chore: clean up import hygiene in src/agentics/core

src/agentics/core/__init__.py

@@ -1 +1,3 @@
 from .agentics import AG
+
+__all__ = ["AG"]

src/agentics/core/agentics.py

@@ -9,7 +9,6 @@
 from functools import partial, reduce
 from typing import (
     Any,
-    Callable,
     Dict,
     Generic,
     List,
@@ -35,7 +34,6 @@
 from agentics.core.async_executor import (
     PydanticTransducerCrewAI,
     PydanticTransducerMellea,
-    PydanticTransducerVLLM,
     aMap,
 )
 from agentics.core.atype import (
@@ -1143,7 +1141,7 @@ def product(self, other: AG) -> AG:
     def merge_states(self, other: AG) -> AG:
         """
         Merge states of two AGs pairwise.
-        
+
         The 'other' AG's fields take precedence over 'self' fields when there are conflicts.
         This ensures that newly generated data (other) overwrites existing data (self).
         """
@@ -1276,13 +1274,15 @@ def rebind_atype(
             "amap_batch_size": self.amap_batch_size,
             "areduce_batches": copy(self.areduce_batches),
             "save_amap_batches_to_path": self.save_amap_batches_to_path,
-            "crew_prompt_params": copy(self.crew_prompt_params) if self.crew_prompt_params else None,
+            "crew_prompt_params": (
+                copy(self.crew_prompt_params) if self.crew_prompt_params else None
+            ),
             "vector_store": self.vector_store,
         }
         # Only include atype_code if it's not None (to avoid validation error)
         if self.atype_code is not None:
             init_params["atype_code"] = self.atype_code
-        
+
         new_ag = AG(**init_params)
 
         for state in self.states:

src/agentics/core/default_types.py

@@ -1,7 +1,6 @@
-from dataclasses import asdict, is_dataclass
-from typing import Any, Callable, Generic, List, Optional, Type, TypeVar, Union
+from typing import Callable, List, Optional, Type, TypeVar, Union
 
-from pydantic import BaseModel, Field, create_model
+from pydantic import BaseModel, Field
 
 
 class Astr(BaseModel):

src/agentics/core/mellea_pydantic_transducer.py

@@ -1,9 +1,8 @@
 # Put this at the top of your script
-import asyncio
 import logging
 import os
 import warnings
-from typing import Optional, Type
+from typing import Type
 
 os.environ["TQDM_DISABLE"] = "1"
 
@@ -21,9 +20,8 @@ def no_bar(iterable=None, *args, **kwargs):
 # Override only the *function*, not the module, not the class
 tqdm.tqdm = no_bar
 
-import mellea
-from mellea.stdlib.sampling import RejectionSamplingStrategy
-from pydantic import BaseModel
+import mellea  # noqa: E402
+from pydantic import BaseModel  # noqa: E402
 
 # Silence asyncio’s “Unclosed client session” ERROR logs
 logging.getLogger("asyncio").setLevel(logging.CRITICAL)

src/agentics/core/semantic_operators.py

@@ -5,11 +5,10 @@
 # sem_agg 	Aggregate across all records (e.g. for summarization)
 # sem_topk 	Order the records by some natural language sorting criteria
 # sem_join 	Join two datasets based on a natural language predicate
-import pathlib
 from typing import Type
 
 import pandas as pd
-from pydantic import BaseModel, Field
+from pydantic import BaseModel
 
 from agentics import AG
 from agentics.core.atype import create_pydantic_model

src/agentics/core/transducible_functions.py

@@ -1,23 +1,24 @@
+import functools
+import inspect
+import logging
 import types
-from typing import Any, Callable, Optional
+from typing import Any, Callable, Optional, Protocol, Type, get_args
 
 from dotenv import load_dotenv
-from pydantic import BaseModel
+from pydantic import BaseModel, create_model
+from pydantic._internal._model_construction import ModelMetaclass  # Pydantic v2
 
 from agentics.core.agentics import AG
+from agentics.core.atype import AGString
+from agentics.core.default_types import GeneratedAtype
 from agentics.core.utils import (
+    get_function_io_types,
     import_last_function_from_code,
     import_pydantic_from_code,
+    percent_non_empty_fields,
 )
 
 load_dotenv()
-import functools
-import inspect
-import logging
-from typing import Any, Callable, Tuple, get_args
-
-from agentics.core.default_types import GeneratedAtype
-from agentics.core.utils import get_function_io_types, percent_non_empty_fields
 
 logging.getLogger("huggingface_hub.utils._http").setLevel(logging.ERROR)
 
@@ -377,10 +378,6 @@ def With(model, **kwargs):
     return TransductionConfig(model, **kwargs)
 
 
-from pydantic import BaseModel
-from pydantic._internal._model_construction import ModelMetaclass  # Pydantic v2
-
-
 def _function_lshift(f, InputType):
     """
     f << X   =   composition
@@ -537,44 +534,38 @@ async def semantic_merge(instance1: BaseModel, instance2: BaseModel) -> BaseMode
     return merged_instance[0]
 
 
-from typing import Type
-
-from pydantic import BaseModel, create_model
-
-from agentics import AG
-from agentics.core.atype import AGString
-
 async def generate_prototypical_instances(
     type: Type[BaseModel],
     n_instances: int = 10,
     llm: Any = AG.get_llm_provider(),
     instructions: str = None,
 ) -> list[BaseModel]:
-    
+
     DynamicModel = create_model(
-            "ListOfObjectsOfGivenType",
-            instances=(list[type] | None, None),  # REQUIRED field
-        )
+        "ListOfObjectsOfGivenType",
+        instances=(list[type] | None, None),  # REQUIRED field
+    )
     if llm:
-    
+
         full_instructions = f"""
                 Generate list of {n_instances} random instances of the following type
                 {type.model_json_schema()}.
                 fill all attributed for each generated instance
                 """
         if instructions:
-            full_instructions += "Adhere to the following instructions \n" + instructions
-
+            full_instructions += (
+                "Adhere to the following instructions \n" + instructions
+            )
+
         target = AG(
             atype=DynamicModel,
             instructions=full_instructions,
             llm=llm,
         )
-        generated = await (target << AGString(string =" "))
+        generated = await (target << AGString(string=" "))
         return generated.states[0].instances
-    else: return [DynamicModel()]
-
-from typing import Any, Awaitable, Protocol
+    else:
+        return [DynamicModel()]
 
 
 class TransducibleFn(Protocol):

src/agentics/core/utils.py

@@ -1,8 +1,14 @@
+import ast
 import asyncio
 import datetime
+import hashlib
 import inspect
+import json
+import math
 import os
 import re
+import textwrap
+import types
 from collections.abc import Iterable
 from typing import (
     Annotated,
@@ -27,8 +33,8 @@
 import httpx
 import pandas as pd
 from dotenv import load_dotenv
+from jsonfinder import jsonfinder
 from loguru import logger
-from numerize.numerize import numerize
 from openai import APIStatusError, AsyncOpenAI
 from pydantic import BaseModel, Field, create_model
 from pydantic.fields import FieldInfo
@@ -166,9 +172,6 @@ def sanitize_field_name(name: str) -> str:
     return re.sub(r"[^\w]", "", name)
 
 
-import math
-
-
 def sanitize_dict_keys(obj):
     """
     Recursively sanitize dictionary keys and values.
@@ -433,11 +436,6 @@ class ATypeList(BaseModel):
     )
 
 
-import json
-
-from jsonfinder import jsonfinder
-
-
 def extract_json_objects(text: str, expected_type: Type) -> List[BaseModel]:
     """
     Scan `text` and return a list of (start_index, end_index, parsed_obj)
@@ -490,12 +488,6 @@ def llm_friendly_json(model: type[BaseModel]) -> str:
     return json.dumps(simple, indent=4)
 
 
-import ast
-import textwrap
-import types
-from typing import Callable
-
-
 def import_last_function_from_code(code: str) -> Callable:
     """
     Execute the code and return ONLY the last user-defined function,
@@ -583,9 +575,6 @@ def import_last_function_from_code(code: str) -> Callable:
     return fn
 
 
-import hashlib
-
-
 def compute_function_hash(
     fn: Callable | str,
     #   SourceModel:Type[BaseModel] | str = None,
@@ -676,11 +665,6 @@ def get_function_io_types(
     return input_types, output_type
 
 
-from typing import Optional, Type
-
-from pydantic import BaseModel, create_model
-
-
 def make_transduction_type(
     source: Type[BaseModel],
     target: Type[BaseModel],

src/agentics/core/vector_store.py

@@ -1,4 +1,4 @@
-from typing import List
+from typing import Any, Dict, List
 
 import numpy as np
 from pydantic import BaseModel, ConfigDict
@@ -23,11 +23,6 @@ def embed(self, texts: List[str]) -> np.ndarray:
         return vecs.astype(np.float32)
 
 
-from typing import Any, Dict, List
-
-from pydantic import BaseModel, ConfigDict
-
-
 class HNSWStore:
     def __init__(
         self,