Create README.md for RAG on pdf

examples/RAG-On-PDF/README.md

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+# FarmerGPT: RAG with PDF for Farmer Data
+
+This project demonstrates how to build a Retrieval-Augmented Generation (RAG) application using farmer-specific data. The data is extracted from a PDF containing crop variety information for sugarcane, turmeric, bamboo, cashew nuts, and more. The application leverages **LangChain** and **LanceDB** to create a customizable and extensible FarmerGPT solution.
+
+---
+
+## Features
+
+- **Customizable Prompts**: Easily adapt prompts to suit specific queries and use cases.
+- **Memory Support**: Incorporates memory capabilities to retain context during interactions.
+- **PDF Integration**: Processes and retrieves data from the provided PDF file.
+
+---
+
+## How to Use
+
+. **Use the Colab Notebook**:
+   - To try FarmerGPT directly without setup, use the provided Google Colab notebook: <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/RAG-On-PDF
+/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+   - Open the notebook, follow the instructions, and run the cells to interact with the application.
+
+---
+
+## Key Technologies
+
+- **LangChain**: Framework for building applications powered by large language models.
+- **LanceDB**: Vector database used for efficient document retrieval.
+---
+
+## Sample Use Cases
+
+- Assisting farmers with queries about crop varieties.
+- Providing tailored advice based on specific crop information.
+- Serving as a reference for agricultural experts and enthusiasts.
+---
+
+## Customization
+
+You can build upon this template to include:
+- Additional crop varieties.
+- New functionalities such as integration with IoT devices.
+- Multi-language support for wider accessibility.
+
+---