> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/NVIDIA-NeMo/Guardrails/llms.txt
> Use this file to discover all available pages before exploring further.
# Configuration Overview
> Learn how to configure NeMo Guardrails for your LLM applications
NeMo Guardrails uses a structured configuration approach to define guardrails for your LLM applications. A guardrails configuration defines the LLM(s) to be used, the rails that should be active, and custom settings.
## Configuration Structure
The standard structure for a guardrails configuration folder looks like this:
```
.
├── config
│ ├── actions.py
│ ├── config.py
│ ├── config.yml
│ ├── rails.co
│ ├── ...
```
### Main Configuration Files
A complete guardrails configuration typically consists of three main components:
General configuration options including LLM models, active rails, and custom settings
Colang definitions that define various types of rails and conversation flows
Custom Python actions that can be called from your guardrails
## Configuration Loading
To use a guardrails configuration in your application:
```python theme={null}
from nemoguardrails import LLMRails, RailsConfig
# Load a guardrails configuration from the specified path
config = RailsConfig.from_path("PATH/TO/CONFIG")
rails = LLMRails(config)
completion = rails.generate(
messages=[{"role": "user", "content": "Hello world!"}]
)
```
**Sample output:**
```json theme={null}
{"role": "assistant", "content": "Hi! How can I help you?"}
```
## Types of Guardrails
NeMo Guardrails supports five main types of guardrails that can be configured:
Applied to user input; can reject or alter input (e.g., mask sensitive data, rephrase)
```yaml theme={null}
rails:
input:
flows:
- check jailbreak
- mask sensitive data on input
```
Influence how the LLM is prompted; operate on canonical form messages and determine conversation flow
```yaml theme={null}
rails:
dialog:
single_call:
enabled: False
```
Applied to retrieved chunks in RAG scenarios; can reject or alter chunks
```yaml theme={null}
rails:
retrieval:
flows:
- check relevance
```
Applied to input/output of custom actions (tools) called by the LLM
Applied to LLM output; can reject or alter output before returning to user
```yaml theme={null}
rails:
output:
flows:
- self check facts
- self check hallucination
```
## Basic Configuration Example
Here's a minimal example from `examples/configs/sample/config.yml`:
```yaml theme={null}
instructions:
- type: general
content: |
Below is a conversation between a bot and a user. The bot is talkative and
quirky. If the bot does not know the answer to a question, it truthfully says it does not know.
sample_conversation: |
user "Hello there!"
express greeting
bot express greeting
"Hello! How can I assist you today?"
user "What can you do for me?"
ask about capabilities
bot respond about capabilities
"I am an AI assistant built to help you."
models:
- type: main
engine: openai
model: gpt-3.5-turbo-instruct
rails:
input:
flows:
- dummy input rail
output:
flows:
- dummy output rail
```
## Next Steps
Learn about all available configuration options
Define custom rails using Colang
Create Python actions for your guardrails
Configure different LLM providers