Day 0

Implementing a Basic Vector Search

Follow along as we build your first collection, insert vectors, and run similarity searches. This guided tutorial walks you through each step.

Step 1: Install the Qdrant Client

To interact with Qdrant, we need the Python client. This enables us to communicate with the Qdrant service, manage collections, and perform vector searches.

!pip install qdrant-client

Step 2: Import Required Libraries

Import the necessary modules from the qdrant-client package. The QdrantClient class establishes connection to Qdrant, while the models module provides configurations for Distance, VectorParams, and PointStruct.

from qdrant_client import QdrantClient, models

Step 3: Connect to Qdrant Cloud

To connect to Qdrant Cloud, you need your cluster URL and API key from your Qdrant Cloud dashboard. Replace with your actual credentials:

import os

client = QdrantClient(url=os.getenv("QDRANT_URL"), api_key=os.getenv("QDRANT_API_KEY"))

# For Colab:
# from google.colab import userdata
# client = QdrantClient(url=userdata.get("QDRANT_URL"), api_key=userdata.get("QDRANT_API_KEY"))

Note: You can also use in-memory mode for testing: client = QdrantClient(":memory:"), but data won’t persist after restart.

Step 4: Create a Collection

A collection in Qdrant is like a table in relational databases - a container for storing vectors and their metadata. When creating a collection, specify:

• Name: A unique identifier for the collection
• Vector Configuration:
  • Size: The dimensionality of the vectors
  • Distance Metric: The method to measure similarity between vectors

# Define the collection name
collection_name = "my_first_collection"

# Create the collection with specified vector parameters
client.create_collection(
    collection_name=collection_name,
    vectors_config=models.VectorParams(
        size=4,  # Dimensionality of the vectors
        distance=models.Distance.COSINE  # Distance metric for similarity search
    )
)

Expected output: True (indicating successful creation)

Distance metrics explained (learn more):

• Euclidean: Measures straight-line distance between points in space
• Cosine: Measures the angle between vectors, focusing on orientation rather than magnitude
• Dot: Measures the dot product of vectors, capturing both magnitude and direction

Step 5: Verify Collection Creation

Confirm that your collection was successfully created by retrieving the list of existing collections:

# Retrieve and display the list of collections
collections = client.get_collections()
print("Existing collections:", collections)

The get_collections() method returns all collections in your Qdrant instance, useful for managing multiple collections dynamically.

Step 6: Insert Points into the Collection

Points are the core data entities in Qdrant. Each point contains:

• ID: A unique identifier
• Vector Data: An array of numerical values representing the data point in vector space
• Payload (Optional): Additional metadata

# Define the vectors to be inserted
points = [
    models.PointStruct(
        id=1,
        vector=[0.1, 0.2, 0.3, 0.4],  # 4D vector
        payload={"category": "example"}  # Metadata (optional)
    ),
    models.PointStruct(
        id=2,
        vector=[0.2, 0.3, 0.4, 0.5],
        payload={"category": "demo"}
    )
]

# Insert vectors into the collection
client.upsert(
    collection_name=collection_name,
    points=points
)

Expected output: UpdateResult(operation_id=2, status=<UpdateStatus.COMPLETED: 'completed'>)

Step 7: Retrieve Collection Details

Now that we’ve inserted vectors, let’s confirm they’re stored correctly by getting collection information:

collection_info = client.get_collection(collection_name)
print("Collection info:", collection_info)

Expected output: Detailed collection information showing points_count=2, vector configuration, and HNSW settings.

Step 8: Run Your First Similarity Search

Find the most similar vector to a given query using Qdrant’s search capabilities:

How Similarity Search Works:

• Qdrant searches the collection to find the vectors that are closest to your query vector.
• The results are ranked by their similarity score, with the best matches appearing first.

query_vector = [0.08, 0.14, 0.33, 0.28]

search_results = client.query_points(
    collection_name=collection_name,
    query=query_vector,
    limit=1  # Return the top 1 most similar vector
)

print("Search results:", search_results)

Expected output: points=[ScoredPoint(id=1, score=0.97642946, payload={'category': 'example'})]