feat: Qdrant support

Signed-off-by: Anush008 <[email protected]>

Author: Anush008

atomic-examples/rag-chatbot/rag_chatbot/.gitignore renamed to atomic-examples/rag-chatbot/.gitignore

@@ -1 +1,2 @@
 chroma_db/
+qdrant_db/

atomic-examples/rag-chatbot/README.md

@@ -5,7 +5,7 @@ This directory contains the RAG (Retrieval-Augmented Generation) Chatbot example
 ## 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
+2. Vector Storage: Supports both [ChromaDB](https://www.trychroma.com/) and [Qdrant](https://qdrant.tech/) 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
@@ -15,73 +15,121 @@ This directory contains the RAG (Retrieval-Augmented Generation) Chatbot example
 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
+   VECTOR_DB_TYPE=chroma  # or 'qdrant'
    ```
+
    Replace `your_openai_api_key` with your actual OpenAI API key.
 
 5. **Run the RAG Chatbot:**
+
    ```bash
    poetry run python rag_chatbot/main.py
    ```
 
+## Vector Database Configuration
+
+The RAG Chatbot supports two vector databases:
+
+### ChromaDB (Default)
+
+- **Local storage**: Data is stored locally in the `chroma_db/` directory
+- **Configuration**: Set `VECTOR_DB_TYPE=chroma` in your `.env` file
+
+### Qdrant
+
+- **Local storage**: Data is stored locally in the `qdrant_db/` directory
+- **Configuration**: Set `VECTOR_DB_TYPE=qdrant` in your `.env` file
+
+## Usage
+
+### Using ChromaDB (Default)
+
+```bash
+export VECTOR_DB_TYPE=chroma
+poetry run python rag_chatbot/main.py
+```
+
+### Using Qdrant (Local)
+
+```bash
+export VECTOR_DB_TYPE=qdrant
+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.
+### 3. Vector Database Services (`services/`)
+
+- **Base Service** (`services/base.py`): Abstract interface for vector database operations
+- **ChromaDB Service** (`services/chroma_db.py`): ChromaDB implementation
+- **Qdrant Service** (`services/qdrant_db.py`): Qdrant implementation
+- **Factory** (`services/factory.py`): Creates the appropriate service based on configuration
 
 ### 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
+   - Storing chunks in the selected vector database with vector embeddings
 
 2. For each user question:
    - The Query Agent generates an optimized semantic search query
-   - Relevant chunks are retrieved from ChromaDB
+   - Relevant chunks are retrieved from the vector database
    - 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
+- Switching between ChromaDB and Qdrant by changing the `VECTOR_DB_TYPE` environment variable
 
 ## 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?"