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README.md

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-# Python FAQs Question Answering App
-
-This repository contains the implementation of a Question Answering (QA) app built using Retrieval Augmented Generation (RAG) techniques. The app leverages Large Language Model `mistralai/Mistral-7B-Instruct-v0.3` to answer questions about Python FAQs by retrieving and utilizing relevant data from `https://docs.python.org/3/faq/index.html`.
-
-## Table of Contents
-- [Introduction to RAG](#introduction-to-rag)
-- [Purpose of RAG](#purpose-of-rag)
-- [Components of a RAG Application](#components-of-a-rag-application)
-- [Workflow of a RAG Application](#workflow-of-a-rag-application)
-- [Tools and Technologies](#tools-and-technologies)
-- [Installation and Setup](#installation-and-setup)
-- [Running the Application](#running-the-application)
-- [Example Application Workflow](#example-application-workflow)
-- [Advanced Techniques and Resources](#advanced-techniques-and-resources)
-
-## Introduction to RAG
-
-**Retrieval Augmented Generation (RAG)** is a technique for building sophisticated question-answering (Q&A) applications using Large Language Models (LLMs). These applications enhance the capabilities of LLMs by augmenting them with additional, relevant data.
-
-## Purpose of RAG
-- **Enhancing LLMs**: Integrates private or recent data to improve the model's reasoning ability.
-- **Application**: Used to create Q&A chatbots for specific data sources.
-
-## Components of a RAG Application
-1. **Indexing**
-   - **Loading Data**: Ingesting data using DocumentLoaders.
-   - **Splitting Text**: Breaking large documents into smaller chunks using text splitters.
-   - **Storing Data**: Indexing and storing text chunks using a VectorStore and an Embeddings model.
-
-2. **Retrieval and Generation**
-   - **Retrieving Data**: Using a Retriever to fetch relevant data chunks based on user queries.
-   - **Generating Answers**: Using a ChatModel or LLM to produce answers by combining user queries with retrieved data.
-
-## Workflow of a RAG Application
-1. **Indexing Phase**
-   - **Load**: Import data using DocumentLoaders.
-   - **Split**: Use text splitters to divide large documents.
-   - **Store**: Index and store chunks in a VectorStore.
-   ![Indexing Diagram](https://python.langchain.com/v0.2/assets/images/rag_indexing-8160f90a90a33253d0154659cf7d453f.png)
-
-2. **Retrieval and Generation Phase**
-   - **Retrieve**: Retrieve relevant chunks based on user queries.
-   - **Generate**: Use retrieved data and user queries to generate answers.
-   ![Retrieval and Generation Diagram](https://python.langchain.com/v0.2/assets/images/rag_retrieval_generation-1046a4668d6bb08786ef73c56d4f228a.png)
-
-## Tools and Technologies
-- **LangChain**: Components for building Q&A applications.
-- **DocumentLoaders**: For loading data.
-- **Text Splitters**: For breaking down documents.
-- **VectorStore**: For storing and indexing data chunks.
-- **Embeddings Model**: For creating searchable vector representations.
-- **Retriever**: For fetching relevant data chunks.
-- **ChatModel/LLM**: For generating answers.
-
-## Installation and Setup
-
-1. Clone the repository:
-    ```bash
-    git clone https://github.com/your-repo/python-faqs-qa-app.git
-    cd python-faqs-qa-app
-    ```
-
-2. Install the required dependencies:
-    ```bash
-    pip install -r requirements.txt
-    ```
-
-3. Set up environment variables:
-    Create a `.env` file in the root directory and add your API keys:
-    ```bash
-    LANGCHAIN_API_KEY=your_langchain_api_key
-    HUGGINGFACEHUB_API_TOKEN=your_huggingfacehub_api_token
-    ```
-
-## Running the Application
-
-1. Run the Streamlit app:
-    ```bash
-    streamlit run app.py
-    ```
-
-2. Open your browser and go to `http://localhost:8501` to use the app.
-
-## Example Application Workflow
-
-1. **Data Ingestion**: Load a large text document using DocumentLoaders.
-2. **Data Preparation**: Split the document into smaller chunks with text splitters.
-3. **Data Indexing**: Store chunks in a VectorStore.
-4. **Query Processing**:
-   - The Retriever searches the VectorStore for relevant data chunks based on user queries.
-   - Retrieved data and user queries are used by the LLM to generate answers.
-
-## Advanced Techniques and Resources
-
-- **LangSmith**: Tool for tracing and understanding the application.
-- **RAG over Structured Data**: Applying RAG to structured data like SQL databases using LangChain.
-
----
-
-Made with ❤️ on python by Raghu
-
-
-
-
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference