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fix(utils.py): return openai streaming prompt caching tokens (#6051)

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* fix(utils.py): return openai streaming prompt caching tokens

Closes https://github.com/BerriAI/litellm/issues/6038

* fix(main.py): fix error in finish_reason updates
This commit is contained in:
Krish Dholakia 2024-10-03 22:20:13 -04:00 • committed by GitHub
parent 04ae095860
commit 09f0c09ba4
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5 changed files with 91 additions and 10 deletions
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46
litellm/main.py
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@ -144,8 +144,10 @@ from .types.llms.openai import HttpxBinaryResponseContent
from .types.utils import (
AdapterCompletionStreamWrapper,
ChatCompletionMessageToolCall,
CompletionTokensDetails,
FileTypes,
HiddenParams,
PromptTokensDetails,
all_litellm_params,
)
@ -5481,7 +5483,13 @@ def stream_chunk_builder(
chunks=chunks, messages=messages
)
role = chunks[0]["choices"][0]["delta"]["role"]
finish_reason = chunks[-1]["choices"][0]["finish_reason"]
finish_reason = "stop"
for chunk in chunks:
if "choices" in chunk and len(chunk["choices"]) > 0:
if hasattr(chunk["choices"][0], "finish_reason"):
finish_reason = chunk["choices"][0].finish_reason
elif "finish_reason" in chunk["choices"][0]:
finish_reason = chunk["choices"][0]["finish_reason"]
# Initialize the response dictionary
response = {
@ -5512,7 +5520,8 @@ def stream_chunk_builder(
tool_call_chunks = [
chunk
for chunk in chunks
if "tool_calls" in chunk["choices"][0]["delta"]
if len(chunk["choices"]) > 0
and "tool_calls" in chunk["choices"][0]["delta"]
and chunk["choices"][0]["delta"]["tool_calls"] is not None
]
@ -5590,7 +5599,8 @@ def stream_chunk_builder(
function_call_chunks = [
chunk
for chunk in chunks
if "function_call" in chunk["choices"][0]["delta"]
if len(chunk["choices"]) > 0
and "function_call" in chunk["choices"][0]["delta"]
and chunk["choices"][0]["delta"]["function_call"] is not None
]
@ -5625,7 +5635,8 @@ def stream_chunk_builder(
content_chunks = [
chunk
for chunk in chunks
if "content" in chunk["choices"][0]["delta"]
if len(chunk["choices"]) > 0
and "content" in chunk["choices"][0]["delta"]
and chunk["choices"][0]["delta"]["content"] is not None
]
@ -5657,6 +5668,8 @@ def stream_chunk_builder(
## anthropic prompt caching information ##
cache_creation_input_tokens: Optional[int] = None
cache_read_input_tokens: Optional[int] = None
completion_tokens_details: Optional[CompletionTokensDetails] = None
prompt_tokens_details: Optional[PromptTokensDetails] = None
for chunk in chunks:
usage_chunk: Optional[Usage] = None
if "usage" in chunk:
@ -5674,6 +5687,26 @@ def stream_chunk_builder(
)
if "cache_read_input_tokens" in usage_chunk:
cache_read_input_tokens = usage_chunk.get("cache_read_input_tokens")
if hasattr(usage_chunk, "completion_tokens_details"):
if isinstance(usage_chunk.completion_tokens_details, dict):
completion_tokens_details = CompletionTokensDetails(
**usage_chunk.completion_tokens_details
)
elif isinstance(
usage_chunk.completion_tokens_details, CompletionTokensDetails
):
completion_tokens_details = (
usage_chunk.completion_tokens_details
)
if hasattr(usage_chunk, "prompt_tokens_details"):
if isinstance(usage_chunk.prompt_tokens_details, dict):
prompt_tokens_details = PromptTokensDetails(
**usage_chunk.prompt_tokens_details
)
elif isinstance(
usage_chunk.prompt_tokens_details, PromptTokensDetails
):
prompt_tokens_details = usage_chunk.prompt_tokens_details
try:
response["usage"]["prompt_tokens"] = prompt_tokens or token_counter(
@ -5700,6 +5733,11 @@ def stream_chunk_builder(
if cache_read_input_tokens is not None:
response["usage"]["cache_read_input_tokens"] = cache_read_input_tokens
if completion_tokens_details is not None:
response["usage"]["completion_tokens_details"] = completion_tokens_details
if prompt_tokens_details is not None:
response["usage"]["prompt_tokens_details"] = prompt_tokens_details
return convert_to_model_response_object(
response_object=response,
model_response_object=model_response,