Skills
AnalysisSkill
Bases: Skill
Analysis skill that analyzes a dataframe and returns a record (e.g. for data analysis purposes). See base class Skill for more information about the attributes.
Source code in adala/skills/_base.py
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apply(input, runtime)
Applies the skill to a dataframe and returns a record.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input |
InternalDataFrame
|
The input data to be processed. |
required |
runtime |
Runtime
|
The runtime instance to be used for processing. |
required |
Returns:
Name | Type | Description |
---|---|---|
InternalSeries |
InternalDataFrame
|
The record containing the analysis results. |
Source code in adala/skills/_base.py
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improve(**kwargs)
Improves the skill.
Source code in adala/skills/_base.py
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SampleTransformSkill
Bases: TransformSkill
Source code in adala/skills/_base.py
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apply(input, runtime)
Applies the skill to a dataframe and returns a dataframe.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input |
InternalDataFrame
|
The input data to be processed. |
required |
runtime |
Runtime
|
The runtime instance to be used for processing. |
required |
Returns:
Name | Type | Description |
---|---|---|
InternalDataFrame |
InternalDataFrame
|
The processed data. |
Source code in adala/skills/_base.py
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Skill
Bases: BaseModelInRegistry
Abstract base class representing a skill.
Provides methods to interact with and obtain information about skills.
Attributes:
Name | Type | Description |
---|---|---|
name |
str
|
Unique name of the skill. |
instructions |
str
|
Instructs agent what to do with the input data. |
input_template |
str
|
Template for the input data. |
output_template |
str
|
Template for the output data. |
description |
Optional[str]
|
Description of the skill. |
field_schema |
Optional[Dict]
|
Field JSON schema to use in the templates. Defaults to all fields are strings, i.e. analogous to {"field_n": {"type": "string"}}. |
extra_fields |
Optional[Dict[str, str]]
|
Extra fields to use in the templates. Defaults to None. |
instructions_first |
bool
|
Flag indicating if instructions should be executed before input. Defaults to True. |
verbose |
bool
|
Flag indicating if runtime outputs should be verbose. Defaults to False. |
frozen |
bool
|
Flag indicating if the skill is frozen. Defaults to False. |
type |
ClassVar[str]
|
Type of the skill. |
Source code in adala/skills/_base.py
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apply(input, runtime)
abstractmethod
Base method for applying the skill.
Source code in adala/skills/_base.py
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get_output_fields()
Retrieves output fields.
Returns:
Type | Description |
---|---|
List[str]: A list of output fields. |
Source code in adala/skills/_base.py
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improve(predictions, train_skill_output, feedback, runtime)
abstractmethod
Base method for improving the skill.
Source code in adala/skills/_base.py
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SynthesisSkill
Bases: Skill
Synthesis skill that synthesize a dataframe from a record (e.g. for dataset generation purposes). See base class Skill for more information about the attributes.
Source code in adala/skills/_base.py
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apply(input, runtime)
Applies the skill to a record and returns a dataframe.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input |
InternalSeries
|
The input data to be processed. |
required |
runtime |
Runtime
|
The runtime instance to be used for processing. |
required |
Returns:
Name | Type | Description |
---|---|---|
InternalDataFrame |
InternalDataFrame
|
The synthesized data. |
Source code in adala/skills/_base.py
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improve(**kwargs)
Improves the skill.
Source code in adala/skills/_base.py
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TransformSkill
Bases: Skill
Transform skill that transforms a dataframe to another dataframe (e.g. for data annotation purposes). See base class Skill for more information about the attributes.
Source code in adala/skills/_base.py
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aapply(input, runtime)
async
Applies the skill to a dataframe and returns another dataframe.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input |
InternalDataFrame
|
The input data to be processed. |
required |
runtime |
Runtime
|
The runtime instance to be used for processing. |
required |
Returns:
Name | Type | Description |
---|---|---|
InternalDataFrame |
InternalDataFrame
|
The transformed data. |
Source code in adala/skills/_base.py
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apply(input, runtime)
Applies the skill to a dataframe and returns another dataframe.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input |
InternalDataFrame
|
The input data to be processed. |
required |
runtime |
Runtime
|
The runtime instance to be used for processing. |
required |
Returns:
Name | Type | Description |
---|---|---|
InternalDataFrame |
InternalDataFrame
|
The transformed data. |
Source code in adala/skills/_base.py
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improve(predictions, train_skill_output, feedback, runtime, add_cot=False)
Improves the skill.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
predictions |
InternalDataFrame
|
The predictions made by the skill. |
required |
train_skill_output |
str
|
The name of the output field of the skill. |
required |
feedback |
InternalDataFrame
|
The feedback provided by the user. |
required |
runtime |
Runtime
|
The runtime instance to be used for processing (CURRENTLY SUPPORTS ONLY |
required |
add_cot |
bool
|
Flag indicating if the skill should be used the Chain-of-Thought strategy. Defaults to False. |
False
|
Source code in adala/skills/_base.py
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LinearSkillSet
Bases: SkillSet
Represents a sequence of skills that are acquired in a specific order to achieve a goal.
LinearSkillSet ensures that skills are applied in a sequential manner.
Attributes:
Name | Type | Description |
---|---|---|
skills |
Union[List[Skill], Dict[str, Skill]]
|
Provided skills |
skill_sequence |
List[str]
|
Ordered list of skill names indicating the order in which they should be acquired. |
Examples:
Create a LinearSkillSet with a list of skills specified as BaseSkill instances:
>>> from adala.skills import LinearSkillSet, TransformSkill, AnalysisSkill, ClassificationSkill
>>> skillset = LinearSkillSet(skills=[TransformSkill(), ClassificationSkill(), AnalysisSkill()])
Source code in adala/skills/skillset.py
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__rich__()
Returns a rich representation of the skill.
Source code in adala/skills/skillset.py
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aapply(input, runtime, improved_skill=None)
async
Sequentially and asynchronously applies each skill on the dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input |
InternalDataFrame
|
Input dataset. |
required |
runtime |
AsyncRuntime
|
The runtime environment in which to apply the skills. |
required |
improved_skill |
Optional[str]
|
Name of the skill to improve. Defaults to None. |
None
|
Returns: InternalDataFrame: Skill predictions.
Source code in adala/skills/skillset.py
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apply(input, runtime, improved_skill=None)
Sequentially applies each skill on the dataset.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input |
InternalDataFrame
|
Input dataset. |
required |
runtime |
Runtime
|
The runtime environment in which to apply the skills. |
required |
improved_skill |
Optional[str]
|
Name of the skill to improve. Defaults to None. |
None
|
Returns: InternalDataFrame: Skill predictions.
Source code in adala/skills/skillset.py
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skill_sequence_validator()
Validates and sets the default order for the skill sequence if not provided.
Returns:
Name | Type | Description |
---|---|---|
LinearSkillSet |
LinearSkillSet
|
The current instance with updated skill_sequence attribute. |
Source code in adala/skills/skillset.py
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ParallelSkillSet
Bases: SkillSet
Represents a set of skills that are acquired simultaneously to reach a goal.
In a ParallelSkillSet, each skill can be developed independently of the others. This is useful for agents that require multiple, diverse capabilities, or tasks where each skill contributes a piece of the overall solution.
Examples:
Create a ParallelSkillSet with a list of skills specified as BaseSkill instances
>>> from adala.skills import ParallelSkillSet, ClassificationSkill, TransformSkill
>>> skillset = ParallelSkillSet(skills=[ClassificationSkill(), TransformSkill()])
Source code in adala/skills/skillset.py
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apply(input, runtime, improved_skill=None)
Applies each skill on the dataset, enhancing the agent's experience.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input |
Union[Record, InternalDataFrame]
|
Input data |
required |
runtime |
Runtime
|
The runtime environment in which to apply the skills. |
required |
improved_skill |
Optional[str]
|
Unused in ParallelSkillSet. Defaults to None. |
None
|
Returns: Union[Record, InternalDataFrame]: Skill predictions.
Source code in adala/skills/skillset.py
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SkillSet
Bases: BaseModel
, ABC
Represents a collection of interdependent skills aiming to achieve a specific goal.
A skill set breaks down the path to achieve a goal into necessary precursor skills. Agents can evolve these skills either in parallel for tasks like self-consistency or sequentially for complex problem decompositions and causal reasoning. In the most generic cases, task decomposition can involve a graph-based approach.
Attributes:
Name | Type | Description |
---|---|---|
skills |
Dict[str, Skill]
|
A dictionary of skills in the skill set. |
Source code in adala/skills/skillset.py
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__getitem__(skill_name)
Select skill by name.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
skill_name |
str
|
Name of the skill to select. |
required |
Returns:
Name | Type | Description |
---|---|---|
BaseSkill |
Skill
|
Skill |
Source code in adala/skills/skillset.py
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__setitem__(skill_name, skill)
Set skill by name.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
skill_name |
str
|
Name of the skill to set. |
required |
skill |
BaseSkill
|
Skill to set. |
required |
Source code in adala/skills/skillset.py
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apply(input, runtime, improved_skill=None)
abstractmethod
Apply the skill set to a dataset using a specified runtime.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input |
Union[Record, InternalDataFrame]
|
Input data to apply the skill set to. |
required |
runtime |
Runtime
|
The runtime environment in which to apply the skills. |
required |
improved_skill |
Optional[str]
|
Name of the skill to start from (to optimize calculations). Defaults to None. |
None
|
Returns: InternalDataFrame: Skill predictions.
Source code in adala/skills/skillset.py
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get_skill_names()
Get list of skill names.
Returns:
Type | Description |
---|---|
List[str]
|
List[str]: List of skill names. |
Source code in adala/skills/skillset.py
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get_skill_outputs()
Get dictionary of skill outputs.
Returns:
Type | Description |
---|---|
Dict[str, str]
|
Dict[str, str]: Dictionary of skill outputs. Keys are output names and values are skill names |
Source code in adala/skills/skillset.py
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skills_validator(v)
Validates and converts the skills attribute to a dictionary of skill names to BaseSkill instances.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
v |
Union[List[Skill], Dict[str, Skill]]
|
The skills attribute to validate and convert. |
required |
Returns:
Type | Description |
---|---|
Dict[str, Skill]
|
Dict[str, BaseSkill]: Dictionary mapping skill names to their corresponding BaseSkill instances. |
Source code in adala/skills/skillset.py
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