487 lines
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487 lines
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<main>
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<h1 id="adr-013-knowledge-graph-temporal-con-surrealdb"><a class="header" href="#adr-013-knowledge-graph-temporal-con-surrealdb">ADR-013: Knowledge Graph Temporal con SurrealDB</a></h1>
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<p><strong>Status</strong>: Accepted | Implemented
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<strong>Date</strong>: 2024-11-01
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<strong>Deciders</strong>: Architecture Team
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<strong>Technical Story</strong>: Enabling collective agent learning through temporal execution history</p>
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<hr />
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<h2 id="decision"><a class="header" href="#decision">Decision</a></h2>
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<p>Implementar <strong>Knowledge Graph temporal</strong> en SurrealDB con historia de ejecución, curvas de aprendizaje, y búsqueda de similaridad.</p>
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<hr />
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<h2 id="rationale"><a class="header" href="#rationale">Rationale</a></h2>
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<ol>
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<li><strong>Collective Learning</strong>: Agentes aprenden de experiencia compartida (no solo individual)</li>
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<li><strong>Temporal History</strong>: Histórico de 30/90 días permite identificar tendencias</li>
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<li><strong>Causal Relationships</strong>: Graph permite rastrear raíces de problemas y soluciones</li>
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<li><strong>Similarity Search</strong>: Encontrar soluciones pasadas para tareas similares</li>
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<li><strong>SurrealDB Native</strong>: Graph queries integradas en mismo DB que relacional</li>
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</ol>
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<hr />
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<h2 id="alternatives-considered"><a class="header" href="#alternatives-considered">Alternatives Considered</a></h2>
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<h3 id="-event-log-only-no-graph"><a class="header" href="#-event-log-only-no-graph">❌ Event Log Only (No Graph)</a></h3>
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<ul>
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<li><strong>Pros</strong>: Simple</li>
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<li><strong>Cons</strong>: Sin relaciones causales, búsqueda ineficiente</li>
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</ul>
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<h3 id="-separate-graph-db-neo4j"><a class="header" href="#-separate-graph-db-neo4j">❌ Separate Graph DB (Neo4j)</a></h3>
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<ul>
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<li><strong>Pros</strong>: Optimizado para graph</li>
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<li><strong>Cons</strong>: Duplicación de datos, sincronización complexity</li>
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</ul>
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<h3 id="-surrealdb-temporal-kg-chosen"><a class="header" href="#-surrealdb-temporal-kg-chosen">✅ SurrealDB Temporal KG (CHOSEN)</a></h3>
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<ul>
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<li>Unificado, temporal, graph queries integradas</li>
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</ul>
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<hr />
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<h2 id="trade-offs"><a class="header" href="#trade-offs">Trade-offs</a></h2>
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<p><strong>Pros</strong>:</p>
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<ul>
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<li>✅ Temporal data (30/90 day retention)</li>
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<li>✅ Causal relationships traceable</li>
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<li>✅ Similarity search for solution discovery</li>
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<li>✅ Learning curves identify improvement trends</li>
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<li>✅ Single database (no sync issues)</li>
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</ul>
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<p><strong>Cons</strong>:</p>
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<ul>
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<li>⚠️ Graph queries more complex than relational</li>
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<li>⚠️ Storage overhead for full history</li>
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<li>⚠️ Retention policy trade-off: longer history = more storage</li>
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</ul>
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<hr />
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<h2 id="implementation"><a class="header" href="#implementation">Implementation</a></h2>
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<p><strong>Temporal Data Model</strong>:</p>
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<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
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</span><span class="boring">fn main() {
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</span>// crates/vapora-knowledge-graph/src/models.rs
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pub struct ExecutionRecord {
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pub id: String,
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pub agent_id: String,
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pub task_id: String,
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pub task_type: String,
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pub success: bool,
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pub quality_score: f32,
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pub latency_ms: u32,
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pub cost_cents: u32,
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pub timestamp: DateTime<Utc>,
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pub daily_window: String, // YYYY-MM-DD for aggregation
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}
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pub struct LearningCurve {
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pub id: String,
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pub agent_id: String,
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pub task_type: String,
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pub day: String, // YYYY-MM-DD
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pub success_rate: f32,
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pub avg_quality: f32,
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pub trend: TrendDirection, // Improving, Stable, Declining
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}
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<span class="boring">}</span></code></pre></pre>
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<p><strong>SurrealDB Schema</strong>:</p>
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<pre><code class="language-surql">-- Define execution records table
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DEFINE TABLE executions;
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DEFINE FIELD agent_id ON TABLE executions TYPE string;
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DEFINE FIELD task_id ON TABLE executions TYPE string;
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DEFINE FIELD task_type ON TABLE executions TYPE string;
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DEFINE FIELD success ON TABLE executions TYPE boolean;
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DEFINE FIELD quality_score ON TABLE executions TYPE float;
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DEFINE FIELD timestamp ON TABLE executions TYPE datetime;
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DEFINE FIELD daily_window ON TABLE executions TYPE string;
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-- Define temporal index for efficient time-range queries
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DEFINE INDEX idx_execution_temporal ON TABLE executions
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COLUMNS timestamp, daily_window;
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-- Define learning curves table
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DEFINE TABLE learning_curves;
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DEFINE FIELD agent_id ON TABLE learning_curves TYPE string;
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DEFINE FIELD task_type ON TABLE learning_curves TYPE string;
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DEFINE FIELD day ON TABLE learning_curves TYPE string;
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DEFINE FIELD success_rate ON TABLE learning_curves TYPE float;
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DEFINE FIELD trend ON TABLE learning_curves TYPE string;
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</code></pre>
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<p><strong>Temporal Query (30-Day Learning Curve)</strong>:</p>
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<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
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</span><span class="boring">fn main() {
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</span>// crates/vapora-knowledge-graph/src/learning.rs
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pub async fn compute_learning_curve(
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db: &Surreal<Ws>,
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agent_id: &str,
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task_type: &str,
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days: u32,
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) -> Result<Vec<LearningCurve>> {
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let since = (Utc::now() - Duration::days(days as i64))
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.format("%Y-%m-%d")
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.to_string();
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let query = format!(
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r#"
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SELECT
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day,
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count(id) as total_tasks,
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count(id WHERE success = true) / count(id) as success_rate,
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avg(quality_score) as avg_quality,
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(avg(quality_score) - LAG(avg(quality_score)) OVER (ORDER BY day)) as trend
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FROM executions
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WHERE agent_id = {} AND task_type = {} AND daily_window >= {}
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GROUP BY daily_window
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ORDER BY daily_window ASC
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"#,
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agent_id, task_type, since
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);
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db.query(query).await?
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.take::<Vec<LearningCurve>>(0)?
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.ok_or(Error::NotFound)
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}
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<span class="boring">}</span></code></pre></pre>
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<p><strong>Similarity Search (Find Past Solutions)</strong>:</p>
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<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
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</span><span class="boring">fn main() {
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</span>pub async fn find_similar_tasks(
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db: &Surreal<Ws>,
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task: &Task,
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limit: u32,
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) -> Result<Vec<(ExecutionRecord, f32)>> {
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// Compute embedding similarity for task description
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let similarity_threshold = 0.85;
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let query = r#"
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SELECT
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executions.*,
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<similarity_score> as score
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FROM executions
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WHERE similarity_score > {} AND success = true
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ORDER BY similarity_score DESC
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LIMIT {}
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"#;
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db.query(query)
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.bind(("similarity_score", similarity_threshold))
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.bind(("limit", limit))
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.await?
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.take::<Vec<(ExecutionRecord, f32)>>(0)?
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.ok_or(Error::NotFound)
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}
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<span class="boring">}</span></code></pre></pre>
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<p><strong>Causal Graph (Problem Resolution)</strong>:</p>
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|
<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
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</span><span class="boring">fn main() {
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|
</span>pub async fn trace_solution_chain(
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|
db: &Surreal<Ws>,
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problem_task_id: &str,
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) -> Result<Vec<ExecutionRecord>> {
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let query = format!(
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|
r#"
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SELECT
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->(resolved_by)->executions AS solutions
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|
FROM tasks
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|
WHERE id = {}
|
|
"#,
|
|
problem_task_id
|
|
);
|
|
|
|
db.query(query)
|
|
.await?
|
|
.take::<Vec<ExecutionRecord>>(0)?
|
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.ok_or(Error::NotFound)
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}
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<span class="boring">}</span></code></pre></pre>
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<p><strong>Key Files</strong>:</p>
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<ul>
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<li><code>/crates/vapora-knowledge-graph/src/learning.rs</code> (learning curve computation)</li>
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<li><code>/crates/vapora-knowledge-graph/src/persistence.rs</code> (DB persistence)</li>
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<li><code>/crates/vapora-knowledge-graph/src/models.rs</code> (temporal models)</li>
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<li><code>/crates/vapora-backend/src/services/</code> (uses KG for task recommendations)</li>
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</ul>
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<hr />
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<h2 id="verification"><a class="header" href="#verification">Verification</a></h2>
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<pre><code class="language-bash"># Test learning curve computation
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cargo test -p vapora-knowledge-graph test_learning_curve_30day
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# Test similarity search
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cargo test -p vapora-knowledge-graph test_similarity_search
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# Test causal graph traversal
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cargo test -p vapora-knowledge-graph test_causal_chain
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# Test retention policy (30-day window)
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cargo test -p vapora-knowledge-graph test_retention_policy
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# Integration test: full KG workflow
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cargo test -p vapora-knowledge-graph test_full_kg_lifecycle
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# Query performance test
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cargo bench -p vapora-knowledge-graph bench_temporal_queries
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</code></pre>
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<p><strong>Expected Output</strong>:</p>
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<ul>
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<li>Learning curves computed correctly</li>
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<li>Similarity search finds relevant past executions</li>
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<li>Causal chains traceable</li>
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<li>Retention policy removes old records</li>
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<li>Temporal queries perform well (<100ms)</li>
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</ul>
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<hr />
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<h2 id="consequences"><a class="header" href="#consequences">Consequences</a></h2>
|
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<h3 id="data-management"><a class="header" href="#data-management">Data Management</a></h3>
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<ul>
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<li>Storage grows ~1MB per 1000 executions (depends on detail level)</li>
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<li>Retention policy: 30 days (users), 90 days (enterprise)</li>
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<li>Archival strategy for historical analysis</li>
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</ul>
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<h3 id="agent-learning"><a class="header" href="#agent-learning">Agent Learning</a></h3>
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<ul>
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<li>Agents access KG to find similar past solutions</li>
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<li>Learning curves inform agent selection (see ADR-014)</li>
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<li>Improvement trends visible for monitoring</li>
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</ul>
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<h3 id="observability"><a class="header" href="#observability">Observability</a></h3>
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<ul>
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<li>Full audit trail of agent decisions</li>
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<li>Trending analysis for capacity planning</li>
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<li>Incident investigation via causal chains</li>
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</ul>
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<h3 id="scalability"><a class="header" href="#scalability">Scalability</a></h3>
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<ul>
|
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<li>Graph queries optimized with indexes</li>
|
|
<li>Temporal queries use daily windows (efficient partition)</li>
|
|
<li>Similarity search scales to millions of records</li>
|
|
</ul>
|
|
<hr />
|
|
<h2 id="references"><a class="header" href="#references">References</a></h2>
|
|
<ul>
|
|
<li><code>/crates/vapora-knowledge-graph/src/learning.rs</code> (implementation)</li>
|
|
<li><code>/crates/vapora-knowledge-graph/src/persistence.rs</code> (persistence layer)</li>
|
|
<li>ADR-004 (SurrealDB)</li>
|
|
<li>ADR-014 (Learning Profiles)</li>
|
|
<li>ADR-019 (Temporal Execution History)</li>
|
|
</ul>
|
|
<hr />
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|
<p><strong>Related ADRs</strong>: ADR-004 (SurrealDB), ADR-014 (Learning Profiles), ADR-019 (Temporal History)</p>
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