feat(vector): Automatic indexing of documents in s3 storage

vector/client.py

@@ -0,0 +1,19 @@
+import requests
+
+class EmbeddedClient:
+    def __init__(self, authorization: str, url: str = "https://openrouter.ai/api/v1/embeddings", model: str = "qwen/qwen3-embedding-8b") -> None:
+        self.url = url
+        self.authorization = authorization
+        self.model = model
+        
+    def embed(self, text: str) -> list[float]:
+        print(f"Embedding text {text}...")
+        response = requests.post(
+            f"{self.url}",
+            headers={"Authorization": self.authorization, "Content-Type": "application/json"},
+            json={"input": text, "model": self.model},
+            timeout=60
+        )
+        response.raise_for_status()
+        embedding = response.json()["data"][0]["embedding"]
+        return embedding