feat: adopt pydantic ai

[PydanticAI](https://ai.pydantic.dev) provides structured output
for Google models and tracing on Logfire. It will also enable
easy switching between model providers.

app/agent.py

@@ -1,101 +1,89 @@
-import json
-import re
 from pathlib import Path
 
-from google import genai
-from google.genai import types
-from pydantic import ValidationError
+import logfire
+from pydantic import BaseModel, Field
+from pydantic_ai import Agent
+from pydantic_ai.messages import (
+    ModelMessage,
+    ModelRequest,
+    ModelResponse,
+    TextPart,
+    ToolReturnPart,
+)
+from pydantic_ai.models.google import GoogleModel, GoogleModelSettings
 
 from app import models
 from app.tools import dailymed, literature
 
-CONFIG = types.GenerateContentConfig(
+
+class Context(BaseModel):
+    thoughts: list[str]
+    sources: dict[str, dict]
+
+
+class Statement(BaseModel):
+    text: str
+    sources: list[str] | None = Field(
+        default=None, description="ID of the sources that support this statement."
+    )
+
+
+model = GoogleModel("gemini-2.5-flash-preview-05-20")
+settings = GoogleModelSettings(
+    google_thinking_config={"thinking_budget": 2048, "include_thoughts": True},
+)
+agent = Agent(
+    model=model,
+    name="elna",
+    model_settings=settings,
+    output_type=list[Statement],
+    system_prompt=(Path(__file__).parent / "system_instruction.txt").read_text(),
     tools=[
         dailymed.find_drug_set_ids,
         dailymed.find_drug_instruction,
         literature.search_medical_literature,
     ],
-    system_instruction=(Path(__file__).parent / "system_instruction.txt").read_text(),
-    thinking_config=types.ThinkingConfig(include_thoughts=True, thinking_budget=1024),
 )
 
-RESPONSE_FORMAT = """\
-
-Return in JSON matching this specification:
-
-Statement = { "text": string, "sources": array<string> }  // the `sources` array contains the ID of the sources
-Return: array<Statement>
-
-Do not return the response in a markdown code block.
-Do not include the citation in the `text` field."""
-
-SOURCE_TOOL_NAMES = {
-    literature.search_medical_literature.__name__,
-    dailymed.find_drug_set_ids.__name__,
-}
-
-
-def hydrate_sources(
-    statements: list[dict], calling_history: list[types.Content]
-) -> models.Statements:
-    sources = {}
-    for call in calling_history:
-        for part in call.parts or []:
-            if (
-                (func := part.function_response)
-                and func.name in SOURCE_TOOL_NAMES
-                and func.response
-            ):
-                for source in func.response.get("result", []):
-                    sources[source["id"]] = source
-
-    for statement in statements:
-        if statement.get("sources"):
-            statement_sources = []
-            for source_id in statement["sources"]:
-                try:
-                    statement_sources.append(sources[source_id])
-                except KeyError:
-                    print("citaion hullucination")
-                    print(source_id)
-                    print(sources)
-            statement["sources"] = statement_sources
-
-    return models.Statements.model_validate({"statements": statements})
-
-
-def validate_response(response: types.GenerateContentResponse) -> models.Statements:
-    thoughts = []
-
-    for part in response.candidates[0].content.parts:  # type: ignore
-        if part.thought:
-            thoughts.append(part.text)
-
-    thoughts = " ".join(thoughts)
-    text = (response.text or "").strip()
-
-    # Extract content inside the first markdown code block (``` or ```json)
-    match = re.match(r"^```(?:json)?\s*([\s\S]*?)\s*```", text)
-    if match:
-        text = match.group(1).strip()
-
-    try:
-        statements = hydrate_sources(
-            json.loads(text), response.automatic_function_calling_history or []
-        )
-        statements.thoughts = thoughts
-    except (json.decoder.JSONDecodeError, ValidationError):
-        statements = models.Statements(
-            statements=[models.Statement(text=text)],
-            thoughts=thoughts,
-        )
-    return statements
-
-
-def respond(client: genai.Client, query: str) -> models.Statements:
-    resp = client.models.generate_content(
-        model="gemini-2.5-flash-preview-05-20",
-        contents=query + RESPONSE_FORMAT,
-        config=CONFIG,
+
+def get_context(messages: list[ModelMessage]) -> Context:
+    thoughts: list[str] = []
+    sources: dict[str, dict] = {}
+
+    for message in messages:
+        if isinstance(message, ModelResponse):
+            for part in message.parts:
+                if isinstance(part, TextPart):
+                    thoughts.append(part.content)
+        elif isinstance(message, ModelRequest):
+            for part in message.parts:
+                if isinstance(part, ToolReturnPart) and part.tool_name in {
+                    "search_medical_literature",
+                    "find_drug_set_ids",
+                }:
+                    for item in part.content:
+                        sources[item["id"]] = item
+
+    return Context(thoughts=thoughts, sources=sources)
+
+
+def respond(query: str) -> models.Statements:
+    result = agent.run_sync(query)
+    context = get_context(result.all_messages())
+
+    statements = []
+    for statement in result.output:
+        sources = []
+        for source_id in statement.sources or []:
+            try:
+                sources.append(context.sources[source_id])
+            except KeyError:
+                logfire.warning(f"citation hallucination '{source_id}'")
+        statements.append({"text": statement.text, "sources": sources})
+
+    return models.Statements.model_validate(
+        {
+            "statements": statements,
+            "thoughts": "\n\n".join(context.thoughts),
+        }
     )
-    return validate_response(resp)

app/main.py

@@ -1,10 +1,16 @@
+import logfire
 from fastapi import FastAPI
-from google import genai
 
-from app import agent, config, models
+from app import agent, models
+
+logfire.configure(
+    service_name="api", scrubbing=False, send_to_logfire="if-token-present"
+)
+logfire.instrument_pydantic_ai()
+
 
-gemini = genai.Client(api_key=config.settings.google_api_key)
 app = FastAPI()
+logfire.instrument_fastapi(app, capture_headers=True)
 
 
 @app.get("/health")
@@ -14,4 +20,4 @@ def health_check():
 
 @app.get("/ask", response_model=models.Statements)
 def ask(query: str):
-    return agent.respond(gemini, query)
+    return agent.respond(query)

main.py

@@ -1,7 +1,12 @@
+import logfire
 import streamlit as st
-from google import genai
 
-from app import agent, config, models
+from app import agent, models
+
+logfire.configure(
+    service_name="streamlit", scrubbing=False, send_to_logfire="if-token-present"
+)
+logfire.instrument_pydantic_ai()
 
 
 def format_output(statements: models.Statements) -> tuple[str, str]:
@@ -34,8 +39,6 @@ def format_output(statements: models.Statements) -> tuple[str, str]:
 
 
 def main():
-    gemini_client = genai.Client(api_key=config.settings.google_api_key)
-
     st.title("Elna")
     with st.form("search", border=False):
         query = st.text_input("Your medical question")
@@ -43,7 +46,7 @@ def main():
 
         if submit:
             with st.spinner("Thinking...", show_time=True):
-                output = agent.respond(gemini_client, query)
+                output = agent.respond(query)
             answer, thoughts = format_output(output)
             with st.expander("Thinking Process"):
                 st.markdown(thoughts)

pyproject.toml

@@ -8,7 +8,9 @@ dependencies = [
     "fastapi[standard]>=0.115.12",
     "google-genai>=1.14.0",
     "httpx>=0.28.1",
+    "logfire>=3.18.0",
     "pydantic>=2.11.4",
+    "pydantic-ai-slim[google]>=0.2.16",
     "pydantic-settings>=2.9.1",
     "python-fasthtml>=0.12.15",
     "streamlit>=1.45.0",

uv.lock

@@ -204,7 +204,9 @@ dependencies = [
     { name = "fastapi", extra = ["standard"] },
     { name = "google-genai" },
     { name = "httpx" },
+    { name = "logfire" },
     { name = "pydantic" },
+    { name = "pydantic-ai-slim", extra = ["google"] },
     { name = "pydantic-settings" },
     { name = "python-fasthtml" },
     { name = "streamlit" },
@@ -216,7 +218,9 @@ requires-dist = [
     { name = "fastapi", extras = ["standard"], specifier = ">=0.115.12" },
     { name = "google-genai", specifier = ">=1.14.0" },
     { name = "httpx", specifier = ">=0.28.1" },
+    { name = "logfire", specifier = ">=3.18.0" },
     { name = "pydantic", specifier = ">=2.11.4" },
+    { name = "pydantic-ai-slim", extras = ["google"], specifier = ">=0.2.16" },
     { name = "pydantic-settings", specifier = ">=2.9.1" },
     { name = "python-fasthtml", specifier = ">=0.12.15" },
     { name = "streamlit", specifier = ">=1.45.0" },
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@@ -344,7 +366,7 @@ wheels = [
 
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+[[package]]
+name = "logfire"
+version = "3.18.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "executing" },
+    { name = "opentelemetry-exporter-otlp-proto-http" },
+    { name = "opentelemetry-instrumentation" },
+    { name = "opentelemetry-sdk" },
+    { name = "protobuf" },
+    { name = "rich" },
+    { name = "typing-extensions" },
+]
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