Build a Semantic Search Engine From Scratch in Python
Ever typed a vague search query and somehow got the perfect result? That's semantic search at work, and in this video we build one from the ground up using Python, sentence embeddings, and a vector index. You'll understand not just how to wire the pieces together, but why each one exists and what trade-offs you're making along the way.
We cover the core difference between keyword and semantic search, how text becomes embeddings, cosine similarity, building an in-memory vector store, scaling up with FAISS for fast approximate nearest neighbour search, chunking strategies for real documents, and finally exposing the whole thing as a FastAPI endpoint. Every concept is backed by real, runnable code — no pseudocode, no hand-waving.
If this helped you finally get how semantic search works, hit subscribe and check out the full AI track playlist on Sukrid LearnHub. Got stuck on embeddings, FAISS, or chunking? Drop your biggest question in the comments — we read every one and often turn them into follow-up videos.
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📚 CHAPTERS
00:00 Introduction
00:00 Keyword vs semantic search
00:45 What is an embedding
01:35 Setting up the project
02:20 Loading the embedding model
03:15 Inspecting a real vector
04:00 How do we compare vectors
04:50 Cosine similarity in code
05:45 Seeing meaning in numbers
06:30 The search engine plan
07:20 Preparing our document corpus
08:10 Batch embedding the corpus
09:00 The naive search function
10:00 Running our first search
10:50 Why brute force breaks down
11:40 Enter FAISS
12:30 Building a FAISS index
13:30 Searching with FAISS
14:25 Verifying FAISS results
15:10 The problem with long documents
16:00 Writing a chunker
17:00 Indexing chunks with metadata
18:00 Searching chunks returns context
18:45 Exposing it as an API
19:45 Running the server
20:30 Calling the live endpoint
21:15 Production considerations
22:10 Choosing better models
22:55 Wrapping up the build
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🔗 LINKS
📺 Full Course Playlist: / @sukridlearnhub
🌐 Website: https://www.sukrid.com/learnhub
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