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🎬Video•25 min

Modern Embedding Approaches

Learn about the latest advances in text embedding technology.

Modern Embedding Approaches

Instruction-Tuned Embeddings

Instructor Model: Prefix text with task instruction: "Represent this document for retrieval: [text]"

Different instructions → different embeddings.

Matryoshka Embeddings

Embeddings that work at multiple dimensions:

  • Full 1536 dimensions: Best quality
  • First 512: Good quality, smaller
  • First 256: Acceptable, very small
  • OpenAI text-embedding-3 supports this.

    Late Interaction

    ColBERT:

  • Separate embeddings per token
  • Compare at query time
  • Better for long documents
  • Multimodal Embeddings

    CLIP:

  • Images and text in same space
  • Search images with text queries
  • ImageBind:

  • Multiple modalities unified
  • Audio, video, depth, thermal
  • Sparse-Dense Hybrid

    Combine:

  • Dense embeddings (semantic)
  • Sparse vectors (keyword matching)
  • Best of both worlds for search.

    🎯 Key Takeaways

    • ✓Instruction-tuning adapts embeddings to tasks
    • ✓Matryoshka allows dimension flexibility
    • ✓Late interaction improves long doc retrieval
    • ✓Hybrid approaches combine strengths

    📚 Additional Resources