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style: repo linting pass (#33089)

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enable docstring-code-format
This commit is contained in:
Mason Daugherty
2025-09-24 15:25:55 -04:00
committed by GitHub
parent 083bb3cdd7
commit b92b394804
33 changed files with 687 additions and 283 deletions
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147
libs/partners/ollama/langchain_ollama/chat_models.py
View File

@@ -262,10 +262,10 @@ class ChatOllama(BaseChatModel):
from langchain_ollama import ChatOllama
llm = ChatOllama(
model = "gpt-oss:20b",
validate_model_on_init = True,
temperature = 0.8,
num_predict = 256,
model="gpt-oss:20b",
validate_model_on_init=True,
temperature=0.8,
num_predict=256,
# other params ...
)
@@ -307,7 +307,22 @@ class ChatOllama(BaseChatModel):
.. code-block:: python
AIMessageChunk(content='Je adore le programmation.(Note: "programmation" is the formal way to say "programming" in French, but informally, people might use the phrase "le développement logiciel" or simply "le code")', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:38:54.933154Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1977300042, 'load_duration': 1345709, 'prompt_eval_duration': 159343000, 'eval_count': 47, 'eval_duration': 1815123000}, id='run-3c81a3ed-3e79-4dd3-a796-04064d804890')
AIMessageChunk(
content='Je adore le programmation.(Note: "programmation" is the formal way to say "programming" in French, but informally, people might use the phrase "le développement logiciel" or simply "le code")',
response_metadata={
"model": "llama3",
"created_at": "2024-07-04T03:38:54.933154Z",
"message": {"role": "assistant", "content": ""},
"done_reason": "stop",
"done": True,
"total_duration": 1977300042,
"load_duration": 1345709,
"prompt_eval_duration": 159343000,
"eval_count": 47,
"eval_duration": 1815123000,
},
id="run-3c81a3ed-3e79-4dd3-a796-04064d804890",
)
Async:
.. code-block:: python
@@ -316,7 +331,23 @@ class ChatOllama(BaseChatModel):
.. code-block:: python
AIMessage(content="Hi there! I'm just an AI, so I don't have feelings or emotions like humans do. But I'm functioning properly and ready to help with any questions or tasks you may have! How can I assist you today?", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:52:08.165478Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 2138492875, 'load_duration': 1364000, 'prompt_eval_count': 10, 'prompt_eval_duration': 297081000, 'eval_count': 47, 'eval_duration': 1838524000}, id='run-29c510ae-49a4-4cdd-8f23-b972bfab1c49-0')
AIMessage(
content="Hi there! I'm just an AI, so I don't have feelings or emotions like humans do. But I'm functioning properly and ready to help with any questions or tasks you may have! How can I assist you today?",
response_metadata={
"model": "llama3",
"created_at": "2024-07-04T03:52:08.165478Z",
"message": {"role": "assistant", "content": ""},
"done_reason": "stop",
"done": True,
"total_duration": 2138492875,
"load_duration": 1364000,
"prompt_eval_count": 10,
"prompt_eval_duration": 297081000,
"eval_count": 47,
"eval_duration": 1838524000,
},
id="run-29c510ae-49a4-4cdd-8f23-b972bfab1c49-0",
)
.. code-block:: python
@@ -332,23 +363,57 @@ class ChatOllama(BaseChatModel):
.. code-block:: python
messages = [
("human", "Say hello world!"),
("human","Say goodbye world!")
]
messages = [("human", "Say hello world!"), ("human", "Say goodbye world!")]
await llm.abatch(messages)
.. code-block:: python
[AIMessage(content='HELLO, WORLD!', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.315396Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1696745458, 'load_duration': 1505000, 'prompt_eval_count': 8, 'prompt_eval_duration': 111627000, 'eval_count': 6, 'eval_duration': 185181000}, id='run-da6c7562-e25a-4a44-987a-2c83cd8c2686-0'),
AIMessage(content="It's been a blast chatting with you! Say goodbye to the world for me, and don't forget to come back and visit us again soon!", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.018076Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1399391083, 'load_duration': 1187417, 'prompt_eval_count': 20, 'prompt_eval_duration': 230349000, 'eval_count': 31, 'eval_duration': 1166047000}, id='run-96cad530-6f3e-4cf9-86b4-e0f8abba4cdb-0')]
[
AIMessage(
content="HELLO, WORLD!",
response_metadata={
"model": "llama3",
"created_at": "2024-07-04T03:55:07.315396Z",
"message": {"role": "assistant", "content": ""},
"done_reason": "stop",
"done": True,
"total_duration": 1696745458,
"load_duration": 1505000,
"prompt_eval_count": 8,
"prompt_eval_duration": 111627000,
"eval_count": 6,
"eval_duration": 185181000,
},
id="run-da6c7562-e25a-4a44-987a-2c83cd8c2686-0",
),
AIMessage(
content="It's been a blast chatting with you! Say goodbye to the world for me, and don't forget to come back and visit us again soon!",
response_metadata={
"model": "llama3",
"created_at": "2024-07-04T03:55:07.018076Z",
"message": {"role": "assistant", "content": ""},
"done_reason": "stop",
"done": True,
"total_duration": 1399391083,
"load_duration": 1187417,
"prompt_eval_count": 20,
"prompt_eval_duration": 230349000,
"eval_count": 31,
"eval_duration": 1166047000,
},
id="run-96cad530-6f3e-4cf9-86b4-e0f8abba4cdb-0",
),
]
JSON mode:
.. code-block:: python
json_llm = ChatOllama(format="json")
llm.invoke("Return a query for the weather in a random location and time of day with two keys: location and time_of_day. Respond using JSON only.").content
llm.invoke(
"Return a query for the weather in a random location and time of day with two keys: location and time_of_day. "
"Respond using JSON only."
).content
.. code-block:: python
@@ -360,19 +425,25 @@ class ChatOllama(BaseChatModel):
from langchain_ollama import ChatOllama
from pydantic import BaseModel, Field
class Multiply(BaseModel):
a: int = Field(..., description="First integer")
b: int = Field(..., description="Second integer")
ans = await chat.invoke("What is 45*67")
ans.tool_calls
.. code-block:: python
[{'name': 'Multiply',
'args': {'a': 45, 'b': 67},
'id': '420c3f3b-df10-4188-945f-eb3abdb40622',
'type': 'tool_call'}]
[
{
"name": "Multiply",
"args": {"a": 45, "b": 67},
"id": "420c3f3b-df10-4188-945f-eb3abdb40622",
"type": "tool_call",
}
]
Thinking / Reasoning:
You can enable reasoning mode for models that support it by setting
@@ -394,9 +465,9 @@ class ChatOllama(BaseChatModel):
from langchain_ollama import ChatOllama
llm = ChatOllama(
model = "deepseek-r1:8b",
validate_model_on_init = True,
reasoning= True,
model="deepseek-r1:8b",
validate_model_on_init=True,
reasoning=True,
)
llm.invoke("how many r in the word strawberry?")
@@ -1118,18 +1189,15 @@ class ChatOllama(BaseChatModel):
answer: str
justification: Optional[str] = Field(
default=..., description="A justification for the answer."
default=...,
description="A justification for the answer.",
)
llm = ChatOllama(model="llama3.1", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification
)
structured_llm = llm.with_structured_output(AnswerWithJustification)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> AnswerWithJustification(
# answer='They weigh the same',
@@ -1153,12 +1221,11 @@ class ChatOllama(BaseChatModel):
llm = ChatOllama(model="llama3.1", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification, include_raw=True
AnswerWithJustification,
include_raw=True,
)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
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.'),
@@ -1180,18 +1247,18 @@ class ChatOllama(BaseChatModel):
answer: str
justification: Optional[str] = Field(
default=..., description="A justification for the answer."
default=...,
description="A justification for the answer.",
)
llm = ChatOllama(model="llama3.1", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification, method="function_calling"
AnswerWithJustification,
method="function_calling",
)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> AnswerWithJustification(
# answer='They weigh the same',
@@ -1213,17 +1280,13 @@ class ChatOllama(BaseChatModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: Annotated[
Optional[str], None, "A justification for the answer."
]
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"
)
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.'

4
libs/partners/ollama/langchain_ollama/embeddings.py
View File

@@ -81,9 +81,7 @@ class OllamaEmbeddings(BaseModel, Embeddings):
from langchain_ollama import OllamaEmbeddings
embed = OllamaEmbeddings(
model="llama3"
)
embed = OllamaEmbeddings(model="llama3")
Embed single text:
.. code-block:: python