masterthesis-playground/raft/rag_chat_merged.py

import argparse
import json
import os
from threading import Thread

import faiss
import numpy as np
import torch
from sentence_transformers import SentenceTransformer
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer

SYSTEM_PERSONA = """You are a culturally interested Bali traveler lead user.

Adopt the perspective of a culturally interested international visitor to Bali who values authenticity, spiritual context, respectful behavior, and meaningful experiences over entertainment or social media appeal.

When answering:
- Prioritize cultural interpretation, atmosphere, and visitor ethics.
- Weigh trade-offs thoughtfully (e.g., crowds vs. significance).
- Avoid generic travel advice and avoid promotional language.
- Do not exaggerate.
- Provide nuanced, reflective reasoning rather than bullet lists.
- Keep answers concise but specific.

Respond as if you are describing your genuine experience and judgment as this type of traveler.

Use the provided CONTEXT to inform your answer, but do not feel obligated to use all of it. If the CONTEXT is not relevant to the question, you can ignore it.
NEVER directly quote the CONTEXT verbatim.
NEVER mention DOC or any context sources you are referring to. Instead, use it to synthesize your own understanding and response.
"""


def load_docstore(path):
    docs = []
    with open(path, "r", encoding="utf-8") as f:
        for line in f:
            docs.append(json.loads(line))
    return docs


def retrieve(index, embedder, query, top_k=12):
    q = embedder.encode([query], normalize_embeddings=True).astype(np.float32)
    scores, ids = index.search(q, top_k)
    return ids[0].tolist(), scores[0].tolist()


@torch.no_grad()
def main():
    ap = argparse.ArgumentParser()
    ap.add_argument(
        "--model_dir", required=True, help="Path to your finetuned model folder"
    )
    ap.add_argument(
        "--out_dir", default="out", help="Where faiss.index and docstore.jsonl live"
    )
    ap.add_argument(
        "--embedding_model", default="sentence-transformers/all-MiniLM-L6-v2"
    )
    ap.add_argument("--top_k", type=int, default=12)
    ap.add_argument("--max_new_tokens", type=int, default=320)
    ap.add_argument("--no_model", action=argparse.BooleanOptionalAction)
    args = ap.parse_args()

    index = faiss.read_index(os.path.join(args.out_dir, "faiss.index"))
    docstore = load_docstore(os.path.join(args.out_dir, "docstore.jsonl"))
    embedder = SentenceTransformer(args.embedding_model)

    # Load your externally finetuned model directly from disk
    tok = AutoTokenizer.from_pretrained(args.model_dir, use_fast=True)

    # Important: ensure pad token exists for generation; Mistral often uses eos as pad
    if tok.pad_token is None:
        tok.pad_token = tok.eos_token

    if not args.no_model:
        model = AutoModelForCausalLM.from_pretrained(
            args.model_dir,
            device_map="auto",
            torch_dtype=torch.float16,
        )
        model.eval()

    print("Type your question (Ctrl+C to exit).")
    while True:
        q = input("\nYou: ").strip()
        if not q:
            continue

        ids, scores = retrieve(index, embedder, q, top_k=args.top_k)

        # Drop irrelevant context
        if scores[0] > 0:
            filtered = [(i, s) for i, s in zip(ids, scores) if s / scores[0] >= 0.75]
            if not filtered:
                print("No relevant context found.")
                continue
            ids, scores = zip(*filtered)
        else:
            print("No relevant context found.")
            continue

        context_docs = [docstore[i]["text"] for i in ids]
        context_blob = "\n\n".join([t for _, t in enumerate(context_docs)])

        print("\nRetrieved Context:")
        for i, (doc, score) in enumerate(zip(context_docs, scores)):
            print(f"\nDoc {i+1} (score: {score:.4f}):\n{doc}")

        messages = [
            {"role": "system", "content": SYSTEM_PERSONA},
            {"role": "user", "content": f"QUESTION: {q}\n\nCONTEXT:\n{context_blob}"},
        ]

        if args.no_model:
            continue

        enc = tok.apply_chat_template(
            messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
        )

        if isinstance(enc, torch.Tensor):
            input_ids = enc.to(model.device)
            attention_mask = torch.ones_like(input_ids, device=model.device)
        else:
            input_ids = enc["input_ids"].to(model.device)
            attention_mask = enc.get("attention_mask")
            if attention_mask is None:
                attention_mask = torch.ones_like(input_ids)
            attention_mask = attention_mask.to(model.device)

        streamer = TextIteratorStreamer(
            tok, skip_special_tokens=True, clean_up_tokenization_spaces=True
        )

        generation_kwargs = dict(
            input_ids=input_ids,
            attention_mask=attention_mask,
            max_new_tokens=args.max_new_tokens,
            do_sample=True,
            temperature=0.7,
            top_p=0.9,
            eos_token_id=tok.eos_token_id,
            pad_token_id=tok.pad_token_id,
            streamer=streamer,
        )

        thread = Thread(target=model.generate, kwargs=generation_kwargs)
        thread.start()

        print("\nBaliTwin: ", end="", flush=True)
        for token in streamer:
            print(token, end="", flush=True)
        print("")
        thread.join()


if __name__ == "__main__":
    main()