

RAG Over Excel Sheets
Overview :
RAG over Excel Sheets is an AI project that combines LlamaIndex and IBM's Docling technology, focusing on implementing retrieval-augmented generation (RAG) for Excel spreadsheets. This project can not only be applied to Excel but can also be extended to PowerPoint presentations and other complex documents. By providing efficient information retrieval and processing capabilities, it greatly enhances the efficiency of data analysis and document management.
Target Users :
The target audience includes data analysts, enterprise information managers, and developers. This project is suitable for them as it provides a powerful tool to handle and retrieve vast amounts of document data, assisting them in more efficient data analysis and decision support.
Use Cases
Data analysts use RAG over Excel Sheets to quickly retrieve market data for trend analysis.
Enterprise information managers leverage this project to manage a large volume of business documents, enhancing information retrieval efficiency.
Developers integrate RAG technology into their applications to provide smarter document processing capabilities.
Features
? Document retrieval using LlamaIndex: The project utilizes LlamaIndex technology to efficiently search for information across large volumes of documents.
? Document parsing with IBM's Docling: The use of Docling technology allows the project to parse and understand document content, providing more accurate retrieval results.
? Support for various file formats: In addition to Excel, the project also supports the processing of PowerPoint presentations and other complex documents.
? Python environment support: The project requires Python 3.11 or a later version, facilitating secondary development and integration for developers.
? Easy installation and deployment: With pip for dependency installation, the project can be rapidly deployed in any Python-supported environment.
? Community support and contributions: The project encourages community contributions via forks and pull requests for collaboration.
How to Use
1. Ensure that Python 3.11 or a later version is installed on your system.
2. Install the required dependencies using pip, including llama-index-core, among others.
3. Clone or download the project code to your local machine.
4. Configure and set up according to the instructions in the project's README.md file.
5. Run the app.py file to start the RAG service.
6. Upload the documents that need to be processed through the interface for retrieval or analysis.
7. Review the retrieval results or analysis reports and take further action as necessary.
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