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docs: Add course content for Day 0 (#1929)
* docs: Add course content for Day 0 * added static * added videos and removed video template files * wording * added youtube links * added deliverable * checked code * added images * added image links * fixed image * linebreak under video * started working on day 1 * updated pitstop structure * pitstop Reflect on Your Findings * pitstop fix * removed filtering from build-simple-vector-search and used create_payload_index instead of unindexed_filtering_retrieve in pitstop --------- Co-authored-by: Kirstin <kirstin.taufertshoefer@qdrant.com>
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Kirstin
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---
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title: Implementing a Basic Vector Search
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weight: 2
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---
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{{< date >}} Day 0 {{< /date >}}
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# Implementing a Basic Vector Search
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<div class="video">
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<iframe
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src="https://www.youtube.com/embed/_83L9ZIoOjM?si=ZTpn6fMXSjc_7JgL"
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frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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referrerpolicy="strict-origin-when-cross-origin"
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allowfullscreen>
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</iframe>
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</div>
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Follow along as we build your first collection, insert vectors, and run similarity searches. This guided tutorial walks you through each step.
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## Step 1: Install the Qdrant Client
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To interact with Qdrant, we need the Python client. This enables us to communicate with the Qdrant service, manage collections, and perform vector searches.
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```python
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!pip install qdrant-client
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```
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## Step 2: Import Required Libraries
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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`.
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```python
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from qdrant_client import QdrantClient, models
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```
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## Step 3: Connect to Qdrant Cloud
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To connect to Qdrant Cloud, you need your cluster URL and API key from your Qdrant Cloud dashboard. Replace with your actual credentials:
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```python
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import os
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from dotenv import load_dotenv
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load_dotenv()
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client = QdrantClient(url=os.getenv("QDRANT_URL"), api_key=os.getenv("QDRANT_API_KEY"))
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# For Colab:
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# from google.colab import userdata
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# client = QdrantClient(url=userdata.get("QDRANT_URL"), api_key=userdata.get("QDRANT_API_KEY"))
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```
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**Note:** You can also use in-memory mode for testing: `client = QdrantClient(":memory:")`, but data won't persist after restart.
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## Step 4: Create a Collection
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A [collection](/documentation/concepts/collections/) in Qdrant is like a table in relational databases - a container for storing vectors and their metadata. When creating a collection, specify:
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- **Name**: A unique identifier for the collection
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- **Vector Configuration**:
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- **Size**: The dimensionality of the vectors
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- **Distance Metric**: The method to measure similarity between vectors
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```python
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# Define the collection name
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collection_name = "my_first_collection"
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# Create the collection with specified vector parameters
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client.create_collection(
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collection_name=collection_name,
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vectors_config=models.VectorParams(
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size=4, # Dimensionality of the vectors
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distance=models.Distance.COSINE # Distance metric for similarity search
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)
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)
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```
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Expected output: `True` (indicating successful creation)
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**Distance metrics explained** ([learn more](/documentation/concepts/collections/#distance-metrics)):
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- **Euclidean**: Measures straight-line distance between points in space
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- **Cosine**: Measures the angle between vectors, focusing on orientation rather than magnitude
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- **Dot**: Measures the dot product of vectors, capturing both magnitude and direction
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## Step 5: Verify Collection Creation
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Confirm that your collection was successfully created by retrieving the list of existing collections:
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```python
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# Retrieve and display the list of collections
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collections = client.get_collections()
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print("Existing collections:", collections)
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```
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The `get_collections()` method returns all collections in your Qdrant instance, useful for managing multiple collections dynamically.
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## Step 6: Insert Points into the Collection
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[Points](/documentation/concepts/points/) are the core data entities in Qdrant. Each point contains:
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- **ID**: A unique identifier
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- **Vector Data**: An array of numerical values representing the data point in vector space
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- **Payload (Optional)**: Additional metadata
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```python
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# Define the vectors to be inserted
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points = [
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models.PointStruct(
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id=1,
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vector=[0.1, 0.2, 0.3, 0.4], # 4D vector
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payload={"category": "example"} # Metadata (optional)
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),
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models.PointStruct(
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id=2,
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vector=[0.2, 0.3, 0.4, 0.5],
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payload={"category": "demo"}
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)
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]
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# Insert vectors into the collection
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client.upsert(
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collection_name=collection_name,
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points=points
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)
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```
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Expected output: `UpdateResult(operation_id=2, status=<UpdateStatus.COMPLETED: 'completed'>)`
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## Step 7: Retrieve Collection Details
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Now that we've inserted vectors, let's confirm they're stored correctly by getting collection information:
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```python
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collection_info = client.get_collection(collection_name)
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print("Collection info:", collection_info)
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```
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Expected output: Detailed collection information showing `points_count=2`, vector configuration, and [HNSW](https://qdrant.tech/articles/filtrable-hnsw/) settings.
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## Step 8: Run Your First Similarity Search
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Find the most similar vector to a given query using Qdrant's search capabilities:
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**How Similarity Search Works:**
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- Qdrant searches the collection to find the vectors that are closest to your query vector.
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- The results are ranked by their similarity score, with the best matches appearing first.
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```python
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query_vector = [0.08, 0.14, 0.33, 0.28]
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search_results = client.query_points(
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collection_name=collection_name,
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query=query_vector,
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limit=1 # Return the top 1 most similar vector
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)
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print("Search results:", search_results)
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```
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Expected output: `points=[ScoredPoint(id=1, score=0.97642946, payload={'category': 'example'})]`
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