refactor: convert f-string logging to lazy %-style format (G004)
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>
This commit is contained in:
Chris Coutinho
co-authored by
Claude Opus 4.7
Claude Opus 4.7
parent
a4e6125d28
commit
665cb9b1eb
@@ -140,8 +140,13 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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doc_types = doc_types_param.split(",") if doc_types_param else None
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logger.info(
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f"Viz search: user={username}, query='{query}', "
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f"algorithm={algorithm}, fusion={fusion}, limit={limit}, doc_types={doc_types}"
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"Viz search: user=%s, query='%s', algorithm=%s, fusion=%s, limit=%s, doc_types=%s",
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username,
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query,
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algorithm,
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fusion,
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limit,
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doc_types,
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)
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try:
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@@ -230,8 +235,9 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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score_range = max_score - min_score if max_score > min_score else 1.0
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logger.info(
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f"Normalizing scores for viz: original range [{min_score:.3f}, {max_score:.3f}] "
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f"→ [0.0, 1.0]"
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"Normalizing scores for viz: original range [%s, %s] → [0.0, 1.0]",
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format(min_score, ".3f"),
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format(max_score, ".3f"),
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)
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# Store original score and rescale to 0-1 for visualization
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@@ -338,7 +344,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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status_code=500,
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)
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logger.info(f"Detected embedding dimension: {embedding_dim}")
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logger.info("Detected embedding dimension: %s", embedding_dim)
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# Build chunk vectors array in search_results order (1:1 mapping)
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chunk_vectors = []
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@@ -349,7 +355,8 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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else:
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# Chunk not found in vectors (shouldn't happen)
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logger.warning(
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f"Chunk {chunk_key} not found in fetched vectors, using zero vector"
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"Chunk %s not found in fetched vectors, using zero vector",
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chunk_key,
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)
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# Use zero vector as fallback
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chunk_vectors.append(np.zeros(embedding_dim))
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@@ -361,14 +368,16 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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if search_algo.query_embedding is not None:
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query_embedding = search_algo.query_embedding
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logger.info(
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f"Reusing query embedding from search algorithm "
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f"(dimension={len(query_embedding)})"
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"Reusing query embedding from search algorithm (dimension=%s)",
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len(query_embedding),
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)
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else:
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# Fallback: generate embedding if not available from search
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embedding_service = get_embedding_service()
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query_embedding = await embedding_service.embed(query)
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logger.info(f"Generated query embedding (dimension={len(query_embedding)})")
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logger.info(
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"Generated query embedding (dimension=%s)", len(query_embedding)
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)
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query_embed_duration = time.perf_counter() - query_embed_start
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# Combine query vector with chunk vectors for PCA
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@@ -387,16 +396,19 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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if zero_norm_mask.any():
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zero_indices = np.where(zero_norm_mask)[0]
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logger.warning(
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f"Found {zero_norm_mask.sum()} zero-norm vectors at indices {zero_indices.tolist()}. "
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"Replacing with small epsilon to avoid division by zero."
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"Found %s zero-norm vectors at indices %s. Replacing with small epsilon to avoid division by zero.",
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zero_norm_mask.sum(),
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zero_indices.tolist(),
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)
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# Replace zero norms with small epsilon to avoid NaN
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norms[zero_norm_mask] = 1e-10
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all_vectors_normalized = all_vectors / norms
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logger.info(
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f"Normalized vectors: query_norm={norms[-1][0]:.3f}, "
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f"doc_norm_range=[{norms[:-1].min():.3f}, {norms[:-1].max():.3f}]"
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"Normalized vectors: query_norm=%s, doc_norm_range=[%s, %s]",
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format(norms[-1][0], ".3f"),
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format(norms[:-1].min(), ".3f"),
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format(norms[:-1].max(), ".3f"),
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)
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# Apply PCA dimensionality reduction (768-dim → 3D) on normalized vectors
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@@ -428,8 +440,9 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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if nan_mask.any():
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nan_rows = np.where(nan_mask.any(axis=1))[0]
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logger.error(
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f"Found NaN values in PCA output at {len(nan_rows)} points: {nan_rows.tolist()[:10]}. "
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"Replacing NaN with 0.0 to prevent JSON serialization error."
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"Found NaN values in PCA output at %s points: %s. Replacing NaN with 0.0 to prevent JSON serialization error.",
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len(nan_rows),
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nan_rows.tolist()[:10],
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)
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# Replace NaN with 0 to allow JSON serialization
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coords_3d = np.nan_to_num(coords_3d, nan=0.0)
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@@ -442,13 +455,16 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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chunk_coords_3d = coords_3d[:-1] # All but last are chunks
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logger.info(
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f"PCA explained variance: PC1={pca.explained_variance_ratio_[0]:.3f}, "
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f"PC2={pca.explained_variance_ratio_[1]:.3f}, "
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f"PC3={pca.explained_variance_ratio_[2]:.3f}"
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"PCA explained variance: PC1=%s, PC2=%s, PC3=%s",
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format(pca.explained_variance_ratio_[0], ".3f"),
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format(pca.explained_variance_ratio_[1], ".3f"),
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format(pca.explained_variance_ratio_[2], ".3f"),
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)
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logger.info(
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f"Embedding stats: chunks={len(chunk_vectors)}, "
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f"query_dim={len(query_embedding)}, chunk_vector_dim={chunk_vectors.shape[1] if chunk_vectors.size > 0 else 0}"
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"Embedding stats: chunks=%s, query_dim=%s, chunk_vector_dim=%s",
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len(chunk_vectors),
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len(query_embedding),
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chunk_vectors.shape[1] if chunk_vectors.size > 0 else 0,
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)
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# Coordinates already match search_results order (1:1 mapping)
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@@ -479,12 +495,18 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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# Log comprehensive timing metrics
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logger.info(
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f"Viz search timing: total={total_duration * 1000:.1f}ms, "
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f"search={search_duration * 1000:.1f}ms ({search_duration / total_duration * 100:.1f}%), "
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f"vector_fetch={vector_fetch_duration * 1000:.1f}ms ({vector_fetch_duration / total_duration * 100:.1f}%), "
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f"query_embed={query_embed_duration * 1000:.1f}ms ({query_embed_duration / total_duration * 100:.1f}%), "
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f"pca={pca_duration * 1000:.1f}ms ({pca_duration / total_duration * 100:.1f}%), "
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f"results={len(search_results)}, chunk_vectors={len(chunk_vectors)}"
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"Viz search timing: total=%sms, search=%sms (%s%%), vector_fetch=%sms (%s%%), query_embed=%sms (%s%%), pca=%sms (%s%%), results=%s, chunk_vectors=%s",
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format(total_duration * 1000, ".1f"),
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format(search_duration * 1000, ".1f"),
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format(search_duration / total_duration * 100, ".1f"),
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format(vector_fetch_duration * 1000, ".1f"),
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format(vector_fetch_duration / total_duration * 100, ".1f"),
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format(query_embed_duration * 1000, ".1f"),
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format(query_embed_duration / total_duration * 100, ".1f"),
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format(pca_duration * 1000, ".1f"),
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format(pca_duration / total_duration * 100, ".1f"),
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len(search_results),
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len(chunk_vectors),
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)
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return JSONResponse(
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@@ -511,7 +533,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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)
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except Exception as e:
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logger.error(f"Viz search error: {e}", exc_info=True)
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logger.error("Viz search error: %s", e, exc_info=True)
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return JSONResponse(
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{"success": False, "error": str(e)},
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status_code=500,
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@@ -637,10 +659,12 @@ async def chunk_context_endpoint(request: Request) -> JSONResponse:
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)
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logger.info(
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f"Fetched chunk context for {doc_type}_{doc_id}: "
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f"chunk_len={len(chunk_context.chunk_text)}, "
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f"before_len={len(chunk_context.before_context)}, "
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f"after_len={len(chunk_context.after_context)}"
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"Fetched chunk context for %s_%s: chunk_len=%s, before_len=%s, after_len=%s",
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doc_type,
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doc_id,
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len(chunk_context.chunk_text),
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len(chunk_context.before_context),
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len(chunk_context.after_context),
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)
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# For PDF files, also fetch the chunk bbox from Qdrant so the client
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@@ -688,7 +712,7 @@ async def chunk_context_endpoint(request: Request) -> JSONResponse:
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status_code=400,
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)
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except Exception as e:
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logger.error(f"Chunk context error: {e}", exc_info=True)
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logger.error("Chunk context error: %s", e, exc_info=True)
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return JSONResponse(
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{"success": False, "error": str(e)},
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status_code=500,
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