langchain[lint]: fix mypy type ignores (#30894)

* Remove unused ignores
* Add type ignore codes
* Add mypy rule `warn_unused_ignores`
* Add ruff rule PGH003

NB: some `type: ignore[unused-ignore]` are added because the ignores are
needed when `extended_testing_deps.txt` deps are installed.

libs/langchain/langchain/agents/agent.py

@@ -1013,7 +1013,7 @@ class Agent(BaseSingleActionAgent):
         }
 
 
-class ExceptionTool(BaseTool):  # type: ignore[override]
+class ExceptionTool(BaseTool):
     """Tool that just returns the query."""
 
     name: str = "_Exception"
@@ -1129,7 +1129,7 @@ class AgentExecutor(Chain):
         """
         agent = self.agent
         tools = self.tools
-        allowed_tools = agent.get_allowed_tools()  # type: ignore
+        allowed_tools = agent.get_allowed_tools()  # type: ignore[union-attr]
         if allowed_tools is not None:
             if set(allowed_tools) != set([tool.name for tool in tools]):
                 raise ValueError(

libs/langchain/langchain/agents/chat/base.py

@@ -120,7 +120,7 @@ class ChatAgent(Agent):
         ]
         if input_variables is None:
             input_variables = ["input", "agent_scratchpad"]
-        return ChatPromptTemplate(input_variables=input_variables, messages=messages)  # type: ignore[arg-type]
+        return ChatPromptTemplate(input_variables=input_variables, messages=messages)
 
     @classmethod
     def from_llm_and_tools(

libs/langchain/langchain/agents/conversational/base.py

@@ -154,7 +154,7 @@ class ConversationalAgent(Agent):
             format_instructions=format_instructions,
             input_variables=input_variables,
         )
-        llm_chain = LLMChain(  # type: ignore[misc]
+        llm_chain = LLMChain(
             llm=llm,
             prompt=prompt,
             callback_manager=callback_manager,

libs/langchain/langchain/agents/conversational_chat/base.py

@@ -114,7 +114,7 @@ class ConversationalChatAgent(Agent):
             HumanMessagePromptTemplate.from_template(final_prompt),
             MessagesPlaceholder(variable_name="agent_scratchpad"),
         ]
-        return ChatPromptTemplate(input_variables=input_variables, messages=messages)  # type: ignore[arg-type]
+        return ChatPromptTemplate(input_variables=input_variables, messages=messages)
 
     def _construct_scratchpad(
         self, intermediate_steps: list[tuple[AgentAction, str]]
@@ -165,7 +165,7 @@ class ConversationalChatAgent(Agent):
             input_variables=input_variables,
             output_parser=_output_parser,
         )
-        llm_chain = LLMChain(  # type: ignore[misc]
+        llm_chain = LLMChain(
             llm=llm,
             prompt=prompt,
             callback_manager=callback_manager,

libs/langchain/langchain/agents/loading.py

@@ -86,7 +86,7 @@ def load_agent_from_config(
         del config["output_parser"]
 
     combined_config = {**config, **kwargs}
-    return agent_cls(**combined_config)  # type: ignore
+    return agent_cls(**combined_config)
 
 
 @deprecated("0.1.0", removal="1.0")

libs/langchain/langchain/agents/mrkl/base.py

@@ -144,7 +144,7 @@ class ZeroShotAgent(Agent):
             format_instructions=format_instructions,
             input_variables=input_variables,
         )
-        llm_chain = LLMChain(  # type: ignore[misc]
+        llm_chain = LLMChain(
             llm=llm,
             prompt=prompt,
             callback_manager=callback_manager,

libs/langchain/langchain/agents/openai_assistant/base.py

@@ -127,7 +127,7 @@ def _get_assistants_tool(
     such as "code_interpreter" and "file_search".
     """
     if _is_assistants_builtin_tool(tool):
-        return tool  # type: ignore
+        return tool  # type: ignore[return-value]
     else:
         return convert_to_openai_tool(tool)
 
@@ -267,7 +267,7 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
         assistant = client.beta.assistants.create(
             name=name,
             instructions=instructions,
-            tools=[_get_assistants_tool(tool) for tool in tools],  # type: ignore
+            tools=[_get_assistants_tool(tool) for tool in tools],  # type: ignore[misc]
             model=model,
         )
         return cls(assistant_id=assistant.id, client=client, **kwargs)
@@ -394,7 +394,7 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
         assistant = await async_client.beta.assistants.create(
             name=name,
             instructions=instructions,
-            tools=openai_tools,  # type: ignore
+            tools=openai_tools,  # type: ignore[arg-type]
             model=model,
         )
         return cls(assistant_id=assistant.id, async_client=async_client, **kwargs)

libs/langchain/langchain/agents/openai_functions_agent/agent_token_buffer_memory.py

@@ -12,7 +12,7 @@ from langchain.agents.format_scratchpad import (
 from langchain.memory.chat_memory import BaseChatMemory
 
 
-class AgentTokenBufferMemory(BaseChatMemory):  # type: ignore[override]
+class AgentTokenBufferMemory(BaseChatMemory):
     """Memory used to save agent output AND intermediate steps.
 
     Parameters:

libs/langchain/langchain/agents/openai_functions_agent/base.py

@@ -241,7 +241,7 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
                 MessagesPlaceholder(variable_name="agent_scratchpad"),
             ]
         )
-        return ChatPromptTemplate(messages=messages)  # type: ignore[arg-type, call-arg]
+        return ChatPromptTemplate(messages=messages)
 
     @classmethod
     def from_llm_and_tools(

libs/langchain/langchain/agents/openai_functions_multi_agent/base.py

@@ -286,7 +286,7 @@ class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
                 MessagesPlaceholder(variable_name="agent_scratchpad"),
             ]
         )
-        return ChatPromptTemplate(messages=messages)  # type: ignore[arg-type, call-arg]
+        return ChatPromptTemplate(messages=messages)
 
     @classmethod
     def from_llm_and_tools(

libs/langchain/langchain/agents/output_parsers/tools.py

@@ -14,7 +14,7 @@ from langchain_core.outputs import ChatGeneration, Generation
 from langchain.agents.agent import MultiActionAgentOutputParser
 
 
-class ToolAgentAction(AgentActionMessageLog):  # type: ignore[override]
+class ToolAgentAction(AgentActionMessageLog):
     tool_call_id: str
     """Tool call that this message is responding to."""
 

libs/langchain/langchain/agents/tools.py

@@ -9,7 +9,7 @@ from langchain_core.callbacks import (
 from langchain_core.tools import BaseTool, tool
 
 
-class InvalidTool(BaseTool):  # type: ignore[override]
+class InvalidTool(BaseTool):
     """Tool that is run when invalid tool name is encountered by agent."""
 
     name: str = "invalid_tool"

libs/langchain/langchain/chains/api/base.py

@@ -199,9 +199,7 @@ try:
         api_docs: str
         question_key: str = "question"  #: :meta private:
         output_key: str = "output"  #: :meta private:
-        limit_to_domains: Optional[Sequence[str]] = Field(
+        limit_to_domains: Optional[Sequence[str]] = Field(default_factory=list)  # type: ignore[arg-type]
-            default_factory=list  # type: ignore
-        )
         """Use to limit the domains that can be accessed by the API chain.
         
         * For example, to limit to just the domain `https://www.example.com`, set

libs/langchain/langchain/chains/base.py

@@ -110,17 +110,13 @@ class Chain(RunnableSerializable[dict[str, Any], dict[str, Any]], ABC):
         self, config: Optional[RunnableConfig] = None
     ) -> type[BaseModel]:
         # This is correct, but pydantic typings/mypy don't think so.
-        return create_model(  # type: ignore[call-overload]
+        return create_model("ChainInput", **{k: (Any, None) for k in self.input_keys})
-            "ChainInput", **{k: (Any, None) for k in self.input_keys}
-        )
 
     def get_output_schema(
         self, config: Optional[RunnableConfig] = None
     ) -> type[BaseModel]:
         # This is correct, but pydantic typings/mypy don't think so.
-        return create_model(  # type: ignore[call-overload]
+        return create_model("ChainOutput", **{k: (Any, None) for k in self.output_keys})
-            "ChainOutput", **{k: (Any, None) for k in self.output_keys}
-        )
 
     def invoke(
         self,

libs/langchain/langchain/chains/combine_documents/base.py

@@ -50,7 +50,7 @@ class BaseCombineDocumentsChain(Chain, ABC):
     ) -> type[BaseModel]:
         return create_model(
             "CombineDocumentsInput",
-            **{self.input_key: (list[Document], None)},  # type: ignore[call-overload]
+            **{self.input_key: (list[Document], None)},
         )
 
     def get_output_schema(
@@ -58,7 +58,7 @@ class BaseCombineDocumentsChain(Chain, ABC):
     ) -> type[BaseModel]:
         return create_model(
             "CombineDocumentsOutput",
-            **{self.output_key: (str, None)},  # type: ignore[call-overload]
+            **{self.output_key: (str, None)},
         )
 
     @property
@@ -249,7 +249,7 @@ class AnalyzeDocumentChain(Chain):
     ) -> type[BaseModel]:
         return create_model(
             "AnalyzeDocumentChain",
-            **{self.input_key: (str, None)},  # type: ignore[call-overload]
+            **{self.input_key: (str, None)},
         )
 
     def get_output_schema(

libs/langchain/langchain/chains/combine_documents/map_reduce.py

@@ -120,7 +120,7 @@ class MapReduceDocumentsChain(BaseCombineDocumentsChain):
                 **{
                     self.output_key: (str, None),
                     "intermediate_steps": (list[str], None),
-                },  # type: ignore[call-overload]
+                },
             )
 
         return super().get_output_schema(config)

libs/langchain/langchain/chains/conversation/base.py

@@ -19,7 +19,7 @@ from langchain.memory.buffer import ConversationBufferMemory
     ),
     removal="1.0",
 )
-class ConversationChain(LLMChain):  # type: ignore[override, override]
+class ConversationChain(LLMChain):
     """Chain to have a conversation and load context from memory.
 
     This class is deprecated in favor of ``RunnableWithMessageHistory``. Please refer

libs/langchain/langchain/chains/elasticsearch_database/base.py

@@ -170,7 +170,7 @@ class ElasticsearchDatabaseChain(Chain):
         except Exception as exc:
             # Append intermediate steps to exception, to aid in logging and later
             # improvement of few shot prompt seeds
-            exc.intermediate_steps = intermediate_steps  # type: ignore
+            exc.intermediate_steps = intermediate_steps  # type: ignore[attr-defined]
             raise exc
 
     @property

libs/langchain/langchain/chains/loading.py

@@ -39,16 +39,14 @@ try:
     from langchain_community.llms.loading import load_llm, load_llm_from_config
 except ImportError:
 
-    def load_llm(*args: Any, **kwargs: Any) -> None:  # type: ignore
+    def load_llm(*args: Any, **kwargs: Any) -> None:
         raise ImportError(
             "To use this load_llm functionality you must install the "
             "langchain_community package. "
             "You can install it with `pip install langchain_community`"
         )
 
-    def load_llm_from_config(  # type: ignore
+    def load_llm_from_config(*args: Any, **kwargs: Any) -> None:
-        *args: Any, **kwargs: Any
-    ) -> None:
         raise ImportError(
             "To use this load_llm_from_config functionality you must install the "
             "langchain_community package. "
@@ -95,9 +93,9 @@ def _load_hyde_chain(config: dict, **kwargs: Any) -> HypotheticalDocumentEmbedde
     else:
         raise ValueError("`embeddings` must be present.")
     return HypotheticalDocumentEmbedder(
-        llm_chain=llm_chain,  # type: ignore[arg-type]
+        llm_chain=llm_chain,
         base_embeddings=embeddings,
-        **config,  # type: ignore[arg-type]
+        **config,
     )
 
 
@@ -160,7 +158,7 @@ def _load_map_reduce_documents_chain(
     )
 
 
-def _load_reduce_documents_chain(config: dict, **kwargs: Any) -> ReduceDocumentsChain:  # type: ignore[valid-type]
+def _load_reduce_documents_chain(config: dict, **kwargs: Any) -> ReduceDocumentsChain:
     combine_documents_chain = None
     collapse_documents_chain = None
 
@@ -213,7 +211,7 @@ def _load_reduce_documents_chain(config: dict, **kwargs: Any) -> ReduceDocuments
             config.pop("collapse_document_chain_path"), **kwargs
         )
 
-    return ReduceDocumentsChain(  # type: ignore[misc]
+    return ReduceDocumentsChain(
         combine_documents_chain=combine_documents_chain,
         collapse_documents_chain=collapse_documents_chain,
         **config,
@@ -245,7 +243,7 @@ def _load_llm_bash_chain(config: dict, **kwargs: Any) -> Any:
     elif "prompt_path" in config:
         prompt = load_prompt(config.pop("prompt_path"))
     if llm_chain:
-        return LLMBashChain(llm_chain=llm_chain, prompt=prompt, **config)  # type: ignore[arg-type]
+        return LLMBashChain(llm_chain=llm_chain, prompt=prompt, **config)
     else:
         return LLMBashChain(llm=llm, prompt=prompt, **config)
 
@@ -347,7 +345,7 @@ def _load_pal_chain(config: dict, **kwargs: Any) -> Any:
         llm_chain = load_chain(config.pop("llm_chain_path"), **kwargs)
     else:
         raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
-    return PALChain(llm_chain=llm_chain, **config)  # type: ignore[arg-type]
+    return PALChain(llm_chain=llm_chain, **config)
 
 
 def _load_refine_documents_chain(config: dict, **kwargs: Any) -> RefineDocumentsChain:
@@ -410,7 +408,7 @@ def _load_sql_database_chain(config: dict, **kwargs: Any) -> Any:
     if "llm_chain" in config:
         llm_chain_config = config.pop("llm_chain")
         chain = load_chain_from_config(llm_chain_config, **kwargs)
-        return SQLDatabaseChain(llm_chain=chain, database=database, **config)  # type: ignore[arg-type]
+        return SQLDatabaseChain(llm_chain=chain, database=database, **config)
     if "llm" in config:
         llm_config = config.pop("llm")
         llm = load_llm_from_config(llm_config, **kwargs)
@@ -563,8 +561,8 @@ def _load_graph_cypher_chain(config: dict, **kwargs: Any) -> GraphCypherQAChain:
         )
     return GraphCypherQAChain(
         graph=graph,
-        cypher_generation_chain=cypher_generation_chain,  # type: ignore[arg-type]
+        cypher_generation_chain=cypher_generation_chain,
-        qa_chain=qa_chain,  # type: ignore[arg-type]
+        qa_chain=qa_chain,
         **config,
     )
 

libs/langchain/langchain/chains/openai_functions/base.py

@@ -51,7 +51,7 @@ def create_openai_fn_chain(
     output_key: str = "function",
     output_parser: Optional[BaseLLMOutputParser] = None,
     **kwargs: Any,
-) -> LLMChain:  # type: ignore[valid-type]
+) -> LLMChain:
     """[Legacy] Create an LLM chain that uses OpenAI functions.
 
     Args:
@@ -131,7 +131,7 @@ def create_openai_fn_chain(
     }
     if len(openai_functions) == 1 and enforce_single_function_usage:
         llm_kwargs["function_call"] = {"name": openai_functions[0]["name"]}
-    llm_chain = LLMChain(  # type: ignore[misc]
+    llm_chain = LLMChain(
         llm=llm,
         prompt=prompt,
         output_parser=output_parser,
@@ -153,7 +153,7 @@ def create_structured_output_chain(
     output_key: str = "function",
     output_parser: Optional[BaseLLMOutputParser] = None,
     **kwargs: Any,
-) -> LLMChain:  # type: ignore[valid-type]
+) -> LLMChain:
     """[Legacy] Create an LLMChain that uses an OpenAI function to get a structured output.
 
     Args:
@@ -216,7 +216,7 @@ def create_structured_output_chain(
         class _OutputFormatter(BaseModel):
             """Output formatter. Should always be used to format your response to the user."""  # noqa: E501
 
-            output: output_schema  # type: ignore
+            output: output_schema  # type: ignore[valid-type]
 
         function = _OutputFormatter
         output_parser = output_parser or PydanticAttrOutputFunctionsParser(

libs/langchain/langchain/chains/openai_functions/citation_fuzzy_match.py

@@ -148,7 +148,7 @@ def create_citation_fuzzy_match_chain(llm: BaseLanguageModel) -> LLMChain:
             )
         ),
     ]
-    prompt = ChatPromptTemplate(messages=messages)  # type: ignore[arg-type, call-arg]
+    prompt = ChatPromptTemplate(messages=messages)  # type: ignore[arg-type]
 
     chain = LLMChain(
         llm=llm,

libs/langchain/langchain/chains/openai_functions/extraction.py

@@ -170,7 +170,7 @@ def create_extraction_chain_pydantic(
     """
 
     class PydanticSchema(BaseModel):
-        info: list[pydantic_schema]  # type: ignore
+        info: list[pydantic_schema]
 
     if hasattr(pydantic_schema, "model_json_schema"):
         openai_schema = pydantic_schema.model_json_schema()

libs/langchain/langchain/chains/openai_functions/openapi.py

@@ -77,7 +77,7 @@ def _openapi_params_to_json_schema(params: list[Parameter], spec: OpenAPISpec) -
         if p.param_schema:
             schema = spec.get_schema(p.param_schema)
         else:
-            media_type_schema = list(p.content.values())[0].media_type_schema  # type: ignore
+            media_type_schema = list(p.content.values())[0].media_type_schema
             schema = spec.get_schema(media_type_schema)
         if p.description and not schema.description:
             schema.description = p.description
@@ -363,7 +363,7 @@ def get_openapi_chain(
             OpenAPISpec.from_text,
         ):
             try:
-                spec = conversion(spec)  # type: ignore[arg-type]
+                spec = conversion(spec)
                 break
             except ImportError as e:
                 raise e

libs/langchain/langchain/chains/openai_functions/qa_with_structure.py

@@ -96,7 +96,7 @@ def create_qa_with_structure_chain(
         HumanMessagePromptTemplate.from_template("Question: {question}"),
         HumanMessage(content="Tips: Make sure to answer in the correct format"),
     ]
-    prompt = prompt or ChatPromptTemplate(messages=messages)  # type: ignore[arg-type, call-arg]
+    prompt = prompt or ChatPromptTemplate(messages=messages)  # type: ignore[arg-type]
 
     chain = LLMChain(
         llm=llm,

libs/langchain/langchain/chains/qa_with_sources/base.py

@@ -69,7 +69,7 @@ class BaseQAWithSourcesChain(Chain, ABC):
             document_prompt=document_prompt,
             document_variable_name="summaries",
         )
-        reduce_documents_chain = ReduceDocumentsChain(  # type: ignore[misc]
+        reduce_documents_chain = ReduceDocumentsChain(
             combine_documents_chain=combine_results_chain
         )
         combine_documents_chain = MapReduceDocumentsChain(

libs/langchain/langchain/chains/query_constructor/parser.py

@@ -11,12 +11,12 @@ try:
     from lark import Lark, Transformer, v_args
 except ImportError:
 
-    def v_args(*args: Any, **kwargs: Any) -> Any:  # type: ignore
+    def v_args(*args: Any, **kwargs: Any) -> Any:  # type: ignore[misc]
         """Dummy decorator for when lark is not installed."""
         return lambda _: None
 
-    Transformer = object  # type: ignore
+    Transformer = object  # type: ignore[assignment,misc]
-    Lark = object  # type: ignore
+    Lark = object  # type: ignore[assignment,misc]
 
 from langchain_core.structured_query import (
     Comparator,

libs/langchain/langchain/chains/question_answering/chain.py

@@ -156,7 +156,7 @@ def _load_map_reduce_chain(
             verbose=verbose,  # type: ignore[arg-type]
             callback_manager=callback_manager,
         )
-    reduce_documents_chain = ReduceDocumentsChain(  # type: ignore[misc]
+    reduce_documents_chain = ReduceDocumentsChain(
         combine_documents_chain=combine_documents_chain,
         collapse_documents_chain=collapse_chain,
         token_max=token_max,

libs/langchain/langchain/chains/router/multi_retrieval_qa.py

@@ -20,7 +20,7 @@ from langchain.chains.router.multi_retrieval_prompt import (
 )
 
 
-class MultiRetrievalQAChain(MultiRouteChain):  # type: ignore[override]
+class MultiRetrievalQAChain(MultiRouteChain):
     """A multi-route chain that uses an LLM router chain to choose amongst retrieval
     qa chains."""
 

libs/langchain/langchain/chains/sql_database/query.py

@@ -134,7 +134,7 @@ def create_sql_query_chain(
         ),
     }
     return (
-        RunnablePassthrough.assign(**inputs)  # type: ignore
+        RunnablePassthrough.assign(**inputs)  # type: ignore[return-value]
         | (
             lambda x: {
                 k: v

libs/langchain/langchain/chains/structured_output/base.py

@@ -520,7 +520,7 @@ def _create_openai_json_runnable(
     """"""
     if isinstance(output_schema, type) and is_basemodel_subclass(output_schema):
         output_parser = output_parser or PydanticOutputParser(
-            pydantic_object=output_schema,  # type: ignore
+            pydantic_object=output_schema,
         )
         schema_as_dict = convert_to_openai_function(output_schema)["parameters"]
     else:
@@ -559,7 +559,7 @@ def _create_openai_functions_structured_output_runnable(
         class _OutputFormatter(BaseModel):
             """Output formatter. Should always be used to format your response to the user."""  # noqa: E501
 
-            output: output_schema  # type: ignore
+            output: output_schema  # type: ignore[valid-type]
 
         function = _OutputFormatter
         output_parser = output_parser or PydanticAttrOutputFunctionsParser(

libs/langchain/langchain/chains/summarize/chain.py

@@ -62,14 +62,14 @@ def _load_map_reduce_chain(
         llm=llm,
         prompt=map_prompt,
         verbose=verbose,  # type: ignore[arg-type]
-        callbacks=callbacks,  # type: ignore[arg-type]
+        callbacks=callbacks,
     )
     _reduce_llm = reduce_llm or llm
     reduce_chain = LLMChain(
         llm=_reduce_llm,
         prompt=combine_prompt,
         verbose=verbose,  # type: ignore[arg-type]
-        callbacks=callbacks,  # type: ignore[arg-type]
+        callbacks=callbacks,
     )
     # TODO: document prompt
     combine_documents_chain = StuffDocumentsChain(

libs/langchain/langchain/chat_models/base.py

@@ -41,7 +41,7 @@ __all__ = [
 
 
 @overload
-def init_chat_model(  # type: ignore[overload-overlap]
+def init_chat_model(
     model: str,
     *,
     model_provider: Optional[str] = None,
@@ -347,7 +347,7 @@ def _init_chat_model_helper(
         _check_pkg("langchain_anthropic")
         from langchain_anthropic import ChatAnthropic
 
-        return ChatAnthropic(model=model, **kwargs)  # type: ignore[call-arg]
+        return ChatAnthropic(model=model, **kwargs)  # type: ignore[call-arg,unused-ignore]
     elif model_provider == "azure_openai":
         _check_pkg("langchain_openai")
         from langchain_openai import AzureChatOpenAI
@@ -402,7 +402,7 @@ def _init_chat_model_helper(
         _check_pkg("langchain_mistralai")
         from langchain_mistralai import ChatMistralAI
 
-        return ChatMistralAI(model=model, **kwargs)  # type: ignore[call-arg]
+        return ChatMistralAI(model=model, **kwargs)  # type: ignore[call-arg,unused-ignore]
     elif model_provider == "huggingface":
         _check_pkg("langchain_huggingface")
         from langchain_huggingface import ChatHuggingFace

libs/langchain/langchain/embeddings/__init__.py

@@ -87,7 +87,7 @@ class HypotheticalDocumentEmbedder:
         )
         from langchain.chains.hyde.base import HypotheticalDocumentEmbedder as H
 
-        return H(*args, **kwargs)  # type: ignore
+        return H(*args, **kwargs)  # type: ignore[return-value]
 
     @classmethod
     def from_llm(cls, *args: Any, **kwargs: Any) -> Any:

libs/langchain/langchain/evaluation/comparison/eval_chain.py

@@ -95,7 +95,7 @@ def resolve_pairwise_criteria(
     return criteria_
 
 
-class PairwiseStringResultOutputParser(BaseOutputParser[dict]):  # type: ignore[override]
+class PairwiseStringResultOutputParser(BaseOutputParser[dict]):
     """A parser for the output of the PairwiseStringEvalChain.
 
     Attributes:
@@ -151,7 +151,7 @@ class PairwiseStringResultOutputParser(BaseOutputParser[dict]):  # type: ignore[
         }
 
 
-class PairwiseStringEvalChain(PairwiseStringEvaluator, LLMEvalChain, LLMChain):  # type: ignore[override]
+class PairwiseStringEvalChain(PairwiseStringEvaluator, LLMEvalChain, LLMChain):
     """A chain for comparing two outputs, such as the outputs
      of two models, prompts, or outputs of a single model on similar inputs.
 
@@ -391,7 +391,7 @@ Performance may be significantly worse with other models."
         return self._prepare_output(result)
 
 
-class LabeledPairwiseStringEvalChain(PairwiseStringEvalChain):  # type: ignore[override]
+class LabeledPairwiseStringEvalChain(PairwiseStringEvalChain):
     """A chain for comparing two outputs, such as the outputs
      of two models, prompts, or outputs of a single model on similar inputs,
      with labeled preferences.

libs/langchain/langchain/evaluation/criteria/eval_chain.py

@@ -165,7 +165,7 @@ def resolve_criteria(
     return criteria_
 
 
-class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):  # type: ignore[override]
+class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
     """LLM Chain for evaluating runs against criteria.
 
     Parameters
@@ -509,7 +509,7 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain):  # type: ignor
         return self._prepare_output(result)
 
 
-class LabeledCriteriaEvalChain(CriteriaEvalChain):  # type: ignore[override]
+class LabeledCriteriaEvalChain(CriteriaEvalChain):
     """Criteria evaluation chain that requires references."""
 
     @classmethod

libs/langchain/langchain/evaluation/exact_match/base.py

@@ -68,7 +68,7 @@ class ExactMatchStringEvaluator(StringEvaluator):
         """
         return "exact_match"
 
-    def _evaluate_strings(  # type: ignore[arg-type,override]
+    def _evaluate_strings(  # type: ignore[override]
         self,
         *,
         prediction: str,

libs/langchain/langchain/evaluation/loading.py

@@ -148,9 +148,7 @@ def load_evaluator(
                         "specify a language model explicitly."
                     )
 
-            llm = llm or ChatOpenAI(  # type: ignore[call-arg]
+            llm = llm or ChatOpenAI(model="gpt-4", seed=42, temperature=0)
-                model="gpt-4", seed=42, temperature=0
-            )
         except Exception as e:
             raise ValueError(
                 f"Evaluation with the {evaluator_cls} requires a "

libs/langchain/langchain/evaluation/regex_match/base.py

@@ -65,7 +65,7 @@ class RegexMatchStringEvaluator(StringEvaluator):
         """
         return "regex_match"
 
-    def _evaluate_strings(  # type: ignore[arg-type,override]
+    def _evaluate_strings(  # type: ignore[override]
         self,
         *,
         prediction: str,

libs/langchain/langchain/evaluation/scoring/eval_chain.py

@@ -144,7 +144,7 @@ class ScoreStringResultOutputParser(BaseOutputParser[dict]):
         }
 
 
-class ScoreStringEvalChain(StringEvaluator, LLMEvalChain, LLMChain):  # type: ignore[override]
+class ScoreStringEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
     """A chain for scoring on a scale of 1-10 the output of a model.
 
     Attributes:
@@ -396,7 +396,7 @@ Performance may be significantly worse with other models."
         return self._prepare_output(result)
 
 
-class LabeledScoreStringEvalChain(ScoreStringEvalChain):  # type: ignore[override]
+class LabeledScoreStringEvalChain(ScoreStringEvalChain):
     """A chain for scoring the output of a model on a scale of 1-10.
 
     Attributes:

libs/langchain/langchain/indexes/_sql_record_manager.py

@@ -45,7 +45,7 @@ try:
     from sqlalchemy.ext.asyncio import async_sessionmaker
 except ImportError:
     # dummy for sqlalchemy < 2
-    async_sessionmaker = type("async_sessionmaker", (type,), {})  # type: ignore
+    async_sessionmaker = type("async_sessionmaker", (type,), {})  # type: ignore[assignment,misc]
 
 Base = declarative_base()
 

libs/langchain/langchain/memory/vectorstore_token_buffer_memory.py

@@ -109,7 +109,7 @@ class ConversationVectorStoreTokenBufferMemory(ConversationTokenBufferMemory):
     previous_history_template: str = DEFAULT_HISTORY_TEMPLATE
     split_chunk_size: int = 1000
 
-    _memory_retriever: VectorStoreRetrieverMemory = PrivateAttr(default=None)  # type: ignore
+    _memory_retriever: VectorStoreRetrieverMemory = PrivateAttr(default=None)  # type: ignore[assignment]
     _timestamps: list[datetime] = PrivateAttr(default_factory=list)
 
     @property

libs/langchain/langchain/pydantic_v1/__init__.py

@@ -12,12 +12,7 @@ from langchain_core._api import warn_deprecated
 # * Creating namespaces for pydantic v1 and v2 should allow us to write code that
 #   unambiguously uses either v1 or v2 API.
 # * This change is easier to roll out and roll back.
-
+from pydantic.v1 import *  # noqa: F403
-try:
-    from pydantic.v1 import *  # noqa: F403
-except ImportError:
-    from pydantic import *  # type: ignore # noqa: F403
-
 
 try:
     _PYDANTIC_MAJOR_VERSION: int = int(metadata.version("pydantic").split(".")[0])

libs/langchain/langchain/pydantic_v1/dataclasses.py

@@ -1,9 +1,5 @@
 from langchain_core._api import warn_deprecated
-
+from pydantic.v1.dataclasses import *  # noqa: F403
-try:
-    from pydantic.v1.dataclasses import *  # noqa: F403
-except ImportError:
-    from pydantic.dataclasses import *  # type: ignore # noqa: F403
 
 warn_deprecated(
     "0.3.0",

libs/langchain/langchain/pydantic_v1/main.py

@@ -1,9 +1,5 @@
 from langchain_core._api import warn_deprecated
-
+from pydantic.v1.main import *  # noqa: F403
-try:
-    from pydantic.v1.main import *  # noqa: F403
-except ImportError:
-    from pydantic.main import *  # type: ignore # noqa: F403
 
 warn_deprecated(
     "0.3.0",

libs/langchain/langchain/retrievers/document_compressors/chain_extract.py

@@ -99,7 +99,7 @@ class LLMChainExtractor(BaseDocumentCompressor):
             if len(outputs[i]) == 0:
                 continue
             compressed_docs.append(
-                Document(page_content=outputs[i], metadata=doc.metadata)  # type: ignore[arg-type]
+                Document(page_content=outputs[i], metadata=doc.metadata)
             )
         return compressed_docs
 

libs/langchain/langchain/retrievers/ensemble.py

@@ -272,7 +272,7 @@ class EnsembleRetriever(BaseRetriever):
         # Enforce that retrieved docs are Documents for each list in retriever_docs
         for i in range(len(retriever_docs)):
             retriever_docs[i] = [
-                Document(page_content=doc) if not isinstance(doc, Document) else doc  # type: ignore[arg-type]
+                Document(page_content=doc) if not isinstance(doc, Document) else doc
                 for doc in retriever_docs[i]
             ]
 

libs/langchain/langchain/retrievers/self_query/base.py

@@ -180,7 +180,7 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
                 return ChromaTranslator()
 
         try:
-            from langchain_postgres import PGVector  # type: ignore[no-redef]
+            from langchain_postgres import PGVector
             from langchain_postgres import PGVectorTranslator as NewPGVectorTranslator
         except ImportError:
             pass

libs/langchain/langchain/smith/evaluation/string_run_evaluator.py

@@ -239,7 +239,7 @@ class StringExampleMapper(Serializable):
         return self.map(example)
 
 
-class StringRunEvaluatorChain(Chain, RunEvaluator):  # type: ignore[override, override]
+class StringRunEvaluatorChain(Chain, RunEvaluator):
     """Evaluate Run and optional examples."""
 
     run_mapper: StringRunMapper

libs/langchain/pyproject.toml

@@ -91,7 +91,7 @@ lint = [
     "cffi; python_version >= \"3.10\"",
 ]
 typing = [
-    "mypy<2.0,>=1.10",
+    "mypy<2.0,>=1.15",
     "types-pyyaml<7.0.0.0,>=6.0.12.2",
     "types-requests<3.0.0.0,>=2.28.11.5",
     "types-toml<1.0.0.0,>=0.10.8.1",
@@ -126,7 +126,7 @@ exclude = ["tests/integration_tests/examples/non-utf8-encoding.py"]
 [tool.mypy]
 ignore_missing_imports = "True"
 disallow_untyped_defs = "True"
-exclude = ["notebooks", "examples", "example_data"]
+warn_unused_ignores = "True"
 
 [tool.codespell]
 skip = ".git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package-lock.json,example_data,_dist,examples,*.trig"
@@ -134,7 +134,7 @@ ignore-regex = ".*(Stati Uniti|Tense=Pres).*"
 ignore-words-list = "momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogyny,unsecure,damon,crate,aadd,symbl,precesses,accademia,nin"
 
 [tool.ruff.lint]
-select = ["E", "F", "I", "T201", "D", "UP"]
+select = ["E", "F", "I", "PGH003", "T201", "D", "UP"]
 ignore = ["UP007", ]
 pydocstyle = { convention = "google" }
 

libs/langchain/tests/mock_servers/robot/server.py

@@ -200,6 +200,6 @@ def custom_openapi() -> dict[str, Any]:
 
 # This lets us prevent the "servers" configuration from being overwritten in
 # the auto-generated OpenAPI schema
-app.openapi = custom_openapi  # type: ignore
+app.openapi = custom_openapi
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=PORT)

libs/langchain/tests/unit_tests/agents/test_agent.py

@@ -392,8 +392,8 @@ def test_agent_with_new_prefix_suffix() -> None:
     )
 
     # avoids "BasePromptTemplate" has no attribute "template" error
-    assert hasattr(agent.agent.llm_chain.prompt, "template")  # type: ignore
+    assert hasattr(agent.agent.llm_chain.prompt, "template")  # type: ignore[union-attr]
-    prompt_str = agent.agent.llm_chain.prompt.template  # type: ignore
+    prompt_str = agent.agent.llm_chain.prompt.template  # type: ignore[union-attr]
     assert prompt_str.startswith(prefix), "Prompt does not start with prefix"
     assert prompt_str.endswith(suffix), "Prompt does not end with suffix"
 
@@ -463,7 +463,7 @@ async def test_runnable_agent() -> None:
         return AgentFinish(return_values={"foo": "meow"}, log="hard-coded-message")
 
     agent = template | model | fake_parse
-    executor = AgentExecutor(agent=agent, tools=[])  # type: ignore[arg-type]
+    executor = AgentExecutor(agent=agent, tools=[])
 
     # Invoke
     result: Any = await asyncio.to_thread(executor.invoke, {"question": "hello"})
@@ -527,7 +527,7 @@ async def test_runnable_agent() -> None:
             run_log = result
         else:
             # `+` is defined for RunLogPatch
-            run_log = run_log + result  # type: ignore[union-attr]
+            run_log = run_log + result
 
     assert isinstance(run_log, RunLog)
 
@@ -583,7 +583,7 @@ async def test_runnable_agent_with_function_calls() -> None:
         return "Spying from under the bed."
 
     agent = template | model | fake_parse
-    executor = AgentExecutor(agent=agent, tools=[find_pet])  # type: ignore[arg-type, list-item]
+    executor = AgentExecutor(agent=agent, tools=[find_pet])
 
     # Invoke
     result = await asyncio.to_thread(executor.invoke, {"question": "hello"})
@@ -701,7 +701,7 @@ async def test_runnable_with_multi_action_per_step() -> None:
         return "purrrr"
 
     agent = template | model | fake_parse
-    executor = AgentExecutor(agent=agent, tools=[find_pet])  # type: ignore[arg-type, list-item]
+    executor = AgentExecutor(agent=agent, tools=[find_pet])
 
     # Invoke
     result = await asyncio.to_thread(executor.invoke, {"question": "hello"})
@@ -852,10 +852,10 @@ async def test_openai_agent_with_streaming() -> None:
     # decorator.
     agent = create_openai_functions_agent(
         model,
-        [find_pet],  # type: ignore[list-item]
+        [find_pet],
         template,
     )
-    executor = AgentExecutor(agent=agent, tools=[find_pet])  # type: ignore[arg-type, list-item]
+    executor = AgentExecutor(agent=agent, tools=[find_pet])
 
     # Invoke
     result = await asyncio.to_thread(executor.invoke, {"question": "hello"})
@@ -1006,7 +1006,7 @@ def _make_tools_invocation(name_to_arguments: dict[str, dict[str, Any]]) -> AIMe
         additional_kwargs={
             "tool_calls": raw_tool_calls,
         },
-        tool_calls=tool_calls,  # type: ignore[arg-type]
+        tool_calls=tool_calls,
     )
 
 
@@ -1024,7 +1024,7 @@ async def test_openai_agent_tools_agent() -> None:
         ]
     )
 
-    GenericFakeChatModel.bind_tools = lambda self, x: self  # type: ignore
+    GenericFakeChatModel.bind_tools = lambda self, x: self  # type: ignore[assignment,misc]
     model = GenericFakeChatModel(messages=infinite_cycle)
 
     @tool
@@ -1053,16 +1053,16 @@ async def test_openai_agent_tools_agent() -> None:
     # decorator.
     openai_agent = create_openai_tools_agent(
         model,
-        [find_pet],  # type: ignore[list-item]
+        [find_pet],
         template,
     )
     tool_calling_agent = create_tool_calling_agent(
         model,
-        [find_pet],  # type: ignore[list-item]
+        [find_pet],
         template,
     )
     for agent in [openai_agent, tool_calling_agent]:
-        executor = AgentExecutor(agent=agent, tools=[find_pet])  # type: ignore[arg-type, list-item]
+        executor = AgentExecutor(agent=agent, tools=[find_pet])
 
         # Invoke
         result = await asyncio.to_thread(executor.invoke, {"question": "hello"})

libs/langchain/tests/unit_tests/agents/test_agent_iterator.py

@@ -238,7 +238,7 @@ def test_agent_iterator_properties_and_setters() -> None:
     assert isinstance(agent_iter.tags, type(None))
     assert isinstance(agent_iter.agent_executor, AgentExecutor)
 
-    agent_iter.inputs = "New input"  # type: ignore
+    agent_iter.inputs = "New input"  # type: ignore[assignment]
     assert isinstance(agent_iter.inputs, dict)
 
     agent_iter.callbacks = [FakeCallbackHandler()]

libs/langchain/tests/unit_tests/agents/test_initialize.py

@@ -17,7 +17,7 @@ def test_initialize_agent_with_str_agent_type() -> None:
     """Test initialize_agent with a string."""
     fake_llm = FakeLLM()
     agent_executor = initialize_agent(
-        [my_tool],  # type: ignore[list-item]
+        [my_tool],
         fake_llm,
         "zero-shot-react-description",  # type: ignore[arg-type]
     )

libs/langchain/tests/unit_tests/agents/test_mrkl_output_parser.py

@@ -16,7 +16,7 @@ def test_valid_action_and_action_input_parse() -> None:
     Action: foo
     Action Input: bar"""
 
-    agent_action: AgentAction = mrkl_output_parser.parse(llm_output)  # type: ignore
+    agent_action: AgentAction = mrkl_output_parser.parse(llm_output)  # type: ignore[assignment]
     assert agent_action.tool == "foo"
     assert agent_action.tool_input == "bar"
 
@@ -24,7 +24,7 @@ def test_valid_action_and_action_input_parse() -> None:
 def test_valid_final_answer_parse() -> None:
     llm_output = """Final Answer: The best pizza to eat is margaritta """
 
-    agent_finish: AgentFinish = mrkl_output_parser.parse(llm_output)  # type: ignore
+    agent_finish: AgentFinish = mrkl_output_parser.parse(llm_output)  # type: ignore[assignment]
     assert (
         agent_finish.return_values.get("output")
         == "The best pizza to eat is margaritta"
@@ -59,7 +59,7 @@ def test_final_answer_before_parsable_action() -> None:
         Action: foo
         Action Input: bar
         """
-    agent_finish: AgentFinish = mrkl_output_parser.parse(llm_output)  # type: ignore
+    agent_finish: AgentFinish = mrkl_output_parser.parse(llm_output)  # type: ignore[assignment]
     assert (
         agent_finish.return_values.get("output")
         == "The best pizza to eat is margaritta"

libs/langchain/tests/unit_tests/agents/test_openai_assistant.py

@@ -11,7 +11,7 @@ def _create_mock_client(*args: Any, use_async: bool = False, **kwargs: Any) -> A
     client = AsyncMock() if use_async else MagicMock()
     mock_assistant = MagicMock()
     mock_assistant.id = "abc123"
-    client.beta.assistants.create.return_value = mock_assistant  # type: ignore
+    client.beta.assistants.create.return_value = mock_assistant
     return client
 
 

libs/langchain/tests/unit_tests/callbacks/fake_callback_handler.py

@@ -254,7 +254,7 @@ class FakeCallbackHandler(BaseCallbackHandler, BaseFakeCallbackHandlerMixin):
     ) -> Any:
         self.on_retriever_error_common()
 
-    def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler":  # type: ignore
+    def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler":  # type: ignore[override]
         return self
 
 
@@ -388,5 +388,5 @@ class FakeAsyncCallbackHandler(AsyncCallbackHandler, BaseFakeCallbackHandlerMixi
     ) -> None:
         self.on_text_common()
 
-    def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler":  # type: ignore
+    def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler":  # type: ignore[override]
         return self

libs/langchain/tests/unit_tests/chat_models/test_base.py

@@ -173,7 +173,7 @@ def test_configurable_with_default() -> None:
     for method in ("get_num_tokens", "get_num_tokens_from_messages", "dict"):
         assert hasattr(model, method)
 
-    assert model.model_name == "gpt-4o"  # type: ignore[attr-defined]
+    assert model.model_name == "gpt-4o"
 
     model_with_tools = model.bind_tools(
         [{"name": "foo", "description": "foo", "parameters": {}}]

libs/langchain/tests/unit_tests/evaluation/agents/test_eval_chain.py

@@ -123,7 +123,7 @@ def test_trajectory_eval_chain(
         },
         sequential_responses=True,
     )
-    chain = TrajectoryEvalChain.from_llm(llm=llm, agent_tools=[foo])  # type: ignore
+    chain = TrajectoryEvalChain.from_llm(llm=llm, agent_tools=[foo])
     # Test when ref is not provided
     res = chain.evaluate_agent_trajectory(
         input="What is your favorite food?",
@@ -151,7 +151,7 @@ def test_trajectory_eval_chain_no_tools(
         },
         sequential_responses=True,
     )
-    chain = TrajectoryEvalChain.from_llm(llm=llm)  # type: ignore
+    chain = TrajectoryEvalChain.from_llm(llm=llm)
     res = chain.evaluate_agent_trajectory(
         input="What is your favorite food?",
         agent_trajectory=intermediate_steps,
@@ -175,7 +175,7 @@ def test_old_api_works(intermediate_steps: list[tuple[AgentAction, str]]) -> Non
         },
         sequential_responses=True,
     )
-    chain = TrajectoryEvalChain.from_llm(llm=llm)  # type: ignore
+    chain = TrajectoryEvalChain.from_llm(llm=llm)
     res = chain(
         {
             "question": "What is your favorite food?",

libs/langchain/tests/unit_tests/evaluation/criteria/test_eval_chain.py

@@ -14,7 +14,6 @@ from tests.unit_tests.llms.fake_llm import FakeLLM
 
 
 def test_resolve_criteria_str() -> None:
-    # type: ignore
     assert CriteriaEvalChain.resolve_criteria("helpfulness") == {
         "helpfulness": _SUPPORTED_CRITERIA[Criteria.HELPFULNESS]
     }

libs/langchain/tests/unit_tests/evaluation/qa/test_eval_chain.py

@@ -61,7 +61,7 @@ def test_load_criteria_evaluator() -> None:
     # Patch the env with an openai-api-key
     with patch.dict(os.environ, {"OPENAI_API_KEY": "foo"}):
         # Check it can load using a string arg (even if that's not how it's typed)
-        load_evaluator("criteria")  # type: ignore
+        load_evaluator("criteria")  # type: ignore[arg-type]
 
 
 @pytest.mark.parametrize("chain_cls", [QAEvalChain, ContextQAEvalChain, CotQAEvalChain])
@@ -78,7 +78,7 @@ def test_returns_expected_results(
     fake_llm = FakeLLM(
         queries={"text": "The meaning of life\nCORRECT"}, sequential_responses=True
     )
-    chain = chain_cls.from_llm(fake_llm)  # type: ignore
+    chain = chain_cls.from_llm(fake_llm)  # type: ignore[attr-defined]
     results = chain.evaluate_strings(
         prediction="my prediction", reference="my reference", input="my input"
     )

libs/langchain/tests/unit_tests/indexes/test_hashed_document.py

@@ -14,9 +14,9 @@ def test_hashed_document_hashing() -> None:
 def test_hashing_with_missing_content() -> None:
     """Check that ValueError is raised if page_content is missing."""
     with pytest.raises(TypeError):
-        _HashedDocument(
+        _HashedDocument(  # type: ignore[call-arg]
             metadata={"key": "value"},
-        )  # type: ignore
+        )
 
 
 def test_uid_auto_assigned_to_hash() -> None:

libs/langchain/tests/unit_tests/indexes/test_indexing.py

@@ -57,7 +57,7 @@ class InMemoryVectorStore(VectorStore):
             for _id in ids:
                 self.store.pop(_id, None)
 
-    def add_documents(  # type: ignore
+    def add_documents(
         self,
         documents: Sequence[Document],
         *,
@@ -140,7 +140,7 @@ def record_manager() -> SQLRecordManager:
     return record_manager
 
 
-@pytest_asyncio.fixture  # type: ignore
+@pytest_asyncio.fixture
 @pytest.mark.requires("aiosqlite")
 async def arecord_manager() -> SQLRecordManager:
     """Timestamped set fixture."""
@@ -292,7 +292,7 @@ def test_index_simple_delete_full(
 
     doc_texts = set(
         # Ignoring type since doc should be in the store and not a None
-        vector_store.store.get(uid).page_content  # type: ignore
+        vector_store.store.get(uid).page_content  # type: ignore[union-attr]
         for uid in vector_store.store
     )
     assert doc_texts == {"mutated document 1", "This is another document."}
@@ -368,7 +368,7 @@ async def test_aindex_simple_delete_full(
 
     doc_texts = set(
         # Ignoring type since doc should be in the store and not a None
-        vector_store.store.get(uid).page_content  # type: ignore
+        vector_store.store.get(uid).page_content  # type: ignore[union-attr]
         for uid in vector_store.store
     )
     assert doc_texts == {"mutated document 1", "This is another document."}
@@ -661,7 +661,7 @@ def test_incremental_delete(
 
     doc_texts = set(
         # Ignoring type since doc should be in the store and not a None
-        vector_store.store.get(uid).page_content  # type: ignore
+        vector_store.store.get(uid).page_content  # type: ignore[union-attr]
         for uid in vector_store.store
     )
     assert doc_texts == {"This is another document.", "This is a test document."}
@@ -720,7 +720,7 @@ def test_incremental_delete(
 
     doc_texts = set(
         # Ignoring type since doc should be in the store and not a None
-        vector_store.store.get(uid).page_content  # type: ignore
+        vector_store.store.get(uid).page_content  # type: ignore[union-attr]
         for uid in vector_store.store
     )
     assert doc_texts == {
@@ -788,7 +788,7 @@ def test_incremental_indexing_with_batch_size(
 
     doc_texts = set(
         # Ignoring type since doc should be in the store and not a None
-        vector_store.store.get(uid).page_content  # type: ignore
+        vector_store.store.get(uid).page_content  # type: ignore[union-attr]
         for uid in vector_store.store
     )
     assert doc_texts == {"1", "2", "3", "4"}
@@ -838,7 +838,7 @@ def test_incremental_delete_with_batch_size(
 
     doc_texts = set(
         # Ignoring type since doc should be in the store and not a None
-        vector_store.store.get(uid).page_content  # type: ignore
+        vector_store.store.get(uid).page_content  # type: ignore[union-attr]
         for uid in vector_store.store
     )
     assert doc_texts == {"1", "2", "3", "4"}
@@ -984,7 +984,7 @@ async def test_aincremental_delete(
 
     doc_texts = set(
         # Ignoring type since doc should be in the store and not a None
-        vector_store.store.get(uid).page_content  # type: ignore
+        vector_store.store.get(uid).page_content  # type: ignore[union-attr]
         for uid in vector_store.store
     )
     assert doc_texts == {"This is another document.", "This is a test document."}
@@ -1043,7 +1043,7 @@ async def test_aincremental_delete(
 
     doc_texts = set(
         # Ignoring type since doc should be in the store and not a None
-        vector_store.store.get(uid).page_content  # type: ignore
+        vector_store.store.get(uid).page_content  # type: ignore[union-attr]
         for uid in vector_store.store
     )
     assert doc_texts == {

libs/langchain/tests/unit_tests/load/test_dump.py

@@ -139,7 +139,7 @@ def test_aliases_hidden() -> None:
         dumped = json.loads(dumps(test_class, pretty=True))
 
     # Check by alias
-    test_class = TestClass(my_favorite_secret_alias="hello", my_other_secret="world")  # type: ignore[call-arg]
+    test_class = TestClass(my_favorite_secret_alias="hello", my_other_secret="world")
     dumped = json.loads(dumps(test_class, pretty=True))
     expected_dump = {
         "lc": 1,

libs/langchain/tests/unit_tests/load/test_load.py

@@ -25,7 +25,7 @@ def test_loads_openai_llm() -> None:
 
     llm = CommunityOpenAI(
         model="davinci", temperature=0.5, openai_api_key="hello", top_p=0.8
-    )  # type: ignore[call-arg]
+    )
     llm_string = dumps(llm)
     llm2 = loads(llm_string, secrets_map={"OPENAI_API_KEY": "hello"})
 
@@ -41,7 +41,7 @@ def test_loads_llmchain() -> None:
 
     llm = CommunityOpenAI(
         model="davinci", temperature=0.5, openai_api_key="hello", top_p=0.8
-    )  # type: ignore[call-arg]
+    )
     prompt = PromptTemplate.from_template("hello {name}!")
     chain = LLMChain(llm=llm, prompt=prompt)
     chain_string = dumps(chain)
@@ -64,7 +64,7 @@ def test_loads_llmchain_env() -> None:
     if not has_env:
         os.environ["OPENAI_API_KEY"] = "env_variable"
 
-    llm = OpenAI(model="davinci", temperature=0.5, top_p=0.8)  # type: ignore[call-arg]
+    llm = OpenAI(model="davinci", temperature=0.5, top_p=0.8)
     prompt = PromptTemplate.from_template("hello {name}!")
     chain = LLMChain(llm=llm, prompt=prompt)
     chain_string = dumps(chain)
@@ -82,7 +82,7 @@ def test_loads_llmchain_env() -> None:
 
 @pytest.mark.requires("openai")
 def test_loads_llmchain_with_non_serializable_arg() -> None:
-    llm = CommunityOpenAI(  # type: ignore[call-arg]
+    llm = CommunityOpenAI(
         model="davinci",
         temperature=0.5,
         openai_api_key="hello",
@@ -99,7 +99,7 @@ def test_loads_llmchain_with_non_serializable_arg() -> None:
 def test_load_openai_llm() -> None:
     from langchain_openai import OpenAI
 
-    llm = CommunityOpenAI(model="davinci", temperature=0.5, openai_api_key="hello")  # type: ignore[call-arg]
+    llm = CommunityOpenAI(model="davinci", temperature=0.5, openai_api_key="hello")
     llm_obj = dumpd(llm)
     llm2 = load(llm_obj, secrets_map={"OPENAI_API_KEY": "hello"})
 
@@ -112,7 +112,7 @@ def test_load_openai_llm() -> None:
 def test_load_llmchain() -> None:
     from langchain_openai import OpenAI
 
-    llm = CommunityOpenAI(model="davinci", temperature=0.5, openai_api_key="hello")  # type: ignore[call-arg]
+    llm = CommunityOpenAI(model="davinci", temperature=0.5, openai_api_key="hello")
     prompt = PromptTemplate.from_template("hello {name}!")
     chain = LLMChain(llm=llm, prompt=prompt)
     chain_obj = dumpd(chain)
@@ -135,7 +135,7 @@ def test_load_llmchain_env() -> None:
     if not has_env:
         os.environ["OPENAI_API_KEY"] = "env_variable"
 
-    llm = CommunityOpenAI(model="davinci", temperature=0.5)  # type: ignore[call-arg]
+    llm = CommunityOpenAI(model="davinci", temperature=0.5)
     prompt = PromptTemplate.from_template("hello {name}!")
     chain = LLMChain(llm=llm, prompt=prompt)
     chain_obj = dumpd(chain)

libs/langchain/tests/unit_tests/test_imports.py

@@ -141,7 +141,7 @@ def _dict_from_ast(node: ast.Dict) -> dict[str, str]:
     """
     result: dict[str, str] = {}
     for key, value in zip(node.keys, node.values):
-        py_key = _literal_eval_str(key)  # type: ignore
+        py_key = _literal_eval_str(key)  # type: ignore[arg-type]
         py_value = _literal_eval_str(value)
         result[py_key] = py_value
     return result

libs/langchain/tests/unit_tests/tools/test_render.py

@@ -21,7 +21,7 @@ def calculator(expression: str) -> str:
 
 @pytest.fixture
 def tools() -> list[BaseTool]:
-    return [search, calculator]  # type: ignore
+    return [search, calculator]
 
 
 def test_render_text_description(tools: list[BaseTool]) -> None:

libs/langchain/uv.lock

@@ -2568,7 +2568,7 @@ test-integration = [
 typing = [
     { name = "langchain-core", editable = "../core" },
     { name = "langchain-text-splitters", editable = "../text-splitters" },
-    { name = "mypy", specifier = ">=1.10,<2.0" },
+    { name = "mypy", specifier = ">=1.15,<2.0" },
     { name = "mypy-protobuf", specifier = ">=3.0.0,<4.0.0" },
     { name = "numpy", marker = "python_full_version < '3.13'", specifier = ">=1.26.4" },
     { name = "numpy", marker = "python_full_version >= '3.13'", specifier = ">=2.1.0" },
@@ -2705,7 +2705,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.51"
+version = "0.3.52"
 source = { editable = "../core" }
 dependencies = [
     { name = "jsonpatch" },
@@ -2745,6 +2745,8 @@ test = [
     { name = "numpy", marker = "python_full_version >= '3.13'", specifier = ">=2.1.0" },
     { name = "pytest", specifier = ">=8,<9" },
     { name = "pytest-asyncio", specifier = ">=0.21.1,<1.0.0" },
+    { name = "pytest-benchmark" },
+    { name = "pytest-codspeed" },
     { name = "pytest-mock", specifier = ">=3.10.0,<4.0.0" },
     { name = "pytest-socket", specifier = ">=0.7.0,<1.0.0" },
     { name = "pytest-watcher", specifier = ">=0.3.4,<1.0.0" },
@@ -2755,8 +2757,7 @@ test = [
 test-integration = []
 typing = [
     { name = "langchain-text-splitters", directory = "../text-splitters" },
-    { name = "mypy", specifier = ">=1.10,<1.11" },
+    { name = "mypy", specifier = ">=1.15,<1.16" },
-    { name = "types-jinja2", specifier = ">=2.11.9,<3.0.0" },
     { name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" },
     { name = "types-requests", specifier = ">=2.28.11.5,<3.0.0.0" },
 ]
@@ -2882,7 +2883,7 @@ wheels = [
 
 [[package]]
 name = "langchain-openai"
-version = "0.3.12"
+version = "0.3.13"
 source = { editable = "../partners/openai" }
 dependencies = [
     { name = "langchain-core" },
@@ -2944,7 +2945,7 @@ wheels = [
 
 [[package]]
 name = "langchain-tests"
-version = "0.3.17"
+version = "0.3.18"
 source = { editable = "../standard-tests" }
 dependencies = [
     { name = "httpx" },