embedding-strategies

Comprehensive guide for selecting, implementing, and optimizing embedding models for vector search and RAG applications. Guide to selecting and optimizing embedding models for vector search applications. - Covers 10+ embedding models with dimensions, token limits, and domain specialization (Voyage AI, OpenAI, open-source options for code, finance, legal, and multilingual content) - Provides four chunking strategies: token-based, sentence-based, semantic sections, and recursive character splitting with overlap handling - Includes three implementation templates for Voyage AI, OpenAI, and local Sentence Transformers with specialized query/document prefixes - Features domain-specific pipelines for general documents and code, plus evaluation metrics (precision, recall, MRR, NDCG) for retrieval quality assessment - Best practices section covers model selection, preprocessing, batching, caching, and common pitfalls - Choosing embedding models for RAG - Optimizing chunking strategies - Fine-tuning embeddings for domains - Comparing embedding model performance - Reducing embedding dimensions - Handling multilingual content - Match model to use case : Code vs prose vs multilingual - Chunk thoughtfully : Preserve semantic boundaries - Normalize embeddings : For cosine similarity search - Batch requests : More efficient than one-by-one - Cache embeddings : Avoid recomputing for static...