feat: rag

16request_vector.py

@@ -0,0 +1,25 @@
+# 使用豆包来向量化文本
+
+import requests
+
+VOLC_EMBEDDINGS_API_URL = "https://ark.cn-beijing.volces.com/api/v3/embeddings"
+VOLC_API_KEY = "79b39c58-56db-4d8a-a8f8-84b95fca08db"
+
+
+def get_doubao_embedding(doc):
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {VOLC_API_KEY}",
+    }
+    params = {"model": "doubao-embedding-text-240715", "input": doc}
+    response = requests.post(VOLC_EMBEDDINGS_API_URL, json=params, headers=headers)
+    if response.status_code == 200:
+        data = response.json()
+        embedding = data["data"][0]["embedding"]
+        return embedding
+    else:
+        raise Exception(f"Embedding API error:{response.text}")
+
+
+embedding = get_doubao_embedding("这是一段文档")
+print(embedding)