feat: entity metadata + diary ingest + BM25 hybrid search

Three features that close the gap between the architecture docs
and the actual codebase:

1. Entity metadata on drawers and closets
   - _extract_entities_for_metadata() pulls names from known_entities.json
     + proper nouns appearing 2+ times
   - Stamped as "entities" field in ChromaDB metadata
   - Enables filterable search by person/project name

2. Day-based diary ingest (diary_ingest.py)
   - ONE drawer per day, upserted as the day grows
   - Closets pack topics atomically, never split mid-topic
   - Tracks entry count in state file, only processes new entries
   - Usage: python -m mempalace.diary_ingest --dir ~/summaries

3. BM25 hybrid search in searcher.py
   - _bm25_score() keyword matching complements vector similarity
   - _hybrid_rank() combines both signals (60% vector, 40% BM25)
   - Catches exact name/term matches that embeddings miss
   - Applied to both closet-first and direct drawer search paths

689/689 tests pass.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

mempalace/diary_ingest.py

@@ -0,0 +1,173 @@
+"""
+diary_ingest.py — Ingest daily summary files into the palace.
+
+Architecture:
+- ONE drawer per day — full verbatim content, upserted as the day grows
+- Closets pack topics up to 1500 chars, never split mid-topic
+- Only new entries are processed (tracks entry count in state file)
+- Entities extracted and stamped on metadata for filterable search
+
+Usage:
+    python -m mempalace.diary_ingest --dir ~/daily_summaries --palace ~/.mempalace/palace
+    python -m mempalace.diary_ingest --dir ~/daily_summaries --palace ~/.mempalace/palace --force
+"""
+
+import hashlib
+import json
+import os
+import re
+from datetime import datetime, timezone
+from pathlib import Path
+
+from .palace import (
+    get_collection,
+    get_closets_collection,
+    build_closet_lines,
+    upsert_closet_lines,
+    CLOSET_CHAR_LIMIT,
+)
+from .miner import _extract_entities_for_metadata
+
+
+DIARY_ENTRY_RE = re.compile(r"^## .+", re.MULTILINE)
+
+
+def _split_entries(text):
+    """Split diary text into (header, body) pairs per ## entry."""
+    parts = DIARY_ENTRY_RE.split(text)
+    headers = DIARY_ENTRY_RE.findall(text)
+    entries = []
+    for i, header in enumerate(headers):
+        body = parts[i + 1] if i + 1 < len(parts) else ""
+        entries.append((header.strip(), body.strip()))
+    return entries
+
+
+def ingest_diaries(
+    diary_dir,
+    palace_path,
+    wing="diary",
+    force=False,
+):
+    """Ingest daily summary files into the palace.
+
+    Each date file gets ONE drawer (upserted as day grows) and
+    closets that pack topics atomically up to 1500 chars.
+    """
+    diary_dir = Path(diary_dir).expanduser().resolve()
+    if not diary_dir.exists():
+        print(f"Diary directory not found: {diary_dir}")
+        return
+
+    diary_files = sorted(diary_dir.glob("*.md"))
+    if not diary_files:
+        print(f"No .md files in {diary_dir}")
+        return
+
+    # State tracks which entries have been closeted per file
+    state_file = diary_dir / ".diary_ingest_state.json"
+    state = {} if force else (
+        json.loads(state_file.read_text()) if state_file.exists() else {}
+    )
+
+    drawers_col = get_collection(palace_path)
+    closets_col = get_closets_collection(palace_path)
+
+    days_updated = 0
+    closets_created = 0
+
+    for diary_path in diary_files:
+        text = diary_path.read_text(encoding="utf-8", errors="replace")
+        if len(text.strip()) < 50:
+            continue
+
+        date_match = re.match(r"(\d{4}-\d{2}-\d{2})", diary_path.stem)
+        if not date_match:
+            continue
+        date_str = date_match.group(1)
+
+        # Skip if content hasn't changed
+        prev_size = state.get(diary_path.name, {}).get("size", 0)
+        curr_size = len(text)
+        if curr_size == prev_size and not force:
+            continue
+
+        now_iso = datetime.now(timezone.utc).isoformat()
+        drawer_id = f"drawer_diary_{date_str}"
+
+        # Extract entities from full day text
+        entities = _extract_entities_for_metadata(text)
+
+        # UPSERT the day's drawer (full verbatim, replaces as day grows)
+        drawer_meta = {
+            "date": date_str,
+            "wing": wing,
+            "room": "daily",
+            "source_file": str(diary_path),
+            "source_session": "daily_diary",
+            "filed_at": now_iso,
+        }
+        if entities:
+            drawer_meta["entities"] = entities
+        drawers_col.upsert(
+            documents=[text],
+            ids=[drawer_id],
+            metadatas=[drawer_meta],
+        )
+
+        # Split into entries and find new ones
+        entries = _split_entries(text)
+        prev_entry_count = state.get(diary_path.name, {}).get("entry_count", 0)
+        new_entries = entries[prev_entry_count:] if not force else entries
+
+        if new_entries:
+            # Build closet lines from new entries
+            all_lines = []
+            for header, body in new_entries:
+                entry_text = f"{header}\n{body}"
+                entry_lines = build_closet_lines(
+                    str(diary_path), [drawer_id], entry_text, wing, "daily"
+                )
+                all_lines.extend(entry_lines)
+
+            if all_lines:
+                closet_id_base = f"closet_diary_{date_str}"
+                closet_meta = {
+                    "date": date_str,
+                    "wing": wing,
+                    "room": "daily",
+                    "source_file": str(diary_path),
+                    "filed_at": now_iso,
+                }
+                if entities:
+                    closet_meta["entities"] = entities
+                n = upsert_closet_lines(
+                    closets_col, closet_id_base, all_lines, closet_meta
+                )
+                closets_created += n
+
+        state[diary_path.name] = {
+            "size": curr_size,
+            "entry_count": len(entries),
+            "ingested_at": now_iso,
+        }
+        days_updated += 1
+
+    state_file.write_text(json.dumps(state, indent=2))
+    if days_updated:
+        print(f"Diary: {days_updated} days updated, {closets_created} new closets")
+
+    return {"days_updated": days_updated, "closets_created": closets_created}
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Ingest daily summaries into the palace")
+    parser.add_argument("--dir", required=True, help="Path to daily_summaries directory")
+    parser.add_argument("--palace", default=os.path.expanduser("~/.mempalace/palace"))
+    parser.add_argument("--wing", default="diary")
+    parser.add_argument("--force", action="store_true")
+    args = parser.parse_args()
+
+    ingest_diaries(args.dir, args.palace, wing=args.wing, force=args.force)

mempalace/miner.py

@@ -371,6 +371,43 @@ def chunk_text(content: str, source_file: str) -> list:
 # =============================================================================
 
 
+def _extract_entities_for_metadata(content: str) -> str:
+    """Extract entity names from content for metadata tagging.
+
+    Returns semicolon-separated string of entity names found in the text,
+    suitable for ChromaDB metadata filtering.
+    """
+    import re
+    # Load known entities from registry if available
+    known_names = set()
+    registry_path = os.path.join(os.path.expanduser("~"), ".mempalace", "known_entities.json")
+    if os.path.exists(registry_path):
+        try:
+            import json
+            kd = json.loads(open(registry_path).read())
+            for cat in kd.values():
+                if isinstance(cat, list):
+                    known_names.update(cat)
+        except Exception:
+            pass
+
+    matched = set()
+    # Match known entities
+    for name in known_names:
+        if re.search(r'(?<!\w)' + re.escape(name) + r'(?!\w)', content):
+            matched.add(name)
+    # Also catch capitalized words appearing 2+ times (likely proper nouns)
+    words = re.findall(r"\b[A-Z][a-z]{2,}\b", content[:5000])
+    freq = {}
+    for w in words:
+        freq[w] = freq.get(w, 0) + 1
+    for w, c in freq.items():
+        if c >= 2 and len(w) > 2:
+            matched.add(w)
+
+    return ";".join(sorted(matched))[:500] if matched else ""
+
+
 def add_drawer(
     collection, wing: str, room: str, content: str, source_file: str, chunk_index: int, agent: str
 ):
@@ -390,6 +427,10 @@ def add_drawer(
             metadata["source_mtime"] = os.path.getmtime(source_file)
         except OSError:
             pass
+        # Tag with entity names for filterable search
+        entities = _extract_entities_for_metadata(content)
+        if entities:
+            metadata["entities"] = entities
         collection.upsert(
             documents=[content],
             ids=[drawer_id],
@@ -479,13 +520,17 @@ def process_file(
             ]
             closet_lines = build_closet_lines(source_file, drawer_ids, content, wing, room)
             closet_id_base = f"closet_{wing}_{room}_{hashlib.sha256(source_file.encode()).hexdigest()[:24]}"
-            upsert_closet_lines(closets_col, closet_id_base, closet_lines, {
+            entities = _extract_entities_for_metadata(content)
+            closet_meta = {
                 "wing": wing,
                 "room": room,
                 "source_file": source_file,
                 "drawer_count": drawers_added,
                 "filed_at": datetime.now().isoformat(),
-            })
+            }
+            if entities:
+                closet_meta["entities"] = entities
+            upsert_closet_lines(closets_col, closet_id_base, closet_lines, closet_meta)
 
     return drawers_added, room
 

mempalace/searcher.py

@@ -2,11 +2,14 @@
 """
 searcher.py — Find anything. Exact words.
 
-Semantic search against the palace.
-Returns verbatim text — the actual words, never summaries.
+Hybrid search: BM25 keyword matching + vector semantic similarity.
+Searches closets first (fast index), then hydrates full drawer content.
+Falls back to direct drawer search for palaces without closets.
 """
 
 import logging
+import math
+import re
 from pathlib import Path
 
 from .palace import get_collection, get_closets_collection
@@ -18,6 +21,59 @@ class SearchError(Exception):
     """Raised when search cannot proceed (e.g. no palace found)."""
 
 
+def _bm25_score(query: str, document: str, k1: float = 1.5, b: float = 0.75, avg_dl: float = 500) -> float:
+    """Simple BM25 score for a single document against a query.
+
+    This is a lightweight keyword-matching signal that complements vector
+    similarity. It catches exact matches that embeddings might miss
+    (e.g., specific names, project codes, error messages).
+    """
+    query_terms = set(re.findall(r'\w{2,}', query.lower()))
+    doc_terms = re.findall(r'\w{2,}', document.lower())
+    if not query_terms or not doc_terms:
+        return 0.0
+    doc_len = len(doc_terms)
+    term_freq = {}
+    for t in doc_terms:
+        term_freq[t] = term_freq.get(t, 0) + 1
+
+    score = 0.0
+    for term in query_terms:
+        tf = term_freq.get(term, 0)
+        if tf > 0:
+            # Simplified IDF — treat each query term as moderately rare
+            idf = math.log(2.0)
+            numerator = tf * (k1 + 1)
+            denominator = tf + k1 * (1 - b + b * doc_len / avg_dl)
+            score += idf * numerator / denominator
+    return score
+
+
+def _hybrid_rank(vector_results, query: str, vector_weight: float = 0.6, bm25_weight: float = 0.4):
+    """Re-rank results using both vector distance and BM25 keyword score.
+
+    Returns results sorted by combined score (higher = better).
+    """
+    if not vector_results:
+        return vector_results
+
+    # Normalize vector distances to 0-1 similarity
+    max_dist = max(r.get("distance", 1.0) for r in vector_results) or 1.0
+    for r in vector_results:
+        vec_sim = max(0.0, 1 - r.get("distance", 1.0) / max(max_dist, 0.001))
+        bm25 = _bm25_score(query, r.get("text", ""))
+        # Normalize BM25 to roughly 0-1 range
+        bm25_norm = min(bm25 / 3.0, 1.0)
+        r["_hybrid_score"] = vector_weight * vec_sim + bm25_weight * bm25_norm
+        r["bm25_score"] = round(bm25, 3)
+
+    vector_results.sort(key=lambda r: r["_hybrid_score"], reverse=True)
+    # Clean up internal field
+    for r in vector_results:
+        del r["_hybrid_score"]
+    return vector_results
+
+
 def build_where_filter(wing: str = None, room: str = None) -> dict:
     """Build ChromaDB where filter for wing/room filtering."""
     if wing and room:
@@ -186,6 +242,8 @@ def search_memories(
                 break
 
         if hits:
+            # Re-rank with BM25 hybrid scoring
+            hits = _hybrid_rank(hits, query)
             return {
                 "query": query,
                 "filters": {"wing": wing, "room": room},
@@ -227,6 +285,8 @@ def search_memories(
             }
         )
 
+    # Re-rank with BM25 hybrid scoring
+    hits = _hybrid_rank(hits, query)
     return {
         "query": query,
         "filters": {"wing": wing, "room": room},