Query vector embeddings with semantic search

Implement semantic similarity queries

In this section, you query the Qdrant vector database using semantic similarity search.

Unlike traditional keyword search, semantic search compares vector embeddings to identify the most relevant results based on meaning and context rather than exact keyword matches.

Semantic search enables AI applications such as chatbots, recommendation systems, and knowledge retrieval platforms.

Architecture overview

The workflow retrieves the most relevant documents using vector similarity.

    

        
        
User Query
      |
      v
Sentence Transformer Model
      |
      v
Query Embedding Vector
      |
      v
Qdrant Vector Database
      |
      v
Similarity Search
      |
      v
Top Matching Documents

    

Create the search script

Navigate to the project directory, then create the Python script used to query the vector database.

    

        
        
cd ~/qdrant-rag-demo
vi search.py

    

Add the following code:

    

        
        
from qdrant_client import QdrantClient
from sentence_transformers import SentenceTransformer

client = QdrantClient(url="http://localhost:6333")

model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

query = "What is vector search?"

query_vector = model.encode(query).tolist()

results = client.query_points(
    collection_name="axion_demo",
    query=query_vector,
    limit=2
)

print("\nTop results:\n")

for point in results.points:
    print(point.payload["text"])

    

What this script does

The script performs the following steps:

• Connects to the Qdrant vector database
• Loads a pretrained transformer embedding model
• Converts the query into a vector embedding
• Performs similarity search against stored vectors
• Returns the most relevant documents

Run the search script

Execute the search script.

    

        
        
python search.py

    

The output is similar to:

    

        
        Vector databases enable semantic search.
Qdrant is optimized for vector similarity search.

        
    

The output confirms that the system successfully retrieved the most semantically relevant documents.

Why semantic search is powerful

Traditional search engines rely on keyword matching, which often fails when queries are phrased differently.

Semantic search uses vector embeddings to capture meaning.

Semantic search allows applications to understand intent rather than exact wording.

What you’ve learned and what’s next

In this section, you learned how to:

• Convert user queries into vector embeddings
• Query the Qdrant vector database
• Retrieve semantically relevant documents
• Understand how semantic similarity search works

In the next section, you will extend this workflow to build a chatbot-style knowledge retrieval system, allowing users to interactively query the vector database using natural language.