(feat) Support Dynamic Params for guardrails (#7415)

* update CustomGuardrail

* unit test custom guardrails

* add dynamic params for aporia

* add dynamic params to bedrock guard

* add dynamic params for all guardrails

* fix linting

* fix should_run_guardrail

* _validate_premium_user

* update guardrail doc

* doc update

* update code q

* should_run_guardrail

docs/my-website/docs/proxy/guardrails/quick_start.md

@@ -114,6 +114,88 @@ curl -i http://localhost:4000/v1/chat/completions \
 
 
 ## Advanced
+
+### ✨ Pass additional parameters to guardrail
+
+:::info
+
+✨ This is an Enterprise only feature [Contact us to get a free trial](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat)
+
+:::
+
+
+
+Use this to pass additional parameters to the guardrail API call. e.g. things like success threshold. **[See `guardrails` spec for more details](#spec-guardrails-parameter)**
+
+
+<Tabs>
+
+<TabItem value="openai" label="OpenAI Python v1.0.0+">
+
+Set `guardrails={"aporia-pre-guard": {"extra_body": {"success_threshold": 0.9}}}` to pass additional parameters to the guardrail
+
+In this example `success_threshold=0.9` is passed to the `aporia-pre-guard` guardrail request body
+
+```python
+import openai
+client = openai.OpenAI(
+    api_key="anything",
+    base_url="http://0.0.0.0:4000"
+)
+
+response = client.chat.completions.create(
+    model="gpt-3.5-turbo",
+    messages = [
+        {
+            "role": "user",
+            "content": "this is a test request, write a short poem"
+        }
+    ],
+    extra_body={
+      "guardrails": [
+        "aporia-pre-guard": {
+          "extra_body": {
+            "success_threshold": 0.9
+          }
+        }
+      ]
+    }
+
+)
+
+print(response)
+```
+</TabItem>
+
+
+<TabItem value="Curl" label="Curl Request">
+
+```shell
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+    --header 'Content-Type: application/json' \
+    --data '{
+    "model": "gpt-3.5-turbo",
+    "messages": [
+        {
+        "role": "user",
+        "content": "what llm are you"
+        }
+    ],
+    "guardrails": [
+      "aporia-pre-guard": {
+        "extra_body": {
+          "success_threshold": 0.9
+        }
+      }
+    ]
+}'
+```
+</TabItem>
+
+
+</Tabs>
+
+
 ### ✨ Control Guardrails per Project (API Key)
 
 :::info
@@ -253,3 +335,42 @@ Expected response
     "guardrails": ["aporia-pre-guard", "aporia-post-guard"]
 }
 ```
+
+## Spec: `guardrails` Parameter
+
+The `guardrails` parameter can be passed to any LiteLLM Proxy endpoint (`/chat/completions`, `/completions`, `/embeddings`).
+
+### Format Options
+
+1. Simple List Format:
+```python
+"guardrails": [
+    "aporia-pre-guard",
+    "aporia-post-guard"
+]
+```
+
+2. Advanced Dictionary Format:
+
+In this format the dictionary key is `guardrail_name` you want to run
+```python
+"guardrails": {
+    "aporia-pre-guard": {
+        "extra_body": {
+            "success_threshold": 0.9,
+            "other_param": "value"
+        }
+    }
+}
+```
+
+### Type Definition
+```python
+guardrails: Union[
+    List[str],                              # Simple list of guardrail names
+    Dict[str, DynamicGuardrailParams]       # Advanced configuration
+]
+
+class DynamicGuardrailParams:
+    extra_body: Dict[str, Any]              # Additional parameters for the guardrail
+```

litellm/integrations/custom_guardrail.py

@@ -1,8 +1,8 @@
-from typing import List, Optional
+from typing import Dict, List, Optional, Union
 
 from litellm._logging import verbose_logger
 from litellm.integrations.custom_logger import CustomLogger
-from litellm.types.guardrails import GuardrailEventHooks
+from litellm.types.guardrails import DynamicGuardrailParams, GuardrailEventHooks
 
 
 class CustomGuardrail(CustomLogger):
@@ -26,9 +26,31 @@ class CustomGuardrail(CustomLogger):
                 )
         super().__init__(**kwargs)
 
-    def should_run_guardrail(self, data, event_type: GuardrailEventHooks) -> bool:
+    def get_guardrail_from_metadata(
+        self, data: dict
+    ) -> Union[List[str], List[Dict[str, DynamicGuardrailParams]]]:
+        """
+        Returns the guardrail(s) to be run from the metadata
+        """
         metadata = data.get("metadata") or {}
         requested_guardrails = metadata.get("guardrails") or []
+        return requested_guardrails
+
+    def _guardrail_is_in_requested_guardrails(
+        self,
+        requested_guardrails: Union[List[str], List[Dict[str, DynamicGuardrailParams]]],
+    ) -> bool:
+        for _guardrail in requested_guardrails:
+            if isinstance(_guardrail, dict):
+                if self.guardrail_name in _guardrail:
+                    return True
+            elif isinstance(_guardrail, str):
+                if self.guardrail_name == _guardrail:
+                    return True
+        return False
+
+    def should_run_guardrail(self, data, event_type: GuardrailEventHooks) -> bool:
+        requested_guardrails = self.get_guardrail_from_metadata(data)
 
         verbose_logger.debug(
             "inside should_run_guardrail for guardrail=%s event_type= %s guardrail_supported_event_hooks= %s requested_guardrails= %s",
@@ -40,7 +62,7 @@ class CustomGuardrail(CustomLogger):
 
         if (
             self.event_hook
-            and self.guardrail_name not in requested_guardrails
+            and not self._guardrail_is_in_requested_guardrails(requested_guardrails)
             and event_type.value != "logging_only"
         ):
             return False
@@ -49,3 +71,51 @@ class CustomGuardrail(CustomLogger):
             return False
 
         return True
+
+    def get_guardrail_dynamic_request_body_params(self, request_data: dict) -> dict:
+        """
+        Returns `extra_body` to be added to the request body for the Guardrail API call
+
+        Use this to pass dynamic params to the guardrail API call - eg. success_threshold, failure_threshold, etc.
+
+        ```
+        [{"lakera_guard": {"extra_body": {"foo": "bar"}}}]
+        ```
+
+        Will return: for guardrail=`lakera-guard`:
+        {
+            "foo": "bar"
+        }
+
+        Args:
+            request_data: The original `request_data` passed to LiteLLM Proxy
+        """
+        requested_guardrails = self.get_guardrail_from_metadata(request_data)
+
+        # Look for the guardrail configuration matching self.guardrail_name
+        for guardrail in requested_guardrails:
+            if isinstance(guardrail, dict) and self.guardrail_name in guardrail:
+                # Get the configuration for this guardrail
+                guardrail_config: DynamicGuardrailParams = DynamicGuardrailParams(
+                    **guardrail[self.guardrail_name]
+                )
+                if self._validate_premium_user() is not True:
+                    return {}
+
+                # Return the extra_body if it exists, otherwise empty dict
+                return guardrail_config.get("extra_body", {})
+
+        return {}
+
+    def _validate_premium_user(self) -> bool:
+        """
+        Returns True if the user is a premium user
+        """
+        from litellm.proxy.proxy_server import CommonProxyErrors, premium_user
+
+        if premium_user is not True:
+            verbose_logger.warning(
+                f"Trying to use premium guardrail without premium user {CommonProxyErrors.not_premium_user.value}"
+            )
+            return False
+        return True

litellm/proxy/guardrails/guardrail_hooks/aporia_ai.py

@@ -86,12 +86,19 @@ class AporiaGuardrail(CustomGuardrail):
         return data
 
     async def make_aporia_api_request(
-        self, new_messages: List[dict], response_string: Optional[str] = None
+        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)
 
         """
@@ -155,7 +162,9 @@ class AporiaGuardrail(CustomGuardrail):
         response_str: Optional[str] = convert_litellm_response_object_to_str(response)
         if response_str is not None:
             await self.make_aporia_api_request(
-                response_string=response_str, new_messages=data.get("messages", [])
+                request_data=data,
+                response_string=response_str,
+                new_messages=data.get("messages", []),
             )
 
             add_guardrail_to_applied_guardrails_header(
@@ -199,7 +208,10 @@ class AporiaGuardrail(CustomGuardrail):
             new_messages = self.transform_messages(messages=data["messages"])
 
         if new_messages is not None:
-            await self.make_aporia_api_request(new_messages=new_messages)
+            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
             )

litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py

@@ -149,7 +149,7 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
     def _prepare_request(
         self,
         credentials,
-        data: BedrockRequest,
+        data: dict,
         optional_params: dict,
         aws_region_name: str,
         extra_headers: Optional[dict] = None,
@@ -186,18 +186,23 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
     ):
 
         credentials, aws_region_name = self._load_credentials()
-        request_data: BedrockRequest = self.convert_to_bedrock_format(
+        bedrock_request_data: dict = dict(
+            self.convert_to_bedrock_format(
                 messages=kwargs.get("messages"), response=response
             )
+        )
+        bedrock_request_data.update(
+            self.get_guardrail_dynamic_request_body_params(request_data=kwargs)
+        )
         prepared_request = self._prepare_request(
             credentials=credentials,
-            data=request_data,
+            data=bedrock_request_data,
             optional_params=self.optional_params,
             aws_region_name=aws_region_name,
         )
         verbose_proxy_logger.debug(
             "Bedrock AI request body: %s, url %s, headers: %s",
-            request_data,
+            bedrock_request_data,
             prepared_request.url,
             prepared_request.headers,
         )

litellm/proxy/guardrails/guardrail_hooks/guardrails_ai.py

@@ -48,10 +48,13 @@ class GuardrailsAI(CustomGuardrail):
         supported_event_hooks = [GuardrailEventHooks.post_call]
         super().__init__(supported_event_hooks=supported_event_hooks, **kwargs)
 
-    async def make_guardrails_ai_api_request(self, llm_output: str):
+    async def make_guardrails_ai_api_request(self, llm_output: str, request_data: dict):
         from httpx import URL
 
-        data = {"llmOutput": llm_output}
+        data = {
+            "llmOutput": llm_output,
+            **self.get_guardrail_dynamic_request_body_params(request_data=request_data),
+        }
         _json_data = json.dumps(data)
         response = await litellm.module_level_aclient.post(
             url=str(
@@ -96,7 +99,9 @@ class GuardrailsAI(CustomGuardrail):
 
         response_str: str = get_content_from_model_response(response)
         if response_str is not None and len(response_str) > 0:
-            await self.make_guardrails_ai_api_request(llm_output=response_str)
+            await self.make_guardrails_ai_api_request(
+                llm_output=response_str, request_data=data
+            )
 
             add_guardrail_to_applied_guardrails_header(
                 request_data=data, guardrail_name=self.guardrail_name

litellm/proxy/guardrails/guardrail_hooks/lakera_ai.py

@@ -216,14 +216,27 @@ class lakeraAI_Moderation(CustomGuardrail):
                     "Skipping lakera prompt injection, no roles with messages found"
                 )
                 return
-            data = {"input": lakera_input}
+            _data = {"input": lakera_input}
-            _json_data = json.dumps(data)
+            _json_data = json.dumps(
+                _data,
+                **self.get_guardrail_dynamic_request_body_params(request_data=data),
+            )
         elif "input" in data and isinstance(data["input"], str):
             text = data["input"]
-            _json_data = json.dumps({"input": text})
+            _json_data = json.dumps(
+                {
+                    "input": text,
+                    **self.get_guardrail_dynamic_request_body_params(request_data=data),
+                }
+            )
         elif "input" in data and isinstance(data["input"], list):
             text = "\n".join(data["input"])
-            _json_data = json.dumps({"input": text})
+            _json_data = json.dumps(
+                {
+                    "input": text,
+                    **self.get_guardrail_dynamic_request_body_params(request_data=data),
+                }
+            )
 
         verbose_proxy_logger.debug("Lakera AI Request Args %s", _json_data)
 

litellm/proxy/guardrails/guardrail_hooks/presidio.py

@@ -132,6 +132,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail):
         text: str,
         output_parse_pii: bool,
         presidio_config: Optional[PresidioPerRequestConfig],
+        request_data: dict,
     ) -> str:
         """
         [TODO] make this more performant for high-throughput scenario
@@ -150,7 +151,11 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail):
                     if self.ad_hoc_recognizers is not None:
                         analyze_payload["ad_hoc_recognizers"] = self.ad_hoc_recognizers
                     # End of constructing Request 1
-
+                    analyze_payload.update(
+                        self.get_guardrail_dynamic_request_body_params(
+                            request_data=request_data
+                        )
+                    )
                     redacted_text = None
                     verbose_proxy_logger.debug(
                         "Making request to: %s with payload: %s",
@@ -235,6 +240,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail):
                                 text=m["content"],
                                 output_parse_pii=self.output_parse_pii,
                                 presidio_config=presidio_config,
+                                request_data=data,
                             )
                         )
                 responses = await asyncio.gather(*tasks)
@@ -311,6 +317,7 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail):
                             text=text_str,
                             output_parse_pii=False,
                             presidio_config=presidio_config,
+                            request_data=kwargs,
                         )
                     )  # need to pass separately b/c presidio has context window limits
             responses = await asyncio.gather(*tasks)

litellm/proxy/proxy_config.yaml

@@ -12,6 +12,14 @@ model_list:
       model: bedrock/*
 
 
+guardrails:
+  - guardrail_name: "bedrock-pre-guard"
+    litellm_params:
+      guardrail: bedrock  # supported values: "aporia", "bedrock", "lakera"
+      mode: "during_call"
+      guardrailIdentifier: ff6ujrregl1q
+      guardrailVersion: "DRAFT"
+
 # for /files endpoints
 # For /fine_tuning/jobs endpoints
 finetune_settings:

litellm/types/guardrails.py

@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Dict, List, Literal, Optional, TypedDict
+from typing import Any, Dict, List, Literal, Optional, TypedDict
 
 from pydantic import BaseModel, ConfigDict
 from typing_extensions import Required, TypedDict
@@ -132,3 +132,7 @@ class BedrockContentItem(TypedDict, total=False):
 class BedrockRequest(TypedDict, total=False):
     source: Literal["INPUT", "OUTPUT"]
     content: List[BedrockContentItem]
+
+
+class DynamicGuardrailParams(TypedDict):
+    extra_body: Dict[str, Any]

tests/logging_callback_tests/test_custom_guardrail.py

@@ -0,0 +1,145 @@
+import io
+import os
+import sys
+
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+import asyncio
+import gzip
+import json
+import logging
+import time
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+import litellm
+from litellm import completion
+from litellm._logging import verbose_logger
+from litellm.integrations.custom_guardrail import CustomGuardrail
+
+
+from typing import Any, Dict, List, Literal, Optional, Union
+
+import litellm
+from litellm._logging import verbose_proxy_logger
+from litellm.caching.caching import DualCache
+from litellm.integrations.custom_guardrail import CustomGuardrail
+from litellm.proxy._types import UserAPIKeyAuth
+from litellm.proxy.guardrails.guardrail_helpers import should_proceed_based_on_metadata
+from litellm.types.guardrails import GuardrailEventHooks
+
+
+def test_get_guardrail_from_metadata():
+    guardrail = CustomGuardrail(guardrail_name="test-guardrail")
+
+    # Test with empty metadata
+    assert guardrail.get_guardrail_from_metadata({}) == []
+
+    # Test with guardrails in metadata
+    data = {"metadata": {"guardrails": ["guardrail1", "guardrail2"]}}
+    assert guardrail.get_guardrail_from_metadata(data) == ["guardrail1", "guardrail2"]
+
+    # Test with dict guardrails
+    data = {
+        "metadata": {
+            "guardrails": [{"test-guardrail": {"extra_body": {"key": "value"}}}]
+        }
+    }
+    assert guardrail.get_guardrail_from_metadata(data) == [
+        {"test-guardrail": {"extra_body": {"key": "value"}}}
+    ]
+
+
+def test_guardrail_is_in_requested_guardrails():
+    guardrail = CustomGuardrail(guardrail_name="test-guardrail")
+
+    # Test with string list
+    assert (
+        guardrail._guardrail_is_in_requested_guardrails(["test-guardrail", "other"])
+        == True
+    )
+    assert guardrail._guardrail_is_in_requested_guardrails(["other"]) == False
+
+    # Test with dict list
+    assert (
+        guardrail._guardrail_is_in_requested_guardrails(
+            [{"test-guardrail": {"extra_body": {"extra_key": "extra_value"}}}]
+        )
+        == True
+    )
+    assert (
+        guardrail._guardrail_is_in_requested_guardrails(
+            [
+                {
+                    "other-guardrail": {"extra_body": {"extra_key": "extra_value"}},
+                    "test-guardrail": {"extra_body": {"extra_key": "extra_value"}},
+                }
+            ]
+        )
+        == True
+    )
+    assert (
+        guardrail._guardrail_is_in_requested_guardrails(
+            [{"other-guardrail": {"extra_body": {"extra_key": "extra_value"}}}]
+        )
+        == False
+    )
+
+
+def test_should_run_guardrail():
+    guardrail = CustomGuardrail(
+        guardrail_name="test-guardrail", event_hook=GuardrailEventHooks.pre_call
+    )
+
+    # Test matching event hook and guardrail
+    assert (
+        guardrail.should_run_guardrail(
+            {"metadata": {"guardrails": ["test-guardrail"]}},
+            GuardrailEventHooks.pre_call,
+        )
+        == True
+    )
+
+    # Test non-matching event hook
+    assert (
+        guardrail.should_run_guardrail(
+            {"metadata": {"guardrails": ["test-guardrail"]}},
+            GuardrailEventHooks.during_call,
+        )
+        == False
+    )
+
+    # Test guardrail not in requested list
+    assert (
+        guardrail.should_run_guardrail(
+            {"metadata": {"guardrails": ["other-guardrail"]}},
+            GuardrailEventHooks.pre_call,
+        )
+        == False
+    )
+
+
+def test_get_guardrail_dynamic_request_body_params():
+    guardrail = CustomGuardrail(guardrail_name="test-guardrail")
+
+    # Test with no extra_body
+    data = {"metadata": {"guardrails": [{"test-guardrail": {}}]}}
+    assert guardrail.get_guardrail_dynamic_request_body_params(data) == {}
+
+    # Test with extra_body
+    data = {
+        "metadata": {
+            "guardrails": [{"test-guardrail": {"extra_body": {"key": "value"}}}]
+        }
+    }
+    assert guardrail.get_guardrail_dynamic_request_body_params(data) == {"key": "value"}
+
+    # Test with non-matching guardrail
+    data = {
+        "metadata": {
+            "guardrails": [{"other-guardrail": {"extra_body": {"key": "value"}}}]
+        }
+    }
+    assert guardrail.get_guardrail_dynamic_request_body_params(data) == {}