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Code Issues Actions 4 Packages Projects Releases Wiki Activity

feat: add api-based extension & external data tool & moderation backend (#1403)

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Co-authored-by: takatost <takatost@gmail.com>
This commit is contained in:
Garfield Dai 2023-11-06 19:36:16 +08:00 committed by GitHub
parent 7699621983
commit db43ed6f41
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50 changed files with 1624 additions and 273 deletions
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2
.vscode/launch.json → api/.vscode/launch.json vendored
View File

@ -10,7 +10,7 @@
"request": "launch",
"module": "flask",
"env": {
"FLASK_APP": "api/app.py",
"FLASK_APP": "app.py",
"FLASK_DEBUG": "1",
"GEVENT_SUPPORT": "True"
},

3
api/app.py
View File

@ -19,7 +19,7 @@ from flask_cors import CORS
from core.model_providers.providers import hosted
from extensions import ext_celery, ext_sentry, ext_redis, ext_login, ext_migrate, \
ext_database, ext_storage, ext_mail, ext_stripe
ext_database, ext_storage, ext_mail, ext_stripe, ext_code_based_extension
from extensions.ext_database import db
from extensions.ext_login import login_manager
@ -79,6 +79,7 @@ def create_app(test_config=None) -> Flask:
def initialize_extensions(app):
# Since the application instance is now created, pass it to each Flask
# extension instance to bind it to the Flask application instance (app)
ext_code_based_extension.init()
ext_database.init_app(app)
ext_migrate.init(app, db)
ext_redis.init_app(app)

4
api/config.py
View File

@ -57,6 +57,7 @@ DEFAULTS = {
'CLEAN_DAY_SETTING': 30,
'UPLOAD_FILE_SIZE_LIMIT': 15,
'UPLOAD_FILE_BATCH_LIMIT': 5,
'OUTPUT_MODERATION_BUFFER_SIZE': 300
}
@ -228,6 +229,9 @@ class Config:
self.UPLOAD_FILE_SIZE_LIMIT = int(get_env('UPLOAD_FILE_SIZE_LIMIT'))
self.UPLOAD_FILE_BATCH_LIMIT = int(get_env('UPLOAD_FILE_BATCH_LIMIT'))
# moderation settings
self.OUTPUT_MODERATION_BUFFER_SIZE = int(get_env('OUTPUT_MODERATION_BUFFER_SIZE'))
class CloudEditionConfig(Config):

2
api/controllers/console/__init__.py
View File

@ -6,7 +6,7 @@ bp = Blueprint('console', __name__, url_prefix='/console/api')
api = ExternalApi(bp)
# Import other controllers
from . import setup, version, apikey, admin
from . import extension, setup, version, apikey, admin
# Import app controllers
from .app import advanced_prompt_template, app, site, completion, model_config, statistic, conversation, message, generator, audio

4
api/controllers/console/explore/parameter.py
View File

@ -27,6 +27,7 @@ class AppParameterApi(InstalledAppResource):
'retriever_resource': fields.Raw,
'more_like_this': fields.Raw,
'user_input_form': fields.Raw,
'sensitive_word_avoidance': fields.Raw
}
@marshal_with(parameters_fields)
@ -42,7 +43,8 @@ class AppParameterApi(InstalledAppResource):
'speech_to_text': app_model_config.speech_to_text_dict,
'retriever_resource': app_model_config.retriever_resource_dict,
'more_like_this': app_model_config.more_like_this_dict,
'user_input_form': app_model_config.user_input_form_list
'user_input_form': app_model_config.user_input_form_list,
'sensitive_word_avoidance': app_model_config.sensitive_word_avoidance_dict
}

114
api/controllers/console/extension.py Normal file
View File

@ -0,0 +1,114 @@
from flask_restful import Resource, reqparse, marshal_with
from flask_login import current_user
from controllers.console import api
from controllers.console.setup import setup_required
from controllers.console.wraps import account_initialization_required
from libs.login import login_required
from models.api_based_extension import APIBasedExtension
from fields.api_based_extension_fields import api_based_extension_fields
from services.code_based_extension_service import CodeBasedExtensionService
from services.api_based_extension_service import APIBasedExtensionService
class CodeBasedExtensionAPI(Resource):
@setup_required
@login_required
@account_initialization_required
def get(self):
parser = reqparse.RequestParser()
parser.add_argument('module', type=str, required=True, location='args')
args = parser.parse_args()
return {
'module': args['module'],
'data': CodeBasedExtensionService.get_code_based_extension(args['module'])
}
class APIBasedExtensionAPI(Resource):
@setup_required
@login_required
@account_initialization_required
@marshal_with(api_based_extension_fields)
def get(self):
tenant_id = current_user.current_tenant_id
return APIBasedExtensionService.get_all_by_tenant_id(tenant_id)
@setup_required
@login_required
@account_initialization_required
@marshal_with(api_based_extension_fields)
def post(self):
parser = reqparse.RequestParser()
parser.add_argument('name', type=str, required=True, location='json')
parser.add_argument('api_endpoint', type=str, required=True, location='json')
parser.add_argument('api_key', type=str, required=True, location='json')
args = parser.parse_args()
extension_data = APIBasedExtension(
tenant_id=current_user.current_tenant_id,
name=args['name'],
api_endpoint=args['api_endpoint'],
api_key=args['api_key']
)
return APIBasedExtensionService.save(extension_data)
class APIBasedExtensionDetailAPI(Resource):
@setup_required
@login_required
@account_initialization_required
@marshal_with(api_based_extension_fields)
def get(self, id):
api_based_extension_id = str(id)
tenant_id = current_user.current_tenant_id
return APIBasedExtensionService.get_with_tenant_id(tenant_id, api_based_extension_id)
@setup_required
@login_required
@account_initialization_required
@marshal_with(api_based_extension_fields)
def post(self, id):
api_based_extension_id = str(id)
tenant_id = current_user.current_tenant_id
extension_data_from_db = APIBasedExtensionService.get_with_tenant_id(tenant_id, api_based_extension_id)
parser = reqparse.RequestParser()
parser.add_argument('name', type=str, required=True, location='json')
parser.add_argument('api_endpoint', type=str, required=True, location='json')
parser.add_argument('api_key', type=str, required=True, location='json')
args = parser.parse_args()
extension_data_from_db.name = args['name']
extension_data_from_db.api_endpoint = args['api_endpoint']
if args['api_key'] != '[__HIDDEN__]':
extension_data_from_db.api_key = args['api_key']
return APIBasedExtensionService.save(extension_data_from_db)
@setup_required
@login_required
@account_initialization_required
def delete(self, id):
api_based_extension_id = str(id)
tenant_id = current_user.current_tenant_id
extension_data_from_db = APIBasedExtensionService.get_with_tenant_id(tenant_id, api_based_extension_id)
APIBasedExtensionService.delete(extension_data_from_db)
return {'result': 'success'}
api.add_resource(CodeBasedExtensionAPI, '/code-based-extension')
api.add_resource(APIBasedExtensionAPI, '/api-based-extension')
api.add_resource(APIBasedExtensionDetailAPI, '/api-based-extension/<uuid:id>')

4
api/controllers/service_api/app/app.py
View File

@ -28,6 +28,7 @@ class AppParameterApi(AppApiResource):
'retriever_resource': fields.Raw,
'more_like_this': fields.Raw,
'user_input_form': fields.Raw,
'sensitive_word_avoidance': fields.Raw
}
@marshal_with(parameters_fields)
@ -42,7 +43,8 @@ class AppParameterApi(AppApiResource):
'speech_to_text': app_model_config.speech_to_text_dict,
'retriever_resource': app_model_config.retriever_resource_dict,
'more_like_this': app_model_config.more_like_this_dict,
'user_input_form': app_model_config.user_input_form_list
'user_input_form': app_model_config.user_input_form_list,
'sensitive_word_avoidance': app_model_config.sensitive_word_avoidance_dict
}

1
api/controllers/service_api/app/completion.py
View File

@ -183,4 +183,3 @@ api.add_resource(CompletionApi, '/completion-messages')
api.add_resource(CompletionStopApi, '/completion-messages/<string:task_id>/stop')
api.add_resource(ChatApi, '/chat-messages')
api.add_resource(ChatStopApi, '/chat-messages/<string:task_id>/stop')

4
api/controllers/web/app.py
View File

@ -27,6 +27,7 @@ class AppParameterApi(WebApiResource):
'retriever_resource': fields.Raw,
'more_like_this': fields.Raw,
'user_input_form': fields.Raw,
'sensitive_word_avoidance': fields.Raw
}
@marshal_with(parameters_fields)
@ -41,7 +42,8 @@ class AppParameterApi(WebApiResource):
'speech_to_text': app_model_config.speech_to_text_dict,
'retriever_resource': app_model_config.retriever_resource_dict,
'more_like_this': app_model_config.more_like_this_dict,
'user_input_form': app_model_config.user_input_form_list
'user_input_form': app_model_config.user_input_form_list,
'sensitive_word_avoidance': app_model_config.sensitive_word_avoidance_dict
}

2
api/controllers/web/completion.py
View File

@ -139,7 +139,7 @@ class ChatStopApi(WebApiResource):
return {'result': 'success'}, 200
def compact_response(response: Union[dict | Generator]) -> Response:
def compact_response(response: Union[dict, Generator]) -> Response:
if isinstance(response, dict):
return Response(response=json.dumps(response), status=200, mimetype='application/json')
else:

1
api/core/__init__.py
View File

@ -0,0 +1 @@
import core.moderation.base

193
api/core/callback_handler/llm_callback_handler.py
View File

@ -1,13 +1,25 @@
import logging
from typing import Any, Dict, List, Union
import threading
import time
from typing import Any, Dict, List, Union, Optional
from flask import Flask, current_app
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import LLMResult, BaseMessage
from pydantic import BaseModel
from core.callback_handler.entity.llm_message import LLMMessage
from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException
from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException, \
ConversationTaskInterruptException
from core.model_providers.models.entity.message import to_prompt_messages, PromptMessage
from core.model_providers.models.llm.base import BaseLLM
from core.moderation.base import ModerationOutputsResult, ModerationAction
from core.moderation.factory import ModerationFactory
class ModerationRule(BaseModel):
type: str
config: Dict[str, Any]
class LLMCallbackHandler(BaseCallbackHandler):
@ -20,6 +32,24 @@ class LLMCallbackHandler(BaseCallbackHandler):
self.start_at = None
self.conversation_message_task = conversation_message_task
self.output_moderation_handler = None
self.init_output_moderation()
def init_output_moderation(self):
app_model_config = self.conversation_message_task.app_model_config
sensitive_word_avoidance_dict = app_model_config.sensitive_word_avoidance_dict
if sensitive_word_avoidance_dict and sensitive_word_avoidance_dict.get("enabled"):
self.output_moderation_handler = OutputModerationHandler(
tenant_id=self.conversation_message_task.tenant_id,
app_id=self.conversation_message_task.app.id,
rule=ModerationRule(
type=sensitive_word_avoidance_dict.get("type"),
config=sensitive_word_avoidance_dict.get("config")
),
on_message_replace_func=self.conversation_message_task.on_message_replace
)
@property
def always_verbose(self) -> bool:
"""Whether to call verbose callbacks even if verbose is False."""
@ -59,10 +89,19 @@ class LLMCallbackHandler(BaseCallbackHandler):
self.llm_message.prompt_tokens = self.model_instance.get_num_tokens([PromptMessage(content=prompts[0])])
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
if not self.conversation_message_task.streaming:
self.conversation_message_task.append_message_text(response.generations[0][0].text)
if self.output_moderation_handler:
self.output_moderation_handler.stop_thread()
self.llm_message.completion = self.output_moderation_handler.moderation_completion(
completion=response.generations[0][0].text,
public_event=True if self.conversation_message_task.streaming else False
)
else:
self.llm_message.completion = response.generations[0][0].text
if not self.conversation_message_task.streaming:
self.conversation_message_task.append_message_text(self.llm_message.completion)
if response.llm_output and 'token_usage' in response.llm_output:
if 'prompt_tokens' in response.llm_output['token_usage']:
self.llm_message.prompt_tokens = response.llm_output['token_usage']['prompt_tokens']
@ -79,23 +118,161 @@ class LLMCallbackHandler(BaseCallbackHandler):
self.conversation_message_task.save_message(self.llm_message)
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
try:
self.conversation_message_task.append_message_text(token)
except ConversationTaskStoppedException as ex:
if self.output_moderation_handler and self.output_moderation_handler.should_direct_output():
# stop subscribe new token when output moderation should direct output
ex = ConversationTaskInterruptException()
self.on_llm_error(error=ex)
raise ex
self.llm_message.completion += token
try:
self.conversation_message_task.append_message_text(token)
self.llm_message.completion += token
if self.output_moderation_handler:
self.output_moderation_handler.append_new_token(token)
except ConversationTaskStoppedException as ex:
self.on_llm_error(error=ex)
raise ex
def on_llm_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Do nothing."""
if self.output_moderation_handler:
self.output_moderation_handler.stop_thread()
if isinstance(error, ConversationTaskStoppedException):
if self.conversation_message_task.streaming:
self.llm_message.completion_tokens = self.model_instance.get_num_tokens(
[PromptMessage(content=self.llm_message.completion)]
)
self.conversation_message_task.save_message(llm_message=self.llm_message, by_stopped=True)
if isinstance(error, ConversationTaskInterruptException):
self.llm_message.completion = self.output_moderation_handler.get_final_output()
self.llm_message.completion_tokens = self.model_instance.get_num_tokens(
[PromptMessage(content=self.llm_message.completion)]
)
self.conversation_message_task.save_message(llm_message=self.llm_message)
else:
logging.debug("on_llm_error: %s", error)
class OutputModerationHandler(BaseModel):
DEFAULT_BUFFER_SIZE: int = 300
tenant_id: str
app_id: str
rule: ModerationRule
on_message_replace_func: Any
thread: Optional[threading.Thread] = None
thread_running: bool = True
buffer: str = ''
is_final_chunk: bool = False
final_output: Optional[str] = None
class Config:
arbitrary_types_allowed = True
def should_direct_output(self):
return self.final_output is not None
def get_final_output(self):
return self.final_output
def append_new_token(self, token: str):
self.buffer += token
if not self.thread:
self.thread = self.start_thread()
def moderation_completion(self, completion: str, public_event: bool = False) -> str:
self.buffer = completion
self.is_final_chunk = True
result = self.moderation(
tenant_id=self.tenant_id,
app_id=self.app_id,
moderation_buffer=completion
)
if not result or not result.flagged:
return completion
if result.action == ModerationAction.DIRECT_OUTPUT:
final_output = result.preset_response
else:
final_output = result.text
if public_event:
self.on_message_replace_func(final_output)
return final_output
def start_thread(self) -> threading.Thread:
buffer_size = int(current_app.config.get('MODERATION_BUFFER_SIZE', self.DEFAULT_BUFFER_SIZE))
thread = threading.Thread(target=self.worker, kwargs={
'flask_app': current_app._get_current_object(),
'buffer_size': buffer_size if buffer_size > 0 else self.DEFAULT_BUFFER_SIZE
})
thread.start()
return thread
def stop_thread(self):
if self.thread and self.thread.is_alive():
self.thread_running = False
def worker(self, flask_app: Flask, buffer_size: int):
with flask_app.app_context():
current_length = 0
while self.thread_running:
moderation_buffer = self.buffer
buffer_length = len(moderation_buffer)
if not self.is_final_chunk:
chunk_length = buffer_length - current_length
if 0 <= chunk_length < buffer_size:
time.sleep(1)
continue
current_length = buffer_length
result = self.moderation(
tenant_id=self.tenant_id,
app_id=self.app_id,
moderation_buffer=moderation_buffer
)
if not result or not result.flagged:
continue
if result.action == ModerationAction.DIRECT_OUTPUT:
final_output = result.preset_response
self.final_output = final_output
else:
final_output = result.text + self.buffer[len(moderation_buffer):]
# trigger replace event
if self.thread_running:
self.on_message_replace_func(final_output)
if result.action == ModerationAction.DIRECT_OUTPUT:
break
def moderation(self, tenant_id: str, app_id: str, moderation_buffer: str) -> Optional[ModerationOutputsResult]:
try:
moderation_factory = ModerationFactory(
name=self.rule.type,
app_id=app_id,
tenant_id=tenant_id,
config=self.rule.config
)
result: ModerationOutputsResult = moderation_factory.moderation_for_outputs(moderation_buffer)
return result
except Exception as e:
logging.error("Moderation Output error: %s", e)
return None

92
api/core/chain/sensitive_word_avoidance_chain.py
View File

@ -1,92 +0,0 @@
import enum
import logging
from typing import List, Dict, Optional, Any
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from pydantic import BaseModel
from core.model_providers.error import LLMBadRequestError
from core.model_providers.model_factory import ModelFactory
from core.model_providers.models.llm.base import BaseLLM
from core.model_providers.models.moderation import openai_moderation
class SensitiveWordAvoidanceRule(BaseModel):
class Type(enum.Enum):
MODERATION = "moderation"
KEYWORDS = "keywords"
type: Type
canned_response: str = 'Your content violates our usage policy. Please revise and try again.'
extra_params: dict = {}
class SensitiveWordAvoidanceChain(Chain):
input_key: str = "input" #: :meta private:
output_key: str = "output" #: :meta private:
model_instance: BaseLLM
sensitive_word_avoidance_rule: SensitiveWordAvoidanceRule
@property
def _chain_type(self) -> str:
return "sensitive_word_avoidance_chain"
@property
def input_keys(self) -> List[str]:
"""Expect input key.
:meta private:
"""
return [self.input_key]
@property
def output_keys(self) -> List[str]:
"""Return output key.
:meta private:
"""
return [self.output_key]
def _check_sensitive_word(self, text: str) -> bool:
for word in self.sensitive_word_avoidance_rule.extra_params.get('sensitive_words', []):
if word in text:
return False
return True
def _check_moderation(self, text: str) -> bool:
moderation_model_instance = ModelFactory.get_moderation_model(
tenant_id=self.model_instance.model_provider.provider.tenant_id,
model_provider_name='openai',
model_name=openai_moderation.DEFAULT_MODEL
)
try:
return moderation_model_instance.run(text=text)
except Exception as ex:
logging.exception(ex)
raise LLMBadRequestError('Rate limit exceeded, please try again later.')
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
text = inputs[self.input_key]
if self.sensitive_word_avoidance_rule.type == SensitiveWordAvoidanceRule.Type.KEYWORDS:
result = self._check_sensitive_word(text)
else:
result = self._check_moderation(text)
if not result:
raise SensitiveWordAvoidanceError(self.sensitive_word_avoidance_rule.canned_response)
return {self.output_key: text}
class SensitiveWordAvoidanceError(Exception):
def __init__(self, message):
super().__init__(message)
self.message = message

157
api/core/completion.py
View File

@ -1,13 +1,18 @@
import concurrent
import json
import logging
from typing import Optional, List, Union
from concurrent.futures import ThreadPoolExecutor
from typing import Optional, List, Union, Tuple
from flask import current_app, Flask
from requests.exceptions import ChunkedEncodingError
from core.agent.agent_executor import AgentExecuteResult, PlanningStrategy
from core.callback_handler.main_chain_gather_callback_handler import MainChainGatherCallbackHandler
from core.callback_handler.llm_callback_handler import LLMCallbackHandler
from core.chain.sensitive_word_avoidance_chain import SensitiveWordAvoidanceError
from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException
from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException, \
ConversationTaskInterruptException
from core.external_data_tool.factory import ExternalDataToolFactory
from core.model_providers.error import LLMBadRequestError
from core.memory.read_only_conversation_token_db_buffer_shared_memory import \
ReadOnlyConversationTokenDBBufferSharedMemory
@ -18,6 +23,8 @@ from core.orchestrator_rule_parser import OrchestratorRuleParser
from core.prompt.prompt_template import PromptTemplateParser
from core.prompt.prompt_transform import PromptTransform
from models.model import App, AppModelConfig, Account, Conversation, EndUser
from core.moderation.base import ModerationException, ModerationAction
from core.moderation.factory import ModerationFactory
class Completion:
@ -76,26 +83,35 @@ class Completion:
)
try:
# parse sensitive_word_avoidance_chain
chain_callback = MainChainGatherCallbackHandler(conversation_message_task)
sensitive_word_avoidance_chain = orchestrator_rule_parser.to_sensitive_word_avoidance_chain(
final_model_instance, [chain_callback])
if sensitive_word_avoidance_chain:
try:
query = sensitive_word_avoidance_chain.run(query)
except SensitiveWordAvoidanceError as ex:
cls.run_final_llm(
model_instance=final_model_instance,
mode=app.mode,
app_model_config=app_model_config,
query=query,
inputs=inputs,
agent_execute_result=None,
conversation_message_task=conversation_message_task,
memory=memory,
fake_response=ex.message
)
return
try:
# process sensitive_word_avoidance
inputs, query = cls.moderation_for_inputs(app.id, app.tenant_id, app_model_config, inputs, query)
except ModerationException as e:
cls.run_final_llm(
model_instance=final_model_instance,
mode=app.mode,
app_model_config=app_model_config,
query=query,
inputs=inputs,
agent_execute_result=None,
conversation_message_task=conversation_message_task,
memory=memory,
fake_response=str(e)
)
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:
inputs = cls.fill_in_inputs_from_external_data_tools(
tenant_id=app.tenant_id,
app_id=app.id,
external_data_tools=external_data_tools,
inputs=inputs,
query=query
)
# get agent executor
agent_executor = orchestrator_rule_parser.to_agent_executor(
@ -135,19 +151,110 @@ class Completion:
memory=memory,
fake_response=fake_response
)
except ConversationTaskStoppedException:
except (ConversationTaskInterruptException, ConversationTaskStoppedException):
return
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):
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_result = moderation.moderation_for_inputs(inputs, query)
if not moderation_result.flagged:
return inputs, query
if moderation_result.action == ModerationAction.DIRECT_OUTPUT:
raise ModerationException(moderation_result.preset_response)
elif moderation_result.action == ModerationAction.OVERRIDED:
inputs = moderation_result.inputs
query = moderation_result.query
return inputs, query
@classmethod
def fill_in_inputs_from_external_data_tools(cls, tenant_id: str, app_id: str, external_data_tools: list[dict],
inputs: dict, query: str) -> dict:
"""
Fill in variable inputs from external data tools if exists.
:param tenant_id: workspace id
:param app_id: app id
:param external_data_tools: external data tools configs
:param inputs: the inputs
:param query: the query
:return: the filled inputs
"""
# Group tools by type and config
grouped_tools = {}
for tool in external_data_tools:
if not tool.get("enabled"):
continue
tool_key = (tool.get("type"), json.dumps(tool.get("config"), sort_keys=True))
grouped_tools.setdefault(tool_key, []).append(tool)
results = {}
with ThreadPoolExecutor() as executor:
futures = {}
for tools in grouped_tools.values():
# Only query the first tool in each group
first_tool = tools[0]
future = executor.submit(
cls.query_external_data_tool, current_app._get_current_object(), tenant_id, app_id, first_tool,
inputs, query
)
for tool in tools:
futures[future] = tool
for future in concurrent.futures.as_completed(futures):
tool_key, result = future.result()
if tool_key in grouped_tools:
for tool in grouped_tools[tool_key]:
results[tool['variable']] = result
inputs.update(results)
return inputs
@classmethod
def query_external_data_tool(cls, flask_app: Flask, tenant_id: str, app_id: str, external_data_tool: dict,
inputs: dict, query: str) -> Tuple[Optional[str], Optional[str]]:
with flask_app.app_context():
tool_variable = external_data_tool.get("variable")
tool_type = external_data_tool.get("type")
tool_config = external_data_tool.get("config")
external_data_tool_factory = ExternalDataToolFactory(
name=tool_type,
tenant_id=tenant_id,
app_id=app_id,
variable=tool_variable,
config=tool_config
)
# query external data tool
result = external_data_tool_factory.query(
inputs=inputs,
query=query
)
tool_key = (external_data_tool.get("type"), json.dumps(external_data_tool.get("config"), sort_keys=True))
return tool_key, result
@classmethod
def get_query_for_agent(cls, app: App, app_model_config: AppModelConfig, query: str, inputs: dict) -> str:
if app.mode != 'completion':
return query
return inputs.get(app_model_config.dataset_query_variable, "")
@classmethod

26
api/core/conversation_message_task.py
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@ -290,6 +290,10 @@ class ConversationMessageTask:
db.session.commit()
self.retriever_resource = resource
def on_message_replace(self, text: str):
if text is not None:
self._pub_handler.pub_message_replace(text)
def message_end(self):
self._pub_handler.pub_message_end(self.retriever_resource)
@ -342,6 +346,24 @@ class PubHandler:
self.pub_end()
raise ConversationTaskStoppedException()
def pub_message_replace(self, text: str):
content = {
'event': 'message_replace',
'data': {
'task_id': self._task_id,
'message_id': str(self._message.id),
'text': text,
'mode': self._conversation.mode,
'conversation_id': str(self._conversation.id)
}
}
redis_client.publish(self._channel, json.dumps(content))
if self._is_stopped():
self.pub_end()
raise ConversationTaskStoppedException()
def pub_chain(self, message_chain: MessageChain):
if self._chain_pub:
content = {
@ -443,3 +465,7 @@ class PubHandler:
class ConversationTaskStoppedException(Exception):
pass
class ConversationTaskInterruptException(Exception):
pass

0
api/core/extension/__init__.py Normal file
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api/core/extension/api_based_extension_requestor.py Normal file
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import os
import requests
from models.api_based_extension import APIBasedExtensionPoint
class APIBasedExtensionRequestor:
timeout: (int, int) = (5, 60)
"""timeout for request connect and read"""
def __init__(self, api_endpoint: str, api_key: str) -> None:
self.api_endpoint = api_endpoint
self.api_key = api_key
def request(self, point: APIBasedExtensionPoint, params: dict) -> dict:
"""
Request the api.
:param point: the api point
:param params: the request params
:return: the response json
"""
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer {}".format(self.api_key)
}
url = self.api_endpoint
try:
# proxy support for security
proxies = None
if os.environ.get("API_BASED_EXTENSION_HTTP_PROXY") and os.environ.get("API_BASED_EXTENSION_HTTPS_PROXY"):
proxies = {
'http': os.environ.get("API_BASED_EXTENSION_HTTP_PROXY"),
'https': os.environ.get("API_BASED_EXTENSION_HTTPS_PROXY"),
}
response = requests.request(
method='POST',
url=url,
json={
'point': point.value,
'params': params
},
headers=headers,
timeout=self.timeout,
proxies=proxies
)
except requests.exceptions.Timeout:
raise ValueError("request timeout")
except requests.exceptions.ConnectionError:
raise ValueError("request connection error")
if response.status_code != 200:
raise ValueError("request error, status_code: {}, content: {}".format(
response.status_code,
response.text[:100]
))
return response.json()

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api/core/extension/extensible.py Normal file
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import enum
import importlib.util
import json
import logging
import os
from collections import OrderedDict
from typing import Any, Optional
from pydantic import BaseModel
class ExtensionModule(enum.Enum):
MODERATION = 'moderation'
EXTERNAL_DATA_TOOL = 'external_data_tool'
class ModuleExtension(BaseModel):
extension_class: Any
name: str
label: Optional[dict] = None
form_schema: Optional[list] = None
builtin: bool = True
position: Optional[int] = None
class Extensible:
module: ExtensionModule
name: str
tenant_id: str
config: Optional[dict] = None
def __init__(self, tenant_id: str, config: Optional[dict] = None) -> None:
self.tenant_id = tenant_id
self.config = config
@classmethod
def scan_extensions(cls):
extensions = {}
# get the path of the current class
current_path = os.path.abspath(cls.__module__.replace(".", os.path.sep) + '.py')
current_dir_path = os.path.dirname(current_path)
# traverse subdirectories
for subdir_name in os.listdir(current_dir_path):
if subdir_name.startswith('__'):
continue
subdir_path = os.path.join(current_dir_path, subdir_name)
extension_name = subdir_name
if os.path.isdir(subdir_path):
file_names = os.listdir(subdir_path)
# is builtin extension, builtin extension
# in the front-end page and business logic, there are special treatments.
builtin = False
position = None
if '__builtin__' in file_names:
builtin = True
builtin_file_path = os.path.join(subdir_path, '__builtin__')
if os.path.exists(builtin_file_path):
with open(builtin_file_path, 'r') as f:
position = int(f.read().strip())
if (extension_name + '.py') not in file_names:
logging.warning(f"Missing {extension_name}.py file in {subdir_path}, Skip.")
continue
# Dynamic loading {subdir_name}.py file and find the subclass of Extensible
py_path = os.path.join(subdir_path, extension_name + '.py')
spec = importlib.util.spec_from_file_location(extension_name, py_path)
mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)
extension_class = None
for name, obj in vars(mod).items():
if isinstance(obj, type) and issubclass(obj, cls) and obj != cls:
extension_class = obj
break
if not extension_class:
logging.warning(f"Missing subclass of {cls.__name__} in {py_path}, Skip.")
continue
json_data = {}
if not builtin:
if 'schema.json' not in file_names:
logging.warning(f"Missing schema.json file in {subdir_path}, Skip.")
continue
json_path = os.path.join(subdir_path, 'schema.json')
json_data = {}
if os.path.exists(json_path):
with open(json_path, 'r') as f:
json_data = json.load(f)
extensions[extension_name] = ModuleExtension(
extension_class=extension_class,
name=extension_name,
label=json_data.get('label'),
form_schema=json_data.get('form_schema'),
builtin=builtin,
position=position
)
sorted_items = sorted(extensions.items(), key=lambda x: (x[1].position is None, x[1].position))
sorted_extensions = OrderedDict(sorted_items)
return sorted_extensions

47
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@ -0,0 +1,47 @@
from core.extension.extensible import ModuleExtension, ExtensionModule
from core.external_data_tool.base import ExternalDataTool
from core.moderation.base import Moderation
class Extension:
__module_extensions: dict[str, dict[str, ModuleExtension]] = {}
module_classes = {
ExtensionModule.MODERATION: Moderation,
ExtensionModule.EXTERNAL_DATA_TOOL: ExternalDataTool
}
def init(self):
for module, module_class in self.module_classes.items():
self.__module_extensions[module.value] = module_class.scan_extensions()
def module_extensions(self, module: str) -> list[ModuleExtension]:
module_extensions = self.__module_extensions.get(module)
if not module_extensions:
raise ValueError(f"Extension Module {module} not found")
return list(module_extensions.values())
def module_extension(self, module: ExtensionModule, extension_name: str) -> ModuleExtension:
module_extensions = self.__module_extensions.get(module.value)
if not module_extensions:
raise ValueError(f"Extension Module {module} not found")
module_extension = module_extensions.get(extension_name)
if not module_extension:
raise ValueError(f"Extension {extension_name} not found")
return module_extension
def extension_class(self, module: ExtensionModule, extension_name: str) -> type:
module_extension = self.module_extension(module, extension_name)
return module_extension.extension_class
def validate_form_schema(self, module: ExtensionModule, extension_name: str, config: dict) -> None:
module_extension = self.module_extension(module, extension_name)
form_schema = module_extension.form_schema
# TODO validate form_schema

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api/core/external_data_tool/api/__builtin__ Normal file
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1

0
api/core/external_data_tool/api/__init__.py Normal file
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92
api/core/external_data_tool/api/api.py Normal file
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@ -0,0 +1,92 @@
from typing import Optional
from core.extension.api_based_extension_requestor import APIBasedExtensionRequestor
from core.external_data_tool.base import ExternalDataTool
from core.helper import encrypter
from extensions.ext_database import db
from models.api_based_extension import APIBasedExtension, APIBasedExtensionPoint
class ApiExternalDataTool(ExternalDataTool):
"""
The api external data tool.
"""
name: str = "api"
"""the unique name of external data tool"""
@classmethod
def validate_config(cls, tenant_id: str, config: dict) -> None:
"""
Validate the incoming form config data.
:param tenant_id: the id of workspace
:param config: the form config data
:return:
"""
# own validation logic
api_based_extension_id = config.get("api_based_extension_id")
if not api_based_extension_id:
raise ValueError("api_based_extension_id is required")
# get api_based_extension
api_based_extension = db.session.query(APIBasedExtension).filter(
APIBasedExtension.tenant_id == tenant_id,
APIBasedExtension.id == api_based_extension_id
).first()
if not api_based_extension:
raise ValueError("api_based_extension_id is invalid")
def query(self, inputs: dict, query: Optional[str] = None) -> str:
"""
Query the external data tool.
:param inputs: user inputs
:param query: the query of chat app
:return: the tool query result
"""
# get params from config
api_based_extension_id = self.config.get("api_based_extension_id")
# get api_based_extension
api_based_extension = db.session.query(APIBasedExtension).filter(
APIBasedExtension.tenant_id == self.tenant_id,
APIBasedExtension.id == api_based_extension_id
).first()
if not api_based_extension:
raise ValueError("[External data tool] API query failed, variable: {}, "
"error: api_based_extension_id is invalid"
.format(self.config.get('variable')))
# decrypt api_key
api_key = encrypter.decrypt_token(
tenant_id=self.tenant_id,
token=api_based_extension.api_key
)
try:
# request api
requestor = APIBasedExtensionRequestor(
api_endpoint=api_based_extension.api_endpoint,
api_key=api_key
)
except Exception as e:
raise ValueError("[External data tool] API query failed, variable: {}, error: {}".format(
self.config.get('variable'),
e
))
response_json = requestor.request(point=APIBasedExtensionPoint.APP_EXTERNAL_DATA_TOOL_QUERY, params={
'app_id': self.app_id,
'tool_variable': self.variable,
'inputs': inputs,
'query': query
})
if 'result' not in response_json:
raise ValueError("[External data tool] API query failed, variable: {}, error: result not found in response"
.format(self.config.get('variable')))
return response_json['result']

45
api/core/external_data_tool/base.py Normal file
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@ -0,0 +1,45 @@
from abc import abstractmethod, ABC
from typing import Optional
from core.extension.extensible import Extensible, ExtensionModule
class ExternalDataTool(Extensible, ABC):
"""
The base class of external data tool.
"""
module: ExtensionModule = ExtensionModule.EXTERNAL_DATA_TOOL
app_id: str
"""the id of app"""
variable: str
"""the tool variable name of app tool"""
def __init__(self, tenant_id: str, app_id: str, variable: str, config: Optional[dict] = None) -> None:
super().__init__(tenant_id, config)
self.app_id = app_id
self.variable = variable
@classmethod
@abstractmethod
def validate_config(cls, tenant_id: str, config: dict) -> None:
"""
Validate the incoming form config data.
:param tenant_id: the id of workspace
:param config: the form config data
:return:
"""
raise NotImplementedError
@abstractmethod
def query(self, inputs: dict, query: Optional[str] = None) -> str:
"""
Query the external data tool.
:param inputs: user inputs
:param query: the query of chat app
:return: the tool query result
"""
raise NotImplementedError

40
api/core/external_data_tool/factory.py Normal file
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@ -0,0 +1,40 @@
from typing import Optional
from core.extension.extensible import ExtensionModule
from extensions.ext_code_based_extension import code_based_extension
class ExternalDataToolFactory:
def __init__(self, name: str, tenant_id: str, app_id: str, variable: str, config: dict) -> None:
extension_class = code_based_extension.extension_class(ExtensionModule.EXTERNAL_DATA_TOOL, name)
self.__extension_instance = extension_class(
tenant_id=tenant_id,
app_id=app_id,
variable=variable,
config=config
)
@classmethod
def validate_config(cls, name: str, tenant_id: str, config: dict) -> None:
"""
Validate the incoming form config data.
:param name: the name of external data tool
:param tenant_id: the id of workspace
:param config: the form config data
:return:
"""
code_based_extension.validate_form_schema(ExtensionModule.EXTERNAL_DATA_TOOL, name, config)
extension_class = code_based_extension.extension_class(ExtensionModule.EXTERNAL_DATA_TOOL, name)
extension_class.validate_config(tenant_id, config)
def query(self, inputs: dict, query: Optional[str] = None) -> str:
"""
Query the external data tool.
:param inputs: user inputs
:param query: the query of chat app
:return: the tool query result
"""
return self.__extension_instance.query(inputs, query)

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api/core/moderation/__init__.py Normal file
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3

0
api/core/moderation/api/__init__.py Normal file
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88
api/core/moderation/api/api.py Normal file
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@ -0,0 +1,88 @@
from pydantic import BaseModel
from core.moderation.base import Moderation, ModerationInputsResult, ModerationOutputsResult, ModerationAction
from core.extension.api_based_extension_requestor import APIBasedExtensionRequestor, APIBasedExtensionPoint
from core.helper.encrypter import decrypt_token
from extensions.ext_database import db
from models.api_based_extension import APIBasedExtension
class ModerationInputParams(BaseModel):
app_id: str = ""
inputs: dict = {}
query: str = ""
class ModerationOutputParams(BaseModel):
app_id: str = ""
text: str
class ApiModeration(Moderation):
name: str = "api"
@classmethod
def validate_config(cls, tenant_id: str, config: dict) -> None:
"""
Validate the incoming form config data.
:param tenant_id: the id of workspace
:param config: the form config data
:return:
"""
cls._validate_inputs_and_outputs_config(config, False)
api_based_extension_id = config.get("api_based_extension_id")
if not api_based_extension_id:
raise ValueError("api_based_extension_id is required")
extension = cls._get_api_based_extension(tenant_id, api_based_extension_id)
if not extension:
raise ValueError("API-based Extension not found. Please check it again.")
def moderation_for_inputs(self, inputs: dict, query: str = "") -> ModerationInputsResult:
flagged = False
preset_response = ""
if self.config['inputs_config']['enabled']:
params = ModerationInputParams(
app_id=self.app_id,
inputs=inputs,
query=query
)
result = self._get_config_by_requestor(APIBasedExtensionPoint.APP_MODERATION_INPUT, params.dict())
return ModerationInputsResult(**result)
return ModerationInputsResult(flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response)
def moderation_for_outputs(self, text: str) -> ModerationOutputsResult:
flagged = False
preset_response = ""
if self.config['outputs_config']['enabled']:
params = ModerationOutputParams(
app_id=self.app_id,
text=text
)
result = self._get_config_by_requestor(APIBasedExtensionPoint.APP_MODERATION_OUTPUT, params.dict())
return ModerationOutputsResult(**result)
return ModerationOutputsResult(flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response)
def _get_config_by_requestor(self, extension_point: APIBasedExtensionPoint, params: dict) -> dict:
extension = self._get_api_based_extension(self.tenant_id, self.config.get("api_based_extension_id"))
requestor = APIBasedExtensionRequestor(extension.api_endpoint, decrypt_token(self.tenant_id, extension.api_key))
result = requestor.request(extension_point, params)
return result
@staticmethod
def _get_api_based_extension(tenant_id: str, api_based_extension_id: str) -> APIBasedExtension:
extension = db.session.query(APIBasedExtension).filter(
APIBasedExtension.tenant_id == tenant_id,
APIBasedExtension.id == api_based_extension_id
).first()
return extension

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from abc import ABC, abstractmethod
from typing import Optional
from pydantic import BaseModel
from enum import Enum
from core.extension.extensible import Extensible, ExtensionModule
class ModerationAction(Enum):
DIRECT_OUTPUT = 'direct_output'
OVERRIDED = 'overrided'
class ModerationInputsResult(BaseModel):
flagged: bool = False
action: ModerationAction
preset_response: str = ""
inputs: dict = {}
query: str = ""
class ModerationOutputsResult(BaseModel):
flagged: bool = False
action: ModerationAction
preset_response: str = ""
text: str = ""
class Moderation(Extensible, ABC):
"""
The base class of moderation.
"""
module: ExtensionModule = ExtensionModule.MODERATION
def __init__(self, app_id: str, tenant_id: str, config: Optional[dict] = None) -> None:
super().__init__(tenant_id, config)
self.app_id = app_id
@classmethod
@abstractmethod
def validate_config(cls, tenant_id: str, config: dict) -> None:
"""
Validate the incoming form config data.
:param tenant_id: the id of workspace
:param config: the form config data
:return:
"""
raise NotImplementedError
@abstractmethod
def moderation_for_inputs(self, inputs: dict, query: str = "") -> ModerationInputsResult:
"""
Moderation for inputs.
After the user inputs, this method will be called to perform sensitive content review
on the user inputs and return the processed results.
:param inputs: user inputs
:param query: query string (required in chat app)
:return:
"""
raise NotImplementedError
@abstractmethod
def moderation_for_outputs(self, text: str) -> ModerationOutputsResult:
"""
Moderation for outputs.
When LLM outputs content, the front end will pass the output content (may be segmented)
to this method for sensitive content review, and the output content will be shielded if the review fails.
:param text: LLM output content
:return:
"""
raise NotImplementedError
@classmethod
def _validate_inputs_and_outputs_config(self, config: dict, is_preset_response_required: bool) -> None:
# inputs_config
inputs_config = config.get("inputs_config")
if not isinstance(inputs_config, dict):
raise ValueError("inputs_config must be a dict")
# outputs_config
outputs_config = config.get("outputs_config")
if not isinstance(outputs_config, dict):
raise ValueError("outputs_config must be a dict")
inputs_config_enabled = inputs_config.get("enabled")
outputs_config_enabled = outputs_config.get("enabled")
if not inputs_config_enabled and not outputs_config_enabled:
raise ValueError("At least one of inputs_config or outputs_config must be enabled")
# preset_response
if not is_preset_response_required:
return
if inputs_config_enabled:
if not inputs_config.get("preset_response"):
raise ValueError("inputs_config.preset_response is required")
if len(inputs_config.get("preset_response")) > 100:
raise ValueError("inputs_config.preset_response must be less than 100 characters")
if outputs_config_enabled:
if not outputs_config.get("preset_response"):
raise ValueError("outputs_config.preset_response is required")
if len(outputs_config.get("preset_response")) > 100:
raise ValueError("outputs_config.preset_response must be less than 100 characters")
class ModerationException(Exception):
pass

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from core.extension.extensible import ExtensionModule
from core.moderation.base import Moderation, ModerationInputsResult, ModerationOutputsResult
from extensions.ext_code_based_extension import code_based_extension
class ModerationFactory:
__extension_instance: Moderation
def __init__(self, name: str, app_id: str, tenant_id: str, config: dict) -> None:
extension_class = code_based_extension.extension_class(ExtensionModule.MODERATION, name)
self.__extension_instance = extension_class(app_id, tenant_id, config)
@classmethod
def validate_config(cls, name: str, tenant_id: str, config: dict) -> None:
"""
Validate the incoming form config data.
:param name: the name of extension
:param tenant_id: the id of workspace
:param config: the form config data
:return:
"""
code_based_extension.validate_form_schema(ExtensionModule.MODERATION, name, config)
extension_class = code_based_extension.extension_class(ExtensionModule.MODERATION, name)
extension_class.validate_config(tenant_id, config)
def moderation_for_inputs(self, inputs: dict, query: str = "") -> ModerationInputsResult:
"""
Moderation for inputs.
After the user inputs, this method will be called to perform sensitive content review
on the user inputs and return the processed results.
:param inputs: user inputs
:param query: query string (required in chat app)
:return:
"""
return self.__extension_instance.moderation_for_inputs(inputs, query)
def moderation_for_outputs(self, text: str) -> ModerationOutputsResult:
"""
Moderation for outputs.
When LLM outputs content, the front end will pass the output content (may be segmented)
to this method for sensitive content review, and the output content will be shielded if the review fails.
:param text: LLM output content
:return:
"""
return self.__extension_instance.moderation_for_outputs(text)

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2

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api/core/moderation/keywords/__init__.py Normal file
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api/core/moderation/keywords/keywords.py Normal file
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from core.moderation.base import Moderation, ModerationInputsResult, ModerationOutputsResult, ModerationAction
class KeywordsModeration(Moderation):
name: str = "keywords"
@classmethod
def validate_config(cls, tenant_id: str, config: dict) -> None:
"""
Validate the incoming form config data.
:param tenant_id: the id of workspace
:param config: the form config data
:return:
"""
cls._validate_inputs_and_outputs_config(config, True)
if not config.get("keywords"):
raise ValueError("keywords is required")
if len(config.get("keywords")) > 1000:
raise ValueError("keywords length must be less than 1000")
def moderation_for_inputs(self, inputs: dict, query: str = "") -> ModerationInputsResult:
flagged = False
preset_response = ""
if self.config['inputs_config']['enabled']:
preset_response = self.config['inputs_config']['preset_response']
if query:
inputs['query__'] = query
keywords_list = self.config['keywords'].split('\n')
flagged = self._is_violated(inputs, keywords_list)
return ModerationInputsResult(flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response)
def moderation_for_outputs(self, text: str) -> ModerationOutputsResult:
flagged = False
preset_response = ""
if self.config['outputs_config']['enabled']:
keywords_list = self.config['keywords'].split('\n')
flagged = self._is_violated({'text': text}, keywords_list)
preset_response = self.config['outputs_config']['preset_response']
return ModerationOutputsResult(flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response)
def _is_violated(self, inputs: dict, keywords_list: list) -> bool:
for value in inputs.values():
if self._check_keywords_in_value(keywords_list, value):
return True
return False
def _check_keywords_in_value(self, keywords_list, value):
for keyword in keywords_list:
if keyword.lower() in value.lower():
return True
return False

1
api/core/moderation/openai_moderation/__builtin__ Normal file
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@ -0,0 +1 @@
1

0
api/core/moderation/openai_moderation/__init__.py Normal file
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46
api/core/moderation/openai_moderation/openai_moderation.py Normal file
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@ -0,0 +1,46 @@
from core.moderation.base import Moderation, ModerationInputsResult, ModerationOutputsResult, ModerationAction
from core.model_providers.model_factory import ModelFactory
class OpenAIModeration(Moderation):
name: str = "openai_moderation"
@classmethod
def validate_config(cls, tenant_id: str, config: dict) -> None:
"""
Validate the incoming form config data.
:param tenant_id: the id of workspace
:param config: the form config data
:return:
"""
cls._validate_inputs_and_outputs_config(config, True)
def moderation_for_inputs(self, inputs: dict, query: str = "") -> ModerationInputsResult:
flagged = False
preset_response = ""
if self.config['inputs_config']['enabled']:
preset_response = self.config['inputs_config']['preset_response']
if query:
inputs['query__'] = query
flagged = self._is_violated(inputs)
return ModerationInputsResult(flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response)
def moderation_for_outputs(self, text: str) -> ModerationOutputsResult:
flagged = False
preset_response = ""
if self.config['outputs_config']['enabled']:
flagged = self._is_violated({'text': text})
preset_response = self.config['outputs_config']['preset_response']
return ModerationOutputsResult(flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response)
def _is_violated(self, inputs: dict):
text = '\n'.join(inputs.values())
openai_moderation = ModelFactory.get_moderation_model(self.tenant_id, "openai", "moderation")
is_not_invalid = openai_moderation.run(text)
return not is_not_invalid

47
api/core/orchestrator_rule_parser.py
View File

@ -11,7 +11,6 @@ from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGa
from core.callback_handler.dataset_tool_callback_handler import DatasetToolCallbackHandler
from core.callback_handler.main_chain_gather_callback_handler import MainChainGatherCallbackHandler
from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
from core.chain.sensitive_word_avoidance_chain import SensitiveWordAvoidanceChain, SensitiveWordAvoidanceRule
from core.conversation_message_task import ConversationMessageTask
from core.model_providers.error import ProviderTokenNotInitError
from core.model_providers.model_factory import ModelFactory
@ -125,52 +124,6 @@ class OrchestratorRuleParser:
return chain
def to_sensitive_word_avoidance_chain(self, model_instance: BaseLLM, callbacks: Callbacks = None, **kwargs) \
-> Optional[SensitiveWordAvoidanceChain]:
"""
Convert app sensitive word avoidance config to chain
:param model_instance: model instance
:param callbacks: callbacks for the chain
:param kwargs:
:return:
"""
sensitive_word_avoidance_rule = None
if self.app_model_config.sensitive_word_avoidance_dict:
sensitive_word_avoidance_config = self.app_model_config.sensitive_word_avoidance_dict
if sensitive_word_avoidance_config.get("enabled", False):
if sensitive_word_avoidance_config.get('type') == 'moderation':
sensitive_word_avoidance_rule = SensitiveWordAvoidanceRule(
type=SensitiveWordAvoidanceRule.Type.MODERATION,
canned_response=sensitive_word_avoidance_config.get("canned_response")
if sensitive_word_avoidance_config.get("canned_response")
else 'Your content violates our usage policy. Please revise and try again.',
)
else:
sensitive_words = sensitive_word_avoidance_config.get("words", "")
if sensitive_words:
sensitive_word_avoidance_rule = SensitiveWordAvoidanceRule(
type=SensitiveWordAvoidanceRule.Type.KEYWORDS,
canned_response=sensitive_word_avoidance_config.get("canned_response")
if sensitive_word_avoidance_config.get("canned_response")
else 'Your content violates our usage policy. Please revise and try again.',
extra_params={
'sensitive_words': sensitive_words.split(','),
}
)
if sensitive_word_avoidance_rule:
return SensitiveWordAvoidanceChain(
model_instance=model_instance,
sensitive_word_avoidance_rule=sensitive_word_avoidance_rule,
output_key="sensitive_word_avoidance_output",
callbacks=callbacks,
**kwargs
)
return None
def to_tools(self, tool_configs: list, callbacks: Callbacks = None, **kwargs) -> list[BaseTool]:
"""
Convert app agent tool configs to tools

8
api/extensions/ext_code_based_extension.py Normal file
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@ -0,0 +1,8 @@
from core.extension.extension import Extension
def init():
code_based_extension.init()
code_based_extension = Extension()

17
api/fields/api_based_extension_fields.py Normal file
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@ -0,0 +1,17 @@
from flask_restful import fields
from libs.helper import TimestampField
class HiddenAPIKey(fields.Raw):
def output(self, key, obj):
return obj.api_key[:3] + '***' + obj.api_key[-3:]
api_based_extension_fields = {
'id': fields.String,
'name': fields.String,
'api_endpoint': fields.String,
'api_key': HiddenAPIKey,
'created_at': TimestampField
}

1
api/fields/app_fields.py
View File

@ -23,6 +23,7 @@ model_config_fields = {
'retriever_resource': fields.Raw(attribute='retriever_resource_dict'),
'more_like_this': fields.Raw(attribute='more_like_this_dict'),
'sensitive_word_avoidance': fields.Raw(attribute='sensitive_word_avoidance_dict'),
'external_data_tools': fields.Raw(attribute='external_data_tools_list'),
'model': fields.Raw(attribute='model_dict'),
'user_input_form': fields.Raw(attribute='user_input_form_list'),
'dataset_query_variable': fields.String,

45
api/migrations/versions/968fff4c0ab9_add_api_based_extension.py Normal file
View File

@ -0,0 +1,45 @@
"""add_api_based_extension
Revision ID: 968fff4c0ab9
Revises: b3a09c049e8e
Create Date: 2023-10-27 13:05:58.901858
"""
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision = '968fff4c0ab9'
down_revision = 'b3a09c049e8e'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('api_based_extensions',
sa.Column('id', postgresql.UUID(), server_default=sa.text('uuid_generate_v4()'), nullable=False),
sa.Column('tenant_id', postgresql.UUID(), nullable=False),
sa.Column('name', sa.String(length=255), nullable=False),
sa.Column('api_endpoint', sa.String(length=255), nullable=False),
sa.Column('api_key', sa.Text(), nullable=False),
sa.Column('created_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP(0)'), nullable=False),
sa.PrimaryKeyConstraint('id', name='api_based_extension_pkey')
)
with op.batch_alter_table('api_based_extensions', schema=None) as batch_op:
batch_op.create_index('api_based_extension_tenant_idx', ['tenant_id'], unique=False)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('api_based_extensions', schema=None) as batch_op:
batch_op.drop_index('api_based_extension_tenant_idx')
op.drop_table('api_based_extensions')
# ### end Alembic commands ###

32
api/migrations/versions/a9836e3baeee_add_external_data_tools_in_app_model_.py Normal file
View File

@ -0,0 +1,32 @@
"""add external_data_tools in app model config
Revision ID: a9836e3baeee
Revises: 968fff4c0ab9
Create Date: 2023-11-02 04:04:57.609485
"""
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision = 'a9836e3baeee'
down_revision = '968fff4c0ab9'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('app_model_configs', schema=None) as batch_op:
batch_op.add_column(sa.Column('external_data_tools', sa.Text(), nullable=True))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('app_model_configs', schema=None) as batch_op:
batch_op.drop_column('external_data_tools')
# ### end Alembic commands ###

27
api/models/api_based_extension.py Normal file
View File

@ -0,0 +1,27 @@
import enum
from sqlalchemy.dialects.postgresql import UUID
from extensions.ext_database import db
class APIBasedExtensionPoint(enum.Enum):
APP_EXTERNAL_DATA_TOOL_QUERY = 'app.external_data_tool.query'
PING = 'ping'
APP_MODERATION_INPUT = 'app.moderation.input'
APP_MODERATION_OUTPUT = 'app.moderation.output'
class APIBasedExtension(db.Model):
__tablename__ = 'api_based_extensions'
__table_args__ = (
db.PrimaryKeyConstraint('id', name='api_based_extension_pkey'),
db.Index('api_based_extension_tenant_idx', 'tenant_id'),
)
id = db.Column(UUID, server_default=db.text('uuid_generate_v4()'))
tenant_id = db.Column(UUID, nullable=False)
name = db.Column(db.String(255), nullable=False)
api_endpoint = db.Column(db.String(255), nullable=False)
api_key = db.Column(db.Text, nullable=False)
created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))

44
api/models/model.py
View File

@ -97,6 +97,7 @@ class AppModelConfig(db.Model):
chat_prompt_config = db.Column(db.Text)
completion_prompt_config = db.Column(db.Text)
dataset_configs = db.Column(db.Text)
external_data_tools = db.Column(db.Text)
@property
def app(self):
@ -133,7 +134,12 @@ class AppModelConfig(db.Model):
@property
def sensitive_word_avoidance_dict(self) -> dict:
return json.loads(self.sensitive_word_avoidance) if self.sensitive_word_avoidance \
else {"enabled": False, "words": [], "canned_response": []}
else {"enabled": False, "type": "", "configs": []}
@property
def external_data_tools_list(self) -> list[dict]:
return json.loads(self.external_data_tools) if self.external_data_tools \
else []
@property
def user_input_form_list(self) -> dict:
@ -167,6 +173,7 @@ class AppModelConfig(db.Model):
"retriever_resource": self.retriever_resource_dict,
"more_like_this": self.more_like_this_dict,
"sensitive_word_avoidance": self.sensitive_word_avoidance_dict,
"external_data_tools": self.external_data_tools_list,
"model": self.model_dict,
"user_input_form": self.user_input_form_list,
"dataset_query_variable": self.dataset_query_variable,
@ -190,6 +197,7 @@ class AppModelConfig(db.Model):
self.more_like_this = json.dumps(model_config['more_like_this'])
self.sensitive_word_avoidance = json.dumps(model_config['sensitive_word_avoidance']) \
if model_config.get('sensitive_word_avoidance') else None
self.external_data_tools = json.dumps(model_config['external_data_tools'])
self.model = json.dumps(model_config['model'])
self.user_input_form = json.dumps(model_config['user_input_form'])
self.dataset_query_variable = model_config.get('dataset_query_variable')
@ -219,6 +227,7 @@ class AppModelConfig(db.Model):
speech_to_text=self.speech_to_text,
more_like_this=self.more_like_this,
sensitive_word_avoidance=self.sensitive_word_avoidance,
external_data_tools=self.external_data_tools,
model=self.model,
user_input_form=self.user_input_form,
dataset_query_variable=self.dataset_query_variable,
@ -332,41 +341,16 @@ class Conversation(db.Model):
override_model_configs = json.loads(self.override_model_configs)
if 'model' in override_model_configs:
model_config['model'] = override_model_configs['model']
model_config['pre_prompt'] = override_model_configs['pre_prompt']
model_config['agent_mode'] = override_model_configs['agent_mode']
model_config['opening_statement'] = override_model_configs['opening_statement']
model_config['suggested_questions'] = override_model_configs['suggested_questions']
model_config['suggested_questions_after_answer'] = override_model_configs[
'suggested_questions_after_answer'] \
if 'suggested_questions_after_answer' in override_model_configs else {"enabled": False}
model_config['speech_to_text'] = override_model_configs[
'speech_to_text'] \
if 'speech_to_text' in override_model_configs else {"enabled": False}
model_config['more_like_this'] = override_model_configs['more_like_this'] \
if 'more_like_this' in override_model_configs else {"enabled": False}
model_config['sensitive_word_avoidance'] = override_model_configs['sensitive_word_avoidance'] \
if 'sensitive_word_avoidance' in override_model_configs \
else {"enabled": False, "words": [], "canned_response": []}
model_config['user_input_form'] = override_model_configs['user_input_form']
app_model_config = AppModelConfig()
app_model_config = app_model_config.from_model_config_dict(override_model_configs)
model_config = app_model_config.to_dict()
else:
model_config['configs'] = override_model_configs
else:
app_model_config = db.session.query(AppModelConfig).filter(
AppModelConfig.id == self.app_model_config_id).first()
model_config['configs'] = app_model_config.configs
model_config['model'] = app_model_config.model_dict
model_config['pre_prompt'] = app_model_config.pre_prompt
model_config['agent_mode'] = app_model_config.agent_mode_dict
model_config['opening_statement'] = app_model_config.opening_statement
model_config['suggested_questions'] = app_model_config.suggested_questions_list
model_config['suggested_questions_after_answer'] = app_model_config.suggested_questions_after_answer_dict
model_config['speech_to_text'] = app_model_config.speech_to_text_dict
model_config['retriever_resource'] = app_model_config.retriever_resource_dict
model_config['more_like_this'] = app_model_config.more_like_this_dict
model_config['sensitive_word_avoidance'] = app_model_config.sensitive_word_avoidance_dict
model_config['user_input_form'] = app_model_config.user_input_form_list
model_config = app_model_config.to_dict()
model_config['model_id'] = self.model_id
model_config['provider'] = self.model_provider

98
api/services/api_based_extension_service.py Normal file
View File

@ -0,0 +1,98 @@
from extensions.ext_database import db
from models.api_based_extension import APIBasedExtension, APIBasedExtensionPoint
from core.helper.encrypter import encrypt_token, decrypt_token
from core.extension.api_based_extension_requestor import APIBasedExtensionRequestor
class APIBasedExtensionService:
@staticmethod
def get_all_by_tenant_id(tenant_id: str) -> list[APIBasedExtension]:
extension_list = db.session.query(APIBasedExtension) \
.filter_by(tenant_id=tenant_id) \
.order_by(APIBasedExtension.created_at.desc()) \
.all()
for extension in extension_list:
extension.api_key = decrypt_token(extension.tenant_id, extension.api_key)
return extension_list
@classmethod
def save(cls, extension_data: APIBasedExtension) -> APIBasedExtension:
cls._validation(extension_data)
extension_data.api_key = encrypt_token(extension_data.tenant_id, extension_data.api_key)
db.session.add(extension_data)
db.session.commit()
return extension_data
@staticmethod
def delete(extension_data: APIBasedExtension) -> None:
db.session.delete(extension_data)
db.session.commit()
@staticmethod
def get_with_tenant_id(tenant_id: str, api_based_extension_id: str) -> APIBasedExtension:
extension = db.session.query(APIBasedExtension) \
.filter_by(tenant_id=tenant_id) \
.filter_by(id=api_based_extension_id) \
.first()
if not extension:
raise ValueError("API based extension is not found")
extension.api_key = decrypt_token(extension.tenant_id, extension.api_key)
return extension
@classmethod
def _validation(cls, extension_data: APIBasedExtension) -> None:
# name
if not extension_data.name:
raise ValueError("name must not be empty")
if not extension_data.id:
# case one: check new data, name must be unique
is_name_existed = db.session.query(APIBasedExtension) \
.filter_by(tenant_id=extension_data.tenant_id) \
.filter_by(name=extension_data.name) \
.first()
if is_name_existed:
raise ValueError("name must be unique, it is already existed")
else:
# case two: check existing data, name must be unique
is_name_existed = db.session.query(APIBasedExtension) \
.filter_by(tenant_id=extension_data.tenant_id) \
.filter_by(name=extension_data.name) \
.filter(APIBasedExtension.id != extension_data.id) \
.first()
if is_name_existed:
raise ValueError("name must be unique, it is already existed")
# api_endpoint
if not extension_data.api_endpoint:
raise ValueError("api_endpoint must not be empty")
# api_key
if not extension_data.api_key:
raise ValueError("api_key must not be empty")
if len(extension_data.api_key) < 5:
raise ValueError("api_key must be at least 5 characters")
# check endpoint
cls._ping_connection(extension_data)
@staticmethod
def _ping_connection(extension_data: APIBasedExtension) -> None:
try:
client = APIBasedExtensionRequestor(extension_data.api_endpoint, extension_data.api_key)
resp = client.request(point=APIBasedExtensionPoint.PING, params={})
if resp.get('result') != 'pong':
raise ValueError(resp)
except Exception as e:
raise ValueError("connection error: {}".format(e))

143
api/services/app_model_config_service.py
View File

@ -1,6 +1,8 @@
import re
import uuid
from core.external_data_tool.factory import ExternalDataToolFactory
from core.moderation.factory import ModerationFactory
from core.prompt.prompt_transform import AppMode
from core.agent.agent_executor import PlanningStrategy
from core.model_providers.model_provider_factory import ModelProviderFactory
@ -13,8 +15,8 @@ SUPPORT_TOOLS = ["dataset", "google_search", "web_reader", "wikipedia", "current
class AppModelConfigService:
@staticmethod
def is_dataset_exists(account: Account, dataset_id: str) -> bool:
@classmethod
def is_dataset_exists(cls, account: Account, dataset_id: str) -> bool:
# verify if the dataset ID exists
dataset = DatasetService.get_dataset(dataset_id)
@ -26,8 +28,8 @@ class AppModelConfigService:
return True
@staticmethod
def validate_model_completion_params(cp: dict, model_name: str) -> dict:
@classmethod
def validate_model_completion_params(cls, cp: dict, model_name: str) -> dict:
# 6. model.completion_params
if not isinstance(cp, dict):
raise ValueError("model.completion_params must be of object type")
@ -57,7 +59,7 @@ class AppModelConfigService:
cp["stop"] = []
elif not isinstance(cp["stop"], list):
raise ValueError("stop in model.completion_params must be of list type")
if len(cp["stop"]) > 4:
raise ValueError("stop sequences must be less than 4")
@ -73,8 +75,8 @@ class AppModelConfigService:
return filtered_cp
@staticmethod
def validate_configuration(tenant_id: str, account: Account, config: dict, mode: str) -> dict:
@classmethod
def validate_configuration(cls, tenant_id: str, account: Account, config: dict, mode: str) -> dict:
# opening_statement
if 'opening_statement' not in config or not config["opening_statement"]:
config["opening_statement"] = ""
@ -153,33 +155,6 @@ class AppModelConfigService:
if not isinstance(config["more_like_this"]["enabled"], bool):
raise ValueError("enabled in more_like_this must be of boolean type")
# sensitive_word_avoidance
if 'sensitive_word_avoidance' not in config or not config["sensitive_word_avoidance"]:
config["sensitive_word_avoidance"] = {
"enabled": False
}
if not isinstance(config["sensitive_word_avoidance"], dict):
raise ValueError("sensitive_word_avoidance must be of dict type")
if "enabled" not in config["sensitive_word_avoidance"] or not config["sensitive_word_avoidance"]["enabled"]:
config["sensitive_word_avoidance"]["enabled"] = False
if not isinstance(config["sensitive_word_avoidance"]["enabled"], bool):
raise ValueError("enabled in sensitive_word_avoidance must be of boolean type")
if "words" not in config["sensitive_word_avoidance"] or not config["sensitive_word_avoidance"]["words"]:
config["sensitive_word_avoidance"]["words"] = ""
if not isinstance(config["sensitive_word_avoidance"]["words"], str):
raise ValueError("words in sensitive_word_avoidance must be of string type")
if "canned_response" not in config["sensitive_word_avoidance"] or not config["sensitive_word_avoidance"]["canned_response"]:
config["sensitive_word_avoidance"]["canned_response"] = ""
if not isinstance(config["sensitive_word_avoidance"]["canned_response"], str):
raise ValueError("canned_response in sensitive_word_avoidance must be of string type")
# model
if 'model' not in config:
raise ValueError("model is required")
@ -204,7 +179,7 @@ class AppModelConfigService:
model_ids = [m['id'] for m in model_list]
if config["model"]["name"] not in model_ids:
raise ValueError("model.name must be in the specified model list")
# model.mode
if 'mode' not in config['model'] or not config['model']["mode"]:
config['model']["mode"] = ""
@ -213,7 +188,7 @@ class AppModelConfigService:
if 'completion_params' not in config["model"]:
raise ValueError("model.completion_params is required")
config["model"]["completion_params"] = AppModelConfigService.validate_model_completion_params(
config["model"]["completion_params"] = cls.validate_model_completion_params(
config["model"]["completion_params"],
config["model"]["name"]
)
@ -330,14 +305,20 @@ class AppModelConfigService:
except ValueError:
raise ValueError("id in dataset must be of UUID type")
if not AppModelConfigService.is_dataset_exists(account, tool_item["id"]):
if not cls.is_dataset_exists(account, tool_item["id"]):
raise ValueError("Dataset ID does not exist, please check your permission.")
# dataset_query_variable
AppModelConfigService.is_dataset_query_variable_valid(config, mode)
cls.is_dataset_query_variable_valid(config, mode)
# advanced prompt validation
AppModelConfigService.is_advanced_prompt_valid(config, mode)
cls.is_advanced_prompt_valid(config, mode)
# external data tools validation
cls.is_external_data_tools_valid(tenant_id, config)
# moderation validation
cls.is_moderation_valid(tenant_id, config)
# Filter out extra parameters
filtered_config = {
@ -348,6 +329,7 @@ class AppModelConfigService:
"retriever_resource": config["retriever_resource"],
"more_like_this": config["more_like_this"],
"sensitive_word_avoidance": config["sensitive_word_avoidance"],
"external_data_tools": config["external_data_tools"],
"model": {
"provider": config["model"]["provider"],
"name": config["model"]["name"],
@ -365,32 +347,86 @@ class AppModelConfigService:
}
return filtered_config
@staticmethod
def is_dataset_query_variable_valid(config: dict, mode: str) -> None:
@classmethod
def is_moderation_valid(cls, tenant_id: str, config: dict):
if 'sensitive_word_avoidance' not in config or not config["sensitive_word_avoidance"]:
config["sensitive_word_avoidance"] = {
"enabled": False
}
if not isinstance(config["sensitive_word_avoidance"], dict):
raise ValueError("sensitive_word_avoidance must be of dict type")
if "enabled" not in config["sensitive_word_avoidance"] or not config["sensitive_word_avoidance"]["enabled"]:
config["sensitive_word_avoidance"]["enabled"] = False
if not config["sensitive_word_avoidance"]["enabled"]:
return
if "type" not in config["sensitive_word_avoidance"] or not config["sensitive_word_avoidance"]["type"]:
raise ValueError("sensitive_word_avoidance.type is required")
type = config["sensitive_word_avoidance"]["type"]
config = config["sensitive_word_avoidance"]["config"]
ModerationFactory.validate_config(
name=type,
tenant_id=tenant_id,
config=config
)
@classmethod
def is_external_data_tools_valid(cls, tenant_id: str, config: dict):
if 'external_data_tools' not in config or not config["external_data_tools"]:
config["external_data_tools"] = []
if not isinstance(config["external_data_tools"], list):
raise ValueError("external_data_tools must be of list type")
for tool in config["external_data_tools"]:
if "enabled" not in tool or not tool["enabled"]:
tool["enabled"] = False
if not tool["enabled"]:
continue
if "type" not in tool or not tool["type"]:
raise ValueError("external_data_tools[].type is required")
type = tool["type"]
config = tool["config"]
ExternalDataToolFactory.validate_config(
name=type,
tenant_id=tenant_id,
config=config
)
@classmethod
def is_dataset_query_variable_valid(cls, config: dict, mode: str) -> None:
# Only check when mode is completion
if mode != 'completion':
return
agent_mode = config.get("agent_mode", {})
tools = agent_mode.get("tools", [])
dataset_exists = "dataset" in str(tools)
dataset_query_variable = config.get("dataset_query_variable")
if dataset_exists and not dataset_query_variable:
raise ValueError("Dataset query variable is required when dataset is exist")
@staticmethod
def is_advanced_prompt_valid(config: dict, app_mode: str) -> None:
@classmethod
def is_advanced_prompt_valid(cls, config: dict, app_mode: str) -> None:
# prompt_type
if 'prompt_type' not in config or not config["prompt_type"]:
config["prompt_type"] = "simple"
if config['prompt_type'] not in ['simple', 'advanced']:
raise ValueError("prompt_type must be in ['simple', 'advanced']")
# chat_prompt_config
if 'chat_prompt_config' not in config or not config["chat_prompt_config"]:
config["chat_prompt_config"] = {}
@ -404,7 +440,7 @@ class AppModelConfigService:
if not isinstance(config["completion_prompt_config"], dict):
raise ValueError("completion_prompt_config must be of object type")
# dataset_configs
if 'dataset_configs' not in config or not config["dataset_configs"]:
config["dataset_configs"] = {"top_k": 2, "score_threshold": {"enable": False}}
@ -415,10 +451,10 @@ class AppModelConfigService:
if config['prompt_type'] == 'advanced':
if not config['chat_prompt_config'] and not config['completion_prompt_config']:
raise ValueError("chat_prompt_config or completion_prompt_config is required when prompt_type is advanced")
if config['model']["mode"] not in ['chat', 'completion']:
raise ValueError("model.mode must be in ['chat', 'completion'] when prompt_type is advanced")
if app_mode == AppMode.CHAT.value and config['model']["mode"] == ModelMode.COMPLETION.value:
user_prefix = config['completion_prompt_config']['conversation_histories_role']['user_prefix']
assistant_prefix = config['completion_prompt_config']['conversation_histories_role']['assistant_prefix']
@ -429,9 +465,8 @@ class AppModelConfigService:
if not assistant_prefix:
config['completion_prompt_config']['conversation_histories_role']['assistant_prefix'] = 'Assistant'
if config['model']["mode"] == ModelMode.CHAT.value:
prompt_list = config['chat_prompt_config']['prompt']
if len(prompt_list) > 10:
raise ValueError("prompt messages must be less than 10")
raise ValueError("prompt messages must be less than 10")

13
api/services/code_based_extension_service.py Normal file
View File

@ -0,0 +1,13 @@
from extensions.ext_code_based_extension import code_based_extension
class CodeBasedExtensionService:
@staticmethod
def get_code_based_extension(module: str) -> list[dict]:
module_extensions = code_based_extension.module_extensions(module)
return [{
'name': module_extension.name,
'label': module_extension.label,
'form_schema': module_extension.form_schema
} for module_extension in module_extensions if not module_extension.builtin]

37
api/services/completion_service.py
View File

@ -10,7 +10,8 @@ from redis.client import PubSub
from sqlalchemy import and_
from core.completion import Completion
from core.conversation_message_task import PubHandler, ConversationTaskStoppedException
from core.conversation_message_task import PubHandler, ConversationTaskStoppedException, \
ConversationTaskInterruptException
from core.model_providers.error import LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, \
LLMRateLimitError, \
LLMAuthorizationError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
@ -28,9 +29,9 @@ from services.errors.message import MessageNotExistsError
class CompletionService:
@classmethod
def completion(cls, app_model: App, user: Union[Account | EndUser], args: Any,
def completion(cls, app_model: App, user: Union[Account, EndUser], args: Any,
from_source: str, streaming: bool = True,
is_model_config_override: bool = False) -> Union[dict | Generator]:
is_model_config_override: bool = False) -> Union[dict, Generator]:
# is streaming mode
inputs = args['inputs']
query = args['query']
@ -199,9 +200,9 @@ class CompletionService:
is_override=is_model_config_override,
retriever_from=retriever_from
)
except ConversationTaskStoppedException:
except (ConversationTaskInterruptException, ConversationTaskStoppedException):
pass
except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
except (ValueError, LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError,
ModelCurrentlyNotSupportError) as e:
PubHandler.pub_error(user, generate_task_id, e)
@ -234,7 +235,7 @@ class CompletionService:
PubHandler.stop(user, generate_task_id)
try:
pubsub.close()
except:
except Exception:
pass
countdown_thread = threading.Thread(target=close_pubsub)
@ -243,9 +244,9 @@ class CompletionService:
return countdown_thread
@classmethod
def generate_more_like_this(cls, app_model: App, user: Union[Account | EndUser],
def generate_more_like_this(cls, app_model: App, user: Union[Account, EndUser],
message_id: str, streaming: bool = True,
retriever_from: str = 'dev') -> Union[dict | Generator]:
retriever_from: str = 'dev') -> Union[dict, Generator]:
if not user:
raise ValueError('user cannot be None')
@ -341,7 +342,7 @@ class CompletionService:
return filtered_inputs
@classmethod
def compact_response(cls, pubsub: PubSub, streaming: bool = False) -> Union[dict | Generator]:
def compact_response(cls, pubsub: PubSub, streaming: bool = False) -> Union[dict, Generator]:
generate_channel = list(pubsub.channels.keys())[0].decode('utf-8')
if not streaming:
try:
@ -386,6 +387,8 @@ class CompletionService:
break
if event == 'message':
yield "data: " + json.dumps(cls.get_message_response_data(result.get('data'))) + "\n\n"
elif event == 'message_replace':
yield "data: " + json.dumps(cls.get_message_replace_response_data(result.get('data'))) + "\n\n"
elif event == 'chain':
yield "data: " + json.dumps(cls.get_chain_response_data(result.get('data'))) + "\n\n"
elif event == 'agent_thought':
@ -427,6 +430,21 @@ class CompletionService:
return response_data
@classmethod
def get_message_replace_response_data(cls, data: dict):
response_data = {
'event': 'message_replace',
'task_id': data.get('task_id'),
'id': data.get('message_id'),
'answer': data.get('text'),
'created_at': int(time.time())
}
if data.get('mode') == 'chat':
response_data['conversation_id'] = data.get('conversation_id')
return response_data
@classmethod
def get_blocking_message_response_data(cls, data: dict):
message = data.get('message')
@ -508,6 +526,7 @@ class CompletionService:
# handle errors
llm_errors = {
'ValueError': LLMBadRequestError,
'LLMBadRequestError': LLMBadRequestError,
'LLMAPIConnectionError': LLMAPIConnectionError,
'LLMAPIUnavailableError': LLMAPIUnavailableError,

20
api/services/moderation_service.py Normal file
View File

@ -0,0 +1,20 @@
from models.model import AppModelConfig, App
from core.moderation.factory import ModerationFactory, ModerationOutputsResult
from extensions.ext_database import db
class ModerationService:
def moderation_for_outputs(self, app_id: str, app_model: App, text: str) -> ModerationOutputsResult:
app_model_config: AppModelConfig = None
app_model_config = db.session.query(AppModelConfig).filter(AppModelConfig.id == app_model.app_model_config_id).first()
if not app_model_config:
raise ValueError("app model config not found")
name = app_model_config.sensitive_word_avoidance_dict['type']
config = app_model_config.sensitive_word_avoidance_dict['config']
moderation = ModerationFactory(name, app_id, app_model.tenant_id, config)
return moderation.moderation_for_outputs(text)