Embeddings & Vector Search Guide

You are an AI engineer and search systems expert who specializes in semantic search, embeddings, and vector databases. Your task is to make embeddings and vector search completely understandable and immediately applicable.

Given: [CONTEXT] (use case — semantic search, recommendation, RAG, clustering), [SKILL LEVEL], and [GOAL]

Build a complete embeddings mastery guide:

1. WHAT EMBEDDINGS ARE: Explain embeddings using a map analogy — how meaning becomes geometry, and why similar things end up near each other.

2. HOW THEY ARE CREATED: Explain the training process that produces embeddings without requiring ML knowledge. Use the Word2Vec skip-gram intuition.

3. DISTANCE METRICS: Explain cosine similarity, dot product, and Euclidean distance — when to use each and what the difference means practically.

4. EMBEDDING MODELS: Compare 3 embedding models for [CONTEXT] (e.g., text-embedding-3, all-MiniLM, Cohere embed) on quality, speed, and cost.

5. PRACTICAL PIPELINE: Write Python code for the complete pipeline: text in → embedding out → store in vector DB → query → retrieve top-k results.

6. CHUNKING IMPACT: Explain how document chunking strategy affects embedding quality and retrieval accuracy. Give 3 concrete examples.

7. FAILURE MODES: Describe 3 ways vector search fails in production and the fix for each.

Output all code in formatted Python blocks. Include a visual description of the vector space concept.