> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/guardrails/_mcp/server.

# Configure Guardrails

## Docs

- [About Configuring Guardrails](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/configure-rails.md): Configure YAML files, Colang flows, custom actions, and other components to control LLM behavior.
- [NeMo Guardrails Library Configuration Overview](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/overview.md): Learn to write config.yml, Colang flows, and custom actions for guardrails.
- [Pre-Configuration Checklist](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/before-configuration.md): Prepare LLM endpoints, NemoGuard NIMs, and knowledge base documents before configuration.
- [Configuring YAML File](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/yaml-schema.md): Define models, guardrails, prompts, and tracing settings in the config.yml file.
- [Model Configuration for NeMo Guardrails](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/yaml-schema/model-configuration.md): Configure LLM engines, embedding models, and task-specific models in config.yml.
- [Guardrails Configuration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/yaml-schema/guardrails-configuration.md): Configure input, output, dialog, retrieval, and execution rails in config.yml to control LLM behavior.
- [Prompt Configuration for the NVIDIA NeMo Guardrails Library](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/yaml-schema/prompt-configuration.md): Customize prompts for self-check, fact-checking, and intent generation tasks.
- [Tracing Configuration for the NVIDIA NeMo Guardrails Library](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/yaml-schema/tracing-configuration.md): Configure FileSystem and OpenTelemetry tracing adapters to monitor guardrails.
- [Streaming Configuration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/yaml-schema/streaming.md): Configure streaming for LLM token generation and output rail processing in config.yml.
- [Streaming LLM Responses in Real-Time](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/yaml-schema/streaming/global-streaming.md): Enable and use streaming mode for LLM responses in real-time in the NeMo Guardrails library.
- [Output Rail Streaming Configuration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/yaml-schema/streaming/output-rail-streaming.md): Configure how output rails process streamed tokens in chunked mode.
- [Configuration YAML Schema Reference](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/configuration-reference.md): Reference for all config.yml options including models, rails, prompts, and advanced settings.
- [Guardrail Catalog](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog.md): Reference for pre-built guardrails including content safety, jailbreak detection, topic control, PII handling, agentic security, and third-party APIs.
- [Content Safety](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/content-safety.md): Reference for pre-built content safety guardrails for protecting against violence, criminal activity, hate speech, sexually explicit content, and similar areas.
- [Jailbreak Protection](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/jailbreak-protection.md): Reference for jailbreak protection guardrails that help prevent adversarial attempts from bypassing safety measures.
- [Topic Control](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/topic-control.md): Reference for topic control guardrails that ensure conversations stay within predefined subject boundaries.
- [PII Detection](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/pii-detection.md): Reference for PII detection guardrails that protect user privacy by detecting and masking sensitive data.
- [Agentic Security](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/agentic-security.md): Reference for agentic security guardrails that protect LLM-based agents using tools and interacting with external systems.
- [Tool Calling](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/tool-calling.md): Validate model-emitted tool calls and application-returned tool results with the IORails tool-calling rails.
- [Hallucinations & Fact-Checking](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/fact-checking.md): Reference for fact-checking and hallucination detection guardrails that ensure LLM output is grounded in evidence.
- [LLM Self-Check](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/self-check.md): Reference for LLM self-checking guardrails that prompt the LLM to perform input checking, output checking, or fact-checking.
- [Third-Party APIs](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party.md): Reference for third-party API integrations that connect with managed services for guardrail use cases.
- [ActiveFence Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/active-fence.md)
- [AlignScore Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/alignscore.md)
- [AutoAlign Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/auto-align.md)
- [Cisco AI Defense Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/ai-defense.md)
- [Clavata Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/clavata.md)
- [Cleanlab Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/cleanlab.md)
- [CrowdStrike AIDR integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/crowdstrike-aidr.md)
- [F5 Guardrails Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/f5-ai-guardrails.md)
- [Fiddler Guardrails Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/fiddler.md)
- [GCP Text Moderation Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/gcp-text-moderations.md)
- [GLiNER Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/gliner.md)
- [GuardrailsAI Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/guardrails-ai.md)
- [HuggingFace Classifier Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/hf-classifier.md)
- [Llama-Guard Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/llama-guard.md)
- [Pangea AI Guard integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/pangea.md)
- [Patronus Evaluate API Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/patronus-evaluate-api.md)
- [Patronus Lynx Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/patronus-lynx.md)
- [PolicyAI Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/policyai.md)
- [Polygraf Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/polygraf.md)
- [Presidio Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/presidio.md)
- [Private AI Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/privateai.md)
- [Prompt Security Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/prompt-security.md)
- [Regular Expression Detection Integration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/regex.md)
- [Trend Micro Vision One AI Application Security](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/guardrail-catalog/third-party/trend-micro.md)
- [Configuring Custom Actions](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/actions.md): Create Python actions to extend guardrails with external APIs and validation logic.
- [Creating Custom Actions](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/actions/creating-actions.md): Create custom actions using the @action decorator to integrate Python logic.
- [Built-in Actions Reference](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/actions/built-in-actions.md): Reference for default actions included in the NeMo Guardrails library.
- [Action Parameters Reference](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/actions/action-parameters.md): Reference for special parameters like context, llm, and config provided to actions.
- [Registering Custom Actions](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/actions/registering-actions.md): Register custom actions via actions.py, LLMRails.register_action(), or config.py.
- [Rail Outcomes](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/actions/rail-outcomes.md): Return engine-neutral allow, block, and transform decisions from rail actions.
- [Outbound HTTP in Actions](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/actions/outbound-http.md): Use the canonical asynchronous HTTP boundary for action requests, retries, telemetry, lifecycle management, and deterministic tests.
- [Configuring Custom Initialization](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/custom-initialization.md): Use config.py to register custom LLM providers, embedding providers, and shared resources at startup.
- [The Init Function for NeMo Guardrails](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/custom-initialization/init-function.md): Define the init() function to initialize resources and register action parameters at startup.
- [Custom LLM Providers for NeMo Guardrails](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/custom-initialization/custom-llm-providers.md): Register LangChain BaseLLM and BaseChatModel providers under the LangChain framework path (requires NEMOGUARDRAILS_LLM_FRAMEWORK=langchain).
- [Custom LLM Models for the NVIDIA NeMo Guardrails library](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/custom-initialization/custom-llm-model.md): Implement the LLMModel protocol to connect a non-OpenAI-compatible backend to the NVIDIA NeMo Guardrails library without depending on LangChain.
- [Testing Your Guardrails Configuration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/custom-initialization/testing-your-config.md): Write fast, deterministic tests for your NeMo Guardrails configuration using the public testing surface.
- [Custom LLM Frameworks for the NVIDIA NeMo Guardrails library](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/custom-initialization/custom-llm-framework.md): Replace the LLM framework layer to connect LiteLLM, an in-house orchestrator, or any non-default LLM stack to the NVIDIA NeMo Guardrails library.
- [Custom Embedding Providers for NeMo Guardrails](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/custom-initialization/custom-embedding-providers.md): Register custom embedding providers for vector similarity search in NeMo Guardrails.
- [Custom Configuration Data for NeMo Guardrails](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/custom-initialization/custom-data.md): Pass and access custom data from config.yml in your initialization code and actions.
- [Colang Guide](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang.md): Learn Colang, the event-driven language for defining guardrails flows and bot behavior.
- [Colang 2.0 Guide](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2.md): Learn Colang 2.0 syntax, concepts, migration guidance, tutorials, and language reference topics.
- [What's Changed](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/whats-changed.md): This guide provides a non-comprehensive overview of the most important changes in Colang 2.0.
- [Migrating from Colang 1 to Colang 2](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/migration-guide.md): Convert Colang 1.0 configurations to Colang 2.x using the nemoguardrails convert tool.
- [Getting Started with Colang 2.0](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/getting-started.md): Build Colang 2.0 guardrails step by step, from Hello World to LLM and multimodal flows.
- [Hello World](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/getting-started/hello-world.md): This section introduces a "Hello World" Colang example.
- [Interaction Loop](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/getting-started/interaction-loop.md): This section explains how to create an interaction loop in Colang 2.0.
- [Input Rails](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/getting-started/input-rails.md): This section explains how to create *input rails* in Colang 2.0.
- [Dialog Rails](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/getting-started/dialog-rails.md): This section explains how to create dialog rails using Colang.
- [LLM Flows](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/getting-started/llm-flows.md): This section explains how to create LLM-driven flows in Colang 2.0.
- [Multimodal Rails](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/getting-started/multimodal-rails.md): This section explains how to create multimodal rails in Colang 2.0.
- [Recommended Next Steps](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/getting-started/recommended-next-steps.md): The Colang 2.0 getting started guide introduces you to a basic Hello World example, and then goes into Dialog Rails,  Input Rails, Multimodal Rails, and other Colang 2.0 concepts l
- [Colang 2.0 Language Reference](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference.md): Reference documentation for Colang 2.0 syntax, events, actions, flows, variables, and the standard library.
- [Introduction](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/introduction.md): Colang is an event-based modeling language to enable the design of highly flexible conversational interactions between a human and a bot. Since learning a new language is not an ea
- [Event Generation & Matching](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/event-generation-and-matching.md): When working with Colang, we assume to have a common event channel that contains all relevant events happening in the interactive system. From a Colang perspective, relevant events
- [Working with Actions](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/working-with-actions.md): You have seen in section Event Generation & Matching how to work with events. We will now introduce actions that will enable you to better manage events and their temporal dependen
- [Defining Flows](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/defining-flows.md): So far you have seen only one flow, the main flow. But in Colang we can define many different flows, like functions in other programming languages. A flow defines a specific intera
- [Working with Variables & Expressions](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/working-with-variables-and-expressions.md): Like Python, Colang supports these fundamental data types: `string`, `int`, `float`, `bool`, `list`, `set`, `dict`, in addition to the Colang specific event, action and flow refere
- [Flow control](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/flow-control.md): Flow control is an essential tool in all programming languages, and Colang supports this as well. It enables branching and repetition of interaction patterns in a single flow.
- [Colang Standard Library (CSL)](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/the-standard-library.md): The Colang Standard Library (CSL) provides an abstraction from the underlying event and action layer and offers a semantic interface to design interaction patterns between the bot
- [Fundamental Core Flows](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/the-standard-library/core.md): The core library that contains all relevant flows related to user and bot utterance events and actions.
- [Timing Flows](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/the-standard-library/timing.md): Flows related to timing and reacting to periods of silence.
- [LLM Flows](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/the-standard-library/lmm.md): Trigger a bot utterance similar to given text
- [Interactive Avatar Modality Flows](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/the-standard-library/avatars.md): **User Event Flows**
- [Guardrail Flows](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/the-standard-library/guardrails.md): Flows to guardrail user inputs and LLM responses.
- [User Attention Flows](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/the-standard-library/attention.md): Flows to handle user attention.
- [Make use of Large Language Models (LLM)](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/make-use-of-llms.md): At its core, Colang does not require a Large Language Model (LLM) as backend. However, many of the more advanced mechanisms in the Colang Standard Library (CSL) depend on one.
- [More on Flows](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/more-on-flows.md): In section Defining Flows, we learned the core mechanisms of flows. In this section will look at more advanced topics that are related to flows.
- [Python Actions](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/python-actions.md): In the section Working with Actions, you have seen how actions can be used in the context of UMIM events. Additionally, you can also use the action concept to call custom python fu
- [Development and Debugging](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-2/language-reference/development-and-debugging.md): Currently, the Colang story designing and building environment is fairly limited, but will be improved on in the future.
- [Colang 1.0 Guide](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-1.md): Reference and tutorials for Colang 1.0 syntax for defining dialog flows and guardrails.
- [Colang 1.0 Language Syntax](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-1/colang-language-syntax-guide.md): Comprehensive syntax guide for Colang 1.0 including messages, flows, variables, and patterns.
- [Colang 1.0 Tutorials](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-1/tutorials.md): Step-by-step tutorials for building guardrails with Colang 1.0 from Hello World to RAG.
- [Hello World](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-1/tutorials/1-hello-world.md): Create your first guardrails configuration to control greeting behavior with Colang 1.0.
- [Core Colang Concepts](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-1/tutorials/2-core-colang-concepts.md): Learn essential Colang concepts including messages, flows, context variables, and LLM integration.
- [Demo Use Case](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-1/tutorials/3-demo-use-case.md): Introduction to the ABC Bot example used throughout the Colang 1.0 tutorial series.
- [Input Rails](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-1/tutorials/4-input-rails.md): Add input rails to validate and filter user messages before LLM processing.
- [Output Rails](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-1/tutorials/5-output-rails.md): Add output rails to filter and validate LLM responses before returning to users.
- [Topical Rails](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-1/tutorials/6-topical-rails.md): Implement topical rails to keep conversations on-topic and prevent off-topic discussions.
- [Retrieval-Augmented Generation](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-1/tutorials/7-rag.md): Apply guardrails to RAG scenarios with knowledge base integration and fact checking.
- [Guardrailing Bot Reasoning Content](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/colang-1/bot-thinking-guardrails.md): Inspect and control LLM reasoning traces to block problematic patterns or enhance moderation decisions.
- [Colang Usage Examples](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/usage-examples.md): Practical examples of Colang patterns for bot messages, value extraction, and flow control.
- [Bot Message Instructions](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/usage-examples/bot-message-instructions.md): Provide custom instructions to control how the LLM generates bot messages in Colang 1.0 and 2.0.
- [Extract User-provided Values](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/colang/usage-examples/extract-user-provided-values.md): Extract and store user-provided values like names, dates, and queries in context variables.
- [Other Configurations](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/other-configurations.md): Additional configuration topics including knowledge base setup and embedding search providers.
- [Knowledge Base Configuration](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/other-configurations/knowledge-base.md): Configure the knowledge base folder for RAG-based responses using markdown documents.
- [Embedding Search Providers](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/other-configurations/embedding-search-providers.md): Configure embedding search providers for vector similarity search using FastEmbed, OpenAI, or custom implementations.
- [Caching Instructions and Prompts](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/caching.md): Configure in-memory caching for LLM calls and KV cache reuse to improve performance and reduce latency.
- [Memory Model Cache](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/caching/model-memory-cache.md): Configure in-memory caching to avoid repeated LLM calls for identical prompts using LFU eviction.
- [KV Cache Reuse for NemoGuard NIM](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/caching/kv-cache-reuse.md): Enable KV cache reuse in NVIDIA NIM for LLMs to reduce inference latency for NemoGuard models.
- [Exceptions and Error Handling](https://docs.nvidia.com/nemo/guardrails/v0.24.0/configure-guardrails/exceptions.md): Raise and handle exceptions in guardrails flows to control error behavior and custom responses.