markdown.py

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.

'''

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