Every article tagged Embeddings, in one place.
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An LLM only knows what was in its training data. Retrieval-augmented generation (RAG) lets it answer questions about your data, such as docs, tickets or policies, by finding the relevant passages first and putting them in the prompt. The pipeline Ingest: split...
An embedding is a list of numbers (a vector) that represents the meaning of a piece of text. Texts with similar meanings get vectors that point in similar directions, even if they share no words. from sentence_transformers import SentenceTransformer model = Se...
Chunking decides what the retriever can find. Chunks that are too small lose context; chunks that are too large mix several topics and dilute the embedding. 1. Fixed-size chunks with overlap The simplest approach: split every N tokens or characters and repeat...
A chunk like "Revenue grew 3% over the previous quarter" is useless on its own: which company, which quarter? Contextual retrieval , a technique published by Anthropic, fixes this by adding a short explanation of where the chunk sits in its document before emb...
