chore(langchain): enable ruff docstring-code-format in langchain_v1 (#32855)

Author: cbornet

libs/langchain_v1/langchain/_internal/_prompts.py

@@ -61,6 +61,7 @@ def resolve_prompt(
         def custom_prompt(state, runtime):
             return [{"role": "system", "content": "Custom"}]
 
+
         messages = resolve_prompt(custom_prompt, state, runtime, "content", "default")
         messages = resolve_prompt("Custom system", state, runtime, "content", "default")
         messages = resolve_prompt(None, state, runtime, "content", "Default")
@@ -128,15 +129,13 @@ async def aresolve_prompt(
         async def async_prompt(state, runtime):
             return [{"role": "system", "content": "Async"}]
 
+
         def sync_prompt(state, runtime):
             return [{"role": "system", "content": "Sync"}]
 
-        messages = await aresolve_prompt(
-            async_prompt, state, runtime, "content", "default"
-        )
-        messages = await aresolve_prompt(
-            sync_prompt, state, runtime, "content", "default"
-        )
+
+        messages = await aresolve_prompt(async_prompt, state, runtime, "content", "default")
+        messages = await aresolve_prompt(sync_prompt, state, runtime, "content", "default")
         messages = await aresolve_prompt("Custom", state, runtime, "content", "default")
         ```
 

libs/langchain_v1/langchain/agents/interrupt.py

@@ -53,15 +53,15 @@ class HumanInterrupt(TypedDict):
         request = HumanInterrupt(
             action_request=ActionRequest(
                 action="run_command",  # The action being requested
-                args={"command": "ls", "args": ["-l"]}  # Arguments for the action
+                args={"command": "ls", "args": ["-l"]},  # Arguments for the action
             ),
             config=HumanInterruptConfig(
-                allow_ignore=True,    # Allow skipping this step
-                allow_respond=True,   # Allow text feedback
-                allow_edit=False,     # Don't allow editing
-                allow_accept=True     # Allow direct acceptance
+                allow_ignore=True,  # Allow skipping this step
+                allow_respond=True,  # Allow text feedback
+                allow_edit=False,  # Don't allow editing
+                allow_accept=True,  # Allow direct acceptance
             ),
-            description="Please review the command before execution"
+            description="Please review the command before execution",
         )
         # Send the interrupt request and get the response
         response = interrupt([request])[0]

libs/langchain_v1/langchain/agents/react_agent.py

@@ -957,14 +957,17 @@ def create_agent(  # noqa: D417
             ```python
             from dataclasses import dataclass
 
+
             @dataclass
             class ModelContext:
                 model_name: str = "gpt-3.5-turbo"
 
+
             # Instantiate models globally
             gpt4_model = ChatOpenAI(model="gpt-4")
             gpt35_model = ChatOpenAI(model="gpt-3.5-turbo")
 
+
             def select_model(state: AgentState, runtime: Runtime[ModelContext]) -> ChatOpenAI:
                 model_name = runtime.context.model_name
                 model = gpt4_model if model_name == "gpt-4" else gpt35_model

libs/langchain_v1/langchain/agents/tool_node.py

@@ -23,10 +23,12 @@
     from langchain_core.tools import tool
     from langchain.agents import ToolNode
 
+
     @tool
     def my_tool(x: int) -> str:
         return f"Result: {x}"
 
+
     tool_node = ToolNode([my_tool])
     ```
 """
@@ -369,6 +371,7 @@ def context_tool(query: str, state: Annotated[dict, InjectedState]) -> str:
         def handle_errors(e: ValueError) -> str:
             return "Invalid input provided"
 
+
         tool_node = ToolNode([my_tool], handle_tool_errors=handle_errors)
         ```
     """
@@ -887,16 +890,18 @@ def tools_condition(
         from langgraph.agents.tool_node import ToolNode, tools_condition
         from typing_extensions import TypedDict
 
+
         class State(TypedDict):
             messages: list
 
+
         graph = StateGraph(State)
         graph.add_node("llm", call_model)
         graph.add_node("tools", ToolNode([my_tool]))
         graph.add_conditional_edges(
             "llm",
             tools_condition,  # Routes to "tools" or "__end__"
-            {"tools": "tools", "__end__": "__end__"}
+            {"tools": "tools", "__end__": "__end__"},
         )
         ```
 
@@ -956,6 +961,7 @@ class AgentState(TypedDict):
             messages: List[BaseMessage]
             foo: str
 
+
         @tool
         def state_tool(x: int, state: Annotated[dict, InjectedState]) -> str:
             '''Do something with state.'''
@@ -964,11 +970,13 @@ def state_tool(x: int, state: Annotated[dict, InjectedState]) -> str:
             else:
                 return "not enough messages"
 
+
         @tool
         def foo_tool(x: int, foo: Annotated[str, InjectedState("foo")]) -> str:
             '''Do something else with state.'''
             return foo + str(x + 1)
 
+
         node = ToolNode([state_tool, foo_tool])
 
         tool_call1 = {"name": "state_tool", "args": {"x": 1}, "id": "1", "type": "tool_call"}
@@ -982,8 +990,8 @@ def foo_tool(x: int, foo: Annotated[str, InjectedState("foo")]) -> str:
 
         ```pycon
         [
-            ToolMessage(content='not enough messages', name='state_tool', tool_call_id='1'),
-            ToolMessage(content='bar2', name='foo_tool', tool_call_id='2')
+            ToolMessage(content="not enough messages", name="state_tool", tool_call_id="1"),
+            ToolMessage(content="bar2", name="foo_tool", tool_call_id="2"),
         ]
         ```
 

libs/langchain_v1/langchain/chat_models/base.py

@@ -191,14 +191,12 @@ def init_chat_model(
             configurable_model = init_chat_model(temperature=0)
 
             configurable_model.invoke(
-                "what's your name",
-                config={"configurable": {"model": "gpt-4o"}}
+                "what's your name", config={"configurable": {"model": "gpt-4o"}}
             )
             # GPT-4o response
 
             configurable_model.invoke(
-                "what's your name",
-                config={"configurable": {"model": "claude-3-5-sonnet-latest"}}
+                "what's your name", config={"configurable": {"model": "claude-3-5-sonnet-latest"}}
             )
             # claude-3.5 sonnet response
 
@@ -213,7 +211,7 @@ def init_chat_model(
                 "openai:gpt-4o",
                 configurable_fields="any",  # this allows us to configure other params like temperature, max_tokens, etc at runtime.
                 config_prefix="foo",
-                temperature=0
+                temperature=0,
             )
 
             configurable_model_with_default.invoke("what's your name")
@@ -224,9 +222,9 @@ def init_chat_model(
                 config={
                     "configurable": {
                         "foo_model": "anthropic:claude-3-5-sonnet-latest",
-                        "foo_temperature": 0.6
+                        "foo_temperature": 0.6,
                     }
-                }
+                },
             )
             # Claude-3.5 sonnet response with temperature 0.6
 
@@ -241,31 +239,34 @@ def init_chat_model(
             from langchain.chat_models import init_chat_model
             from pydantic import BaseModel, Field
 
+
             class GetWeather(BaseModel):
                 '''Get the current weather in a given location'''
 
                 location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
 
+
             class GetPopulation(BaseModel):
                 '''Get the current population in a given location'''
 
                 location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
 
+
             configurable_model = init_chat_model(
-                "gpt-4o",
-                configurable_fields=("model", "model_provider"),
-                temperature=0
+                "gpt-4o", configurable_fields=("model", "model_provider"), temperature=0
             )
 
-            configurable_model_with_tools = configurable_model.bind_tools([GetWeather, GetPopulation])
+            configurable_model_with_tools = configurable_model.bind_tools(
+                [GetWeather, GetPopulation]
+            )
             configurable_model_with_tools.invoke(
                 "Which city is hotter today and which is bigger: LA or NY?"
             )
             # GPT-4o response with tool calls
 
             configurable_model_with_tools.invoke(
                 "Which city is hotter today and which is bigger: LA or NY?",
-                config={"configurable": {"model": "claude-3-5-sonnet-latest"}}
+                config={"configurable": {"model": "claude-3-5-sonnet-latest"}},
             )
             # Claude-3.5 sonnet response with tools
 

libs/langchain_v1/langchain/embeddings/base.py

@@ -162,17 +162,11 @@ def init_embeddings(
             model.embed_query("Hello, world!")
 
             # Using explicit provider
-            model = init_embeddings(
-                model="text-embedding-3-small",
-                provider="openai"
-            )
+            model = init_embeddings(model="text-embedding-3-small", provider="openai")
             model.embed_documents(["Hello, world!", "Goodbye, world!"])
 
             # With additional parameters
-            model = init_embeddings(
-                "openai:text-embedding-3-small",
-                api_key="sk-..."
-            )
+            model = init_embeddings("openai:text-embedding-3-small", api_key="sk-...")
 
     .. versionadded:: 0.3.9
 

libs/langchain_v1/langchain/storage/encoder_backed.py

@@ -22,15 +22,19 @@ class EncoderBackedStore(BaseStore[K, V]):
 
         import json
 
+
         def key_encoder(key: int) -> str:
             return json.dumps(key)
 
+
         def value_serializer(value: float) -> str:
             return json.dumps(value)
 
+
         def value_deserializer(serialized_value: str) -> float:
             return json.loads(serialized_value)
 
+
         # Create an instance of the abstract store
         abstract_store = MyCustomStore()
 
@@ -39,7 +43,7 @@ def value_deserializer(serialized_value: str) -> float:
             store=abstract_store,
             key_encoder=key_encoder,
             value_serializer=value_serializer,
-            value_deserializer=value_deserializer
+            value_deserializer=value_deserializer,
         )
 
         # Use the encoder-backed store methods

libs/langchain_v1/pyproject.toml

@@ -102,6 +102,9 @@ skip = ".git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package
 ignore-regex = ".*(Stati Uniti|Tense=Pres).*"
 ignore-words-list = "momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogyny,unsecure,damon,crate,aadd,symbl,precesses,accademia,nin"
 
+[tool.ruff.format]
+docstring-code-format = true
+
 [tool.ruff.lint]
 select = [
   "ALL"

libs/langchain_v1/tests/unit_tests/conftest.py

@@ -33,8 +33,7 @@ def pytest_collection_modifyitems(config: pytest.Config, items: Sequence[pytest.
     .. code-block:: python
 
         @pytest.mark.requires("package1", "package2")
-        def test_something():
-            ...
+        def test_something(): ...
 
     """
     # Mapping from the name of a package to whether it is installed or not.