About Me
I am a 4-time AI Hackathon winner. I've worked with Large Language Models, Stable Diffusion and Blockchain to create solutions for real world problems. I would like to propose LangChain as a tool.
Introduction
ChatGPT has been a hot topic for the year. It has amazing capabilities in writing high quality content and mimicking a highly intelligent bot with vast amounts of knowledge of various topics.
Some people thought it was sentient but OpenAI (the developers of ChatGPT) made absolutely sure that people knew that it was not.
Instead, it is a simply a Large Language Model (LLM) specialized for text generation. But what is a Large Language Model?
What is a Large Language Model
A Language Model is trained to understand patterns in text. There are two main capabilities offered by Language models:
- Embedding models convert text to a numerical vector representing the semantic meaning of the text
- Generating models predict the next most likely token (or word), given the previous sequence of words
These models may be finetuned to handle cases in specific domains but there are still various limitations to them. For example, OpenAI shares some limitations on ChatGPT:
- It may occasionally generate incorrect information
- It may occasionally produce harmful instructions or biased content
- It has limited knowledge of world and events after 2021
There is more sub context about those limitations, but instead let us discuss a tool to address some of those limitations. The tool is called LangChain.
What is LangChain
LangChain is an interface for connecting LLM services, knowledge services and database services. Some key features of the tool include:
- chaining resources together for problems that require multiple services. For example: getting a latest stock price and calculating it's relative strength against other stocks. This requires Google Search and Wolfram Alpha for math calculations.
- using agents to decide what services to use. For example: getting the latest hit singles. An agent may decide to use the Wikipedia service, Google Search or even a local database.
The tool is based on the idea of chain-of-thought problem solving. The Agent is constantly asking "What do I need to do next to solve x problem?" This make it very easy to debug because you can observe what the model is thinking and what choices it is making.
The tool is completely open source and has a large community for development. The codebase is active and consistently building new features.
Why it would be a great tool for a Hackathon
LangChain is still fairly new (less than 4 months old) but the capabilities are advanced for any complex NLP challenge that requires knowledge from multiple services.
I highly advise checking out the gallery to see some example projects built with LangChain.
Reading the LangChain Ecosystem is a great way to get up to date with the latest features and capabilities.
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