chore: Standardize prompt handling in PydanticAI provider

Author: phil65

src/llmling_agent_providers/pydanticai/provider.py

@@ -20,9 +20,7 @@
 from llmling_agent.common_types import EndStrategy, ModelProtocol
 from llmling_agent.log import get_logger
 from llmling_agent.messaging.messages import ChatMessage, TokenCost
-from llmling_agent.models.content import BaseContent
 from llmling_agent.observability import track_action
-from llmling_agent.prompts.convert import format_prompts
 from llmling_agent.tasks.exceptions import (
     ChainAbortedError,
     RunAbortedError,
@@ -31,6 +29,7 @@
 from llmling_agent.utils.inspection import execute, has_argument_type
 from llmling_agent_providers.base import AgentLLMProvider, ProviderResponse, UsageLimits
 from llmling_agent_providers.pydanticai.utils import (
+    convert_prompts_to_user_content,
     format_part,
     get_tool_calls,
     to_model_message,
@@ -252,25 +251,15 @@ async def generate_response(
             use_model = infer_model(use_model)
             self.model_changed.emit(use_model)
         try:
-            text_prompts = [p for p in prompts if isinstance(p, str)]
-            content_prompts = [p for p in prompts if isinstance(p, BaseContent)]
-
-            # Get normal text prompt
-            prompt = await format_prompts(text_prompts)
-
-            # Convert Content objects to ModelMessages
-            if content_prompts:
-                prompts_msgs = [
-                    ChatMessage(role="user", content=p) for p in content_prompts
-                ]
-                message_history = [*message_history, *prompts_msgs]
+            # Convert prompts to pydantic-ai format
+            converted_prompts = await convert_prompts_to_user_content(prompts)
 
             # Run with complete history
             to_use = model or self.model
             to_use = infer_model(to_use) if isinstance(to_use, str) else to_use
             limits = asdict(usage_limits) if usage_limits else {}
             result: AgentRunResult = await agent.run(
-                prompt,
+                converted_prompts,  # Pass converted prompts
                 deps=self._context,  # type: ignore
                 message_history=[to_model_message(m) for m in message_history],
                 model=to_use,  # type: ignore
@@ -292,10 +281,11 @@ async def generate_response(
                 use_model.model_name if isinstance(use_model, Model) else str(use_model)
             )
             usage = result.usage()
-            cost_str = prompt + str(content_prompts)  # dirty
+            # Create input content representation for cost calculations
+            cost_input = "\n".join(str(p) for p in prompts)
             cost_info = (
                 await TokenCost.from_usage(
-                    usage, resolved_model, cost_str, str(result.data)
+                    usage, resolved_model, cost_input, str(result.data)
                 )
                 if resolved_model and usage
                 else None
@@ -376,22 +366,14 @@ async def stream_response(  # type: ignore[override]
         if model:
             self.model_changed.emit(use_model)
 
-        text_prompts = [p for p in prompts if isinstance(p, str)]
-        content_prompts = [p for p in prompts if isinstance(p, BaseContent)]
-
-        # Get normal text prompt
-        prompt = await format_prompts(text_prompts)
-
-        # Convert Content objects to ChatMessages
-        if content_prompts:
-            prompts_msgs = [ChatMessage(role="user", content=p) for p in content_prompts]
-            message_history = [*message_history, *prompts_msgs]
+        # Convert prompts to pydantic-ai format
+        converted_prompts = await convert_prompts_to_user_content(prompts)
 
         # Convert all messages to pydantic-ai format
         model_messages = [to_model_message(m) for m in message_history]
 
         async with agent.run_stream(
-            prompt,
+            converted_prompts,
             deps=self._context,
             message_history=model_messages,
             model=model or self.model,  # type: ignore

src/llmling_agent_providers/pydanticai/utils.py

@@ -241,7 +241,7 @@ def to_model_message(message: ChatMessage[str | Content]) -> ModelMessage:
             return ModelRequest(parts=[part_cls(content=message.content)])
 
 
-def convert_prompts_to_user_content(
+async def convert_prompts_to_user_content(
     prompts: Sequence[str | Content],
 ) -> list[str | PydanticUserContent]:
     """Convert our prompts to pydantic-ai compatible format.
@@ -252,13 +252,20 @@ def convert_prompts_to_user_content(
     Returns:
         List of strings and pydantic-ai UserContent objects
     """
+    from llmling_agent.prompts.convert import format_prompts
     from llmling_agent_providers.pydanticai.convert_content import content_to_pydantic_ai
 
+    # Special case: if we only have string prompts, format them together
+    # if all(isinstance(p, str) for p in prompts):
+    #     formatted = await format_prompts(prompts)
+    #     return [formatted]
+
+    # Otherwise, process each item individually in order
     result = []
     for p in prompts:
         if isinstance(p, str):
-            # Simply pass through string prompts directly
-            result.append(p)
+            formatted = await format_prompts([p])
+            result.append(formatted)
         elif p_content := content_to_pydantic_ai(p):
             result.append(p_content)
 

tests/test_agent.py

@@ -73,7 +73,7 @@ async def test_agent_streaming_pydanticai_history(test_agent: Agent[None]):
         # Check prompt message
         assert isinstance(new_messages[0], ModelRequest)
         assert isinstance(new_messages[0].parts[0], UserPromptPart)
-        assert new_messages[0].parts[0].content == SIMPLE_PROMPT
+        assert new_messages[0].parts[0].content == [SIMPLE_PROMPT]
 
         # Check response message
         assert isinstance(new_messages[1], ModelResponse)