feat: implement LLM keyword extraction and fusion scoring for table matching

backend/alembic/env.py

@@ -24,14 +24,14 @@
 
 # from apps.system.models.user import SQLModel  # noqa
 # from apps.settings.models.setting_models import SQLModel
-#from apps.chat.models.chat_model import SQLModel
-from apps.terminology.models.terminology_model import SQLModel
-from sqlbot_xpack.custom_prompt.models.custom_prompt_model import SQLModel
+from apps.chat.models.chat_model import SQLModel
+#from apps.terminology.models.terminology_model import SQLModel
+#from sqlbot_xpack.custom_prompt.models.custom_prompt_model import SQLModel
 #from apps.data_training.models.data_training_model import SQLModel
 # from apps.dashboard.models.dashboard_model import SQLModel
 from common.core.config import settings # noqa
 #from apps.datasource.models.datasource import SQLModel
-from apps.system.models.system_model import SQLModel
+#from apps.system.models.system_model import SQLModel
 
 target_metadata = SQLModel.metadata
 

backend/alembic/versions/072_chat_record.py

@@ -0,0 +1,31 @@
+"""072_chat_record
+
+Revision ID: f898322341a5
+Revises: a2e2ecfa5a9c
+Create Date: 2026-09-14 15:54:47.921806
+
+"""
+from alembic import op
+import sqlalchemy as sa
+import sqlmodel.sql.sqltypes
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = 'f898322341a5'
+down_revision = 'a2e2ecfa5a9c'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column('chat_record', sa.Column('extracted_keywords', sa.Text(), nullable=True, comment='关键词'))
+    op.add_column('chat_record', sa.Column('expanded_keywords', sa.Text(), nullable=True, comment='扩展后的关键词'))
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_column('chat_record', 'extracted_keywords')
+    op.drop_column('chat_record', 'expanded_keywords')
+    # ### end Alembic commands ###

backend/apps/chat/curd/chat.py

@@ -933,6 +933,19 @@ def save_sql_answer(session: SessionDep, record_id: int, answer: str) -> ChatRec
     return record
 
 
+def save_extracted_keywords(session: SessionDep, record_id: int, expanded_keywords: str,
+                            extracted_keywords: str = None) -> None:
+    """保存提取的关键词到 record，供后续独立接口复用。"""
+    if not record_id:
+        return
+    values = {'expanded_keywords': expanded_keywords}
+    if extracted_keywords:
+        values['extracted_keywords'] = extracted_keywords
+    stmt = update(ChatRecord).where(and_(ChatRecord.id == record_id)).values(**values)
+    session.execute(stmt)
+    session.commit()
+
+
 def save_analysis_answer(session: SessionDep, record_id: int, answer: str = '') -> ChatRecord:
     if not record_id:
         raise Exception("Record id cannot be None")

backend/apps/chat/models/chat_model.py

@@ -47,6 +47,7 @@ class OperationEnum(Enum):
     FILTER_CUSTOM_PROMPT = '11'
     EXECUTE_SQL = '12'
     GENERATE_PICTURE = '13'
+    EXTRACT_KEYWORDS = '14'
 
 
 class ChatFinishStep(Enum):
@@ -129,6 +130,8 @@ class ChatRecord(SQLModel, table=True):
     analysis_record_id: int = Field(sa_column=Column(BigInteger, nullable=True))
     predict_record_id: int = Field(sa_column=Column(BigInteger, nullable=True))
     regenerate_record_id: int = Field(sa_column=Column(BigInteger, nullable=True))
+    extracted_keywords: str = Field(sa_column=Column(Text, nullable=True))
+    expanded_keywords: str = Field(sa_column=Column(Text, nullable=True))
 
 
 class ChatRecordResult(BaseModel):
@@ -238,6 +241,8 @@ class AiModelQuestion(BaseModel):
     question: str = None
     ai_modal_id: int = None
     ai_modal_name: str = None  # Specific model name
+    extracted_keywords: str = ""  # LLM 提取的核心业务关键词（逗号分隔，未经扩展）
+    expanded_keywords: str = ""  # 经术语同义词扩展后的关键词（用于表匹配）
     engine: str = ""
     db_schema: str = ""
     sql: str = ""
@@ -255,6 +260,10 @@ class AiModelQuestion(BaseModel):
     sample_data: str = ""
     sqlbot_name: str = "SQLBot"
 
+    def extract_keywords_sys_prompt(self) -> str:
+        return get_sql_template().get("extract_keywords").format(lang=self.lang,
+                                                                     sqlbot_name=self.sqlbot_name)
+
     def sql_sys_question(self, db_type: Union[str, DB], enable_query_limit: bool = True):
         templates: dict[str, str] = {}
         _sql_template = get_sql_example_template(db_type)

backend/apps/chat/task/llm.py

@@ -2,6 +2,7 @@
 import concurrent
 import json
 import os
+import re
 import traceback
 import urllib.parse
 import warnings
@@ -30,7 +31,7 @@
 from apps.chat.curd.chat import save_question, save_sql_answer, save_sql, \
     save_error_message, save_sql_exec_data, save_chart_answer, save_chart, \
     finish_record, save_analysis_answer, save_predict_answer, save_predict_data, \
-    save_select_datasource_answer, save_recommend_question_answer, \
+    save_select_datasource_answer, save_recommend_question_answer, save_extracted_keywords, \
     get_old_questions, save_analysis_predict_record, rename_chat, get_chart_config, \
     get_chat_chart_data, list_generate_sql_logs, list_generate_chart_logs, start_log, end_log, \
     get_last_execute_sql_error, format_json_data, format_chart_fields, get_chat_brief_generate, get_chat_predict_data, \
@@ -48,6 +49,7 @@
 from apps.system.crud.parameter_manage import get_groups
 from apps.system.crud.user import user_ws_list
 from apps.system.schemas.system_schema import AssistantOutDsSchema
+from apps.terminology.curd.terminology import expand_with_terminology
 from apps.terminology.curd.terminology import get_terminology_template
 from common.core.config import settings
 from common.core.db import engine
@@ -378,14 +380,16 @@ def filter_terminology_template(self, _session: Session, oid: int = None, ds_id:
             calculate_oid = self.current_assistant.oid if self.current_assistant.type != 4 else self.oid
             if self.current_assistant.type == 1:
                 calculate_ds_id = None
+        # 使用原始提取的关键词（未扩展）进行术语匹配，避免同义词重复查询
+        match_text = self.chat_question.extracted_keywords or self.chat_question.question
         if self.current_assistant and self.current_assistant.type == 1:
             self.chat_question.terminologies, term_list = get_terminology_template(_session,
-                                                                                   self.chat_question.question,
+                                                                                   match_text,
                                                                                    calculate_oid,
                                                                                    None, self.current_assistant.id)
         else:
             self.chat_question.terminologies, term_list = get_terminology_template(_session,
-                                                                                   self.chat_question.question,
+                                                                                   match_text,
                                                                                    calculate_oid,
                                                                                    calculate_ds_id)
 
@@ -447,17 +451,108 @@ def filter_training_template(self, _session: Session, oid: int = None, ds_id: in
                                                                           OperationEnum.FILTER_SQL_EXAMPLE],
                                                                       full_message=example_list)
 
+    def extract_keywords(self, _session: Session) -> tuple[bool, str]:
+        """从用户问题中提取核心业务实体词。
+
+        使用 LLM 提取名词性业务实体，忽略时间词、动作词、修饰词。
+        支持上下文历史，可根据对话上下文理解用户意图。
+        提取结果可复用于表匹配、术语扩展等环节。
+
+        Returns:
+            (is_error, result) 元组：
+                - (False, keywords) 成功，关键词逗号分隔
+                - (True, error_message) 错误（LLM 失败或非查数据意图）
+        """
+        system_prompt = self.chat_question.extract_keywords_sys_prompt()
+
+        keywords_msg: List[Union[BaseMessage, dict[str, Any]]] = []
+        keywords_msg.append(SystemPromptMessage(content=system_prompt))
+
+        # 加载上下文历史（仅取用户的历史提问，不含 AI 生成的 SQL 等回复）
+        last_sql_messages: List[dict[str, Any]] = self.generate_sql_logs[-1].messages if len(
+            self.generate_sql_logs) > 0 else []
+        if self.chat_question.regenerate_record_id:
+            _temp_log = next(
+                filter(lambda obj: obj.pid == self.chat_question.regenerate_record_id, self.generate_sql_logs), None)
+            last_sql_messages: List[dict[str, Any]] = _temp_log.messages if _temp_log else []
+
+        # 排除系统提示词和 AI 回复，只保留用户的历史提问
+        last_user_messages = [
+            obj for obj in last_sql_messages
+            if obj.get("sqlbot_system") != True and obj.get('type') == 'human'
+        ]
+
+        if last_user_messages:
+            last_rounds = get_last_conversation_rounds(last_user_messages, rounds=self.base_message_round_count_limit)
+            for _msg_dict in last_rounds:
+                content = _msg_dict.get('content', '')
+                # 提取 <user-question> 标签内的用户原始提问，排除 error-msg 等干扰信息
+                match = re.search(r'<user-question>(.*?)</user-question>', content, re.DOTALL)
+                if match:
+                    question_text = match.group(1).strip()
+                    if question_text:
+                        keywords_msg.append(HumanMessage(content=question_text))
+
+        # 当前问题
+        keywords_msg.append(HumanMessage(content=self.chat_question.question))
+
+        self.current_logs[OperationEnum.EXTRACT_KEYWORDS] = start_log(
+            session=_session,
+            ai_modal_id=self.chat_question.ai_modal_id,
+            ai_modal_name=self.chat_question.ai_modal_name,
+            operate=OperationEnum.EXTRACT_KEYWORDS,
+            record_id=self.record.id,
+            full_message=[{'type': msg.type, 'content': msg.content} for msg in keywords_msg]
+        )
+
+        full_thinking_text = ''
+        full_text = ''
+        token_usage = {}
+        res = process_stream(self.llm.stream(keywords_msg), token_usage)
+        for chunk in res:
+            if chunk.get('content'):
+                full_text += chunk.get('content')
+            if chunk.get('reasoning_content'):
+                full_thinking_text += chunk.get('reasoning_content')
+
+        result_text = full_text.strip()
+
+        keywords_msg.append(AIMessage(result_text))
+        self.current_logs[OperationEnum.EXTRACT_KEYWORDS] = end_log(
+            session=_session,
+            log=self.current_logs[OperationEnum.EXTRACT_KEYWORDS],
+            full_message=[{'type': msg.type, 'content': msg.content} for msg in keywords_msg],
+            reasoning_content=full_thinking_text if full_thinking_text else None,
+            token_usage=token_usage
+        )
+
+        if result_text.startswith('ERROR:NOT_DATA_QUERY'):
+            # return True, '您的问题似乎不是查询数据的问题，请提出与数据查询相关的问题。'
+            return False, ''
+
+        if result_text == 'EMPTY' or not result_text:
+            self.chat_question.extracted_keywords = self.chat_question.question
+            return False, ''
+
+        self.chat_question.extracted_keywords = result_text
+        return False, result_text
+
     def choose_table_schema(self, _session: Session):
         self.current_logs[OperationEnum.CHOOSE_TABLE] = start_log(session=_session,
                                                                   operate=OperationEnum.CHOOSE_TABLE,
                                                                   record_id=self.record.id,
                                                                   local_operation=True)
+
+        # 使用扩展后的关键词进行表匹配（包含同义词，匹配更全面）
+        keywords = self.chat_question.expanded_keywords or self.chat_question.question
+
         self.chat_question.db_schema, tables = self.out_ds_instance.get_db_schema(
             self.ds.id, self.chat_question.question) if self.out_ds_instance else get_table_schema(
             session=_session,
             current_user=self.current_user,
             ds=self.ds,
-            question=self.chat_question.question)
+            question=self.chat_question.question,
+            keywords=keywords)
 
         # Get sample data for all tables
         if not self.out_ds_instance:
@@ -479,6 +574,12 @@ def generate_analysis(self, _session: Session):
         self.chat_question.data = orjson.dumps(data.get('data')).decode()
         analysis_msg: List[Union[BaseMessage, dict[str, Any]]] = []
 
+        # 从 record 加载已提取的关键词（SQL 生成阶段已持久化）
+        if self.record.extracted_keywords:
+            self.chat_question.extracted_keywords = self.record.extracted_keywords
+        if self.record.expanded_keywords:
+            self.chat_question.expanded_keywords = self.record.expanded_keywords
+
         ds_id = self.ds.id if isinstance(self.ds, CoreDatasource) else None
 
         self.filter_terminology_template(_session, self.oid, ds_id)
@@ -781,6 +882,22 @@ def select_datasource(self, _session: Session):
             oid = self.ds.oid if isinstance(self.ds, CoreDatasource) else 1
             ds_id = self.ds.id if isinstance(self.ds, CoreDatasource) else None
 
+            # 提取关键词（前置步骤，结果复用于术语匹配和表匹配）
+            if settings.TABLE_EMBEDDING_KEYWORD_ENABLED:
+                is_error, kw_result = self.extract_keywords(_session)
+                if is_error:
+                    raise SingleMessageError(kw_result)
+                if kw_result:
+                    self.chat_question.extracted_keywords = kw_result
+                    SQLBotLogUtil.info(f"提取的关键词: {kw_result}")
+                    # 术语同义词扩展（用于表匹配，不用于术语模板查询）
+                    expanded = expand_with_terminology(kw_result, _session, oid)
+                    self.chat_question.expanded_keywords = expanded
+                    SQLBotLogUtil.info(f"扩展后的关键词: {expanded}")
+                    # 持久化到 record，供后续独立接口（如 generate_analysis）使用
+                    save_extracted_keywords(_session, self.record.id,
+                                            expanded_keywords=expanded, extracted_keywords=kw_result)
+
             self.filter_terminology_template(_session, oid, ds_id)
 
             self.filter_training_template(_session, oid, ds_id)
@@ -1247,6 +1364,22 @@ def run_task(self, in_chat: bool = True, stream: bool = True,
                 oid = self.ds.oid if isinstance(self.ds, CoreDatasource) else 1
                 ds_id = self.ds.id if isinstance(self.ds, CoreDatasource) else None
 
+                # 提取关键词（前置步骤，结果复用于术语匹配和表匹配）
+                if settings.TABLE_EMBEDDING_KEYWORD_ENABLED:
+                    is_error, kw_result = self.extract_keywords(_session)
+                    if is_error:
+                        raise SingleMessageError(kw_result)
+                    if kw_result:
+                        self.chat_question.extracted_keywords = kw_result
+                        SQLBotLogUtil.info(f"提取的关键词: {kw_result}")
+                        # 术语同义词扩展（用于表匹配，不用于术语模板查询）
+                        expanded = expand_with_terminology(kw_result, _session, oid)
+                        self.chat_question.expanded_keywords = expanded
+                        SQLBotLogUtil.info(f"扩展后的关键词: {expanded}")
+                        # 持久化到 record，供后续独立接口（如 generate_analysis）使用
+                        save_extracted_keywords(_session, self.record.id,
+                                                expanded_keywords=expanded, extracted_keywords=kw_result)
+
                 self.filter_terminology_template(_session, oid, ds_id)
 
                 self.filter_training_template(_session, oid, ds_id)

backend/apps/datasource/crud/datasource.py

@@ -517,7 +517,8 @@ def get_tables_sample_data(session: SessionDep, current_user: CurrentUser, ds: C
 
 
 def get_table_schema(session: SessionDep, current_user: CurrentUser, ds: CoreDatasource, question: str,
-                     embedding: bool = True, table_list: list[str] = None) -> tuple[str, list]:
+                     embedding: bool = True, table_list: list[str] = None,
+                     keywords: str = None) -> tuple[str, list]:
     schema_str = ""
     table_objs = get_table_obj_by_ds(session=session, current_user=current_user, ds=ds)
     if len(table_objs) == 0:
@@ -538,17 +539,25 @@ def get_table_schema(session: SessionDep, current_user: CurrentUser, ds: CoreDat
         table_comment = ''
         if obj.table.custom_comment:
             table_comment = obj.table.custom_comment.strip()
+        if not table_comment and obj.table.table_comment:
+            table_comment = obj.table.table_comment.strip()
         if table_comment == '':
             schema_table += '\n[\n'
         else:
             schema_table += f", {table_comment}\n[\n"
 
+        fields_info = []
         if obj.fields:
             field_list = []
             for field in obj.fields:
                 field_comment = ''
                 if field.custom_comment:
                     field_comment = field.custom_comment.strip()
+                fields_info.append({
+                    "field_name": field.field_name,
+                    "field_comment": (field.field_comment or '').strip(),
+                    "custom_comment": (field.custom_comment or '').strip()
+                })
                 if field_comment == '':
                     field_list.append(f"({field.field_name}:{field.field_type})")
                 else:
@@ -557,17 +566,22 @@ def get_table_schema(session: SessionDep, current_user: CurrentUser, ds: CoreDat
         schema_table += '\n]\n'
 
         t_obj = {"id": obj.table.id, "table_name": obj.table.table_name, "schema_table": schema_table,
-                 "embedding": obj.table.embedding}
+                 "embedding": obj.table.embedding, "table_comment": table_comment, "fields": fields_info}
         tables.append(t_obj)
         all_tables.append(t_obj)
 
     # 如果没有符合过滤条件的表，直接返回
     if not tables:
         return schema_str, []
 
-    # do table embedding
+    # 执行表 embedding 匹配
     if embedding and tables and settings.TABLE_EMBEDDING_ENABLED:
-        tables = calc_table_embedding(tables, question)
+        try:
+            tables = calc_table_embedding(tables, question, session=session, oid=current_user.oid,
+                                          keywords=keywords)
+        except ValueError as e:
+            # LLM 判断意图不是查数据或关键词提取失败
+            return str(e), []
     # splice schema
     if tables:
         for s in tables: