139 lines
5.5 KiB
Python
139 lines
5.5 KiB
Python
#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import json
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import os
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from openai import AsyncStream
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from openai.types.chat import ChatCompletionChunk
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from pipecat.services.llm_service import FunctionCallFromLLM
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# Suppress gRPC fork warnings
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os.environ["GRPC_ENABLE_FORK_SUPPORT"] = "false"
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from loguru import logger
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from pipecat.frames.frames import LLMTextFrame
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from pipecat.metrics.metrics import LLMTokenUsage
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from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
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from pipecat.services.openai.llm import OpenAILLMService
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class GoogleLLMOpenAIBetaService(OpenAILLMService):
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"""This class implements inference with Google's AI LLM models using the OpenAI format.
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Ref - https://ai.google.dev/gemini-api/docs/openai
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"""
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def __init__(
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self,
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*,
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api_key: str,
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base_url: str = "https://generativelanguage.googleapis.com/v1beta/openai/",
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model: str = "gemini-2.0-flash",
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**kwargs,
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):
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super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs)
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async def _process_context(self, context: OpenAILLMContext):
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functions_list = []
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arguments_list = []
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tool_id_list = []
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func_idx = 0
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function_name = ""
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arguments = ""
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tool_call_id = ""
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await self.start_ttfb_metrics()
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chunk_stream: AsyncStream[ChatCompletionChunk] = await self._stream_chat_completions(
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context
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)
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async for chunk in chunk_stream:
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if chunk.usage:
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tokens = LLMTokenUsage(
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prompt_tokens=chunk.usage.prompt_tokens,
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completion_tokens=chunk.usage.completion_tokens,
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total_tokens=chunk.usage.total_tokens,
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)
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await self.start_llm_usage_metrics(tokens)
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if chunk.choices is None or len(chunk.choices) == 0:
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continue
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await self.stop_ttfb_metrics()
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if not chunk.choices[0].delta:
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continue
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if chunk.choices[0].delta.tool_calls:
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# We're streaming the LLM response to enable the fastest response times.
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# For text, we just yield each chunk as we receive it and count on consumers
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# to do whatever coalescing they need (eg. to pass full sentences to TTS)
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#
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# If the LLM is a function call, we'll do some coalescing here.
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# If the response contains a function name, we'll yield a frame to tell consumers
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# that they can start preparing to call the function with that name.
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# We accumulate all the arguments for the rest of the streamed response, then when
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# the response is done, we package up all the arguments and the function name and
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# yield a frame containing the function name and the arguments.
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logger.debug(f"Tool call: {chunk.choices[0].delta.tool_calls}")
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tool_call = chunk.choices[0].delta.tool_calls[0]
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if tool_call.index != func_idx:
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functions_list.append(function_name)
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arguments_list.append(arguments)
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tool_id_list.append(tool_call_id)
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function_name = ""
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arguments = ""
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tool_call_id = ""
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func_idx += 1
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if tool_call.function and tool_call.function.name:
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function_name += tool_call.function.name
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tool_call_id = tool_call.id
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if tool_call.function and tool_call.function.arguments:
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# Keep iterating through the response to collect all the argument fragments
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arguments += tool_call.function.arguments
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elif chunk.choices[0].delta.content:
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await self.push_frame(LLMTextFrame(chunk.choices[0].delta.content))
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# if we got a function name and arguments, check to see if it's a function with
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# a registered handler. If so, run the registered callback, save the result to
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# the context, and re-prompt to get a chat answer. If we don't have a registered
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# handler, raise an exception.
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if function_name and arguments:
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# added to the list as last function name and arguments not added to the list
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functions_list.append(function_name)
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arguments_list.append(arguments)
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tool_id_list.append(tool_call_id)
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logger.debug(
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f"Function list: {functions_list}, Arguments list: {arguments_list}, Tool ID list: {tool_id_list}"
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)
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function_calls = []
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for function_name, arguments, tool_id in zip(
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functions_list, arguments_list, tool_id_list
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):
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if function_name == "":
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# TODO: Remove the _process_context method once Google resolves the bug
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# where the index is incorrectly set to None instead of returning the actual index,
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# which currently results in an empty function name('').
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continue
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arguments = json.loads(arguments)
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function_calls.append(
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FunctionCallFromLLM(
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context=context,
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tool_call_id=tool_id,
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function_name=function_name,
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arguments=arguments,
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)
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)
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await self.run_function_calls(function_calls)
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