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feat(litellm): Set request parameters #8062

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10 changes: 10 additions & 0 deletions sentry_sdk/integrations/litellm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -187,9 +187,19 @@ def _input_callback(kwargs: "Dict[str, Any]") -> None:
"frequency_penalty": SPANDATA.GEN_AI_REQUEST_FREQUENCY_PENALTY,
"temperature": SPANDATA.GEN_AI_REQUEST_TEMPERATURE,
"top_p": SPANDATA.GEN_AI_REQUEST_TOP_P,
"seed": SPANDATA.GEN_AI_REQUEST_SEED,
"top_k": SPANDATA.GEN_AI_REQUEST_TOP_K,
}
for key, attribute in params.items():
value = kwargs.get(key)
if value is None and isinstance(kwargs.get("optional_params"), dict):
value = kwargs["optional_params"].get(key)
if value is None and isinstance(
kwargs["optional_params"].get("extra_body"), dict
):
value = kwargs["optional_params"]["extra_body"].get(key)
if value is None and isinstance(kwargs.get("extra_body"), dict):
value = kwargs["extra_body"].get(key)
if value is not None:
set_data_normalized(span, attribute, value)

Expand Down
186 changes: 186 additions & 0 deletions tests/integrations/litellm/test_litellm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2273,6 +2273,8 @@ def test_additional_parameters(
top_p=0.9,
frequency_penalty=0.5,
presence_penalty=0.5,
seed=42,
top_k=50,
)

litellm_utils.executor.shutdown(wait=True)
Expand All @@ -2293,6 +2295,8 @@ def test_additional_parameters(
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_TOP_P] == 0.9
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_FREQUENCY_PENALTY] == 0.5
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_PRESENCE_PENALTY] == 0.5
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_SEED] == 42
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_TOP_K] == 50
else:
events = capture_events()

Expand All @@ -2310,6 +2314,8 @@ def test_additional_parameters(
top_p=0.9,
frequency_penalty=0.5,
presence_penalty=0.5,
seed=42,
top_k=50,
)

litellm_utils.executor.shutdown(wait=True)
Expand All @@ -2328,6 +2334,8 @@ def test_additional_parameters(
assert span["data"][SPANDATA.GEN_AI_REQUEST_TOP_P] == 0.9
assert span["data"][SPANDATA.GEN_AI_REQUEST_FREQUENCY_PENALTY] == 0.5
assert span["data"][SPANDATA.GEN_AI_REQUEST_PRESENCE_PENALTY] == 0.5
assert span["data"][SPANDATA.GEN_AI_REQUEST_SEED] == 42
assert span["data"][SPANDATA.GEN_AI_REQUEST_TOP_K] == 50


@pytest.mark.parametrize("span_streaming", [True, False])
Expand Down Expand Up @@ -2387,6 +2395,8 @@ async def test_async_additional_parameters(
top_p=0.9,
frequency_penalty=0.5,
presence_penalty=0.5,
seed=42,
top_k=50,
)

await GLOBAL_LOGGING_WORKER.flush()
Expand All @@ -2408,6 +2418,8 @@ async def test_async_additional_parameters(
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_TOP_P] == 0.9
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_FREQUENCY_PENALTY] == 0.5
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_PRESENCE_PENALTY] == 0.5
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_SEED] == 42
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_TOP_K] == 50
else:
events = capture_events()

Expand All @@ -2425,6 +2437,8 @@ async def test_async_additional_parameters(
top_p=0.9,
frequency_penalty=0.5,
presence_penalty=0.5,
seed=42,
top_k=50,
)

await GLOBAL_LOGGING_WORKER.flush()
Expand All @@ -2444,6 +2458,178 @@ async def test_async_additional_parameters(
assert span["data"][SPANDATA.GEN_AI_REQUEST_TOP_P] == 0.9
assert span["data"][SPANDATA.GEN_AI_REQUEST_FREQUENCY_PENALTY] == 0.5
assert span["data"][SPANDATA.GEN_AI_REQUEST_PRESENCE_PENALTY] == 0.5
assert span["data"][SPANDATA.GEN_AI_REQUEST_SEED] == 42
assert span["data"][SPANDATA.GEN_AI_REQUEST_TOP_K] == 50


@pytest.mark.parametrize("span_streaming", [True, False])
@pytest.mark.parametrize("stream_gen_ai_spans", [True, False])
def test_additional_parameters_zero_values(
reset_litellm_executor,
sentry_init,
capture_events,
capture_items,
get_model_response,
nonstreaming_chat_completions_model_response,
stream_gen_ai_spans,
span_streaming,
):
"""Test that zero values for additional parameters are correctly recorded."""
sentry_init(
integrations=[LiteLLMIntegration()],
disabled_integrations=[StdlibIntegration],
traces_sample_rate=1.0,
stream_gen_ai_spans=stream_gen_ai_spans,
trace_lifecycle="stream" if span_streaming else "static",
)

messages = [{"role": "user", "content": "Hello!"}]
client = OpenAI(api_key="test-key")

model_response = get_model_response(
nonstreaming_chat_completions_model_response(
response_id="chatcmpl-test",
response_model="gpt-3.5-turbo",
message_content="Test response",
created=1234567890,
usage=CompletionUsage(
prompt_tokens=10,
completion_tokens=20,
total_tokens=30,
),
),
serialize_pydantic=True,
request_headers={"X-Stainless-Raw-Response": "true"},
)

if span_streaming or stream_gen_ai_spans:
items = capture_items("span")

with mock.patch.object(
client.completions._client._client,
"send",
return_value=model_response,
), start_transaction(name="litellm test"):
litellm.completion(
model="gpt-3.5-turbo",
messages=messages,
client=client,
temperature=0.0,
max_tokens=100,
top_p=0.0,
frequency_penalty=0.0,
presence_penalty=0.0,
seed=0,
top_k=0,
)

litellm_utils.executor.shutdown(wait=True)

sentry_sdk.flush()
spans = [item.payload for item in items]
chat_spans = list(
x
for x in spans
if x["attributes"]["sentry.op"] == OP.GEN_AI_CHAT
and x["attributes"]["sentry.origin"] == "auto.ai.litellm"
)
assert len(chat_spans) == 1
span = chat_spans[0]

assert span["attributes"][SPANDATA.GEN_AI_REQUEST_TEMPERATURE] == 0.0
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_MAX_TOKENS] == 100
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_TOP_P] == 0.0
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_FREQUENCY_PENALTY] == 0.0
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_PRESENCE_PENALTY] == 0.0
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_SEED] == 0
assert span["attributes"][SPANDATA.GEN_AI_REQUEST_TOP_K] == 0
else:
events = capture_events()

with mock.patch.object(
client.completions._client._client,
"send",
return_value=model_response,
), start_transaction(name="litellm test"):
litellm.completion(
model="gpt-3.5-turbo",
messages=messages,
client=client,
temperature=0.0,
max_tokens=100,
top_p=0.0,
frequency_penalty=0.0,
presence_penalty=0.0,
seed=0,
top_k=0,
)

litellm_utils.executor.shutdown(wait=True)

(event,) = events
chat_spans = list(
x
for x in event["spans"]
if x["op"] == OP.GEN_AI_CHAT and x["origin"] == "auto.ai.litellm"
)
assert len(chat_spans) == 1
span = chat_spans[0]

assert span["data"][SPANDATA.GEN_AI_REQUEST_TEMPERATURE] == 0.0
assert span["data"][SPANDATA.GEN_AI_REQUEST_MAX_TOKENS] == 100
assert span["data"][SPANDATA.GEN_AI_REQUEST_TOP_P] == 0.0
assert span["data"][SPANDATA.GEN_AI_REQUEST_FREQUENCY_PENALTY] == 0.0
assert span["data"][SPANDATA.GEN_AI_REQUEST_PRESENCE_PENALTY] == 0.0
assert span["data"][SPANDATA.GEN_AI_REQUEST_SEED] == 0
assert span["data"][SPANDATA.GEN_AI_REQUEST_TOP_K] == 0


def test_input_callback_fallback_extra_body(sentry_init):
"""Test that seed and top_k are extracted from optional_params and extra_body."""
sentry_init(
integrations=[LiteLLMIntegration()],
traces_sample_rate=1.0,
)

kwargs = {
"model": "gpt-3.5-turbo",
"messages": [{"role": "user", "content": "hi"}],
"optional_params": {
"seed": 999,
"extra_body": {"top_k": 25},
},
}

with start_transaction(name="litellm test"):
_input_callback(kwargs)
span = kwargs.pop("_sentry_span", None)
assert span is not None
data = getattr(span, "attributes", None) or getattr(span, "_data", {})
assert data[SPANDATA.GEN_AI_REQUEST_SEED] == 999
assert data[SPANDATA.GEN_AI_REQUEST_TOP_K] == 25
span.__exit__(None, None, None)


def test_input_callback_omitted_parameters(sentry_init):
"""Test that seed and top_k are omitted when not provided."""
sentry_init(
integrations=[LiteLLMIntegration()],
traces_sample_rate=1.0,
)

kwargs = {
"model": "gpt-3.5-turbo",
"messages": [{"role": "user", "content": "hi"}],
}

with start_transaction(name="litellm test"):
_input_callback(kwargs)
span = kwargs.pop("_sentry_span", None)
assert span is not None
data = getattr(span, "attributes", None) or getattr(span, "_data", {})
assert SPANDATA.GEN_AI_REQUEST_SEED not in data
assert SPANDATA.GEN_AI_REQUEST_TOP_K not in data
span.__exit__(None, None, None)


@pytest.mark.parametrize("span_streaming", [True, False])
Expand Down