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312 lines
8.9 KiB
Plaintext
312 lines
8.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a20c4e38",
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"metadata": {},
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"source": [
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"## ConversationBufferWindowMemory\n",
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"\n",
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"`ConversationBufferWindowMemory` keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so the buffer does not get too large\n",
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"\n",
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"Let's first explore the basic functionality of this type of memory."
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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": "1196da3f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.memory import ConversationBufferWindowMemory"
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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": "2dac7769",
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"metadata": {},
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"outputs": [],
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"source": [
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"memory = ConversationBufferWindowMemory( k=1)\n",
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"memory.save_context({\"input\": \"hi\"}, {\"ouput\": \"whats up\"})\n",
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"memory.save_context({\"input\": \"not much you\"}, {\"ouput\": \"not much\"})"
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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": "0c034a90",
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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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"{'history': 'Human: not much you\\nAI: not much'}"
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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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"memory.load_memory_variables({})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8c5cce1d",
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"metadata": {},
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"source": [
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"We can also get the history as a list of messages (this is useful if you are using this with a chat model)."
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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": "9b15b427",
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"metadata": {},
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"outputs": [],
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"source": [
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"memory = ConversationBufferWindowMemory( k=1, return_messages=True)\n",
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"memory.save_context({\"input\": \"hi\"}, {\"ouput\": \"whats up\"})\n",
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"memory.save_context({\"input\": \"not much you\"}, {\"ouput\": \"not much\"})"
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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": "3bb47191",
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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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"{'history': [HumanMessage(content='not much you', additional_kwargs={}),\n",
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" AIMessage(content='not much', additional_kwargs={})]}"
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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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"memory.load_memory_variables({})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a95af04c",
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"metadata": {},
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"source": [
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"## Using in a chain\n",
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"Let's walk through an example, again setting `verbose=True` so we can see the 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": "0b9da4cd",
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"metadata": {},
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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 ConversationChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\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",
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"\n",
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"Current conversation:\n",
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"\n",
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"Human: Hi, what's up?\n",
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"AI:\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! I'm doing great. I'm currently helping a customer with a technical issue. How about 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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"from langchain.llms import OpenAI\n",
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"from langchain.chains import ConversationChain\n",
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"conversation_with_summary = ConversationChain(\n",
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" llm=OpenAI(temperature=0), \n",
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" # We set a low k=2, to only keep the last 2 interactions in memory\n",
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" memory=ConversationBufferWindowMemory(k=2), \n",
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" verbose=True\n",
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")\n",
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"conversation_with_summary.predict(input=\"Hi, what's up?\")"
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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": "90f73431",
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"metadata": {},
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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 ConversationChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\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",
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"\n",
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"Current conversation:\n",
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"Human: Hi, what's up?\n",
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"AI: Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?\n",
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"Human: What's their issues?\n",
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"AI:\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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"\" The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.\""
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]
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},
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"execution_count": 13,
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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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"conversation_with_summary.predict(input=\"What's their issues?\")"
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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": "cbb499e7",
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"metadata": {},
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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 ConversationChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\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",
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"\n",
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"Current conversation:\n",
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"Human: Hi, what's up?\n",
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"AI: Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?\n",
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"Human: What's their issues?\n",
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"AI: The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.\n",
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"Human: Is it going well?\n",
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"AI:\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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"\" Yes, it's going well so far. We've already identified the problem and are now working on a solution.\""
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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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"conversation_with_summary.predict(input=\"Is it going well?\")"
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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": "0d209cfe",
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"metadata": {},
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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 ConversationChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\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",
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"\n",
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"Current conversation:\n",
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"Human: What's their issues?\n",
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"AI: The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.\n",
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"Human: Is it going well?\n",
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"AI: Yes, it's going well so far. We've already identified the problem and are now working on a solution.\n",
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"Human: What's the solution?\n",
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"AI:\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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"\" The solution is to reset the router and reconfigure the settings. We're currently in the process of doing that.\""
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]
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},
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"execution_count": 15,
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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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"# Notice here that the first interaction does not appear.\n",
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"conversation_with_summary.predict(input=\"What's the solution?\")"
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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": null,
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"id": "8c09a239",
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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.9.1"
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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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