RAG Search with Shiny for Python
Authors: Jason Roberts
Abstract: The RAG Search Pipeline is a powerful tool that combines document analysis with AI-driven insights. This app allows users to upload various document types and process them into a searchable format, and query the content using advanced language models.
Key features:
- Multi-format document upload and processing (pdf, jpg, png, doc, pptx)
- Integration with leading AI models (OpenAI, Claude, HuggingFace) BYOK (Bring Your Own Key)
- Efficient vector-based document retrieval
- User-friendly query interface
- Customizable UI
Built with Shiny for Python (UI) , unstructured.io (Doc Parsing), LangChain (textsplitting, retrievalchain) , and Chroma (Vector Store), it offers a seamless experience for extracting valuable information from document collections. Ideal for research, business intelligence, and information retrieval across various industries.
Easily deployable on platforms like shinyapps.io, RAG Search Pipeline makes AI-powered document analysis accessible to everyone, bridging the gap between vast document libraries and actionable insights.
Full Description:
Shiny app: https://jrobx.shinyapps.io/shiny-rag-app/
Repo: GitHub - jroberts2600/shiny-rag-proj
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