fix(memory): plan-ID searches pin the exact plan via metadata lookup (Patrick ruling) + purge 193 scratchpad junk vectors. Live-verified 100% top-hit, 1011 green

src/aipass/memory/apps/handlers/search/query_executor.py

@@ -18,6 +18,7 @@ Purpose:
     layer to satisfy thin-module standard.
 """
 
+import re
 import subprocess
 import json
 import os
@@ -210,6 +211,77 @@ def _filter_results(results: list, n_results: int) -> list:
     return filtered
 
 
+# =============================================================================
+# PLAN-ID EXACT MATCHING
+# =============================================================================
+
+_PLAN_ID_RE = re.compile(
+    r"(?:^|\b)((?:d|f|p|td|a)plan)[\s\-_]*(\d{3,5})\b",
+    re.IGNORECASE,
+)
+
+
+def _extract_plan_id(query: str) -> str | None:
+    """Extract a normalized plan ID (e.g. 'FPLAN-0332') from a query string."""
+    m = _PLAN_ID_RE.search(query)
+    if not m:
+        return None
+    prefix = m.group(1).upper()
+    number = m.group(2)
+    return f"{prefix}-{number}"
+
+
+def _fetch_plan_by_metadata(plan_id: str, n_results: int) -> list:
+    """Fetch plan chunks directly from ChromaDB by source_file metadata."""
+    input_data = {
+        "operation": "get_by_source",
+        "collection_name": "flow_plans",
+        "source_pattern": plan_id,
+        "n_results": n_results,
+    }
+    try:
+        result = subprocess.run(
+            [str(MEMORY_PYTHON), str(CHROMA_SUBPROCESS_SCRIPT)],
+            input=json.dumps(input_data),
+            capture_output=True,
+            text=True,
+            timeout=30,
+        )
+        if result.returncode != 0:
+            return []
+        data = json.loads(result.stdout)
+        if not data.get("success"):
+            return []
+        return data.get("results", [])
+    except Exception as e:
+        logger.warning(f"[search] Plan metadata fetch failed: {e}")
+        return []
+
+
+def _pin_plan_id_matches(query: str, filtered: list, n_results: int) -> list:
+    """Pin exact plan-ID matches to the top of results.
+
+    If the query contains a plan-ID pattern, fetch matching chunks directly
+    from ChromaDB metadata (bypassing embedding similarity) and pin them.
+    """
+    plan_id = _extract_plan_id(query)
+    if not plan_id:
+        return filtered
+
+    exact = _fetch_plan_by_metadata(plan_id, n_results)
+    if not exact:
+        return filtered
+
+    for r in exact:
+        r["similarity"] = 1.0
+
+    logger.info(f"[search] Pinned {len(exact)} exact matches for {plan_id}")
+
+    seen_ids = {r.get("id") for r in exact}
+    rest = [r for r in filtered if r.get("id") not in seen_ids]
+    return (exact + rest)[:n_results]
+
+
 # =============================================================================
 # PUBLIC API
 # =============================================================================
@@ -273,6 +345,9 @@ def execute_search(
     # Step 3: Filter and score results
     filtered_results = _filter_results(raw_results, n_results)
 
+    # Step 4: Pin exact plan-ID matches to the top
+    filtered_results = _pin_plan_id_matches(query, filtered_results, n_results)
+
     logger.info(f"[search] Filtered to {len(filtered_results)} relevant results")
 
     json_handler.log_operation(

src/aipass/memory/apps/handlers/storage/chroma_subprocess.py

@@ -152,6 +152,79 @@ def _check_plan(plan_label, db_path=None):
     return {"success": True, "found": match_count > 0, "count": match_count, "source_files": sorted(matching_files)}
 
 
+def _get_by_source(collection_name, source_pattern, n_results=5, db_path=None):
+    """Fetch documents whose source_file metadata contains a pattern.
+
+    Args:
+        collection_name: Name of the ChromaDB collection
+        source_pattern: Substring to match in source_file metadata
+        n_results: Maximum number of results to return
+        db_path: Optional path to Chroma database
+
+    Returns:
+        Dict with success, results list (document, metadata, id)
+    """
+    client = _get_client(db_path)
+
+    try:
+        collection = client.get_collection(collection_name, embedding_function=None)
+    except Exception as e:
+        logger.warning(f"[chroma_subprocess] Collection '{collection_name}' not found in get_by_source: {e}")
+        return {"success": False, "error": f"Collection '{collection_name}' not found: {e}"}
+
+    result = collection.get(include=["metadatas", "documents"])
+    matches = []
+    for i, meta in enumerate(result.get("metadatas", [])):
+        source = meta.get("source_file", "")
+        if source_pattern in source:
+            matches.append(
+                {
+                    "collection": collection_name,
+                    "document": result["documents"][i],
+                    "metadata": meta,
+                    "id": result["ids"][i],
+                    "distance": 0.0,
+                }
+            )
+            if len(matches) >= n_results:
+                break
+
+    return {"success": True, "results": matches, "count": len(matches)}
+
+
+def _delete_by_source(collection_name, source_pattern, db_path=None):
+    """Delete vectors whose source_file metadata contains a pattern.
+
+    Args:
+        collection_name: Name of the ChromaDB collection
+        source_pattern: Substring to match in source_file metadata
+        db_path: Optional path to Chroma database
+
+    Returns:
+        Dict with success, deleted count, and matched IDs
+    """
+    client = _get_client(db_path)
+
+    try:
+        collection = client.get_collection(collection_name, embedding_function=None)
+    except Exception as e:
+        logger.warning(f"[chroma_subprocess] Collection '{collection_name}' not found in delete_by_source: {e}")
+        return {"success": False, "error": f"Collection '{collection_name}' not found: {e}"}
+
+    result = collection.get(include=["metadatas"])
+    ids_to_delete = []
+    for i, meta in enumerate(result.get("metadatas", [])):
+        source = meta.get("source_file", "")
+        if source_pattern in source:
+            ids_to_delete.append(result["ids"][i])
+
+    if not ids_to_delete:
+        return {"success": True, "deleted": 0, "ids": [], "message": "No matching vectors found"}
+
+    collection.delete(ids=ids_to_delete)
+    return {"success": True, "deleted": len(ids_to_delete), "ids": ids_to_delete}
+
+
 def _search_vectors(query_embedding, branch=None, memory_type=None, n_results=5, db_path=None):
     """Search for similar vectors."""
     client = _get_client(db_path)
@@ -232,6 +305,19 @@ def main():
             )
         elif operation == "check_plan":
             result = _check_plan(plan_label=input_data.get("plan_label"), db_path=input_data.get("db_path"))
+        elif operation == "get_by_source":
+            result = _get_by_source(
+                collection_name=input_data.get("collection_name"),
+                source_pattern=input_data.get("source_pattern"),
+                n_results=input_data.get("n_results", 5),
+                db_path=input_data.get("db_path"),
+            )
+        elif operation == "delete_by_source":
+            result = _delete_by_source(
+                collection_name=input_data.get("collection_name"),
+                source_pattern=input_data.get("source_pattern"),
+                db_path=input_data.get("db_path"),
+            )
         else:
             result = {"success": False, "error": f"Unknown operation: {operation}"}