From 9430104e70566df63d876dc85ca9bfb898763720 Mon Sep 17 00:00:00 2001 From: Labriji Saad Date: Thu, 18 Apr 2024 19:48:39 +0200 Subject: [PATCH] Updated The Readme file --- README.md | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/README.md b/README.md index f8b63e8..5ca6488 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,35 @@ ## 🌟 Overview This is a Streamlit app leveraging a RAG (Retrieval-Augmented Generation) Language Model (LLM) with FAISS to offer answers from uploaded markdown files πŸ“‚. The app allows users to upload files, ask questions related to the content of these files, and receive relevant answers generated by the RAG LLM πŸ“š. +## ❓How It Works +The LLM RAG Streamlit app is structured into several key areas, each serving a specific function within the application: + +

+ +

+ +- **Setup Knowledge Base** πŸ“‚: Users can establish their **knowledge base** by uploading **markdown documents**. This forms the foundational data that the app will reference for generating answers. + +- **Explore Knowledge Base** πŸ”: After setting up the knowledge base, users can browse and manage the uploaded documents. This allows users to ensure that the data is ready for queries. + +- **RAG Query** πŸ’‘: In this tab, users can pose questions that the app will answer by referencing the content within the knowledge base. The RAG (Retrieval-Augmented Generation) model utilizes both the uploaded documents and the model's own knowledge to generate responses. + +Additionally, the app offers advanced settings to tailor the querying experience: + +

+ +

+ +- **OpenAI Embedding Model Settings**: Users select the desired embedding model for document vectorization. Choices affect the precision of semantic search and the cost per token processed. + +- **OpenAI LLM Settings**: This setting allows users to choose the specific OpenAI language model variant for generating answers. It also displays the associated costs for input and output processing per 1,000 tokens. + +- **Model Temperature**: Adjusting this parameter influences the creativity of the language model’s responses. A higher temperature may yield more varied and creative outputs, while a lower temperature results in more deterministic and conservative text. + +- **Max Completion Tokens**: Users can define the maximum length of the generated response by setting the maximum number of tokens (words and characters) the model should produce. + +- **Drop All Documents in Knowledge Base**: This functionality is crucial for managing the knowledge base. If users need to clear the database, they can do so by typing a confirmatory command. + ## πŸ› οΈ System Architecture The following diagram illustrates the flow of data through the system: