ammirsm/llamaindex-omakase-rag
This project enhances the construction of RAG applications by addressing challenges, improving accessibility, scalability, and managing data and user access. It uses Django, Llamaindex, and Google Drive for effective database management.
This project helps teams build and manage 'Retrieval Augmented Generation' (RAG) applications more easily. It takes documents from Google Drive and processes them to create a searchable knowledge base, which can then be used by an AI system to answer questions or generate content. This is for product managers or technical leads who need to deploy scalable AI applications that can access and understand internal company documents.
148 stars. No commits in the last 6 months.
Use this if you need a web-based RAG application with user management, scheduled data updates from Google Drive, and an API for integrating with other systems.
Not ideal if you're looking for a simple, single-user script or if your primary data sources are not on Google Drive.
Stars
148
Forks
16
Language
Python
License
MIT
Category
Last pushed
Apr 10, 2024
Commits (30d)
0
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