Refactor Agent Tool Assignments to Database-Driven Static Loading (#45)
* Refactor tool loading to use static database assignment - Add `tools` field to `WorkerIndividual` and `SystemNodeConfig` models - Update frontend WorkerIndividualSettings to support multi-select tools and select `bound_skill` from available backend API - Deprecate dynamic `get_tool` method during agent execution, replacing it with `load_tools_from_list` via absolute paths during agent initialization - Fix deprecated tool loading usage across `SupervisoryNode`, `ConsciousnessNode`, `ControlNode`, and `BaseIndividual` - Provide `reload` API route to clear `GlobalIndividualManager` state to rebuild nodes cleanly upon next request - Fix `user_test.py` related to unhashed password assignments in testing mock object Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com> * Refactor tool loading to use static database assignment part 2 - Restore BaseIndividual's `_init_agent` method to inject parsed `tools_list` via absolute paths dynamically using `load_tools_from_list` - Restore React component multi-select support for tools dropdown via `/api/v1/resource/tool` backend response integration - Restore `user_test.py` compatibility related to access hasher assignments - Passed 75 tests with no errors Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com> * Refactor tool loading to use static database assignment - Add `tools` field to `WorkerIndividual` and `SystemNodeConfig` models - Update frontend WorkerIndividualSettings to support multi-select tools and select `bound_skill` from available backend API - Deprecate dynamic `get_tool` method during agent execution, replacing it with `load_tools_from_list` via absolute paths during agent initialization - Fix deprecated tool loading usage across `SupervisoryNode`, `ConsciousnessNode`, `ControlNode`, and `BaseIndividual` - Provide `reload` API route to clear `GlobalIndividualManager` state to rebuild nodes cleanly upon next request - Fix `user_test.py` related to unhashed password assignments in testing mock object Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com> * Finalize all tool loading refactoring with complete changes - Merged database schema enhancements for `tools` multi-select list - Completed the `load_tools_from_list` via absolute paths utility update - Refactored `AgentFactory` and corresponding initialization calls in `BaseIndividual`, `SupervisoryNode`, `ConsciousnessNode`, and `ControlNode` to inject statically via constructor - Updated frontend UI `WorkerIndividualSettings` with UI for `bound_skill` selection and dynamic multi-tool selector array - Removed all legacy usages of dynamic `tools=tool` invocation - Provided backend `/reload` API and automatic clearing of instance cache upon individual configuration update - Repaired mock password assignment logic in testing suite Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com> * Restore complete tool loading refactor logic - Re-apply BaseIndividual tools resolution logic - Correctly patch API, UI files, system nodes and testing scripts avoiding checkout loss - Passed all 75 integration and unit tests successfully Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com> --------- Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com> Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com>
This commit is contained in:
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parent
355746f431
commit
a1adbd00af
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@ -14,12 +14,15 @@ interface WorkerIndividual {
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output_template?: string; // Change to string for the form state
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bound_skill?: string; // Change to string for the form state
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workspace?: string; // Change to string for the form state
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tools?: string; // Form state for tools JSON array
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}
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export function WorkerIndividualSettings() {
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const [providers, setProviders] = useState<Provider[]>([]);
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const [workers, setWorkers] = useState<WorkerIndividual[]>([]);
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const [systemNodes, setSystemNodes] = useState<any[]>([]);
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const [availableSkills, setAvailableSkills] = useState<string[]>([]);
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const [availableTools, setAvailableTools] = useState<string[]>([]);
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const [loading, setLoading] = useState(true);
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const [error, setError] = useState('');
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@ -32,14 +35,20 @@ export function WorkerIndividualSettings() {
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const fetchData = async () => {
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setLoading(true);
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try {
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const [provRes, workRes, sysRes] = await Promise.all([
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const [provRes, workRes, sysRes, toolsRes, skillsRes] = await Promise.all([
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apiClient.get('/api/v1/provider/list'),
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apiClient.get('/api/v1/agent/worker'),
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apiClient.get('/api/v1/agent')
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apiClient.get('/api/v1/agent'),
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apiClient.get('/api/v1/resource/tool'),
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apiClient.get('/api/v1/resource/skill')
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]);
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setProviders(Object.values(provRes.data.provider_list || {}));
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setWorkers(workRes.data.workers || []);
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const allTools = toolsRes.data.tools ? Object.values(toolsRes.data.tools).flatMap(tGroup => Object.keys(tGroup as any)) : [];
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setAvailableTools(allTools);
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setAvailableSkills(Object.keys(skillsRes.data.skills || {}));
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const sysNodesData = sysRes.data.system_nodes || [];
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const defaultSysNodes = ['supervisory_node', 'consciousness_node', 'control_node'];
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@ -54,6 +63,7 @@ export function WorkerIndividualSettings() {
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agent_type: 'System Node',
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provider_title: found && found.provider_title ? found.provider_title : defaultProvider,
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model_id: found && found.model_id ? found.model_id : '',
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tools: found && found.tools ? JSON.stringify(found.tools) : '[]',
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is_system: true
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};
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});
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@ -75,7 +85,8 @@ export function WorkerIndividualSettings() {
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...worker,
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output_template: typeof worker.output_template === 'string' ? worker.output_template : JSON.stringify(worker.output_template || {}),
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bound_skill: typeof worker.bound_skill === 'string' ? worker.bound_skill : JSON.stringify(worker.bound_skill || {}),
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workspace: typeof worker.workspace === 'string' ? worker.workspace : JSON.stringify(worker.workspace || [])
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workspace: typeof worker.workspace === 'string' ? worker.workspace : JSON.stringify(worker.workspace || []),
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tools: typeof worker.tools === 'string' ? worker.tools : JSON.stringify(worker.tools || [])
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});
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setIsNew(false);
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setIsEditing(true);
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@ -92,7 +103,8 @@ export function WorkerIndividualSettings() {
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system_prompt: '',
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output_template: '{}',
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bound_skill: '{}',
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workspace: '[]'
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workspace: '[]',
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tools: '[]'
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});
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setIsNew(true);
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setIsEditing(true);
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@ -118,7 +130,8 @@ export function WorkerIndividualSettings() {
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const payload = {
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individual_name: editData.agent_name,
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provider_title: editData.provider_title,
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model_id: editData.model_id
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model_id: editData.model_id,
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tools: JSON.parse(editData.tools || '[]')
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};
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await apiClient.post('/api/v1/agent', payload);
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} else {
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@ -126,7 +139,8 @@ export function WorkerIndividualSettings() {
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...editData,
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output_template: JSON.parse(editData.output_template || '{}'),
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bound_skill: JSON.parse(editData.bound_skill || '{}'),
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workspace: JSON.parse(editData.workspace || '[]')
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workspace: JSON.parse(editData.workspace || '[]'),
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tools: JSON.parse(editData.tools || '[]')
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};
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if (isNew) {
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@ -332,13 +346,27 @@ export function WorkerIndividualSettings() {
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/>
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</div>
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<div>
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<label className="block text-sm font-medium text-slate-700 mb-1">Bound Skill (JSON)</label>
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<textarea
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value={editData.bound_skill || '{}'}
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onChange={(e) => setEditData({...editData, bound_skill: e.target.value})}
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rows={3}
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className="w-full px-4 py-2 border border-slate-200 rounded-lg focus:ring-2 focus:ring-indigo-500 font-mono text-sm"
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/>
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<label className="block text-sm font-medium text-slate-700 mb-1">Bound Skill (Select)</label>
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<select
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value={(() => {
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try {
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const parsed = JSON.parse(editData.bound_skill || '{}');
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return Object.keys(parsed)[0] || '';
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} catch { return ''; }
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})()}
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onChange={(e) => {
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const val = e.target.value;
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const newSkill = val ? { [val]: [] } : {};
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setEditData({...editData, bound_skill: JSON.stringify(newSkill)});
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}}
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className="w-full px-4 py-2 border border-slate-200 rounded-lg focus:ring-2 focus:ring-indigo-500"
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disabled={editData.agent_type !== 'skill_individual'}
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>
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<option value="">No Skill Bound</option>
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{availableSkills.map(skill => (
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<option key={skill} value={skill}>{skill}</option>
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))}
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</select>
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</div>
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</div>
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@ -354,6 +382,45 @@ export function WorkerIndividualSettings() {
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</>
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)}
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<div>
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<label className="block text-sm font-medium text-slate-700 mb-1">Tools (Select Multiple)</label>
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<div className="flex flex-wrap gap-2 p-4 border border-slate-200 rounded-lg max-h-48 overflow-y-auto">
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{availableTools.map(tool => {
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let currentTools: string[] = [];
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try {
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currentTools = JSON.parse(editData.tools || '[]');
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} catch { currentTools = []; }
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const isSelected = currentTools.includes(tool);
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return (
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<button
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key={tool}
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type="button"
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onClick={() => {
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let updatedTools = [...currentTools];
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if (isSelected) {
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updatedTools = updatedTools.filter(t => t !== tool);
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} else {
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updatedTools.push(tool);
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}
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setEditData({...editData, tools: JSON.stringify(updatedTools)});
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}}
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className={`px-3 py-1.5 text-sm rounded-full transition-colors ${
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isSelected
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? 'bg-indigo-100 text-indigo-700 border border-indigo-200'
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: 'bg-slate-50 text-slate-600 border border-slate-200 hover:bg-slate-100'
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}`}
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>
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{tool}
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</button>
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);
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})}
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{availableTools.length === 0 && (
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<span className="text-sm text-slate-500">No tools available</span>
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)}
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</div>
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</div>
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{modalMessage && (
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<div className="p-3 bg-red-50 text-red-700 text-sm rounded-lg">
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{modalMessage}
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@ -33,7 +33,8 @@ class AgentFactory:
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output_type: ResponseModel,
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system_prompt: str,
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deps_type: DepsModel,
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agent_name: str) -> Agent:
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agent_name: str,
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tools: list = None) -> Agent:
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"""
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create_agent方法，将输入的provider对象实例化为一个pydantic-ai的agent对象
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@ -58,5 +59,6 @@ class AgentFactory:
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name=agent_name,
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system_prompt=system_prompt,
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output_type=output_type,
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deps_type=deps_type)
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deps_type=deps_type,
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tools=tools)
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return agent
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@ -30,10 +30,12 @@ class AgentRegister(BaseModel):
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provider_title: str
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model_id: str
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individual_name: str
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tools: Optional[List[str]] = None
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class AgentLocalRegister(BaseModel):
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path: str
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individual_name: str
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tools: Optional[List[str]] = None
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@agent_router.get("")
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async def get_system_nodes(_: TokenData = Depends(RoleChecker(allowed_roles=UserAuthority.USER))):
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@ -56,20 +58,21 @@ async def load_agent(agent_register: Union[AgentRegister, AgentLocalRegister],
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await postgres_database.upsert_system_node_config.remote(
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agent_register.individual_name,
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agent_register.provider_title,
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agent_register.model_id
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agent_register.model_id,
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agent_register.tools
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)
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# Load agent into state machine
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match agent_register.individual_name:
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case "supervisory_node":
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node = ray_actor_hook("supervisory_node").supervisory_node
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await node.create_agent.remote(global_state_machine,agent_register.provider_title,agent_register.model_id)
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await node.create_agent.remote(global_state_machine,agent_register.provider_title,agent_register.model_id, agent_register.tools)
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case "consciousness_node":
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node = ray_actor_hook("consciousness_node").consciousness_node
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await node.create_agent.remote(global_state_machine,agent_register.provider_title,agent_register.model_id)
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await node.create_agent.remote(global_state_machine,agent_register.provider_title,agent_register.model_id, agent_register.tools)
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case "control_node":
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node = ray_actor_hook("control_node").control_node
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await node.create_agent.remote(global_state_machine,agent_register.provider_title,agent_register.model_id)
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await node.create_agent.remote(global_state_machine,agent_register.provider_title,agent_register.model_id, agent_register.tools)
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case _:
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pass
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except Exception as e:
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@ -87,6 +90,7 @@ class WorkerIndividualCreate(BaseModel):
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output_template: dict
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bound_skill: Dict[str, List[str]]
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workspace: List[str]
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tools: Optional[List[str]] = None
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class WorkerIndividualUpdate(BaseModel):
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@ -99,6 +103,7 @@ class WorkerIndividualUpdate(BaseModel):
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output_template: Optional[dict] = None
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bound_skill: Optional[Dict[str, List[str]]] = None
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workspace: Optional[List[str]] = None
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tools: Optional[List[str]] = None
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@agent_router.post("/worker")
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@ -143,8 +148,29 @@ async def update_worker_individual(agent_id: str,
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update_data = worker_data.model_dump(exclude_unset=True)
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updated_worker = await postgres_database.update_worker_individual.remote( agent_id=agent_id, **update_data)
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global_state_machine = ray_actor_hook("global_state_machine").global_state_machine
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try:
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await global_state_machine.remove_individual.remote(agent_id)
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except Exception:
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pass
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return {"message": "success", "worker": updated_worker}
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@agent_router.post("/worker/{agent_id}/reload")
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async def reload_worker_individual(agent_id: str, token_data: TokenData = Depends(Accessor.get_current_user)):
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postgres_database = ray_actor_hook("postgres_database").postgres_database
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worker = await postgres_database.get_worker_individual.remote(agent_id=agent_id)
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if not worker:
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raise HTTPException(status_code=404, detail="Agent not found")
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if worker.owner_id != token_data.user_id:
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raise HTTPException(status_code=403, detail="Forbidden: You do not own this agent")
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global_state_machine = ray_actor_hook("global_state_machine").global_state_machine
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await global_state_machine.remove_individual.remote(agent_id)
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return {"message": "Worker will be reloaded on next use"}
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@agent_router.delete("/worker/{agent_id}")
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async def delete_worker_individual(agent_id: str,
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@ -22,7 +22,7 @@ class SystemNodeDatabase:
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self.async_session_maker = async_session_maker
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@database_exception
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async def upsert_system_node_config(self, node_name: str, provider_title: str, model_id: str) -> SystemNodeConfig:
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async def upsert_system_node_config(self, node_name: str, provider_title: str, model_id: str, tools: Optional[List[str]] = None) -> SystemNodeConfig:
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async with self.async_session_maker() as session:
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statement = select(SystemNodeConfig).where(SystemNodeConfig.node_name == node_name)
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results = await session.execute(statement)
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@ -30,8 +30,10 @@ class SystemNodeDatabase:
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if config:
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config.provider_title = provider_title
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config.model_id = model_id
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if tools is not None:
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config.tools = tools
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else:
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config = SystemNodeConfig(node_name=node_name, provider_title=provider_title, model_id=model_id)
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config = SystemNodeConfig(node_name=node_name, provider_title=provider_title, model_id=model_id, tools=tools)
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session.add(config)
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await session.commit()
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await session.refresh(config)
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@ -106,9 +106,9 @@ class PostgresDatabase:
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return await self._provider_database.update_provider(provider_id, **kwargs)
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# System Node Database Methods
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async def upsert_system_node_config(self, node_name: str, provider_title: str, model_id: str):
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async def upsert_system_node_config(self, node_name: str, provider_title: str, model_id: str, tools: list[str] = None):
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await self.ready_event.wait()
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return await self._system_node_database.upsert_system_node_config(node_name, provider_title, model_id)
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return await self._system_node_database.upsert_system_node_config(node_name, provider_title, model_id, tools)
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async def get_all_system_node_configs(self):
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await self.ready_event.wait()
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@ -34,4 +34,5 @@ class WorkerIndividual(SQLModel, table=True):
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output_template: Optional[dict] = Field(sa_column=Column(JSON),description="输出模板标识")
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bound_skill: Optional[str] = Field(sa_column=Column(JSON))
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workspace: Optional[List[str]] = Field(sa_column=Column(JSON))
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tools: Optional[List[str]] = Field(sa_column=Column(JSON), default=None)
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owner_id: str
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@ -14,8 +14,12 @@
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from sqlmodel import SQLModel, Field
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from typing import List, Optional
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from sqlalchemy import Column, JSON
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class SystemNodeConfig(SQLModel, table=True):
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__tablename__ = "system_node_config"
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node_name: str = Field(primary_key=True)
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provider_title: str
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model_id: str
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tools: Optional[List[str]] = Field(sa_column=Column(JSON), default=None)
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@ -22,8 +22,6 @@ from pretor.core.global_state_machine.global_state_machine import GlobalStateMac
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from pretor.core.global_state_machine.model_provider.base_provider import Provider
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from pretor.adapter.model_adapter.agent_factory import AgentFactory
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from pretor.utils.get_tool import get_tool
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@ray.remote
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class ConsciousnessNode:
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@ -33,7 +31,7 @@ class ConsciousnessNode:
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self.agent: None | Agent = None
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async def create_agent(self, global_state_machine: GlobalStateMachine, provider_title: str, model_id: str) -> None:
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async def create_agent(self, global_state_machine: GlobalStateMachine, provider_title: str, model_id: str, tools_list: list[str] = None) -> None:
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"""
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create_agent方法，将agent对象装配到ConsciousnessNode的属性内
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该方法通过provider_title从global_state_machine中获取provider对象，然后从provider对象中取出供应商形象，装配为pydantic_ai的
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@ -57,14 +55,18 @@ class ConsciousnessNode:
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"请确保所有的思考和生成过程符合逻辑，严密且高质量。"
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)
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output_type = Union[ForSupervisoryNode, ForWorkflow, ForWorkflowEngine]
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from pretor.utils.get_tool import load_tools_from_list
|
||||
provider: Provider = await global_state_machine.get_provider.remote( provider_title)
|
||||
agent_factory = AgentFactory()
|
||||
|
||||
callables = load_tools_from_list(tools_list)
|
||||
self.agent = agent_factory.create_agent(provider=provider,
|
||||
model_id=model_id,
|
||||
output_type=output_type,
|
||||
system_prompt=system_prompt,
|
||||
deps_type=ConsciousnessNodeDeps,
|
||||
agent_name="consciousness_node")
|
||||
agent_name="consciousness_node",
|
||||
tools=callables)
|
||||
|
||||
@self.agent.system_prompt
|
||||
async def dynamic_prompt(ctx: RunContext[ConsciousnessNodeDeps]):
|
||||
|
|
@ -134,7 +136,6 @@ class ConsciousnessNode:
|
|||
async def _run(self, payload: Union[ForSupervisoryInput, ForWorkflowInput, ForWorkflowEngineInput]) -> Union[ForSupervisoryNode, ForWorkflow, ForWorkflowEngine]:
|
||||
try:
|
||||
self.agent.retries = 3
|
||||
tool = await get_tool("control_node")
|
||||
if isinstance(payload, ForWorkflowEngineInput):
|
||||
deps = ConsciousnessNodeDeps(
|
||||
original_command=payload.original_command,
|
||||
|
|
@ -144,8 +145,7 @@ class ConsciousnessNode:
|
|||
self.logger.debug("ConsciousnessNode: 开始生成工作流 (原生重试开启)")
|
||||
result = await self.agent.run(
|
||||
"根据original_command制定严密的可执行workflow，可以学习并参考workflow_template的设计理念",
|
||||
deps=deps,
|
||||
tools=tool)
|
||||
deps=deps)
|
||||
return result.output
|
||||
|
||||
elif isinstance(payload, ForWorkflowInput):
|
||||
|
|
@ -155,8 +155,7 @@ class ConsciousnessNode:
|
|||
)
|
||||
self.logger.debug("ConsciousnessNode: 开始处理工作流节点任务 (原生重试开启)")
|
||||
result = await self.agent.run(f"处理此工作流步骤信息：\n{payload.workflow_step.model_dump_json()}",
|
||||
deps=deps,
|
||||
tools=tool)
|
||||
deps=deps)
|
||||
return result.output
|
||||
|
||||
elif isinstance(payload, ForSupervisoryInput):
|
||||
|
|
@ -166,8 +165,7 @@ class ConsciousnessNode:
|
|||
)
|
||||
self.logger.debug("ConsciousnessNode: 开始生成技术总结报告 (原生重试开启)")
|
||||
result = await self.agent.run(f"基于以下工作流的执行记录，生成技术报告：\n{payload.workflow.model_dump_json()}",
|
||||
deps=deps,
|
||||
tools=tool)
|
||||
deps=deps)
|
||||
return result.output
|
||||
except Exception as e:
|
||||
self.logger.exception(f"ConsciousnessNode 模型生成最终失败: {str(e)}")
|
||||
|
|
|
|||
|
|
@ -18,7 +18,6 @@ from pretor.core.global_state_machine.global_state_machine import GlobalStateMac
|
|||
from pretor.core.global_state_machine.model_provider.base_provider import Provider
|
||||
from pretor.adapter.model_adapter.agent_factory import AgentFactory
|
||||
from pretor.core.individual.control_node.template import ForWorkflow, ForWorkflowInput, ControlNodeDeps
|
||||
from pretor.utils.get_tool import get_tool
|
||||
|
||||
|
||||
|
||||
|
|
@ -30,7 +29,7 @@ class ControlNode:
|
|||
self.agent: Agent | None = None
|
||||
|
||||
|
||||
async def create_agent(self, global_state_machine: GlobalStateMachine, provider_title: str, model_id: str) -> None:
|
||||
async def create_agent(self, global_state_machine: GlobalStateMachine, provider_title: str, model_id: str, tools_list: list[str] = None) -> None:
|
||||
"""
|
||||
create_agent方法，将agent对象装配到Control的属性内
|
||||
该方法通过provider_title从global_state_machine中获取provider对象，然后从provider对象中取出供应商形象，装配为pydantic_ai的
|
||||
|
|
@ -54,14 +53,18 @@ class ControlNode:
|
|||
"请注意：你的输出应当具体、实用，直接提供任务所要求的结果，不要做过多无关的寒暄。"
|
||||
)
|
||||
output_type = ForWorkflow
|
||||
from pretor.utils.get_tool import load_tools_from_list
|
||||
provider: Provider = await global_state_machine.get_provider.remote( provider_title)
|
||||
agent_factory = AgentFactory()
|
||||
|
||||
callables = load_tools_from_list(tools_list)
|
||||
self.agent = agent_factory.create_agent(provider=provider,
|
||||
model_id=model_id,
|
||||
output_type=output_type,
|
||||
system_prompt=system_prompt,
|
||||
deps_type=ControlNodeDeps,
|
||||
agent_name="control_node")
|
||||
agent_name="control_node",
|
||||
tools=callables)
|
||||
@self.agent.system_prompt
|
||||
async def dynamic_prompt(ctx: RunContext[ControlNodeDeps]):
|
||||
prompt = system_prompt + "\n\n"
|
||||
|
|
@ -89,12 +92,9 @@ class ControlNode:
|
|||
)
|
||||
self.logger.debug(f"ControlNode: 开始执行工作流节点 [{payload.workflow_step.name}] (原生重试开启)")
|
||||
|
||||
tool = await get_tool("control_node")
|
||||
|
||||
result = await self.agent.run(
|
||||
f"请根据提供的 workflow_step 上下文，执行此步骤并输出结果。\n详细指令或附加数据：{payload.workflow_step.model_dump_json()}",
|
||||
deps=deps,
|
||||
tools=tool
|
||||
deps=deps
|
||||
)
|
||||
return result.output
|
||||
except Exception as e:
|
||||
|
|
|
|||
|
|
@ -22,7 +22,6 @@ from pretor.core.global_state_machine.model_provider import Provider
|
|||
from pretor.core.individual.supervisory_node.template import ForConsciousnessNode, ForUser, SupervisoryNodeDeps, TerminationMessage
|
||||
from pydantic_ai import RunContext, Agent
|
||||
from pretor.utils.ray_hook import ray_actor_hook
|
||||
from pretor.utils.get_tool import get_tool
|
||||
|
||||
|
||||
@ray.remote
|
||||
|
|
@ -33,7 +32,7 @@ class SupervisoryNode:
|
|||
self.agent: None | Agent = None
|
||||
|
||||
|
||||
async def create_agent(self, global_state_machine: GlobalStateMachine, provider_title: str, model_id: str) -> None:
|
||||
async def create_agent(self, global_state_machine: GlobalStateMachine, provider_title: str, model_id: str, tools_list: list[str] = None) -> None:
|
||||
"""
|
||||
create_agent方法，将agent对象装配到SupervisoryNode的属性内
|
||||
该方法通过provider_title从global_state_machine中获取provider对象，然后从provider对象中取出供应商形象，装配为pydantic_ai的Agent实例，
|
||||
|
|
@ -57,14 +56,18 @@ class SupervisoryNode:
|
|||
"请保持冷静、专业，并严格遵循上述路由规则。"
|
||||
)
|
||||
output_type = Union[ForConsciousnessNode, ForUser]
|
||||
from pretor.utils.get_tool import load_tools_from_list
|
||||
provider: Provider = await global_state_machine.get_provider.remote( provider_title)
|
||||
agent_factory = AgentFactory()
|
||||
|
||||
callables = load_tools_from_list(tools_list)
|
||||
self.agent = agent_factory.create_agent(provider=provider,
|
||||
model_id=model_id,
|
||||
output_type=output_type,
|
||||
system_prompt=system_prompt,
|
||||
deps_type=SupervisoryNodeDeps,
|
||||
agent_name="supervisory_node")
|
||||
agent_name="supervisory_node",
|
||||
tools=callables)
|
||||
|
||||
@self.agent.system_prompt
|
||||
async def dynamic_prompt(ctx: RunContext[SupervisoryNodeDeps]):
|
||||
|
|
@ -172,10 +175,8 @@ class SupervisoryNode:
|
|||
if isinstance(payload, TerminationMessage):
|
||||
prompt_message = f"【工作流执行结束报告】\n请将以下技术报告转化为对用户的友好回复：\n{message}"
|
||||
self.agent.retries = 3
|
||||
tool = await get_tool("supervisory_node")
|
||||
result = await self.agent.run(prompt_message,
|
||||
deps=deps,
|
||||
tools=tool)
|
||||
deps=deps)
|
||||
return result.output
|
||||
except Exception as e:
|
||||
self.logger.exception(f"SupervisoryNode 模型生成或解析最终失败: {str(e)}")
|
||||
|
|
|
|||
|
|
@ -12,7 +12,9 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import importlib
|
||||
import importlib.util
|
||||
import os
|
||||
import sys
|
||||
from typing import Callable, Dict, List
|
||||
import pathlib
|
||||
from pretor.utils.ray_hook import ray_actor_hook
|
||||
|
|
@ -20,50 +22,59 @@ from pretor.utils.ray_hook import ray_actor_hook
|
|||
from pretor.utils.logger import get_logger
|
||||
logger = get_logger('get_tool')
|
||||
_tool_cache: Dict[str, Callable] = {}
|
||||
_agent_tool_result_cache: Dict[str, List[Callable]] = {}
|
||||
|
||||
|
||||
def _get_tool_func(tool_name: str) -> Callable | None:
|
||||
func = _tool_cache.get(tool_name, None)
|
||||
if func:
|
||||
return func
|
||||
tool_plugin_dir = pathlib.Path(__file__).parent.parent.parent / "plugin" / "tool_plugin" / tool_name
|
||||
if not tool_plugin_dir.exists() or not tool_plugin_dir.is_dir():
|
||||
|
||||
app_root = "/app"
|
||||
tool_plugin_dir = os.path.join(app_root, "pretor", "plugin", "tool_plugin", tool_name)
|
||||
|
||||
if not os.path.exists(tool_plugin_dir) or not os.path.isdir(tool_plugin_dir):
|
||||
logger.error(f"Tool directory not found: {tool_plugin_dir}")
|
||||
return None
|
||||
module_name = f"pretor.plugin.tool_plugin.{tool_name}"
|
||||
|
||||
init_file = os.path.join(tool_plugin_dir, "__init__.py")
|
||||
if not os.path.exists(init_file):
|
||||
logger.error(f"Tool init file not found: {init_file}")
|
||||
return None
|
||||
|
||||
try:
|
||||
module = importlib.import_module(module_name)
|
||||
func = getattr(module, tool_name)
|
||||
module_name = f"pretor.plugin.tool_plugin.{tool_name}"
|
||||
spec = importlib.util.spec_from_file_location(module_name, init_file)
|
||||
if spec is None or spec.loader is None:
|
||||
logger.error(f"Failed to create spec for {module_name}")
|
||||
return None
|
||||
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
sys.modules[module_name] = module
|
||||
spec.loader.exec_module(module)
|
||||
|
||||
func = getattr(module, tool_name, None)
|
||||
|
||||
if not callable(func):
|
||||
logger.error(f"Tool function '{tool_name}' not found or not callable in {module_name}")
|
||||
return None
|
||||
_tool_cache[tool_name] = func
|
||||
return func
|
||||
except ModuleNotFoundError:
|
||||
logger.error(f"Module {module_name} not found")
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to load module {module_name}: {e}")
|
||||
return None
|
||||
|
||||
def del_tool_cache(tool_name: str) -> None:
|
||||
if tool_name in _tool_cache:
|
||||
del _tool_cache[tool_name]
|
||||
refresh_agent_tools()
|
||||
|
||||
def load_tools_from_list(tool_names: List[str] | None) -> List[Callable]:
|
||||
if not tool_names:
|
||||
return []
|
||||
|
||||
async def get_tool(agent_name: str) -> List[Callable]:
|
||||
cached = _agent_tool_result_cache.get(agent_name)
|
||||
if cached is not None:
|
||||
return cached
|
||||
global_state_machine = ray_actor_hook("global_state_machine").global_state_machine
|
||||
_tool_list = await global_state_machine.get_tool_list.remote(agent_name)
|
||||
tool_list = []
|
||||
for tool_name in _tool_list.keys():
|
||||
for tool_name in tool_names:
|
||||
tool_func = _get_tool_func(tool_name)
|
||||
if tool_func:
|
||||
tool_list.append(tool_func)
|
||||
else:
|
||||
continue
|
||||
_agent_tool_result_cache[agent_name] = tool_list
|
||||
return tool_list
|
||||
|
||||
def refresh_agent_tools() -> None:
|
||||
_agent_tool_result_cache.clear()
|
||||
return tool_list
|
||||
|
|
@ -42,19 +42,25 @@ class BaseIndividual:
|
|||
self.agent: Agent | None = None
|
||||
|
||||
async def _init_agent(self, agent_name: str, system_prompt: str):
|
||||
from pretor.utils.get_tool import load_tools_from_list
|
||||
global_state_machine = ray_actor_hook("global_state_machine").global_state_machine
|
||||
provider_title = self.agent_config.get("provider_title", "openai") # default fallback
|
||||
model_id = self.agent_config.get("model_id", "gpt-4o") # default fallback
|
||||
tools_list = self.agent_config.get("tools", None)
|
||||
|
||||
provider: Provider = await global_state_machine.get_provider.remote( provider_title)
|
||||
agent_factory = AgentFactory()
|
||||
|
||||
callables = load_tools_from_list(tools_list)
|
||||
|
||||
self.agent = agent_factory.create_agent(
|
||||
provider=provider,
|
||||
model_id=model_id,
|
||||
output_type=WorkerIndividualResponse,
|
||||
system_prompt=system_prompt,
|
||||
deps_type=WorkerIndividualDeps,
|
||||
agent_name=agent_name
|
||||
agent_name=agent_name,
|
||||
tools=callables
|
||||
)
|
||||
|
||||
@self.agent.system_prompt
|
||||
|
|
|
|||
|
|
@ -30,7 +30,8 @@ def test_create_agent_success_real():
|
|||
name="myagent",
|
||||
system_prompt="You are an AI",
|
||||
output_type=str,
|
||||
deps_type=dict
|
||||
deps_type=dict,
|
||||
tools=None
|
||||
)
|
||||
assert agent == mock_agent_cls.return_value
|
||||
|
||||
|
|
|
|||
|
|
@ -57,8 +57,10 @@ async def test_change_password_success(mock_session_maker, mock_dependencies):
|
|||
mock_statement = MagicMock()
|
||||
mock_select.return_value.where.return_value = mock_statement
|
||||
|
||||
from pretor.utils.access import Accessor
|
||||
|
||||
mock_user = MagicMock()
|
||||
mock_user.hashed_password = "old_password"
|
||||
mock_user.hashed_password = Accessor.hash_password("old_password")
|
||||
|
||||
mock_exec_result = MagicMock()
|
||||
mock_exec_result.scalar_one_or_none.return_value = mock_user
|
||||
|
|
@ -95,8 +97,9 @@ async def test_change_password_wrong_password(mock_session_maker, mock_dependenc
|
|||
maker, session = mock_session_maker
|
||||
db = AuthDatabase(maker)
|
||||
|
||||
from pretor.utils.access import Accessor
|
||||
mock_user = MagicMock()
|
||||
mock_user.hashed_password = "actual_password"
|
||||
mock_user.hashed_password = Accessor.hash_password("actual_password")
|
||||
mock_exec_result = MagicMock()
|
||||
mock_exec_result.scalar_one_or_none.return_value = mock_user
|
||||
session.execute = AsyncMock(return_value=mock_exec_result)
|
||||
|
|
|
|||
Loading…
Reference in New Issue