Texts

Texts

Texts

class vectorai.api.text.ViTextClient(username, api_key, url=None)

Search and Encoding of Texts

search_text(collection_name: str, text, fields: List, metric: str = 'cosine', min_score=None, page: int = 1, page_size: int = 10, include_vector: bool = False, include_count: bool = True, asc: bool = False, return_curl: bool = False, **kwargs)

Search a text field with text using Vector Search with text directly.

For example: “product_description” represents the description of a product:

"AirPods deliver effortless, all-day audio on the go. And AirPods Pro bring Active Noise Cancellation to an in-ear headphone — with a customisable fit"

-> <Encode the text to vector> ->

i.e. text vector, "product_description_vector_": [0.794617772102356, 0.3581121861934662, 0.21113917231559753, 0.24878688156604767, 0.9741804003715515 ...]

-> <Vector Search> ->

Search Results: {...}
Parameters

  • text – Text to encode into vector and vector search with

  • collection_name – Name of Collection

  • search_fields – Vector fields to search through

  • approx – Used for approximate search

  • sum_fields – Whether to sum the multiple vectors similarity search score as 1 or seperate

  • page_size – Size of each page of results

  • page – Page of the results

  • metric – Similarity Metric, choose from [‘cosine’, ‘l1’, ‘l2’, ‘dp’]

  • min_score – Minimum score for similarity metric

  • include_vector – Include vectors in the search results

  • include_count – Include count in the search results

  • hundred_scale –

    Whether to scale up the metric by 100

    asc:

    Whether to sort the score by ascending order (default is false, for getting most similar results)

encode_text_job(collection_name: str, text_field: str, refresh: bool = False, **kwargs)

Encode all texts in a field into vectors

Within a collection encode the specified text field in every document into vectors.

For example, a text field “product_description” represents the description of a product:

document 1 text field: {"product_description" : "AirPods deliver effortless, all-day audio on the go. And AirPods Pro bring Active Noise Cancellation to an in-ear headphone — with a customisable fit."

document 2 text field: {"product_description" : "MacBook Pro elevates the notebook to a whole new level of performance and portability. Wherever your ideas take you, you’ll get there faster than ever with high‑performance processors and memory, advanced graphics, blazing‑fast storage and more — all in a compact package."

-> <Encode the texts to vectors> ->

document 1 text vector: {"product_description_vector_": [0.794617772102356, 0.3581121861934662, 0.21113917231559753, 0.24878688156604767, 0.9741804003715515 ...]}

document 2 text vector: {"product_description_vector_": [0.8364648222923279, 0.6280597448348999, 0.8112713694572449, 0.36105549335479736, 0.005313870031386614 ...]}
Parameters

  • text_field – The text field to encode into vectors

  • refresh – Whether to refresh the whole collection and re-encode all to vectors

  • collection_name – Name of Collection