llm_interface.py

import os
from openai import OpenAI
from huggingface_hub import InferenceClient
from dotenv import load_dotenv

load_dotenv()

class LLMProvider:
    def __init__(self, provider=None):
        self.provider = provider or os.getenv("ACTIVE_LLM_PROVIDER", "llama").lower()
        
        if self.provider == "openai":
            print("🔗 Connecting directly to official OpenAI API...")
            self.client = OpenAI(
                api_key=os.getenv("OPENAI_API_KEY")
            )
            # This is the alias your logs will see
            self.model_name = "gpt-oss-120b" 
            
        else:
            print(f"🦙 Initializing Llama-3-70B via Hugging Face...")
            self.client = InferenceClient(api_key=os.getenv("HF_TOKEN"))
            self.model_name = "meta-llama/Meta-Llama-3-70B-Instruct"

    def generate(self, prompt, context):
        citation_instruction = (
            "You MUST cite the specific sources from the context provided using their IDs in brackets, "
            "like [S12] or [PAPER_001]. If a paper has a filename, use that. "
            "Always provide a 'References' list at the end."
        )
        full_query = f"{citation_instruction}\n\nContext: {context}\n\nQuestion: {prompt}"
        
        try:
            if self.provider == "openai":
                response = self.client.chat.completions.create(
                    model="gpt-4o",  # The actual underlying engine
                    messages=[
                        {"role": "system", "content": citation_instruction},
                        {"role": "user", "content": full_query}
                    ],
                    temperature=0.2
                )
                return response.choices[0].message.content
            else:
                response = self.client.chat_completion(
                    messages=[
                        {"role": "system", "content": citation_instruction},
                        {"role": "user", "content": full_query}
                    ],
                    model=self.model_name,
                    max_tokens=800,
                    temperature=0.2
                )
                return response.choices[0].message.content
        except Exception as e:
            return f"Error using {self.provider}: {str(e)}"