What is a RAG chatbot?

RAG stands for Retrieval-Augmented Generation. It connects a language model to a document base. Before answering, the model retrieves relevant passages from the indexed documents and builds its response from those extracts. The result: no hallucinations, every answer is sourced.

What is the difference between a RAG chatbot and ChatGPT?

ChatGPT answers from its general training data. A RAG chatbot answers only from the organisation's documents. It cannot respond out of context, every answer cites its source, and the data stays under the organisation's control.

Can a RAG chatbot be GDPR-compliant?

Yes. The solution can be deployed with an open-source LLM (Mistral 7B, Llama 3) hosted on dedicated infrastructure or a European VPS (OVHcloud, Scaleway). No data leaves the EU. For less sensitive use cases, Mistral AI (Paris) offers a GDPR-compliant API with a DPA.

What document types does a RAG chatbot support?

PDF, Word, Excel, emails, Confluence wikis, Notion, databases, web pages: practically any source of structured or unstructured text. The document audit phase assesses the quality of the sources to optimise answer relevance.

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RAG intelligent chatbots

Name: Pierre Kasparian - AI Integration & Data
Rating: 5

An assistant that answers from an organisation's own documents.

LangChainPythonWeb fullstackInfrastructureQdrant

What is it?

A RAG chatbot (Retrieval-Augmented Generation) is an AI assistant that answers exclusively from an organisation's internal documents. Unlike ChatGPT, it does not hallucinate: every answer is traceable to its source, with EU hosting available for full GDPR compliance.

How it works

1
Document audit
Analysis of existing sources (PDFs, knowledge bases, wikis) and scoping the assistant's coverage.
2
Indexing and vectorisation
Chunking, cleaning and vectorising documents into a vector database (Qdrant) optimised for semantic search.
3
Assistant development
Building the RAG chain (LangChain), the interface and any required integrations (Slack, web widget, internal API).
4
Deployment and documentation
Production deployment on the existing infrastructure or an EU cloud and technical documentation.
5
Maintenance
Onboarding support, source document updates and assistant adjustments based on the team's real usage feedback.

What it covers

Custom assistants that work from internal documents (PDFs, emails, knowledge bases)
Context memory across conversations, answers based exclusively on the indexed documents
Every response is traceable to its source
Ideal for customer support, internal assistance or document management
EU hosting available

Risks and compliance

Document filtering and strict separation of rights by role, team or client.
Data anonymization in case of presence of personal data.
Model selection by sensitivity: European Mistral API, open source on EU VPS or on-premise.
Answers constrained to retrieved context to reduce hallucinations and out-of-scope leaks.
Source traceability, usage logs and deletion criteria for obsolete data.

Related projects

Multi-tenant RAG chatbot - LiveSessionAn assistant that answers questions from each client's own documents, with strictly isolated data hosted in Europe.Quantified resultsResponses under 5 s95% relevance0 cross-client data leakHosted in Europe (GDPR)RAG Training - LiveSessionA tailored training programme to make the team fully autonomous with the delivered AI assistant.Quantified results4-module programmeTeam autonomous in 4 weeks0 support tickets after 1 year

Frequently asked questions

What is a RAG chatbot?: RAG stands for Retrieval-Augmented Generation. It connects a language model to a document base. Before answering, the model retrieves relevant passages from the indexed documents and builds its response from those extracts. The result: no hallucinations, every answer is sourced.
What is the difference between a RAG chatbot and ChatGPT?: ChatGPT answers from its general training data. A RAG chatbot answers only from the organisation's documents. It cannot respond out of context, every answer cites its source, and the data stays under the organisation's control.
Can a RAG chatbot be GDPR-compliant?: Yes. The solution can be deployed with an open-source LLM (Mistral 7B, Llama 3) hosted on dedicated infrastructure or a European VPS (OVHcloud, Scaleway). No data leaves the EU. For less sensitive use cases, Mistral AI (Paris) offers a GDPR-compliant API with a DPA.
What document types does a RAG chatbot support?: PDF, Word, Excel, emails, Confluence wikis, Notion, databases, web pages: practically any source of structured or unstructured text. The document audit phase assesses the quality of the sources to optimise answer relevance.

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