You will get LLM Chatbot with RAG and LangChain integration | Document Query
Project details
Get a document-based AI chatbot built with LLM, RAG, and LangChain, perfect for answering customer questions, searching company documents, or automating daily tasks.
I will build a fully functional AI chatbot that uses your own documents to give accurate answers in real time. Using advanced tools like LangChain, OpenAI, and vector databases, your chatbot will be fast, reliable, and ready to integrate into your business.
With over 5 years of experience in Python, AI automation, and LLM-based chatbot development, I specialize in:
• RAG (Retrieval-Augmented Generation) for document-based answering
• Smart, context-aware LangChain agents
• Clean UI options using Streamlit or React
• Easy deployment via Docker, cloud platforms, or local servers
Whether you need a customer support chatbot, internal knowledge bot, or AI research assistant, I’ll deliver a solution that’s scalable, secure, and built around your goals.
Let’s turn your documents into a powerful AI assistant that actually gets work done!
I will build a fully functional AI chatbot that uses your own documents to give accurate answers in real time. Using advanced tools like LangChain, OpenAI, and vector databases, your chatbot will be fast, reliable, and ready to integrate into your business.
With over 5 years of experience in Python, AI automation, and LLM-based chatbot development, I specialize in:
• RAG (Retrieval-Augmented Generation) for document-based answering
• Smart, context-aware LangChain agents
• Clean UI options using Streamlit or React
• Easy deployment via Docker, cloud platforms, or local servers
Whether you need a customer support chatbot, internal knowledge bot, or AI research assistant, I’ll deliver a solution that’s scalable, secure, and built around your goals.
Let’s turn your documents into a powerful AI assistant that actually gets work done!
Programming Languages
PHP, Python, JavaCoding Expertise
PSD to HTML, Localization, Performance OptimizationWhat's included
| Service Tiers |
Starter
$100
|
Standard
$300
|
Advanced
$700
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
Number of Revisions | 1 | 3 | 5 |
Design Customization | - | ||
Content Upload | |||
Responsive Design | - | ||
Source Code |
Frequently asked questions
About Nadir
Full-Stack Python Developer | Django, FastAPI, AI/LLM, RAG, SaaS
Lahore, Pakistan - 12:39 am local time
SaaS platforms, AI/LLM applications, and backend systems that actually ship.
I help founders and teams turn complex ideas into working software — from
real-time voice AI agents and RAG-powered SaaS products to optimization
engines and multi-tenant Django backends. I've spent the last 2+ years deep
in LLMs, voice AI, and agent systems, and the years before that building
full-stack web apps end-to-end.
If you need someone who can architect a system, write the backend, integrate
the AI, ship the frontend, and deploy it — that's the lane I work in.
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🔧 WHAT I BUILD
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▸ AI / LLM Applications
RAG pipelines, AI agents, chatbots, document intelligence, fine-tuning
(LoRA), RLHF training pipelines, multi-model orchestration
▸ Voice AI Systems
Real-time STT/TTS, voice agents, inbound/outbound phone calling,
interruption-aware conversation flows, WebSocket audio streaming,
voice cloning
▸ Backend & APIs
Django, FastAPI, Flask, DRF, Node.js, REST + GraphQL, WebSockets,
microservices, async pipelines, OAuth/JWT, Stripe & payment integrations
▸ Full-Stack SaaS Products
Multi-tenant SaaS, embeddable widgets, admin dashboards, billing flows,
role-based access, React/Next.js frontends with Tailwind
▸ Web Scraping & Automation
Selenium, Playwright, Scrapy, BeautifulSoup, anti-bot handling, scheduled
scraping pipelines, data extraction at scale
▸ ML Engineering
Model training, fine-tuning, computer vision, NLP pipelines, RLHF,
deployment with FastAPI/Docker
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💼 SELECTED PROJECTS
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▸ RagAdvise — Multi-Tenant AI Voice SaaS Platform
Full-stack TypeScript SaaS that lets businesses embed AI voice assistants
into any website via a shortcode. Users configure their own assistant,
handle inbound/outbound phone calls, and process payments — all from one
dashboard. Stack: TypeScript, React, Node.js, Cloudflare Workers, Stripe,
multi-tenant architecture.
▸ Autonomous NEMT Dispatch & Assignment Optimizer
End-to-end dispatch system for medical transport: ingests live trips,
drivers, shifts, and runs a constraint-based optimization engine with
multi-leg and shared-ride modes. FastAPI scheduler + React ops UI replaced
manual dispatching. Stack: Python, FastAPI, React, Firebase, Docker.
▸ Moezz AI Voice Dispatcher
Real-time voice agent for medical transport — handles trip status,
scheduling, cancellations, and ETA tracking through natural conversation.
Built with DeepGram (STT), OpenAI (reasoning), Silero VAD (interruption
handling), Kokoro/FasterWhisper.
▸ Intelligent RAG System
Context-aware retrieval pipeline using OpenAI, Llama 3.2, and DeepSeek R1
with vector database integration and a Streamlit interface for testing.
▸ Llama 3.2 Fine-Tuning (LoRA)
Fine-tuned Llama 3.2 on domain-specific data using LoRA for compliant,
accurate Q&A. Hugging Face + Transformers stack.
▸ RLHF Training with Gemini
Built reinforcement learning from human feedback pipelines for Gemini
fine-tuning — reward modeling, preference data curation, evaluation loops.
▸ Speech-to-Speech Assistant
Real-time S2S system: Whisper (deployed on RunPod) for transcription,
gpt-oss-20b for reasoning, XTTSv2 for voice-cloned output — all streamed
over WebSocket for low-latency conversation.
▸ Ride Confirmation Voice Assistant
Outbound AI caller handling ride confirmations, cancellations, voicemail
detection, and bad-number routing. Twilio + DeepGram + OpenAI + Vonage.
▸ Invoice Intelligence (Mobile + LLM)
On-device receipt OCR with ML Kit feeding a local Llama 3.2 agent via
Ollama for structured data extraction.
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🛠 TECH STACK
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Languages — Python, JavaScript, TypeScript, Java, SQL
Backend — Django, DRF, FastAPI, Flask, Node.js, Django Channels
Frontend — React, Next.js, Tailwind, Material-UI, Bootstrap
AI/ML — OpenAI, Anthropic, Gemini, Hugging Face, LangChain, LangGraph,
PyTorch, TensorFlow, Transformers, LoRA, RLHF, Whisper, XTTS,
DeepGram, Silero VAD, Kokoro, Ollama, Llama 3, DeepSeek
Voice/Telephony — Twilio, Vonage, WebRTC, WebSocket audio streaming
Databases — PostgreSQL, MySQL, MongoDB, SQLite, Redis, vector DBs
(Pinecone, Chroma, FAISS)
Payments — Stripe (subscriptions, webhooks, Connect, multi-tenant billing)
Automation — Selenium, Playwright, Scrapy, BeautifulSoup
DevOps — Docker, Docker Compose, Celery, GitHub Actions, GitLab CI/CD
Cloud — AWS (EC2, S3, Lambda, RDS), GCP, Azure, Cloudflare Workers, RunPod
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🎓 BACKGROUND
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▸ MPhil in Artificial Intelligence — Punjab University (in progress)
▸ Master of Computer Science — Islamia University Bahawalpur
▸ 5+ yea
Steps for completing your project
After purchasing the project, send requirements so Nadir can start the project.
Delivery time starts when Nadir receives requirements from you.
Nadir works on your project following the steps below.
Revisions may occur after the delivery date.
Environment Setup & LLM Configuration
Set up LangChain environment, connect the LLM (e.g., OpenAI), and configure API access securely.
Document Ingestion & RAG Setup
Implement the Retrieval-Augmented Generation pipeline using client-provided documents (vector store setup, chunking, embedding).