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Harrison/agent memory (#197)

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add doc for agent with memory
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Harrison Chase 2 years ago committed by GitHub
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325
docs/examples/memory/agent_with_memory.ipynb
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@ -0,0 +1,325 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "fa6802ac",
"metadata": {},
"source": [
"# Adding Memory to an Agent\n",
"\n",
"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",
"\n",
"- [Adding memory to an LLM Chain](adding_memory.ipynb)\n",
"- [Custom Agents](../agents/custom_agent.ipynb)\n",
"\n",
"In order to add a memory to an agent we are going to the the following steps:\n",
"\n",
"1. We are going to create an LLMChain with memory.\n",
"2. We are going to use that LLMChain to create a custom Agent.\n",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8db95912",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import ZeroShotAgent, Tool\n",
"from langchain.chains.conversation.memory import ConversationBufferMemory\n",
"from langchain import OpenAI, SerpAPIChain, LLMChain"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "97ad8467",
"metadata": {},
"outputs": [],
"source": [
"search = SerpAPIChain()\n",
"tools = [\n",
" Tool(\n",
" name = \"Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events\"\n",
" )\n",
"]"
]
},
{
"cell_type": "markdown",
"id": "4ad2e708",
"metadata": {},
"source": [
"Notice the usage of the `chat_history` variable in the PromptTemplate, which matches up with the dynamic key name in the ConversationBufferMemory."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e3439cd6",
"metadata": {},
"outputs": [],
"source": [
"prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
"suffix = \"\"\"Begin!\"\n",
"\n",
"{chat_history}\n",
"Question: {input}\"\"\"\n",
"\n",
"prompt = ZeroShotAgent.create_prompt(\n",
" tools, \n",
" prefix=prefix, \n",
" suffix=suffix, \n",
" input_variables=[\"input\", \"chat_history\"]\n",
")\n",
"memory = ConversationBufferMemory(dynamic_key=\"chat_history\")"
]
},
{
"cell_type": "markdown",
"id": "0021675b",
"metadata": {},
"source": [
"We can now construct the LLMChain, with the Memory object, and then create the agent."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c56a0e73",
"metadata": {},
"outputs": [],
"source": [
"llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt, memory=memory)\n",
"agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ca4bc1fb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"How many people live in canada?\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look up how many people live in canada\n",
"Action: Search\n",
"Action Input: \"How many people live in canada?\"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada 2020 ...\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: The current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data.'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"How many people live in canada?\")"
]
},
{
"cell_type": "markdown",
"id": "45627664",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "eecc0462",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"what is their national anthem called?\n",
"Thought:\u001b[32;1m\u001b[1;3m\n",
"AI: I should look up the name of Canada's national anthem\n",
"Action: Search\n",
"Action Input: \"What is the name of Canada's national anthem?\"\u001b[0m\n",
"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",
"Thought:\u001b[32;1m\u001b[1;3m\n",
"AI: I now know the final answer\n",
"Final Answer: After 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 Lavallée.\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"After 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 Lavallée.\""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"what is their national anthem called?\")"
]
},
{
"cell_type": "markdown",
"id": "cc3d0aa4",
"metadata": {},
"source": [
"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",
"\n",
"For fun, let's compare this to an agent that does NOT have memory."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3359d043",
"metadata": {},
"outputs": [],
"source": [
"prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
"suffix = \"\"\"Begin!\"\n",
"\n",
"Question: {input}\"\"\"\n",
"\n",
"prompt = ZeroShotAgent.create_prompt(\n",
" tools, \n",
" prefix=prefix, \n",
" suffix=suffix, \n",
" input_variables=[\"input\"]\n",
")\n",
"llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
"agent_without_memory = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "970d23df",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"How many people live in canada?\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look up how many people live in canada\n",
"Action: Search\n",
"Action Input: \"How many people live in canada?\"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada 2020 ...\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: The current population of Canada is 38,533,678\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The current population of Canada is 38,533,678'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_without_memory.run(\"How many people live in canada?\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d9ea82f0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"what is their national anthem called?\n",
"Thought:\u001b[32;1m\u001b[1;3m I should probably look this up\n",
"Action: Search\n",
"Action Input: \"What is the national anthem of [country]\"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mMost nation states have an anthem, defined as \"a song, as of praise, devotion, or patriotism\"; most anthems are either marches or hymns in style.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: The national anthem is called \"the national anthem.\"\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The national anthem is called \"the national anthem.\"'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_without_memory.run(\"what is their national anthem called?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5b1f9223",
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

21
langchain/chains/conversation/memory.py
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@ -10,6 +10,15 @@ from langchain.llms.base import LLM
from langchain.prompts.base import BasePromptTemplate
def _get_prompt_input_key(inputs: Dict[str, Any], memory_variables: List[str]) -> str:
# "stop" is a special key that can be passed as input but is not used to
# format the prompt.
prompt_input_keys = list(set(inputs).difference(memory_variables + ["stop"]))
if len(prompt_input_keys) != 1:
raise ValueError(f"One input key expected got {prompt_input_keys}")
return prompt_input_keys[0]
class ConversationBufferMemory(Memory, BaseModel):
"""Buffer for storing conversation memory."""
@ -30,12 +39,10 @@ class ConversationBufferMemory(Memory, BaseModel):
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
prompt_input_keys = list(set(inputs).difference(self.memory_variables))
if len(prompt_input_keys) != 1:
raise ValueError(f"One input key expected got {prompt_input_keys}")
prompt_input_key = _get_prompt_input_key(inputs, self.memory_variables)
if len(outputs) != 1:
raise ValueError(f"One output key expected, got {outputs.keys()}")
human = "Human: " + inputs[prompt_input_keys[0]]
human = "Human: " + inputs[prompt_input_key]
ai = "AI: " + outputs[list(outputs.keys())[0]]
self.buffer += "\n" + "\n".join([human, ai])
@ -74,12 +81,10 @@ class ConversationSummaryMemory(Memory, BaseModel):
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer."""
prompt_input_keys = list(set(inputs).difference(self.memory_variables))
if len(prompt_input_keys) != 1:
raise ValueError(f"One input key expected got {prompt_input_keys}")
prompt_input_key = _get_prompt_input_key(inputs, self.memory_variables)
if len(outputs) != 1:
raise ValueError(f"One output key expected, got {outputs.keys()}")
human = "Human: " + inputs[prompt_input_keys[0]]
human = "Human: " + inputs[prompt_input_key]
ai = "AI: " + list(outputs.values())[0]
new_lines = "\n".join([human, ai])
chain = LLMChain(llm=self.llm, prompt=self.prompt)

16
langchain/prompts/base.py
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@ -1,6 +1,8 @@
"""BasePrompt schema definition."""
from abc import ABC, abstractmethod
from typing import Any, List
from typing import Any, Dict, List
from pydantic import BaseModel, root_validator
from langchain.formatting import formatter
@ -27,12 +29,22 @@ def check_valid_template(
raise ValueError("Invalid prompt schema.")
class BasePromptTemplate(ABC):
class BasePromptTemplate(BaseModel, ABC):
"""Base prompt should expose the format method, returning a prompt."""
input_variables: List[str]
"""A list of the names of the variables the prompt template expects."""
@root_validator()
def validate_variable_names(cls, values: Dict) -> Dict:
"""Validate variable names do not restricted names."""
if "stop" in values["input_variables"]:
raise ValueError(
"Cannot have an input variable named 'stop', as it is used internally,"
" please rename."
)
return values
@abstractmethod
def format(self, **kwargs: Any) -> str:
"""Format the prompt with the inputs.

2
langchain/prompts/prompt.py
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@ -10,7 +10,7 @@ from langchain.prompts.base import (
)
class PromptTemplate(BaseModel, BasePromptTemplate):
class PromptTemplate(BasePromptTemplate, BaseModel):
"""Schema to represent a prompt for an LLM.
Example:

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