And now it’s time to pivot toward the “Learning LangChain” topic…
The most fun introduction to LangChain seems to be from DeepLearning.ai with Andrew Ng and Harrison Chase [1]. You can expect to spend a couple of hours to complete the process of watching the videos and absorbing the content. Make sure you use a browser window large enough to support both the jupyter notebook and the video. You are probably going to want these items to run side by side. This course covers models, prompts, parsers, memory, chains, and agents. The part of this learning package that I was the most interested in learning more about was how people are using agents and of course what sort of plugins could that yield as use cases in the generative AI space. Going forward I think agency will be the defining characteristic of the great generative AI adventure. These applications are going to do things for you and some of those use cases are going to be extremely powerful.
After that course I wanted to dig in more and decided to go ahead and learn everything I could from the LangChain AI Handbook [2]. This handbook has 6 or 7 chapters depending on how you count things. My favorite part about this learning build is that they are using Colab notebooks for hands-on development during the course of the learning adventure. That is awesome and really lets you get going quickly. A side quest spawned out of that handbook learning which involved starting to use Pinecone in general which was interesting. You can do a lot with the Pinecone including building AI agents and chatbots.
I’m going to spend some time working on the udemy course “Develop LLM powered applications with LangChain” later this weekend [3]. You can also find a ton of useful information within the documentation for LangChain including a lot of content about agents [4].
You might now be wondering what alternatives to LangChain exist… I started looking around at AutoChain [5], Auto-GPT [6], AgentGPT [7], BabyAGI [8], LangDock [9], GradientJ [10], Flowise AI [11], and LlamaIndex [12]. Maybe you could also consider TensorFlow to be an alternative. You can tell from the combination of companies and frameworks being built out here a lot of attention is on the space between LLMs and taking action. Getting to the point of agency or taking action is where these spaces are gaining and maintaining value.
Footnotes:
[1] https://learn.deeplearning.ai/langchain/lesson/1/introduction
[2] https://www.pinecone.io/learn/series/langchain/
[3] https://www.udemy.com/course/langchain/
[4] https://python.langchain.com/docs/modules/agents/
[5] https://github.com/Forethought-Technologies/AutoChain
[6] https://github.com/Significant-Gravitas/Auto-GPT
[7] https://github.com/reworkd/AgentGPT
[8] https://github.com/miurla/babyagi-ui
[12] https://www.llamaindex.ai/
What’s next for The Lindahl Letter?
Week 143: Social media analysis
Week 144: Knowledge graphs vs. vector databases
Week 145: Delphi method & Door-to-door canvassing
Week 146: Election simulations & Expert opinions
Week 147: Bayesian Models
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Learning LangChain