Introduction to Vector Databases
Why vector DBs exist, FAISS for local similarity search, storing embeddings for AI retrieval.
Beyond Exact Matches
Traditional databases find rows by exact values (WHERE name = "x"). But AI works with embeddings — vectors that capture meaning — and you often want the items most similar to a query, not exact matches.
Vector databases exist to make this similarity search fast.
What Is an Embedding?
An embedding is a list of numbers (a vector) representing text, an image, or audio so that similar items sit close together in the vector space.
Semantic search, recommendations, and retrieval-augmented generation (RAG) all rest on embeddings.
import numpy as np
# A toy 4-dim embedding for a sentence
vec = np.array([0.12, -0.43, 0.88, 0.05], dtype="float32")
print(vec.shape) # (4,)All lessons in this course
- SQLite with Python's sqlite3 Module
- Pandas and SQL Integration
- Storing and Querying ML Results
- Introduction to Vector Databases