1 files changed, 10 insertions, 15 deletions
diff --git a/autogpt/memory/vector/utils.py b/autogpt/memory/vector/utils.py
index 75d1f69d4..beb2fcf93 100644
--- a/autogpt/memory/vector/utils.py
+++ b/autogpt/memory/vector/utils.py
@@ -1,16 +1,14 @@
 from typing import Any, overload
 
 import numpy as np
-import openai
 
 from autogpt.config import Config
-from autogpt.llm.utils import metered, retry_openai_api
+from autogpt.llm.base import TText
+from autogpt.llm.providers import openai as iopenai
 from autogpt.logs import logger
 
 Embedding = list[np.float32] | np.ndarray[Any, np.dtype[np.float32]]
 """Embedding vector"""
-TText = list[int]
-"""Token array representing text"""
 
 
 @overload
@@ -23,10 +21,8 @@ def get_embedding(input: list[str] | list[TText]) -> list[Embedding]:
     ...
 
 
-@metered
-@retry_openai_api()
 def get_embedding(
-    input: str | TText | list[str] | list[TText],
+    input: str | TText | list[str] | list[TText], config: Config
 ) -> Embedding | list[Embedding]:
     """Get an embedding from the ada model.
 
@@ -37,7 +33,6 @@ def get_embedding(
     Returns:
         List[float]: The embedding.
     """
-    cfg = Config()
     multiple = isinstance(input, list) and all(not isinstance(i, int) for i in input)
 
     if isinstance(input, str):
@@ -45,22 +40,22 @@ def get_embedding(
     elif multiple and isinstance(input[0], str):
         input = [text.replace("\n", " ") for text in input]
 
-    model = cfg.embedding_model
-    if cfg.use_azure:
-        kwargs = {"engine": cfg.get_azure_deployment_id_for_model(model)}
+    model = config.embedding_model
+    if config.use_azure:
+        kwargs = {"engine": config.get_azure_deployment_id_for_model(model)}
     else:
         kwargs = {"model": model}
 
     logger.debug(
         f"Getting embedding{f's for {len(input)} inputs' if multiple else ''}"
         f" with model '{model}'"
-        + (f" via Azure deployment '{kwargs['engine']}'" if cfg.use_azure else "")
+        + (f" via Azure deployment '{kwargs['engine']}'" if config.use_azure else "")
     )
 
-    embeddings = openai.Embedding.create(
-        input=input,
-        api_key=cfg.openai_api_key,
+    embeddings = iopenai.create_embedding(
+        input,
         **kwargs,
+        api_key=config.openai_api_key,
     ).data
 
     if not multiple: