Revolutionizing Conversational AI with MemGPT’s Context Expansion

8 min readOct 28, 2023

The recent innovations in conversation AI have been transformative, and Large Language Models have been at the forefront of these advancements. Improved natural language understanding, contextual conversations, multilingual and multimodal capabilities, personalization, and ethical considerations have all been revolutionized by LLMs. These developments have expanded the potential applications of conversation AI across various domains, benefiting both businesses and individuals.

The story of LLMs begins with the introduction of transformers, a groundbreaking neural network architecture that has paved the way for this AI revolution.

The Rise of Transformers

The term “transformer” in the context of AI refers to a revolutionary architecture for training deep neural networks. Transformers were introduced in the 2017 paper “Attention Is All You Need”. This paper presented a novel approach to natural language processing tasks, departing from the traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs). What made transformers stand out was their ability to handle sequential data, such as text, more effectively through self-attention mechanisms.

Self-attention allows the model to weigh the importance of different words in a sentence, enabling it to capture complex relationships within the text. This breakthrough made transformers the new foundation for NLP tasks and gave birth to models like BERT, GPT-3, and others.

Creation of Large Language Models (LLMs)

The real game-changer came when researchers realized that the more parameters you have in a model, the more powerful it becomes. This led to the development of Large Language Models (LLMs), which are essentially transformers with an enormous number of parameters. They are pre-trained on massive text datasets, which allows them to learn an incredible amount of world knowledge.

This sheer scale has unlocked AI capabilities that were previously unimaginable. LLMs can perform a wide range of tasks, from language translation and text generation to question-answering and even creative content generation.

Major Problem with LLMs: Limited Context Window

However, one of the most significant challenges these models face is their limited context window. Understanding the context window problem is essential to appreciating both the power and the limitations of LLMs.

What is Context Window?

In LLMs, the “context window” refers to the span of text or information that the model can effectively consider when making predictions or generating text. Although this window is quite big in terms of its size, it still has inherent limitations. Models like GPT-3 and Llama 2 can process thousands of tokens in a single pass, but there are practical constraints on the context they can handle.

Impact on Conversational AI:

Long-Term Chat: Engaging in a chat conversation that spans weeks, months, or even years while maintaining context and coherence can be challenging with a limited context window.
Chat with Documents: If you need to refer to extensive documents or datasets during a conversation, the context window quickly becomes a bottleneck. Storing and recalling this information within a conversation is challenging.

Why can’t we just increase the context window size?

Expanding the context window size of LLMs introduces two significant challenges:

1. Escalating Resource Demands:

LLMs have a fixed memory capacity because the self-attention algorithm scales quadratically based on context length. Doubling the memory size makes the LLM’s computations 4x more intensive. Expanding memory becomes computationally infeasible, even for large tech companies.

2. Impairment of Long-Term Context Retention:

LLMs despite their capacity to process long contexts, sometimes face challenges in utilizing longer contexts effectively.
A study shows that LLMs excel when relevant information is located at the beginning or end of input contexts, but struggle to access information in the middle of lengthy contexts which means there might be a decline in performance as context length increases.

To dive deeper into these insights check out this research paper.

One solution is Recursive Summarization

Recursive summarization is a method for handling long context in LLM-based applications, that generates concise representations over a sliding window to fit them within the context window.

However, it’s important to note that while Recursive Summarization is effective in managing lengthy conversations, there is a potential drawback: the unintentional loss of relevant details in longer conversations.

Checkout this research paper for more understanding

Given the significant resources required to train and deploy state-of-the-art LLMs with large context windows, and the diminishing returns of context scaling, alternative techniques to support long context are essential.

Following a comprehensive evaluation of these trade-offs, a team of researchers at UC Berkeley conceived an inventive solution, drawing inspiration from the world of operating systems. Their system, MemGPT applies OS principles like virtual memory and process management to enable more powerful applications of LLMs while remaining within their inherent context window.

What is MemGPT?

MemGPT is an OS-inspired LLM system designed to address the limitations of fixed-length context windows in LLMs. It enables them to effectively remember and recall information during conversations, making them better at long-term chat.

The architecture of MemGPT and its working principles

MemGPT teaches LLMs to manage their own memory using a multi-level memory structure that differentiates the “main context” (similar to RAM) and “external context” (disk storage).

The main context represents the fixed context window used in LLMs, while the external context contains data beyond this window. To access information from the external context, it must be explicitly moved into the main context during inference. This is achieved by built-in functions that allow the LLM processor to autonomously manage its memory without user intervention, enabling the utilization of unbounded context.

In other words, MemGPT allows LLMs to access and process information beyond their limited context window by moving it into and out of the main context as needed. This enables LLMs to have more coherent and long-term interactions and to perform tasks that would otherwise be impossible, such as analyzing large documents or sifting complex legal documents.

Types of External Context

Recall storage: This stores the entire history of events processed by the LLM processor, including the full uncompressed queue from active memory. This helps MemGPT to find past interactions related to a particular query or within a specific time period which is crucial for conversational agents.
Archival storage: This serves as a general read-write datastore that allows MemGPT to store and search over large amounts of data, such as a document database.

Components of Main Context

System instructions: These are the instructions that define the basic functionality of the LLM.
Conversational history: This is a FIFO queue of recent conversational history. If the queue reaches its limit, the oldest entries are truncated or compressed using recursive summarization.
Working Context: This component serves as a dynamic working memory, updated by the LLM processor through function calls.

Updating Working Context

MemGPT uses function calling to manage the flow of data between its main context and external context. This process is entirely self-directed, meaning that MemGPT autonomously decides when to move items between contexts and how to modify its main context based on its current objectives and responsibilities.

This is achieved by following explicit instructions that are provided in the preprompt. These instructions describe the memory hierarchy and function schema, which MemGPT uses to access and modify its memory.

During each inference cycle, the LLM processor receives the main context input and generates an output string. MemGPT parses the output to validate function arguments before execution. This feedback loop helps the system learn and adapt.

MemGPT is aware of its context limits and uses this information to guide its memory management decisions by warning the processor when it is approaching the maximum capacity of the main context.

In MemGPT, events trigger LLM inference:

Events serve as generalized inputs and consist of user messages, system messages (e.g., context capacity warnings), user interactions (e.g., user logins or document uploads), and timed events (for unprompted system operation).
A parser that converts these events into plain text messages, appending them to the main context for LLM processor input.

Support of Function Chaining for Sequential Execution:

Practical tasks often require executing multiple functions in sequence, such as navigating through multiple query result pages or consolidating data from different documents in the main context.
MemGPT supports function chaining to execute multiple functions sequentially before returning control to the user.
A special flag can be used to request an immediate return to the processor after a function completes, adding the function output to the main context.
If the flag is not present (a yield), MemGPT awaits the next external event trigger (e.g., a user message or scheduled interrupt) to resume LLM processor execution.

In summary, MemGPT revolves around the idea of managing memory effectively through function calls, self-directed memory management, and event-based control flow. This allows MemGPT to provide an illusion of an infinite context while using fixed-context models, making it suitable for various applications, including conversational agents and document analysis.

Advantages of MemGPT

MemGPT can greatly enhance the capabilities of conversational AI with:

Unbounded Context: MemGPT addresses the limitation of fixed-length context in language models, allowing it to work with a virtually unlimited context. This is a significant advantage when dealing with long conversations or analyzing extensive documents.
Consistency: MemGPT can query its external memory of prior interactions to remember relevant facts, preferences, and events from past interactions.
Personalization: MemGPT can craft engaging messages in conversations by drawing from the knowledge accumulated in prior interactions. This personalized approach makes dialogue more engaging and user-friendly.

Limitations of MemGPT

Complex Operations: MemGPT’s operations are relatively more complex compared to models with the same token budget, resulting in some token allocation for system instructions, specifically for memory management.
Dependency on GPT-4: MemGPT currently requires GPT-4 models fine-tuned for function calls. However, GPT-4 is expensive, which limits access to MemGPT’s capabilities. Additionally, other language models, such as GPT-3.5 and Llama 2, often generate incorrect function calls.

Applications of MemGPT

Customer Support: A customer service chatbot could use MemGPT to remember a customer’s previous inquiries and preferences so that it can provide more personalized and helpful service.
Document analysis: MemGPT can be used to analyze complex documents, such as legal contracts or medical records, and identify important information that would be difficult to find with traditional methods.
Code generation: MemGPT can be used to generate code that is more efficient and bug-free, by taking into account the entire context of the codebase.
Creative Writing: A creative writing assistant could use MemGPT to generate new ideas and plot twists for a story while staying consistent with the characters and setting.

and many more!!!

In conclusion, MemGPT is a revolutionary system that addresses one of the biggest limitations of LLMs by intelligently managing different memory tiers. This allows MemGPT to have perpetual conversations, regardless of the length or complexity of the topic.

References:

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