Embeddings & Semantic Search

Summary

Converting text into dense vectors where semantic similarity maps to vector distance — the mechanism that lets you search 'by meaning' instead of exact keyword match.

How to Learn This

1Generate embeddings for a set of documents and compute cosine similarity between them.
2Build a simple semantic search over a small document collection.
3Compare embedding-based search results to keyword search on the same queries.

Evaluating Fine-Tuned Models

Vector Databases (Pinecone, Chroma, Weaviate, FAISS)

More topics in Retrieval-Augmented Generation & Vector Databases

Vector Databases (Pinecone, Chroma, Weaviate, FAISS)Chunking Strategies RAG Architecture Hybrid Search (Keyword + Vector)Reranking Context Window Management Evaluating RAG Pipelines Knowledge Graphs (Overview)

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