diff --git a/assembly/bin/build-chat.sh b/assembly/bin/build-chat.sh
index e00ab2fa9..6305f9d8a 100755
--- a/assembly/bin/build-chat.sh
+++ b/assembly/bin/build-chat.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 sbinDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $sbinDir/../)
+baseDir=$(cd "$sbinDir/.." && pwd -P)
 runtimeDir=$baseDir/runtime
 buildDir=$baseDir/build
 
diff --git a/assembly/bin/build-ide.sh b/assembly/bin/build-ide.sh
index b2bcd0ca3..87bfda75e 100755
--- a/assembly/bin/build-ide.sh
+++ b/assembly/bin/build-ide.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 sbinDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $sbinDir/../)
+baseDir=$(cd "$sbinDir/.." && pwd -P)
 buildDir=$baseDir/build
 
 cd $baseDir/bin
diff --git a/assembly/bin/build-semantic.sh b/assembly/bin/build-semantic.sh
index 5eefb3845..1c969081b 100755
--- a/assembly/bin/build-semantic.sh
+++ b/assembly/bin/build-semantic.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 sbinDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $sbinDir/../)
+baseDir=$(cd "$sbinDir/.." && pwd -P)
 runtimeDir=$baseDir/runtime
 buildDir=$baseDir/build
 
diff --git a/assembly/bin/build-standalone.sh b/assembly/bin/build-standalone.sh
index efa2324e8..b14435eca 100755
--- a/assembly/bin/build-standalone.sh
+++ b/assembly/bin/build-standalone.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 sbinDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $sbinDir/../)
+baseDir=$(cd "$sbinDir/.." && pwd -P)
 runtimeDir=$baseDir/runtime
 buildDir=$baseDir/build
 
diff --git a/assembly/bin/start-chat.sh b/assembly/bin/start-chat.sh
index 87e1920b4..0ee1435fe 100755
--- a/assembly/bin/start-chat.sh
+++ b/assembly/bin/start-chat.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 sbinDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $sbinDir/../)
+baseDir=$(cd "$sbinDir/.." && pwd -P)
 runtimeDir=$baseDir/../runtime
 buildDir=$baseDir/build
 
diff --git a/assembly/bin/start-semantic.sh b/assembly/bin/start-semantic.sh
index 1c070b83b..0175de675 100755
--- a/assembly/bin/start-semantic.sh
+++ b/assembly/bin/start-semantic.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 sbinDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $sbinDir/../)
+baseDir=$(cd "$sbinDir/.." && pwd -P)
 runtimeDir=$baseDir/../runtime
 buildDir=$baseDir/build
 
diff --git a/assembly/bin/start-standalone.sh b/assembly/bin/start-standalone.sh
index 80fb3099d..3cb9aff0f 100755
--- a/assembly/bin/start-standalone.sh
+++ b/assembly/bin/start-standalone.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 sbinDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $sbinDir/../)
+baseDir=$(cd "$sbinDir/.." && pwd -P)
 runtimeDir=$baseDir/../runtime
 buildDir=$baseDir/build
 
@@ -29,4 +29,4 @@ rm -fr  ${buildDir}/supersonic-webapp
 #start standalone service
 sh ${runtimeDir}/supersonic-standalone/bin/service.sh restart
 #start llm service
-sh ${runtimeDir}/supersonic-standalone/llm/bin/service.sh restart
\ No newline at end of file
+sh ${runtimeDir}/supersonic-standalone/llm/bin/service.sh restart
diff --git a/auth/api/src/main/java/com/tencent/supersonic/auth/api/authentication/config/AuthenticationConfig.java b/auth/api/src/main/java/com/tencent/supersonic/auth/api/authentication/config/AuthenticationConfig.java
index 78c7cf080..05d1a8991 100644
--- a/auth/api/src/main/java/com/tencent/supersonic/auth/api/authentication/config/AuthenticationConfig.java
+++ b/auth/api/src/main/java/com/tencent/supersonic/auth/api/authentication/config/AuthenticationConfig.java
@@ -19,7 +19,7 @@ public class AuthenticationConfig {
     @Value("${authentication.token.secret:secret}")
     private String tokenSecret;
 
-    @Value("${authentication.token.http.header.key:Auth}")
+    @Value("${authentication.token.http.header.key:Authorization}")
     private String tokenHttpHeaderKey;
 
 
diff --git a/chat/core/src/main/python/bin/install.sh b/chat/core/src/main/python/bin/install.sh
index df8800a19..9e68a6fd2 100755
--- a/chat/core/src/main/python/bin/install.sh
+++ b/chat/core/src/main/python/bin/install.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 binDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $binDir/../)
+baseDir=$(cd "$binDir/.." && pwd -P)
 echo $binDir
 
 source ${binDir}/env.sh
diff --git a/chat/core/src/main/python/bin/run.sh b/chat/core/src/main/python/bin/run.sh
index 8ed1a9d90..8f7af2f23 100755
--- a/chat/core/src/main/python/bin/run.sh
+++ b/chat/core/src/main/python/bin/run.sh
@@ -4,7 +4,7 @@ llm_host=$1
 llm_port=$2
 
 binDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $binDir/../)
+baseDir=$(cd "$binDir/.." && pwd -P)
 
 source ${baseDir}/bin/env.sh
 
diff --git a/chat/core/src/main/python/bin/service.sh b/chat/core/src/main/python/bin/service.sh
index bd3ae8e0a..a1788b5fb 100755
--- a/chat/core/src/main/python/bin/service.sh
+++ b/chat/core/src/main/python/bin/service.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 binDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $binDir/../)
+baseDir=$(cd "$binDir/.." && pwd -P)
 source ${baseDir}/bin/env.sh
 
 command=$1
diff --git a/chat/core/src/main/python/llm/api_service.py b/chat/core/src/main/python/llm/api_service.py
index a5bd975f2..c06748b74 100644
--- a/chat/core/src/main/python/llm/api_service.py
+++ b/chat/core/src/main/python/llm/api_service.py
@@ -25,20 +25,31 @@ app = FastAPI()
 
 @app.post("/query2sql/")
 async def din_query2sql(query_body: Mapping[str, Any]):
-  if 'queryText' not in query_body:
-    raise HTTPException(status_code=400,
+    if 'queryText' not in query_body:
+        raise HTTPException(status_code=400,
                         detail="query_text is not in query_body")
-  else:
-    query_text = query_body['queryText']
+    else:
+        query_text = query_body['queryText']
 
-  if 'schema' not in query_body:
-    raise HTTPException(status_code=400, detail="schema is not in query_body")
-  else:
-    schema = query_body['schema']
+    if 'schema' not in query_body:
+        raise HTTPException(status_code=400, detail="schema is not in query_body")
+    else:
+        schema = query_body['schema']
 
-  resp = query2sql(query_text=query_text, schema=schema)
+    if 'currentDate' not in query_body:
+        raise HTTPException(status_code=400, detail="currentDate is not in query_body")
+    else:
+        current_date = query_body['currentDate']
 
-  return resp
+    if 'linking' not in query_body:
+        linking = None
+    else:
+        linking = query_body['linking']
+
+    resp = query2sql(query_text=query_text,  
+                     schema=schema, current_date=current_date, linking=linking)
+
+    return resp
 
 
 @app.post("/preset_query_retrival/")
diff --git a/chat/core/src/main/python/llm/few_shot_example/sql_exampler.py b/chat/core/src/main/python/llm/few_shot_example/sql_exampler.py
new file mode 100644
index 000000000..87a32dc3d
--- /dev/null
+++ b/chat/core/src/main/python/llm/few_shot_example/sql_exampler.py
@@ -0,0 +1,296 @@
+examplars= [
+    {   "current_date":"2020-12-01",
+        "table_name":"内容库产品",
+        "fields_list":"""["部门", "模块", "用户名", "访问次数", "访问人数", "访问时长", "数据日期"]""",
+        "question":"比较jackjchen和robinlee在内容库的访问次数",
+        "prior_schema_links":"""['jackjchen'->用户名, 'robinlee'->用户名]""",
+        "analysis": """让我们一步一步地思考。在问题“比较jackjchen和robinlee在内容库的访问次数“中，我们被问：
+“比较jackjchen和robinlee”，所以我们需要column=[用户名]
+”内容库的访问次数“，所以我们需要column=[访问次数]
+基于table和columns，可能的cell values 是 = ['jackjchen', 'robinlee']。""",
+        "schema_links":"""["用户名", "访问次数", "'jackjchen'", "'robinlee'"]""",
+        "sql":"""select 用户名, 访问次数 from 内容库产品 where 用户名 in ('jackjchen', 'robinlee') and 数据日期 = '2020-12-01' """
+        },
+    {   "current_date":"2022-11-06",
+        "table_name":"内容库产品",
+        "fields_list":"""["部门", "模块", "用户名", "访问次数", "访问人数", "访问时长", "数据日期"]""",
+        "question":"内容库近12个月访问人数 按部门",
+        "prior_schema_links":"""[]""",
+        "analysis": """让我们一步一步地思考。在问题“内容库近12个月访问人数 按部门“中，我们被问：
+”内容库近12个月“，所以我们需要column=[数据日期]
+“访问人数”，所以我们需要column=[访问人数]
+”按部门“，所以我们需要column=[部门]
+基于table和columns，可能的cell values 是 = [12]。""",
+        "schema_links":"""["访问人数", "部门", "数据日期", 12]""",
+        "sql":"""select 部门, 数据日期, 访问人数 from 内容库产品 where datediff('month', 数据日期, '2022-11-06') <= 12 """
+        },
+    {   "current_date":"2023-04-21",
+        "table_name":"内容库产品",
+        "fields_list":"""["部门", "模块", "用户名", "访问次数", "访问人数", "访问时长", "数据日期"]""",
+        "question":"内容库美术部、技术研发部的访问时长",
+        "prior_schema_links":"""['美术部'->部门, '技术研发部'->部门]""",
+        "analysis": """让我们一步一步地思考。在问题“内容库美术部、技术研发部的访问时长“中，我们被问：
+“访问时长”，所以我们需要column=[访问时长]
+”内容库美术部、技术研发部“，所以我们需要column=[部门]
+基于table和columns，可能的cell values 是 = ['美术部', '技术研发部']。""",
+        "schema_links":"""["访问时长", "部门", "'美术部'", "'技术研发部'"]""",
+        "sql":"""select 部门, 访问时长 from 内容库产品 where 部门 in ('美术部', '技术研发部') and 数据日期 = '2023-04-21' """
+        },
+    {   "current_date":"2023-08-21",
+        "table_name":"严选",
+        "fields_list":"""["严选版权归属系", "付费模式", "结算播放份额", "付费用户结算播放份额", "数据日期"]""",
+        "question":"近3天海田飞系MPPM结算播放份额",
+        "prior_schema_links":"""['海田飞系'->严选版权归属系]""",
+        "analysis": """让我们一步一步地思考。在问题“近3天海田飞系MPPM结算播放份额“中，我们被问：
+“MPPM结算播放份额”，所以我们需要column=[结算播放份额]
+”海田飞系“，所以我们需要column=[严选版权归属系]
+”近3天“，所以我们需要column=[数据日期]
+基于table和columns，可能的cell values 是 = ['海田飞系', 3]。""",
+        "schema_links":"""["结算播放份额", "严选版权归属系", "数据日期", "'海田飞系'", 3]""",
+        "sql":"""select 严选版权归属系, 结算播放份额 from 严选 where 严选版权归属系 = '海田飞系' and datediff('day', 数据日期, '2023-08-21') <= 3 """
+        },
+    {   "current_date":"2023-05-22",
+        "table_name":"歌曲库",
+        "fields_list":"""["是否潮流人歌曲", "C音歌曲ID", "C音歌曲MID", "歌曲名", "歌曲版本", "语种", "歌曲类型", "翻唱类型", "MPPM歌曲ID", "是否严选窄口径歌曲", "是否严选宽口径歌曲", "结算播放量", "运营播放量", "付费用户结算播放量", "历史累计结算播放量", "运营搜播量", "结算搜播量", "运营完播量", "运营推播量", "近7日复播率", "日均搜播量", "数据日期"]""",
+        "question":"对比近7天翻唱版和纯音乐的歌曲播放量",
+        "prior_schema_links":"""['纯音乐'->语种, '翻唱版'->歌曲版本]""",
+        "analysis": """让我们一步一步地思考。在问题“对比近3天翻唱版和纯音乐的歌曲播放量“中，我们被问：
+“歌曲播放量”，所以我们需要column=[结算播放量]
+”翻唱版“，所以我们需要column=[歌曲版本]
+”和纯音乐的歌曲“，所以我们需要column=[语种]
+”近7天“，所以我们需要column=[数据日期]
+基于table和columns，可能的cell values 是 = ['翻唱版', '纯音乐', 7]。""",
+        "schema_links":"""["结算播放量", "歌曲版本", "语种", "数据日期", "'翻唱版'", "'纯音乐'", 7]""",
+        "sql":"""select 歌曲版本, 语种, 结算播放量 from 歌曲库 where 歌曲版本 = '翻唱版' and 语种 = '纯音乐' and datediff('day', 数据日期, '2023-05-22') <= 7 """
+        },
+    {   "current_date":"2023-05-31",
+        "table_name":"艺人库",
+        "fields_list":"""["上下架状态", "歌手名", "歌手等级", "歌手类型", "歌手来源", "MPPM潮流人等级", "活跃区域", "年龄", "歌手才能", "歌手风格", "粉丝数", "潮音粉丝数", "超声波粉丝数", "推博粉丝数", "超声波歌曲数", "在架歌曲数", "超声波分享数", "独占歌曲数", "超声波在架歌曲评论数", "有播放量歌曲数", "数据日期"]""",
+        "question":"对比一下陈拙悬、孟梅琦、赖媚韵的粉丝数",
+        "prior_schema_links":"""['1527896'->MPPM歌手ID, '1565463'->MPPM歌手ID, '2141459'->MPPM歌手ID]""",
+        "analysis": """让我们一步一步地思考。在问题“对比一下陈拙悬、孟梅琦、赖媚韵的粉丝数“中，我们被问：
+“粉丝数”，所以我们需要column=[粉丝数]
+”陈拙悬、孟梅琦、赖媚韵“，所以我们需要column=[歌手名]
+基于table和columns，可能的cell values 是 = ['陈拙悬', '孟梅琦', '赖媚韵']。""",
+        "schema_links":"""["粉丝数", "歌手名", "'陈拙悬'", "'孟梅琦'", "'赖媚韵'"]""",
+        "sql":"""select 歌手名, 粉丝数 from 艺人库 where 歌手名 in ('陈拙悬', '孟梅琦', '赖媚韵') and 数据日期 = '2023-05-31' """
+        },
+    {   "current_date":"2023-07-31",
+        "table_name":"歌曲库",
+        "fields_list":"""["歌曲名", "歌曲版本", "歌曲类型", "MPPM歌曲ID", "是否严选窄口径歌曲", "是否严选宽口径歌曲", "是否潮流人歌曲", "超声波歌曲ID", "C音歌曲ID", "C音歌曲MID", "结算播放量", "运营播放量", "分享量", "收藏量", "运营搜播量", "结算搜播量", "拉新用户数", "拉活用户数", "分享率", "结算播放份额", "数据日期"]""",
+        "question":"播放量大于1万的歌曲有多少",
+        "prior_schema_links":"""[]""",
+        "analysis": """让我们一步一步地思考。在问题“播放量大于1万的歌曲有多少“中，我们被问：
+“歌曲有多少”，所以我们需要column=[歌曲名]
+”播放量大于1万的“，所以我们需要column=[结算播放量]
+基于table和columns，可能的cell values 是 = [10000]。""",
+        "schema_links":"""["歌曲名", "结算播放量", 10000]""",
+        "sql":"""select 歌曲名 from 歌曲库 where 结算播放量 > 10000 and 数据日期 = '2023-07-31' """
+        },
+    {   "current_date":"2023-07-31",
+        "table_name":"内容库产品",
+        "fields_list":"""["用户名", "部门", "模块", "访问时长", "访问次数", "访问人数", "数据日期"]""",
+        "question":"内容库访问时长小于1小时，且来自美术部的用户是哪些",
+        "prior_schema_links":"""['美术部'->部门]""",
+        "analysis": """让我们一步一步地思考。在问题“内容库访问时长小于1小时，且来自美术部的用户是哪些“中，我们被问：
+“用户是哪些”，所以我们需要column=[用户名]
+”美术部的“，所以我们需要column=[部门]
+”访问时长小于1小时“，所以我们需要column=[访问时长]
+基于table和columns，可能的cell values 是 = ['美术部', 1]。""",
+        "schema_links":"""["用户名", "部门", "访问时长", "'美术部'", 1]""",
+        "sql":"""select 用户名 from 内容库产品 where 部门 = '美术部' and 访问时长 < 1 and 数据日期 = '2023-07-31' """
+        },
+    {   "current_date":"2023-08-31",
+        "table_name":"内容库产品",
+        "fields_list":"""["用户名", "部门", "模块", "访问时长", "访问次数", "访问人数", "数据日期"]""",
+        "question":"内容库pv最高的用户有哪些",
+        "prior_schema_links":"""[]""",
+        "analysis": """让我们一步一步地思考。在问题“内容库pv最高的用户有哪些“中，我们被问：
+“用户有哪些”，所以我们需要column=[用户名]
+”pv最高的“，所以我们需要column=[访问次数]
+基于table和columns，可能的cell values 是 = []。""",
+        "schema_links":"""["用户名", "访问次数"]""",
+        "sql":"""select 用户名 from 内容库产品 where 数据日期 = '2023-08-31' order by 访问次数 desc limit 10 """
+        },
+    {   "current_date":"2023-08-31",
+        "table_name":"艺人库",
+        "fields_list":"""["播放量层级", "播放量单调性", "播放量方差", "播放量突增类型", "播放量集中度", "歌手名", "歌手等级", "歌手类型", "歌手来源", "MPPM潮流人等级", "结算播放量", "运营播放量", "历史累计结算播放量", "有播放量歌曲数", "历史累计运营播放量", "付费用户结算播放量", "结算播放量占比", "运营播放份额", "免费用户结算播放占比", "完播量", "数据日期"]""",
+        "question":"近90天袁亚伟播放量平均值是多少",
+        "prior_schema_links":"""['152789226'->MPPM歌手ID]""",
+        "analysis": """让我们一步一步地思考。在问题“近90天袁亚伟播放量平均值是多少“中，我们被问：
+“播放量平均值是多少”，所以我们需要column=[结算播放量]
+”袁亚伟“，所以我们需要column=[歌手名]
+”近90天“，所以我们需要column=[数据日期]
+基于table和columns，可能的cell values 是 = ['袁亚伟', 90]。""",
+        "schema_links":"""["结算播放量", "歌手名", "数据日期", "'袁亚伟'", 90]""",
+        "sql":"""select avg(结算播放量) from 艺人库 where 歌手名 = '袁亚伟' and datediff('day', 数据日期, '2023-08-31') <= 90 """
+        },
+    {   "current_date":"2023-08-31",
+        "table_name":"艺人库",
+        "fields_list":"""["播放量层级", "播放量单调性", "播放量方差", "播放量突增类型", "播放量集中度", "歌手名", "歌手等级", "歌手类型", "歌手来源", "MPPM潮流人等级", "结算播放量", "运营播放量", "历史累计结算播放量", "有播放量歌曲数", "历史累计运营播放量", "付费用户结算播放量", "结算播放量占比", "运营播放份额", "免费用户结算播放占比", "完播量", "数据日期"]""",
+        "question":"周倩倩近7天结算播放量总和是多少",
+        "prior_schema_links":"""['199509'->MPPM歌手ID]""",
+        "analysis": """让我们一步一步地思考。在问题“周倩倩近7天结算播放量总和是多少“中，我们被问：
+“结算播放量总和是多少”，所以我们需要column=[结算播放量]
+”周倩倩“，所以我们需要column=[歌手名]
+”近7天“，所以我们需要column=[数据日期]
+基于table和columns，可能的cell values 是 = ['周倩倩', 7]。""",
+        "schema_links":"""["结算播放量", "歌手名", "数据日期", "'周倩倩'", 7]""",
+        "sql":"""select sum(结算播放量) from 艺人库 where 歌手名 = '周倩倩' and datediff('day', 数据日期, '2023-08-31') <= 7 """
+        },
+    {   "current_date":"2023-09-14",
+        "table_name":"内容库产品",
+        "fields_list":"""["部门", "模块", "用户名", "访问次数", "访问人数", "访问时长", "数据日期"]""",
+        "question":"内容库访问次数大于1k的部门是哪些",
+        "prior_schema_links":"""[]""",
+        "analysis": """让我们一步一步地思考。在问题“内容库访问次数大于1k的部门是哪些“中，我们被问：
+“部门是哪些”，所以我们需要column=[部门]
+”访问次数大于1k的“，所以我们需要column=[访问次数]
+基于table和columns，可能的cell values 是 = [1000]。""",
+        "schema_links":"""["部门", "访问次数", 1000]""",
+        "sql":"""select 部门 from 内容库产品 where 访问次数 > 1000 and 数据日期 = '2023-09-14' """
+        },
+    {   "current_date":"2023-09-18",
+        "table_name":"歌曲库",
+        "fields_list":"""["歌曲名", "MPPM歌手ID", "歌曲版本", "歌曲类型", "MPPM歌曲ID", "是否严选窄口径歌曲", "是否严选宽口径歌曲", "是否潮流人歌曲", "超声波歌曲ID", "C音歌曲ID", "C音歌曲MID", "结算播放量", "运营播放量", "分享量", "收藏量", "运营搜播量", "结算搜播量", "拉新用户数", "拉活用户数", "分享率", "结算播放份额", "数据日期"]""",
+        "question":"陈亿训唱的所有的播放量大于20k的孤勇者有哪些",
+        "prior_schema_links":"""['199509'->MPPM歌手ID, '1527123'->MPPM歌曲ID]""",
+        "analysis": """让我们一步一步地思考。在问题“陈亿训唱的所有的播放量大于20k的孤勇者有哪些“中，我们被问：
+“孤勇者有哪些”，所以我们需要column=[歌曲名]
+”播放量大于20k的“，所以我们需要column=[结算播放量]
+”陈亿训唱的“，所以我们需要column=[歌手名]
+基于table和columns，可能的cell values 是 = [20000, '陈亿训', '孤勇者']。""",
+        "schema_links":"""["歌曲名", "结算播放量", "歌手名", 20000, "'陈亿训'", "'孤勇者'"]""",
+        "sql":"""select 歌曲名 from 歌曲库 where 结算播放量 > 20000 and 歌手名 = '陈亿训' and 歌曲名 = '孤勇者' and 数据日期 = '2023-09-18' """
+        },
+    {   "current_date":"2023-09-18",
+        "table_name":"歌曲库",
+        "fields_list":"""["歌曲名", "歌曲版本", "歌手名", "歌曲类型", "发布时间", "MPPM歌曲ID", "是否严选窄口径歌曲", "是否严选宽口径歌曲", "是否潮流人歌曲", "超声波歌曲ID", "C音歌曲ID", "C音歌曲MID", "结算播放量", "运营播放量", "分享量", "收藏量", "运营搜播量", "结算搜播量", "拉新用户数", "拉活用户数", "分享率", "结算播放份额", "数据日期"]""",
+        "question":"周洁轮去年发布的歌曲有哪些",
+        "prior_schema_links":"""['23109'->MPPM歌手ID]""",
+        "analysis": """让我们一步一步地思考。在问题“周洁轮去年发布的歌曲有哪些“中，我们被问：
+“歌曲有哪些”，所以我们需要column=[歌曲名]
+”去年发布的“，所以我们需要column=[发布时间]
+”周洁轮“，所以我们需要column=[歌手名]
+基于table和columns，可能的cell values 是 = ['周洁轮', 1]。""",
+        "schema_links":"""["歌曲名", "发布时间", "歌手名", 1, "'周洁轮'"]""",
+        "sql":"""select 歌曲名 from 歌曲库 where datediff('year', 发布时间, '2023-09-18') <= 1 and 歌手名 = '周洁轮' and 数据日期 = '2023-09-18' """
+        },
+    {   "current_date":"2023-09-11",
+        "table_name":"艺人库",
+        "fields_list":"""["播放量层级", "播放量单调性", "播放量方差", "播放量突增类型", "播放量集中度", "歌手名", "歌手等级", "歌手类型", "歌手来源", "签约日期", "MPPM潮流人等级", "结算播放量", "运营播放量", "历史累计结算播放量", "有播放量歌曲数", "历史累计运营播放量", "付费用户结算播放量", "结算播放量占比", "运营播放份额", "免费用户结算播放占比", "完播量", "数据日期"]""",
+        "question":"我想要近半年签约的播放量前十的歌手有哪些",
+        "prior_schema_links":"""[]""",
+        "analysis": """让我们一步一步地思考。在问题“我想要近半年签约的播放量前十的歌手“中，我们被问：
+“歌手有哪些”，所以我们需要column=[歌手名]
+”播放量前十的“，所以我们需要column=[结算播放量]
+”近半年签约的“，所以我们需要column=[签约日期]
+基于table和columns，可能的cell values 是 = [0.5, 10]。""",
+        "schema_links":"""["歌手名", "结算播放量", "签约日期", 0.5, 10]""",
+        "sql":"""select 歌手名 from 艺人库 where datediff('year', 签约日期, '2023-09-11') <= 0.5 and 数据日期 = '2023-09-11' order by 结算播放量 desc limit 10"""
+        },
+    {   "current_date":"2023-08-12",
+        "table_name":"歌曲库",
+        "fields_list": """["发行日期", "歌曲语言", "歌曲来源", "歌曲流派", "歌曲名", "歌曲版本", "歌曲类型", "发行时间", "数据日期"]""",
+        "question":"最近一年发行的歌曲中，有哪些在近7天播放超过一千万的",
+        "prior_schema_links":"""[]""",
+        "analysis": """让我们一步一步地思考。在问题“最近一年发行的歌曲中，有哪些在近7天播放超过一千万的“中，我们被问：
+“发行的歌曲中，有哪些”，所以我们需要column=[歌曲名]
+”最近一年发行的“，所以我们需要column=[发行日期]
+”在近7天播放超过一千万的“，所以我们需要column=[数据日期, 结算播放量]
+基于table和columns，可能的cell values 是 = [1, 10000000]""",
+        "schema_links":"""["歌曲名", "发行日期", "数据日期", "结算播放量", 1, 10000000]""",
+        "sql":"""select 歌曲名 from 歌曲库 where datediff('year', 发行日期, '2023-08-12') <= 1 and datediff('day', 数据日期, '2023-08-12') <= 7 and 结算播放量 > 10000000"""
+        },
+    {   "current_date":"2023-08-12",
+        "table_name":"歌曲库",
+        "fields_list": """["发行日期", "歌曲语言", "歌曲来源", "歌曲流派", "歌曲名", "歌曲版本", "歌曲类型", "发行时间", "数据日期"]""",
+        "question":"今年以来发行的歌曲中，有哪些在近7天播放超过一千万的",
+        "prior_schema_links":"""[]""",
+        "analysis": """让我们一步一步地思考。在问题“今年以来发行的歌曲中，有哪些在近7天播放超过一千万的“中，我们被问：
+“发行的歌曲中，有哪些”，所以我们需要column=[歌曲名]
+”今年以来发行的“，所以我们需要column=[发行日期]
+”在近7天播放超过一千万的“，所以我们需要column=[数据日期, 结算播放量]
+基于table和columns，可能的cell values 是 = [0, 7, 10000000]""",
+        "schema_links":"""["歌曲名", "发行日期", "数据日期", "结算播放量", 0, 7, 10000000]""",
+        "sql":"""select 歌曲名 from 歌曲库 where datediff('year', 发行日期, '2023-08-12') <= 0 and datediff('day', 数据日期, '2023-08-12') <= 7 and 结算播放量 > 10000000"""
+        },
+    {   "current_date":"2023-08-12",
+        "table_name":"歌曲库",
+        "fields_list": """["发行日期", "歌曲语言", "歌曲来源", "歌曲流派", "歌曲名", "歌曲版本", "歌曲类型", "发行时间", "数据日期"]""",
+        "question":"2023年以来发行的歌曲中，有哪些在近7天播放超过一千万的",
+        "prior_schema_links":"""['514129144'->MPPM歌曲ID]""",
+        "analysis": """让我们一步一步地思考。在问题“2023年以来发行的歌曲中，有哪些在近7天播放超过一千万的“中，我们被问：
+“发行的歌曲中，有哪些”，所以我们需要column=[歌曲名]
+”2023年以来发行的“，所以我们需要column=[发行日期]
+”在近7天播放超过一千万的“，所以我们需要column=[数据日期, 结算播放量]
+基于table和columns，可能的cell values 是 = [2023, 7, 10000000]""",
+        "schema_links":"""["歌曲名", "发行日期", "数据日期", "结算播放量", 2023, 7, 10000000]""",
+        "sql":"""select 歌曲名 from 歌曲库 where YEAR(发行日期) >= 2023 and datediff('day', 数据日期, '2023-08-12') <= 7 and 结算播放量 > 10000000"""
+        },
+    {   "current_date":"2023-08-01",
+        "table_name":"歌曲库",
+        "fields_list":"""["歌曲名", "歌曲版本", "歌手名", "歌曲类型", "发布时间", "MPPM歌曲ID", "是否严选窄口径歌曲", "是否严选宽口径歌曲", "是否潮流人歌曲", "超声波歌曲ID", "C音歌曲ID", "C音歌曲MID", "结算播放量", "运营播放量", "分享量", "收藏量", "运营搜播量", "结算搜播量", "拉新用户数", "拉活用户数", "分享率", "结算播放份额", "数据日期"]""",
+        "question":"周洁轮2023年6月之后发布的歌曲有哪些",
+        "prior_schema_links":"""['23109'->MPPM歌手ID]""",
+        "analysis": """让我们一步一步地思考。在问题“周洁轮2023年6月之后发布的歌曲有哪些“中，我们被问：
+“歌曲有哪些”，所以我们需要column=[歌曲名]
+”2023年6月之后发布的“，所以我们需要column=[发布时间]
+”周洁轮“，所以我们需要column=[歌手名]
+基于table和columns，可能的cell values 是 = ['周洁轮', 2023, 6]。""",
+        "schema_links":"""["歌曲名", "发布时间", "歌手名", "周洁轮", 2023, 6]""",
+        "sql":"""select 歌曲名 from 歌曲库 where YEAR(发布时间) >= 2023 and MONTH(发布时间) >= 6 and 歌手名 = '周洁轮' and 数据日期 = '2023-08-01' """
+        },
+    {   "current_date":"2023-08-01",
+        "table_name":"歌曲库",
+        "fields_list":"""["歌曲名", "歌曲版本", "歌手名", "歌曲类型", "发布时间", "MPPM歌曲ID", "是否严选窄口径歌曲", "是否严选宽口径歌曲", "是否潮流人歌曲", "超声波歌曲ID", "C音歌曲ID", "C音歌曲MID", "结算播放量", "运营播放量", "分享量", "收藏量", "运营搜播量", "结算搜播量", "拉新用户数", "拉活用户数", "分享率", "结算播放份额", "数据日期"]""",
+        "question":"邓梓琦在2023年1月5日之后发布的歌曲中，有哪些播放量大于500W的？",
+        "prior_schema_links":"""['2312311'->MPPM歌手ID]""",
+        "analysis": """让我们一步一步地思考。在问题“邓梓琦在2023年1月5日之后发布的歌曲中，有哪些播放量大于500W的？“中，我们被问：
+“播放量大于500W的”，所以我们需要column=[结算播放量]
+”邓梓琦在2023年1月5日之后发布的“，所以我们需要column=[发布时间]
+”邓梓琦“，所以我们需要column=[歌手名]
+基于table和columns，可能的cell values 是 = ['邓梓琦', 2023, 1, 5, 5000000]。""",
+        "schema_links":"""["结算播放量", "发布时间", "歌手名", "邓梓琦", 2023, 1, 5, 5000000]""",
+        "sql":"""select 歌曲名 from 歌曲库 where YEAR(发布时间) >= 2023 and MONTH(发布时间) >= 1 and DAY(发布时间) >= 5 and 歌手名 = '邓梓琦' and 结算播放量 > 5000000 and 数据日期 = '2023-08-01'"""
+        },
+    {   "current_date":"2023-09-17",
+        "table_name":"歌曲库",
+        "fields_list":"""["歌曲名", "歌曲版本", "歌手名", "歌曲类型", "发布时间", "MPPM歌曲ID", "是否严选窄口径歌曲", "是否严选宽口径歌曲", "是否潮流人歌曲", "超声波歌曲ID", "C音歌曲ID", "C音歌曲MID", "结算播放量", "运营播放量", "分享量", "收藏量", "运营搜播量", "结算搜播量", "拉新用户数", "拉活用户数", "分享率", "结算播放份额", "数据日期"]""",
+        "question":"2023年6月以后，张亮英播放量大于200万的歌曲有哪些？",
+        "prior_schema_links":"""['45453'->MPPM歌手ID]""",
+        "analysis": """让我们一步一步地思考。在问题“2023年6月以后，张亮英播放量大于200万的歌曲有哪些？“中，我们被问：
+“播放量大于200万的”，所以我们需要column=[结算播放量]
+”2023年6月以后，张亮英“，所以我们需要column=[数据日期, 歌手名]
+”歌曲有哪些“，所以我们需要column=[歌曲名]
+基于table和columns，可能的cell values 是 = ['张亮英', 2023, 6, 2000000]。""",
+        "schema_links":"""["结算播放量", "数据日期", "歌手名", "张亮英", 2023, 6, 2000000]""",
+        "sql":"""select 歌曲名 from 歌曲库 where YEAR(数据日期) >= 2023 and MONTH(数据日期) >= 6 and 歌手名 = '张亮英' and 结算播放量 > 2000000 """
+        },
+    {   "current_date":"2023-08-16",
+        "table_name":"歌曲库",
+        "fields_list":"""["歌曲名", "歌曲版本", "歌手名", "歌曲类型", "发布时间", "MPPM歌曲ID", "是否严选窄口径歌曲", "是否严选宽口径歌曲", "是否潮流人歌曲", "超声波歌曲ID", "C音歌曲ID", "C音歌曲MID", "结算播放量", "运营播放量", "分享量", "收藏量", "运营搜播量", "结算搜播量", "拉新用户数", "拉活用户数", "分享率", "结算播放份额", "数据日期"]""",
+        "question":"2021年6月以后发布的李雨纯的播放量大于20万的歌曲有哪些",
+        "prior_schema_links":"""['23109'->MPPM歌手ID]""",
+        "analysis": """让我们一步一步地思考。在问题“2021年6月以后发布的李雨纯的播放量大于20万的歌曲有哪些“中，我们被问：
+“播放量大于20万的”，所以我们需要column=[结算播放量]
+”2021年6月以后发布的“，所以我们需要column=[发布时间]
+”李雨纯“，所以我们需要column=[歌手名]
+基于table和columns，可能的cell values 是 = ['李雨纯', 2021, 6, 200000]。""",
+        "schema_links":"""["结算播放量", "发布时间", "歌手名", "李雨纯", 2021, 6, 200000]""",
+        "sql":"""select 歌曲名 from 歌曲库 where YEAR(发布时间) >= 2021 and MONTH(发布时间) >= 6 and 歌手名 = '李雨纯' and 结算播放量 > 200000 and 数据日期 = '2023-08-16'"""
+        },
+    {   "current_date":"2023-08-16",
+        "table_name":"歌曲库",
+        "fields_list":"""["歌曲名", "歌曲版本", "歌手名", "歌曲类型", "发布时间", "MPPM歌曲ID", "是否严选窄口径歌曲", "是否严选宽口径歌曲", "是否潮流人歌曲", "超声波歌曲ID", "C音歌曲ID", "C音歌曲MID", "结算播放量", "运营播放量", "分享量", "收藏量", "运营搜播量", "结算搜播量", "拉新用户数", "拉活用户数", "分享率", "结算播放份额", "数据日期"]""",
+        "question":"刘锝桦在1992年4月2日到2020年5月2日之间发布的播放量大于20万的歌曲有哪些",
+        "prior_schema_links":"""['4234234'->MPPM歌手ID]""",
+        "analysis": """让我们一步一步地思考。在问题“刘锝桦在1992年4月2日到2020年5月2日之间发布的播放量大于20万的歌曲有哪些“中，我们被问：
+“播放量大于20万的”，所以我们需要column=[结算播放量]
+”1992年4月2日到2020年5月2日之间发布的“，所以我们需要column=[发布时间]
+”刘锝桦“，所以我们需要column=[歌手名]
+基于table和columns，可能的cell values 是 = ['刘锝桦', 1992, 4, 2, 2020, 5, 2, 200000]。""",
+        "schema_links":"""["结算播放量", "发布时间", "歌手名", "刘锝桦", 1992, 4, 2, 2020, 5, 2, 200000]""",
+        "sql":"""select 歌曲名 from 歌曲库 where YEAR(发布时间) >= 1992 and MONTH(发布时间) >= 4 and DAY(发布时间) >= 2 and YEAR(发布时间) <= 2020 and MONTH(发布时间) <= 5 and DAY(发布时间) <= 2 and 歌手名 = '刘锝桦' and 结算播放量 > 200000 and 数据日期 = '2023-08-16'"""
+        }
+]
\ No newline at end of file
diff --git a/chat/core/src/main/python/llm/preset_retrieval/run.py b/chat/core/src/main/python/llm/preset_retrieval/run.py
index 9027253bf..dc501b49c 100644
--- a/chat/core/src/main/python/llm/preset_retrieval/run.py
+++ b/chat/core/src/main/python/llm/preset_retrieval/run.py
@@ -8,8 +8,7 @@ from typing import Any, List, Mapping, Optional, Union
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 sys.path.append(os.path.dirname(os.path.abspath(__file__)))
 
-import chromadb
-from chromadb.config import Settings
+
 from chromadb.api import Collection, Documents, Embeddings
 
 from langchain.llms import OpenAI
@@ -21,13 +20,9 @@ from preset_query_db import (get_ids, add2preset_query_collection,
 from util.text2vec import Text2VecEmbeddingFunction
 
 from run_config import CHROMA_DB_PERSIST_PATH, PRESET_QUERY_COLLECTION_NAME
+from util.chromadb_instance import client 
 
 
-client = chromadb.Client(Settings(
-    chroma_db_impl="duckdb+parquet",
-    persist_directory=CHROMA_DB_PERSIST_PATH # Optional, defaults to .chromadb/ in the current directory
-))
-
 emb_func = Text2VecEmbeddingFunction()
 
 collection = client.get_or_create_collection(name=PRESET_QUERY_COLLECTION_NAME, 
@@ -35,6 +30,8 @@ collection = client.get_or_create_collection(name=PRESET_QUERY_COLLECTION_NAME,
                                             metadata={"hnsw:space": "cosine"}
                                             ) # Get a collection object from an existing collection, by name. If it doesn't exist, create it.
 
+print("init_preset_query_collection_size: ", preset_query_collection_size(collection))
+
 
 def preset_query_retrieval_run(collection:Collection, query_texts_list:List[str], n_results:int=5):
     retrieval_res = query2preset_query_collection(collection=collection, 
diff --git a/chat/core/src/main/python/llm/run_config.py b/chat/core/src/main/python/llm/run_config.py
index 989b44e5a..e2b47b404 100644
--- a/chat/core/src/main/python/llm/run_config.py
+++ b/chat/core/src/main/python/llm/run_config.py
@@ -9,6 +9,7 @@ TEMPERATURE = 0.0
 
 CHROMA_DB_PERSIST_DIR = 'chm_db'
 PRESET_QUERY_COLLECTION_NAME = "preset_query_collection"
+TEXT2DSL_COLLECTION_NAME = "text2dsl_collection"
 
 CHROMA_DB_PERSIST_PATH = os.path.join(PROJECT_DIR_PATH, CHROMA_DB_PERSIST_DIR)
 
diff --git a/chat/core/src/main/python/llm/sql/constructor.py b/chat/core/src/main/python/llm/sql/constructor.py
new file mode 100644
index 000000000..c6f367492
--- /dev/null
+++ b/chat/core/src/main/python/llm/sql/constructor.py
@@ -0,0 +1,53 @@
+# -*- coding:utf-8 -*-
+import os
+import sys
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from langchain.prompts.few_shot import FewShotPromptTemplate
+from langchain.prompts import PromptTemplate
+from langchain.vectorstores import Chroma
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings
+from langchain.prompts.example_selector import SemanticSimilarityExampleSelector
+
+import chromadb
+from chromadb.config import Settings
+
+from few_shot_example.sql_exampler import examplars as din_sql_examplars
+from util.text2vec import Text2VecEmbeddingFunction, hg_embedding
+from util.chromadb_instance import client as chromadb_client
+
+
+from run_config import TEXT2DSL_COLLECTION_NAME
+
+
+vectorstore = Chroma(collection_name=TEXT2DSL_COLLECTION_NAME, 
+                    embedding_function=hg_embedding,
+                    client=chromadb_client)
+
+example_nums = 15
+
+schema_linking_example_selector = SemanticSimilarityExampleSelector(vectorstore=vectorstore, k=example_nums,
+                                                        input_keys=["question"], 
+                                                        example_keys=["table_name", "fields_list", "prior_schema_links", "question", "analysis", "schema_links"])
+
+sql_example_selector = SemanticSimilarityExampleSelector(vectorstore=vectorstore, k=example_nums,
+                                                        input_keys=["question"],
+                                                        example_keys=["question", "current_date", "table_name", "schema_links", "sql"])
+
+if vectorstore._collection.count() > 0:
+    print("examples already in din_sql_vectorstore")
+    print("init din_sql_vectorstore size:", vectorstore._collection.count()) 
+    if vectorstore._collection.count() < len(din_sql_examplars):
+        print("din_sql_examplars size:", len(din_sql_examplars))
+        vectorstore._collection.delete()
+        print("empty din_sql_vectorstore")
+        for example in din_sql_examplars:
+            schema_linking_example_selector.add_example(example)
+        print("added din_sql_vectorstore size:", vectorstore._collection.count())
+else:
+    for example in din_sql_examplars:
+        schema_linking_example_selector.add_example(example)
+
+    print("added din_sql_vectorstore size:", vectorstore._collection.count())
diff --git a/chat/core/src/main/python/llm/sql/output_parser.py b/chat/core/src/main/python/llm/sql/output_parser.py
index 64df5ba1f..c90388850 100644
--- a/chat/core/src/main/python/llm/sql/output_parser.py
+++ b/chat/core/src/main/python/llm/sql/output_parser.py
@@ -1,15 +1,13 @@
 # -*- coding:utf-8 -*-
 import re
 
-
 def schema_link_parse(schema_link_output):
-  try:
-    schema_link_output = schema_link_output.strip()
-    pattern = r'Schema_links:(.*)'
-    schema_link_output = re.findall(pattern, schema_link_output, re.DOTALL)[
-      0].strip()
-  except Exception as e:
-    print(e)
-    schema_link_output = None
+    try:
+        schema_link_output = schema_link_output.strip()
+        pattern = r'Schema_links:(.*)'
+        schema_link_output = re.findall(pattern, schema_link_output, re.DOTALL)[0].strip()
+    except Exception as e:
+        print(e)
+        schema_link_output = None
 
-  return schema_link_output
+    return schema_link_output
\ No newline at end of file
diff --git a/chat/core/src/main/python/llm/sql/prompt_maker.py b/chat/core/src/main/python/llm/sql/prompt_maker.py
index 6e05f95b3..0cfed83b1 100644
--- a/chat/core/src/main/python/llm/sql/prompt_maker.py
+++ b/chat/core/src/main/python/llm/sql/prompt_maker.py
@@ -1,8 +1,5 @@
 # -*- coding:utf-8 -*-
 from typing import Any, List, Mapping, Optional, Union
-import requests
-import logging
-import json
 import os
 import sys
 
@@ -11,78 +8,68 @@ sys.path.append(os.path.dirname(os.path.abspath(__file__)))
 
 from langchain.prompts import PromptTemplate
 from langchain.prompts.few_shot import FewShotPromptTemplate
-from langchain.llms import OpenAI
-
-from few_shot_example.sql_exampler import examplars
-from output_parser import schema_link_parse
-
-
-def schema_linking_prompt_maker(user_query: str, model_name: str,
-    fields_list: List[str],
-    few_shots_example: str):
-  instruction = "# 根据数据库的表结构,找出为每个问题生成SQL查询语句的schema_links\n"
-
-  schema_linking_prompt = "Table {table_name}, columns = {fields_list}\n问题:{user_query}\n分析: 让我们一步一步地思考。".format(
-    table_name=model_name,
-    fields_list=fields_list,
-    user_query=user_query)
-
-  return instruction + few_shots_example + schema_linking_prompt
+from langchain.prompts.example_selector import SemanticSimilarityExampleSelector
 
 
 def schema_linking_exampler(user_query: str,
-    model_name: str,
-    fields_list: List[str]
-) -> str:
-  example_prompt_template = PromptTemplate(
-    input_variables=["table_name", "fields_list", "question", "analysis",
-                     "schema_links"],
-    template="Table {table_name}, columns = {fields_list}\n问题:{question}\n分析:{analysis} 所以Schema_links是:\nSchema_links:{schema_links}")
+                            domain_name: str,
+                            fields_list: List[str],
+                            prior_schema_links: Mapping[str,str],
+                            example_selector: SemanticSimilarityExampleSelector, 
+                            ) -> str:
 
-  instruction = "# 根据数据库的表结构,找出为每个问题生成SQL查询语句的schema_links"
+    prior_schema_links_str = '['+ ','.join(["""'{}'->{}""".format(k,v) for k,v in prior_schema_links.items()]) + ']'
 
-  schema_linking_prompt = "Table {table_name}, columns = {fields_list}\n问题:{question}\n分析: 让我们一步一步地思考。"
+    example_prompt_template = PromptTemplate(input_variables=["table_name", "fields_list", "prior_schema_links", "question", "analysis", "schema_links"],
+                                template="Table {table_name}, columns = {fields_list}, prior_schema_links = {prior_schema_links}\n问题:{question}\n分析:{analysis} 所以Schema_links是:\nSchema_links:{schema_links}")
 
-  schema_linking_example_prompt_template = FewShotPromptTemplate(
-      examples=examplars,
-      example_prompt=example_prompt_template,
-      example_separator="\n\n",
-      prefix=instruction,
-      input_variables=["table_name", "fields_list", "question"],
-      suffix=schema_linking_prompt
-  )
+    instruction = "# 根据数据库的表结构,参考先验信息,找出为每个问题生成SQL查询语句的schema_links"
 
-  schema_linking_example_prompt = schema_linking_example_prompt_template.format(
-    table_name=model_name,
-    fields_list=fields_list,
-    question=user_query)
+    schema_linking_prompt = "Table {table_name}, columns = {fields_list}, prior_schema_links = {prior_schema_links}\n问题:{question}\n分析: 让我们一步一步地思考。"
 
-  return schema_linking_example_prompt
+    schema_linking_example_prompt_template = FewShotPromptTemplate(
+        example_selector=example_selector,
+        example_prompt=example_prompt_template,
+        example_separator="\n\n", 
+        prefix=instruction,
+        input_variables=["table_name", "fields_list", "prior_schema_links", "question"],
+        suffix=schema_linking_prompt
+        )
+
+    schema_linking_example_prompt = schema_linking_example_prompt_template.format(table_name=domain_name,
+                                                                                    fields_list=fields_list,
+                                                                                    prior_schema_links=prior_schema_links_str,
+                                                                                    question=user_query)
+
+    return schema_linking_example_prompt
 
 
 def sql_exampler(user_query: str,
-    model_name: str,
-    schema_link_str: str
-) -> str:
-  instruction = "# 根据schema_links为每个问题生成SQL查询语句"
+                domain_name: str,
+                schema_link_str: str,
+                data_date: str,
+                example_selector: SemanticSimilarityExampleSelector,
+                ) -> str:
+    
+    instruction = "# 根据schema_links为每个问题生成SQL查询语句"
 
-  sql_example_prompt_template = PromptTemplate(
-    input_variables=["question", "table_name", "schema_links", "sql"],
-    template="问题:{question}\nTable {table_name}\nSchema_links:{schema_links}\nSQL:{sql}")
+    sql_example_prompt_template = PromptTemplate(input_variables=["question", "current_date", "table_name", "schema_links", "sql"],
+                                template="问题:{question}\nCurrent_date:{current_date}\nTable {table_name}\nSchema_links:{schema_links}\nSQL:{sql}")
 
-  sql_prompt = "问题:{question}\nTable {table_name}\nSchema_links:{schema_links}\nSQL:"
+    sql_prompt = "问题:{question}\nCurrent_date:{current_date}\nTable {table_name}\nSchema_links:{schema_links}\nSQL:"
 
-  sql_example_prompt_template = FewShotPromptTemplate(
-      examples=examplars,
-      example_prompt=sql_example_prompt_template,
-      example_separator="\n\n",
-      prefix=instruction,
-      input_variables=["question", "table_name", "schema_links"],
-      suffix=sql_prompt
-  )
+    sql_example_prompt_template = FewShotPromptTemplate(
+        example_selector=example_selector,
+        example_prompt=sql_example_prompt_template,
+        example_separator="\n\n", 
+        prefix=instruction,
+        input_variables=["question", "current_date", "table_name", "schema_links"],
+        suffix=sql_prompt
+        )
 
-  sql_example_prompt = sql_example_prompt_template.format(question=user_query,
-                                                          table_name=model_name,
-                                                          schema_links=schema_link_str)
+    sql_example_prompt = sql_example_prompt_template.format(question=user_query,
+                                                            current_date=data_date,
+                                                            table_name=domain_name,
+                                                            schema_links=schema_link_str)
 
-  return sql_example_prompt
+    return sql_example_prompt
diff --git a/chat/core/src/main/python/llm/sql/run.py b/chat/core/src/main/python/llm/sql/run.py
index ea60d7f36..34919799b 100644
--- a/chat/core/src/main/python/llm/sql/run.py
+++ b/chat/core/src/main/python/llm/sql/run.py
@@ -1,6 +1,4 @@
-# -*- coding:utf-8 -*-
-
-from typing import List, Union
+from typing import List, Union, Mapping
 import logging
 import json
 import os
@@ -9,33 +7,54 @@ import sys
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 sys.path.append(os.path.dirname(os.path.abspath(__file__)))
 
-from sql.prompt_maker import schema_linking_exampler, schema_link_parse, \
-  sql_exampler
+from sql.prompt_maker import schema_linking_exampler, sql_exampler
+from sql.constructor import schema_linking_example_selector, sql_example_selector
+from sql.output_parser import schema_link_parse
 
 from util.llm_instance import llm
 
-def query2sql(query_text: str, schema: dict):
-  print("schema: ", schema)
 
-  model_name = schema['modelName']
-  fields_list = schema['fieldNameList']
+def query2sql(query_text: str, 
+            schema : Union[dict, None] = None,
+            current_date: str = None,
+            linking: Union[List[Mapping[str, str]], None] = None
+            ):
+           
+    print("query_text: ", query_text)
+    print("schema: ", schema)
+    print("current_date: ", current_date)
+    print("prior_schema_links: ", linking)
 
-  schema_linking_prompt = schema_linking_exampler(query_text, model_name,
-                                                  fields_list)
-  schema_link_output = llm(schema_linking_prompt)
-  schema_link_str = schema_link_parse(schema_link_output)
+    if linking is not None:
+          prior_schema_links = {item['fieldValue']:item['fieldName'] for item in linking}
+    else:
+          prior_schema_links = {}
 
-  sql_prompt = sql_exampler(query_text, model_name, schema_link_str)
-  sql_output = llm(sql_prompt)
+    model_name = schema['modelName']
+    fields_list = schema['fieldNameList']
 
-  resp = dict()
-  resp['query'] = query_text
-  resp['model'] = model_name
-  resp['fields'] = fields_list
+    schema_linking_prompt = schema_linking_exampler(query_text, model_name, fields_list, prior_schema_links, schema_linking_example_selector)
+    print("schema_linking_prompt->", schema_linking_prompt)
+    schema_link_output = llm(schema_linking_prompt)
+    schema_link_str = schema_link_parse(schema_link_output)
+    
+    sql_prompt = sql_exampler(query_text, model_name, schema_link_str, current_date, sql_example_selector)
+    print("sql_prompt->", sql_prompt)
+    sql_output = llm(sql_prompt)
 
-  resp['schemaLinkingOutput'] = schema_link_output
-  resp['schemaLinkStr'] = schema_link_str
+    resp = dict()
+    resp['query'] = query_text
+    resp['model'] = model_name
+    resp['fields'] = fields_list
+    resp['priorSchemaLinking'] = linking
+    resp['dataDate'] = current_date
 
-  resp['sqlOutput'] = sql_output
+    resp['schemaLinkingOutput'] = schema_link_output
+    resp['schemaLinkStr'] = schema_link_str
+    
+    resp['sqlOutput'] = sql_output
+
+    print("resp: ", resp)
+
+    return resp
 
-  return resp
diff --git a/chat/core/src/main/python/llm/util/chromadb_instance.py b/chat/core/src/main/python/llm/util/chromadb_instance.py
new file mode 100644
index 000000000..f0fe6ce01
--- /dev/null
+++ b/chat/core/src/main/python/llm/util/chromadb_instance.py
@@ -0,0 +1,10 @@
+# -*- coding:utf-8 -*-
+import chromadb
+from chromadb.config import Settings
+
+from run_config import CHROMA_DB_PERSIST_PATH
+
+client = chromadb.Client(Settings(
+    chroma_db_impl="duckdb+parquet",
+    persist_directory=CHROMA_DB_PERSIST_PATH # Optional, defaults to .chromadb/ in the current directory
+))
\ No newline at end of file
diff --git a/docs/images/supersonic_components.png b/docs/images/supersonic_components.png
index 4db266acc..07ebe188d 100644
Binary files a/docs/images/supersonic_components.png and b/docs/images/supersonic_components.png differ
diff --git a/docs/images/wechat_contact.jpeg b/docs/images/wechat_contact.jpeg
new file mode 100644
index 000000000..1d913ecc0
Binary files /dev/null and b/docs/images/wechat_contact.jpeg differ
diff --git a/launchers/chat/src/main/bin/run.sh b/launchers/chat/src/main/bin/run.sh
index 00d517aea..694b87156 100755
--- a/launchers/chat/src/main/bin/run.sh
+++ b/launchers/chat/src/main/bin/run.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 binDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $binDir/../)
+baseDir=$(cd "$sbinDir/.." && pwd -P)
 libDir=$baseDir/lib
 confDir=$baseDir/conf
 webDir=$baseDir/webapp
diff --git a/launchers/chat/src/main/bin/service.sh b/launchers/chat/src/main/bin/service.sh
index fef8ff3a7..24c32d098 100755
--- a/launchers/chat/src/main/bin/service.sh
+++ b/launchers/chat/src/main/bin/service.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 binDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $binDir/../)
+baseDir=$(cd "$sbinDir/.." && pwd -P)
 confDir=$baseDir/conf
 source ${baseDir}/bin/env.sh
 
diff --git a/launchers/semantic/src/main/bin/run.sh b/launchers/semantic/src/main/bin/run.sh
index 00d517aea..79a06ca90 100755
--- a/launchers/semantic/src/main/bin/run.sh
+++ b/launchers/semantic/src/main/bin/run.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 binDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $binDir/../)
+baseDir=$(cd "$binDir/.." && pwd -P)
 libDir=$baseDir/lib
 confDir=$baseDir/conf
 webDir=$baseDir/webapp
diff --git a/launchers/semantic/src/main/bin/service.sh b/launchers/semantic/src/main/bin/service.sh
index fef8ff3a7..f3374d01e 100755
--- a/launchers/semantic/src/main/bin/service.sh
+++ b/launchers/semantic/src/main/bin/service.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 binDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $binDir/../)
+baseDir=$(cd "$binDir/.." && pwd -P)
 confDir=$baseDir/conf
 source ${baseDir}/bin/env.sh
 
diff --git a/launchers/standalone/src/main/bin/run.sh b/launchers/standalone/src/main/bin/run.sh
index 00d517aea..79a06ca90 100755
--- a/launchers/standalone/src/main/bin/run.sh
+++ b/launchers/standalone/src/main/bin/run.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 binDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $binDir/../)
+baseDir=$(cd "$binDir/.." && pwd -P)
 libDir=$baseDir/lib
 confDir=$baseDir/conf
 webDir=$baseDir/webapp
diff --git a/launchers/standalone/src/main/bin/service.sh b/launchers/standalone/src/main/bin/service.sh
index fef8ff3a7..f3374d01e 100755
--- a/launchers/standalone/src/main/bin/service.sh
+++ b/launchers/standalone/src/main/bin/service.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 binDir=$(cd "$(dirname "$0")"; pwd)
-baseDir=$(readlink -f $binDir/../)
+baseDir=$(cd "$binDir/.." && pwd -P)
 confDir=$baseDir/conf
 source ${baseDir}/bin/env.sh
 
diff --git a/pom.xml b/pom.xml
index 466b96143..0651f854f 100644
--- a/pom.xml
+++ b/pom.xml
@@ -63,7 +63,7 @@
         <calcite.avatica.version>1.23.0</calcite.avatica.version>
         <xk.time.version>3.2.4</xk.time.version>
         <jsqlparser.version>4.5</jsqlparser.version>
-        <revision>0.7.2</revision>
+        <revision>0.7.3</revision>
     </properties>
 
     <dependencyManagement>