Merge pull request #127 from dreadnode/feat/agents

feat: Agents

Author: briangreunke

agent.py

@@ -0,0 +1,20 @@
+from pathlib import Path
+
+from dreadnode.agent.agent import TaskAgent
+from dreadnode.agent.hooks import summarize_when_long
+from dreadnode.agent.tools import tool
+
+
+@tool(truncate=1000, catch=True)
+async def read_file(path: str) -> str:
+    "Read the contents of a file."
+    return (Path("../") / path).read_text()
+
+
+agent = TaskAgent(
+    name="basic",
+    description="A basic agent that can handle simple tasks.",
+    model="gpt-4o-mini",
+    hooks=[summarize_when_long(max_tokens=1000)],
+    tools=[read_file],
+)

docs/sdk/api.mdx

@@ -82,11 +82,10 @@ def __init__(
     }
 
     if api_key:
-        headers["X-Api-Key"] = api_key
+        headers["X-API-Key"] = api_key
 
     self._client = httpx.Client(
         headers=headers,
-        cookies=_cookies,
         base_url=self._base_url,
         timeout=30,
     )
@@ -156,7 +155,7 @@ def export_metrics(
     status: StatusFilter = "completed",
     metrics: list[str] | None = None,
     aggregations: list[MetricAggregationType] | None = None,
-) -> pd.DataFrame:
+) -> "pd.DataFrame":
     """
     Exports metric data for a specific project.
 
@@ -170,6 +169,8 @@ def export_metrics(
     Returns:
         A DataFrame containing the exported metric data.
     """
+    import pandas as pd
+
     response = self.request(
         "GET",
         f"/strikes/projects/{project!s}/export/metrics",
@@ -248,7 +249,7 @@ def export_parameters(
     parameters: list[str] | None = None,
     metrics: list[str] | None = None,
     aggregations: list[MetricAggregationType] | None = None,
-) -> pd.DataFrame:
+) -> "pd.DataFrame":
     """
     Exports parameter data for a specific project.
 
@@ -263,6 +264,8 @@ def export_parameters(
     Returns:
         A DataFrame containing the exported parameter data.
     """
+    import pandas as pd
+
     response = self.request(
         "GET",
         f"/strikes/projects/{project!s}/export/parameters",
@@ -331,7 +334,7 @@ def export_runs(
     # format: ExportFormat = "parquet",
     status: StatusFilter = "completed",
     aggregations: list[MetricAggregationType] | None = None,
-) -> pd.DataFrame:
+) -> "pd.DataFrame":
     """
     Exports run data for a specific project.
 
@@ -344,6 +347,8 @@ def export_runs(
     Returns:
         A DataFrame containing the exported run data.
     """
+    import pandas as pd
+
     response = self.request(
         "GET",
         f"/strikes/projects/{project!s}/export",
@@ -424,7 +429,7 @@ def export_timeseries(
     metrics: list[str] | None = None,
     time_axis: TimeAxisType = "relative",
     aggregations: list[TimeAggregationType] | None = None,
-) -> pd.DataFrame:
+) -> "pd.DataFrame":
     """
     Exports timeseries data for a specific project.
 
@@ -439,6 +444,8 @@ def export_timeseries(
     Returns:
         A DataFrame containing the exported timeseries data.
     """
+    import pandas as pd
+
     response = self.request(
         "GET",
         f"/strikes/projects/{project!s}/export/timeseries",

docs/sdk/data_types.mdx

@@ -72,11 +72,7 @@ def __init__(
         caption: Optional caption for the audio
         format: Optional format to use (default is wav for numpy arrays)
     """
-    if sf is None:
-        raise ImportError(
-            "Audio processing requires optional dependencies. "
-            "Install with: pip install dreadnode[multimodal]"
-        )
+    check_imports()
     self._data = data
     self._sample_rate = sample_rate
     self._caption = caption
@@ -211,10 +207,7 @@ def __init__(
         caption: Optional caption for the image
         format: Optional format to use when saving (png, jpg, etc.)
     """
-    if PILImage is None:
-        raise ImportError(
-            "Image processing requires PIL (Pillow). Install with: pip install dreadnode[multimodal]"
-        )
+    check_imports()
     self._data = data
     self._mode = mode
     self._caption = caption
@@ -650,6 +643,13 @@ def to_serializable(self) -> tuple[bytes, dict[str, t.Any]]:
     Returns:
         A tuple of (video_bytes, metadata_dict)
     """
+    import numpy as np  # type: ignore[import,unused-ignore]
+
+    try:
+        from moviepy.video.VideoClip import VideoClip  # type: ignore[import,unused-ignore]
+    except ImportError:
+        VideoClip = None  # noqa: N806
+
     if isinstance(self._data, (str, Path)) and Path(self._data).exists():
         return self._process_file_path()
     if isinstance(self._data, bytes):

docs/sdk/main.mdx

@@ -460,6 +460,8 @@ def initialize(self) -> None:
 
     This method is called automatically when you call `configure()`.
     """
+    from s3fs import S3FileSystem  # type: ignore [import-untyped]
+
     if self._initialized:
         return
 

docs/sdk/metric.mdx

@@ -136,7 +136,9 @@ def apply_mode(self, mode: MetricAggMode, others: "list[Metric]") -> "Metric":
         self.value = len(others) + 1
     elif mode == "avg" and prior_values:
         current_avg = prior_values[-1]
-        self.value = current_avg + (self.value - current_avg) / (len(prior_values) + 1)
+        self.value = current_avg + (self.value - current_avg) / (
+            len(prior_values) + 1
+        )
 
     return self
 ```

docs/sdk/scorers.mdx

@@ -122,11 +122,20 @@ def zero_shot_classification(
         model_name: The name of the zero-shot model from Hugging Face Hub.
         name: Name of the scorer.
     """
-    if not _TRANSFORMERS_AVAILABLE:
-        warn_at_user_stacklevel(_TRANSFORMERS_ERROR_MSG, UserWarning)
+    transformers_error_msg = (
+        "Hugging Face transformers dependency is not installed. "
+        "Please install with: pip install transformers torch"
+    )
+
+    try:
+        from transformers import (  # type: ignore [attr-defined,import-not-found,unused-ignore]
+            pipeline,
+        )
+    except ImportError:
+        warn_at_user_stacklevel(transformers_error_msg, UserWarning)
 
         def disabled_evaluate(_: t.Any) -> Metric:
-            return Metric(value=0.0, attributes={"error": _TRANSFORMERS_ERROR_MSG})
+            return Metric(value=0.0, attributes={"error": transformers_error_msg})
 
         return Scorer.from_callable(disabled_evaluate, name=name)
 
@@ -816,7 +825,7 @@ def detect_harm_with_openai(
     *,
     api_key: str | None = None,
     model: t.Literal["text-moderation-stable", "text-moderation-latest"] = "text-moderation-stable",
-    client: openai.AsyncOpenAI | None = None,
+    client: "openai.AsyncOpenAI | None" = None,
     name: str = "openai_harm",
 ) -> "Scorer[t.Any]":
     """
@@ -837,6 +846,7 @@ def detect_harm_with_openai(
         model: The moderation model to use.
         name: Name of the scorer.
     """
+    import openai
 
     async def evaluate(data: t.Any) -> Metric:
         text = str(data)
@@ -1800,12 +1810,18 @@ def detect_pii_with_presidio(
         invert: Invert the score (1.0 for no PII, 0.0 for PII detected).
         name: Name of the scorer.
     """
+    presidio_import_error_msg = (
+        "Presidio dependencies are not installed. "
+        "Please install them with: pip install presidio-analyzer presidio-anonymizer 'spacy[en_core_web_lg]'"
+    )
 
-    if not _PRESIDIO_AVAILABLE:
-        warn_at_user_stacklevel(_PRESIDIO_ERROR_MSG, UserWarning)
+    try:
+        import presidio_analyzer  # type: ignore[import-not-found,unused-ignore]  # noqa: F401
+    except ImportError:
+        warn_at_user_stacklevel(presidio_import_error_msg, UserWarning)
 
         def disabled_evaluate(_: t.Any) -> Metric:
-            return Metric(value=0.0, attributes={"error": _PRESIDIO_ERROR_MSG})
+            return Metric(value=0.0, attributes={"error": presidio_import_error_msg})
 
         return Scorer.from_callable(disabled_evaluate, name=name)
 

dreadnode/__init__.py

@@ -1,4 +1,7 @@
-from dreadnode import convert, data_types, scorers
+import importlib
+import typing as t
+
+from dreadnode import convert, data_types
 from dreadnode.data_types import Audio, Code, Image, Markdown, Object3D, Table, Text, Video
 from dreadnode.lookup import Lookup, lookup_input, lookup_output, lookup_param, resolve_lookup
 from dreadnode.main import DEFAULT_INSTANCE, Dreadnode
@@ -8,6 +11,9 @@
 from dreadnode.tracing.span import RunSpan, Span, TaskSpan
 from dreadnode.version import VERSION
 
+if t.TYPE_CHECKING:
+    from dreadnode import scorers  # noqa: F401
+
 configure = DEFAULT_INSTANCE.configure
 shutdown = DEFAULT_INSTANCE.shutdown
 
@@ -77,11 +83,24 @@
     "resolve_lookup",
     "run",
     "scorer",
-    "scorers",
     "shutdown",
     "span",
     "tag",
     "task",
     "task_span",
     "task_span",
 ]
+
+__lazy_submodules__ = ["scorers"]
+
+
+def __getattr__(name: str) -> t.Any:
+    if name in __lazy_submodules__:
+        module = importlib.import_module(f".{name}", __name__)
+        globals()[name] = module
+        return module
+    raise AttributeError(f"module '{__name__}' has no attribute '{name}'")
+
+
+def __dir__() -> list[str]:
+    return sorted(list(globals().keys()) + __lazy_submodules__)

dreadnode/agent/__init__.py

@@ -0,0 +1,13 @@
+from pydantic.dataclasses import rebuild_dataclass
+
+from dreadnode.agent.agent import Agent
+from dreadnode.agent.events import rebuild_event_models
+from dreadnode.agent.result import AgentResult
+from dreadnode.agent.thread import Thread
+
+Agent.model_rebuild()
+Thread.model_rebuild()
+
+rebuild_event_models()
+
+rebuild_dataclass(AgentResult)  # type: ignore[arg-type]