dude, your enthusiasm and the quality of the editing managed to keep my adhd brain hyperfocused for the entire video. Amazing work and I am so subscribed, I even hit the bell. KEEP THIS UP.
I found this very insightful, you did a terrific job of explaing it to someone who is just beginning their journey into the AI, LLM world as a complete noob.
such cool stuff. the analogy I have heard people use for the current state of bare LLMs is that they are just intutition machines. Its as if you are the conscious part of their brain, asking the unconscious part of their brain for split second intuitions. Memgpt is kind of like a front matter for them to get inbetween your requests and their intuitions in a way that lets them be... actually intelligent about their thinking. No memgpt or similar system, and you are essentially a hyptnotist making suggestions to the model that they aren't really aware of, they just respond. With such a system, your suggestions are processed in a context that knows about other recent information and has access to other sources of inspiration and filtration. really really cool stuff.
🎯 Key Takeaways for quick navigation: 00:00 📝 Introduction to MemGPT - Introduction to the MemGPT research paper, which combines concepts from operating systems with large language model applications. - Discusses the novel perspective of retrieval augmented generation and the concept of the language model as the processor behind the operating system. 01:37 🗂️ Retrieval Augmented Generation - Explanation of retrieval augmented generation as a solution to the limitation of large language models. - Provides an example of how retrieval augmented generation works in practice. 02:34 💡 The Big Idea in MemGPT - Discusses the main idea in MemGPT: the large language model being aware of its own input window limitation and taking actions accordingly. - Introduction to the architecture of MemGPT, which is described as the operating system for retrieval augmented generation applications. 03:29 🛠️ Tool Use in MemGPT - Explanation of tool use in MemGPT, which includes functions for reading and writing memory. - Discusses the concept of interrupts and events in MemGPT, which trigger processing. 05:47 🧠 MemGPT as an Agent - Describes MemGPT as an agent that knows how to use memory management tools. - Discusses the idea of endowing the large language model with the option to write to the database. 06:43 💻 Large Language Models as Operating Systems - Discusses the idea of viewing large language models as operating systems. - Quotes Andrej Karpathy's tweet about the emerging picture of large language models as the kernel process of a new operating system. 09:28 📚 Memory Management in MemGPT - Discusses the memory management aspect of MemGPT, which includes functions for appending and replacing the working context. - Explains the different types of context in MemGPT, including system instructions, conversational context, and working context. 12:44 📊 External Storage in MemGPT - Discusses the concept of external storage in MemGPT, which includes recall storage and archival storage. - Explains the different ways of querying the external database in MemGPT, including time-based, text search, and embedding-based search. 15:06 🔄 Self-Directed Editing and Retrieval in MemGPT - Discusses the concept of self-directed editing and retrieval in MemGPT, which involves augmenting the working context with information from the event log or retrieval. - Explains the control flow and function chaining in MemGPT, which involves system events that kick off the MemGPT process and the function chaining involved in paging through search results and reformulating the query. 19:07 📊 MemGPT Experiments and Results - Discusses the experiments conducted to test MemGPT's performance, including the Rouge score and accuracy. - MemGPT was tested on tasks such as conversation opener and nested key value retrieval. - The model was also evaluated on its ability to overcome the "lost in the middle" problem. 22:21 🚀 Future Work and Personal Takeaways - Discusses potential future work directions for MemGPT, including applying it to other domains, integrating different memory tier technologies, and improving control flow and memory management policies. - Shares personal takeaways, including the comparison between MemGPT and Flare, the latency issue, and the potential of parallel asynchronous concurrent processing. - Discusses the use of databases and caches in MemGPT and the potential of synthetic data for training long context models. 29:20 🦍 Connection with Gorilla LLM - Discusses the connection between MemGPT and Gorilla LLM, both coming from the same lab. - Discusses the idea of describing the tool in as few tokens as possible, which is a common theme in both MemGPT and Gorilla LLM. - Discusses the potential of using multiple language models in the intermediate steps. Made with HARPA AI
Thank you for this video. With the explosion of AI over a few years as well as recent developments in accessing and refining its own memory (when I thought it would take years), has got me surprised as to how quickly it's advancing.
Thank you so much! Haha yeah I've interpreted what "refining its own memory" could mean in so many ways -- we've been looking heavily into "writing to databases" with what we are calling "Generative Feedback Loops" (to reference feeding back the resulting generation to the database). Love how MemGPT presents a similar way to manage memory but I still think there is more we can learn from having the LLM write to its external storage.
a yes! finally someone exited about technical awesome things, thank you for your enthusiasm it made it so much easier for my low iq, short attention spam mind
RAG is toast. I would encourage people to take a look at Retrieval Augmented Language Models. The jist is that LLMs become integrated, directly, with their tools, instead of using an orchestration engine to manage an LLM.
Agree and the speaker does point out the main weakness of RAG (limited context). Its a bit of a bummer the conclusion only presented a binary choice. REALM, RETRO, quite well *WHY* RAG is inferior. REALM, RETRO, and atleast 30 other published papers integrate retrieval directly within LLM layers. I hoped to inform the broader community there are many choices, although there are few pre-trained models with direct tool integration.
That video was deeply flawed and didn’t diminish the strategies used by MemGPT. In that video the guy basically describes a technique where an LLM compresses a contextual prompt as to maximize the amount of space in the context window. MemGPT describes the architecture of an LLM operating system more than it any one given strategy to maximize context window size.
I was being a bit felicitous. Also, a follow-up video to the one mentioned comes with a more fleshed out idea and code repo. I've been using the techniques described in it and memgpt for a long time now. Is it fame that drives these people? I don't submit papers but almost everything I keep close to my chest winds up in some paper months later. It's certainly not a desire to share with the world in the spirit of open-source. At least not for a lot of them. I have yet to be able to convince my business partner that writing papers on my work is a good idea.@@gucciburg
dude, your enthusiasm and the quality of the editing managed to keep my adhd brain hyperfocused for the entire video. Amazing work and I am so subscribed, I even hit the bell. KEEP THIS UP.
I found this very insightful, you did a terrific job of explaing it to someone who is just beginning their journey into the AI, LLM world as a complete noob.
such cool stuff. the analogy I have heard people use for the current state of bare LLMs is that they are just intutition machines. Its as if you are the conscious part of their brain, asking the unconscious part of their brain for split second intuitions. Memgpt is kind of like a front matter for them to get inbetween your requests and their intuitions in a way that lets them be... actually intelligent about their thinking. No memgpt or similar system, and you are essentially a hyptnotist making suggestions to the model that they aren't really aware of, they just respond. With such a system, your suggestions are processed in a context that knows about other recent information and has access to other sources of inspiration and filtration. really really cool stuff.
🎯 Key Takeaways for quick navigation:
00:00 📝 Introduction to MemGPT
- Introduction to the MemGPT research paper, which combines concepts from operating systems with large language model applications.
- Discusses the novel perspective of retrieval augmented generation and the concept of the language model as the processor behind the operating system.
01:37 🗂️ Retrieval Augmented Generation
- Explanation of retrieval augmented generation as a solution to the limitation of large language models.
- Provides an example of how retrieval augmented generation works in practice.
02:34 💡 The Big Idea in MemGPT
- Discusses the main idea in MemGPT: the large language model being aware of its own input window limitation and taking actions accordingly.
- Introduction to the architecture of MemGPT, which is described as the operating system for retrieval augmented generation applications.
03:29 🛠️ Tool Use in MemGPT
- Explanation of tool use in MemGPT, which includes functions for reading and writing memory.
- Discusses the concept of interrupts and events in MemGPT, which trigger processing.
05:47 🧠 MemGPT as an Agent
- Describes MemGPT as an agent that knows how to use memory management tools.
- Discusses the idea of endowing the large language model with the option to write to the database.
06:43 💻 Large Language Models as Operating Systems
- Discusses the idea of viewing large language models as operating systems.
- Quotes Andrej Karpathy's tweet about the emerging picture of large language models as the kernel process of a new operating system.
09:28 📚 Memory Management in MemGPT
- Discusses the memory management aspect of MemGPT, which includes functions for appending and replacing the working context.
- Explains the different types of context in MemGPT, including system instructions, conversational context, and working context.
12:44 📊 External Storage in MemGPT
- Discusses the concept of external storage in MemGPT, which includes recall storage and archival storage.
- Explains the different ways of querying the external database in MemGPT, including time-based, text search, and embedding-based search.
15:06 🔄 Self-Directed Editing and Retrieval in MemGPT
- Discusses the concept of self-directed editing and retrieval in MemGPT, which involves augmenting the working context with information from the event log or retrieval.
- Explains the control flow and function chaining in MemGPT, which involves system events that kick off the MemGPT process and the function chaining involved in paging through search results and reformulating the query.
19:07 📊 MemGPT Experiments and Results
- Discusses the experiments conducted to test MemGPT's performance, including the Rouge score and accuracy.
- MemGPT was tested on tasks such as conversation opener and nested key value retrieval.
- The model was also evaluated on its ability to overcome the "lost in the middle" problem.
22:21 🚀 Future Work and Personal Takeaways
- Discusses potential future work directions for MemGPT, including applying it to other domains, integrating different memory tier technologies, and improving control flow and memory management policies.
- Shares personal takeaways, including the comparison between MemGPT and Flare, the latency issue, and the potential of parallel asynchronous concurrent processing.
- Discusses the use of databases and caches in MemGPT and the potential of synthetic data for training long context models.
29:20 🦍 Connection with Gorilla LLM
- Discusses the connection between MemGPT and Gorilla LLM, both coming from the same lab.
- Discusses the idea of describing the tool in as few tokens as possible, which is a common theme in both MemGPT and Gorilla LLM.
- Discusses the potential of using multiple language models in the intermediate steps.
Made with HARPA AI
Haha this is awesome!
That was awesome man, I appreciated your add-ons such as Gorilla, etc.
Nice cover of this subject. Thank you!
Thank you so much for watching, really appreciate the kind words!
Thank you for the good explanation!
Thank you so much Samuel, really appreciate it!
Thank your for sharing video.
Hi
Looking for Vector DB, is any playlist or videos have on ur channel ?
thx
Do the recent OpenAI announcements on Dev Day (e.g. larger context window and threads) alter the requirements for MemGPT?
Thank you for this video. With the explosion of AI over a few years as well as recent developments in accessing and refining its own memory (when I thought it would take years), has got me surprised as to how quickly it's advancing.
Thank you so much! Haha yeah I've interpreted what "refining its own memory" could mean in so many ways -- we've been looking heavily into "writing to databases" with what we are calling "Generative Feedback Loops" (to reference feeding back the resulting generation to the database). Love how MemGPT presents a similar way to manage memory but I still think there is more we can learn from having the LLM write to its external storage.
@@connor-shorten Yes.
Really a cool presentation that covered most important aspects of the paper.
Could you take it step by step and produce a video for MemGPT for dummies ? With a practical example to fully digest it ?
a yes! finally someone exited about technical awesome things, thank you for your enthusiasm it made it so much easier for my low iq, short attention spam mind
Haha really glad to hear it! SUPER excited as always!
this all needs to stay open source
RAG is toast. I would encourage people to take a look at Retrieval Augmented Language Models. The jist is that LLMs become integrated, directly, with their tools, instead of using an orchestration engine to manage an LLM.
To be fair, that was conceptually always the point of RAG
Agree and the speaker does point out the main weakness of RAG (limited context).
Its a bit of a bummer the conclusion only presented a binary choice. REALM, RETRO, quite well *WHY* RAG is inferior. REALM, RETRO, and atleast 30 other published papers integrate retrieval directly within LLM layers. I hoped to inform the broader community there are many choices, although there are few pre-trained models with direct tool integration.
I'm trying to determine if the guy in this video is AI generated or not....
he's real
It's already obsolete lol. We need an AI just to keep up with all the AI.
why? what do you mean?
david shapiro posted a better thing. Technically you can combine the two ideas though, which is what I'm doing.@@MystifulHD
That video was deeply flawed and didn’t diminish the strategies used by MemGPT. In that video the guy basically describes a technique where an LLM compresses a contextual prompt as to maximize the amount of space in the context window. MemGPT describes the architecture of an LLM operating system more than it any one given strategy to maximize context window size.
I was being a bit felicitous. Also, a follow-up video to the one mentioned comes with a more fleshed out idea and code repo. I've been using the techniques described in it and memgpt for a long time now. Is it fame that drives these people? I don't submit papers but almost everything I keep close to my chest winds up in some paper months later. It's certainly not a desire to share with the world in the spirit of open-source. At least not for a lot of them. I have yet to be able to convince my business partner that writing papers on my work is a good idea.@@gucciburg
Yeah definitely not obsolete. It's groundbreaking.