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Document Chunking Strategies for RAG

Learn effective chunking strategies including fixed-size, semantic, recursive, and sentence-window approaches for optimal RAG retrieval.

What are the best chunking strategies for RAG systems?

Chunking determines how documents are split for embedding. Fixed-size chunks are simple but may break semantic units. Semantic chunking splits at natural boundaries. Recursive chunking tries multiple separators hierarchically. Sentence-window chunking embeds sentences but retrieves surrounding context. Most systems use 256-1024 tokens with 10-20% overlap.

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Choosing a Vector Database for RAG

Compare Pinecone, Weaviate, Qdrant, pgvector, and Chroma to find the right vector database for your RAG implementation.

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Document Chunking Strategies for RAG

What are the best chunking strategies for RAG systems?

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