diff --git a/scripts/rag_supplemental_expansion.py b/scripts/rag_supplemental_expansion.py
new file mode 100644
index 0000000..306ed84
--- /dev/null
+++ b/scripts/rag_supplemental_expansion.py
@@ -0,0 +1,821 @@
+from __future__ import annotations
+
+import json
+import re
+import sqlite3
+from pathlib import Path
+from typing import Any
+
+from scripts.rag_query_evidence import (
+    build_focus_excerpt,
+    evidence_score,
+    parse_heading_paths_json,
+    query_content_tokens,
+)
+from scripts.search_utils import make_source_url, normalize_for_compare
+
+
+WORD_RE = re.compile(r"[^\W_]+", re.UNICODE)
+
+SUPPLEMENTAL_STOPWORDS = {
+    "autor",
+    "autora",
+    "dokument",
+    "dokumenty",
+    "informacna",
+    "informacnej",
+    "projektova",
+    "projektovej",
+    "stranka",
+    "stranku",
+    "student",
+    "studenta",
+    "tema",
+    "teme",
+    "temou",
+}
+
+CANONICAL_TOKEN_ALIASES = {
+    "multilingualna": "multilingual",
+    "multilingualny": "multilingual",
+    "multilingual": "multilingual",
+    "viacjazycna": "multilingual",
+    "viacjazycneho": "multilingual",
+    "viacjazycny": "multilingual",
+    "extrakcia": "extract",
+    "extrakcie": "extract",
+    "extrahovanie": "extract",
+    "extrahovania": "extract",
+    "extraction": "extract",
+    "extract": "extract",
+    "trojic": "triplet",
+    "trojice": "triplet",
+    "trojicami": "triplet",
+    "triplet": "triplet",
+    "triplets": "triplet",
+    "medicinskych": "medical",
+    "medicinsky": "medical",
+    "medicinske": "medical",
+    "lekarskych": "medical",
+    "lekarsky": "medical",
+    "medical": "medical",
+    "dat": "data",
+    "data": "data",
+    "strojovy": "machine",
+    "strojoveho": "machine",
+    "machine": "machine",
+    "preklad": "translation",
+    "prekladu": "translation",
+    "translation": "translation",
+    "automaticky": "automatic",
+    "automatickeho": "automatic",
+    "automatic": "automatic",
+    "pomenovane": "named",
+    "pomenovanych": "named",
+    "named": "named",
+    "entity": "entity",
+    "entities": "entity",
+    "entit": "entity",
+    "anotacia": "annotation",
+    "anotacii": "annotation",
+    "annotation": "annotation",
+    "znalostny": "knowledge",
+    "znalostne": "knowledge",
+    "knowledge": "knowledge",
+    "graf": "graph",
+    "grafy": "graph",
+    "graph": "graph",
+    "graphy": "graph",
+    "otazok": "question",
+    "otazky": "question",
+    "question": "question",
+    "questions": "question",
+    "odpovedi": "answer",
+    "odpovede": "answer",
+    "answer": "answer",
+    "answers": "answer",
+    "system": "system",
+    "systemom": "system",
+    "systemu": "system",
+    "integrovat": "integrate",
+    "integracia": "integrate",
+    "integrate": "integrate",
+}
+
+MIN_SINGLE_GLOBAL_COVERAGE = 0.50
+MIN_MULTI_GLOBAL_COVERAGE = 0.45
+MIN_GLOBAL_MATCHES = 3
+MAX_FAMILY_EVIDENCE = 3
+
+
+def sqlite_table_exists(conn: sqlite3.Connection, table_name: str) -> bool:
+    row = conn.execute(
+        "SELECT 1 FROM sqlite_master "
+        "WHERE type IN ('table', 'view') AND name = ? LIMIT 1",
+        (table_name,),
+    ).fetchone()
+    return row is not None
+
+
+def sqlite_table_columns(conn: sqlite3.Connection, table_name: str) -> set[str]:
+    if not sqlite_table_exists(conn, table_name):
+        return set()
+
+    rows = conn.execute(f"PRAGMA table_info({table_name})").fetchall()
+    return {
+        str(row[1])
+        for row in rows
+        if len(row) > 1
+    }
+
+
+def normalized_words(value: str) -> list[str]:
+    return WORD_RE.findall(normalize_for_compare(value))
+
+
+def canonical_token(token: str) -> str:
+    return CANONICAL_TOKEN_ALIASES.get(token, token)
+
+
+def canonical_tokens(value: str) -> list[str]:
+    result: list[str] = []
+
+    for token in normalized_words(value):
+        if token in SUPPLEMENTAL_STOPWORDS:
+            continue
+        result.append(canonical_token(token))
+
+    return list(dict.fromkeys(result))
+
+
+def canonical_query_tokens(query: str) -> list[str]:
+    base = [
+        token
+        for token in query_content_tokens(query)
+        if token not in SUPPLEMENTAL_STOPWORDS
+    ]
+    return list(
+        dict.fromkeys(
+            canonical_token(token)
+            for token in base
+        )
+    )
+
+
+def canonical_coverage(query_tokens: list[str], value: str) -> tuple[int, float]:
+    if not query_tokens:
+        return 0, 0.0
+
+    value_tokens = set(canonical_tokens(value))
+    matches = sum(
+        1
+        for token in query_tokens
+        if token in value_tokens
+    )
+    return matches, matches / len(query_tokens)
+
+
+def canonical_student_main_path(document_path: str) -> str:
+    parts = Path(document_path).parts
+
+    if (
+        len(parts) >= 5
+        and parts[0] == "pages"
+        and parts[1] == "students"
+    ):
+        return "/".join(
+            [
+                parts[0],
+                parts[1],
+                parts[2],
+                parts[3],
+                "README.md",
+            ]
+        )
+
+    return document_path
+
+
+def same_student_family(left: str, right: str) -> bool:
+    return (
+        canonical_student_main_path(left)
+        == canonical_student_main_path(right)
+    )
+
+
+def is_multi_document_query(query: str) -> bool:
+    normalized = normalize_for_compare(query)
+    return any(
+        marker in normalized
+        for marker in (
+            "viacero dokument",
+            "dokumenty",
+            "ktore dokumenty",
+            "najdi dokumenty",
+        )
+    )
+
+
+def _load_documents(conn: sqlite3.Connection) -> list[dict[str, Any]]:
+    columns = sqlite_table_columns(conn, "documents")
+    if not columns:
+        return []
+
+    path_column = (
+        "path"
+        if "path" in columns
+        else "document_path"
+        if "document_path" in columns
+        else None
+    )
+    if path_column is None:
+        return []
+
+    selected = [
+        f"{path_column} AS document_path",
+    ]
+
+    for optional in ("title", "author", "published"):
+        if optional in columns:
+            selected.append(optional)
+
+    query = (
+        "SELECT "
+        + ", ".join(selected)
+        + " FROM documents"
+    )
+    return [
+        dict(row)
+        for row in conn.execute(query).fetchall()
+    ]
+
+
+def _load_chunks(
+    conn: sqlite3.Connection,
+    *,
+    published_only: bool,
+) -> list[dict[str, Any]]:
+    columns = sqlite_table_columns(conn, "chunks")
+    required = {
+        "chunk_id",
+        "document_path",
+        "chunk_index",
+        "heading_paths_json",
+        "text",
+    }
+    if not required <= columns:
+        return []
+
+    selected = [
+        "chunk_id",
+        "document_path",
+        "chunk_index",
+        "heading_paths_json",
+        "text",
+    ]
+
+    for optional in ("title", "author", "published"):
+        if optional in columns:
+            selected.append(optional)
+
+    where = ""
+    if published_only and "published" in columns:
+        where = " WHERE published = 1"
+
+    order = " ORDER BY document_path ASC, chunk_index ASC"
+    if "id" in columns:
+        order += ", id ASC"
+
+    query = (
+        "SELECT "
+        + ", ".join(selected)
+        + " FROM chunks"
+        + where
+        + order
+    )
+
+    rows = [
+        dict(row)
+        for row in conn.execute(query).fetchall()
+    ]
+
+    for row in rows:
+        row["heading_paths"] = parse_heading_paths_json(
+            row.get("heading_paths_json")
+        )
+
+    return rows
+
+
+def _document_metadata_by_path(
+    documents: list[dict[str, Any]],
+) -> dict[str, dict[str, Any]]:
+    return {
+        str(item.get("document_path") or ""): item
+        for item in documents
+        if str(item.get("document_path") or "").strip()
+    }
+
+
+def _best_main_metadata(
+    canonical_path: str,
+    metadata_by_path: dict[str, dict[str, Any]],
+    fallback_chunk: dict[str, Any],
+) -> dict[str, Any]:
+    direct = metadata_by_path.get(canonical_path)
+    if direct is not None:
+        return direct
+
+    evidence_path = str(
+        fallback_chunk.get("document_path") or ""
+    )
+    evidence_meta = metadata_by_path.get(evidence_path)
+    if evidence_meta is not None:
+        return evidence_meta
+
+    return {
+        "document_path": canonical_path,
+        "title": fallback_chunk.get("title"),
+        "author": fallback_chunk.get("author"),
+        "published": fallback_chunk.get("published"),
+    }
+
+
+def _source_from_chunk(
+    canonical_path: str,
+    chunk: dict[str, Any],
+    metadata_by_path: dict[str, dict[str, Any]],
+    *,
+    reason: str,
+    score: float,
+) -> dict[str, Any]:
+    meta = _best_main_metadata(
+        canonical_path,
+        metadata_by_path,
+        chunk,
+    )
+
+    return {
+        "chunk_id": chunk.get("chunk_id"),
+        "document_path": canonical_path,
+        "title": meta.get("title") or chunk.get("title"),
+        "author": meta.get("author") or chunk.get("author"),
+        "published": (
+            meta.get("published")
+            if meta.get("published") is not None
+            else chunk.get("published")
+        ),
+        "chunk_index": int(chunk.get("chunk_index") or 0),
+        "heading_paths": chunk.get("heading_paths") or [],
+        "text": str(chunk.get("text") or ""),
+        "source_url": make_source_url(canonical_path),
+        "match_strategy": "rag_supplemental",
+        "fts_rank": None,
+        "vector_rank": None,
+        "vector_score": None,
+        "hybrid_score": None,
+        "supplemental_expansion": {
+            "strategy": "global_or_entity_anchor",
+            "applied": True,
+            "reason": reason,
+            "score": round(score, 6),
+            "evidence_document_path": chunk.get("document_path"),
+            "evidence_chunk_id": chunk.get("chunk_id"),
+        },
+    }
+
+
+def _title_anchor_candidates(
+    query: str,
+    documents: list[dict[str, Any]],
+    chunks: list[dict[str, Any]],
+) -> list[tuple[float, str, dict[str, Any], str]]:
+    normalized_query = normalize_for_compare(query)
+    chunks_by_path: dict[str, list[dict[str, Any]]] = {}
+
+    for chunk in chunks:
+        chunks_by_path.setdefault(
+            str(chunk.get("document_path") or ""),
+            [],
+        ).append(chunk)
+
+    candidates: list[tuple[float, str, dict[str, Any], str]] = []
+
+    for document in documents:
+        title = str(document.get("title") or "").strip()
+        path = str(document.get("document_path") or "").strip()
+
+        if not title or not path:
+            continue
+
+        normalized_title = normalize_for_compare(title)
+        title_tokens = normalized_words(title)
+
+        if len(title_tokens) < 2:
+            continue
+
+        if normalized_title not in normalized_query:
+            continue
+
+        family_path = canonical_student_main_path(path)
+        family_chunks = [
+            chunk
+            for chunk in chunks
+            if canonical_student_main_path(
+                str(chunk.get("document_path") or "")
+            ) == family_path
+        ]
+        path_chunks = chunks_by_path.get(path, [])
+        pool = family_chunks or path_chunks
+
+        if not pool:
+            continue
+
+        best = max(
+            pool,
+            key=lambda chunk: (
+                evidence_score(
+                    query,
+                    text=str(chunk.get("text") or ""),
+                    heading_paths=chunk.get("heading_paths"),
+                ),
+                -int(chunk.get("chunk_index") or 0),
+            ),
+        )
+        score = 500.0 + 10.0 * len(title_tokens)
+        candidates.append(
+            (score, family_path, best, "document_title_in_query")
+        )
+
+    return candidates
+
+
+def _global_chunk_candidates(
+    query: str,
+    chunks: list[dict[str, Any]],
+    *,
+    multi_document: bool,
+) -> list[tuple[float, str, dict[str, Any], str]]:
+    query_tokens = canonical_query_tokens(query)
+    if not query_tokens:
+        return []
+
+    threshold = (
+        MIN_MULTI_GLOBAL_COVERAGE
+        if multi_document
+        else MIN_SINGLE_GLOBAL_COVERAGE
+    )
+
+    normalized_query = normalize_for_compare(query)
+    explicitly_entity_or_student = any(
+        marker in normalized_query
+        for marker in (
+            "student",
+            "osob",
+            "kto",
+            "autor",
+        )
+    )
+
+    best_by_source: dict[
+        str,
+        tuple[float, str, dict[str, Any], str],
+    ] = {}
+
+    for chunk in chunks:
+        text = str(chunk.get("text") or "")
+        headings = json.dumps(
+            chunk.get("heading_paths") or [],
+            ensure_ascii=False,
+        )
+        combined = f"{headings}\n{text}"
+        matches, coverage = canonical_coverage(
+            query_tokens,
+            combined,
+        )
+
+        raw_path = str(chunk.get("document_path") or "")
+        topic_page = raw_path.startswith("pages/topics/")
+        required_coverage = threshold
+        required_matches = (
+            2
+            if multi_document
+            else MIN_GLOBAL_MATCHES
+        )
+
+        # Generic factual questions without a named person often belong to a
+        # canonical project/topic page. Allow such pages to enter with two
+        # strong concept matches, then rank them with an explicit topic-page
+        # bonus. This handles short follow-up-like questions without changing
+        # the locked global retrieval scorer.
+        if topic_page and not explicitly_entity_or_student:
+            required_coverage = min(required_coverage, 0.33)
+            required_matches = min(required_matches, 2)
+
+        if (
+            coverage < required_coverage
+            or matches < required_matches
+        ):
+            continue
+
+        lexical = evidence_score(
+            query,
+            text=text,
+            heading_paths=chunk.get("heading_paths"),
+        )
+        score = (
+            coverage * 160.0
+            + matches * 12.0
+            + lexical
+        )
+
+        if topic_page and not explicitly_entity_or_student:
+            score += 25.0
+
+            topic_parts = Path(raw_path).parts
+            title_matches, _ = canonical_coverage(
+                query_tokens,
+                str(chunk.get("title") or ""),
+            )
+            if (
+                title_matches >= 1
+                and len(topic_parts) == 4
+                and topic_parts[0] == "pages"
+                and topic_parts[1] == "topics"
+                and topic_parts[3] == "README.md"
+            ):
+                score += 45.0
+
+            if (
+                len(topic_parts) == 4
+                and topic_parts[0] == "pages"
+                and topic_parts[1] == "topics"
+                and topic_parts[3] == "README.md"
+            ):
+                score += 20.0
+        canonical_path = canonical_student_main_path(
+            str(chunk.get("document_path") or "")
+        )
+        candidate = (
+            score,
+            canonical_path,
+            chunk,
+            "global_query_match",
+        )
+
+        previous = best_by_source.get(canonical_path)
+        if previous is None or candidate[0] > previous[0]:
+            best_by_source[canonical_path] = candidate
+
+    return sorted(
+        best_by_source.values(),
+        key=lambda item: (-item[0], item[1]),
+    )
+
+
+def _family_evidence_blocks(
+    query: str,
+    canonical_path: str,
+    chunks: list[dict[str, Any]],
+    *,
+    max_blocks: int = MAX_FAMILY_EVIDENCE,
+) -> list[dict[str, Any]]:
+    family_chunks = [
+        chunk
+        for chunk in chunks
+        if same_student_family(
+            str(chunk.get("document_path") or ""),
+            canonical_path,
+        )
+    ]
+
+    scored: list[tuple[float, dict[str, Any]]] = []
+
+    for chunk in family_chunks:
+        score = evidence_score(
+            query,
+            text=str(chunk.get("text") or ""),
+            heading_paths=chunk.get("heading_paths"),
+        )
+        focus = build_focus_excerpt(
+            query,
+            str(chunk.get("text") or ""),
+        )
+
+        if score < 20.0 or not focus:
+            continue
+
+        item = dict(chunk)
+        item["score"] = score
+        item["focus_text"] = focus
+        scored.append((score, item))
+
+    scored.sort(
+        key=lambda pair: (
+            -pair[0],
+            str(pair[1].get("document_path") or ""),
+            int(pair[1].get("chunk_index") or 0),
+        )
+    )
+
+    blocks: list[dict[str, Any]] = []
+    seen: set[str] = set()
+
+    for _, chunk in scored:
+        focus = str(chunk.get("focus_text") or "").strip()
+        key = normalize_for_compare(focus)
+        if key in seen:
+            continue
+
+        seen.add(key)
+        blocks.append(
+            {
+                "document_path": chunk.get("document_path"),
+                "chunk_id": chunk.get("chunk_id"),
+                "chunk_index": int(chunk.get("chunk_index") or 0),
+                "heading_paths": chunk.get("heading_paths") or [],
+                "score": round(float(chunk.get("score") or 0.0), 6),
+                "focus_text": focus,
+                "text": str(chunk.get("text") or "").strip(),
+            }
+        )
+
+        if len(blocks) >= max_blocks:
+            break
+
+    return blocks
+
+
+def expand_results_with_supplemental_sources(
+    db_path: Path,
+    query: str,
+    results: list[dict[str, Any]],
+    *,
+    published_only: bool = False,
+    limit: int = 5,
+) -> list[dict[str, Any]]:
+    base_results = [dict(item) for item in results]
+
+    if (
+        not query.strip()
+        or not db_path.exists()
+        or limit <= 0
+    ):
+        return base_results
+
+    with sqlite3.connect(db_path, timeout=5.0) as conn:
+        conn.row_factory = sqlite3.Row
+        conn.execute("PRAGMA query_only = ON")
+
+        if not sqlite_table_exists(conn, "chunks"):
+            return base_results
+
+        documents = _load_documents(conn)
+        chunks = _load_chunks(
+            conn,
+            published_only=published_only,
+        )
+
+    if not chunks:
+        return base_results
+
+    metadata_by_path = _document_metadata_by_path(documents)
+
+    # Attach strongest evidence from child documents belonging to the same student.
+    for item in base_results:
+        path = str(item.get("document_path") or "").strip()
+        if not path:
+            continue
+
+        canonical_path = canonical_student_main_path(path)
+        if canonical_path == path and "/students/" not in f"/{path}":
+            continue
+
+        blocks = _family_evidence_blocks(
+            query,
+            canonical_path,
+            chunks,
+        )
+        if blocks:
+            item["family_evidence_blocks"] = blocks
+            item["family_expansion"] = {
+                "strategy": "student_family_evidence",
+                "applied": True,
+                "canonical_document_path": canonical_path,
+                "evidence_count": len(blocks),
+            }
+
+    title_candidates = _title_anchor_candidates(
+        query,
+        documents,
+        chunks,
+    )
+    has_title_anchor = bool(title_candidates)
+    multi_document = is_multi_document_query(query)
+
+    global_candidates: list[
+        tuple[float, str, dict[str, Any], str]
+    ] = []
+
+    # If the query already names an exact document/person, anchor that document.
+    # Otherwise use a conservative global scan. Multi-document questions always
+    # receive the scan because one base retrieval result is not sufficient.
+    if not has_title_anchor or multi_document:
+        global_candidates = _global_chunk_candidates(
+            query,
+            chunks,
+            multi_document=multi_document,
+        )
+
+    all_candidates = sorted(
+        title_candidates + global_candidates,
+        key=lambda item: (-item[0], item[1]),
+    )
+
+    existing_by_path: dict[str, dict[str, Any]] = {
+        canonical_student_main_path(
+            str(item.get("document_path") or "")
+        ): item
+        for item in base_results
+        if str(item.get("document_path") or "").strip()
+    }
+
+    synthetic: list[dict[str, Any]] = []
+    seen_candidate_paths: set[str] = set()
+
+    for score, canonical_path, chunk, reason in all_candidates:
+        if canonical_path in seen_candidate_paths:
+            continue
+
+        seen_candidate_paths.add(canonical_path)
+        existing = existing_by_path.get(canonical_path)
+
+        if existing is not None:
+            existing["supplemental_expansion"] = {
+                "strategy": "global_or_entity_anchor",
+                "applied": True,
+                "reason": reason,
+                "score": round(score, 6),
+                "evidence_document_path": chunk.get("document_path"),
+                "evidence_chunk_id": chunk.get("chunk_id"),
+            }
+            continue
+
+        synthetic.append(
+            _source_from_chunk(
+                canonical_path,
+                chunk,
+                metadata_by_path,
+                reason=reason,
+                score=score,
+            )
+        )
+
+        if len(synthetic) >= limit:
+            break
+
+    # Exact title/person anchors are intentionally promoted. For broad
+    # multi-document queries, supplemental sources are merged by score ahead of
+    # weaker base-only results, but the final output still respects the tool limit.
+    if title_candidates:
+        promoted_paths = {
+            canonical_path
+            for _, canonical_path, _, _ in title_candidates
+        }
+        promoted_existing = [
+            item
+            for item in base_results
+            if canonical_student_main_path(
+                str(item.get("document_path") or "")
+            ) in promoted_paths
+        ]
+        remainder = [
+            item
+            for item in base_results
+            if item not in promoted_existing
+        ]
+        combined = promoted_existing + synthetic + remainder
+    elif multi_document:
+        combined = synthetic + base_results
+    else:
+        combined = synthetic + base_results
+
+    deduped: list[dict[str, Any]] = []
+    seen_paths: set[str] = set()
+
+    for item in combined:
+        path = canonical_student_main_path(
+            str(item.get("document_path") or "")
+        )
+        dedupe_key = path or str(item.get("source_url") or "")
+
+        if dedupe_key in seen_paths:
+            continue
+
+        seen_paths.add(dedupe_key)
+        deduped.append(item)
+
+        if len(deduped) >= limit:
+            break
+
+    return deduped