# How I Built a PDF Chat API in One Day with FastAPI, Gemini, and Qdrant > Source: > Published: 2026-09-11 08:07:01+00:00 Have you ever wanted to just *talk* to a PDF instead of reading through 50 pages? I built a full PDF Chat API in one day — upload any PDF, ask questions in natural language, and get AI-powered answers. Here's how I did it. ## What it does - Upload any PDF document - Ask questions about its content in natural language - Get accurate answers powered by RAG (Retrieval Augmented Generation) - Clean web UI included — no frontend framework needed - REST API with authentication for easy integration ## Tech Stack - **FastAPI** — REST API backend - **Google Gemini** — embeddings (`gemini-embedding-001` ) + chat (`gemini-2.5-flash` ) - **Qdrant** — vector database for semantic search - **LangChain** — RAG pipeline orchestration - **Pure HTML/CSS** — no React, no framework ## How it works The architecture is classic RAG in two phases: **Ingestion (upload):** 1. Extract text from PDF 2. Split into chunks (1000 chars, 200 overlap) 3. Generate embeddings with Gemini 4. Store in Qdrant **Query (chat):** 1. Embed the user's question 2. Search Qdrant for the 4 most relevant chunks 3. Send chunks + question to Gemini 4. Return the answer ## The code The core is surprisingly simple: That's the entire RAG chain — retrieve relevant context, inject into prompt, generate answer. ## What I learned - Gemini embeddings produce 3072-dimensional vectors (not 768 like older models) - `grpcio` on Windows can be a pain — pin to version 1.62.2 - Qdrant Cloud free tier is genuinely useful for side projects - Building a clean UI in pure HTML/CSS takes less time than setting up React ## Try it yourself 👉 [https://elyassdigital.gumroad.com/l/zcgjmmz](https://elyassdigital.gumroad.com/l/zcgjmmz) ## What's next - Multi-user support with separate collections per user - Docker deployment guide - Support for other document types (Word, Excel) Built this as a side project — happy to answer questions in the comments!