You will get a custom Hybrid RAG Document QA Chatbot using FastAPI and FAISS
Project details
I build end-to-end AI systems that let organizations search, query, and extract insights from large document repositories using Hybrid Graph RAG (Retrieval-Augmented Generation). My pipeline combines three retrieval layers — dense vector search (FAISS + Sentence-Transformers), knowledge graph traversal (SpaCy + NetworkX), and topic-based routing (LDA) — into a single grounded, hallucination-resistant system. The result is a production-ready platform with a FastAPI backend, React web UI, multi-user auth, conversation memory, semantic caching, and an admin analytics dashboard. The system works fully offline for data-sensitive environments and has been evaluated on bilingual corpora, achieving a 92.5% end-to-end success rate with zero hallucinations across all test runs.
AI Algorithms
Large Language Model, Linear Discriminant Analysis, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Mobile App Development, AI-Enhanced Classification, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, PyTorch, Streamlit, Word2vecAI Models
BERT, ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$100
|
Standard
$350
|
Advanced
$800
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 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
+$100
Additional Revision
+$30Frequently asked questions
About Anwar
AI Engineer | RAG & LLMs | Flutter & FastAPI Developer
Ksibet el Mediouni, Tunisia - 4:09 pm local time
problems through Artificial Intelligence, Natural Language
Processing (NLP), and robust software architecture.During my
studies and professional projects, I developed a deep expertise
in building intelligent systems, ranging from a Hybrid Graph RAG
platform for the Central Bank of Tunisia (BCT) to a MobileNetV2-
based computer vision tool for agricultural disease detection (96%
accuracy).Alongside my AI background, I have hands-on experience
in mobile development, having architected and delivered responsive,
multilingual frontend interfaces using Flutter and Firebase for
commercial applications.- Core Technologies & Skills: * AI
& Machine Learning: Python, TensorFlow, PyTorch, Scikitlearn,
Pandas, FAISS * NLP & Search: RAG, Graph RAG, LDA,
Word2Vec, SpaCy, Sentence-Transformers, Knowledge Graphs
* Software Engineering: FastAPI, React, Flutter, REST APIs, Git,
Steps for completing your project
After purchasing the project, send requirements so Anwar can start the project.
Delivery time starts when Anwar receives requirements from you.
Anwar works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Architecture Planning
Analyze document structures, define optimal text-chunking strategies, select vector/graph database parameters, and design the FastAPI architecture.
RAG Pipeline & Backend Implementation
Build embedding pipelines, implement hybrid retrieval logic (Vector + Knowledge Graph), set up prompt templates, and expose robust REST API endpoints.