Author(s): Sameer Chauhan

Originally published on Towards AI.

The child turns into an adult

Intro

Imagine a genius who forgets everything the moment a conversation ends. That was the reality of early large language models. Despite their impressive reasoning abilities, they were confined to their training data — unable to search the web, retrieve documents, or adapt to new information. Vector databases changed that.

What is a Vector DB

Vector databases are a key part of building scalable AI-powered applications. Also known as vector similarity search engines, they store, retrieve, and search data represented as vectors in high-dimensional space. They enable efficient similarity searches by comparing vector embeddings rather than relying on exact matches. Vector databases provide long-term memory, context, and the ability to interact with real-world information on top of an existing machine learning model.

How it works (embeddings → storage → similarity)

1. Convert data into embeddings (e.g., documents → vectors using an embedding model).
2. Store the vectors in a vector database along with metadata such as title, source, tags, date, etc.
3. Convert the user’s query into an embedding as well.
4. The database performs a nearest-neighbor search and returns the most similar results (top-K matches).

Example workflow

• Let’s say a company has 50,000 customer support articles (FAQs, troubleshooting guides, policies). Each document is converted into a vector using an embedding model.
• These vectors are then stored in a vector database, which can handle millions of vectors, build specialized indexes, and perform fast nearest-neighbor searches.
• Now, when a user enters a query like “my laptop is blacking out”, the query is also converted into an embedding.
• The system then computes the similarity between the query vector and the stored vectors using methods such as cosine similarity, Euclidean distance, or dot product.
• Finally, the most relevant result is shown to the user, such as:
  “Your laptop may be blacking out due to battery degradation or power adapter issues.”

This is semantic understanding, not text matching.

Usecases

Vector databases also powers Retrieval Augmented Generation (RAG) tasks, and other use cases include:

• Semantic search, used to return results similar to the input of the query.
• Classification, used to return the grouping closest to the input query.
• Recommendation engines, used to return content similar to the input based on different criteria.
• Anomaly detection, identify data points the look “different” from the usual patterns.

This is how LLM apps go from chatbots to real-world intelligent systems.

Vector DB vs Traditional DB

Querying a vector database is different from querying a conventional database. When a query embedding is compared against stored data, the database measures similarity using distance metrics such as cosine similarity, Euclidean distance, or dot product to return the most relevant results.

Without a vector database, semantic search becomes slow because you’d need to compare the query against your entire dataset or rely on keyword matching. In a workflow without a vector database, you would need to pass your entire dataset alongside your query each time, which is neither practical (models have limits on input size) nor efficient, as it would consume significant resources and time.

Traditional are great for exact queries:

“Find users where age = 25”, “Search product where name = iPhone 15”.

Vector DBs solves a different problem:

“Find things that feel similar”

Some commonly used vector DBs are FAISS, Chroma, Pinecone .. etc.

Final Thoughts

Vector databases are becoming a core building block for modern AI apps.

Whether you’re building a recommendation engine, an AI search assistant, or a complete RAG-based chatbot, vector databases help your system retrieve the right knowledge at the right time — fast and accurately

The future is vectorized

References:

1. https://developers.cloudflare.com/vectorize/reference/what-is-a-vector-database/
2. https://www.mongodb.com/resources/basics/databases/vector-databases

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Published via Towards AI