connor
863f5e20ac
Audit found three things compiled in that shouldn't have been: the User-Agent carried the author's domain, the cluster threshold and window were module constants the pipeline never overrode, and the embedding model was pinned -- which blocked the one change most likely to be wanted, swapping in a multilingual model for native-language sources. Configuration is now three files with distinct roles: config.toml (machinery), profile.toml (who the reader is), sources.toml (what to read). All are relocatable via NEWS_CONFIG_DIR. news/*.py now contains no interest, place name, feed URL, or personal identifier. tests/test_config.py keeps it that way: it builds a configuration for an invented reader and asserts none of the author's identity or preferences reaches the running code. Writing it immediately found a real bug -- CONFIG_DIR was resolved at import time, so NEWS_CONFIG_DIR silently did nothing if set after the module was first imported. Now resolved per instantiation. Documented the coupling that portability creates: [clustering].threshold and [scoring].veto_absolute are cosine constants calibrated against one corpus with one model, so changing [embedding].model invalidates both.
54 lines
2.2 KiB
TOML
54 lines
2.2 KiB
TOML
# Runtime configuration and tuning knobs.
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#
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# Three-file split, so a new user knows what to touch:
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# config.toml this file -- machinery. Sane defaults; rarely edited.
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# profile.toml who you are: interests, vetoes, importance, delivery.
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# sources.toml what to read.
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#
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# Nothing here is specific to any person. Everything personal lives in the
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# other two files.
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[http]
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# Sent on every outbound request. Feed operators use it to identify traffic and
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# some will block a generic or absent agent, so put a real contact here.
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# CHANGE THIS if you are not Connor -- it is the contact address feed operators
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# see, and pointing them at someone else's domain is rude and unhelpful.
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user_agent = "news/0.1 (+https://news.rcjohnstone.com; personal feed reader; one user)"
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timeout_secs = 30
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connect_timeout_secs = 10
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concurrency = 8
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max_body_bytes = 10485760
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[embedding]
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# Any model fastembed can serve. `dim` must match the model.
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#
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# The default is English-only. For native-language sources use a multilingual
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# model -- "intfloat/multilingual-e5-small" (384 dim) is the usual choice.
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# Be aware that changing the model INVALIDATES the calibrated constants below
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# and in [scoring]: they are cosine thresholds, and a different model puts
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# similarity on a different scale. Re-run the calibration before trusting them.
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model = "BAAI/bge-small-en-v1.5"
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dim = 384
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[clustering]
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# Calibrated by inspection on a 3,885-item corpus; see CALIBRATION.md.
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# 0.70 merged "Solheim Cup capsules" with "FIBA Women's World Cup Glance";
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# 0.86 under-merged. Over-merging is the worse failure because it inflates the
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# corroboration signal that feeds importance.
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#
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# Re-tune if your sources differ substantially in style or language.
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threshold = 0.82
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window_hours = 48
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[scoring]
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# Compare blocks by z-score WITHIN each block, never by raw cosine: cosine
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# magnitude is not comparable across blocks, and a plain max let one block win
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# 78% of all items. See CALIBRATION.md.
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normalize = "zscore-per-block"
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# Veto is not a plain similarity threshold. A story can be genuinely adjacent to
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# a vetoed topic and still be wanted, so an item is vetoed on unambiguous
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# similarity OR when the veto signal beats the best interest signal by a margin.
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veto_absolute = 0.60
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veto_margin_over_interest = 0.3
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