You will get I will build or integrate a RAG AI chatbot into your application
Project details
I will build a custom RAG AI chatbot that can answer questions using your own documents, files, or business data.
Instead of relying only on an AI model's general knowledge, your chatbot will retrieve relevant information from your data before generating an answer. This helps produce more accurate, context-aware responses.
Depending on your package, I can build a standalone prototype or integrate the chatbot into your existing application using Python, FastAPI, LangChain, and a suitable vector database.
I can help with document ingestion, retrieval setup, prompt engineering, API development, database integration, source-aware responses, testing, and deployment.
This solution is ideal for internal knowledge assistants, document Q&A, customer support, SaaS applications, company knowledge bases, and other AI-powered tools.
You will receive clean source code, setup instructions, and a solution built around your specific use case.
Instead of relying only on an AI model's general knowledge, your chatbot will retrieve relevant information from your data before generating an answer. This helps produce more accurate, context-aware responses.
Depending on your package, I can build a standalone prototype or integrate the chatbot into your existing application using Python, FastAPI, LangChain, and a suitable vector database.
I can help with document ingestion, retrieval setup, prompt engineering, API development, database integration, source-aware responses, testing, and deployment.
This solution is ideal for internal knowledge assistants, document Q&A, customer support, SaaS applications, company knowledge bases, and other AI-powered tools.
You will receive clean source code, setup instructions, and a solution built around your specific use case.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging FaceAI Models
ChatGPT, GPT-4, OpenAI CodexWhat's included
| Service Tiers |
Starter
$49
|
Standard
$149
|
Advanced
$259
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 1 |
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.
Additional data source
+$25
FastAPI API endpoint
+$49
Deployment assistance
+$59Frequently asked questions
About Hamza
Python AI Developer | RAG Chatbots, FastAPI & LLM Integration
Agadir, Morocco - 2:55 am local time
I can help.
I'm a Full-Stack & AI Developer specializing in building practical AI-powered applications using Python, FastAPI, RAG, LangChain, PostgreSQL, and modern web technologies.
My focus is not just creating AI demos — I build systems where AI works with real applications, APIs, databases, and business data.
What I can help you with:
✅ AI & LLM integrations
✅ RAG chatbots connected to documents or databases
✅ LangChain-based applications
✅ OpenAI / LLM API integrations
✅ Python & FastAPI backend development
✅ REST API development and debugging
✅ PostgreSQL / MySQL database integration
✅ Next.js + FastAPI applications
✅ Dockerizing and deploying applications
✅ AI agent and tool integrations
✅ MCP (Model Context Protocol) integrations
✅ NLP and data-processing pipelines
✅ Computer vision integrations
My technical stack:
AI / ML:
Python, LangChain, RAG, TensorFlow, Scikit-learn, NLP, Computer Vision
Backend:
FastAPI, Python, REST APIs, SQLAlchemy
Frontend:
Next.js, React, TypeScript, JavaScript, Tailwind CSS
Databases:
PostgreSQL, MySQL, ChromaDB, Prisma, Drizzle
DevOps / Cloud:
Docker, Kubernetes, GitHub Actions, MLflow, Azure, Oracle Cloud
Relevant experience
I have worked on AI systems involving:
• Retrieval-Augmented Generation systems for improving chatbot response accuracy
• Connecting AI models with business data using MCP
• AI chatbot evaluation pipelines
• Computer vision pipelines using YOLOv8 and EfficientNet
• Machine-learning pricing models
• Full-stack AI applications using Next.js, FastAPI and PostgreSQL
• Dockerized application architectures and ML monitoring
If you already have an application and want to add AI to it, fix an existing AI feature, build an API, or create a small AI MVP, feel free to contact me.
I'm happy to start with a small task so you can evaluate my work before committing to a larger project.
Steps for completing your project
After purchasing the project, send requirements so Hamza can start the project.
Delivery time starts when Hamza receives requirements from you.
Hamza works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1 — Requirements & data review
I review your use case, documents, existing application, and technical requirements.
Step 2 — RAG architecture setup
I prepare document processing, embeddings, retrieval, vector storage, and the AI model connection.