Enhancing AI Conversations with LangChain Memory

Imagine chatting with a virtual assistant that remembers not just your last question but the entire flow of your conversation—personal details, preferences, even follow-up queries. This memory transforms chatbots from simple Q&A machines into sophisticated conversational partners, capable of handling complex topics over multiple interactions. In this article, we dive into the fascinating world of conversational memory in Retrieval-Augmented Generation (RAG) systems, exploring the techniques that allow chatbots to hold onto context, personalize responses, and manage multi-step queries seamlessly. You’ll learn about different memory strategies, their advantages and limitations, and even get hands-on with Python and LangChain to see how these concepts work in real time.

Learning Objectives

• Understand the importance of conversational memory in Retrieval-Augmented Generation (RAG) systems⁠.
• Learn about different types of conversational memory techniques in LangChain, including Conversation Buffer Memory⁠, Conversation Summary Memory, Conversation Buffer Window Memory⁠, Conversation Summary Buffer Memory⁠, Conversation Knowledge Graph Memory, Entity Memory⁠⁠.
• Understand the advantages and disadvantages of each memory technique⁠⁠.
• Learn how to implement these memory techniques by utilizing Python and LangChain⁠.

This article was published as a part of the Data Science Blogathon.

Importance of Conversational Memory in Chatbots

Conversational memory is crucial in chatbots and conversational agents because it enables the system to maintain context over extended interactions, making responses more relevant and personalized. In chatbot based applications, especially when the conversation spans complex topics or multiple queries, memory helps by:

• Maintaining Context: Memory allows the model to remember past inputs, reducing repetitive questioning and enabling smooth, contextually aware responses across multiple turns.
• Improving Relevance: By remembering the specific details of past interactions, such as preferences or key details, the system can retrieve and generate more relevant information, boosting accuracy.
• Enhancing Personalization: A memory of previous exchanges enables chatbot models to tailor responses based on past preferences or choices, improving user engagement and satisfaction.
• Handling Multi-Step Queries: Complex, multi-step inquiries that require information from several sources or documents benefit from memory as it enables the model to “hold” interim responses and build upon them logically.
• Reducing Redundancy: Memory reduces unnecessary repetition by avoiding re-fetching or re-processing already discussed topics, resulting in a smoother user experience.

Conversational Memory using Langchain

There are multiple ways we can incorporate conversational memory in retrieval augmented generation. In LangChain, all these techniques can be executed through ConversationChain.

Implementing Conversational Memory with Python and LangChain

We’ll dive into implementing conversational memory using Python and LangChain, setting up essential components to enable chatbots to remember and refer back to previous exchanges. We’ll cover everything from creating memory types to enhancing response relevance, allowing you to build chatbots that handle extended, context-rich conversations smoothly.

Installing and Importing the Required Libraries

To get started, we’ll install and import the necessary libraries for building conversational memory with Python and LangChain. This setup will provide the tools required to implement and test memory functions effectively.

!pip -q install openai langchain huggingface_hub transformers
!pip install langchain_community
!pip install langchain_openai

from langchain_openai import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
import os

os.environ['OPENAI_API_KEY'] = ''

Conversation Buffer Memory: Storing Full Interaction History

We will explore how to implement Conversation Buffer Memory, which stores the complete interaction history between the user and the system. This memory type helps retain all prior exchanges, ensuring that the chatbot can maintain context throughout the conversation, though it may lead to higher token usage. We’ll walk through the process of setting it up and explain how it enhances the chatbot’s ability to respond with greater relevance.

#Defining the LLM
llm = ChatOpenAI(temperature=0, model="gpt-4o", max_tokens=1000)

conversation = ConversationChain(
    llm=llm,
    verbose=True,
    memory=ConversationBufferMemory()
)
conversation.predict(input="Hi there! I am in Miami Today with my fiancee and want to go for shopping.")conversation.predict(input="How are you today?")
conversation.predict(input="Can you tell me some shopping malls?")
conversation.predict(input="Can you tell me who I am with in Miami?")#import csv 

Output:

> 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 there! I am in Miami Today with my fiancee and want to go for shopping.
AI: Hello! That sounds like a fun day ahead. Miami is a fantastic place for shopping
 with a variety of options to explore. If you're looking for Luxury brands and high-
end fashion, you might want to check out the Bal Harbour Shops. I If you're 
interested in a more eclectic mix of shops, the Miami Design District is a great 
choice. It offers a blend of fashion, art, and design with boutiques like Louis 
Vuitton, Dior, and unique art galleries.

For a more traditional mall experience, you could visit the Aventura Mall, 
which is one of the largest malls in the U.S. It has a wide range of stores from Zara and H&M
 to Nordstrom and Bloomingdale's.
And if you're looking for something more budget-friendly, the Dolphin Mall is an
 outlet mall with a variety of stores offering discounts on popular brands.
No matter where you choose to go, I'm sure you'll have a great time shopping in 
Miami! Let me know if you need more information or have any other questions. Human:
 Can you tell me some shopping malls?
AI: Of course! Here are some popular shopping malls in Miami that you might want to
 consider:
1. **Aventura Mall**: As I mentioned earlier, this is one of the largest malls in 
the U.S. It features over 300 stores, including popular brands like Apple, Zara, and
 Bloomingdale's. It also has a variety of dining options and an imp 2. **Dolphin 
Mall**: This is a great option for outlet shopping, with over 240 stores offering
 discounts on brands like Nike, Calvin Klein, and Tommy Hilfiger. It also has a
 range of dining and entertainment options, including a movi 3. **Bal Harbour
 Shops**: Known for its luxury shopping experience, this open-air mall features
 high-end brands like Chanel, Gucci, and Prada. It's set in a beautiful tropical
 environment, making it a pleasant place to stroll and shop 4. **The Shops at 
Merrick Park**: Located in Coral Gables, this upscale shopping center offers a mix
 of Luxury and contemporary brands such as Neiman Marcus, Nordstrom, and Tiffany &
 Co. It also has a selection of fine dining restaur 5. **Brickell City Centre**: 
Situated in the heart of Miami's financial district, this modern shopping center
 offers a mix of high-end and contemporary brands, including Saks Fifth Avenue, 
AllSaints, and Sephora. It also features a vi 6. **Lincoln Road Mall**: While not a 
traditional mall, this pedestrian street in South Beach is lined with shops, cafes,
 and restaurants. It's a great place to enjoy the Miami weather while shopping at
 stores like H&M, Anthropologie Each of these malls offers a unique shopping 
experience, so it depends on what you're looking for. Enjoy your shopping adventure
 in Miami!
Human: Can you tell me who I am with in Miami?
AI:
> Finished chain.
'You mentioned earlier that you are in Miami with your fiancée. I hope you both have
 a wonderful time exploring the city and enjoying your shopping trip! If there's 
anything else you'd like to know or any other way I can assist you, feel free to 
ask.

Let’s check what is stored in buffer using this function.

print(conversation.memory.buffer)

Output:

Human: Hi there! I am in Miami Today with my fiancee and want to go for shopping.
AI: Hello! That sounds like a fun day ahead. Miami is a great place for shopping
 with a variety of options to explore. If you're looking for high-end fashion and 
luxury brands, you might want to check out Human: Can you tell me some shopping
 malls? AI: Of course! Here are some popular shopping malls in Miami that you might
 want to visit:
1. **Aventura Mall**: As one of the largest malls in the United States, Aventura 
Mall offers a vast selection of stores, including both high-end and more affordable
 brands. You'll find everything from Nords 2. **Dolphin Mall**: This is a great spot
 for outlet shopping, with a wide range of stores offering discounts on popular 
brands. It's a bit more budget-friendly and includes stores like Nike, Calvin Klein,
 3. **Brickell City Centre**: Located in the heart of Miami's financial district,
 this modern shopping center features luxury brands like Saks Fifth Avenue, as well
 as a variety of dining options and a ciner 4. **The Falls**: This is an open-air
 shopping center with a beautiful setting, featuring a waterfall and tropical 
landscaping. It has a mix of popular retailers like Macy's and specialty stores. 5.
 **Dadeland Mall**: Known for its large selection of department stores, including
 Macy's, JCPenney, and Nordstrom, Dadeland Mall also offers a variety of specialty
 shops and dining options.
Each of these malls offers a unique shopping experience, so you can choose based on 
your preferences and what you're looking to buy. Enjoy your time in Miami!
Human: Can you tell me who I am with in Miami?
AI: You mentioned earlier that you are in Miami with your fiancée. I hope you both 
have a wonderful time exploring the city and enjoying your shopping adventure! If 
there's anything else you'd like to know

As we can see, conversational buffer memory saves every interaction in the chat
 history directly. While storing everything gives the LLM the maximum amount of 
information, more tokens mean slowing response times and higher costs.

Conversation Summary Memory: Optimizing Interaction History for Efficiency

Using ConversationBufferMemory, we very quickly use a lot of tokens and even exceed the context window limit of even the most advanced LLMs available today. To avoid excessive token usage, we can use ConversationSummaryMemory. As the name would suggest, this form of memory summarizes the conversation history.

from langchain.chains.conversation.memory import ConversationSummaryMemory
llm = ChatOpenAI(temperature=0, model="gpt-4o", max_tokens=1000)

conversation = ConversationChain(
	llm=llm,
	memory=ConversationSummaryMemory(llm=llm)
)
conversation.predict(input="Hi there! I am in Miami Today with my fiancee and want to go for shopping.")
conversation.predict(input="Can you tell me some shopping malls?")
conversation.predict(input="Can you tell me who I am with in Miami?")

print(conversation.memory.buffer)

Output:

The human is in Miami with their fiancée and wants to go shopping. The AI suggests
 several shopping destinations in Miami, including Bal Harbour Shops for luxury
 brands, the Miami Design District for a mix

We pass the LLM to the ConversationSummaryMemory function as the LLM helps summarize the previous contexts. Let us check out the prompt that is passed to the LLM for summarizing the historical contexts. 

print(conversation.memory.prompt.template)

Output:

Human: Hi there! I am in Miami Today with my fiancee and want to go for shopping.
AI: Hello! That sounds like a fun day ahead. Miami is a great place for shopping
 with a variety of options to explore. If you're looking for high-end fashion and 
luxury brands, you might want to check out Human: Can you tell me some shopping 
malls? AI: Of course! Here are some popular shopping malls in Miami that you might
 want to visit:
1. **Aventura Mall**: As one of the largest malls in the United States, Aventura 
Mall offers a vast selection of stores, including both high-end and more affordable
 brands. You'll find everything from Nords 2. **Dolphin Mall**: This is a great spot
 for outlet shopping, with a wide range of stores offering discounts on popular 
brands. It's a bit more budget-friendly and includes stores like Nike, Calvin Klein,
 3. **Brickell City Centre**: Located in the heart of Miami's financial district, this modern shopping center features luxury brands like Saks Fifth Avenue, as well 
as a variety of dining options and a ciner 4. **The Falls**: This is an open-air 
shopping center with a beautiful setting, featuring a waterfall and tropical 
landscaping. It has a mix of popular retailers like Macy's and specialty stores. 5.
 **Dadeland Mall**: Known for its large selection of department stores, including 
Macy's, JCPenney, and Nordstrom, Dadeland Mall also offers a variety of specialty
 shops and dining options.
Each of these malls offers a unique shopping experience, so you can choose based on 
your preferences and what you're looking to buy. Enjoy your time in Miami!
Human: Can you tell me who I am with in Miami?
AI: You mentioned earlier that you are in Miami with your fiancée. I hope you both 
have a wonderful time exploring the city and enjoying your shopping adventure! If 
there's anything else you'd like to know

Progressively summarize the lines of conversation provided, adding onto the previous
 summary returning a new summary.
EXAMPLE
Current summary:
The human asks what the AI thinks of artificial intelligence. The AI thinks 
artificial intelligence is a force for good.
New lines of conversation:
Human: Why do you think artificial intelligence is a force for good?
AI: Because artificial intelligence will help humans reach their full potential.
New summary:
The human asks what the AI thinks of artificial intelligence. The AI thinks 
artificial intelligence is a force for good because it will help humans reach their 
full potential. END OF EXAMPLE
Current summary: {summary}
New lines of conversation:
{new_lines}
New summary:

While the advantage of using ConversationSummaryMemory is that it reduces the number of tokens for long conversations, the con is that the whole memory is dependent on the saved summarized version of the conversation whose quality again varies with the summarization capability of the LLM used.

Conversation Buffer Window Memory: Retaining Recent Interactions for Contextual Awareness

The Conversation Buffer Window Memory is similar to buffer memory but with a pre-defined window added to the memory. This means we only ask the model to remember ‘n’ number of previous interactions thereby reducing the total number of tokens utilized as compared the ConversationBufferMemory.

from langchain.chains.conversation.memory import ConversationBufferWindowMemory
llm = ChatOpenAI(temperature=0, model="gpt-4o", max_tokens=1000)

conversation = ConversationChain(llm=llm,memory=ConversationBufferWindowMemory(k=3))

conversation.predict(input="Hi there! I am in Miami Today with my fiancee and want to go for shopping.")
conversation.predict(input="Can you tell me some shopping malls?")
conversation.predict(input="Can you tell me who I am with in Miami?")

Output:

'You mentioned earlier that you are in Miami with your fiancée. I hope you both have
 a fantastic time exploring the city and enjoying all the shopping and attractions 
it has to offer! If there's anything e lse you'd like to know or need help with, 
feel free to ask."

As we can see, with ‘k’ set as 3, the model is able to remember the last 3 conversations and hence can remember that the person is with their fiancee in Miami.

If only want our chatbot to remember a number of recent conversations, selecting this model is a good choice. However, this option cant help the chatbot remember very distant interactions.

Conversation Summary Buffer Memory: Combining Recent Interactions with Summarized History

The ConversationSummaryBufferMemory is a combination of ConversationSummaryMemory and ConversationBufferWindowMemory. This memory system saves recent interactions in a buffer and combines older ones into a summary, keeping both stored for use. Rather than removing older interactions just based on their count, it now clears them out based on the total token length.

from langchain.chains.conversation.memory import ConversationSummaryBufferMemory
llm = ChatOpenAI(temperature=0, model="gpt-4o", max_tokens=1000)

conversation_sum_bufw = ConversationChain(
    llm=llm, memory=ConversationSummaryBufferMemory(
        llm=llm,
        max_token_limit=650
))

conversation_sum_bufw.predict(input="Hi there! I am in Miami Today with my fiancee and want to go for shopping.")
conversation_sum_bufw.predict(input="Can you tell me some shopping malls?")
conversation_sum_bufw.predict(input="Can you tell me who I am with in Miami?")

Output:

'You are in Miami with your fiancée. If you need any more information or
 recommendations for your trip, feel free to ask!'

Let us now check how the memory is saved in the buffer for this technique.

print(conversation_sum_bufw.memory.buffer)

Output:

System: The human is in Miami with their fiancée and wants to go shopping. The AI
 suggests several shopping destinations, including Bal Harbour Shops, the Miami
 Design District, Aventura Mall, and Wynwood. Human: Can you tell me who I am with
 in Miami? AI: You are in Miami with your fiancée. If you need any more information 
or recommendations for your trip, feel free to ask!

As we can see in the output above, the buffer memory has a mix of summary of previous distant conversations along with actual interactions saved for the more recent conversations.

The ConversationSummaryBufferMemory requires extra adjustments to decide what to summarize and what to keep in the buffer, but it provides great flexibility in retaining distant interactions while keeping recent interactions in their original, most detailed form.

Conversation Knowledge Graph Memory: Structuring Information for Enhanced Context

In this technique, LangChain builds a mini knowledge graph of connected information by identifying key entities and their relationships, helping the model better understand and respond to different situations.

from langchain.chains.conversation.memory import ConversationKGMemory
from langchain.prompts.prompt import PromptTemplate

llm = ChatOpenAI(temperature=0, model="gpt-4", max_tokens=1000)
template = """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. The AI ONLY uses information contained in the "Relevant Information" section and does not hallucinate.

Relevant Information:

{history}

Conversation:
Human: {input}
AI:"""
prompt = PromptTemplate(
    input_variables=["history", "input"], template=template
)

conversation_with_kg = ConversationChain(
    llm=llm, 
    verbose=True, 
    prompt=prompt,
    memory=ConversationKGMemory(llm=llm)
)

conversation_with_kg.predict(input="Hello, My name is Myra")
conversation_with_kg.predict(input="I am in Miami and need some assistance in booking hotels.")
conversation_with_kg.predict(input="I need hotel recommendations near Miami Beaches")

As seen in the code above, the ConversationChain function is passed with a defined prompt which asks the LLM to focus only on the relevant information for an asked query and not hallucinate. 

import networkx as nx
import matplotlib.pyplot as plt
print(conversation_with_kg.memory.kg.get_triples())

Output:

[('Myra', 'name', 'is'), ('Myra', 'Miami', 'is in'), ('Myra', 'booking hotels',
 'needs assistance in'), ('Human', 'hotel recommendations near Miami Beaches', 
'need')]

As can be seen from the output above, in the memory, key entities and their relationships are saved. Hence, structured information can be very easily extracted using this technique.

Entity Memory: Capturing Key Details for Personalized Responses

Entity memory, like Knowledge Graph memory, pulls specific details from conversations, such as names, objects, or places. This targeted method helps the model respond to user questions with more accuracy.

from langchain.chains.conversation.memory import ConversationEntityMemory
from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
## The propmpt
print(ENTITY_MEMORY_CONVERSATION_TEMPLATE.template)

Output:

'You are an assistant to a human, powered by a large language model trained by 
OpenAI. \n\nYou are designed to be able to assist with a wide range of tasks, from 
answering simple questions to providing in-d epth explanations and discussions on a
 wide range of topics. As a language model, you are able to generate human-like text
 based on the input you receive, allowing you to engage in natural-sounding convers
 ations and provide responses that are coherent and relevant to the topic at hand. 
\n\nYou are constantly learning and improving, and your capabilities are constantly 
evolving. You are able to process and un derstand large amounts of text, and can use 
this knowledge to provide accurate and informative responses to a wide range of
 questions. You have access to some personalized information provided by the human
 in the Context section below. Additionally, you are able to generate your own text 
based on the input you receive, allowing you to engage in discussions and provide
 explanations an

The above output shows the prompt given to the LLM. Let us now see how the ConversationEntityMemory can be implemented taking into account the above prompt template.

llm = ChatOpenAI(temperature=0, model="gpt-4o", max_tokens=200)

conversation = ConversationChain(
    llm=llm, 
    verbose=True,
    prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE,
    memory=ConversationEntityMemory(llm=llm)
)
conversation.predict(input="Hello, My name is Myra")
conversation.predict(input="I am in Miami and need some assistance in booking hotels.")
conversation.predict(input="I need hotel recommendations near Miami Beaches")

from pprint import pprint
pprint(conversation.memory.entity_store)

Output:

InMemoryEntityStore (store={"Myra': "Myra's name is Myra.", 'Miami': 'Miami is a
 city where Myra is currently located and is seeking assistance in booking hotels.',
 'Miami Beaches': 'Miami Beaches is a popul

As can be seen from the output above, all the relevant entities are identified mapped with the associated details like “’Miami is a city where Myra is currently located and is seeking assistance in booking hotels.” is mapped to the entity “Miami”.

Conclusion

In Retrieval-Augmented Generation (RAG) systems, conversational memory is vital for maintaining context and improving relevance. It also helps in personalizing responses and handling multi-step queries. Unlike naive RAG, which processes each query independently, conversational memory builds a continuous experience. Techniques like Conversation Buffer Memory store full interaction histories but may increase token usage. On the other hand, Conversation Summary Memory reduces token usage by summarizing past interactions.

Conversation Buffer Window Memory retains only a set number of recent exchanges, and Conversation Summary Buffer Memory combines recent conversations with summaries of older ones, balancing context and efficiency. More advanced methods, such as Conversation Knowledge Graph Memory, structure information as interconnected entities, while Entity Memory captures specific details like names or locations for precise responses. Together, these techniques enable RAG systems to offer contextually rich and adaptive interactions tailored to user needs.

Key Takeaways

• Conversational memory enables RAG systems to maintain context, enhancing the flow and relevance of interactions.
• Storing complete chat histories, Conversation Buffer Memory is useful but can increase token costs.
• Conversation Summary Memory reduces token usage by summarizing past interactions effectively.
• Hybrid techniques like Conversation Summary Buffer Memory optimize memory by combining recent details and summarized history.
• Advanced memory techniques, such as Knowledge Graph and Entity Memory, improve accuracy by structuring and targeting key information.

Frequently Asked Questions

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Nibedita Dutta

Nibedita completed her master’s in Chemical Engineering from IIT Kharagpur in 2014 and is currently working as a Senior Data Scientist. In her current capacity, she works on building intelligent ML-based solutions to improve business processes.