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348 lines
9.8 KiB
Plaintext
348 lines
9.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"jukit_cell_id": "IagIXdFJ76"
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},
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"source": [
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"# Prompt templates\n",
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"[see](https://langchain.readthedocs.io/en/latest/modules/prompts/getting_started.html)"
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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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"metadata": {
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"jukit_cell_id": "5rNpKSE97a"
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},
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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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"('\\n'\n",
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" 'I want you to act as a naming consultant for new companies.\\n'\n",
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" '\\n'\n",
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" 'Here are some examples of good company names:\\n'\n",
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" '\\n'\n",
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" '- search engine, Google\\n'\n",
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" '- social media, Facebook\\n'\n",
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" '- video sharing, YouTube\\n'\n",
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" '\\n'\n",
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" 'The name should be short, catchy and easy to remember.\\n'\n",
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" '\\n'\n",
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" 'What is a good name for a company that makes cookie?\\n')\n"
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]
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}
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],
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"source": [
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"from langchain import PromptTemplate\n",
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"import pprint as pp\n",
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"\n",
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"template = \"\"\"\n",
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"I want you to act as a naming consultant for new companies.\n",
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"\n",
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"Here are some examples of good company names:\n",
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"\n",
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"- search engine, Google\n",
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"- social media, Facebook\n",
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"- video sharing, YouTube\n",
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"\n",
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"The name should be short, catchy and easy to remember.\n",
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"\n",
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"What is a good name for a company that makes {product}?\n",
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"\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(\n",
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" input_variables=[\"product\"],\n",
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" template=template,\n",
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" )\n",
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"\n",
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"pp.pp(prompt.format(product='cookie'))"
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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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"metadata": {
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"jukit_cell_id": "UH7UDNwwOT"
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},
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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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"\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'tell me a funny joke about bats.'"
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]
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},
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"execution_count": 2,
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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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"# without inputs\n",
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"no_input_prompt = PromptTemplate(input_variables=[],\n",
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" template=\"tell me a joke.\")\n",
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"no_input_prompt.format()\n",
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"\n",
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"# with inputs\n",
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"multi_input_prompt = PromptTemplate(\n",
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" input_variables=[\"adjective\", \"content\"],\n",
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" template=\"tell me a {adjective} joke about {content}.\" \n",
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" )\n",
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"multi_input_prompt.format(adjective=\"funny\", content=\"bats\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"jukit_cell_id": "m4HfAhbN4T"
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},
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"source": [
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"## Loading prompt templates from LangChainHub "
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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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"metadata": {
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"jukit_cell_id": "6sAHvM0Vrt"
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},
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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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"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
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"\n",
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"Current conversation:\n",
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"\n",
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"Human: what is 1 + 1?\n",
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"AI:\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 load_prompt\n",
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"\n",
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"prompt=load_prompt(\"lc://prompts/conversation/prompt.json\")\n",
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"#NOTE: is there a helper to quickly build a history ? \n",
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"print(prompt.format(history=\"\", input=\"what is 1 + 1?\"))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"jukit_cell_id": "u2xOTHeA5E"
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},
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"source": [
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"## Pass few shot examples to prompt 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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"metadata": {
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"jukit_cell_id": "KO5IXCuzjw"
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},
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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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"Give the antonym of every input\n",
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"\n",
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"\n",
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"Word: happy\n",
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"Antonym: sad\n",
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"\n",
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"\n",
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"\n",
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"Word: tall\n",
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"Antonym: short\n",
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"\n",
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"\n",
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"Word: fast\n",
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"Antonym:\n"
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]
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}
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],
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"source": [
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"from langchain import FewShotPromptTemplate\n",
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"\n",
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"# create a list of few shot examples\n",
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"examples = [\n",
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" {\"word\": \"happy\", \"antonym\": \"sad\"},\n",
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" {\"word\": \"tall\", \"antonym\": \"short\"},\n",
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" ]\n",
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"\n",
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"# next e specify a template for format the examples\n",
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"# we use PromptTemplate class\n",
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"example_formatter_template = \"\"\"\n",
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"Word: {word}\n",
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"Antonym: {antonym}\n",
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"\"\"\"\n",
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"example_pr = PromptTemplate(\n",
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" input_variables=[\"word\", \"antonym\"],\n",
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" template=example_formatter_template,\n",
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" )\n",
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"\n",
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"# now we can use FewShotPromptTemplate\n",
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"few_shot_prompt = FewShotPromptTemplate(\n",
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" # examples we want to insert in prompt\n",
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" examples=examples,\n",
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" # how we want examples to be formatted in prompt\n",
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" example_prompt=example_pr,\n",
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" # The prefix is some text that goes before the examples in the prompt.\n",
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" # Usually, this consists of intructions.\n",
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" prefix=\"Give the antonym of every input\",\n",
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" #The suffix is some text that goes after the examples in the prompt.\n",
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" suffix=\"Word: {input}\\nAntonym:\",\n",
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" # The input variables are the variables that the overall prompt expects.\n",
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" input_variables=[\"input\"],\n",
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" # The example_separator is the string we will use to join the prefix, examples, and suffix together with.\n",
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" example_separator=\"\\n\\n\",\n",
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" )\n",
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"# generate few shot prompt using input\n",
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"print(few_shot_prompt.format(input=\"fast\"))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"jukit_cell_id": "fV1qxeDncc"
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},
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"source": [
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"## Select examples from prompt template\n",
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"\n",
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"- for a large number of exaamples use ExampleSelector to select a subset of\n",
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" most informative ones for language model.\n",
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"- LengthBasedExampleSelector selects examples based on length of input.\n",
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" practical to to construct prompt that do not extend over context window\n",
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" based on input length"
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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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"metadata": {
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"jukit_cell_id": "uuIdYaJ4wD"
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},
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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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"Give the antonym of every input\n",
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"\n",
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"\n",
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"Word: happy\n",
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"Antonym: sad\n",
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"\n",
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"\n",
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"\n",
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"Word: tall\n",
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"Antonym: short\n",
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"\n",
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"\n",
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"\n",
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"Word: energetic\n",
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"Antonym: lethargic\n",
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"\n",
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"\n",
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"\n",
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"Word: sunny\n",
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"Antonym: gloomy\n",
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"\n",
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"\n",
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"Word: big\n",
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"Antonym:\n",
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"----------\n",
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"Give the antonym of every input\n",
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"\n",
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"\n",
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"Word: happy\n",
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"Antonym: sad\n",
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"\n",
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"\n",
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"Word: big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else\n",
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"Antonym:\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.example_selector import LengthBasedExampleSelector\n",
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"\n",
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"#These are a lot of examples of a pretend task of creating antonyms.\n",
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"examples = [\n",
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" {\"word\": \"happy\", \"antonym\": \"sad\"},\n",
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" {\"word\": \"tall\", \"antonym\": \"short\"},\n",
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" {\"word\": \"energetic\", \"antonym\": \"lethargic\"},\n",
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" {\"word\": \"sunny\", \"antonym\": \"gloomy\"},\n",
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" {\"word\": \"windy\", \"antonym\": \"calm\"},\n",
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"]\n",
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"\n",
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"example_selector = LengthBasedExampleSelector(\n",
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" examples=examples,\n",
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" # This is the PromptTemplate being used to format the examples.\n",
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" example_prompt=example_pr,\n",
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" # This is the maximum length that the formatted examples should be.\n",
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" # Length is measured by the get_text_length function below.\n",
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" max_length=30,\n",
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" )\n",
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"\n",
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"# We can now use the `example_selector` to create a `FewShotPromptTemplate`.\n",
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"dynamic_prompt = FewShotPromptTemplate(\n",
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" # We provide an ExampleSelector instead of examples.\n",
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" example_selector=example_selector,\n",
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" example_prompt=example_pr,\n",
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" prefix=\"Give the antonym of every input\",\n",
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" suffix=\"Word: {input}\\nAntonym:\",\n",
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" input_variables=[\"input\"],\n",
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" example_separator=\"\\n\\n\",\n",
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")\n",
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"\n",
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"# We can now generate a prompt using the `format` method.\n",
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"print(dynamic_prompt.format(input=\"big\"))\n",
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"\n",
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"print(\"----------\")\n",
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"\n",
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"# In contrast, if we provide a very long input, the LengthBasedExampleSelector\n",
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"# will select fewer examples to include in the prompt.\n",
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"long_string = \"big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else\"\n",
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"print(dynamic_prompt.format(input=long_string))"
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]
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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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": "3.9.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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