# I Built a RAG Document Assistant with FastAPI, React, FAISS and Ollama

Abdraouf Benabdsselem — Fri, 11 Sep 2026 23:29:08 +0000

I Built a RAG Document Assistant with FastAPI, React, FAISS and Ollama

I've been learning more about Retrieval-Augmented Generation (RAG), and I wanted to understand what actually happens inside a RAG application.

So instead of building another simple chatbot, I decided to build a complete application around documents.

The result is CloudRAG:

https://github.com/abderaoufsec/CloudRAG

What does it do?

The idea is straightforward.

You upload documents, then ask questions about them.

Instead of sending the question directly to the LLM, the application first searches the uploaded documents for relevant information. That information is then given to the LLM as context.

The simplified flow looks like this:

Document
   ↓
Text extraction
   ↓
Chunking
   ↓
Embeddings
   ↓
FAISS
   ↓
Relevant chunks
   ↓
Ollama
   ↓
Answer + sources

The stack

For the backend I used:

Python
FastAPI
SQLite
SQLAlchemy

For the RAG pipeline:

SentenceTransformers
FAISS
Ollama

For the frontend:

React
Vite

I also used Docker and Pytest during development.

Why FAISS?

I wanted the project to work locally without depending on a paid external service.

FAISS makes it possible to store and search the embeddings locally, which made it a good fit for the current version of the project.

What I learned

The biggest thing I learned is that RAG isn't simply:

"Send documents to an LLM."

There are several steps that affect the final result.

How you split documents matters.

How you generate embeddings matters.

How you retrieve relevant chunks matters.

And you need a way to evaluate whether your retrieval is actually useful.

That's why I also added retrieval evaluation and testing to the project.

What about Qdrant?

I also experimented with Qdrant Cloud and added an integration for it.

However, I wasn't able to successfully deploy and validate the Qdrant part.

So the current working implementation uses FAISS locally.

I'd like to come back to the Qdrant deployment later and compare it with the local FAISS setup.

Final thoughts

This project was mainly a learning exercise, but it gave me a much better understanding of what goes into building a RAG application.

I now have a better understanding of:

document processing
chunking
embeddings
vector search
retrieval
local LLMs
API design
RAG evaluation

The source code is available here:

https://github.com/abderaoufsec/CloudRAG

I'd be happy to hear feedback, especially from people who have worked on improving RAG retrieval quality.

Building My CCNA Packet Tracer Lab Collection

Abdraouf Benabdsselem — Tue, 08 Sep 2026 08:50:07 +0000

Building My CCNA Packet Tracer Lab Collection

I’ve started organizing my networking practice into a public GitHub repository:

CCNA Packet Tracer Labs

Why I created this repository

Networking concepts become easier to understand when they are tested through practical configurations and troubleshooting.

Instead of keeping every exercise only on my computer, I’m organizing my Packet Tracer labs in one place so I can:

Practice CCNA networking concepts
Review configurations later
Track my progress
Document troubleshooting steps
Share useful exercises with other learners

What the repository contains

The repository is a growing collection of Packet Tracer practice labs.

Each lab may include a topology, configuration work, verification steps, and notes about the concepts being practiced.

I’ll continue improving the organization and adding more exercises as I progress.

My learning approach

For each lab, I try to follow this process:

Understand the networking concept
Build or inspect the topology
Configure the devices
Verify connectivity
Investigate errors
Document what worked and what I learned

What I plan to add

Future updates may include more routing and switching exercises, troubleshooting scenarios, configuration notes, and explanations of the commands used.

Feedback

If you are also learning networking or preparing for CCNA, I would be interested in hearing which lab topics you found most useful.

Repository: CCNA Packet Tracer Labs

DEV Community: Abdraouf Benabdsselem

# I Built a RAG Document Assistant with FastAPI, React, FAISS and Ollama

I Built a RAG Document Assistant with FastAPI, React, FAISS and Ollama

What does it do?

The stack

Why FAISS?

What I learned

What about Qdrant?

Final thoughts

Building My CCNA Packet Tracer Lab Collection

Building My CCNA Packet Tracer Lab Collection

Why I created this repository

What the repository contains

My learning approach

What I plan to add

Feedback