weaviate_agents.query.classes

class weaviate_agents.query.classes.QueryAgentCollectionConfig(*, name, tenant=None, view_properties=None, target_vector=None, additional_filters=None)[source]

Bases: BaseModel

A collection configuration for the QueryAgent.

Parameters:

  • name (str)

  • tenant (str | None)

  • view_properties (list[str] | None)

  • target_vector (str | list[str] | None)

  • additional_filters (Annotated[_Filters, PlainSerializer(func=~weaviate_agents.serialise.serialise_filter, return_type=PydanticUndefined, when_used=always)] | None)

name

The name of the collection to query.

Type:

str

tenant

Tenant name for collections with multi-tenancy enabled.

Type:

str | None

view_properties

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

Type:

list[str] | None

target_vector

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

Type:

str | list[str] | None

additional_filters

Optional filters to apply when the query is executed, in addition to filters selected by the Query Agent (i.e., there are AND combined).

Type:

Annotated[weaviate.collections.classes.filters._Filters, pydantic.functional_serializers.PlainSerializer(func=weaviate_agents.serialise.serialise_filter, return_type=PydanticUndefined, when_used=always)] | None

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.

model_config = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str
tenant: str | None
view_properties: list[str] | None
target_vector: str | list[str] | None
additional_filters: Annotated[_Filters, PlainSerializer(func=serialise_filter, return_type=PydanticUndefined, when_used=always)] | None
class weaviate_agents.query.classes.QueryAgentResponse(*, output_type='final_state', original_query, collection_names, searches, aggregations, usage, total_time, is_partial_answer, missing_information, final_answer, sources)[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.

Parameters:
output_type: Literal['final_state']
original_query: str
collection_names: list[str]
searches: list[list[QueryResultWithCollection]]
aggregations: list[list[AggregationResultWithCollection]]
usage: Usage
total_time: float
is_partial_answer: bool
missing_information: list[str]
final_answer: str
sources: list[Source]
display()[source]

Display a pretty-printed summary of the QueryAgentResponse object.

Return type:

None

model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.Source(*, object_id, collection)[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.

Parameters:

  • object_id (str)

  • collection (str)

object_id: str
collection: str
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.ComparisonOperator(*values)[source]

Bases: str, Enum

EQUALS = '='
LESS_THAN = '<'
GREATER_THAN = '>'
LESS_EQUAL = '<='
GREATER_EQUAL = '>='
NOT_EQUALS = '!='
LIKE = 'LIKE'
CONTAINS_ANY = 'contains_any'
CONTAINS_ALL = 'contains_all'
class weaviate_agents.query.classes.IntegerPropertyFilter(*, filter_type=KnownFilterType.INTEGER, property_name, operator, value)[source]

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.

Parameters:

  • filter_type (Literal[KnownFilterType.INTEGER])

  • property_name (str)

  • operator (ComparisonOperator)

  • value (float)

filter_type: Literal[KnownFilterType.INTEGER]
operator: ComparisonOperator
value: float
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.TextPropertyFilter(*, filter_type=KnownFilterType.TEXT, property_name, operator, value)[source]

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.

Parameters:

  • filter_type (Literal[KnownFilterType.TEXT])

  • property_name (str)

  • operator (ComparisonOperator)

  • value (str)

filter_type: Literal[KnownFilterType.TEXT]
operator: ComparisonOperator
value: str
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.BooleanPropertyFilter(*, filter_type=KnownFilterType.BOOLEAN, property_name, operator, value)[source]

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.

Parameters:

  • filter_type (Literal[KnownFilterType.BOOLEAN])

  • property_name (str)

  • operator (ComparisonOperator)

  • value (bool)

filter_type: Literal[KnownFilterType.BOOLEAN]
operator: ComparisonOperator
value: bool
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.QueryResult(*, queries, filters=[], filter_operators)[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.

Parameters:
queries: list[str | None]
filters: list[list[PropertyFilter]]
filter_operators: Literal['AND', 'OR']
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.NumericMetrics(*values)[source]

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)[source]

Bases: str, Enum

COUNT = 'COUNT'
TYPE = 'TYPE'
TOP_OCCURRENCES = 'TOP_OCCURRENCES'
class weaviate_agents.query.classes.BooleanMetrics(*values)[source]

Bases: str, Enum

COUNT = 'COUNT'
TYPE = 'TYPE'
TOTAL_TRUE = 'TOTAL_TRUE'
TOTAL_FALSE = 'TOTAL_FALSE'
PERCENTAGE_TRUE = 'PERCENTAGE_TRUE'
PERCENTAGE_FALSE = 'PERCENTAGE_FALSE'
class weaviate_agents.query.classes.IntegerPropertyAggregation(*, aggregation_type=KnownAggregationType.INTEGER, property_name, metrics)[source]

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.

Parameters:

  • aggregation_type (Literal[KnownAggregationType.INTEGER])

  • property_name (str)

  • metrics (NumericMetrics)

aggregation_type: Literal[KnownAggregationType.INTEGER]
metrics: NumericMetrics
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.TextPropertyAggregation(*, aggregation_type=KnownAggregationType.TEXT, property_name, metrics, top_occurrences_limit=None)[source]

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.

Parameters:

  • aggregation_type (Literal[KnownAggregationType.TEXT])

  • property_name (str)

  • metrics (TextMetrics)

  • top_occurrences_limit (int | None)

aggregation_type: Literal[KnownAggregationType.TEXT]
metrics: TextMetrics
top_occurrences_limit: int | None
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.BooleanPropertyAggregation(*, aggregation_type=KnownAggregationType.BOOLEAN, property_name, metrics)[source]

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.

Parameters:

  • aggregation_type (Literal[KnownAggregationType.BOOLEAN])

  • property_name (str)

  • metrics (BooleanMetrics)

aggregation_type: Literal[KnownAggregationType.BOOLEAN]
metrics: BooleanMetrics
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.AggregationResult(*, search_query=None, groupby_property=None, aggregations, filters=[])[source]

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.

Parameters:
search_query: str | None
groupby_property: str | None
aggregations: list[PropertyAggregation]
filters: list[PropertyFilter]
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.Usage(*, requests=0, request_tokens=None, response_tokens=None, total_tokens=None, details=None)[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.

Parameters:

  • requests (int | str)

  • request_tokens (int | str | None)

  • response_tokens (int | str | None)

  • total_tokens (int | str | None)

  • details (Dict[str, int] | Dict[str, str] | None)

requests: int | str
request_tokens: int | str | None
response_tokens: int | str | None
total_tokens: int | str | None
details: Dict[str, int] | Dict[str, str] | None
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.AggregationResultWithCollection(*, search_query=None, groupby_property=None, aggregations, filters=[], collection)[source]

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.

Parameters:
collection: str
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.QueryResultWithCollection(*, queries, filters=[], filter_operators, collection)[source]

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.

Parameters:
collection: str
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.BooleanArrayPropertyFilter(*, filter_type=KnownFilterType.BOOLEAN_ARRAY, property_name, operator, value)[source]

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.

Parameters:

  • filter_type (Literal[KnownFilterType.BOOLEAN_ARRAY])

  • property_name (str)

  • operator (ComparisonOperator)

  • value (list[bool])

filter_type: Literal[KnownFilterType.BOOLEAN_ARRAY]
operator: ComparisonOperator
value: list[bool]
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.DateArrayPropertyFilter(*, filter_type=KnownFilterType.DATE_ARRAY, property_name, operator, value)[source]

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.

Parameters:

  • filter_type (Literal[KnownFilterType.DATE_ARRAY])

  • property_name (str)

  • operator (ComparisonOperator)

  • value (list[str])

filter_type: Literal[KnownFilterType.DATE_ARRAY]
operator: ComparisonOperator
value: list[str]
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.DateMetrics(*values)[source]

Bases: str, Enum

COUNT = 'COUNT'
MAX = 'MAXIMUM'
MEDIAN = 'MEDIAN'
MIN = 'MINIMUM'
MODE = 'MODE'
class weaviate_agents.query.classes.DatePropertyAggregation(*, aggregation_type=KnownAggregationType.DATE, property_name, metrics)[source]

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.

Parameters:

  • aggregation_type (Literal[KnownAggregationType.DATE])

  • property_name (str)

  • metrics (DateMetrics)

aggregation_type: Literal[KnownAggregationType.DATE]
metrics: DateMetrics
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.DatePropertyFilter(*, filter_type=KnownFilterType.DATE, property_name, value)[source]

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.

Parameters:

  • filter_type (Literal[KnownFilterType.DATE])

  • property_name (str)

  • value (DateExact | DateRangeFrom | DateRangeTo | DateRangeBetween)

filter_type: Literal[KnownFilterType.DATE]
value: DateExact | DateRangeFrom | DateRangeTo | DateRangeBetween
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.IntegerArrayPropertyFilter(*, filter_type=KnownFilterType.INTEGER_ARRAY, property_name, operator, value)[source]

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.

Parameters:

  • filter_type (Literal[KnownFilterType.INTEGER_ARRAY])

  • property_name (str)

  • operator (ComparisonOperator)

  • value (list[float])

filter_type: Literal[KnownFilterType.INTEGER_ARRAY]
operator: ComparisonOperator
value: list[float]
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.TextArrayPropertyFilter(*, filter_type=KnownFilterType.TEXT_ARRAY, property_name, operator, value)[source]

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.

Parameters:

  • filter_type (Literal[KnownFilterType.TEXT_ARRAY])

  • property_name (str)

  • operator (ComparisonOperator)

  • value (list[str])

filter_type: Literal[KnownFilterType.TEXT_ARRAY]
operator: ComparisonOperator
value: list[str]
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.GeoPropertyFilter(*, filter_type=KnownFilterType.GEO, property_name, latitude, longitude, max_distance_meters)[source]

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.

Parameters:

  • filter_type (Literal[KnownFilterType.GEO])

  • property_name (str)

  • latitude (float)

  • longitude (float)

  • max_distance_meters (float)

filter_type: Literal[KnownFilterType.GEO]
latitude: float
longitude: float
max_distance_meters: float
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.UnknownPropertyAggregation(*, aggregation_type, **extra_data)[source]

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.

Parameters:

  • aggregation_type (None)

  • extra_data (Any)

model_config = {'extra': 'allow'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

aggregation_type: None
classmethod ensure_filter_type_unknown(value)[source]
Parameters:

value (Any)

Return type:

None

model_post_init(context)[source]

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

class weaviate_agents.query.classes.UnknownPropertyFilter(*, filter_type, **extra_data)[source]

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.

Parameters:

  • filter_type (None)

  • extra_data (Any)

model_config = {'extra': 'allow'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

filter_type: None
classmethod ensure_filter_type_unknown(value)[source]
Parameters:

value (Any)

Return type:

None

model_post_init(context)[source]

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

class weaviate_agents.query.classes.ProgressDetails[source]

Bases: TypedDict

queries: list[QueryWithCollection]
class weaviate_agents.query.classes.ProgressMessage(*, output_type='progress_message', stage, message, details={})[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.

Parameters:

  • output_type (Literal['progress_message'])

  • stage (str)

  • message (str)

  • details (ProgressDetails)

output_type: Literal['progress_message']
stage: str
message: str
details: ProgressDetails
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.QueryWithCollection[source]

Bases: TypedDict

query: str
collection: str
class weaviate_agents.query.classes.StreamedThoughts(*, output_type='streamed_thoughts', delta)[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.

Parameters:

  • output_type (Literal['streamed_thoughts'])

  • delta (str)

output_type: Literal['streamed_thoughts']
delta: str
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.StreamedTokens(*, output_type='streamed_tokens', delta)[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.

Parameters:

  • output_type (Literal['streamed_tokens'])

  • delta (str)

output_type: Literal['streamed_tokens']
delta: str
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.IsNullPropertyFilter(*, filter_type=KnownFilterType.IS_NULL, property_name, is_null)[source]

Bases: KnownPropertyFilterBase

Filter by property null state.

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.

Parameters:

  • filter_type (Literal[KnownFilterType.IS_NULL])

  • property_name (str)

  • is_null (bool)

filter_type: Literal[KnownFilterType.IS_NULL]
is_null: bool
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.SearchModeResponseBase(*, searches=None, usage, total_time, search_results)[source]

Bases: BaseModel, ABC, Generic[SearcherT]

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.

Parameters:
searches: list[QueryResultWithCollectionNormalized] | None
usage: ModelUnitUsage
total_time: float
search_results: QueryReturn
abstractmethod next(limit=20, offset=0)[source]
Parameters:

  • self (SearchModeResponseT)

  • limit (int)

  • offset (int)

Return type:

SearchModeResponseT | Coroutine[Any, Any, SearchModeResponseT]

model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context, /)

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

  • self (BaseModel) – The BaseModel instance.

  • context (Any) – The context.

Return type:

None

class weaviate_agents.query.classes.ChatMessage[source]

Bases: TypedDict

role: Literal['user', 'assistant']
content: str
class weaviate_agents.query.classes.AskModeResponse(*, output_type='final_state', searches, aggregations, usage, total_time, is_partial_answer, missing_information, final_answer, sources)[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.

Parameters:
output_type: Literal['final_state']
searches: list[QueryResultWithCollectionNormalized]
aggregations: list[AggregationResultWithCollectionNormalized]
usage: ModelUnitUsage
total_time: float
is_partial_answer: bool | None
missing_information: list[str] | None
final_answer: str
sources: list[Source] | None
display()[source]

Display a pretty-printed summary of the AskModeResponse object.

Return type:

None

model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.ResearchModeResponse(*, output_type='final_state', final_answer, usage, queries, total_time)[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.

Parameters:
output_type: Literal['final_state']
final_answer: str
usage: ModelUnitUsage
queries: list[AskModeResponse]
total_time: float
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.ModelUnitUsage(*, model_units, usage_in_plan, remaining_plan_requests)[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.

Parameters:

  • model_units (int)

  • usage_in_plan (bool)

  • remaining_plan_requests (int)

model_units: int
usage_in_plan: bool
remaining_plan_requests: int
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.FilterAndOr(*, combine, filters)[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.

Parameters:
combine: Literal['AND', 'OR']
filters: list[PropertyFilter | FilterAndOr]
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.QueryResultWithCollectionNormalized(*, query, filters, collection, sort_property=None, uuid_value=None)[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.

Parameters:
query: str | None
filters: PropertyFilter | FilterAndOr | None
collection: str
sort_property: QuerySort | None
uuid_value: UUID | None
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.AggregationResultWithCollectionNormalized(*, groupby_property, aggregation, filters, collection)[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.

Parameters:
groupby_property: str | None
aggregation: PropertyAggregation
filters: PropertyFilter | FilterAndOr | None
collection: str
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.QuerySort(*, property_name, order, tie_break)[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.

Parameters:

  • property_name (str)

  • order (Literal['ascending', 'descending'])

  • tie_break (QuerySort | None)

property_name: str
order: Literal['ascending', 'descending']
tie_break: QuerySort | None
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.UUIDPropertyFilter(*, filter_type=KnownFilterType.UUID, property_name, operator, value)[source]

Bases: KnownPropertyFilterBase

Filter UUID 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.

Parameters:

  • filter_type (Literal[KnownFilterType.UUID])

  • property_name (str)

  • operator (ComparisonOperator)

  • value (UUID)

filter_type: Literal[KnownFilterType.UUID]
property_name: str
operator: ComparisonOperator
value: UUID
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.UUIDArrayPropertyFilter(*, filter_type=KnownFilterType.UUID_ARRAY, property_name, operator, value)[source]

Bases: KnownPropertyFilterBase

Filter UUID array 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.

Parameters:

  • filter_type (Literal[KnownFilterType.UUID_ARRAY])

  • property_name (str)

  • operator (ComparisonOperator)

  • value (list[UUID])

filter_type: Literal[KnownFilterType.UUID_ARRAY]
property_name: str
operator: ComparisonOperator
value: list[UUID]
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.SuggestedQuery(*, query)[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.

Parameters:

query (str)

query: str
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.SuggestQueryResponse(*, queries, collection_count, usage, total_time)[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.

Parameters:
queries: list[SuggestedQuery]
collection_count: int
usage: ModelUnitUsage
total_time: float
model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class weaviate_agents.query.classes.SearchModeResponse(*, searches=None, usage, total_time, search_results)[source]

Bases: SearchModeResponseBase[_SyncSearcher]

Response for the Query Agent search-only mode.

This contains the results of the search, the usage, and the underlying searches performed. You can paginate through the results set by calling the next method on this response with different limit / offset values. This will result in the same underlying searches being performed each time, resulting in a consistent results set across pages.

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.

Parameters:
next(limit=20, offset=0)[source]

Paginate the search-only results with the given limit and offset values.

Parameters:

  • limit (int) – The maximum number of results to return. If not specified, this defaults to 20.

  • offset (int) – The offset to start from. If not specified, the retrieval begins from the first object in the results set.

Returns:

The next SearchModeResponse page.

Return type:

SearchModeResponse

model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context, /)

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

  • self (BaseModel) – The BaseModel instance.

  • context (Any) – The context.

Return type:

None

class weaviate_agents.query.classes.AsyncSearchModeResponse(*, searches=None, usage, total_time, search_results)[source]

Bases: SearchModeResponseBase[_AsyncSearcher]

Response for the Query Agent search-only mode (async).

This contains the results of the search, the usage, and the underlying searches performed. You can paginate through the results set by calling the next method on this response with different limit / offset values. This will result in the same underlying searches being performed each time, resulting in a consistent results set across pages.

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.

Parameters:
async next(limit=20, offset=0)[source]

Paginate the search-only results with the given limit and offset values.

Parameters:

  • limit (int) – The maximum number of results to return. If not specified, this defaults to 20.

  • offset (int) – The offset to start from. If not specified, the retrieval begins from the first object in the results set.

Returns:

The next AsyncSearchModeResponse page.

Return type:

AsyncSearchModeResponse

model_config = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context, /)

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

  • self (BaseModel) – The BaseModel instance.

  • context (Any) – The context.

Return type:

None