From 5fbcd035d5945c7eb5394c96dd143c41b7305976 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Sun, 11 Oct 2026 13:40:11 +0200
Subject: [PATCH 1/3] perf: Embed new chunks in batches across files with
 model2vec 0.10

---
 pyproject.toml             |  3 ++-
 src/semble/index/create.py | 27 +++++++++++++++++----------
 tests/index/test_create.py | 11 +++++++++--
 uv.lock                    | 10 ++++++----
 4 files changed, 34 insertions(+), 17 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index d760a684..5e845098 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,7 +26,7 @@ classifiers = [
     "Natural Language :: English",
 ]
 dependencies = [
-    "model2vec>=0.4.0,<0.10",
+    "model2vec>=0.10.0,<0.11",
     "vicinity>=0.4.4,<0.5",
     "numpy>=1.24.0",
     "pathspec>=0.12",
@@ -147,4 +147,5 @@ exclude_also = ["if __name__ == .__main__.:", "@abstractmethod"]
 exclude-newer = "1 week"
 
 [tool.uv.exclude-newer-package]
+model2vec = false
 semble-grammars = false
diff --git a/src/semble/index/create.py b/src/semble/index/create.py
index 4c354779..129ec8e6 100644
--- a/src/semble/index/create.py
+++ b/src/semble/index/create.py
@@ -40,6 +40,8 @@
 _MIN_FILES_FOR_PROCESSES = 200
 # Files handed to the workers but not yet consumed by the main process.
 _MAX_FILES_IN_FLIGHT = 256
+# New chunks are embedded together once this many have accumulated.
+_EMBED_BATCH_SIZE = 1024
 
 # A file's chunks and their BM25 tokens.
 _ChunkedFile = tuple[list[Chunk], list[list[str]]]
@@ -177,9 +179,10 @@ def create_index_from_path(
 
     chunks: list[Chunk] = []
     chunk_ids: list[str] = []
-    vector_parts: list[EmbeddingMatrix] = []
     manifest: dict[str, FileManifestEntry] = {}
-    embedding_parts: list[tuple[int, int, int]] = []
+    reused_parts: list[tuple[int, EmbeddingMatrix]] = []
+    embedded_parts: list[tuple[list[int], EmbeddingMatrix]] = []
+    new_rows: list[int] = []
 
     skipped_large: list[str] = []
 
@@ -201,23 +204,25 @@ def create_index_from_path(
             miniters=1,  # tqdm's adaptive miniters stalls the bar when fast files are followed by slow ones
             disable=not show_progress_bar,
         ):
+            start = len(chunks)
             if previous is not None and previous_entry is not None and previous_entry.mtime_ns == mtime_ns:
                 file_chunks = previous.chunks[previous_entry.start : previous_entry.end]
-                vector_parts.append(previous.vectors[previous_entry.start : previous_entry.end])
+                reused_parts.append((start, previous.vectors[previous_entry.start : previous_entry.end]))
             else:
                 result = next(chunked)
                 if result is None:
                     continue
                 file_chunks, file_tokens = result
                 _reindex_file(bm25_index, indexed_path, file_tokens, previous_entry)
+                new_rows.extend(range(start, start + len(file_chunks)))
 
-                embedding_parts.append((len(vector_parts), len(chunks), len(file_chunks)))
-                vector_parts.append(embed_chunks(model, file_chunks))
-
-            start = len(chunks)
             chunks.extend(file_chunks)
             chunk_ids.extend(make_chunk_id(indexed_path, slot) for slot in range(len(file_chunks)))
             manifest[indexed_path] = FileManifestEntry(mtime_ns=mtime_ns, start=start, count=len(file_chunks))
+            if len(new_rows) >= _EMBED_BATCH_SIZE:
+                embedded_parts.append((new_rows, embed_chunks(model, [chunks[row] for row in new_rows])))
+                new_rows = []
+    embedded_parts.append((new_rows, embed_chunks(model, [chunks[row] for row in new_rows])))
 
     for indexed_path in previous_manifest.keys() - manifest.keys():
         _reindex_file(bm25_index, indexed_path, [], previous_manifest[indexed_path])
@@ -229,10 +234,12 @@ def create_index_from_path(
 
     if previous is not None and _has_same_vector_layout(manifest, previous_manifest):
         embeddings = previous.vectors
-        for vector_part, start, count in embedding_parts:
-            embeddings[start : start + count] = vector_parts[vector_part]
     else:
-        embeddings = np.vstack(vector_parts)
+        embeddings = np.empty((len(chunks), model.dim), dtype=np.float32)
+        for start, vectors in reused_parts:
+            embeddings[start : start + len(vectors)] = vectors
+    for rows, vectors in embedded_parts:
+        embeddings[rows] = vectors
     bm25_index.set_doc_order(chunk_ids)
     semantic_index = SelectableBasicBackend(embeddings, BasicArgs())
 
diff --git a/tests/index/test_create.py b/tests/index/test_create.py
index 15b0936f..9b94519b 100644
--- a/tests/index/test_create.py
+++ b/tests/index/test_create.py
@@ -26,10 +26,17 @@ def _write_files(root: Path, files: dict[str, str]) -> None:
 
 
 @pytest.mark.parametrize("in_processes", [False, True])
+@pytest.mark.parametrize(("embed_batch_size", "encode_calls"), [(1024, 1), (1, 4)])
 def test_incremental_reindex_reuses_updates_and_prunes(
-    mock_model: Any, tmp_path: Path, monkeypatch: pytest.MonkeyPatch, in_processes: bool
+    mock_model: Any,
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+    in_processes: bool,
+    embed_batch_size: int,
+    encode_calls: int,
 ) -> None:
     """One incremental pass reuses unchanged vectors, re-embeds changes, and keeps BM25 slots current."""
+    monkeypatch.setattr(create, "_EMBED_BATCH_SIZE", embed_batch_size)
     if in_processes:
         monkeypatch.setattr(create, "_MIN_FILES_FOR_PROCESSES", 0)
         monkeypatch.setenv("SEMBLE_INDEX_WORKERS", "2")
@@ -45,7 +52,7 @@ def test_incremental_reindex_reuses_updates_and_prunes(
     bm25_before, semantic_before, chunks_before, manifest_before = create_index_from_path(
         tmp_path, mock_model, display_root=tmp_path
     )
-    assert mock_model.encode.call_count == 4  # once per file, no second full pass
+    assert mock_model.encode.call_count == encode_calls
     a_entry = manifest_before["a.py"]
     b_entry = manifest_before["b.py"]
     a_vectors_before = semantic_before.vectors[a_entry.start : a_entry.end].copy()
diff --git a/uv.lock b/uv.lock
index f089a0ed..d50ade69 100644
--- a/uv.lock
+++ b/uv.lock
@@ -16,6 +16,7 @@ exclude-newer = "0001-01-01T00:00:00Z" # This has no effect and is included for
 exclude-newer-span = "P1W"
 
 [options.exclude-newer-package]
+model2vec = false
 semble-grammars = false
 
 [[package]]
@@ -1881,9 +1882,10 @@ wheels = [
 
 [[package]]
 name = "model2vec"
-version = "0.9.0"
+version = "0.10.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
+    { name = "huggingface-hub" },
     { name = "jinja2" },
     { name = "joblib" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
@@ -1893,9 +1895,9 @@ dependencies = [
     { name = "tokenizers" },
     { name = "tqdm" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/fd/e5/118c4a8af078ff97d9228718fbcd7d4f7e9cae93c3af59652d6f3407010a/model2vec-0.9.0.tar.gz", hash = "sha256:f50229cea128c9db5cfa7b2173478294be3c84e5d3d7fb8487ebd7af285383ab", size = 4590528, upload-time = "2026-08-12T14:24:38.524Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/04/48/2ceace43798ca7cb8c32c50c7956d7446d7f90a3b3f65b201fb7fd87344c/model2vec-0.10.0.tar.gz", hash = "sha256:47119ef1f917b4da7a0010bf10c587c923639a6af4ea51cb2d424fc329d09d2f", size = 4622501, upload-time = "2026-10-11T10:47:59.266Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/af/ea/80246465cafa36a6c8c8ac767778423940e5b826fa57c10ebd957b570c1c/model2vec-0.9.0-py3-none-any.whl", hash = "sha256:8bcf3258d5668678739c13226a562d39eeaeb8fcac9b142bc4aceef8800d5b9d", size = 59865, upload-time = "2026-08-12T14:24:36.626Z" },
+    { url = "https://files.pythonhosted.org/packages/84/c5/2b1b5648195bfcada5de6a1164d560c968ded8fc68f2ddd6118b21cb26b7/model2vec-0.10.0-py3-none-any.whl", hash = "sha256:a39c40de4f82c5e0e50c14bbb87a935c1c62094f3f0f0d8fde8e24f4a4f35b16", size = 89504, upload-time = "2026-10-11T10:47:57.679Z" },
 ]
 
 [[package]]
@@ -3866,7 +3868,7 @@ requires-dist = [
     { name = "einops", marker = "extra == 'benchmark'", specifier = ">=0.8.2" },
     { name = "matplotlib", marker = "extra == 'benchmark'", specifier = ">=3.7" },
     { name = "mcp", marker = "extra == 'mcp'", specifier = ">=2.0,<3.0" },
-    { name = "model2vec", specifier = ">=0.4.0,<0.10" },
+    { name = "model2vec", specifier = ">=0.10.0,<0.11" },
     { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.0" },
     { name = "numpy", specifier = ">=1.24.0" },
     { name = "numpy", marker = "extra == 'benchmark'", specifier = ">=1.24.0" },

From 2b7f0ed89ecda8d8ffda79b8d2a62c534d48b1b5 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Sun, 11 Oct 2026 14:02:37 +0200
Subject: [PATCH 2/3] Rerun semble benchmarks with model2vec 0.10 and batched
 embedding

---
 README.md                                     |    6 +-
 assets/images/speed_vs_ndcg_cold.png          |  Bin 76612 -> 76667 bytes
 assets/images/speed_vs_ndcg_warm.png          |  Bin 71200 -> 71250 bytes
 assets/images/token_efficiency.png            |  Bin 52037 -> 52544 bytes
 benchmarks/README.md                          |   62 +-
 benchmarks/plot.py                            |    6 +-
 .../semble-ablations-5fbcd035d594.json        | 9520 +++++++++++++++++
 .../results/semble-hybrid-5fbcd035d594.json   | 1396 +++
 benchmarks/results/speed-5fbcd035d594.json    | 2152 ++++
 .../token-efficiency-5fbcd035d594.json        |  323 +
 10 files changed, 13428 insertions(+), 37 deletions(-)
 create mode 100644 benchmarks/results/semble-ablations-5fbcd035d594.json
 create mode 100644 benchmarks/results/semble-hybrid-5fbcd035d594.json
 create mode 100644 benchmarks/results/speed-5fbcd035d594.json
 create mode 100644 benchmarks/results/token-efficiency-5fbcd035d594.json

diff --git a/README.md b/README.md
index 545f378e..b73747c3 100644
--- a/README.md
+++ b/README.md
@@ -24,7 +24,7 @@
 
 </div>
 
-Semble is a code search library built for agents. It returns the exact code snippets they need instantly, using ~99% fewer tokens than grep+read. Indexing and searching a full codebase end-to-end takes under a second for most repos, matching the retrieval quality of a code-specialized transformer while indexing ~380x faster and querying ~17x faster (see [benchmarks](#benchmarks)). Everything runs on CPU with no API keys, GPU, or external services. Use it as an MCP server, a CLI tool via AGENTS.md, or a dedicated sub-agent, and any coding agent (Claude Code, Cursor, Codex, OpenCode, etc.) gets instant access to any repo.
+Semble is a code search library built for agents. It returns the exact code snippets they need instantly, using ~99% fewer tokens than grep+read. Indexing and searching a full codebase end-to-end takes under a second for most repos, matching the retrieval quality of a code-specialized transformer while indexing ~480x faster and querying ~23x faster (see [benchmarks](#benchmarks)). Everything runs on CPU with no API keys, GPU, or external services. Use it as an MCP server, a CLI tool via AGENTS.md, or a dedicated sub-agent, and any coding agent (Claude Code, Cursor, Codex, OpenCode, etc.) gets instant access to any repo.
 
 <picture>
   <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/MinishLab/semble/main/assets/images/demo-dark.gif">
@@ -78,7 +78,7 @@ semble install --agent claude --type mcp subagent --yes
 ## Main Features
 
 - **Fast**: indexes an average repo in ~500 ms and answers queries in ~1 ms, all on CPU.
-- **Accurate**: NDCG@10 of 0.854 on our [benchmarks](#benchmarks), on par with code-specialized transformer models, at a fraction of the size and cost.
+- **Accurate**: NDCG@10 of 0.855 on our [benchmarks](#benchmarks), on par with code-specialized transformer models, at a fraction of the size and cost.
 - **Token-efficient**: returns only the relevant chunks, using [~99% fewer tokens than grep+read](#benchmarks).
 - **Zero setup**: runs on CPU with no API keys, GPU, or external services required.
 - **MCP server**: works with Claude Code, Cursor, Codex, OpenCode, VS Code, and any other MCP-compatible agent.
@@ -259,7 +259,7 @@ We benchmark quality and speed across ~1,250 queries over 63 repositories in 19
 </tr>
 </table>
 
-The quality benchmark (left) scores retrieval quality (NDCG@10) against total latency; semble matches the quality of the 137M-parameter [CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed) while indexing 380x faster. The token efficiency benchmark (right) measures how many tokens each method needs to reach a given recall level; semble uses 99% fewer tokens on average and hits 97% recall at only 2k tokens, while grep+read needs a full 100k context window to reach 85%. See [benchmarks](benchmarks/README.md) for per-language results, ablations, and full methodology.
+The quality benchmark (left) scores retrieval quality (NDCG@10) against total latency; semble matches the quality of the 137M-parameter [CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed) while indexing 480x faster. The token efficiency benchmark (right) measures how many tokens each method needs to reach a given recall level; semble uses 99% fewer tokens on average and hits 96% recall at only 2k tokens, while grep+read needs a full 100k context window to reach 85%. See [benchmarks](benchmarks/README.md) for per-language results, ablations, and full methodology.
 
 ## How it works
 
diff --git a/assets/images/speed_vs_ndcg_cold.png b/assets/images/speed_vs_ndcg_cold.png
index ebc55d54198bac4a8ebb28aac676ef774e9cbdcb..0f9932dba61cedbfc9bb4438b64881d355208b59 100644
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diff --git a/assets/images/speed_vs_ndcg_warm.png b/assets/images/speed_vs_ndcg_warm.png
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diff --git a/assets/images/token_efficiency.png b/assets/images/token_efficiency.png
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diff --git a/benchmarks/README.md b/benchmarks/README.md
index bd45ae21..ea230ffa 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -17,7 +17,7 @@ Quality and speed across all methods.
 
 | Method               |   NDCG@10 |      Index |   Query p50 |
 | -------------------- | --------: | ---------: | ----------: |
-| **semble**           | **0.854** | **306 ms** | **0.91 ms** |
+| **semble**           | **0.855** | **240 ms** | **0.69 ms** |
 | CodeRankEmbed        |     0.839 |      116 s |       16 ms |
 | ColGREP              |     0.693 |      5.4 s |      122 ms |
 | BM25                 |     0.673 |      47 ms |     0.17 ms |
@@ -33,7 +33,7 @@ Quality and speed across all methods.
 | :-----------------------------------------------------------------: | :-----------------------------------------------------------------: |
 |          _Time to first result (index + query) vs NDCG@10_          |             _Query latency on a warm index vs NDCG@10_              |
 
-semble matches the NDCG@10 of the 137M-param CodeRankEmbed while winning index time by ~380x and query latency by ~17x.
+semble matches the NDCG@10 of the 137M-param CodeRankEmbed while winning index time by ~480x and query latency by ~23x.
 
 NDCG@10 is averaged across all queries. Speed numbers use one repo per language, CPU only: cold-start index time and warm query p50 (median across 5 consecutive runs).
 
@@ -50,7 +50,7 @@ For each query: tokens consumed at first relevant hit, or 32k if the method neve
 | Method              | Expected tokens |       Savings |
 | ------------------- | --------------: | ------------: |
 | ripgrep + read file |          45,587 |      baseline |
-| **semble**          |         **348** | **99% fewer** |
+| **semble**          |         **353** | **99% fewer** |
 
 ### Recall at fixed token budgets
 
@@ -58,7 +58,7 @@ A relevant file is "covered" once any retrieved unit comes from it.
 
 | Method              |       500 |        1k |        2k |        4k |        8k |       16k |       32k |
 | ------------------- | --------: | --------: | --------: | --------: | --------: | --------: | --------: |
-| **semble**          | **0.842** | **0.923** | **0.967** | **0.988** | **0.995** | **0.995** | **0.995** |
+| **semble**          | **0.843** | **0.921** | **0.963** | **0.989** | **0.995** | **0.996** | **0.996** |
 | ripgrep + read file |     0.001 |     0.008 |     0.037 |     0.086 |     0.207 |     0.374 |     0.583 |
 
 <details>
@@ -74,26 +74,26 @@ NDCG@10 per language, sorted by semble. CRE = CodeRankEmbed, cbm = codebase-memo
 
 | Language    |    semble |       CRE |   ColGREP |  zvec-grep |        ck |       cbm |    grepai |     probe |        cs |   ripgrep |
 | ----------- | --------: | --------: | --------: | ---------: | --------: | --------: | --------: | --------: | --------: | --------: |
-| javascript  |     0.917 | **0.925** |     0.823 |      0.760 |     0.772 |     0.770 |     0.675 |     0.588 |     0.171 |     0.176 |
-| cpp         | **0.915** |     0.897 |     0.626 |      0.677 |     0.687 |     0.630 |     0.731 |     0.375 |     0.262 |     0.126 |
-| zig         | **0.913** |     0.911 |     0.474 |      0.664 |     0.511 |     0.766 |     0.755 |     0.369 |     0.121 |     0.000 |
-| scala       |     0.909 | **0.925** |     0.765 |      0.799 |     0.717 |     0.704 |     0.330 |     0.392 |     0.111 |     0.180 |
+| scala       |     0.921 | **0.925** |     0.765 |      0.799 |     0.717 |     0.704 |     0.330 |     0.392 |     0.111 |     0.180 |
+| javascript  |     0.911 | **0.925** |     0.823 |      0.760 |     0.772 |     0.770 |     0.675 |     0.588 |     0.171 |     0.176 |
+| go          | **0.910** |     0.713 |     0.785 |      0.512 |     0.458 |     0.506 |     0.722 |     0.410 |     0.183 |     0.133 |
 | ruby        | **0.909** |     0.905 |     0.708 |      0.758 |     0.738 |     0.689 |     0.643 |     0.382 |     0.255 |     0.230 |
-| go          | **0.895** |     0.713 |     0.785 |      0.512 |     0.458 |     0.506 |     0.722 |     0.410 |     0.183 |     0.133 |
-| elixir      | **0.894** |     0.893 |     0.808 |      0.811 |     0.786 |     0.506 |     0.669 |     0.412 |     0.397 |     0.134 |
-| csharp      | **0.885** |     0.848 |     0.614 |      0.609 |     0.548 |     0.775 |     0.277 |     0.392 |     0.248 |     0.117 |
-| python      |     0.867 | **0.878** |     0.777 |      0.704 |     0.721 |     0.643 |     0.634 |     0.488 |     0.305 |     0.202 |
-| swift       | **0.860** |     0.845 |     0.710 |      0.709 |     0.672 |     0.630 |     0.429 |     0.280 |     0.151 |     0.160 |
-| php         | **0.858** |     0.847 |     0.663 |      0.583 |     0.615 |     0.608 |     0.402 |     0.340 |     0.180 |     0.123 |
-| rust        | **0.856** |     0.754 |     0.662 |      0.541 |     0.419 |     0.454 |     0.519 |     0.242 |     0.193 |     0.162 |
-| java        | **0.849** |     0.790 |     0.641 |      0.685 |     0.606 |     0.554 |     0.386 |     0.536 |     0.136 |     0.198 |
-| bash        |     0.825 | **0.834** |     0.706 |      0.725 |     0.677 |     0.768 |     0.723 |     0.226 |     0.170 |     0.000 |
-| lua         |     0.823 | **0.829** |     0.798 |      0.736 |     0.738 |     0.591 |     0.699 |     0.336 |     0.050 |     0.000 |
-| kotlin      |     0.821 | **0.823** |     0.637 |      0.628 |     0.587 |     0.611 |     0.478 |     0.335 |     0.170 |     0.166 |
-| haskell     |     0.765 | **0.811** |     0.683 |      0.689 |     0.733 |     0.624 |     0.483 |     0.313 |     0.160 |     0.000 |
-| c           |     0.741 | **0.771** |     0.676 |      0.598 |     0.606 |     0.655 |     0.555 |     0.384 |     0.175 |     0.000 |
-| typescript  | **0.706** |     0.671 |     0.430 |      0.476 |     0.456 |     0.455 |     0.394 |     0.354 |     0.145 |     0.128 |
-| **overall** | **0.854** |     0.839 |     0.693 |      0.670 |     0.642 |     0.630 |     0.561 |     0.387 |     0.200 |     0.126 |
+| elixir      | **0.905** |     0.893 |     0.808 |      0.811 |     0.786 |     0.506 |     0.669 |     0.412 |     0.397 |     0.134 |
+| zig         |     0.899 | **0.911** |     0.474 |      0.664 |     0.511 |     0.766 |     0.755 |     0.369 |     0.121 |     0.000 |
+| cpp         |     0.881 | **0.897** |     0.626 |      0.677 |     0.687 |     0.630 |     0.731 |     0.375 |     0.262 |     0.126 |
+| python      |     0.876 | **0.878** |     0.777 |      0.704 |     0.721 |     0.643 |     0.634 |     0.488 |     0.305 |     0.202 |
+| csharp      | **0.872** |     0.848 |     0.614 |      0.609 |     0.548 |     0.775 |     0.277 |     0.392 |     0.248 |     0.117 |
+| php         | **0.864** |     0.847 |     0.663 |      0.583 |     0.615 |     0.608 |     0.402 |     0.340 |     0.180 |     0.123 |
+| swift       | **0.848** |     0.845 |     0.710 |      0.709 |     0.672 |     0.630 |     0.429 |     0.280 |     0.151 |     0.160 |
+| bash        | **0.840** |     0.834 |     0.706 |      0.725 |     0.677 |     0.768 |     0.723 |     0.226 |     0.170 |     0.000 |
+| lua         | **0.835** |     0.829 |     0.798 |      0.736 |     0.738 |     0.591 |     0.699 |     0.336 |     0.050 |     0.000 |
+| java        | **0.828** |     0.790 |     0.641 |      0.685 |     0.606 |     0.554 |     0.386 |     0.536 |     0.136 |     0.198 |
+| rust        | **0.826** |     0.754 |     0.662 |      0.541 |     0.419 |     0.454 |     0.519 |     0.242 |     0.193 |     0.162 |
+| kotlin      |     0.803 | **0.823** |     0.637 |      0.628 |     0.587 |     0.611 |     0.478 |     0.335 |     0.170 |     0.166 |
+| haskell     |     0.775 | **0.811** |     0.683 |      0.689 |     0.733 |     0.624 |     0.483 |     0.313 |     0.160 |     0.000 |
+| c           |     0.750 | **0.771** |     0.676 |      0.598 |     0.606 |     0.655 |     0.555 |     0.384 |     0.175 |     0.000 |
+| typescript  | **0.742** |     0.671 |     0.430 |      0.476 |     0.456 |     0.455 |     0.394 |     0.354 |     0.145 |     0.128 |
+| **overall** | **0.855** |     0.839 |     0.693 |      0.670 |     0.642 |     0.630 |     0.561 |     0.387 |     0.200 |     0.126 |
 
 ## Ablations
 
@@ -101,20 +101,20 @@ NDCG@10 per language, sorted by semble. CRE = CodeRankEmbed, cbm = codebase-memo
 
 | Retrieval              |   Raw | + ranking |
 | ---------------------- | ----: | --------: |
-| BM25                      | 0.675 |     0.834 |
-| potion-code-16M-v2        | 0.650 |     0.821 |
-| BM25 + potion-code-16M-v2 |     — | **0.854** |
+| BM25                      | 0.686 |     0.838 |
+| potion-code-16M-v2        | 0.652 |     0.824 |
+| BM25 + potion-code-16M-v2 |     — | **0.855** |
 
 <details>
 <summary>By query category</summary>
 
 | Mode                               | Architecture |  Semantic |    Symbol |
 | ---------------------------------- | -----------: | --------: | --------: |
-| BM25 raw                           |        0.628 |     0.676 |     0.719 |
-| potion-code-16M raw                |        0.626 |     0.666 |     0.629 |
-| semble BM25 (+ ranking)            |        0.770 |     0.819 |     0.957 |
-| semble potion-code-16M (+ ranking) |        0.757 |     0.808 |     0.943 |
-| **semble hybrid**                  |    **0.802** | **0.846** | **0.958** |
+| BM25 raw                           |        0.642 |     0.680 |     0.748 |
+| potion-code-16M raw                |        0.636 |     0.671 |     0.620 |
+| semble BM25 (+ ranking)            |        0.794 |     0.817 |     0.962 |
+| semble potion-code-16M (+ ranking) |        0.768 |     0.819 |     0.917 |
+| **semble hybrid**                  |    **0.808** | **0.847** | **0.954** |
 
 </details>
 
diff --git a/benchmarks/plot.py b/benchmarks/plot.py
index 4801611c..378f2523 100644
--- a/benchmarks/plot.py
+++ b/benchmarks/plot.py
@@ -103,9 +103,9 @@ class _Method(TypedDict):
     },
     {
         "name": "semble",
-        "ndcg10": 0.8544,
-        "index_ms": 305.6,
-        "query_p50_ms": 0.91,
+        "ndcg10": 0.8546,
+        "index_ms": 240.3,
+        "query_p50_ms": 0.69,
         "color": "#1a5fa8",
         "params_m": 16,
     },
diff --git a/benchmarks/results/semble-ablations-5fbcd035d594.json b/benchmarks/results/semble-ablations-5fbcd035d594.json
new file mode 100644
index 00000000..fb338f82
--- /dev/null
+++ b/benchmarks/results/semble-ablations-5fbcd035d594.json
@@ -0,0 +1,9520 @@
+{
+  "tool": "semble-ablations",
+  "model": "minishlab/potion-code-16M-v2",
+  "by_mode": {
+    "semble-auto": {
+      "avg_ndcg10": 0.8546,
+      "avg_p50_ms": 1.0,
+      "avg_tokens": 1778.9
+    },
+    "semble-balanced": {
+      "avg_ndcg10": 0.8546,
+      "avg_p50_ms": 1.0,
+      "avg_tokens": 1778.3
+    },
+    "semble-bm25": {
+      "avg_ndcg10": 0.8381,
+      "avg_p50_ms": 0.7,
+      "avg_tokens": 1835.4
+    },
+    "semble-semantic": {
+      "avg_ndcg10": 0.8237,
+      "avg_p50_ms": 0.8,
+      "avg_tokens": 1610.7
+    },
+    "unranked-auto": {
+      "avg_ndcg10": 0.7117,
+      "avg_p50_ms": 0.3,
+      "avg_tokens": 1776.9
+    },
+    "unranked-balanced": {
+      "avg_ndcg10": 0.7098,
+      "avg_p50_ms": 0.3,
+      "avg_tokens": 1778.6
+    },
+    "unranked-bm25": {
+      "avg_ndcg10": 0.6861,
+      "avg_p50_ms": 0.3,
+      "avg_tokens": 1801.8
+    },
+    "unranked-semantic": {
+      "avg_ndcg10": 0.6524,
+      "avg_p50_ms": 0.3,
+      "avg_tokens": 1664.0
+    }
+  },
+  "repos": [
+    {
+      "repo": "abseil-cpp",
+      "language": "cpp",
+      "mode": "semble-auto",
+      "chunks": 16813,
+      "tokens": 1502,
+      "ndcg5": 0.8621567083124564,
+      "ndcg10": 0.8799670676678575,
+      "p50_ms": 1.3537289923988283,
+      "p90_ms": 13.019679504213855,
+      "p95_ms": 13.354321193764918,
+      "p99_ms": 13.599864243878983,
+      "index_ms": 4034.962958015967,
+      "by_category": {
+        "architecture": 1.0,
+        "semantic": 0.8732894235571433,
+        "symbol": 0.8333333333333334
+      }
+    },
+    {
+      "repo": "abseil-cpp",
+      "language": "cpp",
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+      "tokens": 1497,
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+        "semantic": 0.8732894235571433,
+        "symbol": 0.8333333333333334
+      }
+    },
+    {
+      "repo": "abseil-cpp",
+      "language": "cpp",
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+      "ndcg10": 0.859968142854855,
+      "p50_ms": 1.0185620049014688,
+      "p90_ms": 11.718279123306274,
+      "p95_ms": 11.94350971491076,
+      "p99_ms": 12.83223473583348,
+      "index_ms": 4034.962958015967,
+      "by_category": {
+        "architecture": 1.0,
+        "semantic": 0.8466241904731402,
+        "symbol": 0.8333333333333334
+      }
+    },
+    {
+      "repo": "abseil-cpp",
+      "language": "cpp",
+      "mode": "semble-semantic",
+      "chunks": 16813,
+      "tokens": 1380,
+      "ndcg5": 0.8228302093894448,
+      "ndcg10": 0.8228302093894448,
+      "p50_ms": 1.0826459911186248,
+      "p90_ms": 11.996337492018938,
+      "p95_ms": 12.495587480952963,
+      "p99_ms": 12.838917485205457,
+      "index_ms": 4034.962958015967,
+      "by_category": {
+        "architecture": 0.8154648767857288,
+        "semantic": 0.8217116289478296,
+        "symbol": 0.8333333333333334
+      }
+    },
+    {
+      "repo": "abseil-cpp",
+      "language": "cpp",
+      "mode": "unranked-auto",
+      "chunks": 16813,
+      "tokens": 1561,
+      "ndcg5": 0.7765543652555861,
+      "ndcg10": 0.8018249849182094,
+      "p50_ms": 0.7410625112242997,
+      "p90_ms": 0.8540670096408576,
+      "p95_ms": 0.8973294985480608,
+      "p99_ms": 1.0555995092727242,
+      "index_ms": 4034.962958015967,
+      "by_category": {
+        "architecture": 1.0,
+        "semantic": 0.773721542686053,
+        "symbol": 0.8102255193577976
+      }
+    },
+    {
+      "repo": "abseil-cpp",
+      "language": "cpp",
+      "mode": "unranked-balanced",
+      "chunks": 16813,
+      "tokens": 1561,
+      "ndcg5": 0.7765543652555861,
+      "ndcg10": 0.8018249849182094,
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+      }
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+        "architecture": 0.7153382790366966,
+        "semantic": 0.6780328729902405,
+        "symbol": 1.0
+      }
+    },
+    {
+      "repo": "aeson",
+      "language": "haskell",
+      "mode": "semble-auto",
+      "chunks": 788,
+      "tokens": 1990,
+      "ndcg5": 0.7586215664331644,
+      "ndcg10": 0.768292886614028,
+      "p50_ms": 2.9114169883541763,
+      "p90_ms": 5.978920514462522,
+      "p95_ms": 10.443720570765443,
+      "p99_ms": 17.694311323575665,
+      "index_ms": 141.74112502951175,
+      "by_category": {
+        "architecture": 0.6297424628754914,
+        "semantic": 0.81447636119354
+      }
+    },
+    {
+      "repo": "aeson",
+      "language": "haskell",
+      "mode": "semble-balanced",
+      "chunks": 788,
+      "tokens": 1990,
+      "ndcg5": 0.7586215664331644,
+      "ndcg10": 0.768292886614028,
+      "p50_ms": 2.8962290089111775,
+      "p90_ms": 5.922687466954819,
+      "p95_ms": 10.415333329001449,
+      "p99_ms": 17.665100285084907,
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+      "by_category": {
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+      }
+    },
+    {
+      "repo": "aeson",
+      "language": "haskell",
+      "mode": "semble-bm25",
+      "chunks": 788,
+      "tokens": 1933,
+      "ndcg5": 0.7855159313011154,
+      "ndcg10": 0.7855159313011154,
+      "p50_ms": 2.090645517455414,
+      "p90_ms": 4.244424711214387,
+      "p95_ms": 10.27505832025782,
+      "p99_ms": 17.21474530408158,
+      "index_ms": 141.74112502951175,
+      "by_category": {
+        "architecture": 0.5896918237758785,
+        "semantic": 0.8507906338095277
+      }
+    },
+    {
+      "repo": "aeson",
+      "language": "haskell",
+      "mode": "semble-semantic",
+      "chunks": 788,
+      "tokens": 1749,
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diff --git a/benchmarks/results/semble-hybrid-5fbcd035d594.json b/benchmarks/results/semble-hybrid-5fbcd035d594.json
new file mode 100644
index 00000000..bbaf6788
--- /dev/null
+++ b/benchmarks/results/semble-hybrid-5fbcd035d594.json
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diff --git a/benchmarks/results/speed-5fbcd035d594.json b/benchmarks/results/speed-5fbcd035d594.json
new file mode 100644
index 00000000..e3290b3b
--- /dev/null
+++ b/benchmarks/results/speed-5fbcd035d594.json
@@ -0,0 +1,2152 @@
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+      ]
+    },
+    {
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+      "language": "zig",
+      "tool": "semble",
+      "index_ms": 313.9,
+      "p50_ms": 0.62,
+      "p90_ms": 0.74,
+      "p95_ms": 1.34,
+      "p99_ms": 5.29,
+      "latencies_ms": [
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+  ]
+}
diff --git a/benchmarks/results/token-efficiency-5fbcd035d594.json b/benchmarks/results/token-efficiency-5fbcd035d594.json
new file mode 100644
index 00000000..6ef4f851
--- /dev/null
+++ b/benchmarks/results/token-efficiency-5fbcd035d594.json
@@ -0,0 +1,323 @@
+{
+  "tool": "token-efficiency",
+  "tokenizer": "cl100k_base",
+  "budgets": [
+    500,
+    1000,
+    2000,
+    4000,
+    8000,
+    16000,
+    32000
+  ],
+  "n_queries": 1251,
+  "recall_at_budget": {
+    "semble": {
+      "500": 0.8432,
+      "1000": 0.9209,
+      "2000": 0.9632,
+      "4000": 0.9888,
+      "8000": 0.9952,
+      "16000": 0.9956,
+      "32000": 0.9956
+    },
+    "grep+read": {
+      "500": 0.0,
+      "1000": 0.0072,
+      "2000": 0.0228,
+      "4000": 0.042,
+      "8000": 0.0763,
+      "16000": 0.1003,
+      "32000": 0.1251
+    },
+    "grep-kw+read": {
+      "500": 0.0008,
+      "1000": 0.008,
+      "2000": 0.0368,
+      "4000": 0.0871,
+      "8000": 0.2114,
+      "16000": 0.3724,
+      "32000": 0.5837
+    }
+  },
+  "first_hit_reduction": {
+    "grep+read": {
+      "n_paired": 189.0,
+      "median_semble_tokens": 136.0,
+      "median_other_tokens": 7474.0,
+      "median_reduction": 0.9837,
+      "mean_reduction": 0.9665,
+      "semble_better_pct": 1.0,
+      "expected_cost_at_cap": 29726.5276,
+      "expected_cost_cap": 32000.0,
+      "expected_cost_ratio_vs_semble": 84.1753
+    },
+    "grep-kw+read": {
+      "n_paired": 1218.0,
+      "median_semble_tokens": 168.0,
+      "median_other_tokens": 19634.0,
+      "median_reduction": 0.9906,
+      "mean_reduction": 0.9768,
+      "semble_better_pct": 1.0,
+      "expected_cost_at_cap": 47199.2958,
+      "expected_cost_cap": 32000.0,
+      "expected_cost_ratio_vs_semble": 133.6522
+    },
+    "semble": {
+      "expected_cost_at_cap": 353.1503,
+      "expected_cost_cap": 32000.0
+    }
+  },
+  "plot": {
+    "budgets": [
+      100,
+      114,
+      130,
+      149,
+      170,
+      194,
+      222,
+      253,
+      289,
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+      4144,
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+      9205,
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+      115249,
+      131645,
+      150374,
+      171766,
+      196203,
+      224116,
+      255999
+    ],
+    "recall": {
+      "semble": [
+        0.1508,
+        0.186,
+        0.2375,
+        0.3299,
+        0.4684,
+        0.5879,
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+        0.9956,
+        0.9956,
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+        0.9956,
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+        0.9956,
+        0.9956,
+        0.9956,
+        0.9956,
+        0.9956
+      ],
+      "grep+read": [
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+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
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+        0.0,
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+        0.0032,
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+        0.026,
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+        0.0428,
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+        0.0771,
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+        0.0819,
+        0.0895,
+        0.0959,
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+        0.1131,
+        0.1187,
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+        0.1251,
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+        0.1295,
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+        0.1483,
+        0.1483,
+        0.1483,
+        0.1483,
+        0.1491,
+        0.1491,
+        0.1491
+      ],
+      "grep-kw+read": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0008,
+        0.0008,
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+        0.0008,
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+        0.0016,
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+        0.0795,
+        0.0915,
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+        0.1319,
+        0.1547,
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+        0.2126,
+        0.2349,
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+        0.2988,
+        0.3304,
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+        0.4243,
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+        0.5043,
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+        0.8945,
+        0.9089,
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+        0.9313,
+        0.9373
+      ]
+    }
+  }
+}

From 325ac87a2373bbec5a611250faee2c930d61ee19 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Fri, 16 Oct 2026 08:33:12 +0200
Subject: [PATCH 3/3] Check every embedding row across batch boundaries in
 incremental reindex test

---
 tests/index/test_create.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/tests/index/test_create.py b/tests/index/test_create.py
index 9b94519b..a9feed76 100644
--- a/tests/index/test_create.py
+++ b/tests/index/test_create.py
@@ -1,5 +1,6 @@
 import multiprocessing
 import threading
+import zlib
 from pathlib import Path
 from types import SimpleNamespace
 from typing import Any
@@ -26,7 +27,7 @@ def _write_files(root: Path, files: dict[str, str]) -> None:
 
 
 @pytest.mark.parametrize("in_processes", [False, True])
-@pytest.mark.parametrize(("embed_batch_size", "encode_calls"), [(1024, 1), (1, 4)])
+@pytest.mark.parametrize(("embed_batch_size", "encode_calls"), [(1024, 1), (3, 2)])
 def test_incremental_reindex_reuses_updates_and_prunes(
     mock_model: Any,
     tmp_path: Path,
@@ -37,6 +38,9 @@ def test_incremental_reindex_reuses_updates_and_prunes(
 ) -> None:
     """One incremental pass reuses unchanged vectors, re-embeds changes, and keeps BM25 slots current."""
     monkeypatch.setattr(create, "_EMBED_BATCH_SIZE", embed_batch_size)
+    mock_model.encode.side_effect = lambda texts, **_: np.eye(mock_model.dim, dtype=np.float32)[
+        [zlib.crc32(text.encode()) % mock_model.dim for text in texts]
+    ]
     if in_processes:
         monkeypatch.setattr(create, "_MIN_FILES_FOR_PROCESSES", 0)
         monkeypatch.setenv("SEMBLE_INDEX_WORKERS", "2")
@@ -81,7 +85,7 @@ def test_incremental_reindex_reuses_updates_and_prunes(
     (tmp_path / "c.py").unlink()
     (tmp_path / "emptying.py").write_text(" " * 128)
     _write_files(tmp_path, {"d.py": "def brand_new_term():\n    return 4\n"})
-    bm25_after, semantic_after, _, manifest_after = create_index_from_path(
+    bm25_after, semantic_after, chunks_after, manifest_after = create_index_from_path(
         tmp_path, mock_model, display_root=tmp_path, previous=previous
     )
 
@@ -92,6 +96,7 @@ def test_incremental_reindex_reuses_updates_and_prunes(
         b_vectors_before,
         semantic_after.vectors[b_entry_after.start : b_entry_after.end],
     )
+    np.testing.assert_array_equal(semantic_after.vectors, mock_model.encode([chunk.content for chunk in chunks_after]))
     assert "c.py" not in manifest_after
     assert "d.py" in manifest_after
     assert manifest_after["emptying.py"].count == 0