refactor(backend): Introduced Graph Input & Output Schema, Merge Grap…

…hMeta & Graph, Remove subgraph functionality (#8526)

autogpt_platform/backend/backend/blocks/basic.py

@@ -148,9 +148,12 @@ class Input(BlockSchema):
             description="The value to be passed as input.",
             default=None,
         )
-        description: str = SchemaField(
+        title: str | None = SchemaField(
+            description="The title of the input.", default=None, advanced=True
+        )
+        description: str | None = SchemaField(
             description="The description of the input.",
-            default="",
+            default=None,
             advanced=True,
         )
         placeholder_values: List[Any] = SchemaField(
@@ -163,6 +166,16 @@ class Input(BlockSchema):
             default=False,
             advanced=True,
         )
+        advanced: bool = SchemaField(
+            description="Whether to show the input in the advanced section, if the field is not required.",
+            default=False,
+            advanced=True,
+        )
+        secret: bool = SchemaField(
+            description="Whether the input should be treated as a secret.",
+            default=False,
+            advanced=True,
+        )
 
     class Output(BlockSchema):
         result: Any = SchemaField(description="The value passed as input.")
@@ -195,6 +208,7 @@ def __init__(self):
             ],
             categories={BlockCategory.INPUT, BlockCategory.BASIC},
             block_type=BlockType.INPUT,
+            static_output=True,
         )
 
     def run(self, input_data: Input, **kwargs) -> BlockOutput:
@@ -205,35 +219,42 @@ class AgentOutputBlock(Block):
     """
     Records the output of the graph for users to see.
 
-    Attributes:
-        recorded_value: The value to be recorded as output.
-        name: The name of the output.
-        description: The description of the output.
-        fmt_string: The format string to be used to format the recorded_value.
-
-    Outputs:
-        output: The formatted recorded_value if fmt_string is provided and the recorded_value
-                can be formatted, otherwise the raw recorded_value.
-
     Behavior:
-        If fmt_string is provided and the recorded_value is of a type that can be formatted,
-        the block attempts to format the recorded_value using the fmt_string.
-        If formatting fails or no fmt_string is provided, the raw recorded_value is output.
+        If `format` is provided and the `value` is of a type that can be formatted,
+        the block attempts to format the recorded_value using the `format`.
+        If formatting fails or no `format` is provided, the raw `value` is output.
     """
 
     class Input(BlockSchema):
-        value: Any = SchemaField(description="The value to be recorded as output.")
+        value: Any = SchemaField(
+            description="The value to be recorded as output.",
+            default=None,
+            advanced=False,
+        )
         name: str = SchemaField(description="The name of the output.")
-        description: str = SchemaField(
+        title: str | None = SchemaField(
+            description="The title of the input.", default=None, advanced=True
+        )
+        description: str | None = SchemaField(
             description="The description of the output.",
-            default="",
+            default=None,
             advanced=True,
         )
         format: str = SchemaField(
             description="The format string to be used to format the recorded_value.",
             default="",
             advanced=True,
         )
+        advanced: bool = SchemaField(
+            description="Whether to treat the output as advanced.",
+            default=False,
+            advanced=True,
+        )
+        secret: bool = SchemaField(
+            description="Whether the output should be treated as a secret.",
+            default=False,
+            advanced=True,
+        )
 
     class Output(BlockSchema):
         output: Any = SchemaField(description="The value recorded as output.")
@@ -271,6 +292,7 @@ def __init__(self):
             ],
             categories={BlockCategory.OUTPUT, BlockCategory.BASIC},
             block_type=BlockType.OUTPUT,
+            static_output=True,
         )
 
     def run(self, input_data: Input, **kwargs) -> BlockOutput:

autogpt_platform/backend/backend/blocks/branching.py

@@ -71,11 +71,18 @@ def __init__(self):
         )
 
     def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        value1 = input_data.value1
         operator = input_data.operator
+
+        value1 = input_data.value1
+        if isinstance(value1, str):
+            value1 = float(value1.strip())
+
         value2 = input_data.value2
+        if isinstance(value2, str):
+            value2 = float(value2.strip())
+
         yes_value = input_data.yes_value if input_data.yes_value is not None else value1
-        no_value = input_data.no_value if input_data.no_value is not None else value1
+        no_value = input_data.no_value if input_data.no_value is not None else value2
 
         comparison_funcs = {
             ComparisonOperator.EQUAL: lambda a, b: a == b,
@@ -86,17 +93,11 @@ def run(self, input_data: Input, **kwargs) -> BlockOutput:
             ComparisonOperator.LESS_THAN_OR_EQUAL: lambda a, b: a <= b,
         }
 
-        try:
-            result = comparison_funcs[operator](value1, value2)
-
-            yield "result", result
+        result = comparison_funcs[operator](value1, value2)
 
-            if result:
-                yield "yes_output", yes_value
-            else:
-                yield "no_output", no_value
+        yield "result", result
 
-        except Exception:
-            yield "result", None
-            yield "yes_output", None
-            yield "no_output", None
+        if result:
+            yield "yes_output", yes_value
+        else:
+            yield "no_output", no_value

autogpt_platform/backend/backend/data/execution.py

@@ -9,14 +9,11 @@
     AgentNodeExecution,
     AgentNodeExecutionInputOutput,
 )
-from prisma.types import (
-    AgentGraphExecutionInclude,
-    AgentGraphExecutionWhereInput,
-    AgentNodeExecutionInclude,
-)
+from prisma.types import AgentGraphExecutionWhereInput
 from pydantic import BaseModel
 
 from backend.data.block import BlockData, BlockInput, CompletedBlockOutput
+from backend.data.includes import EXECUTION_RESULT_INCLUDE, GRAPH_EXECUTION_INCLUDE
 from backend.util import json, mock
 
 
@@ -110,24 +107,6 @@ def from_db(execution: AgentNodeExecution):
 
 # --------------------- Model functions --------------------- #
 
-EXECUTION_RESULT_INCLUDE: AgentNodeExecutionInclude = {
-    "Input": True,
-    "Output": True,
-    "AgentNode": True,
-    "AgentGraphExecution": True,
-}
-
-GRAPH_EXECUTION_INCLUDE: AgentGraphExecutionInclude = {
-    "AgentNodeExecutions": {
-        "include": {
-            "Input": True,
-            "Output": True,
-            "AgentNode": True,
-            "AgentGraphExecution": True,
-        }
-    }
-}
-
 
 async def create_graph_execution(
     graph_id: str,
@@ -268,21 +247,9 @@ async def update_graph_execution_start_time(graph_exec_id: str):
 
 async def update_graph_execution_stats(
     graph_exec_id: str,
-    error: Exception | None,
-    wall_time: float,
-    cpu_time: float,
-    node_count: int,
+    stats: dict[str, Any],
 ):
-    status = ExecutionStatus.FAILED if error else ExecutionStatus.COMPLETED
-    stats = (
-        {
-            "walltime": wall_time,
-            "cputime": cpu_time,
-            "nodecount": node_count,
-            "error": str(error) if error else None,
-        },
-    )
-
+    status = ExecutionStatus.FAILED if stats.get("error") else ExecutionStatus.COMPLETED
     await AgentGraphExecution.prisma().update(
         where={"id": graph_exec_id},
         data={