Page Comparison

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Call LLM Directly

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RAG (Retrieval-Augmented Generation) in AI for Confluence

The plugin is based on the RAG model and provides intelligent search and query capabilitiescapabilities integration with Confluence knowledge base. During user queries, it leverages content from Confluence and utilizes a large language model (LLM) to deliver answers, along with the sources of the content. Typical use cases are:

• Chat

• Intelligent query

• Define

Content indexing

During the indexing stage, documents are pre-processed in a way that enables efficient search during the retrieval stage.

Indexing Steps

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• Page

• Blog

• Attachments

  • Office docs (.docx, .pptx, .xlsx)

  • PDF (.pdf)

  • Text files (.txt, .xml, .html, .rtf etc)

Will the achieved contents be indexed?

The plugin will call confluence search API to get the new contents to index for embedding, achieved docs can not be searched, so that will not be indexed either.

Where the embeddings stored

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Chroma DB is an open-source tool recommended for use as a database if Confluence has a large volume of data to index. Customer is required to install a Chroma DB that can be accessed by Confluence.

Scopes of Index

All the contents in Confluence will be indexed by default. Confluence administrator can customized scopes in by Spaces and CQL with Knowledge Configuration of the plugin configuration.

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Query Steps:

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A： Query prompts + relevant context

Q： Can user query contents which are blocked by Confluence permissions ?

A： No, the Permission Filter will check the permissions and filter contents to user according to permission settings of confluence (e.g. Space permission, Page permission).

Q： Do you (XDevPod) collect user data?

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