llm_optimizer_processor/decision/
traits.rs

1//! Decision Engine core traits
2
3use async_trait::async_trait;
4
5use super::error::{DecisionError, DecisionResult, DecisionState};
6use super::types::{Decision, DecisionCriteria, DecisionInput, DecisionOutcome, DecisionStats};
7
8/// Core trait for the Decision Engine
9#[async_trait]
10pub trait DecisionEngine: Send + Sync {
11    /// Get the name of this decision engine
12    fn name(&self) -> &str;
13
14    /// Get the current state
15    fn state(&self) -> DecisionState;
16
17    /// Start the decision engine
18    async fn start(&mut self) -> DecisionResult<()>;
19
20    /// Stop the decision engine
21    async fn stop(&mut self) -> DecisionResult<()>;
22
23    /// Make decisions based on analyzer input
24    ///
25    /// # Arguments
26    /// * `input` - Input from the analyzer engine
27    /// * `criteria` - Decision criteria to use
28    ///
29    /// # Returns
30    /// Vector of decisions to execute
31    async fn make_decisions(
32        &mut self,
33        input: DecisionInput,
34        criteria: &DecisionCriteria,
35    ) -> DecisionResult<Vec<Decision>>;
36
37    /// Record the outcome of a decision
38    async fn record_outcome(&mut self, outcome: DecisionOutcome) -> DecisionResult<()>;
39
40    /// Get decision statistics
41    fn get_stats(&self) -> DecisionStats;
42
43    /// Health check
44    async fn health_check(&self) -> DecisionResult<()>;
45
46    /// Reset the decision engine state
47    async fn reset(&mut self) -> DecisionResult<()>;
48}
49
50/// Trait for optimization strategies
51#[async_trait]
52pub trait OptimizationStrategy: Send + Sync {
53    /// Get the name of this strategy
54    fn name(&self) -> &str;
55
56    /// Get the priority of this strategy (higher = more important)
57    fn priority(&self) -> u32;
58
59    /// Check if this strategy is applicable to the given input
60    fn is_applicable(&self, input: &DecisionInput) -> bool;
61
62    /// Evaluate and generate decisions
63    ///
64    /// # Arguments
65    /// * `input` - Input from the analyzer engine
66    /// * `criteria` - Decision criteria to use
67    ///
68    /// # Returns
69    /// Vector of decisions this strategy recommends
70    async fn evaluate(
71        &self,
72        input: &DecisionInput,
73        criteria: &DecisionCriteria,
74    ) -> DecisionResult<Vec<Decision>>;
75
76    /// Validate a decision before execution
77    ///
78    /// # Arguments
79    /// * `decision` - The decision to validate
80    /// * `criteria` - Decision criteria to validate against
81    ///
82    /// # Returns
83    /// Ok if valid, Err with reason if invalid
84    async fn validate(
85        &self,
86        decision: &Decision,
87        criteria: &DecisionCriteria,
88    ) -> DecisionResult<()>;
89
90    /// Learn from a decision outcome
91    ///
92    /// # Arguments
93    /// * `decision` - The original decision
94    /// * `outcome` - The outcome that was observed
95    async fn learn(&mut self, decision: &Decision, outcome: &DecisionOutcome)
96        -> DecisionResult<()>;
97
98    /// Get strategy-specific statistics
99    fn get_stats(&self) -> serde_json::Value;
100}
101
102/// Trait for decision validators
103#[async_trait]
104pub trait DecisionValidator: Send + Sync {
105    /// Validate a decision
106    async fn validate(
107        &self,
108        decision: &Decision,
109        criteria: &DecisionCriteria,
110    ) -> DecisionResult<()>;
111}
112
113/// Trait for decision executors (actuators)
114#[async_trait]
115pub trait DecisionExecutor: Send + Sync {
116    /// Execute a decision
117    async fn execute(&self, decision: &Decision) -> DecisionResult<DecisionOutcome>;
118
119    /// Rollback a decision
120    async fn rollback(&self, decision: &Decision) -> DecisionResult<()>;
121}
llm_optimizer_processor/decision/traits.rs

llm_optimizer_processor/decision/
traits.rs
