How I Built a RAG System with FastAPI

Introduction

Retrieval-Augmented Generation (RAG) has become the go-to approach for grounding Large Language Models in domain-specific knowledge. In this post, I'll walk through how I built a production-ready RAG API using FastAPI.

The Architecture

The system consists of three main components:

1. Document Ingestion Pipeline — Handles PDF, DOCX, and plain text files
2. Vector Store — Uses FAISS for efficient similarity search
3. Generation Layer — Integrates with OpenAI's GPT models

┌──────────┐    ┌───────────┐    ┌──────────┐    ┌───────────┐
│ Documents│───→│ Chunking  │───→│ Embeddings│───→│ FAISS     │
│ (Upload) │    │ (Semantic)│    │ (OpenAI)  │    │ (Vector DB)│
└──────────┘    └───────────┘    └──────────┘    └───────────┘
                                                       │
┌──────────┐    ┌───────────┐    ┌──────────┐          │
│ Response │←───│ LLM       │←───│ Re-ranker│←─────────┘
│ (JSON)   │    │ (GPT-4)   │    │ (Cross)  │
└──────────┘    └───────────┘    └──────────┘

Chunking Strategy

One of the most critical decisions in RAG is how to chunk your documents. I experimented with:

• Fixed-size chunks — 500 tokens with 50-token overlap
• Semantic chunks — Using sentence boundaries
• Recursive chunking — Hierarchical splitting

Semantic chunking with sentence boundaries showed the best retrieval quality.

Quick Start

# Clone and setup
git clone https://github.com/danishsyed-dev/RAG-API.git
cd RAG-API

# Install dependencies
pip install -r requirements.txt

# Set your API key
export OPENAI_API_KEY=your_key_here

# Run the server
uvicorn app.main:app --reload

Key Learnings

1. Re-ranking retrieved chunks significantly improves answer quality
2. Async processing is essential for production workloads
3. Caching embeddings reduces latency by 60%

Conclusion

Building a RAG system requires careful attention to each component in the pipeline. The choices you make in chunking and retrieval directly impact the quality of generated responses.