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    Class LocalEmbedder

    Deterministic, dependency-free hashing embedder (a "hashing vectorizer").

    Implements

    Index
    dimensions: number

    The dimensionality of vectors this embedder produces.

    relevanceThreshold: 0.3 = LEXICAL_RELEVANCE_THRESHOLD

    The cosine similarity at which this model's results turn from unrelated to relevant, used as the admission threshold when MemoryConfig.relevanceThreshold is not set. Declare it for a custom embedder once you have measured it; leave it out to use the library default.

    • Embed a single text into a dense vector.

      Parameters

      • text: string

      Returns Promise<number[]>