feat: new knowledge chat scene

1.add new knowledge chat scene
2.format file format

pilot/embedding_engine/knowledge_embedding.py

@@ -4,28 +4,11 @@ from chromadb.errors import NotEnoughElementsException
 from langchain.embeddings import HuggingFaceEmbeddings
 
 from pilot.configs.config import Config
-from pilot.embedding_engine.csv_embedding import CSVEmbedding
 from pilot.embedding_engine.knowledge_type import get_knowledge_embedding
-from pilot.embedding_engine.markdown_embedding import MarkdownEmbedding
-from pilot.embedding_engine.pdf_embedding import PDFEmbedding
-from pilot.embedding_engine.ppt_embedding import PPTEmbedding
-from pilot.embedding_engine.url_embedding import URLEmbedding
-from pilot.embedding_engine.word_embedding import WordEmbedding
 from pilot.vector_store.connector import VectorStoreConnector
 
 CFG = Config()
 
-# KnowledgeEmbeddingType = {
-#     ".txt": (MarkdownEmbedding, {}),
-#     ".md": (MarkdownEmbedding, {}),
-#     ".pdf": (PDFEmbedding, {}),
-#     ".doc": (WordEmbedding, {}),
-#     ".docx": (WordEmbedding, {}),
-#     ".csv": (CSVEmbedding, {}),
-#     ".ppt": (PPTEmbedding, {}),
-#     ".pptx": (PPTEmbedding, {}),
-# }
-
 
 class KnowledgeEmbedding:
     def __init__(
@@ -57,23 +40,6 @@ class KnowledgeEmbedding:
 
     def init_knowledge_embedding(self):
         return get_knowledge_embedding(self.knowledge_type, self.knowledge_source, self.vector_store_config)
-        # if self.file_type == "url":
-        #     embedding = URLEmbedding(
-        #         file_path=self.file_path,
-        #         vector_store_config=self.vector_store_config,
-        #     )
-        #     return embedding
-        # extension = "." + self.file_path.rsplit(".", 1)[-1]
-        # if extension in KnowledgeEmbeddingType:
-        #     knowledge_class, knowledge_args = KnowledgeEmbeddingType[extension]
-        #     embedding = knowledge_class(
-        #         self.file_path,
-        #         vector_store_config=self.vector_store_config,
-        #         **knowledge_args
-        #     )
-        #     return embedding
-        # raise ValueError(f"Unsupported knowledge file type '{extension}'")
-        # return embedding
 
     def similar_search(self, text, topk):
         vector_client = VectorStoreConnector(

pilot/openapi/api_v1/api_v1.py

@@ -14,6 +14,8 @@ from typing import List
 
 from pilot.server.api_v1.api_view_model import Result, ConversationVo, MessageVo, ChatSceneVo
 from pilot.configs.config import Config
+from pilot.openapi.knowledge.knowledge_service import KnowledgeService
+from pilot.openapi.knowledge.request.knowledge_request import KnowledgeSpaceRequest
 from pilot.scene.base_chat import BaseChat
 from pilot.scene.base import ChatScene
 from pilot.scene.chat_factory import ChatFactory
@@ -27,6 +29,7 @@ router = APIRouter()
 CFG = Config()
 CHAT_FACTORY = ChatFactory()
 logger = build_logger("api_v1", LOGDIR + "api_v1.log")
+knowledge_service = KnowledgeService()
 
 
 async def validation_exception_handler(request: Request, exc: RequestValidationError):
@@ -101,9 +104,8 @@ def plugins_select_info():
 
 
 def knowledge_list():
-    knowledge: dict = {}
+    request = KnowledgeSpaceRequest()
-    ### TODO
+    return knowledge_service.get_knowledge_space(request)
-    return knowledge
 
 
 @router.post('/v1/chat/mode/params/list', response_model=Result[dict])
@@ -164,7 +166,7 @@ async def chat_completions(dialogue: ConversationVo = Body()):
     elif ChatScene.ChatExecution == dialogue.chat_mode:
         chat_param.update("plugin_selector", dialogue.select_param)
     elif ChatScene.ChatKnowledge == dialogue.chat_mode:
-        chat_param.update("knowledge_name", dialogue.select_param)
+        chat_param.update("knowledge_space", dialogue.select_param)
 
     chat: BaseChat = CHAT_FACTORY.get_implementation(dialogue.chat_mode, **chat_param)
     if not chat.prompt_template.stream_out:

pilot/scene/chat_knowledge/v1/chat.py

@@ -0,0 +1,66 @@
+from chromadb.errors import NoIndexException
+
+from pilot.scene.base_chat import BaseChat, logger, headers
+from pilot.scene.base import ChatScene
+from pilot.common.sql_database import Database
+from pilot.configs.config import Config
+
+from pilot.common.markdown_text import (
+    generate_markdown_table,
+    generate_htm_table,
+    datas_to_table_html,
+)
+
+from pilot.configs.model_config import (
+    DATASETS_DIR,
+    KNOWLEDGE_UPLOAD_ROOT_PATH,
+    LLM_MODEL_CONFIG,
+    LOGDIR,
+)
+
+from pilot.scene.chat_knowledge.default.prompt import prompt
+from pilot.embedding_engine.knowledge_embedding import KnowledgeEmbedding
+
+CFG = Config()
+
+
+class ChatKnowledge(BaseChat):
+    chat_scene: str = ChatScene.ChatKnowledge.value
+
+    """Number of results to return from the query"""
+
+    def __init__(self,  chat_session_id, user_input, knowledge_space):
+        """ """
+        super().__init__(
+            chat_mode=ChatScene.ChatKnowledge,
+            chat_session_id=chat_session_id,
+            current_user_input=user_input,
+        )
+        vector_store_config = {
+            "vector_store_name": knowledge_space,
+            "vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH,
+        }
+        self.knowledge_embedding_client = KnowledgeEmbedding(
+            model_name=LLM_MODEL_CONFIG[CFG.EMBEDDING_MODEL],
+            vector_store_config=vector_store_config,
+        )
+
+    def generate_input_values(self):
+        try:
+            docs = self.knowledge_embedding_client.similar_search(
+                self.current_user_input, CFG.KNOWLEDGE_SEARCH_TOP_SIZE
+            )
+            context = [d.page_content for d in docs]
+            context = context[:2000]
+            input_values = {"context": context, "question": self.current_user_input}
+        except NoIndexException:
+            raise ValueError(
+                "you have no knowledge space, please add your knowledge space"
+            )
+        return input_values
+
+
+
+    @property
+    def chat_type(self) -> str:
+        return ChatScene.ChatKnowledge.value

pilot/scene/chat_knowledge/v1/out_parser.py

@@ -0,0 +1,19 @@
+import json
+import re
+from abc import ABC, abstractmethod
+from typing import Dict, NamedTuple
+import pandas as pd
+from pilot.utils import build_logger
+from pilot.out_parser.base import BaseOutputParser, T
+from pilot.configs.model_config import LOGDIR
+
+
+logger = build_logger("webserver", LOGDIR + "DbChatOutputParser.log")
+
+
+class NormalChatOutputParser(BaseOutputParser):
+    def parse_prompt_response(self, model_out_text) -> T:
+        return model_out_text
+
+    def get_format_instructions(self) -> str:
+        pass

pilot/scene/chat_knowledge/v1/prompt.py

@@ -0,0 +1,54 @@
+import builtins
+import importlib
+
+from pilot.prompts.prompt_new import PromptTemplate
+from pilot.configs.config import Config
+from pilot.scene.base import ChatScene
+from pilot.common.schema import SeparatorStyle
+
+from pilot.scene.chat_normal.out_parser import NormalChatOutputParser
+
+
+CFG = Config()
+
+PROMPT_SCENE_DEFINE = """A chat between a curious user and an artificial intelligence assistant, who very familiar with database related knowledge. 
+    The assistant gives helpful, detailed, professional and polite answers to the user's questions. """
+
+
+_DEFAULT_TEMPLATE_ZH = """ 基于以下已知的信息, 专业、简要的回答用户的问题,
+            如果无法从提供的内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题" 禁止胡乱编造。 
+            已知内容: 
+            {context}
+            问题:
+            {question}
+"""
+_DEFAULT_TEMPLATE_EN = """ Based on the known information below, provide users with professional and concise answers to their questions. If the answer cannot be obtained from the provided content, please say: "The information provided in the knowledge base is not sufficient to answer this question." It is forbidden to make up information randomly. 
+            known information: 
+            {context}
+            question:
+            {question}
+"""
+
+_DEFAULT_TEMPLATE = (
+    _DEFAULT_TEMPLATE_EN if CFG.LANGUAGE == "en" else _DEFAULT_TEMPLATE_ZH
+)
+
+
+PROMPT_SEP = SeparatorStyle.SINGLE.value
+
+PROMPT_NEED_NEED_STREAM_OUT = True
+
+prompt = PromptTemplate(
+    template_scene=ChatScene.ChatKnowledge.value,
+    input_variables=["context", "question"],
+    response_format=None,
+    template_define=PROMPT_SCENE_DEFINE,
+    template=_DEFAULT_TEMPLATE,
+    stream_out=PROMPT_NEED_NEED_STREAM_OUT,
+    output_parser=NormalChatOutputParser(
+        sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
+    ),
+)
+
+
+CFG.prompt_templates.update({prompt.template_scene: prompt})

pilot/server/webserver.py

@@ -11,6 +11,8 @@ import uuid
 
 import gradio as gr
 
+from pilot.embedding_engine.knowledge_type import KnowledgeType
+
 ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 sys.path.append(ROOT_PATH)
 
@@ -664,7 +666,8 @@ def knowledge_embedding_store(vs_id, files):
             file.name, os.path.join(KNOWLEDGE_UPLOAD_ROOT_PATH, vs_id, filename)
         )
         knowledge_embedding_client = KnowledgeEmbedding(
-            file_path=os.path.join(KNOWLEDGE_UPLOAD_ROOT_PATH, vs_id, filename),
+            knowledge_source=os.path.join(KNOWLEDGE_UPLOAD_ROOT_PATH, vs_id, filename),
+            knowledge_type=KnowledgeType.DOCUMENT.value,
             model_name=LLM_MODEL_CONFIG["text2vec"],
             vector_store_config={
                 "vector_store_name": vector_store_name["vs_name"],