rag-sentence-transformers

Topics in the source

• Sentence Transformers - State-of-the-Art Embeddings
• When to use Sentence Transformers
• Quick start
• Installation
• Basic usage
• Popular models
• General purpose
• Multilingual

Python framework for sentence and text embeddings using transformers. Use when: Need high-quality embeddings for RAG Semantic similarity and search Text clustering and classification Multilingual embeddings (100+ languages) Running embeddings locally (no API) Cost-effective alternative to OpenAI embeddings Metrics: 15,700+ GitHub stars 5000+ pre-trained models 100+ languages supported Based on PyTorch/Transformers Use alternatives instead: OpenAI Embeddings: Need API-based, highest quality Instructor: Task-specific instructions Cohere Embed: Managed service | Model | Dimensions | Speed | Quality | Use Case | |-------|------------|-------|---------|----------| | all-MiniLM-L6-v2 | 384 | Fast | Good | General, prototyping | | all-mpnet-base-v2 | 768 | Medium | Better | Production RAG | | all-roberta-large-v1 | 1024 | Slow | Best | High accuracy needed | | paraphrase-multilingual | 768 | Medium | Good | Multilingual | 1. Start with all-MiniLM-L6-v2 - Good baseline 2. Normalize embeddings - Better for cosine similarity 3. Use GPU if available - 10× faster encoding 4. Batch encoding - More efficient 5. Cache embeddings - Expensive to recompute 6. Fine-tune for domain - Improves quality

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