pull/1051/head
harshalmore31 6 days ago
parent 3e17704a12
commit cedc7a0062

@ -19,31 +19,44 @@ pip install qdrant-client fastembed swarms-memory litellm
## Tutorial Steps
1. **Install Swarms**: First, install the latest version of Swarms:
### Step 1: Install Swarms
```bash
pip3 install -U swarms
```
First, install the latest version of Swarms:
2. **Environment Setup**: Set up your environment variables in a `.env` file:
```bash
pip3 install -U swarms
```
```plaintext
OPENAI_API_KEY="your-api-key-here"
QDRANT_URL="https://your-cluster.qdrant.io"
QDRANT_API_KEY="your-api-key"
WORKSPACE_DIR="agent_workspace"
```
### Step 2: Environment Setup
Set up your environment variables in a `.env` file:
```plaintext
OPENAI_API_KEY="your-api-key-here"
QDRANT_URL="https://your-cluster.qdrant.io"
QDRANT_API_KEY="your-api-key"
WORKSPACE_DIR="agent_workspace"
```
### Step 3: Choose Deployment
Select your Qdrant deployment option:
- **In-memory**: For testing and development (data is not persisted)
- **Local server**: For production deployments with persistent storage
- **Qdrant Cloud**: Managed cloud service (recommended for production)
### Step 4: Configure Database
Set up the vector database wrapper with your preferred embedding model and collection settings
3. **Choose Deployment**: Select your Qdrant deployment option:
- **In-memory**: For testing and development (data is not persisted)
- **Local server**: For production deployments with persistent storage
- **Qdrant Cloud**: Managed cloud service (recommended for production)
### Step 5: Add Documents
4. **Configure Database**: Set up the vector database wrapper with your preferred embedding model and collection settings
Load documents using individual or batch processing methods
5. **Add Documents**: Load documents using individual or batch processing methods
### Step 6: Create Agent
6. **Create Agent**: Initialize your agent with RAG capabilities and start querying
Initialize your agent with RAG capabilities and start querying
## Code

Loading…
Cancel
Save