ollama[patch]: support structured output (#28629)

- Bump minimum version of `ollama` to 0.4.4 (which also addresses
https://github.com/langchain-ai/langchain/issues/28607).
- Support recently-released [structured
output](https://ollama.com/blog/structured-outputs) feature. This can be
accessed by calling `.with_structured_output` with
`method="json_schema"` (choice of name
[mirrors](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html#langchain_openai.chat_models.base.ChatOpenAI.with_structured_output)
what we have for OpenAI's structured output feature).

`ChatOllama` previously implemented `.with_structured_output` via the
[base
implementation](ec9b41431e/libs/core/langchain_core/language_models/chat_models.py (L1117)).

libs/partners/ollama/langchain_ollama/chat_models.py

@@ -1,6 +1,7 @@
 """Ollama chat models."""
 
 import json
+from operator import itemgetter
 from typing import (
     Any,
     AsyncIterator,
@@ -36,13 +37,24 @@ from langchain_core.messages import (
 )
 from langchain_core.messages.ai import UsageMetadata
 from langchain_core.messages.tool import tool_call
+from langchain_core.output_parsers import (
+    JsonOutputKeyToolsParser,
+    JsonOutputParser,
+    PydanticOutputParser,
+    PydanticToolsParser,
+)
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
-from langchain_core.runnables import Runnable
+from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
 from langchain_core.tools import BaseTool
+from langchain_core.utils.function_calling import (
+    _convert_any_typed_dicts_to_pydantic as convert_any_typed_dicts_to_pydantic,
+)
 from langchain_core.utils.function_calling import convert_to_openai_tool
+from langchain_core.utils.pydantic import TypeBaseModel, is_basemodel_subclass
 from ollama import AsyncClient, Client, Message, Options
-from pydantic import PrivateAttr, model_validator
+from pydantic import BaseModel, PrivateAttr, model_validator
-from typing_extensions import Self
+from pydantic.json_schema import JsonSchemaValue
+from typing_extensions import Self, is_typeddict
 
 
 def _get_usage_metadata_from_generation_info(
@@ -157,6 +169,10 @@ def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict:
     }
 
 
+def _is_pydantic_class(obj: Any) -> bool:
+    return isinstance(obj, type) and is_basemodel_subclass(obj)
+
+
 class ChatOllama(BaseChatModel):
     r"""Ollama chat model integration.
 
@@ -385,8 +401,8 @@ class ChatOllama(BaseChatModel):
     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"] = ""
+    format: Optional[Union[Literal["", "json"], JsonSchemaValue]] = None
-    """Specify the format of the output (options: json)"""
+    """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."""
@@ -815,3 +831,322 @@ class ChatOllama(BaseChatModel):
         """  # noqa: E501
         formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
         return super().bind(tools=formatted_tools, **kwargs)
+
+    def with_structured_output(
+        self,
+        schema: Union[Dict, type],
+        *,
+        method: Literal[
+            "function_calling", "json_mode", "json_schema"
+        ] = "function_calling",
+        include_raw: bool = False,
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
+        """Model wrapper that returns outputs formatted to match the given schema.
+
+        Args:
+            schema:
+                The output schema. Can be passed in as:
+
+                - a Pydantic class,
+                - a JSON schema
+                - a TypedDict class
+                - an OpenAI function/tool schema.
+
+                If ``schema`` is a Pydantic class then the model output will be a
+                Pydantic instance of that class, and the model-generated fields will be
+                validated by the Pydantic class. Otherwise the model output will be a
+                dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
+                for more on how to properly specify types and descriptions of
+                schema fields when specifying a Pydantic or TypedDict class.
+
+            method: The method for steering model generation, one of:
+
+                - "function_calling":
+                    Uses Ollama's tool-calling API
+                - "json_schema":
+                    Uses Ollama's structured output API: https://ollama.com/blog/structured-outputs
+                - "json_mode":
+                    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
+                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".
+
+            kwargs: Additional keyword args aren't supported.
+
+        Returns:
+            A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
+
+            | If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs an instance of ``schema`` (i.e., a Pydantic object). Otherwise, if ``include_raw`` is False then Runnable outputs a dict.
+
+            | If ``include_raw`` is True, then Runnable outputs a dict with keys:
+
+            - "raw": BaseMessage
+            - "parsed": None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
+            - "parsing_error": Optional[BaseException]
+
+        .. versionchanged:: 0.2.2
+
+            Added support for structured output API via ``format`` parameter.
+
+        .. dropdown:: Example: schema=Pydantic class, method="function_calling", include_raw=False
+
+            .. code-block:: python
+
+                from typing import Optional
+
+                from langchain_ollama import ChatOllama
+                from pydantic import BaseModel, Field
+
+
+                class AnswerWithJustification(BaseModel):
+                    '''An answer to the user question along with justification for the answer.'''
+
+                    answer: str
+                    justification: Optional[str] = Field(
+                        default=..., description="A justification for the answer."
+                    )
+
+
+                llm = ChatOllama(model="llama3.1", temperature=0)
+                structured_llm = llm.with_structured_output(
+                    AnswerWithJustification
+                )
+
+                structured_llm.invoke(
+                    "What weighs more a pound of bricks or a pound of feathers"
+                )
+
+                # -> AnswerWithJustification(
+                #     answer='They weigh the same',
+                #     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="function_calling", include_raw=True
+
+            .. code-block:: python
+
+                from langchain_ollama import ChatOllama
+                from pydantic import BaseModel
+
+
+                class AnswerWithJustification(BaseModel):
+                    '''An answer to the user question along with justification for the answer.'''
+
+                    answer: str
+                    justification: str
+
+
+                llm = ChatOllama(model="llama3.1", temperature=0)
+                structured_llm = llm.with_structured_output(
+                    AnswerWithJustification, include_raw=True
+                )
+
+                structured_llm.invoke(
+                    "What weighs more a pound of bricks or a pound of feathers"
+                )
+                # -> {
+                #     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","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."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
+                #     'parsed': AnswerWithJustification(answer='They weigh the same.', 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.'),
+                #     'parsing_error': None
+                # }
+
+        .. dropdown:: Example: schema=Pydantic class, method="json_schema", include_raw=False
+
+            .. code-block:: python
+
+                from typing import Optional
+
+                from langchain_ollama import ChatOllama
+                from pydantic import BaseModel, Field
+
+
+                class AnswerWithJustification(BaseModel):
+                    '''An answer to the user question along with justification for the answer.'''
+
+                    answer: str
+                    justification: Optional[str] = Field(
+                        default=..., description="A justification for the answer."
+                    )
+
+
+                llm = ChatOllama(model="llama3.1", temperature=0)
+                structured_llm = llm.with_structured_output(
+                    AnswerWithJustification, method="json_schema"
+                )
+
+                structured_llm.invoke(
+                    "What weighs more a pound of bricks or a pound of feathers"
+                )
+
+                # -> AnswerWithJustification(
+                #     answer='They weigh the same',
+                #     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=TypedDict class, method="function_calling", include_raw=False
+
+            .. code-block:: python
+
+                # IMPORTANT: If you are using Python <=3.8, you need to import Annotated
+                # from typing_extensions, not from typing.
+                from typing_extensions import Annotated, TypedDict
+
+                from langchain_ollama import ChatOllama
+
+
+                class AnswerWithJustification(TypedDict):
+                    '''An answer to the user question along with justification for the answer.'''
+
+                    answer: str
+                    justification: Annotated[
+                        Optional[str], None, "A justification for the answer."
+                    ]
+
+
+                llm = ChatOllama(model="llama3.1", temperature=0)
+                structured_llm = llm.with_structured_output(AnswerWithJustification)
+
+                structured_llm.invoke(
+                    "What weighs more a pound of bricks or a pound of feathers"
+                )
+                # -> {
+                #     'answer': 'They weigh the same',
+                #     '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
+
+            .. code-block:: python
+
+                from langchain_ollama import ChatOllama
+
+                oai_schema = {
+                    'name': 'AnswerWithJustification',
+                    'description': 'An answer to the user question along with justification for the answer.',
+                    'parameters': {
+                        'type': 'object',
+                        'properties': {
+                            'answer': {'type': 'string'},
+                            'justification': {'description': 'A justification for the answer.', 'type': 'string'}
+                        },
+                       'required': ['answer']
+                   }
+               }
+
+                llm = ChatOllama(model="llama3.1", temperature=0)
+                structured_llm = llm.with_structured_output(oai_schema)
+
+                structured_llm.invoke(
+                    "What weighs more a pound of bricks or a pound of feathers"
+                )
+                # -> {
+                #     'answer': 'They weigh the same',
+                #     '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
+
+            .. code-block::
+
+                from langchain_ollama import ChatOllama
+                from pydantic import BaseModel
+
+                class AnswerWithJustification(BaseModel):
+                    answer: str
+                    justification: str
+
+                llm = ChatOllama(model="llama3.1", temperature=0)
+                structured_llm = llm.with_structured_output(
+                    AnswerWithJustification,
+                    method="json_mode",
+                    include_raw=True
+                )
+
+                structured_llm.invoke(
+                    "Answer the following question. "
+                    "Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
+                    "What's heavier a pound of bricks or a pound of feathers?"
+                )
+                # -> {
+                #     'raw': AIMessage(content='{\\n    "answer": "They are both the same weight.",\\n    "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \\n}'),
+                #     'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
+                #     'parsing_error': None
+                # }
+        """  # noqa: E501, D301
+        if kwargs:
+            raise ValueError(f"Received unsupported arguments {kwargs}")
+        is_pydantic_schema = _is_pydantic_class(schema)
+        if method == "function_calling":
+            if schema is None:
+                raise ValueError(
+                    "schema must be specified when method is not 'json_mode'. "
+                    "Received None."
+                )
+            tool_name = convert_to_openai_tool(schema)["function"]["name"]
+            llm = self.bind_tools([schema], tool_choice=tool_name)
+            if is_pydantic_schema:
+                output_parser: Runnable = PydanticToolsParser(
+                    tools=[schema],  # type: ignore[list-item]
+                    first_tool_only=True,
+                )
+            else:
+                output_parser = JsonOutputKeyToolsParser(
+                    key_name=tool_name, first_tool_only=True
+                )
+        elif method == "json_mode":
+            llm = self.bind(format="json")
+            output_parser = (
+                PydanticOutputParser(pydantic_object=schema)  # type: ignore[arg-type]
+                if is_pydantic_schema
+                else JsonOutputParser()
+            )
+        elif method == "json_schema":
+            if schema is None:
+                raise ValueError(
+                    "schema must be specified when method is not 'json_mode'. "
+                    "Received None."
+                )
+            if is_pydantic_schema:
+                schema = cast(TypeBaseModel, schema)
+                llm = self.bind(format=schema.model_json_schema())
+                output_parser = PydanticOutputParser(pydantic_object=schema)
+            else:
+                if is_typeddict(schema):
+                    schema = cast(type, schema)
+                    response_format = convert_any_typed_dicts_to_pydantic(
+                        schema, visited={}
+                    ).schema()  # type: ignore[attr-defined]
+                    if "required" not in response_format:
+                        response_format["required"] = list(
+                            response_format["properties"].keys()
+                        )
+                else:
+                    # is JSON schema
+                    response_format = schema
+                llm = self.bind(format=response_format)
+                output_parser = JsonOutputParser()
+        else:
+            raise ValueError(
+                f"Unrecognized method argument. Expected one of 'function_calling', "
+                f"'json_schema', or 'json_mode'. Received: '{method}'"
+            )
+
+        if include_raw:
+            parser_assign = RunnablePassthrough.assign(
+                parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
+            )
+            parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
+            parser_with_fallback = parser_assign.with_fallbacks(
+                [parser_none], exception_key="parsing_error"
+            )
+            return RunnableMap(raw=llm) | parser_with_fallback
+        else:
+            return llm | output_parser

libs/partners/ollama/poetry.lock

@@ -13,24 +13,24 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.6.2.post1"
+version = "4.7.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
+    {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"},
-    {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"},
+    {file = "anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48"},
 ]
 
 [package.dependencies]
 exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
-doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -309,7 +309,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.21"
+version = "0.3.22"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -334,7 +334,7 @@ url = "../../core"
 
 [[package]]
 name = "langchain-tests"
-version = "0.3.4"
+version = "0.3.6"
 description = "Standard tests for LangChain implementations"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -342,9 +342,15 @@ files = []
 develop = true
 
 [package.dependencies]
-httpx = "^0.27.0"
+httpx = ">=0.25.0,<1"
-langchain-core = "^0.3.19"
+langchain-core = "^0.3.22"
+numpy = [
+    {version = ">=1.24.0,<2.0.0", markers = "python_version < \"3.12\""},
+    {version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""},
+]
 pytest = ">=7,<9"
+pytest-asyncio = ">=0.20,<1"
+pytest-socket = ">=0.6.0,<1"
 syrupy = "^4"
 
 [package.source]
@@ -353,13 +359,13 @@ url = "../../standard-tests"
 
 [[package]]
 name = "langsmith"
-version = "0.1.144"
+version = "0.1.147"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.144-py3-none-any.whl", hash = "sha256:08ffb975bff2e82fc6f5428837c64c074ea25102d08a25e256361a80812c6100"},
+    {file = "langsmith-0.1.147-py3-none-any.whl", hash = "sha256:7166fc23b965ccf839d64945a78e9f1157757add228b086141eb03a60d699a15"},
-    {file = "langsmith-0.1.144.tar.gz", hash = "sha256:b621f358d5a33441d7b5e7264c376bf4ea82bfc62d7e41aafc0f8094e3bd6369"},
+    {file = "langsmith-0.1.147.tar.gz", hash = "sha256:2e933220318a4e73034657103b3b1a3a6109cc5db3566a7e8e03be8d6d7def7a"},
 ]
 
 [package.dependencies]
@@ -372,6 +378,9 @@ pydantic = [
 requests = ">=2,<3"
 requests-toolbelt = ">=1.0.0,<2.0.0"
 
+[package.extras]
+langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2,<0.2.0)"]
+
 [[package]]
 name = "mypy"
 version = "1.13.0"
@@ -436,15 +445,124 @@ files = [
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
 ]
 
+[[package]]
+name = "numpy"
+version = "1.26.4"
+description = "Fundamental package for array computing in Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"},
+    {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4"},
+    {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a"},
+    {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a4abb4f9001ad2858e7ac189089c42178fcce737e4169dc61321660f1a96c7d2"},
+    {file = "numpy-1.26.4-cp310-cp310-win32.whl", hash = "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07"},
+    {file = "numpy-1.26.4-cp310-cp310-win_amd64.whl", hash = "sha256:b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5"},
+    {file = "numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4c66707fabe114439db9068ee468c26bbdf909cac0fb58686a42a24de1760c71"},
+    {file = "numpy-1.26.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef"},
+    {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7ab55401287bfec946ced39700c053796e7cc0e3acbef09993a9ad2adba6ca6e"},
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+    {file = "orjson-3.10.12.tar.gz", hash = "sha256:0a78bbda3aea0f9f079057ee1ee8a1ecf790d4f1af88dd67493c6b8ee52506ff"},
 ]
 
 [[package]]
@@ -546,13 +681,13 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pydantic"
-version = "2.10.1"
+version = "2.10.3"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pydantic-2.10.1-py3-none-any.whl", hash = "sha256:a8d20db84de64cf4a7d59e899c2caf0fe9d660c7cfc482528e7020d7dd189a7e"},
+    {file = "pydantic-2.10.3-py3-none-any.whl", hash = "sha256:be04d85bbc7b65651c5f8e6b9976ed9c6f41782a55524cef079a34a0bb82144d"},
-    {file = "pydantic-2.10.1.tar.gz", hash = "sha256:a4daca2dc0aa429555e0656d6bf94873a7dc5f54ee42b1f5873d666fb3f35560"},
+    {file = "pydantic-2.10.3.tar.gz", hash = "sha256:cb5ac360ce894ceacd69c403187900a02c4b20b693a9dd1d643e1effab9eadf9"},
 ]
 
 [package.dependencies]
@@ -881,13 +1016,13 @@ files = [
 
 [[package]]
 name = "syrupy"
-version = "4.7.2"
+version = "4.8.0"
 description = "Pytest Snapshot Test Utility"
 optional = false
 python-versions = ">=3.8.1"
 files = [
-    {file = "syrupy-4.7.2-py3-none-any.whl", hash = "sha256:eae7ba6be5aed190237caa93be288e97ca1eec5ca58760e4818972a10c4acc64"},
+    {file = "syrupy-4.8.0-py3-none-any.whl", hash = "sha256:544f4ec6306f4b1c460fdab48fd60b2c7fe54a6c0a8243aeea15f9ad9c638c3f"},
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+    {file = "syrupy-4.8.0.tar.gz", hash = "sha256:648f0e9303aaa8387c8365d7314784c09a6bab0a407455c6a01d6a4f5c6a8ede"},
 ]
 
 [package.dependencies]
@@ -910,13 +1045,43 @@ test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
 [[package]]
 name = "tomli"
-version = "2.1.0"
+version = "2.2.1"
 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "tomli-2.1.0.tar.gz", hash = "sha256:3f646cae2aec94e17d04973e4249548320197cfabdf130015d023de4b74d8ab8"},
+    {file = "tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6"},
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+    {file = "tomli-2.2.1-cp313-cp313-win_amd64.whl", hash = "sha256:a38aa0308e754b0e3c67e344754dff64999ff9b513e691d0e786265c93583c69"},
+    {file = "tomli-2.2.1-py3-none-any.whl", hash = "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc"},
+    {file = "tomli-2.2.1.tar.gz", hash = "sha256:cd45e1dc79c835ce60f7404ec8119f2eb06d38b1deba146f07ced3bbc44505ff"},
 ]
 
 [[package]]
@@ -992,4 +1157,4 @@ watchmedo = ["PyYAML (>=3.10)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "078fb5da0212681a4fc4d02e4e4112fe7a3ebda0923411e72ad706a097a989ae"
+content-hash = "6cdc5d8dc48cecb900f751e3e8f0be9ed5a52fb8e873a9ce2bcf3fd7db36ef79"

libs/partners/ollama/pyproject.toml

@@ -20,7 +20,7 @@ disallow_untyped_defs = "True"
 
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
-ollama = ">=0.3.0,<1"
+ollama = ">=0.4.4,<1"
 langchain-core = "^0.3.20"
 
 [tool.ruff.lint]

libs/partners/ollama/tests/integration_tests/chat_models/test_chat_models.py

@@ -4,10 +4,61 @@ from typing import List, Optional
 
 import pytest
 from pydantic import BaseModel, Field
+from typing_extensions import Annotated, TypedDict
 
 from langchain_ollama import ChatOllama
 
 
+@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."""
+
+    class Joke(BaseModel):
+        """Joke to tell user."""
+
+        setup: str = Field(description="question to set up a joke")
+        punchline: str = Field(description="answer to resolve the joke")
+
+    llm = ChatOllama(model="llama3.1", temperature=0)
+    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)
+
+    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"}
+
+    for chunk in structured_llm.stream(query):
+        assert isinstance(chunk, dict)
+    assert isinstance(chunk, dict)  # for mypy
+    assert set(chunk.keys()) == {"setup", "punchline"}
+
+    # 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"]
+
+    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"}
+
+    for chunk in structured_llm.stream(query):
+        assert isinstance(chunk, dict)
+    assert isinstance(chunk, dict)  # for mypy
+    assert set(chunk.keys()) == {"setup", "punchline"}
+
+
 @pytest.mark.parametrize(("model"), [("llama3.1")])
 def test_structured_output_deeply_nested(model: str) -> None:
     """Test to verify structured output with a nested objects."""