Add simple RAG example

Author: KennyVaneetvelde

README.md

@@ -152,10 +152,20 @@ This snippet showcases how to create a customizable agent that responds to user
 
 In addition to the quickstart examples, we have more complex examples demonstrating the power of Atomic Agents:
 
+- [Basic Multimodal](/atomic-examples/basic-multimodal/README.md): Demonstrates how to analyze images with text, focusing on extracting structured information from nutrition labels using GPT-4 Vision capabilities.
+
+- [Deep Research](/atomic-examples/deep-research/README.md): An advanced example showing how to perform deep research tasks.
+
+- [Orchestration Agent](/atomic-examples/orchestration-agent/README.md): Shows how to create an Orchestrator Agent that intelligently decides between using different tools (search or calculator) based on user input.
+
+- [RAG Chatbot](/atomic-examples/rag-chatbot/README.md): A chatbot implementation using Retrieval-Augmented Generation (RAG) to provide context-aware responses.
+
 - [Web Search Agent](/atomic-examples/web-search-agent/README.md): An intelligent agent that performs web searches and answers questions based on the results.
 
 - [YouTube Summarizer](/atomic-examples/youtube-summarizer/README.md): An agent that extracts and summarizes knowledge from YouTube videos.
 
+- [YouTube to Recipe](/atomic-examples/youtube-to-recipe/README.md): An example that extracts structured recipe information from cooking videos, demonstrating complex information extraction and structuring.
+
 For a complete list of examples, see the [examples directory](/atomic-examples/).
 
 These examples provide a great starting point for understanding and using Atomic Agents.
@@ -262,7 +272,6 @@ from web_search_agent.tools.another_search import AnotherSearchTool
 # Update the output schema
 query_agent.config.output_schema = AnotherSearchTool.input_schema
 ```
-
 This design pattern simplifies the process of chaining agents and tools, making your AI applications more adaptable and easier to maintain.
 
 ## Running the CLI
@@ -339,3 +348,4 @@ This project is licensed under the MIT License—see the [LICENSE](LICENSE) file
 ## Star History
 
 [![Star History Chart](https://api.star-history.com/svg?repos=BrainBlend-AI/atomic-agents&type=Date)](https://star-history.com/#BrainBlend-AI/atomic-agents&Date)
+

atomic-examples/rag-chatbot/README.md

@@ -0,0 +1,95 @@
+# RAG Chatbot
+
+This directory contains the RAG (Retrieval-Augmented Generation) Chatbot example for the Atomic Agents project. This example demonstrates how to build an intelligent chatbot that uses document retrieval to provide context-aware responses using the Atomic Agents framework.
+
+## Features
+
+1. Document Chunking: Automatically splits documents into manageable chunks with configurable overlap
+2. Vector Storage: Uses ChromaDB for efficient storage and retrieval of document chunks
+3. Semantic Search: Generates and executes semantic search queries to find relevant context
+4. Context-Aware Responses: Provides detailed answers based on retrieved document chunks
+5. Interactive UI: Rich console interface with progress indicators and formatted output
+
+## Getting Started
+
+To get started with the RAG Chatbot:
+
+1. **Clone the main Atomic Agents repository:**
+   ```bash
+   git clone https://github.com/BrainBlend-AI/atomic-agents
+   ```
+
+2. **Navigate to the RAG Chatbot directory:**
+   ```bash
+   cd atomic-agents/atomic-examples/rag-chatbot
+   ```
+
+3. **Install the dependencies using Poetry:**
+   ```bash
+   poetry install
+   ```
+
+4. **Set up environment variables:**
+   Create a `.env` file in the `rag-chatbot` directory with the following content:
+   ```env
+   OPENAI_API_KEY=your_openai_api_key
+   ```
+   Replace `your_openai_api_key` with your actual OpenAI API key.
+
+5. **Run the RAG Chatbot:**
+   ```bash
+   poetry run python rag_chatbot/main.py
+   ```
+
+## Components
+
+### 1. Query Agent (`agents/query_agent.py`)
+Generates semantic search queries based on user questions to find relevant document chunks.
+
+### 2. QA Agent (`agents/qa_agent.py`)
+Analyzes retrieved chunks and generates comprehensive answers to user questions.
+
+### 3. ChromaDB Service (`services/chroma_db.py`)
+Manages the vector database for storing and retrieving document chunks.
+
+### 4. Context Provider (`context_providers.py`)
+Provides retrieved document chunks as context to the agents.
+
+### 5. Main Script (`main.py`)
+Orchestrates the entire process, from document processing to user interaction.
+
+## How It Works
+
+1. The system initializes by:
+   - Downloading a sample document (State of the Union address)
+   - Splitting it into chunks with configurable overlap
+   - Storing chunks in ChromaDB with vector embeddings
+
+2. For each user question:
+   - The Query Agent generates an optimized semantic search query
+   - Relevant chunks are retrieved from ChromaDB
+   - The QA Agent analyzes the chunks and generates a detailed answer
+   - The system displays the thought process and final answer
+
+## Customization
+
+You can customize the RAG Chatbot by:
+- Modifying chunk size and overlap in `config.py`
+- Adjusting the number of chunks to retrieve for each query
+- Using different documents as the knowledge base
+- Customizing the system prompts for both agents
+
+## Example Usage
+
+The chatbot can answer questions about the loaded document, such as:
+- "What were the main points about the economy?"
+- "What did the president say about healthcare?"
+- "How did he address foreign policy?"
+
+## Contributing
+
+Contributions are welcome! Please fork the repository and submit a pull request with your enhancements or bug fixes.
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](../../LICENSE) file for details.