RAG cleanup

raft/README.md

@@ -9,7 +9,7 @@
 ## Vorbereiten des Retrieval-Corpus
 
 ```bash
-python prepare_corpus.py --input_tab ../data/intermediate/culture_reviews.csv --out_dir out
+python prepare_corpus.py --input_csv ../data/intermediate/culture_reviews.csv --out_dir out
 ```
 
 ## Erstellen des RAFT-Datensatzes
@@ -26,14 +26,16 @@ python train_mistral_raft.py --train_jsonl out/raft_train.jsonl --out_dir out/mi
 
 ## Inferenz
 
-### Per Baseline Mistral 7B + PEFT-Adapter
-
-```bash
-python rag_chat.py --lora_dir out/mistral_balitwin_lora
-```
-
 ### Pre-Merged Modell + Adapter
 
 ```bash
 python rag_chat_merged.py --model_dir /path/to/model_folder --out_dir out
 ```
+
+### Per Baseline Mistral 7B + PEFT-Adapter
+
+Hinweis: das Skript wurde nach wenigen oberflächlichen Evaluationsrunden nicht weiter verwendet, da der beste Kandidat durch einen Merge des Basismodells und seiner PEFT-Adapter beschleunigt werden konnte und dieses Skript nicht länger relevant war.
+
+```bash
+python deprecated_rag_chat.py --lora_dir out/mistral_balitwin_lora
+```

raft/prepare_corpus.py

@@ -8,6 +8,7 @@ import numpy as np
 import pandas as pd
 from sentence_transformers import SentenceTransformer
 from tqdm import tqdm
+from transformers import AutoTokenizer
 
 
 def simple_clean(text: str) -> str:
@@ -18,21 +19,34 @@ def simple_clean(text: str) -> str:
     return text
 
 
-def chunk_text(text: str, chunk_chars: int = 900, overlap: int = 150):
+def chunk_text(text: str, tokenizer, chunk_tokens: int = 250, overlap_tokens: int = 50):
     """
-    Simple char-based chunking (good enough for reviews).
+    Token-based chunking using a transformer tokenizer.
-    For better chunking, split by sentences and cap token length.
     """
     text = simple_clean(text)
-    if len(text) <= chunk_chars:
+    if not text:
+        return []
+
+    # Tokenize the entire text
+    tokens = tokenizer.encode(text, add_special_tokens=False)
+
+    if len(tokens) <= chunk_tokens:
         return [text] if text else []
+
     chunks = []
     i = 0
-    while i < len(text):
+    while i < len(tokens):
-        chunk = text[i : i + chunk_chars]
+        # Get chunk of tokens
-        if chunk:
+        chunk_token_ids = tokens[i : i + chunk_tokens]
-            chunks.append(chunk)
+
-        i += max(1, chunk_chars - overlap)
+        if chunk_token_ids:
+            # Decode tokens back to text
+            chunk_text_str = tokenizer.decode(chunk_token_ids, skip_special_tokens=True)
+            if chunk_text_str:
+                chunks.append(chunk_text_str)
+
+        i += max(1, chunk_tokens - overlap_tokens)
+
     return chunks
 
 
@@ -48,28 +62,29 @@ def detect_text_col(df: pd.DataFrame) -> str:
             best_score = avg_len
             best_col = col
     if best_col is None:
-        raise ValueError("Could not detect a text column in the .tab file.")
+        raise ValueError("Could not detect a text column in the .csv file.")
     return best_col
 
 
 def main():
     ap = argparse.ArgumentParser()
-    ap.add_argument("--input_tab", required=True, help="Tripadvisor reviews .tab file")
+    ap.add_argument("--input_csv", required=True, help="Tripadvisor reviews .csv file")
     ap.add_argument("--out_dir", default="out")
     ap.add_argument(
         "--embedding_model", default="sentence-transformers/all-MiniLM-L6-v2"
     )
-    ap.add_argument("--chunk_chars", type=int, default=900)
+    ap.add_argument("--chunk_tokens", type=int, default=250)
-    ap.add_argument("--overlap", type=int, default=150)
+    ap.add_argument("--overlap_tokens", type=int, default=50)
     args = ap.parse_args()
 
     os.makedirs(args.out_dir, exist_ok=True)
 
-    # Many .tab files are TSV
+    # Initialize tokenizer
-    df = pd.read_csv(args.input_tab, sep="\t", dtype=str, on_bad_lines="skip")
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-    text_col = detect_text_col(df)
 
-    rows = df[text_col].fillna("").astype(str).tolist()
+    df = pd.read_csv(args.input_csv, sep=",", dtype=str, on_bad_lines="skip")
+
+    rows = df["Original"].fillna("").astype(str).tolist()
 
     corpus_path = os.path.join(args.out_dir, "corpus.jsonl")
     corpus = []
@@ -79,7 +94,12 @@ def main():
         r = simple_clean(r)
         if len(r) < 30:
             continue
-        chunks = chunk_text(r, chunk_chars=args.chunk_chars, overlap=args.overlap)
+        chunks = chunk_text(
+            r,
+            tokenizer,
+            chunk_tokens=args.chunk_tokens,
+            overlap_tokens=args.overlap_tokens,
+        )
         for ch in chunks:
             if len(ch) < 30:
                 continue
@@ -111,9 +131,7 @@ def main():
         for i, c in enumerate(corpus):
             f.write(json.dumps({"faiss_id": i, **c}, ensure_ascii=False) + "\n")
 
-    print(
+    print(f"Saved:\n- {corpus_path}\n- {faiss_path}\n- {mapping_path}")
-        f"Saved:\n- {corpus_path}\n- {faiss_path}\n- {mapping_path}\nText column detected: {text_col}"
-    )
 
 
 if __name__ == "__main__":

raft/rag_chat_merged.py

@@ -37,7 +37,7 @@ def load_docstore(path):
     return docs
 
 
-def retrieve(index, embedder, query, top_k=6):
+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()
@@ -55,8 +55,9 @@ def main():
     ap.add_argument(
         "--embedding_model", default="sentence-transformers/all-MiniLM-L6-v2"
     )
-    ap.add_argument("--top_k", type=int, default=6)
+    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"))
@@ -70,12 +71,13 @@ def main():
     if tok.pad_token is None:
         tok.pad_token = tok.eos_token
 
-    model = AutoModelForCausalLM.from_pretrained(
+    if not args.no_model:
-        args.model_dir,
+        model = AutoModelForCausalLM.from_pretrained(
-        device_map="auto",
+            args.model_dir,
-        torch_dtype=torch.float16,
+            device_map="auto",
-    )
+            torch_dtype=torch.float16,
-    model.eval()
+        )
+        model.eval()
 
     print("Type your question (Ctrl+C to exit).")
     while True:
@@ -83,20 +85,34 @@ def main():
         if not q:
             continue
 
-        ids, _ = retrieve(index, embedder, q, top_k=args.top_k)
+        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(
+        context_blob = "\n\n".join([t for _, t in enumerate(context_docs)])
-            [f"[DOC {i}] {t}" for i, t in enumerate(context_docs)]
-        )
 
         print("\nRetrieved Context:")
-        print(context_blob)
+        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"
         )