legal-ai/mcp-server/src/legal_mcp/services/hybrid_search.py

"""Hybrid (text + image) search wrappers.

Layered on top of ``rerank.maybe_rerank``. When ``MULTIMODAL_ENABLED`` is
true the result comes from a weighted merge of:

  • text side: cosine on chunks → optional rerank-2 cross-encoder
    (precedent search additionally fuses ``ts_rank_cd`` lexical results
    via RRF before this step — see ``BM25_HYBRID_ENABLED``)
  • image side: cosine on per-page voyage-multimodal-3 embeddings

rerank-2 is a *text* cross-encoder, so image-side rows are NOT passed
through it; they keep their cosine score and merge alongside the
(possibly reranked) text rows. Image-only pages with no overlapping
text chunk are surfaced as ``match_type='image'`` so scanned-only or
visual-heavy content still appears in results.

When ``MULTIMODAL_ENABLED`` is false this module degenerates to plain
``rerank.maybe_rerank`` — callers can wrap unconditionally and let env
control behaviour.

BM25/lexical leg (V12 + ``BM25_HYBRID_ENABLED``):
``search_precedent_library_hybrid`` runs ``search_precedent_library_lexical``
in parallel with the semantic side and fuses the two by rank via RRF.
This recovers exact-string recall (case-number citations like "1461/20",
rare planning terms) that voyage embeddings blur. The fused list is
then handed to rerank-2 (if enabled) and to the image RRF (if
multimodal is enabled) exactly as before.
"""
from __future__ import annotations

import logging
from typing import Any
from uuid import UUID

from legal_mcp import config
from legal_mcp.services import db, embeddings, rerank

logger = logging.getLogger(__name__)


async def search_documents_hybrid(
    query: str,
    query_text_embedding: list[float],
    *,
    limit: int,
    case_id: UUID | None = None,
    section_type: str | None = None,
    practice_area: str | None = None,
    appeal_subtype: str | None = None,
) -> list[dict]:
    """Hybrid wrapper for document-chunk search (search_decisions /
    search_case_documents / find_similar_cases)."""
    fetch_k = max(limit, config.VOYAGE_RERANK_FETCH_K) if config.MULTIMODAL_ENABLED else limit
    text_results = await rerank.maybe_rerank(
        query=query,
        base_search=lambda **kw: db.search_similar(
            query_embedding=query_text_embedding, **kw,
        ),
        limit=fetch_k,
        case_id=case_id,
        section_type=section_type,
        practice_area=practice_area,
        appeal_subtype=appeal_subtype,
    )
    if not config.MULTIMODAL_ENABLED:
        return text_results[:limit]

    try:
        query_img_emb = await embeddings.embed_query_for_multimodal(query)
        img_rows = await db.search_document_images_similar(
            query_img_emb,
            limit=fetch_k,
            case_id=case_id,
            practice_area=practice_area,
            appeal_subtype=appeal_subtype,
        )
    except Exception as e:
        logger.warning("Hybrid: image side failed, returning text only: %s", e)
        return text_results[:limit]

    merged = _merge(
        text_results, img_rows,
        id_field="document_id",
        text_weight=config.MULTIMODAL_TEXT_WEIGHT,
    )
    return merged[:limit]


async def search_precedent_library_hybrid(
    query: str,
    query_text_embedding: list[float],
    *,
    limit: int,
    practice_area: str = "",
    court: str = "",
    precedent_level: str = "",
    appeal_subtype: str = "",
    is_binding: bool | None = None,
    subject_tag: str = "",
    include_halachot: bool = True,
    source_kind: str = "",
    district: str = "",
    chair_name: str = "",
    max_per_case_law: int = 2,
) -> list[dict]:
    """Hybrid wrapper for precedent-library search.

    source_kind=''                   → the whole corpus (default, #232)
    source_kind='external_upload'    → court rulings only
    source_kind='internal_committee' → appeals-committee decisions only
    max_per_case_law: MMR-style diversity cap — at most N hits per
        case_law_id in the final ranked list (default 2). Prevents a
        single precedent from monopolizing the result list when many of
        its chunks/halachot are individually relevant.

    When ``config.BM25_HYBRID_ENABLED`` is true (default) ``_base`` fuses
    semantic cosine + lexical ``ts_rank_cd`` via RRF before handing the
    candidates to rerank-2 (if enabled) and the image merge (if
    multimodal is enabled).
    """
    # Fetch deeper so diversity dedup still leaves enough candidates.
    fetch_k = max(limit * max(max_per_case_law, 1), config.VOYAGE_RERANK_FETCH_K) \
        if config.MULTIMODAL_ENABLED else max(limit * max(max_per_case_law, 1), limit)

    async def _base(limit: int) -> list[dict]:
        sem_rows = await db.search_precedent_library_semantic(
            query_embedding=query_text_embedding,
            practice_area=practice_area,
            court=court,
            precedent_level=precedent_level,
            appeal_subtype=appeal_subtype,
            is_binding=is_binding,
            subject_tag=subject_tag,
            limit=limit,
            include_halachot=include_halachot,
            source_kind=source_kind,
            district=district,
            chair_name=chair_name,
        )
        if not config.BM25_HYBRID_ENABLED:
            return sem_rows
        # Fetch lexical with ≥ 2× depth so RRF has reserves at the tail.
        lex_limit = max(limit * 2, limit)
        try:
            lex_rows = await db.search_precedent_library_lexical(
                query=query,
                practice_area=practice_area,
                court=court,
                precedent_level=precedent_level,
                appeal_subtype=appeal_subtype,
                is_binding=is_binding,
                subject_tag=subject_tag,
                source_kind=source_kind,
                district=district,
                chair_name=chair_name,
                limit=lex_limit,
                include_halachot=include_halachot,
            )
        except Exception as e:
            logger.warning(
                "Hybrid precedent: lexical side failed, semantic only: %s", e,
            )
            return sem_rows
        if not lex_rows:
            return sem_rows
        return _merge_sem_lex(sem_rows, lex_rows, limit=limit)

    text_results = await rerank.maybe_rerank(
        query=query, base_search=_base, limit=fetch_k,
    )
    if not config.MULTIMODAL_ENABLED:
        return _diversify_by_case_law(text_results, limit, max_per_case_law)

    try:
        query_img_emb = await embeddings.embed_query_for_multimodal(query)
        img_rows = await db.search_precedent_images_similar(
            query_img_emb,
            limit=fetch_k,
            practice_area=practice_area,
            court=court,
            precedent_level=precedent_level,
            appeal_subtype=appeal_subtype,
            is_binding=is_binding,
        )
    except Exception as e:
        logger.warning("Hybrid: image side failed, returning text only: %s", e)
        return _diversify_by_case_law(text_results, limit, max_per_case_law)

    merged = _merge(
        text_results, img_rows,
        id_field="case_law_id",
        text_weight=config.MULTIMODAL_TEXT_WEIGHT,
    )
    return _diversify_by_case_law(merged, limit, max_per_case_law)


def _diversify_by_case_law(
    rows: list[dict],
    limit: int,
    max_per_case_law: int,
) -> list[dict]:
    """MMR-style diversity cap: at most ``max_per_case_law`` rows per
    case_law_id in the final list. Preserves input order (which is the
    relevance ranking) — for each row, include it only if we haven't
    reached the cap for its case_law_id yet.

    Set max_per_case_law<=0 to disable (returns rows[:limit] unchanged).
    """
    if max_per_case_law <= 0 or not rows:
        return rows[:limit]
    counts: dict[str, int] = {}
    out: list[dict] = []
    for r in rows:
        clid = str(r.get("case_law_id") or "")
        if not clid:
            out.append(r)
            if len(out) >= limit:
                break
            continue
        n = counts.get(clid, 0)
        if n < max_per_case_law:
            out.append(r)
            counts[clid] = n + 1
            if len(out) >= limit:
                break
    return out


def _row_key(r: dict) -> tuple[str, str]:
    """Stable identity for sem/lex RRF.

    Halachot rows have ``halacha_id``; chunk rows have ``chunk_id``.
    Returns ``(type, id)`` so a halacha and a chunk with the same UUID
    (extremely unlikely, but distinct namespaces) don't collide.
    """
    typ = str(r.get("type") or "")
    rid = r.get("halacha_id") if typ == "halacha" else r.get("chunk_id")
    return (typ, str(rid or ""))


def _merge_sem_lex(
    sem_rows: list[dict],
    lex_rows: list[dict],
    *,
    limit: int,
) -> list[dict]:
    """RRF fusion of semantic + lexical precedent results.

    Why RRF (and not weighted score sum): cosine similarities (~0.4-0.7)
    and ``ts_rank_cd`` values (often 0.001-0.5, query-length-dependent)
    live on completely different scales — a weighted sum would let one
    side dominate by accident. RRF combines by *rank*, so a row that
    tops one list and is mid-pack in the other gets a robust boost.

    Per row::

        rrf_score = 1 / (k + sem_rank) + 1 / (k + lex_rank)

    A row that appears in only one list contributes that list's term
    only. Output is sorted by combined score, with extra debug fields
    (``sem_score``, ``sem_rank``, ``lex_score``, ``lex_rank``) attached
    so callers and tests can inspect why a row ranked where it did.

    The row payload (``content``, ``rule_statement``, ``case_*`` joins,
    etc.) is taken from the semantic-side row when available — the two
    sources return identical column shapes, but semantic rows carry the
    confidence-boosted ``score`` that the rest of the pipeline expects.
    """
    k = config.MULTIMODAL_RRF_K
    sem_rank_by_key: dict[tuple, int] = {}
    sem_row_by_key: dict[tuple, dict] = {}
    for rank, r in enumerate(sem_rows, 1):
        key = _row_key(r)
        if not key[1]:
            continue
        sem_rank_by_key[key] = rank
        sem_row_by_key[key] = r

    lex_rank_by_key: dict[tuple, int] = {}
    lex_row_by_key: dict[tuple, dict] = {}
    for rank, r in enumerate(lex_rows, 1):
        key = _row_key(r)
        if not key[1]:
            continue
        lex_rank_by_key[key] = rank
        lex_row_by_key[key] = r

    all_keys = set(sem_rank_by_key) | set(lex_rank_by_key)
    merged: list[dict] = []
    for key in all_keys:
        sem_rank = sem_rank_by_key.get(key)
        lex_rank = lex_rank_by_key.get(key)
        base = sem_row_by_key.get(key) or lex_row_by_key.get(key)
        if base is None:
            continue
        d = dict(base)
        sem_term = 1.0 / (k + sem_rank) if sem_rank else 0.0
        lex_term = 1.0 / (k + lex_rank) if lex_rank else 0.0
        d["sem_score"] = float(sem_row_by_key[key]["score"]) \
            if key in sem_row_by_key else 0.0
        d["sem_rank"] = sem_rank or 0
        d["lex_score"] = float(lex_row_by_key[key]["score"]) \
            if key in lex_row_by_key else 0.0
        d["lex_rank"] = lex_rank or 0
        d["score"] = sem_term + lex_term
        merged.append(d)

    merged.sort(key=lambda x: -float(x["score"]))
    return merged[:limit]


def _merge(
    text_rows: list[dict],
    img_rows: list[dict],
    id_field: str,
    text_weight: float,
) -> list[dict]:
    """Reciprocal Rank Fusion of text + image rows.

    Why RRF: voyage-3 cosine scores (~0.4-0.5) and voyage-multimodal-3
    scores (~0.2-0.25) live on different scales — a direct weighted
    sum lets text always dominate. RRF combines by *rank* in each list,
    making the merge robust to score-scale differences.

    Per item::

        rrf_score = text_weight / (k + text_rank)
                  + image_weight / (k + image_rank)

    A row that appears in only one list contributes that list's term
    only. Rows joined at ``(id_field, page_number)`` get both terms —
    surfaced as ``match_type='text+image'`` with the thumbnail attached.

    Halachot in precedent rows have no page_number; they remain
    text-only under RRF (the case-level image boost is dropped — RRF
    works on rank, not raw scores).
    """
    from legal_mcp import config as _cfg
    img_weight = 1.0 - text_weight
    k = _cfg.MULTIMODAL_RRF_K

    # Index image rows by their join key for boost detection.
    img_rank_by_key: dict[tuple, int] = {}
    img_row_by_key: dict[tuple, dict] = {}
    for rank, r in enumerate(img_rows, 1):
        key = (str(r[id_field]), r.get("page_number"))
        img_rank_by_key[key] = rank
        img_row_by_key[key] = r

    seen_image_keys: set = set()
    merged: list[dict] = []
    for rank, r in enumerate(text_rows, 1):
        rid = str(r[id_field])
        page = r.get("page_number")
        key = (rid, page) if page is not None else None
        img_rank = img_rank_by_key.get(key) if key else None
        text_term = text_weight / (k + rank)
        image_term = img_weight / (k + img_rank) if img_rank else 0.0
        d = dict(r)
        d["text_score"] = float(r.get("score", 0.0))
        d["text_rank"] = rank
        if img_rank:
            img_hit = img_row_by_key[key]
            d["image_score"] = float(img_hit.get("score", 0.0))
            d["image_rank"] = img_rank
            d["image_thumbnail_path"] = img_hit.get("image_thumbnail_path")
            d["match_type"] = "text+image"
            seen_image_keys.add(key)
        else:
            d["image_score"] = 0.0
            d["match_type"] = "text"
        d["score"] = text_term + image_term
        merged.append(d)

    for rank, r in enumerate(img_rows, 1):
        key = (str(r[id_field]), r.get("page_number"))
        if key in seen_image_keys:
            continue
        d = dict(r)
        d["text_score"] = 0.0
        d["image_score"] = float(r.get("score", 0.0))
        d["image_rank"] = rank
        d["score"] = img_weight / (k + rank)
        d["match_type"] = "image"
        d["content"] = ""
        d["section_type"] = "image"
        merged.append(d)

    merged.sort(key=lambda x: -float(x["score"]))
    return merged