mirror of
https://github.com/hwchase17/langchain.git
synced 2025-07-23 13:02:59 +00:00
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)
).
This commit is contained in:
parent
24292c4a31
commit
5c6e2cbcda
@ -1,6 +1,7 @@
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"""Ollama chat models."""
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import json
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from operator import itemgetter
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from typing import (
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Any,
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AsyncIterator,
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@ -36,13 +37,24 @@ from langchain_core.messages import (
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)
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from langchain_core.messages.ai import UsageMetadata
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from langchain_core.messages.tool import tool_call
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from langchain_core.output_parsers import (
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JsonOutputKeyToolsParser,
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JsonOutputParser,
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PydanticOutputParser,
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PydanticToolsParser,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.runnables import Runnable
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from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
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from langchain_core.tools import BaseTool
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from langchain_core.utils.function_calling import (
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_convert_any_typed_dicts_to_pydantic as convert_any_typed_dicts_to_pydantic,
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)
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from langchain_core.utils.function_calling import convert_to_openai_tool
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from langchain_core.utils.pydantic import TypeBaseModel, is_basemodel_subclass
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from ollama import AsyncClient, Client, Message, Options
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from pydantic import PrivateAttr, model_validator
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from typing_extensions import Self
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from pydantic import BaseModel, PrivateAttr, model_validator
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from pydantic.json_schema import JsonSchemaValue
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from typing_extensions import Self, is_typeddict
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def _get_usage_metadata_from_generation_info(
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@ -157,6 +169,10 @@ def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict:
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}
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def _is_pydantic_class(obj: Any) -> bool:
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return isinstance(obj, type) and is_basemodel_subclass(obj)
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class ChatOllama(BaseChatModel):
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r"""Ollama chat model integration.
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@ -385,8 +401,8 @@ class ChatOllama(BaseChatModel):
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to more diverse text, while a lower value (e.g., 0.5) will
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generate more focused and conservative text. (Default: 0.9)"""
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format: Literal["", "json"] = ""
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"""Specify the format of the output (options: json)"""
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format: Optional[Union[Literal["", "json"], JsonSchemaValue]] = None
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"""Specify the format of the output (options: "json", JSON schema)."""
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keep_alive: Optional[Union[int, str]] = None
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"""How long the model will stay loaded into memory."""
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@ -815,3 +831,322 @@ class ChatOllama(BaseChatModel):
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""" # noqa: E501
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formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
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return super().bind(tools=formatted_tools, **kwargs)
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def with_structured_output(
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self,
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schema: Union[Dict, type],
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*,
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method: Literal[
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"function_calling", "json_mode", "json_schema"
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] = "function_calling",
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include_raw: bool = False,
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
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"""Model wrapper that returns outputs formatted to match the given schema.
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Args:
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schema:
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The output schema. Can be passed in as:
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- a Pydantic class,
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- a JSON schema
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- a TypedDict class
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- an OpenAI function/tool schema.
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If ``schema`` is a Pydantic class then the model output will be a
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Pydantic instance of that class, and the model-generated fields will be
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validated by the Pydantic class. Otherwise the model output will be a
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dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
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for more on how to properly specify types and descriptions of
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schema fields when specifying a Pydantic or TypedDict class.
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method: The method for steering model generation, one of:
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- "function_calling":
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Uses Ollama's tool-calling API
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- "json_schema":
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Uses Ollama's structured output API: https://ollama.com/blog/structured-outputs
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- "json_mode":
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Specifies ``format="json"``. Note that if using JSON mode then you
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must include instructions for formatting the output into the
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desired schema into the model call.
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include_raw:
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If False then only the parsed structured output is returned. If
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an error occurs during model output parsing it will be raised. If True
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then both the raw model response (a BaseMessage) and the parsed model
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response will be returned. If an error occurs during output parsing it
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will be caught and returned as well. The final output is always a dict
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with keys "raw", "parsed", and "parsing_error".
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kwargs: Additional keyword args aren't supported.
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Returns:
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A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
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| 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.
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| If ``include_raw`` is True, then Runnable outputs a dict with keys:
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- "raw": BaseMessage
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- "parsed": None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
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- "parsing_error": Optional[BaseException]
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.. versionchanged:: 0.2.2
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Added support for structured output API via ``format`` parameter.
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.. dropdown:: Example: schema=Pydantic class, method="function_calling", include_raw=False
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.. code-block:: python
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from typing import Optional
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from langchain_ollama import ChatOllama
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from pydantic import BaseModel, Field
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class AnswerWithJustification(BaseModel):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: Optional[str] = Field(
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default=..., description="A justification for the answer."
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)
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llm = ChatOllama(model="llama3.1", temperature=0)
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structured_llm = llm.with_structured_output(
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AnswerWithJustification
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)
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structured_llm.invoke(
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"What weighs more a pound of bricks or a pound of feathers"
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)
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# -> AnswerWithJustification(
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# answer='They weigh the same',
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# 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.'
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# )
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.. dropdown:: Example: schema=Pydantic class, method="function_calling", include_raw=True
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.. code-block:: python
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from langchain_ollama import ChatOllama
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from pydantic import BaseModel
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class AnswerWithJustification(BaseModel):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: str
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llm = ChatOllama(model="llama3.1", temperature=0)
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structured_llm = llm.with_structured_output(
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AnswerWithJustification, include_raw=True
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)
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structured_llm.invoke(
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"What weighs more a pound of bricks or a pound of feathers"
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)
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# -> {
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# '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'}]}),
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# '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.'),
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# 'parsing_error': None
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# }
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.. dropdown:: Example: schema=Pydantic class, method="json_schema", include_raw=False
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.. code-block:: python
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from typing import Optional
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from langchain_ollama import ChatOllama
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from pydantic import BaseModel, Field
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class AnswerWithJustification(BaseModel):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: Optional[str] = Field(
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default=..., description="A justification for the answer."
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)
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llm = ChatOllama(model="llama3.1", temperature=0)
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structured_llm = llm.with_structured_output(
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AnswerWithJustification, method="json_schema"
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)
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structured_llm.invoke(
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"What weighs more a pound of bricks or a pound of feathers"
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)
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# -> AnswerWithJustification(
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# answer='They weigh the same',
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# 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.'
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# )
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.. dropdown:: Example: schema=TypedDict class, method="function_calling", include_raw=False
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.. code-block:: python
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# IMPORTANT: If you are using Python <=3.8, you need to import Annotated
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# from typing_extensions, not from typing.
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from typing_extensions import Annotated, TypedDict
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from langchain_ollama import ChatOllama
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class AnswerWithJustification(TypedDict):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: Annotated[
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Optional[str], None, "A justification for the answer."
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]
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llm = ChatOllama(model="llama3.1", temperature=0)
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structured_llm = llm.with_structured_output(AnswerWithJustification)
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structured_llm.invoke(
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"What weighs more a pound of bricks or a pound of feathers"
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)
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# -> {
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# 'answer': 'They weigh the same',
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# '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.'
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# }
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.. dropdown:: Example: schema=OpenAI function schema, method="function_calling", include_raw=False
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.. code-block:: python
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from langchain_ollama import ChatOllama
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oai_schema = {
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'name': 'AnswerWithJustification',
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'description': 'An answer to the user question along with justification for the answer.',
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'parameters': {
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'type': 'object',
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'properties': {
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'answer': {'type': 'string'},
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'justification': {'description': 'A justification for the answer.', 'type': 'string'}
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},
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'required': ['answer']
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}
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}
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llm = ChatOllama(model="llama3.1", temperature=0)
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structured_llm = llm.with_structured_output(oai_schema)
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structured_llm.invoke(
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"What weighs more a pound of bricks or a pound of feathers"
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)
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# -> {
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# 'answer': 'They weigh the same',
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# '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.'
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# }
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.. dropdown:: Example: schema=Pydantic class, method="json_mode", include_raw=True
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.. code-block::
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from langchain_ollama import ChatOllama
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from pydantic import BaseModel
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class AnswerWithJustification(BaseModel):
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answer: str
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justification: str
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llm = ChatOllama(model="llama3.1", temperature=0)
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structured_llm = llm.with_structured_output(
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AnswerWithJustification,
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method="json_mode",
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include_raw=True
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)
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structured_llm.invoke(
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"Answer the following question. "
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"Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
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"What's heavier a pound of bricks or a pound of feathers?"
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)
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# -> {
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# '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}'),
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# '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.'),
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# 'parsing_error': None
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# }
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""" # noqa: E501, D301
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if kwargs:
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raise ValueError(f"Received unsupported arguments {kwargs}")
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is_pydantic_schema = _is_pydantic_class(schema)
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if method == "function_calling":
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if schema is None:
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raise ValueError(
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"schema must be specified when method is not 'json_mode'. "
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"Received None."
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)
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tool_name = convert_to_openai_tool(schema)["function"]["name"]
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llm = self.bind_tools([schema], tool_choice=tool_name)
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if is_pydantic_schema:
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output_parser: Runnable = PydanticToolsParser(
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tools=[schema], # type: ignore[list-item]
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first_tool_only=True,
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)
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else:
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output_parser = JsonOutputKeyToolsParser(
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key_name=tool_name, first_tool_only=True
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)
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elif method == "json_mode":
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llm = self.bind(format="json")
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output_parser = (
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PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type]
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if is_pydantic_schema
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else JsonOutputParser()
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)
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elif method == "json_schema":
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if schema is None:
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raise ValueError(
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"schema must be specified when method is not 'json_mode'. "
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"Received None."
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)
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if is_pydantic_schema:
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schema = cast(TypeBaseModel, schema)
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llm = self.bind(format=schema.model_json_schema())
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output_parser = PydanticOutputParser(pydantic_object=schema)
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else:
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if is_typeddict(schema):
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schema = cast(type, schema)
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response_format = convert_any_typed_dicts_to_pydantic(
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schema, visited={}
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).schema() # type: ignore[attr-defined]
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if "required" not in response_format:
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response_format["required"] = list(
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response_format["properties"].keys()
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)
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else:
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# is JSON schema
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response_format = schema
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llm = self.bind(format=response_format)
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output_parser = JsonOutputParser()
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else:
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raise ValueError(
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f"Unrecognized method argument. Expected one of 'function_calling', "
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f"'json_schema', or 'json_mode'. Received: '{method}'"
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)
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if include_raw:
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parser_assign = RunnablePassthrough.assign(
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parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
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)
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parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
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parser_with_fallback = parser_assign.with_fallbacks(
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[parser_none], exception_key="parsing_error"
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)
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return RunnableMap(raw=llm) | parser_with_fallback
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else:
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return llm | output_parser
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|
335
libs/partners/ollama/poetry.lock
generated
335
libs/partners/ollama/poetry.lock
generated
@ -13,24 +13,24 @@ files = [
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[[package]]
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name = "anyio"
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version = "4.6.2.post1"
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version = "4.7.0"
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description = "High level compatibility layer for multiple asynchronous event loop implementations"
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optional = false
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python-versions = ">=3.9"
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files = [
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{file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
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{file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"},
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{file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"},
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{file = "anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48"},
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]
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[package.dependencies]
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exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
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idna = ">=2.8"
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sniffio = ">=1.1"
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typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
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typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
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[package.extras]
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doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
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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)"]
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doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
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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)"]
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trio = ["trio (>=0.26.1)"]
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[[package]]
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@ -309,7 +309,7 @@ files = [
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[[package]]
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name = "langchain-core"
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||||
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"
|
||||
langchain-core = "^0.3.19"
|
||||
httpx = ">=0.25.0,<1"
|
||||
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 = [
|
||||
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||||
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||||
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||||
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|
||||
]
|
||||
|
||||
[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 = [
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||||
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|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
description = "Fundamental package for array computing in Python"
|
||||
optional = false
|
||||
python-versions = ">=3.10"
|
||||
files = [
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||||
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[[package]]
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||||
@ -546,13 +681,13 @@ testing = ["pytest", "pytest-benchmark"]
|
||||
|
||||
[[package]]
|
||||
name = "pydantic"
|
||||
version = "2.10.1"
|
||||
version = "2.10.3"
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||||
description = "Data validation using Python type hints"
|
||||
optional = false
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python-versions = ">=3.8"
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[package.dependencies]
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@ -881,13 +1016,13 @@ files = [
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|
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[[package]]
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||||
name = "syrupy"
|
||||
version = "4.7.2"
|
||||
version = "4.8.0"
|
||||
description = "Pytest Snapshot Test Utility"
|
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optional = false
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python-versions = ">=3.8.1"
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[package.dependencies]
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@ -910,13 +1045,43 @@ test = ["pytest", "tornado (>=4.5)", "typeguard"]
|
||||
|
||||
[[package]]
|
||||
name = "tomli"
|
||||
version = "2.1.0"
|
||||
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|
||||
{file = "tomli-2.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6972ca9c9cc9f0acaa56a8ca1ff51e7af152a9f87fb64623e31d5c83700080ee"},
|
||||
{file = "tomli-2.2.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c954d2250168d28797dd4e3ac5cf812a406cd5a92674ee4c8f123c889786aa8e"},
|
||||
{file = "tomli-2.2.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8dd28b3e155b80f4d54beb40a441d366adcfe740969820caf156c019fb5c7ec4"},
|
||||
{file = "tomli-2.2.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:e59e304978767a54663af13c07b3d1af22ddee3bb2fb0618ca1593e4f593a106"},
|
||||
{file = "tomli-2.2.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:33580bccab0338d00994d7f16f4c4ec25b776af3ffaac1ed74e0b3fc95e885a8"},
|
||||
{file = "tomli-2.2.1-cp311-cp311-win32.whl", hash = "sha256:465af0e0875402f1d226519c9904f37254b3045fc5084697cefb9bdde1ff99ff"},
|
||||
{file = "tomli-2.2.1-cp311-cp311-win_amd64.whl", hash = "sha256:2d0f2fdd22b02c6d81637a3c95f8cd77f995846af7414c5c4b8d0545afa1bc4b"},
|
||||
{file = "tomli-2.2.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:4a8f6e44de52d5e6c657c9fe83b562f5f4256d8ebbfe4ff922c495620a7f6cea"},
|
||||
{file = "tomli-2.2.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8d57ca8095a641b8237d5b079147646153d22552f1c637fd3ba7f4b0b29167a8"},
|
||||
{file = "tomli-2.2.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4e340144ad7ae1533cb897d406382b4b6fede8890a03738ff1683af800d54192"},
|
||||
{file = "tomli-2.2.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:db2b95f9de79181805df90bedc5a5ab4c165e6ec3fe99f970d0e302f384ad222"},
|
||||
{file = "tomli-2.2.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:40741994320b232529c802f8bc86da4e1aa9f413db394617b9a256ae0f9a7f77"},
|
||||
{file = "tomli-2.2.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:400e720fe168c0f8521520190686ef8ef033fb19fc493da09779e592861b78c6"},
|
||||
{file = "tomli-2.2.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:02abe224de6ae62c19f090f68da4e27b10af2b93213d36cf44e6e1c5abd19fdd"},
|
||||
{file = "tomli-2.2.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b82ebccc8c8a36f2094e969560a1b836758481f3dc360ce9a3277c65f374285e"},
|
||||
{file = "tomli-2.2.1-cp312-cp312-win32.whl", hash = "sha256:889f80ef92701b9dbb224e49ec87c645ce5df3fa2cc548664eb8a25e03127a98"},
|
||||
{file = "tomli-2.2.1-cp312-cp312-win_amd64.whl", hash = "sha256:7fc04e92e1d624a4a63c76474610238576942d6b8950a2d7f908a340494e67e4"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f4039b9cbc3048b2416cc57ab3bda989a6fcf9b36cf8937f01a6e731b64f80d7"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:286f0ca2ffeeb5b9bd4fcc8d6c330534323ec51b2f52da063b11c502da16f30c"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a92ef1a44547e894e2a17d24e7557a5e85a9e1d0048b0b5e7541f76c5032cb13"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9316dc65bed1684c9a98ee68759ceaed29d229e985297003e494aa825ebb0281"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e85e99945e688e32d5a35c1ff38ed0b3f41f43fad8df0bdf79f72b2ba7bc5272"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ac065718db92ca818f8d6141b5f66369833d4a80a9d74435a268c52bdfa73140"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:d920f33822747519673ee656a4b6ac33e382eca9d331c87770faa3eef562aeb2"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a198f10c4d1b1375d7687bc25294306e551bf1abfa4eace6650070a5c1ae2744"},
|
||||
{file = "tomli-2.2.1-cp313-cp313-win32.whl", hash = "sha256:d3f5614314d758649ab2ab3a62d4f2004c825922f9e370b29416484086b264ec"},
|
||||
{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"
|
||||
|
@ -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]
|
||||
|
@ -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."""
|
||||
|
Loading…
Reference in New Issue
Block a user