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provider specific fields preserved now

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Yamac Ay 2026-10-02 14:30:39 +02:00
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1
litellm/llms/sap/chat/handler.py
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@ -99,6 +99,7 @@ class _StreamParser:
"index": c.get("index", 0),
"delta": c.get("delta") or {}, # mutable-ok: transient default, consumed by model_validate
"finish_reason": c.get("finish_reason"),
**({"provider_specific_fields": c["provider_specific_fields"]} if "provider_specific_fields" in c else {}),
}
for c in (orc.get("choices") or []) # mutable-ok: transient default for iteration
],

9
litellm/llms/sap/chat/transformation.py
View file

@ -406,6 +406,15 @@ class GenAIHubOrchestrationConfig(OpenAIGPTConfig):
final_result = normalize_reasoning_content(raw_response.json()["final_result"])
response = ModelResponse.model_validate(final_result)
# Forward provider_specific_fields from each raw choice onto the
# corresponding ModelResponse choice so callers and middleware can
# access provider metadata that doesn't fit the OpenAI schema.
raw_choices: list[dict] = final_result.get("choices") or []
for raw_choice, model_choice in zip(raw_choices, response.choices or []):
psf = raw_choice.get("provider_specific_fields")
if psf is not None:
model_choice.provider_specific_fields = psf # type: ignore[attr-defined]
# Strip markdown code blocks if JSON response_format was used with Anthropic models
# SAP GenAI Hub with Anthropic models sometimes wraps JSON in ```json ... ```
# based on prompt phrasing. GPT/Gemini models don't exhibit this behavior,

255
tests/unit/llms/sap/chat/test_sap_transformation.py
View file

@ -991,3 +991,258 @@ class TestMessagesToSapTemplateWithFileContent:
content = result[0]["content"]
assert content[0]["type"] == "text"
assert content[1]["type"] == "file"
class TestProviderSpecificFields:
"""Tests for provider_specific_fields propagation through normalize_choice,
_StreamParser, and transform_response (spec lines 1161 / 1181)."""
# ------------------------------------------------------------------
# normalize_choice
# ------------------------------------------------------------------
def test_normalize_choice_preserves_provider_specific_fields(self):
"""provider_specific_fields present in a choice must survive normalize_choice."""
from litellm.llms.sap.chat.handler import normalize_choice
choice = {
"index": 0,
"message": {"role": "assistant", "content": "hi"},
"finish_reason": "stop",
"provider_specific_fields": {"raw_finish_reason": "end_turn", "extra": 42},
}
result = normalize_choice(choice)
assert result["provider_specific_fields"] == {"raw_finish_reason": "end_turn", "extra": 42}
def test_normalize_choice_no_provider_specific_fields_unchanged(self):
"""When provider_specific_fields is absent the key must not be injected."""
from litellm.llms.sap.chat.handler import normalize_choice
choice = {"index": 0, "message": {"role": "assistant", "content": "hi"}, "finish_reason": "stop"}
result = normalize_choice(choice)
assert "provider_specific_fields" not in result
def test_normalize_choice_preserves_psf_alongside_reasoning_content(self):
"""provider_specific_fields must survive even when reasoning_content is also normalized."""
from litellm.llms.sap.chat.handler import normalize_choice
choice = {
"index": 0,
"message": {
"role": "assistant",
"content": "answer",
"reasoning_content": [{"content": "thought", "signature": "s1"}],
},
"finish_reason": "stop",
"provider_specific_fields": {"raw_finish_reason": "end_turn"},
}
result = normalize_choice(choice)
assert result["provider_specific_fields"] == {"raw_finish_reason": "end_turn"}
# reasoning normalization must still have happened
assert result["message"]["reasoning_content"] == "thought"
# ------------------------------------------------------------------
# _StreamParser._from_orchestration_result
# ------------------------------------------------------------------
def test_stream_parser_passes_provider_specific_fields(self):
"""_from_orchestration_result must forward provider_specific_fields per choice."""
from litellm.llms.sap.chat.handler import _StreamParser
evt = {
"orchestration_result": {
"id": "chunk-1",
"object": "chat.completion.chunk",
"created": 1700000000,
"model": "gpt-4o",
"choices": [
{
"index": 0,
"delta": {"content": "hello"},
"finish_reason": None,
"provider_specific_fields": {"raw_finish_reason": None, "extra_flag": True},
}
],
}
}
chunk = _StreamParser.to_openai_chunk(evt)
assert chunk is not None
choice = chunk.choices[0]
assert hasattr(choice, "provider_specific_fields")
assert choice.provider_specific_fields == {"raw_finish_reason": None, "extra_flag": True}
def test_stream_parser_omits_key_when_absent(self):
"""When provider_specific_fields is absent the key is not injected into the chunk."""
from litellm.llms.sap.chat.handler import _StreamParser
evt = {
"orchestration_result": {
"id": "chunk-2",
"object": "chat.completion.chunk",
"created": 1700000000,
"model": "gpt-4o",
"choices": [{"index": 0, "delta": {"content": "world"}, "finish_reason": None}],
}
}
chunk = _StreamParser.to_openai_chunk(evt)
assert chunk is not None
choice = chunk.choices[0]
# either absent or None is acceptable — must not be a non-None populated dict
psf = getattr(choice, "provider_specific_fields", None)
assert not psf
# ------------------------------------------------------------------
# transform_response (non-streaming)
# ------------------------------------------------------------------
def test_transform_response_copies_provider_specific_fields(self):
"""transform_response must attach provider_specific_fields from each raw choice
onto the corresponding ModelResponse choice."""
import json
from unittest.mock import MagicMock
from litellm.llms.sap.chat.transformation import GenAIHubOrchestrationConfig
config = GenAIHubOrchestrationConfig()
final_result = {
"id": "chatcmpl-psf-test",
"object": "chat.completion",
"created": 1700000000,
"model": "gpt-4o",
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": "Hello!"},
"finish_reason": "stop",
"provider_specific_fields": {
"finish_reason": "end_turn",
"sap_extra": "metadata",
},
}
],
"usage": {"prompt_tokens": 5, "completion_tokens": 3, "total_tokens": 8},
}
raw_response = MagicMock()
raw_response.text = json.dumps({"final_result": final_result})
raw_response.json.return_value = {"final_result": final_result}
raw_response.headers = {}
response = config.transform_response(
model="gpt-4o",
raw_response=raw_response,
model_response=MagicMock(),
logging_obj=MagicMock(),
api_key="test",
request_data={},
messages=[],
optional_params={},
litellm_params={},
encoding=None,
)
choice = response.choices[0]
assert hasattr(choice, "provider_specific_fields"), "provider_specific_fields missing from choice"
assert choice.provider_specific_fields == {
"finish_reason": "end_turn",
"sap_extra": "metadata",
}
def test_transform_response_no_provider_specific_fields_skipped(self):
"""When provider_specific_fields is absent on raw choices, nothing is attached."""
import json
from unittest.mock import MagicMock
from litellm.llms.sap.chat.transformation import GenAIHubOrchestrationConfig
config = GenAIHubOrchestrationConfig()
final_result = {
"id": "chatcmpl-nopsf",
"object": "chat.completion",
"created": 1700000000,
"model": "gpt-4o",
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": "Hi."},
"finish_reason": "stop",
}
],
"usage": {"prompt_tokens": 2, "completion_tokens": 1, "total_tokens": 3},
}
raw_response = MagicMock()
raw_response.text = json.dumps({"final_result": final_result})
raw_response.json.return_value = {"final_result": final_result}
raw_response.headers = {}
response = config.transform_response(
model="gpt-4o",
raw_response=raw_response,
model_response=MagicMock(),
logging_obj=MagicMock(),
api_key="test",
request_data={},
messages=[],
optional_params={},
litellm_params={},
encoding=None,
)
choice = response.choices[0]
psf = getattr(choice, "provider_specific_fields", None)
assert not psf
def test_transform_response_multiple_choices_each_gets_own_psf(self):
"""Each choice gets its own provider_specific_fields (different values per choice)."""
import json
from unittest.mock import MagicMock
from litellm.llms.sap.chat.transformation import GenAIHubOrchestrationConfig
config = GenAIHubOrchestrationConfig()
final_result = {
"id": "chatcmpl-multi",
"object": "chat.completion",
"created": 1700000000,
"model": "gpt-4o",
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": "A"},
"finish_reason": "stop",
"provider_specific_fields": {"raw_finish_reason": "end_turn"},
},
{
"index": 1,
"message": {"role": "assistant", "content": "B"},
"finish_reason": "length",
"provider_specific_fields": {"raw_finish_reason": "max_tokens"},
},
],
"usage": {"prompt_tokens": 4, "completion_tokens": 2, "total_tokens": 6},
}
raw_response = MagicMock()
raw_response.text = json.dumps({"final_result": final_result})
raw_response.json.return_value = {"final_result": final_result}
raw_response.headers = {}
response = config.transform_response(
model="gpt-4o",
raw_response=raw_response,
model_response=MagicMock(),
logging_obj=MagicMock(),
api_key="test",
request_data={},
messages=[],
optional_params={},
litellm_params={},
encoding=None,
)
assert response.choices[0].provider_specific_fields == {"raw_finish_reason": "end_turn"}
assert response.choices[1].provider_specific_fields == {"raw_finish_reason": "max_tokens"}