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fix: auto-fix code issues (cron)

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- 修复重复导入/字段
- 修复异常处理
- 修复PEP8格式问题
- 添加类型注解
- 修复重复函数定义 (health_check, create_webhook_endpoint, etc)
- 修复未定义名称 (SearchOperator, TenantTier, Query, Body, logger)
- 修复 workflow_manager.py 的类定义重复问题
- 添加缺失的导入
This commit is contained in:
OpenClaw Bot
2026-02-27 09:18:58 +08:00
parent 1d55ae8f1e
commit be22b763fa
39 changed files with 12535 additions and 10327 deletions
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127
backend/test_phase8_task4.py
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@@ -4,6 +4,9 @@ InsightFlow Phase 8 Task 4 测试脚本
测试 AI 能力增强功能
"""
from ai_manager import (
get_ai_manager, ModelType, PredictionType
)
import asyncio
import sys
import os
@@ -11,19 +14,13 @@ import os
# Add backend directory to path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from ai_manager import (
get_ai_manager, CustomModel, TrainingSample, MultimodalAnalysis,
KnowledgeGraphRAG, SmartSummary, PredictionModel, PredictionResult,
ModelType, ModelStatus, MultimodalProvider, PredictionType
)
def test_custom_model():
"""测试自定义模型功能"""
print("\n=== 测试自定义模型 ===")
manager = get_ai_manager()
# 1. 创建自定义模型
print("1. 创建自定义模型...")
model = manager.create_custom_model(
@@ -43,7 +40,7 @@ def test_custom_model():
created_by="user_001"
)
print(f" 创建成功: {model.id}, 状态: {model.status.value}")
# 2. 添加训练样本
print("2. 添加训练样本...")
samples = [
@@ -72,7 +69,7 @@ def test_custom_model():
]
}
]
for sample_data in samples:
sample = manager.add_training_sample(
model_id=model.id,
@@ -81,28 +78,28 @@ def test_custom_model():
metadata={"source": "manual"}
)
print(f" 添加样本: {sample.id}")
# 3. 获取训练样本
print("3. 获取训练样本...")
all_samples = manager.get_training_samples(model.id)
print(f" 共有 {len(all_samples)} 个训练样本")
# 4. 列出自定义模型
print("4. 列出自定义模型...")
models = manager.list_custom_models(tenant_id="tenant_001")
print(f" 找到 {len(models)} 个模型")
for m in models:
print(f" - {m.name} ({m.model_type.value}): {m.status.value}")
return model.id
async def test_train_and_predict(model_id: str):
"""测试训练和预测"""
print("\n=== 测试模型训练和预测 ===")
manager = get_ai_manager()
# 1. 训练模型
print("1. 训练模型...")
try:
@@ -112,7 +109,7 @@ async def test_train_and_predict(model_id: str):
except Exception as e:
print(f" 训练失败: {e}")
return
# 2. 使用模型预测
print("2. 使用模型预测...")
test_text = "赵六患有糖尿病，正在使用胰岛素治疗。"
@@ -127,9 +124,9 @@ async def test_train_and_predict(model_id: str):
def test_prediction_models():
"""测试预测模型"""
print("\n=== 测试预测模型 ===")
manager = get_ai_manager()
# 1. 创建趋势预测模型
print("1. 创建趋势预测模型...")
trend_model = manager.create_prediction_model(
@@ -145,7 +142,7 @@ def test_prediction_models():
}
)
print(f" 创建成功: {trend_model.id}")
# 2. 创建异常检测模型
print("2. 创建异常检测模型...")
anomaly_model = manager.create_prediction_model(
@@ -161,23 +158,23 @@ def test_prediction_models():
}
)
print(f" 创建成功: {anomaly_model.id}")
# 3. 列出预测模型
print("3. 列出预测模型...")
models = manager.list_prediction_models(tenant_id="tenant_001")
print(f" 找到 {len(models)} 个预测模型")
for m in models:
print(f" - {m.name} ({m.prediction_type.value})")
return trend_model.id, anomaly_model.id
async def test_predictions(trend_model_id: str, anomaly_model_id: str):
"""测试预测功能"""
print("\n=== 测试预测功能 ===")
manager = get_ai_manager()
# 1. 训练趋势预测模型
print("1. 训练趋势预测模型...")
historical_data = [
@@ -191,7 +188,7 @@ async def test_predictions(trend_model_id: str, anomaly_model_id: str):
]
trained = await manager.train_prediction_model(trend_model_id, historical_data)
print(f" 训练完成，准确率: {trained.accuracy}")
# 2. 趋势预测
print("2. 趋势预测...")
trend_result = await manager.predict(
@@ -199,7 +196,7 @@ async def test_predictions(trend_model_id: str, anomaly_model_id: str):
{"historical_values": [10, 12, 15, 14, 18, 20, 22]}
)
print(f" 预测结果: {trend_result.prediction_data}")
# 3. 异常检测
print("3. 异常检测...")
anomaly_result = await manager.predict(
@@ -215,9 +212,9 @@ async def test_predictions(trend_model_id: str, anomaly_model_id: str):
def test_kg_rag():
"""测试知识图谱 RAG"""
print("\n=== 测试知识图谱 RAG ===")
manager = get_ai_manager()
# 创建 RAG 配置
print("1. 创建知识图谱 RAG 配置...")
rag = manager.create_kg_rag(
@@ -241,21 +238,21 @@ def test_kg_rag():
}
)
print(f" 创建成功: {rag.id}")
# 列出 RAG 配置
print("2. 列出 RAG 配置...")
rags = manager.list_kg_rags(tenant_id="tenant_001")
print(f" 找到 {len(rags)} 个配置")
return rag.id
async def test_kg_rag_query(rag_id: str):
"""测试 RAG 查询"""
print("\n=== 测试知识图谱 RAG 查询 ===")
manager = get_ai_manager()
# 模拟项目实体和关系
project_entities = [
{"id": "e1", "name": "张三", "type": "PERSON", "definition": "项目经理"},
@@ -264,18 +261,36 @@ async def test_kg_rag_query(rag_id: str):
{"id": "e4", "name": "Kubernetes", "type": "TECH", "definition": "容器编排平台"},
{"id": "e5", "name": "TechCorp", "type": "ORG", "definition": "科技公司"}
]
project_relations = [
{"source_entity_id": "e1", "target_entity_id": "e3", "source_name": "张三", "target_name": "Project Alpha", "relation_type": "works_with", "evidence": "张三负责 Project Alpha 的管理工作"},
{"source_entity_id": "e2", "target_entity_id": "e3", "source_name": "李四", "target_name": "Project Alpha", "relation_type": "works_with", "evidence": "李四负责 Project Alpha 的技术架构"},
{"source_entity_id": "e3", "target_entity_id": "e4", "source_name": "Project Alpha", "target_name": "Kubernetes", "relation_type": "depends_on", "evidence": "项目使用 Kubernetes 进行部署"},
{"source_entity_id": "e1", "target_entity_id": "e5", "source_name": "张三", "target_name": "TechCorp", "relation_type": "belongs_to", "evidence": "张三是 TechCorp 的员工"}
]
project_relations = [{"source_entity_id": "e1",
"target_entity_id": "e3",
"source_name": "张三",
"target_name": "Project Alpha",
"relation_type": "works_with",
"evidence": "张三负责 Project Alpha 的管理工作"},
{"source_entity_id": "e2",
"target_entity_id": "e3",
"source_name": "李四",
"target_name": "Project Alpha",
"relation_type": "works_with",
"evidence": "李四负责 Project Alpha 的技术架构"},
{"source_entity_id": "e3",
"target_entity_id": "e4",
"source_name": "Project Alpha",
"target_name": "Kubernetes",
"relation_type": "depends_on",
"evidence": "项目使用 Kubernetes 进行部署"},
{"source_entity_id": "e1",
"target_entity_id": "e5",
"source_name": "张三",
"target_name": "TechCorp",
"relation_type": "belongs_to",
"evidence": "张三是 TechCorp 的员工"}]
# 执行查询
print("1. 执行 RAG 查询...")
query_text = "Project Alpha 项目有哪些人参与？使用了什么技术？"
try:
result = await manager.query_kg_rag(
rag_id=rag_id,
@@ -283,7 +298,7 @@ async def test_kg_rag_query(rag_id: str):
project_entities=project_entities,
project_relations=project_relations
)
print(f" 查询: {result.query}")
print(f" 回答: {result.answer[:200]}...")
print(f" 置信度: {result.confidence}")
@@ -296,9 +311,9 @@ async def test_kg_rag_query(rag_id: str):
async def test_smart_summary():
"""测试智能摘要"""
print("\n=== 测试智能摘要 ===")
manager = get_ai_manager()
# 模拟转录文本
transcript_text = """
今天的会议主要讨论了 Project Alpha 的进展情况。张三作为项目经理，
@@ -307,7 +322,7 @@ async def test_smart_summary():
会议还讨论了下一步的工作计划，包括测试、文档编写和上线准备。
大家一致认为项目进展顺利，预计可以按时交付。
"""
content_data = {
"text": transcript_text,
"entities": [
@@ -317,10 +332,10 @@ async def test_smart_summary():
{"name": "Kubernetes", "type": "TECH"}
]
}
# 生成不同类型的摘要
summary_types = ["extractive", "abstractive", "key_points"]
for summary_type in summary_types:
print(f"1. 生成 {summary_type} 类型摘要...")
try:
@@ -332,7 +347,7 @@ async def test_smart_summary():
summary_type=summary_type,
content_data=content_data
)
print(f" 摘要类型: {summary.summary_type}")
print(f" 内容: {summary.content[:150]}...")
print(f" 关键要点: {summary.key_points[:3]}")
@@ -346,33 +361,33 @@ async def main():
print("=" * 60)
print("InsightFlow Phase 8 Task 4 - AI 能力增强测试")
print("=" * 60)
try:
# 测试自定义模型
model_id = test_custom_model()
# 测试训练和预测
await test_train_and_predict(model_id)
# 测试预测模型
trend_model_id, anomaly_model_id = test_prediction_models()
# 测试预测功能
await test_predictions(trend_model_id, anomaly_model_id)
# 测试知识图谱 RAG
rag_id = test_kg_rag()
# 测试 RAG 查询
await test_kg_rag_query(rag_id)
# 测试智能摘要
await test_smart_summary()
print("\n" + "=" * 60)
print("所有测试完成！")
print("=" * 60)
except Exception as e:
print(f"\n测试失败: {e}")
import traceback