Search¶

Search

class vectorai.api.search.ViSearchClient(username, api_key, url=None)¶

Search and Advanced Search Operations

hybrid_search(collection_name: str, text: str, vector: List, fields: List, text_fields: List, sum_fields: bool = True, metric: str = 'cosine', min_score: float = None, traditional_weight=0.075, 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 vector and text using Vector Search and Traditional Search

Vector similarity search + Traditional Fuzzy Search with text and vector.

Parameters

text – Text Search Query (not encoded as vector)
vector – Vector, a list/array of floats that represents a piece of data.
text_fields – Text fields to search against
traditional_weight – Multiplier of traditional search. A value of 0.025~0.1 is good.
fuzzy – Fuzziness of the search. A value of 1-3 is good.
join – Whether to consider cases where there is a space in the word. E.g. Go Pro vs GoPro.
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)

search_by_id(collection_name: str, document_id: str, field: str, sum_fields: bool = True, metric: str = 'cosine', min_score=0, page: int = 1, page_size: int = 10, include_vector: bool = False, include_count: bool = True, asc: bool = False, approx: int = 0, hundred_scale: bool = False, return_curl: bool = False, **kwargs)¶

Single Product Recommendations (Search by an id)

Recommendation by retrieving the vector from the specified id’s document. Then performing a search with that vector.

Parameters

document_id – ID of a document
collection_name – Name of Collection
search_field – 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)

return_curl:
Return the CURL statement relevant to the Python requests

search_by_ids(collection_name: str, document_ids: List, field: str, vector_operation: str = 'mean', sum_fields: bool = True, metric: str = 'cosine', min_score=0, page: int = 1, page_size: int = 10, include_vector: bool = False, include_count: bool = True, asc: bool = False, return_curl: bool = False, **kwargs)¶

Multi Product Recommendations (Search by ids)

Recommendation by retrieving the vectors from the specified list of ids documents. Then performing a search with an aggregated vector that is the sum (depends on vector_operation) of those vectors.

Parameters

document_ids – IDs of documents
vector_operation – Aggregation for the vectors, choose from [‘mean’, ‘sum’, ‘min’, ‘max’]
collection_name – Name of Collection
search_field – 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)

search_by_positive_negative_ids(collection_name: str, positive_document_ids: List, negative_document_ids: List, field: str, vector_operation: str = 'mean', sum_fields: bool = True, metric: str = 'cosine', min_score=0, page: int = 1, page_size: int = 10, include_vector: bool = False, include_count: bool = True, asc: bool = False, return_curl: bool = False, **kwargs)¶

Multi Product Recommendations with Likes and Dislikes (Search by ids)

Recommendation by retrieving the vectors from the specified list of positive and negative ids documents. Then performing a search with an aggregated vector that is the sum (depends on vector_operation) of positive id vectors minus the negative id vectors.

Parameters

positive_document_ids – Positive Document IDs to get recommendations for, and the weightings of each document
negative_document_ids – Negative Document IDs to get recommendations for, and the weightings of each document
vector_operation – Aggregation for the vectors, choose from [‘mean’, ‘sum’, ‘min’, ‘max’]
collection_name – Name of Collection
search_field – 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)

search_with_positive_negative_ids_as_history(collection_name: str, vector: List, positive_document_ids: List, negative_document_ids: List, field: str, vector_operation: str = 'mean', sum_fields: bool = True, metric: str = 'cosine', min_score=0, page: int = 1, page_size: int = 10, include_vector: bool = False, include_count: bool = True, asc: bool = False, return_curl: bool = False, **kwargs)¶

Multi Product Recommendations with Likes and Dislikes (Search by ids)

Search by retrieving the vectors from the specified list of positive and negative ids documents. Then performing a search with search query vector and aggregated vector, that is the sum (depends on vector_operation) of positive id vectors minus the negative id vectors.

Parameters

vector – Vector, a list/array of floats that represents a piece of data.
positive_document_ids – Positive Document IDs to get recommendations for, and the weightings of each document
negative_document_ids – Negative Document IDs to get recommendations for, and the weightings of each document
vector_operation – Aggregation for the vectors, choose from [‘mean’, ‘sum’, ‘min’, ‘max’]
collection_name – Name of Collection
search_field – 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)

advanced_search(collection_name: str, multivector_query: Dict, sum_fields: bool = True, facets: List = [], filters: List = [], metric: str = 'cosine', min_score=None, page: int = 1, page_size: int = 10, include_vector: bool = False, include_count: bool = True, include_facets: bool = False, asc: bool = False, approx: int = 0, return_curl: bool = False, **kwargs)¶

Advanced Vector Similarity Search. Support for multiple vectors, vector weightings, facets and filtering

Advance Vector Similarity Search, enables machine learning search with vector search. Search with a multiple vectors for the most similar documents.

For example: Search with a product image and description vectors to find the most similar products by what it looks like and what its described to do.

You can also give weightings of each vector field towards the search, e.g. image_vector_ weights 100%, whilst description_vector_ 50%.

Advanced search also supports filtering to only search through filtered results and facets to get the overview of products available when a minimum score is set.

Parameters

collection_name – Name of Collection
multivector_query – Query for advance search that allows for multiple vector and field querying
page – Page of the results
page_size – Size of each page of 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’]
filters – Query for filtering the search results
facets – Fields to include in the facets, if [] then all
min_score – Minimum score for similarity metric
include_vector – Include vectors in the search results
include_count – Include count in the search results
include_facets – Include facets 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)

Example

>>> vi_client = ViCollectionClient(username, api_key, collection_name, url)
>>> advanced_search_query = {
        'text' : {'vector': encode_question("How do I cluster?"), 'fields' : ['function_vector_']}
    }
>>> vi_client.advanced_search(advanced_search_query)

advanced_hybrid_search(collection_name: str, text: str, multivector_query: Dict, text_fields: List, sum_fields: bool = True, facets: List = [], filters: List = [], metric: str = 'cosine', min_score=None, page: int = 1, page_size: int = 10, include_vector: bool = False, include_count: bool = True, include_facets: bool = False, asc: bool = False, return_curl: bool = False, **kwargs)¶

Advanced Search a text field with vector and text using Vector Search and Traditional Search

Advanced Vector similarity search + Traditional Fuzzy Search with text and vector.

You can also give weightings of each vector field towards the search, e.g. image_vector_ weights 100%, whilst description_vector_ 50%.

Advanced search also supports filtering to only search through filtered results and facets to get the overview of products available when a minimum score is set.

Parameters

collection_name – Name of Collection
page – Page of the results
page_size – Size of each page of 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’]
filters – Query for filtering the search results
facets – Fields to include in the facets, if [] then all
min_score – Minimum score for similarity metric
include_vector – Include vectors in the search results
include_count – Include count in the search results
include_facets – Include facets in the search results
hundred_scale – Whether to scale up the metric by 100
multivector_query – Query for advance search that allows for multiple vector and field querying
text – Text Search Query (not encoded as vector)
text_fields – Text fields to search against
traditional_weight – Multiplier of traditional search. A value of 0.025~0.1 is good.
fuzzy – Fuzziness of the search. A value of 1-3 is good.
join –
Whether to consider cases where there is a space in the word. E.g. Go Pro vs GoPro.

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

advanced_search_by_id(collection_name: str, document_id: str, fields: Dict, sum_fields: bool = True, facets: List = [], filters: List = [], metric: str = 'cosine', min_score=None, page: int = 1, page_size: int = 10, include_vector: bool = False, include_count: bool = True, include_facets: bool = False, asc: bool = False, return_curl: bool = False, **kwargs)¶

Advanced Single Product Recommendations (Search by an id).

For example: Search with id of a product in the database, and using the product’s image and description vectors to find the most similar products by what it looks like and what its described to do.

You can also give weightings of each vector field towards the search, e.g. image_vector_ weights 100%, whilst description_vector_ 50%.

Advanced search also supports filtering to only search through filtered results and facets to get the overview of products available when a minimum score is set.

Parameters

collection_name – Name of Collection
page – Page of the results
page_size – Size of each page of 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’]
filters – Query for filtering the search results
facets – Fields to include in the facets, if [] then all
min_score – Minimum score for similarity metric
include_vector – Include vectors in the search results
include_count – Include count in the search results
include_facets – Include facets in the search results
hundred_scale – Whether to scale up the metric by 100
document_id – ID of a document
search_fields –
Vector fields to search against, and the weightings for them.

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

Example

>>> filter_query = [
        {'field': 'field_name',
        'filter_type': 'text',
        'condition_value': 'monkl',
        'condition': '=='}
    ]
>>> results = client.advanced_search_by_id(document_id=client.random_documents()['documents'][0]['_id'],
fields={'image_url_field_flattened_vector_':1}, filters=filter_query)

advanced_search_by_ids(collection_name: str, document_ids: Dict, fields: Dict, vector_operation: str = 'mean', sum_fields: bool = True, facets: List = [], filters: List = [], metric: str = 'cosine', min_score=None, page: int = 1, page_size: int = 10, include_vector: bool = False, include_count: bool = True, include_facets: bool = False, asc: bool = False, return_curl: bool = False, **kwargs)¶

Advanced Multi Product Recommendations (Search by ids).

For example: Search with multiple ids of products in the database, and using the product’s image and description vectors to find the most similar products by what it looks like and what its described to do.

You can also give weightings of each vector field towards the search, e.g. image_vector_ weights 100%, whilst description_vector_ 50%.

You can also give weightings of on each product as well e.g. product ID-A weights 100% whilst product ID-B 50%.

Advanced search also supports filtering to only search through filtered results and facets to get the overview of products available when a minimum score is set.

Parameters

collection_name – Name of Collection
page – Page of the results
page_size – Size of each page of 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’]
filters – Query for filtering the search results
facets – Fields to include in the facets, if [] then all
min_score – Minimum score for similarity metric
include_vector – Include vectors in the search results
include_count – Include count in the search results
include_facets – Include facets in the search results
hundred_scale – Whether to scale up the metric by 100
document_ids – Document IDs to get recommendations for, and the weightings of each document
search_fields – Vector fields to search against, and the weightings for them.
vector_operation –
Aggregation for the vectors, choose from [‘mean’, ‘sum’, ‘min’, ‘max’]

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

advanced_search_by_positive_negative_ids(collection_name: str, positive_document_ids: Dict, negative_document_ids: Dict, fields: Dict, vector_operation: str = 'mean', sum_fields: bool = True, facets: List = [], filters: List = [], metric: str = 'cosine', min_score=None, page: int = 1, page_size: int = 10, include_vector: bool = False, include_count: bool = True, include_facets: bool = False, asc: bool = False, return_curl: bool = False, **kwargs)¶

Advanced Multi Product Recommendations with likes and dislikes (Search by ids).

For example: Search with multiple ids of liked and dislike products in the database. Then using the product’s image and description vectors to find the most similar products by what it looks like and what its described to do against the positives and most disimilar products for the negatives.

You can also give weightings of each vector field towards the search, e.g. image_vector_ weights 100%, whilst description_vector_ 50%.

You can also give weightings of on each product as well e.g. product ID-A weights 100% whilst product ID-B 50%.

Advanced search also supports filtering to only search through filtered results and facets to get the overview of products available when a minimum score is set.

Parameters

collection_name – Name of Collection
page – Page of the results
page_size – Size of each page of 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’]
filters – Query for filtering the search results
facets – Fields to include in the facets, if [] then all
min_score – Minimum score for similarity metric
include_vector – Include vectors in the search results
include_count – Include count in the search results
include_facets – Include facets in the search results
hundred_scale – Whether to scale up the metric by 100
positive_document_ids – Positive Document IDs to get recommendations for, and the weightings of each document
negative_document_ids – Negative Document IDs to get recommendations for, and the weightings of each document
search_fields – Vector fields to search against, and the weightings for them.
vector_operation –
Aggregation for the vectors, choose from [‘mean’, ‘sum’, ‘min’, ‘max’]

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

advanced_search_with_positive_negative_ids_as_history(collection_name: str, vector: List, positive_document_ids: Dict, negative_document_ids: Dict, fields: Dict, vector_operation: str = 'mean', sum_fields: bool = True, facets: List = [], filters: List = [], metric: str = 'cosine', min_score=None, page: int = 1, page_size: int = 10, include_vector: bool = False, include_count: bool = True, include_facets: bool = False, asc: bool = False, return_curl: bool = False, **kwargs)¶

Advanced Search with Likes and Dislikes as history

For example: Vector search of a query vector with multiple ids of liked and dislike products in the database. Then using the product’s image and description vectors to find the most similar products by what it looks like and what its described to do against the positives and most disimilar products for the negatives.

You can also give weightings of each vector field towards the search, e.g. image_vector_ weights 100%, whilst description_vector_ 50%.

You can also give weightings of on each product as well e.g. product ID-A weights 100% whilst product ID-B 50%.

Advanced search also supports filtering to only search through filtered results and facets to get the overview of products available when a minimum score is set.

Parameters

collection_name – Name of Collection
page – Page of the results
page_size – Size of each page of 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’]
filters – Query for filtering the search results
facets – Fields to include in the facets, if [] then all
min_score – Minimum score for similarity metric
include_vector – Include vectors in the search results
include_count – Include count in the search results
include_facets – Include facets in the search results
hundred_scale – Whether to scale up the metric by 100
positive_document_ids – Positive Document IDs to get recommendations for, and the weightings of each document
negative_document_ids – Negative Document IDs to get recommendations for, and the weightings of each document
search_fields – Vector fields to search against, and the weightings for them.
vector_operation – Aggregation for the vectors, choose from [‘mean’, ‘sum’, ‘min’, ‘max’]
vector –
Vector, a list/array of floats that represents a piece of data

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

chunk_search(collection_name: str, vector: List, search_fields: list, chunk_scoring: str = 'max', facets: List = [], filters: List = [], metric: str = 'cosine', sum_fields: bool = True, approx: int = 0, min_score=None, page: int = 1, page_size: int = 20, include_vector: bool = False, include_count: bool = True, include_facets: bool = False, asc: bool = False, return_curl: bool = False, **kwargs)¶: Chunk search functionality :param collection_name: Name of collection :param vector: A list of values :param Search_fields: A list of fields to search :param chunk_scoring: How each chunk should be scored :param approx: How many approximate neighbors to go through