Building a Production-Grade AI Platform from Scratch
The author documents the process of building a production-ready AI platform called the GenAI DocQA Platform, which allows users to upload documents and ask complex natural language questions. The platform is built entirely from scratch using various technologies like FastAPI, LangGraph, pgvector, and more.
Why it matters
This project demonstrates a comprehensive approach to building a production-ready AI platform from scratch, which can be valuable for developers and AI enthusiasts.
Key Points
- 1Building a production-grade AI platform from scratch, no copy-paste or shortcuts
- 2The GenAI DocQA Platform allows users to upload documents and ask complex questions
- 3The platform is built using a full stack of technologies like FastAPI, LangGraph, pgvector, and more
- 4The project is divided into 13 phases, with this article covering the initial project scaffold
- 5The author's philosophy is to have a justified reason for every file and decision
Details
The author decided to build a production-grade AI platform called the GenAI DocQA Platform from scratch, without relying on tutorials or copy-pasting code. The platform allows users to upload documents (PDF, DOCX, CSV, PPTX) and ask complex natural language questions. A 10-node LangGraph agent retrieves relevant chunks, reasons over them, self-corrects, and streams sourced answers back to the user. The full stack includes technologies like FastAPI, async SQLAlchemy, LangGraph, pgvector, BM25 hybrid search, Cohere reranking, Groq/OpenAI/Anthropic LLMs, Sentence-Transformers, Redis, PostgreSQL, and more. The project is divided into 13 phases, with this article covering the initial project scaffold. The author's philosophy is to have a justified reason for every file and decision, which forces architectural clarity and adaptability.
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