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Merge pull request #5296 from BerriAI/litellm_azure_json_schema_support

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feat(azure.py): support 'json_schema' for older models
This commit is contained in:
Krish Dholakia 2024-08-20 11:41:38 -07:00 • committed by GitHub
commit 969b724615
3 changed files with 101 additions and 31 deletions
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42
litellm/utils.py
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@ -843,13 +843,13 @@ def client(original_function):
and str(original_function.__name__)
in litellm.cache.supported_call_types
):
print_verbose(f"Checking Cache")
print_verbose("Checking Cache")
preset_cache_key = litellm.cache.get_cache_key(*args, **kwargs)
kwargs["preset_cache_key"] = (
preset_cache_key # for streaming calls, we need to pass the preset_cache_key
)
cached_result = litellm.cache.get_cache(*args, **kwargs)
if cached_result != None:
if cached_result is not None:
if "detail" in cached_result:
# implies an error occurred
pass
@ -5907,6 +5907,9 @@ def convert_to_model_response_object(
end_time=None,
hidden_params: Optional[dict] = None,
_response_headers: Optional[dict] = None,
convert_tool_call_to_json_mode: Optional[
bool
] = None, # used for supporting 'json_schema' on older models
):
received_args = locals()
if _response_headers is not None:
@ -5945,7 +5948,7 @@ def convert_to_model_response_object(
):
if response_object is None or model_response_object is None:
raise Exception("Error in response object format")
if stream == True:
if stream is True:
# for returning cached responses, we need to yield a generator
return convert_to_streaming_response(response_object=response_object)
choice_list = []
@ -5955,16 +5958,31 @@ def convert_to_model_response_object(
)
for idx, choice in enumerate(response_object["choices"]):
message = Message(
content=choice["message"].get("content", None),
role=choice["message"]["role"] or "assistant",
function_call=choice["message"].get("function_call", None),
tool_calls=choice["message"].get("tool_calls", None),
)
finish_reason = choice.get("finish_reason", None)
if finish_reason == None:
## HANDLE JSON MODE - anthropic returns single function call]
tool_calls = choice["message"].get("tool_calls", None)
if (
convert_tool_call_to_json_mode
and tool_calls is not None
and len(tool_calls) == 1
):
# to support 'json_schema' logic on older models
json_mode_content_str: Optional[str] = tool_calls[0][
"function"
].get("arguments")
if json_mode_content_str is not None:
message = litellm.Message(content=json_mode_content_str)
finish_reason = "stop"
else:
message = Message(
content=choice["message"].get("content", None),
role=choice["message"]["role"] or "assistant",
function_call=choice["message"].get("function_call", None),
tool_calls=choice["message"].get("tool_calls", None),
)
finish_reason = choice.get("finish_reason", None)
if finish_reason is None:
# gpt-4 vision can return 'finish_reason' or 'finish_details'
finish_reason = choice.get("finish_details")
finish_reason = choice.get("finish_details") or "stop"
logprobs = choice.get("logprobs", None)
enhancements = choice.get("enhancements", None)
choice = Choices(