You will get a custom RAG chatbot that answers questions from your docs and data
Project details
I specialize in building production-ready RAG (Retrieval-Augmented Generation) chatbots that let your users chat directly with your documents, databases, and data sources. Using LangChain, OpenAI GPT-4, and vector databases like Pinecone and ChromaDB, I build intelligent chatbots that retrieve precise answers from your content instead of hallucinating.
Whether you need a simple PDF chatbot, a multi-source knowledge base, or a full enterprise RAG platform with authentication and analytics, I deliver clean Python code, custom embeddings, and a deployed chat UI. My systems handle real business data at scale — PDFs, websites, databases, APIs — and give your users accurate, source-cited answers instantly.
Whether you need a simple PDF chatbot, a multi-source knowledge base, or a full enterprise RAG platform with authentication and analytics, I deliver clean Python code, custom embeddings, and a deployed chat UI. My systems handle real business data at scale — PDFs, websites, databases, APIs — and give your users accurate, source-cited answers instantly.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, StreamlitAI Models
BERT, ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$600
|
Standard
$1,500
|
Advanced
$3,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 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 |
About Mr
Senior AI Native-Built SaaS & AI Platforms | Backend Dev / Tech Lead
Ho Chi Minh City, Vietnam - 7:40 am local time
As a former tech founder and team lead, I've delivered full-stack web products, architected backend systems with TemporalIO, Docker, and Rust, and led the launch of Vietnam's early AI assistants. I enjoy turning ideas into scalable infrastructure and helping businesses automate the boring stuff.
Stack: NodeJS, React, Go, PHP, Python, Rust, Flutter, TemporalIO, PostgreSQL, Redis, AWS, Docker
Focus: Backend Architecture, SaaS, Business Automation, AI Tools
Open to: Backend Dev | Tech Lead | Product Engineer roles (Remote/Hybrid - HCMC)
Open-source highlights (5,700+ contributions on GitHub):
- qwen-code-rust: Blazing-fast AI coding agent in Rust, 45+ tools, multi-provider support
- clawrust: Claude Code CLI rewritten in Rust with full feature parity
- duradx-oss: Durable workflow engine for Rust, DAG-based execution, multi-database support
- ai-instructions-template: Curated best practices for AI agent workflows
Steps for completing your project
After purchasing the project, send requirements so Mr can start the project.
Delivery time starts when Mr receives requirements from you.
Mr works on your project following the steps below.
Revisions may occur after the delivery date.
Data Ingestion & Vector Store Setup
I ingest your documents (PDFs, URLs, databases) using LangChain loaders, split them into optimal chunks, generate OpenAI embeddings, and store them in a vector database (Pinecone or ChromaDB) for fast semantic retrieval.
RAG Pipeline & LLM Integration
I build the retrieval-augmented generation pipeline connecting your vector store to your chosen LLM (GPT-4, Claude, Llama), with custom prompt engineering, context window management, and source citation for accurate, transparent answers.