Initial commit: embedding-chunking v0.1.0

Shared 512-token chunking module for the OB1/vault embedding stack.
Wraps tiktoken cl100k_base; consumed by vault_mcp, ob1-enricher,
ob1-reflector before passing to Ollama (nomic-embed-text, 768-d).

Extracted from /opt/projects/homelab/shared/python/embedding_chunking
to its own repo so consumers can pin via git ref instead of editable
path source, unblocking docker builds outside the homelab tree.

.gitignore

@@ -0,0 +1,34 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+
+# Virtual environments
+.venv/
+venv/
+env/
+
+# Build / distribution
+build/
+dist/
+*.egg-info/
+*.egg
+
+# Test / coverage caches
+.pytest_cache/
+.ruff_cache/
+.mypy_cache/
+.coverage
+.coverage.*
+htmlcov/
+.tox/
+
+# IDE
+.idea/
+.vscode/
+
+# OS
+.DS_Store
+Thumbs.db

README.md

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+# embedding-chunking
+
+Shared 512-token chunking for the OB1 / vault embedding stack.
+
+Wraps `tiktoken` (`cl100k_base`) so all four embedders (`vault_mcp`,
+`ob1-mcp`, `ob1-enricher`, `ob1-reflector`) split source text the same way
+before passing to Ollama (`nomic-embed-text`, 768-d).
+
+`cl100k_base` is not nomic's exact tokenizer but close enough for chunking
+decisions, and far faster than round-tripping every token count through
+Ollama.
+
+## Use
+
+```python
+from embedding_chunking import chunk_text
+
+for chunk in chunk_text(document):
+    vec = embed_text(chunk.text)
+    # upsert with chunk.index, chunk.text, chunk.token_count
+```
+
+Consumers must delete-before-insert keyed on `(source_schema,
+source_table, source_id, embedding_model)` before re-embedding, otherwise a
+shrinking source leaves orphan chunks that silently corrupt retrieval.
+
+## Constants
+
+- `CHUNK_SIZE = 512`
+- `CHUNK_OVERLAP = 64`
+
+Step is `CHUNK_SIZE - CHUNK_OVERLAP = 448`.

pyproject.toml

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+[project]
+name = "embedding-chunking"
+version = "0.1.0"
+description = "Shared 512-token chunking for the OB1/vault embedding stack. Wraps tiktoken cl100k_base; consumed by vault_mcp, ob1-enricher, ob1-reflector."
+requires-python = ">=3.12"
+dependencies = [
+    "tiktoken>=0.8",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/embedding_chunking"]
+
+[dependency-groups]
+dev = [
+    "pytest>=8.0",
+    "pytest-cov>=5.0",
+    "ruff>=0.8",
+]
+
+[tool.ruff]
+line-length = 100
+target-version = "py312"
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "B", "UP", "SIM", "RUF"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]

src/embedding_chunking/__init__.py

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+from dataclasses import dataclass
+
+import tiktoken
+
+CHUNK_SIZE = 512
+CHUNK_OVERLAP = 64
+ENCODER = tiktoken.get_encoding("cl100k_base")
+
+
+@dataclass
+class Chunk:
+    index: int
+    text: str
+    token_count: int
+
+
+def chunk_text(text: str) -> list[Chunk]:
+    tokens = ENCODER.encode(text)
+    if len(tokens) <= CHUNK_SIZE:
+        return [Chunk(index=0, text=text, token_count=len(tokens))]
+
+    chunks: list[Chunk] = []
+    step = CHUNK_SIZE - CHUNK_OVERLAP
+    for i, start in enumerate(range(0, len(tokens), step)):
+        end = start + CHUNK_SIZE
+        chunk_tokens = tokens[start:end]
+        if not chunk_tokens:
+            break
+        chunks.append(
+            Chunk(
+                index=i,
+                text=ENCODER.decode(chunk_tokens),
+                token_count=len(chunk_tokens),
+            )
+        )
+        if end >= len(tokens):
+            break
+    return chunks

tests/test_chunk_text.py

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+from embedding_chunking import CHUNK_OVERLAP, CHUNK_SIZE, ENCODER, chunk_text
+
+
+def _text_of_n_tokens(n: int) -> str:
+    """Build a string that encodes to exactly n cl100k tokens.
+
+    Single ASCII letters encode to one token each, so n letters → n tokens.
+    """
+    token_ids = ENCODER.encode("a " * n)[:n]
+    return ENCODER.decode(token_ids)
+
+
+def test_short_text_returns_single_chunk():
+    text = _text_of_n_tokens(100)
+    chunks = chunk_text(text)
+    assert len(chunks) == 1
+    assert chunks[0].index == 0
+    assert chunks[0].token_count == 100
+    assert chunks[0].text == text
+
+
+def test_1500_token_text_chunks_with_overlap():
+    # Brief asserted 3 chunks for 1500 tokens, but with step=448 the
+    # sliding-window starts are [0, 448, 896, 1344] → 4 chunks (three
+    # full 512-token windows plus a 156-token tail). Keeping the
+    # canonical sliding-window algorithm; updating the expectation.
+    text = _text_of_n_tokens(1500)
+    chunks = chunk_text(text)
+
+    step = CHUNK_SIZE - CHUNK_OVERLAP
+    assert len(chunks) == 4
+    assert [c.index for c in chunks] == [0, 1, 2, 3]
+    assert [c.token_count for c in chunks] == [
+        CHUNK_SIZE,
+        CHUNK_SIZE,
+        CHUNK_SIZE,
+        1500 - 3 * step,
+    ]
+
+    tokens = ENCODER.encode(text)
+    for i, c in enumerate(chunks):
+        start = i * step
+        assert ENCODER.encode(c.text) == tokens[start : start + CHUNK_SIZE]
+
+
+def test_600_token_text_returns_two_chunks():
+    text = _text_of_n_tokens(600)
+    chunks = chunk_text(text)
+
+    assert len(chunks) == 2
+    assert [c.index for c in chunks] == [0, 1]
+
+    step = CHUNK_SIZE - CHUNK_OVERLAP
+    assert chunks[0].token_count == CHUNK_SIZE
+    assert chunks[1].token_count == 600 - step

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