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| from openai import OpenAI | |
| class LlaMa3: | |
| def __init__(self) -> None: | |
| self.client = OpenAI( | |
| base_url="https://integrate.api.nvidia.com/v1", | |
| api_key="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" | |
| ) | |
| self.name = "Llama3" | |
| # Initial greeting and request for decision topic | |
| self.initial_prompt = """ | |
| Hello! I can assist you in making a decision. What decision would you like to make today? | |
| Please describe the decision and provide any relevant details to help me understand. | |
| """ | |
| def chat(self, messages): | |
| # If this is the first message, we use the initial prompt to greet and ask for the decision topic | |
| if len(messages) == 0: # Initial conversation step | |
| messages.append({"role": "system", "content": self.initial_prompt}) | |
| # Call the API to get the model's response | |
| completion = self.client.chat.completions.create( | |
| model="nvidia/llama-3.1-nemotron-70b-instruct", | |
| messages=messages, | |
| temperature=0.5, | |
| top_p=1, | |
| max_tokens=1024, | |
| stream=True | |
| ) | |
| response = "" | |
| for chunk in completion: | |
| if chunk.choices[0].delta.content is not None: | |
| response += chunk.choices[0].delta.content | |
| return response | |