more search improvements

docker-compose.yaml

@@ -179,7 +179,7 @@ services:
       VITE_SERVER_URL: "${SERVER_URL:-http://localhost:8000}"
       STATIC_DIR: "/app/static"
       VOYAGE_API_KEY: ${VOYAGE_API_KEY}
-      ENABLE_BM25_SEARCH: false
+      ENABLE_BM25_SEARCH: true
       OPENAI_API_KEY_FILE: /run/secrets/openai_key
       ANTHROPIC_API_KEY_FILE: /run/secrets/anthropic_key
     secrets: [postgres_password, openai_key, anthropic_key]

requirements/requirements-api.txt

@@ -4,5 +4,4 @@ python-jose==3.3.0
 python-multipart==0.0.9
 sqladmin==0.20.1
 mcp==1.10.0
-bm25s[full]==0.2.13
 slowapi==0.1.9

src/memory/api/search/search.py

@@ -4,7 +4,10 @@ Search endpoints for the knowledge base API.
 
 import asyncio
 import logging
+import math
+import re
 from collections import defaultdict
+from datetime import datetime, timezone
 from typing import Optional
 from sqlalchemy.orm import load_only
 from memory.common import extract, settings
@@ -51,6 +54,76 @@ TITLE_MATCH_BOOST = 0.01
 # This gives a small boost to popular items without dominating relevance
 POPULARITY_BOOST = 0.02
 
+# Recency boost settings
+# Maximum bonus for brand new content (additive)
+RECENCY_BOOST_MAX = 0.005
+# Half-life in days: content loses half its recency boost every N days
+RECENCY_HALF_LIFE_DAYS = 90
+
+# Query expansion: map abbreviations/acronyms to full forms
+# These help match when users search for "ML" but documents say "machine learning"
+QUERY_EXPANSIONS: dict[str, list[str]] = {
+    # AI/ML abbreviations
+    "ai": ["artificial intelligence"],
+    "ml": ["machine learning"],
+    "dl": ["deep learning"],
+    "nlp": ["natural language processing"],
+    "cv": ["computer vision"],
+    "rl": ["reinforcement learning"],
+    "llm": ["large language model", "language model"],
+    "gpt": ["generative pretrained transformer", "language model"],
+    "nn": ["neural network"],
+    "cnn": ["convolutional neural network"],
+    "rnn": ["recurrent neural network"],
+    "lstm": ["long short term memory"],
+    "gan": ["generative adversarial network"],
+    "rag": ["retrieval augmented generation"],
+    # Rationality/EA terms
+    "ea": ["effective altruism"],
+    "lw": ["lesswrong", "less wrong"],
+    "gwwc": ["giving what we can"],
+    "agi": ["artificial general intelligence"],
+    "asi": ["artificial superintelligence"],
+    "fai": ["friendly ai", "ai alignment"],
+    "x-risk": ["existential risk"],
+    "xrisk": ["existential risk"],
+    "p(doom)": ["probability of doom", "ai risk"],
+    # Reverse mappings (full forms -> abbreviations)
+    "artificial intelligence": ["ai"],
+    "machine learning": ["ml"],
+    "deep learning": ["dl"],
+    "natural language processing": ["nlp"],
+    "computer vision": ["cv"],
+    "reinforcement learning": ["rl"],
+    "neural network": ["nn"],
+    "effective altruism": ["ea"],
+    "existential risk": ["x-risk", "xrisk"],
+}
+
+
+def expand_query(query: str) -> str:
+    """
+    Expand query with synonyms and abbreviations.
+
+    This helps match documents that use different terminology for the same concept.
+    For example, "ML algorithms" -> "ML machine learning algorithms"
+    """
+    query_lower = query.lower()
+    expansions = []
+
+    for term, synonyms in QUERY_EXPANSIONS.items():
+        # Check if term appears as a whole word in the query
+        # Use word boundaries to avoid matching partial words
+        pattern = r'\b' + re.escape(term) + r'\b'
+        if re.search(pattern, query_lower):
+            expansions.extend(synonyms)
+
+    if expansions:
+        # Add expansions to the original query
+        return query + " " + " ".join(expansions)
+    return query
+
+
 # Common words to ignore when checking for query term presence
 STOPWORDS = {
     "a", "an", "the", "is", "are", "was", "were", "be", "been", "being",
@@ -116,66 +189,78 @@ def deduplicate_by_source(chunks: list[Chunk]) -> list[Chunk]:
     return list(best_by_source.values())
 
 
-def apply_title_boost(
+def apply_source_boosts(
     chunks: list[Chunk],
     query_terms: set[str],
 ) -> None:
     """
-    Boost chunks when query terms match the source title.
+    Apply title, popularity, and recency boosts to chunks in a single DB query.
 
-    Title matches are a strong signal since titles summarize content.
+    - Title boost: chunks get boosted when query terms appear in source title
-    """
+    - Popularity boost: chunks get boosted based on source karma/popularity
-    if not query_terms or not chunks:
+    - Recency boost: newer content gets a small boost that decays over time
-        return
-
-    # Get unique source IDs
-    source_ids = list({chunk.source_id for chunk in chunks})
-
-    # Fetch full source items (polymorphic) to access title attribute
-    with make_session() as db:
-        sources = db.query(SourceItem).filter(
-            SourceItem.id.in_(source_ids)
-        ).all()
-        titles = {s.id: (getattr(s, 'title', None) or "").lower() for s in sources}
-
-    # Apply boost to chunks whose source title matches query terms
-    for chunk in chunks:
-        title = titles.get(chunk.source_id, "")
-        if not title:
-            continue
-
-        matches = sum(1 for term in query_terms if term in title)
-        if matches > 0:
-            boost = TITLE_MATCH_BOOST * (matches / len(query_terms))
-            chunk.relevance_score = (chunk.relevance_score or 0) + boost
-
-
-def apply_popularity_boost(chunks: list[Chunk]) -> None:
-    """
-    Boost chunks based on source popularity.
-
-    Uses the popularity property from SourceItem subclasses.
-    ForumPost uses karma, others default to 1.0.
     """
     if not chunks:
         return
 
     source_ids = list({chunk.source_id for chunk in chunks})
+    now = datetime.now(timezone.utc)
 
+    # Single query to fetch all source metadata
     with make_session() as db:
         sources = db.query(SourceItem).filter(
             SourceItem.id.in_(source_ids)
         ).all()
-        popularity_map = {s.id: s.popularity for s in sources}
+        source_map = {
+            s.id: {
+                "title": (getattr(s, "title", None) or "").lower(),
+                "popularity": s.popularity,
+                "inserted_at": s.inserted_at,
+            }
+            for s in sources
+        }
 
     for chunk in chunks:
-        popularity = popularity_map.get(chunk.source_id, 1.0)
+        source_data = source_map.get(chunk.source_id, {})
+        score = chunk.relevance_score or 0
+
+        # Apply title boost if query terms match
+        if query_terms:
+            title = source_data.get("title", "")
+            if title:
+                matches = sum(1 for term in query_terms if term in title)
+                if matches > 0:
+                    score += TITLE_MATCH_BOOST * (matches / len(query_terms))
+
+        # Apply popularity boost
+        popularity = source_data.get("popularity", 1.0)
         if popularity != 1.0:
-            # Apply boost: score * (1 + POPULARITY_BOOST * (popularity - 1))
-            # For popularity=2.0: multiplier = 1.02
-            # For popularity=0.5: multiplier = 0.99
             multiplier = 1.0 + POPULARITY_BOOST * (popularity - 1.0)
-            chunk.relevance_score = (chunk.relevance_score or 0) * multiplier
+            score *= multiplier
+
+        # Apply recency boost (exponential decay with half-life)
+        inserted_at = source_data.get("inserted_at")
+        if inserted_at:
+            # Handle timezone-naive timestamps
+            if inserted_at.tzinfo is None:
+                inserted_at = inserted_at.replace(tzinfo=timezone.utc)
+            age_days = (now - inserted_at).total_seconds() / 86400
+            # Exponential decay: boost = max_boost * 0.5^(age/half_life)
+            decay = math.pow(0.5, age_days / RECENCY_HALF_LIFE_DAYS)
+            score += RECENCY_BOOST_MAX * decay
+
+        chunk.relevance_score = score
+
+
+# Keep legacy functions for backwards compatibility and testing
+def apply_title_boost(chunks: list[Chunk], query_terms: set[str]) -> None:
+    """Legacy function - use apply_source_boosts instead."""
+    apply_source_boosts(chunks, query_terms)
+
+
+def apply_popularity_boost(chunks: list[Chunk]) -> None:
+    """Legacy function - use apply_source_boosts instead."""
+    apply_source_boosts(chunks, set())
 
 
 def fuse_scores_rrf(
@@ -242,12 +327,21 @@ async def search_chunks(
     # This helps find results that rank well in one method but not the other
     internal_limit = limit * CANDIDATE_MULTIPLIER
 
-    # Extract query text for HyDE expansion
+    # Extract query text and apply synonym/abbreviation expansion
-    search_data = list(data)  # Copy to avoid modifying original
-    if settings.ENABLE_HYDE_EXPANSION:
     query_text = " ".join(
         c for chunk in data for c in chunk.data if isinstance(c, str)
     )
+    expanded_query = expand_query(query_text)
+
+    # If query was expanded, use expanded version for search
+    if expanded_query != query_text:
+        logger.debug(f"Query expansion: '{query_text}' -> '{expanded_query}'")
+        search_data = [extract.DataChunk(data=[expanded_query])]
+    else:
+        search_data = list(data)  # Copy to avoid modifying original
+
+    # Apply HyDE expansion if enabled
+    if settings.ENABLE_HYDE_EXPANSION:
         # Only expand queries with 4+ words (short queries are usually specific enough)
         if len(query_text.split()) >= 4:
             try:
@@ -316,15 +410,15 @@ async def search_chunks(
         c for chunk in data for c in chunk.data if isinstance(c, str)
     )
 
-    # Apply query term presence boost and title boost
+    # Apply query term presence boost
     if chunks and query_text.strip():
         query_terms = extract_query_terms(query_text)
         apply_query_term_boost(chunks, query_terms)
-        apply_title_boost(chunks, query_terms)
+        # Apply title + popularity boosts (single DB query)
-
+        apply_source_boosts(chunks, query_terms)
-    # Apply popularity boost (karma-based for forum posts)
+    elif chunks:
-    if chunks:
+        # No query terms, just apply popularity boost
-        apply_popularity_boost(chunks)
+        apply_source_boosts(chunks, set())
 
     # Rerank using cross-encoder for better precision
     if settings.ENABLE_RERANKING and chunks and query_text.strip():

src/memory/common/settings.py

@@ -178,6 +178,8 @@ ENABLE_BM25_SEARCH = boolean_env("ENABLE_BM25_SEARCH", True)
 ENABLE_SEARCH_SCORING = boolean_env("ENABLE_SEARCH_SCORING", True)
 ENABLE_HYDE_EXPANSION = boolean_env("ENABLE_HYDE_EXPANSION", True)
 HYDE_TIMEOUT = float(os.getenv("HYDE_TIMEOUT", "3.0"))
+ENABLE_RERANKING = boolean_env("ENABLE_RERANKING", True)
+RERANK_MODEL = os.getenv("RERANK_MODEL", "rerank-2-lite")
 MAX_PREVIEW_LENGTH = int(os.getenv("MAX_PREVIEW_LENGTH", DEFAULT_CHUNK_TOKENS * 16))
 MAX_NON_PREVIEW_LENGTH = int(os.getenv("MAX_NON_PREVIEW_LENGTH", 2000))
 

tests/memory/api/search/test_search.py

@@ -6,17 +6,23 @@ title boosting, and source deduplication.
 import pytest
 from unittest.mock import MagicMock, patch
 
+from datetime import datetime, timedelta, timezone
+
 from memory.api.search.search import (
     extract_query_terms,
     apply_query_term_boost,
     deduplicate_by_source,
     apply_title_boost,
     apply_popularity_boost,
+    apply_source_boosts,
+    expand_query,
     fuse_scores_rrf,
     STOPWORDS,
     QUERY_TERM_BOOST,
     TITLE_MATCH_BOOST,
     POPULARITY_BOOST,
+    RECENCY_BOOST_MAX,
+    RECENCY_HALF_LIFE_DAYS,
     RRF_K,
 )
 
@@ -239,6 +245,8 @@ def test_apply_title_boost(mock_make_session, title, query_terms, initial_score,
     mock_source = MagicMock()
     mock_source.id = 1
     mock_source.title = title
+    mock_source.popularity = 1.0  # Default popularity, no boost
+    mock_source.inserted_at = None  # No recency boost
     mock_session.query.return_value.filter.return_value.all.return_value = [mock_source]
 
     chunks = [_make_title_chunk(1, initial_score)]
@@ -268,6 +276,8 @@ def test_apply_title_boost_none_title(mock_make_session):
     mock_source = MagicMock()
     mock_source.id = 1
     mock_source.title = None
+    mock_source.popularity = 1.0  # Default popularity, no boost
+    mock_source.inserted_at = None  # No recency boost
     mock_session.query.return_value.filter.return_value.all.return_value = [mock_source]
 
     chunks = [_make_title_chunk(1, 0.5)]
@@ -307,6 +317,7 @@ def test_apply_popularity_boost(mock_make_session, popularity, initial_score, ex
     mock_source = MagicMock()
     mock_source.id = 1
     mock_source.popularity = popularity
+    mock_source.inserted_at = None  # No recency boost
     mock_session.query.return_value.filter.return_value.all.return_value = [mock_source]
 
     chunks = [_make_pop_chunk(1, initial_score)]
@@ -331,9 +342,11 @@ def test_apply_popularity_boost_multiple_sources(mock_make_session):
     source1 = MagicMock()
     source1.id = 1
     source1.popularity = 2.0  # High karma
+    source1.inserted_at = None  # No recency boost
     source2 = MagicMock()
     source2.id = 2
     source2.popularity = 1.0  # Default
+    source2.inserted_at = None  # No recency boost
     mock_session.query.return_value.filter.return_value.all.return_value = [source1, source2]
 
     chunks = [_make_pop_chunk(1, 0.5), _make_pop_chunk(2, 0.5)]
@@ -423,3 +436,233 @@ def test_fuse_scores_rrf_only_ranks_matter():
     assert result1["a"] == pytest.approx(result2["a"])
     assert result1["b"] == pytest.approx(result2["b"])
     assert result1["c"] == pytest.approx(result2["c"])
+
+
+# ============================================================================
+# apply_source_boosts recency tests
+# ============================================================================
+
+
+def _make_recency_chunk(source_id: int, score: float = 0.5):
+    """Create a mock chunk for recency boost tests."""
+    chunk = MagicMock()
+    chunk.source_id = source_id
+    chunk.relevance_score = score
+    return chunk
+
+
+@patch("memory.api.search.search.make_session")
+def test_recency_boost_new_content(mock_make_session):
+    """Brand new content should get full recency boost."""
+    mock_session = MagicMock()
+    mock_make_session.return_value.__enter__ = MagicMock(return_value=mock_session)
+    mock_make_session.return_value.__exit__ = MagicMock(return_value=None)
+
+    now = datetime.now(timezone.utc)
+    mock_source = MagicMock()
+    mock_source.id = 1
+    mock_source.title = None
+    mock_source.popularity = 1.0
+    mock_source.inserted_at = now  # Just inserted
+    mock_session.query.return_value.filter.return_value.all.return_value = [mock_source]
+
+    chunks = [_make_recency_chunk(1, 0.5)]
+    apply_source_boosts(chunks, set())
+
+    # Should get nearly full recency boost
+    expected = 0.5 + RECENCY_BOOST_MAX
+    assert chunks[0].relevance_score == pytest.approx(expected, rel=0.01)
+
+
+@patch("memory.api.search.search.make_session")
+def test_recency_boost_half_life_decay(mock_make_session):
+    """Content at half-life age should get half the boost."""
+    mock_session = MagicMock()
+    mock_make_session.return_value.__enter__ = MagicMock(return_value=mock_session)
+    mock_make_session.return_value.__exit__ = MagicMock(return_value=None)
+
+    now = datetime.now(timezone.utc)
+    mock_source = MagicMock()
+    mock_source.id = 1
+    mock_source.title = None
+    mock_source.popularity = 1.0
+    mock_source.inserted_at = now - timedelta(days=RECENCY_HALF_LIFE_DAYS)
+    mock_session.query.return_value.filter.return_value.all.return_value = [mock_source]
+
+    chunks = [_make_recency_chunk(1, 0.5)]
+    apply_source_boosts(chunks, set())
+
+    # Should get half the recency boost
+    expected = 0.5 + RECENCY_BOOST_MAX * 0.5
+    assert chunks[0].relevance_score == pytest.approx(expected, rel=0.01)
+
+
+@patch("memory.api.search.search.make_session")
+def test_recency_boost_old_content(mock_make_session):
+    """Very old content should get minimal recency boost."""
+    mock_session = MagicMock()
+    mock_make_session.return_value.__enter__ = MagicMock(return_value=mock_session)
+    mock_make_session.return_value.__exit__ = MagicMock(return_value=None)
+
+    now = datetime.now(timezone.utc)
+    mock_source = MagicMock()
+    mock_source.id = 1
+    mock_source.title = None
+    mock_source.popularity = 1.0
+    mock_source.inserted_at = now - timedelta(days=365)  # 1 year old
+    mock_session.query.return_value.filter.return_value.all.return_value = [mock_source]
+
+    chunks = [_make_recency_chunk(1, 0.5)]
+    apply_source_boosts(chunks, set())
+
+    # Should get very little boost (about 0.5^4 ≈ 0.0625 of max)
+    assert chunks[0].relevance_score > 0.5
+    assert chunks[0].relevance_score < 0.5 + RECENCY_BOOST_MAX * 0.1
+
+
+@patch("memory.api.search.search.make_session")
+def test_recency_boost_none_timestamp(mock_make_session):
+    """Should handle None inserted_at gracefully."""
+    mock_session = MagicMock()
+    mock_make_session.return_value.__enter__ = MagicMock(return_value=mock_session)
+    mock_make_session.return_value.__exit__ = MagicMock(return_value=None)
+
+    mock_source = MagicMock()
+    mock_source.id = 1
+    mock_source.title = None
+    mock_source.popularity = 1.0
+    mock_source.inserted_at = None
+    mock_session.query.return_value.filter.return_value.all.return_value = [mock_source]
+
+    chunks = [_make_recency_chunk(1, 0.5)]
+    apply_source_boosts(chunks, set())
+
+    # No recency boost applied
+    assert chunks[0].relevance_score == 0.5
+
+
+@patch("memory.api.search.search.make_session")
+def test_recency_boost_timezone_naive(mock_make_session):
+    """Should handle timezone-naive timestamps."""
+    mock_session = MagicMock()
+    mock_make_session.return_value.__enter__ = MagicMock(return_value=mock_session)
+    mock_make_session.return_value.__exit__ = MagicMock(return_value=None)
+
+    # Timezone-naive timestamp
+    naive_dt = datetime.now().replace(tzinfo=None)
+    mock_source = MagicMock()
+    mock_source.id = 1
+    mock_source.title = None
+    mock_source.popularity = 1.0
+    mock_source.inserted_at = naive_dt
+    mock_session.query.return_value.filter.return_value.all.return_value = [mock_source]
+
+    chunks = [_make_recency_chunk(1, 0.5)]
+    apply_source_boosts(chunks, set())  # Should not raise
+
+    # Should get nearly full boost since it's very recent
+    assert chunks[0].relevance_score > 0.5
+
+
+@patch("memory.api.search.search.make_session")
+def test_recency_boost_ordering(mock_make_session):
+    """Newer content should rank higher than older content."""
+    mock_session = MagicMock()
+    mock_make_session.return_value.__enter__ = MagicMock(return_value=mock_session)
+    mock_make_session.return_value.__exit__ = MagicMock(return_value=None)
+
+    now = datetime.now(timezone.utc)
+    source_new = MagicMock()
+    source_new.id = 1
+    source_new.title = None
+    source_new.popularity = 1.0
+    source_new.inserted_at = now - timedelta(days=1)
+
+    source_old = MagicMock()
+    source_old.id = 2
+    source_old.title = None
+    source_old.popularity = 1.0
+    source_old.inserted_at = now - timedelta(days=180)
+
+    mock_session.query.return_value.filter.return_value.all.return_value = [source_new, source_old]
+
+    chunks = [_make_recency_chunk(1, 0.5), _make_recency_chunk(2, 0.5)]
+    apply_source_boosts(chunks, set())
+
+    # Newer content should have higher score
+    assert chunks[0].relevance_score > chunks[1].relevance_score
+
+
+# ============================================================================
+# expand_query tests
+# ============================================================================
+
+
+@pytest.mark.parametrize(
+    "query,expected_expansion",
+    [
+        # AI/ML abbreviations
+        ("ML algorithms", "artificial intelligence"),  # Not "ML" -> won't have AI
+        ("AI safety research", "artificial intelligence"),
+        ("NLP models for text", "natural language processing"),
+        ("deep learning vs DL", "deep learning"),
+        # Rationality/EA terms
+        ("EA organizations", "effective altruism"),
+        ("AGI timeline predictions", "artificial general intelligence"),
+        ("x-risk reduction", "existential risk"),
+        # Reverse mappings
+        ("machine learning basics", "ml"),
+        ("artificial intelligence ethics", "ai"),
+    ],
+)
+def test_expand_query_adds_synonyms(query, expected_expansion):
+    """Should expand abbreviations and add synonyms."""
+    result = expand_query(query)
+    assert expected_expansion in result.lower()
+    assert query in result  # Original query preserved
+
+
+@pytest.mark.parametrize(
+    "query",
+    [
+        "hello world",  # No expansions
+        "python programming",  # No expansions
+        "database optimization",  # No expansions
+    ],
+)
+def test_expand_query_no_match(query):
+    """Should return original query when no expansions match."""
+    result = expand_query(query)
+    assert result == query
+
+
+def test_expand_query_case_insensitive():
+    """Should match terms regardless of case."""
+    assert "artificial intelligence" in expand_query("AI research").lower()
+    assert "artificial intelligence" in expand_query("ai research").lower()
+    assert "artificial intelligence" in expand_query("Ai Research").lower()
+
+
+def test_expand_query_word_boundaries():
+    """Should only match whole words, not partial matches."""
+    # "mail" contains "ai" but shouldn't trigger expansion
+    result = expand_query("email server")
+    assert result == "email server"
+
+    # "claim" contains "ai" but shouldn't trigger expansion
+    result = expand_query("insurance claim")
+    assert result == "insurance claim"
+
+
+def test_expand_query_multiple_terms():
+    """Should expand multiple matching terms."""
+    result = expand_query("AI and ML applications")
+    assert "artificial intelligence" in result.lower()
+    assert "machine learning" in result.lower()
+
+
+def test_expand_query_preserves_original():
+    """Should preserve original query text."""
+    original = "AI safety research"
+    result = expand_query(original)
+    assert result.startswith(original)