Building a RAG API with FastAPI

2026-03-02 · Source: Analytics Vidhya · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

This article details the construction and deployment of a Retrieval-Augmented Generation (RAG) system using FastAPI, enabling users to query PDF and .txt documents. The system leverages FastAPI for API creation, LangChain for LLM capabilities, FAISS for vector storage, and Uvicorn for hosting. It utilizes OpenAI's gpt-4.1-mini model for generation and text-embedding-3-small for embeddings. The implementation includes two primary FastAPI endpoints: `/ingest` for uploading and indexing documents into a FAISS vector store, and `/query` for retrieving relevant text chunks and generating answers using the LLM. The process involves document loading, recursive character splitting into 500-character chunks, embedding, and local storage of the FAISS index. The article also covers setting up a Python virtual environment, installing dependencies like `fastapi==0.129.0` and `langchain==1.2.10`, and testing the API endpoints via Swagger UI.

Key takeaway

For AI Engineers deploying GenAI systems, this guide provides a concrete blueprint for building a RAG-powered API. You should consider FastAPI for its ease of deployment and auto-generated documentation, which streamlines testing and integration. Implementing local FAISS storage ensures data persistence, a critical factor for production systems. Your team can adapt this architecture to create robust, searchable knowledge bases from unstructured data.

Key insights

FastAPI enables efficient deployment of RAG systems, providing API access for document ingestion and AI-powered querying.

Principles

• RAG enhances LLMs with external knowledge.
• FastAPI auto-generates API documentation.
• Vector databases store document embeddings.

Method

Build a RAG system by defining `/ingest` and `/query` FastAPI endpoints. Ingest documents by chunking, embedding, and storing in FAISS. Query by vectorizing the question, retrieving top-k similar chunks, and passing to an LLM for generation.

In practice

• Use `RecursiveCharacterTextSplitter` for document chunking.
• Employ `FAISS` for local vector store persistence.
• Implement `Pydantic` for API request validation.

Topics

• Retrieval-Augmented Generation
• FastAPI
• LangChain
• FAISS
• LLM Deployment

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Analytics Vidhya.