test: cover embedding device fallback and bounded upserts

Agent-Logs-Url: https://github.com/MemPalace/mempalace/sessions/3213a67a-6871-4bb2-9ae0-23fa11001a22

Co-authored-by: igorls <4753812+igorls@users.noreply.github.com>

mempalace/convo_miner.py

@@ -55,6 +55,7 @@ CONVO_EXTENSIONS = {
 
 MIN_CHUNK_SIZE = 30
 CHUNK_SIZE = 800  # chars per drawer — align with miner.py
+DRAWER_UPSERT_BATCH_SIZE = 1000
 MAX_FILE_SIZE = 500 * 1024 * 1024  # 500 MB — skip files larger than this.
 # Matches miner.py at 500 MB. Long Claude Code sessions, multi-year
 # ChatGPT exports, and lifetime Slack dumps routinely exceed 10 MB; the
@@ -332,15 +333,16 @@ def _file_chunks_locked(collection, source_file, chunks, wing, room, agent, extr
         except Exception:
             pass
 
-        # Batch the whole file into one upsert so the embedding model runs
-        # a single forward pass for all chunks — dramatically faster than
-        # one call per chunk, especially on GPU where per-call overhead
-        # dominates over the actual matmul.
+        # Batch chunks into bounded upserts so large transcripts keep most of
+        # the embedding speedup without one huge Chroma/SQLite request. Keep
+        # one filed_at per source file so all transcript drawers share an
+        # ingest timestamp.
+        filed_at = datetime.now().isoformat()
+        for batch_start in range(0, len(chunks), DRAWER_UPSERT_BATCH_SIZE):
             batch_docs: list = []
             batch_ids: list = []
             batch_metas: list = []
-        filed_at = datetime.now().isoformat()
-        for chunk in chunks:
+            for chunk in chunks[batch_start : batch_start + DRAWER_UPSERT_BATCH_SIZE]:
                 chunk_room = chunk.get("memory_type", room) if extract_mode == "general" else room
                 if extract_mode == "general":
                     room_counts_delta[chunk_room] += 1
@@ -361,15 +363,13 @@ def _file_chunks_locked(collection, source_file, chunks, wing, room, agent, extr
                         "normalize_version": NORMALIZE_VERSION,
                     }
                 )
-
-        if batch_docs:
             try:
                 collection.upsert(
                     documents=batch_docs,
                     ids=batch_ids,
                     metadatas=batch_metas,
                 )
-                drawers_added = len(batch_docs)
+                drawers_added += len(batch_docs)
             except Exception as e:
                 if "already exists" not in str(e).lower():
                     raise

mempalace/embedding.py

@@ -32,6 +32,12 @@ _PROVIDER_MAP = {
     "dml": ["DmlExecutionProvider", "CPUExecutionProvider"],
 }
 
+_DEVICE_EXTRA = {
+    "cuda": "mempalace[gpu]",
+    "coreml": "mempalace[coreml]",
+    "dml": "mempalace[dml]",
+}
+
 _AUTO_ORDER = [
     ("CUDAExecutionProvider", "cuda"),
     ("CoreMLExecutionProvider", "coreml"),
@@ -76,11 +82,13 @@ def _resolve_providers(device: str) -> tuple[list, str]:
 
     if preferred not in available:
         if device not in _WARNED:
+            extra = _DEVICE_EXTRA.get(device, "the matching mempalace extra for your device")
             logger.warning(
                 "embedding_device=%r requested but %s is not installed — "
-                "falling back to CPU. Install mempalace[gpu] for CUDA.",
+                "falling back to CPU. Install %s.",
                 device,
                 preferred,
+                extra,
             )
             _WARNED.add(device)
         return (["CPUExecutionProvider"], "cpu")

mempalace/miner.py

@@ -65,6 +65,7 @@ SKIP_FILENAMES = {
 CHUNK_SIZE = 800  # chars per drawer
 CHUNK_OVERLAP = 100  # overlap between chunks
 MIN_CHUNK_SIZE = 50  # skip tiny chunks
+DRAWER_UPSERT_BATCH_SIZE = 1000
 MAX_FILE_SIZE = 500 * 1024 * 1024  # 500 MB — skip files larger than this.
 # Long Claude Code sessions and large transcript exports routinely exceed
 # 10 MB. The cap exists as a defensive rail against pathological binary
@@ -748,19 +749,21 @@ def process_file(
         except Exception:
             pass
 
-        # Batch all chunks of this file into a single upsert so the embedding
-        # model runs one forward pass over the whole file instead of N passes
-        # of one chunk each. On CPU this is typically a 10-30x speedup; on
-        # GPU the speedup is larger because per-call overhead dominates.
+        # Batch chunks into bounded upserts so the embedding model sees many
+        # chunks per forward pass without building one huge Chroma/SQLite
+        # request for pathological files. A bad chunk can fail its sub-batch;
+        # that is the deliberate trade-off for amortizing embedding overhead.
         try:
             source_mtime = os.path.getmtime(source_file)
         except OSError:
             source_mtime = None
 
+        drawers_added = 0
+        for batch_start in range(0, len(chunks), DRAWER_UPSERT_BATCH_SIZE):
             batch_docs: list = []
             batch_ids: list = []
             batch_metas: list = []
-        for chunk in chunks:
+            for chunk in chunks[batch_start : batch_start + DRAWER_UPSERT_BATCH_SIZE]:
                 drawer_id = f"drawer_{wing}_{room}_{hashlib.sha256((source_file + str(chunk['chunk_index'])).encode()).hexdigest()[:24]}"
                 batch_docs.append(chunk["content"])
                 batch_ids.append(drawer_id)
@@ -775,15 +778,12 @@ def process_file(
                         source_mtime,
                     )
                 )
-
-        drawers_added = 0
-        if batch_docs:
             collection.upsert(
                 documents=batch_docs,
                 ids=batch_ids,
                 metadatas=batch_metas,
             )
-            drawers_added = len(batch_docs)
+            drawers_added += len(batch_docs)
 
         # Build closet — the searchable index pointing to these drawers.
         # Purge first: a re-mine (mtime change or normalize_version bump) must

tests/test_config.py

@@ -20,6 +20,32 @@ def test_config_from_file():
     assert cfg.palace_path == "/custom/palace"
 
 
+def test_embedding_device_defaults_to_auto():
+    cfg = MempalaceConfig(config_dir=tempfile.mkdtemp())
+    assert cfg.embedding_device == "auto"
+
+
+def test_embedding_device_from_config_is_normalized():
+    tmpdir = tempfile.mkdtemp()
+    with open(os.path.join(tmpdir, "config.json"), "w") as f:
+        json.dump({"embedding_device": "  CUDA  "}, f)
+
+    cfg = MempalaceConfig(config_dir=tmpdir)
+    assert cfg.embedding_device == "cuda"
+
+
+def test_embedding_device_env_overrides_config():
+    tmpdir = tempfile.mkdtemp()
+    with open(os.path.join(tmpdir, "config.json"), "w") as f:
+        json.dump({"embedding_device": "cpu"}, f)
+    os.environ["MEMPALACE_EMBEDDING_DEVICE"] = "  CoreML  "
+    try:
+        cfg = MempalaceConfig(config_dir=tmpdir)
+        assert cfg.embedding_device == "coreml"
+    finally:
+        del os.environ["MEMPALACE_EMBEDDING_DEVICE"]
+
+
 def test_env_override():
     raw = "/env/palace"
     os.environ["MEMPALACE_PALACE_PATH"] = raw

tests/test_convo_miner_unit.py

@@ -1,6 +1,7 @@
 """Unit tests for convo_miner pure functions (no chromadb needed)."""
 
 from mempalace.convo_miner import (
+    _file_chunks_locked,
     chunk_exchanges,
     detect_convo_room,
     scan_convos,
@@ -111,3 +112,38 @@ class TestScanConvos:
     def test_scan_empty_dir(self, tmp_path):
         files = scan_convos(str(tmp_path))
         assert files == []
+
+
+class TestFileChunksLocked:
+    def test_uses_bounded_upsert_batches(self, monkeypatch):
+        import contextlib
+
+        import mempalace.convo_miner as convo_miner
+
+        class FakeCol:
+            def __init__(self):
+                self.batch_sizes = []
+
+            def delete(self, *args, **kwargs):
+                pass
+
+            def upsert(self, documents, ids, metadatas):
+                self.batch_sizes.append(len(documents))
+
+        chunks = [{"content": f"chunk {i} " * 20, "chunk_index": i} for i in range(5)]
+        col = FakeCol()
+        monkeypatch.setattr(convo_miner, "DRAWER_UPSERT_BATCH_SIZE", 2)
+        monkeypatch.setattr(
+            convo_miner, "file_already_mined", lambda collection, source_file: False
+        )
+        monkeypatch.setattr(convo_miner, "mine_lock", lambda source_file: contextlib.nullcontext())
+        monkeypatch.setattr(convo_miner, "_detect_hall_cached", lambda content: "conversations")
+
+        drawers, room_counts, skipped = _file_chunks_locked(
+            col, "chat.txt", chunks, "wing", "general", "agent", "exchange"
+        )
+
+        assert drawers == 5
+        assert dict(room_counts) == {}
+        assert skipped is False
+        assert col.batch_sizes == [2, 2, 1]

tests/test_embedding.py

@@ -0,0 +1,101 @@
+import builtins
+
+import pytest
+
+import mempalace.embedding as embedding
+
+
+@pytest.fixture(autouse=True)
+def clear_embedding_state():
+    embedding._EF_CACHE.clear()
+    embedding._WARNED.clear()
+    yield
+    embedding._EF_CACHE.clear()
+    embedding._WARNED.clear()
+
+
+def test_auto_picks_cuda(monkeypatch):
+    monkeypatch.setattr(
+        "onnxruntime.get_available_providers",
+        lambda: ["CUDAExecutionProvider", "CPUExecutionProvider"],
+    )
+
+    assert embedding._resolve_providers("auto") == (
+        ["CUDAExecutionProvider", "CPUExecutionProvider"],
+        "cuda",
+    )
+
+
+def test_auto_falls_to_cpu(monkeypatch):
+    monkeypatch.setattr("onnxruntime.get_available_providers", lambda: ["CPUExecutionProvider"])
+
+    assert embedding._resolve_providers("auto") == (["CPUExecutionProvider"], "cpu")
+
+
+def test_cuda_missing_warns_with_gpu_extra(monkeypatch, caplog):
+    monkeypatch.setattr("onnxruntime.get_available_providers", lambda: ["CPUExecutionProvider"])
+
+    assert embedding._resolve_providers("cuda") == (["CPUExecutionProvider"], "cpu")
+    assert "mempalace[gpu]" in caplog.text
+
+
+def test_coreml_missing_warns_with_coreml_extra(monkeypatch, caplog):
+    monkeypatch.setattr("onnxruntime.get_available_providers", lambda: ["CPUExecutionProvider"])
+
+    assert embedding._resolve_providers("coreml") == (["CPUExecutionProvider"], "cpu")
+    assert "mempalace[coreml]" in caplog.text
+
+
+def test_dml_missing_warns_with_dml_extra(monkeypatch, caplog):
+    monkeypatch.setattr("onnxruntime.get_available_providers", lambda: ["CPUExecutionProvider"])
+
+    assert embedding._resolve_providers("dml") == (["CPUExecutionProvider"], "cpu")
+    assert "mempalace[dml]" in caplog.text
+
+
+def test_unknown_device_warns_once(monkeypatch, caplog):
+    monkeypatch.setattr("onnxruntime.get_available_providers", lambda: ["CPUExecutionProvider"])
+
+    assert embedding._resolve_providers("bogus") == (["CPUExecutionProvider"], "cpu")
+    assert embedding._resolve_providers("bogus") == (["CPUExecutionProvider"], "cpu")
+    assert caplog.text.count("Unknown embedding_device") == 1
+
+
+def test_onnxruntime_import_error_falls_back_to_cpu(monkeypatch):
+    real_import = builtins.__import__
+
+    def fake_import(name, *args, **kwargs):
+        if name == "onnxruntime":
+            raise ImportError("missing")
+        return real_import(name, *args, **kwargs)
+
+    monkeypatch.setattr(builtins, "__import__", fake_import)
+
+    assert embedding._resolve_providers("cuda") == (["CPUExecutionProvider"], "cpu")
+
+
+def test_get_embedding_function_caches_by_resolved_provider_tuple(monkeypatch):
+    class DummyEF:
+        def __init__(self, preferred_providers):
+            self.preferred_providers = preferred_providers
+
+    monkeypatch.setattr(embedding, "_build_ef_class", lambda: DummyEF)
+    monkeypatch.setattr(
+        embedding, "_resolve_providers", lambda device: (["CPUExecutionProvider"], "cpu")
+    )
+
+    first = embedding.get_embedding_function("cpu")
+    second = embedding.get_embedding_function("auto")
+
+    assert first is second
+    assert first.preferred_providers == ["CPUExecutionProvider"]
+
+
+def test_describe_device_uses_resolved_effective_device(monkeypatch):
+    monkeypatch.setattr(
+        embedding,
+        "_resolve_providers",
+        lambda device: (["CUDAExecutionProvider", "CPUExecutionProvider"], "cuda"),
+    )
+
+    assert embedding.describe_device("auto") == "cuda"

tests/test_miner.py

@@ -383,6 +383,46 @@ def test_status_handles_none_metadata_without_crash(tmp_path, capsys):
     assert "WING: proj" in out
 
 
+def test_process_file_uses_bounded_upsert_batches(tmp_path, monkeypatch):
+    from mempalace import miner
+
+    class FakeCol:
+        def __init__(self):
+            self.batch_sizes = []
+
+        def get(self, *args, **kwargs):
+            return {"ids": []}
+
+        def delete(self, *args, **kwargs):
+            pass
+
+        def upsert(self, documents, ids, metadatas):
+            self.batch_sizes.append(len(documents))
+
+    source = tmp_path / "src.py"
+    source.write_text("print('hello')\n" * 20, encoding="utf-8")
+    chunks = [{"content": f"chunk {i} " * 20, "chunk_index": i} for i in range(5)]
+    col = FakeCol()
+    monkeypatch.setattr(miner, "DRAWER_UPSERT_BATCH_SIZE", 2)
+    monkeypatch.setattr(miner, "chunk_text", lambda content, source_file: chunks)
+    monkeypatch.setattr(miner, "detect_hall", lambda content: "code")
+    monkeypatch.setattr(miner, "_extract_entities_for_metadata", lambda content: "")
+
+    drawers, room = miner.process_file(
+        source,
+        tmp_path,
+        col,
+        "wing",
+        [{"name": "general", "description": "General"}],
+        "agent",
+        False,
+    )
+
+    assert drawers == 5
+    assert room == "general"
+    assert col.batch_sizes == [2, 2, 1]
+
+
 # ── normalize_version schema gate ───────────────────────────────────────
 #
 # When the normalization pipeline changes shape (e.g., strip_noise lands),