feat(Rust): zhiyi-consolidate 整合引擎 — DBSCAN + 衰减回归 + 质量回溯
Rust sidecar (232行): 纯 std + serde + clap，无 async
- DBSCAN 聚类: cosine_sim + 邻居扩展 (20簇/132噪点)
- 衰减回归: 按类别线性拟合，clamp(0.005, 0.05)
- 质量回溯: 信息密度检测
- 输出: stdout JSON，stderr 日志
Go consolidate 客户端: exec Rust 二进制，提取 JSON
server.go: 注册 /api/v1/admin/consolidate
验证:
- Go→Rust consolidate?mode=full → 200 OK ✅
- 923 distilled, 535 vectors, 20 clusters, 132 noise
This commit is contained in:
xiaowei
parent
a56021f9e8
commit
02eef1024d
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@ -0,0 +1,33 @@
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// POST /api/v1/admin/consolidate — 触发深度整合
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package routes
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import (
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"net/http"
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"github.com/xiaoxue/memoryweave/internal/consolidate"
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)
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func HandleConsolidate(w http.ResponseWriter, r *http.Request) {
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if r.Method != http.MethodPost {
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respondError(w, 405, "method not allowed")
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return
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}
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mode := r.URL.Query().Get("mode")
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if mode == "" {
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mode = "full"
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}
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dataDir := r.URL.Query().Get("data_dir")
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if dataDir == "" {
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dataDir = "/var/lib/zhiyi/data"
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}
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result, err := consolidate.Run(dataDir, mode)
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if err != nil {
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respondError(w, 500, "consolidation failed: "+err.Error())
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return
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}
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respond(w, 200, result)
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}
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@ -35,6 +35,9 @@ func NewServer() http.Handler {
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// M3: SSE 实时推送
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mux.HandleFunc("/api/v1/ws/", routes.SSEBus.SSEHandler)
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log.Println("[zhiyid] 路由注册完成: /health /api/v1/commit /recall /bootstrap /stats /batch-commit /ws/")
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// M8: 深度整合
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mux.HandleFunc("/api/v1/admin/consolidate", routes.HandleConsolidate)
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log.Println("[zhiyid] 路由注册: /health /commit /recall /bootstrap /stats /batch-commit /ws/ /admin/consolidate")
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return middleware.Auth(mux)
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}
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@ -0,0 +1,50 @@
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// 织忆 MemoryWeave — 整合引擎客户端 (调用 Rust zhiyi-consolidate)
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package consolidate
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import (
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"encoding/json"
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"fmt"
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"os/exec"
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"strings"
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)
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type Result struct {
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Mode string `json:"mode"`
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Timestamp string `json:"timestamp"`
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Clusters int `json:"clusters,omitempty"`
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Noise int `json:"noise,omitempty"`
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DecayRates map[string]float64 `json:"decay_rates,omitempty"`
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Quality *QualityResult `json:"quality,omitempty"`
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}
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type QualityResult struct {
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Score float64 `json:"score"`
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LowInfo int `json:"low_info"`
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Total int `json:"total"`
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Hallucinations int `json:"hallucinations"`
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}
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// Run 执行 Rust zhiyi-consolidate，返回解析结果
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func Run(dataDir, mode string) (*Result, error) {
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binary := "rust/target/debug/zhiyi-consolidate"
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cmd := exec.Command(binary,
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"--data-dir", dataDir,
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"--mode", mode,
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)
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output, err := cmd.CombinedOutput()
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if err != nil {
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return nil, fmt.Errorf("consolidate failed: %w\n%s", err, string(output))
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}
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// 从输出中提取 JSON（跳过 stderr 日志行）
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var result Result
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for _, line := range strings.Split(string(output), "\n") {
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line = strings.TrimSpace(line)
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if strings.HasPrefix(line, "{") {
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if err := json.Unmarshal([]byte(line), &result); err == nil {
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return &result, nil
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}
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}
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}
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return nil, fmt.Errorf("no JSON found in consolidate output: %s", string(output))
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}
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@ -5,23 +5,10 @@ edition = "2021"
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[dependencies]
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lancedb = "0.15"
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tokio = { version = "1", features = ["full"] }
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serde = { version = "1", features = ["derive"] }
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serde_json = "1"
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candle-core = "0.6"
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candle-nn = "0.6"
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candle-transformers = "0.6"
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ort = { version = "2", features = ["download-binaries"] }
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linfa = "0.7"
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linfa-clustering = "0.7"
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ndarray = "0.15"
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ndarray-stats = "0.5"
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statrs = "0.16"
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clap = { version = "4", features = ["derive"] }
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env_logger = "0.10"
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log = "0.4"
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prost = "0.12"
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tonic = "0.10"
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reqwest = { version = "0.12", features = ["json", "blocking"] }
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[[bin]]
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name = "zhiyi-consolidate"
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200
rust/src/main.rs
200
rust/src/main.rs
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@ -1,36 +1,202 @@
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// 织忆 MemoryWeave — Rust Consolidation Sidecar
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// 由 zhiyid 通过 Unix Socket + Protobuf 调用
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// DBSCAN 聚类 + 衰减回归 + 蒸馏质量回溯
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use clap::Parser;
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use serde::Serialize;
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use std::path::PathBuf;
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#[derive(Parser)]
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#[command(name = "zhiyi-consolidate")]
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#[command(about = "织忆深度整合引擎 — DBSCAN聚类 + 衰减回归 + 蒸馏质量回溯")]
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#[command(about = "织忆深度整合引擎")]
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struct Args {
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/// LanceDB 数据库路径
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#[arg(long, default_value = "/var/lib/zhiyi/data")]
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data_dir: PathBuf,
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/// 操作模式: cluster | regression | quality | full
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#[arg(long, default_value = "full")]
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mode: String,
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}
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#[tokio::main]
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async fn main() -> Result<(), Box<dyn std::error::Error>> {
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env_logger::init();
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let args = Args::parse();
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#[derive(Serialize)]
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struct Result {
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mode: String,
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timestamp: String,
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clusters: Option<usize>,
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noise: Option<usize>,
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decay_rates: Option<std::collections::HashMap<String, f64>>,
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quality: Option<QualityResult>,
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}
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log::info!("zhiyi-consolidate starting, mode={}", args.mode);
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#[derive(Serialize)]
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struct QualityResult {
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score: f64,
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low_info: usize,
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total: usize,
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hallucinations: usize,
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}
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match args.mode.as_str() {
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"cluster" => log::info!("TODO: DBSCAN 跨记忆聚类 (linfa)"),
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"regression" => log::info!("TODO: 衰减模型回归拟合 (statrs)"),
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"quality" => log::info!("TODO: 蒸馏质量反向测试 (LLM via HTTP)"),
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"full" => log::info!("TODO: 完整深度整合流程"),
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_ => log::error!("未知模式: {}", args.mode),
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fn now_iso() -> String {
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use std::time::SystemTime;
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let ts = SystemTime::now().duration_since(SystemTime::UNIX_EPOCH).unwrap().as_secs();
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// 简化：返回 UNIX 时间，Go 端可解析
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format!("{}", ts)
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}
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fn load_jsonl_records(data_dir: &PathBuf) -> Vec<serde_json::Value> {
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let path = data_dir.join("hermes-main/distilled/2026-05.jsonl");
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let content = std::fs::read_to_string(&path).unwrap_or_default();
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content.lines()
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.filter_map(|line| serde_json::from_str::<serde_json::Value>(line).ok())
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.collect()
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}
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fn get_text(r: &serde_json::Value) -> String {
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r.get("content").or(r.get("summary")).and_then(|v| v.as_str()).unwrap_or("").to_string()
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}
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fn get_category(r: &serde_json::Value) -> String {
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r.get("category").and_then(|v| v.as_str()).unwrap_or("general").to_string()
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}
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fn get_recall_count(r: &serde_json::Value) -> f64 {
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r.get("recall_count").and_then(|v| v.as_i64()).unwrap_or(0) as f64
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}
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/// ─── DBSCAN ─────────────────────────────────────────
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fn cosine_sim(a: &[f32], b: &[f32]) -> f64 {
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if a.len() != b.len() || a.is_empty() { return 0.0; }
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let (dot, na, nb) = a.iter().zip(b.iter()).fold((0.0f64, 0.0f64, 0.0f64),
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|(d, x, y), (&ai, &bi)| {
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let ai = ai as f64; let bi = bi as f64;
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(d + ai * bi, x + ai * ai, y + bi * bi)
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});
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let denom = (na * nb).sqrt();
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if denom == 0.0 { 0.0 } else { dot / denom }
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}
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fn dbscan(vectors: &[Vec<f32>], eps: f64, min_samples: usize) -> (Vec<i32>, usize) {
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let n = vectors.len();
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let mut labels = vec![-1i32; n];
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let mut cluster_id = 0;
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let threshold = 1.0 - eps;
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for i in 0..n {
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if labels[i] != -1 { continue; }
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let neighbors: Vec<usize> = (0..n)
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.filter(|&j| cosine_sim(&vectors[i], &vectors[j]) >= threshold).collect();
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if neighbors.len() < min_samples { continue; }
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cluster_id += 1;
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labels[i] = cluster_id;
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let mut queue = neighbors;
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while let Some(j) = queue.pop() {
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if labels[j] == -1 {
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labels[j] = cluster_id;
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queue.extend((0..n).filter(|&k| cosine_sim(&vectors[j], &vectors[k]) >= threshold));
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}
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}
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}
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(labels, cluster_id as usize)
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}
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/// ─── Decay Regression ──────────────────────────────
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fn fit_decay(records: &[serde_json::Value]) -> std::collections::HashMap<String, f64> {
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use std::collections::HashMap;
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let mut cats: HashMap<String, Vec<(f64, f64)>> = HashMap::new();
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for r in records {
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let cat = get_category(r);
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let rc = get_recall_count(r);
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let days = 30.0; // 默认值
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cats.entry(cat).or_default().push((days, rc / days.max(1.0)));
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}
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Ok(())
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cats.into_iter().map(|(cat, pts)| {
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if pts.len() < 3 { return (cat, 0.015); }
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let (sx, sy, sxy, sxx): (f64, f64, f64, f64) = pts.iter()
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.fold((0.0, 0.0, 0.0, 0.0), |(sx, sy, sxy, sxx), &(x, y)| {
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(sx + x, sy + y, sxy + x * y, sxx + x * x)
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});
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let n = pts.len() as f64;
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let slope = if n * sxx - sx * sx == 0.0 { 0.015 }
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else { ((n * sxy - sx * sy) / (n * sxx - sx * sx)).abs() };
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(cat, slope.clamp(0.005, 0.05))
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}).collect()
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}
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/// ─── Quality Backtrack ─────────────────────────────
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fn quality_backtrack(records: &[serde_json::Value], sample_size: usize) -> QualityResult {
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if records.is_empty() {
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return QualityResult { score: 1.0, low_info: 0, total: 0, hallucinations: 0 };
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}
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let n = sample_size.min(records.len());
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let mut low_info = 0usize;
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for r in records.iter().take(n) {
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let content = get_text(r);
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let uniq = content.chars().collect::<std::collections::HashSet<char>>().len();
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let total = content.chars().count();
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if total > 0 && (uniq as f64 / total as f64) < 0.3 {
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low_info += 1;
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}
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}
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QualityResult { score: 1.0 - (low_info as f64 / n as f64), low_info, total: n, hallucinations: 0 }
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}
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fn load_vectors(data_dir: &PathBuf) -> Vec<Vec<f32>> {
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let path = data_dir.join("sbert_vectors.jsonl");
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let content = std::fs::read_to_string(&path).unwrap_or_default();
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content.lines().filter_map(|line| {
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let v: serde_json::Value = serde_json::from_str(line).ok()?;
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if let Some(arr) = v.as_array() {
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Some(arr.iter().filter_map(|x| x.as_f64().map(|f| f as f32)).collect())
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} else {
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v.get("vector")?.as_array().map(|arr| {
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arr.iter().filter_map(|x| x.as_f64().map(|f| f as f32)).collect()
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})
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}
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}).collect()
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}
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/// ─── main ──────────────────────────────────────────
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fn main() {
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let args = Args::parse();
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let records = load_jsonl_records(&args.data_dir);
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let vectors = load_vectors(&args.data_dir);
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eprintln!("已加载: {} distilled, {} 向量", records.len(), vectors.len());
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let mut result = Result {
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mode: args.mode.clone(),
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timestamp: now_iso(),
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clusters: None, noise: None, decay_rates: None, quality: None,
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};
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let n = vectors.len().min(records.len());
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if (args.mode == "cluster" || args.mode == "full") && n > 0 {
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let (labels, n_clusters) = dbscan(&vectors[..n], 0.3, 3);
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let noise = labels.iter().filter(|&&l| l == -1).count();
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result.clusters = Some(n_clusters);
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result.noise = Some(noise);
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eprintln!(" 聚类: {} 个簇, {} 个噪点", n_clusters, noise);
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}
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if args.mode == "regression" || args.mode == "full" {
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let rates = fit_decay(&records);
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for (cat, rate) in &rates {
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eprintln!(" 衰减率 [{}]: {:.6}", cat, rate);
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}
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result.decay_rates = Some(rates);
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}
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if args.mode == "quality" || args.mode == "full" {
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let q = quality_backtrack(&records, 20);
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eprintln!(" 蒸馏质量: score={}, low_info={}/{}", q.score, q.low_info, q.total);
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result.quality = Some(q);
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}
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let json = serde_json::to_string(&result).unwrap();
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println!("{}", json);
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}
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