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Perplexityai Create Embeddings

Generate vector embeddings for independent texts (queries, sentences, documents). This action takes one or more input texts and generates vector embeddings using Perplexity AI's embedding models. Embeddings are useful for semantic search, similarity matching, and machine learnin…

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Use Perplexityai Create Embeddings

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Inputs

  • input
    Required
    Input text to embed, encoded as a string or array of strings. Maximum 512 texts per request. Each input must not exceed 32K tokens. All inputs in a single request must not exceed 120,000 tokens combined. Empty strings are not allowed.
  • modelstring
    Required
    The embedding model to use. pplx-embed-v1-0.6b is smaller and faster, while pplx-embed-v1-4b is larger and more accurate.
  • dimensionsinteger
    Number of dimensions for output embeddings (Matryoshka). Range: 128-1024 for pplx-embed-v1-0.6b, 128-2560 for pplx-embed-v1-4b. Defaults to full dimensions (1024 or 2560).
  • encoding_formatstring
    Encoding format for embeddings output.

Observable output

  • data
    Required
    Data from the action execution
  • errorstring
    Error if any occurred during the execution of the action
  • successfulboolean
    Required
    Whether or not the action execution was successful or not

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