weaviate_agents.query.classes

pydantic model weaviate_agents.query.classes.QueryAgentCollectionConfig

Bases: BaseModel

A collection configuration for the QueryAgent.

name

The name of the collection to query.

tenant

Tenant name for collections with multi-tenancy enabled.

view_properties

Optional list of property names the agent has the ability to view for this specific collection.

target_vector

Optional target vector name(s) for collections with named vectors. Can be a single vector name or a list of vector names.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field name: str [Required]

field target_vector: str | list[str] | None = None

field tenant: str | None = None

field view_properties: list[str] | None = None

pydantic model weaviate_agents.query.classes.QueryAgentResponse

Bases: BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field aggregation_answer: str | None = None

field aggregations: list[list[AggregationResultWithCollection]] [Required]

field collection_names: list[str] [Required]

field final_answer: str [Required]

field has_aggregation_answer: bool [Required]

field has_search_answer: bool [Required]

field is_partial_answer: bool [Required]

field missing_information: list[str] [Required]

field original_query: str [Required]

field searches: list[list[QueryResultWithCollection]] [Required]

field sources: list[Source] [Required]

field total_time: float [Required]

field usage: Usage [Required]

display()

Display a pretty-printed summary of the QueryAgentResponse object.

Return type:

None

pydantic model weaviate_agents.query.classes.Source

Bases: BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field collection: str [Required]

field object_id: str [Required]

class weaviate_agents.query.classes.ComparisonOperator(*values)

Bases: str, Enum

EQUALS = '='

LESS_THAN = '<'

GREATER_THAN = '>'

LESS_EQUAL = '<='

GREATER_EQUAL = '>='

NOT_EQUALS = '!='

LIKE = 'LIKE'

CONTAINS_ANY = 'contains_any'

CONTAINS_ALL = 'contains_all'

pydantic model weaviate_agents.query.classes.IntegerPropertyFilter

Bases: KnownPropertyFilterBase

Filter numeric properties using comparison operators.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field filter_type: Literal[KnownFilterType.INTEGER] = KnownFilterType.INTEGER

field operator: ComparisonOperator [Required]

field property_name: str [Required]

field value: float [Required]

pydantic model weaviate_agents.query.classes.TextPropertyFilter

Bases: KnownPropertyFilterBase

Filter text properties using equality or LIKE operators.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field filter_type: Literal[KnownFilterType.TEXT] = KnownFilterType.TEXT

field operator: ComparisonOperator [Required]

field property_name: str [Required]

field value: str [Required]

pydantic model weaviate_agents.query.classes.BooleanPropertyFilter

Bases: KnownPropertyFilterBase

Filter boolean properties using equality operators.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field filter_type: Literal[KnownFilterType.BOOLEAN] = KnownFilterType.BOOLEAN

field operator: ComparisonOperator [Required]

field property_name: str [Required]

field value: bool [Required]

pydantic model weaviate_agents.query.classes.QueryResult

Bases: BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field filter_operators: Literal['AND', 'OR'] [Required]

field filters: list[list[IntegerPropertyFilter | IntegerArrayPropertyFilter | TextPropertyFilter | TextArrayPropertyFilter | BooleanPropertyFilter | BooleanArrayPropertyFilter | DatePropertyFilter | DateArrayPropertyFilter | GeoPropertyFilter | UnknownPropertyFilter]] = []

field queries: list[str] [Required]

class weaviate_agents.query.classes.NumericMetrics(*values)

Bases: str, Enum

COUNT = 'COUNT'

MAX = 'MAXIMUM'

MEAN = 'MEAN'

MEDIAN = 'MEDIAN'

MIN = 'MINIMUM'

MODE = 'MODE'

SUM = 'SUM'

TYPE = 'TYPE'

class weaviate_agents.query.classes.TextMetrics(*values)

Bases: str, Enum

COUNT = 'COUNT'

TYPE = 'TYPE'

TOP_OCCURRENCES = 'TOP_OCCURRENCES'

class weaviate_agents.query.classes.BooleanMetrics(*values)

Bases: str, Enum

COUNT = 'COUNT'

TYPE = 'TYPE'

TOTAL_TRUE = 'TOTAL_TRUE'

TOTAL_FALSE = 'TOTAL_FALSE'

PERCENTAGE_TRUE = 'PERCENTAGE_TRUE'

PERCENTAGE_FALSE = 'PERCENTAGE_FALSE'

pydantic model weaviate_agents.query.classes.IntegerPropertyAggregation

Bases: KnownPropertyAggregationBase

Aggregate numeric properties using statistical functions.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field aggregation_type: Literal[KnownAggregationType.INTEGER] = KnownAggregationType.INTEGER

field metrics: NumericMetrics [Required]

field property_name: str [Required]

pydantic model weaviate_agents.query.classes.TextPropertyAggregation

Bases: KnownPropertyAggregationBase

Aggregate text properties using frequency analysis.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field aggregation_type: Literal[KnownAggregationType.TEXT] = KnownAggregationType.TEXT

field metrics: TextMetrics [Required]

field property_name: str [Required]

field top_occurrences_limit: int | None = None

pydantic model weaviate_agents.query.classes.BooleanPropertyAggregation

Bases: KnownPropertyAggregationBase

Aggregate boolean properties using statistical functions.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field aggregation_type: Literal[KnownAggregationType.BOOLEAN] = KnownAggregationType.BOOLEAN

field metrics: BooleanMetrics [Required]

field property_name: str [Required]

pydantic model weaviate_agents.query.classes.AggregationResult

Bases: BaseModel

The aggregations to be performed on a collection in a vector database.

They should be based on the original user query and can include multiple aggregations across different properties and metrics.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field aggregations: list[IntegerPropertyAggregation | TextPropertyAggregation | BooleanPropertyAggregation | DatePropertyAggregation | UnknownPropertyAggregation] [Required]

field filters: list[IntegerPropertyFilter | IntegerArrayPropertyFilter | TextPropertyFilter | TextArrayPropertyFilter | BooleanPropertyFilter | BooleanArrayPropertyFilter | DatePropertyFilter | DateArrayPropertyFilter | GeoPropertyFilter | UnknownPropertyFilter] = []

field groupby_property: str | None = None

field search_query: str | None = None

pydantic model weaviate_agents.query.classes.Usage

Bases: BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field details: Dict[str, int] | Dict[str, str] | None = None

field request_tokens: int | str | None = None

field requests: int | str = 0

field response_tokens: int | str | None = None

field total_tokens: int | str | None = None

pydantic model weaviate_agents.query.classes.AggregationResultWithCollection

Bases: AggregationResult

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field aggregations: list[PropertyAggregation] [Required]

field collection: str [Required]

field filters: list[PropertyFilter] = []

field groupby_property: str | None = None

field search_query: str | None = None

pydantic model weaviate_agents.query.classes.QueryResultWithCollection

Bases: QueryResult

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field collection: str [Required]

field filter_operators: Literal['AND', 'OR'] [Required]

field filters: list[list[PropertyFilter]] = []

field queries: list[str] [Required]

pydantic model weaviate_agents.query.classes.BooleanArrayPropertyFilter

Bases: KnownPropertyFilterBase

Filter boolean-array properties using equality operators.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field filter_type: Literal[KnownFilterType.BOOLEAN_ARRAY] = KnownFilterType.BOOLEAN_ARRAY

field operator: ComparisonOperator [Required]

field property_name: str [Required]

field value: list[bool] [Required]

pydantic model weaviate_agents.query.classes.DateArrayPropertyFilter

Bases: KnownPropertyFilterBase

Filter datetime properties using equality operators.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field filter_type: Literal[KnownFilterType.DATE_ARRAY] = KnownFilterType.DATE_ARRAY

field operator: ComparisonOperator [Required]

field property_name: str [Required]

field value: list[str] [Required]

class weaviate_agents.query.classes.DateMetrics(*values)

Bases: str, Enum

COUNT = 'COUNT'

MAX = 'MAXIMUM'

MEDIAN = 'MEDIAN'

MIN = 'MINIMUM'

MODE = 'MODE'

pydantic model weaviate_agents.query.classes.DatePropertyAggregation

Bases: KnownPropertyAggregationBase

Aggregate datetime properties using statistical functions.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field aggregation_type: Literal[KnownAggregationType.DATE] = KnownAggregationType.DATE

field metrics: DateMetrics [Required]

field property_name: str [Required]

pydantic model weaviate_agents.query.classes.DatePropertyFilter

Bases: KnownPropertyFilterBase

Filter datetime properties using equality operators.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field filter_type: Literal[KnownFilterType.DATE] = KnownFilterType.DATE

field property_name: str [Required]

field value: DateExact | DateRangeFrom | DateRangeTo | DateRangeBetween [Required]

pydantic model weaviate_agents.query.classes.IntegerArrayPropertyFilter

Bases: KnownPropertyFilterBase

Filter numeric-array properties using comparison operators.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field filter_type: Literal[KnownFilterType.INTEGER_ARRAY] = KnownFilterType.INTEGER_ARRAY

field operator: ComparisonOperator [Required]

field property_name: str [Required]

field value: list[float] [Required]

pydantic model weaviate_agents.query.classes.TextArrayPropertyFilter

Bases: KnownPropertyFilterBase

Filter text-array properties using equality or LIKE operators.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field filter_type: Literal[KnownFilterType.TEXT_ARRAY] = KnownFilterType.TEXT_ARRAY

field operator: ComparisonOperator [Required]

field property_name: str [Required]

field value: list[str] [Required]

pydantic model weaviate_agents.query.classes.GeoPropertyFilter

Bases: KnownPropertyFilterBase

Filter geo-coordinates properties.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field filter_type: Literal[KnownFilterType.GEO] = KnownFilterType.GEO

field latitude: float [Required]

field longitude: float [Required]

field max_distance_meters: float [Required]

field property_name: str [Required]

pydantic model weaviate_agents.query.classes.UnknownPropertyAggregation

Bases: BaseModel

Catch-all aggregation for unknown aggregation types, to preserve future back-compatibility.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field aggregation_type: None [Required]

Validated by:

• ensure_filter_type_unknown

model_post_init(context)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Parameters:

context (Any)

Return type:

None

pydantic model weaviate_agents.query.classes.UnknownPropertyFilter

Bases: BaseModel

Catch-all filter for unknown filter types, to preserve future back-compatibility.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field filter_type: None [Required]

Validated by:

• ensure_filter_type_unknown

model_post_init(context)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Parameters:

context (Any)

Return type:

None