RAG
Seraphnet's Retrieval-Augmented Generation (RAG) Module
Overview
Retrieval-Augmented Generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources. This grounds large language models (LLMs) on the most up-to-date information and gives users insight into the LLMs' generative process. Implementing RAG reduces opportunities for LLMs to pull potentially sensitive or incorrect information baked into their parameters, mitigating hallucinations and data leaks.
Seraphnet has integrated RAG with semantic search to further improve search result quality, especially when leveraging sizable information sources in Swarm Pods. Semantic search enables scanning vast databases for specific information in a precise manner, increasing response quality.
Implementation Details
Key Components
Cohere: An efficient and secure toolkit used in Seraphnet's V1 RAG implementation. It allows LLMs to accurately answer questions and solve tasks using enterprise data as the ground truth.
NVIDIA Triton Inference Server: Open-source software that standardizes AI model deployment and execution across workloads.
NVIDIA TensorRT-LLM: Provides an easy-to-use Python API to define LLMs and build optimized TensorRT engines for efficient inference on NVIDIA GPUs. It also includes components to create Python and C++ runtimes for executing these engines.
Hydra: A powerful open-source configuration management framework used to simplify the management of complex configurations in Seraphnet's RAG implementation. Hydra provides:
Structured and hierarchical configuration management across multiple levels (global, application-specific, component-level)
Ability to handle configurations from various sources (files, CLI, environment variables)
Modular design principles for better organization, maintainability and extensibility
Streamlined development and deployment processes
RAG Module Functionality
Seraphnet's RAG module combines semantic search with Cohere, NVIDIA solutions, and the Hydra configuration management framework. This allows:
Reducing the risk of LLM hallucinations and data leakage
Enabling infrastructure to draw accurate, up-to-date information from various sources
Providing ideologically transparent solutions by grounding LLM outputs in verifiable external data
Structured configuration management for efficient development, testing and deployment
Getting Started
To get started with Seraphnet's RAG module, please refer to our GitHub repository: https://github.com/Seraphnetai/hydra-template
This repository contains the implementation details, Hydra configuration instructions, and usage examples for integrating the RAG module into your applications.
Additional Resources
Hydra GitHub Repository: The configuration management framework used in Seraphnet's RAG implementation.
Hydra Documentation: Official documentation for the Hydra configuration framework.
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