Files
LangBot/src/langbot/pkg/rag/knowledge/kbmgr.py
Copilot a8481e43f0 feat: external knowledge bases (#1783)
* Initial plan

* Add backend support for external knowledge bases

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* Add frontend support for external knowledge bases with tabs UI

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* Add i18n translations for all languages (Traditional Chinese and Japanese)

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* Update knowledge base tab list styling to match plugins page

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* perf: margin-top for kb page

* refactor: switch RetrievalResultEntry to langbot_plugin pkg ones

* feat: knowledge retriever listing and creating

* stash

* refactor: unify sync mechanism for polymorphic components

* feat: use unified retireval result struct in retrieval test page

* chore: remove unused methods

* feat: retriever icon displaying

* feat: localagent retrieval with external kbs

* chore: bump version of langbot-plugin to 0.2.0b1

* fix: i18n

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>
Co-authored-by: Junyan Qin <rockchinq@gmail.com>
2025-11-27 23:19:43 +08:00

339 lines
13 KiB
Python

from __future__ import annotations
import traceback
import uuid
import zipfile
import io
from .services import parser, chunker
from langbot.pkg.core import app
from langbot.pkg.rag.knowledge.services.embedder import Embedder
from langbot.pkg.rag.knowledge.services.retriever import Retriever
import sqlalchemy
from langbot.pkg.entity.persistence import rag as persistence_rag
from langbot.pkg.core import taskmgr
from langbot_plugin.api.entities.builtin.rag import context as rag_context
from .base import KnowledgeBaseInterface
from .external import ExternalKnowledgeBase
class RuntimeKnowledgeBase(KnowledgeBaseInterface):
ap: app.Application
knowledge_base_entity: persistence_rag.KnowledgeBase
parser: parser.FileParser
chunker: chunker.Chunker
embedder: Embedder
retriever: Retriever
def __init__(self, ap: app.Application, knowledge_base_entity: persistence_rag.KnowledgeBase):
super().__init__(ap)
self.knowledge_base_entity = knowledge_base_entity
self.parser = parser.FileParser(ap=self.ap)
self.chunker = chunker.Chunker(ap=self.ap)
self.embedder = Embedder(ap=self.ap)
self.retriever = Retriever(ap=self.ap)
# 传递kb_id给retriever
self.retriever.kb_id = knowledge_base_entity.uuid
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')
embedding_model = await self.ap.model_mgr.get_embedding_model_by_uuid(
self.knowledge_base_entity.embedding_model_uuid
)
# embed chunks
await self.embedder.embed_and_store(
kb_id=self.knowledge_base_entity.uuid,
file_id=file.uuid,
chunks=chunks_texts,
embedding_model=embedding_model,
)
# 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.uuid}: {e}')
traceback.print_exc()
# 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
finally:
# delete file from storage
await self.ap.storage_mgr.storage_provider.delete(file.file_name)
async def store_file(self, file_id: str) -> str:
# pre checking
if not await self.ap.storage_mgr.storage_provider.exists(file_id):
raise Exception(f'File {file_id} not found')
file_name = file_id
extension = file_name.split('.')[-1].lower()
if extension == 'zip':
return await self._store_zip_file(file_id)
file_uuid = str(uuid.uuid4())
kb_id = self.knowledge_base_entity.uuid
file_obj_data = {
'uuid': file_uuid,
'kb_id': kb_id,
'file_name': file_name,
'extension': extension,
'status': 'pending',
}
file_obj = persistence_rag.File(**file_obj_data)
await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.File).values(file_obj_data))
# run background task asynchronously
ctx = taskmgr.TaskContext.new()
wrapper = self.ap.task_mgr.create_user_task(
self._store_file_task(file_obj, 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 _store_zip_file(self, zip_file_id: str) -> str:
"""Handle ZIP file by extracting each document and storing them separately."""
self.ap.logger.info(f'Processing ZIP file: {zip_file_id}')
zip_bytes = await self.ap.storage_mgr.storage_provider.load(zip_file_id)
supported_extensions = {'txt', 'pdf', 'docx', 'md', 'html'}
stored_file_tasks = []
# use utf-8 encoding
with zipfile.ZipFile(io.BytesIO(zip_bytes), 'r', metadata_encoding='utf-8') as zip_ref:
for file_info in zip_ref.filelist:
# skip directories and hidden files
if file_info.is_dir() or file_info.filename.startswith('.'):
continue
file_extension = file_info.filename.split('.')[-1].lower()
if file_extension not in supported_extensions:
self.ap.logger.debug(f'Skipping unsupported file in ZIP: {file_info.filename}')
continue
try:
file_content = zip_ref.read(file_info.filename)
base_name = file_info.filename.replace('/', '_').replace('\\', '_')
extension = base_name.split('.')[-1]
file_name = base_name.split('.')[0]
if file_name.startswith('__MACOSX'):
continue
extracted_file_id = file_name + '_' + str(uuid.uuid4())[:8] + '.' + extension
# save file to storage
await self.ap.storage_mgr.storage_provider.save(extracted_file_id, file_content)
task_id = await self.store_file(extracted_file_id)
stored_file_tasks.append(task_id)
self.ap.logger.info(
f'Extracted and stored file from ZIP: {file_info.filename} -> {extracted_file_id}'
)
except Exception as e:
self.ap.logger.warning(f'Failed to extract file {file_info.filename} from ZIP: {e}')
continue
if not stored_file_tasks:
raise Exception('No supported files found in ZIP archive')
self.ap.logger.info(f'Successfully processed ZIP file {zip_file_id}, extracted {len(stored_file_tasks)} files')
await self.ap.storage_mgr.storage_provider.delete(zip_file_id)
return stored_file_tasks[0] if stored_file_tasks else ''
async def retrieve(self, query: str, top_k: int) -> list[rag_context.RetrievalResultEntry]:
embedding_model = await self.ap.model_mgr.get_embedding_model_by_uuid(
self.knowledge_base_entity.embedding_model_uuid
)
return await self.retriever.retrieve(self.knowledge_base_entity.uuid, query, embedding_model, top_k)
async def delete_file(self, file_id: str):
# delete vector
await self.ap.vector_db_mgr.vector_db.delete_by_file_id(self.knowledge_base_entity.uuid, file_id)
# delete chunk
await self.ap.persistence_mgr.execute_async(
sqlalchemy.delete(persistence_rag.Chunk).where(persistence_rag.Chunk.file_id == file_id)
)
await self.ap.persistence_mgr.execute_async(
sqlalchemy.delete(persistence_rag.File).where(persistence_rag.File.uuid == file_id)
)
def get_uuid(self) -> str:
"""Get the UUID of the knowledge base"""
return self.knowledge_base_entity.uuid
def get_name(self) -> str:
"""Get the name of the knowledge base"""
return self.knowledge_base_entity.name
def get_type(self) -> str:
"""Get the type of knowledge base"""
return 'internal'
async def dispose(self):
await self.ap.vector_db_mgr.vector_db.delete_collection(self.knowledge_base_entity.uuid)
class RAGManager:
ap: app.Application
knowledge_bases: list[KnowledgeBaseInterface]
def __init__(self, ap: app.Application):
self.ap = ap
self.knowledge_bases = []
async def initialize(self):
await self.load_knowledge_bases_from_db()
async def load_knowledge_bases_from_db(self):
self.ap.logger.info('Loading knowledge bases from db...')
self.knowledge_bases = []
# Load internal knowledge bases
result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase))
knowledge_bases = result.all()
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()}'
)
# Load external knowledge bases
external_result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_rag.ExternalKnowledgeBase)
)
external_kbs = external_result.all()
for external_kb in external_kbs:
try:
# Don't trigger sync during batch loading - will sync once after LangBot connects to runtime
await self.load_external_knowledge_base(external_kb, trigger_sync=False)
except Exception as e:
self.ap.logger.error(
f'Error loading external knowledge base {external_kb.uuid}: {e}\n{traceback.format_exc()}'
)
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)
runtime_knowledge_base = RuntimeKnowledgeBase(ap=self.ap, knowledge_base_entity=knowledge_base_entity)
await runtime_knowledge_base.initialize()
self.knowledge_bases.append(runtime_knowledge_base)
return runtime_knowledge_base
async def load_external_knowledge_base(
self,
external_kb_entity: persistence_rag.ExternalKnowledgeBase | sqlalchemy.Row | dict,
trigger_sync: bool = True,
) -> ExternalKnowledgeBase:
"""Load external knowledge base into runtime
Args:
external_kb_entity: External KB entity to load
trigger_sync: Whether to trigger sync after loading (default True for manual creation, False for batch loading)
"""
if isinstance(external_kb_entity, sqlalchemy.Row):
external_kb_entity = persistence_rag.ExternalKnowledgeBase(**external_kb_entity._mapping)
elif isinstance(external_kb_entity, dict):
external_kb_entity = persistence_rag.ExternalKnowledgeBase(**external_kb_entity)
external_kb = ExternalKnowledgeBase(ap=self.ap, external_kb_entity=external_kb_entity)
await external_kb.initialize()
self.knowledge_bases.append(external_kb)
# Trigger sync to create the instance immediately (for manual creation)
# Skip sync during batch loading from DB to avoid multiple sync calls
if trigger_sync:
try:
await self.ap.plugin_connector.sync_polymorphic_component_instances()
self.ap.logger.info(f'Triggered sync after loading external KB {external_kb_entity.uuid}')
except Exception as e:
self.ap.logger.error(f'Failed to sync after loading external KB: {e}')
return external_kb
async def get_knowledge_base_by_uuid(self, kb_uuid: str) -> KnowledgeBaseInterface | None:
for kb in self.knowledge_bases:
if kb.get_uuid() == kb_uuid:
return kb
return None
async def remove_knowledge_base_from_runtime(self, kb_uuid: str):
for kb in self.knowledge_bases:
if kb.get_uuid() == kb_uuid:
self.knowledge_bases.remove(kb)
return
async def delete_knowledge_base(self, kb_uuid: str):
for kb in self.knowledge_bases:
if kb.get_uuid() == kb_uuid:
await kb.dispose()
self.knowledge_bases.remove(kb)
return