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Version: v1.1.7

Chat session


A Chat session is a focused interaction between you and Enterprise h2oGPTe, consisting of a series of prompts and answers that are based on a specific Collection.


The default language used to chat with Enterprise h2oGPTe is set to English. However, you can also Chat using a different language by specifying the desired language within the following setting located in the Chat settings page: Personality (System Prompt). For more information about supported languages, see FAQs.

Components of a Chat session

components of a Chat session

i: User feedback

The and icons let you provide feedback on the usefulness of a response. This helps developers improve the model.

ii: LLM Only (no RAG)

The LLM Only (no RAG) section only appears when you select LLM Only (no RAG) as the generation approach (RAG type to use). It shows the generated response to the user's query based on the Large Language Model (LLM) without considering supporting Document contexts from the collection.

iii: HyDE RAG (Hypothetical Document Embedding)

The HyDE RAG (Hypothetical Document Embedding) section provides the response generated from RAG with neural/lexical hybrid search by utilizing the user's query and the LLM response.

iv: Self-reflection

The Self-reflection section appears when you toggle the Include self-reflection for every response option in Chat settings. It asks another LLM for a reflection of the answer given to the question and the context provided in the Collection. It can be used to evaluate the LLM’s performance.

v: References

The References section highlights the sections of the Document from which the context was derived in order to generate the response.