chore: stash

pkg/api/http/controller/group.py

@@ -14,7 +14,7 @@ preregistered_groups: list[type[RouterGroup]] = []
 """RouterGroup 的预注册列表"""
 
 
-def group_class(name: str, path: str) -> None:
+def group_class(name: str, path: str) -> typing.Callable[[typing.Type[RouterGroup]], typing.Type[RouterGroup]]:
     """注册一个 RouterGroup"""
 
     def decorator(cls: typing.Type[RouterGroup]) -> typing.Type[RouterGroup]:
@@ -120,6 +120,6 @@ class RouterGroup(abc.ABC):
             }
         )
 
-    def http_status(self, status: int, code: int, msg: str) -> quart.Response:
+    def http_status(self, status: int, code: int, msg: str) -> typing.Tuple[quart.Response, int]:
         """返回一个指定状态码的响应"""
-        return self.fail(code, msg), status
+        return (self.fail(code, msg), status)

pkg/api/http/controller/groups/knowledge/base.py

@@ -5,77 +5,49 @@ from ... import group
 @group.group_class('knowledge_base', '/api/v1/knowledge/bases')
 class KnowledgeBaseRouterGroup(group.RouterGroup):
     async def initialize(self) -> None:
-        @self.route('', methods=['POST', 'GET'], endpoint='handle_knowledge_bases')
-        async def handle_knowledge_bases() -> str:
+        @self.route('', methods=['POST', 'GET'])
+        async def handle_knowledge_bases() -> quart.Response:
             if quart.request.method == 'GET':
-                knowledge_bases = await self.ap.knowledge_base_service.get_all_knowledge_bases()
-                bases_list = [
-                    {
-                        'uuid': kb.id,
-                        'name': kb.name,
-                        'description': kb.description,
-                        'embedding_model_uuid': kb.embedding_model_uuid,
-                        'top_k': kb.top_k,
-                    }
-                    for kb in knowledge_bases
-                ]
-                return self.success(data={'bases': bases_list})
+                knowledge_bases = await self.ap.knowledge_service.get_knowledge_bases()
+                return self.success(data={'bases': knowledge_bases})
 
             elif quart.request.method == 'POST':
                 json_data = await quart.request.json
-                knowledge_base_uuid = await self.ap.knowledge_base_service.create_knowledge_base(
-                    json_data.get('name'),
-                    json_data.get('description'),
-                    json_data.get('embedding_model_uuid'),
-                    json_data.get('top_k',5),
-                )
+                knowledge_base_uuid = await self.ap.knowledge_service.create_knowledge_base(json_data)
                 return self.success(data={'uuid': knowledge_base_uuid})
 
+            return self.http_status(405, -1, 'Method not allowed')
+
         @self.route(
             '/<knowledge_base_uuid>',
             methods=['GET', 'DELETE'],
-            endpoint='handle_specific_knowledge_base',
         )
-        async def handle_specific_knowledge_base(knowledge_base_uuid: str) -> str:
+        async def handle_specific_knowledge_base(knowledge_base_uuid: str) -> quart.Response:
             if quart.request.method == 'GET':
-                knowledge_base = await self.ap.knowledge_base_service.get_knowledge_base_by_id(knowledge_base_uuid)
+                knowledge_base = await self.ap.knowledge_service.get_knowledge_base(knowledge_base_uuid)
 
                 if knowledge_base is None:
                     return self.http_status(404, -1, 'knowledge base not found')
 
                 return self.success(
                     data={
-                        'base': {
-                            'name': knowledge_base.name,
-                            'description': knowledge_base.description,
-                            'uuid': knowledge_base.id,
-                            'embedding_model_uuid': knowledge_base.embedding_model_uuid,
-                            'top_k': knowledge_base.top_k,
-                        },
+                        'base': knowledge_base,
                     }
                 )
             elif quart.request.method == 'DELETE':
-                await self.ap.knowledge_base_service.delete_kb_by_id(knowledge_base_uuid)
+                await self.ap.knowledge_service.delete_knowledge_base(knowledge_base_uuid)
                 return self.success({})
 
         @self.route(
             '/<knowledge_base_uuid>/files',
             methods=['GET', 'POST'],
-            endpoint='get_knowledge_base_files',
         )
         async def get_knowledge_base_files(knowledge_base_uuid: str) -> str:
             if quart.request.method == 'GET':
-                files = await self.ap.knowledge_base_service.get_files_by_knowledge_base(knowledge_base_uuid)
+                files = await self.ap.knowledge_service.get_files_by_knowledge_base(knowledge_base_uuid)
                 return self.success(
                     data={
-                        'files': [
-                            {
-                                'id': file.id,
-                                'file_name': file.file_name,
-                                'status': file.status,
-                            }
-                            for file in files
-                        ],
+                        'files': files,
                     }
                 )
 
@@ -86,14 +58,17 @@ class KnowledgeBaseRouterGroup(group.RouterGroup):
                     return self.http_status(400, -1, 'File ID is required')
 
                 # 调用服务层方法将文件与知识库关联
-                await self.ap.knowledge_base_service.relate_file_id_with_kb(knowledge_base_uuid, file_id)
-                return self.success({})
+                task_id = await self.ap.knowledge_service.store_file(knowledge_base_uuid, file_id)
+                return self.success(
+                    {
+                        'task_id': task_id,
+                    }
+                )
 
         @self.route(
             '/<knowledge_base_uuid>/files/<file_id>',
             methods=['DELETE'],
-            endpoint='delete_specific_file_in_kb',
         )
-        async def delete_specific_file_in_kb(file_id: str,knowledge_base_uuid: str) -> str:
-            await self.ap.knowledge_base_service.delete_data_by_file_id(file_id)
+        async def delete_specific_file_in_kb(file_id: str, knowledge_base_uuid: str) -> str:
+            await self.ap.knowledge_service.delete_file(knowledge_base_uuid, file_id)
             return self.success({})

pkg/api/http/service/knowledge.py

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+import uuid
+import sqlalchemy
+
+from ....core import app
+from ....entity.persistence import rag as persistence_rag
+
+
+class KnowledgeService:
+    """知识库服务"""
+
+    ap: app.Application
+
+    def __init__(self, ap: app.Application) -> None:
+        self.ap = ap
+
+    async def get_knowledge_bases(self) -> list[dict]:
+        """获取所有知识库"""
+        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase))
+        knowledge_bases = result.all()
+        return [
+            self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base)
+            for knowledge_base in knowledge_bases
+        ]
+
+    async def get_knowledge_base(self, kb_uuid: str) -> dict | None:
+        """获取知识库"""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
+        )
+        knowledge_base = result.first()
+        if knowledge_base is None:
+            return None
+        return self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base)
+
+    async def create_knowledge_base(self, kb_data: dict) -> str:
+        """创建知识库"""
+        kb_data['uuid'] = str(uuid.uuid4())
+        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.KnowledgeBase).values(kb_data))
+
+        kb = await self.get_knowledge_base(kb_data['uuid'])
+
+        await self.ap.rag_mgr.load_knowledge_base(kb)
+
+        return kb_data['uuid']
+
+    async def update_knowledge_base(self, kb_uuid: str, kb_data: dict) -> None:
+        """更新知识库"""
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.update(persistence_rag.KnowledgeBase)
+            .values(kb_data)
+            .where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
+        )
+        await self.ap.rag_mgr.remove_knowledge_base(kb_uuid)
+
+        kb = await self.get_knowledge_base(kb_uuid)
+
+        await self.ap.rag_mgr.load_knowledge_base(kb)
+
+    async def store_file(self, kb_uuid: str, file_id: str) -> int:
+        """存储文件"""
+        # await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.File).values(kb_id=kb_uuid, file_id=file_id))
+        # await self.ap.rag_mgr.store_file(file_id)
+        runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
+        if runtime_kb is None:
+            raise Exception('Knowledge base not found')
+        return await runtime_kb.store_file(file_id)
+
+    async def get_files_by_knowledge_base(self, kb_uuid: str) -> list[dict]:
+        """获取知识库文件"""
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_rag.File).where(persistence_rag.File.kb_id == kb_uuid)
+        )
+        files = result.all()
+        return [self.ap.persistence_mgr.serialize_model(persistence_rag.File, file) for file in files]
+
+    async def delete_file(self, kb_uuid: str, file_id: str) -> None:
+        """删除文件"""
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.delete(persistence_rag.File).where(persistence_rag.File.uuid == file_id)
+        )
+        # TODO: remove from memory
+
+    async def delete_knowledge_base(self, kb_uuid: str) -> None:
+        """删除知识库"""
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.delete(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
+        )
+        # TODO: remove from memory

pkg/core/app.py

@@ -22,6 +22,7 @@ from ..api.http.service import user as user_service
 from ..api.http.service import model as model_service
 from ..api.http.service import pipeline as pipeline_service
 from ..api.http.service import bot as bot_service
+from ..api.http.service import knowledge as knowledge_service
 from ..discover import engine as discover_engine
 from ..storage import mgr as storagemgr
 from ..utils import logcache
@@ -48,6 +49,8 @@ class Application:
 
     model_mgr: llm_model_mgr.ModelManager = None
 
+    rag_mgr: rag_mgr.RAGManager = None
+
     # TODO 移动到 pipeline 里
     tool_mgr: llm_tool_mgr.ToolManager = None
 
@@ -112,7 +115,7 @@ class Application:
 
     bot_service: bot_service.BotService = None
 
-    knowledge_base_service: rag_mgr.RAGManager = None
+    knowledge_service: knowledge_service.KnowledgeService = None
 
     def __init__(self):
         pass

pkg/core/stages/build_app.py

@@ -17,6 +17,7 @@ from ...api.http.service import user as user_service
 from ...api.http.service import model as model_service
 from ...api.http.service import pipeline as pipeline_service
 from ...api.http.service import bot as bot_service
+from ...api.http.service import knowledge as knowledge_service
 from ...discover import engine as discover_engine
 from ...storage import mgr as storagemgr
 from ...utils import logcache
@@ -89,6 +90,10 @@ class BuildAppStage(stage.BootingStage):
         await pipeline_mgr.initialize()
         ap.pipeline_mgr = pipeline_mgr
 
+        rag_mgr_inst = rag_mgr.RAGManager(ap)
+        await rag_mgr_inst.initialize_rag_system()
+        ap.rag_mgr = rag_mgr_inst
+
         http_ctrl = http_controller.HTTPController(ap)
         await http_ctrl.initialize()
         ap.http_ctrl = http_ctrl
@@ -102,15 +107,14 @@ class BuildAppStage(stage.BootingStage):
         embedding_models_service_inst = model_service.EmbeddingModelsService(ap)
         ap.embedding_models_service = embedding_models_service_inst
 
-        knowledge_base_service_inst = rag_mgr.RAGManager(ap)
-        await knowledge_base_service_inst.initialize_rag_system()
-        ap.knowledge_base_service = knowledge_base_service_inst
-
         pipeline_service_inst = pipeline_service.PipelineService(ap)
         ap.pipeline_service = pipeline_service_inst
 
         bot_service_inst = bot_service.BotService(ap)
         ap.bot_service = bot_service_inst
 
+        knowledge_service_inst = knowledge_service.KnowledgeService(ap)
+        ap.knowledge_service = knowledge_service_inst
+
         ctrl = controller.Controller(ap)
         ap.ctrl = ctrl

pkg/entity/persistence/rag.py

@@ -1,51 +1,50 @@
-from sqlalchemy import create_engine, Column, String, Text, DateTime, LargeBinary, Integer
-from sqlalchemy.orm import declarative_base, sessionmaker
-from datetime import datetime
-import os
-import uuid
+import sqlalchemy
+from .base import Base
 
-Base = declarative_base()
-DATABASE_URL = os.getenv('DATABASE_URL', 'sqlite:///./rag_knowledge.db')
-print("Using database URL:", DATABASE_URL)
+# Base = declarative_base()
+# DATABASE_URL = os.getenv('DATABASE_URL', 'sqlite:///./rag_knowledge.db')
+# print("Using database URL:", DATABASE_URL)
 
 
-engine = create_engine(DATABASE_URL, connect_args={'check_same_thread': False})
+# engine = create_engine(DATABASE_URL, connect_args={'check_same_thread': False})
 
-SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+# SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
 
-def create_db_and_tables():
-    """Creates all database tables defined in the Base."""
-    Base.metadata.create_all(bind=engine)
-    print('Database tables created or already exist.')
+# def create_db_and_tables():
+#     """Creates all database tables defined in the Base."""
+#     Base.metadata.create_all(bind=engine)
+#     print('Database tables created or already exist.')
 
 
 class KnowledgeBase(Base):
-    __tablename__ = 'kb'
-    id = Column(String, primary_key=True, index=True)
-    name = Column(String, index=True)
-    description = Column(Text)
-    created_at = Column(DateTime, default=datetime.utcnow)
-    embedding_model_uuid = Column(String, default='')
-    top_k = Column(Integer, default=5)
+    __tablename__ = 'knowledge_bases'
+    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+    name = sqlalchemy.Column(sqlalchemy.String, index=True)
+    description = sqlalchemy.Column(sqlalchemy.Text)
+    created_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now())
+    embedding_model_uuid = sqlalchemy.Column(sqlalchemy.String, default='')
+    top_k = sqlalchemy.Column(sqlalchemy.Integer, default=5)
+
 
 class File(Base):
-    __tablename__ = 'file'
-    id = Column(String, primary_key=True, index=True)
-    kb_id = Column(String, nullable=True)  
-    file_name = Column(String)
-    path = Column(String)
-    created_at = Column(DateTime, default=datetime.utcnow)
-    file_type = Column(String)
-    status = Column(Integer, default=0)  # 0: uploaded and processing, 1: completed, 2: failed
+    __tablename__ = 'knowledge_base_files'
+    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+    kb_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    file_name = sqlalchemy.Column(sqlalchemy.String)
+    extension = sqlalchemy.Column(sqlalchemy.String)
+    created_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now())
+    status = sqlalchemy.Column(sqlalchemy.String, default='pending')  # pending, processing, completed, failed
+
 
 class Chunk(Base):
-    __tablename__ = 'chunks'
-    id = Column(String, primary_key=True, default=lambda: str(uuid.uuid4()))
-    file_id = Column(String, nullable=True)
-    text = Column(Text)
+    __tablename__ = 'knowledge_base_chunks'
+    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+    file_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
+    text = sqlalchemy.Column(sqlalchemy.Text)
 
-class Vector(Base):
-    __tablename__ = 'vectors'
-    id = Column(String, primary_key=True, index=True)
-    chunk_id = Column(String, nullable=True)
-    embedding = Column(LargeBinary)
+
+# class Vector(Base):
+#     __tablename__ = 'knowledge_base_vectors'
+#     uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+#     chunk_id = sqlalchemy.Column(sqlalchemy.String, nullable=True)
+#     embedding = sqlalchemy.Column(sqlalchemy.LargeBinary)

pkg/platform/logger.py

@@ -119,7 +119,7 @@ class EventLogger:
     async def _truncate_logs(self):
         if len(self.logs) > MAX_LOG_COUNT:
             for i in range(DELETE_COUNT_PER_TIME):
-                for image_key in self.logs[i].images:
+                for image_key in self.logs[i].images:  # type: ignore
                     await self.ap.storage_mgr.storage_provider.delete(image_key)
             self.logs = self.logs[DELETE_COUNT_PER_TIME:]
 

pkg/rag/knowledge/mgr.py

@@ -1,149 +1,189 @@
-
 from __future__ import annotations
 import os
 import asyncio
+import traceback
 import uuid
 from pkg.rag.knowledge.services.parser import FileParser
 from pkg.rag.knowledge.services.chunker import Chunker
-from pkg.rag.knowledge.services.database import create_db_and_tables, SessionLocal, KnowledgeBase, File, Chunk
+from pkg.rag.knowledge.services.database import (
+    KnowledgeBase,
+    File,
+    Chunk,
+)
 from pkg.core import app
 from pkg.rag.knowledge.services.embedder import Embedder
 from pkg.rag.knowledge.services.retriever import Retriever
 from pkg.rag.knowledge.services.chroma_manager import ChromaIndexManager
-from ...entity.persistence import model as persistence_model
+from pkg.core import taskmgr
+from ...entity.persistence import rag as persistence_rag
 import sqlalchemy
 
 
+class RuntimeKnowledgeBase:
+    ap: app.Application
+
+    knowledge_base_entity: persistence_rag.KnowledgeBase
+
+    chroma_manager: ChromaIndexManager
+
+    parser: FileParser
+
+    chunker: Chunker
+
+    embedder: Embedder
+
+    retriever: Retriever
+
+    def __init__(self, ap: app.Application, knowledge_base_entity: persistence_rag.KnowledgeBase):
+        self.ap = ap
+        self.knowledge_base_entity = knowledge_base_entity
+        self.chroma_manager = ChromaIndexManager(ap=self.ap)
+        self.parser = FileParser(ap=self.ap)
+        self.chunker = Chunker(ap=self.ap)
+        self.embedder = Embedder(ap=self.ap, chroma_manager=self.chroma_manager)
+        self.retriever = Retriever(ap=self.ap, chroma_manager=self.chroma_manager)
+
+    async def initialize(self):
+        pass
+
+    async def _store_file_task(self, file: persistence_rag.File, task_context: taskmgr.TaskContext):
+        try:
+            # set file status to processing
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.update(persistence_rag.File)
+                .where(persistence_rag.File.uuid == file.uuid)
+                .values(status='processing')
+            )
+
+            task_context.set_current_action('Parsing file')
+            # parse file
+            text = await self.parser.parse(file.file_name, file.extension)
+            if not text:
+                raise Exception(f'No text extracted from file {file.file_name}')
+
+            task_context.set_current_action('Chunking file')
+            # chunk file
+            chunks_texts = await self.chunker.chunk(text)
+            if not chunks_texts:
+                raise Exception(f'No chunks extracted from file {file.file_name}')
+
+            task_context.set_current_action('Embedding chunks')
+            # embed chunks
+            await self.embedder.embed_and_store(
+                file_id=file.uuid, chunks=chunks_texts, embedding_model=self.knowledge_base_entity.embedding_model_uuid
+            )
+
+            # set file status to completed
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.update(persistence_rag.File)
+                .where(persistence_rag.File.uuid == file.uuid)
+                .values(status='completed')
+            )
+
+        except Exception as e:
+            self.ap.logger.error(f'Error storing file {file.file_id}: {e}')
+            # set file status to failed
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.update(persistence_rag.File)
+                .where(persistence_rag.File.uuid == file.uuid)
+                .values(status='failed')
+            )
+
+            raise
+
+    async def store_file(self, file_id: str) -> int:
+        # pre checking
+        if not await self.ap.storage_mgr.storage_provider.exists(file_id):
+            raise Exception(f'File {file_id} not found')
+
+        file_uuid = str(uuid.uuid4())
+        kb_id = self.knowledge_base_entity.uuid
+        file_name = file_id
+        extension = os.path.splitext(file_id)[1].lstrip('.')
+
+        file = persistence_rag.File(
+            uuid=file_uuid,
+            kb_id=kb_id,
+            file_name=file_name,
+            extension=extension,
+            status='pending',
+        )
+
+        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.File).values(**file.to_dict()))
+
+        # run background task asynchronously
+        ctx = taskmgr.TaskContext.new()
+        wrapper = self.ap.task_mgr.create_user_task(
+            self._store_file_task(file, task_context=ctx),
+            kind='knowledge-operation',
+            name=f'knowledge-store-file-{file_id}',
+            label=f'Store file {file_id}',
+            context=ctx,
+        )
+        return wrapper.id
+
+    async def dispose(self):
+        pass
+
+
 class RAGManager:
     ap: app.Application
 
+    knowledge_bases: list[RuntimeKnowledgeBase]
+
     def __init__(self, ap: app.Application):
         self.ap = ap
-        self.chroma_manager = ChromaIndexManager()
-        self.parser = FileParser()
-        self.chunker = Chunker()
-        self.embedder = Embedder(ap=self.ap, chroma_manager=self.chroma_manager)
-        self.retriever = Retriever(ap=self.ap, chroma_manager=self.chroma_manager)
+        self.knowledge_bases = []
 
-    async def initialize_rag_system(self):
-        """Initializes the RAG system by creating database tables."""
-        await asyncio.to_thread(create_db_and_tables)
+    async def initialize(self):
+        pass
 
-    async def create_knowledge_base(
-        self, kb_name: str, kb_description: str, embedding_model_uuid: str = '', top_k: int = 5
-    ):
-        """
-        Creates a new knowledge base if it doesn't already exist.
-        """
-        try:
-            if not kb_name:
-                raise ValueError('Knowledge base name must be set while creating.')
+    async def load_knowledge_bases_from_db(self):
+        self.ap.logger.info('Loading knowledge bases from db...')
 
-            def _create_kb_sync():
-                session = SessionLocal()
-                try:
-                    kb = session.query(KnowledgeBase).filter_by(name=kb_name).first()
-                    if not kb:
-                        id = str(uuid.uuid4())
-                        new_kb = KnowledgeBase(
-                            name=kb_name,
-                            description=kb_description,
-                            embedding_model_uuid=embedding_model_uuid,
-                            top_k=top_k,
-                            id=id,
-                        )
-                        session.add(new_kb)
-                        session.commit()
-                        session.refresh(new_kb)
-                        self.ap.logger.info(f"Knowledge Base '{kb_name}' created.")
-                        print(embedding_model_uuid)
-                        return new_kb.id
-                    else:
-                        self.ap.logger.info(f"Knowledge Base '{kb_name}' already exists.")
-                except Exception as e:
-                    session.rollback()
-                    self.ap.logger.error(f"Error in _create_kb_sync for '{kb_name}': {str(e)}", exc_info=True)
-                    raise
-                finally:
-                    session.close()
+        self.knowledge_bases = []
 
-            return await asyncio.to_thread(_create_kb_sync)
-        except Exception as e:
-            self.ap.logger.error(f"Error creating knowledge base '{kb_name}': {str(e)}", exc_info=True)
-            raise
+        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase))
 
-    async def get_all_knowledge_bases(self):
-        """
-        Retrieves all knowledge bases from the database.
-        """
-        try:
+        knowledge_bases = result.all()
 
-            def _get_all_kbs_sync():
-                session = SessionLocal()
-                try:
-                    return session.query(KnowledgeBase).all()
-                finally:
-                    session.close()
+        for knowledge_base in knowledge_bases:
+            try:
+                await self.load_knowledge_base(knowledge_base)
+            except Exception as e:
+                self.ap.logger.error(
+                    f'Error loading knowledge base {knowledge_base.uuid}: {e}\n{traceback.format_exc()}'
+                )
 
-            return await asyncio.to_thread(_get_all_kbs_sync)
-        except Exception as e:
-            self.ap.logger.error(f'Error retrieving knowledge bases: {str(e)}', exc_info=True)
-            return []
+    async def load_knowledge_base(
+        self,
+        knowledge_base_entity: persistence_rag.KnowledgeBase | sqlalchemy.Row | dict,
+    ) -> RuntimeKnowledgeBase:
+        if isinstance(knowledge_base_entity, sqlalchemy.Row):
+            knowledge_base_entity = persistence_rag.KnowledgeBase(**knowledge_base_entity._mapping)
+        elif isinstance(knowledge_base_entity, dict):
+            knowledge_base_entity = persistence_rag.KnowledgeBase(**knowledge_base_entity)
 
-    async def get_knowledge_base_by_id(self, kb_id: str):
-        """
-        Retrieves a specific knowledge base by its ID.
-        """
-        try:
+        runtime_knowledge_base = RuntimeKnowledgeBase(ap=self.ap, knowledge_base_entity=knowledge_base_entity)
 
-            def _get_kb_sync(kb_id_param):
-                session = SessionLocal()
-                try:
-                    return session.query(KnowledgeBase).filter_by(id=kb_id_param).first()
-                finally:
-                    session.close()
+        await runtime_knowledge_base.initialize()
 
-            return await asyncio.to_thread(_get_kb_sync, kb_id)
-        except Exception as e:
-            self.ap.logger.error(f'Error retrieving knowledge base with ID {kb_id}: {str(e)}', exc_info=True)
-            return None
+        self.knowledge_bases.append(runtime_knowledge_base)
 
-    async def get_files_by_knowledge_base(self, kb_id: str):
-        """
-        Retrieves files associated with a specific knowledge base by querying the File table directly.
-        """
-        try:
+        return runtime_knowledge_base
 
-            def _get_files_sync(kb_id_param):
-                session = SessionLocal()
-                try:
-                    return session.query(File).filter_by(kb_id=kb_id_param).all()
-                finally:
-                    session.close()
+    async def get_knowledge_base_by_uuid(self, kb_uuid: str) -> RuntimeKnowledgeBase | None:
+        for kb in self.knowledge_bases:
+            if kb.knowledge_base_entity.uuid == kb_uuid:
+                return kb
+        return None
 
-            return await asyncio.to_thread(_get_files_sync, kb_id)
-        except Exception as e:
-            self.ap.logger.error(f'Error retrieving files for knowledge base ID {kb_id}: {str(e)}', exc_info=True)
-            return []
-
-    async def get_all_files(self):
-        """
-        Retrieves all files stored in the database, regardless of their association
-        with any specific knowledge base.
-        """
-        try:
-
-            def _get_all_files_sync():
-                session = SessionLocal()
-                try:
-                    return session.query(File).all()
-                finally:
-                    session.close()
-
-            return await asyncio.to_thread(_get_all_files_sync)
-        except Exception as e:
-            self.ap.logger.error(f'Error retrieving all files: {str(e)}', exc_info=True)
-            return []
+    async def remove_knowledge_base(self, kb_uuid: str):
+        for kb in self.knowledge_bases:
+            if kb.knowledge_base_entity.uuid == kb_uuid:
+                await kb.dispose()
+                self.knowledge_bases.remove(kb)
+                return
 
     async def store_data(self, file_path: str, kb_id: str, file_type: str, file_id: str = None):
         """
@@ -220,7 +260,8 @@ class RAGManager:
                     await self.ap.storage_mgr.storage_provider.delete(file_id)
                 except Exception as e:
                     self.ap.logger.error(
-                        f'Error deleting file from storage for file_id {file_id}: {str(e)}', exc_info=True
+                        f'Error deleting file from storage for file_id {file_id}: {str(e)}',
+                        exc_info=True,
                     )
                 self.ap.logger.info(f'Deleted file record for file_id: {file_id}')
             else:
@@ -273,7 +314,10 @@ class RAGManager:
                     )
             except Exception as kb_del_e:
                 session.rollback()
-                self.ap.logger.error(f'Error deleting KnowledgeBase record for ID {kb_id}: {kb_del_e}', exc_info=True)
+                self.ap.logger.error(
+                    f'Error deleting KnowledgeBase record for ID {kb_id}: {kb_del_e}',
+                    exc_info=True,
+                )
                 raise
             finally:
                 session.close()
@@ -283,7 +327,8 @@ class RAGManager:
             if session.is_active:
                 session.rollback()
             self.ap.logger.error(
-                f'Error during overall knowledge base deletion for ID {kb_id}: {str(e)}', exc_info=True
+                f'Error during overall knowledge base deletion for ID {kb_id}: {str(e)}',
+                exc_info=True,
             )
             raise
         finally:

pkg/rag/knowledge/services/chroma_manager.py

@@ -1,43 +1,43 @@
-
 import numpy as np
 import logging
 from chromadb import PersistentClient
-import os
+from pkg.core import app
 
 logger = logging.getLogger(__name__)
 
+
 class ChromaIndexManager:
-    def __init__(self, collection_name: str = "default_collection"):
-        self.logger = logging.getLogger(self.__class__.__name__)
-        chroma_data_path = os.path.abspath(os.path.join(__file__, "../../../../../../data/chroma"))
-        os.makedirs(chroma_data_path, exist_ok=True)
+    def __init__(self, ap: app.Application, collection_name: str = 'default_collection'):
+        self.ap = ap
+        chroma_data_path = './data/chroma'
         self.client = PersistentClient(path=chroma_data_path)
         self._collection_name = collection_name
         self._collection = None
 
-        self.logger.info(f"ChromaIndexManager initialized. Collection name: {self._collection_name}")
+        self.ap.logger.info(f'ChromaIndexManager initialized. Collection name: {self._collection_name}')
 
     @property
     def collection(self):
         if self._collection is None:
             self._collection = self.client.get_or_create_collection(name=self._collection_name)
-            self.logger.info(f"Chroma collection '{self._collection_name}' accessed/created.")
+            self.ap.logger.info(f"Chroma collection '{self._collection_name}' accessed/created.")
         return self._collection
 
-    def add_embeddings_sync(self, file_ids: list[int], chunk_ids: list[int], embeddings: np.ndarray, documents: list[str]):
-        if embeddings.shape[0] != len(chunk_ids) or embeddings.shape[0] != len(file_ids) or embeddings.shape[0] != len(documents):
-            raise ValueError("Embedding, file_id, chunk_id, and document count mismatch.")
+    def add_embeddings_sync(
+        self, file_ids: list[int], chunk_ids: list[int], embeddings: np.ndarray, documents: list[str]
+    ):
+        if (
+            embeddings.shape[0] != len(chunk_ids)
+            or embeddings.shape[0] != len(file_ids)
+            or embeddings.shape[0] != len(documents)
+        ):
+            raise ValueError('Embedding, file_id, chunk_id, and document count mismatch.')
 
-        chroma_ids = [f"{file_id}_{chunk_id}" for file_id, chunk_id in zip(file_ids, chunk_ids)]
-        metadatas = [{"file_id": fid, "chunk_id": cid} for fid, cid in zip(file_ids, chunk_ids)]
+        chroma_ids = [f'{file_id}_{chunk_id}' for file_id, chunk_id in zip(file_ids, chunk_ids)]
+        metadatas = [{'file_id': fid, 'chunk_id': cid} for fid, cid in zip(file_ids, chunk_ids)]
 
         self.logger.debug(f"Adding {len(embeddings)} embeddings to Chroma collection '{self._collection_name}'.")
-        self.collection.add(
-            embeddings=embeddings.tolist(),
-            ids=chroma_ids,
-            metadatas=metadatas,
-            documents=documents
-        )
+        self.collection.add(embeddings=embeddings.tolist(), ids=chroma_ids, metadatas=metadatas, documents=documents)
         self.logger.info(f"Added {len(embeddings)} embeddings to Chroma collection '{self._collection_name}'.")
 
     def search_sync(self, query_embedding: np.ndarray, k: int = 5):
@@ -54,12 +54,14 @@ class ChromaIndexManager:
             query_embeddings=query_embedding.tolist(),
             n_results=k,
             # REMOVE 'ids' from the include list. It's returned by default.
-            include=["metadatas", "distances", "documents"]
+            include=['metadatas', 'distances', 'documents'],
         )
-        self.logger.debug(f"Chroma search returned {len(results.get('ids', [[]])[0])} results.")
+        self.logger.debug(f'Chroma search returned {len(results.get("ids", [[]])[0])} results.')
         return results
 
     def delete_by_file_id_sync(self, file_id: int):
-        self.logger.info(f"Deleting embeddings for file_id: {file_id} from Chroma collection '{self._collection_name}'.")
-        self.collection.delete(where={"file_id": file_id})
-        self.logger.info(f"Deleted embeddings for file_id: {file_id} from Chroma.")
+        self.logger.info(
+            f"Deleting embeddings for file_id: {file_id} from Chroma collection '{self._collection_name}'."
+        )
+        self.collection.delete(where={'file_id': file_id})
+        self.logger.info(f'Deleted embeddings for file_id: {file_id} from Chroma.')

pkg/rag/knowledge/services/chunker.py

@@ -1,21 +1,26 @@
 # services/chunker.py
 import logging
 from typing import List
-from pkg.rag.knowledge.services.base_service import BaseService # Assuming BaseService provides _run_sync
+from pkg.rag.knowledge.services.base_service import BaseService  # Assuming BaseService provides _run_sync
+from pkg.core import app
 
 logger = logging.getLogger(__name__)
 
+
 class Chunker(BaseService):
     """
     A class for splitting long texts into smaller, overlapping chunks.
     """
-    def __init__(self, chunk_size: int = 500, chunk_overlap: int = 50):
-        super().__init__() # Initialize BaseService
-        self.logger = logging.getLogger(self.__class__.__name__)
+
+    def __init__(self, ap: app.Application, chunk_size: int = 500, chunk_overlap: int = 50):
+        super().__init__(ap)  # Initialize BaseService
+        self.ap = ap
         self.chunk_size = chunk_size
         self.chunk_overlap = chunk_overlap
         if self.chunk_overlap >= self.chunk_size:
-            self.logger.warning("Chunk overlap is greater than or equal to chunk size. This may lead to empty or malformed chunks.")
+            self.logger.warning(
+                'Chunk overlap is greater than or equal to chunk size. This may lead to empty or malformed chunks.'
+            )
 
     def _split_text_sync(self, text: str) -> List[str]:
         """
@@ -27,18 +32,19 @@ class Chunker(BaseService):
         # words = text.split()
         # chunks = []
         # current_chunk = []
-        
+
         # for word in words:
         #     current_chunk.append(word)
         #     if len(current_chunk) > self.chunk_size:
         #         chunks.append(" ".join(current_chunk[:self.chunk_size]))
         #         current_chunk = current_chunk[self.chunk_size - self.chunk_overlap:]
-        
+
         # if current_chunk:
         #     chunks.append(" ".join(current_chunk))
-        
+
         # A more robust chunking strategy (e.g., using recursive character text splitter)
         from langchain.text_splitter import RecursiveCharacterTextSplitter
+
         text_splitter = RecursiveCharacterTextSplitter(
             chunk_size=self.chunk_size,
             chunk_overlap=self.chunk_overlap,
@@ -51,8 +57,8 @@ class Chunker(BaseService):
         """
         Asynchronously chunks a given text into smaller pieces.
         """
-        self.logger.info(f"Chunking text (length: {len(text)})...")
+        self.ap.logger.info(f'Chunking text (length: {len(text)})...')
         # Run the synchronous splitting logic in a separate thread
         chunks = await self._run_sync(self._split_text_sync, text)
-        self.logger.info(f"Text chunked into {len(chunks)} pieces.")
-        return chunks
+        self.ap.logger.info(f'Text chunked into {len(chunks)} pieces.')
+        return chunks

pkg/rag/knowledge/services/embedder.py

@@ -7,16 +7,10 @@ from sqlalchemy.orm import Session
 from pkg.rag.knowledge.services.base_service import BaseService
 from pkg.rag.knowledge.services.database import Chunk, SessionLocal
 from pkg.rag.knowledge.services.chroma_manager import ChromaIndexManager
-from sqlalchemy.orm import declarative_base, sessionmaker
 from ....core import app
-from ....entity.persistence import model as persistence_model
-import sqlalchemy
 from ....provider.modelmgr.requester import RuntimeEmbeddingModel
 
 
-base = declarative_base()
-logger = logging.getLogger(__name__)
-
 class Embedder(BaseService):
     def __init__(self, ap: app.Application, chroma_manager: ChromaIndexManager = None) -> None:
         super().__init__()
@@ -30,61 +24,66 @@ class Embedder(BaseService):
         This function assumes it's called within a context where the session
         will be committed/rolled back and closed by the caller.
         """
-        self.logger.debug(f"Saving {len(chunks_texts)} chunks for file_id {file_id} to DB (sync).")
+        self.logger.debug(f'Saving {len(chunks_texts)} chunks for file_id {file_id} to DB (sync).')
         chunk_objects = []
         for text in chunks_texts:
             chunk = Chunk(file_id=file_id, text=text)
             session.add(chunk)
             chunk_objects.append(chunk)
-        session.flush() # This populates the .id attribute for each new chunk object
-        self.logger.debug(f"Successfully added {len(chunk_objects)} chunk entries to DB.")
+        session.flush()  # This populates the .id attribute for each new chunk object
+        self.logger.debug(f'Successfully added {len(chunk_objects)} chunk entries to DB.')
         return chunk_objects
 
-    async def embed_and_store(self, file_id: int, chunks: List[str], embedding_model: RuntimeEmbeddingModel) -> List[Chunk]:
+    async def embed_and_store(
+        self, file_id: int, chunks: List[str], embedding_model: RuntimeEmbeddingModel
+    ) -> List[Chunk]:
         if not embedding_model:
-            raise RuntimeError("Embedding model not loaded. Please check Embedder initialization.")
+            raise RuntimeError('Embedding model not loaded. Please check Embedder initialization.')
 
-        session = SessionLocal() # Start a session that will live for the whole operation
+        session = SessionLocal()  # Start a session that will live for the whole operation
         chunk_objects = []
         try:
             # 1. Save chunks to the relational database first to get their IDs
             #    We call _db_save_chunks_sync directly without _run_sync's session management
             #    because we manage the session here across multiple async calls.
             chunk_objects = await asyncio.to_thread(self._db_save_chunks_sync, session, file_id, chunks)
-            session.commit() # Commit chunks to make their IDs permanent and accessible
+            session.commit()  # Commit chunks to make their IDs permanent and accessible
 
             if not chunk_objects:
-                self.logger.warning(f"No chunk objects created for file_id {file_id}. Skipping embedding and Chroma storage.")
+                self.logger.warning(
+                    f'No chunk objects created for file_id {file_id}. Skipping embedding and Chroma storage.'
+                )
                 return []
-            
+
             # get the embeddings for the chunks
-            embeddings = []
-            i = 0
-            while i <len(chunks):
-                chunk = chunks[i]
+            embeddings: list[list[float]] = []
+
+            for chunk in chunks:
                 result = await embedding_model.requester.invoke_embedding(
                     model=embedding_model,
                     input_text=chunk,
                 )
                 embeddings.append(result)
-                i += 1
-            
+
             embeddings_np = np.array(embeddings, dtype=np.float32)
 
-            self.logger.info("Saving embeddings to Chroma...")
-            chunk_ids = [c.id for c in chunk_objects] 
+            self.logger.info('Saving embeddings to Chroma...')
+            chunk_ids = [c.id for c in chunk_objects]
             file_ids_for_chroma = [file_id] * len(chunk_ids)
 
-            await self._run_sync( # Use _run_sync for the Chroma operation, as it's a sync call
+            await self._run_sync(  # Use _run_sync for the Chroma operation, as it's a sync call
                 self.chroma_manager.add_embeddings_sync,
-                file_ids_for_chroma, chunk_ids, embeddings_np, chunks # Pass original chunks texts for documents
+                file_ids_for_chroma,
+                chunk_ids,
+                embeddings_np,
+                chunks,  # Pass original chunks texts for documents
             )
-            self.logger.info(f"Successfully saved {len(chunk_objects)} embeddings to Chroma.")
+            self.logger.info(f'Successfully saved {len(chunk_objects)} embeddings to Chroma.')
             return chunk_objects
 
         except Exception as e:
-            session.rollback() # Rollback on any error
-            self.logger.error(f"Failed to process and store data for file_id {file_id}: {e}", exc_info=True)
-            raise # Re-raise the exception to propagate it
+            session.rollback()  # Rollback on any error
+            self.logger.error(f'Failed to process and store data for file_id {file_id}: {e}', exc_info=True)
+            raise  # Re-raise the exception to propagate it
         finally:
-            session.close() # Ensure the session is always closed
+            session.close()  # Ensure the session is always closed

pkg/rag/knowledge/services/parser.py

@@ -1,4 +1,5 @@
 import PyPDF2
+import io
 from docx import Document
 import pandas as pd
 import chardet
@@ -10,7 +11,7 @@ import ebooklib
 from ebooklib import epub
 import re
 import asyncio  # Import asyncio for async operations
-import os
+from pkg.core import app
 
 # Configure logging
 logger = logging.getLogger(__name__)
@@ -24,8 +25,8 @@ class FileParser:
     to avoid blocking the asyncio event loop.
     """
 
-    def __init__(self):
-        self.logger = logging.getLogger(self.__class__.__name__)
+    def __init__(self, ap: app.Application):
+        self.ap = ap
 
     async def _run_sync(self, sync_func: Callable, *args: Any, **kwargs: Any) -> Any:
         """
@@ -35,138 +36,160 @@ class FileParser:
         try:
             return await asyncio.to_thread(sync_func, *args, **kwargs)
         except Exception as e:
-            self.logger.error(f'Error running synchronous function {sync_func.__name__}: {e}')
+            self.ap.logger.error(f'Error running synchronous function {sync_func.__name__}: {e}')
             raise
 
-    async def parse(self, file_path: str) -> Union[str, None]:
+    async def parse(self, file_name: str, extension: str) -> Union[str, None]:
         """
         Parses the file based on its extension and returns the extracted text content.
         This is the main asynchronous entry point for parsing.
 
         Args:
-            file_path (str): The path to the file to be parsed.
+            file_name (str): The name of the file to be parsed, get from ap.storage_mgr
 
         Returns:
             Union[str, None]: The extracted text content as a single string, or None if parsing fails.
         """
-        if not file_path or not os.path.exists(file_path):
-            self.logger.error(f'Invalid file path provided: {file_path}')
-            return None
 
-        file_extension = file_path.split('.')[-1].lower()
+        file_extension = extension.lower()
         parser_method = getattr(self, f'_parse_{file_extension}', None)
 
         if parser_method is None:
-            self.logger.error(f'Unsupported file format: {file_extension} for file {file_path}')
+            self.ap.logger.error(f'Unsupported file format: {file_extension} for file {file_name}')
             return None
 
         try:
             # Pass file_path to the specific parser methods
-            return await parser_method(file_path)
+            return await parser_method(file_name)
         except Exception as e:
-            self.logger.error(f'Failed to parse {file_extension} file {file_path}: {e}')
+            self.ap.logger.error(f'Failed to parse {file_extension} file {file_name}: {e}')
             return None
 
     # --- Helper for reading files with encoding detection ---
-    async def _read_file_content(self, file_path: str, mode: str = 'r') -> Union[str, bytes]:
+    async def _read_file_content(self, file_name: str) -> Union[str, bytes]:
         """
         Reads a file with automatic encoding detection, ensuring the synchronous
         file read operation runs in a separate thread.
         """
 
-        def _read_sync():
-            with open(file_path, 'rb') as file:
-                raw_data = file.read()
-                detected = chardet.detect(raw_data)
-                encoding = detected['encoding'] or 'utf-8'
+        # def _read_sync():
+        #     with open(file_path, 'rb') as file:
+        #         raw_data = file.read()
+        #         detected = chardet.detect(raw_data)
+        #         encoding = detected['encoding'] or 'utf-8'
 
-            if mode == 'r':
-                return raw_data.decode(encoding, errors='ignore')
-            return raw_data  # For binary mode
+        #     if mode == 'r':
+        #         return raw_data.decode(encoding, errors='ignore')
+        #     return raw_data  # For binary mode
 
-        return await self._run_sync(_read_sync)
+        # return await self._run_sync(_read_sync)
+        file_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+        detected = chardet.detect(file_bytes)
+        encoding = detected['encoding'] or 'utf-8'
+
+        return file_bytes.decode(encoding, errors='ignore')
 
     # --- Specific Parser Methods ---
 
-    async def _parse_txt(self, file_path: str) -> str:
+    async def _parse_txt(self, file_name: str) -> str:
         """Parses a TXT file and returns its content."""
-        self.logger.info(f'Parsing TXT file: {file_path}')
-        return await self._read_file_content(file_path, mode='r')
+        self.ap.logger.info(f'Parsing TXT file: {file_name}')
+        return await self._read_file_content(file_name)
 
-    async def _parse_pdf(self, file_path: str) -> str:
+    async def _parse_pdf(self, file_name: str) -> str:
         """Parses a PDF file and returns its text content."""
-        self.logger.info(f'Parsing PDF file: {file_path}')
+        self.ap.logger.info(f'Parsing PDF file: {file_name}')
+
+        # def _parse_pdf_sync():
+        #     text_content = []
+        #     with open(file_name, 'rb') as file:
+        #         pdf_reader = PyPDF2.PdfReader(file)
+        #         for page in pdf_reader.pages:
+        #             text = page.extract_text()
+        #             if text:
+        #                 text_content.append(text)
+        #     return '\n'.join(text_content)
+
+        # return await self._run_sync(_parse_pdf_sync)
+
+        pdf_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
 
         def _parse_pdf_sync():
+            pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_bytes))
             text_content = []
-            with open(file_path, 'rb') as file:
-                pdf_reader = PyPDF2.PdfReader(file)
-                for page in pdf_reader.pages:
-                    text = page.extract_text()
-                    if text:
-                        text_content.append(text)
+            for page in pdf_reader.pages:
+                text = page.extract_text()
+                if text:
+                    text_content.append(text)
             return '\n'.join(text_content)
 
         return await self._run_sync(_parse_pdf_sync)
 
-    async def _parse_docx(self, file_path: str) -> str:
+    async def _parse_docx(self, file_name: str) -> str:
         """Parses a DOCX file and returns its text content."""
-        self.logger.info(f'Parsing DOCX file: {file_path}')
+        self.ap.logger.info(f'Parsing DOCX file: {file_name}')
+
+        docx_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
 
         def _parse_docx_sync():
-            doc = Document(file_path)
+            doc = Document(io.BytesIO(docx_bytes))
             text_content = [paragraph.text for paragraph in doc.paragraphs if paragraph.text.strip()]
             return '\n'.join(text_content)
 
         return await self._run_sync(_parse_docx_sync)
 
-    async def _parse_doc(self, file_path: str) -> str:
+    async def _parse_doc(self, file_name: str) -> str:
         """Handles .doc files, explicitly stating lack of direct support."""
-        self.logger.warning(f'Direct .doc parsing is not supported for {file_path}. Please convert to .docx first.')
+        self.ap.logger.warning(f'Direct .doc parsing is not supported for {file_name}. Please convert to .docx first.')
         raise NotImplementedError('Direct .doc parsing not supported. Please convert to .docx first.')
 
-    async def _parse_xlsx(self, file_path: str) -> str:
+    async def _parse_xlsx(self, file_name: str) -> str:
         """Parses an XLSX file, returning text from all sheets."""
-        self.logger.info(f'Parsing XLSX file: {file_path}')
+        self.ap.logger.info(f'Parsing XLSX file: {file_name}')
+
+        xlsx_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
 
         def _parse_xlsx_sync():
-            excel_file = pd.ExcelFile(file_path)
+            excel_file = pd.ExcelFile(io.BytesIO(xlsx_bytes))
             all_sheet_content = []
             for sheet_name in excel_file.sheet_names:
-                df = pd.read_excel(file_path, sheet_name=sheet_name)
+                df = pd.read_excel(io.BytesIO(xlsx_bytes), sheet_name=sheet_name)
                 sheet_text = f'--- Sheet: {sheet_name} ---\n{df.to_string(index=False)}\n'
                 all_sheet_content.append(sheet_text)
             return '\n'.join(all_sheet_content)
 
         return await self._run_sync(_parse_xlsx_sync)
 
-    async def _parse_csv(self, file_path: str) -> str:
+    async def _parse_csv(self, file_name: str) -> str:
         """Parses a CSV file and returns its content as a string."""
-        self.logger.info(f'Parsing CSV file: {file_path}')
+        self.ap.logger.info(f'Parsing CSV file: {file_name}')
+
+        csv_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
 
         def _parse_csv_sync():
             # pd.read_csv can often detect encoding, but explicit detection is safer
-            raw_data = self._read_file_content(
-                file_path, mode='rb'
-            )  # Note: this will need to be await outside this sync function
-            _ = raw_data
+            # raw_data = self._read_file_content(
+            #     file_name, mode='rb'
+            # )  # Note: this will need to be await outside this sync function
+            # _ = raw_data
             # For simplicity, we'll let pandas handle encoding internally after a raw read.
             # A more robust solution might pass encoding directly to pd.read_csv after detection.
-            detected = chardet.detect(open(file_path, 'rb').read())
+            detected = chardet.detect(io.BytesIO(csv_bytes))
             encoding = detected['encoding'] or 'utf-8'
-            df = pd.read_csv(file_path, encoding=encoding)
+            df = pd.read_csv(io.BytesIO(csv_bytes), encoding=encoding)
             return df.to_string(index=False)
 
         return await self._run_sync(_parse_csv_sync)
 
-    async def _parse_markdown(self, file_path: str) -> str:
+    async def _parse_markdown(self, file_name: str) -> str:
         """Parses a Markdown file, converting it to structured plain text."""
-        self.logger.info(f'Parsing Markdown file: {file_path}')
+        self.ap.logger.info(f'Parsing Markdown file: {file_name}')
+
+        md_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
 
         def _parse_markdown_sync():
-            md_content = self._read_file_content(
-                file_path, mode='r'
-            )  # This is a synchronous call within a sync function
+            md_content = io.BytesIO(md_bytes).read().decode('utf-8', errors='ignore')
             html_content = markdown.markdown(
                 md_content, extensions=['extra', 'codehilite', 'tables', 'toc', 'fenced_code']
             )
@@ -200,12 +223,14 @@ class FileParser:
 
         return await self._run_sync(_parse_markdown_sync)
 
-    async def _parse_html(self, file_path: str) -> str:
+    async def _parse_html(self, file_name: str) -> str:
         """Parses an HTML file, extracting structured plain text."""
-        self.logger.info(f'Parsing HTML file: {file_path}')
+        self.ap.logger.info(f'Parsing HTML file: {file_name}')
+
+        html_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
 
         def _parse_html_sync():
-            html_content = self._read_file_content(file_path, mode='r')  # Sync call within sync function
+            html_content = io.BytesIO(html_bytes).read().decode('utf-8', errors='ignore')
             soup = BeautifulSoup(html_content, 'html.parser')
             for script_or_style in soup(['script', 'style']):
                 script_or_style.decompose()
@@ -236,12 +261,14 @@ class FileParser:
 
         return await self._run_sync(_parse_html_sync)
 
-    async def _parse_epub(self, file_path: str) -> str:
+    async def _parse_epub(self, file_name: str) -> str:
         """Parses an EPUB file, extracting metadata and content."""
-        self.logger.info(f'Parsing EPUB file: {file_path}')
+        self.ap.logger.info(f'Parsing EPUB file: {file_name}')
+
+        epub_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
 
         def _parse_epub_sync():
-            book = epub.read_epub(file_path)
+            book = epub.read_epub(io.BytesIO(epub_bytes))
             text_content = []
             title_meta = book.get_metadata('DC', 'title')
             if title_meta:

pkg/vector/mgr.py

@@ -0,0 +1,13 @@
+from __future__ import annotations
+
+from ..core import app
+
+
+class VectorDBManager:
+    ap: app.Application
+
+    def __init__(self, ap: app.Application):
+        self.ap = ap
+
+    async def initialize(self):
+        pass

pkg/vector/vdb.py

@@ -0,0 +1,7 @@
+from __future__ import annotations
+
+import abc
+
+
+class VectorDatabase(abc.ABC):
+    pass