Description

This is a web application chat-bot that is taking on my identity

Accessing the Bot

1. The bot can be accessed via https://intro-ai-jaredpek.vercel.app here
   • This is the chat page for my personal chat-bot
     • It leverages retrieval augmented generation to embed relevant information to any prompt that is received
     • The architecture diagram for the customised retrieval augmented generation pipeline can be found inside the /gai_application/diagram directory here
   • Feel free to ask any questions regarding my profile!
2. The source code for this bot and implementation details can be found inside the /gai_application/src directory here

Tech Stack

1. Framework
   • Next.js
     • Easy and quickly develop both front and back-ends of the application
     • Allows server-side operations and rendering
2. Database
   • MongoDB noSQL database
   • MongoDB vector search index
3. GAI Model
   • Gemini 1.5 Flash (API)
4. Deployment
   • Vercel (AWS)
     • Easy and convenient deployment
       • In-built CI/CD pipeline
       • Free domain name + SSL certification

Approach

Pre-Processing

1. Prepare my profile in markdown format in a .txt file
2. Use langchain's markdown text splitter to split my profile text file into chunks
   • Markdown ensures that chunks are labelled with a specific "category"
   • Preserves the content within each section and prevents content from being mixed with other sections during semantic and vector search
3. Create vector embeddings for each chunk using Google's generative AI text embedding model and save them onto a MongoDB collection
4. Create a vector search index based on the vector embeddings that were saved to the collecion
   • Uses euclidean distance to determine the similarity between the prompt and the chunks

Generation and Operation

1. Users send a message to the bot, which makes an API request to the /api/chat endpoint
2. The chat is first initialised with an initial system prompt
   • Instructions on how the model should respond and the role that it should undertake
   • Vector search is conducted for prompt on the relevant chunks via the MongoDB vector search index and langchain, which allows us to obtain relevant chunks of information from the database
3. Send the initial prompt, together with the user chat history and the newly received message to the Gemini API, and generate a response from the LLM
4. Send the response back to the user