oxirs-chat 0.2.4

RAG chat API with LLM integration and natural language to SPARQL translation
Documentation 
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//! Advanced chat statistics collector stub implementation

use anyhow::Result;
use serde::{Deserialize, Serialize};

use super::config::AdvancedStatisticsConfig;

/// Statistical summary of conversation quality
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConversationStatistics {
    pub total_messages: usize,
    pub average_response_time_ms: f64,
    pub quality_score: f64,
    pub user_satisfaction_score: f64,
    pub topic_coherence: f64,
}

/// Conversation insights extracted through analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConversationInsights {
    pub key_topics: Vec<String>,
    pub sentiment_trend: f64,
    pub engagement_score: f64,
    pub complexity_level: f64,
    pub summary: String,
}

/// Advanced chat statistics collector
///
/// Collects and analyzes conversation statistics for optimization.
/// Full implementation pending scirs2-core API stabilization.
#[derive(Debug)]
pub struct AdvancedChatStatisticsCollector {
    config: AdvancedStatisticsConfig,
}

impl AdvancedChatStatisticsCollector {
    /// Create a new statistics collector
    pub async fn new(config: AdvancedStatisticsConfig) -> Result<Self> {
        Ok(Self { config })
    }

    /// Get the current configuration
    pub fn config(&self) -> &AdvancedStatisticsConfig {
        &self.config
    }

    /// Collect statistics from a conversation (stub)
    pub fn collect(&self, message_count: usize, avg_response_ms: f64) -> ConversationStatistics {
        ConversationStatistics {
            total_messages: message_count,
            average_response_time_ms: avg_response_ms,
            quality_score: 0.5,
            user_satisfaction_score: 0.5,
            topic_coherence: 0.5,
        }
    }

    /// Get conversation insights (stub)
    pub fn get_insights(&self) -> ConversationInsights {
        ConversationInsights {
            key_topics: Vec::new(),
            sentiment_trend: 0.0,
            engagement_score: 0.5,
            complexity_level: 0.5,
            summary: "Analysis pending full implementation".to_string(),
        }
    }
}