CITATION.cff

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title: ERI - External Retrieval Interface
message: >-
  When you want to cite the ERI in your scientific work,
  please use these metadata.
type: software
authors:
  - given-names: Thorsten
    family-names: Sommer
    email: thorsten.sommer@dlr.de
    affiliation: Deutsches Zentrum für Luft- und Raumfahrt (DLR)
    orcid: 'https://orcid.org/0000-0002-3264-9934'
  - name: Open Source Community
repository-code: 'https://github.com/MindWorkAI/ERI'
url: 'https://mindworkai.org/'
abstract: >-
  The ERI is the External Retrieval Interface which could be
  used by AI Studio and other tools. The ERI acts as a
  contract between decentralized data sources and LLM tools.
  The ERI is implemented by the data sources, allowing them
  to be integrated into, e.g., AI Studio later. This means
  that the data sources assume the server role and the LLM
  tool assumes the client role of the API. This approach
  serves to realize a Retrieval-Augmented Generation (RAG)
  process with external data. You can imagine it like this:
  Hypothetically, when Wikipedia implemented the ERI, it
  would vectorize all pages using an embedding method. All
  of Wikipedia's data would remain with Wikipedia, including
  the vector database (decentralized approach). Then, any AI
  Studio user could add Wikipedia as a data source to
  significantly reduce the hallucination of the LLM in
  knowledge questions.


  When you want to integrate your own local data into AI
  Studio, you don't need an ERI. Instead, AI Studio will
  offer an RAG process for this in the future. Is your
  organization interested in integrating internal company
  data into AI Studio? Here you will find the interactive
  documentation of the related OpenAPI interface.
keywords:
  - LLM
  - AI
  - Orchestration
  - Retrieval-Augmented Generation
  - RAG
  - Decentralized