description = """ ## 🕉️ **Project Title: RamayanaGPT & GitaGPT – RAG-based Chatbots for Ancient Indian Epics** --- ### 🔍 **Project Overview** **RamayanaGPT** and **GitaGPT** are knowledge-based conversational AI tools designed to answer questions from the *Valmiki Ramayana* and the *Bhagavad Gita*, respectively. These chatbots use **Retrieval-Augmented Generation (RAG)** architecture to generate accurate, scripture-based responses. They combine powerful **vector search capabilities** with **large language models (LLMs)** to deliver spiritually insightful, context-rich conversations. These tools leverage: * **MongoDB Atlas Vector Search** for embedding-based document retrieval * **Hugging Face** embeddings (`intfloat/multilingual-e5-base`) * **Groq LLaMA 3.1 8B** via API * **LlamaIndex** for orchestration * **Gradio** for user interface --- ### 🏗️ **Key Components** #### 1. **Vector Store: MongoDB Atlas** * Two collections are created in the `RAG` database: * `ramayana` for **Valmiki Ramayana** * `bhagavad_gita` for **Bhagavad Gita** * Each collection contains vector indexes: * `ramayana_vector_index` * `gita_vector_index` * Each document includes: * For Ramayana: `kanda`, `sarga`, `shloka`, `shloka_text`, and `explanation` * For Gita: `Title`, `Chapter`, `Verse`, and `explanation` #### 2. **Vector Embedding: Hugging Face** * Model: `intfloat/multilingual-e5-base` * Used to convert `shloka_text + explanation` or `verse + explanation` into vector representations * These embeddings are indexed into MongoDB for semantic similarity search #### 3. **Language Model: Groq API** * LLM used: `llama-3.1-8b-instant` via **Groq API** * Users input their Groq API key securely * LLM is instantiated per query using `llama_index.llms.groq.Groq` #### 4. **Prompt Engineering** * Custom **PromptTemplates** guide the response structure for each chatbot * **RamayanaGPT Prompt**: * Introduction to the query * Related shlokas with explanations * Summary/overview * **GitaGPT Prompt**: * Context or spiritual background * Relevant verse(s) with meaning * Reflective conclusion #### 5. **Index Initialization** * Vector indexes are loaded **once** at application startup: ```python ramayana_index = get_vector_index("RAG", "ramayana", "ramayana_vector_index") gita_index = get_vector_index("RAG", "bhagavad_gita", "gita_vector_index") ``` * Shared across all user queries for speed and efficiency #### 6. **User Interface: Gradio** * Built with `gr.Blocks` using the `Soft` theme and `Roboto Mono` font * Two tabs: * 🕉️ **RamayanaGPT** * 🕉️ **GitaGPT** * Users enter their Groq API key once; it's stored in `gr.State` * Upon authentication: * API key input and help accordion are hidden * Full chat interface is revealed (`gr.ChatInterface`) --- ### ⚙️ **Technical Stack** | Component | Technology | | --------------- | ------------------------------------- | | Backend LLM | Groq (LLaMA 3.1 8B via API) | | Embedding Model | Hugging Face (`multilingual-e5-base`) | | Vector Store | MongoDB Atlas Vector Search | | Vector Engine | LlamaIndex VectorStoreIndex | | Prompt Engine | LlamaIndex PromptTemplate | | Query Engine | LlamaIndex Query Engine | | UI Framework | Gradio (Blocks + ChatInterface) | | Deployment | Python app using `app.py` | --- ### ✅ **Features Implemented** * [x] Vector search using MongoDB Atlas * `ramayana_vector_index` for Valmiki Ramayana * `gita_vector_index` for Bhagavad Gita * [x] Hugging Face embedding (`e5-base`) integration * [x] API key input and session handling with `gr.State` * [x] LLM integration via Groq API * [x] Prompt templates customized for each scripture * [x] Tabbed interface for seamless switching between RamayanaGPT and GitaGPT * [x] Clean UX with collapsible Groq API key instructions * [x] Logging of each query with timestamp (for debugging/monitoring) """ groq_api_key = """ ### 🔑 How to Get a Groq API Key 1. **Go to** [https://console.groq.com/keys](https://console.groq.com/keys) 2. **Log in or Sign Up** for a Groq account. 3. Click **"API Keys"** from the dashboard. 4. Click **"Create Key"**, name it, and generate. 5. **Copy the API key** and store it securely. 6. **Paste** the key into the RamayanaGPT app to start chatting. --- ⚠️ **Don't share** your API key. Revoke and regenerate if needed. """ RamayanaGPT=''' ## 🕉️ **RamayanaGPT – Overview and Dataset Summary** ### 📖 **Introduction** **RamayanaGPT** is a RAG-based chatbot that draws upon the **Valmiki Ramayana**, the original Sanskrit epic, to answer user queries with reference to shlokas and their commentaries. It aims to offer precise, contextual, and respectful responses using advanced retrieval and generation technologies. ### 🗂️ **Dataset Structure** The uploaded Ramayana dataset includes the following columns: | Column | Description | | ------------- | ------------------------------------------------------------------------------ | | `kanda` | One of the 7 books (kandas) of the Ramayana (e.g., Bala Kanda, Ayodhya Kanda). | | `sarga` | The chapter number within each kanda. | | `shloka` | The shloka (verse) number within the sarga. | | `shloka_text` | Original Sanskrit verse. | | `explanation` | English explanation or interpretation of the shloka. | ### 🔍 **Example** ```text kanda: Bala Kanda sarga: 1 shloka: 1 shloka_text: तपस्स्वाध्यायनिरतं तपस्वी वाग्विदां वरम् । explanation: Ascetic Valmiki enquired of Narada, preeminent among sages, who was engaged in penance and study of the Vedas. ``` ### 💡 **Insights** * The data is well-structured with nearly **1,400+** records. * Each record reflects a deep philosophical or narrative moment from the epic. * Metadata (`kanda`, `sarga`, `shloka`) allows precise retrieval and organization. * Used for vector indexing and semantic retrieval. ''' GitaGPT=''' ## 🕉️ **GitaGPT – Overview and Dataset Summary** ### 📖 **Introduction** **GitaGPT** is a chatbot built to answer spiritual and philosophical questions using the **Bhagavad Gita** as its primary source. It references verses (slokas) directly from the Gita, delivering insights supported by both Sanskrit, Hindi, and English explanations. ### 🗂️ **Dataset Structure** The uploaded Gita dataset contains the following fields: | Column | Description | | --------------------- | --------------------------------------------------- | | `S.No.` | Serial number of the verse. | | `Title` | Title of the chapter (e.g., Arjuna's Vishada Yoga). | | `Chapter` | Gita chapter number (e.g., Chapter 1). | | `Verse` | Verse ID (e.g., Verse 1.1). | | `Sanskrit Anuvad` | Original verse in Devanagari Sanskrit. | | `Hindi Anuvad` | Hindi translation/interpretation. | | `Enlgish Translation` | English translation/interpretation. | ### 🔍 **Example** ```text Chapter: Chapter 1 Verse: Verse 1.1 Sanskrit: धृतराष्ट्र उवाच । धर्मक्षेत्रे कुरुक्षेत्रे समवेता युयुत्सवः... Hindi: धृतराष्ट्र बोले- हे संजय! धर्मभूमि कुरुक्षेत्र में एकत्र हुए युद्ध की इच्छा रखने वाले... English: Dhrtarashtra asked of Sanjaya: O SANJAYA, what did my sons and the sons of Pandu do? ``` ### 💡 **Insights** * The dataset contains **700+ verses** from all 18 chapters. * Multilingual representation (Sanskrit, Hindi, English) enhances usability for diverse users. * The verse structure (`Chapter`, `Verse`) aids in precise referencing and response generation. * Perfectly suited for semantic search via vector embeddings. ''' footer = """

© 2025

This website is made with ❤ by SARATH CHANDRA

GitHub GitHub LinkedIn LinkedIn Portfolio Portfolio
"""