all: test 3.13 ci (#27197)

Co-authored-by: Bagatur <[email protected]>
Co-authored-by: Bagatur <[email protected]>

.github/scripts/check_diff.py

@@ -110,7 +110,7 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
         return _get_pydantic_test_configs(dir_)
 
     if dir_ == "libs/core":
-        py_versions = ["3.9", "3.10", "3.11", "3.12"]
+        py_versions = ["3.9", "3.10", "3.11", "3.12", "3.13"]
     # custom logic for specific directories
     elif dir_ == "libs/partners/milvus":
         # milvus poetry doesn't allow 3.12 because they
@@ -125,8 +125,11 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]:
     elif dir_ == "libs/community" and job == "compile-integration-tests":
         # community integration deps are slow in 3.12
         py_versions = ["3.9", "3.11"]
-    else:
+    elif dir_ == ".":
+        # unable to install with 3.13 because tokenizers doesn't support 3.13 yet
         py_versions = ["3.9", "3.12"]
+    else:
+        py_versions = ["3.9", "3.13"]
 
     return [{"working-directory": dir_, "python-version": py_v} for py_v in py_versions]
 
@@ -305,7 +308,7 @@ def _get_configs_for_multi_dirs(
         ]
     }
     map_job_to_configs["test-doc-imports"] = (
-        [{"python-version": "3.12"}] if docs_edited else []
+        [{"python-version": "3.13"}] if docs_edited else []
     )
 
     for key, value in map_job_to_configs.items():

libs/community/langchain_community/agent_toolkits/nla/tool.py

@@ -13,7 +13,7 @@
 from langchain_community.utilities.requests import Requests
 
 
-class NLATool(Tool):
+class NLATool(Tool):  # type: ignore[override]
     """Natural Language API Tool."""
 
     @classmethod

libs/community/langchain_community/agent_toolkits/openapi/planner.py

@@ -64,7 +64,7 @@ def _get_default_llm_chain_factory(
     return partial(_get_default_llm_chain, prompt)
 
 
-class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool):
+class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Requests GET tool with LLM-instructed extraction of truncated responses."""
 
     name: str = "requests_get"
@@ -98,7 +98,7 @@ async def _arun(self, text: str) -> str:
         raise NotImplementedError()
 
 
-class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):
+class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Requests POST tool with LLM-instructed extraction of truncated responses."""
 
     name: str = "requests_post"
@@ -129,7 +129,7 @@ async def _arun(self, text: str) -> str:
         raise NotImplementedError()
 
 
-class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool):
+class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Requests PATCH tool with LLM-instructed extraction of truncated responses."""
 
     name: str = "requests_patch"
@@ -162,7 +162,7 @@ async def _arun(self, text: str) -> str:
         raise NotImplementedError()
 
 
-class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool):
+class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Requests PUT tool with LLM-instructed extraction of truncated responses."""
 
     name: str = "requests_put"
@@ -193,7 +193,7 @@ async def _arun(self, text: str) -> str:
         raise NotImplementedError()
 
 
-class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool):
+class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool):  # type: ignore[override]
     """Tool that sends a DELETE request and parses the response."""
 
     name: str = "requests_delete"

libs/community/langchain_community/agents/openai_assistant/base.py

@@ -255,7 +255,7 @@ def create_assistant(
             name=name,
             instructions=instructions,
             tools=[_get_assistants_tool(tool) for tool in tools],  # type: ignore
-            tool_resources=tool_resources,
+            tool_resources=tool_resources,  # type: ignore[arg-type]
             model=model,
         )
         return cls(assistant_id=assistant.id, client=client, **kwargs)
@@ -394,7 +394,7 @@ async def acreate_assistant(
             name=name,
             instructions=instructions,
             tools=openai_tools,  # type: ignore
-            tool_resources=tool_resources,
+            tool_resources=tool_resources,  # type: ignore[arg-type]
             model=model,
         )
         return cls(assistant_id=assistant.id, async_client=async_client, **kwargs)

libs/community/langchain_community/chat_models/anyscale.py

@@ -155,7 +155,7 @@ def validate_environment(cls, values: dict) -> Any:
             else:
                 values["openai_api_base"] = values["anyscale_api_base"]
                 values["openai_api_key"] = values["anyscale_api_key"].get_secret_value()
-                values["client"] = openai.ChatCompletion
+                values["client"] = openai.ChatCompletion  # type: ignore[attr-defined]
         except AttributeError as exc:
             raise ValueError(
                 "`openai` has no `ChatCompletion` attribute, this is likely "

libs/community/langchain_community/chat_models/azure_openai.py

@@ -224,7 +224,7 @@ def validate_environment(cls, values: Dict) -> Dict:
                 **client_params
             ).chat.completions
         else:
-            values["client"] = openai.ChatCompletion
+            values["client"] = openai.ChatCompletion  # type: ignore[attr-defined]
         return values
 
     @property

libs/community/langchain_community/chat_models/azureml_endpoint.py

@@ -304,36 +304,38 @@ def _stream(
             "http_client": None,
         }
 
-        client = openai.OpenAI(**client_params)
+        client = openai.OpenAI(**client_params)  # type: ignore[arg-type, arg-type, arg-type, arg-type, arg-type, arg-type]
         message_dicts = [
             CustomOpenAIChatContentFormatter._convert_message_to_dict(m)
             for m in messages
         ]
         params = {"stream": True, "stop": stop, "model": None, **kwargs}
 
         default_chunk_class = AIMessageChunk
-        for chunk in client.chat.completions.create(messages=message_dicts, **params):
+        for chunk in client.chat.completions.create(messages=message_dicts, **params):  # type: ignore[arg-type]
             if not isinstance(chunk, dict):
-                chunk = chunk.dict()
-            if len(chunk["choices"]) == 0:
+                chunk = chunk.dict()  # type: ignore[attr-defined]
+            if len(chunk["choices"]) == 0:  # type: ignore[call-overload]
                 continue
-            choice = chunk["choices"][0]
-            chunk = _convert_delta_to_message_chunk(
-                choice["delta"], default_chunk_class
+            choice = chunk["choices"][0]  # type: ignore[call-overload]
+            chunk = _convert_delta_to_message_chunk(  # type: ignore[assignment]
+                choice["delta"],  # type: ignore[arg-type, index]
+                default_chunk_class,  # type: ignore[arg-type, index]
             )
             generation_info = {}
-            if finish_reason := choice.get("finish_reason"):
+            if finish_reason := choice.get("finish_reason"):  # type: ignore[union-attr]
                 generation_info["finish_reason"] = finish_reason
-            logprobs = choice.get("logprobs")
+            logprobs = choice.get("logprobs")  # type: ignore[union-attr]
             if logprobs:
                 generation_info["logprobs"] = logprobs
-            default_chunk_class = chunk.__class__
-            chunk = ChatGenerationChunk(
-                message=chunk, generation_info=generation_info or None
+            default_chunk_class = chunk.__class__  # type: ignore[assignment]
+            chunk = ChatGenerationChunk(  # type: ignore[assignment]
+                message=chunk,  # type: ignore[arg-type]
+                generation_info=generation_info or None,  # type: ignore[arg-type]
             )
             if run_manager:
-                run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
-            yield chunk
+                run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)  # type: ignore[attr-defined, arg-type]
+            yield chunk  # type: ignore[misc]
 
     async def _astream(
         self,
@@ -357,16 +359,17 @@ async def _astream(
             "http_client": None,
         }
 
-        async_client = openai.AsyncOpenAI(**client_params)
+        async_client = openai.AsyncOpenAI(**client_params)  # type: ignore[arg-type, arg-type, arg-type, arg-type, arg-type, arg-type]
         message_dicts = [
             CustomOpenAIChatContentFormatter._convert_message_to_dict(m)
             for m in messages
         ]
         params = {"stream": True, "stop": stop, "model": None, **kwargs}
 
         default_chunk_class = AIMessageChunk
-        async for chunk in await async_client.chat.completions.create(
-            messages=message_dicts, **params
+        async for chunk in await async_client.chat.completions.create(  # type: ignore[attr-defined]
+            messages=message_dicts,  # type: ignore[arg-type]
+            **params,  # type: ignore[arg-type]
         ):
             if not isinstance(chunk, dict):
                 chunk = chunk.dict()

libs/community/langchain_community/chat_models/everlyai.py

@@ -98,7 +98,7 @@ def validate_environment_override(cls, values: dict) -> Any:
                 "Please install it with `pip install openai`.",
             ) from e
         try:
-            values["client"] = openai.ChatCompletion
+            values["client"] = openai.ChatCompletion  # type: ignore[attr-defined]
         except AttributeError as exc:
             raise ValueError(
                 "`openai` has no `ChatCompletion` attribute, this is likely "

libs/community/langchain_community/chat_models/jinachat.py

@@ -70,11 +70,11 @@ def _create_retry_decorator(llm: JinaChat) -> Callable[[Any], Any]:
         stop=stop_after_attempt(llm.max_retries),
         wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
         retry=(
-            retry_if_exception_type(openai.error.Timeout)
-            | retry_if_exception_type(openai.error.APIError)
-            | retry_if_exception_type(openai.error.APIConnectionError)
-            | retry_if_exception_type(openai.error.RateLimitError)
-            | retry_if_exception_type(openai.error.ServiceUnavailableError)
+            retry_if_exception_type(openai.error.Timeout)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.APIError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.APIConnectionError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.RateLimitError)  # type: ignore[attr-defined]
+            | retry_if_exception_type(openai.error.ServiceUnavailableError)  # type: ignore[attr-defined]
         ),
         before_sleep=before_sleep_log(logger, logging.WARNING),
     )
@@ -234,7 +234,7 @@ def validate_environment(cls, values: Dict) -> Dict:
                 "Please install it with `pip install openai`."
             )
         try:
-            values["client"] = openai.ChatCompletion
+            values["client"] = openai.ChatCompletion  # type: ignore[attr-defined]
         except AttributeError:
             raise ValueError(
                 "`openai` has no `ChatCompletion` attribute, this is likely "
@@ -266,11 +266,11 @@ def _create_retry_decorator(self) -> Callable[[Any], Any]:
             stop=stop_after_attempt(self.max_retries),
             wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
             retry=(
-                retry_if_exception_type(openai.error.Timeout)
-                | retry_if_exception_type(openai.error.APIError)
-                | retry_if_exception_type(openai.error.APIConnectionError)
-                | retry_if_exception_type(openai.error.RateLimitError)
-                | retry_if_exception_type(openai.error.ServiceUnavailableError)
+                retry_if_exception_type(openai.error.Timeout)  # type: ignore[attr-defined]
+                | retry_if_exception_type(openai.error.APIError)  # type: ignore[attr-defined]
+                | retry_if_exception_type(openai.error.APIConnectionError)  # type: ignore[attr-defined]
+                | retry_if_exception_type(openai.error.RateLimitError)  # type: ignore[attr-defined]
+                | retry_if_exception_type(openai.error.ServiceUnavailableError)  # type: ignore[attr-defined]
             ),
             before_sleep=before_sleep_log(logger, logging.WARNING),
         )

libs/community/langchain_community/chat_models/konko.py

@@ -42,7 +42,7 @@
 logger = logging.getLogger(__name__)
 
 
-class ChatKonko(ChatOpenAI):
+class ChatKonko(ChatOpenAI):  # type: ignore[override]
     """`ChatKonko` Chat large language models API.
 
     To use, you should have the ``konko`` python package installed, and the

libs/community/langchain_community/chat_models/moonshot.py

@@ -12,7 +12,7 @@
 from langchain_community.llms.moonshot import MOONSHOT_SERVICE_URL_BASE, MoonshotCommon
 
 
-class MoonshotChat(MoonshotCommon, ChatOpenAI):  # type: ignore[misc]
+class MoonshotChat(MoonshotCommon, ChatOpenAI):  # type: ignore[misc, override, override]
     """Moonshot large language models.
 
     To use, you should have the ``openai`` python package installed, and the

libs/community/langchain_community/chat_models/octoai.py

@@ -98,7 +98,7 @@ def validate_environment(cls, values: Dict) -> Dict:
             else:
                 values["openai_api_base"] = values["octoai_api_base"]
                 values["openai_api_key"] = values["octoai_api_token"].get_secret_value()
-                values["client"] = openai.ChatCompletion
+                values["client"] = openai.ChatCompletion  # type: ignore[attr-defined]
         except ImportError:
             raise ImportError(
                 "Could not import openai python package. "

libs/community/langchain_community/chat_models/openai.py

@@ -87,11 +87,11 @@ def _create_retry_decorator(
     import openai
 
     errors = [
-        openai.error.Timeout,
-        openai.error.APIError,
-        openai.error.APIConnectionError,
-        openai.error.RateLimitError,
-        openai.error.ServiceUnavailableError,
+        openai.error.Timeout,  # type: ignore[attr-defined]
+        openai.error.APIError,  # type: ignore[attr-defined]
+        openai.error.APIConnectionError,  # type: ignore[attr-defined]
+        openai.error.RateLimitError,  # type: ignore[attr-defined]
+        openai.error.ServiceUnavailableError,  # type: ignore[attr-defined]
     ]
     return create_base_retry_decorator(
         error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
@@ -357,7 +357,7 @@ def validate_environment(cls, values: Dict) -> Dict:
                     **client_params
                 ).chat.completions
         elif not values.get("client"):
-            values["client"] = openai.ChatCompletion
+            values["client"] = openai.ChatCompletion  # type: ignore[attr-defined]
         else:
             pass
         return values
@@ -594,7 +594,7 @@ def _client_params(self) -> Dict[str, Any]:
         if self.openai_proxy:
             import openai
 
-            openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy}
+            openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy}  # type: ignore[attr-defined]
         return {**self._default_params, **openai_creds}
 
     def _get_invocation_params(

libs/community/langchain_community/chat_models/solar.py

@@ -13,7 +13,7 @@
 @deprecated(  # type: ignore[arg-type]
     since="0.0.34", removal="1.0", alternative_import="langchain_upstage.ChatUpstage"
 )
-class SolarChat(SolarCommon, ChatOpenAI):
+class SolarChat(SolarCommon, ChatOpenAI):  # type: ignore[override, override]
     """Wrapper around Solar large language models.
     To use, you should have the ``openai`` python package installed, and the
     environment variable ``SOLAR_API_KEY`` set with your API key.

libs/community/langchain_community/chat_models/vertexai.py

@@ -209,7 +209,7 @@ def _get_question(messages: List[BaseMessage]) -> HumanMessage:
     removal="1.0",
     alternative_import="langchain_google_vertexai.ChatVertexAI",
 )
-class ChatVertexAI(_VertexAICommon, BaseChatModel):
+class ChatVertexAI(_VertexAICommon, BaseChatModel):  # type: ignore[override]
     """`Vertex AI` Chat large language models API."""
 
     model_name: str = "chat-bison"

libs/community/langchain_community/document_loaders/blob_loaders/cloud_blob_loader.py

@@ -224,7 +224,7 @@ def _yield_paths(self) -> Iterable["AnyPath"]:
             yield self.path
             return
 
-        paths = self.path.glob(self.glob)
+        paths = self.path.glob(self.glob)  # type: ignore[attr-defined]
         for path in paths:
             if self.exclude:
                 if any(path.match(glob) for glob in self.exclude):

libs/community/langchain_community/document_loaders/csv_loader.py

@@ -219,4 +219,4 @@ def __init__(
     def _get_elements(self) -> List:
         from unstructured.partition.csv import partition_csv
 
-        return partition_csv(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_csv(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]

libs/community/langchain_community/document_loaders/email.py

@@ -60,16 +60,16 @@ def __init__(
     def _get_elements(self) -> List:
         from unstructured.file_utils.filetype import FileType, detect_filetype
 
-        filetype = detect_filetype(self.file_path)
+        filetype = detect_filetype(self.file_path)  # type: ignore[arg-type]
 
         if filetype == FileType.EML:
             from unstructured.partition.email import partition_email
 
-            return partition_email(filename=self.file_path, **self.unstructured_kwargs)
+            return partition_email(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
         elif satisfies_min_unstructured_version("0.5.8") and filetype == FileType.MSG:
             from unstructured.partition.msg import partition_msg
 
-            return partition_msg(filename=self.file_path, **self.unstructured_kwargs)
+            return partition_msg(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]
         else:
             raise ValueError(
                 f"Filetype {filetype} is not supported in UnstructuredEmailLoader."

libs/community/langchain_community/document_loaders/epub.py

@@ -39,4 +39,4 @@ def _get_elements(self) -> List:
             )
         from unstructured.partition.epub import partition_epub
 
-        return partition_epub(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_epub(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]

libs/community/langchain_community/document_loaders/excel.py

@@ -48,4 +48,4 @@ def __init__(
     def _get_elements(self) -> List:
         from unstructured.partition.xlsx import partition_xlsx
 
-        return partition_xlsx(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_xlsx(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]

libs/community/langchain_community/document_loaders/git.py

@@ -74,7 +74,7 @@ def lazy_load(self) -> Iterator[Document]:
 
             file_path = os.path.join(self.repo_path, item.path)
 
-            ignored_files = repo.ignored([file_path])
+            ignored_files = repo.ignored([file_path])  # type: ignore[arg-type]
             if len(ignored_files):
                 continue
 

libs/community/langchain_community/document_loaders/html.py

@@ -30,4 +30,4 @@ class UnstructuredHTMLLoader(UnstructuredFileLoader):
     def _get_elements(self) -> List:
         from unstructured.partition.html import partition_html
 
-        return partition_html(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_html(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]

libs/community/langchain_community/document_loaders/image.py

@@ -30,4 +30,4 @@ class UnstructuredImageLoader(UnstructuredFileLoader):
     def _get_elements(self) -> List:
         from unstructured.partition.image import partition_image
 
-        return partition_image(filename=self.file_path, **self.unstructured_kwargs)
+        return partition_image(filename=self.file_path, **self.unstructured_kwargs)  # type: ignore[arg-type]