0be354a920
Single-commit clean baseline after security scrub of niche-tells, project codenames, internal jargon, and contributor-email leaks. Contents: - 100 Rust crates (_primitives/_rust/) - 37 agent manifests (_manifests/) + generated specs (_generated/) - 67 user-invocable skills (skills/) - 33 hooks (hooks/) - Composition blocks (_blocks/) - Documentation (docs/, README.md) - TS adapter packages (_ts_packages/) - Assembler (_assembler/) - Roles (_roles/) - Templates (_templates/) - Forgejo CI (.forgejo/) Author: Denis Parfionovich <info@greendragon.info> License: see LICENSE.
56 lines
1.8 KiB
Rust
56 lines
1.8 KiB
Rust
//! [`TokenEvent`] data shape — one row per LLM turn / tool call.
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//!
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//! `micro_cents` matches the `kei-ledger` cost unit (1 cent = 1_000_000
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//! micro-cents) so cross-store rollups stay coherent without conversion
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//! tables. The optional `category` / `source_kind` fields allow the
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//! sleep-report to bucket usage by call site without forcing every
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//! caller to populate them.
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use serde::{Deserialize, Serialize};
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/// One LLM turn worth of telemetry. `record_event` accepts a borrowed
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/// reference; the store copies fields into prepared-statement params,
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/// so the caller keeps ownership of the struct after recording.
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#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
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pub struct TokenEvent {
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pub ts: i64,
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pub agent_id: String,
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pub conversation_id: Option<String>,
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pub model: String,
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pub role: String,
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pub input_tokens: u32,
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pub output_tokens: u32,
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pub micro_cents: u64,
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pub category: Option<String>,
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pub source_kind: Option<String>,
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pub latency_ms: Option<u32>,
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}
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impl TokenEvent {
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/// Convenience constructor for the common chat-turn shape — fills in
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/// optional columns as `None`. Tests + CLI seeding paths use this so
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/// they don't have to spell out every Option<…> at every call site.
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pub fn chat_turn(
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ts: i64,
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agent_id: impl Into<String>,
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model: impl Into<String>,
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role: impl Into<String>,
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input_tokens: u32,
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output_tokens: u32,
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micro_cents: u64,
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) -> Self {
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Self {
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ts,
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agent_id: agent_id.into(),
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conversation_id: None,
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model: model.into(),
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role: role.into(),
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input_tokens,
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output_tokens,
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micro_cents,
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category: None,
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source_kind: None,
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latency_ms: None,
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}
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}
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}
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