community[patch]: import flattening fix (#20110)

This PR should make it easier for linters to do type checking and for IDEs to jump to definition of code.

See #20050 as a template for this PR.
- As a byproduct: Added 3 missed `test_imports`.
- Added missed `SolarChat` in to __init___.py Added it into test_import
ut.
- Added `# type: ignore` to fix linting. It is not clear, why linting
errors appear after ^ changes.

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>

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

@@ -116,7 +116,7 @@ class ConversationalAgent(Agent):
             format_instructions=format_instructions,
             input_variables=input_variables,
         )
-        llm_chain = LLMChain(
+        llm_chain = LLMChain(  # type: ignore[misc]
             llm=llm,
             prompt=prompt,
             callback_manager=callback_manager,

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

@@ -125,7 +125,7 @@ class ConversationalChatAgent(Agent):
             input_variables=input_variables,
             output_parser=_output_parser,
         )
-        llm_chain = LLMChain(
+        llm_chain = LLMChain(  # type: ignore[misc]
             llm=llm,
             prompt=prompt,
             callback_manager=callback_manager,

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

@@ -110,7 +110,7 @@ class ZeroShotAgent(Agent):
             format_instructions=format_instructions,
             input_variables=input_variables,
         )
-        llm_chain = LLMChain(
+        llm_chain = LLMChain(  # type: ignore[misc]
             llm=llm,
             prompt=prompt,
             callback_manager=callback_manager,

libs/langchain/langchain/chains/llm_requests.py

@@ -27,7 +27,7 @@ class LLMRequestsChain(Chain):
         See https://python.langchain.com/docs/security for more information.
     """
 
-    llm_chain: LLMChain
+    llm_chain: LLMChain  # type: ignore[valid-type]
     requests_wrapper: TextRequestsWrapper = Field(
         default_factory=lambda: TextRequestsWrapper(headers=DEFAULT_HEADERS),
         exclude=True,
@@ -87,7 +87,7 @@ class LLMRequestsChain(Chain):
         # extract the text from the html
         soup = BeautifulSoup(res, "html.parser")
         other_keys[self.requests_key] = soup.get_text()[: self.text_length]
-        result = self.llm_chain.predict(
+        result = self.llm_chain.predict(  # type: ignore[attr-defined]
             callbacks=_run_manager.get_child(), **other_keys
         )
         return {self.output_key: result}

libs/langchain/langchain/chains/loading.py

@@ -134,7 +134,7 @@ def _load_map_reduce_documents_chain(
     )
 
 
-def _load_reduce_documents_chain(config: dict, **kwargs: Any) -> ReduceDocumentsChain:
+def _load_reduce_documents_chain(config: dict, **kwargs: Any) -> ReduceDocumentsChain:  # type: ignore[valid-type]
     combine_documents_chain = None
     collapse_documents_chain = None
 
@@ -187,7 +187,7 @@ def _load_reduce_documents_chain(config: dict, **kwargs: Any) -> ReduceDocuments
             config.pop("collapse_document_chain_path"), **kwargs
         )
 
-    return ReduceDocumentsChain(
+    return ReduceDocumentsChain(  # type: ignore[misc]
         combine_documents_chain=combine_documents_chain,
         collapse_documents_chain=collapse_documents_chain,
         **config,

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

@@ -52,7 +52,7 @@ def create_openai_fn_chain(
     output_key: str = "function",
     output_parser: Optional[BaseLLMOutputParser] = None,
     **kwargs: Any,
-) -> LLMChain:
+) -> LLMChain:  # type: ignore[valid-type]
     """[Legacy] Create an LLM chain that uses OpenAI functions.
 
     Args:
@@ -132,7 +132,7 @@ 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(
+    llm_chain = LLMChain(  # type: ignore[misc]
         llm=llm,
         prompt=prompt,
         output_parser=output_parser,
@@ -154,7 +154,7 @@ def create_structured_output_chain(
     output_key: str = "function",
     output_parser: Optional[BaseLLMOutputParser] = None,
     **kwargs: Any,
-) -> LLMChain:
+) -> LLMChain:  # type: ignore[valid-type]
     """[Legacy] Create an LLMChain that uses an OpenAI function to get a structured output.
 
     Args:

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

@@ -59,7 +59,7 @@ class BaseQAWithSourcesChain(Chain, ABC):
             document_prompt=document_prompt,
             document_variable_name="summaries",
         )
-        reduce_documents_chain = ReduceDocumentsChain(
+        reduce_documents_chain = ReduceDocumentsChain(  # type: ignore[misc]
             combine_documents_chain=combine_results_chain
         )
         combine_documents_chain = MapReduceDocumentsChain(

libs/langchain/langchain/chains/question_answering/__init__.py

@@ -153,7 +153,7 @@ def _load_map_reduce_chain(
             verbose=verbose,  # type: ignore[arg-type]
             callback_manager=callback_manager,
         )
-    reduce_documents_chain = ReduceDocumentsChain(
+    reduce_documents_chain = ReduceDocumentsChain(  # type: ignore[misc]
         combine_documents_chain=combine_documents_chain,
         collapse_documents_chain=collapse_chain,
         token_max=token_max,

libs/langchain/tests/integration_tests/agent/test_powerbi_agent.py

@@ -27,10 +27,10 @@ def test_daxquery() -> None:
 
     fast_llm = ChatOpenAI(
         temperature=0.5, max_tokens=1000, model_name="gpt-3.5-turbo", verbose=True
-    )
+    )  # type: ignore[call-arg]
     smart_llm = ChatOpenAI(
         temperature=0, max_tokens=100, model_name="gpt-4", verbose=True
-    )
+    )  # type: ignore[call-arg]
 
     toolkit = PowerBIToolkit(
         powerbi=PowerBIDataset(