• embedding — A list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.
• context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.

Smaller vectors at comparable retrieval accuracy cut vector database cost directly, and 16K context lets you embed longer chunks without splitting them.

• postVoyage's rerankers start taking instructions at query time
• postWhy code retrieval benchmarks mislead, and how to rebuild them
• postWhen embedding a chunk should include the whole document
• postLong context windows do not fix LLM reranking

• postVoyage 4 puts every model size in one embedding spaceVoyage AI by MongoDB
• postvoyage-multilingual-2 targets non-English retrieval with 32K contextVoyage AI by MongoDB
• postvoyage-context-3 embeds chunks with the whole document in viewVoyage AI by MongoDB

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