"""Prompt template that contains few shot examples."""
from __future__ import annotations
from pathlib import Path
from typing import Any, Dict, List, Literal, Optional, Union
from langchain_core.example_selectors import BaseExampleSelector
from langchain_core.messages import BaseMessage, get_buffer_string
from langchain_core.prompts.chat import (
BaseChatPromptTemplate,
BaseMessagePromptTemplate,
)
from langchain_core.prompts.prompt import PromptTemplate
from langchain_core.prompts.string import (
DEFAULT_FORMATTER_MAPPING,
StringPromptTemplate,
check_valid_template,
get_template_variables,
)
from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
class _FewShotPromptTemplateMixin(BaseModel):
"""Prompt template that contains few shot examples."""
examples: Optional[List[dict]] = None
"""Examples to format into the prompt.
Either this or example_selector should be provided."""
example_selector: Optional[BaseExampleSelector] = None
"""ExampleSelector to choose the examples to format into the prompt.
Either this or examples should be provided."""
class Config:
arbitrary_types_allowed = True
extra = Extra.forbid
@root_validator(pre=True)
def check_examples_and_selector(cls, values: Dict) -> Dict:
"""Check that one and only one of examples/example_selector are provided.
Args:
values: The values to check.
Returns:
The values if they are valid.
Raises:
ValueError: If neither or both examples and example_selector are provided.
ValueError: If both examples and example_selector are provided.
"""
examples = values.get("examples", None)
example_selector = values.get("example_selector", None)
if examples and example_selector:
raise ValueError(
"Only one of 'examples' and 'example_selector' should be provided"
)
if examples is None and example_selector is None:
raise ValueError(
"One of 'examples' and 'example_selector' should be provided"
)
return values
def _get_examples(self, **kwargs: Any) -> List[dict]:
"""Get the examples to use for formatting the prompt.
Args:
**kwargs: Keyword arguments to be passed to the example selector.
Returns:
List of examples.
Raises:
ValueError: If neither examples nor example_selector are provided.
"""
if self.examples is not None:
return self.examples
elif self.example_selector is not None:
return self.example_selector.select_examples(kwargs)
else:
raise ValueError(
"One of 'examples' and 'example_selector' should be provided"
)
async def _aget_examples(self, **kwargs: Any) -> List[dict]:
"""Async get the examples to use for formatting the prompt.
Args:
**kwargs: Keyword arguments to be passed to the example selector.
Returns:
List of examples.
Raises:
ValueError: If neither examples nor example_selector are provided.
"""
if self.examples is not None:
return self.examples
elif self.example_selector is not None:
return await self.example_selector.aselect_examples(kwargs)
else:
raise ValueError(
"One of 'examples' and 'example_selector' should be provided"
)
[docs]class FewShotPromptTemplate(_FewShotPromptTemplateMixin, StringPromptTemplate):
"""Prompt template that contains few shot examples."""
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether or not the class is serializable."""
return False
validate_template: bool = False
"""Whether or not to try validating the template."""
example_prompt: PromptTemplate
"""PromptTemplate used to format an individual example."""
suffix: str
"""A prompt template string to put after the examples."""
example_separator: str = "\n\n"
"""String separator used to join the prefix, the examples, and suffix."""
prefix: str = ""
"""A prompt template string to put before the examples."""
template_format: Literal["f-string", "jinja2"] = "f-string"
"""The format of the prompt template. Options are: 'f-string', 'jinja2'."""
def __init__(self, **kwargs: Any) -> None:
"""Initialize the few shot prompt template."""
if "input_variables" not in kwargs and "example_prompt" in kwargs:
kwargs["input_variables"] = kwargs["example_prompt"].input_variables
super().__init__(**kwargs)
@root_validator(pre=False, skip_on_failure=True)
def template_is_valid(cls, values: Dict) -> Dict:
"""Check that prefix, suffix, and input variables are consistent."""
if values["validate_template"]:
check_valid_template(
values["prefix"] + values["suffix"],
values["template_format"],
values["input_variables"] + list(values["partial_variables"]),
)
elif values.get("template_format"):
values["input_variables"] = [
var
for var in get_template_variables(
values["prefix"] + values["suffix"], values["template_format"]
)
if var not in values["partial_variables"]
]
return values
class Config:
arbitrary_types_allowed = True
extra = Extra.forbid
@property
def _prompt_type(self) -> str:
"""Return the prompt type key."""
return "few_shot"
[docs] def save(self, file_path: Union[Path, str]) -> None:
"""Save the prompt template to a file.
Args:
file_path: The path to save the prompt template to.
Raises:
ValueError: If example_selector is provided.
"""
if self.example_selector:
raise ValueError("Saving an example selector is not currently supported")
return super().save(file_path)
[docs]class FewShotChatMessagePromptTemplate(
BaseChatPromptTemplate, _FewShotPromptTemplateMixin
):
"""Chat prompt template that supports few-shot examples.
The high level structure of produced by this prompt template is a list of messages
consisting of prefix message(s), example message(s), and suffix message(s).
This structure enables creating a conversation with intermediate examples like:
System: You are a helpful AI Assistant
Human: What is 2+2?
AI: 4
Human: What is 2+3?
AI: 5
Human: What is 4+4?
This prompt template can be used to generate a fixed list of examples or else
to dynamically select examples based on the input.
Examples:
Prompt template with a fixed list of examples (matching the sample
conversation above):
.. code-block:: python
from langchain_core.prompts import (
FewShotChatMessagePromptTemplate,
ChatPromptTemplate
)
examples = [
{"input": "2+2", "output": "4"},
{"input": "2+3", "output": "5"},
]
example_prompt = ChatPromptTemplate.from_messages(
[('human', '{input}'), ('ai', '{output}')]
)
few_shot_prompt = FewShotChatMessagePromptTemplate(
examples=examples,
# This is a prompt template used to format each individual example.
example_prompt=example_prompt,
)
final_prompt = ChatPromptTemplate.from_messages(
[
('system', 'You are a helpful AI Assistant'),
few_shot_prompt,
('human', '{input}'),
]
)
final_prompt.format(input="What is 4+4?")
Prompt template with dynamically selected examples:
.. code-block:: python
from langchain_core.prompts import SemanticSimilarityExampleSelector
from langchain_core.embeddings import OpenAIEmbeddings
from langchain_core.vectorstores import Chroma
examples = [
{"input": "2+2", "output": "4"},
{"input": "2+3", "output": "5"},
{"input": "2+4", "output": "6"},
# ...
]
to_vectorize = [
" ".join(example.values())
for example in examples
]
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_texts(
to_vectorize, embeddings, metadatas=examples
)
example_selector = SemanticSimilarityExampleSelector(
vectorstore=vectorstore
)
from langchain_core import SystemMessage
from langchain_core.prompts import HumanMessagePromptTemplate
from langchain_core.prompts.few_shot import FewShotChatMessagePromptTemplate
few_shot_prompt = FewShotChatMessagePromptTemplate(
# Which variable(s) will be passed to the example selector.
input_variables=["input"],
example_selector=example_selector,
# Define how each example will be formatted.
# In this case, each example will become 2 messages:
# 1 human, and 1 AI
example_prompt=(
HumanMessagePromptTemplate.from_template("{input}")
+ AIMessagePromptTemplate.from_template("{output}")
),
)
# Define the overall prompt.
final_prompt = (
SystemMessagePromptTemplate.from_template(
"You are a helpful AI Assistant"
)
+ few_shot_prompt
+ HumanMessagePromptTemplate.from_template("{input}")
)
# Show the prompt
print(final_prompt.format_messages(input="What's 3+3?")) # noqa: T201
# 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?"})
"""
input_variables: List[str] = Field(default_factory=list)
"""A list of the names of the variables the prompt template will use
to pass to the example_selector, if provided."""
example_prompt: Union[BaseMessagePromptTemplate, BaseChatPromptTemplate]
"""The class to format each example."""
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether or not the class is serializable."""
return False
class Config:
arbitrary_types_allowed = True
extra = Extra.forbid
[docs] def pretty_repr(self, html: bool = False) -> str:
"""Return a pretty representation of the prompt template.
Args:
html: Whether or not to return an HTML formatted string.
Returns:
A pretty representation of the prompt template.
"""
raise NotImplementedError()
