langchain: Bump ruff version to 0.9

libs/langchain/langchain/agents/initialize.py

@@ -84,8 +84,7 @@ def initialize_agent(
             pass
     else:
         raise ValueError(
-            "Somehow both `agent` and `agent_path` are None, "
-            "this should never happen."
+            "Somehow both `agent` and `agent_path` are None, this should never happen."
         )
     return AgentExecutor.from_agent_and_tools(
         agent=agent_obj,

libs/langchain/langchain/agents/loading.py

@@ -58,8 +58,7 @@ def load_agent_from_config(
     if load_from_tools:
         if llm is None:
             raise ValueError(
-                "If `load_from_llm_and_tools` is set to True, "
-                "then LLM must be provided"
+                "If `load_from_llm_and_tools` is set to True, then LLM must be provided"
             )
         if tools is None:
             raise ValueError(

libs/langchain/langchain/callbacks/tracers/logging.py

@@ -41,6 +41,6 @@ def on_text(
         except TracerException:
             crumbs_str = ""
         self.function_callback(
-            f'{get_colored_text("[text]", color="blue")}'
-            f' {get_bolded_text(f"{crumbs_str}New text:")}\n{text}'
+            f"{get_colored_text('[text]', color='blue')}"
+            f" {get_bolded_text(f'{crumbs_str}New text:')}\n{text}"
         )

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

@@ -370,6 +370,7 @@ def from_llm_and_api_docs(
         @property
         def _chain_type(self) -> str:
             return "api_chain"
+
 except ImportError:
 
     class APIChain:  # type: ignore[no-redef]

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

@@ -233,7 +233,7 @@ def _call(
             response = (
                 f"{api_response.status_code}: {api_response.reason}"
                 + f"\nFor {name} "
-                + f"Called with args: {args.get('params','')}"
+                + f"Called with args: {args.get('params', '')}"
             )
         else:
             try:

libs/langchain/langchain/chains/openai_tools/extraction.py

@@ -68,7 +68,10 @@ def create_extraction_chain_pydantic(
     if not isinstance(pydantic_schemas, list):
         pydantic_schemas = [pydantic_schemas]
     prompt = ChatPromptTemplate.from_messages(
-        [("system", system_message), ("user", "{input}")]
+        [
+            ("system", system_message),
+            ("user", "{input}"),
+        ]
     )
     functions = [convert_pydantic_to_openai_function(p) for p in pydantic_schemas]
     tools = [{"type": "function", "function": d} for d in functions]

libs/langchain/langchain/chains/question_answering/refine_prompts.py

@@ -33,7 +33,11 @@
     "If the context isn't useful, return the original answer."
 )
 CHAT_REFINE_PROMPT = ChatPromptTemplate.from_messages(
-    [("human", "{question}"), ("ai", "{existing_answer}"), ("human", refine_template)]
+    [
+        ("human", "{question}"),
+        ("ai", "{existing_answer}"),
+        ("human", refine_template),
+    ]
 )
 REFINE_PROMPT_SELECTOR = ConditionalPromptSelector(
     default_prompt=DEFAULT_REFINE_PROMPT,
@@ -60,7 +64,10 @@
     "answer any questions"
 )
 CHAT_QUESTION_PROMPT = ChatPromptTemplate.from_messages(
-    [("system", chat_qa_prompt_template), ("human", "{question}")]
+    [
+        ("system", chat_qa_prompt_template),
+        ("human", "{question}"),
+    ]
 )
 QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(
     default_prompt=DEFAULT_TEXT_QA_PROMPT,

libs/langchain/langchain/chains/sequential.py

@@ -178,7 +178,9 @@ def _call(
         _input = inputs[self.input_key]
         color_mapping = get_color_mapping([str(i) for i in range(len(self.chains))])
         for i, chain in enumerate(self.chains):
-            _input = chain.run(_input, callbacks=_run_manager.get_child(f"step_{i+1}"))
+            _input = chain.run(
+                _input, callbacks=_run_manager.get_child(f"step_{i + 1}")
+            )
             if self.strip_outputs:
                 _input = _input.strip()
             _run_manager.on_text(
@@ -196,7 +198,7 @@ async def _acall(
         color_mapping = get_color_mapping([str(i) for i in range(len(self.chains))])
         for i, chain in enumerate(self.chains):
             _input = await chain.arun(
-                _input, callbacks=_run_manager.get_child(f"step_{i+1}")
+                _input, callbacks=_run_manager.get_child(f"step_{i + 1}")
             )
             if self.strip_outputs:
                 _input = _input.strip()

libs/langchain/langchain/chat_models/base.py

@@ -590,7 +590,11 @@ def with_config(
         queued_declarative_operations = list(self._queued_declarative_operations)
         if remaining_config:
             queued_declarative_operations.append(
-                ("with_config", (), {"config": remaining_config})
+                (
+                    "with_config",
+                    (),
+                    {"config": remaining_config},
+                )
             )
         return _ConfigurableModel(
             default_config={**self._default_config, **model_params},

libs/langchain/langchain/embeddings/base.py

@@ -174,8 +174,7 @@ def init_embeddings(
     if not model:
         providers = _SUPPORTED_PROVIDERS.keys()
         raise ValueError(
-            "Must specify model name. "
-            f"Supported providers are: {', '.join(providers)}"
+            f"Must specify model name. Supported providers are: {', '.join(providers)}"
         )
 
     provider, model_name = _infer_model_and_provider(model, provider=provider)

libs/langchain/langchain/evaluation/embedding_distance/base.py

@@ -310,7 +310,10 @@ async def _acall(
             Dict[str, Any]: The computed score.
         """
         embedded = await self.embeddings.aembed_documents(
-            [inputs["prediction"], inputs["reference"]]
+            [
+                inputs["prediction"],
+                inputs["reference"],
+            ]
         )
         vectors = np.array(embedded)
         score = self._compute_score(vectors)
@@ -427,7 +430,10 @@ def _call(
         """
         vectors = np.array(
             self.embeddings.embed_documents(
-                [inputs["prediction"], inputs["prediction_b"]]
+                [
+                    inputs["prediction"],
+                    inputs["prediction_b"],
+                ]
             )
         )
         score = self._compute_score(vectors)
@@ -449,7 +455,10 @@ async def _acall(
             Dict[str, Any]: The computed score.
         """
         embedded = await self.embeddings.aembed_documents(
-            [inputs["prediction"], inputs["prediction_b"]]
+            [
+                inputs["prediction"],
+                inputs["prediction_b"],
+            ]
         )
         vectors = np.array(embedded)
         score = self._compute_score(vectors)

libs/langchain/langchain/memory/chat_memory.py

@@ -71,7 +71,10 @@ def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
         """Save context from this conversation to buffer."""
         input_str, output_str = self._get_input_output(inputs, outputs)
         self.chat_memory.add_messages(
-            [HumanMessage(content=input_str), AIMessage(content=output_str)]
+            [
+                HumanMessage(content=input_str),
+                AIMessage(content=output_str),
+            ]
         )
 
     async def asave_context(
@@ -80,7 +83,10 @@ async def asave_context(
         """Save context from this conversation to buffer."""
         input_str, output_str = self._get_input_output(inputs, outputs)
         await self.chat_memory.aadd_messages(
-            [HumanMessage(content=input_str), AIMessage(content=output_str)]
+            [
+                HumanMessage(content=input_str),
+                AIMessage(content=output_str),
+            ]
         )
 
     def clear(self) -> None:

libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py

@@ -92,7 +92,10 @@ def rerank(
         result_dicts = []
         for res in results:
             result_dicts.append(
-                {"index": res.index, "relevance_score": res.relevance_score}
+                {
+                    "index": res.index,
+                    "relevance_score": res.relevance_score,
+                }
             )
         return result_dicts
 

libs/langchain/langchain/retrievers/ensemble.py

@@ -223,7 +223,7 @@ def rank_fusion(
             retriever.invoke(
                 query,
                 patch_config(
-                    config, callbacks=run_manager.get_child(tag=f"retriever_{i+1}")
+                    config, callbacks=run_manager.get_child(tag=f"retriever_{i + 1}")
                 ),
             )
             for i, retriever in enumerate(self.retrievers)
@@ -265,7 +265,8 @@ async def arank_fusion(
                 retriever.ainvoke(
                     query,
                     patch_config(
-                        config, callbacks=run_manager.get_child(tag=f"retriever_{i+1}")
+                        config,
+                        callbacks=run_manager.get_child(tag=f"retriever_{i + 1}"),
                     ),
                 )
                 for i, retriever in enumerate(self.retrievers)

libs/langchain/langchain/smith/evaluation/runner_utils.py

@@ -247,8 +247,7 @@ def _get_prompt(inputs: Dict[str, Any]) -> str:
     if "prompt" in inputs:
         if not isinstance(inputs["prompt"], str):
             raise InputFormatError(
-                "Expected string for 'prompt', got"
-                f" {type(inputs['prompt']).__name__}"
+                f"Expected string for 'prompt', got {type(inputs['prompt']).__name__}"
             )
         prompts = [inputs["prompt"]]
     elif "prompts" in inputs: