Merge pull request #26144 from BerriAI/litellm_post_call_non_streaming

fix(bedrock_guardrails): use Bedrock OUTPUT source for apply_guardrail when scanning model responses
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yuneng-jiang 2026-04-21 17:50:16 -07:00 committed by GitHub
commit d9494b6990
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2 changed files with 104 additions and 4 deletions

View file

@ -62,6 +62,7 @@ from litellm.types.utils import (
CallTypesLiteral,
Choices,
GuardrailStatus,
Message,
ModelResponse,
ModelResponseStream,
StreamingChoices,
@ -1563,11 +1564,43 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM):
# Bedrock will throw an error if there is no text to process
if filtered_messages:
bedrock_response = await self.make_bedrock_api_request(
source="INPUT",
messages=filtered_messages,
request_data=request_data,
# Map the abstract input_type to the Bedrock source parameter.
# "request" -> INPUT (scan user-supplied content)
# "response" -> OUTPUT (scan model-generated content)
# Bedrock guardrail policies are often configured differently
# for Input vs Output (e.g. PII blocking only on Output), so
# the source MUST match where the text originated.
bedrock_source: Literal["INPUT", "OUTPUT"] = (
"OUTPUT" if input_type == "response" else "INPUT"
)
if bedrock_source == "OUTPUT":
# Build a synthetic ModelResponse whose choices carry the
# text(s) to scan, so _create_bedrock_output_content_request
# can produce the correct Bedrock OUTPUT payload.
synthetic_response = ModelResponse(
choices=[
Choices(
index=_idx,
message=Message(
role="assistant",
content=str(_msg.get("content") or ""),
),
finish_reason="stop",
)
for _idx, _msg in enumerate(filtered_messages)
]
)
bedrock_response = await self.make_bedrock_api_request(
source="OUTPUT",
response=synthetic_response,
request_data=request_data,
)
else:
bedrock_response = await self.make_bedrock_api_request(
source="INPUT",
messages=filtered_messages,
request_data=request_data,
)
# Apply any masking that was applied by the guardrail
output_list = bedrock_response.get("output")

View file

@ -17,6 +17,7 @@ from litellm.proxy.guardrails.guardrail_hooks.bedrock_guardrails import (
BedrockGuardrail,
_redact_pii_matches,
)
from litellm.types.utils import ModelResponse
@pytest.mark.asyncio
@ -1113,6 +1114,72 @@ async def test_bedrock_apply_guardrail_with_only_tool_calls_response():
print("✅ apply_guardrail with tool_calls test passed - no API call made")
@pytest.mark.asyncio
async def test_bedrock_apply_guardrail_response_uses_OUTPUT_source():
"""input_type='response' must call Bedrock with source=OUTPUT and assistant content.
Regression: apply_guardrail used to always use source=INPUT. Output-only Bedrock
policies (e.g. PII on model output) then returned action=NONE for non-streaming
completions that go through unified_guardrail -> process_output_response.
"""
guardrail = BedrockGuardrail(
guardrailIdentifier="test-guardrail", guardrailVersion="DRAFT"
)
bedrock_none = {"action": "NONE", "output": [], "outputs": []}
with patch.object(
guardrail, "make_bedrock_api_request", new_callable=AsyncMock
) as mock_api:
mock_api.return_value = bedrock_none
await guardrail.apply_guardrail(
inputs={"texts": ["first line", "second line"]},
request_data={"model": "gpt-4o"},
input_type="response",
)
mock_api.assert_called_once()
kwargs = mock_api.call_args.kwargs
assert kwargs["source"] == "OUTPUT"
assert kwargs["request_data"] == {"model": "gpt-4o"}
synthetic = kwargs["response"]
assert isinstance(synthetic, ModelResponse)
assert len(synthetic.choices) == 2
assert synthetic.choices[0].message.content == "first line"
assert synthetic.choices[0].message.role == "assistant"
assert synthetic.choices[1].message.content == "second line"
assert synthetic.choices[1].message.role == "assistant"
@pytest.mark.asyncio
async def test_bedrock_apply_guardrail_request_uses_INPUT_source():
"""input_type='request' must call Bedrock with source=INPUT and user messages."""
guardrail = BedrockGuardrail(
guardrailIdentifier="test-guardrail", guardrailVersion="DRAFT"
)
bedrock_none = {"action": "NONE", "output": [], "outputs": []}
with patch.object(
guardrail, "make_bedrock_api_request", new_callable=AsyncMock
) as mock_api:
mock_api.return_value = bedrock_none
await guardrail.apply_guardrail(
inputs={"texts": ["user prompt"]},
request_data={},
input_type="request",
)
mock_api.assert_called_once()
kwargs = mock_api.call_args.kwargs
assert kwargs["source"] == "INPUT"
assert kwargs["messages"] is not None
assert len(kwargs["messages"]) == 1
assert kwargs["messages"][0]["role"] == "user"
assert kwargs["messages"][0]["content"] == "user prompt"
assert kwargs.get("response") is None
@pytest.mark.asyncio
async def test_bedrock_guardrail_blocked_content_with_masking_enabled():
"""Test that BLOCKED content raises exception even when masking is enabled