diff --git a/.github/workflows/api_doc_build.yml b/.github/workflows/api_doc_build.yml index c6c4c90edda..e3eab91c962 100644 --- a/.github/workflows/api_doc_build.yml +++ b/.github/workflows/api_doc_build.yml @@ -52,7 +52,6 @@ jobs: run: | # Get unique repositories REPOS=$(echo "$REPOS_UNSORTED" | sort -u) - # Checkout each unique repository for repo in $REPOS; do # Validate repository format (allow any org with proper format) @@ -68,7 +67,6 @@ jobs: echo "Error: Invalid repository name: $REPO_NAME" exit 1 fi - echo "Checking out $repo to $REPO_NAME" git clone --depth 1 https://github.com/$repo.git $REPO_NAME done diff --git a/.github/workflows/check_diffs.yml b/.github/workflows/check_diffs.yml index 73ecb813353..0c81880442d 100644 --- a/.github/workflows/check_diffs.yml +++ b/.github/workflows/check_diffs.yml @@ -30,6 +30,7 @@ jobs: build: name: 'Detect Changes & Set Matrix' runs-on: ubuntu-latest + if: ${{ !contains(github.event.pull_request.labels.*.name, 'ci-ignore') }} steps: - name: '📋 Checkout Code' uses: actions/checkout@v4 diff --git a/.github/workflows/codspeed.yml b/.github/workflows/codspeed.yml index c6836d192c4..6d6025082df 100644 --- a/.github/workflows/codspeed.yml +++ b/.github/workflows/codspeed.yml @@ -20,6 +20,7 @@ jobs: codspeed: name: 'Benchmark' runs-on: ubuntu-latest + if: ${{ !contains(github.event.pull_request.labels.*.name, 'codspeed-ignore') }} strategy: matrix: include: diff --git a/.vscode/settings.json b/.vscode/settings.json index 93dae04eff7..29072f76c0b 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -21,7 +21,7 @@ "[python]": { "editor.formatOnSave": true, "editor.codeActionsOnSave": { - "source.organizeImports": "explicit", + "source.organizeImports.ruff": "explicit", "source.fixAll": "explicit" }, "editor.defaultFormatter": "charliermarsh.ruff" @@ -77,4 +77,6 @@ "editor.tabSize": 2, "editor.insertSpaces": true }, + "python.terminal.activateEnvironment": false, + "python.defaultInterpreterPath": "./.venv/bin/python" } diff --git a/docs/api_reference/create_api_rst.py b/docs/api_reference/create_api_rst.py index f4c5f977b3f..6af4114721b 100644 --- a/docs/api_reference/create_api_rst.py +++ b/docs/api_reference/create_api_rst.py @@ -97,7 +97,7 @@ def _load_module_members(module_path: str, namespace: str) -> ModuleMembers: if type(type_) is typing_extensions._TypedDictMeta: # type: ignore kind: ClassKind = "TypedDict" elif type(type_) is typing._TypedDictMeta: # type: ignore - kind: ClassKind = "TypedDict" + kind = "TypedDict" elif ( issubclass(type_, Runnable) and issubclass(type_, BaseModel) @@ -189,7 +189,7 @@ def _load_package_modules( if isinstance(package_directory, str) else package_directory ) - modules_by_namespace = {} + modules_by_namespace: Dict[str, ModuleMembers] = {} # Get the high level package name package_name = package_path.name @@ -283,7 +283,7 @@ def _construct_doc( .. toctree:: :hidden: :maxdepth: 2 - + """ index_autosummary = """ """ @@ -365,9 +365,9 @@ def _construct_doc( module_doc += f"""\ :template: {template} - + {class_["qualified_name"]} - + """ index_autosummary += f""" {class_["qualified_name"]} @@ -550,8 +550,8 @@ def _build_index(dirs: List[str]) -> None: integrations = sorted(dir_ for dir_ in dirs if dir_ not in main_) doc = """# LangChain Python API Reference -Welcome to the LangChain Python API reference. This is a reference for all -`langchain-x` packages. +Welcome to the LangChain Python API reference. This is a reference for all +`langchain-x` packages. For user guides see [https://python.langchain.com](https://python.langchain.com). diff --git a/docs/docs/contributing/how_to/testing.mdx b/docs/docs/contributing/how_to/testing.mdx index cc5a1155c32..853ad44e7e3 100644 --- a/docs/docs/contributing/how_to/testing.mdx +++ b/docs/docs/contributing/how_to/testing.mdx @@ -124,6 +124,47 @@ start "" htmlcov/index.html || open htmlcov/index.html ``` +## Snapshot Testing + +Some tests use [syrupy](https://github.com/tophat/syrupy) for snapshot testing, which captures the output of functions and compares them to stored snapshots. This is particularly useful for testing JSON schema generation and other structured outputs. + +### Updating Snapshots + +To update snapshots when the expected output has legitimately changed: + +```bash +uv run --group test pytest path/to/test.py --snapshot-update +``` + +### Pydantic Version Compatibility Issues + +Pydantic generates different JSON schemas across versions, which can cause snapshot test failures in CI when tests run with different Pydantic versions than what was used to generate the snapshots. + +**Symptoms:** +- CI fails with snapshot mismatches showing differences like missing or extra fields. +- Tests pass locally but fail in CI with different Pydantic versions + +**Solution:** +Locally update snapshots using the same Pydantic version that CI uses: + +1. **Identify the failing Pydantic version** from CI logs (e.g., `2.7.0`, `2.8.0`, `2.9.0`) + +2. **Update snapshots with that version:** + ```bash + uv run --with "pydantic==2.9.0" --group test pytest tests/unit_tests/path/to/test.py::test_name --snapshot-update + ``` + +3. **Verify compatibility across supported versions:** + ```bash + # Test with the version you used to update + uv run --with "pydantic==2.9.0" --group test pytest tests/unit_tests/path/to/test.py::test_name + + # Test with other supported versions + uv run --with "pydantic==2.8.0" --group test pytest tests/unit_tests/path/to/test.py::test_name + ``` + +**Note:** Some tests use `@pytest.mark.skipif` decorators to only run with specific Pydantic version ranges (e.g., `PYDANTIC_VERSION_AT_LEAST_210`). Make sure to understand these constraints when updating snapshots. + ## Coverage Code coverage (i.e. the amount of code that is covered by unit tests) helps identify areas of the code that are potentially more or less brittle. diff --git a/libs/core/langchain_core/_api/beta_decorator.py b/libs/core/langchain_core/_api/beta_decorator.py index f4a9260c1bc..76955b435f4 100644 --- a/libs/core/langchain_core/_api/beta_decorator.py +++ b/libs/core/langchain_core/_api/beta_decorator.py @@ -144,7 +144,7 @@ def beta( obj.__init__ = functools.wraps(obj.__init__)( # type: ignore[misc] warn_if_direct_instance ) - return cast("T", obj) + return obj elif isinstance(obj, property): if not _obj_type: diff --git a/libs/core/langchain_core/_api/deprecation.py b/libs/core/langchain_core/_api/deprecation.py index 762c76f7845..195812236c6 100644 --- a/libs/core/langchain_core/_api/deprecation.py +++ b/libs/core/langchain_core/_api/deprecation.py @@ -225,7 +225,7 @@ def deprecated( obj.__init__ = functools.wraps(obj.__init__)( # type: ignore[misc] warn_if_direct_instance ) - return cast("T", obj) + return obj elif isinstance(obj, FieldInfoV1): wrapped = None diff --git a/libs/core/langchain_core/messages/modifier.py b/libs/core/langchain_core/messages/modifier.py index 08b7e79b69c..5f1602a4908 100644 --- a/libs/core/langchain_core/messages/modifier.py +++ b/libs/core/langchain_core/messages/modifier.py @@ -13,7 +13,7 @@ class RemoveMessage(BaseMessage): def __init__( self, - id: str, # noqa: A002 + id: str, **kwargs: Any, ) -> None: """Create a RemoveMessage. diff --git a/libs/core/langchain_core/messages/tool.py b/libs/core/langchain_core/messages/tool.py index 1f8a519a7dc..10e2d18917a 100644 --- a/libs/core/langchain_core/messages/tool.py +++ b/libs/core/langchain_core/messages/tool.py @@ -212,7 +212,7 @@ def tool_call( *, name: str, args: dict[str, Any], - id: Optional[str], # noqa: A002 + id: Optional[str], ) -> ToolCall: """Create a tool call. @@ -260,7 +260,7 @@ def tool_call_chunk( *, name: Optional[str] = None, args: Optional[str] = None, - id: Optional[str] = None, # noqa: A002 + id: Optional[str] = None, index: Optional[int] = None, ) -> ToolCallChunk: """Create a tool call chunk. @@ -298,7 +298,7 @@ def invalid_tool_call( *, name: Optional[str] = None, args: Optional[str] = None, - id: Optional[str] = None, # noqa: A002 + id: Optional[str] = None, error: Optional[str] = None, ) -> InvalidToolCall: """Create an invalid tool call. diff --git a/libs/core/langchain_core/messages/utils.py b/libs/core/langchain_core/messages/utils.py index 37f16c632bd..e84dc6c0191 100644 --- a/libs/core/langchain_core/messages/utils.py +++ b/libs/core/langchain_core/messages/utils.py @@ -213,7 +213,7 @@ def _create_message_from_message_type( name: Optional[str] = None, tool_call_id: Optional[str] = None, tool_calls: Optional[list[dict[str, Any]]] = None, - id: Optional[str] = None, # noqa: A002 + id: Optional[str] = None, **additional_kwargs: Any, ) -> BaseMessage: """Create a message from a message type and content string. diff --git a/libs/core/langchain_core/output_parsers/transform.py b/libs/core/langchain_core/output_parsers/transform.py index 876e66b5556..0c864805b93 100644 --- a/libs/core/langchain_core/output_parsers/transform.py +++ b/libs/core/langchain_core/output_parsers/transform.py @@ -32,7 +32,7 @@ class BaseTransformOutputParser(BaseOutputParser[T]): def _transform( self, - input: Iterator[Union[str, BaseMessage]], # noqa: A002 + input: Iterator[Union[str, BaseMessage]], ) -> Iterator[T]: for chunk in input: if isinstance(chunk, BaseMessage): @@ -42,7 +42,7 @@ class BaseTransformOutputParser(BaseOutputParser[T]): async def _atransform( self, - input: AsyncIterator[Union[str, BaseMessage]], # noqa: A002 + input: AsyncIterator[Union[str, BaseMessage]], ) -> AsyncIterator[T]: async for chunk in input: if isinstance(chunk, BaseMessage): diff --git a/libs/core/langchain_core/output_parsers/xml.py b/libs/core/langchain_core/output_parsers/xml.py index c6a46f3de1a..483d9871c1a 100644 --- a/libs/core/langchain_core/output_parsers/xml.py +++ b/libs/core/langchain_core/output_parsers/xml.py @@ -105,10 +105,11 @@ class _StreamingParser: self.buffer = "" # yield all events try: - for event, elem in self.pull_parser.read_events(): + events = self.pull_parser.read_events() + for event, elem in events: # type: ignore[misc] if event == "start": # update current path - self.current_path.append(elem.tag) + self.current_path.append(elem.tag) # type: ignore[union-attr] self.current_path_has_children = False elif event == "end": # remove last element from current path @@ -116,7 +117,7 @@ class _StreamingParser: self.current_path.pop() # yield element if not self.current_path_has_children: - yield nested_element(self.current_path, elem) + yield nested_element(self.current_path, elem) # type: ignore[arg-type] # prevent yielding of parent element if self.current_path: self.current_path_has_children = True diff --git a/libs/core/langchain_core/runnables/base.py b/libs/core/langchain_core/runnables/base.py index 6e0be997af6..38da82fcda3 100644 --- a/libs/core/langchain_core/runnables/base.py +++ b/libs/core/langchain_core/runnables/base.py @@ -1,4 +1,4 @@ -"""Base classes and utilities for Runnables.""" +"""Base classes and utilities for ``Runnable``s.""" from __future__ import annotations @@ -111,17 +111,17 @@ class Runnable(ABC, Generic[Input, Output]): Key Methods =========== - - **invoke/ainvoke**: Transforms a single input into an output. - - **batch/abatch**: Efficiently transforms multiple inputs into outputs. - - **stream/astream**: Streams output from a single input as it's produced. - - **astream_log**: Streams output and selected intermediate results from an input. + - **``invoke``/``ainvoke``**: Transforms a single input into an output. + - **``batch``/``abatch``**: Efficiently transforms multiple inputs into outputs. + - **``stream``/``astream``**: Streams output from a single input as it's produced. + - **``astream_log``**: Streams output and selected intermediate results from an input. Built-in optimizations: - **Batch**: By default, batch runs invoke() in parallel using a thread pool executor. Override to optimize batching. - - **Async**: Methods with "a" suffix are asynchronous. By default, they execute + - **Async**: Methods with ``'a'`` suffix are asynchronous. By default, they execute the sync counterpart using asyncio's thread pool. Override for native async. @@ -129,24 +129,24 @@ class Runnable(ABC, Generic[Input, Output]): execution, add tags and metadata for tracing and debugging etc. Runnables expose schematic information about their input, output and config via - the input_schema property, the output_schema property and config_schema method. + the ``input_schema`` property, the ``output_schema`` property and ``config_schema`` method. LCEL and Composition ==================== - The LangChain Expression Language (LCEL) is a declarative way to compose Runnables + The LangChain Expression Language (LCEL) is a declarative way to compose ``Runnables`` into chains. Any chain constructed this way will automatically have sync, async, batch, and streaming support. - The main composition primitives are RunnableSequence and RunnableParallel. + The main composition primitives are ``RunnableSequence`` and ``RunnableParallel``. - **RunnableSequence** invokes a series of runnables sequentially, with + **``RunnableSequence``** invokes a series of runnables sequentially, with one Runnable's output serving as the next's input. Construct using - the `|` operator or by passing a list of runnables to RunnableSequence. + the ``|`` operator or by passing a list of runnables to ``RunnableSequence``. - **RunnableParallel** invokes runnables concurrently, providing the same input + **``RunnableParallel``** invokes runnables concurrently, providing the same input to each. Construct it using a dict literal within a sequence or by passing a - dict to RunnableParallel. + dict to ``RunnableParallel``. For example, @@ -171,11 +171,11 @@ class Runnable(ABC, Generic[Input, Output]): Standard Methods ================ - All Runnables expose additional methods that can be used to modify their behavior + All ``Runnable``s expose additional methods that can be used to modify their behavior (e.g., add a retry policy, add lifecycle listeners, make them configurable, etc.). - These methods will work on any Runnable, including Runnable chains constructed - by composing other Runnables. See the individual methods for details. + These methods will work on any ``Runnable``, including ``Runnable`` chains constructed + by composing other ``Runnable``s. See the individual methods for details. For example, @@ -232,17 +232,17 @@ class Runnable(ABC, Generic[Input, Output]): config={'callbacks': [ConsoleCallbackHandler()]} ) - For a UI (and much more) checkout LangSmith: https://docs.smith.langchain.com/ + For a UI (and much more) checkout `LangSmith `__. """ # noqa: E501 name: Optional[str] - """The name of the Runnable. Used for debugging and tracing.""" + """The name of the ``Runnable``. Used for debugging and tracing.""" def get_name( self, suffix: Optional[str] = None, *, name: Optional[str] = None ) -> str: - """Get the name of the Runnable.""" + """Get the name of the ``Runnable``.""" if name: name_ = name elif hasattr(self, "name") and self.name: @@ -273,7 +273,7 @@ class Runnable(ABC, Generic[Input, Output]): @property def InputType(self) -> type[Input]: # noqa: N802 - """The type of input this Runnable accepts specified as a type annotation.""" + """The type of input this ``Runnable`` accepts specified as a type annotation.""" # noqa: E501 # First loop through all parent classes and if any of them is # a pydantic model, we will pick up the generic parameterization # from that model via the __pydantic_generic_metadata__ attribute. @@ -299,7 +299,7 @@ class Runnable(ABC, Generic[Input, Output]): @property def OutputType(self) -> type[Output]: # noqa: N802 - """The type of output this Runnable produces specified as a type annotation.""" + """The type of output this ``Runnable`` produces specified as a type annotation.""" # noqa: E501 # First loop through bases -- this will help generic # any pydantic models. for base in self.__class__.mro(): @@ -321,7 +321,7 @@ class Runnable(ABC, Generic[Input, Output]): @property def input_schema(self) -> type[BaseModel]: - """The type of input this Runnable accepts specified as a pydantic model.""" + """The type of input this ``Runnable`` accepts specified as a pydantic model.""" return self.get_input_schema() def get_input_schema( @@ -330,9 +330,9 @@ class Runnable(ABC, Generic[Input, Output]): ) -> type[BaseModel]: """Get a pydantic model that can be used to validate input to the Runnable. - Runnables that leverage the configurable_fields and configurable_alternatives - methods will have a dynamic input schema that depends on which - configuration the Runnable is invoked with. + ``Runnable``s that leverage the ``configurable_fields`` and + ``configurable_alternatives`` methods will have a dynamic input schema that + depends on which configuration the ``Runnable`` is invoked with. This method allows to get an input schema for a specific configuration. @@ -367,13 +367,13 @@ class Runnable(ABC, Generic[Input, Output]): def get_input_jsonschema( self, config: Optional[RunnableConfig] = None ) -> dict[str, Any]: - """Get a JSON schema that represents the input to the Runnable. + """Get a JSON schema that represents the input to the ``Runnable``. Args: config: A config to use when generating the schema. Returns: - A JSON schema that represents the input to the Runnable. + A JSON schema that represents the input to the ``Runnable``. Example: @@ -395,18 +395,18 @@ class Runnable(ABC, Generic[Input, Output]): @property def output_schema(self) -> type[BaseModel]: - """The type of output this Runnable produces specified as a pydantic model.""" + """The type of output this ``Runnable`` produces specified as a pydantic model.""" # noqa: E501 return self.get_output_schema() def get_output_schema( self, config: Optional[RunnableConfig] = None, # noqa: ARG002 ) -> type[BaseModel]: - """Get a pydantic model that can be used to validate output to the Runnable. + """Get a pydantic model that can be used to validate output to the ``Runnable``. - Runnables that leverage the configurable_fields and configurable_alternatives - methods will have a dynamic output schema that depends on which - configuration the Runnable is invoked with. + ``Runnable``s that leverage the ``configurable_fields`` and + ``configurable_alternatives`` methods will have a dynamic output schema that + depends on which configuration the ``Runnable`` is invoked with. This method allows to get an output schema for a specific configuration. @@ -441,13 +441,13 @@ class Runnable(ABC, Generic[Input, Output]): def get_output_jsonschema( self, config: Optional[RunnableConfig] = None ) -> dict[str, Any]: - """Get a JSON schema that represents the output of the Runnable. + """Get a JSON schema that represents the output of the ``Runnable``. Args: config: A config to use when generating the schema. Returns: - A JSON schema that represents the output of the Runnable. + A JSON schema that represents the output of the ``Runnable``. Example: @@ -469,22 +469,23 @@ class Runnable(ABC, Generic[Input, Output]): @property def config_specs(self) -> list[ConfigurableFieldSpec]: - """List configurable fields for this Runnable.""" + """List configurable fields for this ``Runnable``.""" return [] def config_schema( self, *, include: Optional[Sequence[str]] = None ) -> type[BaseModel]: - """The type of config this Runnable accepts specified as a pydantic model. + """The type of config this ``Runnable`` accepts specified as a pydantic model. - To mark a field as configurable, see the `configurable_fields` - and `configurable_alternatives` methods. + To mark a field as configurable, see the ``configurable_fields`` + and ``configurable_alternatives`` methods. Args: include: A list of fields to include in the config schema. Returns: A pydantic model that can be used to validate config. + """ include = include or [] config_specs = self.config_specs @@ -519,20 +520,21 @@ class Runnable(ABC, Generic[Input, Output]): def get_config_jsonschema( self, *, include: Optional[Sequence[str]] = None ) -> dict[str, Any]: - """Get a JSON schema that represents the config of the Runnable. + """Get a JSON schema that represents the config of the ``Runnable``. Args: include: A list of fields to include in the config schema. Returns: - A JSON schema that represents the config of the Runnable. + A JSON schema that represents the config of the ``Runnable``. .. versionadded:: 0.3.0 + """ return self.config_schema(include=include).model_json_schema() def get_graph(self, config: Optional[RunnableConfig] = None) -> Graph: - """Return a graph representation of this Runnable.""" + """Return a graph representation of this ``Runnable``.""" graph = Graph() try: input_node = graph.add_node(self.get_input_schema(config)) @@ -552,7 +554,7 @@ class Runnable(ABC, Generic[Input, Output]): def get_prompts( self, config: Optional[RunnableConfig] = None ) -> list[BasePromptTemplate]: - """Return a list of prompts used by this Runnable.""" + """Return a list of prompts used by this ``Runnable``.""" from langchain_core.prompts.base import BasePromptTemplate return [ @@ -571,7 +573,7 @@ class Runnable(ABC, Generic[Input, Output]): Mapping[str, Union[Runnable[Any, Other], Callable[[Any], Other], Any]], ], ) -> RunnableSerializable[Input, Other]: - """Compose this Runnable with another object to create a RunnableSequence.""" + """Compose this ``Runnable`` with another object to create a ``RunnableSequence``.""" # noqa: E501 return RunnableSequence(self, coerce_to_runnable(other)) def __ror__( @@ -584,7 +586,7 @@ class Runnable(ABC, Generic[Input, Output]): Mapping[str, Union[Runnable[Other, Any], Callable[[Other], Any], Any]], ], ) -> RunnableSerializable[Other, Output]: - """Compose this Runnable with another object to create a RunnableSequence.""" + """Compose this ``Runnable`` with another object to create a ``RunnableSequence``.""" # noqa: E501 return RunnableSequence(coerce_to_runnable(other), self) def pipe( @@ -592,9 +594,9 @@ class Runnable(ABC, Generic[Input, Output]): *others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None, ) -> RunnableSerializable[Input, Other]: - """Compose this Runnable with Runnable-like objects to make a RunnableSequence. + """Compose this ``Runnable`` with ``Runnable``-like objects to make a ``RunnableSequence``. - Equivalent to `RunnableSequence(self, *others)` or `self | others[0] | ...` + Equivalent to ``RunnableSequence(self, *others)`` or ``self | others[0] | ...`` Example: .. code-block:: python @@ -621,11 +623,11 @@ class Runnable(ABC, Generic[Input, Output]): await sequence.abatch([1, 2, 3]) # -> [4, 6, 8] - """ + """ # noqa: E501 return RunnableSequence(self, *others, name=name) def pick(self, keys: Union[str, list[str]]) -> RunnableSerializable[Any, Any]: - """Pick keys from the output dict of this Runnable. + """Pick keys from the output dict of this ``Runnable``. Pick single key: .. code-block:: python @@ -688,9 +690,9 @@ class Runnable(ABC, Generic[Input, Output]): ], ], ) -> RunnableSerializable[Any, Any]: - """Assigns new fields to the dict output of this Runnable. + """Assigns new fields to the dict output of this ``Runnable``. - Returns a new Runnable. + Returns a new ``Runnable``. .. code-block:: python @@ -728,36 +730,38 @@ class Runnable(ABC, Generic[Input, Output]): @abstractmethod def invoke( self, - input: Input, # noqa: A002 + input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> Output: """Transform a single input into an output. Args: - input: The input to the Runnable. - config: A config to use when invoking the Runnable. + input: The input to the ``Runnable``. + config: A config to use when invoking the ``Runnable``. The config supports standard keys like ``'tags'``, ``'metadata'`` for tracing purposes, ``'max_concurrency'`` for controlling how much work to - do in parallel, and other keys. Please refer to the RunnableConfig + do in parallel, and other keys. Please refer to the ``RunnableConfig`` for more details. Defaults to None. Returns: - The output of the Runnable. + The output of the ``Runnable``. + """ async def ainvoke( self, - input: Input, # noqa: A002 + input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> Output: - """Default implementation of ainvoke, calls invoke from a thread. + """Default implementation of ``ainvoke``, calls ``invoke`` from a thread. The default implementation allows usage of async code even if - the Runnable did not implement a native async version of invoke. + the ``Runnable`` did not implement a native async version of ``invoke``. Subclasses should override this method if they can run asynchronously. + """ return await run_in_executor(config, self.invoke, input, config, **kwargs) @@ -774,7 +778,8 @@ class Runnable(ABC, Generic[Input, Output]): The default implementation of batch works well for IO bound runnables. Subclasses should override this method if they can batch more efficiently; - e.g., if the underlying Runnable uses an API which supports a batch mode. + e.g., if the underlying ``Runnable`` uses an API which supports a batch mode. + """ if not inputs: return [] @@ -825,9 +830,10 @@ class Runnable(ABC, Generic[Input, Output]): return_exceptions: bool = False, **kwargs: Optional[Any], ) -> Iterator[tuple[int, Union[Output, Exception]]]: - """Run invoke in parallel on a list of inputs. + """Run ``invoke`` in parallel on a list of inputs. Yields results as they complete. + """ if not inputs: return @@ -876,26 +882,27 @@ class Runnable(ABC, Generic[Input, Output]): return_exceptions: bool = False, **kwargs: Optional[Any], ) -> list[Output]: - """Default implementation runs ainvoke in parallel using asyncio.gather. + """Default implementation runs ``ainvoke`` in parallel using ``asyncio.gather``. - The default implementation of batch works well for IO bound runnables. + The default implementation of ``batch`` works well for IO bound runnables. Subclasses should override this method if they can batch more efficiently; - e.g., if the underlying Runnable uses an API which supports a batch mode. + e.g., if the underlying ``Runnable`` uses an API which supports a batch mode. Args: - inputs: A list of inputs to the Runnable. - config: A config to use when invoking the Runnable. + inputs: A list of inputs to the ``Runnable``. + config: A config to use when invoking the ``Runnable``. The config supports standard keys like ``'tags'``, ``'metadata'`` for tracing purposes, ``'max_concurrency'`` for controlling how much work to - do in parallel, and other keys. Please refer to the RunnableConfig + do in parallel, and other keys. Please refer to the ``RunnableConfig`` for more details. Defaults to None. return_exceptions: Whether to return exceptions instead of raising them. Defaults to False. - kwargs: Additional keyword arguments to pass to the Runnable. + kwargs: Additional keyword arguments to pass to the ``Runnable``. Returns: - A list of outputs from the Runnable. + A list of outputs from the ``Runnable``. + """ if not inputs: return [] @@ -944,23 +951,24 @@ class Runnable(ABC, Generic[Input, Output]): return_exceptions: bool = False, **kwargs: Optional[Any], ) -> AsyncIterator[tuple[int, Union[Output, Exception]]]: - """Run ainvoke in parallel on a list of inputs. + """Run ``ainvoke`` in parallel on a list of inputs. Yields results as they complete. Args: - inputs: A list of inputs to the Runnable. - config: A config to use when invoking the Runnable. + inputs: A list of inputs to the ``Runnable``. + config: A config to use when invoking the ``Runnable``. The config supports standard keys like ``'tags'``, ``'metadata'`` for tracing purposes, ``'max_concurrency'`` for controlling how much work to - do in parallel, and other keys. Please refer to the RunnableConfig + do in parallel, and other keys. Please refer to the ``RunnableConfig`` for more details. Defaults to None. return_exceptions: Whether to return exceptions instead of raising them. Defaults to False. - kwargs: Additional keyword arguments to pass to the Runnable. + kwargs: Additional keyword arguments to pass to the ``Runnable``. Yields: - A tuple of the index of the input and the output from the Runnable. + A tuple of the index of the input and the output from the ``Runnable``. + """ if not inputs: return @@ -996,41 +1004,43 @@ class Runnable(ABC, Generic[Input, Output]): def stream( self, - input: Input, # noqa: A002 + input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any], ) -> Iterator[Output]: - """Default implementation of stream, which calls invoke. + """Default implementation of ``stream``, which calls ``invoke``. Subclasses should override this method if they support streaming output. Args: - input: The input to the Runnable. - config: The config to use for the Runnable. Defaults to None. - kwargs: Additional keyword arguments to pass to the Runnable. + input: The input to the ``Runnable``. + config: The config to use for the ``Runnable``. Defaults to None. + kwargs: Additional keyword arguments to pass to the ``Runnable``. Yields: - The output of the Runnable. + The output of the ``Runnable``. + """ yield self.invoke(input, config, **kwargs) async def astream( self, - input: Input, # noqa: A002 + input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any], ) -> AsyncIterator[Output]: - """Default implementation of astream, which calls ainvoke. + """Default implementation of ``astream``, which calls ``ainvoke``. Subclasses should override this method if they support streaming output. Args: - input: The input to the Runnable. - config: The config to use for the Runnable. Defaults to None. - kwargs: Additional keyword arguments to pass to the Runnable. + input: The input to the ``Runnable``. + config: The config to use for the ``Runnable``. Defaults to None. + kwargs: Additional keyword arguments to pass to the ``Runnable``. Yields: - The output of the Runnable. + The output of the ``Runnable``. + """ yield await self.ainvoke(input, config, **kwargs) @@ -1070,7 +1080,7 @@ class Runnable(ABC, Generic[Input, Output]): async def astream_log( self, - input: Any, # noqa: A002 + input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, @@ -1083,7 +1093,7 @@ class Runnable(ABC, Generic[Input, Output]): exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any, ) -> Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]: - """Stream all output from a Runnable, as reported to the callback system. + """Stream all output from a ``Runnable``, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc. @@ -1094,20 +1104,21 @@ class Runnable(ABC, Generic[Input, Output]): The Jsonpatch ops can be applied in order to construct state. Args: - input: The input to the Runnable. - config: The config to use for the Runnable. + input: The input to the ``Runnable``. + config: The config to use for the ``Runnable``. diff: Whether to yield diffs between each step or the current state. - with_streamed_output_list: Whether to yield the streamed_output list. + with_streamed_output_list: Whether to yield the ``streamed_output`` list. include_names: Only include logs with these names. include_types: Only include logs with these types. include_tags: Only include logs with these tags. exclude_names: Exclude logs with these names. exclude_types: Exclude logs with these types. exclude_tags: Exclude logs with these tags. - kwargs: Additional keyword arguments to pass to the Runnable. + kwargs: Additional keyword arguments to pass to the ``Runnable``. Yields: - A RunLogPatch or RunLog object. + A ``RunLogPatch`` or ``RunLog`` object. + """ from langchain_core.tracers.log_stream import ( LogStreamCallbackHandler, @@ -1141,7 +1152,7 @@ class Runnable(ABC, Generic[Input, Output]): async def astream_events( self, - input: Any, # noqa: A002 + input: Any, config: Optional[RunnableConfig] = None, *, version: Literal["v1", "v2"] = "v2", @@ -1155,73 +1166,73 @@ class Runnable(ABC, Generic[Input, Output]): ) -> AsyncIterator[StreamEvent]: """Generate a stream of events. - Use to create an iterator over StreamEvents that provide real-time information - about the progress of the Runnable, including StreamEvents from intermediate + Use to create an iterator over ``StreamEvents`` that provide real-time information + about the progress of the ``Runnable``, including ``StreamEvents`` from intermediate results. - A StreamEvent is a dictionary with the following schema: + A ``StreamEvent`` is a dictionary with the following schema: - ``event``: **str** - Event names are of the format: - on_[runnable_type]_(start|stream|end). - - ``name``: **str** - The name of the Runnable that generated the event. + ``on_[runnable_type]_(start|stream|end)``. + - ``name``: **str** - The name of the ``Runnable`` that generated the event. - ``run_id``: **str** - randomly generated ID associated with the given - execution of the Runnable that emitted the event. A child Runnable that gets - invoked as part of the execution of a parent Runnable is assigned its own + execution of the ``Runnable`` that emitted the event. A child ``Runnable`` that gets + invoked as part of the execution of a parent ``Runnable`` is assigned its own unique ID. - ``parent_ids``: **list[str]** - The IDs of the parent runnables that generated - the event. The root Runnable will have an empty list. The order of the parent + the event. The root ``Runnable`` will have an empty list. The order of the parent IDs is from the root to the immediate parent. Only available for v2 version of the API. The v1 version of the API will return an empty list. - - ``tags``: **Optional[list[str]]** - The tags of the Runnable that generated + - ``tags``: **Optional[list[str]]** - The tags of the ``Runnable`` that generated the event. - - ``metadata``: **Optional[dict[str, Any]]** - The metadata of the Runnable that + - ``metadata``: **Optional[dict[str, Any]]** - The metadata of the ``Runnable`` that generated the event. - ``data``: **dict[str, Any]** - Below is a table that illustrates some events that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table. - .. NOTE:: This reference table is for the V2 version of the schema. + .. note:: + This reference table is for the v2 version of the schema. - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | event | name | chunk | input | output | - +======================+==================+=================================+===============================================+=================================================+ - | on_chat_model_start | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | on_chat_model_stream | [model name] | AIMessageChunk(content="hello") | | | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | on_chat_model_end | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | AIMessageChunk(content="hello world") | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | on_llm_start | [model name] | | {'input': 'hello'} | | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | on_llm_stream | [model name] | 'Hello' | | | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | on_llm_end | [model name] | | 'Hello human!' | | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | on_chain_start | format_docs | | | | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | on_chain_stream | format_docs | "hello world!, goodbye world!" | | | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | on_chain_end | format_docs | | [Document(...)] | "hello world!, goodbye world!" | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | on_tool_start | some_tool | | {"x": 1, "y": "2"} | | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | on_tool_end | some_tool | | | {"x": 1, "y": "2"} | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | on_retriever_start | [retriever name] | | {"query": "hello"} | | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | on_retriever_end | [retriever name] | | {"query": "hello"} | [Document(...), ..] | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | on_prompt_start | [template_name] | | {"question": "hello"} | | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ - | on_prompt_end | [template_name] | | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) | - +----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+ + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | event | name | chunk | input | output | + +==========================+==================+=====================================+===================================================+=====================================================+ + | ``on_chat_model_start`` | [model name] | | ``{"messages": [[SystemMessage, HumanMessage]]}`` | | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | ``on_chat_model_stream`` | [model name] | ``AIMessageChunk(content="hello")`` | | | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | ``on_chat_model_end`` | [model name] | | ``{"messages": [[SystemMessage, HumanMessage]]}`` | ``AIMessageChunk(content="hello world")`` | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | ``on_llm_start`` | [model name] | | ``{'input': 'hello'}`` | | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | ``on_llm_stream`` | [model name] | ``'Hello' `` | | | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | ``on_llm_end`` | [model name] | | ``'Hello human!'`` | | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | ``on_chain_start`` | format_docs | | | | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | ``on_chain_stream`` | format_docs | ``'hello world!, goodbye world!'`` | | | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | ``on_chain_end`` | format_docs | | ``[Document(...)]`` | ``'hello world!, goodbye world!'`` | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | ``on_tool_start`` | some_tool | | ``{"x": 1, "y": "2"}`` | | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | ``on_tool_end`` | some_tool | | | ``{"x": 1, "y": "2"}`` | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | ``on_retriever_start`` | [retriever name] | | ``{"query": "hello"}`` | | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | ``on_retriever_end`` | [retriever name] | | ``{"query": "hello"}`` | ``[Document(...), ..]`` | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | ``on_prompt_start`` | [template_name] | | ``{"question": "hello"}`` | | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ + | ``on_prompt_end`` | [template_name] | | ``{"question": "hello"}`` | ``ChatPromptValue(messages: [SystemMessage, ...])`` | + +--------------------------+------------------+-------------------------------------+---------------------------------------------------+-----------------------------------------------------+ In addition to the standard events, users can also dispatch custom events (see example below). - Custom events will be only be surfaced with in the `v2` version of the API! + Custom events will be only be surfaced with in the v2 version of the API! A custom event has following format: @@ -1235,7 +1246,7 @@ class Runnable(ABC, Generic[Input, Output]): Here are declarations associated with the standard events shown above: - `format_docs`: + ``format_docs``: .. code-block:: python @@ -1245,7 +1256,7 @@ class Runnable(ABC, Generic[Input, Output]): format_docs = RunnableLambda(format_docs) - `some_tool`: + ``some_tool``: .. code-block:: python @@ -1254,7 +1265,7 @@ class Runnable(ABC, Generic[Input, Output]): '''Some_tool.''' return {"x": x, "y": y} - `prompt`: + ``prompt``: .. code-block:: python @@ -1339,29 +1350,29 @@ class Runnable(ABC, Generic[Input, Output]): print(event) Args: - input: The input to the Runnable. - config: The config to use for the Runnable. - version: The version of the schema to use either `v2` or `v1`. - Users should use `v2`. - `v1` is for backwards compatibility and will be deprecated + input: The input to the ``Runnable``. + config: The config to use for the ``Runnable``. + version: The version of the schema to use either ``'v2'`` or ``'v1'``. + Users should use ``'v2'``. + ``'v1'`` is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. - custom events will only be surfaced in `v2`. - include_names: Only include events from runnables with matching names. - include_types: Only include events from runnables with matching types. - include_tags: Only include events from runnables with matching tags. - exclude_names: Exclude events from runnables with matching names. - exclude_types: Exclude events from runnables with matching types. - exclude_tags: Exclude events from runnables with matching tags. - kwargs: Additional keyword arguments to pass to the Runnable. - These will be passed to astream_log as this implementation - of astream_events is built on top of astream_log. + custom events will only be surfaced in ``'v2'``. + include_names: Only include events from ``Runnables`` with matching names. + include_types: Only include events from ``Runnables`` with matching types. + include_tags: Only include events from ``Runnables`` with matching tags. + exclude_names: Exclude events from ``Runnables`` with matching names. + exclude_types: Exclude events from ``Runnables`` with matching types. + exclude_tags: Exclude events from ``Runnables`` with matching tags. + kwargs: Additional keyword arguments to pass to the ``Runnable``. + These will be passed to ``astream_log`` as this implementation + of ``astream_events`` is built on top of ``astream_log``. Yields: - An async stream of StreamEvents. + An async stream of ``StreamEvents``. Raises: - NotImplementedError: If the version is not `v1` or `v2`. + NotImplementedError: If the version is not ``'v1'`` or ``'v2'``. """ # noqa: E501 from langchain_core.tracers.event_stream import ( @@ -1407,23 +1418,24 @@ class Runnable(ABC, Generic[Input, Output]): def transform( self, - input: Iterator[Input], # noqa: A002 + input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any], ) -> Iterator[Output]: - """Default implementation of transform, which buffers input and calls astream. + """Default implementation of transform, which buffers input and calls ``astream``. Subclasses should override this method if they can start producing output while input is still being generated. Args: - input: An iterator of inputs to the Runnable. - config: The config to use for the Runnable. Defaults to None. - kwargs: Additional keyword arguments to pass to the Runnable. + input: An iterator of inputs to the ``Runnable``. + config: The config to use for the ``Runnable``. Defaults to None. + kwargs: Additional keyword arguments to pass to the ``Runnable``. Yields: - The output of the Runnable. - """ + The output of the ``Runnable``. + + """ # noqa: E501 final: Input got_first_val = False @@ -1449,23 +1461,24 @@ class Runnable(ABC, Generic[Input, Output]): async def atransform( self, - input: AsyncIterator[Input], # noqa: A002 + input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any], ) -> AsyncIterator[Output]: - """Default implementation of atransform, which buffers input and calls astream. + """Default implementation of atransform, which buffers input and calls ``astream``. Subclasses should override this method if they can start producing output while input is still being generated. Args: - input: An async iterator of inputs to the Runnable. - config: The config to use for the Runnable. Defaults to None. - kwargs: Additional keyword arguments to pass to the Runnable. + input: An async iterator of inputs to the ``Runnable``. + config: The config to use for the ``Runnable``. Defaults to None. + kwargs: Additional keyword arguments to pass to the ``Runnable``. Yields: - The output of the Runnable. - """ + The output of the ``Runnable``. + + """ # noqa: E501 final: Input got_first_val = False @@ -1491,16 +1504,16 @@ class Runnable(ABC, Generic[Input, Output]): yield output def bind(self, **kwargs: Any) -> Runnable[Input, Output]: - """Bind arguments to a Runnable, returning a new Runnable. + """Bind arguments to a ``Runnable``, returning a new ``Runnable``. - Useful when a Runnable in a chain requires an argument that is not - in the output of the previous Runnable or included in the user input. + Useful when a ``Runnable`` in a chain requires an argument that is not + in the output of the previous ``Runnable`` or included in the user input. Args: - kwargs: The arguments to bind to the Runnable. + kwargs: The arguments to bind to the ``Runnable``. Returns: - A new Runnable with the arguments bound. + A new ``Runnable`` with the arguments bound. Example: @@ -1538,14 +1551,15 @@ class Runnable(ABC, Generic[Input, Output]): # Sadly Unpack is not well-supported by mypy so this will have to be untyped **kwargs: Any, ) -> Runnable[Input, Output]: - """Bind config to a Runnable, returning a new Runnable. + """Bind config to a ``Runnable``, returning a new ``Runnable``. Args: - config: The config to bind to the Runnable. - kwargs: Additional keyword arguments to pass to the Runnable. + config: The config to bind to the ``Runnable``. + kwargs: Additional keyword arguments to pass to the ``Runnable``. Returns: - A new Runnable with the config bound. + A new ``Runnable`` with the config bound. + """ return RunnableBinding( bound=self, @@ -1569,22 +1583,22 @@ class Runnable(ABC, Generic[Input, Output]): Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]] ] = None, ) -> Runnable[Input, Output]: - """Bind lifecycle listeners to a Runnable, returning a new Runnable. + """Bind lifecycle listeners to a ``Runnable``, returning a new ``Runnable``. - The Run object contains information about the run, including its id, - type, input, output, error, start_time, end_time, and any tags or metadata - added to the run. + The Run object contains information about the run, including its ``id``, + ``type``, ``input``, ``output``, ``error``, ``start_time``, ``end_time``, and + any tags or metadata added to the run. Args: - on_start: Called before the Runnable starts running, with the Run object. - Defaults to None. - on_end: Called after the Runnable finishes running, with the Run object. - Defaults to None. - on_error: Called if the Runnable throws an error, with the Run object. - Defaults to None. + on_start: Called before the ``Runnable`` starts running, with the ``Run`` + object. Defaults to None. + on_end: Called after the ``Runnable`` finishes running, with the ``Run`` + object. Defaults to None. + on_error: Called if the ``Runnable`` throws an error, with the ``Run`` + object. Defaults to None. Returns: - A new Runnable with the listeners bound. + A new ``Runnable`` with the listeners bound. Example: @@ -1636,22 +1650,22 @@ class Runnable(ABC, Generic[Input, Output]): on_end: Optional[AsyncListener] = None, on_error: Optional[AsyncListener] = None, ) -> Runnable[Input, Output]: - """Bind async lifecycle listeners to a Runnable, returning a new Runnable. + """Bind async lifecycle listeners to a ``Runnable``, returning a new ``Runnable``. - The Run object contains information about the run, including its id, - type, input, output, error, start_time, end_time, and any tags or metadata - added to the run. + The Run object contains information about the run, including its ``id``, + ``type``, ``input``, ``output``, ``error``, ``start_time``, ``end_time``, and + any tags or metadata added to the run. Args: - on_start: Called asynchronously before the Runnable starts running, - with the Run object. Defaults to None. - on_end: Called asynchronously after the Runnable finishes running, - with the Run object. Defaults to None. - on_error: Called asynchronously if the Runnable throws an error, - with the Run object. Defaults to None. + on_start: Called asynchronously before the ``Runnable`` starts running, + with the ``Run`` object. Defaults to None. + on_end: Called asynchronously after the ``Runnable`` finishes running, + with the ``Run`` object. Defaults to None. + on_error: Called asynchronously if the ``Runnable`` throws an error, + with the ``Run`` object. Defaults to None. Returns: - A new Runnable with the listeners bound. + A new ``Runnable`` with the listeners bound. Example: @@ -1702,7 +1716,7 @@ class Runnable(ABC, Generic[Input, Output]): on end callback ends at 2025-03-01T07:05:29.883893+00:00 on end callback ends at 2025-03-01T07:05:30.884831+00:00 - """ + """ # noqa: E501 from langchain_core.tracers.root_listeners import AsyncRootListenersTracer return RunnableBinding( @@ -1727,11 +1741,11 @@ class Runnable(ABC, Generic[Input, Output]): input_type: Optional[type[Input]] = None, output_type: Optional[type[Output]] = None, ) -> Runnable[Input, Output]: - """Bind input and output types to a Runnable, returning a new Runnable. + """Bind input and output types to a ``Runnable``, returning a new ``Runnable``. Args: - input_type: The input type to bind to the Runnable. Defaults to None. - output_type: The output type to bind to the Runnable. Defaults to None. + input_type: The input type to bind to the ``Runnable``. Defaults to None. + output_type: The output type to bind to the ``Runnable``. Defaults to None. Returns: A new Runnable with the types bound. @@ -1810,12 +1824,12 @@ class Runnable(ABC, Generic[Input, Output]): ) def map(self) -> Runnable[list[Input], list[Output]]: - """Return a new Runnable that maps a list of inputs to a list of outputs. + """Return a new ``Runnable`` that maps a list of inputs to a list of outputs. - Calls invoke() with each input. + Calls ``invoke`` with each input. Returns: - A new Runnable that maps a list of inputs to a list of outputs. + A new ``Runnable`` that maps a list of inputs to a list of outputs. Example: @@ -1839,22 +1853,22 @@ class Runnable(ABC, Generic[Input, Output]): exceptions_to_handle: tuple[type[BaseException], ...] = (Exception,), exception_key: Optional[str] = None, ) -> RunnableWithFallbacksT[Input, Output]: - """Add fallbacks to a Runnable, returning a new Runnable. + """Add fallbacks to a ``Runnable``, returning a new ``Runnable``. - The new Runnable will try the original Runnable, and then each fallback + The new ``Runnable`` will try the original ``Runnable``, and then each fallback in order, upon failures. Args: - fallbacks: A sequence of runnables to try if the original Runnable fails. + fallbacks: A sequence of runnables to try if the original ``Runnable`` fails. exceptions_to_handle: A tuple of exception types to handle. - Defaults to (Exception,). + Defaults to ``(Exception,)``. exception_key: If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, - exceptions will not be passed to fallbacks. If used, the base Runnable + exceptions will not be passed to fallbacks. If used, the base ``Runnable`` and its fallbacks must accept a dictionary as input. Defaults to None. Returns: - A new Runnable that will try the original Runnable, and then each + A new ``Runnable`` that will try the original ``Runnable``, and then each fallback in order, upon failures. Example: @@ -1881,18 +1895,18 @@ class Runnable(ABC, Generic[Input, Output]): print(''.join(runnable.stream({}))) #foo bar Args: - fallbacks: A sequence of runnables to try if the original Runnable fails. + fallbacks: A sequence of runnables to try if the original ``Runnable`` fails. exceptions_to_handle: A tuple of exception types to handle. exception_key: If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, - exceptions will not be passed to fallbacks. If used, the base Runnable + exceptions will not be passed to fallbacks. If used, the base ``Runnable`` and its fallbacks must accept a dictionary as input. Returns: - A new Runnable that will try the original Runnable, and then each + A new ``Runnable`` that will try the original ``Runnable``, and then each fallback in order, upon failures. - """ + """ # noqa: E501 from langchain_core.runnables.fallbacks import RunnableWithFallbacks return RunnableWithFallbacks( @@ -1917,10 +1931,11 @@ class Runnable(ABC, Generic[Input, Output]): serialized: Optional[dict[str, Any]] = None, **kwargs: Optional[Any], ) -> Output: - """Helper method to transform an Input value to an Output value, with callbacks. + """Helper method to transform an ``Input`` value to an ``Output`` value, with callbacks. - Use this method to implement invoke() in subclasses. - """ + Use this method to implement ``invoke`` in subclasses. + + """ # noqa: E501 config = ensure_config(config) callback_manager = get_callback_manager_for_config(config) run_manager = callback_manager.on_chain_start( @@ -1967,10 +1982,10 @@ class Runnable(ABC, Generic[Input, Output]): serialized: Optional[dict[str, Any]] = None, **kwargs: Optional[Any], ) -> Output: - """Helper method to transform an Input value to an Output value, with callbacks. + """Helper method to transform an ``Input`` value to an ``Output`` value, with callbacks. - Use this method to implement ainvoke() in subclasses. - """ + Use this method to implement ``ainvoke`` in subclasses. + """ # noqa: E501 config = ensure_config(config) callback_manager = get_async_callback_manager_for_config(config) run_manager = await callback_manager.on_chain_start( @@ -2016,8 +2031,9 @@ class Runnable(ABC, Generic[Input, Output]): ) -> list[Output]: """Transform a list of inputs to a list of outputs, with callbacks. - Helper method to transform an Input value to an Output value, - with callbacks. Use this method to implement invoke() in subclasses. + Helper method to transform an ``Input`` value to an ``Output`` value, + with callbacks. Use this method to implement ``invoke`` in subclasses. + """ if not inputs: return [] @@ -2089,9 +2105,11 @@ class Runnable(ABC, Generic[Input, Output]): ) -> list[Output]: """Transform a list of inputs to a list of outputs, with callbacks. - Helper method to transform an Input value to an Output value, + Helper method to transform an ``Input`` value to an ``Output`` value, with callbacks. - Use this method to implement invoke() in subclasses. + + Use this method to implement ``invoke`` in subclasses. + """ if not inputs: return [] @@ -2163,9 +2181,11 @@ class Runnable(ABC, Generic[Input, Output]): ) -> Iterator[Output]: """Transform a stream with config. - Helper method to transform an Iterator of Input values into an Iterator of - Output values, with callbacks. - Use this to implement `stream()` or `transform()` in Runnable subclasses. + Helper method to transform an ``Iterator`` of ``Input`` values into an + ``Iterator`` of ``Output`` values, with callbacks. + + Use this to implement ``stream`` or ``transform`` in ``Runnable`` subclasses. + """ # Mixin that is used by both astream log and astream events implementation from langchain_core.tracers._streaming import _StreamingCallbackHandler @@ -2267,9 +2287,11 @@ class Runnable(ABC, Generic[Input, Output]): ) -> AsyncIterator[Output]: """Transform a stream with config. - Helper method to transform an Async Iterator of Input values into an Async - Iterator of Output values, with callbacks. - Use this to implement `astream()` or `atransform()` in Runnable subclasses. + Helper method to transform an Async ``Iterator`` of ``Input`` values into an + Async ``Iterator`` of ``Output`` values, with callbacks. + + Use this to implement ``astream`` or ``atransform`` in ``Runnable`` subclasses. + """ # Mixin that is used by both astream log and astream events implementation from langchain_core.tracers._streaming import _StreamingCallbackHandler @@ -2362,12 +2384,12 @@ class Runnable(ABC, Generic[Input, Output]): description: Optional[str] = None, arg_types: Optional[dict[str, type]] = None, ) -> BaseTool: - """Create a BaseTool from a Runnable. + """Create a ``BaseTool`` from a ``Runnable``. - ``as_tool`` will instantiate a BaseTool with a name, description, and - ``args_schema`` from a Runnable. Where possible, schemas are inferred + ``as_tool`` will instantiate a ``BaseTool`` with a name, description, and + ``args_schema`` from a ``Runnable``. Where possible, schemas are inferred from ``runnable.get_input_schema``. Alternatively (e.g., if the - Runnable takes a dict as input and the specific dict keys are not typed), + ``Runnable`` takes a dict as input and the specific dict keys are not typed), the schema can be specified directly with ``args_schema``. You can also pass ``arg_types`` to just specify the required arguments and their types. @@ -2376,9 +2398,11 @@ class Runnable(ABC, Generic[Input, Output]): name: The name of the tool. Defaults to None. description: The description of the tool. Defaults to None. arg_types: A dictionary of argument names to types. Defaults to None. + message_version: Version of ``ToolMessage`` to return given + :class:`~langchain_core.messages.content_blocks.ToolCall` input. Returns: - A BaseTool instance. + A ``BaseTool`` instance. Typed dict input: @@ -2477,10 +2501,11 @@ class RunnableSerializable(Serializable, Runnable[Input, Output]): @override def to_json(self) -> Union[SerializedConstructor, SerializedNotImplemented]: - """Serialize the Runnable to JSON. + """Serialize the ``Runnable`` to JSON. Returns: - A JSON-serializable representation of the Runnable. + A JSON-serializable representation of the ``Runnable``. + """ dumped = super().to_json() with contextlib.suppress(Exception): @@ -2490,13 +2515,13 @@ class RunnableSerializable(Serializable, Runnable[Input, Output]): def configurable_fields( self, **kwargs: AnyConfigurableField ) -> RunnableSerializable[Input, Output]: - """Configure particular Runnable fields at runtime. + """Configure particular ``Runnable`` fields at runtime. Args: - **kwargs: A dictionary of ConfigurableField instances to configure. + **kwargs: A dictionary of ``ConfigurableField`` instances to configure. Returns: - A new Runnable with the fields configured. + A new ``Runnable`` with the fields configured. .. code-block:: python @@ -2545,20 +2570,20 @@ class RunnableSerializable(Serializable, Runnable[Input, Output]): prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]], ) -> RunnableSerializable[Input, Output]: - """Configure alternatives for Runnables that can be set at runtime. + """Configure alternatives for ``Runnables`` that can be set at runtime. Args: - which: The ConfigurableField instance that will be used to select the + which: The ``ConfigurableField`` instance that will be used to select the alternative. default_key: The default key to use if no alternative is selected. Defaults to ``'default'``. - prefix_keys: Whether to prefix the keys with the ConfigurableField id. + prefix_keys: Whether to prefix the keys with the ``ConfigurableField`` id. Defaults to False. - **kwargs: A dictionary of keys to Runnable instances or callables that - return Runnable instances. + **kwargs: A dictionary of keys to ``Runnable`` instances or callables that + return ``Runnable`` instances. Returns: - A new Runnable with the alternatives configured. + A new ``Runnable`` with the alternatives configured. .. code-block:: python @@ -2672,25 +2697,26 @@ def _seq_output_schema( class RunnableSequence(RunnableSerializable[Input, Output]): - """Sequence of Runnables, where the output of each is the input of the next. + """Sequence of ``Runnables``, where the output of each is the input of the next. - **RunnableSequence** is the most important composition operator in LangChain + **``RunnableSequence``** is the most important composition operator in LangChain as it is used in virtually every chain. - A RunnableSequence can be instantiated directly or more commonly by using the `|` - operator where either the left or right operands (or both) must be a Runnable. + A ``RunnableSequence`` can be instantiated directly or more commonly by using the + ``|`` operator where either the left or right operands (or both) must be a + ``Runnable``. - Any RunnableSequence automatically supports sync, async, batch. + Any ``RunnableSequence`` automatically supports sync, async, batch. - The default implementations of `batch` and `abatch` utilize threadpools and - asyncio gather and will be faster than naive invocation of invoke or ainvoke - for IO bound Runnables. + The default implementations of ``batch`` and ``abatch`` utilize threadpools and + asyncio gather and will be faster than naive invocation of ``invoke`` or ``ainvoke`` + for IO bound ``Runnable``s. Batching is implemented by invoking the batch method on each component of the - RunnableSequence in order. + ``RunnableSequence`` in order. - A RunnableSequence preserves the streaming properties of its components, so if all - components of the sequence implement a `transform` method -- which + A ``RunnableSequence`` preserves the streaming properties of its components, so if + all components of the sequence implement a ``transform`` method -- which is the method that implements the logic to map a streaming input to a streaming output -- then the sequence will be able to stream input to output! @@ -2704,10 +2730,10 @@ class RunnableSequence(RunnableSerializable[Input, Output]): ``RunnableSequence`` (if you need to use the ``stream``/``astream`` methods). If you need arbitrary logic and need streaming, you can subclass - Runnable, and implement `transform` for whatever logic you need. + Runnable, and implement ``transform`` for whatever logic you need. Here is a simple example that uses simple functions to illustrate the use of - RunnableSequence: + ``RunnableSequence``: .. code-block:: python @@ -2756,11 +2782,11 @@ class RunnableSequence(RunnableSerializable[Input, Output]): # purposes. It allows specifying the `Input` on the first type, the `Output` of # the last type. first: Runnable[Input, Any] - """The first Runnable in the sequence.""" + """The first ``Runnable`` in the sequence.""" middle: list[Runnable[Any, Any]] = Field(default_factory=list) - """The middle Runnables in the sequence.""" + """The middle ``Runnable`` in the sequence.""" last: Runnable[Any, Output] - """The last Runnable in the sequence.""" + """The last ``Runnable`` in the sequence.""" def __init__( self, @@ -2770,13 +2796,13 @@ class RunnableSequence(RunnableSerializable[Input, Output]): middle: Optional[list[Runnable[Any, Any]]] = None, last: Optional[Runnable[Any, Any]] = None, ) -> None: - """Create a new RunnableSequence. + """Create a new ``RunnableSequence``. Args: steps: The steps to include in the sequence. - name: The name of the Runnable. Defaults to None. - first: The first Runnable in the sequence. Defaults to None. - middle: The middle Runnables in the sequence. Defaults to None. + name: The name of the ``Runnable``. Defaults to None. + first: The first ``Runnable`` in the sequence. Defaults to None. + middle: The middle ``Runnables`` in the sequence. Defaults to None. last: The last Runnable in the sequence. Defaults to None. Raises: @@ -2807,10 +2833,10 @@ class RunnableSequence(RunnableSerializable[Input, Output]): @property def steps(self) -> list[Runnable[Any, Any]]: - """All the Runnables that make up the sequence in order. + """All the ``Runnable``s that make up the sequence in order. Returns: - A list of Runnables. + A list of ``Runnable``s. """ return [self.first, *self.middle, self.last] @@ -2821,7 +2847,9 @@ class RunnableSequence(RunnableSerializable[Input, Output]): Returns: True if the object is serializable, False otherwise. - Defaults to True. + + Defaults to True. + """ return True @@ -2832,26 +2860,27 @@ class RunnableSequence(RunnableSerializable[Input, Output]): @property @override def InputType(self) -> type[Input]: - """The type of the input to the Runnable.""" + """The type of the input to the ``Runnable``.""" return self.first.InputType @property @override def OutputType(self) -> type[Output]: - """The type of the output of the Runnable.""" + """The type of the output of the ``Runnable``.""" return self.last.OutputType @override def get_input_schema( self, config: Optional[RunnableConfig] = None ) -> type[BaseModel]: - """Get the input schema of the Runnable. + """Get the input schema of the ``Runnable``. Args: config: The config to use. Defaults to None. Returns: - The input schema of the Runnable. + The input schema of the ``Runnable``. + """ return _seq_input_schema(self.steps, config) @@ -2859,23 +2888,25 @@ class RunnableSequence(RunnableSerializable[Input, Output]): def get_output_schema( self, config: Optional[RunnableConfig] = None ) -> type[BaseModel]: - """Get the output schema of the Runnable. + """Get the output schema of the ``Runnable``. Args: config: The config to use. Defaults to None. Returns: - The output schema of the Runnable. + The output schema of the ``Runnable``. + """ return _seq_output_schema(self.steps, config) @property @override def config_specs(self) -> list[ConfigurableFieldSpec]: - """Get the config specs of the Runnable. + """Get the config specs of the ``Runnable``. Returns: - The config specs of the Runnable. + The config specs of the ``Runnable``. + """ from langchain_core.beta.runnables.context import ( CONTEXT_CONFIG_PREFIX, @@ -2923,16 +2954,17 @@ class RunnableSequence(RunnableSerializable[Input, Output]): @override def get_graph(self, config: Optional[RunnableConfig] = None) -> Graph: - """Get the graph representation of the Runnable. + """Get the graph representation of the ``Runnable``. Args: config: The config to use. Defaults to None. Returns: - The graph representation of the Runnable. + The graph representation of the ``Runnable``. Raises: - ValueError: If a Runnable has no first or last node. + ValueError: If a ``Runnable`` has no first or last node. + """ from langchain_core.runnables.graph import Graph @@ -3470,19 +3502,19 @@ class RunnableSequence(RunnableSerializable[Input, Output]): class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]): - """Runnable that runs a mapping of Runnables in parallel. + """Runnable that runs a mapping of ``Runnable``s in parallel. Returns a mapping of their outputs. - RunnableParallel is one of the two main composition primitives for the LCEL, - alongside RunnableSequence. It invokes Runnables concurrently, providing the same - input to each. + ``RunnableParallel`` is one of the two main composition primitives for the LCEL, + alongside ``RunnableSequence``. It invokes ``Runnable``s concurrently, providing the + same input to each. - A RunnableParallel can be instantiated directly or by using a dict literal within a - sequence. + A ``RunnableParallel`` can be instantiated directly or by using a dict literal + within a sequence. Here is a simple example that uses functions to illustrate the use of - RunnableParallel: + ``RunnableParallel``: .. code-block:: python @@ -3521,8 +3553,8 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]): sequence.batch([1, 2, 3]) await sequence.abatch([1, 2, 3]) - RunnableParallel makes it easy to run Runnables in parallel. In the below example, - we simultaneously stream output from two different Runnables: + ``RunnableParallel`` makes it easy to run ``Runnable``s in parallel. In the below + example, we simultaneously stream output from two different ``Runnables``: .. code-block:: python @@ -3571,11 +3603,12 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]): Mapping[str, Union[Runnable[Input, Any], Callable[[Input], Any]]], ], ) -> None: - """Create a RunnableParallel. + """Create a ``RunnableParallel``. Args: steps__: The steps to include. Defaults to None. **kwargs: Additional steps to include. + """ merged = {**steps__} if steps__ is not None else {} merged.update(kwargs) @@ -3601,14 +3634,15 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]): def get_name( self, suffix: Optional[str] = None, *, name: Optional[str] = None ) -> str: - """Get the name of the Runnable. + """Get the name of the ``Runnable``. Args: suffix: The suffix to use. Defaults to None. name: The name to use. Defaults to None. Returns: - The name of the Runnable. + The name of the ``Runnable``. + """ name = name or self.name or f"RunnableParallel<{','.join(self.steps__.keys())}>" return super().get_name(suffix, name=name) @@ -3616,7 +3650,7 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]): @property @override def InputType(self) -> Any: - """The type of the input to the Runnable.""" + """The type of the input to the ``Runnable``.""" for step in self.steps__.values(): if step.InputType: return step.InputType @@ -3627,13 +3661,14 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]): def get_input_schema( self, config: Optional[RunnableConfig] = None ) -> type[BaseModel]: - """Get the input schema of the Runnable. + """Get the input schema of the ``Runnable``. Args: config: The config to use. Defaults to None. Returns: - The input schema of the Runnable. + The input schema of the ``Runnable``. + """ if all( s.get_input_schema(config).model_json_schema().get("type", "object") @@ -3657,13 +3692,14 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]): def get_output_schema( self, config: Optional[RunnableConfig] = None ) -> type[BaseModel]: - """Get the output schema of the Runnable. + """Get the output schema of the ``Runnable``. Args: config: The config to use. Defaults to None. Returns: - The output schema of the Runnable. + The output schema of the ``Runnable``. + """ fields = {k: (v.OutputType, ...) for k, v in self.steps__.items()} return create_model_v2(self.get_name("Output"), field_definitions=fields) @@ -3671,10 +3707,11 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]): @property @override def config_specs(self) -> list[ConfigurableFieldSpec]: - """Get the config specs of the Runnable. + """Get the config specs of the ``Runnable``. Returns: - The config specs of the Runnable. + The config specs of the ``Runnable``. + """ return get_unique_config_specs( spec for step in self.steps__.values() for spec in step.config_specs @@ -3682,16 +3719,17 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]): @override def get_graph(self, config: Optional[RunnableConfig] = None) -> Graph: - """Get the graph representation of the Runnable. + """Get the graph representation of the ``Runnable``. Args: config: The config to use. Defaults to None. Returns: - The graph representation of the Runnable. + The graph representation of the ``Runnable``. Raises: - ValueError: If a Runnable has no first or last node. + ValueError: If a ``Runnable`` has no first or last node. + """ from langchain_core.runnables.graph import Graph @@ -3988,22 +4026,24 @@ RunnableMap = RunnableParallel class RunnableGenerator(Runnable[Input, Output]): - """Runnable that runs a generator function. + """``Runnable`` that runs a generator function. - RunnableGenerators can be instantiated directly or by using a generator within + ``RunnableGenerator``s can be instantiated directly or by using a generator within a sequence. - RunnableGenerators can be used to implement custom behavior, such as custom output - parsers, while preserving streaming capabilities. Given a generator function with - a signature Iterator[A] -> Iterator[B], wrapping it in a RunnableGenerator allows - it to emit output chunks as soon as they are streamed in from the previous step. + ``RunnableGenerator``s can be used to implement custom behavior, such as custom + output parsers, while preserving streaming capabilities. Given a generator function + with a signature ``Iterator[A] -> Iterator[B]``, wrapping it in a + ``RunnableGenerator`` allows it to emit output chunks as soon as they are streamed + in from the previous step. - Note that if a generator function has a signature A -> Iterator[B], such that it - requires its input from the previous step to be completed before emitting chunks - (e.g., most LLMs need the entire prompt available to start generating), it can - instead be wrapped in a RunnableLambda. + .. note:: + If a generator function has a ``signature A -> Iterator[B]``, such that it + requires its input from the previous step to be completed before emitting chunks + (e.g., most LLMs need the entire prompt available to start generating), it can + instead be wrapped in a ``RunnableLambda``. - Here is an example to show the basic mechanics of a RunnableGenerator: + Here is an example to show the basic mechanics of a ``RunnableGenerator``: .. code-block:: python @@ -4032,7 +4072,7 @@ class RunnableGenerator(Runnable[Input, Output]): await runnable.ainvoke(None) # "Have a nice day" [p async for p in runnable.astream(None)] # ["Have", " a", " nice", " day"] - RunnableGenerator makes it easy to implement custom behavior within a streaming + ``RunnableGenerator`` makes it easy to implement custom behavior within a streaming context. Below we show an example: .. code-block:: python @@ -4086,15 +4126,16 @@ class RunnableGenerator(Runnable[Input, Output]): *, name: Optional[str] = None, ) -> None: - """Initialize a RunnableGenerator. + """Initialize a ``RunnableGenerator``. Args: transform: The transform function. atransform: The async transform function. Defaults to None. - name: The name of the Runnable. Defaults to None. + name: The name of the ``Runnable``. Defaults to None. Raises: TypeError: If the transform is not a generator function. + """ if atransform is not None: self._atransform = atransform @@ -4289,12 +4330,12 @@ class RunnableGenerator(Runnable[Input, Output]): class RunnableLambda(Runnable[Input, Output]): - """RunnableLambda converts a python callable into a Runnable. + """``RunnableLambda`` converts a python callable into a ``Runnable``. - Wrapping a callable in a RunnableLambda makes the callable usable + Wrapping a callable in a ``RunnableLambda`` makes the callable usable within either a sync or async context. - RunnableLambda can be composed as any other Runnable and provides + ``RunnableLambda`` can be composed as any other ``Runnable`` and provides seamless integration with LangChain tracing. ``RunnableLambda`` is best suited for code that does not need to support @@ -4371,7 +4412,7 @@ class RunnableLambda(Runnable[Input, Output]): ] = None, name: Optional[str] = None, ) -> None: - """Create a RunnableLambda from a callable, and async callable or both. + """Create a ``RunnableLambda`` from a callable, and async callable or both. Accepts both sync and async variants to allow providing efficient implementations for sync and async execution. @@ -4380,11 +4421,12 @@ class RunnableLambda(Runnable[Input, Output]): func: Either sync or async callable afunc: An async callable that takes an input and returns an output. Defaults to None. - name: The name of the Runnable. Defaults to None. + name: The name of the ``Runnable``. Defaults to None. Raises: - TypeError: If the func is not a callable type. - TypeError: If both func and afunc are provided. + TypeError: If the ``func`` is not a callable type. + TypeError: If both ``func`` and ``afunc`` are provided. + """ if afunc is not None: self.afunc = afunc @@ -4423,7 +4465,7 @@ class RunnableLambda(Runnable[Input, Output]): @property @override def InputType(self) -> Any: - """The type of the input to this Runnable.""" + """The type of the input to this ``Runnable``.""" func = getattr(self, "func", None) or self.afunc try: params = inspect.signature(func).parameters @@ -4438,13 +4480,14 @@ class RunnableLambda(Runnable[Input, Output]): def get_input_schema( self, config: Optional[RunnableConfig] = None ) -> type[BaseModel]: - """The pydantic schema for the input to this Runnable. + """The pydantic schema for the input to this ``Runnable``. Args: config: The config to use. Defaults to None. Returns: - The input schema for this Runnable. + The input schema for this ``Runnable``. + """ func = getattr(self, "func", None) or self.afunc @@ -4482,10 +4525,11 @@ class RunnableLambda(Runnable[Input, Output]): @property @override def OutputType(self) -> Any: - """The type of the output of this Runnable as a type annotation. + """The type of the output of this ``Runnable`` as a type annotation. Returns: - The type of the output of this Runnable. + The type of the output of this ``Runnable``. + """ func = getattr(self, "func", None) or self.afunc try: @@ -4531,11 +4575,12 @@ class RunnableLambda(Runnable[Input, Output]): @functools.cached_property def deps(self) -> list[Runnable]: - """The dependencies of this Runnable. + """The dependencies of this ``Runnable``. Returns: - The dependencies of this Runnable. If the function has nonlocal - variables that are Runnables, they are considered dependencies. + The dependencies of this ``Runnable``. If the function has nonlocal + variables that are ``Runnable``s, they are considered dependencies. + """ if hasattr(self, "func"): objects = get_function_nonlocals(self.func) @@ -4599,7 +4644,7 @@ class RunnableLambda(Runnable[Input, Output]): __hash__ = None # type: ignore[assignment] def __repr__(self) -> str: - """A string representation of this Runnable.""" + """A string representation of this ``Runnable``.""" if self._repr is None: if hasattr(self, "func") and isinstance(self.func, itemgetter): self._repr = f"RunnableLambda({str(self.func)[len('operator.') :]})" @@ -4765,18 +4810,19 @@ class RunnableLambda(Runnable[Input, Output]): config: Optional[RunnableConfig] = None, **kwargs: Optional[Any], ) -> Output: - """Invoke this Runnable synchronously. + """Invoke this ``Runnable`` synchronously. Args: - input: The input to this Runnable. + input: The input to this ``Runnable``. config: The config to use. Defaults to None. kwargs: Additional keyword arguments. Returns: - The output of this Runnable. + The output of this ``Runnable``. Raises: - TypeError: If the Runnable is a coroutine function. + TypeError: If the ``Runnable`` is a coroutine function. + """ if hasattr(self, "func"): return self._call_with_config( @@ -4795,15 +4841,16 @@ class RunnableLambda(Runnable[Input, Output]): config: Optional[RunnableConfig] = None, **kwargs: Optional[Any], ) -> Output: - """Invoke this Runnable asynchronously. + """Invoke this ``Runnable`` asynchronously. Args: - input: The input to this Runnable. + input: The input to this ``Runnable``. config: The config to use. Defaults to None. kwargs: Additional keyword arguments. Returns: - The output of this Runnable. + The output of this ``Runnable``. + """ return await self._acall_with_config( self._ainvoke, @@ -5033,12 +5080,13 @@ class RunnableLambda(Runnable[Input, Output]): class RunnableEachBase(RunnableSerializable[list[Input], list[Output]]): - """Runnable that calls another Runnable for each element of the input sequence. + """``Runnable`` that calls another ``Runnable`` for each element of the input sequence. - Use only if creating a new RunnableEach subclass with different __init__ args. + Use only if creating a new ``RunnableEach`` subclass with different ``__init__`` args. - See documentation for RunnableEach for more details. - """ + See documentation for ``RunnableEach`` for more details. + + """ # noqa: E501 bound: Runnable[Input, Output] @@ -5163,13 +5211,13 @@ class RunnableEachBase(RunnableSerializable[list[Input], list[Output]]): class RunnableEach(RunnableEachBase[Input, Output]): - """Runnable that calls another Runnable for each element of the input sequence. + """``Runnable`` that calls another ``Runnable`` for each element of the input sequence. - It allows you to call multiple inputs with the bounded Runnable. + It allows you to call multiple inputs with the bounded ``Runnable``. - RunnableEach makes it easy to run multiple inputs for the Runnable. + ``RunnableEach`` makes it easy to run multiple inputs for the ``Runnable``. In the below example, we associate and run three inputs - with a Runnable: + with a ``Runnable``: .. code-block:: python @@ -5188,7 +5236,7 @@ class RunnableEach(RunnableEachBase[Input, Output]): {'topic':'Biology'}]) print(output) # noqa: T201 - """ + """ # noqa: E501 @override def get_name( @@ -5221,22 +5269,23 @@ class RunnableEach(RunnableEachBase[Input, Output]): Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]] ] = None, ) -> RunnableEach[Input, Output]: - """Bind lifecycle listeners to a Runnable, returning a new Runnable. + """Bind lifecycle listeners to a ``Runnable``, returning a new ``Runnable``. - The Run object contains information about the run, including its id, - type, input, output, error, start_time, end_time, and any tags or metadata - added to the run. + The ``Run`` object contains information about the run, including its ``id``, + ``type``, ``input``, ``output``, ``error``, ``start_time``, ``end_time``, and + any tags or metadata added to the run. Args: - on_start: Called before the Runnable starts running, with the Run object. - Defaults to None. - on_end: Called after the Runnable finishes running, with the Run object. - Defaults to None. - on_error: Called if the Runnable throws an error, with the Run object. - Defaults to None. + on_start: Called before the ``Runnable`` starts running, with the ``Run`` + object. Defaults to None. + on_end: Called after the ``Runnable`` finishes running, with the ``Run`` + object. Defaults to None. + on_error: Called if the ``Runnable`` throws an error, with the ``Run`` + object. Defaults to None. Returns: - A new Runnable with the listeners bound. + A new ``Runnable`` with the listeners bound. + """ return RunnableEach( bound=self.bound.with_listeners( @@ -5251,23 +5300,24 @@ class RunnableEach(RunnableEachBase[Input, Output]): on_end: Optional[AsyncListener] = None, on_error: Optional[AsyncListener] = None, ) -> RunnableEach[Input, Output]: - """Bind async lifecycle listeners to a Runnable, returning a new Runnable. + """Bind async lifecycle listeners to a ``Runnable``, returning a new ``Runnable``. - The Run object contains information about the run, including its id, - type, input, output, error, start_time, end_time, and any tags or metadata - added to the run. + The ``Run`` object contains information about the run, including its ``id``, + ``type``, ``input``, ``output``, ``error``, ``start_time``, ``end_time``, and + any tags or metadata added to the run. Args: - on_start: Called asynchronously before the Runnable starts running, - with the Run object. Defaults to None. - on_end: Called asynchronously after the Runnable finishes running, - with the Run object. Defaults to None. - on_error: Called asynchronously if the Runnable throws an error, - with the Run object. Defaults to None. + on_start: Called asynchronously before the ``Runnable`` starts running, + with the ``Run`` object. Defaults to None. + on_end: Called asynchronously after the ``Runnable`` finishes running, + with the ``Run`` object. Defaults to None. + on_error: Called asynchronously if the ``Runnable`` throws an error, + with the ``Run`` object. Defaults to None. Returns: - A new Runnable with the listeners bound. - """ + A new ``Runnable`` with the listeners bound. + + """ # noqa: E501 return RunnableEach( bound=self.bound.with_alisteners( on_start=on_start, on_end=on_end, on_error=on_error @@ -5276,43 +5326,46 @@ class RunnableEach(RunnableEachBase[Input, Output]): class RunnableBindingBase(RunnableSerializable[Input, Output]): - """Runnable that delegates calls to another Runnable with a set of kwargs. + """``Runnable`` that delegates calls to another ``Runnable`` with a set of kwargs. - Use only if creating a new RunnableBinding subclass with different __init__ args. + Use only if creating a new ``RunnableBinding`` subclass with different ``__init__`` + args. + + See documentation for ``RunnableBinding`` for more details. - See documentation for RunnableBinding for more details. """ bound: Runnable[Input, Output] - """The underlying Runnable that this Runnable delegates to.""" + """The underlying ``Runnable`` that this ``Runnable`` delegates to.""" kwargs: Mapping[str, Any] = Field(default_factory=dict) - """kwargs to pass to the underlying Runnable when running. + """kwargs to pass to the underlying ``Runnable`` when running. - For example, when the Runnable binding is invoked the underlying - Runnable will be invoked with the same input but with these additional + For example, when the ``Runnable`` binding is invoked the underlying + ``Runnable`` will be invoked with the same input but with these additional kwargs. + """ - config: RunnableConfig = Field(default_factory=RunnableConfig) # type: ignore[arg-type] - """The config to bind to the underlying Runnable.""" + config: RunnableConfig = Field(default_factory=RunnableConfig) + """The config to bind to the underlying ``Runnable``.""" config_factories: list[Callable[[RunnableConfig], RunnableConfig]] = Field( default_factory=list ) - """The config factories to bind to the underlying Runnable.""" + """The config factories to bind to the underlying ``Runnable``.""" # Union[Type[Input], BaseModel] + things like list[str] custom_input_type: Optional[Any] = None - """Override the input type of the underlying Runnable with a custom type. + """Override the input type of the underlying ``Runnable`` with a custom type. - The type can be a pydantic model, or a type annotation (e.g., `list[str]`). + The type can be a pydantic model, or a type annotation (e.g., ``list[str]``). """ # Union[Type[Output], BaseModel] + things like list[str] custom_output_type: Optional[Any] = None - """Override the output type of the underlying Runnable with a custom type. + """Override the output type of the underlying ``Runnable`` with a custom type. - The type can be a pydantic model, or a type annotation (e.g., `list[str]`). + The type can be a pydantic model, or a type annotation (e.g., ``list[str]``). """ model_config = ConfigDict( @@ -5332,25 +5385,25 @@ class RunnableBindingBase(RunnableSerializable[Input, Output]): custom_output_type: Optional[Union[type[Output], BaseModel]] = None, **other_kwargs: Any, ) -> None: - """Create a RunnableBinding from a Runnable and kwargs. + """Create a ``RunnableBinding`` from a ``Runnable`` and kwargs. Args: - bound: The underlying Runnable that this Runnable delegates calls to. - kwargs: optional kwargs to pass to the underlying Runnable, when running - the underlying Runnable (e.g., via `invoke`, `batch`, - `transform`, or `stream` or async variants) + bound: The underlying ``Runnable`` that this ``Runnable`` delegates calls to. + kwargs: optional kwargs to pass to the underlying ``Runnable``, when running + the underlying ``Runnable`` (e.g., via ``invoke``, ``batch``, + ``transform``, or ``stream`` or async variants) Defaults to None. - config: optional config to bind to the underlying Runnable. + config: optional config to bind to the underlying ``Runnable``. Defaults to None. config_factories: optional list of config factories to apply to the - config before binding to the underlying Runnable. + config before binding to the underlying ``Runnable``. Defaults to None. custom_input_type: Specify to override the input type of the underlying - Runnable with a custom type. Defaults to None. + ``Runnable`` with a custom type. Defaults to None. custom_output_type: Specify to override the output type of the underlying - Runnable with a custom type. Defaults to None. + ``Runnable`` with a custom type. Defaults to None. **other_kwargs: Unpacked into the base class. - """ + """ # noqa: E501 super().__init__( # type: ignore[call-arg] bound=bound, kwargs=kwargs or {}, @@ -5424,7 +5477,7 @@ class RunnableBindingBase(RunnableSerializable[Input, Output]): def get_lc_namespace(cls) -> list[str]: """Get the namespace of the langchain object. - Defaults to ["langchain", "schema", "runnable"]. + Defaults to ``["langchain", "schema", "runnable"]``. """ return ["langchain", "schema", "runnable"] @@ -5677,28 +5730,28 @@ class RunnableBindingBase(RunnableSerializable[Input, Output]): class RunnableBinding(RunnableBindingBase[Input, Output]): - """Wrap a Runnable with additional functionality. + """Wrap a ``Runnable`` with additional functionality. - A RunnableBinding can be thought of as a "runnable decorator" that - preserves the essential features of Runnable; i.e., batching, streaming, + A ``RunnableBinding`` can be thought of as a "runnable decorator" that + preserves the essential features of ``Runnable``; i.e., batching, streaming, and async support, while adding additional functionality. - Any class that inherits from Runnable can be bound to a `RunnableBinding`. - Runnables expose a standard set of methods for creating `RunnableBindings` - or sub-classes of `RunnableBindings` (e.g., `RunnableRetry`, - `RunnableWithFallbacks`) that add additional functionality. + Any class that inherits from ``Runnable`` can be bound to a ``RunnableBinding``. + Runnables expose a standard set of methods for creating ``RunnableBindings`` + or sub-classes of ``RunnableBindings`` (e.g., ``RunnableRetry``, + ``RunnableWithFallbacks``) that add additional functionality. These methods include: - - ``bind``: Bind kwargs to pass to the underlying Runnable when running it. - - ``with_config``: Bind config to pass to the underlying Runnable when running it. - - ``with_listeners``: Bind lifecycle listeners to the underlying Runnable. - - ``with_types``: Override the input and output types of the underlying Runnable. - - ``with_retry``: Bind a retry policy to the underlying Runnable. - - ``with_fallbacks``: Bind a fallback policy to the underlying Runnable. + - ``bind``: Bind kwargs to pass to the underlying ``Runnable`` when running it. + - ``with_config``: Bind config to pass to the underlying ``Runnable`` when running it. + - ``with_listeners``: Bind lifecycle listeners to the underlying ``Runnable``. + - ``with_types``: Override the input and output types of the underlying ``Runnable``. + - ``with_retry``: Bind a retry policy to the underlying ``Runnable``. + - ``with_fallbacks``: Bind a fallback policy to the underlying ``Runnable``. Example: - `bind`: Bind kwargs to pass to the underlying Runnable when running it. + `bind`: Bind kwargs to pass to the underlying ``Runnable`` when running it. .. code-block:: python @@ -5712,7 +5765,8 @@ class RunnableBinding(RunnableBindingBase[Input, Output]): runnable_binding = model.bind(stop=['-']) runnable_binding.invoke('Say "Parrot-MAGIC"') # Should return `Parrot` - Can also be done by instantiating a RunnableBinding directly (not recommended): + Can also be done by instantiating a ``RunnableBinding`` directly (not + recommended): .. code-block:: python @@ -5723,18 +5777,19 @@ class RunnableBinding(RunnableBindingBase[Input, Output]): ) runnable_binding.invoke('Say "Parrot-MAGIC"') # Should return `Parrot` - """ + """ # noqa: E501 @override def bind(self, **kwargs: Any) -> Runnable[Input, Output]: - """Bind additional kwargs to a Runnable, returning a new Runnable. + """Bind additional kwargs to a ``Runnable``, returning a new ``Runnable``. Args: - **kwargs: The kwargs to bind to the Runnable. + **kwargs: The kwargs to bind to the ``Runnable``. Returns: - A new Runnable with the same type and config as the original, + A new ``Runnable`` with the same type and config as the original, but with the additional kwargs bound. + """ return self.__class__( bound=self.bound, @@ -5775,22 +5830,22 @@ class RunnableBinding(RunnableBindingBase[Input, Output]): Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]] ] = None, ) -> Runnable[Input, Output]: - """Bind lifecycle listeners to a Runnable, returning a new Runnable. + """Bind lifecycle listeners to a ``Runnable``, returning a new ``Runnable``. - The Run object contains information about the run, including its id, - type, input, output, error, start_time, end_time, and any tags or metadata - added to the run. + The ``Run`` object contains information about the run, including its ``id``, + ``type``, ``input``, ``output``, ``error``, ``start_time``, ``end_time``, and + any tags or metadata added to the run. Args: - on_start: Called before the Runnable starts running, with the Run object. - Defaults to None. - on_end: Called after the Runnable finishes running, with the Run object. - Defaults to None. - on_error: Called if the Runnable throws an error, with the Run object. - Defaults to None. + on_start: Called before the ``Runnable`` starts running, with the ``Run`` + object. Defaults to None. + on_end: Called after the ``Runnable`` finishes running, with the ``Run`` + object. Defaults to None. + on_error: Called if the ``Runnable`` throws an error, with the ``Run`` + object. Defaults to None. Returns: - A new Runnable with the listeners bound. + A new ``Runnable`` with the listeners bound. """ from langchain_core.tracers.root_listeners import RootListenersTracer @@ -5918,16 +5973,16 @@ RunnableLike = Union[ def coerce_to_runnable(thing: RunnableLike) -> Runnable[Input, Output]: - """Coerce a Runnable-like object into a Runnable. + """Coerce a ``Runnable``-like object into a ``Runnable``. Args: - thing: A Runnable-like object. + thing: A ``Runnable``-like object. Returns: - A Runnable. + A ``Runnable``. Raises: - TypeError: If the object is not Runnable-like. + TypeError: If the object is not ``Runnable``-like. """ if isinstance(thing, Runnable): return thing @@ -5976,16 +6031,16 @@ def chain( Callable[[Input], AsyncIterator[Output]], ], ) -> Runnable[Input, Output]: - """Decorate a function to make it a Runnable. + """Decorate a function to make it a ``Runnable``. - Sets the name of the Runnable to the name of the function. + Sets the name of the ``Runnable`` to the name of the function. Any runnables called by the function will be traced as dependencies. Args: - func: A callable. + func: A ``Callable``. Returns: - A Runnable. + A ``Runnable``. Example: diff --git a/libs/core/langchain_core/runnables/config.py b/libs/core/langchain_core/runnables/config.py index 4ac7bda7b46..cc36622b914 100644 --- a/libs/core/langchain_core/runnables/config.py +++ b/libs/core/langchain_core/runnables/config.py @@ -402,7 +402,7 @@ def call_func_with_variable_args( Callable[[Input, CallbackManagerForChainRun], Output], Callable[[Input, CallbackManagerForChainRun, RunnableConfig], Output], ], - input: Input, # noqa: A002 + input: Input, config: RunnableConfig, run_manager: Optional[CallbackManagerForChainRun] = None, **kwargs: Any, @@ -439,7 +439,7 @@ def acall_func_with_variable_args( Awaitable[Output], ], ], - input: Input, # noqa: A002 + input: Input, config: RunnableConfig, run_manager: Optional[AsyncCallbackManagerForChainRun] = None, **kwargs: Any, diff --git a/libs/core/langchain_core/runnables/fallbacks.py b/libs/core/langchain_core/runnables/fallbacks.py index dd9a9fdf390..ab280790c8e 100644 --- a/libs/core/langchain_core/runnables/fallbacks.py +++ b/libs/core/langchain_core/runnables/fallbacks.py @@ -5,7 +5,7 @@ import inspect import typing from collections.abc import AsyncIterator, Iterator, Sequence from functools import wraps -from typing import TYPE_CHECKING, Any, Optional, Union +from typing import TYPE_CHECKING, Any, Optional, Union, cast from pydantic import BaseModel, ConfigDict from typing_extensions import override @@ -397,7 +397,7 @@ class RunnableWithFallbacks(RunnableSerializable[Input, Output]): ) ) - to_return = {} + to_return: dict[int, Union[Output, BaseException]] = {} run_again = dict(enumerate(inputs)) handled_exceptions: dict[int, BaseException] = {} first_to_raise = None @@ -447,7 +447,7 @@ class RunnableWithFallbacks(RunnableSerializable[Input, Output]): if not return_exceptions and sorted_handled_exceptions: raise sorted_handled_exceptions[0][1] to_return.update(handled_exceptions) - return [output for _, output in sorted(to_return.items())] # type: ignore[misc] + return [cast("Output", output) for _, output in sorted(to_return.items())] @override def stream( @@ -569,7 +569,7 @@ class RunnableWithFallbacks(RunnableSerializable[Input, Output]): async for chunk in stream: yield chunk try: - output = output + chunk + output = output + chunk # type: ignore[operator] except TypeError: output = None except BaseException as e: diff --git a/libs/core/langchain_core/runnables/graph.py b/libs/core/langchain_core/runnables/graph.py index 3e22494bad7..20a841d51a8 100644 --- a/libs/core/langchain_core/runnables/graph.py +++ b/libs/core/langchain_core/runnables/graph.py @@ -114,7 +114,7 @@ class Node(NamedTuple): def copy( self, *, - id: Optional[str] = None, # noqa: A002 + id: Optional[str] = None, name: Optional[str] = None, ) -> Node: """Return a copy of the node with optional new id and name. @@ -187,7 +187,7 @@ class MermaidDrawMethod(Enum): def node_data_str( - id: str, # noqa: A002 + id: str, data: Union[type[BaseModel], RunnableType, None], ) -> str: """Convert the data of a node to a string. @@ -328,7 +328,7 @@ class Graph: def add_node( self, data: Union[type[BaseModel], RunnableType, None], - id: Optional[str] = None, # noqa: A002 + id: Optional[str] = None, *, metadata: Optional[dict[str, Any]] = None, ) -> Node: diff --git a/libs/core/langchain_core/tracers/log_stream.py b/libs/core/langchain_core/tracers/log_stream.py index 5246ea8456e..dbf5ef2a6ae 100644 --- a/libs/core/langchain_core/tracers/log_stream.py +++ b/libs/core/langchain_core/tracers/log_stream.py @@ -176,7 +176,7 @@ class RunLog(RunLogPatch): # Then compare that the ops are the same return super().__eq__(other) - __hash__ = None # type: ignore[assignment] + __hash__ = None T = TypeVar("T") diff --git a/libs/core/langchain_core/utils/function_calling.py b/libs/core/langchain_core/utils/function_calling.py index 609129ac58b..4775a06c5c5 100644 --- a/libs/core/langchain_core/utils/function_calling.py +++ b/libs/core/langchain_core/utils/function_calling.py @@ -277,7 +277,7 @@ def _convert_any_typed_dicts_to_pydantic( ) fields: dict = {} for arg, arg_type in annotations_.items(): - if get_origin(arg_type) is Annotated: + if get_origin(arg_type) is Annotated: # type: ignore[comparison-overlap] annotated_args = get_args(arg_type) new_arg_type = _convert_any_typed_dicts_to_pydantic( annotated_args[0], depth=depth + 1, visited=visited @@ -627,7 +627,7 @@ def convert_to_json_schema( @beta() def tool_example_to_messages( - input: str, # noqa: A002 + input: str, tool_calls: list[BaseModel], tool_outputs: Optional[list[str]] = None, *, @@ -640,15 +640,16 @@ def tool_example_to_messages( The list of messages per example by default corresponds to: - 1) HumanMessage: contains the content from which content should be extracted. - 2) AIMessage: contains the extracted information from the model - 3) ToolMessage: contains confirmation to the model that the model requested a tool - correctly. + 1. ``HumanMessage``: contains the content from which content should be extracted. + 2. ``AIMessage``: contains the extracted information from the model + 3. ``ToolMessage``: contains confirmation to the model that the model requested a + tool correctly. - If `ai_response` is specified, there will be a final AIMessage with that response. + If ``ai_response`` is specified, there will be a final ``AIMessage`` with that + response. - The ToolMessage is required because some chat models are hyper-optimized for agents - rather than for an extraction use case. + The ``ToolMessage`` is required because some chat models are hyper-optimized for + agents rather than for an extraction use case. Arguments: input: string, the user input @@ -657,7 +658,7 @@ def tool_example_to_messages( tool_outputs: Optional[list[str]], a list of tool call outputs. Does not need to be provided. If not provided, a placeholder value will be inserted. Defaults to None. - ai_response: Optional[str], if provided, content for a final AIMessage. + ai_response: Optional[str], if provided, content for a final ``AIMessage``. Returns: A list of messages @@ -739,6 +740,7 @@ def _parse_google_docstring( """Parse the function and argument descriptions from the docstring of a function. Assumes the function docstring follows Google Python style guide. + """ if docstring: docstring_blocks = docstring.split("\n\n") diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml index e329e0d3c5c..4917b2a52a9 100644 --- a/libs/core/pyproject.toml +++ b/libs/core/pyproject.toml @@ -28,7 +28,7 @@ repository = "https://github.com/langchain-ai/langchain" [dependency-groups] lint = ["ruff<0.13,>=0.12.2"] typing = [ - "mypy<1.16,>=1.15", + "mypy<1.18,>=1.17.1", "types-pyyaml<7.0.0.0,>=6.0.12.2", "types-requests<3.0.0.0,>=2.28.11.5", "langchain-text-splitters", @@ -86,6 +86,7 @@ ignore = [ "FIX002", # Line contains TODO "ISC001", # Messes with the formatter "PERF203", # Rarely useful + "PLC0414", # Enable re-export "PLR09", # Too many something (arg, statements, etc) "RUF012", # Doesn't play well with Pydantic "TC001", # Doesn't play well with Pydantic @@ -105,6 +106,7 @@ unfixable = ["PLW1510",] flake8-annotations.allow-star-arg-any = true flake8-annotations.mypy-init-return = true +flake8-builtins.ignorelist = ["id", "input", "type"] flake8-type-checking.runtime-evaluated-base-classes = ["pydantic.BaseModel","langchain_core.load.serializable.Serializable","langchain_core.runnables.base.RunnableSerializable"] pep8-naming.classmethod-decorators = [ "classmethod", "langchain_core.utils.pydantic.pre_init", "pydantic.field_validator", "pydantic.v1.root_validator",] pydocstyle.convention = "google" diff --git a/libs/core/tests/unit_tests/load/test_serializable.py b/libs/core/tests/unit_tests/load/test_serializable.py index 59bb6d54697..e782709fd41 100644 --- a/libs/core/tests/unit_tests/load/test_serializable.py +++ b/libs/core/tests/unit_tests/load/test_serializable.py @@ -1,6 +1,9 @@ +import json + +import pytest from pydantic import BaseModel, ConfigDict, Field -from langchain_core.load import Serializable, dumpd, load +from langchain_core.load import Serializable, dumpd, dumps, load from langchain_core.load.serializable import _is_field_useful from langchain_core.messages import AIMessage from langchain_core.outputs import ChatGeneration, Generation @@ -276,3 +279,92 @@ def test_serialization_with_ignore_unserializable_fields() -> None: ] ] } + + +# Tests for dumps() function +def test_dumps_basic_serialization() -> None: + """Test basic string serialization with `dumps()`.""" + foo = Foo(bar=42, baz="test") + json_str = dumps(foo) + + # Should be valid JSON + parsed = json.loads(json_str) + assert parsed == { + "id": ["tests", "unit_tests", "load", "test_serializable", "Foo"], + "kwargs": {"bar": 42, "baz": "test"}, + "lc": 1, + "type": "constructor", + } + + +def test_dumps_pretty_formatting() -> None: + """Test pretty printing functionality.""" + foo = Foo(bar=1, baz="hello") + + # Test pretty=True with default indent + pretty_json = dumps(foo, pretty=True) + assert " " in pretty_json + + # Test custom indent (4-space) + custom_indent = dumps(foo, pretty=True, indent=4) + assert " " in custom_indent + + # Verify it's still valid JSON + parsed = json.loads(pretty_json) + assert parsed["kwargs"]["bar"] == 1 + + +def test_dumps_invalid_default_kwarg() -> None: + """Test that passing `'default'` as kwarg raises ValueError.""" + foo = Foo(bar=1, baz="test") + + with pytest.raises(ValueError, match="`default` should not be passed to dumps"): + dumps(foo, default=lambda x: x) + + +def test_dumps_additional_json_kwargs() -> None: + """Test that additional JSON kwargs are passed through.""" + foo = Foo(bar=1, baz="test") + + compact_json = dumps(foo, separators=(",", ":")) + assert ", " not in compact_json # Should be compact + + # Test sort_keys + sorted_json = dumps(foo, sort_keys=True) + parsed = json.loads(sorted_json) + assert parsed == dumpd(foo) + + +def test_dumps_non_serializable_object() -> None: + """Test `dumps()` behavior with non-serializable objects.""" + + class NonSerializable: + def __init__(self, value: int) -> None: + self.value = value + + obj = NonSerializable(42) + json_str = dumps(obj) + + # Should create a "not_implemented" representation + parsed = json.loads(json_str) + assert parsed["lc"] == 1 + assert parsed["type"] == "not_implemented" + assert "NonSerializable" in parsed["repr"] + + +def test_dumps_mixed_data_structure() -> None: + """Test `dumps()` with complex nested data structures.""" + data = { + "serializable": Foo(bar=1, baz="test"), + "list": [1, 2, {"nested": "value"}], + "primitive": "string", + } + + json_str = dumps(data) + parsed = json.loads(json_str) + + # Serializable object should be properly serialized + assert parsed["serializable"]["type"] == "constructor" + # Primitives should remain unchanged + assert parsed["list"] == [1, 2, {"nested": "value"}] + assert parsed["primitive"] == "string" diff --git a/libs/core/tests/unit_tests/prompts/test_structured.py b/libs/core/tests/unit_tests/prompts/test_structured.py index fda2d00b367..4f5cbc9ab79 100644 --- a/libs/core/tests/unit_tests/prompts/test_structured.py +++ b/libs/core/tests/unit_tests/prompts/test_structured.py @@ -81,7 +81,7 @@ def test_structured_prompt_dict() -> None: chain = loads(dumps(prompt)) | model - assert chain.invoke({"hello": "there"}) == {"name": 1, "value": 42} # type: ignore[comparison-overlap] + assert chain.invoke({"hello": "there"}) == {"name": 1, "value": 42} def test_structured_prompt_kwargs() -> None: @@ -104,7 +104,7 @@ def test_structured_prompt_kwargs() -> None: assert chain.invoke({"hello": "there"}) == {"name": 1, "value": 7} # type: ignore[comparison-overlap] assert loads(dumps(prompt)).model_dump() == prompt.model_dump() chain = loads(dumps(prompt)) | model - assert chain.invoke({"hello": "there"}) == {"name": 1, "value": 7} # type: ignore[comparison-overlap] + assert chain.invoke({"hello": "there"}) == {"name": 1, "value": 7} class OutputSchema(BaseModel): name: str diff --git a/libs/core/tests/unit_tests/runnables/test_graph.py b/libs/core/tests/unit_tests/runnables/test_graph.py index 7944d0b1da4..fd9ff2f813e 100644 --- a/libs/core/tests/unit_tests/runnables/test_graph.py +++ b/libs/core/tests/unit_tests/runnables/test_graph.py @@ -14,9 +14,7 @@ from langchain_core.runnables import RunnableConfig from langchain_core.runnables.base import Runnable from langchain_core.runnables.graph import Edge, Graph, Node from langchain_core.runnables.graph_mermaid import _escape_node_label -from langchain_core.utils.pydantic import ( - PYDANTIC_VERSION, -) +from langchain_core.utils.pydantic import PYDANTIC_VERSION from tests.unit_tests.pydantic_utils import _normalize_schema diff --git a/libs/core/tests/unit_tests/runnables/test_runnable.py b/libs/core/tests/unit_tests/runnables/test_runnable.py index 4b63fb50ae2..d9a9db349e8 100644 --- a/libs/core/tests/unit_tests/runnables/test_runnable.py +++ b/libs/core/tests/unit_tests/runnables/test_runnable.py @@ -6,13 +6,7 @@ import warnings from collections.abc import AsyncIterator, Awaitable, Iterator, Sequence from functools import partial from operator import itemgetter -from typing import ( - Any, - Callable, - Optional, - Union, - cast, -) +from typing import Any, Callable, Optional, Union, cast from uuid import UUID import pytest @@ -37,11 +31,7 @@ from langchain_core.language_models import ( ) from langchain_core.load import dumpd, dumps from langchain_core.load.load import loads -from langchain_core.messages import ( - AIMessageChunk, - HumanMessage, - SystemMessage, -) +from langchain_core.messages import AIMessageChunk, HumanMessage, SystemMessage from langchain_core.messages.base import BaseMessage from langchain_core.output_parsers import ( BaseOutputParser, @@ -90,9 +80,7 @@ from langchain_core.tracers import ( RunLogPatch, ) from langchain_core.tracers.context import collect_runs -from langchain_core.utils.pydantic import ( - PYDANTIC_VERSION, -) +from langchain_core.utils.pydantic import PYDANTIC_VERSION from tests.unit_tests.pydantic_utils import _normalize_schema, _schema from tests.unit_tests.stubs import AnyStr, _any_id_ai_message, _any_id_ai_message_chunk @@ -243,7 +231,11 @@ def test_schemas(snapshot: SnapshotAssertion) -> None: } assert fake.get_config_jsonschema(include=["tags", "metadata", "run_name"]) == { "properties": { - "metadata": {"default": None, "title": "Metadata", "type": "object"}, + "metadata": { + "default": None, + "title": "Metadata", + "type": "object", + }, "run_name": {"default": None, "title": "Run Name", "type": "string"}, "tags": { "default": None, diff --git a/libs/core/uv.lock b/libs/core/uv.lock index 7ae9f48264b..d9282614cdf 100644 --- a/libs/core/uv.lock +++ b/libs/core/uv.lock @@ -1,9 +1,11 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.9" resolution-markers = [ - "python_full_version >= '3.13' and platform_python_implementation == 'PyPy'", - "python_full_version >= '3.13' and platform_python_implementation != 'PyPy'", + "python_full_version >= '3.14' and platform_python_implementation == 'PyPy'", + 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platform_python_implementation == 'PyPy'", "python_full_version == '3.10.*' and platform_python_implementation == 'PyPy'", "python_full_version < '3.10' and platform_python_implementation == 'PyPy'", @@ -3094,7 +3260,8 @@ name = "urllib3" version = "2.5.0" source = { registry = "https://pypi.org/simple" } resolution-markers = [ - "python_full_version >= '3.13' and platform_python_implementation != 'PyPy'", + "python_full_version >= '3.14' and platform_python_implementation != 'PyPy'", + "python_full_version == '3.13.*' and platform_python_implementation != 'PyPy'", "python_full_version >= '3.11' and python_full_version < '3.13' and platform_python_implementation != 'PyPy'", "python_full_version == '3.10.*' and platform_python_implementation != 'PyPy'", ] diff --git a/libs/langchain/langchain/evaluation/embedding_distance/base.py b/libs/langchain/langchain/evaluation/embedding_distance/base.py index bf65305114b..bf9e0a798a0 100644 --- a/libs/langchain/langchain/evaluation/embedding_distance/base.py +++ b/libs/langchain/langchain/evaluation/embedding_distance/base.py @@ -201,15 +201,41 @@ class _EmbeddingDistanceChainMixin(Chain): np.ndarray: The cosine distance. """ try: - from langchain_community.utils.math import cosine_similarity - except ImportError as e: - msg = ( - "The cosine_similarity function is required to compute cosine distance." - " Please install the langchain-community package using" - " `pip install langchain-community`." - ) - raise ImportError(msg) from e - return 1.0 - cosine_similarity(a, b) + from langchain_core.vectorstores.utils import _cosine_similarity + + return 1.0 - _cosine_similarity(a, b) + except ImportError: + # Fallback to scipy if available + try: + from scipy.spatial.distance import cosine + + return cosine(a.flatten(), b.flatten()) + except ImportError: + # Pure numpy fallback + if _check_numpy(): + np = _import_numpy() + a_flat = a.flatten() + b_flat = b.flatten() + dot_product = np.dot(a_flat, b_flat) + norm_a = np.linalg.norm(a_flat) + norm_b = np.linalg.norm(b_flat) + if norm_a == 0 or norm_b == 0: + return 0.0 + return 1.0 - (dot_product / (norm_a * norm_b)) + # Pure Python implementation + a_flat = a if hasattr(a, "__len__") else [a] + b_flat = b if hasattr(b, "__len__") else [b] + if hasattr(a, "flatten"): + a_flat = a.flatten() + if hasattr(b, "flatten"): + b_flat = b.flatten() + + dot_product = sum(x * y for x, y in zip(a_flat, b_flat)) + norm_a = sum(x * x for x in a_flat) ** 0.5 + norm_b = sum(x * x for x in b_flat) ** 0.5 + if norm_a == 0 or norm_b == 0: + return 0.0 + return 1.0 - (dot_product / (norm_a * norm_b)) @staticmethod def _euclidean_distance(a: Any, b: Any) -> Any: @@ -222,12 +248,17 @@ class _EmbeddingDistanceChainMixin(Chain): Returns: np.floating: The Euclidean distance. """ - if _check_numpy(): - import numpy as np + try: + from scipy.spatial.distance import euclidean - return np.linalg.norm(a - b) + return euclidean(a.flatten(), b.flatten()) + except ImportError: + if _check_numpy(): + import numpy as np - return sum((x - y) * (x - y) for x, y in zip(a, b)) ** 0.5 + return np.linalg.norm(a - b) + + return sum((x - y) * (x - y) for x, y in zip(a, b)) ** 0.5 @staticmethod def _manhattan_distance(a: Any, b: Any) -> Any: @@ -240,11 +271,16 @@ class _EmbeddingDistanceChainMixin(Chain): Returns: np.floating: The Manhattan distance. """ - if _check_numpy(): - np = _import_numpy() - return np.sum(np.abs(a - b)) + try: + from scipy.spatial.distance import cityblock - return sum(abs(x - y) for x, y in zip(a, b)) + return cityblock(a.flatten(), b.flatten()) + except ImportError: + if _check_numpy(): + np = _import_numpy() + return np.sum(np.abs(a - b)) + + return sum(abs(x - y) for x, y in zip(a, b)) @staticmethod def _chebyshev_distance(a: Any, b: Any) -> Any: @@ -257,11 +293,16 @@ class _EmbeddingDistanceChainMixin(Chain): Returns: np.floating: The Chebyshev distance. """ - if _check_numpy(): - np = _import_numpy() - return np.max(np.abs(a - b)) + try: + from scipy.spatial.distance import chebyshev - return max(abs(x - y) for x, y in zip(a, b)) + return chebyshev(a.flatten(), b.flatten()) + except ImportError: + if _check_numpy(): + np = _import_numpy() + return np.max(np.abs(a - b)) + + return max(abs(x - y) for x, y in zip(a, b)) @staticmethod def _hamming_distance(a: Any, b: Any) -> Any: @@ -274,11 +315,16 @@ class _EmbeddingDistanceChainMixin(Chain): Returns: np.floating: The Hamming distance. """ - if _check_numpy(): - np = _import_numpy() - return np.mean(a != b) + try: + from scipy.spatial.distance import hamming - return sum(1 for x, y in zip(a, b) if x != y) / len(a) + return hamming(a.flatten(), b.flatten()) + except ImportError: + if _check_numpy(): + np = _import_numpy() + return np.mean(a != b) + + return sum(1 for x, y in zip(a, b) if x != y) / len(a) def _compute_score(self, vectors: Any) -> float: """Compute the score based on the distance metric. diff --git a/libs/langchain/pyproject.toml b/libs/langchain/pyproject.toml index 1ec57de2480..1b96744cbad 100644 --- a/libs/langchain/pyproject.toml +++ b/libs/langchain/pyproject.toml @@ -25,9 +25,9 @@ readme = "README.md" community = ["langchain-community"] anthropic = ["langchain-anthropic"] openai = ["langchain-openai"] -azure-ai = ["langchain-azure-ai"] -cohere = ["langchain-cohere"] -google-vertexai = ["langchain-google-vertexai"] +# azure-ai = ["langchain-azure-ai"] +# cohere = ["langchain-cohere"] +# google-vertexai = ["langchain-google-vertexai"] google-genai = ["langchain-google-genai"] fireworks = ["langchain-fireworks"] ollama = ["langchain-ollama"] @@ -35,9 +35,9 @@ together = ["langchain-together"] mistralai = ["langchain-mistralai"] huggingface = ["langchain-huggingface"] groq = ["langchain-groq"] -aws = ["langchain-aws"] +# aws = ["langchain-aws"] deepseek = ["langchain-deepseek"] -xai = ["langchain-xai"] +# xai = ["langchain-xai"] perplexity = ["langchain-perplexity"] [project.urls] diff --git a/libs/langchain/tests/unit_tests/agents/format_scratchpad/test_openai_tools.py b/libs/langchain/tests/unit_tests/agents/format_scratchpad/test_openai_tools.py index 04d33d12f4e..089f31f47f7 100644 --- a/libs/langchain/tests/unit_tests/agents/format_scratchpad/test_openai_tools.py +++ b/libs/langchain/tests/unit_tests/agents/format_scratchpad/test_openai_tools.py @@ -53,7 +53,12 @@ def test_calls_convert_agent_action_to_messages() -> None: message4 = AIMessage( content="", tool_calls=[ - ToolCall(name="exponentiate", args={"a": 3, "b": 5}, id="call_abc02468"), + ToolCall( + name="exponentiate", + args={"a": 3, "b": 5}, + id="call_abc02468", + type="tool_call", + ), ], ) actions4 = parse_ai_message_to_openai_tool_action(message4) diff --git a/libs/langchain/tests/unit_tests/agents/test_agent.py b/libs/langchain/tests/unit_tests/agents/test_agent.py index ea6c455db69..d1bc7e6c2a0 100644 --- a/libs/langchain/tests/unit_tests/agents/test_agent.py +++ b/libs/langchain/tests/unit_tests/agents/test_agent.py @@ -1008,7 +1008,7 @@ def _make_tools_invocation(name_to_arguments: dict[str, dict[str, Any]]) -> AIMe for idx, (name, arguments) in enumerate(name_to_arguments.items()) ] tool_calls = [ - ToolCall(name=name, args=args, id=str(idx)) + ToolCall(name=name, args=args, id=str(idx), type="tool_call") for idx, (name, args) in enumerate(name_to_arguments.items()) ] return AIMessage( diff --git a/libs/langchain/uv.lock b/libs/langchain/uv.lock index e7cf3e95dfe..c569cf265ab 100644 --- a/libs/langchain/uv.lock +++ b/libs/langchain/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.9, <4.0" resolution-markers = [ "python_full_version >= '3.13' and platform_python_implementation == 'PyPy'", @@ -272,81 +272,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/fc/30/d4986a882011f9df997a55e6becd864812ccfcd821d64aac8570ee39f719/attrs-25.1.0-py3-none-any.whl", hash = "sha256:c75a69e28a550a7e93789579c22aa26b0f5b83b75dc4e08fe092980051e1090a", size = 63152, upload-time = "2025-01-25T11:30:10.164Z" }, ] -[[package]] -name = "azure-ai-inference" -version = "1.0.0b9" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - 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{ name = "mypy", specifier = ">=1.15,<1.16" }, + { name = "mypy", specifier = ">=1.17.1,<1.18" }, { name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" }, { name = "types-requests", specifier = ">=2.28.11.5,<3.0.0.0" }, ] @@ -598,7 +598,7 @@ requires-dist = [ [package.metadata.requires-dev] codespell = [{ name = "codespell", specifier = ">=2.2.0,<3.0.0" }] -lint = [{ name = "ruff", specifier = ">=0.12.2,<0.13" }] +lint = [{ name = "ruff", specifier = ">=0.12.8,<0.13" }] test = [{ name = "langchain-core", editable = "../../core" }] test-integration = [] typing = [ diff --git a/libs/partners/fireworks/pyproject.toml b/libs/partners/fireworks/pyproject.toml index 93dd12fd84b..fad2a199cdb 100644 --- a/libs/partners/fireworks/pyproject.toml +++ b/libs/partners/fireworks/pyproject.toml @@ -95,6 +95,8 @@ ignore = [ "D104", # Missing docstring in public package "D105", # Missing docstring in magic method "D107", # Missing docstring in __init__ + "D203", + "D213", "COM812", # Messes with the formatter "ISC001", # Messes with the formatter "PERF203", # Rarely useful @@ -121,4 +123,4 @@ asyncio_mode = "auto" "tests/**/*.py" = [ "S101", # Tests need assertions "S311", # Standard pseudo-random generators are not suitable for cryptographic purposes -] \ No newline at end of file +] diff --git a/libs/partners/fireworks/uv.lock b/libs/partners/fireworks/uv.lock index 41b62b0c51d..98879efafcf 100644 --- a/libs/partners/fireworks/uv.lock +++ b/libs/partners/fireworks/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.9" resolution-markers = [ "python_full_version >= '3.13' and platform_python_implementation == 'PyPy'", @@ -704,7 +704,7 @@ test = [ test-integration = [] typing = [ { name = "langchain-text-splitters", directory = "../../text-splitters" }, - { name = "mypy", specifier = ">=1.15,<1.16" }, + { name = "mypy", specifier = ">=1.17.1,<1.18" }, { name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" }, { name = "types-requests", specifier = ">=2.28.11.5,<3.0.0.0" }, ] diff --git a/libs/partners/groq/pyproject.toml b/libs/partners/groq/pyproject.toml index 570383ac4cb..46845695580 100644 --- a/libs/partners/groq/pyproject.toml +++ b/libs/partners/groq/pyproject.toml @@ -88,6 +88,8 @@ ignore = [ "D104", # Missing docstring in public package "D105", # Missing docstring in magic method "D107", # Missing docstring in __init__ + "D203", + "D213", "COM812", # Messes with the formatter "ISC001", # Messes with the formatter "PERF203", # Rarely useful diff --git a/libs/partners/groq/uv.lock b/libs/partners/groq/uv.lock index 2820d1a6b29..ea02583c522 100644 --- a/libs/partners/groq/uv.lock +++ b/libs/partners/groq/uv.lock @@ -331,7 +331,7 @@ wheels = [ [[package]] name = "langchain-core" -version = "0.3.72" +version = "0.3.74" source = { editable = "../../core" } dependencies = [ { name = "jsonpatch" }, @@ -382,7 +382,7 @@ test = [ test-integration = [] typing = [ { name = "langchain-text-splitters", directory = "../../text-splitters" }, - { name = "mypy", specifier = ">=1.15,<1.16" }, + { name = "mypy", specifier = ">=1.17.1,<1.18" }, { name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" }, { name = "types-requests", specifier = ">=2.28.11.5,<3.0.0.0" }, ] @@ -485,7 +485,7 @@ requires-dist = [ [package.metadata.requires-dev] codespell = [{ name = "codespell", specifier = ">=2.2.0,<3.0.0" }] -lint = [{ name = "ruff", specifier = ">=0.12.2,<0.13" }] +lint = [{ name = "ruff", specifier = ">=0.12.8,<0.13" }] test = [{ name = "langchain-core", editable = "../../core" }] test-integration = [] typing = [ diff --git a/libs/partners/huggingface/pyproject.toml b/libs/partners/huggingface/pyproject.toml index 48ee6132566..13ed737ba80 100644 --- a/libs/partners/huggingface/pyproject.toml +++ b/libs/partners/huggingface/pyproject.toml @@ -102,6 +102,8 @@ ignore = [ "D104", # pydocstyle: Missing docstring in public package "D105", # pydocstyle: Missing docstring in magic method "D107", # pydocstyle: Missing docstring in __init__ + "D203", + "D213", "D407", # pydocstyle: Missing-dashed-underline-after-section "COM812", # Messes with the formatter "ISC001", # Messes with the formatter diff --git a/libs/partners/huggingface/uv.lock b/libs/partners/huggingface/uv.lock index 2720641279b..d12b463ac53 100644 --- a/libs/partners/huggingface/uv.lock +++ b/libs/partners/huggingface/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.9" resolution-markers = [ "python_full_version >= '3.13' and platform_python_implementation == 'PyPy'", @@ -976,7 +976,7 @@ test = [ test-integration = [] typing = [ { name = "langchain-text-splitters", directory = "../../text-splitters" }, - { name = "mypy", specifier = ">=1.15,<1.16" }, + { name = "mypy", specifier = ">=1.17.1,<1.18" }, { name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" }, { name = "types-requests", specifier = ">=2.28.11.5,<3.0.0.0" }, ] diff --git a/libs/partners/ollama/Makefile b/libs/partners/ollama/Makefile index 68f307e27e1..de0768ab599 100644 --- a/libs/partners/ollama/Makefile +++ b/libs/partners/ollama/Makefile @@ -8,10 +8,14 @@ UV_FROZEN = true # Define a variable for the test file path. TEST_FILE ?= tests/unit_tests/ -integration_test: TEST_FILE = tests/integration_tests/ +integration_test integration_tests: TEST_FILE = tests/integration_tests/ # note: leaving out integration_tests (with s) command to skip release testing for now # TODO(erick) configure ollama server to run in CI, in separate repo +# Define variables for test model configuration +OLLAMA_TEST_MODEL ?= llama3.1 +OLLAMA_REASONING_TEST_MODEL ?= deepseek-r1:1.5b + # unit tests are run with the --disable-socket flag to prevent network calls test tests: @@ -22,10 +26,9 @@ test_watch: # integration tests are run without the --disable-socket flag to allow network calls -integration_test: - uv run --group test --group test_integration pytest $(TEST_FILE) +integration_test integration_tests: + OLLAMA_TEST_MODEL=$(OLLAMA_TEST_MODEL) OLLAMA_REASONING_TEST_MODEL=$(OLLAMA_REASONING_TEST_MODEL) uv run --group test --group test_integration pytest $(TEST_FILE) # note: leaving out integration_tests (with s) command to skip release testing for now -# TODO(erick) configure ollama server to run in CI, in separate repo ###################### # LINTING AND FORMATTING @@ -70,3 +73,6 @@ help: @echo 'test - run unit tests' @echo 'tests - run unit tests' @echo 'test TEST_FILE= - run all tests in file' + @echo 'integration_test - run integration tests' + @echo 'integration_test OLLAMA_TEST_MODEL= - run integration tests with specific model' + @echo ' Example: make integration_test OLLAMA_TEST_MODEL=llama3.1' diff --git a/libs/partners/ollama/langchain_ollama/__init__.py b/libs/partners/ollama/langchain_ollama/__init__.py index 4d9864fc6a2..3789c6de8a4 100644 --- a/libs/partners/ollama/langchain_ollama/__init__.py +++ b/libs/partners/ollama/langchain_ollama/__init__.py @@ -10,6 +10,7 @@ service. exist locally. This is useful for ensuring that the model is available before attempting to use it, especially in environments where models may not be pre-downloaded. + """ from importlib import metadata @@ -19,6 +20,8 @@ from langchain_ollama.embeddings import OllamaEmbeddings from langchain_ollama.llms import OllamaLLM try: + if __package__ is None: + raise metadata.PackageNotFoundError __version__ = metadata.version(__package__) except metadata.PackageNotFoundError: # Case where package metadata is not available. diff --git a/libs/partners/ollama/langchain_ollama/_utils.py b/libs/partners/ollama/langchain_ollama/_utils.py index f3cd6fe9a4d..8d08ed87a41 100644 --- a/libs/partners/ollama/langchain_ollama/_utils.py +++ b/libs/partners/ollama/langchain_ollama/_utils.py @@ -1,11 +1,11 @@ -"""Utility functions for validating Ollama models.""" +"""Utility function to validate Ollama models.""" from httpx import ConnectError from ollama import Client, ResponseError def validate_model(client: Client, model_name: str) -> None: - """Validate that a model exists in the Ollama instance. + """Validate that a model exists in the local Ollama instance. Args: client: The Ollama client. @@ -29,7 +29,10 @@ def validate_model(client: Client, model_name: str) -> None: ) raise ValueError(msg) except ConnectError as e: - msg = "Failed to connect to Ollama. Please check that Ollama is downloaded, running and accessible. https://ollama.com/download" # noqa: E501 + msg = ( + "Failed to connect to Ollama. Please check that Ollama is downloaded, " + "running and accessible. https://ollama.com/download" + ) raise ValueError(msg) from e except ResponseError as e: msg = ( diff --git a/libs/partners/ollama/langchain_ollama/chat_models.py b/libs/partners/ollama/langchain_ollama/chat_models.py index ae836ed5d08..aa350643101 100644 --- a/libs/partners/ollama/langchain_ollama/chat_models.py +++ b/libs/partners/ollama/langchain_ollama/chat_models.py @@ -87,8 +87,8 @@ def _parse_json_string( ) -> Any: """Attempt to parse a JSON string for tool calling. - It first tries to use the standard json.loads. If that fails, it falls - back to ast.literal_eval to safely parse Python literals, which is more + It first tries to use the standard ``json.loads``. If that fails, it falls + back to ``ast.literal_eval`` to safely parse Python literals, which is more robust against models using single quotes or containing apostrophes. Args: @@ -100,7 +100,8 @@ def _parse_json_string( The parsed JSON string or Python literal. Raises: - OutputParserException: If the string is invalid and skip=False. + OutputParserException: If the string is invalid and ``skip=False``. + """ try: return json.loads(json_string) @@ -138,14 +139,20 @@ def _parse_arguments_from_tool_call( Band-aid fix for issue in Ollama with inconsistent tool call argument structure. Should be removed/changed if fixed upstream. + See https://github.com/ollama/ollama/issues/6155 + """ if "function" not in raw_tool_call: return None + function_name = raw_tool_call["function"]["name"] arguments = raw_tool_call["function"]["arguments"] parsed_arguments: dict = {} if isinstance(arguments, dict): for key, value in arguments.items(): + # Filter out metadata fields like 'functionName' that echo function name + if key == "functionName" and value == function_name: + continue if isinstance(value, str): parsed_value = _parse_json_string( value, skip=True, raw_tool_call=raw_tool_call @@ -446,26 +453,26 @@ class ChatOllama(BaseChatModel): mirostat: Optional[int] = None """Enable Mirostat sampling for controlling perplexity. - (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)""" + (default: ``0``, ``0`` = disabled, ``1`` = Mirostat, ``2`` = Mirostat 2.0)""" mirostat_eta: Optional[float] = None """Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make - the algorithm more responsive. (Default: 0.1)""" + the algorithm more responsive. (Default: ``0.1``)""" mirostat_tau: Optional[float] = None """Controls the balance between coherence and diversity of the output. A lower value will result in more focused and - coherent text. (Default: 5.0)""" + coherent text. (Default: ``5.0``)""" num_ctx: Optional[int] = None """Sets the size of the context window used to generate the - next token. (Default: 2048) """ + next token. (Default: ``2048``) """ num_gpu: Optional[int] = None - """The number of GPUs to use. On macOS it defaults to 1 to - enable metal support, 0 to disable.""" + """The number of GPUs to use. On macOS it defaults to ``1`` to + enable metal support, ``0`` to disable.""" num_thread: Optional[int] = None """Sets the number of threads to use during computation. @@ -475,20 +482,20 @@ class ChatOllama(BaseChatModel): num_predict: Optional[int] = None """Maximum number of tokens to predict when generating text. - (Default: 128, -1 = infinite generation, -2 = fill context)""" + (Default: ``128``, ``-1`` = infinite generation, ``-2`` = fill context)""" repeat_last_n: Optional[int] = None """Sets how far back for the model to look back to prevent - repetition. (Default: 64, 0 = disabled, -1 = num_ctx)""" + repetition. (Default: ``64``, ``0`` = disabled, ``-1`` = ``num_ctx``)""" repeat_penalty: Optional[float] = None - """Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) - will penalize repetitions more strongly, while a lower value (e.g., 0.9) - will be more lenient. (Default: 1.1)""" + """Sets how strongly to penalize repetitions. A higher value (e.g., ``1.5``) + will penalize repetitions more strongly, while a lower value (e.g., ``0.9``) + will be more lenient. (Default: ``1.1``)""" temperature: Optional[float] = None """The temperature of the model. Increasing the temperature will - make the model answer more creatively. (Default: 0.8)""" + make the model answer more creatively. (Default: ``0.8``)""" seed: Optional[int] = None """Sets the random number seed to use for generation. Setting this @@ -500,21 +507,21 @@ class ChatOllama(BaseChatModel): tfs_z: Optional[float] = None """Tail free sampling is used to reduce the impact of less probable - tokens from the output. A higher value (e.g., 2.0) will reduce the - impact more, while a value of 1.0 disables this setting. (default: 1)""" + tokens from the output. A higher value (e.g., ``2.0``) will reduce the + impact more, while a value of ``1.0`` disables this setting. (default: ``1``)""" top_k: Optional[int] = None - """Reduces the probability of generating nonsense. A higher value (e.g. 100) - will give more diverse answers, while a lower value (e.g. 10) - will be more conservative. (Default: 40)""" + """Reduces the probability of generating nonsense. A higher value (e.g. ``100``) + will give more diverse answers, while a lower value (e.g. ``10``) + will be more conservative. (Default: ``40``)""" top_p: Optional[float] = None - """Works together with top-k. A higher value (e.g., 0.95) will lead - to more diverse text, while a lower value (e.g., 0.5) will - generate more focused and conservative text. (Default: 0.9)""" + """Works together with top-k. A higher value (e.g., ``0.95``) will lead + to more diverse text, while a lower value (e.g., ``0.5``) will + generate more focused and conservative text. (Default: ``0.9``)""" format: Optional[Union[Literal["", "json"], JsonSchemaValue]] = None - """Specify the format of the output (options: "json", JSON schema).""" + """Specify the format of the output (options: ``'json'``, JSON schema).""" keep_alive: Optional[Union[int, str]] = None """How long the model will stay loaded into memory.""" @@ -524,32 +531,35 @@ class ChatOllama(BaseChatModel): client_kwargs: Optional[dict] = {} """Additional kwargs to pass to the httpx clients. + These arguments are passed to both synchronous and async clients. - Use sync_client_kwargs and async_client_kwargs to pass different arguments + + Use ``sync_client_kwargs`` and ``async_client_kwargs`` to pass different arguments to synchronous and asynchronous clients. + """ async_client_kwargs: Optional[dict] = {} - """Additional kwargs to merge with client_kwargs before + """Additional kwargs to merge with ``client_kwargs`` before passing to the httpx AsyncClient. + `Full list of params. `__ + """ sync_client_kwargs: Optional[dict] = {} - """Additional kwargs to merge with client_kwargs before + """Additional kwargs to merge with ``client_kwargs`` before passing to the httpx Client. + `Full list of params. `__ + """ _client: Client = PrivateAttr() - """ - The client to use for making requests. - """ + """The client to use for making requests.""" _async_client: AsyncClient = PrivateAttr() - """ - The async client to use for making requests. - """ + """The async client to use for making requests.""" def _chat_params( self, @@ -1064,14 +1074,14 @@ class ChatOllama(BaseChatModel): - ``'function_calling'``: Uses Ollama's tool-calling API - ``'json_mode'``: - Specifies ``format="json"``. Note that if using JSON mode then you + Specifies ``format='json'``. Note that if using JSON mode then you must include instructions for formatting the output into the desired schema into the model call. include_raw: If False then only the parsed structured output is returned. If an error occurs during model output parsing it will be raised. If True - then both the raw model response (a BaseMessage) and the parsed model + then both the raw model response (a ``BaseMessage``) and the parsed model response will be returned. If an error occurs during output parsing it will be caught and returned as well. The final output is always a dict with keys ``'raw'``, ``'parsed'``, and ``'parsing_error'``. @@ -1085,7 +1095,7 @@ class ChatOllama(BaseChatModel): If ``include_raw`` is True, then Runnable outputs a dict with keys: - - ``'raw'``: BaseMessage + - ``'raw'``: ``BaseMessage`` - ``'parsed'``: None if there was a parsing error, otherwise the type depends on the ``schema`` as described above. - ``'parsing_error'``: Optional[BaseException] @@ -1130,7 +1140,7 @@ class ChatOllama(BaseChatModel): # justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.' # ) - .. dropdown:: Example: schema=Pydantic class, method="json_schema", include_raw=True + .. dropdown:: Example: ``schema=Pydantic`` class, ``method='json_schema'``, ``include_raw=True`` .. code-block:: python @@ -1159,7 +1169,7 @@ class ChatOllama(BaseChatModel): # 'parsing_error': None # } - .. dropdown:: Example: schema=Pydantic class, method="function_calling", include_raw=False + .. dropdown:: Example: ``schema=Pydantic`` class, ``method='function_calling'``, ``include_raw=False`` .. code-block:: python @@ -1223,7 +1233,7 @@ class ChatOllama(BaseChatModel): # 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.' # } - .. dropdown:: Example: schema=OpenAI function schema, method="function_calling", include_raw=False + .. dropdown:: Example: ``schema=OpenAI`` function schema, ``method='function_calling'``, ``include_raw=False`` .. code-block:: python @@ -1253,7 +1263,7 @@ class ChatOllama(BaseChatModel): # 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.' # } - .. dropdown:: Example: schema=Pydantic class, method="json_mode", include_raw=True + .. dropdown:: Example: ``schema=Pydantic`` class, ``method='json_mode'``, ``include_raw=True`` .. code-block:: diff --git a/libs/partners/ollama/langchain_ollama/embeddings.py b/libs/partners/ollama/langchain_ollama/embeddings.py index ac5619a3b06..5f6c96a2997 100644 --- a/libs/partners/ollama/langchain_ollama/embeddings.py +++ b/libs/partners/ollama/langchain_ollama/embeddings.py @@ -21,12 +21,12 @@ class OllamaEmbeddings(BaseModel, Embeddings): """Ollama embedding model integration. Set up a local Ollama instance: - Install the Ollama package and set up a local Ollama instance - using the instructions here: https://github.com/ollama/ollama . + `Install the Ollama package `__ and set up a + local Ollama instance. You will need to choose a model to serve. - You can view a list of available models via the model library (https://ollama.com/library). + You can view a list of available models via `the model library `__. To fetch a model from the Ollama model library use ``ollama pull ``. @@ -39,8 +39,8 @@ class OllamaEmbeddings(BaseModel, Embeddings): This will download the default tagged version of the model. Typically, the default points to the latest, smallest sized-parameter model. - * On Mac, the models will be downloaded to ~/.ollama/models - * On Linux (or WSL), the models will be stored at /usr/share/ollama/.ollama/models + * On Mac, the models will be downloaded to ``~/.ollama/models`` + * On Linux (or WSL), the models will be stored at ``/usr/share/ollama/.ollama/models`` You can specify the exact version of the model of interest as such ``ollama pull vicuna:13b-v1.5-16k-q4_0``. @@ -132,6 +132,7 @@ class OllamaEmbeddings(BaseModel, Embeddings): """Whether to validate the model exists in ollama locally on initialization. .. versionadded:: 0.3.4 + """ base_url: Optional[str] = None @@ -139,60 +140,62 @@ class OllamaEmbeddings(BaseModel, Embeddings): client_kwargs: Optional[dict] = {} """Additional kwargs to pass to the httpx clients. + These arguments are passed to both synchronous and async clients. - Use sync_client_kwargs and async_client_kwargs to pass different arguments + + Use ``sync_client_kwargs`` and ``async_client_kwargs`` to pass different arguments to synchronous and asynchronous clients. + """ async_client_kwargs: Optional[dict] = {} - """Additional kwargs to merge with client_kwargs before passing to the httpx + """Additional kwargs to merge with ``client_kwargs`` before passing to the httpx AsyncClient. For a full list of the params, see the `HTTPX documentation `__. + """ sync_client_kwargs: Optional[dict] = {} - """Additional kwargs to merge with client_kwargs before passing to the HTTPX Client. + """Additional kwargs to merge with ``client_kwargs`` before + passing to the HTTPX Client. For a full list of the params, see the `HTTPX documentation `__. + """ _client: Optional[Client] = PrivateAttr(default=None) - """ - The client to use for making requests. - """ + """The client to use for making requests.""" _async_client: Optional[AsyncClient] = PrivateAttr(default=None) - """ - The async client to use for making requests. - """ + """The async client to use for making requests.""" mirostat: Optional[int] = None """Enable Mirostat sampling for controlling perplexity. - (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)""" + (default: ``0``, ``0`` = disabled, ``1`` = Mirostat, ``2`` = Mirostat 2.0)""" mirostat_eta: Optional[float] = None """Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make - the algorithm more responsive. (Default: 0.1)""" + the algorithm more responsive. (Default: ``0.1``)""" mirostat_tau: Optional[float] = None """Controls the balance between coherence and diversity of the output. A lower value will result in more focused and - coherent text. (Default: 5.0)""" + coherent text. (Default: ``5.0``)""" num_ctx: Optional[int] = None """Sets the size of the context window used to generate the - next token. (Default: 2048) """ + next token. (Default: ``2048``) """ num_gpu: Optional[int] = None - """The number of GPUs to use. On macOS it defaults to 1 to - enable metal support, 0 to disable.""" + """The number of GPUs to use. On macOS it defaults to ``1`` to + enable metal support, ``0`` to disable.""" keep_alive: Optional[int] = None - """controls how long the model will stay loaded into memory - following the request (default: 5m) + """Controls how long the model will stay loaded into memory + following the request (default: ``5m``) """ num_thread: Optional[int] = None @@ -203,34 +206,34 @@ class OllamaEmbeddings(BaseModel, Embeddings): repeat_last_n: Optional[int] = None """Sets how far back for the model to look back to prevent - repetition. (Default: 64, 0 = disabled, -1 = num_ctx)""" + repetition. (Default: ``64``, ``0`` = disabled, ``-1`` = ``num_ctx``)""" repeat_penalty: Optional[float] = None - """Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) - will penalize repetitions more strongly, while a lower value (e.g., 0.9) - will be more lenient. (Default: 1.1)""" + """Sets how strongly to penalize repetitions. A higher value (e.g., ``1.5``) + will penalize repetitions more strongly, while a lower value (e.g., ``0.9``) + will be more lenient. (Default: ``1.1``)""" temperature: Optional[float] = None """The temperature of the model. Increasing the temperature will - make the model answer more creatively. (Default: 0.8)""" + make the model answer more creatively. (Default: ``0.8``)""" stop: Optional[list[str]] = None """Sets the stop tokens to use.""" tfs_z: Optional[float] = None """Tail free sampling is used to reduce the impact of less probable - tokens from the output. A higher value (e.g., 2.0) will reduce the - impact more, while a value of 1.0 disables this setting. (default: 1)""" + tokens from the output. A higher value (e.g., ``2.0``) will reduce the + impact more, while a value of ``1.0`` disables this setting. (default: ``1``)""" top_k: Optional[int] = None - """Reduces the probability of generating nonsense. A higher value (e.g. 100) - will give more diverse answers, while a lower value (e.g. 10) - will be more conservative. (Default: 40)""" + """Reduces the probability of generating nonsense. A higher value (e.g. ``100``) + will give more diverse answers, while a lower value (e.g. ``10``) + will be more conservative. (Default: ``40``)""" top_p: Optional[float] = None - """Works together with top-k. A higher value (e.g., 0.95) will lead - to more diverse text, while a lower value (e.g., 0.5) will - generate more focused and conservative text. (Default: 0.9)""" + """Works together with top-k. A higher value (e.g., ``0.95``) will lead + to more diverse text, while a lower value (e.g., ``0.5``) will + generate more focused and conservative text. (Default: ``0.9``)""" model_config = ConfigDict( extra="forbid", @@ -257,7 +260,7 @@ class OllamaEmbeddings(BaseModel, Embeddings): @model_validator(mode="after") def _set_clients(self) -> Self: - """Set clients to use for ollama.""" + """Set clients to use for Ollama.""" client_kwargs = self.client_kwargs or {} sync_client_kwargs = client_kwargs diff --git a/libs/partners/ollama/langchain_ollama/llms.py b/libs/partners/ollama/langchain_ollama/llms.py index b433606340d..5ca2c961a2d 100644 --- a/libs/partners/ollama/langchain_ollama/llms.py +++ b/libs/partners/ollama/langchain_ollama/llms.py @@ -61,26 +61,26 @@ class OllamaLLM(BaseLLM): mirostat: Optional[int] = None """Enable Mirostat sampling for controlling perplexity. - (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)""" + (default: ``0``, ``0`` = disabled, ``1`` = Mirostat, ``2`` = Mirostat 2.0)""" mirostat_eta: Optional[float] = None """Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make - the algorithm more responsive. (Default: 0.1)""" + the algorithm more responsive. (Default: ``0.1``)""" mirostat_tau: Optional[float] = None """Controls the balance between coherence and diversity of the output. A lower value will result in more focused and - coherent text. (Default: 5.0)""" + coherent text. (Default: ``5.0``)""" num_ctx: Optional[int] = None """Sets the size of the context window used to generate the - next token. (Default: 2048)""" + next token. (Default: ``2048``)""" num_gpu: Optional[int] = None - """The number of GPUs to use. On macOS it defaults to 1 to - enable metal support, 0 to disable.""" + """The number of GPUs to use. On macOS it defaults to ``1`` to + enable metal support, ``0`` to disable.""" num_thread: Optional[int] = None """Sets the number of threads to use during computation. @@ -90,20 +90,20 @@ class OllamaLLM(BaseLLM): num_predict: Optional[int] = None """Maximum number of tokens to predict when generating text. - (Default: 128, -1 = infinite generation, -2 = fill context)""" + (Default: ``128``, ``-1`` = infinite generation, ``-2`` = fill context)""" repeat_last_n: Optional[int] = None """Sets how far back for the model to look back to prevent - repetition. (Default: 64, 0 = disabled, -1 = num_ctx)""" + repetition. (Default: ``64``, ``0`` = disabled, ``-1`` = ``num_ctx``)""" repeat_penalty: Optional[float] = None - """Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) - will penalize repetitions more strongly, while a lower value (e.g., 0.9) - will be more lenient. (Default: 1.1)""" + """Sets how strongly to penalize repetitions. A higher value (e.g., ``1.5``) + will penalize repetitions more strongly, while a lower value (e.g., ``0.9``) + will be more lenient. (Default: ``1.1``)""" temperature: Optional[float] = None """The temperature of the model. Increasing the temperature will - make the model answer more creatively. (Default: 0.8)""" + make the model answer more creatively. (Default: ``0.8``)""" seed: Optional[int] = None """Sets the random number seed to use for generation. Setting this @@ -115,21 +115,21 @@ class OllamaLLM(BaseLLM): tfs_z: Optional[float] = None """Tail free sampling is used to reduce the impact of less probable - tokens from the output. A higher value (e.g., 2.0) will reduce the - impact more, while a value of 1.0 disables this setting. (default: 1)""" + tokens from the output. A higher value (e.g., ``2.0``) will reduce the + impact more, while a value of 1.0 disables this setting. (default: ``1``)""" top_k: Optional[int] = None - """Reduces the probability of generating nonsense. A higher value (e.g. 100) - will give more diverse answers, while a lower value (e.g. 10) - will be more conservative. (Default: 40)""" + """Reduces the probability of generating nonsense. A higher value (e.g. ``100``) + will give more diverse answers, while a lower value (e.g. ``10``) + will be more conservative. (Default: ``40``)""" top_p: Optional[float] = None - """Works together with top-k. A higher value (e.g., 0.95) will lead - to more diverse text, while a lower value (e.g., 0.5) will - generate more focused and conservative text. (Default: 0.9)""" + """Works together with top-k. A higher value (e.g., ``0.95``) will lead + to more diverse text, while a lower value (e.g., ``0.5``) will + generate more focused and conservative text. (Default: ``0.9``)""" format: Literal["", "json"] = "" - """Specify the format of the output (options: json)""" + """Specify the format of the output (options: ``'json'``)""" keep_alive: Optional[Union[int, str]] = None """How long the model will stay loaded into memory.""" @@ -139,33 +139,35 @@ class OllamaLLM(BaseLLM): client_kwargs: Optional[dict] = {} """Additional kwargs to pass to the httpx clients. + These arguments are passed to both synchronous and async clients. - Use sync_client_kwargs and async_client_kwargs to pass different arguments + + Use ``sync_client_kwargs`` and ``async_client_kwargs`` to pass different arguments to synchronous and asynchronous clients. + """ async_client_kwargs: Optional[dict] = {} - """Additional kwargs to merge with client_kwargs before passing to the HTTPX + """Additional kwargs to merge with ``client_kwargs`` before passing to the HTTPX AsyncClient. For a full list of the params, see the `HTTPX documentation `__. + """ sync_client_kwargs: Optional[dict] = {} - """Additional kwargs to merge with client_kwargs before passing to the HTTPX Client. + """Additional kwargs to merge with ``client_kwargs`` before + passing to the HTTPX Client. For a full list of the params, see the `HTTPX documentation `__. + """ _client: Optional[Client] = PrivateAttr(default=None) - """ - The client to use for making requests. - """ + """The client to use for making requests.""" _async_client: Optional[AsyncClient] = PrivateAttr(default=None) - """ - The async client to use for making requests. - """ + """The async client to use for making requests.""" def _generate_params( self, diff --git a/libs/partners/ollama/pyproject.toml b/libs/partners/ollama/pyproject.toml index 2cafc288491..7d7a6e35387 100644 --- a/libs/partners/ollama/pyproject.toml +++ b/libs/partners/ollama/pyproject.toml @@ -124,4 +124,4 @@ asyncio_mode = "auto" "tests/**/*.py" = [ "S101", # Tests need assertions "S311", # Standard pseudo-random generators are not suitable for cryptographic purposes -] \ No newline at end of file +] diff --git a/libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models.py b/libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models.py index c24a75297f1..650f77f184d 100644 --- a/libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models.py +++ b/libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models.py @@ -15,7 +15,7 @@ DEFAULT_MODEL_NAME = "llama3.1" @pytest.mark.parametrize(("method"), [("function_calling"), ("json_schema")]) def test_structured_output(method: str) -> None: - """Test to verify structured output via tool calling and ``format`` parameter.""" + """Test to verify structured output via tool calling and `format` parameter.""" class Joke(BaseModel): """Joke to tell user.""" @@ -27,40 +27,44 @@ def test_structured_output(method: str) -> None: query = "Tell me a joke about cats." # Pydantic - structured_llm = llm.with_structured_output(Joke, method=method) # type: ignore[arg-type] - result = structured_llm.invoke(query) - assert isinstance(result, Joke) + if method == "function_calling": + structured_llm = llm.with_structured_output(Joke, method="function_calling") + result = structured_llm.invoke(query) + assert isinstance(result, Joke) - for chunk in structured_llm.stream(query): - assert isinstance(chunk, Joke) + for chunk in structured_llm.stream(query): + assert isinstance(chunk, Joke) # JSON Schema - structured_llm = llm.with_structured_output(Joke.model_json_schema(), method=method) # type: ignore[arg-type] - result = structured_llm.invoke(query) - assert isinstance(result, dict) - assert set(result.keys()) == {"setup", "punchline"} + if method == "json_schema": + structured_llm = llm.with_structured_output( + Joke.model_json_schema(), method="json_schema" + ) + result = structured_llm.invoke(query) + assert isinstance(result, dict) + assert set(result.keys()) == {"setup", "punchline"} - for chunk in structured_llm.stream(query): + for chunk in structured_llm.stream(query): + assert isinstance(chunk, dict) assert isinstance(chunk, dict) - assert isinstance(chunk, dict) - assert set(chunk.keys()) == {"setup", "punchline"} + assert set(chunk.keys()) == {"setup", "punchline"} - # Typed Dict - class JokeSchema(TypedDict): - """Joke to tell user.""" + # Typed Dict + class JokeSchema(TypedDict): + """Joke to tell user.""" - setup: Annotated[str, "question to set up a joke"] - punchline: Annotated[str, "answer to resolve the joke"] + setup: Annotated[str, "question to set up a joke"] + punchline: Annotated[str, "answer to resolve the joke"] - structured_llm = llm.with_structured_output(JokeSchema, method=method) # type: ignore[arg-type] - result = structured_llm.invoke(query) - assert isinstance(result, dict) - assert set(result.keys()) == {"setup", "punchline"} + structured_llm = llm.with_structured_output(JokeSchema, method="json_schema") + result = structured_llm.invoke(query) + assert isinstance(result, dict) + assert set(result.keys()) == {"setup", "punchline"} - for chunk in structured_llm.stream(query): + for chunk in structured_llm.stream(query): + assert isinstance(chunk, dict) assert isinstance(chunk, dict) - assert isinstance(chunk, dict) - assert set(chunk.keys()) == {"setup", "punchline"} + assert set(chunk.keys()) == {"setup", "punchline"} @pytest.mark.parametrize(("model"), [(DEFAULT_MODEL_NAME)]) diff --git a/libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models_reasoning.py b/libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models_reasoning.py index 19e2106e9ce..82198050b40 100644 --- a/libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models_reasoning.py +++ b/libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models_reasoning.py @@ -1,29 +1,17 @@ -"""Ollama specific chat model integration tests for reasoning models.""" +"""Ollama integration tests for reasoning chat models.""" import pytest -from langchain_core.messages import ( - AIMessageChunk, - BaseMessageChunk, - HumanMessage, -) -from pydantic import BaseModel, Field +from langchain_core.messages import AIMessageChunk, BaseMessageChunk, HumanMessage from langchain_ollama import ChatOllama SAMPLE = "What is 3^3?" -class MathAnswer(BaseModel): - """A mathematical expression and its numerical answer.""" - - expression: str = Field(description="The mathematical expression to evaluate.") - answer: int = Field(description="The numerical answer to the expression.") - - @pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) def test_stream_no_reasoning(model: str) -> None: """Test streaming with `reasoning=False`""" - llm = ChatOllama(model=model, num_ctx=2**12) + llm = ChatOllama(model=model, num_ctx=2**12, reasoning=False) messages = [ { "role": "user", @@ -39,16 +27,14 @@ def test_stream_no_reasoning(model: str) -> None: result += chunk assert isinstance(result, AIMessageChunk) assert result.content - assert "reasoning_content" not in result.additional_kwargs assert "" not in result.content and "" not in result.content - assert "" not in result.additional_kwargs["reasoning_content"] - assert "" not in result.additional_kwargs["reasoning_content"] + assert "reasoning_content" not in result.additional_kwargs @pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) async def test_astream_no_reasoning(model: str) -> None: """Test async streaming with `reasoning=False`""" - llm = ChatOllama(model=model, num_ctx=2**12) + llm = ChatOllama(model=model, num_ctx=2**12, reasoning=False) messages = [ { "role": "user", @@ -64,10 +50,8 @@ async def test_astream_no_reasoning(model: str) -> None: result += chunk assert isinstance(result, AIMessageChunk) assert result.content - assert "reasoning_content" not in result.additional_kwargs assert "" not in result.content and "" not in result.content - assert "" not in result.additional_kwargs["reasoning_content"] - assert "" not in result.additional_kwargs["reasoning_content"] + assert "reasoning_content" not in result.additional_kwargs @pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) @@ -89,10 +73,10 @@ def test_stream_reasoning_none(model: str) -> None: result += chunk assert isinstance(result, AIMessageChunk) assert result.content - assert "reasoning_content" not in result.additional_kwargs assert "" in result.content and "" in result.content - assert "" not in result.additional_kwargs["reasoning_content"] - assert "" not in result.additional_kwargs["reasoning_content"] + assert "reasoning_content" not in result.additional_kwargs + assert "" not in result.additional_kwargs.get("reasoning_content", "") + assert "" not in result.additional_kwargs.get("reasoning_content", "") @pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) @@ -114,10 +98,10 @@ async def test_astream_reasoning_none(model: str) -> None: result += chunk assert isinstance(result, AIMessageChunk) assert result.content - assert "reasoning_content" not in result.additional_kwargs assert "" in result.content and "" in result.content - assert "" not in result.additional_kwargs["reasoning_content"] - assert "" not in result.additional_kwargs["reasoning_content"] + assert "reasoning_content" not in result.additional_kwargs + assert "" not in result.additional_kwargs.get("reasoning_content", "") + assert "" not in result.additional_kwargs.get("reasoning_content", "") @pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) @@ -175,27 +159,23 @@ async def test_reasoning_astream(model: str) -> None: @pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) def test_invoke_no_reasoning(model: str) -> None: """Test using invoke with `reasoning=False`""" - llm = ChatOllama(model=model, num_ctx=2**12) + llm = ChatOllama(model=model, num_ctx=2**12, reasoning=False) message = HumanMessage(content=SAMPLE) result = llm.invoke([message]) assert result.content assert "reasoning_content" not in result.additional_kwargs assert "" not in result.content and "" not in result.content - assert "" not in result.additional_kwargs["reasoning_content"] - assert "" not in result.additional_kwargs["reasoning_content"] @pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) async def test_ainvoke_no_reasoning(model: str) -> None: """Test using async invoke with `reasoning=False`""" - llm = ChatOllama(model=model, num_ctx=2**12) + llm = ChatOllama(model=model, num_ctx=2**12, reasoning=False) message = HumanMessage(content=SAMPLE) result = await llm.ainvoke([message]) assert result.content assert "reasoning_content" not in result.additional_kwargs assert "" not in result.content and "" not in result.content - assert "" not in result.additional_kwargs["reasoning_content"] - assert "" not in result.additional_kwargs["reasoning_content"] @pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) @@ -207,8 +187,8 @@ def test_invoke_reasoning_none(model: str) -> None: assert result.content assert "reasoning_content" not in result.additional_kwargs assert "" in result.content and "" in result.content - assert "" not in result.additional_kwargs["reasoning_content"] - assert "" not in result.additional_kwargs["reasoning_content"] + assert "" not in result.additional_kwargs.get("reasoning_content", "") + assert "" not in result.additional_kwargs.get("reasoning_content", "") @pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) @@ -220,8 +200,8 @@ async def test_ainvoke_reasoning_none(model: str) -> None: assert result.content assert "reasoning_content" not in result.additional_kwargs assert "" in result.content and "" in result.content - assert "" not in result.additional_kwargs["reasoning_content"] - assert "" not in result.additional_kwargs["reasoning_content"] + assert "" not in result.additional_kwargs.get("reasoning_content", "") + assert "" not in result.additional_kwargs.get("reasoning_content", "") @pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) @@ -250,3 +230,43 @@ async def test_reasoning_ainvoke(model: str) -> None: assert "" not in result.content and "" not in result.content assert "" not in result.additional_kwargs["reasoning_content"] assert "" not in result.additional_kwargs["reasoning_content"] + + +@pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) +def test_think_tag_stripping_necessity(model: str) -> None: + """Test that demonstrates why ``_strip_think_tags`` is necessary. + + DeepSeek R1 models include reasoning/thinking as their default behavior. + When ``reasoning=False`` is set, the user explicitly wants no reasoning content, + but Ollama cannot disable thinking at the API level for these models. + Therefore, post-processing is required to strip the ```` tags. + + This test documents the specific behavior that necessitates the + ``_strip_think_tags`` function in the chat_models.py implementation. + """ + # Test with reasoning=None (default behavior - should include think tags) + llm_default = ChatOllama(model=model, reasoning=None, num_ctx=2**12) + message = HumanMessage(content=SAMPLE) + + result_default = llm_default.invoke([message]) + + # With reasoning=None, the model's default behavior includes tags + # This demonstrates why we need the stripping logic + assert "" in result_default.content + assert "" in result_default.content + assert "reasoning_content" not in result_default.additional_kwargs + + # Test with reasoning=False (explicit disable - should NOT include think tags) + llm_disabled = ChatOllama(model=model, reasoning=False, num_ctx=2**12) + + result_disabled = llm_disabled.invoke([message]) + + # With reasoning=False, think tags should be stripped from content + # This verifies that _strip_think_tags is working correctly + assert "" not in result_disabled.content + assert "" not in result_disabled.content + assert "reasoning_content" not in result_disabled.additional_kwargs + + # Verify the difference: same model, different reasoning settings + # Default includes tags, disabled strips them + assert result_default.content != result_disabled.content diff --git a/libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models_standard.py b/libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models_standard.py index d596011bfb4..95742e924f2 100644 --- a/libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models_standard.py +++ b/libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models_standard.py @@ -40,7 +40,7 @@ class TestChatOllama(ChatModelIntegrationTests): @property def has_tool_choice(self) -> bool: # TODO: update after Ollama implements - # https://github.com/ollama/ollama/blob/main/docs/openai.md + # https://github.com/ollama/ollama/blob/main/docs/openai.md#supported-request-fields return False @property @@ -168,7 +168,7 @@ class TestChatOllama(ChatModelIntegrationTests): with pytest.raises(ValidationError) as excinfo: ChatOllama(model="any-model", validate_model_on_init=True) - assert "not found in Ollama" in str(excinfo.value) + assert "Failed to connect to Ollama" in str(excinfo.value) @patch("langchain_ollama.chat_models.Client.list") def test_init_response_error(self, mock_list: MagicMock) -> None: diff --git a/libs/partners/ollama/tests/integration_tests/test_embeddings.py b/libs/partners/ollama/tests/integration_tests/test_embeddings.py index bf8e3a04bec..65f242f8bab 100644 --- a/libs/partners/ollama/tests/integration_tests/test_embeddings.py +++ b/libs/partners/ollama/tests/integration_tests/test_embeddings.py @@ -1,10 +1,12 @@ """Test Ollama embeddings.""" +import os + from langchain_tests.integration_tests import EmbeddingsIntegrationTests from langchain_ollama.embeddings import OllamaEmbeddings -MODEL_NAME = "llama3.1" +MODEL_NAME = os.environ.get("OLLAMA_TEST_MODEL", "llama3.1") class TestOllamaEmbeddings(EmbeddingsIntegrationTests): diff --git a/libs/partners/ollama/tests/integration_tests/test_llms.py b/libs/partners/ollama/tests/integration_tests/test_llms.py index 0ee236e62e5..34136f0862d 100644 --- a/libs/partners/ollama/tests/integration_tests/test_llms.py +++ b/libs/partners/ollama/tests/integration_tests/test_llms.py @@ -1,24 +1,27 @@ """Test OllamaLLM llm.""" +import os + import pytest from langchain_core.outputs import GenerationChunk from langchain_core.runnables import RunnableConfig from langchain_ollama.llms import OllamaLLM -MODEL_NAME = "llama3.1" +MODEL_NAME = os.environ.get("OLLAMA_TEST_MODEL", "llama3.1") +REASONING_MODEL_NAME = os.environ.get("OLLAMA_REASONING_TEST_MODEL", "deepseek-r1:1.5b") SAMPLE = "What is 3^3?" def test_stream_text_tokens() -> None: - """Test streaming raw string tokens from OllamaLLM.""" + """Test streaming raw string tokens from `OllamaLLM`.""" llm = OllamaLLM(model=MODEL_NAME) for token in llm.stream("I'm Pickle Rick"): assert isinstance(token, str) -@pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) +@pytest.mark.parametrize(("model"), [(REASONING_MODEL_NAME)]) def test__stream_no_reasoning(model: str) -> None: """Test low-level chunk streaming of a simple prompt with `reasoning=False`.""" llm = OllamaLLM(model=model, num_ctx=2**12) @@ -39,7 +42,7 @@ def test__stream_no_reasoning(model: str) -> None: assert "reasoning_content" not in result_chunk.generation_info # type: ignore[operator] -@pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) +@pytest.mark.parametrize(("model"), [(REASONING_MODEL_NAME)]) def test__stream_with_reasoning(model: str) -> None: """Test low-level chunk streaming with `reasoning=True`.""" llm = OllamaLLM(model=model, num_ctx=2**12, reasoning=True) @@ -64,14 +67,14 @@ def test__stream_with_reasoning(model: str) -> None: async def test_astream_text_tokens() -> None: - """Test async streaming raw string tokens from OllamaLLM.""" + """Test async streaming raw string tokens from `OllamaLLM`.""" llm = OllamaLLM(model=MODEL_NAME) async for token in llm.astream("I'm Pickle Rick"): assert isinstance(token, str) -@pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) +@pytest.mark.parametrize(("model"), [(REASONING_MODEL_NAME)]) async def test__astream_no_reasoning(model: str) -> None: """Test low-level async chunk streaming with `reasoning=False`.""" llm = OllamaLLM(model=model, num_ctx=2**12) @@ -89,7 +92,7 @@ async def test__astream_no_reasoning(model: str) -> None: assert "reasoning_content" not in result_chunk.generation_info # type: ignore[operator] -@pytest.mark.parametrize(("model"), [("deepseek-r1:1.5b")]) +@pytest.mark.parametrize(("model"), [(REASONING_MODEL_NAME)]) async def test__astream_with_reasoning(model: str) -> None: """Test low-level async chunk streaming with `reasoning=True`.""" llm = OllamaLLM(model=model, num_ctx=2**12, reasoning=True) @@ -109,7 +112,7 @@ async def test__astream_with_reasoning(model: str) -> None: async def test_abatch() -> None: - """Test batch sync token generation from OllamaLLM.""" + """Test batch sync token generation from `OllamaLLM`.""" llm = OllamaLLM(model=MODEL_NAME) result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"]) @@ -129,7 +132,7 @@ async def test_abatch_tags() -> None: def test_batch() -> None: - """Test batch token generation from OllamaLLM.""" + """Test batch token generation from `OllamaLLM`.""" llm = OllamaLLM(model=MODEL_NAME) result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"]) diff --git a/libs/partners/ollama/tests/unit_tests/test_chat_models.py b/libs/partners/ollama/tests/unit_tests/test_chat_models.py index b32ad638cb8..5bcb8262908 100644 --- a/libs/partners/ollama/tests/unit_tests/test_chat_models.py +++ b/libs/partners/ollama/tests/unit_tests/test_chat_models.py @@ -1,4 +1,4 @@ -"""Test chat model integration.""" +"""Unit tests for ChatOllama.""" import json import logging @@ -33,6 +33,16 @@ class TestChatOllama(ChatModelUnitTests): def test__parse_arguments_from_tool_call() -> None: + """Test that string arguments are preserved as strings in tool call parsing. + + This test verifies the fix for PR #30154 which addressed an issue where + string-typed tool arguments (like IDs or long strings) were being incorrectly + processed. The parser should preserve string values as strings rather than + attempting to parse them as JSON when they're already valid string arguments. + + The test uses a long string ID to ensure string arguments maintain their + original type after parsing, which is critical for tools expecting string inputs. + """ raw_response = '{"model":"sample-model","message":{"role":"assistant","content":"","tool_calls":[{"function":{"name":"get_profile_details","arguments":{"arg_1":"12345678901234567890123456"}}}]},"done":false}' # noqa: E501 raw_tool_calls = json.loads(raw_response)["message"]["tool_calls"] response = _parse_arguments_from_tool_call(raw_tool_calls[0]) @@ -40,6 +50,41 @@ def test__parse_arguments_from_tool_call() -> None: assert isinstance(response["arg_1"], str) +def test__parse_arguments_from_tool_call_with_function_name_metadata() -> None: + """Test that functionName metadata is filtered out from tool arguments. + + Some models may include metadata like ``functionName`` in the arguments + that just echoes the function name. This should be filtered out for + no-argument tools to return an empty dictionary. + """ + # Test case where arguments contain functionName metadata + raw_tool_call_with_metadata = { + "function": { + "name": "magic_function_no_args", + "arguments": {"functionName": "magic_function_no_args"}, + } + } + response = _parse_arguments_from_tool_call(raw_tool_call_with_metadata) + assert response == {} + + # Test case where arguments contain both real args and metadata + raw_tool_call_mixed = { + "function": { + "name": "some_function", + "arguments": {"functionName": "some_function", "real_arg": "value"}, + } + } + response_mixed = _parse_arguments_from_tool_call(raw_tool_call_mixed) + assert response_mixed == {"real_arg": "value"} + + # Test case where functionName has different value (should be preserved) + raw_tool_call_different = { + "function": {"name": "function_a", "arguments": {"functionName": "function_b"}} + } + response_different = _parse_arguments_from_tool_call(raw_tool_call_different) + assert response_different == {"functionName": "function_b"} + + @contextmanager def _mock_httpx_client_stream( *args: Any, **kwargs: Any @@ -54,6 +99,7 @@ def _mock_httpx_client_stream( def test_arbitrary_roles_accepted_in_chatmessages( monkeypatch: pytest.MonkeyPatch, ) -> None: + """Test that `ChatOllama` accepts arbitrary roles in `ChatMessage`.""" monkeypatch.setattr(Client, "stream", _mock_httpx_client_stream) llm = ChatOllama( model=MODEL_NAME, @@ -94,9 +140,6 @@ dummy_raw_tool_call = { } -# --- Regression tests for tool-call argument parsing (see #30910) --- - - @pytest.mark.parametrize( "input_string, expected_output", [ @@ -113,14 +156,14 @@ dummy_raw_tool_call = { def test_parse_json_string_success_cases( input_string: str, expected_output: Any ) -> None: - """Tests that _parse_json_string correctly parses valid and fixable strings.""" + """Tests that `_parse_json_string` correctly parses valid and fixable strings.""" raw_tool_call = {"function": {"name": "test_func", "arguments": input_string}} result = _parse_json_string(input_string, raw_tool_call=raw_tool_call, skip=False) assert result == expected_output def test_parse_json_string_failure_case_raises_exception() -> None: - """Tests that _parse_json_string raises an exception for truly malformed strings.""" + """Tests that `_parse_json_string` raises an exception for malformed strings.""" malformed_string = "{'key': 'value',,}" raw_tool_call = {"function": {"name": "test_func", "arguments": malformed_string}} with pytest.raises(OutputParserException): @@ -132,7 +175,7 @@ def test_parse_json_string_failure_case_raises_exception() -> None: def test_parse_json_string_skip_returns_input_on_failure() -> None: - """Tests that skip=True returns the original string on parse failure.""" + """Tests that `skip=True` returns the original string on parse failure.""" malformed_string = "{'not': valid,,,}" raw_tool_call = {"function": {"name": "test_func", "arguments": malformed_string}} result = _parse_json_string( diff --git a/libs/partners/ollama/tests/unit_tests/test_embeddings.py b/libs/partners/ollama/tests/unit_tests/test_embeddings.py index 93f996f59bc..feca762deb6 100644 --- a/libs/partners/ollama/tests/unit_tests/test_embeddings.py +++ b/libs/partners/ollama/tests/unit_tests/test_embeddings.py @@ -32,7 +32,7 @@ def test_validate_model_on_init(mock_validate_model: Any) -> None: @patch("langchain_ollama.embeddings.Client") def test_embed_documents_passes_options(mock_client_class: Any) -> None: - """Test that embed_documents method passes options including num_gpu.""" + """Test that `embed_documents()` passes options, including `num_gpu`.""" # Create a mock client instance mock_client = Mock() mock_client_class.return_value = mock_client diff --git a/libs/partners/ollama/uv.lock b/libs/partners/ollama/uv.lock index bb34f95c883..5e1e262b834 100644 --- a/libs/partners/ollama/uv.lock +++ b/libs/partners/ollama/uv.lock @@ -1,5 +1,5 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a/libs/partners/openai/pyproject.toml +++ b/libs/partners/openai/pyproject.toml @@ -71,6 +71,9 @@ ignore = [ "UP007", "UP045" ] docstring-code-format = true skip-magic-trailing-comma = true +[tool.ruff.lint.isort] +split-on-trailing-comma = false + [tool.coverage.run] omit = ["tests/*"] diff --git a/libs/partners/openai/tests/integration_tests/chat_models/test_base.py b/libs/partners/openai/tests/integration_tests/chat_models/test_base.py index b18bd5f97e9..b914af07cba 100644 --- a/libs/partners/openai/tests/integration_tests/chat_models/test_base.py +++ b/libs/partners/openai/tests/integration_tests/chat_models/test_base.py @@ -474,6 +474,7 @@ def test_manual_tool_call_msg(use_responses_api: bool) -> None: name="GenerateUsername", args={"name": "Sally", "hair_color": "green"}, id="foo", + type="tool_call", ) ], ), @@ -495,6 +496,7 @@ def test_manual_tool_call_msg(use_responses_api: bool) -> None: name="GenerateUsername", args={"name": "Sally", "hair_color": "green"}, id="bar", + type="tool_call", ) ], ), diff --git a/libs/partners/openai/uv.lock b/libs/partners/openai/uv.lock index 9d28251fa2e..f626c1bacdc 100644 --- a/libs/partners/openai/uv.lock +++ b/libs/partners/openai/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.9" resolution-markers = [ "python_full_version >= '3.13' and platform_python_implementation == 'PyPy'", @@ -531,7 +531,7 @@ test = [ test-integration = [] typing = [ { name = "langchain-text-splitters", directory = "../../text-splitters" }, - { name = "mypy", specifier = ">=1.15,<1.16" }, + { name = "mypy", specifier = ">=1.17.1,<1.18" }, { name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" }, { name = "types-requests", specifier = ">=2.28.11.5,<3.0.0.0" }, ] @@ -588,7 +588,7 @@ typing = [ [package.metadata] requires-dist = [ { name = "langchain-core", editable = "../../core" }, - { name = "openai", specifier = ">=1.99.3,<2.0.0" }, + { name = "openai", specifier = ">=1.86.0,<2.0.0" }, { name = "tiktoken", specifier = ">=0.7,<1" }, ] @@ -662,7 +662,7 @@ requires-dist = [ [package.metadata.requires-dev] codespell = [{ name = "codespell", specifier = ">=2.2.0,<3.0.0" }] -lint = [{ name = "ruff", specifier = ">=0.12.2,<0.13" }] +lint = [{ name = "ruff", specifier = ">=0.12.8,<0.13" }] test = [{ name = "langchain-core", editable = "../../core" }] test-integration = [] typing = [ diff --git a/libs/standard-tests/README.md b/libs/standard-tests/README.md index 77f6780062a..fe34759e3ac 100644 --- a/libs/standard-tests/README.md +++ b/libs/standard-tests/README.md @@ -14,62 +14,68 @@ also break your CI if we introduce tests that your integration doesn't pass. Pip: - ```bash - pip install -U langchain-tests - ``` +```bash +pip install -U langchain-tests +``` Poetry: - ```bash - poetry add langchain-tests - ``` +```bash +poetry add langchain-tests +``` + +uv: + +```bash +uv add langchain-tests +``` ## Usage -To add standard tests to an integration package's e.g. ChatModel, you need to create +To add standard tests to an integration package (e.g., for a ChatModel), you need to create -1. A unit test class that inherits from ChatModelUnitTests -2. An integration test class that inherits from ChatModelIntegrationTests +1. A unit test class that inherits from `ChatModelUnitTests` +2. An integration test class that inherits from `ChatModelIntegrationTests` `tests/unit_tests/test_standard.py`: - ```python - """Standard LangChain interface tests""" +```python +"""Standard LangChain interface tests""" - from typing import Type +from typing import Type - import pytest - from langchain_core.language_models import BaseChatModel - from langchain_tests.unit_tests import ChatModelUnitTests +import pytest +from langchain_core.language_models import BaseChatModel +from langchain_tests.unit_tests import ChatModelUnitTests - from langchain_parrot_chain import ChatParrotChain +from langchain_parrot_chain import ChatParrotChain - class TestParrotChainStandard(ChatModelUnitTests): - @pytest.fixture - def chat_model_class(self) -> Type[BaseChatModel]: - return ChatParrotChain - ``` +class TestParrotChainStandard(ChatModelUnitTests): + @pytest.fixture + def chat_model_class(self) -> Type[BaseChatModel]: + return ChatParrotChain +``` `tests/integration_tests/test_standard.py`: - ```python - """Standard LangChain interface tests""" +```python +"""Standard LangChain interface tests""" - from typing import Type +from typing import Type - import pytest - from langchain_core.language_models import BaseChatModel - from langchain_tests.integration_tests import ChatModelIntegrationTests +import pytest +from langchain_core.language_models import BaseChatModel +from langchain_tests.integration_tests import ChatModelIntegrationTests - from langchain_parrot_chain import ChatParrotChain +from langchain_parrot_chain import ChatParrotChain - class TestParrotChainStandard(ChatModelIntegrationTests): - @pytest.fixture - def chat_model_class(self) -> Type[BaseChatModel]: - return ChatParrotChain - ``` +class TestParrotChainStandard(ChatModelIntegrationTests): + @pytest.fixture + def chat_model_class(self) -> Type[BaseChatModel]: + return ChatParrotChain +``` ## Reference diff --git a/libs/standard-tests/langchain_tests/base.py b/libs/standard-tests/langchain_tests/base.py index bc262b31c19..c54b84055a7 100644 --- a/libs/standard-tests/langchain_tests/base.py +++ b/libs/standard-tests/langchain_tests/base.py @@ -9,7 +9,7 @@ class BaseStandardTests(ABC): :private: """ - # find path to standard test implementations + # Find path to standard test implementations comparison_class = None def explore_bases(cls: type) -> None: diff --git a/libs/standard-tests/langchain_tests/integration_tests/chat_models.py b/libs/standard-tests/langchain_tests/integration_tests/chat_models.py index 090b1f6dc73..d6666ed55a6 100644 --- a/libs/standard-tests/langchain_tests/integration_tests/chat_models.py +++ b/libs/standard-tests/langchain_tests/integration_tests/chat_models.py @@ -32,9 +32,7 @@ from pytest_benchmark.fixture import BenchmarkFixture # type: ignore[import-unt from typing_extensions import TypedDict from vcr.cassette import Cassette -from langchain_tests.unit_tests.chat_models import ( - ChatModelTests, -) +from langchain_tests.unit_tests.chat_models import ChatModelTests from langchain_tests.utils.pydantic import PYDANTIC_MAJOR_VERSION @@ -110,6 +108,7 @@ def magic_function_no_args() -> int: def _validate_tool_call_message(message: BaseMessage) -> None: assert isinstance(message, AIMessage) assert len(message.tool_calls) == 1 + tool_call = message.tool_calls[0] assert tool_call["name"] == "magic_function" assert tool_call["args"] == {"input": 3} @@ -120,6 +119,7 @@ def _validate_tool_call_message(message: BaseMessage) -> None: def _validate_tool_call_message_no_args(message: BaseMessage) -> None: assert isinstance(message, AIMessage) assert len(message.tool_calls) == 1 + tool_call = message.tool_calls[0] assert tool_call["name"] == "magic_function_no_args" assert tool_call["args"] == {} @@ -137,6 +137,7 @@ def unicode_customer(customer_name: str, description: str) -> str: Returns: A confirmation message about the customer creation. + """ return f"Created customer: {customer_name} - {description}" @@ -173,7 +174,7 @@ class ChatModelIntegrationTests(ChatModelTests): API references for individual test methods include troubleshooting tips. - Test subclasses must implement the following two properties: + Test subclasses **must** implement the following two properties: chat_model_class The chat model class to test, e.g., ``ChatParrotLink``. @@ -426,10 +427,10 @@ class ChatModelIntegrationTests(ChatModelTests): .. dropdown:: returns_usage_metadata Boolean property indicating whether the chat model returns usage metadata - on invoke and streaming responses. + on invoke and streaming responses. Defaults to ``True``. - ``usage_metadata`` is an optional dict attribute on AIMessages that track input - and output tokens: https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html + ``usage_metadata`` is an optional dict attribute on ``AIMessage``s that track input + and output tokens. `See more. `__ Example: @@ -440,7 +441,7 @@ class ChatModelIntegrationTests(ChatModelTests): return False Models supporting ``usage_metadata`` should also return the name of the - underlying model in the ``response_metadata`` of the AIMessage. + underlying model in the ``response_metadata`` of the ``AIMessage``. .. dropdown:: supports_anthropic_inputs @@ -525,8 +526,8 @@ class ChatModelIntegrationTests(ChatModelTests): Property controlling what usage metadata details are emitted in both invoke and stream. - ``usage_metadata`` is an optional dict attribute on AIMessages that track input - and output tokens: https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html + ``usage_metadata`` is an optional dict attribute on ``AIMessage``s that track input + and output tokens. `See more. `__ It includes optional keys ``input_token_details`` and ``output_token_details`` that can track usage details associated with special types of tokens, such as @@ -682,13 +683,13 @@ class ChatModelIntegrationTests(ChatModelTests): return {} def test_invoke(self, model: BaseChatModel) -> None: - """Test to verify that `model.invoke(simple_message)` works. + """Test to verify that ``model.invoke(simple_message)`` works. This should pass for all integrations. .. dropdown:: Troubleshooting - If this test fails, you should make sure your _generate method + If this test fails, you should make sure your ``_generate`` method does not raise any exceptions, and that it returns a valid :class:`~langchain_core.outputs.chat_result.ChatResult` like so: @@ -708,7 +709,7 @@ class ChatModelIntegrationTests(ChatModelTests): assert len(result.content) > 0 async def test_ainvoke(self, model: BaseChatModel) -> None: - """Test to verify that `await model.ainvoke(simple_message)` works. + """Test to verify that ``await model.ainvoke(simple_message)`` works. This should pass for all integrations. Passing this test does not indicate a "natively async" implementation, but rather that the model can be used @@ -718,7 +719,7 @@ class ChatModelIntegrationTests(ChatModelTests): First, debug :meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_invoke`. - because `ainvoke` has a default implementation that calls `invoke` in an + because ``ainvoke`` has a default implementation that calls ``invoke`` in an async context. If that test passes but not this one, you should make sure your _agenerate @@ -741,7 +742,7 @@ class ChatModelIntegrationTests(ChatModelTests): assert len(result.content) > 0 def test_stream(self, model: BaseChatModel) -> None: - """Test to verify that `model.stream(simple_message)` works. + """Test to verify that ``model.stream(simple_message)`` works. This should pass for all integrations. Passing this test does not indicate a "streaming" implementation, but rather that the model can be used in a @@ -751,10 +752,10 @@ class ChatModelIntegrationTests(ChatModelTests): First, debug :meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_invoke`. - because `stream` has a default implementation that calls `invoke` and yields - the result as a single chunk. + because ``stream`` has a default implementation that calls ``invoke`` and + yields the result as a single chunk. - If that test passes but not this one, you should make sure your _stream + If that test passes but not this one, you should make sure your ``_stream`` method does not raise any exceptions, and that it yields valid :class:`~langchain_core.outputs.chat_generation.ChatGenerationChunk` objects like so: @@ -770,11 +771,12 @@ class ChatModelIntegrationTests(ChatModelTests): for chunk in model.stream("Hello"): assert chunk is not None assert isinstance(chunk, AIMessageChunk) + assert isinstance(chunk.content, (str, list)) num_chunks += 1 assert num_chunks > 0 async def test_astream(self, model: BaseChatModel) -> None: - """Test to verify that `await model.astream(simple_message)` works. + """Test to verify that ``await model.astream(simple_message)`` works. This should pass for all integrations. Passing this test does not indicate a "natively async" or "streaming" implementation, but rather that the model can @@ -786,11 +788,11 @@ class ChatModelIntegrationTests(ChatModelTests): :meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_stream`. and :meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_ainvoke`. - because `astream` has a default implementation that calls `_stream` in an - async context if it is implemented, or `ainvoke` and yields the result as a - single chunk if not. + because ``astream`` has a default implementation that calls ``_stream`` in + an async context if it is implemented, or ``ainvoke`` and yields the result + as a single chunk if not. - If those tests pass but not this one, you should make sure your _astream + If those tests pass but not this one, you should make sure your ``_astream`` method does not raise any exceptions, and that it yields valid :class:`~langchain_core.outputs.chat_generation.ChatGenerationChunk` objects like so: @@ -811,7 +813,7 @@ class ChatModelIntegrationTests(ChatModelTests): assert num_chunks > 0 def test_batch(self, model: BaseChatModel) -> None: - """Test to verify that `model.batch([messages])` works. + """Test to verify that ``model.batch([messages])`` works. This should pass for all integrations. Tests the model's ability to process multiple prompts in a single batch. @@ -820,12 +822,13 @@ class ChatModelIntegrationTests(ChatModelTests): First, debug :meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_invoke` - because `batch` has a default implementation that calls `invoke` for each - message in the batch. + because ``batch`` has a default implementation that calls ``invoke`` for + each message in the batch. - If that test passes but not this one, you should make sure your `batch` + If that test passes but not this one, you should make sure your ``batch`` method does not raise any exceptions, and that it returns a list of valid :class:`~langchain_core.messages.AIMessage` objects. + """ batch_results = model.batch(["Hello", "Hey"]) assert batch_results is not None @@ -838,7 +841,7 @@ class ChatModelIntegrationTests(ChatModelTests): assert len(result.content) > 0 async def test_abatch(self, model: BaseChatModel) -> None: - """Test to verify that `await model.abatch([messages])` works. + """Test to verify that ``await model.abatch([messages])`` works. This should pass for all integrations. Tests the model's ability to process multiple prompts in a single batch asynchronously. @@ -849,12 +852,13 @@ class ChatModelIntegrationTests(ChatModelTests): :meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_batch` and :meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_ainvoke` - because `abatch` has a default implementation that calls `ainvoke` for each - message in the batch. + because ``abatch`` has a default implementation that calls ``ainvoke`` for + each message in the batch. - If those tests pass but not this one, you should make sure your `abatch` + If those tests pass but not this one, you should make sure your ``abatch`` method does not raise any exceptions, and that it returns a list of valid :class:`~langchain_core.messages.AIMessage` objects. + """ batch_results = await model.abatch(["Hello", "Hey"]) assert batch_results is not None @@ -877,18 +881,20 @@ class ChatModelIntegrationTests(ChatModelTests): First, debug :meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_invoke` - because this test also uses `model.invoke()`. + because this test also uses ``model.invoke()``. If that test passes but not this one, you should verify that: 1. Your model correctly processes the message history 2. The model maintains appropriate context from previous messages 3. The response is a valid :class:`~langchain_core.messages.AIMessage` + """ messages = [ HumanMessage("hello"), AIMessage("hello"), HumanMessage("how are you"), ] + result = model.invoke(messages) assert result is not None assert isinstance(result, AIMessage) @@ -906,17 +912,17 @@ class ChatModelIntegrationTests(ChatModelTests): First, debug :meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_invoke` - because this test also uses `model.invoke()`. + because this test also uses ``model.invoke()``. Second, debug :meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_conversation` because this test is the "basic case" without double messages. If that test passes those but not this one, you should verify that: - 1. Your model API can handle double messages, or the integration should - merge messages before sending them to the API. + 1. Your model API can handle double messages, or the integration should merge messages before sending them to the API. 2. The response is a valid :class:`~langchain_core.messages.AIMessage` - """ + + """ # noqa: E501 messages = [ SystemMessage("hello"), SystemMessage("hello"), @@ -926,6 +932,7 @@ class ChatModelIntegrationTests(ChatModelTests): AIMessage("hello"), HumanMessage("how are you"), ] + result = model.invoke(messages) assert result is not None assert isinstance(result, AIMessage) @@ -940,13 +947,14 @@ class ChatModelIntegrationTests(ChatModelTests): .. versionchanged:: 0.3.17 - Additionally check for the presence of `model_name` in the response + Additionally check for the presence of ``model_name`` in the response metadata, which is needed for usage tracking in callback handlers. .. dropdown:: Configuration By default, this test is run. - To disable this feature, set `returns_usage_metadata` to False in your + + To disable this feature, set ``returns_usage_metadata`` to ``False`` in your test class: .. code-block:: python @@ -957,7 +965,7 @@ class ChatModelIntegrationTests(ChatModelTests): return False This test can also check the format of specific kinds of usage metadata - based on the `supported_usage_metadata_details` property. This property + based on the ``supported_usage_metadata_details`` property. This property should be configured as follows with the types of tokens that the model supports tracking: @@ -988,7 +996,7 @@ class ChatModelIntegrationTests(ChatModelTests): If this test fails, first verify that your model returns :class:`~langchain_core.messages.ai.UsageMetadata` dicts - attached to the returned AIMessage object in `_generate`: + attached to the returned AIMessage object in ``_generate``: .. code-block:: python @@ -1020,9 +1028,11 @@ class ChatModelIntegrationTests(ChatModelTests): """ if not self.returns_usage_metadata: pytest.skip("Not implemented.") + result = model.invoke("Hello") assert result is not None assert isinstance(result, AIMessage) + assert result.usage_metadata is not None assert isinstance(result.usage_metadata["input_tokens"], int) assert isinstance(result.usage_metadata["output_tokens"], int) @@ -1106,13 +1116,13 @@ class ChatModelIntegrationTests(ChatModelTests): .. versionchanged:: 0.3.17 - Additionally check for the presence of `model_name` in the response + Additionally check for the presence of ``model_name`` in the response metadata, which is needed for usage tracking in callback handlers. .. dropdown:: Configuration By default, this test is run. - To disable this feature, set `returns_usage_metadata` to False in your + To disable this feature, set ``returns_usage_metadata`` to ``False`` in your test class: .. code-block:: python @@ -1123,7 +1133,7 @@ class ChatModelIntegrationTests(ChatModelTests): return False This test can also check the format of specific kinds of usage metadata - based on the `supported_usage_metadata_details` property. This property + based on the ``supported_usage_metadata_details`` property. This property should be configured as follows with the types of tokens that the model supports tracking: @@ -1153,16 +1163,16 @@ class ChatModelIntegrationTests(ChatModelTests): If this test fails, first verify that your model yields :class:`~langchain_core.messages.ai.UsageMetadata` dicts - attached to the returned AIMessage object in `_stream` + attached to the returned AIMessage object in ``_stream`` that sum up to the total usage metadata. - Note that `input_tokens` should only be included on one of the chunks - (typically the first or the last chunk), and the rest should have 0 or None - to avoid counting input tokens multiple times. + Note that ``input_tokens`` should only be included on one of the chunks + (typically the first or the last chunk), and the rest should have ``0`` or + ``None`` to avoid counting input tokens multiple times. - `output_tokens` typically count the number of tokens in each chunk, not the - sum. This test will pass as long as the sum of `output_tokens` across all - chunks is not 0. + ``output_tokens`` typically count the number of tokens in each chunk, not + the sum. This test will pass as long as the sum of ``output_tokens`` across + all chunks is not ``0``. .. code-block:: python @@ -1198,6 +1208,7 @@ class ChatModelIntegrationTests(ChatModelTests): """ if not self.returns_usage_metadata: pytest.skip("Not implemented.") + full: Optional[AIMessageChunk] = None for chunk in model.stream("Write me 2 haikus. Only include the haikus."): assert isinstance(chunk, AIMessageChunk) @@ -1262,7 +1273,7 @@ class ChatModelIntegrationTests(ChatModelTests): """Test that model does not fail when invoked with the ``stop`` parameter, which is a standard parameter for stopping generation at a certain token. - More on standard parameters here: https://python.langchain.com/docs/concepts/chat_models/#standard-parameters + `More on standard parameters `__ This should pass for all integrations. @@ -1336,6 +1347,7 @@ class ChatModelIntegrationTests(ChatModelTests): """ if not self.has_tool_calling: pytest.skip("Test requires tool calling.") + tool_choice_value = None if not self.has_tool_choice else "any" # Emit warning if tool_choice_value property is overridden if inspect.getattr_static( @@ -1410,6 +1422,7 @@ class ChatModelIntegrationTests(ChatModelTests): """ if not self.has_tool_calling: pytest.skip("Test requires tool calling.") + tool_choice_value = None if not self.has_tool_choice else "any" model_with_tools = model.bind_tools( [magic_function], tool_choice=tool_choice_value @@ -1519,10 +1532,10 @@ class ChatModelIntegrationTests(ChatModelTests): If this test fails, check that: - 1. The model can correctly handle message histories that include AIMessage objects with ``""`` content. - 2. The ``tool_calls`` attribute on AIMessage objects is correctly handled and passed to the model in an appropriate format. - 3. The model can correctly handle ToolMessage objects with string content and arbitrary string values for ``tool_call_id``. - assert tool_call.get("type") == "tool_call" + 1. The model can correctly handle message histories that include ``AIMessage`` objects with ``""`` content. + 2. The ``tool_calls`` attribute on ``AIMessage`` objects is correctly handled and passed to the model in an appropriate format. + 3. The model can correctly handle ``ToolMessage`` objects with string content and arbitrary string values for ``tool_call_id``. + You can ``xfail`` the test if tool calling is implemented but this format is not supported. @@ -1535,6 +1548,7 @@ class ChatModelIntegrationTests(ChatModelTests): """ # noqa: E501 if not self.has_tool_calling: pytest.skip("Test requires tool calling.") + model_with_tools = model.bind_tools([my_adder_tool]) function_name = "my_adder_tool" function_args = {"a": "1", "b": "2"} @@ -1570,7 +1584,7 @@ class ChatModelIntegrationTests(ChatModelTests): """Test that message histories are compatible with list tool contents (e.g. Anthropic format). - These message histories will include AIMessage objects with "tool use" and + These message histories will include ``AIMessage`` objects with "tool use" and content blocks, e.g., .. code-block:: python @@ -1604,8 +1618,8 @@ class ChatModelIntegrationTests(ChatModelTests): If this test fails, check that: - 1. The model can correctly handle message histories that include AIMessage objects with list content. - 2. The ``tool_calls`` attribute on AIMessage objects is correctly handled and passed to the model in an appropriate format. + 1. The model can correctly handle message histories that include ``AIMessage`` objects with list content. + 2. The ``tool_calls`` attribute on ``AIMessage`` objects is correctly handled and passed to the model in an appropriate format. 3. The model can correctly handle ToolMessage objects with string content and arbitrary string values for ``tool_call_id``. You can ``xfail`` the test if tool calling is implemented but this format @@ -1620,6 +1634,7 @@ class ChatModelIntegrationTests(ChatModelTests): """ # noqa: E501 if not self.has_tool_calling: pytest.skip("Test requires tool calling.") + model_with_tools = model.bind_tools([my_adder_tool]) function_name = "my_adder_tool" function_args = {"a": 1, "b": 2} @@ -1692,7 +1707,7 @@ class ChatModelIntegrationTests(ChatModelTests): pytest.skip("Test requires tool choice.") @tool - def get_weather(location: str) -> str: # pylint: disable=unused-argument + def get_weather(location: str) -> str: """Get weather at a location.""" return "It's sunny." @@ -1750,6 +1765,7 @@ class ChatModelIntegrationTests(ChatModelTests): """ # noqa: E501 if not self.has_tool_calling: pytest.skip("Test requires tool calling.") + tool_choice_value = None if not self.has_tool_choice else "any" model_with_tools = model.bind_tools( [magic_function_no_args], tool_choice=tool_choice_value @@ -1767,7 +1783,7 @@ class ChatModelIntegrationTests(ChatModelTests): def test_tool_message_error_status( self, model: BaseChatModel, my_adder_tool: BaseTool ) -> None: - """Test that ToolMessage with ``status="error"`` can be handled. + """Test that ``ToolMessage`` with ``status="error"`` can be handled. These messages may take the form: @@ -1806,6 +1822,7 @@ class ChatModelIntegrationTests(ChatModelTests): """ if not self.has_tool_calling: pytest.skip("Test requires tool calling.") + model_with_tools = model.bind_tools([my_adder_tool]) messages = [ HumanMessage("What is 1 + 2"), @@ -1860,8 +1877,9 @@ class ChatModelIntegrationTests(ChatModelTests): .. dropdown:: Troubleshooting - This test uses a utility function in ``langchain_core`` to generate a - sequence of messages representing "few-shot" examples: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.tool_example_to_messages.html + This test uses `a utility function `__ + in ``langchain_core`` to generate a sequence of messages representing + "few-shot" examples. If this test fails, check that the model can correctly handle this sequence of messages. @@ -1878,6 +1896,7 @@ class ChatModelIntegrationTests(ChatModelTests): """ if not self.has_tool_calling: pytest.skip("Test requires tool calling.") + model_with_tools = model.bind_tools([my_adder_tool], tool_choice="any") function_result = json.dumps({"result": 3}) @@ -1921,10 +1940,12 @@ class ChatModelIntegrationTests(ChatModelTests): If this test fails, ensure that the model's ``bind_tools`` method properly handles both JSON Schema and Pydantic V2 models. - ``langchain_core`` implements a utility function that will accommodate - most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html - See example implementation of ``with_structured_output`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output + ``langchain_core`` implements `a utility function `__ + that will accommodate most formats. + + See `example implementation `__ + of ``with_structured_output``. """ if not self.has_structured_output: @@ -2000,10 +2021,12 @@ class ChatModelIntegrationTests(ChatModelTests): If this test fails, ensure that the model's ``bind_tools`` method properly handles both JSON Schema and Pydantic V2 models. - ``langchain_core`` implements a utility function that will accommodate - most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html - See example implementation of ``with_structured_output`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output + ``langchain_core`` implements `a utility function `__ + that will accommodate most formats. + + See `example implementation `__ + of ``with_structured_output``. """ if not self.has_structured_output: @@ -2052,10 +2075,9 @@ class ChatModelIntegrationTests(ChatModelTests): @pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 2, reason="Test requires pydantic 2.") def test_structured_output_pydantic_2_v1(self, model: BaseChatModel) -> None: - """Test to verify we can generate structured output using - pydantic.v1.BaseModel. + """Test to verify we can generate structured output using ``pydantic.v1.BaseModel``. - pydantic.v1.BaseModel is available in the pydantic 2 package. + ``pydantic.v1.BaseModel`` is available in the Pydantic 2 package. This test is optional and should be skipped if the model does not support structured output (see Configuration below). @@ -2079,12 +2101,14 @@ class ChatModelIntegrationTests(ChatModelTests): If this test fails, ensure that the model's ``bind_tools`` method properly handles both JSON Schema and Pydantic V1 models. - ``langchain_core`` implements a utility function that will accommodate - most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html - See example implementation of ``with_structured_output`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output + ``langchain_core`` implements `a utility function `__ + that will accommodate most formats. - """ + See `example implementation `__ + of ``with_structured_output``. + + """ # noqa: E501 if not self.has_structured_output: pytest.skip("Test requires structured output.") @@ -2141,10 +2165,12 @@ class ChatModelIntegrationTests(ChatModelTests): If this test fails, ensure that the model's ``bind_tools`` method properly handles Pydantic V2 models with optional parameters. - ``langchain_core`` implements a utility function that will accommodate - most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html - See example implementation of ``with_structured_output`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output + ``langchain_core`` implements `a utility function `__ + that will accommodate most formats. + + See `example implementation `__ + of ``with_structured_output``. """ if not self.has_structured_output: @@ -2225,7 +2251,7 @@ class ChatModelIntegrationTests(ChatModelTests): # Type ignoring since the interface only officially supports pydantic 1 # or pydantic.v1.BaseModel but not pydantic.BaseModel from pydantic 2. # We'll need to do a pass updating the type signatures. - chat = model.with_structured_output(Joke, method="json_mode") # type: ignore[arg-type] + chat = model.with_structured_output(Joke, method="json_mode") msg = ( "Tell me a joke about cats. Return the result as a JSON with 'setup' and " "'punchline' keys. Return nothing other than JSON." @@ -2288,6 +2314,7 @@ class ChatModelIntegrationTests(ChatModelTests): """ if not self.supports_pdf_inputs: pytest.skip("Model does not support PDF inputs.") + url = "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" pdf_data = base64.b64encode(httpx.get(url).content).decode("utf-8") @@ -2364,6 +2391,7 @@ class ChatModelIntegrationTests(ChatModelTests): """ if not self.supports_audio_inputs: pytest.skip("Model does not support audio inputs.") + url = "https://upload.wikimedia.org/wikipedia/commons/3/3d/Alcal%C3%A1_de_Henares_%28RPS_13-04-2024%29_canto_de_ruise%C3%B1or_%28Luscinia_megarhynchos%29_en_el_Soto_del_Henares.wav" audio_data = base64.b64encode(httpx.get(url).content).decode("utf-8") @@ -2465,6 +2493,7 @@ class ChatModelIntegrationTests(ChatModelTests): """ if not self.supports_image_inputs: pytest.skip("Model does not support image message.") + image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8") @@ -2572,6 +2601,7 @@ class ChatModelIntegrationTests(ChatModelTests): """ if not self.supports_image_tool_message: pytest.skip("Model does not support image tool message.") + image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8") @@ -2687,7 +2717,7 @@ class ChatModelIntegrationTests(ChatModelTests): 1. The model can correctly handle message histories that include message objects with list content. 2. The ``tool_calls`` attribute on AIMessage objects is correctly handled and passed to the model in an appropriate format. - 3. HumanMessages with "tool_result" content blocks are correctly handled. + 3. ``HumanMessage``s with "tool_result" content blocks are correctly handled. Otherwise, if Anthropic tool call and result formats are not supported, set the ``supports_anthropic_inputs`` property to False. @@ -2793,7 +2823,7 @@ class ChatModelIntegrationTests(ChatModelTests): assert isinstance(response, AIMessage) def test_message_with_name(self, model: BaseChatModel) -> None: - """Test that HumanMessage with values for the ``name`` field can be handled. + """Test that ``HumanMessage`` with values for the ``name`` field can be handled. These messages may take the form: @@ -2842,7 +2872,7 @@ class ChatModelIntegrationTests(ChatModelTests): chat model. Check also that all required information (e.g., tool calling identifiers) - from AIMessage objects is propagated correctly to model payloads. + from ``AIMessage`` objects is propagated correctly to model payloads. This test may fail if the chat model does not consistently generate tool calls in response to an appropriate query. In these cases you can ``xfail`` @@ -2859,7 +2889,7 @@ class ChatModelIntegrationTests(ChatModelTests): pytest.skip("Test requires tool calling.") @tool - def get_weather(location: str) -> str: # pylint: disable=unused-argument + def get_weather(location: str) -> str: """Call to surf the web.""" return "It's sunny." @@ -2953,12 +2983,13 @@ class ChatModelIntegrationTests(ChatModelTests): Args: model: The chat model to test - tool_choice: Tool choice parameter to pass to bind_tools (provider-specific) - force_tool_call: Whether to force a tool call (use tool_choice=True if None) + tool_choice: Tool choice parameter to pass to ``bind_tools()`` (provider-specific) + force_tool_call: Whether to force a tool call (use ``tool_choice=True`` if None) Tests that Unicode characters in tool call arguments are preserved correctly, - not escaped as \\uXXXX sequences. - """ + not escaped as ``\\uXXXX`` sequences. + + """ # noqa: E501 if not self.has_tool_calling: pytest.skip("Test requires tool calling support.") diff --git a/libs/standard-tests/langchain_tests/unit_tests/chat_models.py b/libs/standard-tests/langchain_tests/unit_tests/chat_models.py index 320d2b491f1..a42201f2bce 100644 --- a/libs/standard-tests/langchain_tests/unit_tests/chat_models.py +++ b/libs/standard-tests/langchain_tests/unit_tests/chat_models.py @@ -12,17 +12,11 @@ from langchain_core.load import dumpd, load from langchain_core.runnables import RunnableBinding from langchain_core.tools import BaseTool, tool from pydantic import BaseModel, Field, SecretStr -from pydantic.v1 import ( - BaseModel as BaseModelV1, -) -from pydantic.v1 import ( - Field as FieldV1, -) -from pydantic.v1 import ( - ValidationError as ValidationErrorV1, -) +from pydantic.v1 import BaseModel as BaseModelV1 +from pydantic.v1 import Field as FieldV1 +from pydantic.v1 import ValidationError as ValidationErrorV1 from pytest_benchmark.fixture import BenchmarkFixture # type: ignore[import-untyped] -from syrupy import SnapshotAssertion +from syrupy.assertion import SnapshotAssertion from langchain_tests.base import BaseStandardTests from langchain_tests.utils.pydantic import PYDANTIC_MAJOR_VERSION @@ -32,6 +26,7 @@ def generate_schema_pydantic_v1_from_2() -> Any: """Use to generate a schema from v1 namespace in pydantic 2. :private: + """ if PYDANTIC_MAJOR_VERSION != 2: msg = "This function is only compatible with Pydantic v2." @@ -50,6 +45,7 @@ def generate_schema_pydantic() -> Any: """Works with either pydantic 1 or 2. :private: + """ class PersonA(BaseModel): @@ -71,6 +67,7 @@ class ChatModelTests(BaseStandardTests): """Base class for chat model tests. :private: + """ @property @@ -154,16 +151,12 @@ class ChatModelTests(BaseStandardTests): @property def supports_image_inputs(self) -> bool: - """(bool) whether the chat model supports image inputs, defaults to - ``False``. - """ + """(bool) whether the chat model supports image inputs, defaults to ``False``.""" # noqa: E501 return False @property def supports_image_urls(self) -> bool: - """(bool) whether the chat model supports image inputs from URLs, defaults to - ``False``. - """ + """(bool) whether the chat model supports image inputs from URLs, defaults to ``False``.""" # noqa: E501 return False @property @@ -173,23 +166,21 @@ class ChatModelTests(BaseStandardTests): @property def supports_audio_inputs(self) -> bool: - """(bool) whether the chat model supports audio inputs, defaults to - ``False``. - """ + """(bool) whether the chat model supports audio inputs, defaults to ``False``.""" # noqa: E501 return False @property def supports_video_inputs(self) -> bool: """(bool) whether the chat model supports video inputs, defaults to ``False``. + No current tests are written for this feature. + """ return False @property def returns_usage_metadata(self) -> bool: - """(bool) whether the chat model returns usage metadata on invoke and streaming - responses. - """ + """(bool) whether the chat model returns usage metadata on invoke and streaming responses.""" # noqa: E501 return True @property @@ -199,9 +190,7 @@ class ChatModelTests(BaseStandardTests): @property def supports_image_tool_message(self) -> bool: - """(bool) whether the chat model supports ToolMessages that include image - content. - """ + """(bool) whether the chat model supports ``ToolMessage``s that include image content.""" # noqa: E501 return False @property @@ -211,6 +200,7 @@ class ChatModelTests(BaseStandardTests): .. important:: See ``enable_vcr_tests`` dropdown :class:`above ` for more information. + """ return False @@ -267,7 +257,7 @@ class ChatModelUnitTests(ChatModelTests): API references for individual test methods include troubleshooting tips. - Test subclasses must implement the following two properties: + Test subclasses **must** implement the following two properties: chat_model_class The chat model class to test, e.g., ``ChatParrotLink``. @@ -299,7 +289,7 @@ class ChatModelUnitTests(ChatModelTests): Boolean property indicating whether the chat model supports tool calling. - By default, this is determined by whether the chat model's `bind_tools` method + By default, this is determined by whether the chat model's ``bind_tools`` method is overridden. It typically does not need to be overridden on the test class. Example override: @@ -401,7 +391,7 @@ class ChatModelUnitTests(ChatModelTests): Defaults to ``False``. If set to ``True``, the chat model will be tested using content blocks of the - form + form. .. code-block:: python @@ -437,7 +427,7 @@ class ChatModelUnitTests(ChatModelTests): URLs. Defaults to ``False``. If set to ``True``, the chat model will be tested using content blocks of the - form + form. .. code-block:: python @@ -463,7 +453,7 @@ class ChatModelUnitTests(ChatModelTests): Defaults to ``False``. If set to ``True``, the chat model will be tested using content blocks of the - form + form. .. code-block:: python @@ -490,7 +480,7 @@ class ChatModelUnitTests(ChatModelTests): Defaults to ``False``. If set to ``True``, the chat model will be tested using content blocks of the - form + form. .. code-block:: python @@ -519,10 +509,10 @@ class ChatModelUnitTests(ChatModelTests): .. dropdown:: returns_usage_metadata Boolean property indicating whether the chat model returns usage metadata - on invoke and streaming responses. + on invoke and streaming responses. Defaults to ``True``. - ``usage_metadata`` is an optional dict attribute on AIMessages that track input - and output tokens: https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html + ``usage_metadata`` is an optional dict attribute on ``AIMessage``s that track input + and output tokens. `See more. `__ Example: @@ -533,7 +523,7 @@ class ChatModelUnitTests(ChatModelTests): return False Models supporting ``usage_metadata`` should also return the name of the - underlying model in the ``response_metadata`` of the AIMessage. + underlying model in the ``response_metadata`` of the ``AIMessage``. .. dropdown:: supports_anthropic_inputs @@ -567,7 +557,7 @@ class ChatModelUnitTests(ChatModelTests): .. dropdown:: supports_image_tool_message - Boolean property indicating whether the chat model supports ToolMessages + Boolean property indicating whether the chat model supports ``ToolMessage``s that include image content, e.g., .. code-block:: python @@ -615,11 +605,11 @@ class ChatModelUnitTests(ChatModelTests): .. dropdown:: supported_usage_metadata_details - Property controlling what usage metadata details are emitted in both invoke - and stream. + Property controlling what usage metadata details are emitted in both ``invoke`` + and ``stream``. - ``usage_metadata`` is an optional dict attribute on AIMessages that track input - and output tokens: https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html + ``usage_metadata`` is an optional dict attribute on ``AIMessage``s that track input + and output tokens. `See more. `__ It includes optional keys ``input_token_details`` and ``output_token_details`` that can track usage details associated with special types of tokens, such as @@ -812,6 +802,7 @@ class ChatModelUnitTests(ChatModelTests): def init_from_env_params(self) -> tuple[dict, dict, dict]: """(tuple) environment variables, additional initialization args, and expected instance attributes for testing initialization from environment variables. + """ return {}, {}, {} @@ -823,7 +814,8 @@ class ChatModelUnitTests(ChatModelTests): If this test fails, ensure that: 1. ``chat_model_params`` is specified and the model can be initialized from those params; - 2. The model accommodates standard parameters: https://python.langchain.com/docs/concepts/chat_models/#standard-parameters + 2. The model accommodates `standard parameters `__ + """ # noqa: E501 model = self.chat_model_class( **{ @@ -843,6 +835,7 @@ class ChatModelUnitTests(ChatModelTests): If this test fails, ensure that ``init_from_env_params`` is specified correctly and that model parameters are properly set from environment variables during initialization. + """ env_params, model_params, expected_attrs = self.init_from_env_params if not env_params: @@ -867,6 +860,7 @@ class ChatModelUnitTests(ChatModelTests): If this test fails, ensure that the model can be initialized with a boolean ``streaming`` parameter. + """ model = self.chat_model_class( **{ @@ -893,6 +887,7 @@ class ChatModelUnitTests(ChatModelTests): a utility function that will accommodate most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html See example implementation of ``bind_tools`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.bind_tools + """ if not self.has_tool_calling: return @@ -933,6 +928,7 @@ class ChatModelUnitTests(ChatModelTests): a utility function that will accommodate most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html See example implementation of ``with_structured_output`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output + """ if not self.has_structured_output: return @@ -955,6 +951,7 @@ class ChatModelUnitTests(ChatModelTests): Check also that the model class is named according to convention (e.g., ``ChatProviderName``). + """ class ExpectedParams(BaseModelV1): @@ -992,6 +989,7 @@ class ChatModelUnitTests(ChatModelTests): If this test fails, check that the ``init_from_env_params`` property is correctly set on the test class. + """ if not self.chat_model_class.is_lc_serializable(): pytest.skip("Model is not serializable.") @@ -1011,6 +1009,7 @@ class ChatModelUnitTests(ChatModelTests): def test_init_time(self, benchmark: BenchmarkFixture) -> None: """Test initialization time of the chat model. If this test fails, check that we are not introducing undue overhead in the model's initialization. + """ def _init_in_loop() -> None: diff --git a/libs/standard-tests/tests/unit_tests/custom_chat_model.py b/libs/standard-tests/tests/unit_tests/custom_chat_model.py index cc9be763989..737745ec27e 100644 --- a/libs/standard-tests/tests/unit_tests/custom_chat_model.py +++ b/libs/standard-tests/tests/unit_tests/custom_chat_model.py @@ -1,15 +1,9 @@ from collections.abc import Iterator from typing import Any, Optional -from langchain_core.callbacks import ( - CallbackManagerForLLMRun, -) +from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models import BaseChatModel -from langchain_core.messages import ( - AIMessage, - AIMessageChunk, - BaseMessage, -) +from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage from langchain_core.messages.ai import UsageMetadata from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult from pydantic import Field diff --git a/libs/standard-tests/uv.lock b/libs/standard-tests/uv.lock index 5c009076c25..8bf6cb597c8 100644 --- a/libs/standard-tests/uv.lock +++ b/libs/standard-tests/uv.lock @@ -356,7 +356,7 @@ test = [ test-integration = [] typing = [ { name = "langchain-text-splitters", directory = "../text-splitters" }, - { name = "mypy", specifier = ">=1.15,<1.16" }, + { name = "mypy", specifier = ">=1.17.1,<1.18" }, { name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" }, { name = "types-requests", specifier = ">=2.28.11.5,<3.0.0.0" }, ] diff --git a/uv.lock b/uv.lock index bb8f22f98e7..28ffabb41d2 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.9" resolution-markers = [ "python_full_version >= '3.13' and platform_python_implementation == 'PyPy'", @@ -181,7 +181,7 @@ wheels = [ [[package]] name = "anthropic" -version = "0.57.1" +version = "0.60.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "anyio" }, @@ -192,9 +192,9 @@ dependencies = [ { name = "sniffio" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/d7/75/6261a1a8d92aed47e27d2fcfb3a411af73b1435e6ae1186da02b760565d0/anthropic-0.57.1.tar.gz", hash = "sha256:7815dd92245a70d21f65f356f33fc80c5072eada87fb49437767ea2918b2c4b0", size = 423775, upload-time = "2025-07-03T16:57:35.932Z" } +sdist = { url = "https://files.pythonhosted.org/packages/4e/03/3334921dc54ed822b3dd993ae72d823a7402588521bbba3e024b3333a1fd/anthropic-0.60.0.tar.gz", hash = "sha256:a22ba187c6f4fd5afecb2fc913b960feccf72bc0d25c1b7ce0345e87caede577", size = 425983, upload-time = "2025-07-28T19:53:47.685Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/e5/cf/ca0ba77805aec6171629a8b665c7dc224dab374539c3d27005b5d8c100a0/anthropic-0.57.1-py3-none-any.whl", hash = "sha256:33afc1f395af207d07ff1bffc0a3d1caac53c371793792569c5d2f09283ea306", size = 292779, upload-time = "2025-07-03T16:57:34.636Z" }, + { url = "https://files.pythonhosted.org/packages/da/bb/d84f287fb1c217b30c328af987cf8bbe3897edf0518dcc5fa39412f794ec/anthropic-0.60.0-py3-none-any.whl", hash = "sha256:65ad1f088a960217aaf82ba91ff743d6c89e9d811c6d64275b9a7c59ee9ac3c6", size = 293116, upload-time = "2025-07-28T19:53:45.944Z" }, ] [[package]] @@ -2259,15 +2259,11 @@ dependencies = [ requires-dist = [ { name = "async-timeout", marker = "python_full_version < '3.11'", specifier = ">=4.0.0,<5.0.0" }, { name = "langchain-anthropic", marker = "extra == 'anthropic'" }, - { name = "langchain-aws", marker = "extra == 'aws'" }, - { name = "langchain-azure-ai", marker = "extra == 'azure-ai'" }, - { name = "langchain-cohere", marker = "extra == 'cohere'" }, { name = "langchain-community", marker = "extra == 'community'" }, { name = "langchain-core", editable = "libs/core" }, { name = "langchain-deepseek", marker = "extra == 'deepseek'" }, { name = "langchain-fireworks", marker = "extra == 'fireworks'" }, { name = "langchain-google-genai", marker = "extra == 'google-genai'" }, - { name = "langchain-google-vertexai", marker = "extra == 'google-vertexai'" }, { name = "langchain-groq", marker = "extra == 'groq'" }, { name = "langchain-huggingface", marker = "extra == 'huggingface'" }, { name = "langchain-mistralai", marker = "extra == 'mistralai'" }, @@ -2276,14 +2272,13 @@ requires-dist = [ { name = "langchain-perplexity", marker = "extra == 'perplexity'" }, { name = "langchain-text-splitters", editable = "libs/text-splitters" }, { name = "langchain-together", marker = "extra == 'together'" }, - { name = "langchain-xai", marker = "extra == 'xai'" }, { name = "langsmith", specifier = ">=0.1.17" }, { name = "pydantic", specifier = ">=2.7.4,<3.0.0" }, { name = "pyyaml", specifier = ">=5.3" }, { name = "requests", specifier = ">=2,<3" }, { name = "sqlalchemy", specifier = ">=1.4,<3" }, ] -provides-extras = ["community", "anthropic", "openai", "azure-ai", "cohere", "google-vertexai", "google-genai", "fireworks", "ollama", "together", "mistralai", "huggingface", "groq", "aws", "deepseek", "xai", "perplexity"] +provides-extras = ["community", "anthropic", "openai", "google-genai", "fireworks", "ollama", "together", "mistralai", "huggingface", "groq", "deepseek", "perplexity"] [package.metadata.requires-dev] codespell = [{ name = "codespell", specifier = ">=2.2.0,<3.0.0" }] @@ -2354,7 +2349,7 @@ typing = [ [[package]] name = "langchain-anthropic" -version = "0.3.17" +version = "0.3.18" source = { editable = "libs/partners/anthropic" } dependencies = [ { name = "anthropic" }, @@ -2364,7 +2359,7 @@ dependencies = [ [package.metadata] requires-dist = [ - { name = "anthropic", specifier = ">=0.57.0,<1" }, + { name = "anthropic", specifier = ">=0.60.0,<1" }, { name = "langchain-core", editable = "libs/core" }, { name = "pydantic", specifier = ">=2.7.4,<3.0.0" }, ] @@ -2483,7 +2478,7 @@ wheels = [ [[package]] name = "langchain-core" -version = "0.3.72" +version = "0.3.74" source = { editable = "libs/core" } dependencies = [ { name = "jsonpatch" }, @@ -2534,7 +2529,7 @@ test = [ test-integration = [] typing = [ { name = "langchain-text-splitters", directory = "libs/text-splitters" }, - { name = "mypy", specifier = ">=1.15,<1.16" }, + { name = "mypy", specifier = ">=1.17.1,<1.18" }, { name = "types-pyyaml", specifier = ">=6.0.12.2,<7.0.0.0" }, { name = "types-requests", specifier = ">=2.28.11.5,<3.0.0.0" }, ] @@ -2618,7 +2613,7 @@ dependencies = [ [[package]] name = "langchain-groq" -version = "0.3.6" +version = "0.3.7" source = { editable = "libs/partners/groq" } dependencies = [ { name = "groq" }, @@ -2627,7 +2622,7 @@ dependencies = [ [package.metadata] requires-dist = [ - { name = "groq", specifier = ">=0.29.0,<1" }, + { name = "groq", specifier = ">=0.30.0,<1" }, { name = "langchain-core", editable = "libs/core" }, ] @@ -2819,7 +2814,7 @@ typing = [] [[package]] name = "langchain-openai" -version = "0.3.28" +version = "0.3.29" source = { editable = "libs/partners/openai" } dependencies = [ { name = "langchain-core" }, @@ -2889,10 +2884,14 @@ version = "0.3.9" source = { editable = "libs/text-splitters" } dependencies = [ { name = "langchain-core" }, + { name = "pip" }, ] [package.metadata] -requires-dist = [{ name = "langchain-core", editable = "libs/core" }] +requires-dist = [ + { name = "langchain-core", editable = "libs/core" }, + { name = "pip", specifier = ">=25.2" }, +] [package.metadata.requires-dev] dev = [ @@ -2901,7 +2900,7 @@ dev = [ ] lint = [ { name = "langchain-core", editable = "libs/core" }, - { name = "ruff", specifier = ">=0.12.2,<0.13" }, + { name = "ruff", specifier = ">=0.12.8,<0.13" }, ] test = [ { name = "freezegun", specifier = ">=1.2.2,<2.0.0" }, @@ -2918,6 +2917,7 @@ test-integration = [ { name = "sentence-transformers", specifier = ">=3.0.1" }, { name = "spacy", specifier = ">=3.8.7,<4.0.0" }, { name = "thinc", specifier = ">=8.3.6,<9.0.0" }, + { name = "tiktoken", specifier = ">=0.8.0,<1.0.0" }, { name = "transformers", specifier = ">=4.51.3,<5.0.0" }, ] typing = [ @@ -4438,6 +4438,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/41/67/936f9814bdd74b2dfd4822f1f7725ab5d8ff4103919a1664eb4874c58b2f/pillow-11.1.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:4637b88343166249fe8aa94e7c4a62a180c4b3898283bb5d3d2fd5fe10d8e4e0", size = 2626353, upload-time = "2025-01-02T08:13:52.725Z" }, ] +[[package]] +name = "pip" +version = "25.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/20/16/650289cd3f43d5a2fadfd98c68bd1e1e7f2550a1a5326768cddfbcedb2c5/pip-25.2.tar.gz", hash = "sha256:578283f006390f85bb6282dffb876454593d637f5d1be494b5202ce4877e71f2", size = 1840021, upload-time = "2025-07-30T21:50:15.401Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b7/3f/945ef7ab14dc4f9d7f40288d2df998d1837ee0888ec3659c813487572faa/pip-25.2-py3-none-any.whl", hash = "sha256:6d67a2b4e7f14d8b31b8b52648866fa717f45a1eb70e83002f4331d07e953717", size = 1752557, upload-time = "2025-07-30T21:50:13.323Z" }, +] + [[package]] name = "platformdirs" version = "4.3.6"