claw/insightflow
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix: auto-fix code issues (cron)

Browse Source 
- 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
This commit is contained in:
AutoFix Bot
2026-03-02 12:14:39 +08:00
parent e23f1fec08
commit 98527c4de4
39 changed files with 8109 additions and 8147 deletions
Download Patch File Download Diff File Expand all files Collapse all files

154
backend/test_phase8_task4.py
View File

@@ -18,27 +18,27 @@ def test_custom_model() -> None:
"""测试自定义模型功能"""
print("\n=== 测试自定义模型 ===")
manager = get_ai_manager()
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(
tenant_id = "tenant_001",
name = "领域实体识别模型",
description = "用于识别医疗领域实体的自定义模型",
model_type = ModelType.CUSTOM_NER,
training_data = {
model = manager.create_custom_model(
tenant_id="tenant_001",
name="领域实体识别模型",
description="用于识别医疗领域实体的自定义模型",
model_type=ModelType.CUSTOM_NER,
training_data={
"entity_types": ["DISEASE", "SYMPTOM", "DRUG", "TREATMENT"],
"domain": "medical",
},
hyperparameters = {"epochs": 15, "learning_rate": 0.001, "batch_size": 32},
created_by = "user_001",
hyperparameters={"epochs": 15, "learning_rate": 0.001, "batch_size": 32},
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 = [
samples = [
{
"text": "患者张三患有高血压，正在服用降压药治疗。",
"entities": [
@@ -66,22 +66,22 @@ def test_custom_model() -> None:
]
for sample_data in samples:
sample = manager.add_training_sample(
model_id = model.id,
text = sample_data["text"],
entities = sample_data["entities"],
metadata = {"source": "manual"},
sample = manager.add_training_sample(
model_id=model.id,
text=sample_data["text"],
entities=sample_data["entities"],
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)
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")
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}")
@@ -93,12 +93,12 @@ async def test_train_and_predict(model_id: str) -> None:
"""测试训练和预测"""
print("\n=== 测试模型训练和预测 ===")
manager = get_ai_manager()
manager = get_ai_manager()
# 1. 训练模型
print("1. 训练模型...")
try:
trained_model = await manager.train_custom_model(model_id)
trained_model = await manager.train_custom_model(model_id)
print(f" 训练完成: {trained_model.status.value}")
print(f" 指标: {trained_model.metrics}")
except Exception as e:
@@ -107,9 +107,9 @@ async def test_train_and_predict(model_id: str) -> None:
# 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。"
test_text = "赵六患有糖尿病，正在使用胰岛素治疗。"
try:
entities = await manager.predict_with_custom_model(model_id, test_text)
entities = await manager.predict_with_custom_model(model_id, test_text)
print(f" 输入: {test_text}")
print(f" 预测实体: {entities}")
except Exception as e:
@@ -120,37 +120,37 @@ def test_prediction_models() -> None:
"""测试预测模型"""
print("\n=== 测试预测模型 ===")
manager = get_ai_manager()
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(
tenant_id = "tenant_001",
project_id = "project_001",
name = "实体数量趋势预测",
prediction_type = PredictionType.TREND,
target_entity_type = "PERSON",
features = ["entity_count", "time_period", "document_count"],
model_config = {"algorithm": "linear_regression", "window_size": 7},
trend_model = manager.create_prediction_model(
tenant_id="tenant_001",
project_id="project_001",
name="实体数量趋势预测",
prediction_type=PredictionType.TREND,
target_entity_type="PERSON",
features=["entity_count", "time_period", "document_count"],
model_config={"algorithm": "linear_regression", "window_size": 7},
)
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(
tenant_id = "tenant_001",
project_id = "project_001",
name = "实体增长异常检测",
prediction_type = PredictionType.ANOMALY,
target_entity_type = None,
features = ["daily_growth", "weekly_growth"],
model_config = {"threshold": 2.5, "sensitivity": "medium"},
anomaly_model = manager.create_prediction_model(
tenant_id="tenant_001",
project_id="project_001",
name="实体增长异常检测",
prediction_type=PredictionType.ANOMALY,
target_entity_type=None,
features=["daily_growth", "weekly_growth"],
model_config={"threshold": 2.5, "sensitivity": "medium"},
)
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")
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})")
@@ -162,11 +162,11 @@ async def test_predictions(trend_model_id: str, anomaly_model_id: str) -> None:
"""测试预测功能"""
print("\n=== 测试预测功能 ===")
manager = get_ai_manager()
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 = [
historical_data = [
{"date": "2024-01-01", "value": 10},
{"date": "2024-01-02", "value": 12},
{"date": "2024-01-03", "value": 15},
@@ -175,19 +175,19 @@ async def test_predictions(trend_model_id: str, anomaly_model_id: str) -> None:
{"date": "2024-01-06", "value": 20},
{"date": "2024-01-07", "value": 22},
]
trained = await manager.train_prediction_model(trend_model_id, historical_data)
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(
trend_result = await manager.predict(
trend_model_id, {"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(
anomaly_result = await manager.predict(
anomaly_model_id, {"value": 50, "historical_values": [10, 12, 11, 13, 12, 14, 13]}
)
print(f" 检测结果: {anomaly_result.prediction_data}")
@@ -197,27 +197,27 @@ def test_kg_rag() -> None:
"""测试知识图谱 RAG"""
print("\n=== 测试知识图谱 RAG ===")
manager = get_ai_manager()
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(
tenant_id = "tenant_001",
project_id = "project_001",
name = "项目知识问答",
description = "基于项目知识图谱的智能问答",
kg_config = {
rag = manager.create_kg_rag(
tenant_id="tenant_001",
project_id="project_001",
name="项目知识问答",
description="基于项目知识图谱的智能问答",
kg_config={
"entity_types": ["PERSON", "ORG", "PROJECT", "TECH"],
"relation_types": ["works_with", "belongs_to", "depends_on"],
},
retrieval_config = {"top_k": 5, "similarity_threshold": 0.7, "expand_relations": True},
generation_config = {"temperature": 0.3, "max_tokens": 1000, "include_sources": True},
retrieval_config={"top_k": 5, "similarity_threshold": 0.7, "expand_relations": True},
generation_config={"temperature": 0.3, "max_tokens": 1000, "include_sources": True},
)
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")
rags = manager.list_kg_rags(tenant_id="tenant_001")
print(f" 找到 {len(rags)} 个配置")
return rag.id
@@ -227,10 +227,10 @@ async def test_kg_rag_query(rag_id: str) -> None:
"""测试 RAG 查询"""
print("\n=== 测试知识图谱 RAG 查询 ===")
manager = get_ai_manager()
manager = get_ai_manager()
# 模拟项目实体和关系
project_entities = [
project_entities = [
{"id": "e1", "name": "张三", "type": "PERSON", "definition": "项目经理"},
{"id": "e2", "name": "李四", "type": "PERSON", "definition": "技术负责人"},
{"id": "e3", "name": "Project Alpha", "type": "PROJECT", "definition": "核心产品项目"},
@@ -238,7 +238,7 @@ async def test_kg_rag_query(rag_id: str) -> None:
{"id": "e5", "name": "TechCorp", "type": "ORG", "definition": "科技公司"},
]
project_relations = [
project_relations = [
{
"source_entity_id": "e1",
"target_entity_id": "e3",
@@ -275,14 +275,14 @@ async def test_kg_rag_query(rag_id: str) -> None:
# 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？"
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,
query = query_text,
project_entities = project_entities,
project_relations = project_relations,
result = await manager.query_kg_rag(
rag_id=rag_id,
query=query_text,
project_entities=project_entities,
project_relations=project_relations,
)
print(f" 查询: {result.query}")
@@ -298,10 +298,10 @@ async def test_smart_summary() -> None:
"""测试智能摘要"""
print("\n=== 测试智能摘要 ===")
manager = get_ai_manager()
manager = get_ai_manager()
# 模拟转录文本
transcript_text = """
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，
汇报了当前的项目进度，表示已经完成了 80% 的开发工作。李四提出了
一些关于 Kubernetes 部署的问题，建议我们采用新的部署策略。
@@ -309,7 +309,7 @@ async def test_smart_summary() -> None:
大家一致认为项目进展顺利，预计可以按时交付。
"""
content_data = {
content_data = {
"text": transcript_text,
"entities": [
{"name": "张三", "type": "PERSON"},
@@ -320,18 +320,18 @@ async def test_smart_summary() -> None:
}
# 生成不同类型的摘要
summary_types = ["extractive", "abstractive", "key_points"]
summary_types = ["extractive", "abstractive", "key_points"]
for summary_type in summary_types:
print(f"1. 生成 {summary_type} 类型摘要...")
try:
summary = await manager.generate_smart_summary(
tenant_id = "tenant_001",
project_id = "project_001",
source_type = "transcript",
source_id = "transcript_001",
summary_type = summary_type,
content_data = content_data,
summary = await manager.generate_smart_summary(
tenant_id="tenant_001",
project_id="project_001",
source_type="transcript",
source_id="transcript_001",
summary_type=summary_type,
content_data=content_data,
)
print(f" 摘要类型: {summary.summary_type}")
@@ -350,19 +350,19 @@ async def main() -> None:
try:
# 测试自定义模型
model_id = test_custom_model()
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()
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()
rag_id = test_kg_rag()
# 测试 RAG 查询
await test_kg_rag_query(rag_id)