Day 0

Project: Building Your First Vector Search System

Time to apply what you’ve learned. You’ll create a complete, working vector search system from scratch.

Your Mission

Build a functional vector search system that demonstrates the core concepts: collections, points, similarity search, and filtering. You’ll design simple 4-dimensional vectors that represent different concepts or items.

Estimated Time: 30 minutes

What You’ll Build

A working search system with:

• One collection with 4-dimensional vectors and Cosine distance
• 5–10 points with hand-crafted vectors and meaningful payloads
• Basic similarity search to find nearest neighbors
• Filtered search combining similarity with payload conditions

Setup

Prerequisites

• Qdrant Cloud cluster (URL + API key)
• Python 3.9+ (or Colab)
• Required packages: qdrant-client, python-dotenv.

Models

• None. We will create vectors by hand.

Dataset

• None. We will create our own data points.

Before creating data, decide what each of the four dimensions in your vectors will represent. This is the creative part of vector search!

Example Ideas:

• Product categories: Create vectors where each dimension represents a feature (affordability, quality, popularity, innovation). Electronics might be [0.8, 0.7, 0.9, 0.6], while books could be [0.3, 0.9, 0.4, 0.8].
• Color palettes: Each dimension represents color (red, green, blue). Bright red: [0.9, 0.1, 0.1], forest green: [0.1, 0.8, 0.2].
• Data types: Dimensions for structure, size, complexity, frequency. Spreadsheets: [0.9, 0.6, 0.3, 0.7], images: [0.2, 0.8, 0.5, 0.4].
• Movie genres: Action, drama, comedy, sci-fi intensities. Action thriller: [0.9, 0.3, 0.1, 0.7], romantic comedy: [0.1, 0.6, 0.9, 0.2].

For this tutorial, we’ll use the Product Categories concept.

Build Steps

Step 1: Initialize Client

from qdrant_client import QdrantClient, models
import os
from dotenv import load_dotenv

load_dotenv()
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"))

Step 2: Create Collection

collection_name = "day0_first_system"
client.create_collection(
    collection_name=collection_name,
    vectors_config=models.VectorParams(size=4, distance=models.Distance.COSINE),
)

# Create payload index right after creating the collection and before uploading any data to enable filtering.
# If you add it later, HNSW won't rebuild automatically—bump ef_construct (e.g., 100→101) to trigger a safe rebuild.
client.create_payload_index(
    collection_name=collection_name,
    field_name="category",
    field_schema=models.PayloadSchemaType.KEYWORD,
)

Step 3: Insert Points

We use dummy embeddings.

points=[
    models.PointStruct(
        id=1,
        vector=[0.9, 0.1, 0.1, 0.8], # High affordability, high innovation
        payload={"name": "Budget Smartphone", "category": "electronics", "price": 299},
    ),
    models.PointStruct(
        id=2,
        vector=[0.2, 0.9, 0.8, 0.5], # High quality, high popularity
        payload={"name": "Bestselling Novel", "category": "books", "price": 19},
    ),
    models.PointStruct(
        id=3,
        vector=[0.8, 0.3, 0.2, 0.9], # High affordability, high innovation (similar to ID 1)
        payload={"name": "Smart Home Hub", "category": "electronics", "price": 89},
    ),
    # Add 2-5 more points to experiment with...
]

client.upsert(collection_name=collection_name, points=points)

Step 5: Test Searches

# Define a query vector for "affordable and innovative"
query_vector = [0.85, 0.2, 0.1, 0.9]

# 1. Basic similarity search
basic_results = client.query_points(collection_name, query=query_vector)

# 2. Filtered search (only find electronics)
filtered_results = client.query_points(
    collection_name,
    query=query_vector,
    query_filter=models.Filter(
        must=[models.FieldCondition(key="category", match=models.MatchValue(value="electronics"))]
    ),
)
print("Filtered search results:", filtered_results)

Success Criteria

You’ll know you’ve succeeded when:

Collection created without errors
Search returns results ranked by similarity score
Filtered search works and returns appropriate subsets
You can explain why certain items are more similar than others

Share Your Discovery

Step 1: Reflect on Your Findings

For a new concept (not the Product Categories concept) run the code above and do the following:

• Vector Meaning: What did each of your four dimensions represent?
• Query & Results: Pick one query vector you tried. Which items were the top matches, and why does that make sense based on your vector design?
• Filtering: How did adding a filter change your results?
• Surprise: Was there anything unexpected about the results? (e.g., “a small change in one dimension affected the score a lot”)

Step 2: Post Your Results

Show what you built and compare notes with others. Post your results in using this short template—copy, fill, and send:

**[Day 0] Building Your First Vector Search System**

**High-Level Summary**
- **Domain:** “I built a vector search for [topic]”
- **Key Finding:** “[one sentence on what your vectors captured well]”

**Project-Specific Details**
- **Vector meaning:** d1=…, d2=…, d3=…, d4=…
- **Collection:** day0_first_system (Cosine), points: [count]
- **Query vector:** [a, b, c, d]
- **Top matches (id → score):**
  1) [id] → [score]
  2) [id] → [score]
  3) [id] → [score]
- **Filter used:** category=electronics
- **Filtered result:** [ids returned]

**Why these matched**
- [brief note about direction in 4D space]

**Surprise**
- “[one thing you didn’t expect]”

**Next step**
- “[what you’ll try tomorrow]”

Troubleshooting

No results from filtered search? If no vectors satisfy the filter conditions, Qdrant returns an empty result set. Try adjusting your filter values or checking your payload data.

Collection already exists? Use client.delete_collection(collection_name) to remove it, then recreate.

Vector dimension mismatch? Ensure all vectors have exactly the same number of dimensions as specified in your collection configuration.

Connection issues? Verify your Qdrant Cloud credentials and ensure your cluster is running.

Congratulations! 🎉 You’ve completed Day 0!