feat: add hosted moderation (#1158)

api/core/chain/sensitive_word_avoidance_chain.py

@@ -1,15 +1,38 @@
+import enum
+import logging
 from typing import List, Dict, Optional, Any
 
+import openai
+from flask import current_app
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
+from openai import InvalidRequestError
+from openai.error import APIConnectionError, APIError, ServiceUnavailableError, Timeout, RateLimitError, \
+    AuthenticationError, OpenAIError
+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:
 
-    sensitive_words: List[str] = []
-    canned_response: str = None
+    model_instance: BaseLLM
+    sensitive_word_avoidance_rule: SensitiveWordAvoidanceRule
 
     @property
     def _chain_type(self) -> str:
@@ -31,11 +54,24 @@ class SensitiveWordAvoidanceChain(Chain):
         """
         return [self.output_key]
 
-    def _check_sensitive_word(self, text: str) -> str:
-        for word in self.sensitive_words:
+    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 self.canned_response
-        return 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,
@@ -43,5 +79,13 @@ class SensitiveWordAvoidanceChain(Chain):
             run_manager: Optional[CallbackManagerForChainRun] = None,
     ) -> Dict[str, Any]:
         text = inputs[self.input_key]
-        output = self._check_sensitive_word(text)
-        return {self.output_key: output}
+
+        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 LLMBadRequestError(self.sensitive_word_avoidance_rule.canned_response)
+
+        return {self.output_key: text}