Abstract: System and methods are described for generating training data for a natural language search system, the training data including synthetic user queries and associated structured query language (SQL) statements. A method includes generating an abstract query representation from a probabilistic context-free grammar (PCFG), selecting a sample record from a database using the abstract query representation, generating a tagged user query from the abstract query representation and the sample record, and generating the one or more SQL statements from the tagged user query and one or more SQL templates.

Abstract: A database system may receive a natural language query that is associated with a tenant of a multi-tenant system. The natural language query may be parsed into a set of tokens, and the set of tokens may be tagged, using a tenant specific tagging model associated with the tenant, the set of tokens with at least one pre-configured data type identifier that is configured for the plurality of tenants. A global tagging model that supports the plurality of tenants of the multi-tenant system may tag the set of tokens with at least one category identifier. The global tagging model may use the natural language query and the pre-configured data type identifier to identify the at least one category identifier. The system may execute a database query on a database associated with the tenant using the at least one pre-configured data type identifier and the at least one category identifier.

Abstract: In disclosed techniques, a computing system causes presentation of a user interface having an input field operable to receive, from a user, a search query for a database. The computing system may classify the search query by: determining whether the search query includes terms that are within a specified vocabulary indicative of a natural language query and determining whether the search query includes terms that identify an object defined in a schema of the database. In response to classifying the search query as a natural language query, the computing system returns query results determined by identifying values in the database corresponding to the object defined in the schema. In response to classifying the search query as a keyword query, the computing system returns query results determined by comparing terms of the search query to values within records in the database.

Abstract: Methods, systems, and devices supporting determining user and data record relationships based on vector space embeddings are described. Some database systems may receive data record access indications corresponding to data records accessed by users. A database system may generate, based on the data record access indications, user sessions for the users, data record sessions for the data records, or a combination for users and data records. For example, a user session may correspond to a respective user and include a record identifier associated with each data record accessed by the user. The system may generate, in a vector space, vectors from the sessions using an embedding operation, where each vector corresponds to a respective user or data record. The system may determine relationships between the users, data records, or both based on the vectors and may transmit an indication of at least one data record based on the relationships.

Abstract: A database system may receive a natural language query that is associated with a tenant of a multi-tenant system. The natural language query may be parsed into a set of tokens, and the set of tokens may be tagged, using a tenant specific tagging model associated with the tenant, the set of tokens with at least one pre-configured data type identifier that is configured for the plurality of tenants. A global tagging model that supports the plurality of tenants of the multi-tenant system may tag the set of tokens with at least one category identifier. The global tagging model may use the natural language query and the pre-configured data type identifier to identify the at least one category identifier. The system may execute a database query on a database associated with the tenant using the at least one pre-configured data type identifier and the at least one category identifier.

Abstract: System and methods are described for generating training data for a natural language search system, the training data including synthetic user queries and associated structured query language (SQL) statements. A method includes generating an abstract query representation from a probabilistic context-free grammar (PCFG), selecting a sample record from a database using the abstract query representation, generating a tagged user query from the abstract query representation and the sample record, and generating the one or more SQL statements from the tagged user query and one or more SQL templates.

Abstract: Methods, systems, and devices supporting determining user and data record relationships based on vector space embeddings are described. Some database systems may receive data record access indications corresponding to data records accessed by users. A database system may generate, based on the data record access indications, user sessions for the users, data record sessions for the data records, or a combination for users and data records. For example, a user session may correspond to a respective user and include a record identifier associated with each data record accessed by the user. The system may generate, in a vector space, vectors from the sessions using an embedding operation, where each vector corresponds to a respective user or data record. The system may determine relationships between the users, data records, or both based on the vectors and may transmit an indication of at least one data record based on the relationships.

Abstract: In disclosed techniques, a computing system causes presentation of a user interface having an input field operable to receive, from a user, a search query for a database. The computing system may classify the search query by: determining whether the search query includes terms that are within a specified vocabulary indicative of a natural language query and determining whether the search query includes terms that identify an object defined in a schema of the database. In response to classifying the search query as a natural language query, the computing system returns query results determined by identifying values in the database corresponding to the object defined in the schema. In response to classifying the search query as a keyword query, the computing system returns query results determined by comparing terms of the search query to values within records in the database.