agcore/examples/knowledge_graph_demo.rs

//! knowledge_graph_demo -- 知识图谱 + 双通道检索演示。
//!
//! 演示：
//! 1. 构建 KnowledgeGraph（实体 + 关系）
//! 2. BFS 图遍历（get_related）
//! 3. MemoryRetriever 双通道检索（Hybrid / GraphOnly / KnowledgeOnly）
//! 4. 标签管理（set_entity_tags / find_tags）
//!
//! 运行：`cargo run --example knowledge_graph_demo`

use std::sync::Arc;

use agcore::memory::{
    GraphEntity, GraphRelation, InMemoryGraph, InMemoryStore, KnowledgeGraph, KnowledgePage,
    KnowledgeStore, MemoryRetriever, MemoryStore, RelationDirection, RetrievalItem,
    RetrievalStrategy, RetrieverConfig,
};
use time::OffsetDateTime;

fn make_page(id: &str, title: &str, content: &str) -> KnowledgePage {
    let now = OffsetDateTime::now_utc();
    KnowledgePage {
        id: id.to_string(),
        title: title.to_string(),
        summary: content.chars().take(40).collect(),
        content: content.to_string(),
        tags: Vec::new(),
        references: Vec::new(),
        created_at: now,
        updated_at: now,
    }
}

#[tokio::main]
async fn main() {
    // ── 1. 构建知识图谱 ──
    println!("=== 1. 构建知识图谱 ===");
    let graph = Arc::new(InMemoryGraph::new());

    let mut langchain = GraphEntity::new("langchain", "LangChain", "framework");
    langchain.description = "LLM application framework".to_string();
    let mut langgraph = GraphEntity::new("langgraph", "LangGraph", "framework");
    langgraph.description = "Graph-based agent runtime from LangChain".to_string();
    let mut langsmith = GraphEntity::new("langsmith", "LangSmith", "tool");
    langsmith.description = "Tracing and evaluation platform".to_string();
    let mut python = GraphEntity::new("python", "Python", "language");
    python.description = "Programming language".to_string();
    let mut rust = GraphEntity::new("rust", "Rust", "language");
    rust.description = "Systems programming language".to_string();

    for e in [&langchain, &langgraph, &langsmith, &python, &rust] {
        graph.add_entity(e.clone()).await.unwrap();
    }
    graph
        .add_relation(GraphRelation::new("langchain", "langgraph", "includes", 0.9))
        .await
        .unwrap();
    graph
        .add_relation(GraphRelation::new("langchain", "langsmith", "includes", 0.7))
        .await
        .unwrap();
    graph
        .add_relation(GraphRelation::new("langchain", "python", "built_with", 0.95))
        .await
        .unwrap();
    graph
        .add_relation(GraphRelation::new("langgraph", "python", "depends_on", 0.8))
        .await
        .unwrap();
    println!("已添加 5 个实体 + 4 条关系");

    // ── 2. BFS 图遍历 ──
    println!("\n=== 2. BFS 图遍历：从 LangChain 出发，depth=2 ===");
    let related = graph
        .get_related("langchain", 2, RelationDirection::Outgoing, None)
        .await
        .unwrap();
    for se in &related {
        println!(
            "  {} (score={:.3}, path={:?})",
            se.entity.name, se.score, se.path
        );
    }
    assert!(!related.is_empty(), "应找到关联实体");

    // ── 3. 标签管理 ──
    println!("\n=== 3. 标签管理 ===");
    graph
        .set_entity_tags("langchain", vec!["ai".into(), "framework".into(), "llm".into()])
        .await
        .unwrap();
    graph
        .set_entity_tags("langgraph", vec!["ai".into(), "agent".into()])
        .await
        .unwrap();
    let tags = graph.find_tags("a").await.unwrap();
    println!("前缀 'a' 查找标签: {:?}", tags);
    let count = graph.entity_count_by_tag("ai").await.unwrap();
    println!("标签 'ai' 下实体数: {}", count);

    // ── 4. 双通道检索 ──
    println!("\n=== 4. 双通道检索 ===");
    let store: Arc<dyn MemoryStore> = Arc::new(InMemoryStore::new());
    let ks = KnowledgeStore::new(store);
    ks.add_page(make_page(
        "p1",
        "LangChain 框架介绍",
        "LangChain 是用于构建 LLM 应用的开源框架",
    ))
    .await
    .unwrap();
    ks.add_page(make_page(
        "p2",
        "Rust 异步编程",
        "Rust 异步基于 tokio 与 futures 抽象",
    ))
    .await
    .unwrap();

    // Hybrid 策略（默认）
    let retriever = MemoryRetriever::new(ks, RetrieverConfig::default())
        .with_knowledge_graph(graph.clone());
    println!("\n--- Hybrid 检索: 'langchain' ---");
    let result = retriever.retrieve("langchain").await.unwrap();
    println!("策略: {:?}", result.strategy);
    for item in &result.items {
        match item {
            RetrievalItem::KnowledgePage { page, score } => {
                println!("  [Store] {} (score={:.3})", page.title, score);
            }
            RetrievalItem::GraphEntity {
                entity, score, path, ..
            } => {
                println!(
                    "  [Graph] {} (score={:.3}, path={:?})",
                    entity.name, score, path
                );
            }
        }
    }
    let has_store = result
        .items
        .iter()
        .any(|i| matches!(i, RetrievalItem::KnowledgePage { .. }));
    let has_graph = result
        .items
        .iter()
        .any(|i| matches!(i, RetrievalItem::GraphEntity { .. }));
    assert!(has_store, "Hybrid 应有 Store 结果");
    assert!(has_graph, "Hybrid 应有 Graph 结果");

    // GraphOnly 策略
    let store2: Arc<dyn MemoryStore> = Arc::new(InMemoryStore::new());
    let ks2 = KnowledgeStore::new(store2);
    ks2.add_page(make_page("p1", "LangChain", "LLM framework"))
        .await
        .unwrap();
    let retriever_g = MemoryRetriever::new(ks2, RetrieverConfig::default())
        .with_knowledge_graph(graph.clone())
        .with_strategy(RetrievalStrategy::GraphOnly);
    println!("\n--- GraphOnly 检索: 'langchain' ---");
    let result = retriever_g.retrieve("langchain").await.unwrap();
    println!("策略: {:?}", result.strategy);
    for item in &result.items {
        match item {
            RetrievalItem::GraphEntity { entity, score, .. } => {
                println!("  [Graph] {} (score={:.3})", entity.name, score);
            }
            RetrievalItem::KnowledgePage { page, score, .. } => {
                println!("  [Store] {} (score={:.3})", page.title, score);
            }
        }
    }
    assert!(
        result.items.iter().all(|i| matches!(i, RetrievalItem::GraphEntity { .. })),
        "GraphOnly 应只返回 Graph 结果"
    );

    println!("\n✓ knowledge_graph_demo 完成");
}