70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
import json
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from typing import Dict, List, Optional
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from groq import Groq
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from mem0.llms.base import LLMBase
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class GroqLLM(LLMBase):
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def __init__(self, model="llama3-70b-8192"):
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self.client = Groq()
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self.model = model
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def _parse_response(self, response, tools):
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"""
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Process the response based on whether tools are used or not.
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Args:
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response: The raw response from API.
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tools: The list of tools provided in the request.
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Returns:
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str or dict: The processed response.
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"""
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if tools:
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processed_response = {
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"content": response.choices[0].message.content,
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"tool_calls": []
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}
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if response.choices[0].message.tool_calls:
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for tool_call in response.choices[0].message.tool_calls:
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processed_response["tool_calls"].append({
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"name": tool_call.function.name,
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"arguments": json.loads(tool_call.function.arguments)
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})
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return processed_response
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else:
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return response.choices[0].message.content
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def generate_response(
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self,
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messages: List[Dict[str, str]],
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response_format=None,
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tools: Optional[List[Dict]] = None,
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tool_choice: str = "auto",
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):
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"""
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Generate a response based on the given messages using Groq.
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Args:
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messages (list): List of message dicts containing 'role' and 'content'.
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response_format (str or object, optional): Format of the response. Defaults to "text".
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tools (list, optional): List of tools that the model can call. Defaults to None.
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tool_choice (str, optional): Tool choice method. Defaults to "auto".
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Returns:
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str: The generated response.
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"""
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params = {"model": self.model, "messages": messages}
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if response_format:
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params["response_format"] = response_format
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if tools:
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params["tools"] = tools
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params["tool_choice"] = tool_choice
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response = self.client.chat.completions.create(**params)
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return self._parse_response(response, tools)
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