mirror of
https://github.com/langbot-app/LangBot.git
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351 lines
13 KiB
Python
351 lines
13 KiB
Python
from __future__ import annotations
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import datetime
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from types import SimpleNamespace
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from unittest.mock import AsyncMock, Mock
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import pytest
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import sqlalchemy
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from sqlalchemy.ext.asyncio import create_async_engine
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from langbot.pkg.api.http.authz import WorkspaceRequiredError
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from langbot.pkg.api.http.context import ExecutionContext
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from langbot.pkg.api.http.service.knowledge import KnowledgeService
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from langbot.pkg.entity.persistence.base import Base
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from langbot.pkg.entity.persistence.rag import File, KnowledgeBase
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from langbot.pkg.entity.persistence.workspace import Workspace, WorkspaceExecutionState
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from langbot.pkg.rag.knowledge.kbmgr import RAGManager
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from langbot.pkg.rag.service.runtime import RAGRuntimeService
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from langbot.pkg.vector.mgr import VectorDBManager
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from langbot.pkg.workspace.errors import WorkspaceNotFoundError
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from langbot.pkg.workspace.policy import CloudWorkspacePolicy, SingleWorkspacePolicy
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from langbot.pkg.workspace.service import WorkspaceService
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pytestmark = pytest.mark.asyncio
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INSTANCE_UUID = 'instance-rag-isolation'
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WORKSPACE_A = '00000000-0000-0000-0000-00000000000a'
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WORKSPACE_B = '00000000-0000-0000-0000-00000000000b'
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class _PersistenceManager:
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def __init__(self, engine):
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self.engine = engine
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def get_db_engine(self):
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return self.engine
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async def execute_async(self, *args, **kwargs):
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async with self.engine.connect() as connection:
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result = await connection.execute(*args, **kwargs)
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await connection.commit()
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return result
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@staticmethod
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def serialize_model(model, row, masked_columns=()):
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return {
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column.name: (
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getattr(row, column.name).isoformat()
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if isinstance(getattr(row, column.name), datetime.datetime)
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else getattr(row, column.name)
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)
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for column in model.__table__.columns
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if column.name not in masked_columns
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}
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class _RecordingVectorDatabase:
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def __init__(self):
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self.collections: list[str] = []
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self.metadatas: list[list[dict]] = []
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self.calls: list[tuple[str, str]] = []
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async def add_embeddings(self, *, collection, ids, embeddings_list, metadatas, documents):
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self.collections.append(collection)
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self.metadatas.append(metadatas)
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self.calls.append(('upsert', collection))
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async def search(self, *, collection, **_kwargs):
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self.calls.append(('search', collection))
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return {'ids': [[]], 'distances': [[]], 'metadatas': [[]]}
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async def delete_by_file_id(self, collection, _file_id):
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self.calls.append(('delete_by_file_id', collection))
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async def delete_collection(self, collection):
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self.calls.append(('delete_collection', collection))
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async def delete_by_filter(self, collection, _filter):
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self.calls.append(('delete_by_filter', collection))
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return 1
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async def list_by_filter(self, collection, _filter, _limit, _offset):
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self.calls.append(('list_by_filter', collection))
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return [], 0
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@pytest.fixture
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async def tenant_rag(tmp_path):
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engine = create_async_engine(f'sqlite+aiosqlite:///{tmp_path / "rag-tenant.db"}')
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async with engine.begin() as connection:
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await connection.run_sync(Base.metadata.create_all)
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await connection.execute(
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sqlalchemy.insert(Workspace),
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[
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{
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'uuid': WORKSPACE_A,
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'instance_uuid': INSTANCE_UUID,
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'name': 'Workspace A',
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'slug': 'workspace-a',
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'source': 'local',
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},
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{
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'uuid': WORKSPACE_B,
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'instance_uuid': INSTANCE_UUID,
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'name': 'Workspace B',
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'slug': 'workspace-b',
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'source': 'cloud_projection',
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},
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],
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)
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await connection.execute(
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sqlalchemy.insert(WorkspaceExecutionState),
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[
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{
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'workspace_uuid': WORKSPACE_A,
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'instance_uuid': INSTANCE_UUID,
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'active_generation': 3,
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'state': 'active',
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'source': 'local',
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'write_fenced': False,
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},
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{
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'workspace_uuid': WORKSPACE_B,
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'instance_uuid': INSTANCE_UUID,
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'active_generation': 3,
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'state': 'active',
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'source': 'cloud',
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'write_fenced': False,
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},
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],
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)
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await connection.execute(
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sqlalchemy.insert(KnowledgeBase),
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[
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{
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'uuid': 'kb-a',
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'workspace_uuid': WORKSPACE_A,
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'name': 'Same Knowledge Base',
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'description': 'A',
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'knowledge_engine_plugin_id': 'author/engine',
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'collection_id': 'kb-a',
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'creation_settings': {},
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'retrieval_settings': {},
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},
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{
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'uuid': 'kb-b',
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'workspace_uuid': WORKSPACE_B,
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'name': 'Same Knowledge Base',
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'description': 'B',
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'knowledge_engine_plugin_id': 'author/engine',
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'collection_id': 'kb-b',
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'creation_settings': {},
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'retrieval_settings': {},
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},
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],
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)
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await connection.execute(
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sqlalchemy.insert(File),
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[
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{
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'uuid': 'file-a',
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'workspace_uuid': WORKSPACE_A,
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'kb_id': 'kb-a',
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'file_name': 'a.pdf',
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'extension': 'pdf',
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},
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{
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'uuid': 'file-b',
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'workspace_uuid': WORKSPACE_B,
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'kb_id': 'kb-b',
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'file_name': 'b.pdf',
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'extension': 'pdf',
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},
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],
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)
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app = SimpleNamespace()
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app.persistence_mgr = _PersistenceManager(engine)
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app.logger = Mock()
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app.workspace_policy = SingleWorkspacePolicy()
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app.plugin_connector = SimpleNamespace(
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is_enable_plugin=False,
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rag_on_kb_create=AsyncMock(),
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rag_on_kb_delete=AsyncMock(),
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)
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app.workspace_service = WorkspaceService(app, instance_uuid=INSTANCE_UUID)
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app.rag_mgr = RAGManager(app)
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await app.rag_mgr.initialize()
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app.knowledge_service = KnowledgeService(app)
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yield app, engine
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await engine.dispose()
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def _context(workspace_uuid: str) -> ExecutionContext:
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return ExecutionContext(
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instance_uuid=INSTANCE_UUID,
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workspace_uuid=workspace_uuid,
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placement_generation=3,
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)
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async def test_context_is_mandatory_and_same_names_are_isolated(tenant_rag):
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app, _engine = tenant_rag
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with pytest.raises(WorkspaceRequiredError):
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await app.knowledge_service.get_knowledge_bases(None)
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bases_a = await app.knowledge_service.get_knowledge_bases(_context(WORKSPACE_A))
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bases_b = await app.knowledge_service.get_knowledge_bases(_context(WORKSPACE_B))
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assert [(item['uuid'], item['name']) for item in bases_a] == [('kb-a', 'Same Knowledge Base')]
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assert [(item['uuid'], item['name']) for item in bases_b] == [('kb-b', 'Same Knowledge Base')]
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async def test_cross_workspace_uuid_and_file_guessing_return_not_found(tenant_rag):
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app, engine = tenant_rag
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context_a = _context(WORKSPACE_A)
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assert await app.knowledge_service.get_knowledge_base(context_a, 'kb-b') is None
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with pytest.raises(WorkspaceNotFoundError):
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await app.knowledge_service.update_knowledge_base(context_a, 'kb-b', {'name': 'stolen'})
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with pytest.raises(WorkspaceNotFoundError):
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await app.knowledge_service.delete_knowledge_base(context_a, 'kb-b')
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with pytest.raises(WorkspaceNotFoundError):
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await app.knowledge_service.get_files_by_knowledge_base(context_a, 'kb-b')
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with pytest.raises(WorkspaceNotFoundError):
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await app.knowledge_service.delete_file(context_a, 'kb-b', 'file-b')
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async with engine.connect() as connection:
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assert (
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await connection.scalar(sqlalchemy.select(KnowledgeBase.name).where(KnowledgeBase.uuid == 'kb-b'))
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== 'Same Knowledge Base'
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)
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assert await connection.scalar(sqlalchemy.select(File.uuid).where(File.uuid == 'file-b')) == 'file-b'
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async def test_runtime_rejects_cross_workspace_collection_reference(tenant_rag):
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app, _engine = tenant_rag
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app.vector_db_mgr = SimpleNamespace(upsert=AsyncMock())
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service = RAGRuntimeService(app)
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with pytest.raises(WorkspaceNotFoundError):
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await service.vector_upsert(
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_context(WORKSPACE_A),
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'kb-b',
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vectors=[[0.1, 0.2]],
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ids=['chunk-1'],
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)
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app.vector_db_mgr.upsert.assert_not_awaited()
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async def test_physical_vector_handles_do_not_collide_across_workspaces(tenant_rag):
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app, _engine = tenant_rag
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database = _RecordingVectorDatabase()
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manager = VectorDBManager(app)
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manager.vector_db = database
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context_a = _context(WORKSPACE_A)
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context_b = _context(WORKSPACE_B)
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assert manager.physical_collection_name(context_a, 'same-kb-id') != manager.physical_collection_name(
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context_b,
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'same-kb-id',
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)
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await manager.upsert(context_a, 'kb-a', [[0.1]], ['a'], metadata=[{'source': 'client'}])
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await manager.upsert(context_b, 'kb-b', [[0.2]], ['b'], metadata=[{'source': 'client'}])
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assert len(set(database.collections)) == 2
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assert database.metadatas[0][0]['_langbot_workspace_uuid'] == WORKSPACE_A
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assert database.metadatas[1][0]['_langbot_workspace_uuid'] == WORKSPACE_B
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async def test_migrated_local_kb_keeps_legacy_collection_for_every_vector_operation(tenant_rag):
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app, engine = tenant_rag
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legacy_collection = 'legacy-collection-kb-a'
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async with engine.begin() as connection:
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await connection.execute(
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sqlalchemy.update(KnowledgeBase)
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.where(KnowledgeBase.uuid == 'kb-a')
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.values(
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collection_id=legacy_collection,
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legacy_vector_collection=True,
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)
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)
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database = _RecordingVectorDatabase()
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manager = VectorDBManager(app)
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manager.vector_db = database
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context = _context(WORKSPACE_A)
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await manager.upsert(context, 'kb-a', [[0.1]], ['chunk-a'])
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await manager.search(context, 'kb-a', [0.1], 3)
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await manager.delete_by_file_id(context, 'kb-a', ['file-a'])
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assert await manager.delete_by_filter(context, 'kb-a', {'file_id': 'file-a'}) == 1
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assert await manager.list_by_filter(context, 'kb-a', {'file_id': 'file-a'}) == ([], 0)
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await manager.delete_collection(context, 'kb-a')
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assert database.calls == [
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('upsert', legacy_collection),
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('search', legacy_collection),
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('delete_by_file_id', legacy_collection),
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('delete_by_filter', legacy_collection),
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('list_by_filter', legacy_collection),
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('delete_collection', legacy_collection),
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]
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@pytest.mark.parametrize('deny_by', ['projected_workspace', 'multi_workspace_policy'])
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async def test_legacy_marker_is_ignored_outside_single_local_workspace(tenant_rag, deny_by):
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app, engine = tenant_rag
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workspace_uuid = WORKSPACE_B if deny_by == 'projected_workspace' else WORKSPACE_A
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kb_uuid = 'kb-b' if deny_by == 'projected_workspace' else 'kb-a'
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legacy_collection = f'legacy-{deny_by}'
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async with engine.begin() as connection:
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await connection.execute(
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sqlalchemy.update(KnowledgeBase)
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.where(KnowledgeBase.uuid == kb_uuid)
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.values(
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collection_id=legacy_collection,
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legacy_vector_collection=True,
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)
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)
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if deny_by == 'multi_workspace_policy':
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app.workspace_policy = CloudWorkspacePolicy()
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database = _RecordingVectorDatabase()
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manager = VectorDBManager(app)
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manager.vector_db = database
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context = _context(workspace_uuid)
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await manager.search(context, kb_uuid, [0.1], 3)
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assert database.calls == [('search', manager.physical_collection_name(context, kb_uuid))]
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assert database.calls[0][1] != legacy_collection
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app.logger.warning.assert_called_once()
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async def test_stale_generation_is_rejected_before_vector_access(tenant_rag):
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app, _engine = tenant_rag
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database = _RecordingVectorDatabase()
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manager = VectorDBManager(app)
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manager.vector_db = database
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stale = ExecutionContext(
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instance_uuid=INSTANCE_UUID,
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workspace_uuid=WORKSPACE_A,
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placement_generation=2,
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)
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with pytest.raises(Exception, match='generation'):
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await manager.upsert(stale, 'kb-a', [[0.1]], ['a'])
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assert database.collections == []
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