altair823
a8ec354188
사용자 실제 config(주석·대안 줄·score_gate=0.3000…1192 포함)를 fixture 로. 값·주석 보존 + v3 파싱 일치 + 멱등 검증. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
150 lines
3.5 KiB
TOML
150 lines
3.5 KiB
TOML
# kebab config — `~/.config/kebab/config.toml`.
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#
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## `workspace.root` accepts:
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# • absolute paths (`/home/me/KnowledgeBase`)
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# • tilde (`~/KnowledgeBase`) ← default
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# • env vars (`${XDG_DATA_HOME}/kebab`)
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# • relative paths (`./notes`, `notes`, `../shared/x`)
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# — relative paths resolve against the directory of THIS
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# config file, NOT the user's `cwd` at invocation time.
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#
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# 처리 가능한 형식 (extractor 가 자동 결정 — config 에 명시할 수 없음):
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# • Markdown: .md
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# • 이미지: .png .jpg .jpeg (OCR + caption)
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# • PDF: .pdf
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# 다른 확장자는 ingest 시 자동 skip + warning. 처리 대상 폴더의
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# 일부만 ingest 하고 싶으면 `kebab ingest <path>` 로 root 명시
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# 또는 `.kebabignore` 파일 / 본 `workspace.exclude` 로 denylist.
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#
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# Override individual keys at runtime with `KEBAB_*` env vars
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# (e.g. `KEBAB_WORKSPACE_ROOT=/tmp/test kebab ingest`).
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schema_version = 2
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[workspace]
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root = "/Users/user/Obsidian/Default"
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exclude = [
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".git/**",
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"node_modules/**",
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".obsidian/**",
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]
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[storage]
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data_dir = "${XDG_DATA_HOME:-~/.local/share}/kebab"
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sqlite = "{data_dir}/kebab.sqlite"
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vector_dir = "{data_dir}/lancedb"
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asset_dir = "{data_dir}/assets"
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artifact_dir = "{data_dir}/artifacts"
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model_dir = "{data_dir}/models"
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runs_dir = "{data_dir}/runs"
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copy_threshold_mb = 100
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[indexing]
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max_parallel_extractors = 2
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max_parallel_embeddings = 1
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watch_filesystem = false
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[chunking]
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target_tokens = 500
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overlap_tokens = 80
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respect_markdown_headings = true
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chunker_version = "md-heading-v1"
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[models.embedding]
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provider = "ollama"
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endpoint = "http://127.0.0.1:11434"
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# endpoint = "http://192.168.0.2:11943"
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model = "snowflake-arctic-embed2"
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# provider = "candle"
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# model = "snowflake-arctic-embed-l-v2.0"
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version = "v1"
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dimensions = 1024
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batch_size = 64
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num_threads = 0
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[models.llm]
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provider = "ollama"
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model = "gemma4:e4b"
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context_tokens = 32768
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# endpoint = "http://127.0.0.1:11434"
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endpoint = "http://192.168.0.2:11943"
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temperature = 0.0
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seed = 0
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request_timeout_secs = 300
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# NLI(groundedness) 모델.
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[models.nli]
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model = "Xenova/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7"
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provider = "onnx"
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[search]
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default_k = 10
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hybrid_fusion = "rrf"
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rrf_k = 60
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snippet_chars = 220
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cache_capacity = 256
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stale_threshold_days = 30
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[rag]
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prompt_template_version = "rag-v3"
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score_gate = 0.30000001192092896
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explain_default = false
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max_context_tokens = 8000
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multi_hop_max_depth = 3
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multi_hop_max_sub_queries_per_iter = 5
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multi_hop_max_pool_chunks = 15
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nli_threshold = 0.0
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[image.ocr]
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enabled = true
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engine = "paddle-onnx"
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# engine = "ollama-vision"
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model = "gemma4:e4b"
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languages = [
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"eng",
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"kor",
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]
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max_pixels = 1600
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request_timeout_secs = 300
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[image.caption]
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enabled = true
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max_pixels = 768
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prompt_template_version = "caption-v1"
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[ui]
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theme = "dark"
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# code ingest skip 정책(.gitignore 자동 honor).
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[ingest.code]
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skip_generated_header = false
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max_file_bytes = 262144
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max_file_lines = 5000
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extra_skip_globs = []
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ast_chunk_max_lines = 200
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fallback_lines_per_chunk = 80
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fallback_lines_overlap = 20
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# scanned PDF page-단위 OCR(기본 off).
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[pdf.ocr]
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enabled = false
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always_on = false
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engine = "paddle-onnx"
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# engine = "ollama-vision"
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model = "qwen2.5vl:3b"
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languages = [
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"eng",
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"kor",
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]
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max_pixels = 2048
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request_timeout_secs = 180
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valid_ratio_threshold = 0.5
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min_char_count = 20
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lang_hint = "kor"
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# ingest 로그(기본 on, ~/.local/state/kebab/logs).
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[logging]
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ingest_log_enabled = true
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ingest_log_dir = "{state_dir}/logs"
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keep_recent_runs = 100
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retention_days = 30
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