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chore(core): enable ruff docstring-code-format (#32834)

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See https://docs.astral.sh/ruff/settings/#format_docstring-code-format

---------

Co-authored-by: Mason Daugherty <mason@langchain.dev>
This commit is contained in:
Christophe Bornet
2025-09-08 17:13:50 +02:00
committed by GitHub
parent e3b6c9bb66
commit 5840dad40b
45 changed files with 797 additions and 651 deletions
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111
libs/core/langchain_core/prompts/chat.py
View File

@@ -67,10 +67,10 @@ class MessagesPlaceholder(BaseMessagePromptTemplate):
from langchain_core.prompts import MessagesPlaceholder
prompt = MessagesPlaceholder("history")
prompt.format_messages() # raises KeyError
prompt.format_messages() # raises KeyError
prompt = MessagesPlaceholder("history", optional=True)
prompt.format_messages() # returns empty list []
prompt.format_messages() # returns empty list []
prompt.format_messages(
history=[
@@ -93,14 +93,14 @@ class MessagesPlaceholder(BaseMessagePromptTemplate):
[
("system", "You are a helpful assistant."),
MessagesPlaceholder("history"),
("human", "{question}")
("human", "{question}"),
]
)
prompt.invoke(
{
"history": [("human", "what's 5 + 2"), ("ai", "5 + 2 is 7")],
"question": "now multiply that by 4"
}
{
"history": [("human", "what's 5 + 2"), ("ai", "5 + 2 is 7")],
"question": "now multiply that by 4",
}
)
# -> ChatPromptValue(messages=[
# SystemMessage(content="You are a helpful assistant."),
@@ -795,18 +795,17 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
from langchain_core.prompts import ChatPromptTemplate
template = ChatPromptTemplate([
("system", "You are a helpful AI bot. Your name is {name}."),
("human", "Hello, how are you doing?"),
("ai", "I'm doing well, thanks!"),
("human", "{user_input}"),
])
template = ChatPromptTemplate(
[
("system", "You are a helpful AI bot. Your name is {name}."),
("human", "Hello, how are you doing?"),
("ai", "I'm doing well, thanks!"),
("human", "{user_input}"),
]
)
prompt_value = template.invoke(
{
"name": "Bob",
"user_input": "What is your name?"
}
{"name": "Bob", "user_input": "What is your name?"}
)
# Output:
# ChatPromptValue(
@@ -816,7 +815,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
# AIMessage(content="I'm doing well, thanks!"),
# HumanMessage(content='What is your name?')
# ]
#)
# )
Messages Placeholder:
@@ -826,14 +825,16 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
# you can initialize the template with a MessagesPlaceholder
# either using the class directly or with the shorthand tuple syntax:
template = ChatPromptTemplate([
("system", "You are a helpful AI bot."),
# Means the template will receive an optional list of messages under
# the "conversation" key
("placeholder", "{conversation}")
# Equivalently:
# MessagesPlaceholder(variable_name="conversation", optional=True)
])
template = ChatPromptTemplate(
[
("system", "You are a helpful AI bot."),
# Means the template will receive an optional list of messages under
# the "conversation" key
("placeholder", "{conversation}"),
# Equivalently:
# MessagesPlaceholder(variable_name="conversation", optional=True)
]
)
prompt_value = template.invoke(
{
@@ -841,7 +842,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
("human", "Hi!"),
("ai", "How can I assist you today?"),
("human", "Can you make me an ice cream sundae?"),
("ai", "No.")
("ai", "No."),
]
}
)
@@ -855,7 +856,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
# HumanMessage(content='Can you make me an ice cream sundae?'),
# AIMessage(content='No.'),
# ]
#)
# )
Single-variable template:
@@ -868,10 +869,12 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
from langchain_core.prompts import ChatPromptTemplate
template = ChatPromptTemplate([
("system", "You are a helpful AI bot. Your name is Carl."),
("human", "{user_input}"),
])
template = ChatPromptTemplate(
[
("system", "You are a helpful AI bot. Your name is Carl."),
("human", "{user_input}"),
]
)
prompt_value = template.invoke("Hello, there!")
# Equivalent to
@@ -930,20 +933,24 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
.. code-block:: python
template = ChatPromptTemplate([
("human", "Hello, how are you?"),
("ai", "I'm doing well, thanks!"),
("human", "That's good to hear."),
])
template = ChatPromptTemplate(
[
("human", "Hello, how are you?"),
("ai", "I'm doing well, thanks!"),
("human", "That's good to hear."),
]
)
Instantiation from mixed message formats:
.. code-block:: python
template = ChatPromptTemplate([
SystemMessage(content="hello"),
("human", "Hello, how are you?"),
])
template = ChatPromptTemplate(
[
SystemMessage(content="hello"),
("human", "Hello, how are you?"),
]
)
"""
messages_ = [
@@ -1137,20 +1144,24 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
.. code-block:: python
template = ChatPromptTemplate.from_messages([
("human", "Hello, how are you?"),
("ai", "I'm doing well, thanks!"),
("human", "That's good to hear."),
])
template = ChatPromptTemplate.from_messages(
[
("human", "Hello, how are you?"),
("ai", "I'm doing well, thanks!"),
("human", "That's good to hear."),
]
)
Instantiation from mixed message formats:
.. code-block:: python
template = ChatPromptTemplate.from_messages([
SystemMessage(content="hello"),
("human", "Hello, how are you?"),
])
template = ChatPromptTemplate.from_messages(
[
SystemMessage(content="hello"),
("human", "Hello, how are you?"),
]
)
Args:
messages: sequence of message representations.

14
libs/core/langchain_core/prompts/few_shot.py
View File

@@ -272,7 +272,7 @@ class FewShotChatMessagePromptTemplate(
from langchain_core.prompts import (
FewShotChatMessagePromptTemplate,
ChatPromptTemplate
ChatPromptTemplate,
)
examples = [
@@ -281,7 +281,7 @@ class FewShotChatMessagePromptTemplate(
]
example_prompt = ChatPromptTemplate.from_messages(
[('human', 'What is {input}?'), ('ai', '{output}')]
[("human", "What is {input}?"), ("ai", "{output}")]
)
few_shot_prompt = FewShotChatMessagePromptTemplate(
@@ -292,9 +292,9 @@ class FewShotChatMessagePromptTemplate(
final_prompt = ChatPromptTemplate.from_messages(
[
('system', 'You are a helpful AI Assistant'),
("system", "You are a helpful AI Assistant"),
few_shot_prompt,
('human', '{input}'),
("human", "{input}"),
]
)
final_prompt.format(input="What is 4+4?")
@@ -314,10 +314,7 @@ class FewShotChatMessagePromptTemplate(
# ...
]
to_vectorize = [
" ".join(example.values())
for example in examples
]
to_vectorize = [" ".join(example.values()) for example in examples]
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_texts(
to_vectorize, embeddings, metadatas=examples
@@ -355,6 +352,7 @@ class FewShotChatMessagePromptTemplate(
# Use within an LLM
from langchain_core.chat_models import ChatAnthropic
chain = final_prompt | ChatAnthropic(model="claude-3-haiku-20240307")
chain.invoke({"input": "What's 3+3?"})

2
libs/core/langchain_core/prompts/structured.py
View File

@@ -89,10 +89,12 @@ class StructuredPrompt(ChatPromptTemplate):
from langchain_core.prompts import StructuredPrompt
class OutputSchema(BaseModel):
name: str
value: int
template = StructuredPrompt(
[
("human", "Hello, how are you?"),