Chris CoutinhoandClaude Opus 4.7
665cb9b1eb
Sweep all 1676 G004 violations across 112 files, converting
`logger.<level>(f"…{x}…")` to `logger.<level>("…%s…", x)`.
Why: ruff rule G004 was added to pyproject.toml to enforce lazy
%-style logging — defers formatting until the log level is enabled
and lets structured log tooling match the unformatted template.
Conversion preserves rendered output byte-for-byte:
- `{x}` → `%s` + `x`
- `{x!r}` / `{x!s}` / `{x!a}` → `%r` / `%s` / `%a`
- Format specs (`{x:.2f}`, `{x:>10}`) → `%s` + `format(x, 'spec')`
(printf-style specs aren't 1:1 with Python format specs, so we
delegate to `format()` to keep identical output)
- Literal `%` → `%%`
- Concatenated f-strings (`f"a {x} " "b"`) flattened
- Trailing kwargs (`exc_info=True`) preserved
Verified:
- `uv run ruff check --select G004` → 0 violations
- `uv run ty check -- nextcloud_mcp_server` → passes
- `uv run pytest tests/unit/` → 1010 passed
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
"""BM25 sparse embedding provider using FastEmbed."""
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import logging
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from typing import Any
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import anyio
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from fastembed import SparseTextEmbedding
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logger = logging.getLogger(__name__)
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class BM25SparseEmbeddingProvider:
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"""
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BM25 sparse embedding provider for hybrid search.
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Uses FastEmbed's BM25 model to generate sparse vectors for keyword-based
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retrieval. These sparse vectors are combined with dense semantic vectors
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in Qdrant using Reciprocal Rank Fusion (RRF) for hybrid search.
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Unlike dense embeddings which have fixed dimensions, sparse embeddings
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have variable-length vectors with (index, value) pairs representing
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term frequencies in the BM25 vocabulary.
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"""
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def __init__(self, model_name: str = "Qdrant/bm25"):
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"""
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Initialize BM25 sparse embedding provider.
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Args:
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model_name: FastEmbed BM25 model name (default: Qdrant/bm25)
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"""
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self.model_name = model_name
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logger.info("Initializing BM25 sparse embedding provider: %s", model_name)
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# Initialize FastEmbed sparse embedding model
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self.model = SparseTextEmbedding(model_name=model_name)
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logger.info("BM25 sparse embedding model loaded: %s", model_name)
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def encode(self, text: str) -> dict[str, Any]:
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"""
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Generate BM25 sparse embedding for a single text (synchronous).
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Note: For async contexts, prefer encode_async() to avoid blocking the event loop.
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Args:
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text: Input text to encode
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Returns:
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Dictionary with 'indices' and 'values' keys for Qdrant sparse vector
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"""
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# FastEmbed returns a generator, take first result
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sparse_embedding = next(iter(self.model.embed([text])))
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return {
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"indices": sparse_embedding.indices.tolist(),
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"values": sparse_embedding.values.tolist(),
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}
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async def encode_async(self, text: str) -> dict[str, Any]:
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"""
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Generate BM25 sparse embedding for a single text (async).
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Runs CPU-bound BM25 encoding in thread pool to avoid blocking the event loop.
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Args:
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text: Input text to encode
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Returns:
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Dictionary with 'indices' and 'values' keys for Qdrant sparse vector
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"""
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# Run CPU-bound BM25 encoding in thread pool
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return await anyio.to_thread.run_sync(lambda: self.encode(text)) # type: ignore[attr-defined]
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async def encode_batch(self, texts: list[str]) -> list[dict[str, Any]]:
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"""
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Generate BM25 sparse embeddings for multiple texts (batched).
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Args:
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texts: List of texts to encode
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Returns:
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List of dictionaries with 'indices' and 'values' for each text
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"""
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# Run CPU-bound BM25 encoding in thread pool to avoid blocking event loop
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sparse_embeddings = await anyio.to_thread.run_sync( # type: ignore[attr-defined]
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lambda: list(self.model.embed(texts))
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)
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return [
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{
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"indices": emb.indices.tolist(),
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"values": emb.values.tolist(),
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}
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for emb in sparse_embeddings
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]
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