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chore(app_runner): Remove some dead code

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Signed-off-by: -LAN- <laipz8200@outlook.com>
pull/21739/head
-LAN- 10 months ago
parent a0204e084f
commit 8260a4dea3
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2 changed files with 646 additions and 12 deletions
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13
api/core/app/apps/advanced_chat/app_runner.py
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@ -31,7 +31,7 @@ from core.workflow.workflow_entry import WorkflowEntry
from extensions.ext_database import db from extensions.ext_database import db
from models import Workflow from models import Workflow
from models.enums import UserFrom from models.enums import UserFrom
from models.model import App, Conversation, EndUser, Message, MessageAnnotation from models.model import App, Conversation, Message, MessageAnnotation
from models.workflow import ConversationVariable, WorkflowType from models.workflow import ConversationVariable, WorkflowType
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -76,17 +76,6 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
if not app_record: if not app_record:
raise ValueError("App not found") raise ValueError("App not found")
if not self._workflow:
raise ValueError("Workflow not initialized")
user_id: str | None = None
if self.application_generate_entity.invoke_from in {InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API}:
end_user = db.session.query(EndUser).filter(EndUser.id == self.application_generate_entity.user_id).first()
if end_user:
user_id = end_user.session_id
else:
user_id = self.application_generate_entity.user_id
workflow_callbacks: list[WorkflowCallback] = [] workflow_callbacks: list[WorkflowCallback] = []
if dify_config.DEBUG: if dify_config.DEBUG:
workflow_callbacks.append(WorkflowLoggingCallback()) workflow_callbacks.append(WorkflowLoggingCallback())

645
queue_based_graph_engine_spec.md
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@ -0,0 +1,645 @@
# Queue-Based Graph Engine Specification
## Overview
This document specifies the design and implementation of a new queue-based graph engine for workflow execution. The new engine will replace the current thread-based execution model with a more scalable and manageable queue-based approach.
## Architecture Components
### 1. Graph
A data structure representing the workflow definition, independent of execution.
**Responsibilities:**
- Store nodes and edges
- Provide graph traversal methods
- Parse from dictionary representation
- Validate graph structure (cycles, connectivity)
**Key Methods:**
```python
class Graph:
nodes: Dict[str, BaseNode]
edges: List[Edge]
@classmethod
def from_dict(cls, data: dict[str, Any]) -> 'Graph':
"""Parse a dictionary to create a Graph instance"""
def get_node(self, node_id: str) -> BaseNode:
"""Get node by ID"""
def get_edges_from(self, node_id: str) -> List[Edge]:
"""Get all outgoing edges from a node"""
def get_edges_to(self, node_id: str) -> List[Edge]:
"""Get all incoming edges to a node"""
def validate(self) -> None:
"""Validate graph structure"""
```
### 2. GraphEngine
The execution engine that processes a Graph using queue-based scheduling.
**Responsibilities:**
- Execute graphs using queue-based scheduling
- Manage execution queues (ready_q, running_q, completed_q)
- Schedule nodes based on preconditions
- Handle event propagation
- Support suspend/resume operations
**Key Methods:**
```python
class GraphEngine:
def __init__(self, graph: Graph, runtime_state: GraphRuntimeState):
"""Initialize with graph and runtime state"""
def run(self, start_node_id: str) -> None:
"""Start execution from specified node"""
def resume(self) -> None:
"""Resume execution from saved state"""
def suspend(self) -> GraphRuntimeState:
"""Suspend execution and return current state"""
def schedule(self) -> None:
"""Main scheduling loop - moves nodes between queues"""
def check_preconditions(self, node: BaseNode) -> bool:
"""Check if node's preconditions are satisfied"""
def execute_node(self, node: ExecutableNode) -> None:
"""Execute a single node"""
```
**Queue Management:**
- `ready_q`: Nodes whose preconditions are satisfied
- `running_q`: Currently executing nodes
- `completed_q`: Successfully completed nodes
- `failed_q`: Failed nodes for retry or error handling
### 3. GraphRuntimeState
Captures the complete execution state for suspend/resume functionality.
**Responsibilities:**
- Store queue states
- Track node execution status
- Maintain variable pool state
- Support serialization/deserialization
```python
@dataclass
class GraphRuntimeState:
# Queue states
ready_nodes: List[str]
running_nodes: List[str]
completed_nodes: List[str]
failed_nodes: List[str]
# Node execution states
node_states: Dict[str, NodeExecutionState]
# Variable pool
variables: Dict[str, Any]
# Execution metadata
start_time: datetime
last_checkpoint: datetime
execution_id: str
def to_dict(self) -> dict[str, Any]:
"""Serialize state to dictionary"""
@classmethod
def from_dict(cls, data: dict[str, Any]) -> 'GraphRuntimeState':
"""Deserialize state from dictionary"""
```
### 4. Edge
Represents connections between nodes in the graph.
```python
@dataclass
class Edge:
source_node_id: str
target_node_id: str
condition: Optional[Callable[[GraphEngine], bool]] = None
priority: int = 0
```
### 5. Node Hierarchy
#### BaseNode
Abstract base class for all nodes.
```python
class BaseNode(ABC):
node_id: str
node_type: NodeType
data: dict[str, Any]
@abstractmethod
def preconditions(self) -> List[Callable[[GraphEngine], bool]]:
"""Return list of precondition checks"""
@classmethod
@abstractmethod
def from_dict(cls, data: dict[str, Any]) -> Self:
"""Factory method for node creation with validation"""
def get_dependencies(self) -> List[str]:
"""Return list of node IDs this node depends on"""
```
#### ExecutableNode
Nodes that perform actual work.
```python
class ExecutableNode(BaseNode):
@abstractmethod
async def execute(self, context: ExecutionContext) -> NodeResult:
"""Execute the node's logic"""
def preconditions(self) -> List[Callable[[GraphEngine], bool]]:
"""Default: all upstream nodes must be completed"""
return [
lambda engine: all(
engine.is_node_completed(dep_id)
for dep_id in self.get_dependencies()
)
]
```
### 6. Special Node Types
#### StartNode
- **Characteristics:**
- Always has satisfied preconditions
- No dependencies
- Triggers initial execution
- Only one per workflow
- Cannot be skipped
```python
class StartNode(ExecutableNode):
def preconditions(self) -> List[Callable[[GraphEngine], bool]]:
return [] # Always ready
async def execute(self, context: ExecutionContext) -> NodeResult:
return NodeResult(status="success", outputs={})
```
#### EndNode
- **Characteristics:**
- Marks workflow completion
- Can have multiple end nodes (different paths)
- Triggers cleanup operations
- Collects final outputs
```python
class EndNode(ExecutableNode):
def preconditions(self) -> List[Callable[[GraphEngine], bool]]:
# At least one path must reach this node
return [lambda engine: any(
engine.is_node_completed(dep_id)
for dep_id in self.get_dependencies()
)]
```
## Execution Flow
### 1. Graph Creation Phase
```python
# Parse from dictionary
graph_data = {
"nodes": [...],
"edges": [...]
}
graph = Graph.from_dict(graph_data)
graph.validate()
```
### 2. Engine Initialization
```python
# Fresh start
runtime_state = GraphRuntimeState.create_new()
engine = GraphEngine(graph, runtime_state)
engine.run(start_node_id="start_node")
# Resume from suspended state
saved_state = GraphRuntimeState.from_dict(saved_state_dict)
engine = GraphEngine(graph, saved_state)
engine.resume()
```
### 3. Scheduling Loop
```
while not all_nodes_processed():
# Check completed nodes and update downstream
for node_id in completed_q:
for edge in graph.get_edges_from(node_id):
target_node = graph.get_node(edge.target_node_id)
if check_preconditions(target_node):
ready_q.add(target_node)
# Execute ready nodes
while ready_q and has_available_workers():
node = ready_q.pop()
running_q.add(node)
submit_to_worker(node)
# Handle timeouts and failures
check_timeouts()
process_failed_nodes()
# Checkpoint state periodically
if should_checkpoint():
runtime_state.update_from_engine(engine)
```
### 4. Node Execution
```
1. Worker picks up node from queue
2. Validates preconditions (double-check)
3. Execute node logic
4. Update variable pool in runtime state
5. Emit events
6. Move to completed_q or failed_q
```
### 5. Suspend/Resume Operations
```python
# Suspend
current_state = engine.suspend()
state_dict = current_state.to_dict()
# Save state_dict to database/storage
# Resume
saved_state = GraphRuntimeState.from_dict(state_dict)
engine = GraphEngine(graph, saved_state)
engine.resume()
```
## Implementation Plan
### Phase 1: Core Infrastructure
1. Implement Graph class with `from_dict` parsing
2. Implement base node classes (BaseNode, ExecutableNode)
3. Create GraphRuntimeState for state management
4. Build precondition framework
5. Implement GraphEngine with queue management
### Phase 2: Node Migration
1. Convert existing nodes to ExecutableNode
2. Implement `from_dict` methods for each node type
3. Add precondition logic to each node type
4. Update node execution to async
### Phase 3: State Management
1. Implement suspend/resume functionality
2. Add checkpoint mechanisms
3. Create state persistence layer
4. Add recovery mechanisms
### Phase 4: Advanced Features
1. Priority-based scheduling
2. Resource-aware execution
3. Distributed execution support
4. Advanced retry mechanisms
5. Performance optimizations
## Benefits
1. **Scalability**: Easy to distribute across workers
2. **Resource Management**: Better control over concurrent executions
3. **Observability**: Clear queue states for monitoring
4. **Fault Tolerance**: Failed nodes can be retried independently
5. **Flexibility**: Easy to add new scheduling strategies
## Migration Strategy
1. **Compatibility Layer**: Maintain backward compatibility during transition
2. **Feature Flag**: Toggle between old and new engines
3. **Gradual Rollout**: Test with simple workflows first
4. **Performance Monitoring**: Compare metrics between implementations
5. **Rollback Plan**: Easy switch back to old engine if needed
## Key Design Decisions
1. **Graph and GraphEngine Separation**: Graph is a pure data structure, GraphEngine handles execution
2. **GraphRuntimeState**: Enables suspend/resume functionality with complete state capture
3. **No ContainerNode Initially**: Focus on core execution model first
4. **Queue-Based Scheduling**: Better resource management and scalability than thread-based approach
## Open Questions
1. **Queue Backend**: In-memory, Redis, or database-backed?
2. **Worker Pool**: Thread-based, process-based, or distributed?
3. **State Persistence**: How frequently to checkpoint?
4. **Event System**: Keep current or migrate to message queue?
5. **Priority Algorithm**: Simple numeric or complex scoring?
6. **Graph Parsing**: Should we reuse existing node parsing logic or create new?
## Additional Components
### 7. ExecutionContext
Provides runtime context for node execution.
```python
@dataclass
class ExecutionContext:
graph_engine: GraphEngine
runtime_state: GraphRuntimeState
variable_pool: VariablePool
user_id: str
workflow_id: str
execution_id: str
def get_variable(self, key: str) -> Any:
"""Get variable from pool"""
return self.variable_pool.get(key)
def set_variable(self, key: str, value: Any) -> None:
"""Set variable in pool"""
self.variable_pool.set(key, value)
def emit_event(self, event: BaseEvent) -> None:
"""Emit event to engine"""
self.graph_engine.handle_event(event)
```
### 8. NodeExecutionState
Tracks individual node execution state.
```python
@dataclass
class NodeExecutionState:
node_id: str
status: Literal["pending", "running", "completed", "failed", "skipped"]
start_time: Optional[datetime] = None
end_time: Optional[datetime] = None
error: Optional[str] = None
outputs: Optional[dict[str, Any]] = None
retry_count: int = 0
def to_dict(self) -> dict[str, Any]:
"""Serialize to dictionary"""
return {
"node_id": self.node_id,
"status": self.status,
"start_time": self.start_time.isoformat() if self.start_time else None,
"end_time": self.end_time.isoformat() if self.end_time else None,
"error": self.error,
"outputs": self.outputs,
"retry_count": self.retry_count
}
```
### 9. Event System Integration
```python
class GraphEngine:
def handle_event(self, event: BaseEvent) -> None:
"""Handle events from nodes"""
if isinstance(event, NodeRunStartedEvent):
self._on_node_started(event)
elif isinstance(event, NodeRunSucceededEvent):
self._on_node_succeeded(event)
elif isinstance(event, NodeRunFailedEvent):
self._on_node_failed(event)
# Propagate to external listeners
self._event_emitter.emit(event)
```
### 10. VariablePool
Manages shared variables between nodes.
```python
class VariablePool:
def __init__(self, initial_vars: Optional[dict[str, Any]] = None):
self._variables: dict[str, Any] = initial_vars or {}
self._locks: dict[str, threading.Lock] = defaultdict(threading.Lock)
def get(self, key: str, default: Any = None) -> Any:
"""Thread-safe variable retrieval"""
with self._locks[key]:
return self._variables.get(key, default)
def set(self, key: str, value: Any) -> None:
"""Thread-safe variable setting"""
with self._locks[key]:
self._variables[key] = value
def to_dict(self) -> dict[str, Any]:
"""Serialize for state persistence"""
return self._variables.copy()
```
## Example: Graph.from_dict Implementation
Based on the existing workflow structure, here's how Graph.from_dict would parse the data:
```python
@classmethod
def from_dict(cls, data: dict[str, Any]) -> 'Graph':
"""
Parse workflow data dictionary to create a Graph instance.
Expected format:
{
"nodes": [
{
"id": "node_123",
"data": {
"type": "llm",
"title": "LLM Node",
...node specific config...
}
}
],
"edges": [
{
"id": "edge_123",
"source": "node_1",
"target": "node_2",
"sourceHandle": "source",
"targetHandle": "target"
}
]
}
"""
nodes = {}
edges = []
# Parse nodes
for node_data in data.get("nodes", []):
node_id = node_data["id"]
node_type = node_data["data"]["type"]
# Get the appropriate node class based on type
node_class = NODE_TYPE_MAPPING.get(node_type)
if not node_class:
raise ValueError(f"Unknown node type: {node_type}")
# Create node instance
node = node_class.from_dict(node_data["data"])
node.node_id = node_id
nodes[node_id] = node
# Parse edges
for edge_data in data.get("edges", []):
edge = Edge(
source_node_id=edge_data["source"],
target_node_id=edge_data["target"],
# Additional edge properties can be added here
)
edges.append(edge)
graph = cls(nodes=nodes, edges=edges)
graph.validate()
return graph
```
## Detailed Implementation Example
### Queue-Based Scheduling Algorithm
```python
class GraphEngine:
def __init__(self, graph: Graph, runtime_state: GraphRuntimeState):
self.graph = graph
self.runtime_state = runtime_state
# Initialize queues from runtime state
self.ready_q = deque(runtime_state.ready_nodes)
self.running_q = set(runtime_state.running_nodes)
self.completed_q = set(runtime_state.completed_nodes)
self.failed_q = set(runtime_state.failed_nodes)
# Worker pool
self.executor = ThreadPoolExecutor(max_workers=10)
self.futures: Dict[str, Future] = {}
def schedule(self) -> None:
"""Main scheduling loop"""
while self._has_pending_nodes():
# Move nodes from completed to ready if preconditions met
self._update_ready_queue()
# Submit ready nodes to workers
while self.ready_q and self._has_available_workers():
node_id = self.ready_q.popleft()
self._submit_node(node_id)
# Wait for at least one node to complete
if self.futures:
done, _ = concurrent.futures.wait(
self.futures.values(),
timeout=0.1,
return_when=concurrent.futures.FIRST_COMPLETED
)
for future in done:
node_id = self._get_node_id_from_future(future)
self._handle_node_completion(node_id, future)
# Checkpoint periodically
if self._should_checkpoint():
self._checkpoint()
def _update_ready_queue(self) -> None:
"""Check all nodes and move to ready queue if preconditions met"""
for node_id in self.graph.nodes:
if (node_id not in self.ready_q and
node_id not in self.running_q and
node_id not in self.completed_q and
node_id not in self.failed_q):
node = self.graph.get_node(node_id)
if self._check_preconditions(node):
self.ready_q.append(node_id)
def _check_preconditions(self, node: BaseNode) -> bool:
"""Check if all preconditions are satisfied"""
for condition in node.preconditions():
if not condition(self):
return False
return True
def _submit_node(self, node_id: str) -> None:
"""Submit node for execution"""
self.running_q.add(node_id)
self.runtime_state.node_states[node_id].status = "running"
self.runtime_state.node_states[node_id].start_time = datetime.now()
future = self.executor.submit(self._execute_node, node_id)
self.futures[node_id] = future
```
### Node Result Handling
```python
@dataclass
class NodeResult:
status: Literal["success", "failed", "skipped"]
outputs: Optional[dict[str, Any]] = None
error: Optional[Exception] = None
class GraphEngine:
def _execute_node(self, node_id: str) -> NodeResult:
"""Execute a single node"""
try:
node = self.graph.get_node(node_id)
context = self._create_execution_context(node_id)
# Emit start event
context.emit_event(NodeRunStartedEvent(
node_id=node_id,
node_type=node.node_type,
timestamp=datetime.now()
))
# Execute node
if isinstance(node, ExecutableNode):
result = asyncio.run(node.execute(context))
else:
raise ValueError(f"Node {node_id} is not executable")
# Emit success event
context.emit_event(NodeRunSucceededEvent(
node_id=node_id,
outputs=result.outputs,
timestamp=datetime.now()
))
return result
except Exception as e:
# Emit failure event
context.emit_event(NodeRunFailedEvent(
node_id=node_id,
error=str(e),
timestamp=datetime.now()
))
return NodeResult(status="failed", error=e)
```
## Next Steps
1. Review and approve specification
2. Create detailed API documentation
3. Set up development environment
4. Begin Phase 1 implementation
5. Create comprehensive test suite
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