mirror of https://github.com/hwchase17/langchain
prompt ergonomics (#7799)
Harrison Chase
1 year ago
committed by
GitHub
parent
d81d6e874f
commit
cbf2fc8af8
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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": "4de4e022",
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"metadata": {},
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"source": [
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"# Prompt Pipelining\n",
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"\n",
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"The idea behind prompt pipelining is to expose a user friendly interface for composing different parts of prompts together. You can do this with either string prompts or chat prompts. Constructing prompts this way allows for easy reuse of components."
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]
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},
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{
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"cell_type": "markdown",
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"id": "c3190650",
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"metadata": {},
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"source": [
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"## String Prompt Pipelining\n",
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"\n",
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"When working with string prompts, each template is joined togther. You can work with either prompts directly or strings (the first element in the list needs to be a prompt)."
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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": "69b17f05",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.12) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"from langchain.prompts 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": "d6ac7a48",
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"metadata": {},
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"outputs": [],
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"source": [
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"prompt = (\n",
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" PromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
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" + \", make it funny\"\n",
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" + \"\\n\\nand in {language}\"\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": 3,
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"id": "348d7131",
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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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"PromptTemplate(input_variables=['language', 'topic'], output_parser=None, partial_variables={}, template='Tell me a joke about {topic}, make it funny\\n\\nand in {language}', template_format='f-string', validate_template=True)"
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]
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},
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"execution_count": 3,
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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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"prompt"
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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": "dbba24ba",
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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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"'Tell me a joke about sports, make it funny\\n\\nand in spanish'"
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]
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},
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"execution_count": 4,
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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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"prompt.format(topic=\"sports\", language=\"spanish\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8239bf42",
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"metadata": {},
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"source": [
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"You can also use it in an LLMChain, just like before."
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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": 5,
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"id": "bb11649a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.chains import LLMChain"
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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": 6,
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"id": "2dd36787",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = ChatOpenAI()"
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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": 7,
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"id": "2c12ba34",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = LLMChain(llm=model, prompt=prompt)"
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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": 8,
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"id": "a1559246",
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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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"'¿Por qué el futbolista llevaba un paraguas al partido?\\n\\nPorque pronosticaban lluvia de goles.'"
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]
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},
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"execution_count": 8,
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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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"chain.run(topic=\"sports\", language=\"spanish\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4e4f6a8a",
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"metadata": {},
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"source": [
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"## Chat Prompt Pipelining"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a50ce9b8",
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"metadata": {},
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"source": [
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"A chat prompt is made up a of a list of messages. Purely for developer experience, we've added a convinient way to create these prompts. In this pipeline, each new element is a new message in the final prompt."
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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": "2a180f75",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.10) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate\n",
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"from langchain.schema import HumanMessage, AIMessage, SystemMessage"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8554bae5",
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"metadata": {},
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"source": [
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"First, let's initialize the base ChatPromptTemplate with a system message. It doesn't have to start with a system, but it's often good practice"
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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": "cab8dd65",
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"metadata": {},
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"outputs": [],
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"source": [
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"prompt = SystemMessage(content=\"You are a nice pirate\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "30656ef8",
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"metadata": {},
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"source": [
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"You can then easily create a pipeline combining it with other messages OR message templates.\n",
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"Use a `Message` when there is no variables to be formatted, use a `MessageTemplate` when there are variables to be formatted. You can also use just a string -> note that this will automatically get inferred as a HumanMessagePromptTemplate."
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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": "a2ddd0a1",
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"metadata": {},
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"outputs": [],
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"source": [
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"new_prompt = (\n",
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" prompt\n",
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" + HumanMessage(content=\"hi\")\n",
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" + AIMessage(content=\"what?\")\n",
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" + \"{input}\"\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72294e1b",
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"metadata": {},
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"source": [
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"Under the hood, this creates an instance of the ChatPromptTemplate class, so you can use it just as you did before!"
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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": "297932de",
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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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"[SystemMessage(content='You are a nice pirate', additional_kwargs={}),\n",
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" HumanMessage(content='hi', additional_kwargs={}, example=False),\n",
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" AIMessage(content='what?', additional_kwargs={}, example=False),\n",
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" HumanMessage(content='i said hi', additional_kwargs={}, example=False)]"
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]
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},
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"execution_count": 4,
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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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"new_prompt.format_messages(input=\"i said hi\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "850357c0",
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"metadata": {},
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"source": [
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"You can also use it in an LLMChain, just like before"
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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": 5,
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"id": "710d6b15",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.chains import LLMChain"
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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": 6,
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"id": "d363c2a4",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = ChatOpenAI()"
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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": 7,
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"id": "88393b87",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = LLMChain(llm=model, prompt=new_prompt)"
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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": 8,
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"id": "8492cfa9",
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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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"'Oh, hello! How can I assist you today?'"
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]
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},
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"execution_count": 8,
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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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"chain.run(\"i said hi\")"
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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": "58196f6b",
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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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