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What is Qdrant? Build AI Search Engines in 30 Minutes

Qdrant is an open-source vector database (a specialized tool for storing and searching complex data like images or text) that allows you to build AI-powered search engines in under 30 minutes. By using high-performance vector similarity search (finding items that are "similar" rather than exact matches), Qdrant can process millions of data points with millisecond response times. In our experience, it is one of the most reliable tools for beginners because it offers a simple "local mode" that doesn't require complex server setup to start.

Why do you need a vector database?

Traditional databases like MySQL or PostgreSQL search for exact matches, such as finding a specific username or a price. However, AI models like Claude Sonnet 4 or GPT-5 understand the "meaning" of data, which is much harder to search for using keywords.

When you convert a sentence or an image into a long list of numbers, this is called an embedding (a mathematical representation of meaning). A vector database like Qdrant is designed specifically to store these lists of numbers and find which ones are closest to each other.

This technology is what powers "Recommended for you" sections on shopping sites or the "Search by Image" feature on your phone. Without a vector database, your AI application would be incredibly slow and struggle to find relevant information.

How does Qdrant actually work?

Qdrant works by organizing your data into "Collections" (similar to a folder or a table in a spreadsheet). Inside these collections, you store "Points" (individual items like a single paragraph of text or one product image).

Each Point contains two main parts: a Vector (the mathematical numbers) and a Payload (the actual data, like the name of the product or the text of a blog post). When you ask Qdrant a question, it compares your question's vector against all stored vectors to find the best match.

The search process uses an algorithm called HNSW (Hierarchical Navigable Small World - a way to quickly navigate through data without checking every single item). This ensures that even if you have a billion items, Qdrant finds the answer almost instantly.

What do you need to get started?

Before you start building, you should have a few basic tools ready on your computer. Don't worry if you haven't used all of these before; they are standard in the world of AI development.

  • Python 3.12+: This is the programming language we will use to talk to Qdrant.
  • Docker Desktop: A tool that lets you run software in a "container" (a pre-packaged environment) so you don't have to worry about complex installations.
  • An API Key: If you want to create embeddings automatically, you might want an account with Anthropic (for Claude) or OpenAI.

If you don't want to use Docker yet, Qdrant also offers a "Cloud" version with a free tier that is perfect for learning.

Step 1: How to install the Qdrant client?

The first step is to install the Python library that allows your code to communicate with the Qdrant database. You can do this using your computer's terminal (the text-based command window).

Run this command to install the necessary tools:

# This installs the main Qdrant library
pip install qdrant-client

What you should see: Your terminal will show several bars filling up as it downloads the software. Once it finishes, you'll see a message saying "Successfully installed qdrant-client."

Step 2: How to start Qdrant locally?

The easiest way to run Qdrant is to use an "in-memory" database. This means the database runs inside your Python script and disappears when you close the program, which is perfect for your first experiment.

Create a new file named app.py and add the following code:

from qdrant_client import QdrantClient

# Create a client that stores data in your computer's RAM
# This is the easiest way to start for beginners!
client = QdrantClient(":memory:")

print("Qdrant is ready and running in memory!")

What you should see: When you run python app.py, the terminal should print the success message. You have officially started your first vector database!

Step 3: How to create your first collection?

Now that the database is running, you need to create a place to store your data. In Qdrant, we call this a Collection. You must tell Qdrant how "wide" your vectors are (the size) and how to measure distance (the metric).

Update your app.py with this code:

from qdrant_client.models import Distance, VectorParams

# Define a collection named 'my_library'
# size=4 means each piece of data is represented by 4 numbers
# Distance.COSINE is a common way to measure how similar two vectors are
client.create_collection(
    collection_name="my_library",
    vectors_config=VectorParams(size=4, distance=Distance.COSINE),
)

print("Collection 'my_library' has been created.")

What you should see: The script will run and confirm the collection is ready. Note that in a real project using Claude Opus 4.5, your vector size would be much larger (like 1536 or 3072), but we are using 4 to keep it simple.

Step 4: How to add data to the database?

Adding data involves giving Qdrant a unique ID, the vector numbers, and some extra information (the payload). Think of the payload as the label on a box.

from qdrant_client.models import PointStruct

# We are adding two 'Points' to our collection
client.upsert(
    collection_name="my_library",
    points=[
        PointStruct(
            id=1, 
            vector=[0.05, 0.61, 0.76, 0.74], 
            payload={"name": "The History of AI"}
        ),
        PointStruct(
            id=2, 
            vector=[0.19, 0.81, 0.75, 0.11], 
            payload={"name": "Cooking Italian Food"}
        ),
    ],
)

print("Data has been successfully added!")

What you should see: Qdrant will return a status message saying "UpdateStatus.COMPLETED." This means your "points" are now safely stored and searchable.

This is the most exciting part! You can now ask Qdrant to find the data that is most similar to a new set of numbers.

# We are searching for something similar to this new vector
search_result = client.search(
    collection_name="my_library",
    query_vector=[0.02, 0.63, 0.77, 0.73], # This is close to our 'History of AI' vector
    limit=1 # Tell Qdrant to only give us the single best match
)

for hit in search_result:
    print(f"Found match: {hit.payload['name']} with score: {hit.score}")

What you should see: The output should show "The History of AI." This happened because the numbers in our query_vector were mathematically closer to the first item we added than the second one.

What are the common gotchas for beginners?

It is normal to feel a bit confused when your code doesn't work the first time. Here are a few things we've seen that often trip people up:

  • Vector Size Mismatch: If you create a collection with a size of 1536 but try to upload data with a size of 4, Qdrant will throw an error. The size must be exactly the same every time.
  • Forgetting to start Docker: If you aren't using the :memory: mode and are using a server version, make sure Docker is actually running. If it's not, your code won't be able to connect.
  • Re-creating Collections: If you try to run create_collection twice with the same name, Qdrant will give you an error. You can add a check to see if it exists first or just delete the old one.

Next steps

Now that you've built a basic search system, you're ready to explore more advanced topics. You might want to try connecting Qdrant to a real AI model. Instead of making up numbers like [0.1, 0.2...], you can use a library like sentence-transformers to turn real sentences into vectors automatically.

You can also explore "Filtering," which allows you to search for similar items but only within a certain category (like "Show me similar books, but only those written after 2024").

To continue your journey, we recommend looking at the official Qdrant documentation.

Read the Qdrant Documentation