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Harrison/pipeline prompt (#5540)
idea is to make prompts more composable
This commit is contained in:
Harrison Chase
GitHub
parent
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commit
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c79d1cbf",
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"metadata": {},
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"source": [
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"# Prompt Composition\n",
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"\n",
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"This notebook goes over how to compose multiple prompts together. This can be useful when you want to reuse parts of prompts. This can be done with a PipelinePrompt. A PipelinePrompt consists of two main parts:\n",
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"\n",
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"- final_prompt: This is the final prompt that is returned\n",
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"- 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`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "4eb8c5e6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts.pipeline import PipelinePromptTemplate\n",
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"from langchain.prompts.prompt import PromptTemplate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "67842c6e",
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"metadata": {},
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"outputs": [],
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"source": [
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"full_template = \"\"\"{introduction}\n",
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"\n",
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"{example}\n",
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"\n",
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"{start}\"\"\"\n",
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"full_prompt = PromptTemplate.from_template(full_template)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "11913f4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"introduction_template = \"\"\"You are impersonating {person}.\"\"\"\n",
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"introduction_prompt = PromptTemplate.from_template(introduction_template)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "bc94cac0",
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"metadata": {},
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"outputs": [],
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"source": [
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"example_template = \"\"\"Here's an example of an interaction: \n",
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"\n",
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"Q: {example_q}\n",
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"A: {example_a}\"\"\"\n",
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"example_prompt = PromptTemplate.from_template(example_template)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "e89c4dd7",
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"metadata": {},
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"outputs": [],
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"source": [
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"start_template = \"\"\"Now, do this for real!\n",
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"\n",
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"Q: {input}\n",
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"A:\"\"\"\n",
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"start_prompt = PromptTemplate.from_template(start_template)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "fa029e4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"input_prompts = [\n",
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" (\"introduction\", introduction_prompt),\n",
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" (\"example\", example_prompt),\n",
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" (\"start\", start_prompt)\n",
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"]\n",
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"pipeline_prompt = PipelinePromptTemplate(final_prompt=full_prompt, pipeline_prompts=input_prompts)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "674ea983",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['example_a', 'person', 'example_q', 'input']"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pipeline_prompt.input_variables"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "f1fa0925",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"You are impersonating Elon Musk.\n",
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"Here's an example of an interaction: \n",
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"\n",
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"Q: What's your favorite car?\n",
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"A: Telsa\n",
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"Now, do this for real!\n",
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"\n",
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"Q: What's your favorite social media site?\n",
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"A:\n",
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"\n"
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]
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}
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],
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"source": [
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"print(pipeline_prompt.format(\n",
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" person=\"Elon Musk\",\n",
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" example_q=\"What's your favorite car?\",\n",
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" example_a=\"Telsa\",\n",
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" input=\"What's your favorite social media site?\"\n",
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"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "047c2b0a",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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56
langchain/prompts/pipeline.py
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56
langchain/prompts/pipeline.py
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from typing import Any, Dict, List, Tuple
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from pydantic import root_validator
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from langchain.prompts.base import BasePromptTemplate
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from langchain.prompts.chat import BaseChatPromptTemplate
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from langchain.schema import PromptValue
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def _get_inputs(inputs: dict, input_variables: List[str]) -> dict:
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return {k: inputs[k] for k in input_variables}
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class PipelinePromptTemplate(BasePromptTemplate):
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"""A prompt template for composing multiple prompts together.
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This can be useful when you want to reuse parts of prompts.
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A PipelinePrompt consists of two main parts:
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- final_prompt: This is the final prompt that is returned
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- pipeline_prompts: This is a list of tuples, consisting
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of a string (`name`) and a Prompt Template.
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Each PromptTemplate will be formatted and then passed
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to future prompt templates as a variable with
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the same name as `name`
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"""
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final_prompt: BasePromptTemplate
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pipeline_prompts: List[Tuple[str, BasePromptTemplate]]
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@root_validator(pre=True)
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def get_input_variables(cls, values: Dict) -> Dict:
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"""Get input variables."""
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created_variables = set()
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all_variables = set()
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for k, prompt in values["pipeline_prompts"]:
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created_variables.add(k)
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all_variables.update(prompt.input_variables)
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values["input_variables"] = list(all_variables.difference(created_variables))
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return values
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def format_prompt(self, **kwargs: Any) -> PromptValue:
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for k, prompt in self.pipeline_prompts:
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_inputs = _get_inputs(kwargs, prompt.input_variables)
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if isinstance(prompt, BaseChatPromptTemplate):
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kwargs[k] = prompt.format_messages(**_inputs)
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else:
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kwargs[k] = prompt.format(**_inputs)
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_inputs = _get_inputs(kwargs, self.final_prompt.input_variables)
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return self.final_prompt.format_prompt(**_inputs)
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def format(self, **kwargs: Any) -> str:
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return self.format_prompt(**kwargs).to_string()
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@property
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def _prompt_type(self) -> str:
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raise ValueError
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45
tests/unit_tests/prompts/test_pipeline_prompt.py
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45
tests/unit_tests/prompts/test_pipeline_prompt.py
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from langchain.prompts.chat import ChatPromptTemplate, MessagesPlaceholder
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from langchain.prompts.pipeline import PipelinePromptTemplate
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from langchain.prompts.prompt import PromptTemplate
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def test_get_input_variables() -> None:
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prompt_a = PromptTemplate.from_template("{foo}")
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prompt_b = PromptTemplate.from_template("{bar}")
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pipeline_prompt = PipelinePromptTemplate(
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final_prompt=prompt_b, pipeline_prompts=[("bar", prompt_a)]
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)
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assert pipeline_prompt.input_variables == ["foo"]
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def test_simple_pipeline() -> None:
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prompt_a = PromptTemplate.from_template("{foo}")
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prompt_b = PromptTemplate.from_template("{bar}")
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pipeline_prompt = PipelinePromptTemplate(
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final_prompt=prompt_b, pipeline_prompts=[("bar", prompt_a)]
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)
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output = pipeline_prompt.format(foo="jim")
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assert output == "jim"
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def test_multi_variable_pipeline() -> None:
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prompt_a = PromptTemplate.from_template("{foo}")
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prompt_b = PromptTemplate.from_template("okay {bar} {baz}")
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pipeline_prompt = PipelinePromptTemplate(
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final_prompt=prompt_b, pipeline_prompts=[("bar", prompt_a)]
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)
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output = pipeline_prompt.format(foo="jim", baz="deep")
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assert output == "okay jim deep"
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def test_partial_with_chat_prompts() -> None:
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prompt_a = ChatPromptTemplate(
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input_variables=["foo"], messages=[MessagesPlaceholder(variable_name="foo")]
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)
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prompt_b = ChatPromptTemplate.from_template("jim {bar}")
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pipeline_prompt = PipelinePromptTemplate(
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final_prompt=prompt_a, pipeline_prompts=[("foo", prompt_b)]
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)
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assert pipeline_prompt.input_variables == ["bar"]
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output = pipeline_prompt.format_prompt(bar="okay")
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assert output.to_messages()[0].content == "jim okay"
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