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RAG Foundations: Ingestion & Indexing: sections and modules
T5.4

Embeddings & Model Selection

Explain dense embeddings, dimensions and cosine / dot-product / Euclidean similarity.

Lessons
7 lessons
By
Sourav Banerjee
Prerequisites
None

Lessons

Start module
7 lessons
  1. 1
    Embeddings & Model Selection: overview

  2. 2
    Embedding fundamentals, similarity metrics and model selection

  3. 3
    Hosted embeddings: OpenAI models and dimensions

  4. 4
    Open-source embeddings with Hugging Face

  5. 5
    Production concerns: batching, caching and similarity behaviour

  6. 6
    Benchmarking embedding models with MLflow on a managed platform

  7. 7
    Embeddings & Model Selection: interview questions

Practise and Check

Deliverable
Embedding shoot-out: compare one hosted and two open-source models on a 30-question golden set and pick one, writing down the cost and latency per 1,000 chunks.
Assessment
Answer the five Interview questions without notes, then run the lab.
Interview probe
Larkspur's support assistant was fine on Friday. On Monday every answer is vague, and the logged top-1 similarity for golden questions has fallen from its usual range to near zero. Nobody touched the prompt. What do you check first?
Common mistakes
  • Shipping 64 dimensions because recall@1 is perfect on six queries
  • Scoring by dot product with HuggingFaceEmbeddings left unnormalised
  • Sharing the cache's default empty namespace across two embedding models

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