Embedding
Embedding fundamentals, fine-tuning, and model selection for RAG systems
Embeddings are the foundation of semantic search in RAG systems. This section covers how to choose, fine-tune, and optimize embedding models for your specific domain.
Start Here
Embedding Fundamentals
Level: Beginner
Introduction to embeddings and vector representations. Learn how embeddings capture semantic meaning and enable semantic search.
Topics: vector-representations • semantic-similarity • cosine-distance • embedding-models
Choosing Embedding Models
Level: Intermediate
Guide to selecting the right embedding model for your use case. Compare open-source vs. API models, speed vs. quality tradeoffs, and domain-specific considerations.
Topics: model-selection • benchmarking • cost-analysis • performance-optimization
Fine-Tuning Embeddings
Level: Intermediate
Learn how to fine-tune embedding models with domain-specific data to achieve 15-30% improvements in retrieval metrics using sentence-transformers.
Topics: fine-tuning • sentence-transformers • domain-adaptation • hard-negatives
Multilingual Embeddings
Level: Advanced
Handle multiple languages in RAG systems with multilingual embedding models. Enable cross-lingual retrieval and support global users.
Topics: multilingual-models • cross-lingual-retrieval • language-detection • translation