Design of On-Premises Version of RAG with AI Agent for Framework Selection Together with Dify and DSL as Well as Ollama for LLM

Currently, most RAGs are cloud-based and include Bedrock. However, there is a trend to return from the cloud to on-premises due to security concerns. In addition, it is common for APIs to call Lambda or EC2 for data access, but it is not easy to select the optimal framework depending on the data att...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International journal of advanced computer science & applications Ročník 15; číslo 12
Hlavní autor: Arai, Kohei
Médium: Journal Article
Jazyk:angličtina
Vydáno: West Yorkshire Science and Information (SAI) Organization Limited 2024
Témata:
Agents (artificial intelligence)
Application programming interface
Classification
Computer science
Cost analysis
Cost reduction
Generative artificial intelligence
Language
Large language models
Natural language processing
Optimization
Performance evaluation
Query expansion
Text categorization
User interface
ISSN:2158-107X, 2156-5570
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Currently, most RAGs are cloud-based and include Bedrock. However, there is a trend to return from the cloud to on-premises due to security concerns. In addition, it is common for APIs to call Lambda or EC2 for data access, but it is not easy to select the optimal framework depending on the data attributes. For this reason, the author devised a system for selecting the optimal framework using an AI agent. Furthermore, the author decided to use Dify, which is based on a DSL, as the user interface for the on-premises version of RAG, and ollama as a large-scale language model that can be installed on-premises as well. The author also considered the specifications of the hardware required to build this RAG and confirmed the feasibility of implementation.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2024.0151212