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[Feat]: Guardrails - Add streaming for bedrock post guard (#11247)

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* feat: add streaming for bedrock post guard

* fix: bedrock guardrails

* fix: add clear comments

* Update litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Update litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* fix: clean up bedrock guardrails

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
Ishaan Jaff
2025-05-29 20:01:44 -07:00
committed by GitHub
co-authored by Copilot
parent 62a083de02
commit f24d8919c4
3 changed files with 291 additions and 17 deletions
Download Patch File Download Diff File Expand all files Collapse all files
litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py
+52 -2
View File
@@ -13,7 +13,7 @@ sys.path.insert(
) # Adds the parent directory to the system path
import json
import sys
from typing import Any, List, Literal, Optional, Tuple, Union
from typing import Any, AsyncGenerator, List, Literal, Optional, Tuple, Union
from fastapi import HTTPException
@@ -40,7 +40,7 @@ from litellm.types.proxy.guardrails.guardrail_hooks.bedrock_guardrails import (
BedrockRequest,
BedrockTextContent,
)
from litellm.types.utils import ModelResponse
from litellm.types.utils import ModelResponse, ModelResponseStream
GUARDRAIL_NAME = "bedrock"
@@ -475,6 +475,56 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
messages=messages, masked_texts=masked_texts
)
async def async_post_call_streaming_iterator_hook(
self,
user_api_key_dict: UserAPIKeyAuth,
response: Any,
request_data: dict,
) -> AsyncGenerator[ModelResponseStream, None]:
"""
Process streaming response chunks.
Collect content from the stream and make a bedrock api request to get the guardrail response.
"""
# Import here to avoid circular imports
from litellm.llms.base_llm.base_model_iterator import MockResponseIterator
from litellm.main import stream_chunk_builder
from litellm.types.utils import TextCompletionResponse
# Collect all chunks to process them together
all_chunks: List[ModelResponseStream] = []
async for chunk in response:
all_chunks.append(chunk)
assembled_model_response: Optional[
Union[ModelResponse, TextCompletionResponse]
] = stream_chunk_builder(
chunks=all_chunks,
)
if isinstance(assembled_model_response, ModelResponse):
####################################################################
########## 1. Make the Bedrock Apply Guardrail API request ##########
# Bedrock will raise an exception if this violates the guardrail policy
###################################################################
await self.make_bedrock_api_request(
kwargs=request_data, response=assembled_model_response
)
#########################################################################
########## If guardrail passed, then return the collected chunks ##########
#########################################################################
mock_response = MockResponseIterator(
model_response=assembled_model_response
)
# Return the reconstructed stream
async for chunk in mock_response:
yield chunk
else:
for chunk in all_chunks:
yield chunk
def _extract_masked_texts_from_response(
self, bedrock_guardrail_response: BedrockGuardrailResponse
) -> List[str]:
litellm/proxy/proxy_config.yaml
+11 -1
View File
@@ -1,4 +1,14 @@
model_list:
- model_name: openai/*
litellm_params:
model: openai/*
model: openai/*
guardrails:
- guardrail_name: "bedrock-pre-guard"
litellm_params:
guardrail: bedrock # supported values: "aporia", "bedrock", "lakera"
mode: "post_call"
guardrailIdentifier: wf0hkdb5x07f # your guardrail ID on bedrock
guardrailVersion: "DRAFT" # your guardrail version on bedrock
default_on: true
tests/guardrails_tests/test_bedrock_guardrails.py
+228 -14
View File
@@ -5,16 +5,21 @@ import pytest
sys.path.insert(0, os.path.abspath("../.."))
import litellm
from litellm.proxy.guardrails.guardrail_hooks.bedrock_guardrails import BedrockGuardrail
from litellm.proxy._types import UserAPIKeyAuth
from litellm.caching import DualCache
from unittest.mock import MagicMock
@pytest.mark.asyncio
async def test_bedrock_guardrails():
# Create proper mock objects
mock_user_api_key_dict = UserAPIKeyAuth()
guardrail = BedrockGuardrail(
guardrailIdentifier="wf0hkdb5x07f",
guardrailVersion="DRAFT",
mask_request_content=True,
)
request_data = {
"model": "gpt-4o",
"messages": [
@@ -27,20 +32,23 @@ async def test_bedrock_guardrails():
response = await guardrail.async_moderation_hook(
data=request_data,
user_api_key_dict={},
user_api_key_dict=mock_user_api_key_dict,
call_type="completion"
)
print(response)
assert response["messages"][0]["content"] == "Hello, my phone number is {PHONE}"
assert response["messages"][1]["content"] == "Hello, how can I help you today?"
assert response["messages"][2]["content"] == "I need to cancel my order"
assert response["messages"][3]["content"] == "ok, my credit card number is {CREDIT_DEBIT_CARD_NUMBER}"
if response: # Only assert if response is not None
assert response["messages"][0]["content"] == "Hello, my phone number is {PHONE}"
assert response["messages"][1]["content"] == "Hello, how can I help you today?"
assert response["messages"][2]["content"] == "I need to cancel my order"
assert response["messages"][3]["content"] == "ok, my credit card number is {CREDIT_DEBIT_CARD_NUMBER}"
@pytest.mark.asyncio
async def test_bedrock_guardrails_content_list():
# Create proper mock objects
mock_user_api_key_dict = UserAPIKeyAuth()
guardrail = BedrockGuardrail(
guardrailIdentifier="wf0hkdb5x07f",
guardrailVersion="DRAFT",
@@ -64,16 +72,222 @@ async def test_bedrock_guardrails_content_list():
response = await guardrail.async_moderation_hook(
data=request_data,
user_api_key_dict={},
user_api_key_dict=mock_user_api_key_dict,
call_type="completion"
)
print(response)
# Verify that the list content is properly masked
assert isinstance(response["messages"][0]["content"], list)
assert response["messages"][0]["content"][0]["text"] == "Hello, my phone number is {PHONE}"
assert response["messages"][0]["content"][1]["text"] == "what time is it?"
assert response["messages"][1]["content"] == "Hello, how can I help you today?"
assert response["messages"][2]["content"] == "who is the president of the united states?"
if response: # Only assert if response is not None
# Verify that the list content is properly masked
assert isinstance(response["messages"][0]["content"], list)
assert response["messages"][0]["content"][0]["text"] == "Hello, my phone number is {PHONE}"
assert response["messages"][0]["content"][1]["text"] == "what time is it?"
assert response["messages"][1]["content"] == "Hello, how can I help you today?"
assert response["messages"][2]["content"] == "who is the president of the united states?"
@pytest.mark.asyncio
async def test_bedrock_guardrails_with_streaming():
from litellm.proxy.utils import ProxyLogging
from litellm.types.guardrails import GuardrailEventHooks
# Create proper mock objects
mock_user_api_key_cache = MagicMock(spec=DualCache)
mock_user_api_key_dict = UserAPIKeyAuth()
with pytest.raises(Exception): # Assert that this raises an exception
proxy_logging_obj = ProxyLogging(
user_api_key_cache=mock_user_api_key_cache,
premium_user=True,
)
guardrail = BedrockGuardrail(
guardrailIdentifier="wf0hkdb5x07f",
guardrailVersion="DRAFT",
supported_event_hooks=[GuardrailEventHooks.post_call],
guardrail_name="bedrock-post-guard",
)
litellm.callbacks.append(guardrail)
request_data = {
"model": "gpt-4o",
"messages": [
{
"role": "user",
"content": "My name is ishaan@gmail.com"
}
],
"stream": True,
"metadata": {"guardrails": ["bedrock-post-guard"]}
}
response = await litellm.acompletion(
**request_data,
)
response = proxy_logging_obj.async_post_call_streaming_iterator_hook(
user_api_key_dict=mock_user_api_key_dict,
response=response,
request_data=request_data,
)
async for chunk in response:
print(chunk)
@pytest.mark.asyncio
async def test_bedrock_guardrails_with_streaming_no_violation():
from litellm.proxy.utils import ProxyLogging
from litellm.types.guardrails import GuardrailEventHooks
# Create proper mock objects
mock_user_api_key_cache = MagicMock(spec=DualCache)
mock_user_api_key_dict = UserAPIKeyAuth()
proxy_logging_obj = ProxyLogging(
user_api_key_cache=mock_user_api_key_cache,
premium_user=True,
)
guardrail = BedrockGuardrail(
guardrailIdentifier="wf0hkdb5x07f",
guardrailVersion="DRAFT",
supported_event_hooks=[GuardrailEventHooks.post_call],
guardrail_name="bedrock-post-guard",
)
litellm.callbacks.append(guardrail)
request_data = {
"model": "gpt-4o",
"messages": [
{
"role": "user",
"content": "hi"
}
],
"stream": True,
"metadata": {"guardrails": ["bedrock-post-guard"]}
}
response = await litellm.acompletion(
**request_data,
)
response = proxy_logging_obj.async_post_call_streaming_iterator_hook(
user_api_key_dict=mock_user_api_key_dict,
response=response,
request_data=request_data,
)
async for chunk in response:
print(chunk)
@pytest.mark.asyncio
async def test_bedrock_guardrails_streaming_request_body_mock():
"""Test that the exact request body sent to Bedrock matches expected format when using streaming"""
import json
from unittest.mock import AsyncMock, MagicMock, patch
from litellm.proxy._types import UserAPIKeyAuth
from litellm.caching import DualCache
from litellm.types.guardrails import GuardrailEventHooks
# Create mock objects
mock_user_api_key_dict = UserAPIKeyAuth()
mock_cache = MagicMock(spec=DualCache)
# Create the guardrail
guardrail = BedrockGuardrail(
guardrailIdentifier="wf0hkdb5x07f",
guardrailVersion="DRAFT",
supported_event_hooks=[GuardrailEventHooks.post_call],
guardrail_name="bedrock-post-guard",
)
# Mock the assembled response from streaming
mock_response = litellm.ModelResponse(
id="test-id",
choices=[
litellm.Choices(
index=0,
message=litellm.Message(
role="assistant",
content="The capital of Spain is Madrid."
),
finish_reason="stop"
)
],
created=1234567890,
model="gpt-4o",
object="chat.completion"
)
# Mock Bedrock API response
mock_bedrock_response = MagicMock()
mock_bedrock_response.status_code = 200
mock_bedrock_response.json.return_value = {
"action": "NONE",
"outputs": []
}
# Patch the async_handler.post method to capture the request body
with patch.object(guardrail, 'async_handler') as mock_async_handler:
mock_async_handler.post = AsyncMock(return_value=mock_bedrock_response)
# Test data - simulating request data and assembled response
request_data = {
"model": "gpt-4o",
"messages": [
{
"role": "user",
"content": "what's the capital of spain?"
}
],
"stream": True,
"metadata": {"guardrails": ["bedrock-post-guard"]}
}
# Call the method that should make the Bedrock API request
await guardrail.make_bedrock_api_request(
kwargs=request_data,
response=mock_response
)
# Verify the API call was made
mock_async_handler.post.assert_called_once()
# Get the request data that was passed
call_args = mock_async_handler.post.call_args
# The data should be in the 'data' parameter of the prepared request
# We need to parse the JSON from the prepared request body
prepared_request_body = call_args.kwargs.get('data')
# Parse the JSON body
if isinstance(prepared_request_body, bytes):
actual_body = json.loads(prepared_request_body.decode('utf-8'))
else:
actual_body = json.loads(prepared_request_body)
# Expected body based on the convert_to_bedrock_format method behavior
expected_body = {
'source': 'OUTPUT',
'content': [
{'text': {'text': "what's the capital of spain?"}},
{'text': {'text': 'The capital of Spain is Madrid.'}}
]
}
print("Actual Bedrock request body:", json.dumps(actual_body, indent=2))
print("Expected Bedrock request body:", json.dumps(expected_body, indent=2))
# Assert the request body matches exactly
assert actual_body == expected_body, f"Request body mismatch. Expected: {expected_body}, Got: {actual_body}"