Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (6.9%) to scientific vocabulary
Repository
(E)xternal (R)etrieval (I)nterface
Basic Info
- Host: GitHub
- Owner: MindWorkAI
- Language: C#
- Default Branch: main
- Homepage: https://MindWorkAI.org/
- Size: 2.4 MB
Statistics
- Stars: 4
- Watchers: 1
- Forks: 3
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
ERI - (E)xternal (R)etrieval (I)nterface
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.
Links: - Interactive documentation aka Swagger UI - ERI specification, which you could use with tools like OpenAPI Generator.
Owner
- Name: MindWork AI
- Login: MindWorkAI
- Kind: organization
- Location: Germany
- Website: https://MindWorkAI.org/
- Repositories: 1
- Profile: https://github.com/MindWorkAI
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
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
GitHub Events
Total
- Watch event: 2
- Push event: 3
- Pull request review event: 2
- Pull request event: 11
- Fork event: 1
Last Year
- Watch event: 2
- Push event: 3
- Pull request review event: 2
- Pull request event: 11
- Fork event: 1
Dependencies
- Microsoft.AspNetCore.OpenApi 9.0.0-rc.2.24474.3
- Swashbuckle.AspNetCore 6.9.0