You built a chatbot that answers questions about your documents.
Someone asks about "refund policy." Nothing.
You know it's in there. The document says "return procedures" and "money back guarantees."
Same meaning. Different words. Your AI doesn't get it.
Your search is only as smart as how it stores meaning.
FOUNDATIONAL - The storage layer that makes semantic search possible.
When you ask an AI system a question about your business, it needs to find relevant information from your documents. But here's the problem: traditional databases are designed for exact matches. If you search for "refund policy," you'll only find documents that contain those exact words. not documents about "return procedures" or "money back guarantees" that mean the same thing.
Vector databases solve this by storing meaning, not just words. They take the numerical representations of your content (embeddings) and index them in a way that lets you ask "what's similar to this?" instead of "what matches this exactly?" It's the difference between finding documents that look like your query and finding documents that mean the same thing as your query.
Without a vector database, your AI is blind to meaning. With one, it finds exactly what users need. even when they don't use the "right" words.
Vector databases aren't just about AI. They're a pattern that appears whenever you need to find things by what they're like, not what they're called.
Similarity is often more useful than equality. Traditional search finds exact matches. Similarity search finds "close enough" - which is what humans actually want most of the time.
Select a query to see what traditional keyword search finds versus what vector search finds.
Refund processing time and procedures
Keywords: refund, processing, time, procedures
Only finds documents with exact word matches
What is your return policy for damaged items?
96% matchHow do I get my money back for a purchase?
96% matchRefund processing time and procedures
96% matchFinds semantically similar documents by meaning
Let someone else handle the infrastructure
Services like Pinecone, Weaviate Cloud, or Qdrant Cloud handle scaling, backups, and maintenance. You get an API endpoint and start storing vectors. Best for teams that want to focus on building, not managing databases.
Add vector search to your existing database
If you're already running PostgreSQL, pgvector adds vector similarity search without a new database. Stores embeddings alongside your regular data, queries work with your existing tools. Good for simpler use cases.
Run purpose-built vector databases yourself
Deploy Milvus, Qdrant, or Weaviate on your own infrastructure. Full control over data, customization, and costs at scale. Requires DevOps expertise to manage clustering, replication, and upgrades.
This flow turns raw documents into AI-searchable knowledge. The vector database stores embeddings so that when someone asks a question, the system finds semantically similar content in milliseconds. no keyword matching required.
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OpenAI's embeddings and Cohere's embeddings aren't compatible - they live in different vector spaces. Mixing them is like measuring some things in miles and others in kilometers, then sorting by the numbers.
Instead: Use one embedding model per collection. If you switch models, re-embed everything.
A vector is meaningless to humans. If you only store embeddings, you can find similar items but can't show users what those items actually are. You'll need a separate lookup for every result.
Instead: Store the original text (or a reference to it) alongside each vector. Most vector databases support metadata fields for this.
Vector databases use approximate nearest neighbor (ANN) algorithms. Default settings work for small datasets but fall apart at scale. You get slow queries or inaccurate results - sometimes both.
Instead: Tune your index parameters (like HNSW ef_construction and M values) based on your dataset size and accuracy requirements. Test with realistic data.
Larger embedding models produce higher-dimensional vectors. But higher dimensions mean more storage, slower queries, and sometimes worse results due to the "curse of dimensionality."
Instead: Match embedding dimensions to your use case. For most text search, 768-1536 dimensions work well. Only go higher if you've tested and confirmed better results.
You've learned how vector databases store embeddings and enable similarity search. The natural next step is understanding how to create those embeddings in the first place.