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Annotation management (#1767)

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Co-authored-by: jyong <jyong@dify.ai>
This commit is contained in:
Jyong
2023-12-18 13:10:05 +08:00
committed by GitHub
parent a9b942981d
commit a71f2863ac
41 changed files with 1871 additions and 67 deletions
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89
api/core/completion.py
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@@ -12,8 +12,10 @@ from core.callback_handler.main_chain_gather_callback_handler import MainChainGa
from core.callback_handler.llm_callback_handler import LLMCallbackHandler
from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException, \
ConversationTaskInterruptException
from core.embedding.cached_embedding import CacheEmbedding
from core.external_data_tool.factory import ExternalDataToolFactory
from core.file.file_obj import FileObj
from core.index.vector_index.vector_index import VectorIndex
from core.model_providers.error import LLMBadRequestError
from core.memory.read_only_conversation_token_db_buffer_shared_memory import \
ReadOnlyConversationTokenDBBufferSharedMemory
@@ -23,9 +25,12 @@ from core.model_providers.models.llm.base import BaseLLM
from core.orchestrator_rule_parser import OrchestratorRuleParser
from core.prompt.prompt_template import PromptTemplateParser
from core.prompt.prompt_transform import PromptTransform
from models.dataset import Dataset
from models.model import App, AppModelConfig, Account, Conversation, EndUser
from core.moderation.base import ModerationException, ModerationAction
from core.moderation.factory import ModerationFactory
from services.annotation_service import AppAnnotationService
from services.dataset_service import DatasetCollectionBindingService
class Completion:
@@ -33,7 +38,7 @@ class Completion:
def generate(cls, task_id: str, app: App, app_model_config: AppModelConfig, query: str, inputs: dict,
files: List[FileObj], user: Union[Account, EndUser], conversation: Optional[Conversation],
streaming: bool, is_override: bool = False, retriever_from: str = 'dev',
auto_generate_name: bool = True):
auto_generate_name: bool = True, from_source: str = 'console'):
"""
errors: ProviderTokenNotInitError
"""
@@ -109,7 +114,10 @@ class Completion:
fake_response=str(e)
)
return
# check annotation reply
annotation_reply = cls.query_app_annotations_to_reply(conversation_message_task, from_source)
if annotation_reply:
return
# fill in variable inputs from external data tools if exists
external_data_tools = app_model_config.external_data_tools_list
if external_data_tools:
@@ -166,17 +174,18 @@ class Completion:
except ChunkedEncodingError as e:
# Interrupt by LLM (like OpenAI), handle it.
logging.warning(f'ChunkedEncodingError: {e}')
conversation_message_task.end()
return
@classmethod
def moderation_for_inputs(cls, app_id: str, tenant_id: str, app_model_config: AppModelConfig, inputs: dict, query: str):
def moderation_for_inputs(cls, app_id: str, tenant_id: str, app_model_config: AppModelConfig, inputs: dict,
query: str):
if not app_model_config.sensitive_word_avoidance_dict['enabled']:
return inputs, query
type = app_model_config.sensitive_word_avoidance_dict['type']
moderation = ModerationFactory(type, app_id, tenant_id, app_model_config.sensitive_word_avoidance_dict['config'])
moderation = ModerationFactory(type, app_id, tenant_id,
app_model_config.sensitive_word_avoidance_dict['config'])
moderation_result = moderation.moderation_for_inputs(inputs, query)
if not moderation_result.flagged:
@@ -324,6 +333,76 @@ class Completion:
external_context = memory.load_memory_variables({})
return external_context[memory_key]
@classmethod
def query_app_annotations_to_reply(cls, conversation_message_task: ConversationMessageTask,
from_source: str) -> bool:
"""Get memory messages."""
app_model_config = conversation_message_task.app_model_config
app = conversation_message_task.app
annotation_reply = app_model_config.annotation_reply_dict
if annotation_reply['enabled']:
score_threshold = annotation_reply.get('score_threshold', 1)
embedding_provider_name = annotation_reply['embedding_model']['embedding_provider_name']
embedding_model_name = annotation_reply['embedding_model']['embedding_model_name']
# get embedding model
embedding_model = ModelFactory.get_embedding_model(
tenant_id=app.tenant_id,
model_provider_name=embedding_provider_name,
model_name=embedding_model_name
)
embeddings = CacheEmbedding(embedding_model)
dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
embedding_provider_name,
embedding_model_name,
'annotation'
)
dataset = Dataset(
id=app.id,
tenant_id=app.tenant_id,
indexing_technique='high_quality',
embedding_model_provider=embedding_provider_name,
embedding_model=embedding_model_name,
collection_binding_id=dataset_collection_binding.id
)
vector_index = VectorIndex(
dataset=dataset,
config=current_app.config,
embeddings=embeddings
)
documents = vector_index.search(
conversation_message_task.query,
search_type='similarity_score_threshold',
search_kwargs={
'k': 1,
'score_threshold': score_threshold,
'filter': {
'group_id': [dataset.id]
}
}
)
if documents:
annotation_id = documents[0].metadata['annotation_id']
score = documents[0].metadata['score']
annotation = AppAnnotationService.get_annotation_by_id(annotation_id)
if annotation:
conversation_message_task.annotation_end(annotation.content, annotation.id, annotation.account.name)
# insert annotation history
AppAnnotationService.add_annotation_history(annotation.id,
app.id,
annotation.question,
annotation.content,
conversation_message_task.query,
conversation_message_task.user.id,
conversation_message_task.message.id,
from_source,
score)
return True
return False
@classmethod
def get_memory_from_conversation(cls, tenant_id: str, app_model_config: AppModelConfig,
conversation: Conversation,