diff --git a/.hooks/generate_docs.py b/.hooks/generate_docs.py
index 1ab5b499..b38679be 100644
--- a/.hooks/generate_docs.py
+++ b/.hooks/generate_docs.py
@@ -96,7 +96,7 @@ def simple_convert_markdown(
             html_id: str = "",
             **kwargs: t.Any,
         ) -> t.Any:
-            return Markup(md.convert(text) if text else "")  # noqa: S704 # nosec
+            return Markup(md.convert(text) if text else "")  # nosec  # noqa: S704
 
         self.handler.env.filters["convert_markdown"] = simple_convert_markdown
 
diff --git a/dreadnode/__init__.py b/dreadnode/__init__.py
index 77de7bbb..0337148d 100644
--- a/dreadnode/__init__.py
+++ b/dreadnode/__init__.py
@@ -32,7 +32,7 @@
 from dreadnode.version import VERSION
 
 if t.TYPE_CHECKING:
-    from dreadnode import scorers  # noqa: F401
+    from dreadnode import scorers
 
 logger.disable("dreadnode")
 
diff --git a/dreadnode/agent/agent.py b/dreadnode/agent/agent.py
index 22016af1..0a987c20 100644
--- a/dreadnode/agent/agent.py
+++ b/dreadnode/agent/agent.py
@@ -51,6 +51,8 @@
 from dreadnode.agent.stop import StopCondition, stop_never
 from dreadnode.agent.thread import Thread
 from dreadnode.agent.tools import AnyTool, Tool, Toolset, discover_tools_on_obj
+from dreadnode.agent.tools.planning import update_todo
+from dreadnode.agent.tools.tasking import finish_task, give_up_on_task
 from dreadnode.agent.types import Message, ToolCall
 from dreadnode.meta import Component, Config, Model, component
 from dreadnode.scorers import ScorersLike
@@ -745,9 +747,6 @@ class TaskAgent(Agent):
     """
 
     def model_post_init(self, _: t.Any) -> None:
-        from dreadnode.agent.tools.planning import update_todo
-        from dreadnode.agent.tools.tasking import finish_task, give_up_on_task
-
         if not any(tool for tool in self.tools if tool.name == "finish_task"):
             self.tools.append(finish_task)
 
diff --git a/dreadnode/agent/console.py b/dreadnode/agent/console.py
index 517a60c2..e73c01da 100644
--- a/dreadnode/agent/console.py
+++ b/dreadnode/agent/console.py
@@ -48,13 +48,13 @@ def _handle_tool_start(self, event: ToolStart) -> None:
                 self._status_table, console=self.console, transient=True, auto_refresh=True
             )
             self._live_status.start()
+        if self._status_table is not None:
+            # Add a new row for the running tool with a spinner.
+            self._status_table.add_row(
+                Text(f"Running [bold]{event.tool_call.name}[/bold]...", style="yellow")
+            )
 
-        # Add a new row for the running tool with a spinner.
-        self._status_table.add_row(
-            Text(f"Running [bold]{event.tool_call.name}[/bold]...", style="yellow")
-        )
-
-    def _handle_tool_end(self, event: ToolEnd):
+    def _handle_tool_end(self, event: ToolEnd) -> None:
         """Prints the tool's result and cleans up the status board."""
         # First, print the static result panel. This ensures it's in the
         # console history even after the live display is gone.
diff --git a/dreadnode/agent/hooks/summarize.py b/dreadnode/agent/hooks/summarize.py
index 0fac958a..82ccc080 100644
--- a/dreadnode/agent/hooks/summarize.py
+++ b/dreadnode/agent/hooks/summarize.py
@@ -1,6 +1,8 @@
 import contextlib
 import typing as t
 
+from litellm.exceptions import ContextWindowExceededError
+
 from dreadnode.agent.events import AgentError, AgentEvent, GenerationEnd, StepStart
 from dreadnode.agent.prompts import summarize_conversation
 from dreadnode.agent.reactions import Continue, Reaction, Retry
@@ -23,8 +25,6 @@
 def _is_context_length_error(error: BaseException) -> bool:
     """Checks if an exception is likely due to exceeding the context window."""
     with contextlib.suppress(ImportError):
-        from litellm.exceptions import ContextWindowExceededError
-
         if isinstance(error, ContextWindowExceededError):
             return True
 
diff --git a/dreadnode/agent/tools/fs.py b/dreadnode/agent/tools/fs.py
index 7c31157f..7b76aac0 100644
--- a/dreadnode/agent/tools/fs.py
+++ b/dreadnode/agent/tools/fs.py
@@ -8,7 +8,7 @@
 import rigging as rg
 from fsspec import AbstractFileSystem  # type: ignore[import-untyped]
 from pydantic import PrivateAttr
-from upath import UPath
+from upath import UPath  # type: ignore[import-not-found]
 
 from dreadnode.agent.tools import Toolset, tool_method
 from dreadnode.meta import Config
@@ -127,7 +127,7 @@ def read_file(
         content = _path.read_bytes()
 
         try:
-            return content.decode("utf-8")
+            return content.decode("utf-8")  # type: ignore[no-any-return]
         except UnicodeDecodeError as e:
             if self.multi_modal:
                 return rg.ContentImageUrl.from_file(path)
diff --git a/dreadnode/agent/tools/tasking.py b/dreadnode/agent/tools/tasking.py
index 8af798fd..0f971d5a 100644
--- a/dreadnode/agent/tools/tasking.py
+++ b/dreadnode/agent/tools/tasking.py
@@ -5,7 +5,7 @@
 
 
 @tool
-async def finish_task(success: bool, summary: str) -> None:  # noqa: ARG001, FBT001
+async def finish_task(success: bool, summary: str) -> None:  # noqa: ARG001
     """
     Mark your task as complete with a success/failure status and markdown summary of actions taken.
 
diff --git a/dreadnode/airt/attack/prompt.py b/dreadnode/airt/attack/prompt.py
index 98b90f80..2b72941a 100644
--- a/dreadnode/airt/attack/prompt.py
+++ b/dreadnode/airt/attack/prompt.py
@@ -28,7 +28,7 @@ def prompt_attack(
     beam_width: int = 3,
     branching_factor: int = 3,
     max_steps: int = 10,
-    additional_scorers: list[Scorer] | None = None,
+    additional_scorers: list[Scorer[t.Any]] | None = None,
     name: str | None = None,
 ) -> Attack[str, str]:
     """
@@ -88,7 +88,7 @@ def prompt_attack(
 
     objective = weighted_avg(
         (judge_scorer, 1),
-        *[(scorer, 1) for scorer in additional_scorers],
+        *[(scorer, 1) for scorer in (additional_scorers or [])],
         name="prompt_objective",
     )
 
diff --git a/dreadnode/airt/attack/tap.py b/dreadnode/airt/attack/tap.py
index 1fa06438..12aaa76c 100644
--- a/dreadnode/airt/attack/tap.py
+++ b/dreadnode/airt/attack/tap.py
@@ -18,7 +18,7 @@ def tap_attack(
     beam_width: int = 10,
     branching_factor: int = 3,
     max_steps: int = 10,
-    additional_constraints: list[Scorer] | None = None,
+    additional_constraints: list[Scorer[t.Any]] | None = None,
 ) -> Attack[str, str]:
     """
     Creates a Generative Attack optimized for the TAP (Tree-of-thought Attack Prompting) pattern,
diff --git a/dreadnode/airt/target/llm.py b/dreadnode/airt/target/llm.py
index 33a0359c..963865db 100644
--- a/dreadnode/airt/target/llm.py
+++ b/dreadnode/airt/target/llm.py
@@ -35,6 +35,8 @@ class LLMTarget(Model, Target[t.Any, str]):
 
     @property
     def name(self) -> str:
+        if self._generator is None:
+            return "unknown"
         return self._generator.to_identifier(short=True)
 
     def as_task(self, input: t.Any) -> Task[[], str]:
@@ -51,9 +53,12 @@ def as_task(self, input: t.Any) -> Task[[], str]:
 
         @task(name=f"generate - {self.name}", label="llm_target_generate", tags=["target"])
         async def generate(
-            messages: list[rg.Message] = messages, params: rg.GenerateParams = params
+            messages: list[rg.Message] = messages, params: rg.GenerateParams | None = params
         ) -> str:
-            generated = (await self._generator.generate_messages([messages], [params]))[0]
+            if self._generator is None:
+                raise ValueError("Generator not initialized")
+            params_list = [params] if params is not None else [rg.GenerateParams()]
+            generated = (await self._generator.generate_messages([messages], params_list))[0]
             if isinstance(generated, BaseException):
                 raise generated
             return generated.message.content
diff --git a/dreadnode/artifact/credential_manager.py b/dreadnode/artifact/credential_manager.py
index 242ce227..10116493 100644
--- a/dreadnode/artifact/credential_manager.py
+++ b/dreadnode/artifact/credential_manager.py
@@ -3,7 +3,7 @@
 from datetime import datetime, timezone
 from typing import TYPE_CHECKING, TypeVar
 
-from botocore.exceptions import ClientError
+from botocore.exceptions import ClientError  # type: ignore[import-untyped]
 from loguru import logger
 from s3fs import S3FileSystem  # type: ignore[import-untyped]
 
diff --git a/dreadnode/cli/agent/cli.py b/dreadnode/cli/agent/cli.py
index f27c12b4..f2e37245 100644
--- a/dreadnode/cli/agent/cli.py
+++ b/dreadnode/cli/agent/cli.py
@@ -8,6 +8,7 @@
 import rich
 
 from dreadnode import log_input
+from dreadnode import run as run_span
 from dreadnode.agent import Agent
 from dreadnode.agent.format import format_agent, format_agents_table
 from dreadnode.discovery import DEFAULT_SEARCH_PATHS, discover
@@ -118,8 +119,6 @@ async def agent_cli(
         *,
         config: t.Any = config_default,
     ) -> None:
-        from dreadnode import run as run_span
-
         flat_config = {k: v for k, v in flatten_model(config).items() if v is not None}
         agent = hydrate(agent_blueprint, config)
 
diff --git a/dreadnode/cli/main.py b/dreadnode/cli/main.py
index 9ddca31c..3a4030f1 100644
--- a/dreadnode/cli/main.py
+++ b/dreadnode/cli/main.py
@@ -1,5 +1,7 @@
 import contextlib
+import importlib.metadata
 import pathlib
+import platform
 import shutil
 import sys
 import typing as t
@@ -202,10 +204,6 @@ def clone(
 
 @cli.command(help="Show versions and exit.", group="Meta")
 def version() -> None:
-    import importlib.metadata
-    import platform
-    import sys
-
     version = importlib.metadata.version("dreadnode")
     python_version = f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"
 
diff --git a/dreadnode/cli/profile/cli.py b/dreadnode/cli/profile/cli.py
index 45c2642f..9ed1ccf8 100644
--- a/dreadnode/cli/profile/cli.py
+++ b/dreadnode/cli/profile/cli.py
@@ -3,6 +3,7 @@
 import cyclopts
 import rich
 from rich import box
+from rich.prompt import Prompt
 from rich.table import Table
 
 from dreadnode.cli.api import Token
@@ -59,8 +60,6 @@ def switch(
 
     # If no profile provided, prompt user to choose
     if profile is None:
-        from rich.prompt import Prompt
-
         profiles = list(config.servers.keys())
         rich.print("\nAvailable profiles:")
         for i, p in enumerate(profiles, 1):
diff --git a/dreadnode/convert.py b/dreadnode/convert.py
index c1fca0cd..ef762306 100644
--- a/dreadnode/convert.py
+++ b/dreadnode/convert.py
@@ -1,19 +1,17 @@
 import typing as t
 
-if t.TYPE_CHECKING:
-    import networkx as nx  # type: ignore [import-untyped]
+import networkx as nx  # type: ignore[import-untyped]
 
+if t.TYPE_CHECKING:
     from dreadnode.tracing.span import RunSpan
 
 
 def run_span_to_graph(run: "RunSpan") -> "nx.DiGraph":
     try:
-        import networkx as nx  # pyright: ignore[reportMissingModuleSource]
-    except ImportError as e:
+        graph = nx.DiGraph()
+    except NameError as e:
         raise RuntimeError("The `networkx` package is required for graph conversion") from e
 
-    graph = nx.DiGraph()
-
     graph.add_node(
         run.run_id,
         name=run.name,
diff --git a/dreadnode/data_types/audio.py b/dreadnode/data_types/audio.py
index 53ebf4e6..a992443d 100644
--- a/dreadnode/data_types/audio.py
+++ b/dreadnode/data_types/audio.py
@@ -1,27 +1,23 @@
+import importlib.util
 import io
 import typing as t
 from pathlib import Path
 
-from dreadnode.data_types.base import DataType
+import numpy as np
 
-if t.TYPE_CHECKING:
-    import numpy as np
+from dreadnode.data_types.base import DataType
 
 
 def check_imports() -> None:
-    try:
-        import soundfile as sf  # type: ignore[import-untyped,unused-ignore]  # noqa: F401
-    except ImportError as e:
+    if importlib.util.find_spec("soundfile") is None:
         raise ImportError(
             "Audio processing requires SoundFile. Install with: pip install dreadnode[multimodal]"
-        ) from e
+        )
 
-    try:
-        import numpy as np  # type: ignore[import-untyped,unused-ignore]  # noqa: F401
-    except ImportError as e:
+    if importlib.util.find_spec("numpy") is None:
         raise ImportError(
             "Audio processing requires NumPy. Install with: pip install dreadnode[multimodal]"
-        ) from e
+        )
 
 
 AudioDataType: t.TypeAlias = "str | Path | np.ndarray[t.Any, t.Any] | bytes"
@@ -78,7 +74,6 @@ def _process_audio_data(self) -> tuple[bytes, str, int | None, float | None]:
         Returns:
             A tuple of (audio_bytes, format_name, sample_rate, duration)
         """
-        import numpy as np
 
         if isinstance(self._data, str | Path) and Path(self._data).exists():
             return self._process_file_path()
@@ -94,7 +89,7 @@ def _process_file_path(self) -> tuple[bytes, str, int | None, float | None]:
         Returns:
             A tuple of (audio_bytes, format_name, sample_rate, duration)
         """
-        import soundfile as sf  # type: ignore[import-not-found,unused-ignore]
+        import soundfile as sf  # type: ignore  # noqa: PGH003
 
         path_str = str(self._data)
         audio_bytes = Path(path_str).read_bytes()
@@ -113,8 +108,7 @@ def _process_numpy_array(self) -> tuple[bytes, str, int | None, float | None]:
         Returns:
             A tuple of (audio_bytes, format_name, sample_rate, duration)
         """
-        import numpy as np  # type: ignore[import-not-found,unused-ignore]
-        import soundfile as sf  # type: ignore[import-not-found,unused-ignore]
+        import soundfile as sf
 
         if self._sample_rate is None:
             raise ValueError('Argument "sample_rate" is required when using numpy arrays.')
@@ -151,8 +145,6 @@ def _generate_metadata(
         Returns:
             A dictionary of metadata
         """
-        import numpy as np  # type: ignore[import-not-found,unused-ignore]
-
         metadata: dict[str, str | int | float | None] = {
             "extension": format_name.lower(),
             "x-python-datatype": "dreadnode.Audio.bytes",
diff --git a/dreadnode/data_types/base.py b/dreadnode/data_types/base.py
index 9d566b16..ac16c4cd 100644
--- a/dreadnode/data_types/base.py
+++ b/dreadnode/data_types/base.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import typing as t
 from abc import ABC, abstractmethod
 
diff --git a/dreadnode/data_types/image.py b/dreadnode/data_types/image.py
index 55ab8204..caa1ef12 100644
--- a/dreadnode/data_types/image.py
+++ b/dreadnode/data_types/image.py
@@ -3,27 +3,11 @@
 import typing as t
 from pathlib import Path
 
-from dreadnode.data_types.base import DataType
-
-if t.TYPE_CHECKING:
-    import numpy as np
-
-
-def check_imports() -> None:
-    try:
-        import PIL  # type: ignore[import,unused-ignore]  # noqa: F401
-    except ImportError as e:
-        raise ImportError(
-            "Image processing requires Pillow. Install with: pip install dreadnode[multimodal]"
-        ) from e
-
-    try:
-        import numpy as np  # type: ignore[import,unused-ignore]  # noqa: F401
-    except ImportError as e:
-        raise ImportError(
-            "Image processing requires NumPy. Install with: pip install dreadnode[multimodal]"
-        ) from e
+import numpy as np
+import PIL
+import PIL.Image
 
+from dreadnode.data_types.base import DataType
 
 ImageDataType = t.Union[t.Any, "np.ndarray[t.Any, t.Any]"]
 ImageDataOrPathType = str | Path | bytes | ImageDataType
@@ -61,7 +45,6 @@ def __init__(
             caption: Optional caption for the image
             format: Optional format to use when saving (png, jpg, etc.)
         """
-        check_imports()
         self._data = data
         self._mode = mode
         self._caption = caption
@@ -83,10 +66,8 @@ def _process_image_data(self) -> tuple[bytes, str, str | None, int | None, int |
         Returns:
             A tuple of (image_bytes, image_format, mode, width, height)
         """
-        import numpy as np  # type: ignore[import,unused-ignore]
-        import PIL.Image  # type: ignore[import,unused-ignore]
 
-        if isinstance(self._data, (str, Path)) and Path(self._data).exists():
+        if isinstance(self._data, str | Path) and Path(self._data).exists():
             return self._process_file_path()
         if isinstance(self._data, PIL.Image.Image):
             return self._process_pil_image()
@@ -104,7 +85,6 @@ def _process_file_path(self) -> tuple[bytes, str, str | None, int | None, int |
         Returns:
             A tuple of (image_bytes, image_format, mode, width, height)
         """
-        import PIL.Image  # type: ignore[import,unused-ignore]
 
         path_str = str(self._data)
         image_bytes = Path(path_str).read_bytes()
@@ -122,7 +102,6 @@ def _process_pil_image(self) -> tuple[bytes, str, str | None, int | None, int |
         Returns:
             A tuple of (image_bytes, image_format, mode, width, height)
         """
-        import PIL.Image  # type: ignore[import,unused-ignore]
 
         if not isinstance(self._data, PIL.Image.Image):
             raise TypeError(f"Expected PIL.Image, got {type(self._data)}")
@@ -160,8 +139,6 @@ def _process_numpy_array(self) -> tuple[bytes, str, str | None, int | None, int
         Returns:
             A tuple of (image_bytes, image_format, mode, width, height)
         """
-        import numpy as np  # type: ignore[import,unused-ignore]
-        import PIL.Image  # type: ignore[import,unused-ignore]
 
         buffer = io.BytesIO()
         image_format = self._format or "png"
@@ -191,7 +168,6 @@ def _process_raw_bytes(self) -> tuple[bytes, str, str | None, int | None, int |
         Returns:
             A tuple of (image_bytes, image_format, mode, width, height)
         """
-        import PIL.Image  # type: ignore[import,unused-ignore]
 
         if not isinstance(self._data, bytes):
             raise TypeError(f"Expected bytes, got {type(self._data)}")
@@ -216,7 +192,6 @@ def _process_base64_string(self) -> tuple[bytes, str, str | None, int | None, in
         Returns:
             A tuple of (image_bytes, image_format, mode, width, height)
         """
-        import PIL.Image  # type: ignore[import,unused-ignore]
 
         if not isinstance(self._data, str):
             raise TypeError(f"Expected str, got {type(self._data)}")
@@ -253,15 +228,13 @@ def _generate_metadata(
         self, image_format: str, mode: str | None, width: int | None, height: int | None
     ) -> dict[str, str | int | None]:
         """Generate metadata for the image."""
-        import numpy as np  # type: ignore[import,unused-ignore]
-        import PIL.Image  # type: ignore[import,unused-ignore]
 
         metadata: dict[str, str | int | None] = {
             "extension": image_format.lower(),
             "x-python-datatype": "dreadnode.Image.bytes",
         }
 
-        if isinstance(self._data, (str, Path)) and Path(self._data).exists():
+        if isinstance(self._data, str | Path) and Path(self._data).exists():
             metadata["source-type"] = "file"
             metadata["source-path"] = str(self._data)
         elif isinstance(self._data, PIL.Image.Image):
@@ -313,7 +286,6 @@ def _ensure_valid_image_array(
         self, array: "np.ndarray[t.Any, np.dtype[t.Any]]"
     ) -> "np.ndarray[t.Any, np.dtype[t.Any]]":
         """Convert numpy array to a format suitable for PIL."""
-        import numpy as np  # type: ignore[import,unused-ignore]
 
         grayscale_dim = 2
         rgb_dim = 3
diff --git a/dreadnode/data_types/object_3d.py b/dreadnode/data_types/object_3d.py
index d4000983..bfaf745d 100644
--- a/dreadnode/data_types/object_3d.py
+++ b/dreadnode/data_types/object_3d.py
@@ -54,7 +54,7 @@ def to_serializable(self) -> tuple[bytes, dict[str, t.Any]]:
         Returns:
             A tuple of (object_bytes, metadata_dict)
         """
-        if isinstance(self._data, (str, Path)) and Path(self._data).exists():
+        if isinstance(self._data, str | Path) and Path(self._data).exists():
             return self._process_file_path()
         if isinstance(self._data, bytes):
             format_name = self._format or "glb"
@@ -67,7 +67,7 @@ def _process_file_path(self) -> tuple[bytes, dict[str, t.Any]]:
         Returns:
             A tuple of (object_bytes, metadata_dict)
         """
-        if not isinstance(self._data, (str, Path)):
+        if not isinstance(self._data, str | Path):
             raise TypeError(f"Expected str or Path for file path, got {type(self._data)}")
         path = Path(self._data)
         object_bytes = path.read_bytes()
@@ -92,7 +92,7 @@ def _generate_metadata(self, format_name: str) -> dict[str, t.Any]:
         if self._caption:
             metadata["caption"] = self._caption
 
-        if isinstance(self._data, (str, Path)):
+        if isinstance(self._data, str | Path):
             metadata["source-type"] = "file"
             metadata["source-path"] = str(self._data)
         elif isinstance(self._data, bytes):
diff --git a/dreadnode/data_types/table.py b/dreadnode/data_types/table.py
index 2446ff7f..46dbab58 100644
--- a/dreadnode/data_types/table.py
+++ b/dreadnode/data_types/table.py
@@ -3,11 +3,10 @@
 from pathlib import Path
 from typing import ClassVar
 
-from dreadnode.data_types.base import DataType
+import numpy as np
+import pandas as pd
 
-if t.TYPE_CHECKING:
-    import numpy as np
-    import pandas as pd
+from dreadnode.data_types.base import DataType
 
 TableDataType = t.Union[
     "pd.DataFrame", dict[t.Any, t.Any], list[t.Any], str, Path, "np.ndarray[t.Any, t.Any]"
@@ -78,12 +77,10 @@ def _to_dataframe(self) -> "pd.DataFrame":
         Returns:
             A pandas DataFrame representation of the input data
         """
-        import numpy as np
-        import pandas as pd
 
         if isinstance(self._data, pd.DataFrame):
             return self._data
-        if isinstance(self._data, (str, Path)) and Path(self._data).exists():
+        if isinstance(self._data, str | Path) and Path(self._data).exists():
             path = Path(self._data)
             suffix = path.suffix.lower()
 
@@ -98,7 +95,7 @@ def _to_dataframe(self) -> "pd.DataFrame":
         if isinstance(self._data, dict):
             return pd.DataFrame.from_dict(self._data)
 
-        if isinstance(self._data, (list, np.ndarray)):
+        if isinstance(self._data, list | np.ndarray):
             return pd.DataFrame(self._data)
 
         raise ValueError(f"Unsupported table data type: {type(self._data)}")
@@ -134,8 +131,6 @@ def _generate_metadata(self, data_frame: "pd.DataFrame") -> dict[str, t.Any]:
         Returns:
             A dictionary of metadata
         """
-        import numpy as np
-        import pandas as pd
 
         metadata = {
             "extension": self._format,
@@ -151,7 +146,7 @@ def _generate_metadata(self, data_frame: "pd.DataFrame") -> dict[str, t.Any]:
 
         if isinstance(self._data, pd.DataFrame):
             metadata["source-type"] = "pandas.DataFrame"
-        elif isinstance(self._data, (str, Path)):
+        elif isinstance(self._data, str | Path):
             metadata["source-type"] = "file"
             metadata["source-path"] = str(self._data)
         elif isinstance(self._data, dict):
diff --git a/dreadnode/data_types/video.py b/dreadnode/data_types/video.py
index 54ef0f69..89e48925 100644
--- a/dreadnode/data_types/video.py
+++ b/dreadnode/data_types/video.py
@@ -71,11 +71,11 @@ def to_serializable(self) -> tuple[bytes, dict[str, t.Any]]:
         except ImportError:
             VideoClip = None  # noqa: N806
 
-        if isinstance(self._data, (str, Path)) and Path(self._data).exists():
+        if isinstance(self._data, str | Path) and Path(self._data).exists():
             return self._process_file_path()
         if isinstance(self._data, bytes):
             return self._process_bytes()
-        if isinstance(self._data, (np.ndarray, list)):
+        if isinstance(self._data, np.ndarray | list):
             return self._process_numpy_array()
         if VideoClip is not None and isinstance(self._data, VideoClip):
             return self._process_moviepy_clip()
@@ -92,7 +92,7 @@ def _process_file_path(self) -> tuple[bytes, dict[str, t.Any]]:
         Returns:
             A tuple of (video_bytes, metadata_dict)
         """
-        if not isinstance(self._data, (str, Path)):
+        if not isinstance(self._data, str | Path):
             raise TypeError("Expected file path as str or Path")
         video_bytes = Path(self._data).read_bytes()
         format_name = self._format
@@ -126,7 +126,7 @@ def _process_numpy_array(self) -> tuple[bytes, dict[str, t.Any]]:
 
         if not self._fps:
             raise ValueError("fps is required for numpy array video frames")
-        if not isinstance(self._data, (np.ndarray, list)):
+        if not isinstance(self._data, np.ndarray | list):
             raise TypeError("data must be a numpy array or list of numpy arrays")
 
         frames = self._extract_frames_from_data()
diff --git a/dreadnode/eval/console.py b/dreadnode/eval/console.py
index f58dbe87..541b3931 100644
--- a/dreadnode/eval/console.py
+++ b/dreadnode/eval/console.py
@@ -19,7 +19,6 @@
 from rich.table import Table
 from rich.text import Text
 
-from dreadnode.eval.eval import In, Out
 from dreadnode.eval.events import (
     EvalEnd,
     EvalEvent,
@@ -38,7 +37,7 @@
 EvalT = t.TypeVar("EvalT", bound="Eval")
 
 
-class EvalConsoleAdapter(t.Generic[In, Out]):
+class EvalConsoleAdapter:
     """
     Consumes an Eval's event stream and renders a live progress dashboard.
     """
diff --git a/dreadnode/eval/dataset.py b/dreadnode/eval/dataset.py
index 5c8db2ce..d5d4e515 100644
--- a/dreadnode/eval/dataset.py
+++ b/dreadnode/eval/dataset.py
@@ -3,6 +3,8 @@
 import typing as t
 from pathlib import Path
 
+import yaml  # type: ignore[import-untyped]
+
 from dreadnode.types import AnyDict
 
 FileFormat = t.Literal["jsonl", "csv", "json", "yaml", "yml"]
@@ -49,13 +51,6 @@ def load_dataset(path: Path, *, file_format: FileFormat | None = None) -> list[A
             raise ValueError("JSON file must contain a list of objects.")
 
     elif file_format in {"yaml", "yml"}:
-        try:
-            import yaml  # type: ignore[import-untyped,unused-ignore]
-        except ImportError as e:
-            raise ImportError(
-                "Loading YAML datasets requires PyYAML. Install with: pip install pyyaml"
-            ) from e
-
         dataset = yaml.safe_load(content)
         if not isinstance(dataset, list):
             raise ValueError("YAML file must contain a list of objects.")
diff --git a/dreadnode/eval/eval.py b/dreadnode/eval/eval.py
index 7d0ae034..7e77cd14 100644
--- a/dreadnode/eval/eval.py
+++ b/dreadnode/eval/eval.py
@@ -9,6 +9,7 @@
 from pydantic import ConfigDict, FilePath, TypeAdapter
 
 from dreadnode.discovery import find
+from dreadnode.eval.console import EvalConsoleAdapter
 from dreadnode.eval.dataset import load_dataset
 from dreadnode.eval.events import (
     EvalEnd,
@@ -350,7 +351,6 @@ async def run(self) -> EvalResult[In, Out]:
 
     async def console(self) -> EvalResult:
         """Run the evaluation with a live display in the console."""
-        from dreadnode.eval.console import EvalConsoleAdapter
 
         adapter = EvalConsoleAdapter(self)
         return await adapter.run()
diff --git a/dreadnode/eval/result.py b/dreadnode/eval/result.py
index 58a2dff2..bde39733 100644
--- a/dreadnode/eval/result.py
+++ b/dreadnode/eval/result.py
@@ -5,6 +5,7 @@
 from dataclasses import dataclass, field
 from pathlib import Path
 
+import pandas as pd
 import typing_extensions as te
 
 from dreadnode.eval.sample import Sample
@@ -119,7 +120,6 @@ def to_dataframe(self) -> "t.Any":
         """
         Converts the results into a pandas DataFrame for analysis.
         """
-        import pandas as pd
 
         return pd.DataFrame(self.to_dicts())  # type: ignore[misc]
 
@@ -129,8 +129,7 @@ def to_jsonl(self, path: str | Path) -> None:
         """
         records = self.to_dicts()  # type: ignore[misc]
         with Path(path).open("w", encoding="utf-8") as f:
-            for record in records:
-                f.write(json.dumps(record) + "\n")
+            f.writelines(json.dumps(record) + "\n" for record in records)
 
 
 @dataclass
diff --git a/dreadnode/eval/sample.py b/dreadnode/eval/sample.py
index a5d0bcf8..cd657c8f 100644
--- a/dreadnode/eval/sample.py
+++ b/dreadnode/eval/sample.py
@@ -88,7 +88,8 @@ def from_task(
         context: dict[str, t.Any] | None = None,
     ) -> "Sample[In, Out]":
         # Assume false for all
-        assertions = dict.fromkeys(task.assert_scores, False)
+        assert_scores: t.Any = getattr(task, "assert_scores", [])
+        assertions = dict.fromkeys(assert_scores, False)
 
         # If a score was reported, assume true
         for name in set(span.metrics.keys()) & set(assertions.keys()):
diff --git a/dreadnode/meta/hydrate.py b/dreadnode/meta/hydrate.py
index 83ff95de..a0873894 100644
--- a/dreadnode/meta/hydrate.py
+++ b/dreadnode/meta/hydrate.py
@@ -101,10 +101,12 @@ def _hydrate_recursive(obj: t.Any, override: t.Any) -> t.Any:  # noqa: PLR0911,
             hydrated_dict[key] = _hydrate_recursive(item, item_overrides)
         return hydrated_dict
 
-    if not isinstance(obj, (str, int, float, bool, type(None), type)) and hasattr(obj, "__dict__"):
+    if not isinstance(obj, str | int | float | bool | type(None) | type) and hasattr(
+        obj, "__dict__"
+    ):
         with contextlib.suppress(Exception):
             for attr_name, attr_value in obj.__dict__.items():
-                if attr_name.startswith("__") or not isinstance(attr_value, (Component, Model)):
+                if attr_name.startswith("__") or not isinstance(attr_value, Component | Model):
                     continue
 
                 hydrated = _hydrate_recursive(attr_value, override)
diff --git a/dreadnode/meta/introspect.py b/dreadnode/meta/introspect.py
index 8e36dd32..ce0a35db 100644
--- a/dreadnode/meta/introspect.py
+++ b/dreadnode/meta/introspect.py
@@ -152,10 +152,12 @@ def flatten_model(model: PydanticBaseModel, prefix: str = "") -> dict[str, t.Any
 
 
 def _find_nested_configurable(obj: t.Any) -> t.Any | None:
-    if isinstance(obj, (Component, Model)):
+    if isinstance(obj, Component | Model):
         return obj
 
-    if isinstance(obj, (str, int, float, bool, type(None), type)) or not hasattr(obj, "__dict__"):
+    if isinstance(obj, str | int | float | bool | type(None) | type) or not hasattr(
+        obj, "__dict__"
+    ):
         return None
 
     with contextlib.suppress(Exception):
@@ -163,7 +165,7 @@ def _find_nested_configurable(obj: t.Any) -> t.Any | None:
             if attr_name.startswith("__"):
                 continue
 
-            if isinstance(attr_value, (Component, Model)):
+            if isinstance(attr_value, Component | Model):
                 return attr_value
 
     return None
@@ -187,11 +189,11 @@ def _resolve_type_and_default(obj: t.Any, annotation: t.Any, name: str) -> tuple
     nested_fields: AnyDict = {}
     nested_default: t.Any
 
-    if isinstance(obj, (list, tuple)):
+    if isinstance(obj, list | tuple):
         used_names = set()
 
         for item in obj:
-            if not isinstance(item, (Model, Component)) and not (
+            if not isinstance(item, Model | Component) and not (
                 item := _find_nested_configurable(item)
             ):
                 continue
@@ -221,7 +223,7 @@ def _resolve_type_and_default(obj: t.Any, annotation: t.Any, name: str) -> tuple
 
     elif isinstance(obj, dict):
         for key, value in obj.items():
-            if not isinstance(value, (Model, Component)) and not (
+            if not isinstance(value, Model | Component) and not (
                 value := _find_nested_configurable(value)
             ):
                 continue
@@ -238,7 +240,7 @@ def _resolve_type_and_default(obj: t.Any, annotation: t.Any, name: str) -> tuple
         with contextlib.suppress(Exception):
             obj_default = obj_type()
 
-    elif isinstance(obj, (Model, Component)):
+    elif isinstance(obj, Model | Component):
         obj_type = get_config_model(obj, name)
         obj_default = Ellipsis
         with contextlib.suppress(Exception):
diff --git a/dreadnode/meta/types.py b/dreadnode/meta/types.py
index 20876c37..18e361ae 100644
--- a/dreadnode/meta/types.py
+++ b/dreadnode/meta/types.py
@@ -279,7 +279,7 @@ def Config(  # noqa: N802
 )
 class ConfigurableMeta(ModelMetaclass):
     def __new__(
-        mcs,
+        cls,
         name: str,
         bases: tuple[type[t.Any], ...],
         namespace: dict[str, t.Any],
@@ -297,11 +297,11 @@ def __new__(
             }
             namespace[attr_name] = PydanticField(**field_kwargs)  # type: ignore[arg-type]
 
-        cls = super().__new__(mcs, name, bases, namespace, **kwargs)
+        new_cls = super().__new__(cls, name, bases, namespace, **kwargs)
 
         # Merge config from all base classes
         merged_configs = {}
-        for base in reversed(cls.__mro__):  # Go from most base to most derived
+        for base in reversed(new_cls.__mro__):  # Go from most base to most derived
             if hasattr(base, "__dn_config__"):
                 merged_configs.update(base.__dn_config__)
 
@@ -309,13 +309,13 @@ def __new__(
 
         # If pydantic resolved any of our field descriptions, we need to
         # reflect those back into the ConfigInfo objects
-        for field_name, field_info in cls.model_fields.items():  # type: ignore[attr-defined]
+        for field_name, field_info in new_cls.model_fields.items():  # type: ignore[attr-defined]
             if field_name in configs:
                 configs[field_name].field_kwargs["description"] = field_info.description
 
-        cls.__dn_config__ = merged_configs  # type: ignore[attr-defined]
+        new_cls.__dn_config__ = merged_configs  # type: ignore[attr-defined]
 
-        return cls
+        return new_cls
 
 
 class Model(PydanticBaseModel, metaclass=ConfigurableMeta):
diff --git a/dreadnode/optimization/collectors.py b/dreadnode/optimization/collectors.py
index 418a6ad9..ad8cb578 100644
--- a/dreadnode/optimization/collectors.py
+++ b/dreadnode/optimization/collectors.py
@@ -23,13 +23,13 @@ def get_parent(trial: Trial[T]) -> Trial[T] | None:
             else None
         )
 
-    trials: Trials[T] = []
+    trials: Trials[T] = []  # type: ignore[assignment]
     parent = get_parent(current_trial)
     while parent:
         trials.insert(0, parent)
         parent = get_parent(parent)
 
-    return trials[:depth]
+    return trials[:depth]  # type: ignore[return-value]
 
 
 @component
@@ -37,7 +37,7 @@ def all_successful(_: Trial[T], all_trials: Trials[T]) -> Trials[T]:
     """
     Collects all successful trials, regardless of lineage.
     """
-    return [t for t in all_trials if t.status == "success"]
+    return [t for t in all_trials if t.status == "success"]  # type: ignore[return-value]
 
 
 @component
@@ -53,14 +53,15 @@ def local_neighborhood(
     The maximum distance for any discovered node is `2h-1`.
     """
     if not all_trials:
-        return []
+        return []  # type: ignore[return-value]
 
     # 1 - Build a bi-directional graph for efficient traversal
 
     all_trials_map: dict[UUID, Trial] = {t.id: t for t in all_trials}
     children_map: dict[UUID, list[UUID]] = {tid: [] for tid in all_trials_map}
     for trial in [t_ for t_ in all_trials if t_.parent_id]:
-        children_map.setdefault(trial.parent_id, []).append(trial.id)
+        if trial.parent_id:
+            children_map.setdefault(trial.parent_id, []).append(trial.id)
 
     # 2 - Perform a BFS staying within 2h-1
 
@@ -92,4 +93,4 @@ def local_neighborhood(
                 visited.add(child_id)
                 queue.append((child_id, distance + 1))
 
-    return [all_trials_map[tid] for tid in neighborhood_ids]
+    return [all_trials_map[tid] for tid in neighborhood_ids]  # type: ignore[return-value]
diff --git a/dreadnode/optimization/console.py b/dreadnode/optimization/console.py
index 0fcde8be..d8342d33 100644
--- a/dreadnode/optimization/console.py
+++ b/dreadnode/optimization/console.py
@@ -33,7 +33,7 @@
     from dreadnode.optimization.study import Study
     from dreadnode.optimization.trial import Trial
 
-StudyT = t.TypeVar("StudyT", bound="Study")
+StudyT = t.TypeVar("StudyT", bound="Study[t.Any]")
 
 
 class StudyConsoleAdapter(t.Generic[StudyT]):
@@ -61,7 +61,7 @@ def __init__(
             expand=True,
         )
         self._trial_log: deque[Trial] = deque(maxlen=max_log_entries)
-        self._best_trial_summary = Text("No successful trials yet.", style="dim")
+        self._best_trial_summary: Text | Table = Text("No successful trials yet.", style="dim")
         self._summary_stats = {
             "Successful": 0,
             "Failed": 0,
@@ -143,7 +143,8 @@ def _build_dashboard(self) -> Panel:
             Layout(trials_panel, name="trials_log", ratio=1, minimum_size=5),
         )
 
-        name = self.study.name or get_callable_name(self.study.objective, short=True)
+        objective_func = t.cast("t.Callable[..., t.Any]", self.study.objective)
+        name = self.study.name or get_callable_name(objective_func, short=True)
 
         return Panel(
             layout,
@@ -151,7 +152,7 @@ def _build_dashboard(self) -> Panel:
             border_style="cyan",
         )
 
-    def _handle_event(self, event: StudyEvent) -> None:  # noqa: PLR0912
+    def _handle_event(self, event: StudyEvent[t.Any]) -> None:  # noqa: PLR0912
         if isinstance(event, StudyStart):
             self._steps_task_id = self._progress.add_task(
                 "[bold]Overall Steps", total=self.study.max_steps
@@ -206,7 +207,7 @@ def _handle_event(self, event: StudyEvent) -> None:  # noqa: PLR0912
             if self._steps_task_id is not None:
                 self._progress.update(self._steps_task_id, advance=1)
             if self._patience_task_id is not None:
-                if self.study._steps_since_best > 0:
+                if self.study._steps_since_best > 0:  # noqa: SLF001
                     self._progress.update(self._patience_task_id, advance=1)
                 else:
                     self._progress.reset(self._patience_task_id)
diff --git a/dreadnode/optimization/sampling.py b/dreadnode/optimization/sampling.py
index a725e178..acc670ff 100644
--- a/dreadnode/optimization/sampling.py
+++ b/dreadnode/optimization/sampling.py
@@ -17,7 +17,7 @@ def top_k(
     Selects the top k trials by score (highest first).
     """
     sorted_trials = sorted(trials, key=lambda t: t.score, reverse=True)
-    return sorted_trials[:k]
+    return sorted_trials[:k]  # type: ignore[return-value]
 
 
 @component
@@ -27,7 +27,7 @@ def random_(
     """
     Selects k random trials from the pool.
     """
-    return random.sample(trials, min(k, len(trials))) if trials else []  # nosec
+    return random.sample(trials, min(k, len(trials))) if trials else []  # type: ignore[return-value]
 
 
 @component
@@ -47,9 +47,9 @@ def epsilon_greedy(
     if random.random() < epsilon and len(sorted_trials) >= k:  # noqa: S311 # nosec
         k_minus_1 = sorted_trials[: k - 1]
         random_choice = random.choice(sorted_trials[k - 1 :])  # noqa: S311 # nosec
-        return [*k_minus_1, random_choice]
+        return [*k_minus_1, random_choice]  # type: ignore[return-value]
 
-    return sorted_trials[:k]
+    return sorted_trials[:k]  # type: ignore[return-value]
 
 
 @component
@@ -72,7 +72,7 @@ def competitive(trials: Trials[T], *, k: int = Config(5), pool_size: int = Confi
         winners.append(winner)
         pool.remove(winner)
 
-    return winners
+    return winners  # type: ignore[return-value]
 
 
 @component
@@ -93,7 +93,7 @@ def proportional(
         A list of selected trials.
     """
     if not trials:
-        return []
+        return []  # type: ignore[return-value]
 
     # 1 - Normalize scores for use as weights
 
@@ -104,7 +104,7 @@ def proportional(
 
     # If all trials have the same score - take the fast route
     if total_weight == 0:
-        return random.sample(trials, min(k, len(trials)))  # nosec
+        return random.sample(trials, min(k, len(trials)))  # type: ignore[return-value]
 
     # 2 - Select k winners one by one, without replacement
 
@@ -128,7 +128,7 @@ def proportional(
         if sum(current_weights) == 0:
             break
 
-    return winners
+    return winners  # type: ignore[return-value]
 
 
 # Utils
@@ -144,7 +144,7 @@ def interleave_by_parent(trials: Trials[T]) -> Trials[T]:
     Example: `[P1, P1, P2, P2, P3]` -> [P1, P2, P3, P1, P2]
     """
     if not trials:
-        return []
+        return []  # type: ignore[return-value]
 
     parent_to_children = defaultdict(list)
     for trial in trials:
@@ -156,4 +156,4 @@ def interleave_by_parent(trials: Trials[T]) -> Trials[T]:
             if trial is not None:
                 interleaved_list.append(trial)  # noqa: PERF401
 
-    return interleaved_list
+    return interleaved_list  # type: ignore[return-value]
diff --git a/dreadnode/optimization/search/graph.py b/dreadnode/optimization/search/graph.py
index fb00279e..f909159e 100644
--- a/dreadnode/optimization/search/graph.py
+++ b/dreadnode/optimization/search/graph.py
@@ -50,8 +50,8 @@ class GraphSearch(Model, Search[T]):
     pruning_sampler: TrialSampler[T] = Config(top_k)
     """A trial sampler to prune new children after each branching."""
 
-    _trials: Trials[T] = PrivateAttr(default_factory=list)
-    _leaves: Trials[T] = PrivateAttr(default_factory=list)
+    _trials: Trials[T] = PrivateAttr(default_factory=list)  # type: ignore[arg-type]
+    _leaves: Trials[T] = PrivateAttr(default_factory=list)  # type: ignore[arg-type]
 
     def __repr__(self) -> str:
         parts = [
@@ -63,17 +63,17 @@ def __repr__(self) -> str:
         return f"GraphSearch({', '.join(parts)})"
 
     def reset(self) -> None:
-        self._trials = []
-        self._leaves = []
+        self._trials = []  # type: ignore[assignment]
+        self._leaves = []  # type: ignore[assignment]
 
     async def suggest(self, step: int) -> Trials[T]:
         if not self._leaves:
-            return [Trial(candidate=self.initial_candidate, step=step)]
+            return [Trial(candidate=self.initial_candidate, step=step)]  # type: ignore[return-value]
 
-        trials: Trials[T] = []
+        trials: Trials[T] = []  # type: ignore[assignment]
         for leaf in self._leaves:
             context = [leaf, *self.context_collector(leaf, self._trials)]
-            coroutines = [self.transform(context) for _ in range(self.branching_factor)]
+            coroutines = [self.transform(context) for _ in range(self.branching_factor)]  # type: ignore[arg-type]
             for candidate in await asyncio.gather(*coroutines):
                 trials.append(Trial(candidate=candidate, parent_id=leaf.id, step=step))
 
@@ -82,8 +82,8 @@ async def suggest(self, step: int) -> Trials[T]:
     async def observe(self, trials: list[Trial[T]]) -> None:
         successful_trials = [t for t in trials if t.status == "success"]
         self._trials.extend(successful_trials)
-        interleaved_trials = interleave_by_parent(successful_trials)  # Prevent parent bias
-        self._leaves = self.pruning_sampler(interleaved_trials)
+        interleaved_trials: list[Trial[T]] = interleave_by_parent(successful_trials)  # type: ignore[arg-type]
+        self._leaves = self.pruning_sampler(interleaved_trials)  # type: ignore[arg-type]
 
 
 def iterative_search(
@@ -109,11 +109,11 @@ def iterative_search(
         A pre-configured GraphSearch instance.
     """
     return GraphSearch[T](
-        transform=transform,
+        transform=transform,  # type: ignore[arg-type]
         initial_candidate=initial_candidate,
         branching_factor=branching_factor,
-        context_collector=lineage(),
-        pruning_sampler=top_k(k=1),
+        context_collector=lineage(),  # type: ignore[arg-type,call-arg]
+        pruning_sampler=top_k(k=1),  # type: ignore[arg-type,call-arg]
     )
 
 
@@ -141,7 +141,7 @@ def beam_search(
         A pre-configured GraphSearch instance.
     """
     return GraphSearch[T](
-        transform=transform,
+        transform=transform,  # type: ignore[arg-type]
         initial_candidate=initial_candidate,
         branching_factor=branching_factor,
         context_collector=lineage,
@@ -176,7 +176,7 @@ def graph_search(
         A pre-configured GraphSearch instance.
     """
     return GraphSearch[T](
-        transform=transform,
+        transform=transform,  # type: ignore[arg-type]
         initial_candidate=initial_candidate,
         branching_factor=branching_factor,
         context_collector=local_neighborhood.configure(depth=neighborhood_depth),
diff --git a/dreadnode/optimization/search/optuna_.py b/dreadnode/optimization/search/optuna_.py
index d4862e29..1dc2e214 100644
--- a/dreadnode/optimization/search/optuna_.py
+++ b/dreadnode/optimization/search/optuna_.py
@@ -42,7 +42,7 @@ def __init__(
         self.optuna_search_space = _convert_search_space(search_space)
         self._trial_map: dict[UUID, optuna.trial.Trial] = {}
 
-    async def suggest(self) -> list[Trial[AnyDict]]:
+    async def suggest(self, step: int) -> list[Trial[AnyDict]]:  # type: ignore[override]  # noqa: ARG002
         optuna_trial = self.optuna_study.ask(self.optuna_search_space)
         candidate_params = optuna_trial.params
 
@@ -53,7 +53,7 @@ async def suggest(self) -> list[Trial[AnyDict]]:
 
         return [trial]
 
-    def observe(self, trials: list[Trial[AnyDict]]) -> None:
+    async def observe(self, trials: list[Trial[AnyDict]]) -> None:
         for trial in trials:
             optuna_trial = self._trial_map[trial.id]
             if trial.status == "success":
diff --git a/dreadnode/optimization/study.py b/dreadnode/optimization/study.py
index d21d88b6..81cf2113 100644
--- a/dreadnode/optimization/study.py
+++ b/dreadnode/optimization/study.py
@@ -9,6 +9,7 @@
 from dreadnode.eval.sample import InputDataset
 from dreadnode.meta import Model
 from dreadnode.meta.types import Config
+from dreadnode.optimization.console import StudyConsoleAdapter
 from dreadnode.optimization.events import (
     CandidatePruned,
     CandidatesSuggested,
@@ -26,12 +27,10 @@
 from dreadnode.optimization.trial import Trial, Trials
 from dreadnode.scorers import Scorer, ScorerLike
 from dreadnode.task import Task
+from dreadnode.tracing.span import current_run_span
 from dreadnode.types import AnyDict
 from dreadnode.util import concurrent_gen, get_callable_name
 
-if t.TYPE_CHECKING:
-    from dreadnode.eval.events import EvalStopReason
-
 Direction = t.Literal["maximize", "minimize"]
 """The direction of optimization for the objective score."""
 
@@ -52,7 +51,7 @@ class Study(Model, t.Generic[CandidateT]):
     """The search strategy to use for suggesting new trials."""
     task_factory: t.Callable[[CandidateT], Task[..., t.Any]]
     """A function that accepts a candidate and returns a configured Task ready for evaluation."""
-    objective: ScorerLike[t.Any] | str = Config(default_factory=list)
+    objective: ScorerLike[t.Any] | str = Config(default_factory=list)  # type: ignore[assignment]
     """The objective to optimize. Can be a scorer instance, a scorer-like callable, or a string name of scorer already on the task."""
     dataset: InputDataset[t.Any] | list[AnyDict] | FilePath | None = Config(
         default=None, expose_as=FilePath | None
@@ -142,8 +141,8 @@ async def _stream(self) -> t.AsyncGenerator[StudyEvent[CandidateT], None]:  # no
         self._steps_since_best = 0
         self.strategy.reset()
 
-        stop_reason: EvalStopReason = "unknown"
-        all_trials: Trials[CandidateT] = []
+        stop_reason: t.Any = "unknown"
+        all_trials: Trials[CandidateT] = []  # type: ignore[assignment]
         best_trial: Trial[CandidateT] | None = None
 
         yield StudyStart(study=self, trials=all_trials, max_steps=self.max_steps)
@@ -232,7 +231,7 @@ async def _stream(self) -> t.AsyncGenerator[StudyEvent[CandidateT], None]:  # no
             result=StudyResult(trials=all_trials, stop_reason=stop_reason),
         )
 
-    def _log_event_metrics(self, event: StudyEvent) -> None:
+    def _log_event_metrics(self, event: StudyEvent[t.Any]) -> None:
         from dreadnode import log_metric
 
         if isinstance(event, TrialComplete):
@@ -249,7 +248,6 @@ def _log_event_metrics(self, event: StudyEvent) -> None:
 
     async def _stream_traced(self) -> t.AsyncGenerator[StudyEvent[CandidateT], None]:
         from dreadnode import log_inputs, log_outputs, log_params, run, task_span
-        from dreadnode.tracing.span import current_run_span
 
         objective_name = (
             self.objective
@@ -267,7 +265,7 @@ async def _stream_traced(self) -> t.AsyncGenerator[StudyEvent[CandidateT], None]
 
         # config_model = get_config_model(self)
         # flat_config = {k: v for k, v in flatten_model(config_model()).items() if v is not None}
-        flat_config = {}
+        flat_config: dict[str, t.Any] = {}
 
         with trace_context:
             if run_using_tasks:
@@ -275,7 +273,7 @@ async def _stream_traced(self) -> t.AsyncGenerator[StudyEvent[CandidateT], None]
             else:
                 log_params(**flat_config)
 
-            last_event: StudyEvent | None = None
+            last_event: StudyEvent[t.Any] | None = None
             try:
                 async with contextlib.aclosing(self._stream()) as stream:
                     async for event in stream:
@@ -287,10 +285,10 @@ async def _stream_traced(self) -> t.AsyncGenerator[StudyEvent[CandidateT], None]
                     result = last_event.result
                     outputs = {"stop_reason": result.stop_reason}
                     if result.best_trial:
-                        outputs["best_score"] = result.best_trial.score
+                        outputs["best_score"] = result.best_trial.score  # type: ignore[assignment]
                         outputs["best_candidate"] = result.best_trial.candidate
-                        outputs["best_output"] = result.best_trial.output
-                    log_outputs(**outputs)
+                        outputs["best_output"] = result.best_trial.output  # type: ignore[assignment]
+                    log_outputs(**outputs)  # type: ignore[arg-type]
 
     @contextlib.asynccontextmanager
     async def stream(self) -> t.AsyncIterator[t.AsyncGenerator[StudyEvent[CandidateT], None]]:
@@ -335,7 +333,6 @@ async def run(self) -> StudyResult[CandidateT]:
 
     async def console(self) -> StudyResult[CandidateT]:
         """Runs the optimization study with a live progress dashboard in the console."""
-        from dreadnode.optimization.console import StudyConsoleAdapter
 
         adapter = StudyConsoleAdapter(self)
         return await adapter.run()
diff --git a/dreadnode/optimization/trial.py b/dreadnode/optimization/trial.py
index 5ca4d36a..f00720df 100644
--- a/dreadnode/optimization/trial.py
+++ b/dreadnode/optimization/trial.py
@@ -39,7 +39,6 @@ def __repr__(self) -> str:
         return f"Trial(id={self.id}, status='{self.status}', score={self.score:.3f})"
 
     @computed_field
-    @property
     def output(self) -> t.Any | None:
         """Get the output of the trial."""
         if self.eval_result and self.eval_result.samples:
diff --git a/dreadnode/scorers/classification.py b/dreadnode/scorers/classification.py
index a96a0162..4c2b6577 100644
--- a/dreadnode/scorers/classification.py
+++ b/dreadnode/scorers/classification.py
@@ -1,5 +1,7 @@
 import typing as t
 
+from transformers import pipeline
+
 from dreadnode.meta import Config
 from dreadnode.metric import Metric
 from dreadnode.scorers import Scorer
@@ -35,9 +37,7 @@ def zero_shot_classification(
     )
 
     try:
-        from transformers import (  # type: ignore [attr-defined,import-not-found,unused-ignore]
-            pipeline,
-        )
+        pipeline("zero-shot-classification", model=model_name)
     except ImportError:
         warn_at_user_stacklevel(transformers_error_msg, UserWarning)
 
@@ -45,6 +45,12 @@ def disabled_evaluate(_: t.Any) -> Metric:
             return Metric(value=0.0, attributes={"error": transformers_error_msg})
 
         return Scorer(disabled_evaluate, name=name)
+    except Exception:  # noqa: BLE001
+
+        def disabled_evaluate(_: t.Any) -> Metric:
+            return Metric(value=0.0, attributes={"error": "Failed to create pipeline"})
+
+        return Scorer(disabled_evaluate, name=name)
 
     def evaluate(
         data: t.Any,
diff --git a/dreadnode/scorers/harm.py b/dreadnode/scorers/harm.py
index 80e66b76..2ac52821 100644
--- a/dreadnode/scorers/harm.py
+++ b/dreadnode/scorers/harm.py
@@ -1,15 +1,11 @@
 import typing as t
 
+import openai
+
 from dreadnode.meta import Config
 from dreadnode.metric import Metric
 from dreadnode.scorers import Scorer
 
-if t.TYPE_CHECKING:
-    import openai
-
-if t.TYPE_CHECKING:
-    import openai
-
 
 def detect_harm_with_openai(
     *,
@@ -36,7 +32,6 @@ def detect_harm_with_openai(
         model: The moderation model to use.
         name: Name of the scorer.
     """
-    import openai
 
     async def evaluate(
         data: t.Any, *, api_key: str | None = Config(api_key), model: str = Config(model)
diff --git a/dreadnode/scorers/pii.py b/dreadnode/scorers/pii.py
index c96a2131..ba1c9c35 100644
--- a/dreadnode/scorers/pii.py
+++ b/dreadnode/scorers/pii.py
@@ -1,14 +1,15 @@
 import re
 import typing as t
 
+from presidio_analyzer import AnalyzerEngine
+from presidio_analyzer.nlp_engine import NlpEngineProvider
+
 from dreadnode.metric import Metric
 from dreadnode.scorers import Scorer
 from dreadnode.scorers.contains import contains
 from dreadnode.util import warn_at_user_stacklevel
 
 if t.TYPE_CHECKING:
-    from presidio_analyzer import AnalyzerEngine  # type: ignore[import-not-found,unused-ignore]
-
     from dreadnode.types import JsonDict
 
 
@@ -65,9 +66,6 @@ def _get_presidio_analyzer() -> "AnalyzerEngine":
     """Lazily initializes and returns a singleton Presidio AnalyzerEngine instance."""
     global g_analyzer_engine  # noqa: PLW0603
 
-    from presidio_analyzer import AnalyzerEngine
-    from presidio_analyzer.nlp_engine import NlpEngineProvider
-
     if g_analyzer_engine is None:
         provider = NlpEngineProvider(
             nlp_configuration={
@@ -109,8 +107,8 @@ def detect_pii_with_presidio(
     )
 
     try:
-        import presidio_analyzer  # type: ignore[import-not-found,unused-ignore]  # noqa: F401
-    except ImportError:
+        _get_presidio_analyzer()
+    except (ImportError, OSError):
         warn_at_user_stacklevel(presidio_import_error_msg, UserWarning)
 
         def disabled_evaluate(_: t.Any) -> Metric:
diff --git a/dreadnode/scorers/readability.py b/dreadnode/scorers/readability.py
index e8fa3373..259f5feb 100644
--- a/dreadnode/scorers/readability.py
+++ b/dreadnode/scorers/readability.py
@@ -1,5 +1,7 @@
 import typing as t
 
+import textstat  # type: ignore[import-untyped]
+
 from dreadnode.metric import Metric
 from dreadnode.scorers.base import Scorer
 from dreadnode.util import warn_at_user_stacklevel
@@ -27,8 +29,8 @@ def readability(
     )
 
     try:
-        import textstat  # type: ignore[import-not-found,import-untyped,unused-ignore]
-    except ImportError:
+        textstat.flesch_kincaid_grade("test")
+    except (ImportError, AttributeError):
         warn_at_user_stacklevel(textstat_import_error_msg, UserWarning)
 
         def disabled_evaluate(_: t.Any) -> Metric:
diff --git a/dreadnode/scorers/rigging.py b/dreadnode/scorers/rigging.py
index 48f76e03..9d3a3bd6 100644
--- a/dreadnode/scorers/rigging.py
+++ b/dreadnode/scorers/rigging.py
@@ -1,10 +1,11 @@
 import typing as t
 
+from rigging.chat import Chat
+
 from dreadnode.metric import Metric
 from dreadnode.scorers.base import Scorer
 
 if t.TYPE_CHECKING:
-    from rigging.chat import Chat
     from rigging.message import Message
 
 ChatFilterMode = t.Literal[
@@ -44,8 +45,6 @@ def wrap_chat(
     """
 
     async def evaluate(chat: "Chat") -> Metric:
-        from rigging.chat import Chat
-
         # Fall through to the inner scorer if chat is not a Chat instance
         if not isinstance(chat, Chat):
             return await inner_scorer(chat)
diff --git a/dreadnode/scorers/sentiment.py b/dreadnode/scorers/sentiment.py
index 895d4b36..d9151da8 100644
--- a/dreadnode/scorers/sentiment.py
+++ b/dreadnode/scorers/sentiment.py
@@ -2,6 +2,7 @@
 import typing as t
 
 import httpx
+from textblob import TextBlob  # type: ignore[import-untyped]
 
 from dreadnode.meta import Config
 from dreadnode.metric import Metric
@@ -32,8 +33,8 @@ def sentiment(
     textblob_import_error_msg = "TextBlob dependency is not installed. Install with: pip install textblob && python -m textblob.download_corpora"
 
     try:
-        from textblob import TextBlob  # type: ignore[import-not-found,unused-ignore,import-untyped]
-    except ImportError:
+        TextBlob("test").sentiment  # noqa: B018
+    except (ImportError, AttributeError):
         warn_at_user_stacklevel(textblob_import_error_msg, UserWarning)
 
         def disabled_evaluate(_: t.Any) -> Metric:
diff --git a/dreadnode/scorers/similarity.py b/dreadnode/scorers/similarity.py
index 0bd2db80..de199751 100644
--- a/dreadnode/scorers/similarity.py
+++ b/dreadnode/scorers/similarity.py
@@ -1,17 +1,23 @@
 import typing as t
 from difflib import SequenceMatcher
 
+import litellm
+import nltk  # type: ignore[import-untyped]
+from nltk.tokenize import word_tokenize  # type: ignore[import-untyped]
+from nltk.translate.bleu_score import sentence_bleu  # type: ignore[import-untyped]
+from rapidfuzz import distance, fuzz, utils
+from sentence_transformers import SentenceTransformer, util
+from sklearn.feature_extraction.text import TfidfVectorizer  # type: ignore[import-untyped]
+from sklearn.metrics.pairwise import (  # type: ignore  # noqa: PGH003
+    cosine_similarity as sklearn_cosine_similarity,
+)
+
 from dreadnode.meta import Config
 from dreadnode.metric import Metric
 from dreadnode.scorers.base import Scorer
 from dreadnode.scorers.util import cosine_similarity
 from dreadnode.util import warn_at_user_stacklevel
 
-if t.TYPE_CHECKING:
-    from sentence_transformers import (  # type: ignore[import-not-found,import-untyped,unused-ignore]
-        SentenceTransformer,
-    )
-
 
 def similarity(
     reference: str,
@@ -93,7 +99,7 @@ def similarity_with_rapidfuzz(
     )
 
     try:
-        from rapidfuzz import fuzz, utils  # type: ignore[import-not-found,unused-ignore]
+        fuzz.ratio("test", "test")
     except ImportError:
         warn_at_user_stacklevel(rapidfuzz_import_error_msg, UserWarning)
 
@@ -162,7 +168,7 @@ def evaluate(
     return Scorer(evaluate, name=name, catch=True)
 
 
-def distance(
+def string_distance(
     reference: str,
     *,
     method: t.Literal[
@@ -190,7 +196,7 @@ def distance(
     )
 
     try:
-        from rapidfuzz import distance  # type: ignore[import-not-found,unused-ignore]
+        distance.Levenshtein.distance("test", "test")
     except ImportError:
         warn_at_user_stacklevel(rapidfuzz_import_error_msg, UserWarning)
 
@@ -259,12 +265,7 @@ def similarity_with_tf_idf(reference: str, *, name: str = "similarity") -> "Scor
     )
 
     try:
-        from sklearn.feature_extraction.text import (  # type: ignore[import-not-found,unused-ignore]
-            TfidfVectorizer,
-        )
-        from sklearn.metrics.pairwise import (  # type: ignore[import-not-found,unused-ignore]
-            cosine_similarity as sklearn_cosine_similarity,
-        )
+        TfidfVectorizer()
     except ImportError:
         warn_at_user_stacklevel(sklearn_import_error_msg, UserWarning)
 
@@ -311,10 +312,7 @@ def similarity_with_sentence_transformers(
     sentence_transformers_error_msg = "Sentence transformers dependency is not installed. Please install it with: pip install sentence-transformers"
 
     try:
-        from sentence_transformers import (  # type: ignore[import-not-found,import-untyped,unused-ignore]
-            SentenceTransformer,
-            util,
-        )
+        SentenceTransformer(model_name)
     except ImportError:
         warn_at_user_stacklevel(sentence_transformers_error_msg, UserWarning)
 
@@ -372,7 +370,6 @@ def similarity_with_litellm(
                   or self-hosted models.
         name: Name of the scorer.
     """
-    import litellm
 
     async def evaluate(
         data: t.Any,
@@ -429,21 +426,17 @@ def bleu(
     nltk_import_error_msg = "NLTK dependency is not installed. Install with: pip install nltk && python -m nltk.downloader punkt"
 
     try:
-        import nltk  # type: ignore[import-not-found,unused-ignore]
-        from nltk.tokenize import word_tokenize  # type: ignore[import-not-found,unused-ignore]
-        from nltk.translate.bleu_score import (  # type: ignore[import-not-found,unused-ignore]
-            sentence_bleu,
-        )
-
         # Check for the 'punkt' tokenizer data
         try:
             nltk.data.find("tokenizers/punkt")
+            word_tokenize("test")
+            sentence_bleu([["test"]], ["test"])
         except LookupError as e:
             nltk_import_error_msg = (
                 "NLTK 'punkt' tokenizer not found. Please run: python -m nltk.downloader punkt"
             )
             raise ImportError(nltk_import_error_msg) from e
-    except ImportError:
+    except (ImportError, AttributeError):
         warn_at_user_stacklevel(nltk_import_error_msg, UserWarning)
 
         def disabled_evaluate(_: t.Any) -> Metric:
diff --git a/dreadnode/serialization.py b/dreadnode/serialization.py
index 462acf0e..834ce5b7 100644
--- a/dreadnode/serialization.py
+++ b/dreadnode/serialization.py
@@ -23,6 +23,14 @@
 from re import Pattern
 from uuid import UUID
 
+import attrs
+import datasets  # type: ignore[import-untyped]
+import numpy as np
+import pandas as pd
+import pydantic
+import pydantic.dataclasses
+from pydantic import TypeAdapter
+
 from dreadnode.data_types.base import DataType
 from dreadnode.types import JsonDict, JsonValue
 from dreadnode.util import safe_repr
@@ -317,16 +325,11 @@ def _handle_dataclass(obj: t.Any, seen: set[int]) -> tuple[JsonValue, JsonDict]:
 
 
 def _handle_attrs(obj: t.Any, seen: set[int]) -> tuple[JsonValue, JsonDict]:
-    import attrs
-
     keys = [f.name for f in attrs.fields(obj.__class__)]
     return _handle_custom_object(obj, keys, seen, "attrs")
 
 
 def _handle_pydantic_dataclass(obj: t.Any, _seen: set[int]) -> tuple[JsonValue, JsonDict]:
-    import pydantic.dataclasses
-    from pydantic import TypeAdapter
-
     if not pydantic.dataclasses.is_pydantic_dataclass(obj.__class__):
         return safe_repr(obj), UNKNOWN_OBJECT_SCHEMA
 
@@ -341,8 +344,6 @@ def _handle_pydantic_dataclass(obj: t.Any, _seen: set[int]) -> tuple[JsonValue,
 
 
 def _handle_pydantic_model(obj: t.Any, _seen: set[int]) -> tuple[JsonValue, JsonDict]:
-    import pydantic
-
     if not isinstance(obj, pydantic.BaseModel):
         return safe_repr(obj), UNKNOWN_OBJECT_SCHEMA
 
@@ -362,9 +363,7 @@ def _handle_numpy_array(
     obj: t.Any,
     seen: set[int],
 ) -> tuple[JsonValue, JsonDict]:
-    import numpy  # noqa: ICN001
-
-    if not isinstance(obj, numpy.ndarray):
+    if not isinstance(obj, np.ndarray):
         return safe_repr(obj), UNKNOWN_OBJECT_SCHEMA
 
     serialized, schema = _handle_bytes(obj.tobytes(), seen)
@@ -380,8 +379,6 @@ def _handle_pandas_dataframe(
     obj: t.Any,
     seen: set[int],
 ) -> tuple[JsonValue, JsonDict]:
-    import pandas as pd
-
     if not isinstance(obj, pd.DataFrame):
         return safe_repr(obj), UNKNOWN_OBJECT_SCHEMA
 
@@ -395,8 +392,6 @@ def _handle_pandas_series(
     obj: t.Any,
     seen: set[int],
 ) -> tuple[JsonValue, JsonDict]:
-    import pandas as pd
-
     if not isinstance(obj, pd.Series):
         return safe_repr(obj), UNKNOWN_OBJECT_SCHEMA
 
@@ -407,8 +402,6 @@ def _handle_pandas_series(
 
 
 def _handle_dataset(obj: t.Any, _seen: set[int]) -> tuple[JsonValue, JsonDict]:
-    import datasets  # type: ignore[import-untyped]
-
     if not isinstance(obj, datasets.Dataset):
         return safe_repr(obj), UNKNOWN_OBJECT_SCHEMA
 
@@ -470,8 +463,6 @@ def _get_handlers() -> dict[type, HandlerFunc]:
     # Pydantic
 
     with contextlib.suppress(Exception):
-        import pydantic
-
         handlers[pydantic.NameEmail] = lambda o, s: _handle_str_based(
             o,
             s,
@@ -495,8 +486,6 @@ def _get_handlers() -> dict[type, HandlerFunc]:
         handlers[pydantic.BaseModel] = _handle_pydantic_model
 
     with contextlib.suppress(Exception):
-        import numpy as np
-
         handlers[np.ndarray] = _handle_numpy_array
         handlers[np.floating] = lambda o, s: _serialize(float(o), s)
         handlers[np.integer] = lambda o, s: _serialize(int(o), s)
@@ -513,14 +502,10 @@ def _get_handlers() -> dict[type, HandlerFunc]:
         )
 
     with contextlib.suppress(Exception):
-        import pandas as pd
-
         handlers[pd.DataFrame] = _handle_pandas_dataframe
         handlers[pd.Series] = _handle_pandas_series
 
     with contextlib.suppress(Exception):
-        import datasets
-
         handlers[datasets.Dataset] = _handle_dataset
 
     with contextlib.suppress(Exception):
@@ -572,8 +557,6 @@ def _serialize(obj: t.Any, seen: set[int] | None = None) -> tuple[JsonValue, Jso
 
         if dataclasses.is_dataclass(obj) and not isinstance(obj, type):
             with contextlib.suppress(Exception):
-                import pydantic.dataclasses
-
                 if pydantic.dataclasses.is_pydantic_dataclass(obj.__class__):
                     return _handle_pydantic_dataclass(obj, seen)
 
diff --git a/dreadnode/task.py b/dreadnode/task.py
index 0e6cda0d..fe05285d 100644
--- a/dreadnode/task.py
+++ b/dreadnode/task.py
@@ -403,7 +403,7 @@ def as_target(
         self,
         input_param_name: str | None = None,
     ) -> "CustomTarget[R]":
-        from dreadnode.airt.target import CustomTarget
+        from dreadnode.airt.target.custom import CustomTarget
 
         return CustomTarget(task=self, input_param_name=input_param_name)
 
diff --git a/dreadnode/transforms/ascii_art.py b/dreadnode/transforms/ascii_art.py
index 7e0178ec..0c62af7e 100644
--- a/dreadnode/transforms/ascii_art.py
+++ b/dreadnode/transforms/ascii_art.py
@@ -1,3 +1,5 @@
+from art import text2art  # type: ignore[import-untyped]
+
 from dreadnode.meta import Config
 from dreadnode.transforms.base import Transform
 
@@ -6,11 +8,11 @@ def ascii_art(font: str = "rand", *, name: str = "ascii_art") -> Transform[str,
     """Converts text into ASCII art using the 'art' library."""
 
     try:
-        from art import text2art  # type: ignore[import-not-found,unused-ignore,import-untyped]
-    except ImportError as e:
+        text2art("test")  # Test if art is working
+    except (ImportError, AttributeError):
         raise ImportError(
             "ASCII art dependency is not installed. Install with: pip install art"
-        ) from e
+        ) from ImportError("art library not available")
 
     def transform(text: str, *, font: str = Config(font, help="The font to use")) -> str:
         return str(text2art(text, font=font))
diff --git a/dreadnode/transforms/llm_refine.py b/dreadnode/transforms/llm_refine.py
index f3088463..b49f8d2f 100644
--- a/dreadnode/transforms/llm_refine.py
+++ b/dreadnode/transforms/llm_refine.py
@@ -4,10 +4,12 @@
 import rigging as rg
 
 from dreadnode.meta import Config
-from dreadnode.optimization.trial import Trials
 from dreadnode.transforms.base import Transform
 from dreadnode.types import AnyDict
 
+if t.TYPE_CHECKING:
+    from dreadnode.optimization.trial import Trials
+
 T = t.TypeVar("T")
 
 
diff --git a/dreadnode/transforms/perturbation.py b/dreadnode/transforms/perturbation.py
index 59d82bd5..cf9bf01f 100644
--- a/dreadnode/transforms/perturbation.py
+++ b/dreadnode/transforms/perturbation.py
@@ -3,6 +3,8 @@
 import typing as t
 import unicodedata
 
+from confusables import confusable_characters  # type: ignore[import-untyped]
+
 from dreadnode.meta import Config
 from dreadnode.transforms.base import Transform
 
@@ -224,13 +226,11 @@ def unicode_confusable(
     """
 
     try:
-        from confusables import (  # type: ignore[import-not-found,unused-ignore,import-untyped]
-            confusable_characters,
-        )
-    except ImportError as e:
+        confusable_characters("a")
+    except (ImportError, AttributeError):
         raise ImportError(
             "Confusables dependency is not installed. Install with: pip install confusables"
-        ) from e
+        ) from ImportError("confusables library not available")
 
     if not 0.0 <= ratio <= 1.0:
         raise ValueError("Application ratio must be between 0.0 and 1.0.")
diff --git a/poetry.lock b/poetry.lock
index 273a34d8..0a331377 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand.
 
 [[package]]
 name = "aiobotocore"
@@ -285,7 +285,7 @@ description = "Timeout context manager for asyncio programs"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.10\""
 files = [
     {file = "async_timeout-5.0.1-py3-none-any.whl", hash = "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c"},
     {file = "async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3"},
@@ -1623,7 +1623,7 @@ description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 groups = ["main"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.10\""
 files = [
     {file = "ipython-8.37.0-py3-none-any.whl", hash = "sha256:ed87326596b878932dbcb171e3e698845434d8c61b8d8cd474bf663041a9dcf2"},
     {file = "ipython-8.37.0.tar.gz", hash = "sha256:ca815841e1a41a1e6b73a0b08f3038af9b2252564d01fc405356d34033012216"},
@@ -1872,7 +1872,7 @@ description = "Lightweight pipelining with Python functions"
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "extra == \"text\""
+markers = "extra == \"training\" or extra == \"text\""
 files = [
     {file = "joblib-1.5.2-py3-none-any.whl", hash = "sha256:4e1f0bdbb987e6d843c70cf43714cb276623def372df3c22fe5266b2670bc241"},
     {file = "joblib-1.5.2.tar.gz", hash = "sha256:3faa5c39054b2f03ca547da9b2f52fde67c06240c31853f306aea97f13647b55"},
@@ -2568,6 +2568,25 @@ doc = ["Sphinx (==6.*)", "numpydoc (<2.0)", "pydata-sphinx-theme (==0.13)", "sph
 lint = ["black (>=23.7.0)", "flake8 (>=6.0.0)", "flake8-absolute-import (>=1.0)", "flake8-docstrings (>=1.7.0)", "flake8-implicit-str-concat (==0.4.0)", "flake8-rst-docstrings (>=0.3)", "isort (>=5.12)", "pre-commit (>=3.3)"]
 test = ["coveralls (>=3.0,<4.0)", "pytest (>=3.0.0,<7.0.0)", "pytest-cov (>=2.5.1,<3.0)"]
 
+[[package]]
+name = "mpmath"
+version = "1.3.0"
+description = "Python library for arbitrary-precision floating-point arithmetic"
+optional = true
+python-versions = "*"
+groups = ["main"]
+markers = "extra == \"training\""
+files = [
+    {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
+    {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
+]
+
+[package.extras]
+develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
+docs = ["sphinx"]
+gmpy = ["gmpy2 (>=2.1.0a4) ; platform_python_implementation != \"PyPy\""]
+tests = ["pytest (>=4.6)"]
+
 [[package]]
 name = "multidict"
 version = "6.6.4"
@@ -2851,6 +2870,49 @@ files = [
     {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"},
 ]
 
+[[package]]
+name = "networkx"
+version = "3.4.2"
+description = "Python package for creating and manipulating graphs and networks"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "python_version == \"3.10\""
+files = [
+    {file = "networkx-3.4.2-py3-none-any.whl", hash = "sha256:df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f"},
+    {file = "networkx-3.4.2.tar.gz", hash = "sha256:307c3669428c5362aab27c8a1260aa8f47c4e91d3891f48be0141738d8d053e1"},
+]
+
+[package.extras]
+default = ["matplotlib (>=3.7)", "numpy (>=1.24)", "pandas (>=2.0)", "scipy (>=1.10,!=1.11.0,!=1.11.1)"]
+developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
+doc = ["intersphinx-registry", "myst-nb (>=1.1)", "numpydoc (>=1.8.0)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.15)", "sphinx (>=7.3)", "sphinx-gallery (>=0.16)", "texext (>=0.6.7)"]
+example = ["cairocffi (>=1.7)", "contextily (>=1.6)", "igraph (>=0.11)", "momepy (>=0.7.2)", "osmnx (>=1.9)", "scikit-learn (>=1.5)", "seaborn (>=0.13)"]
+extra = ["lxml (>=4.6)", "pydot (>=3.0.1)", "pygraphviz (>=1.14)", "sympy (>=1.10)"]
+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
+[[package]]
+name = "networkx"
+version = "3.5"
+description = "Python package for creating and manipulating graphs and networks"
+optional = false
+python-versions = ">=3.11"
+groups = ["main"]
+markers = "python_version >= \"3.11\""
+files = [
+    {file = "networkx-3.5-py3-none-any.whl", hash = "sha256:0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec"},
+    {file = "networkx-3.5.tar.gz", hash = "sha256:d4c6f9cf81f52d69230866796b82afbccdec3db7ae4fbd1b65ea750feed50037"},
+]
+
+[package.extras]
+default = ["matplotlib (>=3.8)", "numpy (>=1.25)", "pandas (>=2.0)", "scipy (>=1.11.2)"]
+developer = ["mypy (>=1.15)", "pre-commit (>=4.1)"]
+doc = ["intersphinx-registry", "myst-nb (>=1.1)", "numpydoc (>=1.8.0)", "pillow (>=10)", "pydata-sphinx-theme (>=0.16)", "sphinx (>=8.0)", "sphinx-gallery (>=0.18)", "texext (>=0.6.7)"]
+example = ["cairocffi (>=1.7)", "contextily (>=1.6)", "igraph (>=0.11)", "momepy (>=0.7.2)", "osmnx (>=2.0.0)", "scikit-learn (>=1.5)", "seaborn (>=0.13)"]
+extra = ["lxml (>=4.6)", "pydot (>=3.0.1)", "pygraphviz (>=1.14)", "sympy (>=1.10)"]
+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)", "pytest-xdist (>=3.0)"]
+test-extras = ["pytest-mpl", "pytest-randomly"]
+
 [[package]]
 name = "nltk"
 version = "3.9.1"
@@ -2898,7 +2960,7 @@ description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.10"
 groups = ["main"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.10\""
 files = [
     {file = "numpy-2.2.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b412caa66f72040e6d268491a59f2c43bf03eb6c96dd8f0307829feb7fa2b6fb"},
     {file = "numpy-2.2.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8e41fd67c52b86603a91c1a505ebaef50b3314de0213461c7a6e99c9a3beff90"},
@@ -3042,6 +3104,214 @@ files = [
     {file = "numpy-2.3.2.tar.gz", hash = "sha256:e0486a11ec30cdecb53f184d496d1c6a20786c81e55e41640270130056f8ee48"},
 ]
 
+[[package]]
+name = "nvidia-cublas-cu12"
+version = "12.8.4.1"
+description = "CUBLAS native runtime libraries"
+optional = true
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:b86f6dd8935884615a0683b663891d43781b819ac4f2ba2b0c9604676af346d0"},
+    {file = "nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:8ac4e771d5a348c551b2a426eda6193c19aa630236b418086020df5ba9667142"},
+    {file = "nvidia_cublas_cu12-12.8.4.1-py3-none-win_amd64.whl", hash = "sha256:47e9b82132fa8d2b4944e708049229601448aaad7e6f296f630f2d1a32de35af"},
+]
+
+[[package]]
+name = "nvidia-cuda-cupti-cu12"
+version = "12.8.90"
+description = "CUDA profiling tools runtime libs."
+optional = true
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:4412396548808ddfed3f17a467b104ba7751e6b58678a4b840675c56d21cf7ed"},
+    {file = "nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ea0cb07ebda26bb9b29ba82cda34849e73c166c18162d3913575b0c9db9a6182"},
+    {file = "nvidia_cuda_cupti_cu12-12.8.90-py3-none-win_amd64.whl", hash = "sha256:bb479dcdf7e6d4f8b0b01b115260399bf34154a1a2e9fe11c85c517d87efd98e"},
+]
+
+[[package]]
+name = "nvidia-cuda-nvrtc-cu12"
+version = "12.8.93"
+description = "NVRTC native runtime libraries"
+optional = true
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:a7756528852ef889772a84c6cd89d41dfa74667e24cca16bb31f8f061e3e9994"},
+    {file = "nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fc1fec1e1637854b4c0a65fb9a8346b51dd9ee69e61ebaccc82058441f15bce8"},
+    {file = "nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-win_amd64.whl", hash = "sha256:7a4b6b2904850fe78e0bd179c4b655c404d4bb799ef03ddc60804247099ae909"},
+]
+
+[[package]]
+name = "nvidia-cuda-runtime-cu12"
+version = "12.8.90"
+description = "CUDA Runtime native Libraries"
+optional = true
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:52bf7bbee900262ffefe5e9d5a2a69a30d97e2bc5bb6cc866688caa976966e3d"},
+    {file = "nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:adade8dcbd0edf427b7204d480d6066d33902cab2a4707dcfc48a2d0fd44ab90"},
+    {file = "nvidia_cuda_runtime_cu12-12.8.90-py3-none-win_amd64.whl", hash = "sha256:c0c6027f01505bfed6c3b21ec546f69c687689aad5f1a377554bc6ca4aa993a8"},
+]
+
+[[package]]
+name = "nvidia-cudnn-cu12"
+version = "9.10.2.21"
+description = "cuDNN runtime libraries"
+optional = true
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:c9132cc3f8958447b4910a1720036d9eff5928cc3179b0a51fb6d167c6cc87d8"},
+    {file = "nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:949452be657fa16687d0930933f032835951ef0892b37d2d53824d1a84dc97a8"},
+    {file = "nvidia_cudnn_cu12-9.10.2.21-py3-none-win_amd64.whl", hash = "sha256:c6288de7d63e6cf62988f0923f96dc339cea362decb1bf5b3141883392a7d65e"},
+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+
+[[package]]
+name = "nvidia-cufft-cu12"
+version = "11.3.3.83"
+description = "CUFFT native runtime libraries"
+optional = true
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:848ef7224d6305cdb2a4df928759dca7b1201874787083b6e7550dd6765ce69a"},
+    {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74"},
+    {file = "nvidia_cufft_cu12-11.3.3.83-py3-none-win_amd64.whl", hash = "sha256:7a64a98ef2a7c47f905aaf8931b69a3a43f27c55530c698bb2ed7c75c0b42cb7"},
+]
+
+[package.dependencies]
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-cufile-cu12"
+version = "1.13.1.3"
+description = "cuFile GPUDirect libraries"
+optional = true
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1d069003be650e131b21c932ec3d8969c1715379251f8d23a1860554b1cb24fc"},
+    {file = "nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:4beb6d4cce47c1a0f1013d72e02b0994730359e17801d395bdcbf20cfb3bb00a"},
+]
+
+[[package]]
+name = "nvidia-curand-cu12"
+version = "10.3.9.90"
+description = "CURAND native runtime libraries"
+optional = true
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:dfab99248034673b779bc6decafdc3404a8a6f502462201f2f31f11354204acd"},
+    {file = "nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:b32331d4f4df5d6eefa0554c565b626c7216f87a06a4f56fab27c3b68a830ec9"},
+    {file = "nvidia_curand_cu12-10.3.9.90-py3-none-win_amd64.whl", hash = "sha256:f149a8ca457277da854f89cf282d6ef43176861926c7ac85b2a0fbd237c587ec"},
+]
+
+[[package]]
+name = "nvidia-cusolver-cu12"
+version = "11.7.3.90"
+description = "CUDA solver native runtime libraries"
+optional = true
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:db9ed69dbef9715071232caa9b69c52ac7de3a95773c2db65bdba85916e4e5c0"},
+    {file = "nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450"},
+    {file = "nvidia_cusolver_cu12-11.7.3.90-py3-none-win_amd64.whl", hash = "sha256:4a550db115fcabc4d495eb7d39ac8b58d4ab5d8e63274d3754df1c0ad6a22d34"},
+]
+
+[package.dependencies]
+nvidia-cublas-cu12 = "*"
+nvidia-cusparse-cu12 = "*"
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-cusparse-cu12"
+version = "12.5.8.93"
+description = "CUSPARSE native runtime libraries"
+optional = true
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9b6c161cb130be1a07a27ea6923df8141f3c295852f4b260c65f18f3e0a091dc"},
+    {file = "nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b"},
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+]
+
+[package.dependencies]
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-cusparselt-cu12"
+version = "0.7.1"
+description = "NVIDIA cuSPARSELt"
+optional = true
+python-versions = "*"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:8878dce784d0fac90131b6817b607e803c36e629ba34dc5b433471382196b6a5"},
+    {file = "nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f1bb701d6b930d5a7cea44c19ceb973311500847f81b634d802b7b539dc55623"},
+    {file = "nvidia_cusparselt_cu12-0.7.1-py3-none-win_amd64.whl", hash = "sha256:f67fbb5831940ec829c9117b7f33807db9f9678dc2a617fbe781cac17b4e1075"},
+]
+
+[[package]]
+name = "nvidia-nccl-cu12"
+version = "2.27.3"
+description = "NVIDIA Collective Communication Library (NCCL) Runtime"
+optional = true
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "nvidia_nccl_cu12-2.27.3-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9ddf1a245abc36c550870f26d537a9b6087fb2e2e3d6e0ef03374c6fd19d984f"},
+    {file = "nvidia_nccl_cu12-2.27.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:adf27ccf4238253e0b826bce3ff5fa532d65fc42322c8bfdfaf28024c0fbe039"},
+]
+
+[[package]]
+name = "nvidia-nvjitlink-cu12"
+version = "12.8.93"
+description = "Nvidia JIT LTO Library"
+optional = true
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:81ff63371a7ebd6e6451970684f916be2eab07321b73c9d244dc2b4da7f73b88"},
+    {file = "nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:adccd7161ace7261e01bb91e44e88da350895c270d23f744f0820c818b7229e7"},
+    {file = "nvidia_nvjitlink_cu12-12.8.93-py3-none-win_amd64.whl", hash = "sha256:bd93fbeeee850917903583587f4fc3a4eafa022e34572251368238ab5e6bd67f"},
+]
+
+[[package]]
+name = "nvidia-nvtx-cu12"
+version = "12.8.90"
+description = "NVIDIA Tools Extension"
+optional = true
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d7ad891da111ebafbf7e015d34879f7112832fc239ff0d7d776b6cb685274615"},
+    {file = "nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5b17e2001cc0d751a5bc2c6ec6d26ad95913324a4adb86788c944f8ce9ba441f"},
+    {file = "nvidia_nvtx_cu12-12.8.90-py3-none-win_amd64.whl", hash = "sha256:619c8304aedc69f02ea82dd244541a83c3d9d40993381b3b590f1adaed3db41e"},
+]
+
 [[package]]
 name = "openai"
 version = "1.102.0"
@@ -3396,7 +3666,7 @@ description = "Python Imaging Library (Fork)"
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "extra == \"multimodal\""
+markers = "extra == \"training\" or extra == \"multimodal\""
 files = [
     {file = "pillow-11.3.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:1b9c17fd4ace828b3003dfd1e30bff24863e0eb59b535e8f80194d9cc7ecf860"},
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@@ -5027,6 +5298,7 @@ files = [
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@@ -5043,6 +5316,7 @@ files = [
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@@ -5051,6 +5325,7 @@ files = [
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@@ -5154,7 +5429,7 @@ description = "A set of python modules for machine learning and data mining"
 optional = true
 python-versions = ">=3.10"
 groups = ["main"]
-markers = "extra == \"text\""
+markers = "extra == \"training\" or extra == \"text\""
 files = [
     {file = "scikit_learn-1.7.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:406204dd4004f0517f0b23cf4b28c6245cbd51ab1b6b78153bc784def214946d"},
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@@ -5206,7 +5481,7 @@ description = "Fundamental algorithms for scientific computing in Python"
 optional = true
 python-versions = ">=3.10"
 groups = ["main"]
-markers = "python_version < \"3.11\" and extra == \"text\""
+markers = "python_version == \"3.10\" and (extra == \"training\" or extra == \"text\")"
 files = [
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@@ -5271,7 +5546,7 @@ description = "Fundamental algorithms for scientific computing in Python"
 optional = true
 python-versions = ">=3.11"
 groups = ["main"]
-markers = "python_version >= \"3.11\" and extra == \"text\""
+markers = "python_version >= \"3.11\" and (extra == \"training\" or extra == \"text\")"
 files = [
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@@ -5338,6 +5613,36 @@ dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodest
 doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.19.1)", "jupytext", "linkify-it-py", "matplotlib (>=3.5)", "myst-nb (>=1.2.0)", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.2.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)"]
 test = ["Cython", "array-api-strict (>=2.3.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja ; sys_platform != \"emscripten\"", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
+[[package]]
+name = "sentence-transformers"
+version = "5.1.0"
+description = "Embeddings, Retrieval, and Reranking"
+optional = true
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "extra == \"training\""
+files = [
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+    {file = "sentence_transformers-5.1.0.tar.gz", hash = "sha256:70c7630697cc1c64ffca328d6e8688430ebd134b3c2df03dc07cb3a016b04739"},
+]
+
+[package.dependencies]
+huggingface-hub = ">=0.20.0"
+Pillow = "*"
+scikit-learn = "*"
+scipy = "*"
+torch = ">=1.11.0"
+tqdm = "*"
+transformers = ">=4.41.0,<5.0.0"
+typing_extensions = ">=4.5.0"
+
+[package.extras]
+dev = ["accelerate (>=0.20.3)", "datasets", "peft", "pre-commit", "pytest", "pytest-cov"]
+onnx = ["optimum[onnxruntime] (>=1.23.1)"]
+onnx-gpu = ["optimum[onnxruntime-gpu] (>=1.23.1)"]
+openvino = ["optimum-intel[openvino] (>=1.20.0)"]
+train = ["accelerate (>=0.20.3)", "datasets"]
+
 [[package]]
 name = "setuptools"
 version = "80.9.0"
@@ -5345,7 +5650,7 @@ description = "Easily download, build, install, upgrade, and uninstall Python pa
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "extra == \"text\""
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (extra == \"training\" or extra == \"text\") or extra == \"text\" or python_version >= \"3.12\" and (extra == \"text\" or extra == \"training\")"
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@@ -5786,6 +6091,25 @@ typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""
 [package.extras]
 full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
 
+[[package]]
+name = "sympy"
+version = "1.14.0"
+description = "Computer algebra system (CAS) in Python"
+optional = true
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "extra == \"training\""
+files = [
+    {file = "sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5"},
+    {file = "sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517"},
+]
+
+[package.dependencies]
+mpmath = ">=1.1.0,<1.4"
+
+[package.extras]
+dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
+
 [[package]]
 name = "taskgroup"
 version = "0.2.2"
@@ -5935,7 +6259,7 @@ description = "threadpoolctl"
 optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "extra == \"text\""
+markers = "extra == \"training\" or extra == \"text\""
 files = [
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@@ -6052,7 +6376,7 @@ description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.10\""
 files = [
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@@ -6088,6 +6412,70 @@ files = [
     {file = "tomli-2.2.1.tar.gz", hash = "sha256:cd45e1dc79c835ce60f7404ec8119f2eb06d38b1deba146f07ced3bbc44505ff"},
 ]
 
+[[package]]
+name = "torch"
+version = "2.8.0"
+description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
+optional = true
+python-versions = ">=3.9.0"
+groups = ["main"]
+markers = "extra == \"training\""
+files = [
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+    {file = "torch-2.8.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:da6afa31c13b669d4ba49d8a2169f0db2c3ec6bec4af898aa714f401d4c38904"},
+    {file = "torch-2.8.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:06fcee8000e5c62a9f3e52a688b9c5abb7c6228d0e56e3452983416025c41381"},
+    {file = "torch-2.8.0-cp39-cp39-win_amd64.whl", hash = "sha256:5128fe752a355d9308e56af1ad28b15266fe2da5948660fad44de9e3a9e36e8c"},
+    {file = "torch-2.8.0-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:e9f071f5b52a9f6970dc8a919694b27a91ae9dc08898b2b988abbef5eddfd1ae"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = "*"
+jinja2 = "*"
+networkx = "*"
+nvidia-cublas-cu12 = {version = "12.8.4.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-cupti-cu12 = {version = "12.8.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-nvrtc-cu12 = {version = "12.8.93", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-runtime-cu12 = {version = "12.8.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cudnn-cu12 = {version = "9.10.2.21", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cufft-cu12 = {version = "11.3.3.83", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cufile-cu12 = {version = "1.13.1.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-curand-cu12 = {version = "10.3.9.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusolver-cu12 = {version = "11.7.3.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparse-cu12 = {version = "12.5.8.93", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparselt-cu12 = {version = "0.7.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.27.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvjitlink-cu12 = {version = "12.8.93", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvtx-cu12 = {version = "12.8.90", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+setuptools = {version = "*", markers = "python_version >= \"3.12\""}
+sympy = ">=1.13.3"
+triton = {version = "3.4.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+typing-extensions = ">=4.10.0"
+
+[package.extras]
+opt-einsum = ["opt-einsum (>=3.3)"]
+optree = ["optree (>=0.13.0)"]
+pyyaml = ["pyyaml"]
+
 [[package]]
 name = "tornado"
 version = "6.5.2"
@@ -6224,6 +6612,31 @@ torchhub = ["filelock", "huggingface-hub (>=0.34.0,<1.0)", "importlib-metadata",
 video = ["av"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
+[[package]]
+name = "triton"
+version = "3.4.0"
+description = "A language and compiler for custom Deep Learning operations"
+optional = true
+python-versions = "<3.14,>=3.9"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"training\""
+files = [
+    {file = "triton-3.4.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7ff2785de9bc02f500e085420273bb5cc9c9bb767584a4aa28d6e360cec70128"},
+    {file = "triton-3.4.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7b70f5e6a41e52e48cfc087436c8a28c17ff98db369447bcaff3b887a3ab4467"},
+    {file = "triton-3.4.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:31c1d84a5c0ec2c0f8e8a072d7fd150cab84a9c239eaddc6706c081bfae4eb04"},
+    {file = "triton-3.4.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00be2964616f4c619193cb0d1b29a99bd4b001d7dc333816073f92cf2a8ccdeb"},
+    {file = "triton-3.4.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7936b18a3499ed62059414d7df563e6c163c5e16c3773678a3ee3d417865035d"},
+    {file = "triton-3.4.0-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:98e5c1442eaeabae2e2452ae765801bd53cd4ce873cab0d1bdd59a32ab2d9397"},
+]
+
+[package.dependencies]
+setuptools = ">=40.8.0"
+
+[package.extras]
+build = ["cmake (>=3.20,<4.0)", "lit"]
+tests = ["autopep8", "isort", "llnl-hatchet", "numpy", "pytest", "pytest-forked", "pytest-xdist", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
+
 [[package]]
 name = "typer"
 version = "0.17.3"
@@ -6889,9 +7302,9 @@ type = ["pytest-mypy"]
 dev = ["datasets", "ipykernel", "markdown", "markdownify", "mkdocstrings-python", "mypy", "pandas-stubs", "pre-commit", "pyarrow", "pytest", "pytest-asyncio", "ruff", "typer", "types-protobuf", "types-requests"]
 multimodal = ["moviepy", "pillow", "soundfile"]
 text = ["art", "confusables", "nltk", "presidio-analyzer", "rapidfuzz", "scikit-learn", "textblob", "textstat"]
-training = ["transformers"]
+training = ["sentence-transformers", "transformers"]
 
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.14"
-content-hash = "1a3928fff7148fab6b2f47998bb6eba98c13dd87e810ae502d5f1cd8bd3394da"
+content-hash = "c2495ca89db7e42c85802bf951614fb8cf1648363ce9f94edd71616ef6e242d7"
diff --git a/pyproject.toml b/pyproject.toml
index c7f330b3..68b4dd56 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,10 +24,11 @@ dependencies = [
     "markdown[dev] (>=3.8.2,<4.0.0)",
     "mkdocstrings-python[dev] (>=1.18.2,<2.0.0)",
     "markdownify[dev] (>=1.2.0,<2.0.0)",
+    "networkx>=3.3,<4.0.0",
 ]
 
 [project.optional-dependencies]
-training = ["transformers>=4.41.0,<5.0.0"]
+training = ["transformers>=4.41.0,<5.0.0", "sentence-transformers>=5.1.0,<6.0.0",]
 
 multimodal = [
     "pillow>=11.2.1,<12.0.0",
@@ -149,6 +150,10 @@ ignore = [
     "FIX002",  # contains todo, consider fixing
     "COM812",  # disabled for formatting
     "ISC001",  # disabled for formatting
+    "PLC0415",  # import should be at top-level (lazy imports)
+    "FBT001",   # boolean positional argument (legitimate cases)  
+    "FURB122",  # use f.writelines (minor optimization)
+    "F401", # ignore imported but unused
 ]
 
 [tool.ruff.format]