AI with TypeScript: Deno, Llama, Jupyter

The Dino 🦕, the Llama 🦙, and the Whale 🐋

• An environment for our language model – while you can connect up to various LLM hosting environments via APIs, we are going to leverage the Ollama framework for running language models on your local machine.
• A large language model – we will use a resized version of DeepSeek R1 that can run locally.
• A notebook – Jupyter Notebook for interactive code and text.
• Deno – a runtime that includes a built-in Jupyter kernel. We assume a recent version is installed.
• An IDE – we’ll use VSCode with built-in Jupyter Notebook support and the Deno extension (extension link).
• An AI library/framework – LangChain.js to simplify interactions with the LLM.
• A schema validator – we’ll structure LLM output. We will use zod for this.

Build a custom RAG AI agent in TypeScript and Jupyter

• Retrieve and prepare several blog posts to be used by our AI agent.
• Create an AI agent which has several tools:
• A tool to query the blog posts in the database.
• A tool to grade if the documents are relevant to the query.
• The ability to rewrite and improve the query if required.
• Finally we generate a response to the query based on our collection of information.