LangChain Intro, Keymate.AI Search Plugin for ChatGPT, How to use langchain library? How to implement similar functionality in programming language of your choice? Example LangChain applications.
The presentation revolves around the concept of "langChain", This innovative framework is designed to "chain" together different components to create more advanced use cases around Large Language Models (LLMs). The idea is to leverage the power of LLMs to tackle complex problems and generate solutions that are more than the sum of their parts.
One of the key features of the presentation is the application of the "Keymate.AI Search" plugin in conjunction with the Reasoning and Acting Chain of Thought (ReAct) framework. The presenter encourages the audience to utilize these tools to generate reasoning traces and actions. The ReAct framework, learned from an initial search, is then applied to these traces and actions, demonstrating the potential of LLMs to learn and apply complex frameworks.
The presentation also delves into the impact of climate change on biodiversity. The presenter prompts the audience to look up the latest research on this topic and summarize the key findings. This exercise not only highlights the importance of climate change but also demonstrates the capabilities of LLMs in researching and summarizing complex topics.
The presentation concludes with several key takeaways. The presenter emphasizes that specialized custom solutions work best and suggests a bottom-up approach to expert systems. However, they caution that over-abstraction can lead to leakages, causing time and money limits to hit early and tasks to fail or require many iterations. The presenter also notes that while prompt engineering is important, it's not necessary to over-optimize if the LLM is clever. The presentation ends on a hopeful note, expressing a need for more clever LLMs and acknowledging that good applications are rare but achievable.
Overall, the presentation provides a comprehensive overview of the LanGCHAIN framework, its applications, and the potential of LLMs in solving complex problems. It serves as a call to action for the audience to explore these tools and frameworks.
2. MANAGING EXPECTATIONS
Who am I?
Startup Background
Educational Background
NEO AI
Conversationalist
Software Engineer Contractor @ Pollinate
Founder @ Keymate.AI
CTO in several tech startups one AI startup
Reminis and one generative AI product in 2016
Masters in AI (Facial Recognition / Similarity Search)
Bsc in CS
My twitter bio set in 2019: I may continue to tweet
even after I die thanks to future AI version of
myself. Not dead, yet.
ChatGPT rated this keynote 7/10
3. LANGCHAIN
PHILOSOPHY
1.Connect a language model to other sources of data
2.Allow a language model to interact with its
environment
CLIK HERE
NumPy and Pandas for LLMs, greatly increasing their
usability and functionality
Examples that leverages Langchain:
AgentGPT, babyAGI
Minecraft playing GPT4 (Voyager)***
4. LANGCHAIN
COMPONENTS
Schema: Text structure
Model:OpenAI completion, text-in text-out or embedded
Prompt templates
Indexes and Document Loaders
Memory: Long term and short term
Chains: LLMChain
Agents: which tools should be called or used
Components
Chains may consist of multiple components from
several modules
5. Minecraft playing GPT-4 (Voyager)
ReAct : REASONING AND ACTING IN
LANGUAGE MODELS
Plan-and-solve
Chain-of-thought reasoning
Had to develop my own langchain in iOS, tool using agent, chat history,
decision prioritisation, message type handler
ELEMENTS
Focus on things on
top
Research Papers
about prompting
methods
Langchain:LLM interaction framework but easy to
adapt to another Programming Language and System
9. Can we run ReAct
(Reason + Act) on
ChatGPT Plus?
It seems YES! we just need to find a way to inform LLM about thought patterns.
Human structured/abstracted execution may not be the best option. (langchain)
You as a human is just a tool for LLM. Langchain is already baked in ChatGPT.
10.
11. REACT ON CHATGPT
PLUS
Utilize the "internetSearch" plugin and search for
the Reasoning and Acting Chain of Thought
framework. Generate reasoning traces and actions,
then apply the ReAct Framework that you'll learn
from the initial search.
Subsequently, look up the latest research on the
impact of climate change on biodiversity, and
summarize the key findings.
Too long, Didn't scan and read:
Select the Keymate.AI search plugin and prompt in the
following manner:
1.
2.
Magic keyword is continue. Continues the loop.
internetSearch Plugin (Keymate.AI) + 2 Extra
tools of your choice
12. COMPONENTS CONT.
LLMs (GPT4 , Hugging Face etc)
In LLMs we trust all others manage time, resources, limits, autonomy
Chat Models (Many built on top of GPT3)
Embeddings (To store and search/retrieve big data)
Toolkits (specialized agent for particular use case)
Tools (agents can use to achieve certain tasks)
Tokenizers (To count text size before passing to LLM)
Document Loaders (Text document processing)
Vectorstores (To store and index information to pass to agent)
Agent Strategy (Prompt engineering / Research Papers )
The more general an agent is the less powerful in terms of task handling
unless it has a very clever LLM.
Good plugins can still work really well with ChatGPT.
Interesting ideas:
Add user based memory to your plugin. (auth and good vectorstore is
needed)
Add smart GPT4 based chains to your plugin ( time limit :( )
15. TAKEAWAYS
Can you observe the the thought reactions?
When it awaits for input it wants to use you as a tool.
Force trigger tools
Using continue keyword
Chain is derived from a dynamic state machine and it's endless
You were part of the chain in ChatGPT and starting prompt
Langchain is limited to two programming languages and limited platforms
Build your own langchain. Good to grasp the concept.
Amazing applications on top of langchain.
Your language model can run structured or unstructured other models so
that when right tools provided it can achieve anything.
Personal opinion: Customised prompt templates and chains is better than
using a framework, performance is limited with 30 seconds on the internet
as sockets are short lived but LLM needs more chaining and time to
execute sometimes.
Using 10 tools at the same time is possible but not over the Web.
Unstructured models should become structured and time-framed.
16. TAKEAWAYS
Point 1: It's important to leverage human tool correctly. Users should be
aware of using keywords and triggering specific tools when needed.
ChatGPT is actually half-GPT, when you increase human UX and usability
and prompt knowledge ChatGPT performs better.
Human Tool can be enhanced to pass beyond the limits of chatgpt :
Zero-shot, one-shot, few-shots learning concepts
Tool triggering
Usage of human memory to pass the context from one chat to another
Transfer Learning
Usage of Vector Databases for local memory, manual usage of vector
databases to enhance human input.
Naming entities and giving example to concepts.
Forcing ChatGPT to review and rate itself. Although it sounds harsh to a
human it makes chatGPT go beyond it's initial reasoning and pushes
forward.
User should know more about underlying agents: should I use the one that
does constant self-critique or should I use the one that hallucinates a bit
more.
Time limits are the bottleneck of AI systems
You need constant smart summarisation, divide n conquer techniques to
overcome the issues.
17. TAKEAWAYS
Specialized custom solutions work best; I suggest going bottom-up on
expert systems, but structuring and abstracting things may not be
beneficial.
Langchain is abstraction and it leaks a lot; leakages cause time and money
limits to hit early, and tasks usually either fail or require many iterations.
You don't have to overoptimize on prompt engineering if LLM is clever.
GPT4 learned how to apply ReAct framework with just a google search
plugin.
We need more clever LLMs.
Good applications are very rare.
