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