langchain: upgrade mypy

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

@@ -97,7 +97,7 @@ def create_prompt(
         ]
         if input_variables is None:
             input_variables = ["input", "agent_scratchpad"]
-        return ChatPromptTemplate(input_variables=input_variables, messages=messages)
+        return ChatPromptTemplate(input_variables=input_variables, messages=messages)  # type: ignore[arg-type]
 
     @classmethod
     def from_llm_and_tools(

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

@@ -88,7 +88,7 @@ def create_prompt(
             HumanMessagePromptTemplate.from_template(final_prompt),
             MessagesPlaceholder(variable_name="agent_scratchpad"),
         ]
-        return ChatPromptTemplate(input_variables=input_variables, messages=messages)
+        return ChatPromptTemplate(input_variables=input_variables, messages=messages)  # type: ignore[arg-type]
 
     def _construct_scratchpad(
         self, intermediate_steps: List[Tuple[AgentAction, str]]

libs/langchain/langchain/agents/load_tools.py

@@ -406,7 +406,7 @@ def _get_eleven_labs_text2speech(**kwargs: Any) -> BaseTool:
 
 
 def _get_memorize(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
-    return Memorize(llm=llm)
+    return Memorize(llm=llm)  # type: ignore[arg-type]
 
 
 def _get_google_cloud_texttospeech(**kwargs: Any) -> BaseTool:

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

@@ -201,7 +201,7 @@ def create_prompt(
                 MessagesPlaceholder(variable_name="agent_scratchpad"),
             ]
         )
-        return ChatPromptTemplate(messages=messages)
+        return ChatPromptTemplate(messages=messages)  # type: ignore[arg-type, call-arg]
 
     @classmethod
     def from_llm_and_tools(
@@ -220,7 +220,7 @@ def from_llm_and_tools(
             extra_prompt_messages=extra_prompt_messages,
             system_message=system_message,
         )
-        return cls(
+        return cls(  # type: ignore[call-arg]
             llm=llm,
             prompt=prompt,
             tools=tools,

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

@@ -279,7 +279,7 @@ def create_prompt(
                 MessagesPlaceholder(variable_name="agent_scratchpad"),
             ]
         )
-        return ChatPromptTemplate(messages=messages)
+        return ChatPromptTemplate(messages=messages)  # type: ignore[arg-type, call-arg]
 
     @classmethod
     def from_llm_and_tools(
@@ -298,7 +298,7 @@ def from_llm_and_tools(
             extra_prompt_messages=extra_prompt_messages,
             system_message=system_message,
         )
-        return cls(
+        return cls(  # type: ignore[call-arg]
             llm=llm,
             prompt=prompt,
             tools=tools,

libs/langchain/langchain/agents/structured_chat/base.py

@@ -103,7 +103,7 @@ def create_prompt(
             *_memory_prompts,
             HumanMessagePromptTemplate.from_template(human_message_template),
         ]
-        return ChatPromptTemplate(input_variables=input_variables, messages=messages)
+        return ChatPromptTemplate(input_variables=input_variables, messages=messages)  # type: ignore[arg-type]
 
     @classmethod
     def from_llm_and_tools(

libs/langchain/langchain/chains/graph_qa/cypher.py

@@ -199,10 +199,11 @@ def from_llm(
                 cypher_prompt if cypher_prompt is not None else CYPHER_GENERATION_PROMPT
             )
 
-        qa_chain = LLMChain(llm=qa_llm or llm, **use_qa_llm_kwargs)
+        qa_chain = LLMChain(llm=qa_llm or llm, **use_qa_llm_kwargs)  # type: ignore[arg-type]
 
         cypher_generation_chain = LLMChain(
-            llm=cypher_llm or llm, **use_cypher_llm_kwargs
+            llm=cypher_llm or llm,  # type: ignore[arg-type]
+            **use_cypher_llm_kwargs,  # type: ignore[arg-type]
         )
 
         if exclude_types and include_types:

libs/langchain/langchain/chains/graph_qa/neptune_sparql.py

@@ -135,7 +135,7 @@ def from_llm(
             )
         sparql_generation_chain = LLMChain(llm=llm, prompt=sparql_prompt)
 
-        return cls(
+        return cls(  # type: ignore[call-arg]
             qa_chain=qa_chain,
             sparql_generation_chain=sparql_generation_chain,
             examples=examples,

libs/langchain/langchain/chains/llm_checker/base.py

@@ -54,7 +54,7 @@ def _load_question_to_checked_assertions_chain(
         revised_answer_chain,
     ]
     question_to_checked_assertions_chain = SequentialChain(
-        chains=chains,
+        chains=chains,  # type: ignore[arg-type]
         input_variables=["question"],
         output_variables=["revised_statement"],
         verbose=True,

libs/langchain/langchain/chains/loading.py

@@ -69,7 +69,9 @@ def _load_hyde_chain(config: dict, **kwargs: Any) -> HypotheticalDocumentEmbedde
     else:
         raise ValueError("`embeddings` must be present.")
     return HypotheticalDocumentEmbedder(
-        llm_chain=llm_chain, base_embeddings=embeddings, **config
+        llm_chain=llm_chain,  # type: ignore[arg-type]
+        base_embeddings=embeddings,
+        **config,  # type: ignore[arg-type]
     )
 
 
@@ -125,7 +127,7 @@ def _load_map_reduce_documents_chain(
 
     return MapReduceDocumentsChain(
         llm_chain=llm_chain,
-        reduce_documents_chain=reduce_documents_chain,
+        reduce_documents_chain=reduce_documents_chain,  # type: ignore[arg-type]
         **config,
     )
 
@@ -207,7 +209,7 @@ def _load_llm_bash_chain(config: dict, **kwargs: Any) -> Any:
     elif "prompt_path" in config:
         prompt = load_prompt(config.pop("prompt_path"))
     if llm_chain:
-        return LLMBashChain(llm_chain=llm_chain, prompt=prompt, **config)
+        return LLMBashChain(llm_chain=llm_chain, prompt=prompt, **config)  # type: ignore[arg-type]
     else:
         return LLMBashChain(llm=llm, prompt=prompt, **config)
 
@@ -250,10 +252,10 @@ def _load_llm_checker_chain(config: dict, **kwargs: Any) -> LLMCheckerChain:
         revised_answer_prompt = load_prompt(config.pop("revised_answer_prompt_path"))
     return LLMCheckerChain(
         llm=llm,
-        create_draft_answer_prompt=create_draft_answer_prompt,
-        list_assertions_prompt=list_assertions_prompt,
-        check_assertions_prompt=check_assertions_prompt,
-        revised_answer_prompt=revised_answer_prompt,
+        create_draft_answer_prompt=create_draft_answer_prompt,  # type: ignore[arg-type]
+        list_assertions_prompt=list_assertions_prompt,  # type: ignore[arg-type]
+        check_assertions_prompt=check_assertions_prompt,  # type: ignore[arg-type]
+        revised_answer_prompt=revised_answer_prompt,  # type: ignore[arg-type]
         **config,
     )
 
@@ -281,7 +283,7 @@ def _load_llm_math_chain(config: dict, **kwargs: Any) -> LLMMathChain:
     elif "prompt_path" in config:
         prompt = load_prompt(config.pop("prompt_path"))
     if llm_chain:
-        return LLMMathChain(llm_chain=llm_chain, prompt=prompt, **config)
+        return LLMMathChain(llm_chain=llm_chain, prompt=prompt, **config)  # type: ignore[arg-type]
     else:
         return LLMMathChain(llm=llm, prompt=prompt, **config)
 
@@ -296,7 +298,7 @@ def _load_map_rerank_documents_chain(
         llm_chain = load_chain(config.pop("llm_chain_path"))
     else:
         raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
-    return MapRerankDocumentsChain(llm_chain=llm_chain, **config)
+    return MapRerankDocumentsChain(llm_chain=llm_chain, **config)  # type: ignore[arg-type]
 
 
 def _load_pal_chain(config: dict, **kwargs: Any) -> Any:
@@ -309,7 +311,7 @@ def _load_pal_chain(config: dict, **kwargs: Any) -> Any:
         llm_chain = load_chain(config.pop("llm_chain_path"))
     else:
         raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
-    return PALChain(llm_chain=llm_chain, **config)
+    return PALChain(llm_chain=llm_chain, **config)  # type: ignore[arg-type]
 
 
 def _load_refine_documents_chain(config: dict, **kwargs: Any) -> RefineDocumentsChain:
@@ -337,8 +339,8 @@ def _load_refine_documents_chain(config: dict, **kwargs: Any) -> RefineDocuments
     elif "document_prompt_path" in config:
         document_prompt = load_prompt(config.pop("document_prompt_path"))
     return RefineDocumentsChain(
-        initial_llm_chain=initial_llm_chain,
-        refine_llm_chain=refine_llm_chain,
+        initial_llm_chain=initial_llm_chain,  # type: ignore[arg-type]
+        refine_llm_chain=refine_llm_chain,  # type: ignore[arg-type]
         document_prompt=document_prompt,
         **config,
     )
@@ -355,7 +357,7 @@ def _load_qa_with_sources_chain(config: dict, **kwargs: Any) -> QAWithSourcesCha
             "One of `combine_documents_chain` or "
             "`combine_documents_chain_path` must be present."
         )
-    return QAWithSourcesChain(combine_documents_chain=combine_documents_chain, **config)
+    return QAWithSourcesChain(combine_documents_chain=combine_documents_chain, **config)  # type: ignore[arg-type]
 
 
 def _load_sql_database_chain(config: dict, **kwargs: Any) -> Any:
@@ -368,7 +370,7 @@ def _load_sql_database_chain(config: dict, **kwargs: Any) -> Any:
     if "llm_chain" in config:
         llm_chain_config = config.pop("llm_chain")
         chain = load_chain_from_config(llm_chain_config)
-        return SQLDatabaseChain(llm_chain=chain, database=database, **config)
+        return SQLDatabaseChain(llm_chain=chain, database=database, **config)  # type: ignore[arg-type]
     if "llm" in config:
         llm_config = config.pop("llm")
         llm = load_llm_from_config(llm_config)
@@ -403,7 +405,7 @@ def _load_vector_db_qa_with_sources_chain(
             "`combine_documents_chain_path` must be present."
         )
     return VectorDBQAWithSourcesChain(
-        combine_documents_chain=combine_documents_chain,
+        combine_documents_chain=combine_documents_chain,  # type: ignore[arg-type]
         vectorstore=vectorstore,
         **config,
     )
@@ -425,7 +427,7 @@ def _load_retrieval_qa(config: dict, **kwargs: Any) -> RetrievalQA:
             "`combine_documents_chain_path` must be present."
         )
     return RetrievalQA(
-        combine_documents_chain=combine_documents_chain,
+        combine_documents_chain=combine_documents_chain,  # type: ignore[arg-type]
         retriever=retriever,
         **config,
     )
@@ -449,7 +451,7 @@ def _load_retrieval_qa_with_sources_chain(
             "`combine_documents_chain_path` must be present."
         )
     return RetrievalQAWithSourcesChain(
-        combine_documents_chain=combine_documents_chain,
+        combine_documents_chain=combine_documents_chain,  # type: ignore[arg-type]
         retriever=retriever,
         **config,
     )
@@ -471,7 +473,7 @@ def _load_vector_db_qa(config: dict, **kwargs: Any) -> VectorDBQA:
             "`combine_documents_chain_path` must be present."
         )
     return VectorDBQA(
-        combine_documents_chain=combine_documents_chain,
+        combine_documents_chain=combine_documents_chain,  # type: ignore[arg-type]
         vectorstore=vectorstore,
         **config,
     )
@@ -495,8 +497,8 @@ def _load_graph_cypher_chain(config: dict, **kwargs: Any) -> GraphCypherQAChain:
 
     return GraphCypherQAChain(
         graph=graph,
-        cypher_generation_chain=cypher_generation_chain,
-        qa_chain=qa_chain,
+        cypher_generation_chain=cypher_generation_chain,  # type: ignore[arg-type]
+        qa_chain=qa_chain,  # type: ignore[arg-type]
         **config,
     )
 
@@ -525,8 +527,8 @@ def _load_api_chain(config: dict, **kwargs: Any) -> APIChain:
     else:
         raise ValueError("`requests_wrapper` must be present.")
     return APIChain(
-        api_request_chain=api_request_chain,
-        api_answer_chain=api_answer_chain,
+        api_request_chain=api_request_chain,  # type: ignore[arg-type]
+        api_answer_chain=api_answer_chain,  # type: ignore[arg-type]
         requests_wrapper=requests_wrapper,
         **config,
     )

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

@@ -95,7 +95,7 @@ def create_citation_fuzzy_match_chain(llm: BaseLanguageModel) -> LLMChain:
             )
         ),
     ]
-    prompt = ChatPromptTemplate(messages=messages)
+    prompt = ChatPromptTemplate(messages=messages)  # type: ignore[arg-type, call-arg]
 
     chain = LLMChain(
         llm=llm,

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

@@ -82,7 +82,7 @@ def create_qa_with_structure_chain(
         HumanMessagePromptTemplate.from_template("Question: {question}"),
         HumanMessage(content="Tips: Make sure to answer in the correct format"),
     ]
-    prompt = prompt or ChatPromptTemplate(messages=messages)
+    prompt = prompt or ChatPromptTemplate(messages=messages)  # type: ignore[arg-type, call-arg]
 
     chain = LLMChain(
         llm=llm,

libs/langchain/langchain/chains/qa_with_sources/loading.py

@@ -59,12 +59,12 @@ def _load_stuff_chain(
     verbose: Optional[bool] = None,
     **kwargs: Any,
 ) -> StuffDocumentsChain:
-    llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
+    llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)  # type: ignore[arg-type]
     return StuffDocumentsChain(
         llm_chain=llm_chain,
         document_variable_name=document_variable_name,
         document_prompt=document_prompt,
-        verbose=verbose,
+        verbose=verbose,  # type: ignore[arg-type]
         **kwargs,
     )
 
@@ -83,14 +83,14 @@ def _load_map_reduce_chain(
     token_max: int = 3000,
     **kwargs: Any,
 ) -> MapReduceDocumentsChain:
-    map_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose)
+    map_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose)  # type: ignore[arg-type]
     _reduce_llm = reduce_llm or llm
-    reduce_chain = LLMChain(llm=_reduce_llm, prompt=combine_prompt, verbose=verbose)
+    reduce_chain = LLMChain(llm=_reduce_llm, prompt=combine_prompt, verbose=verbose)  # type: ignore[arg-type]
     combine_documents_chain = StuffDocumentsChain(
         llm_chain=reduce_chain,
         document_variable_name=combine_document_variable_name,
         document_prompt=document_prompt,
-        verbose=verbose,
+        verbose=verbose,  # type: ignore[arg-type]
     )
     if collapse_prompt is None:
         collapse_chain = None
@@ -105,7 +105,7 @@ def _load_map_reduce_chain(
             llm_chain=LLMChain(
                 llm=_collapse_llm,
                 prompt=collapse_prompt,
-                verbose=verbose,
+                verbose=verbose,  # type: ignore[arg-type]
             ),
             document_variable_name=combine_document_variable_name,
             document_prompt=document_prompt,
@@ -114,13 +114,13 @@ def _load_map_reduce_chain(
         combine_documents_chain=combine_documents_chain,
         collapse_documents_chain=collapse_chain,
         token_max=token_max,
-        verbose=verbose,
+        verbose=verbose,  # type: ignore[arg-type]
     )
     return MapReduceDocumentsChain(
         llm_chain=map_chain,
         reduce_documents_chain=reduce_documents_chain,
         document_variable_name=map_reduce_document_variable_name,
-        verbose=verbose,
+        verbose=verbose,  # type: ignore[arg-type]
         **kwargs,
     )
 
@@ -136,16 +136,16 @@ def _load_refine_chain(
     verbose: Optional[bool] = None,
     **kwargs: Any,
 ) -> RefineDocumentsChain:
-    initial_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose)
+    initial_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose)  # type: ignore[arg-type]
     _refine_llm = refine_llm or llm
-    refine_chain = LLMChain(llm=_refine_llm, prompt=refine_prompt, verbose=verbose)
+    refine_chain = LLMChain(llm=_refine_llm, prompt=refine_prompt, verbose=verbose)  # type: ignore[arg-type]
     return RefineDocumentsChain(
         initial_llm_chain=initial_chain,
         refine_llm_chain=refine_chain,
         document_variable_name=document_variable_name,
         initial_response_name=initial_response_name,
         document_prompt=document_prompt,
-        verbose=verbose,
+        verbose=verbose,  # type: ignore[arg-type]
         **kwargs,
     )