Parallel skill merge branch (#35)

Co-authored-by: alex-medicratic <137122049+alex-medicratic@users.noreply.github.com>
Co-authored-by: nik <nik@heartex.net>

Author: 3 people

adala/skills/skillset.py

@@ -244,6 +244,95 @@ def __rich__(self):
 class ParallelSkillSet(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, TextClassificationSkill, TextGenerationSkill
+        >>> skillset = ParallelSkillSet(skills=[TextClassificationSkill(name='Classify sentiment', instructions='Classify the sentiment'), TextGenerationSkill(name='Summarize text', instructions='Generate a summar')])
+
+        Create a ParallelSkillSet with a dictionary of skill names to BaseSkill instances
+        >>> from adala.skills import ParallelSkillSet, TextClassificationSkill, TextGenerationSkill
+        >>> skillset = ParallelSkillSet(skills={'sentiment_analysis': TextClassificationSkill(name='Classify sentiment', instructions='Classify the sentiment'),'text_summary': TextGenerationSkill(name='Summarize text', instructions='Generate a summary')})
     """
-    
-    pass
+
+    @field_validator("skills", mode="before")
+    @classmethod
+    def skills_validator(
+        cls, v: Union[List[BaseSkill], Dict[str, BaseSkill]]
+    ) -> Dict[str, BaseSkill]:
+        """
+        Validates and converts the skills attribute to a dictionary of skill names to BaseSkill instances.
+
+        Args:
+            v (List[BaseSkill], Dict[str, BaseSkill]]): The skills attribute to validate.
+
+        Returns:
+            Dict[str, BaseSkill]: Dictionary mapping skill names to their corresponding BaseSkill instances.
+        """
+        skills = OrderedDict()
+        if not v:
+            return skills
+
+        if isinstance(v, list) and isinstance(v[0], BaseSkill):
+            # convert list of skill names to dictionary
+            for skill in v:
+                skills[skill.name] = skill
+        elif isinstance(v, dict):
+            skills = v
+        else:
+            raise ValidationError(
+                f"skills must be a list or dictionary, not {type(skills)}"
+            )
+        return skills
+
+    def apply(
+        self,
+        dataset: Union[Dataset, InternalDataFrame],
+        runtime: Runtime,
+        improved_skill: Optional[str] = None,
+    ) -> InternalDataFrame:
+        """
+        Applies each skill on the dataset, enhancing the agent's experience.
+
+        Args:
+            dataset (Dataset): The dataset to apply the skills on.
+            runtime (Runtime): The runtime environment in which to apply the skills.
+            improved_skill (Optional[str], optional): Unused in ParallelSkillSet. Defaults to None.
+        Returns:
+            InternalDataFrame: Skill predictions.
+        """
+        predictions = None
+
+        for i, skill_name in enumerate(self.skills.keys()):
+            skill = self.skills[skill_name]
+            # use input dataset for the first node in the pipeline
+            input_dataset = dataset if i == 0 else predictions
+            print_text(f"Applying skill: {skill_name}")
+            predictions = skill.apply(input_dataset, runtime)
+
+        return predictions
+
+    def select_skill_to_improve(
+        self, accuracy: Mapping, accuracy_threshold: Optional[float] = 0.9
+    ) -> Optional[BaseSkill]:
+        """
+        Selects the skill with the lowest accuracy to improve.
+
+        Args:
+            accuracy (Mapping): Accuracy of each skill.
+            accuracy_threshold (Optional[float], optional): Accuracy threshold. Defaults to 1.0.
+        Returns:
+            Optional[BaseSkill]: Skill to improve. None if no skill to improve.
+        """
+        skills_below_threshold = [
+            skill_name
+            for skill_name in self.skills.keys()
+            if accuracy[skill_name] < accuracy_threshold
+        ]
+        if skills_below_threshold:
+            weakest_skill_name = min(skills_below_threshold, key=accuracy.get)
+            return self.skills[weakest_skill_name]

tests/test_llm_parallel_skillset.py

@@ -0,0 +1,85 @@
+import pandas as pd
+
+from utils import patching, PatchedCalls
+
+@patching(
+    target_function=PatchedCalls.OPENAI_MODEL_LIST.value,
+    data=[{'input': {}, 'output': {'data': [{'id': 'gpt-3.5-turbo-instruct'}]}}],
+)
+@patching(
+    target_function=PatchedCalls.GUIDANCE.value,
+    data=[
+        # Responses for the first text entry
+        {
+        'input': {"text_": "Apple's latest product, the iPhone 15, was released in September 2023."},
+        'output': {"predictions": ""}  # No person mentioned
+        },
+        {
+        'input': {"text_": "Barack Obama was the 44th president of the United States."},
+        'output': {"predictions": "Barack Obama"}
+        },
+        {
+        'input': {"text_": "Apple's latest product, the iPhone 15, was released in September 2023."},
+        'output': {"predictions": "iPhone 15"}
+        },
+        {
+        'input': {"text_": "Barack Obama was the 44th president of the United States."},
+        'output': {"predictions": ""}  # No product mentioned
+        },
+        {
+        'input': {"text_": "Apple's latest product, the iPhone 15, was released in September 2023."},
+        'output': {"predictions": "September 2023"}
+        },
+        {
+        'input': {"text_": "Barack Obama was the 44th president of the United States."},
+        'output': {"predictions": ""}  # No date mentioned
+        },
+        {
+        'input': {"text_": "Apple's latest product, the iPhone 15, was released in September 2023."},
+        'output': {"predictions": ""}  # No location mentioned
+        },
+        {
+        'input': {"text_": "Barack Obama was the 44th president of the United States."},
+        'output': {"predictions": "United States"}
+        }
+    ],
+    strict=False
+)
+def test_llm_parallel_skillset():
+    from adala.skills.skillset import ParallelSkillSet, LLMSkill
+    from adala.datasets import DataFrameDataset, InternalDataFrame
+    from adala.runtimes import OpenAIRuntime
+
+    skillset = ParallelSkillSet(
+        skills=[
+            LLMSkill(name="skill_person", instructions="Extract person's name", input_data_field="text"),
+            LLMSkill(name="skill_product", instructions="Extract product name", input_data_field="text"),
+            LLMSkill(name="skill_date", instructions="Extract date", input_data_field="text"),
+            LLMSkill(name="skill_location", instructions="Extract location", input_data_field="text"),
+        ]
+    )
+    dataset = DataFrameDataset(df=InternalDataFrame([
+        "Apple's latest product, the iPhone 15, was released in September 2023.",
+        "Barack Obama was the 44th president of the United States.",
+    ], columns=["text"]))
+    predictions = skillset.apply(
+        dataset=dataset,
+        runtime=OpenAIRuntime(verbose=True),
+    )
+
+    pd.testing.assert_frame_equal(InternalDataFrame.from_records([
+        {
+        'text': "Apple's latest product, the iPhone 15, was released in September 2023.",
+        'skill_person': "",  # No person mentioned
+        'skill_product': 'iPhone 15',
+        'skill_date': 'September 2023',
+        'skill_location': ""  # No location mentioned
+        },
+        {
+        'text': 'Barack Obama was the 44th president of the United States.',
+        'skill_person': 'Barack Obama',
+        'skill_product': "",  # No product mentioned
+        'skill_date': "",  # No date mentioned
+        'skill_location': 'United States'
+        }
+    ]), predictions)