Array & Dictionary¶

Array & Dictionary

class vectorai.api.array_dict_vectorizer.ViArrayDictClient(username, api_key, url=None)¶

Search and Encoding for Array & Dictionary

encode_dictionary_field(collection_name: str, dictionary_fields: List, return_curl: bool = False, **kwargs)¶

Encode all dictionaries in a field for collection into vectors

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

For example: a dictionary that represents a person’s characteristics visiting a store, field “person_characteristics”:

document 1 field: {"person_characteristics" : {"height":180, "age":40, "weight":70}}

document 2 field: {"person_characteristics" : {"age":32, "purchases":10, "visits": 24}}

-> <Encode the dictionaries to vectors> ->

| height | age | weight | purchases | visits |
|--------|-----|--------|-----------|--------|
| 180    | 40  | 70     | 0         | 0      |
| 0      | 32  | 0      | 10        | 24     |

document 1 dictionary vector: {"person_characteristics_vector_": [180, 40, 70, 0, 0]}

document 2 dictionary vector: {"person_characteristics_vector_": [0, 32, 0, 10, 24]}

Parameters

dictionary_fields – The dictionary field to train on to encode into vectors
collection_name – Name of Collection

encode_dictionary(collection_name: str, dictionary: Dict, dictionary_field: str, return_curl=False, **kwargs)¶

Encode an dictionary into a vector

For example: a dictionary that represents a person’s characteristics visiting a store, field “person_characteristics”:

{"height":180, "age":40, "weight":70}

-> <Encode the dictionary to vector> ->

| height | age | weight | purchases | visits |
|--------|-----|--------|-----------|--------|
| 180    | 40  | 70     | 0         | 0      |

dictionary vector: [180, 40, 70, 0, 0]

Parameters

collection_name – Name of Collection
dictionary – A dictionary to encode into vectors
dictionary_field – The dictionary field that encoding of the dictionary is trained on

search_with_dictionary(collection_name: str, dictionary: Dict, dictionary_field: str, fields: List, sum_fields: bool = True, 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 dictionary field with a dictionary using Vector Search with a dictionary directly.

For example: a dictionary that represents a person’s characteristics visiting a store, field “person_characteristics”:

{"height":180, "age":40, "weight":70}

-> <Encode the dictionary to vector> ->

| height | age | weight | purchases | visits |
|--------|-----|--------|-----------|--------|
| 180    | 40  | 70     | 0         | 0      |

dictionary vector: [180, 40, 70, 0, 0]

-> <Vector Search> ->

Search Results: {...}

Parameters

collection_name – Name of Collection
search_fields – Vector fields to search against
page_size – Size of each page of results
page – Page of the results
approx – Used for approximate search
sum_fields – Whether to sum the multiple vectors similarity search score as 1 or seperate
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
dictionary – A dictionary to encode into vectors
dictionary_field –
The dictionary field that encoding of the dictionary is trained on

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

encode_array_field(collection_name: str, array_fields: List, return_curl: bool = False, **kwargs)¶

Encode all arrays in a field for a collection into vectors

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

For example, array that represents a **movie’s categories, field “movie_categories”:

document 1 array field: {"category" : ["sci-fi", "thriller", "comedy"]}

document 2 array field: {"category" : ["sci-fi", "romance", "drama"]}

-> <Encode the arrays to vectors> ->

| sci-fi | thriller | comedy | romance | drama |
|--------|----------|--------|---------|-------|
| 1      | 1        | 1      | 0       | 0     |
| 1      | 0        | 0      | 1       | 1     |

document 1 array vector: {"movie_categories_vector_": [1, 1, 1, 0, 0]}

document 2 array vector: {"movie_categories_vector_": [1, 0, 0, 1, 1]}

Parameters

array_fields – The array field to train on to encode into vectors
collection_name – Name of Collection

encode_array(collection_name: str, array: List, array_field: str, return_curl: bool = False, **kwargs)¶

Encode an array into a vector

For example: an array that represents a movie’s categories, field “movie_categories”:

["sci-fi", "thriller", "comedy"]

-> <Encode the arrays to vectors> ->

| sci-fi | thriller | comedy | romance | drama |
|--------|----------|--------|---------|-------|
| 1      | 1        | 1      | 0       | 0     |

array vector: [1, 1, 1, 0, 0]

Parameters

array_field – The array field that encoding of the dictionary is trained on
array – The array to encode into vectors
collection_name – Name of Collection

search_with_array(collection_name: str, array: List, array_field: str, fields: List, sum_fields: bool = True, 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 an array field with an array using Vector Search with an array directly.

For example: an array that represents a movie’s categories, field “movie_categories”:

["sci-fi", "thriller", "comedy"]

-> <Encode the arrays to vectors> ->

| sci-fi | thriller | comedy | romance | drama |
|--------|----------|--------|---------|-------|
| 1      | 1        | 1      | 0       | 0     |

array vector: [1, 1, 1, 0, 0]

-> <Vector Search> ->

Search Results: {...}

Parameters

array_field – The array field that encoding of the dictionary is trained on
array – The array to encode into vectors
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)