FAQ

How does the integrated Chatbot work?

Chatbot system

• Provision of raw data from the wiki for processing in the RAG pipeline.
• Request to the chatbot via the chat interface.
• Transfer of the request to the RAG pipeline. There, the data is processed accordingly.
• Transfer to the LLM. Text generation by the language model.
• Transfer to the chatbot.
• Transfer to the chat interface. Output there.

 

What are the LLM requirements?

• BlueSpice does not provide the LLM that is a prerequisite for the chatbot feature. The LLM must be provided by the customer and the wikimust be able to communicate with it by exchanging API keys.
• Currently, the following LLMs are supported:
  • OpenAI and OpenAI-compatible systems (including, for example, Microsoft Azure, DeepSeek, Google Gemini)
  • Ollama, Mistral AI (open source, e.g., via IONOS)
• Cloud hosting requirement: Internet access for the LLM is required (common encryption).

RAG pipeline

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Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information. With RAG, LLMs do not respond to user queries until they refer to a specified set of documents. These documents supplement information from the LLM's pre-existing training data. This allows LLMs to use domain-specific and/or updated information that is not available in the training data. For example, this helps LLM-based chatbots access internal company data or generate responses based on authoritative sources. wikipedia

A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) and provide the core capabilities of chatbots such as ChatGPT, Gemini and Claude. wikipedia