diff --git a/rag.py b/rag.py
index 2ecac8f..a223bb6 100644
--- a/rag.py
+++ b/rag.py
@@ -1,107 +1,91 @@
 #!/usr/bin/env python3
 """
-Mini RAG interactif :
-• Recherche sémantique FAISS sur le corpus.idx / corpus.meta.json
-• Contexte (top‑k=4 passages) envoyé à Mistral‑7B via Ollama.
-
-Robuste aux différentes sorties de BGEM3FlagModel.encode.
+RAG interactif robuste.
+• Recharge les passages à partir des fiches (même découpe que l'index) pour disposer du texte.
+• Recherche FAISS top‑k=4 et génération via mistral7b-fast.
 """
-import json
-import readline
+import os, json, readline, re
 from pathlib import Path
 
-import faiss
-import numpy as np
-import requests
+import faiss, numpy as np, requests
 from FlagEmbedding import BGEM3FlagModel
 from rich import print
 
 # ---------------------------------------------------------------------------
-# Chargements initiaux
-# ---------------------------------------------------------------------------
-IDX_FILE = Path("corpus.idx")
-META_FILE = Path("corpus.meta.json")
+ROOT = Path("Fiches")            # dossier des fiches sur l'hôte
+CHUNK, OVERLAP = 800, 100         # identiques à l'indexation
+K = 4                             # nb de passages remis au modèle
 
-if not IDX_FILE.exists() or not META_FILE.exists():
-    raise SystemExit("[bold red]Erreur :[/] index absent. Lancez d'abord index.py !")
+# --- découpe ---------------------------------------------------------------
 
-index = faiss.read_index(str(IDX_FILE))
-meta = json.loads(META_FILE.read_text())
+def split(text: str):
+    sents = re.split(r"(?<=[.!?]) +", text)
+    buf, out = [], []
+    for s in sents:
+        buf.append(s)
+        if len(" ".join(buf).split()) > CHUNK:   # approx 1 mot = 1 token
+            out.append(" ".join(buf))
+            buf = buf[-OVERLAP:]
+    if buf:
+        out.append(" ".join(buf))
+    return out
+
+# --- charger docs + meta dans le même ordre que l'index --------------------
+
+docs, meta = [], []
+for fp in ROOT.rglob("*.md"):
+    for i, chunk in enumerate(split(fp.read_text(encoding="utf-8"))):
+        docs.append(chunk)
+        meta.append({"file": fp.name, "part": i})
+
+print(f"[dim]Chargé {len(docs)} passages depuis {ROOT}.[/]")
+
+# --- FAISS index existant ---------------------------------------------------
+
+idx = faiss.read_index("corpus.idx")
 model = BGEM3FlagModel("BAAI/bge-m3", device="cpu")
 
-# ---------------------------------------------------------------------------
-# Utilitaires
-# ---------------------------------------------------------------------------
+# --- boucle Q/A -------------------------------------------------------------
 
-def _normalize(x: np.ndarray) -> np.ndarray:
-    """L2‑normalize each row (tokens=float32)."""
-    return x / (np.linalg.norm(x, axis=1, keepdims=True) + 1e-12)
-
-
-def embed(texts):
-    """Encode list[str] → ndarray (n, dim), quelle que soit la sortie lib."""
-    out = model.encode(texts)
-    # Possible shapes :
-    #  • ndarray
-    #  • dict {"embedding": ndarray} ou {"embeddings": ndarray}
-    #  • dict {"sentence_embeds": [...]} etc.
-    if isinstance(out, np.ndarray):
-        arr = out
-    elif isinstance(out, dict):
-        # pick the first ndarray-like value
-        for v in out.values():
-            if isinstance(v, (list, tuple)) or hasattr(v, "shape"):
-                arr = np.asarray(v)
-                break
-        else:
-            raise TypeError("encode() dict sans clé embedding !")
-    else:  # list[list[float]] etc.
-        arr = np.asarray(out)
-    return _normalize(arr.astype("float32"))
-
-
-def fetch_passage(i: int) -> str:
+def fetch_passage(i: int):
     m = meta[i]
-    return f"[{m['file']} · part {m['part']}] {m['text']}"
+    return f"[{m['file']} · part {m['part']}] {docs[i][:200]}…"
 
-
-def ask_llm(prompt: str) -> str:
-    r = requests.post(
-        "http://127.0.0.1:11434/api/generate",
-        json={
-            "model": "mistral7b-fast",
-            "prompt": prompt,
-            "stream": False,
-            "options": {"temperature": 0.2, "num_predict": 512},
-        },
-        timeout=300,
-    )
+def ask_llm(prompt: str):
+    r = requests.post("http://127.0.0.1:11434/api/generate", json={
+        "model": "mistral7b-fast",
+        "prompt": prompt,
+        "stream": False,
+        "options": {"temperature": 0.2, "num_predict": 512}
+    }, timeout=300)
     return r.json()["response"]
 
-# ---------------------------------------------------------------------------
-# Boucle interactive
-# ---------------------------------------------------------------------------
-print("[bold green]RAG prêt.[/] Posez vos questions ! (Ctrl‑D pour sortir)")
-while True:
-    try:
-        q = input("❓ > ").strip()
-        if not q:
-            continue
-    except (EOFError, KeyboardInterrupt):
-        print("\n[dim]Bye.[/]")
-        break
+print("RAG prêt. Posez vos questions ! (Ctrl‑D pour sortir)")
+try:
+    while True:
+        try:
+            q = input("❓ > ").strip()
+            if not q:
+                continue
+        except (EOFError, KeyboardInterrupt):
+            print("\nBye."); break
 
-    q_emb = embed([q])  # (1, dim)
-    D, I = index.search(q_emb, 4)
-    ctx = "\n\n".join(fetch_passage(int(idx)) for idx in I[0])
+        emb = model.encode([q])
+        if isinstance(emb, dict):
+            emb = next(v for v in emb.values() if isinstance(v, np.ndarray))
+        q_emb = emb[0] / np.linalg.norm(emb[0])
 
-    prompt = (
-        "<system>Réponds en français, précis et factuel.</system>\n"
-        f"<context>{ctx}</context>\n"
-        f"<user>{q}</user>"
-    )
+        D, I = idx.search(q_emb.astype("float32").reshape(1, -1), K)
 
-    print("\n[bold]Réponse :[/]\n")
-    print(ask_llm(prompt))
-    print("\n[dim]--- contexte utilisé ---[/]")
-    print(ctx)
+        context = "\n\n".join(docs[int(idx_id)] for idx_id in I[0])
+        prompt = f"""<system>Réponds en français, précis et factuel.</system>\n<context>{context}</context>\n<user>{q}</user>"""
+
+        print("\n[bold]Réponse :[/]")
+        print(ask_llm(prompt))
+
+        # petite trace des sources
+        print("\n[dim]--- contexte utilisé ---[/]")
+        for idx_id in I[0]:
+            print(fetch_passage(int(idx_id)))
+except Exception as e:
+    print("[red]Erreur :", e)