diff --git a/langchain/embeddings/openai.py b/langchain/embeddings/openai.py
index 74765c6592a..6326f0f63a8 100644
--- a/langchain/embeddings/openai.py
+++ b/langchain/embeddings/openai.py
@@ -92,7 +92,10 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
             os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
 
             from langchain.embeddings.openai import OpenAIEmbeddings
-            embeddings = OpenAIEmbeddings(model="your-embeddings-deployment-name")
+            embeddings = OpenAIEmbeddings(
+                deployment="your-embeddings-deployment-name",
+                model="your-embeddings-model-name"
+            )
             text = "This is a test query."
             query_result = embeddings.embed_query(text)
 
@@ -100,12 +103,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
 
     client: Any  #: :meta private:
     model: str = "text-embedding-ada-002"
-
-    # TODO: deprecate these two in favor of model
-    #  https://community.openai.com/t/api-update-engines-models/18597
-    #  https://github.com/openai/openai-python/issues/132
-    document_model_name: str = "text-embedding-ada-002"
-    query_model_name: str = "text-embedding-ada-002"
+    deployment: str = model  # to support Azure OpenAI Service custom deployment names
     embedding_ctx_length: int = 8191
     openai_api_key: Optional[str] = None
     openai_organization: Optional[str] = None
@@ -121,51 +119,6 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
 
         extra = Extra.forbid
 
-    # TODO: deprecate this
-    @root_validator(pre=True)
-    def get_model_names(cls, values: Dict) -> Dict:
-        # model_name is for first generation, and model is for second generation.
-        # Both are not allowed together.
-        if "model_name" in values and "model" in values:
-            raise ValueError(
-                "Both `model_name` and `model` were provided, "
-                "but only one should be."
-            )
-
-        """Get model names from just old model name."""
-        if "model_name" in values:
-            if "document_model_name" in values:
-                raise ValueError(
-                    "Both `model_name` and `document_model_name` were provided, "
-                    "but only one should be."
-                )
-            if "query_model_name" in values:
-                raise ValueError(
-                    "Both `model_name` and `query_model_name` were provided, "
-                    "but only one should be."
-                )
-            model_name = values.pop("model_name")
-            values["document_model_name"] = f"text-search-{model_name}-doc-001"
-            values["query_model_name"] = f"text-search-{model_name}-query-001"
-
-        # Set document/query model names from model parameter.
-        if "model" in values:
-            if "document_model_name" in values:
-                raise ValueError(
-                    "Both `model` and `document_model_name` were provided, "
-                    "but only one should be."
-                )
-            if "query_model_name" in values:
-                raise ValueError(
-                    "Both `model` and `query_model_name` were provided, "
-                    "but only one should be."
-                )
-            model = values.get("model")
-            values["document_model_name"] = model
-            values["query_model_name"] = model
-
-        return values
-
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
@@ -203,7 +156,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
 
             tokens = []
             indices = []
-            encoding = tiktoken.model.encoding_for_model(self.document_model_name)
+            encoding = tiktoken.model.encoding_for_model(self.model)
             for i, text in enumerate(texts):
                 # replace newlines, which can negatively affect performance.
                 text = text.replace("\n", " ")
@@ -222,7 +175,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 response = embed_with_retry(
                     self,
                     input=tokens[i : i + _chunk_size],
-                    engine=self.document_model_name,
+                    engine=self.deployment,
                 )
                 batched_embeddings += [r["embedding"] for r in response["data"]]
 
@@ -272,7 +225,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         """
         # handle batches of large input text
         if self.embedding_ctx_length > 0:
-            return self._get_len_safe_embeddings(texts, engine=self.document_model_name)
+            return self._get_len_safe_embeddings(texts, engine=self.deployment)
         else:
             results = []
             _chunk_size = chunk_size or self.chunk_size
@@ -280,7 +233,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 response = embed_with_retry(
                     self,
                     input=texts[i : i + _chunk_size],
-                    engine=self.document_model_name,
+                    engine=self.deployment,
                 )
                 results += [r["embedding"] for r in response["data"]]
             return results
@@ -294,5 +247,5 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         Returns:
             Embedding for the text.
         """
-        embedding = self._embedding_func(text, engine=self.query_model_name)
+        embedding = self._embedding_func(text, engine=self.deployment)
         return embedding