Upload app.py

app.py

@@ -1,746 +1,727 @@
-# -*- coding: utf-8 -*--
-# Db2 z/OS • RAG (NVIDIA NIM)
-# Sidebar + Diagnóstico + Extração robusta (com OCR opcional) + Chunkização por caracteres
-# Requisitos:
-#   pip install openai gradio numpy pypdf pdfminer.six pymupdf
-# (OCR opcional) + dependências de SO:
-#   pip install pytesseract pdf2image pillow
-#   Linux: apt-get install -y tesseract-ocr poppler-utils
-
-import os
-import re
-import json
-from pathlib import Path
-from typing import List, Tuple, Dict, Optional
-
-import numpy as np
-import gradio as gr
-
-# ==============================
-# Config (Db2 + NVIDIA NIM)
-# ==============================
-BASE_DIR = Path(__file__).parent if "__file__" in globals() else Path.cwd()
-
-# Altere se necessário; o app também aceita PDFs no diretório atual (*.pdf)
-USER_PDF = Path("db2z_13_utilities.pdf")
-PDFS = [USER_PDF] if USER_PDF.exists() else sorted([p for p in BASE_DIR.glob("*.pdf") if p.is_file() and p.suffix.lower()==".pdf"])
-
-NVCF_BASE = os.getenv("NVCF_BASE", "https://integrate.api.nvidia.com/v1")
-NVCF_API_KEY = os.getenv("NVCF_API_KEY", "")
-EMBED_MODEL = os.getenv("EMBED_MODEL", "nvidia/nv-embed-v1")           # 4096-dim atualmente
-CHAT_MODEL = os.getenv("CHAT_MODEL", "meta/llama-3.1-8b-instruct")
-
-SAFE_IDX = f".db2_index_{EMBED_MODEL.replace('/','__')}"
-INDEX_DIR = BASE_DIR / SAFE_IDX
-INDEX_DIR.mkdir(exist_ok=True)
-VEC_FILE = INDEX_DIR / "vectors.npy"
-META_FILE = INDEX_DIR / "meta.json"
-
-TOP_K_RETRIEVE = 3
-TARGET_CONTEXT_CHARS = 1500
-
-# ==============================
-# NVIDIA (OpenAI-compatible)
-# ==============================
-try:
-    from openai import OpenAI
-except ImportError:
-    raise RuntimeError("Instale: pip install openai gradio numpy pypdf pdfminer.six pymupdf")
-
-def get_client():
-    if not NVCF_API_KEY or NVCF_API_KEY == "xxxxxxxxxxxxxxxxxxx":
-        raise RuntimeError("NVCF_API_KEY não definido. export/set NVCF_API_KEY='SUA_CHAVE'")
-    return OpenAI(base_url=NVCF_BASE, api_key=NVCF_API_KEY)
-
-# ==============================
-# PDF utils (robusto + OCR opcional)
-# ==============================
-try:
-    import fitz  # PyMuPDF
-except Exception:
-    fitz = None
-
-try:
-    from pdfminer.high_level import extract_text as pdfminer_extract_text
-except Exception:
-    pdfminer_extract_text = None
-
-try:
-    from pypdf import PdfReader
-except Exception:
-    PdfReader = None
-
-NBSP = "\u00A0"
-def _normalize_text(t: str) -> str:
-    if not isinstance(t, str):
-        t = str(t or "")
-    t = t.replace(NBSP, " ")
-    t = re.sub(r"[\u0000-\u001F]", " ", t)  # remove controles
-    # mantém quebras simples e colapsa espaços longos
-    t = re.sub(r"[ \t]{2,}", " ", t)
-    t = re.sub(r"\n{3,}", "\n\n", t)
-    return t.strip()
-
-def _extract_with_pymupdf(path: Path) -> List[Tuple[int, str]]:
-    out: List[Tuple[int, str]] = []
-    with fitz.open(path) as doc:
-        for i, page in enumerate(doc):
-            text = page.get_text("text") or ""
-            out.append((i + 1, _normalize_text(text)))
-    return out
-
-def _extract_with_pdfminer(path: Path) -> List[Tuple[int, str]]:
-    text_all = pdfminer_extract_text(str(path)) or ""
-    pages = re.split(r"\f", text_all)
-    out: List[Tuple[int, str]] = []
-    for i, tx in enumerate(pages):
-        out.append((i + 1, _normalize_text(tx)))
-    return out
-
-def _extract_with_pypdf(path: Path) -> List[Tuple[int, str]]:
-    if PdfReader is None:
-        return []
-    try:
-        r = PdfReader(str(path), strict=False)
-    except Exception as e:
-        print(f"[WARN] pypdf: falha ao abrir {path.name}: {e}")
-        return []
-    out: List[Tuple[int, str]] = []
-    for i in range(len(r.pages)):
-        try:
-            page = r.pages[i]
-            t = page.extract_text() or ""
-        except Exception as e:
-            print(f"[WARN] pypdf: falha ao extrair pag {i+1}: {e}")
-            t = ""
-        out.append((i + 1, _normalize_text(t)))
-    return out
-
-def _maybe_ocr_images(path: Path) -> List[Tuple[int, str]]:
-    try:
-        import pytesseract
-        from pdf2image import convert_from_path
-    except Exception:
-        return []
-    out: List[Tuple[int, str]] = []
-    try:
-        images = convert_from_path(str(path))
-        for i, img in enumerate(images):
-            txt = pytesseract.image_to_string(img) or ""
-            out.append((i + 1, _normalize_text(txt)))
-    except Exception as e:
-        print(f"[WARN] OCR: falha ao converter/ler {path.name}: {e}")
-    return out
-
-def read_pdf_pages(path: Path) -> List[Tuple[int, str]]:
-    if not path.exists():
-        return []
-    # 1) PyMuPDF
-    if fitz is not None:
-        try:
-            pages = _extract_with_pymupdf(path)
-            if any((tx or "").strip() for _, tx in pages):
-                return pages
-        except Exception as e:
-            print(f"[WARN] PyMuPDF falhou: {e}")
-    # 2) pdfminer
-    if pdfminer_extract_text is not None:
-        try:
-            pages = _extract_with_pdfminer(path)
-            if any((tx or "").strip() for _, tx in pages):
-                return pages
-        except Exception as e:
-            print(f"[WARN] pdfminer falhou: {e}")
-    # 3) pypdf
-    try:
-        pages = _extract_with_pypdf(path)
-        if any((tx or "").strip() for _, tx in pages):
-            return pages
-    except Exception as e:
-        print(f"[WARN] pypdf falhou: {e}")
-    # 4) OCR quando nada foi extraído
-    ocr_pages = _maybe_ocr_images(path)
-    if not any((tx or "").strip() for _, tx in ocr_pages):
-        print("[ERRO] Nenhum texto extraído, nem com OCR.")
-    return ocr_pages
-
-# ==============================
-# Segmentação (detecção de seções para metadados)
-# ==============================
-DB2_HEADER_RE = re.compile(
-    r"^(Part\s+\d+\.|Chapter\s+\d+\.)|"
-    r"\b(BACKUP SYSTEM|CATMAINT|CHECK DATA|CHECK INDEX|CHECK LOB|COPY|COPYTOCOPY|DIAGNOSE|LISTDEF|LOAD|"
-    r"MERGECOPY|MODIFY RECOVERY|MODIFY STATISTICS|OPTIONS|QUIESCE|REBUILD INDEX|RECOVER|REORG INDEX|REORG TABLESPACE|"
-    r"REPAIR|REPORT|RESTORE SYSTEM|RUNSTATS|STOSPACE|TEMPLATE|UNLOAD)\b",
-    re.IGNORECASE
-)
-
-def split_db2_docs(pages: List[Tuple[int, str]], doc_label: str) -> List[Dict]:
-    """Agrupa páginas por possíveis cabeçalhos (capítulos/utilities) para compor metadados de seção."""
-    blocks: List[Dict] = []
-    current = {"doc": doc_label, "section": "INTRO", "start_page": 1, "texts": []}
-    for pg, tx in pages:
-        head = (tx or "")[:300]
-        if DB2_HEADER_RE.search(head):
-            if current["texts"]:
-                current["end_page"] = current["texts"][-1][0]
-                blocks.append(current)
-            m = re.search(r"(Chapter\s+\d+\.\s*[^\n]+|^[^\n]{1,200})", tx or "")
-            title = (m.group(1).strip() if m else f"Section@{pg}")
-            current = {"doc": doc_label, "section": title, "start_page": pg, "texts": []}
-        current["texts"].append((pg, tx or ""))
-    if current["texts"]:
-        current["end_page"] = current["texts"][-1][0]
-        blocks.append(current)
-    return blocks
-
-# ==============================
-# Chunkização por caracteres (robusta)
-# ==============================
-def make_chunks_by_chars(blocks: List[Dict], max_chars: int = 1500, min_chars: int = 180) -> List[Dict]:
-    """Concatena o texto das páginas de cada bloco e fatia por janelas de caracteres com overlap."""
-    out: List[Dict] = []
-    for b in blocks:
-        pieces: List[str] = []
-        pages: List[int] = []
-        for pg, tx in b["texts"]:
-            txn = _normalize_text(tx or "")
-            if txn:
-                pieces.append(txn)
-                pages.append(pg)
-        if not pieces:
-            continue
-        blob = "\n".join(pieces).strip()
-        if not blob:
-            continue
-        start_page = min(pages) if pages else b.get("start_page", 0)
-        end_page = max(pages) if pages else b.get("end_page", start_page)
-
-        if len(blob) <= max_chars and len(blob) >= min_chars:
-            out.append({
-                "doc": b["doc"],
-                "section": b["section"],
-                "start_page": start_page,
-                "end_page": end_page,
-                "text": blob
-            })
-            continue
-
-        overlap = 120
-        i, n = 0, len(blob)
-        while i < n:
-            j = min(i + max_chars, n)
-            chunk_text = blob[i:j].strip()
-            if len(chunk_text) >= min_chars:
-                out.append({
-                    "doc": b["doc"],
-                    "section": b["section"],
-                    "start_page": start_page,
-                    "end_page": end_page,
-                    "text": chunk_text
-                })
-            new_i = j - overlap
-            i = j if new_i <= i else new_i
-    # filtro final
-    out = [c for c in out if (c.get("text") or "").strip()]
-    return out
-
-# ==============================
-# Embeddings
-# ==============================
-def embed_texts(texts: List[str], batch_size: int = 16) -> np.ndarray:
-    client = get_client()
-    clean = [(i, t) for i, t in enumerate(texts) if isinstance(t, str) and t.strip()]
-    if not clean:
-        return np.zeros((0, 0), dtype=np.float32)
-    order, payload = zip(*clean)
-    vecs: Dict[int, np.ndarray] = {}
-    for i in range(0, len(payload), batch_size):
-        batch = list(payload[i:i + batch_size])
-        resp = client.embeddings.create(model=EMBED_MODEL, input=batch)
-        for k, item in enumerate(resp.data):
-            vecs[int(order[i + k])] = np.array(item.embedding, dtype=np.float32)
-    rows: List[np.ndarray] = []
-    for idx in range(len(texts)):
-        if idx in vecs:
-            rows.append(vecs[idx])
-    if not rows:
-        return np.zeros((0, 0), dtype=np.float32)
-    mat = np.vstack(rows).astype(np.float32)
-    norms = np.linalg.norm(mat, axis=1, keepdims=True)
-    norms[norms == 0] = 1.0
-    return mat / norms
-
-def embed_query(q: str) -> np.ndarray:
-    client = get_client()
-    resp = client.embeddings.create(model=EMBED_MODEL, input=[q])
-    v = np.array(resp.data[0].embedding, dtype=np.float32)
-    n = np.linalg.norm(v)
-    return (v / (n if n > 0 else 1.0)).astype(np.float32)
-
-# ==============================
-# Indexação
-# ==============================
-def build_index() -> Tuple[np.ndarray, List[Dict]]:
-    all_blocks: List[Dict] = []
-    for p in PDFS:
-        pages = read_pdf_pages(p)
-        if not pages or not any((tx or "").strip() for _, tx in pages):
-            print(f"[WARN] Sem texto legível em {p.name}; ignorando.")
-            continue
-        blks = split_db2_docs(pages, p.name)
-        all_blocks.extend(blks)
-
-    all_chunks = make_chunks_by_chars(all_blocks, max_chars=1500, min_chars=180)
-    all_chunks = [c for c in all_chunks if (c.get("text") or "").strip()]
-
-    if not all_chunks:
-        with open(META_FILE, "w", encoding="utf-8") as f:
-            json.dump({"chunks": [], "embed_model": EMBED_MODEL, "embed_dim": 0, "total_chars": 0}, f, ensure_ascii=False, indent=2)
-        np.save(VEC_FILE, np.zeros((0, 0), dtype=np.float32))
-        raise RuntimeError("Nenhum chunk foi criado. Verifique extração/ OCR.")
-
-    texts = [c["text"] for c in all_chunks]
-    total_chars = sum(len(t) for t in texts)
-
-    mat = embed_texts(texts) if texts else np.zeros((0, 0), dtype=np.float32)
-    embed_dim = int(mat.shape[1]) if mat.size else 0
-
-    np.save(VEC_FILE, mat)
-    with open(META_FILE, "w", encoding="utf-8") as f:
-        json.dump(
-            {"chunks": all_chunks, "embed_model": EMBED_MODEL, "embed_dim": embed_dim, "total_chars": total_chars},
-            f, ensure_ascii=False, indent=2
-        )
-    return mat, all_chunks
-
-def load_index() -> Tuple[np.ndarray, List[Dict]]:
-    if VEC_FILE.exists() and META_FILE.exists():
-        mat = np.load(VEC_FILE)
-        dd = json.loads(META_FILE.read_text(encoding="utf-8"))
-        chunks = dd.get("chunks", [])
-        return mat, chunks
-    return build_index()
-
-def wipe_index() -> str:
-    try:
-        if INDEX_DIR.exists():
-            for p in INDEX_DIR.glob("*"):
-                p.unlink()
-            INDEX_DIR.rmdir()
-        INDEX_DIR.mkdir(exist_ok=True)
-        return "Índice limpo."
-    except Exception as e:
-        return f"Erro ao limpar índice: {e}"
-
-# ==============================
-# Recuperação + LLM
-# ==============================
-def _check_embed_dim(mat: np.ndarray) -> Optional[str]:
-    try:
-        dd = json.loads(META_FILE.read_text(encoding="utf-8"))
-        idx_dim = int(dd.get("embed_dim", 0))
-    except Exception:
-        idx_dim = 0
-    try:
-        v = embed_query("dim_test")
-        cur_dim = int(v.shape[0])
-    except Exception as e:
-        return f"Falha ao checar dimensão do embedding: {e}"
-    if idx_dim and cur_dim and idx_dim != cur_dim:
-        return (f"Incompatibilidade de dimensão do embedding: índice={idx_dim}, modelo atual={cur_dim}. "
-                f"Reindexe com o mesmo EMBED_MODEL. (Atual EMBED_MODEL: {EMBED_MODEL})")
-    return None
-
-def retrieve_topk(query: str, doc_filter: Optional[str] = None, k: int = TOP_K_RETRIEVE) -> List[Dict]:
-    mat, chunks = load_index()
-    if mat.shape[0] == 0 or not chunks:
-        return []
-    qv = embed_query(query)
-    if mat.shape[1] != qv.shape[0]:
-        raise RuntimeError(
-            f"Dimensão incompatível mat={mat.shape} vs query={qv.shape}. "
-            f"Provável troca de EMBED_MODEL após criar o índice. Clique 'Reindexar'."
-        )
-    sims = (mat @ qv).astype(float)
-    if doc_filter and doc_filter != "(Todos)":
-        mask = np.array([1.0 if c["doc"] == doc_filter else 0.0 for c in chunks], dtype=float)
-        sims *= mask
-    idxs = np.argsort(-sims)[:k]
-    out = []
-    for i in idxs:
-        c = chunks[int(i)]
-        out.append({
-            "doc": c["doc"],
-            "section": c.get("section", ""),
-            "start_page": c.get("start_page", "?"),
-            "end_page": c.get("end_page", "?"),
-            "text": c["text"],
-            "score": float(sims[int(i)]),
-            "idx": int(i)
-        })
-    return out
-
-def expand_context(hits: List[Dict], all_chunks: List[Dict], target_chars: int = TARGET_CONTEXT_CHARS) -> Tuple[str, List[Tuple[str, str, str]]]:
-    if not hits:
-        return "", []
-    best = max(hits, key=lambda x: x["score"])
-    ctx = best["text"]
-    srcs = {(best["doc"], best["section"], f"{best['start_page']}–{best['end_page']}")}
-    doc, section, best_idx = best["doc"], best["section"], best["idx"]
-    indices = [i for i, c in enumerate(all_chunks) if c["doc"] == doc and c.get("section", "") == section]
-    if not indices:
-        return ctx, sorted(list(srcs))
-    indices.sort()
-    if best_idx not in indices:
-        return ctx, sorted(list(srcs))
-    pos = indices.index(best_idx)
-    left, right = pos - 1, pos + 1
-    while len(ctx) < target_chars and (left >= 0 or right < len(indices)):
-        if right < len(indices) and len(ctx) < target_chars:
-            rch = all_chunks[indices[right]]
-            ctx += "\n\n" + rch["text"]
-            srcs.add((doc, section, f"{rch.get('start_page', '?')}–{rch.get('end_page', '?')}"))
-            right += 1
-        if left >= 0 and len(ctx) < target_chars:
-            lch = all_chunks[indices[left]]
-            ctx = lch["text"] + "\n\n" + ctx
-            srcs.add((doc, section, f"{lch.get('start_page', '?')}–{lch.get('end_page', '?')}"))
-            left -= 1
-    return ctx, sorted(list(srcs))
-
-def answer_with_llm(question: str, context: str) -> str:
-    client = get_client()
-    system = ("Você é um assistente especialista em IBM Db2 para z/OS. "
-              "Responda em português, com exemplos de comandos SQL/JCL completos e corretos. "
-              "Use apenas o contexto fornecido; se algo não estiver nele, diga que não está disponível.")
-    user = (f"Pergunta:\n{question}\n\n"
-            f"Contexto do(s) manual(is):\n{context}\n\n"
-            "Regras de resposta:\n"
-            "- Explique o necessário e como fazer.\n"
-            "- Inclua pelo menos um exemplo de comando Db2 utilitário, SQL ou JCL (auto-contido), se aplicável.\n"
-            "- Liste observações/pré-requisitos, se houver.\n"
-            "- Cite as fontes (Documento e páginas) ao final.")
-    chat = client.chat.completions.create(
-        model=CHAT_MODEL,
-        messages=[{"role": "system", "content": system}, {"role": "user", "content": user}],
-        temperature=0.2,
-    )
-    return chat.choices[0].message.content.strip()
-
-def format_sources_md(sources: List[Tuple[str, str, str]]) -> str:
-    if not sources:
-        return ""
-    lines = [
-        f"- **Documento:** {d}  \n  **Seção:** {s}  \n  **Páginas:** {p}"
-        for (d, s, p) in sources
-    ]
-    return "\n".join(lines)
-
-# ==============================
-# Templates Db2 (exemplos)
-# ==============================
-DB2_TEMPLATES: Dict[str, str] = {
-    "RUNSTATS_TABLESPACE": (
-        "//RUNSTAT JOB (ACCT),'RUNSTATS',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
-        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='RUNSTATS',UTPROC=''\n"
-        "//SYSIN   DD *\n"
-        "  RUNSTATS TABLESPACE(DBNAME.TSNAME) TABLE(ALL) INDEX(ALL)\n"
-        "/*\n"
-    ),
-    "REORG_TABLESPACE": (
-        "//REORG   JOB (ACCT),'REORG',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
-        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='REORGTS',UTPROC=''\n"
-        "//SYSIN   DD *\n"
-        "  REORG TABLESPACE(DBNAME.TSNAME) SHRLEVEL CHANGE\n"
-        "/*\n"
-    ),
-    "EXPLAIN_SQL": (
-        "//EXPLAIN JOB (ACCT),'EXPLAIN',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
-        "//STEP1   EXEC DSNTEP2,SYSTEM=DSN1\n"
-        "//SYSIN   DD *\n"
-        "  EXPLAIN PLAN FOR\n"
-        "  SELECT COL1, COL2 FROM DBNAME.TBNAME WHERE COL3 = 'X';\n"
-        "/*\n"
-    ),
-    "DISPLAY_BUFFERPOOL": (
-        "//DISPBP  JOB (ACCT),'DISPLAY BP',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
-        "//STEP1   EXEC PGM=IKJEFT01\n"
-        "//SYSTSPRT DD SYSOUT=*\n"
-        "//SYSIN    DD *\n"
-        "  DSN SYSTEM(DSN1)\n"
-        "  -DISPLAY BUFFERPOOL(BP0) DETAIL\n"
-        "  END\n"
-        "/*\n"
-    ),
-    "DSNTEP2_SELECT": (
-        "//SELECT  JOB (ACCT),'DSNTEP2',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
-        "//STEP1   EXEC DSNTEP2,SYSTEM=DSN1\n"
-        "//SYSIN   DD *\n"
-        "  SELECT FIRSTNME, LASTNAME FROM DSN8810.EMP\n"
-        "  WHERE WORKDEPT = 'A00';\n"
-        "/*\n"
-    ),
-    "COPY_TABLESPACE": (
-        "//COPYTS  JOB (ACCT),'COPY',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
-        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='COPYTS',UTPROC=''\n"
-        "//SYSIN   DD *\n"
-        "  COPY TABLESPACE(DBNAME.TSNAME) FULL YES SHRLEVEL CHANGE\n"
-        "/*\n"
-    ),
-    "LOAD_TABLE": (
-        "//LOADTBL JOB (ACCT),'LOAD',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
-        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='LOADTBL',UTPROC=''\n"
-        "//SYSIN   DD *\n"
-        "  LOAD DATA INDDN SYSREC INTO TABLE DBNAME.TBNAME\n"
-        "  REPLACE\n"
-        "/*\n"
-    ),
-    "RECOVER_TABLESPACE": (
-        "//RECOVTS JOB (ACCT),'RECOVER',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
-        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='RECOVTS',UTPROC=''\n"
-        "//SYSIN   DD *\n"
-        "  RECOVER TABLESPACE(DBNAME.TSNAME)\n"
-        "/*\n"
-    ),
-    "STATS_INDEX": (
-        "//STATSIX JOB (ACCT),'STATS INDEX',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
-        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='STATSIX',UTPROC=''\n"
-        "//SYSIN   DD *\n"
-        "  RUNSTATS INDEX(DBNAME.IXNAME) ALL\n"
-        "/*\n"
-    ),
-    "MODIFY_RECOVERY": (
-        "//MODREC  JOB (ACCT),'MODIFY RECOVERY',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
-        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='MODREC',UTPROC=''\n"
-        "//SYSIN   DD *\n"
-        "  MODIFY RECOVERY TABLESPACE(DBNAME.TSNAME) AGE(30)\n"
-        "/*\n"
-    ),
-    "CHECK_DATA": (
-        "//CHKDATA JOB (ACCT),'CHECK DATA',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
-        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='CHKDATA',UTPROC=''\n"
-        "//SYSIN   DD *\n"
-        "  CHECK DATA TABLESPACE(DBNAME.TSNAME) SCOPE ALL\n"
-        "/*\n"
-    ),
-}
-
-def template_for_db2(cmd: str) -> str:
-    return DB2_TEMPLATES.get(cmd, "//GENERIC ...\n")
-
-# ==============================
-# UI — layout com SIDEBAR + diagnóstico
-# ==============================
-CUSTOM_CSS = """
-:root{ --ink:#0f172a; --muted:#475569; }
-.gradio-container { max-width: 1200px !important; margin: 0 auto !important; }
-.section-card { background: #fff; border: 1px solid #e2e8f0; border-radius: 16px; padding: 16px; box-shadow: 0 10px 30px rgba(2,6,23,.05); }
-.section-title { font-size: 1.05rem; font-weight: 800; color: var(--ink); display: flex; gap: .6rem; align-items: center; }
-.subtitle { color:var(--muted); font-size:.95rem; margin-top:.25rem; }
-.result-card { background:#fcfdff; border:1px solid #e2e8f0; border-radius:12px; padding:12px; }
-hr.sep { border:none; border-top:1px dashed #e2e8f0; margin:10px 0 14px; }
-.small { font-size:.9rem; color:var(--muted); }
-"""
-
-def build_app():
-    doc_label = PDFS[0].name if PDFS else "(Nenhum PDF)"
-    all_doc_names = [p.name for p in PDFS] or ["(Nenhum PDF encontrado)"]
-    default_doc = all_doc_names[0] if all_doc_names else "(Todos)"
-
-    with gr.Blocks(title="Db2 z/OS • RAG (NVIDIA NIM)", css=CUSTOM_CSS, fill_height=True) as demo:
-
-        # ===== Sidebar =====
-        with gr.Sidebar():
-            gr.Markdown(
-                f"""
-<div class="section-title">💼 Db2 RAG</div>
-<div class="small">Contexto: <code>{doc_label}</code></div>
-"""
-            )
-            status_box = gr.Markdown("Pronto ✅" if PDFS else "⚠️ Nenhum PDF encontrado.")
-            gr.Markdown("<hr class='sep'/>")
-
-            with gr.Group():
-                gr.Markdown("**Ações**")
-                test_btn = gr.Button("🧪 Testar conexão NVIDIA", variant="secondary")
-                rebuild_btn = gr.Button("🔁 Reindexar (NIM)")
-                diag_btn = gr.Button("🛠️ Diagnóstico do Índice")
-
-            gr.Markdown("<hr class='sep'/>")
-
-            with gr.Accordion("Configuração", open=False):
-                gr.Markdown(f"- **Embeddings:** `{EMBED_MODEL}`\n- **LLM:** `{CHAT_MODEL}`\n- **Índice:** `{INDEX_DIR.name}`")
-                doc_choice = gr.Dropdown(
-                    choices=(["(Todos)"] + all_doc_names),
-                    value=default_doc if PDFS else "(Todos)",
-                    label="Documento"
-                )
-
-        # ===== Main content =====
-        gr.Markdown(
-            f"""
-<div class="section-card" style="padding:18px; display:flex; gap:16px; align-items:center;">
-  <div style="font-size:26px;">🧭</div>
-  <div style="flex:1">
-    <div style="font-size:1.2rem; font-weight:800; color:#0f172a;">DB2 -Z/OS UTILITIES | RAG + NVIDIA NIM</div>
-    <div class="subtitle">Pergunte sobre utilidades (COPY, LOAD, REORG, RUNSTATS, RECOVER, etc.). As respostas vêm do manual: <code>{doc_label}</code>.</div>
-  </div>
-</div>
-            """
-        )
-
-        with gr.Row():
-            q = gr.Textbox(
-                label="Pergunta (Db2 Utilities)",
-                placeholder="Ex.: Como usar COPY FULL com SHRLEVEL CHANGE? • Quando rodar RUNSTATS INDEX? • REORG TABLESPACE SHRLEVEL CHANGE • RECOVER PITR...",
-                scale=8
-            )
-        with gr.Row():
-            ask_btn = gr.Button("🔍 Buscar", variant="primary", scale=2)
-            clear_btn = gr.Button("🧹 Limpar", scale=1)
-
-        out = gr.Markdown(label="Resposta (Db2)")
-
-        gr.Markdown("<hr class='sep'/>")
-
-        with gr.Accordion("🧩 Templates Db2 executáveis", open=False):
-            db2_choice = gr.Dropdown(
-                choices=list(DB2_TEMPLATES.keys()),
-                value="RUNSTATS_TABLESPACE",
-                label="Comando / Padrão"
-            )
-            db2_btn = gr.Button("📄 Gerar exemplo")
-            db2_out = gr.Textbox(label="Exemplo (copiar/ajustar)", lines=18, show_copy_button=True)
-
-        with gr.Accordion("🧪 Log / Diagnóstico", open=False):
-            diag_out = gr.Markdown()
-
-        # ===== Callbacks =====
-        def _test_conn():
-            try:
-                dim = len(get_client().embeddings.create(model=EMBED_MODEL, input=["ping"]).data[0].embedding)
-                return f"Conexão ok ✅ — dimensão do embedding: **{dim}**"
-            except Exception as e:
-                return f"⚠️ Falha na conexão/credenciais NVIDIA: `{type(e).__name__}` — {e}"
-
-        def _rebuild():
-            try:
-                msg = wipe_index()
-                mat, chunks = build_index()
-                return msg + f" Reindexação concluída ✅ PDFs: {len(PDFS)} • Chunks: {len(chunks)} • Vetores: {mat.shape}"
-            except Exception as e:
-                return f"⚠️ Erro ao reindexar: `{type(e).__name__}` — {e}"
-
-        def _diagnose(dsel: str) -> str:
-            try:
-                if not (VEC_FILE.exists() and META_FILE.exists()):
-                    return "❌ Nenhum índice encontrado. Clique **Reindexar (NIM)**."
-                mat = np.load(VEC_FILE)
-                meta = json.loads(META_FILE.read_text(encoding="utf-8"))
-                chunks = meta.get("chunks", [])
-                embed_dim = meta.get("embed_dim", 0)
-                total_chars = int(meta.get("total_chars", 0))
-                dim_msg = _check_embed_dim(mat)
-                # primeiras seções
-                first_secs = []
-                for c in chunks[:12]:
-                    if dsel == "(Todos)" or c["doc"] == dsel:
-                        first_secs.append(f"- {c['doc']} • {c.get('section','?')} • p.{c.get('start_page','?')}-{c.get('end_page','?')}")
-                if not first_secs:
-                    first_secs = ["(Filtro de documento não encontra seções no índice.)"]
-                # prévia do primeiro chunk
-                preview = ""
-                for c in chunks:
-                    t = (c.get("text") or "").strip()
-                    if t:
-                        preview = t[:400].replace("\n", " ")
-                        break
-                if not preview:
-                    preview = "(Nenhum chunk contém texto — verifique extração/OCR.)"
-                msg = [
-                    f"**Índice**: Vetores `{mat.shape}` • embed_dim(meta): `{embed_dim}` • Modelo atual: `{EMBED_MODEL}`",
-                    f"**Chunks**: **{len(chunks)}** • **Total de caracteres**: {total_chars}",
-                    f"**Documento selecionado**: `{dsel}`",
-                    f"**Primeiras seções**:\n" + "\n".join(first_secs),
-                    f"\n**Prévia (400 chars)**:\n```\n{preview}\n```"
-                ]
-                if dim_msg:
-                    msg.append(f"\n⚠️ {dim_msg}")
-                return "\n".join(msg)
-            except Exception as e:
-                return f"⚠️ Diagnóstico falhou: `{type(e).__name__}` — {e}"
-
-        def _search_answer(qstr: str, d: str) -> str:
-            try:
-                if not qstr or qstr.strip() == "":
-                    return "_Informe uma pergunta._"
-                if not (VEC_FILE.exists() and META_FILE.exists()):
-                    return "_Nenhum conteúdo indexado. Use **Reindexar**._"
-                mat = np.load(VEC_FILE)
-                meta = json.loads(META_FILE.read_text(encoding="utf-8"))
-                chunks = meta.get("chunks", [])
-                if mat.size == 0 or not chunks:
-                    return "_Índice vazio. Reindexe (pode ser necessário OCR)._"
-                dim_msg = _check_embed_dim(mat)
-                if dim_msg:
-                    return f"⚠️ {dim_msg}"
-                # retrieve
-                hits = retrieve_topk(qstr, None if d == "(Todos)" else d, k=TOP_K_RETRIEVE)
-                hits = [h for h in hits if (h.get("text") or "").strip()]
-                if not hits:
-                    return "_Nada encontrado para a consulta (verifique o filtro de documento ou reindexe)._"
-                context, sources = expand_context(hits, chunks, TARGET_CONTEXT_CHARS)
-                if not context.strip():
-                    return "_Contexto insuficiente encontrado._"
-                answer = answer_with_llm(qstr, context)
-                src_md = format_sources_md(sources)
-                return f"<div class='result-card'>{answer}</div>\n\n### Fontes\n{src_md}"
-            except Exception as e:
-                return f"⚠️ Erro ao buscar: `{type(e).__name__}` — {e}"
-
-        def _clear(doc_default: str) -> Tuple[str, str]:
-            return "", (doc_default if PDFS else "(Todos)")
-
-        def ui_db2_template(cmd_choice: str) -> str:
-            return template_for_db2(cmd_choice)
-
-        test_btn.click(_test_conn, outputs=[status_box])
-        rebuild_btn.click(_rebuild, outputs=[status_box])
-        diag_btn.click(_diagnose, inputs=[doc_choice], outputs=[diag_out])
-
-        ask_btn.click(_search_answer, inputs=[q, doc_choice], outputs=[out])
-        clear_btn.click(_clear, inputs=[gr.State(default_doc)], outputs=[q, doc_choice])
-        db2_btn.click(ui_db2_template, inputs=[db2_choice], outputs=[db2_out])
-
-    return demo
-
-# ==============================
-# Main (robusto: bind público, respeita $PORT, SSR off, queue opcional)
-# ==============================
-if __name__ == "__main__":
-    PORT = int(os.getenv("PORT", "7860"))
-    ROOT_PATH = os.getenv("GRADIO_ROOT_PATH", "/")
-    SERVER_NAME = os.getenv("GRADIO_SERVER_NAME", "0.0.0.0")
-
-    try:
-        _ = load_index()  # constrói o índice se não existir
-    except Exception as e:
-        print(f"[AVISO] Índice não carregado: {e}")
-
-    demo = build_app()
-    try:
-        demo = demo.queue()  # algumas versões não aceitam kwargs; se falhar, segue sem fila
-    except Exception as e:
-        print(f"[INFO] Queue não habilitada ({type(e).__name__}: {e}). Continuando sem fila.")
-
-    print(f"[INFO] Iniciando em http://{SERVER_NAME}:{PORT}{ROOT_PATH} (SSR off)")
-    demo.launch(
-        server_name=SERVER_NAME,     # 0.0.0.0 para acesso externo
-        server_port=PORT,            # usa $PORT quando presente
-        root_path=ROOT_PATH,         # necessário se estiver atrás de subpath
-        show_error=True,
-        ssr_mode=False,              # evita tela branca em alguns proxies
-        share=False,
-        inbrowser=False
-    )
+# -*- coding: utf-8 -*-
+# Db2 z/OS • RAG (NVIDIA NIM)
+# Sidebar + Diagnóstico + Extração robusta (com OCR opcional) + Chunkização por caracteres
+# Requisitos:
+#   pip install openai gradio numpy pypdf pdfminer.six pymupdf
+# (OCR opcional) + dependências de SO:
+#   pip install pytesseract pdf2image pillow
+#   Linux: apt-get install -y tesseract-ocr poppler-utils
+
+import os
+import re
+import json
+from pathlib import Path
+from typing import List, Tuple, Dict, Optional
+
+import numpy as np
+import gradio as gr
+
+# ==============================
+# Config (Db2 + NVIDIA NIM)
+# ==============================
+BASE_DIR = Path(__file__).parent if "__file__" in globals() else Path.cwd()
+
+# Altere se necessário; o app também aceita PDFs no diretório atual (*.pdf)
+USER_PDF = Path("db2z_13_utilities.pdf")
+PDFS = [USER_PDF] if USER_PDF.exists() else sorted([p for p in BASE_DIR.glob("*.pdf") if p.is_file() and p.suffix.lower()==".pdf"])
+
+NVCF_BASE = os.getenv("NVCF_BASE", "https://integrate.api.nvidia.com/v1")
+NVCF_API_KEY = os.getenv("NVCF_API_KEY", "")
+EMBED_MODEL = os.getenv("EMBED_MODEL", "nvidia/nv-embed-v1")           # 4096-dim atualmente
+CHAT_MODEL = os.getenv("CHAT_MODEL", "meta/llama-3.1-8b-instruct")
+
+SAFE_IDX = f".db2_index_{EMBED_MODEL.replace('/','__')}"
+INDEX_DIR = BASE_DIR / SAFE_IDX
+INDEX_DIR.mkdir(exist_ok=True)
+VEC_FILE = INDEX_DIR / "vectors.npy"
+META_FILE = INDEX_DIR / "meta.json"
+
+TOP_K_RETRIEVE = 3
+TARGET_CONTEXT_CHARS = 1500
+
+# ==============================
+# NVIDIA (OpenAI-compatible)
+# ==============================
+try:
+    from openai import OpenAI
+except ImportError:
+    raise RuntimeError("Instale: pip install openai gradio numpy pypdf pdfminer.six pymupdf")
+
+def get_client():
+    if not NVCF_API_KEY or NVCF_API_KEY == "xxxxxxxxxxxxxxxxxxx":
+        raise RuntimeError("NVCF_API_KEY não definido. export/set NVCF_API_KEY='SUA_CHAVE'")
+    return OpenAI(base_url=NVCF_BASE, api_key=NVCF_API_KEY)
+
+# ==============================
+# PDF utils (robusto + OCR opcional)
+# ==============================
+try:
+    import fitz  # PyMuPDF
+except Exception:
+    fitz = None
+
+try:
+    from pdfminer.high_level import extract_text as pdfminer_extract_text
+except Exception:
+    pdfminer_extract_text = None
+
+try:
+    from pypdf import PdfReader
+except Exception:
+    PdfReader = None
+
+NBSP = "\u00A0"
+def _normalize_text(t: str) -> str:
+    if not isinstance(t, str):
+        t = str(t or "")
+    t = t.replace(NBSP, " ")
+    t = re.sub(r"[\u0000-\u001F]", " ", t)  # remove controles
+    # mantém quebras simples e colapsa espaços longos
+    t = re.sub(r"[ \t]{2,}", " ", t)
+    t = re.sub(r"\n{3,}", "\n\n", t)
+    return t.strip()
+
+def _extract_with_pymupdf(path: Path) -> List[Tuple[int, str]]:
+    out: List[Tuple[int, str]] = []
+    with fitz.open(path) as doc:
+        for i, page in enumerate(doc):
+            text = page.get_text("text") or ""
+            out.append((i + 1, _normalize_text(text)))
+    return out
+
+def _extract_with_pdfminer(path: Path) -> List[Tuple[int, str]]:
+    text_all = pdfminer_extract_text(str(path)) or ""
+    pages = re.split(r"\f", text_all)
+    out: List[Tuple[int, str]] = []
+    for i, tx in enumerate(pages):
+        out.append((i + 1, _normalize_text(tx)))
+    return out
+
+def _extract_with_pypdf(path: Path) -> List[Tuple[int, str]]:
+    if PdfReader is None:
+        return []
+    try:
+        r = PdfReader(str(path), strict=False)
+    except Exception as e:
+        print(f"[WARN] pypdf: falha ao abrir {path.name}: {e}")
+        return []
+    out: List[Tuple[int, str]] = []
+    for i in range(len(r.pages)):
+        try:
+            page = r.pages[i]
+            t = page.extract_text() or ""
+        except Exception as e:
+            print(f"[WARN] pypdf: falha ao extrair pag {i+1}: {e}")
+            t = ""
+        out.append((i + 1, _normalize_text(t)))
+    return out
+
+def _maybe_ocr_images(path: Path) -> List[Tuple[int, str]]:
+    try:
+        import pytesseract
+        from pdf2image import convert_from_path
+    except Exception:
+        return []
+    out: List[Tuple[int, str]] = []
+    try:
+        images = convert_from_path(str(path))
+        for i, img in enumerate(images):
+            txt = pytesseract.image_to_string(img) or ""
+            out.append((i + 1, _normalize_text(txt)))
+    except Exception as e:
+        print(f"[WARN] OCR: falha ao converter/ler {path.name}: {e}")
+    return out
+
+def read_pdf_pages(path: Path) -> List[Tuple[int, str]]:
+    if not path.exists():
+        return []
+    # 1) PyMuPDF
+    if fitz is not None:
+        try:
+            pages = _extract_with_pymupdf(path)
+            if any((tx or "").strip() for _, tx in pages):
+                return pages
+        except Exception as e:
+            print(f"[WARN] PyMuPDF falhou: {e}")
+    # 2) pdfminer
+    if pdfminer_extract_text is not None:
+        try:
+            pages = _extract_with_pdfminer(path)
+            if any((tx or "").strip() for _, tx in pages):
+                return pages
+        except Exception as e:
+            print(f"[WARN] pdfminer falhou: {e}")
+    # 3) pypdf
+    try:
+        pages = _extract_with_pypdf(path)
+        if any((tx or "").strip() for _, tx in pages):
+            return pages
+    except Exception as e:
+        print(f"[WARN] pypdf falhou: {e}")
+    # 4) OCR quando nada foi extraído
+    ocr_pages = _maybe_ocr_images(path)
+    if not any((tx or "").strip() for _, tx in ocr_pages):
+        print("[ERRO] Nenhum texto extraído, nem com OCR.")
+    return ocr_pages
+
+# ==============================
+# Segmentação (detecção de seções para metadados)
+# ==============================
+DB2_HEADER_RE = re.compile(
+    r"^(Part\s+\d+\.|Chapter\s+\d+\.)|"
+    r"\b(BACKUP SYSTEM|CATMAINT|CHECK DATA|CHECK INDEX|CHECK LOB|COPY|COPYTOCOPY|DIAGNOSE|LISTDEF|LOAD|"
+    r"MERGECOPY|MODIFY RECOVERY|MODIFY STATISTICS|OPTIONS|QUIESCE|REBUILD INDEX|RECOVER|REORG INDEX|REORG TABLESPACE|"
+    r"REPAIR|REPORT|RESTORE SYSTEM|RUNSTATS|STOSPACE|TEMPLATE|UNLOAD)\b",
+    re.IGNORECASE
+)
+
+def split_db2_docs(pages: List[Tuple[int, str]], doc_label: str) -> List[Dict]:
+    """Agrupa páginas por possíveis cabeçalhos (capítulos/utilities) para compor metadados de seção."""
+    blocks: List[Dict] = []
+    current = {"doc": doc_label, "section": "INTRO", "start_page": 1, "texts": []}
+    for pg, tx in pages:
+        head = (tx or "")[:300]
+        if DB2_HEADER_RE.search(head):
+            if current["texts"]:
+                current["end_page"] = current["texts"][-1][0]
+                blocks.append(current)
+            m = re.search(r"(Chapter\s+\d+\.\s*[^\n]+|^[^\n]{1,200})", tx or "")
+            title = (m.group(1).strip() if m else f"Section@{pg}")
+            current = {"doc": doc_label, "section": title, "start_page": pg, "texts": []}
+        current["texts"].append((pg, tx or ""))
+    if current["texts"]:
+        current["end_page"] = current["texts"][-1][0]
+        blocks.append(current)
+    return blocks
+
+# ==============================
+# Chunkização por caracteres (robusta)
+# ==============================
+def make_chunks_by_chars(blocks: List[Dict], max_chars: int = 1500, min_chars: int = 180) -> List[Dict]:
+    """Concatena o texto das páginas de cada bloco e fatia por janelas de caracteres com overlap."""
+    out: List[Dict] = []
+    for b in blocks:
+        pieces: List[str] = []
+        pages: List[int] = []
+        for pg, tx in b["texts"]:
+            txn = _normalize_text(tx or "")
+            if txn:
+                pieces.append(txn)
+                pages.append(pg)
+        if not pieces:
+            continue
+        blob = "\n".join(pieces).strip()
+        if not blob:
+            continue
+        start_page = min(pages) if pages else b.get("start_page", 0)
+        end_page = max(pages) if pages else b.get("end_page", start_page)
+
+        if len(blob) <= max_chars and len(blob) >= min_chars:
+            out.append({
+                "doc": b["doc"],
+                "section": b["section"],
+                "start_page": start_page,
+                "end_page": end_page,
+                "text": blob
+            })
+            continue
+
+        overlap = 120
+        i, n = 0, len(blob)
+        while i < n:
+            j = min(i + max_chars, n)
+            chunk_text = blob[i:j].strip()
+            if len(chunk_text) >= min_chars:
+                out.append({
+                    "doc": b["doc"],
+                    "section": b["section"],
+                    "start_page": start_page,
+                    "end_page": end_page,
+                    "text": chunk_text
+                })
+            new_i = j - overlap
+            i = j if new_i <= i else new_i
+    # filtro final
+    out = [c for c in out if (c.get("text") or "").strip()]
+    return out
+
+# ==============================
+# Embeddings
+# ==============================
+def embed_texts(texts: List[str], batch_size: int = 16) -> np.ndarray:
+    client = get_client()
+    clean = [(i, t) for i, t in enumerate(texts) if isinstance(t, str) and t.strip()]
+    if not clean:
+        return np.zeros((0, 0), dtype=np.float32)
+    order, payload = zip(*clean)
+    vecs: Dict[int, np.ndarray] = {}
+    for i in range(0, len(payload), batch_size):
+        batch = list(payload[i:i + batch_size])
+        resp = client.embeddings.create(model=EMBED_MODEL, input=batch)
+        for k, item in enumerate(resp.data):
+            vecs[int(order[i + k])] = np.array(item.embedding, dtype=np.float32)
+    rows: List[np.ndarray] = []
+    for idx in range(len(texts)):
+        if idx in vecs:
+            rows.append(vecs[idx])
+    if not rows:
+        return np.zeros((0, 0), dtype=np.float32)
+    mat = np.vstack(rows).astype(np.float32)
+    norms = np.linalg.norm(mat, axis=1, keepdims=True)
+    norms[norms == 0] = 1.0
+    return mat / norms
+
+def embed_query(q: str) -> np.ndarray:
+    client = get_client()
+    resp = client.embeddings.create(model=EMBED_MODEL, input=[q])
+    v = np.array(resp.data[0].embedding, dtype=np.float32)
+    n = np.linalg.norm(v)
+    return (v / (n if n > 0 else 1.0)).astype(np.float32)
+
+# ==============================
+# Indexação
+# ==============================
+def build_index() -> Tuple[np.ndarray, List[Dict]]:
+    all_blocks: List[Dict] = []
+    for p in PDFS:
+        pages = read_pdf_pages(p)
+        if not pages or not any((tx or "").strip() for _, tx in pages):
+            print(f"[WARN] Sem texto legível em {p.name}; ignorando.")
+            continue
+        blks = split_db2_docs(pages, p.name)
+        all_blocks.extend(blks)
+
+    all_chunks = make_chunks_by_chars(all_blocks, max_chars=1500, min_chars=180)
+    all_chunks = [c for c in all_chunks if (c.get("text") or "").strip()]
+
+    if not all_chunks:
+        with open(META_FILE, "w", encoding="utf-8") as f:
+            json.dump({"chunks": [], "embed_model": EMBED_MODEL, "embed_dim": 0, "total_chars": 0}, f, ensure_ascii=False, indent=2)
+        np.save(VEC_FILE, np.zeros((0, 0), dtype=np.float32))
+        raise RuntimeError("Nenhum chunk foi criado. Verifique extração/ OCR.")
+
+    texts = [c["text"] for c in all_chunks]
+    total_chars = sum(len(t) for t in texts)
+
+    mat = embed_texts(texts) if texts else np.zeros((0, 0), dtype=np.float32)
+    embed_dim = int(mat.shape[1]) if mat.size else 0
+
+    np.save(VEC_FILE, mat)
+    with open(META_FILE, "w", encoding="utf-8") as f:
+        json.dump(
+            {"chunks": all_chunks, "embed_model": EMBED_MODEL, "embed_dim": embed_dim, "total_chars": total_chars},
+            f, ensure_ascii=False, indent=2
+        )
+    return mat, all_chunks
+
+def load_index() -> Tuple[np.ndarray, List[Dict]]:
+    if VEC_FILE.exists() and META_FILE.exists():
+        mat = np.load(VEC_FILE)
+        dd = json.loads(META_FILE.read_text(encoding="utf-8"))
+        chunks = dd.get("chunks", [])
+        return mat, chunks
+    return build_index()
+
+def wipe_index() -> str:
+    try:
+        if INDEX_DIR.exists():
+            for p in INDEX_DIR.glob("*"):
+                p.unlink()
+            INDEX_DIR.rmdir()
+        INDEX_DIR.mkdir(exist_ok=True)
+        return "Índice limpo."
+    except Exception as e:
+        return f"Erro ao limpar índice: {e}"
+
+# ==============================
+# Recuperação + LLM
+# ==============================
+def _check_embed_dim(mat: np.ndarray) -> Optional[str]:
+    try:
+        dd = json.loads(META_FILE.read_text(encoding="utf-8"))
+        idx_dim = int(dd.get("embed_dim", 0))
+    except Exception:
+        idx_dim = 0
+    try:
+        v = embed_query("dim_test")
+        cur_dim = int(v.shape[0])
+    except Exception as e:
+        return f"Falha ao checar dimensão do embedding: {e}"
+    if idx_dim and cur_dim and idx_dim != cur_dim:
+        return (f"Incompatibilidade de dimensão do embedding: índice={idx_dim}, modelo atual={cur_dim}. "
+                f"Reindexe com o mesmo EMBED_MODEL. (Atual EMBED_MODEL: {EMBED_MODEL})")
+    return None
+
+def retrieve_topk(query: str, doc_filter: Optional[str] = None, k: int = TOP_K_RETRIEVE) -> List[Dict]:
+    mat, chunks = load_index()
+    if mat.shape[0] == 0 or not chunks:
+        return []
+    qv = embed_query(query)
+    if mat.shape[1] != qv.shape[0]:
+        raise RuntimeError(
+            f"Dimensão incompatível mat={mat.shape} vs query={qv.shape}. "
+            f"Provável troca de EMBED_MODEL após criar o índice. Clique 'Reindexar'."
+        )
+    sims = (mat @ qv).astype(float)
+    if doc_filter and doc_filter != "(Todos)":
+        mask = np.array([1.0 if c["doc"] == doc_filter else 0.0 for c in chunks], dtype=float)
+        sims *= mask
+    idxs = np.argsort(-sims)[:k]
+    out = []
+    for i in idxs:
+        c = chunks[int(i)]
+        out.append({
+            "doc": c["doc"],
+            "section": c.get("section", ""),
+            "start_page": c.get("start_page", "?"),
+            "end_page": c.get("end_page", "?"),
+            "text": c["text"],
+            "score": float(sims[int(i)]),
+            "idx": int(i)
+        })
+    return out
+
+def expand_context(hits: List[Dict], all_chunks: List[Dict], target_chars: int = TARGET_CONTEXT_CHARS) -> Tuple[str, List[Tuple[str, str, str]]]:
+    if not hits:
+        return "", []
+    best = max(hits, key=lambda x: x["score"])
+    ctx = best["text"]
+    srcs = {(best["doc"], best["section"], f"{best['start_page']}–{best['end_page']}")}
+    doc, section, best_idx = best["doc"], best["section"], best["idx"]
+    indices = [i for i, c in enumerate(all_chunks) if c["doc"] == doc and c.get("section", "") == section]
+    if not indices:
+        return ctx, sorted(list(srcs))
+    indices.sort()
+    if best_idx not in indices:
+        return ctx, sorted(list(srcs))
+    pos = indices.index(best_idx)
+    left, right = pos - 1, pos + 1
+    while len(ctx) < target_chars and (left >= 0 or right < len(indices)):
+        if right < len(indices) and len(ctx) < target_chars:
+            rch = all_chunks[indices[right]]
+            ctx += "\n\n" + rch["text"]
+            srcs.add((doc, section, f"{rch.get('start_page', '?')}–{rch.get('end_page', '?')}"))
+            right += 1
+        if left >= 0 and len(ctx) < target_chars:
+            lch = all_chunks[indices[left]]
+            ctx = lch["text"] + "\n\n" + ctx
+            srcs.add((doc, section, f"{lch.get('start_page', '?')}–{lch.get('end_page', '?')}"))
+            left -= 1
+    return ctx, sorted(list(srcs))
+
+def answer_with_llm(question: str, context: str) -> str:
+    client = get_client()
+    system = ("Você é um assistente especialista em IBM Db2 para z/OS. "
+              "Responda em português, com exemplos de comandos SQL/JCL completos e corretos. "
+              "Use apenas o contexto fornecido; se algo não estiver nele, diga que não está disponível.")
+    user = (f"Pergunta:\n{question}\n\n"
+            f"Contexto do(s) manual(is):\n{context}\n\n"
+            "Regras de resposta:\n"
+            "- Explique o necessário e como fazer.\n"
+            "- Inclua pelo menos um exemplo de comando Db2 utilitário, SQL ou JCL (auto-contido), se aplicável.\n"
+            "- Liste observações/pré-requisitos, se houver.\n"
+            "- Cite as fontes (Documento e páginas) ao final.")
+    chat = client.chat.completions.create(
+        model=CHAT_MODEL,
+        messages=[{"role": "system", "content": system}, {"role": "user", "content": user}],
+        temperature=0.2,
+    )
+    return chat.choices[0].message.content.strip()
+
+def format_sources_md(sources: List[Tuple[str, str, str]]) -> str:
+    if not sources:
+        return ""
+    lines = [
+        f"- **Documento:** {d}  \n  **Seção:** {s}  \n  **Páginas:** {p}"
+        for (d, s, p) in sources
+    ]
+    return "\n".join(lines)
+
+# ==============================
+# Templates Db2 (exemplos)
+# ==============================
+DB2_TEMPLATES: Dict[str, str] = {
+    "RUNSTATS_TABLESPACE": (
+        "//RUNSTAT JOB (ACCT),'RUNSTATS',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
+        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='RUNSTATS',UTPROC=''\n"
+        "//SYSIN   DD *\n"
+        "  RUNSTATS TABLESPACE(DBNAME.TSNAME) TABLE(ALL) INDEX(ALL)\n"
+        "/*\n"
+    ),
+    "REORG_TABLESPACE": (
+        "//REORG   JOB (ACCT),'REORG',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
+        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='REORGTS',UTPROC=''\n"
+        "//SYSIN   DD *\n"
+        "  REORG TABLESPACE(DBNAME.TSNAME) SHRLEVEL CHANGE\n"
+        "/*\n"
+    ),
+    "EXPLAIN_SQL": (
+        "//EXPLAIN JOB (ACCT),'EXPLAIN',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
+        "//STEP1   EXEC DSNTEP2,SYSTEM=DSN1\n"
+        "//SYSIN   DD *\n"
+        "  EXPLAIN PLAN FOR\n"
+        "  SELECT COL1, COL2 FROM DBNAME.TBNAME WHERE COL3 = 'X';\n"
+        "/*\n"
+    ),
+    "DISPLAY_BUFFERPOOL": (
+        "//DISPBP  JOB (ACCT),'DISPLAY BP',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
+        "//STEP1   EXEC PGM=IKJEFT01\n"
+        "//SYSTSPRT DD SYSOUT=*\n"
+        "//SYSIN    DD *\n"
+        "  DSN SYSTEM(DSN1)\n"
+        "  -DISPLAY BUFFERPOOL(BP0) DETAIL\n"
+        "  END\n"
+        "/*\n"
+    ),
+    "DSNTEP2_SELECT": (
+        "//SELECT  JOB (ACCT),'DSNTEP2',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
+        "//STEP1   EXEC DSNTEP2,SYSTEM=DSN1\n"
+        "//SYSIN   DD *\n"
+        "  SELECT FIRSTNME, LASTNAME FROM DSN8810.EMP\n"
+        "  WHERE WORKDEPT = 'A00';\n"
+        "/*\n"
+    ),
+    "COPY_TABLESPACE": (
+        "//COPYTS  JOB (ACCT),'COPY',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
+        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='COPYTS',UTPROC=''\n"
+        "//SYSIN   DD *\n"
+        "  COPY TABLESPACE(DBNAME.TSNAME) FULL YES SHRLEVEL CHANGE\n"
+        "/*\n"
+    ),
+    "LOAD_TABLE": (
+        "//LOADTBL JOB (ACCT),'LOAD',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
+        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='LOADTBL',UTPROC=''\n"
+        "//SYSIN   DD *\n"
+        "  LOAD DATA INDDN SYSREC INTO TABLE DBNAME.TBNAME\n"
+        "  REPLACE\n"
+        "/*\n"
+    ),
+    "RECOVER_TABLESPACE": (
+        "//RECOVTS JOB (ACCT),'RECOVER',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
+        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='RECOVTS',UTPROC=''\n"
+        "//SYSIN   DD *\n"
+        "  RECOVER TABLESPACE(DBNAME.TSNAME)\n"
+        "/*\n"
+    ),
+    "STATS_INDEX": (
+        "//STATSIX JOB (ACCT),'STATS INDEX',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
+        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='STATSIX',UTPROC=''\n"
+        "//SYSIN   DD *\n"
+        "  RUNSTATS INDEX(DBNAME.IXNAME) ALL\n"
+        "/*\n"
+    ),
+    "MODIFY_RECOVERY": (
+        "//MODREC  JOB (ACCT),'MODIFY RECOVERY',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
+        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='MODREC',UTPROC=''\n"
+        "//SYSIN   DD *\n"
+        "  MODIFY RECOVERY TABLESPACE(DBNAME.TSNAME) AGE(30)\n"
+        "/*\n"
+    ),
+    "CHECK_DATA": (
+        "//CHKDATA JOB (ACCT),'CHECK DATA',CLASS=A,MSGCLASS=X,NOTIFY=&SYSUID\n"
+        "//STEP1   EXEC DSNUPROC,SYSTEM=DSN1,UID='CHKDATA',UTPROC=''\n"
+        "//SYSIN   DD *\n"
+        "  CHECK DATA TABLESPACE(DBNAME.TSNAME) SCOPE ALL\n"
+        "/*\n"
+    ),
+}
+
+def template_for_db2(cmd: str) -> str:
+    return DB2_TEMPLATES.get(cmd, "//GENERIC ...\n")
+
+# ==============================
+# UI — layout com SIDEBAR + diagnóstico
+# ==============================
+CUSTOM_CSS = """
+:root{ --ink:#0f172a; --muted:#475569; }
+.gradio-container { max-width: 1200px !important; margin: 0 auto !important; }
+.section-card { background: #fff; border: 1px solid #e2e8f0; border-radius: 16px; padding: 16px; box-shadow: 0 10px 30px rgba(2,6,23,.05); }
+.section-title { font-size: 1.05rem; font-weight: 800; color: var(--ink); display: flex; gap: .6rem; align-items: center; }
+.subtitle { color:var(--muted); font-size:.95rem; margin-top:.25rem; }
+.result-card { background:#fcfdff; border:1px solid #e2e8f0; border-radius:12px; padding:12px; }
+hr.sep { border:none; border-top:1px dashed #e2e8f0; margin:10px 0 14px; }
+.small { font-size:.9rem; color:var(--muted); }
+"""
+
+def build_app():
+    doc_label = PDFS[0].name if PDFS else "(Nenhum PDF)"
+    all_doc_names = [p.name for p in PDFS] or ["(Nenhum PDF encontrado)"]
+    default_doc = all_doc_names[0] if all_doc_names else "(Todos)"
+
+    with gr.Blocks(title="Db2 z/OS • RAG (NVIDIA NIM)", css=CUSTOM_CSS, fill_height=True) as demo:
+
+        # ===== Sidebar =====
+        with gr.Sidebar():
+            gr.Markdown(
+                f"""
+<div class="section-title">💼 Db2 RAG</div>
+<div class="small">Contexto: <code>{doc_label}</code></div>
+"""
+            )
+            status_box = gr.Markdown("Pronto ✅" if PDFS else "⚠️ Nenhum PDF encontrado.")
+            gr.Markdown("<hr class='sep'/>")
+
+            with gr.Group():
+                gr.Markdown("**Ações**")
+                test_btn = gr.Button("🧪 Testar conexão NVIDIA", variant="secondary")
+                rebuild_btn = gr.Button("🔁 Reindexar (NIM)")
+                diag_btn = gr.Button("🛠️ Diagnóstico do Índice")
+
+            gr.Markdown("<hr class='sep'/>")
+
+            with gr.Accordion("Configuração", open=False):
+                gr.Markdown(f"- **Embeddings:** `{EMBED_MODEL}`\n- **LLM:** `{CHAT_MODEL}`\n- **Índice:** `{INDEX_DIR.name}`")
+                doc_choice = gr.Dropdown(
+                    choices=(["(Todos)"] + all_doc_names),
+                    value=default_doc if PDFS else "(Todos)",
+                    label="Documento"
+                )
+
+        # ===== Main content =====
+        gr.Markdown(
+            f"""
+<div class="section-card" style="padding:18px; display:flex; gap:16px; align-items:center;">
+  <div style="font-size:26px;">🧭</div>
+  <div style="flex:1">
+    <div style="font-size:1.2rem; font-weight:800; color:#0f172a;">DB2 -Z/OS UTILITIES | RAG + NVIDIA NIM</div>
+    <div class="subtitle">Pergunte sobre utilidades (COPY, LOAD, REORG, RUNSTATS, RECOVER, etc.). As respostas vêm do manual: <code>{doc_label}</code>.</div>
+  </div>
+</div>
+            """
+        )
+
+        with gr.Row():
+            q = gr.Textbox(
+                label="Pergunta (Db2 Utilities)",
+                placeholder="Ex.: Como usar COPY FULL com SHRLEVEL CHANGE? • Quando rodar RUNSTATS INDEX? • REORG TABLESPACE SHRLEVEL CHANGE • RECOVER PITR...",
+                scale=8
+            )
+        with gr.Row():
+            ask_btn = gr.Button("🔍 Buscar", variant="primary", scale=2)
+            clear_btn = gr.Button("🧹 Limpar", scale=1)
+
+        out = gr.Markdown(label="Resposta (Db2)")
+
+        gr.Markdown("<hr class='sep'/>")
+
+        with gr.Accordion("🧩 Templates Db2 executáveis", open=False):
+            db2_choice = gr.Dropdown(
+                choices=list(DB2_TEMPLATES.keys()),
+                value="RUNSTATS_TABLESPACE",
+                label="Comando / Padrão"
+            )
+            db2_btn = gr.Button("📄 Gerar exemplo")
+            db2_out = gr.Textbox(label="Exemplo (copiar/ajustar)", lines=18, show_copy_button=True)
+
+        with gr.Accordion("🧪 Log / Diagnóstico", open=False):
+            diag_out = gr.Markdown()
+
+        # ===== Callbacks =====
+        def _test_conn():
+            try:
+                dim = len(get_client().embeddings.create(model=EMBED_MODEL, input=["ping"]).data[0].embedding)
+                return f"Conexão ok ✅ — dimensão do embedding: **{dim}**"
+            except Exception as e:
+                return f"⚠️ Falha na conexão/credenciais NVIDIA: `{type(e).__name__}` — {e}"
+
+        def _rebuild():
+            try:
+                msg = wipe_index()
+                mat, chunks = build_index()
+                return msg + f" Reindexação concluída ✅ PDFs: {len(PDFS)} • Chunks: {len(chunks)} • Vetores: {mat.shape}"
+            except Exception as e:
+                return f"⚠️ Erro ao reindexar: `{type(e).__name__}` — {e}"
+
+        def _diagnose(dsel: str) -> str:
+            try:
+                if not (VEC_FILE.exists() and META_FILE.exists()):
+                    return "❌ Nenhum índice encontrado. Clique **Reindexar (NIM)**."
+                mat = np.load(VEC_FILE)
+                meta = json.loads(META_FILE.read_text(encoding="utf-8"))
+                chunks = meta.get("chunks", [])
+                embed_dim = meta.get("embed_dim", 0)
+                total_chars = int(meta.get("total_chars", 0))
+                dim_msg = _check_embed_dim(mat)
+                # primeiras seções
+                first_secs = []
+                for c in chunks[:12]:
+                    if dsel == "(Todos)" or c["doc"] == dsel:
+                        first_secs.append(f"- {c['doc']} • {c.get('section','?')} • p.{c.get('start_page','?')}-{c.get('end_page','?')}")
+                if not first_secs:
+                    first_secs = ["(Filtro de documento não encontra seções no índice.)"]
+                # prévia do primeiro chunk
+                preview = ""
+                for c in chunks:
+                    t = (c.get("text") or "").strip()
+                    if t:
+                        preview = t[:400].replace("\n", " ")
+                        break
+                if not preview:
+                    preview = "(Nenhum chunk contém texto — verifique extração/OCR.)"
+                msg = [
+                    f"**Índice**: Vetores `{mat.shape}` • embed_dim(meta): `{embed_dim}` • Modelo atual: `{EMBED_MODEL}`",
+                    f"**Chunks**: **{len(chunks)}** • **Total de caracteres**: {total_chars}",
+                    f"**Documento selecionado**: `{dsel}`",
+                    f"**Primeiras seções**:\n" + "\n".join(first_secs),
+                    f"\n**Prévia (400 chars)**:\n```\n{preview}\n```"
+                ]
+                if dim_msg:
+                    msg.append(f"\n⚠️ {dim_msg}")
+                return "\n".join(msg)
+            except Exception as e:
+                return f"⚠️ Diagnóstico falhou: `{type(e).__name__}` — {e}"
+
+        def _search_answer(qstr: str, d: str) -> str:
+            try:
+                if not qstr or qstr.strip() == "":
+                    return "_Informe uma pergunta._"
+                if not (VEC_FILE.exists() and META_FILE.exists()):
+                    return "_Nenhum conteúdo indexado. Use **Reindexar**._"
+                mat = np.load(VEC_FILE)
+                meta = json.loads(META_FILE.read_text(encoding="utf-8"))
+                chunks = meta.get("chunks", [])
+                if mat.size == 0 or not chunks:
+                    return "_Índice vazio. Reindexe (pode ser necessário OCR)._"
+                dim_msg = _check_embed_dim(mat)
+                if dim_msg:
+                    return f"⚠️ {dim_msg}"
+                # retrieve
+                hits = retrieve_topk(qstr, None if d == "(Todos)" else d, k=TOP_K_RETRIEVE)
+                hits = [h for h in hits if (h.get("text") or "").strip()]
+                if not hits:
+                    return "_Nada encontrado para a consulta (verifique o filtro de documento ou reindexe)._"
+                context, sources = expand_context(hits, chunks, TARGET_CONTEXT_CHARS)
+                if not context.strip():
+                    return "_Contexto insuficiente encontrado._"
+                answer = answer_with_llm(qstr, context)
+                src_md = format_sources_md(sources)
+                return f"<div class='result-card'>{answer}</div>\n\n### Fontes\n{src_md}"
+            except Exception as e:
+                return f"⚠️ Erro ao buscar: `{type(e).__name__}` — {e}"
+
+        def _clear(doc_default: str) -> Tuple[str, str]:
+            return "", (doc_default if PDFS else "(Todos)")
+
+        def ui_db2_template(cmd_choice: str) -> str:
+            return template_for_db2(cmd_choice)
+
+        test_btn.click(_test_conn, outputs=[status_box])
+        rebuild_btn.click(_rebuild, outputs=[status_box])
+        diag_btn.click(_diagnose, inputs=[doc_choice], outputs=[diag_out])
+
+        ask_btn.click(_search_answer, inputs=[q, doc_choice], outputs=[out])
+        clear_btn.click(_clear, inputs=[gr.State(default_doc)], outputs=[q, doc_choice])
+        db2_btn.click(ui_db2_template, inputs=[db2_choice], outputs=[db2_out])
+
+    return demo
+
+# ==============================
+# Main (robusto: bind público, respeita $PORT, SSR off, queue opcional)
+# ==============================
+if __name__ == "__main__":
+    try:
+        _ = load_index()
+    except Exception as e:
+        print(f"[AVISO] Índice não carregado: {e}")
+    app = build_app()
+    app.launch(server_name="0.0.0.0", server_port=7860)