mirror of
https://github.com/hwchase17/langchain
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332 lines
9.0 KiB
Plaintext
332 lines
9.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "00695447",
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"metadata": {
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"tags": []
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},
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"source": [
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"# Memory in LLMChain\n",
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"\n",
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"This notebook goes over how to use the Memory class with an LLMChain. \n",
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"\n",
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"We will add the [ConversationBufferMemory](https://api.python.langchain.com/en/latest/memory/langchain.memory.buffer.ConversationBufferMemory.html#langchain.memory.buffer.ConversationBufferMemory) class, although this can be any memory class."
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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": "9f1aaf47",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.chains import LLMChain\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.memory import ConversationBufferMemory\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": "4b066ced",
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"metadata": {},
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"source": [
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"The most important step is setting up the prompt correctly. In the below prompt, we have two input keys: one for the actual input, another for the input from the Memory class. Importantly, we make sure the keys in the PromptTemplate and the ConversationBufferMemory match up (`chat_history`)."
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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": "e5501eda",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"template = \"\"\"You are a chatbot having a conversation with a human.\n",
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"\n",
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"{chat_history}\n",
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"Human: {human_input}\n",
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"Chatbot:\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(\n",
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" input_variables=[\"chat_history\", \"human_input\"], template=template\n",
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")\n",
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"memory = ConversationBufferMemory(memory_key=\"chat_history\")"
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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": "f6566275",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"llm = OpenAI()\n",
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"llm_chain = LLMChain(\n",
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" llm=llm,\n",
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" prompt=prompt,\n",
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" verbose=True,\n",
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" memory=memory,\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": 4,
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"id": "e2b189dc",
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"metadata": {
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"tags": []
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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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"\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
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"\n",
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"\n",
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"Human: Hi there my friend\n",
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"Chatbot:\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\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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"' Hi there! How can I help you today?'"
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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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"llm_chain.predict(human_input=\"Hi there my friend\")"
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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": "a902729f",
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"metadata": {
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"tags": []
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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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"\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
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"\n",
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"Human: Hi there my friend\n",
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"AI: Hi there! How can I help you today?\n",
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"Human: Not too bad - how are you?\n",
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"Chatbot:\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\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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"\" I'm doing great, thanks for asking! How are you doing?\""
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]
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},
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"execution_count": 5,
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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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"llm_chain.predict(human_input=\"Not too bad - how are 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": "33978824-0048-4e75-9431-1b2c02c169b0",
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"metadata": {},
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"source": [
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"## Adding Memory to a Chat Model-based LLMChain\n",
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"\n",
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"The above works for completion-style `LLM`s, but if you are using a chat model, you will likely get better performance using structured chat messages. Below is an example."
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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": "ae5309bb",
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"metadata": {
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"tags": []
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},
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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.schema import SystemMessage\n",
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"from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a237bbb8-e448-4238-8420-004e046ef84e",
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"metadata": {},
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"source": [
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"We will use the [ChatPromptTemplate](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.ChatPromptTemplate.html) class to set up the chat prompt.\n",
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"\n",
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"The [from_messages](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.ChatPromptTemplate.html#langchain.prompts.chat.ChatPromptTemplate.from_messages) method creates a ChatPromptTemplate from a list of messages (e.g., SystemMessage, HumanMessage, AIMessage, ChatMessage, etc.) or message templates, such as the [MessagesPlaceholder](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.MessagesPlaceholder.html#langchain.prompts.chat.MessagesPlaceholder) below.\n",
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"\n",
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"The configuration below makes it so the memory will be injected to the middle of the chat prompt, in the \"chat_history\" key, and the user's inputs will be added in a human/user message to the end of the chat 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": 7,
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"id": "9bb8cde1-67c2-4133-b453-5c34fb36ff74",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"prompt = ChatPromptTemplate.from_messages([\n",
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" SystemMessage(content=\"You are a chatbot having a conversation with a human.\"), # The persistent system prompt\n",
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" MessagesPlaceholder(variable_name=\"chat_history\"), # Where the memory will be stored.\n",
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" HumanMessagePromptTemplate.from_template(\"{human_input}\"), # Where the human input will injected\n",
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"])\n",
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" \n",
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"memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=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": 8,
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"id": "9f77e466-a1a3-4c69-a001-ac5b7a40e219",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"llm = ChatOpenAI()\n",
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"\n",
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"chat_llm_chain = LLMChain(\n",
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" llm=llm,\n",
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" prompt=prompt,\n",
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" verbose=True,\n",
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" memory=memory,\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": 9,
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"id": "f9709647-be82-43d5-b076-2a7da344ce8a",
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"metadata": {
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"tags": []
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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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"\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mSystem: You are a chatbot having a conversation with a human.\n",
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"Human: Hi there my friend\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\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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"'Hello! How can I assist you today, my friend?'"
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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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"chat_llm_chain.predict(human_input=\"Hi there my friend\")"
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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": "bdf04ebe-525a-4156-a3a7-65fd2df8d6fc",
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"metadata": {
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"tags": []
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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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"\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mSystem: You are a chatbot having a conversation with a human.\n",
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"Human: Hi there my friend\n",
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"AI: Hello! How can I assist you today, my friend?\n",
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"Human: Not too bad - how are you?\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\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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"\"I'm an AI chatbot, so I don't have feelings, but I'm here to help and chat with you! Is there something specific you would like to talk about or any questions I can assist you with?\""
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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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"chat_llm_chain.predict(human_input=\"Not too bad - how are you?\")"
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]
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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": "3.10.12"
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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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