support of OpenAI package v1.X.X for utils.OpenAIEmbeddingFunction, d…

…eployment_id parameter for openai v0.X.X (#1338)

## Description of changes
- Add support of OpenAI package v1.X.X for utils.OpenAIEmbeddingFunction
- Add Azure OpenAI Deployment ID parameter for openai v0.X.X lib in
utils.OpenAIEmbeddingFunction

## Test plan
*How are these changes tested?*

Tested as dependency of https://github.com/Nayjest/ai-microcore with
Azure & openai packages v0.28.1 & v1.0.1, v1.1.0

chromadb/utils/embedding_functions.py

@@ -85,6 +85,7 @@ def __init__(
         api_base: Optional[str] = None,
         api_type: Optional[str] = None,
         api_version: Optional[str] = None,
+        deployment_id: Optional[str] = None,
     ):
         """
         Initialize the OpenAIEmbeddingFunction.
@@ -103,6 +104,7 @@ def __init__(
             api_version (str, optional): The api version for the API. If not provided,
                 it will use the api version for the OpenAI API. This can be used to
                 point to a different deployment, such as an Azure deployment.
+            deployment_id (str, optional): Deployment ID for Azure OpenAI.
 
         """
         try:
@@ -126,27 +128,61 @@ def __init__(
         if api_version is not None:
             openai.api_version = api_version
 
+        self._api_type = api_type
         if api_type is not None:
             openai.api_type = api_type
 
         if organization_id is not None:
             openai.organization = organization_id
 
-        self._client = openai.Embedding
+        self._v1 = openai.__version__.startswith('1.')
+        if self._v1:
+            if api_type == "azure":
+                self._client = openai.AzureOpenAI(
+                    api_key=api_key,
+                    api_version=api_version,
+                    azure_endpoint=api_base
+                ).embeddings
+            else:
+                self._client = openai.OpenAI(
+                    api_key=api_key,
+                    base_url=api_base
+                ).embeddings
+        else:
+            self._client = openai.Embedding
         self._model_name = model_name
+        self._deployment_id = deployment_id
 
     def __call__(self, input: Documents) -> Embeddings:
         # replace newlines, which can negatively affect performance.
         input = [t.replace("\n", " ") for t in input]
 
         # Call the OpenAI Embedding API
-        embeddings = self._client.create(input=input, engine=self._model_name)["data"]
-
-        # Sort resulting embeddings by index
-        sorted_embeddings = sorted(embeddings, key=lambda e: e["index"])  # type: ignore
-
-        # Return just the embeddings
-        return [result["embedding"] for result in sorted_embeddings]
+        if self._v1:
+            embeddings = self._client.create(
+                input=input,
+                model=self._deployment_id or self._model_name
+            ).data
+
+            # Sort resulting embeddings by index
+            sorted_embeddings = sorted(embeddings, key=lambda e: e.index)  # type: ignore
+
+            # Return just the embeddings
+            return [result.embedding for result in sorted_embeddings]
+        else:
+            if self._api_type == "azure":
+                embeddings = self._client.create(
+                    input=input,
+                    engine=self._deployment_id or self._model_name
+                )["data"]
+            else:
+                embeddings = self._client.create(input=input, model=self._model_name)["data"]
+
+            # Sort resulting embeddings by index
+            sorted_embeddings = sorted(embeddings, key=lambda e: e["index"])  # type: ignore
+
+            # Return just the embeddings
+            return [result["embedding"] for result in sorted_embeddings]
 
 
 class CohereEmbeddingFunction(EmbeddingFunction[Documents]):