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|
# +-------------------------------------------------------------+
#
# Use AporiaAI for your LLM calls
#
# +-------------------------------------------------------------+
# Thank you users! We ❤️ you! - Krrish & Ishaan
import os
import sys
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system path
import json
import sys
from typing import Any, List, Literal, Optional
from fastapi import HTTPException
import litellm
from litellm._logging import verbose_proxy_logger
from litellm.integrations.custom_guardrail import (
CustomGuardrail,
log_guardrail_information,
)
from litellm.litellm_core_utils.logging_utils import (
convert_litellm_response_object_to_str,
)
from litellm.llms.custom_httpx.http_handler import (
get_async_httpx_client,
httpxSpecialProvider,
)
from litellm.proxy._types import UserAPIKeyAuth
from litellm.proxy.guardrails.guardrail_helpers import should_proceed_based_on_metadata
from litellm.types.guardrails import GuardrailEventHooks
litellm.set_verbose = True
GUARDRAIL_NAME = "aporia"
class AporiaGuardrail(CustomGuardrail):
def __init__(
self, api_key: Optional[str] = None, api_base: Optional[str] = None, **kwargs
):
self.async_handler = get_async_httpx_client(
llm_provider=httpxSpecialProvider.GuardrailCallback
)
self.aporia_api_key = api_key or os.environ["APORIO_API_KEY"]
self.aporia_api_base = api_base or os.environ["APORIO_API_BASE"]
super().__init__(**kwargs)
#### CALL HOOKS - proxy only ####
def transform_messages(self, messages: List[dict]) -> List[dict]:
supported_openai_roles = ["system", "user", "assistant"]
default_role = "other" # for unsupported roles - e.g. tool
new_messages = []
for m in messages:
if m.get("role", "") in supported_openai_roles:
new_messages.append(m)
else:
new_messages.append(
{
"role": default_role,
**{key: value for key, value in m.items() if key != "role"},
}
)
return new_messages
async def prepare_aporia_request(
self, new_messages: List[dict], response_string: Optional[str] = None
) -> dict:
data: dict[str, Any] = {}
if new_messages is not None:
data["messages"] = new_messages
if response_string is not None:
data["response"] = response_string
# Set validation target
if new_messages and response_string:
data["validation_target"] = "both"
elif new_messages:
data["validation_target"] = "prompt"
elif response_string:
data["validation_target"] = "response"
verbose_proxy_logger.debug("Aporia AI request: %s", data)
return data
async def make_aporia_api_request(
self,
request_data: dict,
new_messages: List[dict],
response_string: Optional[str] = None,
):
data = await self.prepare_aporia_request(
new_messages=new_messages, response_string=response_string
)
data.update(
self.get_guardrail_dynamic_request_body_params(request_data=request_data)
)
_json_data = json.dumps(data)
"""
export APORIO_API_KEY=<your key>
curl https://gr-prd-trial.aporia.com/some-id \
-X POST \
-H "X-APORIA-API-KEY: $APORIO_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"messages": [
{
"role": "user",
"content": "This is a test prompt"
}
],
}
'
"""
response = await self.async_handler.post(
url=self.aporia_api_base + "/validate",
data=_json_data,
headers={
"X-APORIA-API-KEY": self.aporia_api_key,
"Content-Type": "application/json",
},
)
verbose_proxy_logger.debug("Aporia AI response: %s", response.text)
if response.status_code == 200:
# check if the response was flagged
_json_response = response.json()
action: str = _json_response.get(
"action"
) # possible values are modify, passthrough, block, rephrase
if action == "block":
raise HTTPException(
status_code=400,
detail={
"error": "Violated guardrail policy",
"aporia_ai_response": _json_response,
},
)
@log_guardrail_information
async def async_post_call_success_hook(
self,
data: dict,
user_api_key_dict: UserAPIKeyAuth,
response,
):
from litellm.proxy.common_utils.callback_utils import (
add_guardrail_to_applied_guardrails_header,
)
"""
Use this for the post call moderation with Guardrails
"""
event_type: GuardrailEventHooks = GuardrailEventHooks.post_call
if self.should_run_guardrail(data=data, event_type=event_type) is not True:
return
response_str: Optional[str] = convert_litellm_response_object_to_str(response)
if response_str is not None:
await self.make_aporia_api_request(
request_data=data,
response_string=response_str,
new_messages=data.get("messages", []),
)
add_guardrail_to_applied_guardrails_header(
request_data=data, guardrail_name=self.guardrail_name
)
pass
@log_guardrail_information
async def async_moderation_hook(
self,
data: dict,
user_api_key_dict: UserAPIKeyAuth,
call_type: Literal[
"completion",
"embeddings",
"image_generation",
"moderation",
"audio_transcription",
"responses",
],
):
from litellm.proxy.common_utils.callback_utils import (
add_guardrail_to_applied_guardrails_header,
)
event_type: GuardrailEventHooks = GuardrailEventHooks.during_call
if self.should_run_guardrail(data=data, event_type=event_type) is not True:
return
# old implementation - backwards compatibility
if (
await should_proceed_based_on_metadata(
data=data,
guardrail_name=GUARDRAIL_NAME,
)
is False
):
return
new_messages: Optional[List[dict]] = None
if "messages" in data and isinstance(data["messages"], list):
new_messages = self.transform_messages(messages=data["messages"])
if new_messages is not None:
await self.make_aporia_api_request(
request_data=data,
new_messages=new_messages,
)
add_guardrail_to_applied_guardrails_header(
request_data=data, guardrail_name=self.guardrail_name
)
else:
verbose_proxy_logger.warning(
"Aporia AI: not running guardrail. No messages in data"
)
pass
|
