Merge pull request #273 from akashAD98/rag/farmergpt

example: rag/farmerGPT

README.md

@@ -73,6 +73,7 @@ Develop a Retrieval-Augmented Generation (RAG) application using LanceDB for eff
 | RAG    | Interactive Notebook & Scripts | Blog |
 | --------- | -------------------------- | ----------- |
 ||||
+| [RAG On PDF](/examples/RAG-On-PDF/) | <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> [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)|
 | [RAG with Contextual Retrieval and Hybrid search](./examples/Contextual-RAG/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-RAG/Anthropic_Contextual_RAG.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> [![LLM](https://img.shields.io/badge/openai-api-white)](#)  [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/guide-to-use-contextual-retrieval-and-prompt-caching-with-lancedb/) |
 | [RAG with Matryoshka Embeddings and LlamaIndex](./tutorials/RAG-with_MatryoshkaEmbed-Llamaindex/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/RAG-with_MatryoshkaEmbed-Llamaindex/RAG_with_MatryoshkaEmbedding_and_Llamaindex.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)||
 | [RAG with IBM Watsonx](./examples/RAG-with-watsonx/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/RAG-with-watsonx/Watsonx_example.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![watsonx LLM](https://img.shields.io/badge/watsonx-api-lightblue)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)||

examples/RAG-On-PDF/Dataset/info.txt

@@ -0,0 +1 @@
+Data is collected from open search.

examples/RAG-On-PDF/README.md

@@ -0,0 +1,45 @@
+# FarmerGPT: RAG with PDF for Farmer Data
+
+![image](../../assets/RAG_PDF_FarmerQA.png)
+
+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.
+
+---