agentscope.pipelines

Import all pipeline related modules in the package.

class agentscope.pipelines.PipelineBase[源代码]

基类：Operator

Base interface of all pipelines.

The pipeline is a special kind of operator that includes multiple operators and the interaction logic among them.

__init__() → None[源代码]

class agentscope.pipelines.SequentialPipeline(operators: Sequence[Operator])[源代码]

基类：PipelineBase

A template pipeline for implementing sequential logic.

Sequential(operators) represents the following workflow:

x = operators[0](x)
x = operators[1](x)
...
x = operators[n](x)

__init__(operators: Sequence[Operator]) → None[源代码]

Initialize a Sequential pipeline.

参数:: operators (Sequence[Operator]) – A Sequence of operators to be executed sequentially.

class agentscope.pipelines.IfElsePipeline(condition_func: ~typing.Callable[[dict], bool], if_body_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator], else_body_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator] = <function placeholder>)[源代码]

基类：PipelineBase

A template pipeline for implementing control flow like if-else.

IfElsePipeline(condition_func, if_body_operators, else_body_operators) represents the following workflow:

if condition_func(x):
    if_body_operators(x)
else:
    else_body_operators(x)

__init__(condition_func: ~typing.Callable[[dict], bool], if_body_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator], else_body_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator] = <function placeholder>) → None[源代码]

Initialize an IfElsePipeline.

参数:

condition_func (Callable[[dict], bool]) – A function that determines whether to execute if_body_operators or else_body_operators based on the input x.
if_body_operators (Operators) – Operators executed when condition_func returns True.
else_body_operators (Operators) – Operators executed when condition_func returns False, does nothing and just return the input by default.

class agentscope.pipelines.SwitchPipeline(condition_func: ~typing.Callable[[dict], ~typing.Any], case_operators: ~typing.Mapping[~typing.Any, ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator]], default_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator] = <function placeholder>)[源代码]

基类：PipelineBase

A template pipeline for implementing control flow like switch-case.

SwitchPipeline(condition_func, case_operators, default_operators) represents the following workflow:

switch condition_func(x):
    case k1: return case_operators[k1](x)
    case k2: return case_operators[k2](x)
    ...
    default: return default_operators(x)

__init__(condition_func: ~typing.Callable[[dict], ~typing.Any], case_operators: ~typing.Mapping[~typing.Any, ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator]], default_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator] = <function placeholder>) → None[源代码]

Initialize a SwitchPipeline.

参数:

condition_func (Callable[[dict], Any]) – A function that determines which case_operator to execute based on the input x.
case_operators (dict[Any, Operators]) – A dictionary containing multiple operators and their corresponding trigger conditions.
default_operators (Operators, defaults to placeholder) – Operators that are executed when the actual condition do not meet any of the case_operators, does nothing and just return the input by default.

class agentscope.pipelines.ForLoopPipeline(loop_body_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator], max_loop: int, break_func: ~typing.Callable[[dict], bool] = <function ForLoopPipeline.<lambda>>)[源代码]

基类：PipelineBase

A template pipeline for implementing control flow like for-loop

ForLoopPipeline(loop_body_operators, max_loop) represents the following workflow:

for i in range(max_loop):
    x = loop_body_operators(x)

ForLoopPipeline(loop_body_operators, max_loop, break_func) represents the following workflow:

for i in range(max_loop):
    x = loop_body_operators(x)
    if break_func(x):
        break

__init__(loop_body_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator], max_loop: int, break_func: ~typing.Callable[[dict], bool] = <function ForLoopPipeline.<lambda>>)[源代码]

Initialize a ForLoopPipeline.

参数:

loop_body_operators (Operators) – Operators executed as the body of the loop.
max_loop (int) – Maximum number of loop executions.
(`Callable[[dict] (break_func) –
bool]`
_ (defaults to `lambda) –
False`) – A function used to determine whether to break out of the loop based on the output of the loop_body_operators.

class agentscope.pipelines.WhileLoopPipeline(loop_body_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator], condition_func: ~typing.Callable[[int, dict], bool] = <function WhileLoopPipeline.<lambda>>)[源代码]

基类：PipelineBase

A template pipeline for implementing control flow like while-loop

WhileLoopPipeline(loop_body_operators, condition_operator, condition_func) represents the following workflow:

i = 0
while (condition_func(i, x))
    x = loop_body_operators(x)
    i += 1

__init__(loop_body_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator], condition_func: ~typing.Callable[[int, dict], bool] = <function WhileLoopPipeline.<lambda>>)[源代码]

Initialize a WhileLoopPipeline.

参数:

loop_body_operators (Operators) – Operators executed as the body of the loop.
(`Callable[[int (condition_func) –
dict]
bool]`
to (defaults)
_ (`lambda) –
False`): A function that determines whether to continue executing the loop body based on the current loop number and output of the loop_body_operator
__ – False`): A function that determines whether to continue executing the loop body based on the current loop number and output of the loop_body_operator

agentscope.pipelines.sequentialpipeline(operators: Sequence[Operator], x: dict | None = None) → dict[源代码]

Functional version of SequentialPipeline.

参数:

operators (Sequence[Operator]) – Participating operators.
x (Optional[dict], defaults to None) – The input dictionary.

返回:

the output dictionary.

返回类型:

dict

agentscope.pipelines.ifelsepipeline(condition_func: ~typing.Callable, if_body_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator], else_body_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator] = <function placeholder>, x: dict | None = None) → dict[源代码]

Functional version of IfElsePipeline.

参数:

condition_func (Callable) – A function that determines whether to exeucte if_body_operator or else_body_operator based on x.
if_body_operator (Operators) – Operators executed when condition_func returns True.
else_body_operator (Operators, defaults to placeholder) – Operators executed when condition_func returns False, does nothing and just return the input by default.
x (Optional[dict], defaults to None) – The input dictionary.

返回:

the output dictionary.

返回类型:

dict

agentscope.pipelines.switchpipeline(condition_func: ~typing.Callable[[~typing.Any], ~typing.Any], case_operators: ~typing.Mapping[~typing.Any, ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator]], default_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator] = <function placeholder>, x: dict | None = None) → dict[源代码]

Functional version of SwitchPipeline.

参数:

condition_func (Callable[[Any], Any]) – A function that determines which case_operator to execute based on the input x.
case_operators (Mapping[Any, Operator]) – A dictionary containing multiple operators and their corresponding trigger conditions.
default_operators (Operators, defaults to placeholder) – Operators that are executed when the actual condition do not meet any of the case_operators, does nothing and just return the input by default.
x (Optional[dict], defaults to None) – The input dictionary.

返回:

the output dictionary.

返回类型:

dict

agentscope.pipelines.forlooppipeline(loop_body_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator], max_loop: int, break_func: ~typing.Callable[[dict], bool] = <function <lambda>>, x: dict | None = None) → dict[源代码]

Functional version of ForLoopPipeline.

参数:

loop_body_operators (Operators) – Operators executed as the body of the loop.
max_loop (int) – maximum number of loop executions.
break_func (Callable[[dict], bool]) – A function used to determine whether to break out of the loop based on the output of the loop_body_operator, defaults to lambda _: False
x (Optional[dict], defaults to None) – The input dictionary.

返回:

The output dictionary.

返回类型:

dict

agentscope.pipelines.whilelooppipeline(loop_body_operators: ~agentscope.agents.operator.Operator | ~typing.Sequence[~agentscope.agents.operator.Operator], condition_func: ~typing.Callable[[int, ~typing.Any], bool] = <function <lambda>>, x: dict | None = None) → dict[源代码]

Functional version of WhileLoopPipeline.

参数:

loop_body_operators (Operators) – Operators executed as the body of the loop.
condition_func (Callable[[int, Any], bool], optional) – A function that determines whether to continue executing the loop body based on the current loop number and output of the loop_body_operator, defaults to lambda _,__: False
x (Optional[dict], defaults to None) – The input dictionary.

返回:

the output dictionary.

返回类型:

dict