I will build a private local rag chatbot to chat with your documents using ollama


About this gig
Are you working with confidential documents, legal contracts, or proprietary company data that cannot be uploaded to cloud APIs like OpenAI or Anthropic?
I will build a 100% Private, Offline RAG (Retrieval-Augmented Generation) System that allows you to chat with your documents locally on your laptop or private server. Zero API keys, zero cloud latency, and complete data privacy.
What You Get:
- 100% Offline & Private: Powered by Ollama running locally (Llama 3.1, Qwen 2.5, DeepSeek, or Mistral).
- Source Attribution & Citations: Every answer highlights the exact page number and text snippet used from your documents.
- Fast Local Vector Search: Uses ChromaDB or FAISS for instant semantic search across thousands of pages.
- Multi-Format Support: Query PDFs, Word documents, CSVs, TXT files, and technical manuals.
- User-Friendly Web Interface: Chat via an intuitive Web UI (Streamlit, Chainlit, or Open WebUI).
Tech Stack Used:
- LLM Engine: Ollama / Llama.cpp
- Orchestration: LlamaIndex / LangChain
- Vector Store: ChromaDB / FAISS / Qdrant
- Frontend: Streamlit / Chainlit / Python
Keep your data completely secure while unlocking the full power of conversational AI over your private files!
Get to know Younes Belhadj
Data Science and AI Engineer
- FromAlgeria
- Member sinceMay 2020
- Avg. response time1 hour
Languages
English, Arabic, French
FAQ
Do I need an internet connection to run this chatbot?
No! Once installed, the entire RAG system runs completely offline on your machine with zero external network requests.
What hardware do I need to run this locally?
A system with 8GB RAM can easily run 3B/7B quantized models (like Qwen 2.5 or Llama 3.1). For optimal performance, a discrete GPU (NVIDIA RTX series) is recommended.
Can this handle large PDF files with hundreds of pages?
Yes. The system chunks and embeds your documents into a local vector database, allowing instant retrieval regardless of total page count.
