feat(phase3): Task1 embedding链路验证 - embo-01(1536维)+pgvector检索打通

2026-05-26 10:33:25 +08:00
parent d40d46a434
commit 1325807257
6 changed files with 390 additions and 0 deletions
@@ -0,0 +1,70 @@
+"""
+探路脚本 — 调 MiniMax embo-01，打印原始返回 JSON
+确认向量字段位置和维度后再写正式 service。
+"""
+
+import httpx
+import json
+import os
+from pathlib import Path
+
+# 加载 .env
+from dotenv import load_dotenv
+_env_path = Path(__file__).parent.parent / ".env"
+load_dotenv(str(_env_path))
+
+api_key = os.environ.get("MINIMAX_EMBED_API_KEY", "")
+group_id = os.environ.get("MINIMAX_GROUP_ID", "")
+
+if not api_key or api_key == "your_api_key_here":
+    print("[ERROR] MINIMAX_EMBED_API_KEY not configured, please edit backend/.env")
+    exit(1)
+if not group_id or group_id == "your_group_id_here":
+    print("[ERROR] MINIMAX_GROUP_ID not configured, please edit backend/.env")
+    exit(1)
+
+print(f"API Key (first 4 chars): {api_key[:4]}...")
+print(f"GroupId: {group_id}")
+print()
+
+# 最小调用
+test_text = "这是一段测试文本，用于验证 embo-01 接口返回结构。"
+
+print(f"Sending request, test text: {test_text}")
+print("-" * 60)
+
+try:
+    resp = httpx.post(
+        "https://api.minimax.chat/v1/embeddings",
+        headers={
+            "Authorization": f"Bearer {api_key}",
+            "GroupId": group_id,
+            "Content-Type": "application/json",
+        },
+        json={"model": "embo-01", "texts": [test_text], "type": "db"},
+        timeout=30.0,
+    )
+    print(f"HTTP status: {resp.status_code}")
+    print()
+    data = resp.json()
+    print(json.dumps(data, indent=2, ensure_ascii=False))
+
+    # 提取向量，验证维度
+    print()
+    print("-" * 60)
+    vectors = data.get("vectors", [])
+    if vectors and len(vectors) > 0:
+        embedding = vectors[0]
+        dim = len(embedding)
+        print(f"[OK] Embedding field: vectors[0]")
+        print(f"[OK] Embedding dimension: {dim}")
+        if dim != 1536:
+            print(f"[STOP] Dimension is NOT 1536! Got {dim} - stopping here")
+        else:
+            print(f"[OK] Dimension correct: 1536")
+            print(f"[OK] API call successful, structure confirmed.")
+    else:
+        print("[WARNING] vectors not found in response")
+
+except Exception as e:
+    print(f"[ERROR] Request failed: {e}")
@@ -0,0 +1,79 @@
+"""
+全链路验证脚本 — TPS 知识库 embedding 最小链路
+
+验证步骤：
+1. 读取 backend/sample_md/ 下的 5 篇 .md 文件
+2. 调用 embo-01 转成向量（打印维度）
+3. 存入 knowledge_items + knowledge_embeddings（打印行数）
+4. 执行语义检索（打印查询句 + 最相似笔记）
+5. 查 episodes 表行数（打印，只读不动）
+"""
+
+import os
+from pathlib import Path
+
+from dotenv import load_dotenv
+from sqlmodel import text
+
+# 加载 .env
+_env_path = Path(__file__).parent.parent / ".env"
+load_dotenv(str(_env_path))
+
+from app.services.knowledge_service import KnowledgeService
+from app.db.session import engine
+
+
+def main():
+    print("=" * 60)
+    print("TPS Knowledge Base — Embedding Full链路验证")
+    print("=" * 60)
+
+    sample_dir = Path(__file__).parent.parent / "sample_md"
+    md_files = sorted(sample_dir.glob("*.md"))
+    print(f"\n[FIND] Found {len(md_files)} .md files in sample_md/")
+
+    ks = KnowledgeService()
+
+    # 1. 写入知识库
+    print("\n[STEP 1] Storing MD files into knowledge base...")
+    items_stored = []
+    for mf in md_files:
+        title = mf.stem  # 文件名（不含扩展名）作为标题
+        content = mf.read_text(encoding="utf-8")
+        item = ks.store_md_file(
+            title=title,
+            content_md=content,
+            source_file_name=mf.name,
+            source_type="manual",
+        )
+        items_stored.append(item)
+        print(f"  - Stored: {item.title} (id={item.id})")
+
+    ki_count = ks.get_item_count()
+    ke_count = ks.get_embedding_count()
+    print(f"\n[OK] knowledge_items rows: {ki_count}")
+    print(f"[OK] knowledge_embeddings rows: {ke_count}")
+
+    # 2. 语义检索
+    print("\n[STEP 2] Semantic search test...")
+    query = "五代战斗机的隐身技术有哪些关键要素？"
+    print(f"Query: {query}")
+    results = ks.search_similar(query, top_k=3)
+    print(f"\n[OK] Top 3 similar notes:")
+    for i, r in enumerate(results, 1):
+        print(f"  {i}. [{r['similarity']}] {r['title']}")
+
+    # 3. 查 episodes 表行数（只读不动）
+    print("\n[STEP 3] Episodes table (read-only)...")
+    with engine.connect() as conn:
+        result = conn.execute(text("SELECT COUNT(*) FROM episodes"))
+        episode_count = result.scalar()
+    print(f"[OK] episodes table row count: {episode_count}")
+
+    print("\n" + "=" * 60)
+    print("Verification complete.")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()