Documentation
¶
Overview ¶
Package scoring owns the article importance model: the rubric dimensions an LLM rates, the deterministic aggregation of those dimensions into a 0-100 score, and the tier thresholds used to bucket articles in digests.
The LLM no longer chooses the final score directly. It rates a handful of narrow, anchored sub-dimensions (0-4 each) and Compute combines them with tunable weights here. Because the raw dimensions are persisted alongside the computed score, the weights can be retuned later and scores recomputed in a batch without re-running the LLM.
Index ¶
Constants ¶
const ( // AggregatorScore is the fixed score forced for aggregator/roundup articles, // preserving the previous prompt's "always set the score to exactly 40" rule. AggregatorScore = 40 // EvergreenCap caps the score of pure-evergreen articles (Specificity == 0), // preserving the previous prompt's "generic/evergreen must score ≤60" rule. EvergreenCap = 60 )
const ( TierMustRead = 90 TierShouldRead = 75 TierMayRead = 60 )
Read-tier thresholds (inclusive lower bounds) on the 0-100 score.
Variables ¶
var Weights = struct { Specificity float64 Severity float64 Breadth float64 Novelty float64 Actionability float64 Credibility float64 }{ Specificity: 0.20, Severity: 0.25, Breadth: 0.20, Novelty: 0.10, Actionability: 0.15, Credibility: 0.10, }
Per-dimension weights, summing to 1.0. This is the single place to retune the relative influence of each dimension on the final score.
Functions ¶
func Compute ¶
func Compute(d Dimensions) int
Compute aggregates rubric dimensions into a 0-100 importance score.
Each dimension is normalised to 0-1, combined via Weights into a weighted average, and scaled to 0-100. Overrides are then applied: aggregator articles are forced to AggregatorScore, and pure-evergreen articles (Specificity == 0) are capped at EvergreenCap.
func PriorityKey ¶
PriorityKey returns the short bucket key for a 0-100 score, used for digest manifest priority tallies. Unlike ReadTier it has no "unscored" bucket; anything below TierMayRead falls into "opt".
Types ¶
type Dimensions ¶
type Dimensions struct {
// Specificity: generic/evergreen concept (0) → single concrete, recent event (4).
Specificity int `json:"specificity"`
// Severity: informational (0) → active exploitation / critical patch / major breach (4).
Severity int `json:"severity"`
// Breadth: niche product (0) → ubiquitous software or whole sector affected (4).
Breadth int `json:"breadth"`
// Novelty: rehash of known facts (0) → genuinely new disclosure/finding (4).
Novelty int `json:"novelty"`
// Actionability: nothing to do (0) → clear defensive action, patch, IOCs, detection (4).
Actionability int `json:"actionability"`
// Credibility: unsourced blogspam (0) → primary source / vendor advisory / named researcher (4).
Credibility int `json:"credibility"`
// IsAggregator marks roundups / weekly recaps / link digests, which are forced
// to AggregatorScore regardless of the other dimensions.
IsAggregator bool `json:"is_aggregator"`
}
Dimensions are the rubric sub-scores rated by the LLM. Each numeric field is on a 0-4 scale; values outside that range are clamped by Compute.
Source Files