updated readme.md file with instructions

LICENSE

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+MIT License
+
+Copyright (c) 2024 Raghu Rami Reddy Konda
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README copy.md

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-# Python-FAQs-Chatbot

README.md

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 license: mit
 ---
 
+# 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