embedding-strategies
Optimize Embeddings for Semantic Search
Également disponible depuis: wshobson
This skill helps developers choose and implement the right embedding models for vector search, providing code templates for OpenAI, Sentence Transformers, and domain-specific pipelines.
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Tester
Utilisation de "embedding-strategies". How do I choose between text-embedding-3-large and text-embedding-3-small?
RĂ©sultat attendu:
- text-embedding-3-large: 3072 dimensions, 8191 tokens - Best for high accuracy needs
- text-embedding-3-small: 1536 dimensions, 8191 tokens - Cost-effective option
- Choose large for complex semantic tasks, small for high-volume applications
Utilisation de "embedding-strategies". What is the best chunking strategy for code?
RĂ©sultat attendu:
- Use tree-sitter to parse code structure
- Chunk by functions, classes, and methods
- Include surrounding context for better retrieval
- Consider language-specific separators
Audit de sécurité
SûrThis skill provides educational content about embedding strategies for RAG applications. All static findings are false positives: code blocks are markdown documentation examples, URLs are legitimate documentation links, and no cryptographic algorithms or system reconnaissance behavior is present. Safe for publication.
Score de qualité
Ce que vous pouvez construire
Build RAG Applications
Set up embedding pipelines for retrieval-augmented generation with optimal chunking and model selection
Compare Embedding Models
Evaluate different embedding models based on dimensions, cost, and performance for specific domains
Optimize Vector Search
Improve semantic search quality through proper preprocessing, chunking, and embedding configuration
Essayez ces prompts
Show me how to get started with OpenAI embeddings for a RAG application. Include batching and dimension reduction.
How do I set up local embedding models using sentence-transformers? Include BGE and E5 models with proper preprocessing.
What chunking strategy should I use for technical documentation? Include token-based, sentence-based, and semantic approaches.
How do I evaluate the quality of my embeddings for retrieval? Show metrics like precision, recall, MRR, and NDCG.
Bonnes pratiques
- Match embedding model to your specific use case (code, prose, multilingual)
- Normalize embeddings when using cosine similarity for retrieval
- Cache embeddings to avoid recomputing for repeated queries
Éviter
- Using the wrong model for your domain (e.g., general embeddings for code)
- Ignoring token limits which causes truncation and lost information
- Mixing embedding models in production which creates incompatible vector spaces