A warm welcome to Vector Databases in Action: FAISS, Pinecone, Chroma & Weaviate course by Uplatz.
What Are Vector Databases?
Vector databases are specialized data systems designed to store and search high-dimensional vectors — numerical representations of data such as text, images, audio, or code. These vectors (embeddings) capture semantic meaning, allowing machines to compare similarity between items using distance metrics like cosine similarity. Unlike traditional databases that search by exact matches or SQL filters, vector databases enable semantic retrieval, powering AI applications such as chatbots, recommendation engines, RAG pipelines, document search, and multimodal understanding.
How They Work
When data is converted into embeddings (vectors), these are stored in an index optimized for fast Approximate Nearest Neighbor (ANN) search. During a query, the user input is also transformed into a vector, and the database retrieves the most similar vectors based on distance calculations. Various indexing algorithms (e.g., HNSW, IVF, PQ) allow sub-second responses even with millions of vectors. Vector databases can also combine keyword filtering, metadata search, and semantic search for hybrid querying — making them ideal for production-grade AI systems.
Popular Vector Databases
This course dives deep into the four most widely used vector databases.
- 1. FAISS, developed by Facebook AI Research, is a high-performance local library ideal for fast similarity search and prototyping.
- 2. Chroma is a lightweight, open-source vector database built for LLM workflows and integrates smoothly with LangChain.
- 3. Pinecone is a fully managed cloud platform offering high scalability, enterprise-grade performance, and production-ready infrastructure.
- 4. Weaviate is an open-source vector database with both local and cloud deployment options, featuring GraphQL APIs, hybrid search, schema design, and strong multimodal capabilities. Together, these platforms cover everything from local experimentation to real-world AI deployment at scale.
The rise of Generative AI and LLMs has made vector databases the new backbone of intelligent applications. Instead of searching by keywords, vector databases enable semantic search — retrieving results based on meaning and context. This course takes you from the mathematical foundations of embeddings all the way to building real-world AI apps using FAISS, Chroma, Pinecone, and Weaviate.
You’ll learn how embeddings work, how Approximate Nearest Neighbor (ANN) algorithms power high-speed search, and how to design production-ready Retrieval-Augmented Generation (RAG) pipelines with LLMs. By the end of the course, you’ll know exactly which vector database to use, when, and why — and how to deploy AI search systems at scale.
