fix: guard against non-dict artifacts in output log building

When a component like Chroma (using chromadb internally) stores a
threading.Lock object as an artifact value, the code in
ResultData.validate_model() and build_output_logs() would crash with
TypeError because it tried to use the 'in' operator on a non-dict object.

- ResultData.validate_model(): skip artifacts that are not dicts before
  checking for 'stream_url' and 'type' keys
- build_output_logs(): guard STREAM case with isinstance(message, dict)
  check, and guard ARRAY case against None message

Fixes #12591

src/lfx/src/lfx/graph/schema.py

@@ -39,6 +39,9 @@ class ResultData(BaseModel):
                 if message is None:
                     continue
 
+                if not isinstance(message, dict):
+                    continue
+
                 if "stream_url" in message and "type" in message:
                     stream_url = StreamURL(location=message["stream_url"])
                     values["outputs"].update({key: OutputValue(message=stream_url, type=message["type"])})

src/lfx/src/lfx/schema/schema.py

@@ -110,7 +110,7 @@ def build_output_logs(vertex, result) -> dict:
         type_ = get_type(output_result)
 
         match type_:
-            case LogType.STREAM if "stream_url" in message:
+            case LogType.STREAM if isinstance(message, dict) and "stream_url" in message:
                 message = StreamURL(location=message["stream_url"])
 
             case LogType.STREAM:
@@ -123,9 +123,13 @@ def build_output_logs(vertex, result) -> dict:
                 message = ""
 
             case LogType.ARRAY:
-                if isinstance(message, DataFrame):
+                if message is None:
+                    message = []
+                elif isinstance(message, DataFrame):
                     message = message.to_dict(orient="records")
-                message = [serialize(item) for item in message]
+                    message = [serialize(item) for item in message]
+                else:
+                    message = [serialize(item) for item in message]
         name = output.get("name", f"output_{index}")
         outputs |= {name: OutputValue(message=message, type=type_).model_dump()}