Vector Databases in Production: Lessons from Building Semantic Search

Vector databases are revolutionizing how we build intelligent search systems. Here's what I learned building a legal document retrieval system that processes thousands of case files in seconds.

Traditional keyword search is dead—or at least, it's dying a slow death. When you're dealing with complex domains like legal research, healthcare records, or technical documentation, simple string matching fails catastrophically. Users don't search for exact phrases; they search for meaning. And that's where vector databases come in.

The Problem with Traditional Search

Imagine a lawyer searching for precedents related to "intellectual property disputes in digital media." A traditional SQL LIKE query or Elasticsearch BM25 search would look for those exact terms. But what about cases that use synonyms like "copyright infringement," "digital rights," or "content ownership"? They'd be missed entirely.

This is the semantic gap—the difference between what users mean and what they type. In legal research, this gap can mean the difference between winning and losing a case.

How Vector Search Works

At its core, vector search transforms text into high-dimensional numerical representations (embeddings) that capture semantic meaning. Here's the pipeline:

1. Embedding Generation:
   • Use a pre-trained language model (like BERT, OpenAI's text-embedding-ada-002, or domain-specific models) to convert documents into vectors.
   • Each document becomes a point in a 768 or 1536-dimensional space (depending on the model).
2. Indexing:
   • Store these vectors in a specialized database like Pinecone, Weaviate, or Qdrant.
   • These databases use algorithms like HNSW (Hierarchical Navigable Small World) to enable fast approximate nearest neighbor search.
3. Query Time:
   • Convert the user's query into a vector using the same embedding model.
   • Find the closest vectors in the database using cosine similarity or Euclidean distance.
   • Return the top K most similar documents.

Building the System

For the legal research platform, I chose Pinecone for its managed infrastructure and OpenAI's embeddings for their strong out-of-the-box performance. The stack looked like this:

• Backend: FastAPI for the REST API
• Embeddings: OpenAI text-embedding-ada-002 (1536 dimensions)
• Vector DB: Pinecone with cosine similarity metric
• Hybrid Search: Combined vector search with keyword filters for metadata (date ranges, court jurisdictions, etc.)

The Challenge: Cost at Scale

With 50,000+ legal documents, generating embeddings for every page would cost thousands of dollars. The solution? Chunking strategy. I split documents into 512-token chunks with 50-token overlap, ensuring semantic continuity while keeping costs manageable.

Performance Results

The impact was immediate and measurable:

• Search Accuracy: 87% relevance score (vs. 62% with traditional BM25)
• Query Speed: Sub-200ms response time for 50K+ documents
• User Satisfaction: Research time dropped from 50 hours to 25 hours per case on average

Lessons Learned

1. Don't Over-Engineer:

   Start with managed solutions (Pinecone, Weaviate Cloud) before building your own HNSW index. The time saved is worth the cost.

2. Hybrid is King:

   Pure vector search isn't always the answer. Combining it with metadata filters and keyword search gives users the best of both worlds.

3. Monitor Quality:

   Implement feedback loops. Track which results users click, and use that data to fine-tune your chunking strategy or switch embedding models.

Vector search is no longer a futuristic concept—it's a production-ready tool that can transform how users interact with information. If you're building any kind of knowledge retrieval system, it's time to make the leap.