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348 lines
10 KiB
Plaintext
348 lines
10 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": "2XVP2VXIL1"
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},
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"source": [
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"# Chains\n",
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"\n",
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"Chaining LLMs with each other or with other experts.\n",
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"\n",
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"## Getting Started\n",
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"\n",
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"- Using the simple LLM chain\n",
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"- Creating sequential chains\n",
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"- Creating a custom chain\n",
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"\n",
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"### Why Use Chains ?\n",
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"\n",
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"- combine multiple components together\n",
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"- ex: take user input, format with PromptTemplate, pass formatted text to LLM.\n",
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"\n",
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"## Query an LLM with LLMChain"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"jukit_cell_id": "DPRWRo3fl7"
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},
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"source": [
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"from langchain.prompts import PromptTemplate\n",
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"from langchain.llms import OpenAI\n",
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"import pprint as pp\n",
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"\n",
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"llm = OpenAI(temperature=0.9)\n",
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"prompt = PromptTemplate(\n",
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" input_variables=[\"product\"],\n",
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" template=\"What is a good name for a company that makes {product}\"\n",
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" )"
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],
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"outputs": [],
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"execution_count": null
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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": "tOpTb9idHh"
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},
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"source": [
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"We can now create a simple chain that takes user input format it and pass to LLM"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"jukit_cell_id": "QXu2N1dEEC"
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},
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"source": [
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"from langchain.chains import LLMChain\n",
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"chain = LLMChain(llm=llm, prompt=prompt, output_key='company_name')\n",
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"\n",
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"# run the chain only specifying input variables\n",
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"print(chain.run(\"hand crafted handbags\"))\n",
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"\n",
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"# NOTE: we pass data to the run of the entry chain (see sequence under)"
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],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "\n\nUrban Crafts Co.\n"
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}
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],
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"execution_count": 1
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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": "Kv6bj1l9I3"
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},
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"source": [
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"## Combining chains with SequentialChain\n",
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"\n",
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"Chains that execute their links in predefined order.\n",
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"\n",
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"- SimpleSequentialChain: simplest form, each step has a single input/output. \n",
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"Output of one step is input to next.\n",
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"- SequentialChain: More advanced, multiple inputs/outputs.\n",
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"\n",
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"\n",
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"Following tutorial uses SimpleSequentialChain and SequentialChain, each chains output is input to the next one.\n",
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"This sequential chain will:\n",
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" 1. create company name for a product. We just use LLMChain for that\n",
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" 2. Create a catchphrase for the product. We will use a new LLMChain for the catchphrase, as show below."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"jukit_cell_id": "BMZLsdY9VP"
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},
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"source": [
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"second_prompt = PromptTemplate(\n",
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" input_variables=[\"company_name\"],\n",
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" template=\"Write a catchphrase for the following company: {company_name}\",\n",
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" )\n",
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"chain_two = LLMChain(llm=llm, prompt=second_prompt, output_key='catchphrase')"
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],
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"outputs": [],
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"execution_count": null
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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": "epQHxmeWCP"
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},
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"source": [
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"We now combine the two chains to create company name and catch phrase."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"jukit_cell_id": "SHwDHjVCxb"
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},
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"source": [
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"from langchain.chains import SimpleSequentialChain, SequentialChain"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"metadata": {
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"jukit_cell_id": "lKgp9HR0VX"
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},
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"source": [
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"full_chain = SimpleSequentialChain(\n",
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" chains=[chain, chain_two], verbose=True,\n",
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" )\n",
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"\n",
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"print(full_chain.run(\"hand crafted handbags\"))"
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],
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"outputs": [],
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"execution_count": null
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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": "RiYcYwJhdC"
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},
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"source": [
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"---\n",
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"\n",
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"In the third prompt we create an small advertisement with the title and the product description"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"jukit_cell_id": "RhnqOumOtX"
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},
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"source": [
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"ad_template = \"\"\"Create a small advertisement destined for reddit. \n",
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"The advertisement is for a company with the following details:\n",
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"\n",
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"name: {company_name}\n",
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"product: {product}\n",
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"catchphrase: {catchphrase}\n",
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"\n",
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"advertisement:\n",
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"\"\"\"\n",
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"ad_prompt = PromptTemplate(\n",
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" input_variables=[\"product\", \"company_name\", \"catchphrase\"],\n",
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" template=ad_template,\n",
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" )"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"metadata": {
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"jukit_cell_id": "MsQnieyxgL"
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},
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"source": [
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"#Connet the three chains together\n",
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"\n",
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"ad_chain = LLMChain(llm=llm, prompt=ad_prompt, output_key='advertisement')"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"metadata": {
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"jukit_cell_id": "4PYfwOxTlq"
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},
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"source": [
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"final_chain = SequentialChain(\n",
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" chains=[chain, chain_two, ad_chain],\n",
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" input_variables=['product'],\n",
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" output_variables=['advertisement'],\n",
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" verbose=True\n",
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" )\n",
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"\n",
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"ad = final_chain.run('Professional Cat Cuddler')"
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],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "\n\n\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n\n\u001b[1m> Finished chain.\u001b[0m\n"
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}
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],
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"execution_count": 2
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},
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{
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"cell_type": "code",
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"metadata": {
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"jukit_cell_id": "2akm8eB1EV"
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},
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"source": [
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"print(ad)"
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],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "Are you in need of a little indulgence? Then come to Purr-fect Pampering! Our professional cat cuddler will provide you with the ultimate relaxation experience. We guarantee that after a session with us, you'll be feeling more purr-fect than ever! Treat yourself to the luxurious indulgence of Purr-fect Pampering!\n"
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}
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],
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"execution_count": 3
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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": "1iT7gBMABZ"
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},
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"source": [
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"## Creating a custom chain\n",
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"\n",
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"Example: create a custom chain that concats output of 2 LLMChain\n",
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"\n",
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"Steps:\n",
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" 1. Subclass Chain class\n",
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" 2. Fill out `input_keys` and `output_keys`\n",
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" 3. add the `_call` method that shows how to execute chain"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"jukit_cell_id": "OUXv7kGtDH"
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},
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"source": [
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"from langchain.chains import LLMChain\n",
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"from langchain.chains.base import Chain\n",
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"\n",
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"from typing import Dict, List\n",
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"\n",
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"class ConcatenateChain(Chain):\n",
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" chain_1: LLMChain\n",
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" chain_2: LLMChain\n",
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"\n",
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" @property\n",
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" def input_keys(self) -> List[str]:\n",
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" # Union of the input keys of the two chains\n",
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" all_inputs_vars = set(self.chain_1.input_keys).union(\n",
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" set(self.chain_2.input_keys))\n",
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" return list(all_inputs_vars)\n",
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"\n",
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" @property\n",
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" def output_keys(self) -> List[str]:\n",
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" return ['concat_output']\n",
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"\n",
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" def _call(self, inputs: Dict[str, str]) -> Dict[str,str]:\n",
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" output_1 = self.chain_1.run(inputs)\n",
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" output_2 = self.chain_2.run(inputs)\n",
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" return {'concat_output': output_1 + output_2}"
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],
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"outputs": [],
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"execution_count": null
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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": "MUOMbKovF6"
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},
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"source": [
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"Running the custom chain"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"jukit_cell_id": "kBfPU3rB6L"
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},
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"source": [
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"prompt_1 = PromptTemplate(\n",
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" input_variables=['product'],\n",
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" template='what is a good name for a company that makes {product}?'\n",
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" )\n",
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"chain_1 = LLMChain(llm=llm, prompt=prompt_1)\n",
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"\n",
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"prompt_2 = PromptTemplate(\n",
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" input_variables=['product'],\n",
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" template='what is a good slogan for a company that makes {product} ?'\n",
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" )\n",
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"chain_2 = LLMChain(llm=llm, prompt=prompt_2)\n",
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"\n",
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"concat_chain = ConcatenateChain(chain_1=chain_1, chain_2=chain_2)\n",
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"\n",
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"concat_output = concat_chain.run('leather handbags')\n",
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"print(f'Concatenated output:\\n{concat_output}')"
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],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "Concatenated output:\n\n\nLeather Luxury Boutique.\n\n\"Handcrafted Leather: The Perfect Accent for Any Look.\"\n"
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}
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],
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"execution_count": 4
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},
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{
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"cell_type": "code",
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"metadata": {
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"jukit_cell_id": "9CdH3GtsmW"
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},
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"source": [],
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"outputs": [],
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"execution_count": null
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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",
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"language": "python",
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"name": "python3"
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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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} |