Implement Layer 3: Big Five personality analysis system
Layer 3 evaluates the user based on their Layer 2 memories using the Big Five personality model (OCEAN), which is the most reliable psychological model for personality assessment. Changes: - Add UserAnalysis struct with Big Five traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) - Create user_analyses table in SQLite for storing analyses - Add storage methods: save_analysis, get_latest_analysis, list_analyses - Add MCP tools: save_user_analysis and get_user_analysis - Include helper methods: dominant_trait(), is_high() - All scores are f32 clamped to 0.0-1.0 range Architecture: - Layer 3 analyzes patterns from Layer 2 memories (AI interpretations and priority scores) to build psychological profile - AI judges personality, tool records the analysis - Independent from memory storage but references Layer 2 data
This commit is contained in:
Claude
161
src/core/analysis.rs
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161
src/core/analysis.rs
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@@ -0,0 +1,161 @@
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use chrono::{DateTime, Utc};
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use serde::{Deserialize, Serialize};
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use ulid::Ulid;
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/// User personality analysis based on Big Five model (OCEAN)
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct UserAnalysis {
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/// Unique identifier using ULID
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pub id: String,
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/// Openness to Experience (0.0-1.0)
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/// Curiosity, imagination, willingness to try new things
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pub openness: f32,
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/// Conscientiousness (0.0-1.0)
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/// Organization, responsibility, self-discipline
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pub conscientiousness: f32,
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/// Extraversion (0.0-1.0)
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/// Sociability, assertiveness, energy level
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pub extraversion: f32,
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/// Agreeableness (0.0-1.0)
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/// Compassion, cooperation, trust
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pub agreeableness: f32,
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/// Neuroticism (0.0-1.0)
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/// Emotional stability, anxiety, mood swings
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pub neuroticism: f32,
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/// AI-generated summary of the personality analysis
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pub summary: String,
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/// When this analysis was performed
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pub analyzed_at: DateTime<Utc>,
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}
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impl UserAnalysis {
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/// Create a new personality analysis
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pub fn new(
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openness: f32,
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conscientiousness: f32,
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extraversion: f32,
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agreeableness: f32,
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neuroticism: f32,
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summary: String,
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) -> Self {
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let id = Ulid::new().to_string();
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let analyzed_at = Utc::now();
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Self {
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id,
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openness: openness.clamp(0.0, 1.0),
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conscientiousness: conscientiousness.clamp(0.0, 1.0),
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extraversion: extraversion.clamp(0.0, 1.0),
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agreeableness: agreeableness.clamp(0.0, 1.0),
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neuroticism: neuroticism.clamp(0.0, 1.0),
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summary,
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analyzed_at,
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}
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}
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/// Get the dominant trait (highest score)
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pub fn dominant_trait(&self) -> &str {
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let scores = [
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(self.openness, "Openness"),
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(self.conscientiousness, "Conscientiousness"),
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(self.extraversion, "Extraversion"),
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(self.agreeableness, "Agreeableness"),
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(self.neuroticism, "Neuroticism"),
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];
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scores
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.iter()
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.max_by(|a, b| a.0.partial_cmp(&b.0).unwrap())
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.map(|(_, name)| *name)
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.unwrap_or("Unknown")
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}
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/// Check if a trait is high (>= 0.6)
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pub fn is_high(&self, trait_name: &str) -> bool {
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let score = match trait_name.to_lowercase().as_str() {
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"openness" | "o" => self.openness,
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"conscientiousness" | "c" => self.conscientiousness,
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"extraversion" | "e" => self.extraversion,
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"agreeableness" | "a" => self.agreeableness,
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"neuroticism" | "n" => self.neuroticism,
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_ => return false,
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};
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score >= 0.6
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn test_new_analysis() {
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let analysis = UserAnalysis::new(
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0.8,
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0.7,
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0.4,
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0.6,
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0.3,
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"Test summary".to_string(),
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);
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assert_eq!(analysis.openness, 0.8);
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assert_eq!(analysis.conscientiousness, 0.7);
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assert_eq!(analysis.extraversion, 0.4);
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assert_eq!(analysis.agreeableness, 0.6);
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assert_eq!(analysis.neuroticism, 0.3);
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assert!(!analysis.id.is_empty());
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}
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#[test]
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fn test_score_clamping() {
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let analysis = UserAnalysis::new(
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1.5, // Should clamp to 1.0
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-0.2, // Should clamp to 0.0
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0.5,
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0.5,
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0.5,
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"Test".to_string(),
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);
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assert_eq!(analysis.openness, 1.0);
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assert_eq!(analysis.conscientiousness, 0.0);
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}
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#[test]
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fn test_dominant_trait() {
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let analysis = UserAnalysis::new(
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0.9, // Highest
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0.5,
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0.4,
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0.6,
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0.3,
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"Test".to_string(),
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);
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assert_eq!(analysis.dominant_trait(), "Openness");
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}
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#[test]
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fn test_is_high() {
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let analysis = UserAnalysis::new(
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0.8, // High
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0.4, // Low
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0.6, // Threshold
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0.5,
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0.3,
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"Test".to_string(),
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);
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assert!(analysis.is_high("openness"));
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assert!(!analysis.is_high("conscientiousness"));
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assert!(analysis.is_high("extraversion")); // 0.6 is high
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}
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}
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@@ -1,7 +1,9 @@
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pub mod analysis;
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pub mod error;
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pub mod memory;
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pub mod store;
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pub use analysis::UserAnalysis;
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pub use error::{MemoryError, Result};
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pub use memory::Memory;
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pub use store::MemoryStore;
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@@ -2,6 +2,7 @@ use chrono::{DateTime, Utc};
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use rusqlite::{params, Connection};
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use std::path::PathBuf;
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use super::analysis::UserAnalysis;
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use super::error::{MemoryError, Result};
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use super::memory::Memory;
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@@ -61,6 +62,26 @@ impl MemoryStore {
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[],
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)?;
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// Create user_analyses table (Layer 3)
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conn.execute(
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"CREATE TABLE IF NOT EXISTS user_analyses (
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id TEXT PRIMARY KEY,
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openness REAL NOT NULL,
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conscientiousness REAL NOT NULL,
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extraversion REAL NOT NULL,
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agreeableness REAL NOT NULL,
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neuroticism REAL NOT NULL,
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summary TEXT NOT NULL,
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analyzed_at TEXT NOT NULL
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)",
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[],
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)?;
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conn.execute(
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"CREATE INDEX IF NOT EXISTS idx_analyzed_at ON user_analyses(analyzed_at)",
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[],
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)?;
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Ok(Self { conn })
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}
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@@ -249,6 +270,102 @@ impl MemoryStore {
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.query_row("SELECT COUNT(*) FROM memories", [], |row| row.get(0))?;
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Ok(count)
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}
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// ========== Layer 3: User Analysis Methods ==========
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/// Save a new user personality analysis
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pub fn save_analysis(&self, analysis: &UserAnalysis) -> Result<()> {
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self.conn.execute(
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"INSERT INTO user_analyses (id, openness, conscientiousness, extraversion, agreeableness, neuroticism, summary, analyzed_at)
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VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8)",
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params![
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&analysis.id,
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&analysis.openness,
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&analysis.conscientiousness,
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&analysis.extraversion,
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&analysis.agreeableness,
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&analysis.neuroticism,
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&analysis.summary,
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analysis.analyzed_at.to_rfc3339(),
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],
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)?;
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Ok(())
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}
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/// Get the most recent user analysis
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pub fn get_latest_analysis(&self) -> Result<Option<UserAnalysis>> {
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let mut stmt = self.conn.prepare(
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"SELECT id, openness, conscientiousness, extraversion, agreeableness, neuroticism, summary, analyzed_at
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FROM user_analyses
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ORDER BY analyzed_at DESC
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LIMIT 1",
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)?;
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let result = stmt.query_row([], |row| {
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let analyzed_at: String = row.get(7)?;
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Ok(UserAnalysis {
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id: row.get(0)?,
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openness: row.get(1)?,
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conscientiousness: row.get(2)?,
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extraversion: row.get(3)?,
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agreeableness: row.get(4)?,
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neuroticism: row.get(5)?,
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summary: row.get(6)?,
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analyzed_at: DateTime::parse_from_rfc3339(&analyzed_at)
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.map(|dt| dt.with_timezone(&Utc))
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.map_err(|e| {
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rusqlite::Error::FromSqlConversionFailure(
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7,
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rusqlite::types::Type::Text,
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Box::new(e),
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)
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})?,
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})
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});
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match result {
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Ok(analysis) => Ok(Some(analysis)),
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Err(rusqlite::Error::QueryReturnedNoRows) => Ok(None),
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Err(e) => Err(e.into()),
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}
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}
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/// Get all user analyses, ordered by date (newest first)
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pub fn list_analyses(&self) -> Result<Vec<UserAnalysis>> {
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let mut stmt = self.conn.prepare(
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"SELECT id, openness, conscientiousness, extraversion, agreeableness, neuroticism, summary, analyzed_at
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FROM user_analyses
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ORDER BY analyzed_at DESC",
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)?;
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let analyses = stmt
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.query_map([], |row| {
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let analyzed_at: String = row.get(7)?;
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Ok(UserAnalysis {
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id: row.get(0)?,
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openness: row.get(1)?,
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conscientiousness: row.get(2)?,
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extraversion: row.get(3)?,
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agreeableness: row.get(4)?,
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neuroticism: row.get(5)?,
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summary: row.get(6)?,
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analyzed_at: DateTime::parse_from_rfc3339(&analyzed_at)
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.map(|dt| dt.with_timezone(&Utc))
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.map_err(|e| {
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rusqlite::Error::FromSqlConversionFailure(
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7,
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rusqlite::types::Type::Text,
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Box::new(e),
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)
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})?,
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})
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})?
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.collect::<std::result::Result<Vec<_>, _>>()?;
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Ok(analyses)
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}
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}
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#[cfg(test)]
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117
src/mcp/base.rs
117
src/mcp/base.rs
@@ -2,7 +2,7 @@ use anyhow::Result;
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use serde_json::{json, Value};
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use std::io::{self, BufRead, Write};
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use crate::core::{Memory, MemoryStore};
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use crate::core::{Memory, MemoryStore, UserAnalysis};
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pub struct BaseMCPServer {
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store: MemoryStore,
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@@ -192,6 +192,58 @@ impl BaseMCPServer {
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"required": ["id"]
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}
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}),
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json!({
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"name": "save_user_analysis",
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"description": "Save a Big Five personality analysis based on user's memories (Layer 3)",
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"inputSchema": {
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"type": "object",
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"properties": {
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"openness": {
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"type": "number",
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"description": "Openness to Experience (0.0-1.0)",
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"minimum": 0.0,
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"maximum": 1.0
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},
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"conscientiousness": {
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"type": "number",
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"description": "Conscientiousness (0.0-1.0)",
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"minimum": 0.0,
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"maximum": 1.0
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},
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"extraversion": {
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"type": "number",
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"description": "Extraversion (0.0-1.0)",
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"minimum": 0.0,
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"maximum": 1.0
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},
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"agreeableness": {
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"type": "number",
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"description": "Agreeableness (0.0-1.0)",
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"minimum": 0.0,
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"maximum": 1.0
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},
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"neuroticism": {
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"type": "number",
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"description": "Neuroticism (0.0-1.0)",
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"minimum": 0.0,
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"maximum": 1.0
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},
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"summary": {
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"type": "string",
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"description": "AI-generated summary of the personality analysis"
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}
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},
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"required": ["openness", "conscientiousness", "extraversion", "agreeableness", "neuroticism", "summary"]
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}
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}),
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json!({
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"name": "get_user_analysis",
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"description": "Get the most recent Big Five personality analysis (Layer 3)",
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"inputSchema": {
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"type": "object",
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"properties": {}
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}
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}),
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]
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}
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@@ -222,6 +274,8 @@ impl BaseMCPServer {
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"list_memories" => self.tool_list_memories(),
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"update_memory" => self.tool_update_memory(arguments),
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"delete_memory" => self.tool_delete_memory(arguments),
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"save_user_analysis" => self.tool_save_user_analysis(arguments),
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"get_user_analysis" => self.tool_get_user_analysis(),
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_ => json!({
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"success": false,
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"error": format!("Unknown tool: {}", tool_name)
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@@ -374,6 +428,67 @@ impl BaseMCPServer {
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}
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}
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// ========== Layer 3: User Analysis Tools ==========
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fn tool_save_user_analysis(&self, arguments: &Value) -> Value {
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let openness = arguments["openness"].as_f64().unwrap_or(0.5) as f32;
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let conscientiousness = arguments["conscientiousness"].as_f64().unwrap_or(0.5) as f32;
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let extraversion = arguments["extraversion"].as_f64().unwrap_or(0.5) as f32;
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let agreeableness = arguments["agreeableness"].as_f64().unwrap_or(0.5) as f32;
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let neuroticism = arguments["neuroticism"].as_f64().unwrap_or(0.5) as f32;
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let summary = arguments["summary"].as_str().unwrap_or("").to_string();
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let analysis = UserAnalysis::new(
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openness,
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conscientiousness,
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extraversion,
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agreeableness,
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neuroticism,
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summary,
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);
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match self.store.save_analysis(&analysis) {
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Ok(()) => json!({
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"success": true,
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"id": analysis.id,
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"message": "User analysis saved successfully",
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"dominant_trait": analysis.dominant_trait()
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}),
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Err(e) => json!({
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"success": false,
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"error": e.to_string()
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}),
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}
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}
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fn tool_get_user_analysis(&self) -> Value {
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match self.store.get_latest_analysis() {
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Ok(Some(analysis)) => json!({
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"success": true,
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"analysis": {
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"id": analysis.id,
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"openness": analysis.openness,
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"conscientiousness": analysis.conscientiousness,
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"extraversion": analysis.extraversion,
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"agreeableness": analysis.agreeableness,
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"neuroticism": analysis.neuroticism,
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"summary": analysis.summary,
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"dominant_trait": analysis.dominant_trait(),
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"analyzed_at": analysis.analyzed_at
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}
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}),
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Ok(None) => json!({
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"success": true,
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"analysis": null,
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"message": "No analysis found. Run personality analysis first."
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}),
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Err(e) => json!({
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"success": false,
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"error": e.to_string()
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}),
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}
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}
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fn handle_unknown_method(&self, id: Value) -> Value {
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json!({
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"jsonrpc": "2.0",
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Reference in New Issue
Block a user