MemGPT 🧠 Giving AI Unlimited Prompt Size (Big Step Towards AGI?)

The Challenge of Memory in Artificial Intelligence

This section discusses the limitations of memory in artificial intelligence models and the need for improved context windows.

Memory Limitations in AI Models

• AI models lack memory once they are trained, limiting their ability to retain information beyond what was provided in the training data.

• Context windows, which determine the size of prompt and response, are highly limited. Previous models had context windows of 2,000 tokens (approximately 1,500 words), but newer models like GPT-4 can handle up to 32,000 tokens.

• However, even with larger context windows, there is still a need for improved memory management due to long-term chat conversations and working with large document sets.

Introducing MemGPT - A Solution for Memory Management

This section introduces MemGPT as a solution for managing memory in AI models and provides an overview of the research paper.

Introduction to MemGPT

• MemGPT is a research project that addresses the limitations of context windows by proposing a virtual context management system.

• The authors have open-sourced their code, allowing users to install and utilize MemGPT effectively.

• The research paper titled "MemGPT: Towards Large-Scale Multimodal Language Models as Operating Systems" is authored by Charles Packer et al. from UC Berkeley.

Key Points from the Research Paper

This section highlights key points from the research paper on MemGPT's approach to memory management.

Use Cases for Improved Context Window

• Long-term chat conversations spanning weeks or months require consistent conversation flow that is challenging with limited context windows.

• Chatting with documents becomes difficult when dealing with large document sets, as the context window quickly becomes insufficient.

Challenges with Increasing Context Window Size

• Simply increasing the context window size incurs a quadratic increase in computational time and memory cost due to the Transformer architecture's self-attention mechanism.

• Large language models tend to forget parts of a large context, even within an expanded context window.

The Illusion of Infinite Context

• MemGPT proposes the illusion of an infinite context while still using fixed-context models.

• It aims to mimic an operating system's memory management by creating virtual contexts with different token limits.

• The main context has a fixed token limit, while an external context allows for unlimited tokens and larger context sizes.

Autonomous Memory Management in MemGPT

This section explains how MemGPT autonomously manages its own memory through function calls.

Function Calls for Memory Management

• MemGPT utilizes function calls to enable autonomous memory management.

• Function calls allow AI models to execute different tasks and define functions that facilitate memory operations.

• GPT-4 excels at function calls, but previous models like GPT 3.5 lack this capability.

Diagram of MemGPT's Proposed System

This section presents a diagram illustrating the proposed system architecture of MemGPT.

System Architecture Overview

• Inputs include user messages, uploaded documents, system messages, and timers.

• The inputs go through a parser for proper formatting before entering the virtual context.

• The virtual context consists of a main context with a fixed token limit and an external context with unlimited tokens and larger size.

• The LLM (Large Language Model) processor performs inference on the virtual context.

• Output goes through another parser for validation before being presented as the final result.

These notes provide a comprehensive summary of the transcript, highlighting the key points discussed in each section. The timestamps are used to associate the bullet points with specific parts of the transcript, allowing for easy reference and study.

Processing Module and User Design

This section discusses the design of the processing module and user interaction in a conversational agent.

Memory Hierarchy for LLMS

• The design allows for repeated context modifications during a single task, enabling the agent to effectively utilize its limited context.

• Context windows are treated as constrained memory resources, similar to memory tiers in traditional operating systems.

• The main context represents short-term memory (RAM), while the external context represents long-term memory (hard drive).

Maximum Number of Tokens

• A table shows the maximum number of tokens for popular models like Llama 2, GPT models, and Cloud models.

• The maximum conversation length varies depending on the model, assuming an average message size.

Importance of Pre-Prompt

• Pre-prompts dictate system behavior and are commonly used in llm-based conversational agents.

• Larger pre-prompts are common for complex tasks like coding.

Recursive Summarization and Lossy Compression

This section explores recursive summarization as a solution to overflowing context windows but highlights its limitations.

Reflecting on Memories

• Agents store memories in a vector database but face limitations in providing all necessary memories to the llm.

• Reflecting on memories involves compressing memories into a summarized version.

Limitations of Recursive Summarization

• Recursive summarization is inherently lossy and leads to large holes in the system's memory.

• Lossy compression can be compared to video compression where repeated compression results in pixelation and loss of information.

Main Context Components

This section discusses the three components of the main context: system instructions, conversational context, and user messages.

System Instructions

• System instructions guide how the llm should behave or what role it should take on.

Conversational Context

• The conversational context represents recent event history and serves as a cue for the llm.

User Messages

• User messages are part of the conversation data stored in the main context.

The transcript has been summarized based on the provided timestamps.

Context and Working Context

This section discusses the context and working context in the conversational model.

Context and Working Context

• The conversational context is read-only with a special eviction policy, meaning it continues to put information until it runs out of space.

• The working context is both readable and writable by the LLM processor via function calls.

• The working context allows storing information that can be accessed later.

Storing Information in Working Context

This section explains how M GPT writes details from a conversation to the working context without a memory warning.

Storing Information in Working Context

• M GPT can store additional information from a conversation in its working context.

• An example is given where M GPT stores birthday information and favorite cake details provided by the user.

• Function calls are used to store this information in the working context for later use.

External Context

This section introduces the concept of external context, which refers to out-of-context storage outside the limited window of the LLM processor.

External Context

• External context refers to storage outside the limited window of the LLM processor, similar to disk memory in traditional operating systems.

• It includes recall storage, which stores past events processed by the LLM processor, and archival storage, which serves as a general data store for overflow from the main context.

• Different ways are provided for querying external context, including time-based search, text-based search, and embeddings-based search.

Memory Management in MGPT

This section discusses how MGPT manages its memory through memory edits and retrieval executed via function calls.

Memory Management in MGPT

• Memory edits and retrieval in MGPT are self-directed and executed through function calls.

• Instructions within the pre-prompt guide the system on how to interact with its memory systems.

• The instructions include a detailed description of the memory hierarchy, utilities, and a function schema with natural language descriptions.

Managing Memory Overflow

This section explains how MGPT manages memory overflow when the maximum context length is reached.

Managing Memory Overflow

• When the maximum context length is reached, MGPT starts managing its memory.

• It appends information to the working context key, such as personality traits or user preferences.

• False information can be corrected by updating the working context.

Timestamps for other sections are not provided in the transcript.

Testing MGPT for Conversational Agents

The transcript discusses how OpenAI tested MGPT (Multi-Modal Generative Pre-trained Transformer) for conversational agents. They conducted tests using long-term chat dialogues and document retrieval, focusing on consistency and engagement as key factors.

Testing Methods

• Tested against long-term chat dialogues with thousands of messages.

• Tested against document retrieval or chat with your docs.

• Two main factors evaluated: consistency and engagement.

Consistency

• Agent should maintain conversational coherence.

• New facts, preferences, and events mentioned should align with prior statements from both the user and agent.

Engagement

• Agent should draw on long-term knowledge about the user.

• Referencing prior conversations makes dialogue more natural and engaging.

Example of MGPT in Action

An example is provided to illustrate how MGPT works in a conversation. The user asks about a past conversation on music, and MGPT uses its recall memory to retrieve the relevant information.

• User asks about past conversation on music.

• MGPT searches its recall memory for the artist's name.

• Function call: music search recall storage

• Finds Taylor Swift as the mentioned artist from prior conversation history.

• Bot confirms Taylor Swift as the answer.

Deep Memory Retrieval (DMR)

DMR is introduced as a concept where a conversational agent is asked a question that explicitly refers back to a prior conversation with a narrow expected answer range. It enhances accuracy by leveraging past memory.

Performance Comparison of GPT Models

A performance comparison is shown between GPT 3.5 alone, GPT 4 alone, and using M GPT for accuracy of responses. M GPT outperforms the AI models alone, indicating its effectiveness in conversation.

Crafting Engaging Conversation Openers

The ability of an agent to craft engaging messages as conversation openers is evaluated. Referencing facts and information from prior conversations enhances engagement.

Performance Comparison of Conversation Openers

Performance comparison of different conversation openers is shown, including MGPT with working context and recall storage, MGPT with working context only, and recall storage only. MGPT with both working context and recall storage performs the best.

Document Analysis Challenges

The challenges of document analysis are discussed, highlighting the limitations of existing models in handling lengthy documents. Many real document analysis tasks require drawing connections across multiple lengthy documents.

Importance of Context in Document Analysis

The importance of providing all the necessary context for document analysis is emphasized. Existing models have token limits that make it difficult to handle large documents or multiple documents simultaneously.

Timestamps were used to associate bullet points with specific parts of the transcript as requested.

The Power of Primacy and Recency in Memory

The speaker recalls an experiment from their school days where the teacher listed 10 words and asked students to write down the ones they remembered. They found that people tend to remember the first and last words more than those in the middle. This phenomenon is similar to what large language models demonstrate.

• People tend to remember the first and last items in a list more than those in the middle.

• Large language models, like GPT-4, show a similar pattern of remembering information.

• This reflects how the human mind processes and retains information.

Accuracy of Answers Based on Document Retrieval

The speaker discusses charts showing the accuracy of answers based on document retrieval. They compare different models' performance based on the number of documents retrieved and nesting levels.

• Charts show the accuracy of answers based on document retrieval.

• GPT-4 performs well initially but drops significantly when it reaches its context window limit.

• MGPT maintains consistent accuracy regardless of the number of documents retrieved.

• Nesting more information affects GPT-3.5 and GPT-4's performance negatively, while MGPT's performance remains stable.

Tradeoff in Retrieved Document Capacity for MGPT

The speaker mentions a tradeoff in retrieved document capacity for MGPT due to its complex operation and token budget limitations. They explain that a portion of MGPT's token budget is consumed by system instructions required for managing memory.

• There is a tradeoff in retrieved document capacity for MGPT due to its complex operation.

• A non-trivial portion of MGPT's token budget is used for system instructions related to memory management.

Adding Memory to Language Models

The speaker references a paper by Park et al. that proposes adding memory to large language models (LLMs) and using them as planners to observe emergent social behaviors in a multi-agent sandbox environment.

• Park et al. propose adding memory to LLMs and using them as planners.

• The goal is to observe emergent social behaviors in a multi-agent sandbox environment.

Limitations of Function Calling in GPT Models

The speaker discusses limitations related to function calling in GPT models. They mention that while GPT-4 fine-tuned models rarely make errors, GPT-3.5 fine-tuned models consistently generate incorrect function calls. Even the most popular LLM model variants struggle with generating correct function calls.

• GPT-4 fine-tuned models rarely make syntactic or semantic errors on the M GPT function set.

• GPT-3.5 fine-tuned models consistently generate incorrect function calls.

• Popular LLM model variants also struggle with generating correct function calls.

Improving Open Source Models for Function Calling

The speaker emphasizes the importance of open-source models being able to perform function calling effectively, as it is currently a challenge even for fine-tuned models. They mention autogen as an example and express hope for future advancements in this area.

• Open-source models need to improve their ability to perform function calling effectively.

• Autogen is mentioned as an example of a project that relies on function calling.

Installation and Usage of MGPT

The speaker provides instructions on how to install and use MGPT, including cloning the repository, creating a new conda environment, and accessing the GitHub page for demonstrations and documentation.

• Instructions are provided for installing and using MGPT.

• Cloning the repository is the first step.

• Creating a new conda environment is necessary.

• The GitHub page provides demonstrations and documentation.

Memory in Language Models and Autonomous Agents

The speaker mentions the concept of adding memory to language models (LLMs) and references the paper by Park et al. They express excitement about the potential combination of autogen and MGPT to give agents unlimited memory.

• Adding memory to LLMs is an ongoing area of research.

• Combining autogen with MGPT could enable agents to have unlimited memory.

Conclusion on Function Calling and Open Source Models

The speaker concludes by reiterating the importance of open-source models being able to perform function calling effectively, as it can help reduce reliance on expensive models like GPT-4.

• Open-source models need to improve their ability to perform function calling effectively.

• Effective function calling can reduce reliance on expensive models like GPT-4.

Setting up the Environment

In this section, the speaker explains how to set up the environment for using MGPT.

Installing Requirements and API Key

• Change directory to the MGPT folder: cd mem GPT

• Install all requirements: pip install -r requirements.txt

• Set OpenAI API key: export OPENAI_API_KEY=<your_api_key>

Document Retrieval with MGPT

This section demonstrates how to perform document retrieval using MGPT.

Computing Embeddings

• Run the command: Python 3 main.py D- archival storage files compute embeddings=<location_of_documents>

• Use wildcard (*) to select all text documents in a folder

Querying Documents

• Ask questions based on archival memory: "How much revenue did each company make?"

• Customize Persona to reference archival memory for queries

Asking Questions and Getting Responses

This section shows examples of asking questions and receiving responses from MGPT.

Example 1: Revenue Details

• Ask about revenue of specific companies based on archival memory

• Query: "Based on your archival memory, how much revenue did Lyft and Uber make last year?"

• Response: Revenue for Lyft was $8.399 billion and Uber was $31.875 billion

Example 2: Word Frequency

• Ask about word frequency in specific documents based on archival memory

• Query (stopped prematurely): "Based on your archival memory, how many times did the word 'driver' appear in Lyft's SEC filings?"

Future Plans for MGPT

The speaker discusses the future plans for MGPT.

• OpenAI is working on adding open-source models to MGPT

• Excitement about upcoming updates and features

• Possibility of creating another tutorial when open-source models are included

Interview with MGPT Authors

The speaker interviews the authors of MGPT.

• Charles and Vivian are part of the team behind MGPT

• Goal of addressing memory limitations in current language models

• Memory system in MGPT allows saving important facts during conversations

• Short-term roadmap includes supporting more user workflows