Provide CAII batch embedding for better performance

llm-service/app/services/CaiiEmbeddingModel.py

@@ -38,7 +38,7 @@
 import http.client as http_client
 import json
 import os
-from typing import Any, Dict
+from typing import Any, Dict, List
 
 from llama_index.core.base.embeddings.base import BaseEmbedding, Embedding
 from pydantic import Field
@@ -86,6 +86,33 @@ def _get_embedding(self, query: str, input_type: str) -> Embedding:
 
         return embedding
 
+    def _get_text_embeddings(self, texts: List[str]) -> List[Embedding]:
+        model = self.endpoint["endpointmetadata"]["model_name"]
+        domain = os.environ["CAII_DOMAIN"]
+
+        connection = http_client.HTTPSConnection(domain, 443)
+        headers = self.build_auth_headers()
+        headers["Content-Type"] = "application/json"
+        body = json.dumps(
+            {
+                "input": texts,
+                "input_type": "passage",
+                "truncate": "END",
+                "model": model,
+            }
+        )
+        connection.request("POST", self.endpoint["url"], body=body, headers=headers)
+        res = connection.getresponse()
+        data = res.read()
+        json_response = data.decode("utf-8")
+        structured_response = json.loads(json_response)
+        embeddings = structured_response["data"][0]["embedding"]
+        assert isinstance(embeddings, list)
+        assert all(isinstance(x, list) for x in embeddings)
+        assert all(all(isinstance(y, float) for y in x) for x in embeddings)
+
+        return embeddings
+
     def build_auth_headers(self) -> Dict[str, str]:
         with open("/tmp/jwt", "r") as file:
             jwt_contents = json.load(file)