fix: OpenAI Vector Model Using Openai Supplier

[shaohuzhang1]

fix: OpenAI Vector Model Using Openai Supplier

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[shaohuzhang1]

The code has a few issues, main ones being:

1. The openai module does not directly call any class method, but rather uses its attributes to perform operations (create).

2. Using dynamic method calls (like in .embed_documents) can be less readable and maintainable.

3. It lacks validation checks, particularly for required parameters such as apiKey, model_name, etc.

4. There is no exception handling.

To improve the code, you could do these changes:

@@ -6,18 +6,34 @@
     @date：2024/7/12 17:44
     @desc:
 """
-from typing import Dict
+from typing import Dict, List

-import openai
+from setting.models_provider.base_model_provider import MaxKBBaseModel


-class OpenAIEmbeddingModel(MaxKBBaseModel, openai.Embeddings):
+class OpenAIEmbeddingModel(MaxKBBaseModel):
     _client: openai.Completion
     api_key: str
     base_url: str
     model_name: str

+    def __init__(self, api_key, base_url, model_name: str = "text-davinci-003"):
         if not isinstance(api_key, str) or not api_key:
             raise ValueError("API key cannot be empty")
         if not isinstance(base_url, str) or not base_url:
             raise ValueError("Base URL cannot be empty")

         self.api_key = api_key
         self.base_url = base_url
-        super().__init__()
+        self._client = openai.OpenAI(api_key=api_key, base_url=base_url)

+        # set default model name if one wasn't provided
+        if not self.model_name:
+            self.model_name = "text-davinci-003"

     @staticmethod
     def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
         return OpenAIEmbeddingModel(
@@ -34,7 +49,7 @@
         return result.choices[0].message.content.split("\n\n")[:-1]

     def embed_query(self, text: str) -> List[float]:
-        response = self._client.create(prompt=text)
+        response = self.client.completions.create(prompt=text)
         token_ids = [token["id"] for token in response.choices[0]["tokens"]]
         embedding_values = [token["logprobs"]["probs"][i] for i in range(0, len(token_ids), 512)]
@@ -43,20 +58,24 @@

This revised version improves readability and error-handling by using explicit initialization and proper parameter checking.