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feat(backend): pass workspace context through executor
Update executor to propagate workspace context: - Pass workspace_id in execution kwargs - Update test utilities with workspace support Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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@@ -236,7 +236,12 @@ async def execute_node(
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input_size = len(input_data_str)
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log_metadata.debug("Executed node with input", input=input_data_str)
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# Update execution_context with node-level info
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execution_context.node_id = node_id
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execution_context.node_exec_id = node_exec_id
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# Inject extra execution arguments for the blocks via kwargs
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# Keep individual kwargs for backwards compatibility with existing blocks
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extra_exec_kwargs: dict = {
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"graph_id": graph_id,
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"graph_version": graph_version,
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@@ -892,11 +892,19 @@ async def add_graph_execution(
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settings = await gdb.get_graph_settings(user_id=user_id, graph_id=graph_id)
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execution_context = ExecutionContext(
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# Execution identity
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user_id=user_id,
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graph_id=graph_id,
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graph_exec_id=graph_exec.id,
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graph_version=graph_version,
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# Safety settings
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human_in_the_loop_safe_mode=settings.human_in_the_loop_safe_mode,
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sensitive_action_safe_mode=settings.sensitive_action_safe_mode,
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# User settings
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user_timezone=(
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user.timezone if user.timezone != USER_TIMEZONE_NOT_SET else "UTC"
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),
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# Execution hierarchy
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root_execution_id=graph_exec.id,
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)
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@@ -140,14 +140,29 @@ async def execute_block_test(block: Block):
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setattr(block, mock_name, mock_obj)
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# Populate credentials argument(s)
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# Generate IDs for execution context
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graph_id = str(uuid.uuid4())
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node_id = str(uuid.uuid4())
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graph_exec_id = str(uuid.uuid4())
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node_exec_id = str(uuid.uuid4())
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user_id = str(uuid.uuid4())
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graph_version = 1 # Default version for tests
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extra_exec_kwargs: dict = {
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"graph_id": str(uuid.uuid4()),
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"node_id": str(uuid.uuid4()),
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"graph_exec_id": str(uuid.uuid4()),
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"node_exec_id": str(uuid.uuid4()),
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"user_id": str(uuid.uuid4()),
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"graph_version": 1, # Default version for tests
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"execution_context": ExecutionContext(),
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"graph_id": graph_id,
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"node_id": node_id,
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"graph_exec_id": graph_exec_id,
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"node_exec_id": node_exec_id,
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"user_id": user_id,
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"graph_version": graph_version,
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"execution_context": ExecutionContext(
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user_id=user_id,
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graph_id=graph_id,
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graph_exec_id=graph_exec_id,
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graph_version=graph_version,
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node_id=node_id,
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node_exec_id=node_exec_id,
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),
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}
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input_model = cast(type[BlockSchema], block.input_schema)
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