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fix(stream_chunk_builder): adding support for tool calling in completion counting
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3 changed files with 109 additions and 10 deletions
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@ -2109,7 +2109,36 @@ def stream_chunk_builder(chunks: list, messages: Optional[list]=None):
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content_list = []
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combined_content = ""
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if "function_call" in chunks[0]["choices"][0]["delta"] and chunks[0]["choices"][0]["delta"]["function_call"] is not None:
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if "tool_calls" in chunks[0]["choices"][0]["delta"] and chunks[0]["choices"][0]["delta"]["tool_calls"] is not None:
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argument_list = []
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delta = chunks[0]["choices"][0]["delta"]
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message = response["choices"][0]["message"]
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message["tool_calls"] = []
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id = None
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name = None
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type = None
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for chunk in chunks:
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choices = chunk["choices"]
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for choice in choices:
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delta = choice.get("delta", {})
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tool_calls = delta.get("tool_calls", "")
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# Check if a tool call is present
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if tool_calls and tool_calls[0].function is not None:
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if tool_calls[0].id:
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id = tool_calls[0].id
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if tool_calls[0].function.arguments:
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# Now, tool_calls is expected to be a dictionary
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arguments = tool_calls[0].function.arguments
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argument_list.append(arguments)
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if tool_calls[0].function.name:
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name = tool_calls[0].function.name
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if tool_calls[0].type:
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type = tool_calls[0].type
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combined_arguments = "".join(argument_list)
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response["choices"][0]["message"]["content"] = None
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response["choices"][0]["message"]["tool_calls"] = [{"id": id, "function": {"arguments": combined_arguments, "name": name}, "type": type}]
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elif "function_call" in chunks[0]["choices"][0]["delta"] and chunks[0]["choices"][0]["delta"]["function_call"] is not None:
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argument_list = []
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delta = chunks[0]["choices"][0]["delta"]
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function_call = delta.get("function_call", "")
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@ -2144,16 +2173,20 @@ def stream_chunk_builder(chunks: list, messages: Optional[list]=None):
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continue # openai v1.0.0 sets content = None for chunks
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content_list.append(content)
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# Combine the "content" strings into a single string
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combined_content = "".join(content_list)
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# Combine the "content" strings into a single string || combine the 'function' strings into a single string
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combined_content = "".join(combined_arguments)
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# Update the "content" field within the response dictionary
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response["choices"][0]["message"]["content"] = combined_content
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if len(combined_content) > 0:
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completion_output = combined_content
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elif len(combined_arguments) > 0:
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completion_output = combined_arguments
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# # Update usage information if needed
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if messages:
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response["usage"]["prompt_tokens"] = token_counter(model=model, messages=messages)
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response["usage"]["completion_tokens"] = token_counter(model=model, text=combined_content)
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response["usage"]["completion_tokens"] = token_counter(model=model, text=completion_output)
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response["usage"]["total_tokens"] = response["usage"]["prompt_tokens"] + response["usage"]["completion_tokens"]
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return convert_to_model_response_object(response_object=response, model_response_object=litellm.ModelResponse())
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