feat(vector): pluggable multimodal vector space

核心暴露 MultimodalEmbedder 接口，两条路径共享同一套 L0/L2/L3
向量缓存、media.Store 坐标、QueryMemoryMediaScored 检索：
  - onnx：内嵌 ONNX 模型（CLIP 等），通过 build tag 编译
  - http：外部向量 API 服务（Jina v5 / OpenAI / 自建）

跨模态融合权重改为 CrossModalFusionConfig 可配置结构体，
移除所有模型特定硬编码（CLIP/Jina），版本切换只需改配置。

模型切换自动迁移：
  - StaleVecDigestsAll 支持全模态（image+audio+video）
  - 启动时并发重算（ONNX 4 workers / API 8 workers）
  - 修复 SQL 运算符优先级导致 kind 过滤失效的 bug

实测对比（492 篇生产文档 + 3 张真实图片）：
  - TF-IDF：MRR 0.457（精确匹配快，语义差）
  - fastText：MRR 0.530（语义中等，延迟 8ms）
  - Jina v5-omni：MRR 0.900（全面领先，延迟 40ms）
  - 中文文本→图片：Jina MRR 0.833 vs CLIP 0.611

See docs/embedding-comparison.md for full benchmark.

tmp_jinabench.py

@@ -0,0 +1,204 @@
+#!/usr/bin/env python3
+"""Real Jina v5-omni benchmark on HomeAgent production documents and media.
+
+Inputs never leave the host. Reports Recall/Hit@K, MRR, margins, and latency for:
+- Jina unified dense space: text query -> text document
+- Jina unified dense space: text query -> production image
+"""
+from __future__ import annotations
+
+import glob
+import json
+import os
+import time
+from pathlib import Path
+
+import numpy as np
+import torch
+from PIL import Image
+from transformers import AutoModel, AutoProcessor
+
+MODEL = "/home/newqqagent/models/jina-v5-omni-nano"
+DOC_DIR = "/home/newqqagent/memory/documents"
+MEDIA_DB = "/home/newqqagent/memory/media/media.db"
+BLOB_ROOT = "/home/newqqagent/memory/media/blobs"
+
+CASES = [
+    ("mail-semantic", "邮件代理是否已经成功接入", ["AgentMail 接入验证"]),
+    ("fox-cross-language", "生成一张雪地红狐狸的图片", ["red fox in snowy forest"]),
+    ("plugin-semantic", "升级安装 QQ 插件包", ["plugin_install"]),
+    ("weather-paraphrase", "我所在城市的天气预报", ["河南新乡"]),
+    ("textarea-paraphrase", "聊天输入区域文字多了会不会自动增高", ["输入框在内容超过一行"]),
+    ("devices-paraphrase", "检查当前接入了哪些终端设备", ["你看看现在你都有哪些设备"]),
+    ("memory-health", "长期文档记忆功能是否健康", ["文档记忆系统是否正常工作"]),
+    ("reload-plugins", "重新加载全部扩展组件", ["热重载所有插件", "plgreload"]),
+    ("exact-agentmail", "AgentMail 接入验证", ["AgentMail 接入验证"]),
+    ("exact-plugin", "plugin_install", ["plugin_install"]),
+]
+
+MEDIA_QUERIES = [
+    ("captcha-cn", "一张带有干扰线和字符的验证码图片", "7e689211"),
+    ("captcha-en", "captcha with distorted letters and noise", "7e689211"),
+    ("news-cn", "中文新闻报道页面截图", "f6f17229"),
+    ("news-en", "a screenshot of a Chinese news article", "f6f17229"),
+    ("notes-cn", "手机深色模式备忘录截图", "ee5bdb2a"),
+    ("notes-en", "dark mode phone notes application screenshot", "ee5bdb2a"),
+]
+
+
+def load_docs():
+    docs = []
+    for p in sorted(glob.glob(DOC_DIR + "/doc_*.json")):
+        try:
+            d = json.load(open(p, encoding="utf-8"))
+        except Exception:
+            continue
+        if not d.get("id"):
+            continue
+        docs.append({"id": d["id"], "text": d.get("summary", "") + "\n" + d.get("content", "")})
+    return docs
+
+
+def relevant(docs, seeds):
+    seeds = [s.lower() for s in seeds]
+    return {d["id"] for d in docs if any(s in d["text"].lower() for s in seeds)}
+
+
+def embed_text(model, proc, texts, side, batch_size=4):
+    result = []
+    prefix = "Query: " if side == "query" else "Document: "
+    for start in range(0, len(texts), batch_size):
+        batch = [prefix + x for x in texts[start:start + batch_size]]
+        inp = proc(text=batch, padding=True, truncation=True, max_length=1024, return_tensors="pt")
+        with torch.inference_mode():
+            vec = model.embed(**inp)
+        result.append(vec.float().cpu().numpy())
+    return np.concatenate(result, axis=0)
+
+
+def measure_text(docs, doc_vecs, model, proc):
+    out = []
+    for name, query, seeds in CASES:
+        rel = relevant(docs, seeds)
+        t0 = time.perf_counter()
+        qv = embed_text(model, proc, [query], "query", 1)[0]
+        latency = (time.perf_counter() - t0) * 1000
+        scores = doc_vecs @ qv
+        order = np.argsort(-scores)
+        rank = 0
+        for pos, idx in enumerate(order, 1):
+            if docs[int(idx)]["id"] in rel:
+                rank = pos
+                break
+        best_rel = max((float(scores[i]) for i, d in enumerate(docs) if d["id"] in rel), default=float("nan"))
+        best_neg = max((float(scores[i]) for i, d in enumerate(docs) if d["id"] not in rel), default=float("nan"))
+        out.append({
+            "name": name, "query": query, "relevant": len(rel), "rank": rank,
+            "reciprocal_rank": 1.0 / rank if rank else 0.0,
+            "hit_at_1": 0 < rank <= 1, "hit_at_5": 0 < rank <= 5,
+            "query_latency_ms": latency,
+            "best_relevant": best_rel, "best_negative": best_neg,
+            "margin": best_rel - best_neg,
+            "top": [{"id": docs[int(i)]["id"], "score": float(scores[i])} for i in order[:5]],
+        })
+    return out
+
+
+def load_images():
+    rows = [
+        ("7e689211", "7e/689211bf78bc2f6366405b2a1f2fe0f7ec9ccdc59f368c13175c421c8e4018"),
+        ("ee5bdb2a", "ee/5bdb2a5fa3927ede79384ee6ec7657428896e2690b622f2409daa4b3fa9fa3"),
+        ("f6f17229", "f6/f17229b3513ea1f9c660353ee4ab021bec88e2b423780e8ed4445bc9aaf2fb"),
+    ]
+    return [(name, Image.open(os.path.join(BLOB_ROOT, rel)).convert("RGB")) for name, rel in rows]
+
+
+def embed_image(model, proc, img):
+    # Retrieval document side; nano's image geometry is nearly side-invariant, but use the documented prefix.
+    inp = proc(images=img, text="Document: <image>", return_tensors="pt")
+    with torch.inference_mode():
+        return model.embed(**inp).float().cpu().numpy()[0]
+
+
+def measure_media(images, image_vecs, model, proc):
+    out = []
+    names = [x[0] for x in images]
+    for name, query, want in MEDIA_QUERIES:
+        t0 = time.perf_counter()
+        qv = embed_text(model, proc, [query], "query", 1)[0]
+        latency = (time.perf_counter() - t0) * 1000
+        scores = image_vecs @ qv
+        order = np.argsort(-scores)
+        want_idx = names.index(want)
+        rank = int(np.where(order == want_idx)[0][0]) + 1
+        best_neg = max(float(scores[i]) for i in range(len(names)) if i != want_idx)
+        out.append({
+            "name": name, "query": query, "want": want, "rank": rank,
+            "query_latency_ms": latency, "positive": float(scores[want_idx]),
+            "best_negative": best_neg, "margin": float(scores[want_idx]) - best_neg,
+            "ranking": [{"id": names[int(i)], "score": float(scores[i])} for i in order],
+        })
+    return out
+
+
+def summary(rows):
+    return {
+        "queries": len(rows),
+        "hit_at_1": sum(r["hit_at_1"] for r in rows) / len(rows),
+        "hit_at_5": sum(r["hit_at_5"] for r in rows) / len(rows),
+        "mrr": sum(r["reciprocal_rank"] for r in rows) / len(rows),
+        "mean_query_latency_ms": sum(r["query_latency_ms"] for r in rows) / len(rows),
+        "mean_margin": sum(r["margin"] for r in rows) / len(rows),
+    }
+
+
+def main():
+    torch.set_num_threads(min(8, os.cpu_count() or 1))
+    print("loading Jina v5-omni-nano (vision + text)...", flush=True)
+    t0 = time.perf_counter()
+    model = AutoModel.from_pretrained(
+        MODEL, trust_remote_code=True, local_files_only=True,
+        default_task="retrieval", modality="vision", dtype=torch.float32,
+    ).eval()
+    proc = AutoProcessor.from_pretrained(MODEL, trust_remote_code=True, local_files_only=True)
+    load_s = time.perf_counter() - t0
+    docs = load_docs()
+    print(f"loaded model in {load_s:.2f}s, embedding {len(docs)} docs", flush=True)
+    t0 = time.perf_counter()
+    doc_vecs = embed_text(model, proc, [d["text"] for d in docs], "document", batch_size=4)
+    doc_embed_s = time.perf_counter() - t0
+    text_rows = measure_text(docs, doc_vecs, model, proc)
+
+    images = load_images()
+    t0 = time.perf_counter()
+    image_vecs = np.stack([embed_image(model, proc, img) for _, img in images])
+    image_embed_s = time.perf_counter() - t0
+    media_rows = measure_media(images, image_vecs, model, proc)
+
+    report = {
+        "model": "jina-v5-omni-nano", "dimension": int(doc_vecs.shape[1]),
+        "model_load_seconds": load_s, "documents": len(docs),
+        "document_embedding_seconds": doc_embed_s,
+        "document_embedding_ms_per_doc": doc_embed_s * 1000 / len(docs),
+        "text_retrieval": text_rows, "text_summary": summary(text_rows),
+        "images": len(images), "image_embedding_seconds": image_embed_s,
+        "image_embedding_ms_per_image": image_embed_s * 1000 / len(images),
+        "media_retrieval": media_rows,
+        "media_summary": {
+            "queries": len(media_rows),
+            "hit_at_1": sum(r["rank"] == 1 for r in media_rows) / len(media_rows),
+            "mrr": sum(1.0 / r["rank"] for r in media_rows) / len(media_rows),
+            "mean_margin": sum(r["margin"] for r in media_rows) / len(media_rows),
+            "mean_query_latency_ms": sum(r["query_latency_ms"] for r in media_rows) / len(media_rows),
+        },
+    }
+    Path("/tmp/homeagent-jina-benchmark.json").write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
+    print(json.dumps({k: report[k] for k in ["model", "dimension", "model_load_seconds", "documents", "document_embedding_seconds", "document_embedding_ms_per_doc", "text_summary", "images", "image_embedding_seconds", "image_embedding_ms_per_image", "media_summary"]}, ensure_ascii=False, indent=2))
+    for r in text_rows:
+        print(f"TEXT {r['name']:20s} rank={r['rank']:3d} margin={r['margin']:+.4f} latency={r['query_latency_ms']:.1f}ms")
+    for r in media_rows:
+        print(f"MEDIA {r['name']:20s} rank={r['rank']} pos={r['positive']:+.4f} neg={r['best_negative']:+.4f} margin={r['margin']:+.4f}")
+
+
+if __name__ == "__main__":
+    main()