mirror of
https://github.com/langgenius/dify.git
synced 2024-11-16 11:42:29 +08:00
feat: add hosted moderation (#1158)
This commit is contained in:
takatost
GitHub
parent
983834cd52
commit
f9082104ed
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@ -61,6 +61,8 @@ DEFAULTS = {
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'HOSTED_ANTHROPIC_PAID_INCREASE_QUOTA': 1000000,
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'HOSTED_ANTHROPIC_PAID_MIN_QUANTITY': 20,
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'HOSTED_ANTHROPIC_PAID_MAX_QUANTITY': 100,
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'HOSTED_MODERATION_ENABLED': 'False',
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'HOSTED_MODERATION_PROVIDERS': '',
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'TENANT_DOCUMENT_COUNT': 100,
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'CLEAN_DAY_SETTING': 30,
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'UPLOAD_FILE_SIZE_LIMIT': 15,
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@ -230,6 +232,9 @@ class Config:
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self.HOSTED_ANTHROPIC_PAID_MIN_QUANTITY = int(get_env('HOSTED_ANTHROPIC_PAID_MIN_QUANTITY'))
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self.HOSTED_ANTHROPIC_PAID_MAX_QUANTITY = int(get_env('HOSTED_ANTHROPIC_PAID_MAX_QUANTITY'))
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self.HOSTED_MODERATION_ENABLED = get_bool_env('HOSTED_MODERATION_ENABLED')
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self.HOSTED_MODERATION_PROVIDERS = get_env('HOSTED_MODERATION_PROVIDERS')
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self.STRIPE_API_KEY = get_env('STRIPE_API_KEY')
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self.STRIPE_WEBHOOK_SECRET = get_env('STRIPE_WEBHOOK_SECRET')
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@ -16,6 +16,8 @@ from core.agent.agent.structed_multi_dataset_router_agent import StructuredMulti
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from core.agent.agent.structured_chat import AutoSummarizingStructuredChatAgent
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from langchain.agents import AgentExecutor as LCAgentExecutor
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from core.helper import moderation
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from core.model_providers.error import LLMError
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from core.model_providers.models.llm.base import BaseLLM
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from core.tool.dataset_retriever_tool import DatasetRetrieverTool
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@ -116,6 +118,18 @@ class AgentExecutor:
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return self.agent.should_use_agent(query)
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def run(self, query: str) -> AgentExecuteResult:
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moderation_result = moderation.check_moderation(
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self.configuration.model_instance.model_provider,
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query
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)
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if not moderation_result:
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return AgentExecuteResult(
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output="I apologize for any confusion, but I'm an AI assistant to be helpful, harmless, and honest.",
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strategy=self.configuration.strategy,
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configuration=self.configuration
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)
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agent_executor = LCAgentExecutor.from_agent_and_tools(
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agent=self.agent,
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tools=self.configuration.tools,
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@ -128,7 +142,9 @@ class AgentExecutor:
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try:
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output = agent_executor.run(query)
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except Exception:
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except LLMError as ex:
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raise ex
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except Exception as ex:
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logging.exception("agent_executor run failed")
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output = None
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@ -6,7 +6,7 @@ from typing import Any, Dict, List, Union, Optional
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from langchain.agents import openai_functions_agent, openai_functions_multi_agent
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from langchain.callbacks.base import BaseCallbackHandler
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from langchain.schema import AgentAction, AgentFinish, LLMResult, ChatGeneration
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from langchain.schema import AgentAction, AgentFinish, LLMResult, ChatGeneration, BaseMessage
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from core.callback_handler.entity.agent_loop import AgentLoop
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from core.conversation_message_task import ConversationMessageTask
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@ -18,9 +18,9 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
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"""Callback Handler that prints to std out."""
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raise_error: bool = True
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def __init__(self, model_instant: BaseLLM, conversation_message_task: ConversationMessageTask) -> None:
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def __init__(self, model_instance: BaseLLM, conversation_message_task: ConversationMessageTask) -> None:
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"""Initialize callback handler."""
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self.model_instant = model_instant
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self.model_instance = model_instance
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self.conversation_message_task = conversation_message_task
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self._agent_loops = []
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self._current_loop = None
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@ -46,6 +46,21 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
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"""Whether to ignore chain callbacks."""
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return True
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def on_chat_model_start(
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self,
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serialized: Dict[str, Any],
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messages: List[List[BaseMessage]],
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**kwargs: Any
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) -> Any:
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if not self._current_loop:
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# Agent start with a LLM query
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self._current_loop = AgentLoop(
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position=len(self._agent_loops) + 1,
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prompt="\n".join([message.content for message in messages[0]]),
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status='llm_started',
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started_at=time.perf_counter()
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)
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def on_llm_start(
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self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
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) -> None:
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@ -70,7 +85,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
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if response.llm_output:
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self._current_loop.prompt_tokens = response.llm_output['token_usage']['prompt_tokens']
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else:
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self._current_loop.prompt_tokens = self.model_instant.get_num_tokens(
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self._current_loop.prompt_tokens = self.model_instance.get_num_tokens(
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[PromptMessage(content=self._current_loop.prompt)]
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)
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completion_generation = response.generations[0][0]
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@ -87,7 +102,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
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if response.llm_output:
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self._current_loop.completion_tokens = response.llm_output['token_usage']['completion_tokens']
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else:
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self._current_loop.completion_tokens = self.model_instant.get_num_tokens(
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self._current_loop.completion_tokens = self.model_instance.get_num_tokens(
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[PromptMessage(content=self._current_loop.completion)]
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)
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@ -162,7 +177,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
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self._current_loop.latency = self._current_loop.completed_at - self._current_loop.started_at
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self.conversation_message_task.on_agent_end(
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self._message_agent_thought, self.model_instant, self._current_loop
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self._message_agent_thought, self.model_instance, self._current_loop
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)
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self._agent_loops.append(self._current_loop)
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@ -193,7 +208,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
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)
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self.conversation_message_task.on_agent_end(
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self._message_agent_thought, self.model_instant, self._current_loop
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self._message_agent_thought, self.model_instance, self._current_loop
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)
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self._agent_loops.append(self._current_loop)
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@ -6,4 +6,3 @@ class LLMMessage(BaseModel):
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prompt_tokens: int = 0
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completion: str = ''
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completion_tokens: int = 0
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latency: float = 0.0
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@ -1,5 +1,4 @@
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import logging
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import time
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from typing import Any, Dict, List, Union
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from langchain.callbacks.base import BaseCallbackHandler
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@ -32,7 +31,6 @@ class LLMCallbackHandler(BaseCallbackHandler):
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messages: List[List[BaseMessage]],
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**kwargs: Any
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) -> Any:
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self.start_at = time.perf_counter()
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real_prompts = []
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for message in messages[0]:
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if message.type == 'human':
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@ -53,8 +51,6 @@ class LLMCallbackHandler(BaseCallbackHandler):
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def on_llm_start(
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self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
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) -> None:
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self.start_at = time.perf_counter()
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self.llm_message.prompt = [{
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"role": 'user',
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"text": prompts[0]
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@ -63,9 +59,6 @@ class LLMCallbackHandler(BaseCallbackHandler):
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self.llm_message.prompt_tokens = self.model_instance.get_num_tokens([PromptMessage(content=prompts[0])])
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def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
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end_at = time.perf_counter()
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self.llm_message.latency = end_at - self.start_at
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if not self.conversation_message_task.streaming:
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self.conversation_message_task.append_message_text(response.generations[0][0].text)
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self.llm_message.completion = response.generations[0][0].text
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@ -89,8 +82,6 @@ class LLMCallbackHandler(BaseCallbackHandler):
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"""Do nothing."""
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if isinstance(error, ConversationTaskStoppedException):
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if self.conversation_message_task.streaming:
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end_at = time.perf_counter()
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self.llm_message.latency = end_at - self.start_at
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self.llm_message.completion_tokens = self.model_instance.get_num_tokens(
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[PromptMessage(content=self.llm_message.completion)]
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)
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@ -1,15 +1,38 @@
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import enum
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import logging
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from typing import List, Dict, Optional, Any
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import openai
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from flask import current_app
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from langchain.callbacks.manager import CallbackManagerForChainRun
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from langchain.chains.base import Chain
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from openai import InvalidRequestError
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from openai.error import APIConnectionError, APIError, ServiceUnavailableError, Timeout, RateLimitError, \
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AuthenticationError, OpenAIError
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from pydantic import BaseModel
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from core.model_providers.error import LLMBadRequestError
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from core.model_providers.model_factory import ModelFactory
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from core.model_providers.models.llm.base import BaseLLM
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from core.model_providers.models.moderation import openai_moderation
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class SensitiveWordAvoidanceRule(BaseModel):
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class Type(enum.Enum):
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MODERATION = "moderation"
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KEYWORDS = "keywords"
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type: Type
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canned_response: str = 'Your content violates our usage policy. Please revise and try again.'
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extra_params: dict = {}
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class SensitiveWordAvoidanceChain(Chain):
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input_key: str = "input" #: :meta private:
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output_key: str = "output" #: :meta private:
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sensitive_words: List[str] = []
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canned_response: str = None
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model_instance: BaseLLM
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sensitive_word_avoidance_rule: SensitiveWordAvoidanceRule
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@property
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def _chain_type(self) -> str:
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@ -31,11 +54,24 @@ class SensitiveWordAvoidanceChain(Chain):
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"""
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return [self.output_key]
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def _check_sensitive_word(self, text: str) -> str:
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for word in self.sensitive_words:
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def _check_sensitive_word(self, text: str) -> bool:
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for word in self.sensitive_word_avoidance_rule.extra_params.get('sensitive_words', []):
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if word in text:
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return self.canned_response
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return text
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return False
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return True
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def _check_moderation(self, text: str) -> bool:
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moderation_model_instance = ModelFactory.get_moderation_model(
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tenant_id=self.model_instance.model_provider.provider.tenant_id,
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model_provider_name='openai',
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model_name=openai_moderation.DEFAULT_MODEL
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)
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try:
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return moderation_model_instance.run(text=text)
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except Exception as ex:
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logging.exception(ex)
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raise LLMBadRequestError('Rate limit exceeded, please try again later.')
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def _call(
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self,
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@ -43,5 +79,13 @@ class SensitiveWordAvoidanceChain(Chain):
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, Any]:
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text = inputs[self.input_key]
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output = self._check_sensitive_word(text)
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return {self.output_key: output}
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if self.sensitive_word_avoidance_rule.type == SensitiveWordAvoidanceRule.Type.KEYWORDS:
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result = self._check_sensitive_word(text)
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else:
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result = self._check_moderation(text)
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if not result:
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raise LLMBadRequestError(self.sensitive_word_avoidance_rule.canned_response)
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return {self.output_key: text}
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@ -1,9 +1,7 @@
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import json
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import logging
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import re
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from typing import Optional, List, Union, Tuple
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from typing import Optional, List, Union
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from langchain.schema import BaseMessage
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from requests.exceptions import ChunkedEncodingError
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from core.agent.agent_executor import AgentExecuteResult, PlanningStrategy
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@ -14,11 +12,10 @@ from core.model_providers.error import LLMBadRequestError
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from core.memory.read_only_conversation_token_db_buffer_shared_memory import \
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ReadOnlyConversationTokenDBBufferSharedMemory
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from core.model_providers.model_factory import ModelFactory
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from core.model_providers.models.entity.message import PromptMessage, to_prompt_messages
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from core.model_providers.models.entity.message import PromptMessage
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from core.model_providers.models.llm.base import BaseLLM
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from core.orchestrator_rule_parser import OrchestratorRuleParser
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from core.prompt.prompt_builder import PromptBuilder
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from core.prompt.prompt_template import JinjaPromptTemplate
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from core.prompt.prompts import MORE_LIKE_THIS_GENERATE_PROMPT
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from models.dataset import DocumentSegment, Dataset, Document
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from models.model import App, AppModelConfig, Account, Conversation, Message, EndUser
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@ -81,7 +78,7 @@ class Completion:
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# parse sensitive_word_avoidance_chain
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chain_callback = MainChainGatherCallbackHandler(conversation_message_task)
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sensitive_word_avoidance_chain = orchestrator_rule_parser.to_sensitive_word_avoidance_chain([chain_callback])
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sensitive_word_avoidance_chain = orchestrator_rule_parser.to_sensitive_word_avoidance_chain(final_model_instance, [chain_callback])
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if sensitive_word_avoidance_chain:
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query = sensitive_word_avoidance_chain.run(query)
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@ -1,5 +1,5 @@
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import decimal
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import json
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import time
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from typing import Optional, Union, List
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from core.callback_handler.entity.agent_loop import AgentLoop
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@ -23,6 +23,8 @@ class ConversationMessageTask:
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def __init__(self, task_id: str, app: App, app_model_config: AppModelConfig, user: Account,
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inputs: dict, query: str, streaming: bool, model_instance: BaseLLM,
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conversation: Optional[Conversation] = None, is_override: bool = False):
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self.start_at = time.perf_counter()
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self.task_id = task_id
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self.app = app
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@ -61,6 +63,7 @@ class ConversationMessageTask:
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)
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def init(self):
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override_model_configs = None
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if self.is_override:
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override_model_configs = self.app_model_config.to_dict()
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@ -165,7 +168,7 @@ class ConversationMessageTask:
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self.message.answer_tokens = answer_tokens
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self.message.answer_unit_price = answer_unit_price
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self.message.answer_price_unit = answer_price_unit
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self.message.provider_response_latency = llm_message.latency
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self.message.provider_response_latency = time.perf_counter() - self.start_at
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self.message.total_price = total_price
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db.session.commit()
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@ -220,18 +223,18 @@ class ConversationMessageTask:
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return message_agent_thought
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def on_agent_end(self, message_agent_thought: MessageAgentThought, agent_model_instant: BaseLLM,
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def on_agent_end(self, message_agent_thought: MessageAgentThought, agent_model_instance: BaseLLM,
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agent_loop: AgentLoop):
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agent_message_unit_price = agent_model_instant.get_tokens_unit_price(MessageType.HUMAN)
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agent_message_price_unit = agent_model_instant.get_price_unit(MessageType.HUMAN)
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agent_answer_unit_price = agent_model_instant.get_tokens_unit_price(MessageType.ASSISTANT)
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agent_answer_price_unit = agent_model_instant.get_price_unit(MessageType.ASSISTANT)
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agent_message_unit_price = agent_model_instance.get_tokens_unit_price(MessageType.HUMAN)
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agent_message_price_unit = agent_model_instance.get_price_unit(MessageType.HUMAN)
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agent_answer_unit_price = agent_model_instance.get_tokens_unit_price(MessageType.ASSISTANT)
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agent_answer_price_unit = agent_model_instance.get_price_unit(MessageType.ASSISTANT)
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loop_message_tokens = agent_loop.prompt_tokens
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loop_answer_tokens = agent_loop.completion_tokens
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loop_message_total_price = agent_model_instant.calc_tokens_price(loop_message_tokens, MessageType.HUMAN)
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loop_answer_total_price = agent_model_instant.calc_tokens_price(loop_answer_tokens, MessageType.ASSISTANT)
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loop_message_total_price = agent_model_instance.calc_tokens_price(loop_message_tokens, MessageType.HUMAN)
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loop_answer_total_price = agent_model_instance.calc_tokens_price(loop_answer_tokens, MessageType.ASSISTANT)
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loop_total_price = loop_message_total_price + loop_answer_total_price
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message_agent_thought.observation = agent_loop.tool_output
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@ -245,7 +248,7 @@ class ConversationMessageTask:
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message_agent_thought.latency = agent_loop.latency
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message_agent_thought.tokens = agent_loop.prompt_tokens + agent_loop.completion_tokens
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message_agent_thought.total_price = loop_total_price
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message_agent_thought.currency = agent_model_instant.get_currency()
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message_agent_thought.currency = agent_model_instance.get_currency()
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db.session.flush()
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def on_dataset_query_end(self, dataset_query_obj: DatasetQueryObj):
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|
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32
api/core/helper/moderation.py
Normal file
32
api/core/helper/moderation.py
Normal file
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@ -0,0 +1,32 @@
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import logging
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import openai
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from flask import current_app
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from core.model_providers.error import LLMBadRequestError
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from core.model_providers.providers.base import BaseModelProvider
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from models.provider import ProviderType
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def check_moderation(model_provider: BaseModelProvider, text: str) -> bool:
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if current_app.config['HOSTED_MODERATION_ENABLED'] and current_app.config['HOSTED_MODERATION_PROVIDERS']:
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moderation_providers = current_app.config['HOSTED_MODERATION_PROVIDERS'].split(',')
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if model_provider.provider.provider_type == ProviderType.SYSTEM.value \
|
||||
and model_provider.provider_name in moderation_providers:
|
||||
# 2000 text per chunk
|
||||
length = 2000
|
||||
chunks = [text[i:i + length] for i in range(0, len(text), length)]
|
||||
|
||||
try:
|
||||
moderation_result = openai.Moderation.create(input=chunks,
|
||||
api_key=current_app.config['HOSTED_OPENAI_API_KEY'])
|
||||
except Exception as ex:
|
||||
logging.exception(ex)
|
||||
raise LLMBadRequestError('Rate limit exceeded, please try again later.')
|
||||
|
||||
for result in moderation_result.results:
|
||||
if result['flagged'] is True:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
|
@ -8,6 +8,7 @@ from core.model_providers.models.base import BaseProviderModel
|
|||
from core.model_providers.models.embedding.base import BaseEmbedding
|
||||
from core.model_providers.models.entity.model_params import ModelKwargs, ModelType
|
||||
from core.model_providers.models.llm.base import BaseLLM
|
||||
from core.model_providers.models.moderation.base import BaseModeration
|
||||
from core.model_providers.models.speech2text.base import BaseSpeech2Text
|
||||
from extensions.ext_database import db
|
||||
from models.provider import TenantDefaultModel
|
||||
|
|
@ -180,7 +181,7 @@ class ModelFactory:
|
|||
def get_moderation_model(cls,
|
||||
tenant_id: str,
|
||||
model_provider_name: str,
|
||||
model_name: str) -> Optional[BaseProviderModel]:
|
||||
model_name: str) -> Optional[BaseModeration]:
|
||||
"""
|
||||
get moderation model.
|
||||
|
||||
|
|
|
|||
|
|
@ -10,6 +10,7 @@ from langchain.memory.chat_memory import BaseChatMemory
|
|||
from langchain.schema import LLMResult, SystemMessage, AIMessage, HumanMessage, BaseMessage, ChatGeneration
|
||||
|
||||
from core.callback_handler.std_out_callback_handler import DifyStreamingStdOutCallbackHandler, DifyStdOutCallbackHandler
|
||||
from core.helper import moderation
|
||||
from core.model_providers.models.base import BaseProviderModel
|
||||
from core.model_providers.models.entity.message import PromptMessage, MessageType, LLMRunResult, to_prompt_messages
|
||||
from core.model_providers.models.entity.model_params import ModelType, ModelKwargs, ModelMode, ModelKwargsRules
|
||||
|
|
@ -116,6 +117,15 @@ class BaseLLM(BaseProviderModel):
|
|||
:param callbacks:
|
||||
:return:
|
||||
"""
|
||||
moderation_result = moderation.check_moderation(
|
||||
self.model_provider,
|
||||
"\n".join([message.content for message in messages])
|
||||
)
|
||||
|
||||
if not moderation_result:
|
||||
kwargs['fake_response'] = "I apologize for any confusion, " \
|
||||
"but I'm an AI assistant to be helpful, harmless, and honest."
|
||||
|
||||
if self.deduct_quota:
|
||||
self.model_provider.check_quota_over_limit()
|
||||
|
||||
|
|
|
|||
29
api/core/model_providers/models/moderation/base.py
Normal file
29
api/core/model_providers/models/moderation/base.py
Normal file
|
|
@ -0,0 +1,29 @@
|
|||
from abc import abstractmethod
|
||||
from typing import Any
|
||||
|
||||
from core.model_providers.models.base import BaseProviderModel
|
||||
from core.model_providers.models.entity.model_params import ModelType
|
||||
from core.model_providers.providers.base import BaseModelProvider
|
||||
|
||||
|
||||
class BaseModeration(BaseProviderModel):
|
||||
name: str
|
||||
type: ModelType = ModelType.MODERATION
|
||||
|
||||
def __init__(self, model_provider: BaseModelProvider, client: Any, name: str):
|
||||
super().__init__(model_provider, client)
|
||||
self.name = name
|
||||
|
||||
def run(self, text: str) -> bool:
|
||||
try:
|
||||
return self._run(text)
|
||||
except Exception as ex:
|
||||
raise self.handle_exceptions(ex)
|
||||
|
||||
@abstractmethod
|
||||
def _run(self, text: str) -> bool:
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def handle_exceptions(self, ex: Exception) -> Exception:
|
||||
raise NotImplementedError
|
||||
|
|
@ -4,29 +4,35 @@ import openai
|
|||
|
||||
from core.model_providers.error import LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, \
|
||||
LLMRateLimitError, LLMAuthorizationError
|
||||
from core.model_providers.models.base import BaseProviderModel
|
||||
from core.model_providers.models.entity.model_params import ModelType
|
||||
from core.model_providers.models.moderation.base import BaseModeration
|
||||
from core.model_providers.providers.base import BaseModelProvider
|
||||
|
||||
DEFAULT_AUDIO_MODEL = 'whisper-1'
|
||||
DEFAULT_MODEL = 'whisper-1'
|
||||
|
||||
|
||||
class OpenAIModeration(BaseProviderModel):
|
||||
type: ModelType = ModelType.MODERATION
|
||||
class OpenAIModeration(BaseModeration):
|
||||
|
||||
def __init__(self, model_provider: BaseModelProvider, name: str):
|
||||
super().__init__(model_provider, openai.Moderation)
|
||||
super().__init__(model_provider, openai.Moderation, name)
|
||||
|
||||
def run(self, text):
|
||||
def _run(self, text: str) -> bool:
|
||||
credentials = self.model_provider.get_model_credentials(
|
||||
model_name=DEFAULT_AUDIO_MODEL,
|
||||
model_name=self.name,
|
||||
model_type=self.type
|
||||
)
|
||||
|
||||
try:
|
||||
return self._client.create(input=text, api_key=credentials['openai_api_key'])
|
||||
except Exception as ex:
|
||||
raise self.handle_exceptions(ex)
|
||||
# 2000 text per chunk
|
||||
length = 2000
|
||||
chunks = [text[i:i + length] for i in range(0, len(text), length)]
|
||||
|
||||
moderation_result = self._client.create(input=chunks,
|
||||
api_key=credentials['openai_api_key'])
|
||||
|
||||
for result in moderation_result.results:
|
||||
if result['flagged'] is True:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def handle_exceptions(self, ex: Exception) -> Exception:
|
||||
if isinstance(ex, openai.error.InvalidRequestError):
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import math
|
||||
from typing import Optional
|
||||
|
||||
from flask import current_app
|
||||
from langchain import WikipediaAPIWrapper
|
||||
from langchain.callbacks.manager import Callbacks
|
||||
from langchain.memory.chat_memory import BaseChatMemory
|
||||
|
|
@ -12,7 +13,7 @@ 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
|
||||
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
|
||||
|
|
@ -26,6 +27,7 @@ from core.tool.web_reader_tool import WebReaderTool
|
|||
from extensions.ext_database import db
|
||||
from models.dataset import Dataset, DatasetProcessRule
|
||||
from models.model import AppModelConfig
|
||||
from models.provider import ProviderType
|
||||
|
||||
|
||||
class OrchestratorRuleParser:
|
||||
|
|
@ -63,7 +65,7 @@ class OrchestratorRuleParser:
|
|||
|
||||
# add agent callback to record agent thoughts
|
||||
agent_callback = AgentLoopGatherCallbackHandler(
|
||||
model_instant=agent_model_instance,
|
||||
model_instance=agent_model_instance,
|
||||
conversation_message_task=conversation_message_task
|
||||
)
|
||||
|
||||
|
|
@ -123,23 +125,45 @@ class OrchestratorRuleParser:
|
|||
|
||||
return chain
|
||||
|
||||
def to_sensitive_word_avoidance_chain(self, callbacks: Callbacks = None, **kwargs) \
|
||||
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:
|
||||
"""
|
||||
if not self.app_model_config.sensitive_word_avoidance_dict:
|
||||
return None
|
||||
sensitive_word_avoidance_rule = None
|
||||
|
||||
sensitive_word_avoidance_config = self.app_model_config.sensitive_word_avoidance_dict
|
||||
sensitive_words = sensitive_word_avoidance_config.get("words", "")
|
||||
if sensitive_word_avoidance_config.get("enabled", False) and sensitive_words:
|
||||
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(
|
||||
sensitive_words=sensitive_words.split(","),
|
||||
canned_response=sensitive_word_avoidance_config.get("canned_response", ''),
|
||||
model_instance=model_instance,
|
||||
sensitive_word_avoidance_rule=sensitive_word_avoidance_rule,
|
||||
output_key="sensitive_word_avoidance_output",
|
||||
callbacks=callbacks,
|
||||
**kwargs
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ import json
|
|||
import os
|
||||
from unittest.mock import patch
|
||||
|
||||
from core.model_providers.models.moderation.openai_moderation import OpenAIModeration, DEFAULT_AUDIO_MODEL
|
||||
from core.model_providers.models.moderation.openai_moderation import OpenAIModeration, DEFAULT_MODEL
|
||||
from core.model_providers.providers.openai_provider import OpenAIProvider
|
||||
from models.provider import Provider, ProviderType
|
||||
|
||||
|
|
@ -23,7 +23,7 @@ def get_mock_openai_moderation_model():
|
|||
openai_provider = OpenAIProvider(provider=get_mock_provider(valid_openai_api_key))
|
||||
return OpenAIModeration(
|
||||
model_provider=openai_provider,
|
||||
name=DEFAULT_AUDIO_MODEL
|
||||
name=DEFAULT_MODEL
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -36,5 +36,4 @@ def test_run(mock_decrypt):
|
|||
model = get_mock_openai_moderation_model()
|
||||
rst = model.run('hello')
|
||||
|
||||
assert isinstance(rst, dict)
|
||||
assert 'id' in rst
|
||||
assert rst is True
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user