

# vLLM Integration Guide

!!! info "Overview"
    vLLM is a high-performance and easy-to-use library for LLM inference and serving. This guide explains how to integrate vLLM with Swarms for efficient, production-grade language model deployment.


## Installation

!!! note "Prerequisites"
    Before you begin, make sure you have Python 3.8+ installed on your system.

=== "pip"
    ```bash
    pip install -U vllm swarms
    ```

=== "poetry"
    ```bash
    poetry add vllm swarms
    ```

## Basic Usage

Here's a simple example of how to use vLLM with Swarms:

```python title="basic_usage.py"
from swarms.utils.vllm_wrapper import VLLMWrapper

# Initialize the vLLM wrapper
vllm = VLLMWrapper(
    model_name="meta-llama/Llama-2-7b-chat-hf",
    system_prompt="You are a helpful assistant.",
    temperature=0.7,
    max_tokens=4000
)

# Run inference
response = vllm.run("What is the capital of France?")
print(response)
```

## VLLMWrapper Class

!!! abstract "Class Overview"
    The `VLLMWrapper` class provides a convenient interface for working with vLLM models.

### Key Parameters

| Parameter | Type | Description | Default |
|-----------|------|-------------|---------|
| `model_name` | str | Name of the model to use | "meta-llama/Llama-2-7b-chat-hf" |
| `system_prompt` | str | System prompt to use | None |
| `stream` | bool | Whether to stream the output | False |
| `temperature` | float | Sampling temperature | 0.5 |
| `max_tokens` | int | Maximum number of tokens to generate | 4000 |

### Example with Custom Parameters

```python title="custom_parameters.py"
vllm = VLLMWrapper(
    model_name="meta-llama/Llama-2-13b-chat-hf",
    system_prompt="You are an expert in artificial intelligence.",
    temperature=0.8,
    max_tokens=2000
)
```

## Integration with Agents

You can easily integrate vLLM with Swarms agents for more complex workflows:

```python title="agent_integration.py"
from swarms import Agent
from swarms.utils.vllm_wrapper import VLLMWrapper

# Initialize vLLM
vllm = VLLMWrapper(
    model_name="meta-llama/Llama-2-7b-chat-hf",
    system_prompt="You are a helpful assistant."
)

# Create an agent with vLLM
agent = Agent(
    agent_name="Research-Agent",
    agent_description="Expert in conducting research and analysis",
    system_prompt="""You are an expert research agent. Your tasks include:
    1. Analyzing complex topics
    2. Providing detailed summaries
    3. Making data-driven recommendations""",
    llm=vllm,
    max_loops=1
)

# Run the agent
response = agent.run("Research the impact of AI on healthcare")
```

## Advanced Features

### Batch Processing

!!! tip "Performance Optimization"
    Use batch processing for efficient handling of multiple tasks simultaneously.

```python title="batch_processing.py"
tasks = [
    "What is machine learning?",
    "Explain neural networks",
    "Describe deep learning"
]

results = vllm.batched_run(tasks, batch_size=3)
```

### Error Handling

!!! warning "Error Management"
    Always implement proper error handling in production environments.

```python title="error_handling.py"
from loguru import logger

try:
    response = vllm.run("Complex task")
except Exception as error:
    logger.error(f"Error occurred: {error}")
```

## Best Practices

!!! success "Recommended Practices"
    === "Model Selection"
        - Choose appropriate model sizes based on your requirements
        - Consider the trade-off between model size and inference speed

    === "System Resources"
        - Ensure sufficient GPU memory for your chosen model
        - Monitor resource usage during batch processing

    === "Prompt Engineering"
        - Use clear and specific system prompts
        - Structure user prompts for optimal results

    === "Error Handling"
        - Implement proper error handling and logging
        - Set up monitoring for production deployments

    === "Performance"
        - Use batch processing for multiple tasks
        - Adjust max_tokens based on your use case
        - Fine-tune temperature for optimal output quality

## Example: Multi-Agent System

Here's an example of creating a multi-agent system using vLLM:

```python title="multi_agent_system.py"
from swarms import Agent, ConcurrentWorkflow
from swarms.utils.vllm_wrapper import VLLMWrapper

# Initialize vLLM
vllm = VLLMWrapper(
    model_name="meta-llama/Llama-2-7b-chat-hf",
    system_prompt="You are a helpful assistant."
)

# Create specialized agents
research_agent = Agent(
    agent_name="Research-Agent",
    agent_description="Expert in research",
    system_prompt="You are a research expert.",
    llm=vllm
)

analysis_agent = Agent(
    agent_name="Analysis-Agent",
    agent_description="Expert in analysis",
    system_prompt="You are an analysis expert.",
    llm=vllm
)

# Create a workflow
agents = [research_agent, analysis_agent]
workflow = ConcurrentWorkflow(
    name="Research-Analysis-Workflow",
    description="Comprehensive research and analysis workflow",
    agents=agents
)

# Run the workflow
result = workflow.run("Analyze the impact of renewable energy")
```