langchain/docs/getting_started/memory.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d31df93e",
   "metadata": {},
   "source": [
    "# Memory\n",
    "So far, all the chains and agents we've gone through have been stateless. But often, you may want a chain or agent to have some concept of \"memory\" so that it may remember information about its previous interactions. The clearest and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of \"short-term memory\". On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of \"long-term memory\". For more concrete ideas on the latter, see this [awesome paper](https://memprompt.com/).\n",
    "\n",
    "LangChain provides several specially created chains just for this purpose. This notebook walks through using one of those chains (the `ConversationChain`) with two different types of memory."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d051c1da",
   "metadata": {},
   "source": [
    "### ConversationChain with default memory\n",
    "By default, the `ConversationChain` has a simple type of memory that remembers all previous inputs/outputs and adds them to the context that is passed. Let's take a look at using this chain (setting `verbose=True` so we can see the prompt)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ae046bff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mThe 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",
      "\n",
      "Current conversation:\n",
      "\n",
      "Human: Hi there!\n",
      "AI:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "' Hello! How are you today?'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain import OpenAI, ConversationChain\n",
    "\n",
    "llm = OpenAI(temperature=0)\n",
    "conversation = ConversationChain(llm=llm, verbose=True)\n",
    "\n",
    "conversation.predict(input=\"Hi there!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d8e2a6ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mThe 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",
      "\n",
      "Current conversation:\n",
      "\n",
      "Human: Hi there!\n",
      "AI:  Hello! How are you today?\n",
      "Human: I'm doing well! Just having a conversation with an AI.\n",
      "AI:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\" That's great! What would you like to talk about?\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversation.predict(input=\"I'm doing well! Just having a conversation with an AI.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "15eda316",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mThe 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",
      "\n",
      "Current conversation:\n",
      "\n",
      "Human: Hi there!\n",
      "AI:  Hello! How are you today?\n",
      "Human: I'm doing well! Just having a conversation with an AI.\n",
      "AI:  That's great! What would you like to talk about?\n",
      "Human: Tell me about yourself.\n",
      "AI:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'  I am an AI created to provide information and support to humans. I enjoy learning and exploring new things.'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversation.predict(input=\"Tell me about yourself.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c8735cc",
   "metadata": {},
   "source": [
    "### More Resources on Memory\n",
    "\n",
    "This just scratches the surface of what you can do with memory. \n",
    "\n",
    "For more concrete examples of conversational memory, please see [this notebook](../examples/memory/conversational_memory)\n",
    "\n",
    "For more examples on things like how to implement custom memory classes, how to add memory to a custom LLM chain and how to use memory with an agent, please see the [How-To: Memory](../examples/memoryrst) section. \n",
    "\n",
    "For even more advanced ideas on memory (which will hopefully be included in LangChain soon!) see the [MemPrompt](https://memprompt.com/) paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "436dda66",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}