Is the Text Embeddings & Cosine Similarity lesson free?

The interactive Text Embeddings & Cosine Similarity lesson is part of Pro (₹299/month). The first 5 lessons of the track are free — no account or card needed.

How long does the Text Embeddings & Cosine Similarity lesson take?

About 14 minutes end-to-end. It's a hands-on lesson — you read a paragraph, edit the code, and run it in the same tab.

Do I need to install Python to try the Text Embeddings & Cosine Similarity lesson?

No install required. PyRun runs real CPython in your browser via Pyodide (Python compiled to WebAssembly). Works on Chrome, Firefox, Safari, and Edge — desktop or mobile.

What level is the Text Embeddings & Cosine Similarity lesson?

Advanced level. Assumes comfort with classes, functions, and standard library. Not for first-time Python learners.

Text Embeddings & Cosine Similarity

Advanced
14 min
GenAI & RAG
genai · embeddings · numpy · rag
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Lesson 1 of 6

In this track

Text Embeddings & Cosine Similarity14 min
Building a Tiny Vector Store15 min
Chunking Documents for Retrieval13 min
Assembling a RAG Pipeline18 min
Prompt Engineering in Python14 min
Evaluating Retrieval: Precision & Recall @ k15 min

Advanced · 15 minBuilding a Tiny Vector StoreStore documents as (id, vector) pairs and retrieve the top-k most similar to a query — the core of every RAG retriever.Advanced · 13 minChunking Documents for RetrievalSplit a long document into overlapping chunks by characters or words so each piece is small enough to embed and retrieve.Advanced · 18 minAssembling a RAG PipelineWire embeddings, retrieval and prompt-building into one flow, then see exactly where the LLM call slots in.

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