completed VectorizationService to get embeddings

src/ctp_slack_bot/services/VectorizationService.py

@@ -2,18 +2,18 @@ from pydantic import BaseModel, validator
 from typing import List, Optional
 from ctp_slack_bot.core.config import settings
 import numpy as np
-from openai import AsyncOpenAI  # Updated import
+from openai import OpenAI
 
 
-class VectorizationService(BaseModel):
+class VectorizationService():
     """
     Service for vectorizing chunks of text data.
     """
     def __init__(self):
-        self.client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
+        self.client = OpenAI(api_key=settings.OPENAI_API_KEY)
 
         
-    async def get_embeddings(self, texts: List[str]) -> np.ndarray:
+    def get_embeddings(self, texts: List[str]) -> np.ndarray:
         """Get embeddings for a list of texts using OpenAI's API.
         
         Args:
@@ -27,7 +27,7 @@ class VectorizationService(BaseModel):
         """
         try:
             # Use the initialized client instead of the global openai module
-            response = await self.client.embeddings.create(
+            response = self.client.embeddings.create(
                 model=settings.EMBEDDING_MODEL,
                 input=texts,
                 encoding_format="float"  # Ensure we get raw float values
@@ -47,18 +47,13 @@ class VectorizationService(BaseModel):
             
         except Exception as e:
             print(f"Error getting embeddings: {str(e)}")
-            raise
+            pass
 
-    def _test(self):
+    def _test(self, list_of_strings: List[str] = ['Hello my sweet Svetlana.', 'You mean the world to me.']):
         """
         Test the vectorization service.
         """
-
-        pass
-
-
-
-vs = VectorizationService()
-vs._test()
-    
-    
+        print('embedding list', list_of_strings)
+        embeddings = self.get_embeddings(list_of_strings)
+        print(embeddings)
+        return embeddings