import json from typing import Dict, List, Optional from together import Together from mem0.llms.base import LLMBase class TogetherLLM(LLMBase): def __init__(self, model="mistralai/Mixtral-8x7B-Instruct-v0.1"): self.client = Together() self.model = model def _parse_response(self, response, tools): """ Process the response based on whether tools are used or not. Args: response: The raw response from API. tools: The list of tools provided in the request. Returns: str or dict: The processed response. """ if tools: processed_response = { "content": response.choices[0].message.content, "tool_calls": [] } if response.choices[0].message.tool_calls: for tool_call in response.choices[0].message.tool_calls: processed_response["tool_calls"].append({ "name": tool_call.function.name, "arguments": json.loads(tool_call.function.arguments) }) return processed_response else: return response.choices[0].message.content def generate_response( self, messages: List[Dict[str, str]], response_format=None, tools: Optional[List[Dict]] = None, tool_choice: str = "auto", ): """ Generate a response based on the given messages using TogetherAI. Args: messages (list): List of message dicts containing 'role' and 'content'. response_format (str or object, optional): Format of the response. Defaults to "text". tools (list, optional): List of tools that the model can call. Defaults to None. tool_choice (str, optional): Tool choice method. Defaults to "auto". Returns: str: The generated response. """ params = {"model": self.model, "messages": messages} if response_format: params["response_format"] = response_format if tools: params["tools"] = tools params["tool_choice"] = tool_choice response = self.client.chat.completions.create(**params) return self._parse_response(response, tools)