Source code for langchain_core.prompts.pipeline
from typing import Any, Dict, List, Tuple
from langchain_core.prompt_values import PromptValue
from langchain_core.prompts.base import BasePromptTemplate
from langchain_core.prompts.chat import BaseChatPromptTemplate
from langchain_core.pydantic_v1 import root_validator
def _get_inputs(inputs: dict, input_variables: List[str]) -> dict:
return {k: inputs[k] for k in input_variables}
[docs]class PipelinePromptTemplate(BasePromptTemplate):
"""A prompt template for composing multiple prompt templates together.
This can be useful when you want to reuse parts of prompts.
A PipelinePrompt consists of two main parts:
- final_prompt: This is the final prompt that is returned
- pipeline_prompts: This is a list of tuples, consisting
of a string (`name`) and a Prompt Template.
Each PromptTemplate will be formatted and then passed
to future prompt templates as a variable with
the same name as `name`
"""
final_prompt: BasePromptTemplate
"""The final prompt that is returned."""
pipeline_prompts: List[Tuple[str, BasePromptTemplate]]
"""A list of tuples, consisting of a string (`name`) and a Prompt Template."""
[docs] @classmethod
def get_lc_namespace(cls) -> List[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "prompts", "pipeline"]
@root_validator(pre=True)
def get_input_variables(cls, values: Dict) -> Dict:
"""Get input variables."""
created_variables = set()
all_variables = set()
for k, prompt in values["pipeline_prompts"]:
created_variables.add(k)
all_variables.update(prompt.input_variables)
values["input_variables"] = list(all_variables.difference(created_variables))
return values
@property
def _prompt_type(self) -> str:
raise ValueError