273 consistent styling

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.0.1
+    rev: v5.0.0
     hooks:
       - id: trailing-whitespace
       - id: end-of-file-fixer
@@ -10,28 +10,30 @@ repos:
       - id: detect-private-key
 
   - repo: https://github.com/psf/black
-    rev: 24.4.2
+    rev: 24.10.0
     hooks:
       - id: black
-        args: ['--line-length=88']
+        args: ['--config=pyproject.toml']
         exclude: ^docs/|.*\.(json|yaml|md|txt)$
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.2
+    rev: v0.8.2
     hooks:
       # Run the linter.
       - id: ruff
-        args: ['--fix']
+        args: ['--fix', '--config=pyproject.toml']
         exclude: ^docs/|.*\.(json|yaml|md|txt)$
 
-  # Add local hooks to run custom commands
+  # stage files after ruff
   - repo: local
     hooks:
       - id: run-make-format
         name: Run Make Format
         entry: make format
         language: system
+        stages: [commit]
         pass_filenames: false
+
   # - repo: https://github.com/pycqa/flake8
   #   rev: 4.0.1
   #   hooks:

Makefile

@@ -22,12 +22,14 @@ setup:
 # Format code using Black and Ruff
 .PHONY: format
 format:
-	$(PYTHON) black $(SRC_DIR)
-	git ls-files | xargs pre-commit run black --files
+	$(PYTHON) black $(SRC_DIR) --config pyproject.toml
+	$(PYTHON) ruff check --fix $(SRC_DIR)
+# remove git ls-files | xargs pre-commit run black --files, causes a circular dependency
 
 # Run lint checks using Ruff
 .PHONY: lint
 lint:
+	$(PYTHON) black --check $(SRC_DIR) --config pyproject.toml
 	$(PYTHON) ruff check $(SRC_DIR)
 
 # Run all pre-commit hooks on all files

adalflow/adalflow/components/agent/react.py

@@ -224,7 +224,6 @@ def _execute_action(self, action_step: StepOutput) -> Optional[StepOutput]:
         """Parse the action string to a function call and execute it. Update the action_step with the result."""
         action = action_step.action
         try:
-
             fun: Function = self.tool_manager.parse_func_expr(action)
             result: FunctionOutput = self.tool_manager.execute_func(fun)
             # TODO: optimize the action_step

adalflow/adalflow/components/model_client/cohere_client.py

@@ -105,7 +105,6 @@ def call(self, api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINE
         if (
             model_type == ModelType.RERANKER
         ):  # query -> # scores for top_k documents, index for the top_k documents, return as tuple
-
             response = self.sync_client.rerank(**api_kwargs)
             top_k_scores = [result.relevance_score for result in response.results]
             top_k_indices = [result.index for result in response.results]

adalflow/adalflow/components/model_client/google_client.py

@@ -117,7 +117,6 @@ def convert_inputs_to_api_kwargs(
                 raise TypeError("input must be a sequence of text")
             final_model_kwargs["input"] = input
         elif model_type == ModelType.LLM:
-
             final_model_kwargs["prompt"] = input
         else:
             raise ValueError(f"model_type {model_type} is not supported")

adalflow/adalflow/components/model_client/ollama_client.py

@@ -16,9 +16,9 @@
 import warnings
 from adalflow.core.types import ModelType, GeneratorOutput
 
-from adalflow.utils.lazy_import import safe_import, OptionalPackages
 
-ollama = safe_import(OptionalPackages.OLLAMA.value[0], OptionalPackages.OLLAMA.value[1])
+# need to pick either safe or regular import
+# ollama = safe_import(OptionalPackages.OLLAMA.value[0], OptionalPackages.OLLAMA.value[1])
 import ollama
 from ollama import RequestError, ResponseError, GenerateResponse
 

adalflow/adalflow/components/model_client/transformers_client.py

@@ -12,13 +12,10 @@
 from adalflow.core.functional import get_top_k_indices_scores
 
 # optional import
-from adalflow.utils.lazy_import import safe_import, OptionalPackages
 
-
-transformers = safe_import(
-    OptionalPackages.TRANSFORMERS.value[0], OptionalPackages.TRANSFORMERS.value[1]
-)
-torch = safe_import(OptionalPackages.TORCH.value[0], OptionalPackages.TORCH.value[1])
+# need to pick either safe or regular import
+# transformers = safe_import(OptionalPackages.TRANSFORMERS.value[0], OptionalPackages.TRANSFORMERS.value[1])
+# torch = safe_import(OptionalPackages.TORCH.value[0], OptionalPackages.TORCH.value[1])
 
 import torch
 
@@ -201,7 +198,6 @@ def infer_bge_reranker_base(
         input = [(query, doc) for doc in documents]
 
         with torch.no_grad():
-
             inputs = self.tokenizer(
                 input,
                 padding=True,
@@ -358,7 +354,6 @@ def init_model(self, model_name: str):
             raise ValueError(f"Model {model_name} is not supported")
 
     def _parse_chat_completion_from_pipeline(self, completion: Any) -> str:
-
         text = completion[0]["generated_text"]
 
         pattern = r"(?<=\|assistant\|>).*"
@@ -407,7 +402,6 @@ def _infer_from_pipeline(
         )
 
         if model == "HuggingFaceH4/zephyr-7b-beta":
-
             prompt = model_to_use.tokenizer.apply_chat_template(
                 messages, tokenize=False, add_generation_prompt=True
             )
@@ -728,7 +722,6 @@ def convert_inputs_to_api_kwargs(
     }
 
     class CustomizeLLM:
-
         def __init__(self) -> None:
             pass
 

adalflow/adalflow/components/model_client/utils.py

@@ -1,4 +1,5 @@
 "Helpers for model client for integrating models and parsing the output."
+
 from adalflow.core.types import EmbedderOutput, Embedding, Usage
 
 

adalflow/adalflow/components/output_parsers/outputs.py

@@ -145,7 +145,6 @@ def __init__(
         exclude_fields: ExcludeType = None,
         return_data_class: bool = False,
     ):
-
         super().__init__()
         if not is_dataclass(data_class):
             raise TypeError(f"Provided class is not a dataclass: {data_class}")
@@ -349,7 +348,6 @@ def format_instructions(self) -> str:
         return "The output should be a boolean value. True or False."
 
     def call(self, input: str) -> bool:
-
         input = input.strip()
         output = None
         # evaluate the expression to get the boolean value

adalflow/adalflow/components/retriever/bm25_retriever.py

@@ -1,4 +1,4 @@
-"""BM25 retriever implementation. """
+"""BM25 retriever implementation."""
 
 from typing import List, Dict, Optional, Callable, Any, Sequence
 import numpy as np

adalflow/adalflow/core/base_data_class.py

@@ -189,10 +189,8 @@ class MyOutputs(DataClass):
     __output_fields__: List[str] = []
 
     def __post_init__(self):
-
         for f in fields(self):
             if "desc" not in f.metadata and "description" not in f.metadata:
-
                 logger.debug(
                     f"Class {  self.__class__.__name__} Field {f.name} is missing 'desc' in metadata"
                 )

adalflow/adalflow/core/component.py

@@ -840,7 +840,6 @@ def remove_from(*dicts_or_sets):
                 )
             self.register_parameter(name, value)
         else:  # set component
-
             components = self.__dict__.get("_components")
             if isinstance(value, Component):
                 if components is None:

adalflow/adalflow/core/embedder.py

@@ -48,7 +48,6 @@ def __init__(
         model_kwargs: Dict[str, Any] = {},
         output_processors: Optional[Component] = None,
     ) -> None:
-
         super().__init__(model_kwargs=model_kwargs)
         if not isinstance(model_kwargs, Dict):
             raise TypeError(

adalflow/adalflow/core/func_tool.py

@@ -259,7 +259,6 @@ def _extra_repr(self) -> str:
 
 
 if __name__ == "__main__":
-
     import asyncio
     import time
 

adalflow/adalflow/core/functional.py

@@ -252,7 +252,6 @@ class TrecDataList:
     if is_dataclass(cls) or is_potential_dataclass(
         cls
     ):  # Optional[Address] will be false, and true for each check
-
         log.debug(
             f"{is_dataclass(cls)} of {cls}, {is_potential_dataclass(cls)} of {cls}"
         )
@@ -922,7 +921,6 @@ def get_top_k_indices_scores(
 
 
 def generate_readable_key_for_function(fn: Callable) -> str:
-
     module_name = fn.__module__
     function_name = fn.__name__
     return f"{module_name}.{function_name}"
@@ -1236,7 +1234,6 @@ def parse_json_str_to_obj(json_str: str) -> Union[Dict[str, Any], List[Any]]:
         except json.JSONDecodeError:
             # 3rd attemp using yaml
             try:
-
                 # NOTE: parsing again with pyyaml
                 #       pyyaml is less strict, and allows for trailing commas
                 #       right now we rely on this since guidance program generates

adalflow/adalflow/core/generator.py

@@ -188,7 +188,6 @@ def get_cache_path(self) -> str:
     def _get_default_mapping(
         output: "GeneratorOutput" = None,
     ) -> Tuple[Dict[str, Callable], List[str]]:
-
         if (
             output.data
             and isinstance(output.data, DataClass)
@@ -546,7 +545,6 @@ def backward(
         backward_engine: Optional["Generator"] = None,
         id: Optional[str] = None,  # the id of the input
     ) -> Parameter:
-
         log.info(f"Generator: Backward: {response}")
 
         children_params = response.predecessors
@@ -678,7 +676,6 @@ def _backward_through_one_predecessor(
                 data=manual_response, raw_response=manual_response
             )
         else:
-
             gradient_output: GeneratorOutput = backward_engine(
                 prompt_kwargs=backward_engine_prompt_kwargs
             )
@@ -881,7 +878,6 @@ def failure_message_to_backward_engine(
 
 
 class BackwardEngine(Generator):  # it is a generator with defaule template
-
     __doc__ = """The backward engine is a Generator with a default template for the backward pass.
 
     If you want to customize the template, you can create your own backward engine"""

adalflow/adalflow/core/prompt_builder.py

@@ -169,9 +169,7 @@ def _convert_prompt_kwargs_to_str(prompt_kwargs: Dict) -> Dict[str, str]:
     prompt_kwargs_str: Dict[str, str] = {}
 
     for key, p in prompt_kwargs.items():
-
         if isinstance(p, Parameter):
-
             prompt_kwargs_str[key] = p.data
         else:
             prompt_kwargs_str[key] = p

adalflow/adalflow/core/tool_manager.py

@@ -129,7 +129,6 @@ def execute_func_expr(self, expr: FunctionExpression) -> FunctionOutput:
         r"""Execute the function expression. Support both sync and async functions."""
         func: Function = self.parse_func_expr(expr)
         try:
-
             return self.execute_func(func)
         except Exception as e:
             # NOTE: if the function expression is not a function call, try to execute it as a function expression

adalflow/adalflow/database/sqlalchemy/pipeline/inject_data.py

@@ -83,7 +83,6 @@ def process_batch(self, documents: List[Document]):
     def __call__(self, documents: List[Document]):
         batch_size = self.batch_size
         for i in range(0, len(documents), batch_size):
-
             List = documents[i : i + batch_size]
             print(i, len(List))
             self.process_batch(List)

adalflow/adalflow/datasets/big_bench_hard.py

@@ -41,7 +41,6 @@ def __init__(
         *args,
         **kwargs,
     ):
-
         if split not in ["train", "val", "test"]:
             raise ValueError("Split must be one of 'train', 'val', 'test'")
 
@@ -65,7 +64,6 @@ def __init__(
                 )  # dont use a tuple, use a dict {"x": ..., "y": ...}
 
     def _check_or_download_dataset(self, data_path: str = None, split: str = "train"):
-
         if data_path is None:
             raise ValueError("data_path must be specified")
         json_path = os.path.join(data_path, f"{self.task_name}.json")

adalflow/adalflow/datasets/trec.py

@@ -28,7 +28,6 @@ def calculate_class_weights(labels: torch.Tensor) -> torch.Tensor:
 
 
 def sample_subset_dataset(dataset, num_samples: int, sample_weights):
-
     # Create a WeightedRandomSampler to get 400 samples
     sampler = WeightedRandomSampler(
         weights=sample_weights, num_samples=num_samples, replacement=False
@@ -171,7 +170,6 @@ def __init__(
                 )
 
     def _check_or_download_dataset(self, data_path: str = None, split: str = "train"):
-
         if data_path is None:
             raise ValueError("data_path must be specified")
         split_csv_path = os.path.join(data_path, f"{split}.csv")

adalflow/adalflow/eval/g_eval.py

@@ -18,7 +18,6 @@
 
 
 class GEvalMetric(Enum):
-
     RELEVANCE = "Relevance"  # range [1, 5]
     FLUENCY = "Fluency"  # range [1, 3]
     CONSISTENCY = "Consistency"  # range [1, 5]

adalflow/adalflow/optim/_llm_optimizer.py

@@ -10,7 +10,6 @@
 from adalflow.core.base_data_class import DataClass
 
 if TYPE_CHECKING:
-
     from adalflow.core.model_client import ModelClient
 
 

adalflow/adalflow/optim/few_shot/bootstrap_optimizer.py

@@ -71,7 +71,6 @@ def add_scores(self, ids: List[str], scores: List[float], is_teacher: bool = Tru
             )
 
         for score in scores:
-
             if not isinstance(score, float):
                 raise ValueError(
                     f"score must be a float, got {type(score)}, score: {score}"
@@ -198,7 +197,6 @@ def samples_to_str(
         sample_strs = []
         for sample in samples:
             try:
-
                 # process the input fields
                 if augmented:
                     exclude_fields = ["id", "score"]
@@ -239,7 +237,6 @@ def propose(self):
 
                     demo_str = ""
                     if len(sampled_augmented_demos) > 0:
-
                         demo_str = self.samples_to_str(
                             samples=sampled_augmented_demos,
                             augmented=True,

adalflow/adalflow/optim/optimizer.py

@@ -47,7 +47,6 @@ def zero_grad(self):
 
 
 class DemoOptimizer(Optimizer):
-
     __doc__ = r"""Base class for all demo optimizers.
 
     Demo optimizer are few-shot optimization, where it will sample raw examples from train dataset or bootstrap examples from the model's output.

adalflow/adalflow/optim/parameter.py

@@ -419,7 +419,6 @@ def build_graph(node: "Parameter"):
     def backward(
         self,
     ):  # engine should be the llm or customized backwards function to pass feedback
-
         # topological sort of all the predecessors of the current parameter in the graph
         log.debug(f"Backward pass for {self.data}, backward function: {self.grad_fn}")
         topo: List[Parameter] = []
@@ -577,7 +576,6 @@ def wrap_and_escape(text, width=40):
             log.info(f"Node: {n.name}, {n.to_dict()}")
             # track gradients
             for g in n.gradients:
-
                 log.info(f"Gradient: {g.name}, {g.to_dict()}")
                 log.info(f"Gradient prompt: {g.gradient_prompt}")
         for n1, n2 in edges:
@@ -685,7 +683,6 @@ def __repr__(self):
 
 
 def _check_and_reduce_gradients(variable: Parameter) -> Set[Parameter]:
-
     if variable.get_gradient_and_context_text() == "":
         log.debug(f"No gradients detected for {variable.data}")
         return variable.gradients