fixed api key client issue

vision_agent/utils/sim.py

@@ -5,10 +5,10 @@
 from openai import Client
 from scipy.spatial.distance import cosine # type: ignore
 
-client = Client()
 
-
-def get_embedding(text: str, model: str = "text-embedding-3-small") -> List[float]:
+def get_embedding(
+ client: Client, text: str, model: str = "text-embedding-3-small"
+) -> List[float]:
  text = text.replace("\n", " ")
  return client.embeddings.create(input=[text], model=model).data[0].embedding
 
@@ -18,6 +18,7 @@ def __init__(
  self,
  df: pd.DataFrame,
  sim_key: Optional[str] = None,
+ api_key: Optional[str] = None,
  model: str = "text-embedding-3-small",
  ) -> None:
  """Creates a similarity object that can be used to find similar items in a
@@ -30,13 +31,18 @@ def __init__(
  model: str: The model to use for embeddings.
  """
  self.df = df
+ if not api_key:
+ self.client = Client()
+ else:
+ self.client = Client(api_key=api_key)
+
  self.model = model
  if "embs" not in df.columns and sim_key is None:
  raise ValueError("key is required if no column 'embs' is present.")
 
  if sim_key is not None:
  self.df["embs"] = self.df[sim_key].apply(
- lambda x: get_embedding(x, model=self.model)
+ lambda x: get_embedding(self.client, x, model=self.model)
  )
 
  def save(self, sim_file: Union[str, Path]) -> None:
@@ -53,7 +59,7 @@ def top_k(self, query: str, k: int = 5) -> Sequence[Dict]:
  Sequence[Dict]: The top k most similar items.
  """
 
- embedding = get_embedding(query, model=self.model)
+ embedding = get_embedding(self.client, query, model=self.model)
  self.df["sim"] = self.df.embs.apply(lambda x: 1 - cosine(x, embedding))
  res = self.df.sort_values("sim", ascending=False).head(k)
  return res[[c for c in res.columns if c != "embs"]].to_dict(orient="records")