RAG cleanup

raft/deprecated_rag_chat.py

@@ -0,0 +1,98 @@
+import argparse
+import json
+import os
+
+import faiss
+import numpy as np
+import torch
+from peft import PeftModel
+from sentence_transformers import SentenceTransformer
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+SYSTEM_PERSONA = """You are simulating a culturally interested Bali traveler segment for evaluation purposes.
+
+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.
+
+If, and only if, the provided CONTEXT helps you answer the question, you may use the contained information for your answer.
+"""
+
+
+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=6):
+    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("--base_model", default="mistralai/Mistral-7B-Instruct-v0.2")
+    ap.add_argument("--lora_dir", default="out/mistral_balitwin_lora")
+    ap.add_argument("--out_dir", default="out")
+    ap.add_argument(
+        "--embedding_model", default="sentence-transformers/all-MiniLM-L6-v2"
+    )
+    ap.add_argument("--top_k", type=int, default=6)
+    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)
+
+    tok = AutoTokenizer.from_pretrained(args.base_model, use_fast=True)
+    base = AutoModelForCausalLM.from_pretrained(
+        args.base_model, device_map="auto", torch_dtype=torch.float16
+    )
+    model = PeftModel.from_pretrained(base, args.lora_dir)
+    model.eval()
+
+    print("Type your question (Ctrl+C to exit).")
+    while True:
+        q = input("\nYou: ").strip()
+        if not q:
+            continue
+
+        ids, _ = retrieve(index, embedder, q, top_k=args.top_k)
+        context_docs = [docstore[i]["text"] for i in ids]
+        context_blob = "\n\n".join(
+            [f"[DOC {i}] {t}" for i, t in enumerate(context_docs)]
+        )
+
+        messages = [
+            {"role": "system", "content": SYSTEM_PERSONA},
+            {"role": "user", "content": f"QUESTION: {q}\n\nCONTEXT:\n{context_blob}"},
+        ]
+        inp = tok.apply_chat_template(messages, return_tensors="pt").to(model.device)
+
+        out = model.generate(
+            inp,
+            max_new_tokens=320,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+            eos_token_id=tok.eos_token_id,
+        )
+        ans = tok.decode(out[0][inp.shape[1] :], skip_special_tokens=True).strip()
+        print(f"\nBaliTwin: {ans}")
+
+
+if __name__ == "__main__":
+    main()