Initial commit with .gitignore

basket.py

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+import requests
+from dashvector import Client, Doc
+
+# === 配置 ===
+DASHVECTOR_API_KEY = 'sk-55x6oBXypSlPHQ8NvPHfyBABcMIMUE0407A0FCC2A11F0B9C802831A608ABB'
+DASHVECTOR_ENDPOINT = 'vrs-cn-2ml4jm42o0001r.dashvector.cn-hangzhou.aliyuncs.com'
+
+# 从 DashScope 控制台获取（不是百炼 Model Studio！）
+DASHSCOPE_API_KEY = 'sk-d3f235925afa4e4e83d707dde04b9e52'  # 👈 替换这里！
+
+def get_embedding(text):
+    url = "https://dashscope.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding-v1"
+    headers = {
+        "Authorization": f"Bearer {DASHSCOPE_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    data = {
+        "input": {"texts": [text]},
+        "model": "text-embedding-v1"
+    }
+    resp = requests.post(url, headers=headers, json=data)
+    if resp.status_code == 200:
+        return resp.json()["output"]["embeddings"][0]["embedding"]
+    else:
+        raise Exception(f"❌ Embedding API 错误: {resp.status_code} - {resp.text}")
+
+# === 初始化 DashVector 客户端 ===
+client = Client(api_key=DASHVECTOR_API_KEY, endpoint=DASHVECTOR_ENDPOINT)
+
+# === 创建集合（注意维度是 1536！）===
+collection_name = "medical_topics"
+try:
+    client.delete(collection_name)
+except:
+    pass
+
+client.create(name=collection_name, dimension=1536)  # text-embedding-v1 输出 1536 维
+collection = client.get(collection_name)
+print("✅ 集合已创建并获取")
+
+# === 插入数据 ===
+topics = [
+    "如何治疗阳痿、早泄和肾虚？",
+    "早泄可以吃哪些中药？",
+    "该如何治疗早泄？",
+    "前列腺肥大是什么原因引起的？"
+]
+
+docs = []
+for i, text in enumerate(topics, 1):
+    emb = get_embedding(text)
+    docs.append(Doc(id=f"topic_{i}", vector=emb, fields={"content": text}))
+
+resp = collection.insert(docs)
+if resp.success:
+    print("✅ 4 条中文话题已成功插入！")
+else:
+    print("❌ 插入失败:", resp)
+    exit(1)
+
+# === 查询测试 ===
+query_text = "早泄的治疗方法有哪些？"
+query_vec = get_embedding(query_text)
+
+rets = collection.query(vector=query_vec, topk=3, output_fields=["content"])
+if rets.success:
+    print(f"\n🔍 查询 '{query_text}' 的结果:")
+    for doc in rets.documents:
+        print(f"  ID: {doc.id} | 相似度: {doc.score:.4f} | 内容: {doc.fields['content']}")
+else:
+    print("❌ 查询失败:", rets)
+