Vector database sharding
Multimodal search
JaguarDB quantization
JaguarDB Vector API
Best Vector databases
JaguarDB in Docker
Setup JaguarDB with tar package
Setup JaguarDB on multiple nodes
Vector index sharing
How zeromove works
Video introduction
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Example: Hybrid Search
In various application scenarios, there arises a need for users to perform targeted queries on a dataset, ensuring that the retrieved data records not only adhere to certain criteria but also exhibit a certain level of similarity to a provided data sample. This intricate task demands the identification of vectors that are both closely related and satisfy specific prerequisites. With the innovative capabilities of JaguarDB, this complex process can be streamlined into a single step. Through the integration of similarity search alongside selective criteria, JaguarDB facilitates the discovery of nearest neighbors that fulfill predefined qualifications. This advanced functionality empowers users to seamlessly locate a subset of data records and subsequently assess their likeness to a reference vector, resulting in the assignment of similarity rankings. By accounting both aspects of similarity and tailored selection, this approach significantly mitigates the potential for inaccuracies, making it particularly well-suited for environments characterized by stringent business requirements.
The following similarity search statement is extended with the "where" clause to filter the nearest neighbors of the query vector:
select similarity(v, '0.1, 0.2, 0.3, 0.4, 0.5, 0.3, 0.1',
'topk=100,type=manhatten_fraction_float')
from vectab
where customer_region=’NE’ and marriage_status=’single’
In this context, the "customer_region" and "marriage_status" are two fields in the table "vectab" that may describe two attributes of a patient. The field "v" may represent a vector from history records or imagery data. The search process involves the establishment of a topK records subset, containing a specified count of 100 records, which are inspected to see if they match the criteria given by the where predicates. The intersection of the two sets of records is returned to the user. In production environments, any type and any number of attributes can contribute to the exact matching process.
The unique convergence of similarity-based search and tailored qualification selection brings unprecedented efficiency to the intricate task of querying and comparison. Once a cohort of relevant data records is extracted, their alignment with a given vector is precisely evaluated, generating a hierarchy of similarity rankings. This integrated approach is instrumental in refining the matching process, ensuring that data records not only exhibit the desired attributes but also possess a designated degree of resemblance to a reference sample. This holistic functionality carries substantial benefits, especially in high-stakes scenarios where precision is paramount. By converging the twin challenges of similarity and criterion-based filtering, JaguarDB effectively minimizes the potential for inaccuracies, offering a robust solution for industries demanding precise data retrieval and analysis.
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