services/prompt_builder.py

# prompt_builder.py
from typing import Protocol, List, Tuple
from transformers import AutoTokenizer


class PromptTemplate(Protocol):
    """Protocol for prompt templates."""
    def format(self, context: str, user_input: str, chat_history: List[Tuple[str, str]], **kwargs) -> str:
        pass


class LlamaPromptTemplate:
    def format(self, context: str, user_input: str, chat_history: List[Tuple[str, str]], max_history_turns: int = 1) -> str:
        system_message = f"Please assist based on the following context: {context}"
        prompt = f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_message}<|eot_id|>"
        
        for user_msg, assistant_msg in chat_history[-max_history_turns:]:
            prompt += f"<|start_header_id|>user<|end_header_id|>\n\n{user_msg}<|eot_id|>"
            prompt += f"<|start_header_id|>assistant<|end_header_id|>\n\n{assistant_msg}<|eot_id|>"
            
        prompt += f"<|start_header_id|>user<|end_header_id|>\n\n{user_input}<|eot_id|>"
        prompt += "<|start_header_id|>assistant<|end_header_id|>\n\n"
        return prompt
        

class TransformersPromptTemplate:
    def __init__(self, model_path: str):
        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
        
    def format(self, context: str, user_input: str, chat_history: List[Tuple[str, str]], **kwargs) -> str:
        messages = [
            {
                "role": "system",
                "content": f"Please assist based on the following context: {context}",
            }
        ]
        
        for user_msg, assistant_msg in chat_history:
            messages.extend([
                {"role": "user", "content": user_msg},
                {"role": "assistant", "content": assistant_msg}
            ])
            
        messages.append({"role": "user", "content": user_input})
        
        tokenized_chat = self.tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True
        )
        return tokenized_chat