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# Config

## Overview

In `synthora`, configurations are defined using YAML files, enabling a modular and reusable approach to setting up models, agents, and tools. Below is a detailed explanation of the configurations and their use cases.

---

### **Basic Model Configuration**
The `basic_model.yaml` file defines the foundational configuration for a model.

```yaml
name: gpt-4o
backend: openai
backend_config:
  api_key: YOUR_API_KEY
  url: https://api.openai.com
config:
  temperature: 0
  top_p: 1.0
```

#### Key Parameters:
- **`name`**: The name of the model, here `gpt-4o`.
- **`backend`**: Specifies the backend provider, such as `openai`.
- **`backend_config`**: Contains the necessary configuration for the backend, like API keys and endpoint URLs.
- **`config`**: Defines model-specific parameters like `temperature` and `top_p` for controlling response randomness and diversity.

---

### **Referencing a Model Configuration in an Agent**
The `basic_agent.yaml` file demonstrates how to use the `basic_model.yaml` configuration in an agent.

```yaml
name: test
type: vanilla
model: !include basic_model.yaml
prompt: "Your name is {x}"
```

#### Key Parameters:
- **`name`**: The name of the agent, here `test`.
- **`type`**: The type of the agent, such as `vanilla`.
- **`model`**: References the model configuration using `!include basic_model.yaml`.
- **`prompt`**: The agent-specific prompt template, e.g., `"Your name is {x}"`.

#### Loading the Agent Configuration:
You can load the agent configuration programmatically:
```python
config = AgentConfig.load("YOUR_PATH/basic_agent.yaml")
```

---

### **Using an Agent as a Tool**
The `vanilla_agent.yaml` file demonstrates how to embed an agent within another agent or toolkit.

```yaml
name: Vanilla
type: vanilla
model: !include basic_model.yaml
prompt: "You are an AI Assistant. You are here to help me with my queries. Your name is vanilla."
tools:
  - synthora.toolkits.basic_math_toolkit.BasicMathToolkit
  - target: synthora.agents.VanillaAgent
    args: !include basic_agent.yaml
```

#### Key Additions:
- **`tools`**: Specifies tools or other agents that the current agent can use.
  - **`synthora.toolkits.basic_math_toolkit.BasicMathToolkit`**: A toolkit providing basic math functionalities.
  - **`target`**: Points to another agent class, here `synthora.agents.VanillaAgent`.
  - **`args`**: Includes configuration for the target agent using `!include`.

---

### **Referencing Predefined Prompts**
The `searcher.yaml` file illustrates how to use predefined prompts.

```yaml
name: WebSearcher
description: "This agent is a simple AI assistant that can help you search the web."
type: react
model: !include basic_model.yaml
prompt: !prompt FewShotReactPrompt
```

#### Highlights:
- **`prompt`**: Uses `!prompt` to reference a predefined prompt (`FewShotReactPrompt`), allowing for prompt standardization across agents.

---

### **Building an Agent from Config**
To construct an agent using its configuration, you can use the appropriate agent class and call the `from_config` method. The class is determined by the `type` of the agent.

#### Example:
For a `VanillaAgent`:
```python
agent = VanillaAgent.from_config(config)
```

---

This configuration-based setup ensures flexibility, reusability, and a clear separation of concerns, allowing you to define, extend, and manage agents and tools efficiently.