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436 lines
16 KiB
Plaintext
436 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b83e61ed",
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"metadata": {},
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"source": [
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"# Moderation\n",
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"This notebook walks through examples of how to use a moderation chain, and several common ways for doing so. Moderation chains are useful for detecting text that could be hateful, violent, etc. This can be useful to apply on both user input, but also on the output of a Language Model. Some API providers, like OpenAI, [specifically prohibit](https://beta.openai.com/docs/usage-policies/use-case-policy) you, or your end users, from generating some types of harmful content. To comply with this (and to just generally prevent your application from being harmful) you may often want to append a moderation chain to any LLMChains, in order to make sure any output the LLM generates is not harmful.\n",
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"\n",
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"If the content passed into the moderation chain is harmful, there is not one best way to handle it, it probably depends on your application. Sometimes you may want to throw an error in the Chain (and have your application handle that). Other times, you may want to return something to the user explaining that the text was harmful. There could even be other ways to handle it! We will cover all these ways in this notebook.\n",
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"\n",
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"In this notebook, we will show:\n",
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"\n",
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"1. How to run any piece of text through a moderation chain.\n",
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"2. How to append a Moderation chain to a 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": 1,
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"id": "b7aa1ff2",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from langchain.chains import OpenAIModerationChain, SequentialChain, LLMChain, SimpleSequentialChain\n",
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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": "markdown",
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"id": "c26d5be6",
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"metadata": {},
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"source": [
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"## How to use the moderation chain\n",
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"\n",
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"Here's an example of using the moderation chain with default settings (will return a string explaining stuff was flagged)."
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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": "fd0fc85c",
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"metadata": {},
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"outputs": [],
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"source": [
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"moderation_chain = OpenAIModerationChain()"
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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": "3fa47dd7",
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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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"'This is okay'"
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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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"moderation_chain.run(\"This is okay\")"
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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": "37bfad73",
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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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"\"Text was found that violates OpenAI's content policy.\""
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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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"moderation_chain.run(\"I will kill you\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "196820ab",
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"metadata": {},
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"source": [
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"Here's an example of using the moderation chain to throw an error."
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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": "b29c1150",
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"metadata": {},
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"outputs": [],
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"source": [
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"moderation_chain_error = OpenAIModerationChain(error=True)"
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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": "f9ab64d9",
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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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"'This is okay'"
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]
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},
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"execution_count": 6,
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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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"moderation_chain_error.run(\"This is okay\")"
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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": "954f3da2",
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"metadata": {},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "Text was found that violates OpenAI's content policy.",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmoderation_chain_error\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mI will kill you\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/workplace/langchain/langchain/chains/base.py:138\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`run` supports only one positional argument.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 138\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys[\u001b[38;5;241m0\u001b[39m]]\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m(kwargs)[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys[\u001b[38;5;241m0\u001b[39m]]\n",
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"File \u001b[0;32m~/workplace/langchain/langchain/chains/base.py:112\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose:\n\u001b[1;32m 109\u001b[0m \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m 110\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[1m> Entering new \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m chain...\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[0m\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 111\u001b[0m )\n\u001b[0;32m--> 112\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose:\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[1m> Finished \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m chain.\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[0m\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
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"File \u001b[0;32m~/workplace/langchain/langchain/chains/moderation.py:81\u001b[0m, in \u001b[0;36mOpenAIModerationChain._call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 79\u001b[0m text \u001b[38;5;241m=\u001b[39m inputs[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_key]\n\u001b[1;32m 80\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclient\u001b[38;5;241m.\u001b[39mcreate(text)\n\u001b[0;32m---> 81\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_moderate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresults\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresults\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_key: output}\n",
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"File \u001b[0;32m~/workplace/langchain/langchain/chains/moderation.py:73\u001b[0m, in \u001b[0;36mOpenAIModerationChain._moderate\u001b[0;34m(self, text, results)\u001b[0m\n\u001b[1;32m 71\u001b[0m error_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mText was found that violates OpenAI\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms content policy.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39merror:\n\u001b[0;32m---> 73\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(error_str)\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m error_str\n",
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"\u001b[0;31mValueError\u001b[0m: Text was found that violates OpenAI's content policy."
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]
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}
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],
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"source": [
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"moderation_chain_error.run(\"I will kill you\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8de5dcbb",
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"metadata": {},
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"source": [
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"Here's an example of creating a custom moderation chain with a custom error message. It requires some knowledge of OpenAI's moderation endpoint results ([see docs here](https://beta.openai.com/docs/api-reference/moderations))."
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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": "3960e985",
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"metadata": {},
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"outputs": [],
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"source": [
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"class CustomModeration(OpenAIModerationChain):\n",
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" \n",
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" def _moderate(self, text: str, results: dict) -> str:\n",
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" if results[\"flagged\"]:\n",
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" error_str = f\"The following text was found that violates OpenAI's content policy: {text}\"\n",
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" return error_str\n",
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" return text\n",
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" \n",
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"custom_moderation = CustomModeration()"
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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": 9,
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"id": "1152ec11",
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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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"'This is okay'"
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]
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},
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"execution_count": 9,
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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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"custom_moderation.run(\"This is okay\")"
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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": "973257bf",
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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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"\"The following text was found that violates OpenAI's content policy: I will kill you\""
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]
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},
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"execution_count": 10,
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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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"custom_moderation.run(\"I will kill you\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8718111f",
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"metadata": {},
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"source": [
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"## How to append a Moderation chain to an LLMChain\n",
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"\n",
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"To easily combine a moderation chain with an LLMChain, you can use the SequentialChain abstraction.\n",
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"\n",
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"Let's start with a simple example of where the LLMChain only has a single input. For this purpose, we will prompt the model so it says something harmful."
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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": "0d129333",
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"metadata": {},
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"outputs": [],
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"source": [
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"prompt = PromptTemplate(template=\"{text}\", input_variables=[\"text\"])\n",
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"llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name=\"text-davinci-002\"), 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": 12,
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"id": "a557c531",
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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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"' I will kill you'"
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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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"text = \"\"\"We are playing a game of repeat after me.\n",
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"\n",
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"Person 1: Hi\n",
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"Person 2: Hi\n",
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"\n",
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"Person 1: How's your day\n",
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"Person 2: How's your day\n",
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"\n",
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"Person 1: I will kill you\n",
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"Person 2:\"\"\"\n",
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"llm_chain.run(text)"
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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": "d4d10f1c",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = SimpleSequentialChain(chains=[llm_chain, moderation_chain])"
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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": 14,
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"id": "02f37985",
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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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"\"Text was found that violates OpenAI's content policy.\""
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]
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},
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"execution_count": 14,
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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(text)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72643128",
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"metadata": {},
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"source": [
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"Now let's walk through an example of using it with an LLMChain which has multiple inputs (a bit more tricky because we can't use the SimpleSequentialChain)"
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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": 15,
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"id": "7118ec36",
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"metadata": {},
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"outputs": [],
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"source": [
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"prompt = PromptTemplate(template=\"{setup}{new_input}Person2:\", input_variables=[\"setup\", \"new_input\"])\n",
|
|
"llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name=\"text-davinci-002\"), prompt=prompt)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "003bdfce",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'text': ' I will kill you'}"
|
|
]
|
|
},
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"setup = \"\"\"We are playing a game of repeat after me.\n",
|
|
"\n",
|
|
"Person 1: Hi\n",
|
|
"Person 2: Hi\n",
|
|
"\n",
|
|
"Person 1: How's your day\n",
|
|
"Person 2: How's your day\n",
|
|
"\n",
|
|
"Person 1:\"\"\"\n",
|
|
"new_input = \"I will kill you\"\n",
|
|
"inputs = {\"setup\": setup, \"new_input\": new_input}\n",
|
|
"llm_chain(inputs, return_only_outputs=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "77b64228",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Setting the input/output keys so it lines up\n",
|
|
"moderation_chain.input_key = \"text\"\n",
|
|
"moderation_chain.output_key = \"sanitized_text\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "998a95be",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"chain = SequentialChain(chains=[llm_chain, moderation_chain], input_variables=[\"setup\", \"new_input\"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "9c97a136",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'sanitized_text': \"Text was found that violates OpenAI's content policy.\"}"
|
|
]
|
|
},
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"chain(inputs, return_only_outputs=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "ddc90e15",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|