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Draft from Daniel Baldassare :

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Draft Daniel Burkhardt : 

• Definition of RAG 
• Types of RAG 
• Applications for RAG 

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Draft Robert David:

• Initial RAG idea: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• RAG is commonly used with vector databases.
  • can only grasp semantic similarity represented in the document content
  • only unstructured data
  • vector distance instead of a DB search limits the retrieval capabilities
• Graph RAG uses knowledge graphs as part of the RAG system
  • KGs for retrieval (directly), meaning the database is storing KG data
  • KGs for retrieval via a semantic layer, potentially retrieving over different data sources of structured and unstructured data
  • KGs for augmenting the retrieval, meaning the queries to some database is modified via KG data
• Via Graph RAG, we can
  • ingest additional semantic background knowledge (knowledge model) not represented in the data itself
    • additional related knowledge based on defined paths (rule-based inference)
    • focus on certain aspects of a data set for the retrieval (search configuration)
    • personalization: represent different roles for retrieval via ingesting role description data into the retrieval (especially important in an enterprise environment)
  • reasoning
  • linked data makes factual knowledge related to the LLM-generated knowledge and thereby provide a means to check for correctness
  • explainable AI: provide justifications via KG
  • consolidate different data sources: unstructured, semi-structured, structured (enterprise knowledge graph scenario)
  • doing the actual retrieval via KG queries: SPARQL
  • hybrid retrieval: combine KG-based retrieval with vector databases or search indexes

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Draft from Daniel Burkhardt: 

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Draft from Daniel Burkhardt: 

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