partners/openai: fix depracation errors of pydantic's .dict() function (reopen #16629)

libs/partners/openai/langchain_openai/chat_models/azure.py

@@ -7,7 +7,7 @@
 
 import openai
 from langchain_core.outputs import ChatResult
-from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
+from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
 
 from langchain_openai.chat_models.base import ChatOpenAI
@@ -209,9 +209,11 @@ def lc_attributes(self) -> Dict[str, Any]:
             "openai_api_version": self.openai_api_version,
         }
 
-    def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
+    def _create_chat_result(
+        self, response: Union[dict, openai.BaseModel]
+    ) -> ChatResult:
         if not isinstance(response, dict):
-            response = response.dict()
+            response = response.model_dump()
         for res in response["choices"]:
             if res.get("finish_reason", None) == "content_filter":
                 raise ValueError(

libs/partners/openai/langchain_openai/chat_models/base.py

@@ -394,7 +394,7 @@ def _stream(
         default_chunk_class = AIMessageChunk
         for chunk in self.client.create(messages=message_dicts, **params):
             if not isinstance(chunk, dict):
-                chunk = chunk.dict()
+                chunk = chunk.model_dump()
             if len(chunk["choices"]) == 0:
                 continue
             choice = chunk["choices"][0]
@@ -449,10 +449,12 @@ def _create_message_dicts(
         message_dicts = [_convert_message_to_dict(m) for m in messages]
         return message_dicts, params
 
-    def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
+    def _create_chat_result(
+        self, response: Union[dict, openai.BaseModel]
+    ) -> ChatResult:
         generations = []
         if not isinstance(response, dict):
-            response = response.dict()
+            response = response.model_dump()
         for res in response["choices"]:
             message = _convert_dict_to_message(res["message"])
             generation_info = dict(finish_reason=res.get("finish_reason"))
@@ -486,7 +488,7 @@ async def _astream(
             messages=message_dicts, **params
         ):
             if not isinstance(chunk, dict):
-                chunk = chunk.dict()
+                chunk = chunk.model_dump()
             if len(chunk["choices"]) == 0:
                 continue
             choice = chunk["choices"][0]

libs/partners/openai/langchain_openai/embeddings/base.py

@@ -324,7 +324,7 @@ def _get_len_safe_embeddings(
                 input=tokens[i : i + _chunk_size], **self._invocation_params
             )
             if not isinstance(response, dict):
-                response = response.dict()
+                response = response.model_dump()
             batched_embeddings.extend(r["embedding"] for r in response["data"])
 
         results: List[List[List[float]]] = [[] for _ in range(len(texts))]
@@ -343,7 +343,7 @@ def _get_len_safe_embeddings(
                     input="", **self._invocation_params
                 )
                 if not isinstance(average_embedded, dict):
-                    average_embedded = average_embedded.dict()
+                    average_embedded = average_embedded.model_dump()
                 average = average_embedded["data"][0]["embedding"]
             else:
                 average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
@@ -436,7 +436,7 @@ async def _aget_len_safe_embeddings(
             )
 
             if not isinstance(response, dict):
-                response = response.dict()
+                response = response.model_dump()
             batched_embeddings.extend(r["embedding"] for r in response["data"])
 
         results: List[List[List[float]]] = [[] for _ in range(len(texts))]
@@ -453,7 +453,7 @@ async def _aget_len_safe_embeddings(
                     input="", **self._invocation_params
                 )
                 if not isinstance(average_embedded, dict):
-                    average_embedded = average_embedded.dict()
+                    average_embedded = average_embedded.model_dump()
                 average = average_embedded["data"][0]["embedding"]
             else:
                 average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])

libs/partners/openai/langchain_openai/llms/base.py

@@ -251,7 +251,7 @@ def _stream(
         self.get_sub_prompts(params, [prompt], stop)  # this mutates params
         for stream_resp in self.client.create(prompt=prompt, **params):
             if not isinstance(stream_resp, dict):
-                stream_resp = stream_resp.dict()
+                stream_resp = stream_resp.model_dump()
             chunk = _stream_response_to_generation_chunk(stream_resp)
             yield chunk
             if run_manager:
@@ -279,7 +279,7 @@ async def _astream(
             prompt=prompt, **params
         ):
             if not isinstance(stream_resp, dict):
-                stream_resp = stream_resp.dict()
+                stream_resp = stream_resp.model_dump()
             chunk = _stream_response_to_generation_chunk(stream_resp)
             yield chunk
             if run_manager:
@@ -357,7 +357,7 @@ def _generate(
                 if not isinstance(response, dict):
                     # V1 client returns the response in an PyDantic object instead of
                     # dict. For the transition period, we deep convert it to dict.
-                    response = response.dict()
+                    response = response.model_dump()
 
                 choices.extend(response["choices"])
                 _update_token_usage(_keys, response, token_usage)
@@ -420,7 +420,7 @@ async def _agenerate(
             else:
                 response = await self.async_client.create(prompt=_prompts, **params)
                 if not isinstance(response, dict):
-                    response = response.dict()
+                    response = response.model_dump()
                 choices.extend(response["choices"])
                 _update_token_usage(_keys, response, token_usage)
         return self.create_llm_result(