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description = """
## 🕉️ **Project Title: RamayanaGPT – A RAG-based Chatbot for Valmiki Ramayana**
---
### 🔍 **Project Overview**
**RamayanaGPT** is a knowledge-based conversational chatbot designed to answer questions from the *Valmiki Ramayana*. It leverages advanced **Retrieval-Augmented Generation (RAG)** principles to provide accurate and context-rich responses by referencing canonical verses (*shlokas*) and their explanations. This project integrates **MongoDB Atlas**, **LlamaIndex**, **Groq LLM**, and **Gradio UI**, offering an intuitive and scholarly digital assistant for users curious about the ancient epic.
---
### 🏗️ **Key Components**
#### 1. **Vector Store: MongoDB Atlas**
* The *Valmiki Ramayana* dataset is stored in a MongoDB Atlas collection.
* Each document consists of metadata fields: `kanda`, `sarga`, `shloka`, and `shloka_text`, with an `explanation` used for semantic retrieval.
* MongoDB Atlas Vector Search is configured using `MongoDBAtlasVectorSearch` for efficient similarity-based queries.
#### 2. **Embeddings: Hugging Face**
* Embedding model: `intfloat/multilingual-e5-base` from HuggingFace.
* Converts shloka and explanation texts into vector representations for similarity search.
#### 3. **Language Model: Groq API**
* Model used: `llama-3.1-8b-instant`.
* API key is provided at runtime by the user.
* Integrates with the query engine to synthesize responses based on context-relevant documents.
#### 4. **Prompt Engineering**
* A custom `PromptTemplate` guides the LLM to:
* Provide an introduction.
* Quote relevant shlokas.
* Explain them.
* Give a closing summary relevant to the query.
* Prompt ensures scholarly tone and contextual accuracy.
#### 5. **Vector Index**
* Built once during app startup using `VectorStoreIndex.from_vector_store()`.
* Shared across user queries to prevent repeated MongoDB connections (for efficiency and speed).
#### 6. **User Interface: Gradio**
* Tabbed interface using `gr.Blocks` with a clean `Soft` theme and Google Fonts.
* Users input their Groq API key.
* After key submission:
* API key input and button are hidden.
* Chat interface is shown using `gr.ChatInterface`.
* Uses `gr.State` to hold the Groq API key during the session.
---
### ⚙️ **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 |
| Query Engine | LlamaIndex |
| Prompt Engine | LlamaIndex PromptTemplate |
| UI Framework | Gradio (Blocks + ChatInterface) |
| Deployment | Python app using `app.py` |
---
### ✅ **Features Implemented**
* [x] Connection to MongoDB Atlas (once during app startup).
* [x] API key input and secure state handling using `gr.State`.
* [x] Vector search over embedded shloka data.
* [x] Chat interface with dynamic UI (hides API key and button post-auth).
* [x] RAG-based responses tailored to Valmiki Ramayana structure.
* [x] Modular, clean design for future extensibility (e.g., Bhagavad Gita, Mahabharata).
---
"""
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.
"""
footer = """
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<p style="margin: 0;">© 2025 </p>
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""" |