community: Add ruff rule PGH003 (#30812)

See https://docs.astral.sh/ruff/rules/blanket-type-ignore/

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>

libs/community/langchain_community/agent_toolkits/sql/base.py

@@ -196,7 +196,7 @@ def create_sql_agent(
             ]
             prompt = ChatPromptTemplate.from_messages(messages)
         agent = RunnableAgent(
-            runnable=create_openai_functions_agent(llm, tools, prompt),  # type: ignore
+            runnable=create_openai_functions_agent(llm, tools, prompt),  # type: ignore[arg-type]
             input_keys_arg=["input"],
             return_keys_arg=["output"],
             **kwargs,
@@ -211,9 +211,9 @@ def create_sql_agent(
             ]
             prompt = ChatPromptTemplate.from_messages(messages)
         if agent_type == "openai-tools":
-            runnable = create_openai_tools_agent(llm, tools, prompt)  # type: ignore
+            runnable = create_openai_tools_agent(llm, tools, prompt)  # type: ignore[arg-type]
         else:
-            runnable = create_tool_calling_agent(llm, tools, prompt)  # type: ignore
+            runnable = create_tool_calling_agent(llm, tools, prompt)  # type: ignore[arg-type]
         agent = RunnableMultiActionAgent(  # type: ignore[assignment]
             runnable=runnable,
             input_keys_arg=["input"],

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

@@ -135,7 +135,7 @@ def _get_assistants_tool(
         Dict[str, Any]: A dictionary of tools that are converted into OpenAI tools.
     """
     if _is_assistants_builtin_tool(tool):
-        return tool  # type: ignore
+        return tool  # type: ignore[return-value]
     else:
         return convert_to_openai_tool(tool)
 
@@ -288,7 +288,7 @@ class OpenAIAssistantV2Runnable(OpenAIAssistantRunnable):
         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],
             tool_resources=tool_resources,  # type: ignore[arg-type]
             model=model,
             extra_body=extra_body,
@@ -430,7 +430,7 @@ class OpenAIAssistantV2Runnable(OpenAIAssistantRunnable):
         assistant = await async_client.beta.assistants.create(
             name=name,
             instructions=instructions,
-            tools=openai_tools,  # type: ignore
+            tools=openai_tools,
             tool_resources=tool_resources,  # type: ignore[arg-type]
             model=model,
         )

libs/community/langchain_community/cache.py

@@ -238,7 +238,7 @@ class InMemoryCache(BaseCache):
 Base = declarative_base()
 
 
-class FullLLMCache(Base):  # type: ignore
+class FullLLMCache(Base):  # type: ignore[misc,valid-type]
     """SQLite table for full LLM Cache (all generations)."""
 
     __tablename__ = "full_llm_cache"
@@ -261,7 +261,7 @@ class SQLAlchemyCache(BaseCache):
         """Look up based on prompt and llm_string."""
         stmt = (
             select(self.cache_schema.response)
-            .where(self.cache_schema.prompt == prompt)  # type: ignore
+            .where(self.cache_schema.prompt == prompt)
             .where(self.cache_schema.llm == llm_string)
             .order_by(self.cache_schema.idx)
         )
@@ -1531,7 +1531,7 @@ class CassandraSemanticCache(BaseCache):
         await self.table.aclear()
 
 
-class FullMd5LLMCache(Base):  # type: ignore
+class FullMd5LLMCache(Base):  # type: ignore[misc,valid-type]
     """SQLite table for full LLM Cache (all generations)."""
 
     __tablename__ = "full_md5_llm_cache"
@@ -1583,7 +1583,7 @@ class SQLAlchemyMd5Cache(BaseCache):
     def _delete_previous(self, session: Session, prompt: str, llm_string: str) -> None:
         stmt = (
             delete(self.cache_schema)
-            .where(self.cache_schema.prompt_md5 == self.get_md5(prompt))  # type: ignore
+            .where(self.cache_schema.prompt_md5 == self.get_md5(prompt))
             .where(self.cache_schema.llm == llm_string)
             .where(self.cache_schema.prompt == prompt)
         )
@@ -1593,7 +1593,7 @@ class SQLAlchemyMd5Cache(BaseCache):
         prompt_pd5 = self.get_md5(prompt)
         stmt = (
             select(self.cache_schema.response)
-            .where(self.cache_schema.prompt_md5 == prompt_pd5)  # type: ignore
+            .where(self.cache_schema.prompt_md5 == prompt_pd5)
             .where(self.cache_schema.llm == llm_string)
             .where(self.cache_schema.prompt == prompt)
             .order_by(self.cache_schema.idx)
@@ -1796,7 +1796,7 @@ class _CachedAwaitable:
     def __await__(self) -> Generator:
         if self.result is _unset:
             self.result = yield from self.awaitable.__await__()
-        return self.result  # type: ignore
+        return self.result  # type: ignore[return-value]
 
 
 def _reawaitable(func: Callable) -> Callable:

libs/community/langchain_community/callbacks/comet_ml_callback.py

@@ -584,7 +584,7 @@ class CometCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         )
         _custom_metrics = custom_metrics if custom_metrics else self.custom_metrics
 
-        self.__init__(  # type: ignore
+        self.__init__(  # type: ignore[misc]
             task_type=_task_type,
             workspace=_workspace,
             project_name=_project_name,

libs/community/langchain_community/callbacks/wandb_callback.py

@@ -580,7 +580,7 @@ class WandbCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
             self.temp_dir.cleanup()
             self.reset_callback_meta()
         if reset:
-            self.__init__(  # type: ignore
+            self.__init__(  # type: ignore[misc]
                 job_type=job_type if job_type else self.job_type,
                 project=project if project else self.project,
                 entity=entity if entity else self.entity,

libs/community/langchain_community/chains/ernie_functions/base.py

@@ -352,7 +352,7 @@ def create_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(
@@ -537,7 +537,7 @@ 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/community/langchain_community/chains/graph_qa/cypher.py

@@ -316,7 +316,7 @@ class GraphCypherQAChain(Chain):
                         MessagesPlaceholder(variable_name="function_response"),
                     ]
                 )
-                qa_chain = response_prompt | qa_llm | StrOutputParser()  # type: ignore
+                qa_chain = response_prompt | qa_llm | StrOutputParser()  # type: ignore[operator]
             except (NotImplementedError, AttributeError):
                 raise ValueError("Provided LLM does not support native tools/functions")
         else:
@@ -404,15 +404,15 @@ class GraphCypherQAChain(Chain):
             intermediate_steps.append({"context": context})
             if self.use_function_response:
                 function_response = get_function_response(question, context)
-                final_result = self.qa_chain.invoke(  # type: ignore
+                final_result = self.qa_chain.invoke(  # type: ignore[assignment]
                     {"question": question, "function_response": function_response},
                 )
             else:
-                result = self.qa_chain.invoke(  # type: ignore
+                result = self.qa_chain.invoke(
                     {"question": question, "context": context},
                     callbacks=callbacks,
                 )
-                final_result = result[self.qa_chain.output_key]  # type: ignore
+                final_result = result[self.qa_chain.output_key]  # type: ignore[union-attr]
 
         chain_result: Dict[str, Any] = {self.output_key: final_result}
         if self.return_intermediate_steps:

libs/community/langchain_community/chains/graph_qa/memgraph.py

@@ -225,11 +225,11 @@ class MemgraphQAChain(Chain):
                         MessagesPlaceholder(variable_name="function_response"),
                     ]
                 )
-                qa_chain = response_prompt | qa_llm | StrOutputParser()  # type: ignore
+                qa_chain = response_prompt | qa_llm | StrOutputParser()  # type: ignore[operator]
             except (NotImplementedError, AttributeError):
                 raise ValueError("Provided LLM does not support native tools/functions")
         else:
-            qa_chain = use_qa_llm_kwargs["prompt"] | qa_llm | StrOutputParser()  # type: ignore
+            qa_chain = use_qa_llm_kwargs["prompt"] | qa_llm | StrOutputParser()
 
         prompt = use_cypher_llm_kwargs["prompt"]
         llm_to_use = cypher_llm if cypher_llm is not None else llm
@@ -300,11 +300,11 @@ class MemgraphQAChain(Chain):
             intermediate_steps.append({"context": context})
             if self.use_function_response:
                 function_response = get_function_response(question, context)
-                result = self.qa_chain.invoke(  # type: ignore
+                result = self.qa_chain.invoke(
                     {"question": question, "function_response": function_response},
                 )
             else:
-                result = self.qa_chain.invoke(  # type: ignore
+                result = self.qa_chain.invoke(
                     {"question": question, "context": context},
                     callbacks=callbacks,
                 )

libs/community/langchain_community/chains/graph_qa/neptune_cypher.py

@@ -67,11 +67,11 @@ def extract_cypher(text: str) -> str:
 
 def use_simple_prompt(llm: BaseLanguageModel) -> bool:
     """Decides whether to use the simple prompt"""
-    if llm._llm_type and "anthropic" in llm._llm_type:  # type: ignore
+    if llm._llm_type and "anthropic" in llm._llm_type:  # type: ignore[attr-defined]
         return True
 
     # Bedrock anthropic
-    if hasattr(llm, "model_id") and "anthropic" in llm.model_id:  # type: ignore
+    if hasattr(llm, "model_id") and "anthropic" in llm.model_id:
         return True
 
     return False

libs/community/langchain_community/chains/pebblo_retrieval/base.py

@@ -313,8 +313,12 @@ class PebbloRetrievalQA(Chain):
         )
 
     @staticmethod
-    def _get_app_details(  # type: ignore
-        app_name: str, owner: str, description: str, llm: BaseLanguageModel, **kwargs
+    def _get_app_details(
+        app_name: str,
+        owner: str,
+        description: str,
+        llm: BaseLanguageModel,
+        **kwargs: Any,
     ) -> App:
         """Fetch app details. Internal method.
         Returns:

libs/community/langchain_community/chat_message_histories/cassandra.py

@@ -81,7 +81,7 @@ class CassandraChatMessageHistory(BaseChatMessageHistory):
         )
 
     @property
-    def messages(self) -> List[BaseMessage]:  # type: ignore
+    def messages(self) -> List[BaseMessage]:  # type: ignore[override]
         """Retrieve all session messages from DB"""
         # The latest are returned, in chronological order
         rows = self.table.get_partition(

libs/community/langchain_community/chat_message_histories/file.py

@@ -35,7 +35,7 @@ class FileChatMessageHistory(BaseChatMessageHistory):
             )
 
     @property
-    def messages(self) -> List[BaseMessage]:  # type: ignore
+    def messages(self) -> List[BaseMessage]:  # type: ignore[override]
         """Retrieve the messages from the local file"""
         items = json.loads(self.file_path.read_text(encoding=self.encoding))
         messages = messages_from_dict(items)

libs/community/langchain_community/chat_message_histories/kafka.py

@@ -334,7 +334,7 @@ class KafkaChatMessageHistory(BaseChatMessageHistory):
         )
 
     @property
-    def messages(self) -> List[BaseMessage]:  # type: ignore
+    def messages(self) -> List[BaseMessage]:  # type: ignore[override]
         """
         Retrieve the messages for the session, from Kafka topic continuously
         from last consumed message. This method is stateful and maintains

libs/community/langchain_community/chat_message_histories/mongodb.py

@@ -60,7 +60,7 @@ class MongoDBChatMessageHistory(BaseChatMessageHistory):
             self.collection.create_index("SessionId")
 
     @property
-    def messages(self) -> List[BaseMessage]:  # type: ignore
+    def messages(self) -> List[BaseMessage]:  # type: ignore[override]
         """Retrieve the messages from MongoDB"""
         from pymongo import errors
 

libs/community/langchain_community/chat_message_histories/postgres.py

@@ -65,7 +65,7 @@ class PostgresChatMessageHistory(BaseChatMessageHistory):
         self.connection.commit()
 
     @property
-    def messages(self) -> List[BaseMessage]:  # type: ignore
+    def messages(self) -> List[BaseMessage]:  # type: ignore[override]
         """Retrieve the messages from PostgreSQL"""
         query = (
             f"SELECT message FROM {self.table_name} WHERE session_id = %s ORDER BY id;"

libs/community/langchain_community/chat_message_histories/rocksetdb.py

@@ -215,7 +215,7 @@ class RocksetChatMessageHistory(BaseChatMessageHistory):
             self._create_empty_doc()
 
     @property
-    def messages(self) -> List[BaseMessage]:  # type: ignore
+    def messages(self) -> List[BaseMessage]:  # type: ignore[override]
         """Messages in this chat history."""
         return messages_from_dict(
             self._query(

libs/community/langchain_community/chat_message_histories/singlestoredb.py

@@ -212,7 +212,7 @@ class SingleStoreDBChatMessageHistory(BaseChatMessageHistory):
             conn.close()
 
     @property
-    def messages(self) -> List[BaseMessage]:  # type: ignore
+    def messages(self) -> List[BaseMessage]:  # type: ignore[override]
         """Retrieve the messages from SingleStoreDB"""
         self._create_table_if_not_exists()
         conn = self.connection_pool.connect()

libs/community/langchain_community/chat_message_histories/sql.py

@@ -47,7 +47,7 @@ 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]
 
 logger = logging.getLogger(__name__)
 
@@ -242,7 +242,7 @@ class SQLChatMessageHistory(BaseChatMessageHistory):
             self._table_created = True
 
     @property
-    def messages(self) -> List[BaseMessage]:  # type: ignore
+    def messages(self) -> List[BaseMessage]:  # type: ignore[override]
         """Retrieve all messages from db"""
         with self._make_sync_session() as session:
             result = (

libs/community/langchain_community/chat_message_histories/upstash_redis.py

@@ -51,7 +51,7 @@ class UpstashRedisChatMessageHistory(BaseChatMessageHistory):
         return self.key_prefix + self.session_id
 
     @property
-    def messages(self) -> List[BaseMessage]:  # type: ignore
+    def messages(self) -> List[BaseMessage]:  # type: ignore[override]
         """Retrieve the messages from Upstash Redis"""
         _items = self.redis_client.lrange(self.key, 0, -1)
         items = [json.loads(m) for m in _items[::-1]]

libs/community/langchain_community/chat_message_histories/xata.py

@@ -83,7 +83,7 @@ class XataChatMessageHistory(BaseChatMessageHistory):
             raise Exception(f"Error adding message to Xata: {r.status_code} {r}")
 
     @property
-    def messages(self) -> List[BaseMessage]:  # type: ignore
+    def messages(self) -> List[BaseMessage]:  # type: ignore[override]
         r = self._client.data().query(
             self._table_name,
             payload={

libs/community/langchain_community/chat_message_histories/zep.py

@@ -87,7 +87,7 @@ class ZepChatMessageHistory(BaseChatMessageHistory):
         self.session_id = session_id
 
     @property
-    def messages(self) -> List[BaseMessage]:  # type: ignore
+    def messages(self) -> List[BaseMessage]:  # type: ignore[override]
         """Retrieve messages from Zep memory"""
         zep_memory: Optional[Memory] = self._get_memory()
         if not zep_memory:

libs/community/langchain_community/chat_message_histories/zep_cloud.py

@@ -134,7 +134,7 @@ class ZepCloudChatMessageHistory(BaseChatMessageHistory):
         self.summary_instruction = summary_instruction
 
     @property
-    def messages(self) -> List[BaseMessage]:  # type: ignore
+    def messages(self) -> List[BaseMessage]:  # type: ignore[override]
         """Retrieve messages from Zep memory"""
         zep_memory: Optional[Memory] = self._get_memory()
         if not zep_memory:

libs/community/langchain_community/chat_models/litellm_router.py

@@ -42,7 +42,7 @@ class ChatLiteLLMRouter(ChatLiteLLM):
 
     def __init__(self, *, router: Any, **kwargs: Any) -> None:
         """Construct Chat LiteLLM Router."""
-        super().__init__(router=router, **kwargs)  # type: ignore
+        super().__init__(router=router, **kwargs)  # type: ignore[call-arg]
         self.router = router
 
     @property

libs/community/langchain_community/chat_models/llamacpp.py

@@ -815,4 +815,4 @@ def _convert_delta_to_message_chunk(
     elif role or default_class == ChatMessageChunk:
         return ChatMessageChunk(content=content, role=role, id=id_)
     else:
-        return default_class(content=content, id=id_)  # type: ignore
+        return default_class(content=content, id=id_)  # type: ignore[call-arg]

libs/community/langchain_community/chat_models/oci_generative_ai.py

@@ -716,7 +716,7 @@ class ChatOCIGenAI(BaseChatModel, OCIGenAIBase):
             if is_pydantic_schema:
                 output_parser: OutputParserLike = PydanticToolsParser(
                     tools=[schema],  # type: ignore[list-item]
-                    first_tool_only=True,  # type: ignore[list-item]
+                    first_tool_only=True,
                 )
             else:
                 output_parser = JsonOutputKeyToolsParser(

libs/community/langchain_community/chat_models/premai.py

@@ -158,9 +158,9 @@ def _convert_delta_response_to_message_chunk(
     Optional[str],
 ]:
     """Converts delta response to message chunk"""
-    _delta = response.choices[0].delta  # type: ignore
-    role = _delta.get("role", "")  # type: ignore
-    content = _delta.get("content", "")  # type: ignore
+    _delta = response.choices[0].delta
+    role = _delta.get("role", "")
+    content = _delta.get("content", "")
     additional_kwargs: Dict = {}
     finish_reasons: Optional[str] = response.choices[0].finish_reason
 
@@ -398,7 +398,7 @@ class ChatPremAI(BaseChatModel, BaseModel):
                 messages, template_id=kwargs["template_id"]
             )
         else:
-            system_prompt, messages_to_pass = _messages_to_prompt_dict(messages)  # type: ignore
+            system_prompt, messages_to_pass = _messages_to_prompt_dict(messages)
 
         if system_prompt is not None and system_prompt != "":
             kwargs["system_prompt"] = system_prompt
@@ -425,9 +425,9 @@ class ChatPremAI(BaseChatModel, BaseModel):
         if "template_id" in kwargs:
             system_prompt, messages_to_pass = _messages_to_prompt_dict(
                 messages, template_id=kwargs["template_id"]
-            )  # type: ignore
+            )
         else:
-            system_prompt, messages_to_pass = _messages_to_prompt_dict(messages)  # type: ignore
+            system_prompt, messages_to_pass = _messages_to_prompt_dict(messages)
 
         if stop is not None:
             logger.warning("stop is not supported in langchain streaming")

libs/community/langchain_community/document_loaders/blackboard.py

@@ -218,7 +218,7 @@ class BlackboardLoader(WebBaseLoader):
         loader = DirectoryLoader(
             path=self.folder_path,
             glob="*.pdf",
-            loader_cls=PyPDFLoader,  # type: ignore
+            loader_cls=PyPDFLoader,  # type: ignore[arg-type]
         )
         # Load the documents
         documents = loader.load()

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

@@ -35,7 +35,7 @@ class _CloudBlob(Blob):
         from cloudpathlib import AnyPath
 
         if self.data is None and self.path:
-            return AnyPath(self.path).read_text(encoding=self.encoding)  # type: ignore
+            return AnyPath(self.path).read_text(encoding=self.encoding)
         elif isinstance(self.data, bytes):
             return self.data.decode(self.encoding)
         elif isinstance(self.data, str):
@@ -52,7 +52,7 @@ class _CloudBlob(Blob):
         elif isinstance(self.data, str):
             return self.data.encode(self.encoding)
         elif self.data is None and self.path:
-            return AnyPath(self.path).read_bytes()  # type: ignore
+            return AnyPath(self.path).read_bytes()
         else:
             raise ValueError(f"Unable to get bytes for blob {self}")
 
@@ -64,7 +64,7 @@ class _CloudBlob(Blob):
         if isinstance(self.data, bytes):
             yield BytesIO(self.data)
         elif self.data is None and self.path:
-            return AnyPath(self.path).read_bytes()  # type: ignore
+            return AnyPath(self.path).read_bytes()
         else:
             raise NotImplementedError(f"Unable to convert blob {self}")
 
@@ -79,7 +79,7 @@ def _url_to_filename(url: str) -> str:
     url_parsed = urlparse(url)
     suffix = Path(url_parsed.path).suffix
     if url_parsed.scheme in ["s3", "az", "gs"]:
-        with AnyPath(url).open("rb") as f:  # type: ignore
+        with AnyPath(url).open("rb") as f:
             temp_file = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
             while True:
                 buf = f.read()
@@ -116,7 +116,7 @@ def _make_iterator(
 
         iterator = _with_tqdm
     else:
-        iterator = iter  # type: ignore
+        iterator = iter  # type: ignore[assignment]
 
     return iterator
 
@@ -220,7 +220,7 @@ class CloudBlobLoader(BlobLoader):
 
     def _yield_paths(self) -> Iterable["AnyPath"]:
         """Yield paths that match the requested pattern."""
-        if self.path.is_file():  # type: ignore
+        if self.path.is_file():
             yield self.path
             return
 
@@ -269,7 +269,7 @@ class CloudBlobLoader(BlobLoader):
             Blob instance
         """
         if mime_type is None and guess_type:
-            _mimetype = mimetypes.guess_type(path)[0] if guess_type else None  # type: ignore
+            _mimetype = mimetypes.guess_type(path)[0] if guess_type else None
         else:
             _mimetype = mime_type
 

libs/community/langchain_community/document_loaders/googledrive.py

@@ -252,7 +252,7 @@ class GoogleDriveLoader(BaseLoader, BaseModel):
         files = self._fetch_files_recursive(service, folder_id)
         # If file types filter is provided, we'll filter by the file type.
         if file_types:
-            _files = [f for f in files if f["mimeType"] in file_types]  # type: ignore
+            _files = [f for f in files if f["mimeType"] in file_types]
         else:
             _files = files
 
@@ -261,14 +261,14 @@ class GoogleDriveLoader(BaseLoader, BaseModel):
             if file["trashed"] and not self.load_trashed_files:
                 continue
             elif file["mimeType"] == "application/vnd.google-apps.document":
-                returns.append(self._load_document_from_id(file["id"]))  # type: ignore
+                returns.append(self._load_document_from_id(file["id"]))  # type: ignore[arg-type]
             elif file["mimeType"] == "application/vnd.google-apps.spreadsheet":
-                returns.extend(self._load_sheet_from_id(file["id"]))  # type: ignore
+                returns.extend(self._load_sheet_from_id(file["id"]))  # type: ignore[arg-type]
             elif (
                 file["mimeType"] == "application/pdf"
                 or self.file_loader_cls is not None
             ):
-                returns.extend(self._load_file_from_id(file["id"]))  # type: ignore
+                returns.extend(self._load_file_from_id(file["id"]))  # type: ignore[arg-type]
             else:
                 pass
         return returns

libs/community/langchain_community/document_loaders/parsers/docai.py

@@ -267,7 +267,7 @@ class DocAIParser(BaseBlobParser):
         """Initializes Long-Running Operations from their names."""
         try:
             from google.longrunning.operations_pb2 import (
-                GetOperationRequest,  # type: ignore
+                GetOperationRequest,
             )
         except ImportError as exc:
             raise ImportError(

libs/community/langchain_community/document_loaders/parsers/documentloader_adapter.py

@@ -59,9 +59,9 @@ class DocumentLoaderAsParser(BaseBlobParser):
         """
         Use underlying DocumentLoader to lazily parse the blob.
         """
-        doc_loader = self.DocumentLoaderClass(
+        doc_loader = self.DocumentLoaderClass(  # type: ignore[call-arg]
             file_path=blob.path, **self.document_loader_kwargs
-        )  # type: ignore
+        )
         for document in doc_loader.lazy_load():
             document.metadata.update(blob.metadata)
             yield document

libs/community/langchain_community/document_loaders/parsers/images.py

@@ -107,7 +107,7 @@ class RapidOCRBlobParser(BaseImageBlobParser):
                     "`rapidocr-onnxruntime` package not found, please install it with "
                     "`pip install rapidocr-onnxruntime`"
                 )
-        ocr_result, _ = self.ocr(np.array(img))  # type: ignore
+        ocr_result, _ = self.ocr(np.array(img))  # type: ignore[misc]
         content = ""
         if ocr_result:
             content = ("\n".join([text[1] for text in ocr_result])).strip()

libs/community/langchain_community/document_loaders/parsers/language/tree_sitter_segmenter.py

@@ -82,7 +82,7 @@ class TreeSitterSegmenter(CodeSegmenter):
             )
 
             for line_num in range(start_line + 1, end_line + 1):
-                simplified_lines[line_num] = None  # type: ignore
+                simplified_lines[line_num] = None  # type: ignore[call-overload]
 
             processed_lines.update(lines)
 

libs/community/langchain_community/document_loaders/sharepoint.py

@@ -6,7 +6,7 @@ import json
 from pathlib import Path
 from typing import Any, Dict, Iterator, List, Optional
 
-import requests  # type: ignore
+import requests
 from langchain_core.document_loaders import BaseLoader
 from langchain_core.documents import Document
 from pydantic import Field

libs/community/langchain_community/document_loaders/trello.py

@@ -78,7 +78,7 @@ class TrelloLoader(BaseLoader):
         """
 
         try:
-            from trello import TrelloClient  # type: ignore
+            from trello import TrelloClient
         except ImportError as ex:
             raise ImportError(
                 "Could not import trello python package. "
@@ -124,7 +124,7 @@ class TrelloLoader(BaseLoader):
         return board
 
     def _card_to_doc(self, card: Card, list_dict: dict) -> Document:
-        from bs4 import BeautifulSoup  # type: ignore
+        from bs4 import BeautifulSoup
 
         text_content = ""
         if self.include_card_name:

libs/community/langchain_community/document_loaders/unstructured.py

@@ -245,8 +245,8 @@ def get_elements_from_api(
         from unstructured.partition.api import partition_multiple_via_api
 
         _doc_elements = partition_multiple_via_api(
-            filenames=file_path,  # type: ignore
-            files=file,  # type: ignore
+            filenames=file_path,
+            files=file,
             api_key=api_key,
             api_url=api_url,
             **unstructured_kwargs,

libs/community/langchain_community/document_loaders/web_base.py

@@ -393,7 +393,7 @@ class WebBaseLoader(BaseLoader):
             "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html"  # noqa: E501
         ),
     )
-    def aload(self) -> List[Document]:  # type: ignore
+    def aload(self) -> List[Document]:  # type: ignore[override]
         """Load text from the urls in web_path async into Documents."""
 
         results = self.scrape_all(self.web_paths)

libs/community/langchain_community/embeddings/__init__.py

@@ -439,7 +439,7 @@ 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/community/langchain_community/embeddings/infinity_local.py

@@ -68,7 +68,7 @@ class InfinityEmbeddingsLocal(BaseModel, Embeddings):
         """Validate that api key and python package exists in environment."""
 
         try:
-            from infinity_emb import AsyncEmbeddingEngine  # type: ignore
+            from infinity_emb import AsyncEmbeddingEngine
         except ImportError:
             raise ImportError(
                 "Please install the "

libs/community/langchain_community/embeddings/jina.py

@@ -76,7 +76,7 @@ class JinaEmbeddings(BaseModel, Embeddings):
 
     def _embed(self, input: Any) -> List[List[float]]:
         # Call Jina AI Embedding API
-        resp = self.session.post(  # type: ignore
+        resp = self.session.post(
             JINA_API_URL, json={"input": input, "model": self.model_name}
         ).json()
         if "data" not in resp:
@@ -85,7 +85,7 @@ class JinaEmbeddings(BaseModel, Embeddings):
         embeddings = resp["data"]
 
         # Sort resulting embeddings by index
-        sorted_embeddings = sorted(embeddings, key=lambda e: e["index"])  # type: ignore
+        sorted_embeddings = sorted(embeddings, key=lambda e: e["index"])
 
         # Return just the embeddings
         return [result["embedding"] for result in sorted_embeddings]

libs/community/langchain_community/embeddings/textembed.py

@@ -309,7 +309,7 @@ class AsyncOpenAITextEmbedEmbeddingClient:
         Raises:
             Exception: If the response status is not 200.
         """
-        async with session.post(**kwargs) as response:  # type: ignore
+        async with session.post(**kwargs) as response:  # type: ignore[arg-type]
             if response.status != 200:
                 raise Exception(
                     f"TextEmbed responded with an unexpected status message "

libs/community/langchain_community/example_selectors/ngram_overlap.py

@@ -22,7 +22,7 @@ def ngram_overlap_score(source: List[str], example: List[str]) -> float:
     https://aclanthology.org/P02-1040.pdf
     """
     from nltk.translate.bleu_score import (
-        SmoothingFunction,  # type: ignore
+        SmoothingFunction,
         sentence_bleu,
     )
 

libs/community/langchain_community/indexes/_sql_record_manager.py

@@ -54,7 +54,7 @@ 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]
 
 from langchain_community.indexes.base import RecordManager
 
@@ -308,8 +308,8 @@ class SQLRecordManager(RecordManager):
                     [UpsertionRecord.key, UpsertionRecord.namespace],
                     set_=dict(
                         # attr-defined type ignore
-                        updated_at=insert_stmt.excluded.updated_at,  # type: ignore
-                        group_id=insert_stmt.excluded.group_id,  # type: ignore
+                        updated_at=insert_stmt.excluded.updated_at,
+                        group_id=insert_stmt.excluded.group_id,
                     ),
                 )
             elif self.dialect == "postgresql":
@@ -322,8 +322,8 @@ class SQLRecordManager(RecordManager):
                     "uix_key_namespace",  # Name of constraint
                     set_=dict(
                         # attr-defined type ignore
-                        updated_at=insert_stmt.excluded.updated_at,  # type: ignore
-                        group_id=insert_stmt.excluded.group_id,  # type: ignore
+                        updated_at=insert_stmt.excluded.updated_at,
+                        group_id=insert_stmt.excluded.group_id,
                     ),
                 )
             else:
@@ -383,8 +383,8 @@ class SQLRecordManager(RecordManager):
                     [UpsertionRecord.key, UpsertionRecord.namespace],
                     set_=dict(
                         # attr-defined type ignore
-                        updated_at=insert_stmt.excluded.updated_at,  # type: ignore
-                        group_id=insert_stmt.excluded.group_id,  # type: ignore
+                        updated_at=insert_stmt.excluded.updated_at,
+                        group_id=insert_stmt.excluded.group_id,
                     ),
                 )
             elif self.dialect == "postgresql":
@@ -397,8 +397,8 @@ class SQLRecordManager(RecordManager):
                     "uix_key_namespace",  # Name of constraint
                     set_=dict(
                         # attr-defined type ignore
-                        updated_at=insert_stmt.excluded.updated_at,  # type: ignore
-                        group_id=insert_stmt.excluded.group_id,  # type: ignore
+                        updated_at=insert_stmt.excluded.updated_at,
+                        group_id=insert_stmt.excluded.group_id,
                     ),
                 )
             else:

libs/community/langchain_community/llms/azureml_endpoint.py

@@ -486,10 +486,10 @@ class AzureMLBaseEndpoint(BaseModel):
         timeout = values.get("timeout", DEFAULT_TIMEOUT)
 
         http_client = AzureMLEndpointClient(
-            endpoint_url,  # type: ignore
-            endpoint_key.get_secret_value(),  # type: ignore
-            deployment_name,  # type: ignore
-            timeout,  # type: ignore
+            endpoint_url,  # type: ignore[arg-type]
+            endpoint_key.get_secret_value(),  # type: ignore[union-attr]
+            deployment_name,  # type: ignore[arg-type]
+            timeout,
         )
 
         return http_client

libs/community/langchain_community/llms/beam.py

@@ -201,7 +201,7 @@ class Beam(LLM):  # type: ignore[override, override, override, override]
     def _deploy(self) -> str:
         """Call to Beam."""
         try:
-            import beam  # type: ignore
+            import beam
 
             if beam.__path__ == "":
                 raise ImportError

libs/community/langchain_community/llms/gigachat.py

@@ -181,7 +181,7 @@ class _BaseGigaChat(Serializable):
     def get_num_tokens(self, text: str) -> int:
         """Count approximate number of tokens"""
         if self.use_api_for_tokens:
-            return self.tokens_count([text])[0].tokens  # type: ignore
+            return self.tokens_count([text])[0].tokens
         else:
             return round(len(text) / 4.6)
 

libs/community/langchain_community/llms/huggingface_hub.py

@@ -142,7 +142,7 @@ class HuggingFaceHub(LLM):
         if "error" in response:
             raise ValueError(f"Error raised by inference API: {response['error']}")
 
-        response_key = VALID_TASKS_DICT[self.task]  # type: ignore
+        response_key = VALID_TASKS_DICT[self.task]  # type: ignore[index]
         if isinstance(response, list):
             text = response[0][response_key]
         else:

libs/community/langchain_community/llms/ipex_llm.py

@@ -172,7 +172,7 @@ class IpexLLM(LLM):
         if not low_bit_model:
             if load_in_low_bit is not None:
                 load_function_name = "from_pretrained"
-                load_kwargs["load_in_low_bit"] = load_in_low_bit  # type: ignore
+                load_kwargs["load_in_low_bit"] = load_in_low_bit  # type: ignore[assignment]
             else:
                 load_function_name = "from_pretrained"
                 load_kwargs["load_in_4bit"] = load_in_4bit

libs/community/langchain_community/llms/openai.py

@@ -246,7 +246,7 @@ class BaseOpenAI(BaseLLM):
     http_client: Union[Any, None] = None
     """Optional httpx.Client."""
 
-    def __new__(cls, **data: Any) -> Union[OpenAIChat, BaseOpenAI]:  # type: ignore
+    def __new__(cls, **data: Any) -> Union[OpenAIChat, BaseOpenAI]:  # type: ignore[misc]
         """Initialize the OpenAI object."""
         model_name = data.get("model_name", "")
         if (

libs/community/langchain_community/llms/yi.py

@@ -47,7 +47,7 @@ class YiLLM(LLM):
     def _post(self, request: Any) -> Any:
         headers = {
             "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.yi_api_key.get_secret_value()}",  # type: ignore
+            "Authorization": f"Bearer {self.yi_api_key.get_secret_value()}",  # type: ignore[union-attr]
         }
 
         urls = []

libs/community/langchain_community/output_parsers/ernie_functions.py

@@ -161,11 +161,11 @@ class PydanticOutputFunctionsParser(OutputFunctionsParser):
     def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
         _result = super().parse_result(result)
         if self.args_only:
-            pydantic_args = self.pydantic_schema.parse_raw(_result)  # type: ignore
+            pydantic_args = self.pydantic_schema.parse_raw(_result)  # type: ignore[union-attr]
         else:
             fn_name = _result["name"]
             _args = _result["arguments"]
-            pydantic_args = self.pydantic_schema[fn_name].parse_raw(_args)  # type: ignore
+            pydantic_args = self.pydantic_schema[fn_name].parse_raw(_args)  # type: ignore[index]
         return pydantic_args
 
 

libs/community/langchain_community/query_constructors/deeplake.py

@@ -53,9 +53,9 @@ class DeepLakeTranslator(Visitor):
     def _format_func(self, func: Union[Operator, Comparator]) -> str:
         self._validate_func(func)
         if isinstance(func, Operator):
-            value = OPERATOR_TO_TQL[func.value]  # type: ignore
+            value = OPERATOR_TO_TQL[func.value]  # type: ignore[index]
         elif isinstance(func, Comparator):
-            value = COMPARATOR_TO_TQL[func.value]  # type: ignore
+            value = COMPARATOR_TO_TQL[func.value]  # type: ignore[index]
         return f"{value}"
 
     def visit_operation(self, operation: Operation) -> str:

libs/community/langchain_community/query_constructors/timescalevector.py

@@ -42,9 +42,9 @@ class TimescaleVectorTranslator(Visitor):
     def _format_func(self, func: Union[Operator, Comparator]) -> str:
         self._validate_func(func)
         if isinstance(func, Operator):
-            value = self.OPERATOR_MAP[func.value]  # type: ignore
+            value = self.OPERATOR_MAP[func.value]  # type: ignore[index]
         elif isinstance(func, Comparator):
-            value = self.COMPARATOR_MAP[func.value]  # type: ignore
+            value = self.COMPARATOR_MAP[func.value]  # type: ignore[index]
         return f"{value}"
 
     def visit_operation(self, operation: Operation) -> client.Predicates:

libs/community/langchain_community/storage/sql.py

@@ -41,7 +41,7 @@ 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()
 
@@ -255,7 +255,7 @@ class SQLStore(BaseStore[str, bytes]):
 
     def yield_keys(self, *, prefix: Optional[str] = None) -> Iterator[str]:
         with self._make_sync_session() as session:
-            for v in session.query(LangchainKeyValueStores).filter(  # type: ignore
+            for v in session.query(LangchainKeyValueStores).filter(
                 LangchainKeyValueStores.namespace == self.namespace
             ):
                 if str(v.key).startswith(prefix or ""):

libs/community/langchain_community/tools/databricks/_execution.py

@@ -178,7 +178,7 @@ def execute_function(
         statement=parametrized_statement.statement,
         warehouse_id=warehouse_id,
         parameters=parametrized_statement.parameters,
-        **execute_statement_args,  # type: ignore
+        **execute_statement_args,
     )
     if response.status and job_pending(response.status.state) and response.statement_id:
         statement_id = response.statement_id
@@ -197,7 +197,7 @@ def execute_function(
                 f"status after {wait} seconds."
             )
             time.sleep(wait)
-            response = ws.statement_execution.get_statement(statement_id)  # type: ignore
+            response = ws.statement_execution.get_statement(statement_id)
             if response.status is None or not job_pending(response.status.state):
                 break
             wait_time += wait
@@ -228,7 +228,7 @@ def execute_function(
     if is_scalar(function):
         value = None
         if data_array and len(data_array) > 0 and len(data_array[0]) > 0:
-            value = str(data_array[0][0])  # type: ignore
+            value = str(data_array[0][0])
         return FunctionExecutionResult(
             format="SCALAR", value=value, truncated=truncated
         )

libs/community/langchain_community/tools/databricks/tool.py

@@ -51,8 +51,8 @@ def _uc_type_to_pydantic_type(uc_type_json: Union[str, Dict[str, Any]]) -> Type:
         if tpe == "array":
             element_type = _uc_type_to_pydantic_type(uc_type_json["elementType"])
             if uc_type_json["containsNull"]:
-                element_type = Optional[element_type]  # type: ignore
-            return List[element_type]  # type: ignore
+                element_type = Optional[element_type]  # type: ignore[assignment]
+            return List[element_type]  # type: ignore[valid-type]
         elif tpe == "map":
             key_type = uc_type_json["keyType"]
             assert key_type == "string", TypeError(
@@ -60,14 +60,14 @@ def _uc_type_to_pydantic_type(uc_type_json: Union[str, Dict[str, Any]]) -> Type:
             )
             value_type = _uc_type_to_pydantic_type(uc_type_json["valueType"])
             if uc_type_json["valueContainsNull"]:
-                value_type: Type = Optional[value_type]  # type: ignore
-            return Dict[str, value_type]  # type: ignore
+                value_type: Type = Optional[value_type]  # type: ignore[no-redef]
+            return Dict[str, value_type]  # type: ignore[valid-type]
         elif tpe == "struct":
             fields = {}
             for field in uc_type_json["fields"]:
                 field_type = _uc_type_to_pydantic_type(field["type"])
                 if field.get("nullable"):
-                    field_type = Optional[field_type]  # type: ignore
+                    field_type = Optional[field_type]  # type: ignore[assignment]
                 comment = (
                     uc_type_json["metadata"].get("comment")
                     if "metadata" in uc_type_json
@@ -76,7 +76,7 @@ def _uc_type_to_pydantic_type(uc_type_json: Union[str, Dict[str, Any]]) -> Type:
                 fields[field["name"]] = (field_type, Field(..., description=comment))
             uc_type_json_str = json.dumps(uc_type_json, sort_keys=True)
             type_hash = md5(uc_type_json_str.encode()).hexdigest()[:8]
-            return create_model(f"Struct_{type_hash}", **fields)  # type: ignore
+            return create_model(f"Struct_{type_hash}", **fields)  # type: ignore[call-overload]
         else:
             raise TypeError(f"Unknown type {uc_type_json}. Try upgrading this package.")
 
@@ -94,7 +94,7 @@ def _generate_args_schema(function: "FunctionInfo") -> Type[BaseModel]:
         description = p.comment
         default: Any = ...
         if p.parameter_default:
-            pydantic_type = Optional[pydantic_type]  # type: ignore
+            pydantic_type = Optional[pydantic_type]  # type: ignore[assignment]
             default = None
             # TODO: Convert default value string to the correct type.
             # We might need to use statement execution API
@@ -108,9 +108,9 @@ def _generate_args_schema(function: "FunctionInfo") -> Type[BaseModel]:
             pydantic_type,
             Field(default=default, description=description),
         )
-    return create_model(
+    return create_model(  # type: ignore[call-overload]
         f"{function.catalog_name}__{function.schema_name}__{function.name}__params",
-        **fields,  # type: ignore
+        **fields,
     )
 
 

libs/community/langchain_community/tools/nuclia/tool.py

@@ -217,10 +217,10 @@ class NucliaUnderstandingAPI(BaseTool):  # type: ignore[override, override]
                 logger.info(f"No matching id for {uuid}")
             else:
                 self._results[matching_id]["status"] = "done"
-                data = MessageToJson(
+                data = MessageToJson(  # type: ignore[call-arg]
                     pb,
                     preserving_proto_field_name=True,
-                    including_default_value_fields=True,  # type: ignore
+                    including_default_value_fields=True,
                 )
                 self._results[matching_id]["data"] = data
 

libs/community/langchain_community/tools/zapier/tool.py

@@ -161,9 +161,7 @@ class ZapierNLARunAction(BaseTool):  # type: ignore[override]
         )
 
 
-ZapierNLARunAction.__doc__ = (
-    ZapierNLAWrapper.run.__doc__ + ZapierNLARunAction.__doc__  # type: ignore
-)
+ZapierNLARunAction.__doc__ = ZapierNLAWrapper.run.__doc__ + ZapierNLARunAction.__doc__  # type: ignore[operator]
 
 
 # other useful actions
@@ -210,5 +208,5 @@ class ZapierNLAListActions(BaseTool):  # type: ignore[override]
 
 
 ZapierNLAListActions.__doc__ = (
-    ZapierNLAWrapper.list.__doc__ + ZapierNLAListActions.__doc__  # type: ignore
+    ZapierNLAWrapper.list.__doc__ + ZapierNLAListActions.__doc__  # type: ignore[operator]
 )

libs/community/langchain_community/utilities/bing_search.py

@@ -50,7 +50,7 @@ class BingSearchAPIWrapper(BaseModel):
         response = requests.get(
             self.bing_search_url,
             headers=headers,
-            params=params,  # type: ignore
+            params=params,
         )
         response.raise_for_status()
         search_results = response.json()

libs/community/langchain_community/utilities/google_scholar.py

@@ -82,7 +82,7 @@ class GoogleScholarAPIWrapper(BaseModel):
             # 0 is the first page of results, 20 is the 2nd page of results,
             # 40 is the 3rd page of results, etc.
             results = (
-                self.google_scholar_engine(  # type: ignore
+                self.google_scholar_engine(
                     {
                         "q": query,
                         "start": page,
@@ -106,7 +106,7 @@ class GoogleScholarAPIWrapper(BaseModel):
         ):  # From the last page we would only need top_k_results%20 results
             # if k is not divisible by 20.
             results = (
-                self.google_scholar_engine(  # type: ignore
+                self.google_scholar_engine(
                     {
                         "q": query,
                         "start": page,

libs/community/langchain_community/utilities/metaphor_search.py

@@ -49,7 +49,6 @@ class MetaphorSearchAPIWrapper(BaseModel):
             "useAutoprompt": use_autoprompt,
         }
         response = requests.post(
-            # type: ignore
             f"{METAPHOR_API_URL}/search",
             headers=headers,
             json=params,

libs/community/langchain_community/utilities/openapi.py

@@ -53,7 +53,7 @@ if TYPE_CHECKING:
 try:
     from openapi_pydantic import OpenAPI
 except ImportError:
-    OpenAPI = object  # type: ignore
+    OpenAPI = object
 
 
 class OpenAPISpec(OpenAPI):

libs/community/langchain_community/utilities/passio_nutrition_ai.py

@@ -134,7 +134,7 @@ class NutritionAIAPI(BaseModel):
         return requests.get(
             self.nutritionai_api_url,
             headers=self.auth_.headers,
-            params=params,  # type: ignore
+            params=params,
         )
 
     def _api_call_results(self, search_term: str) -> dict:

libs/community/langchain_community/utilities/sql_database.py

@@ -395,7 +395,7 @@ class SQLDatabase:
         try:
             # get the sample rows
             with self._engine.connect() as connection:
-                sample_rows_result = connection.execute(command)  # type: ignore
+                sample_rows_result = connection.execute(command)
                 # shorten values in the sample rows
                 sample_rows = list(
                     map(lambda ls: [str(i)[:100] for i in ls], sample_rows_result)

libs/community/langchain_community/utilities/steam.py

@@ -123,10 +123,10 @@ class SteamWebAPIWrapper(BaseModel):
         except ImportError:
             raise ImportError("steamspypi library is not installed.")
         users_games = self.get_users_games(steam_id)
-        result = {}  # type: ignore
+        result: dict[str, int] = {}
         most_popular_genre = ""
         most_popular_genre_count = 0
-        for game in users_games["games"]:  # type: ignore
+        for game in users_games["games"]:  # type: ignore[call-overload]
             appid = game["appid"]
             data_request = {"request": "appdetails", "appid": appid}
             genreStore = steamspypi.download(data_request)
@@ -148,7 +148,7 @@ class SteamWebAPIWrapper(BaseModel):
         sorted_data = sorted(
             data.values(), key=lambda x: x.get("average_forever", 0), reverse=True
         )
-        owned_games = [game["appid"] for game in users_games["games"]]  # type: ignore
+        owned_games = [game["appid"] for game in users_games["games"]]  # type: ignore[call-overload]
         remaining_games = [
             game for game in sorted_data if game["appid"] not in owned_games
         ]

libs/community/langchain_community/utilities/tavily_search.py

@@ -58,7 +58,6 @@ class TavilySearchAPIWrapper(BaseModel):
             "include_images": include_images,
         }
         response = requests.post(
-            # type: ignore
             f"{TAVILY_API_URL}/search",
             json=params,
         )

libs/community/langchain_community/utilities/you.py

@@ -240,7 +240,6 @@ class YouSearchAPIWrapper(BaseModel):
         if self.endpoint_type == "snippet":
             self.endpoint_type = "search"
         response = requests.get(
-            # type: ignore
             f"{YOU_API_URL}/{self.endpoint_type}",
             params=params,
             headers=headers,

libs/community/langchain_community/utils/math.py

@@ -71,4 +71,4 @@ def cosine_similarity_top_k(
     top_k_idxs = top_k_idxs[np.argsort(score_array.ravel()[top_k_idxs])][::-1]
     ret_idxs = np.unravel_index(top_k_idxs, score_array.shape)
     scores = score_array.ravel()[top_k_idxs].tolist()
-    return list(zip(*ret_idxs)), scores  # type: ignore
+    return list(zip(*ret_idxs)), scores  # type: ignore[return-value]

libs/community/langchain_community/vectorstores/azure_cosmos_db.py

@@ -407,7 +407,7 @@ class AzureCosmosDBVectorSearch(VectorStore):
             for t, m, embedding in zip(texts, metadatas, embeddings)
         ]
         # insert the documents in Cosmos DB
-        insert_result = self._collection.insert_many(to_insert)  # type: ignore
+        insert_result = self._collection.insert_many(to_insert)
         return insert_result.inserted_ids
 
     @classmethod

libs/community/langchain_community/vectorstores/azuresearch.py

@@ -1571,7 +1571,7 @@ class AzureSearch(VectorStore):
         azure_search.add_embeddings(text_embeddings, metadatas, **kwargs)
         return azure_search
 
-    def as_retriever(self, **kwargs: Any) -> AzureSearchVectorStoreRetriever:  # type: ignore
+    def as_retriever(self, **kwargs: Any) -> AzureSearchVectorStoreRetriever:  # type: ignore[override]
         """Return AzureSearchVectorStoreRetriever initialized from this VectorStore.
 
         Args:
@@ -1781,7 +1781,7 @@ async def _areorder_results_with_maximal_marginal_relevance(
         # Function can return -1 index
         if x == -1:
             break
-        ret.append((documents[x], scores[x]))  # type: ignore
+        ret.append((documents[x], scores[x]))
 
     return ret
 
@@ -1816,7 +1816,7 @@ def _reorder_results_with_maximal_marginal_relevance(
         # Function can return -1 index
         if x == -1:
             break
-        ret.append((documents[x], scores[x]))  # type: ignore
+        ret.append((documents[x], scores[x]))
 
     return ret
 

libs/community/langchain_community/vectorstores/bigquery_vector_search.py

@@ -656,7 +656,7 @@ class BigQueryVectorSearch(VectorStore):
         Returns:
             List of Documents most similar to the query vector, with similarity scores.
         """
-        emb = self.embedding_model.embed_query(query)  # type: ignore
+        emb = self.embedding_model.embed_query(query)
         return self.similarity_search_with_score_by_vector(
             emb, k, filter, brute_force, fraction_lists_to_search, **kwargs
         )
@@ -738,9 +738,7 @@ class BigQueryVectorSearch(VectorStore):
         Returns:
             List of Documents selected by maximal marginal relevance.
         """
-        query_embedding = self.embedding_model.embed_query(  # type: ignore
-            query
-        )
+        query_embedding = self.embedding_model.embed_query(query)
         doc_tuples = self._search_with_score_and_embeddings_by_vector(
             query_embedding, fetch_k, filter, brute_force, fraction_lists_to_search
         )

libs/community/langchain_community/vectorstores/clarifai.py

@@ -183,7 +183,7 @@ class Clarifai(VectorStore):
         try:
             from clarifai.client.search import Search
             from clarifai_grpc.grpc.api import resources_pb2
-            from google.protobuf import json_format  # type: ignore
+            from google.protobuf import json_format
         except ImportError as e:
             raise ImportError(
                 "Could not import clarifai python package. "

libs/community/langchain_community/vectorstores/deeplake.py

@@ -275,7 +275,7 @@ class DeepLake(VectorStore):
                 metadata=metadatas,
                 embedding_data=texts,
                 embedding_tensor="embedding",
-                embedding_function=self._embedding_function.embed_documents,  # type: ignore
+                embedding_function=self._embedding_function.embed_documents,  # type: ignore[union-attr]
                 return_ids=True,
                 **kwargs,
             )
@@ -464,8 +464,8 @@ class DeepLake(VectorStore):
 
         if use_maximal_marginal_relevance:
             lambda_mult = kwargs.get("lambda_mult", 0.5)
-            indices = maximal_marginal_relevance(  # type: ignore
-                embedding,  # type: ignore
+            indices = maximal_marginal_relevance(
+                embedding,  # type: ignore[arg-type]
                 embeddings,
                 k=min(k, len(texts)),
                 lambda_mult=lambda_mult,
@@ -829,7 +829,7 @@ class DeepLake(VectorStore):
             use_maximal_marginal_relevance=True,
             lambda_mult=lambda_mult,
             exec_option=exec_option,
-            embedding_function=embedding_function,  # type: ignore
+            embedding_function=embedding_function,  # type: ignore[arg-type]
             **kwargs,
         )
 

libs/community/langchain_community/vectorstores/docarray/base.py

@@ -103,7 +103,7 @@ class DocArrayIndex(VectorStore, ABC):
             Lower score represents more similarity.
         """
         query_embedding = self.embedding.embed_query(query)
-        query_doc = self.doc_cls(embedding=query_embedding)  # type: ignore
+        query_doc = self.doc_cls(embedding=query_embedding)
         docs, scores = self.doc_index.find(query_doc, search_field="embedding", limit=k)
 
         result = [
@@ -152,7 +152,7 @@ class DocArrayIndex(VectorStore, ABC):
             List of Documents most similar to the query vector.
         """
 
-        query_doc = self.doc_cls(embedding=embedding)  # type: ignore
+        query_doc = self.doc_cls(embedding=embedding)
         docs = self.doc_index.find(
             query_doc, search_field="embedding", limit=k
         ).documents
@@ -187,7 +187,7 @@ class DocArrayIndex(VectorStore, ABC):
             List of Documents selected by maximal marginal relevance.
         """
         query_embedding = self.embedding.embed_query(query)
-        query_doc = self.doc_cls(embedding=query_embedding)  # type: ignore
+        query_doc = self.doc_cls(embedding=query_embedding)
 
         docs = self.doc_index.find(
             query_doc, search_field="embedding", limit=fetch_k

libs/community/langchain_community/vectorstores/docarray/hnsw.py

@@ -71,7 +71,7 @@ class DocArrayHnswSearch(DocArrayIndex):
             num_threads=num_threads,
             **kwargs,
         )
-        doc_index = HnswDocumentIndex[doc_cls](work_dir=work_dir)  # type: ignore
+        doc_index = HnswDocumentIndex[doc_cls](work_dir=work_dir)
         return cls(doc_index, embedding)
 
     @classmethod

libs/community/langchain_community/vectorstores/docarray/in_memory.py

@@ -41,7 +41,7 @@ class DocArrayInMemorySearch(DocArrayIndex):
         from docarray.index import InMemoryExactNNIndex
 
         doc_cls = cls._get_doc_cls(space=metric, **kwargs)
-        doc_index = InMemoryExactNNIndex[doc_cls]()  # type: ignore
+        doc_index = InMemoryExactNNIndex[doc_cls]()
         return cls(doc_index, embedding)
 
     @classmethod

libs/community/langchain_community/vectorstores/documentdb.py

@@ -265,7 +265,7 @@ class DocumentDBVectorSearch(VectorStore):
             for t, m, embedding in zip(texts, metadatas, embeddings)
         ]
         # insert the documents in DocumentDB
-        insert_result = self._collection.insert_many(to_insert)  # type: ignore
+        insert_result = self._collection.insert_many(to_insert)
         return insert_result.inserted_ids
 
     @classmethod

libs/community/langchain_community/vectorstores/duckdb.py

@@ -220,7 +220,7 @@ class DuckDB(VectorStore):
         except ImportError:
             warnings.warn("You may need to `pip install pandas` to use this method.")
 
-        embedding = self._embedding.embed_query(query)  # type: ignore
+        embedding = self._embedding.embed_query(query)
         list_cosine_similarity = self.duckdb.FunctionExpression(
             "list_cosine_similarity",
             self.duckdb.ColumnExpression(self._vector_key),
@@ -265,7 +265,7 @@ class DuckDB(VectorStore):
             A list of Documents most similar to the query.
         """
 
-        embedding = self._embedding.embed_query(query)  # type: ignore
+        embedding = self._embedding.embed_query(query)
         list_cosine_similarity = self.duckdb.FunctionExpression(
             "list_cosine_similarity",
             self.duckdb.ColumnExpression(self._vector_key),

libs/community/langchain_community/vectorstores/infinispanvs.py

@@ -254,7 +254,7 @@ repeated float %s = 1;
         texts_l = list(texts)
         if last_vector:
             texts_l.pop()
-        embeds = self._embedding.embed_documents(texts_l)  # type: ignore
+        embeds = self._embedding.embed_documents(texts_l)  # type: ignore[union-attr]
         if last_vector:
             embeds.append(last_vector)
         if not metadatas:
@@ -288,7 +288,7 @@ repeated float %s = 1;
         Returns:
             List[Tuple[Document, float]]
         """
-        embed = self._embedding.embed_query(query)  # type: ignore
+        embed = self._embedding.embed_query(query)  # type: ignore[union-attr]
         documents = self.similarity_search_with_score_by_vector(embedding=embed, k=k)
         return documents
 

libs/community/langchain_community/vectorstores/lancedb.py

@@ -201,7 +201,7 @@ class LanceDB(VectorStore):
         """
         docs = []
         ids = ids or [str(uuid.uuid4()) for _ in texts]
-        embeddings = self._embedding.embed_documents(list(texts))  # type: ignore
+        embeddings = self._embedding.embed_documents(list(texts))  # type: ignore[union-attr]
         for idx, text in enumerate(texts):
             embedding = embeddings[idx]
             metadata = metadatas[idx] if metadatas else {"id": ids[idx]}
@@ -490,7 +490,7 @@ class LanceDB(VectorStore):
                     embedding = self._embedding.embed_query(query)
                     _query = (embedding, query)
                 else:
-                    _query = query  # type: ignore
+                    _query = query  # type: ignore[assignment]
 
                 res = self._query(_query, k, filter=filter, name=name, **kwargs)
                 return self.results_to_docs(res, score=score)

libs/community/langchain_community/vectorstores/lantern.py

@@ -58,9 +58,9 @@ def get_embedding_store(
     embedding_type = None
 
     if distance_strategy == DistanceStrategy.HAMMING:
-        embedding_type = sqlalchemy.INTEGER  # type: ignore
+        embedding_type = sqlalchemy.INTEGER
     else:
-        embedding_type = sqlalchemy.REAL  # type: ignore
+        embedding_type = sqlalchemy.REAL  # type: ignore[assignment]
 
     DynamicBase = declarative_base(class_registry=dict())  # type: Any
 
@@ -74,7 +74,7 @@ def get_embedding_store(
         cmetadata = sqlalchemy.Column(JSON, nullable=True)
         # custom_id : any user defined id
         custom_id = sqlalchemy.Column(sqlalchemy.String, nullable=True)
-        embedding = sqlalchemy.Column(sqlalchemy.ARRAY(embedding_type))  # type: ignore
+        embedding = sqlalchemy.Column(sqlalchemy.ARRAY(embedding_type))  # type: ignore[arg-type,var-annotated]
 
     return EmbeddingStore
 

libs/community/langchain_community/vectorstores/momento_vector_index.py

@@ -397,7 +397,7 @@ class MomentoVectorIndex(VectorStore):
         )
         selected = [response.hits[i].metadata for i in mmr_selected]
         return [
-            Document(page_content=metadata.pop(self.text_field, ""), metadata=metadata)  # type: ignore
+            Document(page_content=metadata.pop(self.text_field, ""), metadata=metadata)
             for metadata in selected
         ]
 
@@ -484,6 +484,6 @@ class MomentoVectorIndex(VectorStore):
                 configuration=VectorIndexConfigurations.Default.latest(),
                 credential_provider=CredentialProvider.from_string(api_key),
             )
-        vector_db = cls(embedding=embedding, client=client, **kwargs)  # type: ignore
+        vector_db = cls(embedding=embedding, client=client, **kwargs)  # type: ignore[call-arg]
         vector_db.add_texts(texts=texts, metadatas=metadatas, **kwargs)
         return vector_db

libs/community/langchain_community/vectorstores/mongodb_atlas.py

@@ -183,7 +183,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
             for t, m, embedding in zip(texts, metadatas, embeddings)
         ]
         # insert the documents in MongoDB Atlas
-        insert_result = self._collection.insert_many(to_insert)  # type: ignore
+        insert_result = self._collection.insert_many(to_insert)
         return insert_result.inserted_ids
 
     def _similarity_search_with_score(

libs/community/langchain_community/vectorstores/oraclevs.py

@@ -857,7 +857,7 @@ class OracleVS(VectorStore):
                     )
 
                     documents.append((document, distance, current_embedding))
-        return documents  # type: ignore
+        return documents
 
     @_handle_exceptions
     def max_marginal_relevance_search_with_score_by_vector(

libs/community/langchain_community/vectorstores/pgembedding.py

@@ -49,7 +49,7 @@ class CollectionStore(BaseModel):
 
     @classmethod
     def get_by_name(cls, session: Session, name: str) -> Optional["CollectionStore"]:
-        return session.query(cls).filter(cls.name == name).first()  # type: ignore
+        return session.query(cls).filter(cls.name == name).first()
 
     @classmethod
     def get_or_create(
@@ -88,7 +88,7 @@ class EmbeddingStore(BaseModel):
     )
     collection = relationship(CollectionStore, back_populates="embeddings")
 
-    embedding = sqlalchemy.Column(sqlalchemy.ARRAY(sqlalchemy.REAL))  # type: ignore
+    embedding = sqlalchemy.Column(sqlalchemy.ARRAY(sqlalchemy.REAL))  # type: ignore[var-annotated]
     document = sqlalchemy.Column(sqlalchemy.String, nullable=True)
     cmetadata = sqlalchemy.Column(JSON, nullable=True)
 

libs/community/langchain_community/vectorstores/pgvector.py

@@ -33,7 +33,7 @@ try:
     from sqlalchemy import SQLColumnExpression
 except ImportError:
     # for sqlalchemy < 2
-    SQLColumnExpression = Any  # type: ignore
+    SQLColumnExpression = Any  # type: ignore[assignment,misc]
 
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
@@ -126,7 +126,7 @@ def _get_embedding_collection_store(
         def get_by_name(
             cls, session: Session, name: str
         ) -> Optional["CollectionStore"]:
-            return session.query(cls).filter(cls.name == name).first()  # type: ignore
+            return session.query(cls).filter(cls.name == name).first()
 
         @classmethod
         def get_or_create(
@@ -956,7 +956,7 @@ class PGVector(VectorStore):
             results: List[Any] = (
                 session.query(
                     self.EmbeddingStore,
-                    self.distance_strategy(embedding).label("distance"),  # type: ignore
+                    self.distance_strategy(embedding).label("distance"),
                 )
                 .filter(*filter_by)
                 .order_by(sqlalchemy.asc("distance"))

libs/community/langchain_community/vectorstores/redis/base.py

@@ -419,7 +419,7 @@ class Redis(VectorStore):
 
         # type check for metadata
         if metadatas:
-            if isinstance(metadatas, list) and len(metadatas) != len(texts):  # type: ignore
+            if isinstance(metadatas, list) and len(metadatas) != len(texts):
                 raise ValueError("Number of metadatas must match number of texts")
             if not (isinstance(metadatas, list) and isinstance(metadatas[0], dict)):
                 raise ValueError("Metadatas must be a list of dicts")
@@ -427,7 +427,7 @@ class Redis(VectorStore):
             generated_schema = _generate_field_schema(metadatas[0])
             if index_schema:
                 # read in the schema solely to compare to the generated schema
-                user_schema = read_schema(index_schema)  # type: ignore
+                user_schema = read_schema(index_schema)
 
                 # the very rare case where a super user decides to pass the index
                 # schema and a document loader is used that has metadata which
@@ -722,7 +722,7 @@ class Redis(VectorStore):
 
         # type check for metadata
         if metadatas:
-            if isinstance(metadatas, list) and len(metadatas) != len(texts):  # type: ignore
+            if isinstance(metadatas, list) and len(metadatas) != len(texts):  # type: ignore[arg-type]
                 raise ValueError("Number of metadatas must match number of texts")
             if not (isinstance(metadatas, list) and isinstance(metadatas[0], dict)):
                 raise ValueError("Metadatas must be a list of dicts")
@@ -850,7 +850,7 @@ class Redis(VectorStore):
         # Perform vector search
         # ignore type because redis-py is wrong about bytes
         try:
-            results = self.client.ft(self.index_name).search(redis_query, params_dict)  # type: ignore
+            results = self.client.ft(self.index_name).search(redis_query, params_dict)
         except redis.exceptions.ResponseError as e:
             # split error message and see if it starts with "Syntax"
             if str(e).split(" ")[0] == "Syntax":
@@ -966,7 +966,7 @@ class Redis(VectorStore):
         # Perform vector search
         # ignore type because redis-py is wrong about bytes
         try:
-            results = self.client.ft(self.index_name).search(redis_query, params_dict)  # type: ignore
+            results = self.client.ft(self.index_name).search(redis_query, params_dict)
         except redis.exceptions.ResponseError as e:
             # split error message and see if it starts with "Syntax"
             if str(e).split(" ")[0] == "Syntax":
@@ -1206,7 +1206,7 @@ class Redis(VectorStore):
         # read in schema (yaml file or dict) and
         # pass to the Pydantic validators
         if index_schema:
-            schema_values = read_schema(index_schema)  # type: ignore
+            schema_values = read_schema(index_schema)
             schema = RedisModel(**schema_values)
 
             # ensure user did not exclude the content field
@@ -1242,7 +1242,7 @@ class Redis(VectorStore):
 
     def _create_index_if_not_exist(self, dim: int = 1536) -> None:
         try:
-            from redis.commands.search.indexDefinition import (  # type: ignore
+            from redis.commands.search.indexDefinition import (
                 IndexDefinition,
                 IndexType,
             )

libs/community/langchain_community/vectorstores/redis/filters.py

@@ -140,7 +140,7 @@ class RedisTag(RedisFilterField):
         elif isinstance(other, str):
             other = [other]
 
-        self._set_value(other, self.SUPPORTED_VAL_TYPES, operator)  # type: ignore
+        self._set_value(other, self.SUPPORTED_VAL_TYPES, operator)  # type: ignore[arg-type]
 
     @check_operator_misuse
     def __eq__(
@@ -240,7 +240,7 @@ class RedisNum(RedisFilterField):
             >>> from langchain_community.vectorstores.redis import RedisNum
             >>> filter = RedisNum("zipcode") == 90210
         """
-        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.EQ)  # type: ignore
+        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.EQ)  # type: ignore[arg-type]
         return RedisFilterExpression(str(self))
 
     @check_operator_misuse
@@ -254,7 +254,7 @@ class RedisNum(RedisFilterField):
             >>> from langchain_community.vectorstores.redis import RedisNum
             >>> filter = RedisNum("zipcode") != 90210
         """
-        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.NE)  # type: ignore
+        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.NE)  # type: ignore[arg-type]
         return RedisFilterExpression(str(self))
 
     def __gt__(self, other: Union[int, float]) -> "RedisFilterExpression":
@@ -267,7 +267,7 @@ class RedisNum(RedisFilterField):
             >>> from langchain_community.vectorstores.redis import RedisNum
             >>> filter = RedisNum("age") > 18
         """
-        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.GT)  # type: ignore
+        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.GT)  # type: ignore[arg-type]
         return RedisFilterExpression(str(self))
 
     def __lt__(self, other: Union[int, float]) -> "RedisFilterExpression":
@@ -280,7 +280,7 @@ class RedisNum(RedisFilterField):
             >>> from langchain_community.vectorstores.redis import RedisNum
             >>> filter = RedisNum("age") < 18
         """
-        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.LT)  # type: ignore
+        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.LT)  # type: ignore[arg-type]
         return RedisFilterExpression(str(self))
 
     def __ge__(self, other: Union[int, float]) -> "RedisFilterExpression":
@@ -293,7 +293,7 @@ class RedisNum(RedisFilterField):
             >>> from langchain_community.vectorstores.redis import RedisNum
             >>> filter = RedisNum("age") >= 18
         """
-        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.GE)  # type: ignore
+        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.GE)  # type: ignore[arg-type]
         return RedisFilterExpression(str(self))
 
     def __le__(self, other: Union[int, float]) -> "RedisFilterExpression":
@@ -306,7 +306,7 @@ class RedisNum(RedisFilterField):
             >>> from langchain_community.vectorstores.redis import RedisNum
             >>> filter = RedisNum("age") <= 18
         """
-        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.LE)  # type: ignore
+        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.LE)  # type: ignore[arg-type]
         return RedisFilterExpression(str(self))
 
 
@@ -336,7 +336,7 @@ class RedisText(RedisFilterField):
             >>> from langchain_community.vectorstores.redis import RedisText
             >>> filter = RedisText("job") == "engineer"
         """
-        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.EQ)  # type: ignore
+        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.EQ)  # type: ignore[arg-type]
         return RedisFilterExpression(str(self))
 
     @check_operator_misuse
@@ -350,7 +350,7 @@ class RedisText(RedisFilterField):
             >>> from langchain_community.vectorstores.redis import RedisText
             >>> filter = RedisText("job") != "engineer"
         """
-        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.NE)  # type: ignore
+        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.NE)  # type: ignore[arg-type]
         return RedisFilterExpression(str(self))
 
     def __mod__(self, other: str) -> "RedisFilterExpression":
@@ -366,7 +366,7 @@ class RedisText(RedisFilterField):
             >>> filter = RedisText("job") % "engineer|doctor" # contains either term
             >>> filter = RedisText("job") % "engineer doctor" # contains both terms
         """
-        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.LIKE)  # type: ignore
+        self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.LIKE)  # type: ignore[arg-type]
         return RedisFilterExpression(str(self))
 
     def __str__(self) -> str:

libs/community/langchain_community/vectorstores/redis/schema.py

@@ -14,7 +14,7 @@ from typing_extensions import TYPE_CHECKING, Literal
 from langchain_community.vectorstores.redis.constants import REDIS_VECTOR_DTYPE_MAP
 
 if TYPE_CHECKING:
-    from redis.commands.search.field import (  # type: ignore
+    from redis.commands.search.field import (
         NumericField,
         TagField,
         TextField,
@@ -47,13 +47,13 @@ class TextFieldSchema(RedisField):
     sortable: Optional[bool] = False
 
     def as_field(self) -> TextField:
-        from redis.commands.search.field import TextField  # type: ignore
+        from redis.commands.search.field import TextField
 
         return TextField(
             self.name,
             weight=self.weight,
             no_stem=self.no_stem,
-            phonetic_matcher=self.phonetic_matcher,  # type: ignore
+            phonetic_matcher=self.phonetic_matcher,
             sortable=self.sortable,
             no_index=self.no_index,
         )
@@ -68,7 +68,7 @@ class TagFieldSchema(RedisField):
     sortable: Optional[bool] = False
 
     def as_field(self) -> TagField:
-        from redis.commands.search.field import TagField  # type: ignore
+        from redis.commands.search.field import TagField
 
         return TagField(
             self.name,
@@ -86,7 +86,7 @@ class NumericFieldSchema(RedisField):
     sortable: Optional[bool] = False
 
     def as_field(self) -> NumericField:
-        from redis.commands.search.field import NumericField  # type: ignore
+        from redis.commands.search.field import NumericField
 
         return NumericField(self.name, sortable=self.sortable, no_index=self.no_index)
 
@@ -131,7 +131,7 @@ class FlatVectorField(RedisVectorField):  # type: ignore[override]
     block_size: Optional[int] = None
 
     def as_field(self) -> VectorField:
-        from redis.commands.search.field import VectorField  # type: ignore
+        from redis.commands.search.field import VectorField
 
         field_data = super()._fields()
         if self.block_size is not None:
@@ -149,7 +149,7 @@ class HNSWVectorField(RedisVectorField):  # type: ignore[override]
     epsilon: float = Field(default=0.01)
 
     def as_field(self) -> VectorField:
-        from redis.commands.search.field import VectorField  # type: ignore
+        from redis.commands.search.field import VectorField
 
         field_data = super()._fields()
         field_data.update(
@@ -193,9 +193,9 @@ class RedisModel(BaseModel):
 
         # ignore types as pydantic is handling type validation and conversion
         if vector_field["algorithm"] == "FLAT":
-            self.vector.append(FlatVectorField(**vector_field))  # type: ignore
+            self.vector.append(FlatVectorField(**vector_field))
         elif vector_field["algorithm"] == "HNSW":
-            self.vector.append(HNSWVectorField(**vector_field))  # type: ignore
+            self.vector.append(HNSWVectorField(**vector_field))
         else:
             raise ValueError(
                 f"algorithm must be either FLAT or HNSW. Got "

libs/community/langchain_community/vectorstores/supabase.py

@@ -257,7 +257,7 @@ class SupabaseVectorStore(VectorStore):
         match_result = [
             (
                 Document(
-                    metadata=search.get("metadata", {}),  # type: ignore
+                    metadata=search.get("metadata", {}),
                     page_content=search.get("content", ""),
                 ),
                 search.get("similarity", 0.0),
@@ -302,7 +302,7 @@ class SupabaseVectorStore(VectorStore):
         match_result = [
             (
                 Document(
-                    metadata=search.get("metadata", {}),  # type: ignore
+                    metadata=search.get("metadata", {}),
                     page_content=search.get("content", ""),
                 ),
                 search.get("similarity", 0.0),
@@ -351,7 +351,7 @@ class SupabaseVectorStore(VectorStore):
                 "id": ids[idx],
                 "content": documents[idx].page_content,
                 "embedding": embedding,
-                "metadata": documents[idx].metadata,  # type: ignore
+                "metadata": documents[idx].metadata,
                 **kwargs,
             }
             for idx, embedding in enumerate(vectors)
@@ -360,7 +360,7 @@ class SupabaseVectorStore(VectorStore):
         for i in range(0, len(rows), chunk_size):
             chunk = rows[i : i + chunk_size]
 
-            result = client.from_(table_name).upsert(chunk).execute()  # type: ignore
+            result = client.from_(table_name).upsert(chunk).execute()
 
             if len(result.data) == 0:
                 raise Exception("Error inserting: No rows added")

libs/community/langchain_community/vectorstores/upstash.py

@@ -153,7 +153,7 @@ class UpstashVectorStore(VectorStore):
         self._namespace = namespace
 
     @property
-    def embeddings(self) -> Optional[Union[Embeddings, bool]]:  # type: ignore
+    def embeddings(self) -> Optional[Union[Embeddings, bool]]:  # type: ignore[override]
         """Access the query embedding object if available."""
         return self._embeddings
 
@@ -730,7 +730,7 @@ class UpstashVectorStore(VectorStore):
         )
         selected = [results[i].metadata for i in mmr_selected]
         return [
-            Document(page_content=metadata.pop((self._text_key)), metadata=metadata)  # type: ignore
+            Document(page_content=metadata.pop((self._text_key)), metadata=metadata)
             for metadata in selected
         ]
 
@@ -798,7 +798,7 @@ class UpstashVectorStore(VectorStore):
         )
         selected = [results[i].metadata for i in mmr_selected]
         return [
-            Document(page_content=metadata.pop((self._text_key)), metadata=metadata)  # type: ignore
+            Document(page_content=metadata.pop((self._text_key)), metadata=metadata)
             for metadata in selected
         ]
 

libs/community/langchain_community/vectorstores/vectara.py

@@ -467,7 +467,7 @@ class Vectara(VectorStore):
         }
 
         if config.lambda_val > 0:
-            body["query"][0]["corpusKey"][0]["lexicalInterpolationConfig"] = {  # type: ignore
+            body["query"][0]["corpusKey"][0]["lexicalInterpolationConfig"] = {  # type: ignore[index]
                 "lambda": config.lambda_val
             }
 
@@ -495,7 +495,7 @@ class Vectara(VectorStore):
                 }
             ]
             if chat:
-                body["query"][0]["summary"][0]["chat"] = {  # type: ignore
+                body["query"][0]["summary"][0]["chat"] = {  # type: ignore[index]
                     "store": True,
                     "conversationId": chat_conv_id,
                 }

libs/community/pyproject.toml

@@ -65,7 +65,7 @@ lint = [
 ]
 dev = ["jupyter<2.0.0,>=1.0.0", "setuptools<68.0.0,>=67.6.1", "langchain-core"]
 typing = [
-    "mypy<2.0,>=1.12",
+    "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",
@@ -103,7 +103,7 @@ 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,cann"
 
 [tool.ruff.lint]
-select = ["E", "F", "I", "T201"]
+select = ["E", "F", "I", "PGH003", "T201"]
 
 [tool.coverage.run]
 omit = ["tests/*"]

libs/community/tests/integration_tests/cache/test_gptcache.py

@@ -27,7 +27,7 @@ def init_gptcache_map(cache_obj: Any) -> None:
         pre_embedding_func=get_prompt,
         data_manager=get_data_manager(data_path=cache_path),
     )
-    init_gptcache_map._i = i + 1  # type: ignore
+    init_gptcache_map._i = i + 1  # type: ignore[attr-defined]
 
 
 def init_gptcache_map_with_llm(cache_obj: Any, llm: str) -> None:

libs/community/tests/integration_tests/chat_message_histories/test_zep.py

@@ -37,7 +37,7 @@ def test_messages(mocker: MockerFixture, zep_chat: ZepChatMessageHistory) -> Non
             Message(content="message2", role="human", metadata={"key2": "value2"}),
         ],
     )
-    zep_chat.zep_client.memory.get_memory.return_value = mock_memory  # type: ignore
+    zep_chat.zep_client.memory.get_memory.return_value = mock_memory
 
     result = zep_chat.messages
 
@@ -52,25 +52,25 @@ def test_add_user_message(
     mocker: MockerFixture, zep_chat: ZepChatMessageHistory
 ) -> None:
     zep_chat.add_user_message("test message")
-    zep_chat.zep_client.memory.add_memory.assert_called_once()  # type: ignore
+    zep_chat.zep_client.memory.add_memory.assert_called_once()
 
 
 @pytest.mark.requires("zep_python")
 def test_add_ai_message(mocker: MockerFixture, zep_chat: ZepChatMessageHistory) -> None:
     zep_chat.add_ai_message("test message")
-    zep_chat.zep_client.memory.add_memory.assert_called_once()  # type: ignore
+    zep_chat.zep_client.memory.add_memory.assert_called_once()
 
 
 @pytest.mark.requires("zep_python")
 def test_append(mocker: MockerFixture, zep_chat: ZepChatMessageHistory) -> None:
     zep_chat.add_message(AIMessage(content="test message"))
-    zep_chat.zep_client.memory.add_memory.assert_called_once()  # type: ignore
+    zep_chat.zep_client.memory.add_memory.assert_called_once()
 
 
 @pytest.mark.requires("zep_python")
 def test_search(mocker: MockerFixture, zep_chat: ZepChatMessageHistory) -> None:
     zep_chat.search("test query")
-    zep_chat.zep_client.memory.search_memory.assert_called_once_with(  # type: ignore
+    zep_chat.zep_client.memory.search_memory.assert_called_once_with(
         "test_session", mocker.ANY, limit=None
     )
 
@@ -78,6 +78,4 @@ def test_search(mocker: MockerFixture, zep_chat: ZepChatMessageHistory) -> None:
 @pytest.mark.requires("zep_python")
 def test_clear(mocker: MockerFixture, zep_chat: ZepChatMessageHistory) -> None:
     zep_chat.clear()
-    zep_chat.zep_client.memory.delete_memory.assert_called_once_with(  # type: ignore
-        "test_session"
-    )
+    zep_chat.zep_client.memory.delete_memory.assert_called_once_with("test_session")

libs/community/tests/integration_tests/chat_models/test_minimax.py

@@ -67,7 +67,7 @@ class AnswerWithJustification(BaseModel):
 
 def test_chat_minimax_with_structured_output() -> None:
     """Test MiniMaxChat with structured output."""
-    llm = MiniMaxChat()  # type: ignore
+    llm = MiniMaxChat()  # type: ignore[call-arg]
     structured_llm = llm.with_structured_output(AnswerWithJustification)
     response = structured_llm.invoke(
         "What weighs more a pound of bricks or a pound of feathers"
@@ -77,7 +77,7 @@ def test_chat_minimax_with_structured_output() -> None:
 
 def test_chat_tongyi_with_structured_output_include_raw() -> None:
     """Test MiniMaxChat with structured output."""
-    llm = MiniMaxChat()  # type: ignore
+    llm = MiniMaxChat()  # type: ignore[call-arg]
     structured_llm = llm.with_structured_output(
         AnswerWithJustification, include_raw=True
     )

libs/community/tests/integration_tests/chat_models/test_tongyi.py

@@ -170,7 +170,7 @@ class GenerateUsername(BaseModel):
 
 
 def test_tool_use() -> None:
-    llm = ChatTongyi(model="qwen-turbo", temperature=0)  # type: ignore
+    llm = ChatTongyi(model="qwen-turbo", temperature=0)  # type: ignore[call-arg]
     llm_with_tool = llm.bind_tools(tools=[GenerateUsername])
     msgs: List = [
         HumanMessage(content="Sally has green hair, what would her username be?")
@@ -187,7 +187,7 @@ def test_tool_use() -> None:
 
     tool_msg = ToolMessage(
         content="sally_green_hair",
-        tool_call_id=ai_msg.tool_calls[0]["id"],  # type: ignore
+        tool_call_id=ai_msg.tool_calls[0]["id"],
         name=ai_msg.tool_calls[0]["name"],
     )
     msgs.extend([ai_msg, tool_msg])
@@ -201,7 +201,7 @@ def test_tool_use() -> None:
             gathered = message
             first = False
         else:
-            gathered = gathered + message  # type: ignore
+            gathered = gathered + message  # type: ignore[assignment]
     assert isinstance(gathered, AIMessageChunk)
 
     streaming_tool_msg = ToolMessage(
@@ -215,7 +215,7 @@ def test_tool_use() -> None:
 
 def test_manual_tool_call_msg() -> None:
     """Test passing in manually construct tool call message."""
-    llm = ChatTongyi(model="qwen-turbo", temperature=0)  # type: ignore
+    llm = ChatTongyi(model="qwen-turbo", temperature=0)  # type: ignore[call-arg]
     llm_with_tool = llm.bind_tools(tools=[GenerateUsername])
     msgs: List = [
         HumanMessage(content="Sally has green hair, what would her username be?"),
@@ -246,7 +246,7 @@ class AnswerWithJustification(BaseModel):
 
 def test_chat_tongyi_with_structured_output() -> None:
     """Test ChatTongyi with structured output."""
-    llm = ChatTongyi()  # type: ignore
+    llm = ChatTongyi()  # type: ignore[call-arg]
     structured_llm = llm.with_structured_output(AnswerWithJustification)
     response = structured_llm.invoke(
         "What weighs more a pound of bricks or a pound of feathers"
@@ -256,7 +256,7 @@ def test_chat_tongyi_with_structured_output() -> None:
 
 def test_chat_tongyi_with_structured_output_include_raw() -> None:
     """Test ChatTongyi with structured output."""
-    llm = ChatTongyi()  # type: ignore
+    llm = ChatTongyi()  # type: ignore[call-arg]
     structured_llm = llm.with_structured_output(
         AnswerWithJustification, include_raw=True
     )

libs/community/tests/integration_tests/chat_models/test_vertexai.py

@@ -75,7 +75,7 @@ async def test_vertexai_agenerate(model_name: str) -> None:
     message = HumanMessage(content="Hello")
     response = await model.agenerate([[message]])
     assert isinstance(response, LLMResult)
-    assert isinstance(response.generations[0][0].message, AIMessage)  # type: ignore
+    assert isinstance(response.generations[0][0].message, AIMessage)  # type: ignore[union-attr]
 
     sync_response = model.generate([[message]])
     assert response.generations[0][0] == sync_response.generations[0][0]

libs/community/tests/integration_tests/document_loaders/test_quip.py

@@ -1,4 +1,6 @@
-from typing import Dict
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Dict
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -6,6 +8,9 @@ from langchain_core.documents import Document
 
 from langchain_community.document_loaders.quip import QuipLoader
 
+if TYPE_CHECKING:
+    from collections.abc import Iterator
+
 try:
     from quip_api.quip import QuipClient  # noqa: F401
 
@@ -15,7 +20,7 @@ except ImportError:
 
 
 @pytest.fixture
-def mock_quip():  # type: ignore
+def mock_quip() -> Iterator[MagicMock]:
     # mock quip_client
     with patch("quip_api.quip.QuipClient") as mock_quip:
         yield mock_quip

libs/community/tests/integration_tests/document_loaders/test_unstructured.py

@@ -105,7 +105,7 @@ def test_unstructured_api_file_loader_io_multiple_files() -> None:
         files = [stack.enter_context(open(file_path, "rb")) for file_path in file_paths]
 
         loader = UnstructuredAPIFileIOLoader(
-            file=files,  # type: ignore
+            file=files,
             api_key="FAKE_API_KEY",
             strategy="fast",
             mode="elements",