- Implementation of specialist execution api, including SSE protocol

- eveai_chat becomes deprecated and should be replaced with SSE - Adaptation of STANDARD_RAG specialist - Base class definition allowing to realise specialists with crewai framework - Implementation of SPIN_SPECIALIST - Implementation of test app for testing specialists (test_specialist_client). Also serves as an example for future SSE-based client - Improvements to startup scripts to better handle and scale multiple connections - Small improvements to the interaction forms and views - Caching implementation improved and augmented with additional caches
2025-02-20 05:50:16 +01:00
parent d106520d22
commit 25213f2004
79 changed files with 2791 additions and 347 deletions
--- a/common/utils/cache/crewai_configuration.py
+++ b/common/utils/cache/crewai_configuration.py
@@ -0,0 +1,126 @@
+from dataclasses import dataclass
+from typing import Dict, Any, Optional
+
+
+@dataclass
+class ProcessedAgentConfig:
+    """Processed and ready-to-use agent configuration"""
+    role: str
+    goal: str
+    backstory: str
+    name: str
+    type: str
+    description: Optional[str] = None
+    verbose: bool = False
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for serialization"""
+        return {
+            'role': self.role,
+            'goal': self.goal,
+            'backstory': self.backstory,
+            'name': self.name,
+            'type': self.type,
+            'description': self.description,
+            'verbose': self.verbose
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> 'ProcessedAgentConfig':
+        """Create from dictionary"""
+        return cls(**data)
+
+
+@dataclass
+class ProcessedTaskConfig:
+    """Processed and ready-to-use task configuration"""
+    task_description: str
+    expected_output: str
+    name: str
+    type: str
+    description: Optional[str] = None
+    verbose: bool = False
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for serialization"""
+        return {
+            'task_description': self.task_description,
+            'expected_output': self.expected_output,
+            'name': self.name,
+            'type': self.type,
+            'description': self.description,
+            'verbose': self.verbose
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> 'ProcessedTaskConfig':
+        """Create from dictionary"""
+        return cls(**data)
+
+
+@dataclass
+class ProcessedToolConfig:
+    """Processed and ready-to-use tool configuration"""
+    name: str
+    type: str
+    description: Optional[str] = None
+    configuration: Optional[Dict[str, Any]] = None
+    verbose: bool = False
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for serialization"""
+        return {
+            'name': self.name,
+            'type': self.type,
+            'description': self.description,
+            'configuration': self.configuration,
+            'verbose': self.verbose
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> 'ProcessedToolConfig':
+        """Create from dictionary"""
+        return cls(**data)
+
+
+@dataclass
+class SpecialistProcessedConfig:
+    """Complete processed configuration for a specialist"""
+    agents: Dict[str, ProcessedAgentConfig]
+    tasks: Dict[str, ProcessedTaskConfig]
+    tools: Dict[str, ProcessedToolConfig]
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert entire configuration to dictionary"""
+        return {
+            'agents': {
+                agent_type: config.to_dict()
+                for agent_type, config in self.agents.items()
+            },
+            'tasks': {
+                task_type: config.to_dict()
+                for task_type, config in self.tasks.items()
+            },
+            'tools': {
+                tool_type: config.to_dict()
+                for tool_type, config in self.tools.items()
+            }
+        }
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> 'SpecialistProcessedConfig':
+        """Create from dictionary"""
+        return cls(
+            agents={
+                agent_type: ProcessedAgentConfig.from_dict(config)
+                for agent_type, config in data['agents'].items()
+            },
+            tasks={
+                task_type: ProcessedTaskConfig.from_dict(config)
+                for task_type, config in data['tasks'].items()
+            },
+            tools={
+                tool_type: ProcessedToolConfig.from_dict(config)
+                for tool_type, config in data['tools'].items()
+            }
+        )