RAG Pipelines (Retrieval-Augmented Generation)

RAG Pipelines (Retrieval-Augmented Generation) are a technology that integrates retrieval-based and generative models to produce high-quality content. These pipelines first retrieve relevant information from external sources and then augment this with generative capabilities to create more accurate and contextually rich text.

RAG Pipelines address the limitations of purely generative models by leveraging external knowledge sources to improve the accuracy and relevance of generated content. This is particularly useful in scenarios where high-quality, context-specific information is required, such as in content generation or knowledge retrieval tasks.

In RAG Pipelines, the process begins by using a retrieval model to find relevant documents or passages from an external knowledge base that match the input query. This retrieved content is then used as context for a generative model, which generates the final output. The combination of these two models allows for more accurate and contextually rich text generation.

• retrieval models
• generative models
• external knowledge bases

Manufacturing RAG Pipelines involves developing and integrating both retrieval and generative models. The key components include a robust external knowledge base, efficient retrieval algorithms, and advanced natural language processing techniques for generating text.

The build process for RAG Pipelines typically includes the following steps: defining the problem domain, selecting or training a retrieval model, selecting or training a generative model, integrating both models into a pipeline, and validating the system with diverse datasets to ensure it can handle various input queries effectively.

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Retrieval-Augmented Generation (RAG)

A technology that combines retrieval-based and generative models to produce high-quality content by leveraging external knowledge sources.

retrieval model

A component of RAG Pipelines responsible for finding relevant information from an external knowledge base based on the input query.

generative model

A component of RAG Pipelines that uses the retrieved context to generate high-quality, contextually rich text.

external knowledge base

A repository of information used by retrieval models to find relevant content for generating text in RAG Pipelines.

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