You will get I will build a RAG chatbot with LangChain, FAISS, and FastAPI


Project details
I build custom RAG (Retrieval-Augmented Generation) chatbots that let your users ask questions and get accurate, source-cited answers from your own documents — not from generic AI training data. Using LangChain, FAISS, and FastAPI, I deliver a fully working pipeline from document ingestion to API deployment. I have built and shipped this stack in real projects (see portfolio). You get clean, documented source code and a tested REST endpoint ready to integrate into any app. No hallucinations — every answer is grounded in your content.
AI Algorithms
Convolutional Neural Network, Generative Adversarial Network, Large Language Model, Transformer Model, YOLOAI Applications
AI Chatbot, AI-Generated Code, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Gradio, Hugging FaceAI Models
BERT, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$150
|
Standard
$250
|
Advanced
$450
|
|---|---|---|---|
| Delivery Time | 1 day | 7 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | |
Model Documentation | - | ||
Model Monitoring | - | - | - |
Model Testing & Optimization | - | ||
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | ||
Setup File | |||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$30 - $35
Additional Revision
+$20Frequently asked questions
About Mohanned
Machine Learning Engineer | Python | PyTorch | Computer Vision | NLP
Zeramedine, Tunisia - 3:31 am local time
Recent work includes a full RAG system combining FAISS vector search with LLM inference via FastAPI, a GAN-based 3D shape completion pipeline trained on ShapeNet (Bachelor's final project, Distinction grade), and a real-time tennis analytics system using YOLOv8 and OpenCV.
What I deliver:
→ Custom ML models (classification, object detection, NLP, generative AI)
→ LLM integration and RAG pipelines (LangChain, Hugging Face, FAISS)
→ REST API backends with FastAPI + PostgreSQL
→ Full-stack AI apps (React frontend + cloud deployment on Vercel/Render)
→ Computer vision systems with YOLO, OpenCV, and CNNs
Certified by NVIDIA Deep Learning Institute (Deep Learning Fundamentals) and Hugging Face (LLM Fundamentals). Fluent in English, French, and Arabic — available 30+ hrs/week.
Let's discuss your project.
Steps for completing your project
After purchasing the project, send requirements so Mohanned can start the project.
Delivery time starts when Mohanned receives requirements from you.
Mohanned works on your project following the steps below.
Revisions may occur after the delivery date.
You share your documents and answer the requirement questions
I process and index your documents into a FAISS vector store