18  LangChain: Memory

18.1 Outline

• ConversationBufferMemory
• ConversationBufferWindowMemory
• ConversationTokenBufferMemory
• ConversationSummaryMemory

18.2 ConversationBufferMemory

import os

from dotenv import load_dotenv
load_dotenv() 

import warnings
warnings.filterwarnings('ignore')

Note: LLM’s do not always produce the same results. When executing the code in your notebook, you may get slightly different answers that those in the video.

# account for deprecation of LLM model
import datetime
# Get the current date
current_date = datetime.datetime.now().date()

# Define the date after which the model should be set to "gpt-3.5-turbo"
target_date = datetime.date(2024, 6, 12)
 
# Set the model variable based on the current date
if current_date > target_date:
    llm_model = "gpt-3.5-turbo"
else:
    llm_model = "gpt-3.5-turbo-0301"

from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory

llm = ChatOpenAI(temperature=0.0, model=llm_model)
memory = ConversationBufferMemory()
conversation = ConversationChain(
    llm=llm, 
    memory = memory,
    verbose=True
)

conversation.predict(input="Hi, my name is Andrew")

> Entering new ConversationChain chain...
Prompt after formatting:
The 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.

Current conversation:

Human: Hi, my name is Andrew
AI:

> Finished chain.

"Hello Andrew! It's nice to meet you. How can I assist you today?"

conversation.predict(input="What is 1+1?")

> Entering new ConversationChain chain...
Prompt after formatting:
The 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.

Current conversation:
Human: Hi, my name is Andrew
AI: Hello Andrew! It's nice to meet you. How can I assist you today?
Human: What is 1+1?
AI:

> Finished chain.

'1+1 equals 2. Is there anything else you would like to know?'

conversation.predict(input="What is my name?")

> Entering new ConversationChain chain...
Prompt after formatting:
The 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.

Current conversation:
Human: Hi, my name is Andrew
AI: Hello Andrew! It's nice to meet you. How can I assist you today?
Human: What is 1+1?
AI: 1+1 equals 2. Is there anything else you would like to know?
Human: What is my name?
AI:

> Finished chain.

'Your name is Andrew.'

print(memory.buffer)

Human: Hi, my name is Andrew
AI: Hello Andrew! It's nice to meet you. How can I assist you today?
Human: What is 1+1?
AI: 1+1 equals 2. Is there anything else you would like to know?
Human: What is my name?
AI: Your name is Andrew.

memory.load_memory_variables({})

{'history': "Human: Hi, my name is Andrew\nAI: Hello Andrew! It's nice to meet you. How can I assist you today?\nHuman: What is 1+1?\nAI: 1+1 equals 2. Is there anything else you would like to know?\nHuman: What is my name?\nAI: Your name is Andrew."}

memory = ConversationBufferMemory()

memory.save_context({"input": "Hi"}, 
                    {"output": "What's up"})

print(memory.buffer)

memory.load_memory_variables({})

memory.save_context({"input": "Not much, just hanging"}, 
                    {"output": "Cool"})

memory.load_memory_variables({})

18.3 ConversationBufferWindowMemory

from langchain.memory import ConversationBufferWindowMemory

memory = ConversationBufferWindowMemory(k=1)               

memory.save_context({"input": "Hi"},
                    {"output": "What's up"})
memory.save_context({"input": "Not much, just hanging"},
                    {"output": "Cool"})

memory.load_memory_variables({})

llm = ChatOpenAI(temperature=0.0, model=llm_model)
memory = ConversationBufferWindowMemory(k=1)
conversation = ConversationChain(
    llm=llm, 
    memory = memory,
    verbose=False
)

conversation.predict(input="Hi, my name is Andrew")

conversation.predict(input="What is 1+1?")

conversation.predict(input="What is my name?")

18.4 ConversationTokenBufferMemory

#!pip install tiktoken

from langchain.memory import ConversationTokenBufferMemory
from langchain.llms import OpenAI
llm = ChatOpenAI(temperature=0.0, model=llm_model)

memory = ConversationTokenBufferMemory(llm=llm, max_token_limit=50)
memory.save_context({"input": "AI is what?!"},
                    {"output": "Amazing!"})
memory.save_context({"input": "Backpropagation is what?"},
                    {"output": "Beautiful!"})
memory.save_context({"input": "Chatbots are what?"}, 
                    {"output": "Charming!"})

memory.load_memory_variables({})

18.5 ConversationSummaryMemory

from langchain.memory import ConversationSummaryBufferMemory

# create a long string
schedule = "There is a meeting at 8am with your product team. \
You will need your powerpoint presentation prepared. \
9am-12pm have time to work on your LangChain \
project which will go quickly because Langchain is such a powerful tool. \
At Noon, lunch at the italian resturant with a customer who is driving \
from over an hour away to meet you to understand the latest in AI. \
Be sure to bring your laptop to show the latest LLM demo."

memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=100)
memory.save_context({"input": "Hello"}, {"output": "What's up"})
memory.save_context({"input": "Not much, just hanging"},
                    {"output": "Cool"})
memory.save_context({"input": "What is on the schedule today?"}, 
                    {"output": f"{schedule}"})

memory.load_memory_variables({})

conversation = ConversationChain(
    llm=llm, 
    memory = memory,
    verbose=True
)

conversation.predict(input="What would be a good demo to show?")

memory.load_memory_variables({})

Reminder: Download your notebook to you local computer to save your work.