"""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.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: Any = None """ExampleSelector to choose the examples to format into the prompt. Either this or examples should be provided.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @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.""" 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. """ 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" ) 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.""" input_variables: List[str] """A list of the names of the variables the prompt template expects.""" 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'.""" @root_validator() 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: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True def format(self, **kwargs: Any) -> str: """Format the prompt with the inputs. Args: **kwargs: Any arguments to be passed to the prompt template. Returns: A formatted string. Example: .. code-block:: python prompt.format(variable1="foo") """ kwargs = self._merge_partial_and_user_variables(**kwargs) # Get the examples to use. examples = self._get_examples(**kwargs) examples = [ {k: e[k] for k in self.example_prompt.input_variables} for e in examples ] # Format the examples. example_strings = [ self.example_prompt.format(**example) for example in examples ] # Create the overall template. pieces = [self.prefix, *example_strings, self.suffix] template = self.example_separator.join([piece for piece in pieces if piece]) # Format the template with the input variables. return DEFAULT_FORMATTER_MAPPING[self.template_format](template, **kwargs) @property def _prompt_type(self) -> str: """Return the prompt type key.""" return "few_shot" def save(self, file_path: Union[Path, str]) -> None: if self.example_selector: raise ValueError("Saving an example selector is not currently supported") return super().save(file_path) 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() chain.invoke({"input": "What's 3+3?"}) """ @classmethod def is_lc_serializable(cls) -> bool: """Return whether or not the class is serializable.""" return False 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.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True def format_messages(self, **kwargs: Any) -> List[BaseMessage]: """Format kwargs into a list of messages. Args: **kwargs: keyword arguments to use for filling in templates in messages. Returns: A list of formatted messages with all template variables filled in. """ # Get the examples to use. examples = self._get_examples(**kwargs) examples = [ {k: e[k] for k in self.example_prompt.input_variables} for e in examples ] # Format the examples. messages = [ message for example in examples for message in self.example_prompt.format_messages(**example) ] return messages def format(self, **kwargs: Any) -> str: """Format the prompt with inputs generating a string. Use this method to generate a string representation of a prompt consisting of chat messages. Useful for feeding into a string based completion language model or debugging. Args: **kwargs: keyword arguments to use for formatting. Returns: A string representation of the prompt """ messages = self.format_messages(**kwargs) return get_buffer_string(messages) def pretty_repr(self, html: bool = False) -> str: raise NotImplementedError()