fix(retrieval): switch hybrid merge to Reciprocal Rank Fusion (RRF)

Cosine scores in voyage-3 (~0.4-0.5) and voyage-multimodal-3
(~0.2-0.25) live on different scales. The previous weighted-sum
merge let text always dominate — verified empirically: 0 image-only
hits across 7 queries on case 8174-24, image side contributed nothing.

RRF combines by *rank* in each list rather than raw score, robust
to scale differences. Per-item score:

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

A row that appears in both lists (joined on (id_field, page_number))
gets both terms — surfaced as match_type='text+image'.

After fix on 8174-24 (146 image rows): 2 image-only hits land in
top-5 across all 7 test queries, surfacing actual table/diagram/
signature pages (p12, p13 of שומת המשיבה for 'טבלת השוואת ערכי שומה',
p25 of שומת השגה for 'תרשים גוש וחלקה', etc).

On 8137-24 (273 image rows): 'חישוב היוון של דמי החכירה' goes from
0 baseline results → 5 hybrid results (3 text + 2 image), opening
recall on scanned content the OCR layer misses.

Default MULTIMODAL_TEXT_WEIGHT 0.65 → 0.5 (vanilla RRF) since the
prior 0.65 was tuned for raw cosine scales that no longer apply.
New env knob MULTIMODAL_RRF_K (default 60, standard literature).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

mcp-server/src/legal_mcp/config.py

@@ -73,13 +73,19 @@ MULTIMODAL_DPI = int(os.environ.get("MULTIMODAL_DPI", "144"))
 # Separate, lower DPI for the JPEG thumbnail saved to disk for UI
 # preview. ~96dpi → ~20KB/page; ingestion-time, no re-render at view.
 MULTIMODAL_THUMB_DPI = int(os.environ.get("MULTIMODAL_THUMB_DPI", "96"))
-# Hybrid merge weight for the *text* side. The image side gets
-# (1 - this). POC found text dominates most queries; image wins only
-# on table/visual queries — slight text bias starting point, tunable
-# per env without redeploy.
+# Hybrid merge: Reciprocal Rank Fusion (RRF) bias for the *text* side.
+# voyage-3 cosine scores (~0.4-0.5) and voyage-multimodal-3 scores
+# (~0.20-0.25) live on different scales; a direct weighted sum lets
+# text always dominate. RRF is rank-based and robust to that. The
+# weight here biases the contribution of each side: 0.5 = balanced
+# (vanilla RRF), >0.5 favours text, <0.5 favours image. Tunable per
+# env without redeploy.
 MULTIMODAL_TEXT_WEIGHT = float(
-    os.environ.get("MULTIMODAL_TEXT_WEIGHT", "0.65")
+    os.environ.get("MULTIMODAL_TEXT_WEIGHT", "0.5")
 )
+# RRF damping constant. Standard literature value is 60: lower values
+# concentrate weight at top ranks; higher values flatten the curve.
+MULTIMODAL_RRF_K = int(os.environ.get("MULTIMODAL_RRF_K", "60"))
 
 # Halacha extraction — auto-approve threshold. Halachot with extractor
 # confidence >= this value are inserted with review_status='approved'

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

@@ -140,59 +140,72 @@ def _merge(
     id_field: str,
     text_weight: float,
 ) -> list[dict]:
-    """Weighted merge of text + image rows.
+    """Reciprocal Rank Fusion of text + image rows.
 
-    Joins on ``(id_field, page_number)``. Halachot in precedent rows
-    have no page_number; for those, image_score = max page score in
-    the same case_law row (case-level boost).
+    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.
 
-    Image-only rows (no matching text hit) appear with match_type='image'
-    and empty content — UI shows the thumbnail instead of a snippet.
+    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
-    img_by_key: dict[tuple, dict] = {}
-    img_max_by_id: dict[str, float] = {}
-    for r in img_rows:
-        rid = str(r[id_field])
-        page = r.get("page_number")
-        img_by_key[(rid, page)] = r
-        score = float(r.get("score", 0.0))
-        img_max_by_id[rid] = max(img_max_by_id.get(rid, 0.0), score)
+    k = _cfg.MULTIMODAL_RRF_K
 
-    seen: set = set()
+    # 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 r in text_rows:
+    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_hit = img_by_key.get(key) if key else None
-        text_score = float(r.get("score", 0.0))
-        if img_hit:
-            image_score = float(img_hit["score"])
-        elif r.get("type") == "halacha":
-            image_score = img_max_by_id.get(rid, 0.0)
-        else:
-            image_score = 0.0
+        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"] = text_score
-        d["image_score"] = image_score
-        d["score"] = text_score * text_weight + image_score * img_weight
-        d["match_type"] = "text+image" if img_hit else "text"
-        if img_hit:
+        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")
-        if key:
-            seen.add(key)
+            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 r in img_rows:
-        rid = str(r[id_field])
-        key = (rid, r.get("page_number"))
-        if key in seen:
+    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["score"] = float(r.get("score", 0.0)) * img_weight
+        d["image_rank"] = rank
+        d["score"] = img_weight / (k + rank)
         d["match_type"] = "image"
         d["content"] = ""
         d["section_type"] = "image"