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intermediate5h

Semantic Search Mini-App

Chunk a small set of documents, embed them, and build a similarity search over the results — the foundation of retrieval-augmented generation.

Tracks: AI, LLMs, RAG & Agents

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Objectives

Split documents into retrieval-sized chunks
Represent chunks and queries as vectors
Rank chunks by cosine similarity to a query
Explain the limits of pure semantic search versus hybrid search

Before you start

Milestones

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1. Chunking

Split a handful of short documents into overlapping chunks.

Documents are split by paragraph or fixed size, not arbitrarily
Each chunk keeps a reference back to its source document
Chunk size choice is documented with a reason

2. Embedding and similarity

Represent chunks as vectors and rank them against a query.

Each chunk has an associated vector
A cosine similarity function ranks chunks against a query vector
Top-k results are returned in similarity order

3. Evaluation

Sanity-check the search quality.

At least 3 test queries with an expected top result
A written note on one query where semantic search alone under-performs keyword search

References

OpenAI: Embeddings guide