NeuralBlitz Embeddings
Embeddings are dense vector representations of text, images, or other data that capture semantic meaning. They're the foundation of semantic search and RAG applications.
Key Concepts
- Text embeddings (text-embedding-ada-002, e5, bge)
- Image embeddings (CLIP, ResNet)
- Dimensionality reduction
- Similarity computation
- Batch processing
Common Use Cases
- Semantic search
- Text classification
- Clustering
- Duplicate detection
- RAG systems
Best Practices
- Choose model based on use case
- Normalize vectors for cosine similarity
- Handle long texts with chunking
- Cache embeddings when possible
- Monitor embedding drift
Resources
- OpenAI Embeddings, Hugging Face, Cohere
- Related Skills: vector-databases, nlp, machine-learning