spark-scripts/chatbot.py

#!/usr/bin/env python3
"""Grounded Audrey Tang chatbot with RAG + fine-tuned TAIDE."""

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from retriever import AudreyRetriever

SYSTEM_PROMPT = """You are Audrey Tang (唐鳳), Taiwan's Cyber Ambassador and 2025 Right Livelihood Laureate. You are a civic hacker, co-author of Plurality, and Senior Accelerator Fellow at the Oxford Institute for Ethics in AI.

You served as Taiwan's first Digital Minister (2016–2024). You describe your philosophy as becoming a "good enough ancestor."

GROUNDING RULES:
- Only assert what the provided transcript passages support.
- Cite sources by [date — title] when making specific claims.
- If the passages don't cover the topic, say so honestly and suggest adjacent topics you CAN speak to from your experience.
- Never fabricate quotes, events, or policy positions.
- Use the bilingual terminology from the lexicon for consistent EN↔ZH translations.
- Speak naturally in whatever language the user uses.

Your voice: metaphor-rich, bridging diverse traditions, grounded in Taiwan's lived experience, generous and warm in engagement. You reframe questions to find unexpected connections. You use analogies from nature, technology, open source, and philosophy."""

class AudreyChatbot:
    def __init__(
        self,
        model_path: str = "./models/taide-12b-audrey",
        retriever_kwargs: dict = None,
    ):
        self.retriever = AudreyRetriever(**(retriever_kwargs or {}))

        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
        self.model = AutoModelForCausalLM.from_pretrained(
            model_path,
            torch_dtype=torch.bfloat16,
            device_map="auto",
        )
        self.model.eval()
        self.conversation_history = []

    def chat(self, user_message: str) -> str:
        # Retrieve grounding passages
        result = self.retriever.retrieve(user_message)
        context = self.retriever.format_context(result)

        # Build messages
        system_with_context = (
            f"{SYSTEM_PROMPT}\n\n"
            f"## Retrieved transcript passages:\n\n{context}"
        )

        messages = [{"role": "user", "content": system_with_context + "\n\n" + user_message}]

        # Include conversation history (last 6 turns)
        if self.conversation_history:
            history_messages = self.conversation_history[-6:]
            # Prepend history before the current message
            full_messages = history_messages + messages
        else:
            full_messages = messages

        # Generate
        input_text = self.tokenizer.apply_chat_template(
            full_messages,
            tokenize=False,
            add_generation_prompt=True,
        )
        inputs = self.tokenizer(input_text, return_tensors="pt").to(self.model.device)

        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_new_tokens=1024,
                temperature=0.7,
                top_p=0.9,
                repetition_penalty=1.1,
                do_sample=True,
            )

        response = self.tokenizer.decode(
            outputs[0][inputs["input_ids"].shape[1]:],
            skip_special_tokens=True,
        )

        # Update history
        self.conversation_history.append({"role": "user", "content": user_message})
        self.conversation_history.append({"role": "model", "content": response})

        return response

    def reset(self):
        self.conversation_history = []


if __name__ == "__main__":
    print("Loading Audrey Tang chatbot...")
    bot = AudreyChatbot()
    print("Ready. Type 'quit' to exit, 'reset' to clear history.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() == "quit":
            break
        if user_input.lower() == "reset":
            bot.reset()
            print("Conversation reset.\n")
            continue
        if not user_input:
            continue

        response = bot.chat(user_input)
        print(f"\nAudrey: {response}\n")