Redesign Python AI engine (#5991)

# Description of Changes
Redesign the Python AI engine to be properly agentic and make use of
`pydantic-ai` instead of `langchain` for correctness and ergonomics.
This should be a good foundation for us to build our AI engine on going
forwards.

engine/src/stirling/agents/pdf_questions.py

@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+from pydantic_ai import Agent
+from pydantic_ai.output import NativeOutput
+
+from stirling.contracts import (
+    PdfQuestionAnswerResponse,
+    PdfQuestionNeedTextResponse,
+    PdfQuestionNotFoundResponse,
+    PdfQuestionRequest,
+    PdfQuestionResponse,
+)
+from stirling.services import AppRuntime
+
+
+class PdfQuestionAgent:
+    def __init__(self, runtime: AppRuntime) -> None:
+        self.runtime = runtime
+        self.agent = Agent(
+            model=runtime.smart_model,
+            output_type=NativeOutput(
+                [
+                    PdfQuestionAnswerResponse,
+                    PdfQuestionNotFoundResponse,
+                ]
+            ),
+            system_prompt=(
+                "Answer questions about a PDF using only the extracted text provided in the prompt. "
+                "Do not guess or use outside knowledge. "
+                "If the answer is not supported by the provided text, return not_found. "
+                "When answering, include a short list of evidence snippets copied from the provided text."
+            ),
+            model_settings=runtime.smart_model_settings,
+        )
+
+    async def handle(self, request: PdfQuestionRequest) -> PdfQuestionResponse:
+        if not request.extracted_text.strip():
+            return PdfQuestionNeedTextResponse(
+                reason="No extracted PDF text was provided, so the question cannot be answered yet."
+            )
+        return await self._run_answer_agent(request)
+
+    async def _run_answer_agent(self, request: PdfQuestionRequest) -> PdfQuestionResponse:
+        result = await self.agent.run(self._build_prompt(request))
+        return result.output
+
+    def _build_prompt(self, request: PdfQuestionRequest) -> str:
+        file_name = request.file_name or "Unknown file"
+        return f"File: {file_name}\nQuestion: {request.question}\nExtracted text:\n{request.extracted_text}"