feat(ChatKnowledge): return topk document source (KBQA) (#609)

1. Support score feedback
2. Add method to return topk document source (KBQA)

assets/schema/history.sql

@@ -0,0 +1,18 @@
+CREATE DATABASE history;
+use history;
+CREATE TABLE `chat_feed_back` (
+  `id` bigint(20) NOT NULL AUTO_INCREMENT,
+  `conv_uid` varchar(128) DEFAULT NULL COMMENT '会话id',
+  `conv_index` int(4) DEFAULT NULL COMMENT '第几轮会话',
+  `score` int(1) DEFAULT NULL COMMENT '评分',
+  `ques_type` varchar(32) DEFAULT NULL COMMENT '用户问题类别',
+  `question` longtext DEFAULT NULL COMMENT '用户问题',
+  `knowledge_space` varchar(128) DEFAULT NULL COMMENT '知识库',
+  `messages` longtext DEFAULT NULL COMMENT '评价详情',
+  `user_name` varchar(128) DEFAULT NULL COMMENT '评价人',
+  `gmt_created` datetime DEFAULT NULL,
+  `gmt_modified` datetime DEFAULT NULL,
+  PRIMARY KEY (`id`),
+  UNIQUE KEY `uk_conv` (`conv_uid`,`conv_index`),
+  KEY `idx_conv` (`conv_uid`,`conv_index`)
+) ENGINE=InnoDB AUTO_INCREMENT=0 DEFAULT CHARSET=utf8mb4 COMMENT='用户评分反馈表';

pilot/configs/config.py

@@ -185,6 +185,9 @@ def __init__(self) -> None:
         self.KNOWLEDGE_SEARCH_MAX_TOKEN = int(
             os.getenv("KNOWLEDGE_SEARCH_MAX_TOKEN", 2000)
         )
+        ### Control whether to display the source document of knowledge on the front end.
+        self.KNOWLEDGE_CHAT_SHOW_RELATIONS = False
+
         ### SUMMARY_CONFIG Configuration
         self.SUMMARY_CONFIG = os.getenv("SUMMARY_CONFIG", "FAST")
 

pilot/openapi/api_v1/api_v1.py

@@ -85,6 +85,26 @@ def plugins_select_info():
     return plugins_infos
 
 
+def get_db_list_info():
+    dbs = CFG.LOCAL_DB_MANAGE.get_db_list()
+    params: dict = {}
+    for item in dbs:
+        comment = item["comment"]
+        if comment is not None and len(comment) > 0:
+            params.update({item["db_name"]: comment})
+    return params
+
+
+def knowledge_list_info():
+    """return knowledge space list"""
+    params: dict = {}
+    request = KnowledgeSpaceRequest()
+    spaces = knowledge_service.get_knowledge_space(request)
+    for space in spaces:
+        params.update({space.name: space.desc})
+    return params
+
+
 def knowledge_list():
     """return knowledge space list"""
     params: dict = {}

pilot/openapi/api_v1/feedback/api_fb_v1.py

@@ -0,0 +1,48 @@
+from fastapi import APIRouter, Body, Request
+
+from pilot.openapi.api_v1.feedback.feed_back_model import FeedBackBody
+from pilot.openapi.api_v1.feedback.feed_back_db import (
+    ChatFeedBackDao,
+    ChatFeedBackEntity,
+)
+from pilot.openapi.api_view_model import Result
+
+router = APIRouter()
+chat_feed_back = ChatFeedBackDao()
+
+
+@router.get("/v1/feedback/find", response_model=Result[FeedBackBody])
+async def feed_back_find(conv_uid: str, conv_index: int):
+    rt = chat_feed_back.get_chat_feed_back(conv_uid, conv_index)
+    if rt is not None:
+        return Result.succ(
+            FeedBackBody(
+                conv_uid=rt.conv_uid,
+                conv_index=rt.conv_index,
+                question=rt.question,
+                knowledge_space=rt.knowledge_space,
+                score=rt.score,
+                ques_type=rt.ques_type,
+                messages=rt.messages,
+            )
+        )
+    else:
+        return Result.succ(None)
+
+
+@router.post("/v1/feedback/commit", response_model=Result[bool])
+async def feed_back_commit(request: Request, feed_back_body: FeedBackBody = Body()):
+    chat_feed_back.create_or_update_chat_feed_back(feed_back_body)
+    return Result.succ(True)
+
+
+@router.get("/v1/feedback/select", response_model=Result[dict])
+async def feed_back_select():
+    return Result.succ(
+        {
+            "information": "信息查询",
+            "work_study": "工作学习",
+            "just_fun": "互动闲聊",
+            "others": "其他",
+        }
+    )

pilot/openapi/api_v1/feedback/feed_back_db.py

@@ -0,0 +1,84 @@
+from datetime import datetime
+
+from sqlalchemy import Column, Integer, Text, String, DateTime
+from sqlalchemy.ext.declarative import declarative_base
+
+from pilot.connections.rdbms.base_dao import BaseDao
+from pilot.openapi.api_v1.feedback.feed_back_model import FeedBackBody
+
+Base = declarative_base()
+
+
+class ChatFeedBackEntity(Base):
+    __tablename__ = "chat_feed_back"
+    id = Column(Integer, primary_key=True)
+    conv_uid = Column(String(128))
+    conv_index = Column(Integer)
+    score = Column(Integer)
+    ques_type = Column(String(32))
+    question = Column(Text)
+    knowledge_space = Column(String(128))
+    messages = Column(Text)
+    user_name = Column(String(128))
+    gmt_created = Column(DateTime)
+    gmt_modified = Column(DateTime)
+
+    def __repr__(self):
+        return (
+            f"ChatFeekBackEntity(id={self.id}, conv_index='{self.conv_index}', conv_index='{self.conv_index}', "
+            f"score='{self.score}', ques_type='{self.ques_type}', question='{self.question}', knowledge_space='{self.knowledge_space}', "
+            f"messages='{self.messages}', user_name='{self.user_name}', gmt_created='{self.gmt_created}', gmt_modified='{self.gmt_modified}')"
+        )
+
+
+class ChatFeedBackDao(BaseDao):
+    def __init__(self):
+        super().__init__(database="history", orm_base=Base, create_not_exist_table=True)
+
+    def create_or_update_chat_feed_back(self, feed_back: FeedBackBody):
+        # Todo: We need to have user information first.
+        def_user_name = ""
+
+        session = self.Session()
+        chat_feed_back = ChatFeedBackEntity(
+            conv_uid=feed_back.conv_uid,
+            conv_index=feed_back.conv_index,
+            score=feed_back.score,
+            ques_type=feed_back.ques_type,
+            question=feed_back.question,
+            knowledge_space=feed_back.knowledge_space,
+            messages=feed_back.messages,
+            user_name=def_user_name,
+            gmt_created=datetime.now(),
+            gmt_modified=datetime.now(),
+        )
+        result = (
+            session.query(ChatFeedBackEntity)
+            .filter(ChatFeedBackEntity.conv_uid == feed_back.conv_uid)
+            .filter(ChatFeedBackEntity.conv_index == feed_back.conv_index)
+            .first()
+        )
+        if result is not None:
+            result.score = feed_back.score
+            result.ques_type = feed_back.ques_type
+            result.question = feed_back.question
+            result.knowledge_space = feed_back.knowledge_space
+            result.messages = feed_back.messages
+            result.user_name = def_user_name
+            result.gmt_created = datetime.now()
+            result.gmt_modified = datetime.now()
+        else:
+            session.merge(chat_feed_back)
+        session.commit()
+        session.close()
+
+    def get_chat_feed_back(self, conv_uid: str, conv_index: int):
+        session = self.Session()
+        result = (
+            session.query(ChatFeedBackEntity)
+            .filter(ChatFeedBackEntity.conv_uid == conv_uid)
+            .filter(ChatFeedBackEntity.conv_index == conv_index)
+            .first()
+        )
+        session.close()
+        return result

pilot/openapi/api_v1/feedback/feed_back_model.py

@@ -0,0 +1,25 @@
+from pydantic.main import BaseModel
+
+
+class FeedBackBody(BaseModel):
+    """conv_uid: conversation id"""
+
+    conv_uid: str
+
+    """conv_index: conversation index"""
+    conv_index: int
+
+    """question: human question"""
+    question: str
+
+    """knowledge_space: knowledge space"""
+    knowledge_space: str
+
+    """score: rating of the llm's answer"""
+    score: int
+
+    """ques_type: question type"""
+    ques_type: str
+
+    """messages: rating detail"""
+    messages: str

pilot/scene/chat_knowledge/v1/chat.py

@@ -1,3 +1,4 @@
+import os
 from typing import Dict
 
 from pilot.scene.base_chat import BaseChat
@@ -55,6 +56,21 @@ def __init__(self, chat_param: Dict):
             vector_store_config=vector_store_config,
             embedding_factory=embedding_factory,
         )
+        self.prompt_template.template_is_strict = False
+
+    async def stream_call(self):
+        input_values = self.generate_input_values()
+        async for output in super().stream_call():
+            # Source of knowledge file
+            relations = input_values.get("relations")
+            if (
+                CFG.KNOWLEDGE_CHAT_SHOW_RELATIONS
+                and type(relations) == list
+                and len(relations) > 0
+                and hasattr(output, "text")
+            ):
+                output.text = output.text + "\trelations:" + ",".join(relations)
+            yield output
 
     def generate_input_values(self):
         if self.space_context:
@@ -69,7 +85,14 @@ def generate_input_values(self):
             )
         context = [d.page_content for d in docs]
         context = context[: self.max_token]
-        input_values = {"context": context, "question": self.current_user_input}
+        relations = list(
+            set([os.path.basename(d.metadata.get("source")) for d in docs])
+        )
+        input_values = {
+            "context": context,
+            "question": self.current_user_input,
+            "relations": relations,
+        }
         return input_values
 
     @property

pilot/server/dbgpt_server.py

@@ -29,6 +29,7 @@
 from pilot.openapi.api_v1.api_v1 import router as api_v1
 from pilot.openapi.base import validation_exception_handler
 from pilot.openapi.api_v1.editor.api_editor_v1 import router as api_editor_route_v1
+from pilot.openapi.api_v1.feedback.api_fb_v1 import router as api_fb_v1
 from pilot.commands.disply_type.show_chart_gen import static_message_img_path
 from pilot.model.cluster import initialize_worker_manager_in_client
 from pilot.utils.utils import setup_logging, logging_str_to_uvicorn_level
@@ -72,6 +73,7 @@ def swagger_monkey_patch(*args, **kwargs):
 app.include_router(api_v1, prefix="/api")
 app.include_router(knowledge_router, prefix="/api")
 app.include_router(api_editor_route_v1, prefix="/api")
+app.include_router(api_fb_v1, prefix="/api")
 
 # app.include_router(api_v1)
 app.include_router(knowledge_router)