Building Scalable Search Systems with Vector Databases¶

Published: March 2025 • 8 min read

Introduction¶

Modern AI applications demand search systems that can handle millions of users while maintaining sub-second response times. This article explores the architecture patterns and implementation strategies for building scalable hybrid search systems, drawing from real-world experience with code search applications.

System Architecture¶

Core Components¶

A scalable search system consists of several key components working together:

Vector Database: Specialized storage for high-dimensional embeddings
Embedding Models: Separate models for text and code domains
Hybrid Search Engine: Combining dense and sparse retrieval methods
Query Processing Pipeline: Handling multi-modal search requests

Technology Stack¶

For production-ready systems, consider this proven stack:

# Vector Database: Qdrant
from qdrant_client import QdrantClient
from qdrant_client.models import VectorParams, Distance

# Embedding Models
text_model = "sentence-transformers/all-MiniLM-L6-v2"  # 384 dimensions
code_model = "jinaai/jina-embeddings-v2-base-code"     # 768 dimensions

Hybrid Search Implementation¶

Dual Embedding Strategy¶

The key to effective hybrid search is using specialized embeddings for different content types:

def create_hybrid_embeddings(text_content, code_content):
    # Text embedding for natural language
    text_embedding = text_model.encode(text_content)

    # Code embedding for technical content
    code_embedding = code_model.encode(code_content)

    return text_embedding, code_embedding

Reciprocal Rank Fusion (RRF)¶

Combine results from multiple search strategies using RRF:

def hybrid_search(query, k=10):
    # Search across both embedding spaces
    text_results = search_text_embeddings(query)
    code_results = search_code_embeddings(query)

    # Apply RRF to combine rankings
    combined_results = reciprocal_rank_fusion(
        [text_results, code_results], 
        k=k
    )

    return combined_results

Performance Optimization¶

Quantization for Speed¶

Implementing scalar quantization can dramatically improve search performance:

11x faster search times for code search tasks
Reduced memory footprint with minimal accuracy loss
On-disk storage with in-memory quantized vectors

# Configure quantized collection
vector_params = VectorParams(
    size=768,
    distance=Distance.COSINE,
    quantization=ScalarQuantization(
        type=ScalarType.INT8,
        quantile=0.99,
        always_ram=True
    )
)

Batch Processing¶

Optimize data ingestion with parallel processing:

def batch_embed_documents(documents, batch_size=100):
    with ThreadPoolExecutor(max_workers=4) as executor:
        futures = []
        for batch in chunks(documents, batch_size):
            future = executor.submit(embed_batch, batch)
            futures.append(future)

        results = [future.result() for future in futures]
    return results

Scaling Patterns¶

Collection Strategy¶

Separate collections by use case and performance requirements:

Normal Collection: High accuracy, slower queries
Quantized Collection: Fast queries, slight accuracy trade-off
Specialized Collections: Domain-specific optimizations

Memory Management¶

Balance between speed and resource usage:

# Configure memory-efficient settings
collection_config = {
    "vectors": {
        "size": 768,
        "distance": "Cosine",
        "on_disk": True,  # Store on disk
        "quantization": {
            "scalar": {
                "type": "int8",
                "quantile": 0.99,
                "always_ram": True  # Keep quantized in memory
            }
        }
    }
}

Data Preprocessing¶

Chunking Strategy¶

Effective document chunking is crucial for search quality:

def preprocess_code_data(problem_description, code_solution):
    # Combine problem context with solution
    combined_text = f"{problem_description}\n\nSolution:\n{code_solution}"

    # Filter and clean data
    if len(combined_text) > 10000:  # Skip very long documents
        return None

    return {
        "text": problem_description,
        "code": code_solution,
        "combined": combined_text
    }

Evaluation and Monitoring¶

Performance Metrics¶

Track key metrics for production systems:

Search Latency: Target sub-100ms response times
Throughput: Queries per second under load
Accuracy: Relevance of search results
NDCG Score: Normalized Discounted Cumulative Gain