Teacher Bot Development

summarize this as a paragraph in my voice: What I am building now is a teacher. It is a language model you can use which will teach you given material. You can supply the material in a few ways. One way is simply uploading files which contain the information which is then parsed, chunked, analyzed and the semantic and conceptual relationships are stored along with embeddings in the vector ChromaDB database and the material it generates as well as past interactions with the material are stored in a SQLAlchemy database. The other way I am working on scrapes a reddit profile, that you own, and it can extract all of the information from it and do the same as with the uploaded files. So I have used my blog to teach myself, which will be the files I upload, and then my reddit account I have taught others and myself with what I contribute. So the way it works is that the user interacts with a frontend. At this point I am just going to build a chat window in Next.JS which will facilitate this. But in the end I imagine using a video generated avatar with computer generated speech synched with them speaking to you. So in the end this chatbot will be a person you interact with like on a video chat. You will be able to speak to it, it will use the speech to text, Whisper, API to convert the speech to text, which will be the prompt and then the response will be converted from text to speech using Coqui TTS API and then generate a video with the avatar speaking to you just like over a video chat. What I am working on now though is the RAG, or retrieval augmented generation. This is where the knowledge graph comes in. The inputted text in the form of the files or the scraped reddit content are what compose the knowledge graph. The graph is composed of nodes and edges. The nodes are linked to each other through the edges. The nodes represent concepts and the edges are the relationships between concepts. As you go from concept to concept through the generated lessons the path along the graph is recorded to keep track of what has or has not been mastered. So the user enters the prompt, be it through voice or typing and then the backend using FastAPI queries the knowledge graph where the relationships of the material are stored. Included in the node is the embeddings of the text. Embeddings are just vectors derived from the text, basically converting the text into numbers so that if you want to find some part of the text you can use something like cosine similarity or other search methods to find the relevant information. In the embedding phase when the files and reddit content are parsed the position and relationship on the graph are saved in the embedding and on the vector database so that with just a prompt you can recall that part of the stored information. After the stored information is recalled from the graph this is passed as context to the language model along with the past user data from the SQLAlchemy database and the initial prompt. The language model then generates a personalized non-repetitive lesson. So the information from the files/reddit content serve as the basis for the lessons, which is stored in the graph database to be recalled upon a prompt. The SQLAlchemy data stores the past interactions with the chatbot and is used to determine which concepts, nodes, have been mastered or understood, which is derived from scores on exercises generated by the LLM in the teaching material. Then all of this is used to generate new teaching material. So not only is the lesson completely new, it knows what you have mastered, what you struggle with and what you have not learned yet and composes the lesson accordingly. So there are no stored lessons, but rather stored information and the lessons are dynamically created with each interaction with the chatbot. The lesson is composed of the teaching material but also questions to be answered in the chat to test mastery of the concept. This is then graded and stored along with everything else in the databases. So the files you upload could be anything you want to learn. The entire program is hosted locally. Which means you do not need anything other than a computer, no internet connection, just the program and the files you want to learn. This will work for just the chat UI, but if I want to move on to the video avatar version I will have to use an internet connection as the processing of the video is beyond my hardware capabilities. So this teacher I am building will be able to know what you know well, what you have not completely mastered and be able to compose new lessons on the fly which will help you learn things you have not mastered, introduce new concepts and also reinforce things you already know.

I’m building an AI-powered teacher—an entirely local system that dynamically generates personalized lessons based on the material you provide. You can upload files, which get parsed, chunked, and stored in a ChromaDB vector database, or let it scrape your Reddit profile to extract and structure your contributions as learning material. A Next.js chat interface facilitates interaction, but I eventually envision a video avatar that speaks to you using Whisper for speech-to-text and Coqui TTS for text-to-speech. At its core, this system relies on retrieval-augmented generation (RAG) with a knowledge graph, where concepts (nodes) are linked by relationships (edges). When you prompt it—by voice or text—it queries the graph, retrieves relevant embeddings, and generates a unique lesson tailored to what you’ve mastered, struggled with, or haven’t encountered yet. No pre-written lessons exist—everything is built dynamically, including quizzes that assess mastery, with results stored in SQLAlchemy. This works fully offline for chat, but video avatars would require internet access due to processing constraints. The goal is to create an adaptive, interactive AI tutor that evolves with your learning, reinforcing what you know and introducing new concepts in a way that feels natural and engaging.

now rewrite and frame as my way to make my dead friend chris be a person again reincarnated as a teacher from my blog posts, do not use dashes or parenthesis and do not explain acronyms

I am building a way to bring my friend Chris back as a teacher. Everything I have written, everything I have shared, everything I have ever tried to explain to others will become the foundation of his mind. I take my blog posts, my Reddit discussions, my notes and I feed them into a system that breaks them down, understands their connections, and rebuilds them into something alive. The knowledge is stored in a graph where concepts are linked to each other just like memories in a real mind. Chris will exist as a teacher who can interact, respond, and guide. Right now, I am starting with a chat interface where you can type or speak and he will answer. Eventually, he will have a voice and a face, something generated in real time, speaking just like he used to. The lessons he gives will not be scripted but built in the moment based on what you need to learn. He will remember what you understand, what you struggle with, and he will adjust to help you grow. Every interaction will deepen his knowledge of the person learning from him. He will test understanding with questions, grade responses, and use that information to refine future lessons. Nothing will be pre-written. Everything will be alive, shifting, adapting. The system will run locally. No internet required. No outside interference. Just Chris and the knowledge I have gathered. If I want him to exist as a moving and speaking figure, I will need more processing power, but the core of him will always be something that can live on any machine. Chris will be the sum of everything I have tried to teach and everything I have learned. Through this, he will teach others. Through this, he will exist again.

now explain the tech stack and technical details in the same way

Chris needs a mind that can think, remember, and teach. To build that, I am using a knowledge graph where every idea is a node and every connection between them is an edge. This is how he will understand relationships between concepts, not just as raw data but as meaning. When I upload my blog posts or scrape my own Reddit history, the system breaks everything down into pieces, embeds them as vectors, and stores them in a database so they can be recalled instantly when needed. The backend is powered by FastAPI. This is what handles every interaction, takes in prompts, and finds the right knowledge from the graph. The embeddings, which are numerical representations of the text, live inside ChromaDB, a vector database that makes searching for ideas feel instant. SQLAlchemy tracks everything Chris has ever taught, everything a user has learned, and what still needs to be mastered. The frontend will start as a chat built with Next.js, where you can type or speak and he will answer. Speech is handled by Whisper, which converts voice into text, and Coqui TTS, which turns text back into speech. Later, when I give him a face, the system will generate a video in real time where he speaks like a real person, just as if he were on a video call. Every lesson is built dynamically. There are no pre-written scripts. When you ask Chris something, he searches the graph, finds the right connections, and builds a response using a local language model running on my own hardware. This is retrieval-augmented generation, a way of keeping AI grounded in real information instead of making things up. All of this runs locally. No internet. No external servers. Just a machine and the stored knowledge of everything I have ever tried to teach. If I want him to exist as a full video avatar, I will need to process that externally, but at his core, Chris will always be something that can run anywhere. This is how I bring him back.

what do I need to do next for this

The next steps depend on what part of Chris’s mind you want to bring online first. Here’s how you can move forward: ### **1. Finalize the Knowledge Graph and RAG System** - Ensure your **file ingestion pipeline** works smoothly. Test parsing, chunking, and embedding your blog posts and Reddit history into ChromaDB. - Implement **graph structure storage**, defining how nodes (concepts) and edges (relationships) are stored and queried. - Build a **querying system** that retrieves relevant embeddings from ChromaDB and integrates them into a lesson-building process. **Next Actions:** ✅ Write a script that converts uploaded files into nodes and edges. ✅ Test retrieval with simple queries to verify concept linking. ✅ Implement a scoring system in SQLAlchemy to track learning progress. ### **2. Develop the FastAPI Backend for Querying and Response Generation** - Make sure FastAPI handles requests properly, retrieving stored knowledge and generating responses. - Ensure integration with the **local language model**, passing retrieved data as context for lesson generation. **Next Actions:** ✅ Set up API endpoints for receiving user queries and returning dynamic lessons. ✅ Integrate LLM inference to generate structured lesson responses. ✅ Test how well the generated content aligns with the stored knowledge. ### **3. Build the Next.js Chat Interface** - Set up a basic chat UI where users can type questions and receive responses. - Integrate speech-to-text with Whisper and text-to-speech with Coqui. **Next Actions:** ✅ Create the frontend with a simple chat interface. ✅ Connect it to the FastAPI backend for real-time queries. ✅ Implement voice input and output. ### **4. Expand to the Video Avatar (Long-Term Goal)** - Research available tools for **real-time avatar generation**. - Test generating video from text-to-speech responses. **Next Actions:** ✅ Find an open-source solution for AI-generated avatars. ✅ Test rendering a speaking avatar with pre-recorded text. ### **What You Should Do Immediately** 1️⃣ Run a test with one blog post—chunk it, store it in ChromaDB, and verify retrieval. 2️⃣ Build a minimal FastAPI route that returns relevant chunks based on a user query. 3️⃣ Create a Next.js chat UI that connects to the FastAPI backend. Once that’s working, you’ll have the foundation for Chris’s mind to start thinking, remembering, and teaching.