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

@@ -6,9 +6,11 @@ same architectural rule as the other extractors (local MCP only).
 """
 from __future__ import annotations
 import logging
+from uuid import UUID
 from legal_mcp import config
 from legal_mcp.config import parse_llm_json
 from legal_mcp.services import claude_session
+from legal_mcp.services import db, embeddings
 
 logger = logging.getLogger(__name__)
 
@@ -75,3 +77,28 @@ async def classify_treatment(cited_citation: str, context: str) -> str:
         logger.warning("classify_treatment failed: %s", e)
         return "mentioned"
     return _coerce_treatment(result if isinstance(result, dict) else {})
+
+
+async def build_for_precedent(case_law_id: str | UUID) -> dict:
+    """For one cited precedent: classify+match+store each incoming citation. Idempotent."""
+    if isinstance(case_law_id, str):
+        case_law_id = UUID(case_law_id)
+    cits = await db.incoming_citations_for_precedent(case_law_id)
+    linked = 0
+    for c in cits:
+        ctx = (c.get("context") or "").strip()
+        if not ctx:
+            continue
+        vecs = await embeddings.embed_texts([ctx], input_type="query")
+        best = await db.nearest_halacha_for_vector(case_law_id, vecs[0])
+        halacha_id = accept_match(best)
+        if not halacha_id:
+            continue
+        treatment = await classify_treatment(c.get("citing_case_law_id") or c.get("citing_decision_id") or "", ctx)
+        await db.store_corroboration(
+            halacha_id, c["source_id"],
+            c.get("citing_case_law_id"), c.get("citing_decision_id"),
+            treatment, best[1], ctx,
+        )
+        linked += 1
+    return {"citations": len(cits), "linked": linked}

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

@@ -3398,6 +3398,47 @@ async def nearest_halacha_for_vector(case_law_id: UUID, vec: list[float]) -> tup
     return (row["id"], float(row["sim"])) if row else None
 
 
+async def incoming_citations_for_precedent(case_law_id: UUID) -> list[dict]:
+    """All incoming citations (both graphs) with their context + source id."""
+    pool = await get_pool()
+    rows = await pool.fetch(
+        "SELECT id::text AS source_id, source_case_law_id::text AS citing_case_law_id, "
+        "       NULL::text AS citing_decision_id, match_context AS context "
+        "FROM precedent_internal_citations WHERE cited_case_law_id = $1 "
+        "UNION ALL "
+        "SELECT id::text, NULL, decision_id::text, context_text "
+        "FROM case_law_citations WHERE case_law_id = $1",
+        case_law_id,
+    )
+    return [dict(r) for r in rows]
+
+
+async def store_corroboration(
+    halacha_id: str,
+    source_id: str,
+    citing_case_law_id,
+    citing_decision_id,
+    treatment: str,
+    score: float,
+    context: str,
+) -> None:
+    from uuid import UUID as _UUID
+    pool = await get_pool()
+    # asyncpg requires UUID objects for uuid-typed columns; convert non-None strings.
+    h_id = _UUID(halacha_id) if isinstance(halacha_id, str) else halacha_id
+    s_id = _UUID(source_id) if isinstance(source_id, str) else source_id
+    cl_id = _UUID(citing_case_law_id) if (citing_case_law_id and isinstance(citing_case_law_id, str)) else citing_case_law_id
+    d_id = _UUID(citing_decision_id) if (citing_decision_id and isinstance(citing_decision_id, str)) else citing_decision_id
+    await pool.execute(
+        "INSERT INTO halacha_citation_corroboration "
+        "(halacha_id, citing_case_law_id, citing_decision_id, source_citation_id, treatment, match_score, match_context) "
+        "VALUES ($1,$2,$3,$4,$5,$6,$7) "
+        "ON CONFLICT (halacha_id, source_citation_id) DO UPDATE SET "
+        "treatment=EXCLUDED.treatment, match_score=EXCLUDED.match_score",
+        h_id, cl_id, d_id, s_id, treatment, score, context,
+    )
+
+
 async def search_precedent_library_semantic(
     query_embedding: list[float],
     practice_area: str = "",