantic nuances of text, making it ideal for applications like semantic search or Q&A systems. It takes a text chunk as input and outputs a numerical vector that encapsulates the meaning of that text.

Step 5: Initialize Qdrant Client

First, initialize the Qdrant client with the appropriate host and API key.

from qdrant_client import QdrantClient
qdrant_client = QdrantClient(

Step 6: Create or Recreate Collection

Create a new collection in Qdrant to store the embeddings. If the collection already exists, you can recreate it.

    vectors_config=models.VectorParams(size=1536, distance=models.Distance.COSINE),

Step 7: Index Embeddings

Now, index the embeddings in the collection you've just created.

operation_info = qdrant_client.upsert(

Why Use Qdrant for Indexing?

Qdrant offers a robust, production-ready service tailored for extended filtering support. Its API is convenient for storing, searching, and managing points, which in this case are the embeddings. By indexing the embeddings in Qdrant, you can later perform efficient similarity searches based on user input.


In summary, the data-driven analysis reveals that Qdrant excels in key performance metrics such as latency, RPS, and precision. While it may take longer for data upload and indexing, the trade-off is a high-throughput, low-latency engine that delivers accurate results. These attributes make Qdrant a compelling choice for organizations that prioritize search performance and result accuracy in their vector search engine requirements.

Frequently Asked Questions (FAQs)

  1. What makes Qdrant stand out in terms of latency and RPS?

    Qdrant employs an optimized Hierarchical Navigable Small World (HNSW) graph for search, which contributes to its low latency and high RPS. The engine parameters, particularly the hnsw_ef set to 64, are fine-tuned to achieve this performance.

  2. Why does Qdrant take longer for data upload and indexing?

    The extended time for data upload and indexing in Qdrant is a trade-off for its high performance in search queries. The engine focuses on creating an optimized index structure, which although time-consuming, results in faster and more accurate searches later on.

  3. How does Qdrant's precision compare with other engines?

    Qdrant has a high precision score of 0.96, which is comparable to Milvus at 0.97. This indicates that Qdrant is highly reliable for similarity search tasks, returning accurate results most of the time.

  4. Is Qdrant suitable for large-scale deployments?

    Given its high RPS and low latency, Qdrant is well-suited for large-scale deployments where high throughput and quick response times are critical. However, organizations should consider the longer upload and indexing times when planning their data pipeline.

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