Merge branch 'main' into fix-typos

Author: hangfei

src/google/adk/flows/llm_flows/functions.py

@@ -310,9 +310,7 @@ async def _process_function_live_helper(
       function_response = {
           'status': f'No active streaming function named {function_name} found'
       }
-  elif inspect.isasyncgenfunction(tool.func):
-    print('is async')
-
+  elif hasattr(tool, "func") and inspect.isasyncgenfunction(tool.func):
     # for streaming tool use case
     # we require the function to be a async generator function
     async def run_tool_and_update_queue(tool, function_args, tool_context):

src/google/adk/planners/built_in_planner.py

@@ -56,6 +56,7 @@ def apply_thinking_config(self, llm_request: LlmRequest) -> None:
       llm_request: The LLM request to apply the thinking config to.
     """
     if self.thinking_config:
+      llm_request.config = llm_request.config or types.GenerateContentConfig()
       llm_request.config.thinking_config = self.thinking_config
 
   @override

src/google/adk/sessions/database_session_service.py

@@ -17,10 +17,10 @@
 from datetime import datetime
 import json
 import logging
-from typing import Any
-from typing import Optional
+from typing import Any, Optional
 import uuid
 
+from google.genai import types
 from sqlalchemy import Boolean
 from sqlalchemy import delete
 from sqlalchemy import Dialect
@@ -136,7 +136,7 @@ class StorageEvent(Base):
   author: Mapped[str] = mapped_column(String)
   branch: Mapped[str] = mapped_column(String, nullable=True)
   timestamp: Mapped[DateTime] = mapped_column(DateTime(), default=func.now())
-  content: Mapped[dict[str, Any]] = mapped_column(DynamicJSON)
+  content: Mapped[dict[str, Any]] = mapped_column(DynamicJSON, nullable=True)
   actions: Mapped[MutableDict[str, Any]] = mapped_column(PickleType)
 
   long_running_tool_ids_json: Mapped[Optional[str]] = mapped_column(
@@ -576,8 +576,12 @@ def _merge_state(app_state, user_state, session_state):
   return merged_state
 
 
-def _decode_content(content: dict[str, Any]) -> dict[str, Any]:
+def _decode_content(
+    content: Optional[dict[str, Any]],
+) -> Optional[types.Content]:
+  if not content:
+    return None
   for p in content["parts"]:
     if "inline_data" in p:
       p["inline_data"]["data"] = base64.b64decode(p["inline_data"]["data"][0])
-  return content
+  return types.Content.model_validate(content)