Part 1: Experience Vector Search¶

In this part, you'll experience how Vector Search works in practice.

Goals of This Part¶

Understand what Vector Search is
Actually run Vector Search
Experience the convenience of "semantic search"

Step 1: What is Vector Search?¶

Problems with Traditional Search¶

Example: Searching for Products on an E-Commerce Site¶

Your search: "red sneakers"

Traditional search results:

"red sneakers" → Found
"red running shoes" → Not found
"red sports shoes" → Not found

Why not found?

Traditional search only looks for "characters"

How Vector Search Works¶

Vector Search searches by understanding "meaning".

Step 1: Text Input

User Input
'red sneakers'

Text

Step 2: Vector Conversion

Embedding Model
Text → Vector

Convert

Step 3: Vector Representation

Vector (384 dimensions)
[0.2, 0.8, 0.1, 0.5, ...]

Search Query

Step 4: Similarity Search

Milvus
Vector DB

Similar Vectors

Step 5: Search Results

Similar Products List
• Red Running Shoes (0.8268)
• Red Sports Shoes (0.8122)
• Red Training Shoes (0.7203)

Key Point

Similar meanings result in similar vectors
Computers can quickly calculate numerical similarity

Your search: "red sneakers"

Vector Search results:

"red sneakers" → Found
"red running shoes" → Found (similar meaning)
"red sports shoes" → Found (similar meaning)

Why found?

Vector Search understands "meaning"
"sneakers" "running shoes" "sports shoes" → Understood as similar meanings

How Vector Search Operates¶

Step 1: Convert text to numbers
"red sneakers" → [0.2, 0.8, 0.1, 0.5, ...] (vector)

Step 2: Find similar numbers
Search for similar numerical patterns from the database

Step 3: Return results
Return products with similar meanings

Key points:

"Vector" = array of numbers
Similar meanings result in similar numerical patterns
Computers can quickly calculate numerical similarity

Step 2: Run Connection Test¶

Practice: Let's get hands-on

Before running Vector Search, verify that you can connect to the required services.

Enter the following in IBM Bob's chat screen:

Test the Milvus connection.

IBM Bob runs the connection test script. If IBM Bob asks for approval to run a command, approve it.

If Running Manually

Enter the following in the terminal:

cd setup/participant
python test_connection.py

Verify Results¶

If Successful¶

==================================================
Milvus Connection Test
==================================================

=== Environment Variable Check ===
✓ MILVUS_HOST: 192.168.1.100
✓ MILVUS_PORT: 19530
✓ MILVUS_USER: root
✓ MILVUS_PASSWORD: ********

=== Milvus Connection Test ===
Connecting to: 192.168.1.100:19530
Auth: user/password auth
✓ Connected to Milvus successfully
✓ Existing collections: 0

==================================================
Test Results
==================================================
Milvus connection: ✓ success

✓ Milvus connection test passed!
  Next step: Create vector collection

The connection test, sample data insertion script, and demo application all read the same .env connection settings. If this test succeeds, the next steps use the same Milvus host, port, and authentication method.

If Failed¶

✗ Milvus connection error: Connection refused

Solution:

Check the .env file
- Verify that the IP address distributed by the instructor is correctly entered in MILVUS_HOST ( Configuration method)
- Verify that MILVUS_PASSWORD is the password distributed by the instructor — an authentication error such as "auth check failure" means the password is wrong or still the template placeholder
Check internet connection
For other errors, refer to FAQ

Step 3: Insert Sample Data¶

Practice: Insert Sample Data into Milvus

To experience Vector Search, first insert sample product data.

Enter the following in IBM Bob's chat screen:

Insert the sample data into Milvus with the provided script.

IBM Bob runs the script and inserts the sample data.

If Running Manually

Enter the following in the terminal:

# If you are in the project root folder
cd setup/participant
python insert_sample_data.py

If you are already in the setup/participant folder, skip cd setup/participant.

Verify Insertion Results¶

If you see the following display, it's successful:

==================================================
✓ Sample data insertion completed
==================================================

Collection name: products_taro  # the unique name you set in .env
Entity count: 12

You can start the demo application:
  venv/bin/python app.py
==================================================

Inserted data:

Number of products: 12
Categories: Sneakers, Cameras, Computers, Bags
Each product includes product name, price, description, and embedding vector
The collection schema and search field names are shared with the demo application, so the inserted data is ready to search immediately

Step 4: Experience Vector Search¶

Practice: Let's run Vector Search

Once sample data insertion is successful, let's experience Vector Search.

Launch Demo Application¶

Run this step in the terminal.

Run this after activating the virtual environment created during preparation and installing the packages in requirements.txt.

Mac Windows

cd ~/Desktop/vector-search-builder-en/setup/participant
venv/bin/python app.py

cd %USERPROFILE%\Desktop\vector-search-builder-en\setup\participant
venv\Scripts\python app.py

If you are already in the setup/participant folder, skip the cd ... line.

After running venv/bin/python app.py or venv\Scripts\python app.py, it may look like nothing is happening at first. Startup can take a little time, so wait until the terminal shows the command output.

If Launch Succeeds¶

If you see output like the following, the application is running.

==================================================
✓ Application started successfully
==================================================

Search screen: http://localhost:8002
==================================================

INFO:     Application startup complete.

Keep the Terminal Open

Closing the terminal stops the application. Please be careful.

If Launch Fails¶

If ModuleNotFoundError: No module named 'fastapi' appears, the required packages are not installed in the virtual environment. Install the required packages ( installation steps), then start the demo application again ( launch steps).

Verify Launch¶

Open the following URL in your web browser and verify that the search screen (Product Search Demo) is displayed:

http://localhost:8002

Launch Successful

If the search screen is displayed, the application has started successfully.

Try Searching¶

Step 1: Enter a Search Query¶

Type the following in the search box at the top of the screen:

red sneakers

Step 2: Click "Search"¶

Click the Search button (or press Enter)

Step 3: Verify Results¶

Products are displayed as cards, most similar first. The top three are the three red shoes: Red Running Shoes (0.8268), Red Sports Shoes (0.8122), and Red Training Shoes (0.7203), followed by Blue Casual Sneakers. Scores may vary slightly depending on your environment and model version.

How to read results:

Rank (#1, #2, ...): Order of similarity, most similar first
Category (category): Product category
Product name (product_name): Product name
Price (price): Price in US dollars
Description (description): Description
Similarity (similarity_score): Similarity (0.0-1.0, higher is more similar). The bar is green at 0.7 and above, blue from 0.4 to 0.7, and gray below 0.4

Results (top_k) sets how many products are returned (default: 5). The price filter stays unavailable until you add it in Part 2.

Optional: See the raw API response

To see the JSON that the search screen displays, open Swagger UI at http://localhost:8002/docs, open /search, click "Try it out", enter the request body below, and click "Execute". Swagger UI loads its files from the internet, while the search screen works offline.

{
  "query": "red sneakers",
  "top_k": 1
}

Response (the score may vary slightly):

{
  "results": [
    {
      "product_name": "Red Running Shoes",
      "similarity_score": 0.8268,
      "price": 89,
      "category": "Sneakers",
      "description": "Lightweight and breathable running shoes."
    }
  ]
}

Try Various Searches¶

Try the following searches as well. You can type them, or click the matching example under the search box.

Example 1: Search for beginner-friendly products¶

beginner camera

Example 2: Search for business-oriented products¶

business laptop

Example 3: Search for high-performance products¶

high-performance gaming PC

Experience the Power of Vector Search¶

As you try various searches, you should notice the following:

Observation 1: Found even with different phrasing

"beginner" → "entry-level" "for beginners" are also found

Observation 2: Similarity scores are useful

Higher score = more similar
You can see the reliability of results

Observation 3: Descriptions are also considered

Understands not just product names but also the meaning of descriptions
"business laptop" also finds the Lightweight Business Bag, whose description says it fits a laptop

Part 1 Completion Check¶

FAQ¶

Cannot open the search screen

Verify the application is running
Verify the URL is correct (http://localhost:8002)
Try a different browser

Search results are 0

Verify sample data has been inserted
Try changing the search query

Similarity scores are extremely low

Reinsert sample data with the latest insert_sample_data.py
Restart the demo application manually
1. Press Ctrl+C in the terminal running the application (stop)
2. Start the application ( How to start)
Search again on the search screen

If the existing collection was created with an older search metric, scores may appear very low, such as 0.06.

Next →