One of the challenges in working with an LLM API, like OpenAI’s is that you have to limit the request and the response to a certain number of tokens. This limits the amount of context you can work with. One of the most popular solutions to work with larger amounts of data is a python and Typescript library called LangChain. LangChain is a framework for apps powered by large langage models.

LangChain works with a number of additional models and APIs including Hugging Face. This is the documentation for LangChain in JavaScript.

Langchain chains together document loaders, calls to different LLMs to create more complex prompts. These lower-level abstractions are useful on their own and can also be combined to create agents capable of performing certain tasks.

I spent some time with Langchain earlier this year, which was a fun way to learn more Python. But if you’re wondering why I haven’t updated my AI writing a plugin in a while, it’s because I tried to rewrite the backend in Python. That’s a challenging side project, especially because I only sort of know Python.

So instead, I started using the Typescript version. This post, which I will update as I learn more, is my list of links for developing with LangChain and Typescript.

Template Repo For LangChain

I made a template Github repo for LangChain development with Typescript: https://github.com/imaginarymachines/langchainjs-template

Helpful Langchian TypeScript Examples

• Example of using LangChain to load a PDF, create embeddings, and then ask the PDF questions.
  • https://github.com/bdcorps/langchain-pdf-qa
• Create a Typescript CLI with Langchain
  • https://langchainers.hashnode.dev/getting-started-with-langchainjs
• Connect Langchain to a MySQL database and ask it questions.
  • https://www.langchainers.com/integrate-llms-using-mysql-and-langchain/

Featured image is CCO licensed photo by me via the WordPress Photo Directory.