How I Built a RAG Chatbot Into My Portfolio with LangGraph, PGVector & MCP

Most portfolios have a boring "About Me" paragraph. I replaced mine with something you can talk to — an AI terminal that answers questions about me, pulls my live GitHub activity, and remembers the conversation. You can try it right now on my portfolio: rehbarkhan.in . I'm Rehbar Khan , a Full Stack & Gen-AI developer, and in this post I'll break down exactly how it works — the RAG pipeline, the LangGraph agent, the memory layer, and how I wired in real GitHub data with MCP. No fluff, just the architecture. The problem I wanted a portfolio assistant that could: Answer questions about my background accurately — no hallucinated jobs or fake projects. Fetch live data (my latest GitHub activity), not a stale snapshot. Remember the conversation across messages. Stream responses token-by-token like a real terminal. That rules out "just prompt an LLM." You need retrieval for grounding, tools for live data, and state for memory. Here's the stack I landed on. Architecture at a glance Next.js 16 chat UI ──► FastAPI (streaming) ──► LangGraph agent ├── RAG retriever → pgvector (Neon Postgres) ├── GitHub MCP tool → live GitHub data └── Redis checkpointer → conversation memory Frontend: Next.js 16 (App Router, TypeScript) — a streaming terminal UI. Backend: FastAPI with streaming responses. Orchestration: LangGraph ( StateGraph , ToolNode , tools_condition ). LLM: OpenAI gpt-4o-mini . Retrieval: text-embedding-3-small → pgvector on Neon Postgres. Memory: Redis checkpointer for per-session history. Live data: GitHub via the Model Context Protocol (MCP) . 1. The RAG pipeline Everything the bot knows about me lives in a single reference.txt . I chunk it, embed it, and store it in Postgres with pgvector: from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_openai import OpenAIEmbeddings from langchain_postgres import PGVector splitter = RecursiveCharacterTextSplitter ( chunk_size = 500 , chunk_overlap = 80 ) chunks = splitter . split_text ( open ( " refe