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wangning
ciecc-agent
Commits
6c04f4ff
Commit
6c04f4ff
authored
Jan 21, 2026
by
wangning
Browse files
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fix 智能体知识库 问题
parent
2c8526bc
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10 changed files
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183 additions
and
26 deletions
+183
-26
workspace.xml
.idea/workspace.xml
+27
-24
ChatServiceImpl.java
...pss/server/agent/service/chatService/ChatServiceImpl.java
+4
-1
ChatSessionManager.java
...rver/agent/service/sessionService/ChatSessionManager.java
+3
-1
algo_knowledge1.jsonl
.../resources/testcases/algo_knowledge/algo_knowledge1.jsonl
+0
-0
algo_knowledge2.jsonl
.../resources/testcases/algo_knowledge/algo_knowledge2.jsonl
+0
-0
algo_knowledge3.jsonl
.../resources/testcases/algo_knowledge/algo_knowledge3.jsonl
+0
-0
algo_knowledge4.jsonl
.../resources/testcases/algo_knowledge/algo_knowledge4.jsonl
+47
-0
suggest_algorithm.jsonl
...es/testcases/algo_suggest_request/suggest_algorithm.jsonl
+0
-0
suggest_algorithm1.jsonl
...s/testcases/algo_suggest_request/suggest_algorithm1.jsonl
+102
-0
suggest_algorithm_bak.jsonl
...estcases/algo_suggest_request/suggest_algorithm_bak.jsonl
+0
-0
No files found.
.idea/workspace.xml
View file @
6c04f4ff
...
@@ -4,29 +4,17 @@
...
@@ -4,29 +4,17 @@
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...
...
src/main/java/pro/spss/server/agent/service/chatService/ChatServiceImpl.java
View file @
6c04f4ff
...
@@ -6,6 +6,7 @@ import org.springframework.beans.factory.annotation.Autowired;
...
@@ -6,6 +6,7 @@ import org.springframework.beans.factory.annotation.Autowired;
import
org.springframework.beans.factory.annotation.Value
;
import
org.springframework.beans.factory.annotation.Value
;
import
org.springframework.scheduling.annotation.Async
;
import
org.springframework.scheduling.annotation.Async
;
import
org.springframework.stereotype.Service
;
import
org.springframework.stereotype.Service
;
import
pro.spss.server.agent.domain.constant.ChatConstants
;
import
pro.spss.server.agent.domain.enums.ChatStatusEnum
;
import
pro.spss.server.agent.domain.enums.ChatStatusEnum
;
import
pro.spss.server.agent.domain.enums.ConversationStateEnum
;
import
pro.spss.server.agent.domain.enums.ConversationStateEnum
;
import
pro.spss.server.agent.domain.enums.CreateWayEnum
;
import
pro.spss.server.agent.domain.enums.CreateWayEnum
;
...
@@ -81,6 +82,8 @@ public class ChatServiceImpl implements BaseChatService {
...
@@ -81,6 +82,8 @@ public class ChatServiceImpl implements BaseChatService {
}
}
chatSessionManager
.
initSession
(
sessionId
,
CreateWayEnum
.
ALGO_FIRST
);
chatSessionManager
.
initSession
(
sessionId
,
CreateWayEnum
.
ALGO_FIRST
);
JSONArray
messages
=
chatSessionManager
.
getMessages
(
sessionId
);
messages
.
add
(
ChatConstants
.
createMessage
(
ChatConstants
.
ROLE_SYSTEM
,
initPrompt
));
sseService
.
sendMessage
(
ResponseMessageType
.
INIT
.
getType
(),
sessionId
,
userChatMessage
.
getToken
(),
initPrompt
);
sseService
.
sendMessage
(
ResponseMessageType
.
INIT
.
getType
(),
sessionId
,
userChatMessage
.
getToken
(),
initPrompt
);
return
true
;
return
true
;
...
@@ -127,7 +130,7 @@ public class ChatServiceImpl implements BaseChatService {
...
@@ -127,7 +130,7 @@ public class ChatServiceImpl implements BaseChatService {
*/
*/
private
ResponseMessage
executeTool
(
UserChatMessage
userChatMessage
,
RequestParams
requestParams
,
String
toolName
,
long
startTimestamp
)
{
private
ResponseMessage
executeTool
(
UserChatMessage
userChatMessage
,
RequestParams
requestParams
,
String
toolName
,
long
startTimestamp
)
{
String
sessionId
=
userChatMessage
.
getUserId
();
String
sessionId
=
userChatMessage
.
getUserId
();
JSONArray
historyCopy
=
new
JSONArray
(
chatSessionManager
.
getMessages
(
sessionId
)
);
JSONArray
historyCopy
=
chatSessionManager
.
getMessages
(
sessionId
);
String
prompt
=
userChatMessage
.
getPrompt
();
String
prompt
=
userChatMessage
.
getPrompt
();
ResponseMessage
responseMessage
=
conversationHandler
.
toolExecutor
(
requestParams
,
userChatMessage
,
toolName
,
historyCopy
,
prompt
);
ResponseMessage
responseMessage
=
conversationHandler
.
toolExecutor
(
requestParams
,
userChatMessage
,
toolName
,
historyCopy
,
prompt
);
responseMessage
.
setStartTimestamp
(
startTimestamp
);
responseMessage
.
setStartTimestamp
(
startTimestamp
);
...
...
src/main/java/pro/spss/server/agent/service/sessionService/ChatSessionManager.java
View file @
6c04f4ff
...
@@ -2,6 +2,7 @@ package pro.spss.server.agent.service.sessionService;
...
@@ -2,6 +2,7 @@ package pro.spss.server.agent.service.sessionService;
import
com.alibaba.fastjson2.JSONArray
;
import
com.alibaba.fastjson2.JSONArray
;
import
lombok.Getter
;
import
lombok.Getter
;
import
org.springframework.beans.factory.annotation.Value
;
import
org.springframework.stereotype.Component
;
import
org.springframework.stereotype.Component
;
import
pro.spss.server.agent.domain.constant.ChatConstants
;
import
pro.spss.server.agent.domain.constant.ChatConstants
;
import
pro.spss.server.agent.domain.enums.ConversationStateEnum
;
import
pro.spss.server.agent.domain.enums.ConversationStateEnum
;
...
@@ -25,7 +26,8 @@ public class ChatSessionManager {
...
@@ -25,7 +26,8 @@ public class ChatSessionManager {
}
}
public
void
initSession
(
String
sessionId
,
CreateWayEnum
createWay
)
{
public
void
initSession
(
String
sessionId
,
CreateWayEnum
createWay
)
{
JSONArray
messages
=
new
JSONArray
(
ChatConstants
.
SYSTEM_MESSAGES
);
JSONArray
messages
=
new
JSONArray
();
messages
.
add
(
ChatConstants
.
createMessage
(
ChatConstants
.
ROLE_SYSTEM
,
ChatConstants
.
WELCOME_MESSAGE
));
chatSessions
.
put
(
sessionId
,
messages
);
chatSessions
.
put
(
sessionId
,
messages
);
RequestParams
params
=
new
RequestParams
();
RequestParams
params
=
new
RequestParams
();
...
...
src/main/resources/testcases/algo_knowledge1.jsonl
→
src/main/resources/testcases/algo_knowledge
/algo_knowledge
1.jsonl
View file @
6c04f4ff
File moved
src/main/resources/testcases/algo_knowledge2.jsonl
→
src/main/resources/testcases/algo_knowledge
/algo_knowledge
2.jsonl
View file @
6c04f4ff
File moved
src/main/resources/testcases/algo_knowledge3.jsonl
→
src/main/resources/testcases/algo_knowledge
/algo_knowledge
3.jsonl
View file @
6c04f4ff
File moved
src/main/resources/testcases/algo_knowledge/algo_knowledge4.jsonl
0 → 100644
View file @
6c04f4ff
[
{"name":"最小二乘回归","task":["回归","预测"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["可解释性强","对多重共线性敏感"]},
{"name":"岭回归","task":["回归","预测"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["抗多重共线性","模型稳定"]},
{"name":"套索回归","task":["回归","预测"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["特征选择","模型稀疏"]},
{"name":"多项式回归","task":["回归","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["拟合弯曲关系","易过拟合"]},
{"name":"支持向量机回归","task":["回归","预测"],"data":"非时间序列","relation":"非线性","sample":"偏小样本","complexity":"复杂","property":["小样本表现好","对参数敏感"]},
{"name":"BP神经网络","task":["回归","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["表达能力强","可解释性弱"]},
{"name":"梯度提升树","task":["回归","分类","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["精度高","对噪声敏感"]},
{"name":"移动平均法","task":["预测"],"data":"时间序列","relation":"不限","sample":"偏小样本","complexity":"简单","property":["平滑波动","对突变反应慢"]},
{"name":"指数平滑法","task":["预测"],"data":"时间序列","relation":"不限","sample":"偏小样本","complexity":"简单","property":["重视近期数据","适合短期预测"]},
{"name":"自回归模型(AR)","task":["预测"],"data":"时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["依赖历史值","要求平稳"]},
{"name":"滑动平均模型(MA)","task":["预测"],"data":"时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["刻画随机扰动","短期效果好"]},
{"name":"自回归滑动平均模型","task":["预测"],"data":"时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["综合AR和MA","预测能力强"]},
{"name":"差分自回归移动平均模型","task":["预测"],"data":"时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["处理非平稳序列","建模步骤多"]},
{"name":"灰色模型","task":["预测"],"data":"时间序列","relation":"线性","sample":"小样本","complexity":"简单","property":["小样本适用","抗随机性弱"]},
{"name":"决策树","task":["分类","决策"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"简单","property":["结构直观","易过拟合"]},
{"name":"随机森林","task":["分类","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["稳定性高","计算量大"]},
{"name":"自适应增强算法","task":["分类","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["关注难样本","对噪声敏感"]},
{"name":"判别分析","task":["分类"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["模型简洁","分布假设强"]},
{"name":"二分类逻辑回归","task":["分类","回归"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["概率输出","可解释性好"]},
{"name":"皮尔逊相关分析","task":["相关"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["衡量线性关系","对异常值敏感"]},
{"name":"斯皮尔曼相关分析","task":["相关"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["基于秩次","对异常值不敏感"]},
{"name":"独立样本t检验","task":["检验"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["比较均值差异","要求正态性"]},
{"name":"正态检验","task":["检验"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["检验分布形态","对样本量敏感"]},
{"name":"K-均值聚类","task":["聚类"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["速度快","对初始中心敏感"]},
{"name":"层次聚类","task":["聚类"],"data":"非时间序列","relation":"不限","sample":"小样本","complexity":"复杂","property":["结构直观","计算量大"]},
{"name":"因子分析","task":["降维","评价"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["提取潜在因子","解释性强"]},
{"name":"主成分分析","task":["降维"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["信息压缩","可解释性弱"]},
{"name":"优劣解距离法","task":["排序","评价"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["结果直观","依赖权重"]},
{"name":"秩和比评价法","task":["排序","评价"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["计算简单","信息损失多"]},
{"name":"模糊综合评价","task":["评价","排序"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"复杂","property":["处理模糊信息","主观性强"]},
{"name":"数据包络分析(CCR)","task":["效率","评价"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["规模报酬不变","适合同规模对象"]},
{"name":"数据包络分析(BCC)","task":["效率","评价"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["规模报酬可变","区分技术效率"]},
{"name":"柯布-道格拉斯生产函数","task":["效率","回归"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["形式简单","函数假设强"]}
]
算法标签体系简要说明
通过标签对算法的适用场景和特性进行结构化描述,用于支持算法自动推荐、条件筛选和规则匹配。
任务类型:说明算法能解决的问题,如预测、回归、分类、排序、聚类、相关、检验、降维、评价、效率等。
数据类型:区分是否为时间序列数据。
关系形式:描述模型刻画关系的方式,包括线性、非线性或不限。
样本规模:表示算法对样本数量的适应程度,如小样本或不限。
复杂度:表示算法实现和计算难度,分为简单或复杂。
通过这些标签,可以将用户需求与算法能力快速匹配,实现智能推荐与筛选。
样本很小、特征少 → 优先简单模型
特征多、关系明显非线性 → 偏向复杂模型
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