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324 lines
10 KiB
Plaintext
324 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fa6802ac",
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"metadata": {},
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"source": [
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"# Adding Memory to an Agent\n",
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"\n",
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"This notebook goes over adding memory to an Agent. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:\n",
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"\n",
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"- [Adding memory to an LLM Chain](adding_memory.ipynb)\n",
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"- [Custom Agents](../agents/custom_agent.ipynb)\n",
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"\n",
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"In order to add a memory to an agent we are going to the the following steps:\n",
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"\n",
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"1. We are going to create an LLMChain with memory.\n",
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"2. We are going to use that LLMChain to create a custom Agent.\n",
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"\n",
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"For the purposes of this exercise, we are going to create a simple custom Agent that has access to a search tool and utilizes the `ConversationBufferMemory` 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": 6,
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"id": "8db95912",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n",
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"from langchain.chains.conversation.memory import ConversationBufferMemory\n",
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"from langchain import OpenAI, SerpAPIWrapper, 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": 2,
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"id": "97ad8467",
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"metadata": {},
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"outputs": [],
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"source": [
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"search = SerpAPIWrapper()\n",
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"tools = [\n",
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" Tool(\n",
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" name = \"Search\",\n",
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" func=search.run,\n",
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" description=\"useful for when you need to answer questions about current events\"\n",
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" )\n",
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"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4ad2e708",
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"metadata": {},
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"source": [
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"Notice the usage of the `chat_history` variable in the PromptTemplate, which matches up with the dynamic key name in the ConversationBufferMemory."
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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": "e3439cd6",
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"metadata": {},
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"outputs": [],
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"source": [
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"prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
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"suffix = \"\"\"Begin!\"\n",
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"\n",
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"{chat_history}\n",
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"Question: {input}\n",
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"{agent_scratchpad}\"\"\"\n",
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"\n",
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"prompt = ZeroShotAgent.create_prompt(\n",
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" tools, \n",
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" prefix=prefix, \n",
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" suffix=suffix, \n",
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" input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"]\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": "markdown",
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"id": "0021675b",
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"metadata": {},
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"source": [
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"We can now construct the LLMChain, with the Memory object, and then create the agent."
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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": 16,
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"id": "c56a0e73",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
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"agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)\n",
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"agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=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": 17,
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"id": "ca4bc1fb",
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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 AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I need to find out the population of Canada\n",
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"Action: Search\n",
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"Action Input: Population of Canada\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,555,354 as of Monday, December 19, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada 2020 ...\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: The current population of Canada is 38,555,354.\u001b[0m\n",
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"\u001b[1m> Finished AgentExecutor 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 current population of Canada is 38,555,354.'"
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]
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},
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"execution_count": 17,
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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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"agent_chain.run(input=\"How many people live in canada?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "45627664",
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"metadata": {},
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"source": [
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"To test the memory of this agent, we can ask a followup question that relies on information in the previous exchange to be answered correctly."
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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": 20,
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"id": "eecc0462",
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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 AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I need to find out what the national anthem of Canada is called.\n",
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"Action: Search\n",
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"Action Input: National anthem of Canada\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mAfter 100 years of tradition, O Canada was proclaimed Canada's national anthem in 1980. The music for O Canada was composed in 1880 by Calixa ...\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: O Canada is the national anthem of Canada.\u001b[0m\n",
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"\u001b[1m> Finished AgentExecutor 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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"'O Canada is the national anthem of Canada.'"
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]
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},
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"execution_count": 20,
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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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"agent_chain.run(input=\"what is their national anthem called?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cc3d0aa4",
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"metadata": {},
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"source": [
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"We can see that the agent remembered that the previous question was about Canada, and properly asked Google Search what the name of Canada's national anthem was.\n",
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"\n",
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"For fun, let's compare this to an agent that does NOT have 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": 23,
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"id": "3359d043",
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"metadata": {},
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"outputs": [],
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"source": [
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"prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
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"suffix = \"\"\"Begin!\"\n",
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"\n",
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"Question: {input}\n",
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"{agent_scratchpad}\"\"\"\n",
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"\n",
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"prompt = ZeroShotAgent.create_prompt(\n",
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" tools, \n",
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" prefix=prefix, \n",
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" suffix=suffix, \n",
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" input_variables=[\"input\", \"agent_scratchpad\"]\n",
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")\n",
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"llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
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"agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)\n",
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"agent_without_memory = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=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": 24,
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"id": "970d23df",
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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 AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I should look up the answer\n",
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"Action: Search\n",
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"Action Input: population of Canada\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,555,354 as of Monday, December 19, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada 2020 ...\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: The current population of Canada is 38,555,354.\u001b[0m\n",
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"\u001b[1m> Finished AgentExecutor 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 current population of Canada is 38,555,354.'"
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]
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},
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"execution_count": 24,
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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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"agent_without_memory.run(\"How many people live in canada?\")"
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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": 25,
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"id": "d9ea82f0",
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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 AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I should look up the answer\n",
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"Action: Search\n",
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"Action Input: national anthem of [country]\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m\"Himno Nacional\" (\"National Anthem\") · \"Pátria\" (\"Fatherland\") · \"Salve, Oh Patria\" (\"We Salute You, Our Homeland\") · \"Bilady, Bilady, Bilady\" (\"My Country, My ...\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: The national anthem of [country] is called \"Himno Nacional\", \"Pátria\", \"Salve, Oh Patria\", and \"Bilady, Bilady, Bilady\".\u001b[0m\n",
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"\u001b[1m> Finished AgentExecutor 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 national anthem of [country] is called \"Himno Nacional\", \"Pátria\", \"Salve, Oh Patria\", and \"Bilady, Bilady, Bilady\".'"
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]
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},
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"execution_count": 25,
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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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"agent_without_memory.run(\"what is their national anthem called?\")"
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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": "5b1f9223",
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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": "3.10.8"
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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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