About this video

• Video Title: Add Memory to your AI Agents | Context Management for LLMs | LangChain
• Channel: Saurav Prateek
• Speakers: Saurav Prateek
• Duration: 00:28:17

Overview

This video explains how to add memory and context management to large language models (LLMs) within agentic workflows. The presenter, Saurav Prateek, demonstrates two primary methods: passing the entire chat history as context and passing a summarized version of the chat history. The goal is to enable LLMs to retain information from previous interactions, leading to more accurate and context-aware responses. The video includes a code walkthrough illustrating these concepts using LangChain.

Key takeaways

• Importance of Memory: LLMs benefit greatly from memory, allowing them to retain context from previous conversations, which leads to more accurate responses.
• Two Memory Strategies:
  • Full Chat History: Directly providing the entire conversation history to the LLM as context. This is straightforward but can be limited by context window size.
  • Summarized Chat History: Condensing the past conversation into a summary before providing it to the LLM. This is more efficient for longer conversations and respects context window limitations.
• Code Implementation: The video demonstrates practical implementation using LangChain, showing how to manage chat history and pass it to the LLM.
• Case Study: No Memory vs. Memory: The presentation contrasts how an LLM without memory fails to answer context-dependent questions, while one equipped with memory can recall and respond accurately.
• Code Walkthrough: A detailed code demonstration covers setting up the model, defining prompts, creating chains, and implementing functions to manage and pass chat history (both full and summarized) to the LLM.

The two main methods discussed for adding memory to LLMs are:

1. Providing the chat history as context: This involves sending the entire conversation history to the model so it can remember previous interactions.
2. Providing a summarized chat history: Instead of the full history, a condensed summary of the past conversation is provided to the model. This is particularly useful for managing the LLM's context window limitations.

The limitation of passing the entire chat history to an LLM is the context window size, which is limited. This means you cannot pass an excessive amount of information at once.

This is addressed by using a summarized chat history. Instead of sending the entire conversation, the history is condensed into a smaller summary, which is then provided to the LLM. This approach is more efficient and respects the LLM's context window.

The preserve_chat_history flag is a boolean that controls whether the chat history is included when querying the model.

• If set to True, the chat history is passed along with the current message, allowing the model to retain context from previous interactions.
• If set to False, only the current message is sent, and the model will not have access to past conversations, treating each interaction independently.

Essentially, this flag allows you to control the memory behavior of the agent – whether it should remember past exchanges or not.

The video demonstrates the difference through two scenarios:

1. Without Memory: A user asks, "What is John's profession?" If the LLM does not have memory (i.e., the chat history is not preserved), it responds with something like, "I don't have a context on this" or "Currently, I don't have a context," because it has no record of previous discussions about John.

2. With Memory: In a scenario where the LLM does have memory, the previous chat history indicates that "John is a software engineer." When the user asks the same question, "What is John's profession?", the LLM correctly responds, "John is a software engineer," because it can recall the information from its stored context.

This contrast highlights how memory allows the LLM to provide accurate and contextually relevant answers, whereas a stateless model cannot.