pydantic ai fixes

deepeval/integrations/pydantic_ai/__init__.py

@@ -1,3 +1,5 @@
+from .agent import DeepEvalPydanticAIAgent as Agent
 from .patcher import instrument as instrument_pydantic_ai
+from .otel import instrument_pydantic_ai as otel_instrument_pydantic_ai 
 
-__all__ = ["instrument_pydantic_ai"]
+__all__ = ["instrument_pydantic_ai", "Agent", otel_instrument_pydantic_ai]

deepeval/integrations/pydantic_ai/agent.py

@@ -0,0 +1,280 @@
+import inspect
+from typing import Optional, List, Generic, TypeVar
+from contextvars import ContextVar
+from contextlib import asynccontextmanager
+
+from deepeval.prompt import Prompt
+from deepeval.tracing.types import AgentSpan
+from deepeval.tracing.tracing import Observer
+from deepeval.metrics.base_metric import BaseMetric
+from deepeval.tracing.context import current_span_context
+from deepeval.integrations.pydantic_ai.utils import extract_tools_called
+
+try:
+    from pydantic_ai.agent import Agent
+    from pydantic_ai.tools import AgentDepsT
+    from pydantic_ai.output import OutputDataT
+    from deepeval.integrations.pydantic_ai.utils import create_patched_tool, update_trace_context, patch_llm_model
+    is_pydantic_ai_installed = True
+except:
+    is_pydantic_ai_installed = False
+
+def pydantic_ai_installed():
+    if not is_pydantic_ai_installed:
+        raise ImportError(
+            "Pydantic AI is not installed. Please install it with `pip install pydantic-ai`."
+        )
+
+_IS_RUN_SYNC = ContextVar("deepeval_is_run_sync", default=False)   
+
+class DeepEvalPydanticAIAgent(Agent[AgentDepsT, OutputDataT], Generic[AgentDepsT, OutputDataT]):
+
+    trace_name: Optional[str] = None
+    trace_tags: Optional[List[str]] = None
+    trace_metadata: Optional[dict] = None
+    trace_thread_id: Optional[str] = None
+    trace_user_id: Optional[str] = None
+    trace_metric_collection: Optional[str] = None
+    trace_metrics: Optional[List[BaseMetric]] = None 
+
+    llm_prompt: Optional[Prompt] = None
+    llm_metrics: Optional[List[BaseMetric]] = None
+    llm_metric_collection: Optional[str] = None
+
+    agent_metrics: Optional[List[BaseMetric]] = None
+    agent_metric_collection: Optional[str] = None
+
+    def __init__(
+        self,
+        *args,
+        trace_name: Optional[str] = None,
+        trace_tags: Optional[List[str]] = None,
+        trace_metadata: Optional[dict] = None,
+        trace_thread_id: Optional[str] = None,
+        trace_user_id: Optional[str] = None,
+        trace_metric_collection: Optional[str] = None,
+        trace_metrics: Optional[List[BaseMetric]] = None, 
+        llm_metric_collection: Optional[str] = None,
+        llm_metrics: Optional[List[BaseMetric]] = None,
+        llm_prompt: Optional[Prompt] = None,
+        agent_metric_collection: Optional[str] = None,
+        agent_metrics: Optional[List[BaseMetric]] = None,
+        **kwargs
+    ):
+        pydantic_ai_installed()
+
+        self.trace_name = trace_name
+        self.trace_tags = trace_tags
+        self.trace_metadata = trace_metadata
+        self.trace_thread_id = trace_thread_id
+        self.trace_user_id = trace_user_id
+        self.trace_metric_collection = trace_metric_collection
+        self.trace_metrics = trace_metrics
+
+        self.llm_metric_collection = llm_metric_collection
+        self.llm_metrics = llm_metrics
+        self.llm_prompt = llm_prompt
+
+        self.agent_metric_collection = agent_metric_collection
+        self.agent_metrics = agent_metrics
+
+        super().__init__(*args, **kwargs)
+
+        patch_llm_model(self._model, llm_metric_collection, llm_metrics, llm_prompt) #TODO: Add dual patch guards
+
+
+    async def run(
+        self,
+        *args,
+
+        name: Optional[str] = None,
+        tags: Optional[List[str]] = None,
+        user_id: Optional[str] = None,
+        metadata: Optional[dict] = None,
+        thread_id: Optional[str] = None,
+        metrics: Optional[List[BaseMetric]] = None,
+        metric_collection: Optional[str] = None,
+
+        **kwargs
+    ):
+        sig = inspect.signature(super().run)
+        bound = sig.bind_partial(*args, **kwargs)
+        bound.apply_defaults()
+        input = bound.arguments.get("user_prompt", None)
+
+        agent_name = super().name if super().name is not None else "Agent"
+
+        with Observer(
+            span_type="agent" if not _IS_RUN_SYNC.get() else "custom",
+            func_name=agent_name if not _IS_RUN_SYNC.get() else "run",
+            function_kwargs={"input": input},
+            metrics=self.agent_metrics if not _IS_RUN_SYNC.get() else None,
+            metric_collection=self.agent_metric_collection if not _IS_RUN_SYNC.get() else None,
+        ) as observer:
+            result = await super().run(*args, **kwargs)
+            observer.result = result.output
+            update_trace_context(
+
+                trace_name=name if name is not None else self.trace_name,
+                trace_tags=tags if tags is not None else self.trace_tags,
+                trace_metadata=metadata if metadata is not None else self.trace_metadata,
+                trace_thread_id=thread_id if thread_id is not None else self.trace_thread_id,
+                trace_user_id=user_id if user_id is not None else self.trace_user_id,
+                trace_metric_collection=metric_collection if metric_collection is not None else self.trace_metric_collection,
+                trace_metrics=metrics if metrics is not None else self.trace_metrics,
+
+                trace_input=input,
+                trace_output=result.output,
+            )
+
+            agent_span: AgentSpan = current_span_context.get()
+            try: 
+                agent_span.tools_called = extract_tools_called(result)
+            except:
+                pass
+            # TODO: available tools 
+            # TODO: agent handoffs 
+
+        return result
+
+    def run_sync(
+        self,   
+        *args,
+
+        name: Optional[str] = None,
+        tags: Optional[List[str]] = None,
+        metadata: Optional[dict] = None,
+        thread_id: Optional[str] = None,
+        user_id: Optional[str] = None,
+        metric_collection: Optional[str] = None,
+        metrics: Optional[List[BaseMetric]] = None,
+
+        **kwargs
+    ):
+        sig = inspect.signature(super().run_sync)
+        bound = sig.bind_partial(*args, **kwargs)
+        bound.apply_defaults()
+        input = bound.arguments.get("user_prompt", None)
+
+        token = _IS_RUN_SYNC.set(True)
+
+        agent_name = super().name if super().name is not None else "Agent"
+
+        with Observer(
+            span_type="agent",
+            func_name=agent_name,
+            function_kwargs={"input": input},
+            metrics=self.agent_metrics,
+            metric_collection=self.agent_metric_collection,
+        ) as observer:
+            try:
+                result = super().run_sync(*args, **kwargs)
+            finally:
+                _IS_RUN_SYNC.reset(token)
+
+            observer.result = result.output
+            update_trace_context(
+
+                trace_name=name if name is not None else self.trace_name,
+                trace_tags=tags if tags is not None else self.trace_tags,
+                trace_metadata=metadata if metadata is not None else self.trace_metadata,
+                trace_thread_id=thread_id if thread_id is not None else self.trace_thread_id,
+                trace_user_id=user_id if user_id is not None else self.trace_user_id,
+                trace_metric_collection=metric_collection if metric_collection is not None else self.trace_metric_collection,
+                trace_metrics=metrics if metrics is not None else self.trace_metrics,
+
+                trace_input=input,
+                trace_output=result.output,
+            )
+
+            agent_span: AgentSpan = current_span_context.get()
+            try: 
+                agent_span.tools_called = extract_tools_called(result)
+            except:
+                pass
+
+            # TODO: available tools 
+            # TODO: agent handoffs 
+
+        return result
+
+    @asynccontextmanager
+    async def run_stream(
+        self,
+        *args,
+
+        name: Optional[str] = None,
+        tags: Optional[List[str]] = None,
+        metadata: Optional[dict] = None,
+        thread_id: Optional[str] = None,
+        user_id: Optional[str] = None,
+        metric_collection: Optional[str] = None,
+        metrics: Optional[List[BaseMetric]] = None,
+
+        **kwargs
+    ):
+        sig = inspect.signature(super().run_stream)
+        super_params = sig.parameters
+        super_kwargs = {k: v for k, v in kwargs.items() if k in super_params}
+        bound = sig.bind_partial(*args, **super_kwargs)
+        bound.apply_defaults()
+        input = bound.arguments.get("user_prompt", None)
+
+        agent_name = super().name if super().name is not None else "Agent"
+
+        with Observer(
+            span_type="agent",
+            func_name=agent_name,
+            function_kwargs={"input": input},
+            metrics=self.agent_metrics,
+            metric_collection=self.agent_metric_collection,
+        ) as observer:
+            final_result = None
+            async with super().run_stream(*args, **super_kwargs) as result:
+                try:
+                    yield result
+                finally:
+                    try:
+                        final_result = await result.get_output()
+                        observer.result = final_result
+                    except Exception:
+                        pass
+
+                    update_trace_context(
+
+                        trace_name=name if name is not None else self.trace_name,
+                        trace_tags=tags if tags is not None else self.trace_tags,
+                        trace_metadata=metadata if metadata is not None else self.trace_metadata,
+                        trace_thread_id=thread_id if thread_id is not None else self.trace_thread_id,
+                        trace_user_id=user_id if user_id is not None else self.trace_user_id,
+                        trace_metric_collection=metric_collection if metric_collection is not None else self.trace_metric_collection,
+                        trace_metrics=metrics if metrics is not None else self.trace_metrics,
+
+                        trace_input=input,
+                        trace_output=(final_result if final_result is not None else None),
+                    )
+                    agent_span: AgentSpan = current_span_context.get()
+                    try:
+                        if final_result is not None:
+                            agent_span.tools_called = extract_tools_called(final_result)
+                    except:
+                        pass
+
+    def tool(
+        self,
+        *args,
+        metrics: Optional[List[BaseMetric]] = None,
+        metric_collection: Optional[str] = None,
+        **kwargs
+    ):
+        # Direct decoration: @agent.tool
+        if args and callable(args[0]):
+            patched_func = create_patched_tool(args[0], metrics, metric_collection)
+            new_args = (patched_func,) + args[1:]
+            return super(DeepEvalPydanticAIAgent, self).tool(*new_args, **kwargs)
+        # Decoration with args: @agent.tool(...)
+        super_tool = super(DeepEvalPydanticAIAgent, self).tool
+        def decorator(func):
+            patched_func = create_patched_tool(func, metrics, metric_collection)
+            return super_tool(*args, **kwargs)(patched_func)
+        return decorator

deepeval/integrations/pydantic_ai/otel.py

@@ -26,7 +26,6 @@ def is_opentelemetry_available():
 
 OTLP_ENDPOINT = "https://otel.confident-ai.com/v1/traces"
 
-
 def instrument_pydantic_ai(api_key: Optional[str] = None):
     with capture_tracing_integration("pydantic_ai"):
         is_opentelemetry_available()