rag-chatbot
rag-chatbot is an open-source Python application that enables local, conversational interaction with multiple PDF documents using retrieval-augmented generation (RAG). It supports models from Hugging Face and Ollama, with a Gradio-based web interface for ease of use.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | datvodinh/rag-chatbot |
| Owner | datvodinh |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 672 |
| Forks | 105 |
| Open issues | 8 |
| Latest release | v0.1.13 (2025-10-23) |
| Last updated | 2025-10-23 |
| Source | https://github.com/datvodinh/rag-chatbot |
What rag-chatbot is
Built on LlamaIndex, the project implements RAG workflows to retrieve relevant PDF content and generate contextual responses. It supports both cloud (Kaggle) and local deployment via Docker or direct Python execution, integrating with Ollama for local LLM inference.
Get the rag-chatbot source
Clone the repository and explore it locally.
git clone https://github.com/datvodinh/rag-chatbot.gitcd rag-chatbot# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Local inference via Ollama requires sufficient GPU/CPU resources and memory; model selection (e.g., Llama 3, Mistral) affects latency and quality.
- PDF parsing and chunking strategy (not detailed in README) critically impacts retrieval quality; may need tuning for domain-specific documents.
- Ngrok is a tunneling solution for remote access; not intended for production traffic; consider reverse proxy or VPN for secure long-term exposure.
- Python dependency management uses `uv`; ensure lock file is maintained across team deployments to avoid version drift.
- UI is Gradio-based; suitable for internal tools but lacks theming and branding customization for white-label scenarios.
When to avoid it — and what to weigh
- Production Multi-Tenant SaaS — No evidence of user authentication, access control, audit logging, or multi-tenant isolation. Not suitable for hosting user data in a shared environment without substantial engineering.
- High-Volume or Real-Time Requirements — Relies on local or Ollama inference; no horizontal scaling, load balancing, or caching layer described. Query latency and throughput likely constrain enterprise-scale deployments.
- Strict Regulatory Compliance — No evidence of compliance certifications (SOC 2, HIPAA, GDPR), data encryption at rest/transit, or formal security audit. Requires external validation for regulated industries.
- Hands-Off Administration — Requires manual setup of Ollama, Ngrok, and Python dependencies. No managed service option or turnkey deployment reduces operational burden.
License & commercial use
Apache License 2.0 (Apache-2.0) is a permissive OSI-approved open-source license permitting commercial use, modification, and distribution with attribution and liability waiver.
Apache-2.0 permits commercial use without royalty or license fee. However, you are responsible for understanding and complying with dependencies' licenses (LlamaIndex, Ollama, Gradio, etc.). No warranty is provided; production deployment requires independent security and compliance review.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
No authentication or authorization mechanisms; PDFs and chat history stored locally without encryption. Ngrok tunnel exposes the app publicly; credentials must be guarded. Model inference on local hardware reduces third-party data exposure but requires secure network isolation. No formal security audit or vulnerability disclosure process documented.
Alternatives to consider
LangChain + Streamlit
Similar RAG functionality with broader LLM API support (OpenAI, Anthropic, Bedrock), stronger documentation, and larger community ecosystem. Requires more setup but offers more flexibility.
ChatPDF / Perplexity (Proprietary SaaS)
Managed PDF chat without infrastructure overhead, but locks you into a vendor's model and pricing. No local control or customization.
Retrieval Augmented Generation (RAG) on Hugging Face Spaces
Templates and examples for RAG pipelines with free hosting. Less turn-key than rag-chatbot but more transparent regarding dependencies and model availability.
Build on rag-chatbot with DEV.co software developers
rag-chatbot is a solid foundation for internal document Q&A or RAG prototyping. For production use cases or integrations with proprietary LLMs, our AI application development team can extend and harden the codebase for your needs.
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rag-chatbot FAQ
Can I use proprietary LLMs (OpenAI, Claude) instead of Ollama?
Is my data sent to the cloud?
Can I use this in production without modifications?
What happens to uploaded PDFs?
Software development & web development with DEV.co
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If rag-chatbot is part of your rag frameworks roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Build a RAG Solution?
rag-chatbot is a solid foundation for internal document Q&A or RAG prototyping. For production use cases or integrations with proprietary LLMs, our AI application development team can extend and harden the codebase for your needs.