Training Custom Embedding Models
Fine-tune or train embedding models for your domain. Improve retrieval quality with domain-specific embeddings.
TL;DR
Train domain-specific embeddings to improve retrieval. Collect query-document pairs, fine-tune Sentence Transformers with contrastive learning, and evaluate with retrieval metrics.
Why custom embeddings?
General embeddings (OpenAI, Sentence Transformers) work well but:
- May miss domain jargon
- Don't capture your specific semantics
- Custom models can be 10-30% better on domain tasks
Data collection
Pairs needed:
- (Query, relevant doc) positive pairs
- (Query, irrelevant doc) negative pairs
- 1000-10,000+ pairs recommended
Collection methods:
- User click data
- Expert annotations
- Synthetic generation
Training approaches
Contrastive learning: Pull similar closer, push dissimilar apart
Triplet loss: (anchor, positive, negative)
In-batch negatives: Efficient training on batches
Implementation
Libraries:
- Sentence Transformers (easiest)
- OpenAI fine-tuning (embeddings API)
- Custom PyTorch
Evaluation
- Recall@K: % of relevant docs in top K
- MRR (Mean Reciprocal Rank)
- NDCG
- Compare to baseline embeddings
Deployment
- Self-host or use managed inference
- Reindex documents with new embeddings
- A/B test vs baseline
Challenges
- Data collection effort
- Training infrastructure
- Evaluation complexity
- Maintenance (retraining)
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Key Terms Used in This Guide
Embedding
A list of numbers that represents the meaning of text. Similar meanings have similar numbers, so computers can compare by 'closeness'.
Model
The trained AI system that contains all the patterns it learned from data. Think of it as the 'brain' that makes predictions or decisions.
Training
The process of feeding data to an AI system so it learns patterns and improves its predictions over time.
AI (Artificial Intelligence)
Making machines perform tasks that typically require human intelligenceālike understanding language, recognizing patterns, or making decisions.
Fine-Tuning
Taking a pre-trained AI model and training it further on your specific data to make it better at your particular task.
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