NVIDIA/GenerativeAIExamples
GenerativeAIExamples
This project is a library of reference implementations and blueprints for deploying large language models and generative AI workflows. It provides a collection of practical examples designed to guide the deployment of generative systems.
The repository features architectural patterns for autonomous agentic workflows that utilize reasoning and tool integration to execute multi-step tasks. It also includes frameworks and templates for building retrieval-augmented generation pipelines that connect language models to vector databases and external data sources.
The codebase covers several functional areas, including the construction of knowledge graphs for structured relational data and the integration of voice interfaces for speech-to-text and text-to-speech processing. It also provides tools for evaluating the quality and reliability of model outputs and optimizing retrieval performance.
Features
- LLM and Transformer Examples - Serves as a collection of practical code implementations and blueprints for deploying large language models and workflows.
- Agentic Workflow Automations - Ships platforms for designing and executing stateful, multi-step sequences that integrate tools and human oversight.
- Autonomous Agent Orchestration - Provides frameworks for deploying modular agents that automate complex, multi-step workflows with human oversight.
- RAG Pipelines - Implements workflows that tune data retrieval and processing stages to optimize generative results for business needs.
- RAG Frameworks - Provides reference architectures and development environments for building retrieval-augmented generation applications.
- Retrieval Augmented Generation Pipelines - Provides a framework for connecting language models to external data sources and vector databases via RAG pipelines.
- Agentic Workflows - Provides architectural patterns and templates for building autonomous agents that use reasoning and tool integration.
- AI Voice and Video Integration - Offers examples for integrating speech-to-text and text-to-speech interfaces into generative AI applications.
- Conversational Voice Pipelines - Implements coordinated workflows integrating speech-to-text and text-to-speech for real-time voice interactions.
- Generative Model Evaluation - Includes frameworks for assessing the quality and reliability of responses produced within generative pipelines.
- Knowledge Graph Construction - Implements automated processes for building graph structures from datasets to improve retrieval precision.
- Retrieval Optimization - Provides strategies for refining data splitting and querying to enhance the precision and speed of retrieval.
- Multimodal Voice Integrations - Integrates speech-to-text, language models, and text-to-speech for real-time multimodal voice interactions.
- Knowledge Graph Construction Tools - Provides frameworks for building interconnected relational data structures to enhance information retrieval accuracy.
- RAG Pipeline Optimizers - Provides configurations and tools for tuning document parsing and retrieval latency to improve response precision.
