RAG Pipelines in Production

RAG pipelines in production, or Retrieval-Augmented Generation pipelines, are a method that combines the strengths of retrieval-based models and generative models to improve the efficiency and accuracy of information retrieval. In this approach, a retrieval model first fetches relevant documents from a knowledge base, which are then used by a generative model to produce context-aware responses.

RAG pipelines address the limitations of purely generative models by leveraging retrieval to incorporate factual knowledge into the generation process. This helps in reducing hallucinations, improving the relevance of generated content, and enhancing overall user satisfaction in applications like chatbots, virtual assistants, and AI agents.

The process begins with a user query that is fed into a retrieval model. This model searches through a pre-indexed database or knowledge graph and selects the most relevant documents based on semantic similarity. These selected documents are then passed to a generative model, which uses them as context to produce a more accurate and coherent response compared to traditional generation models that do not have access to external information.

• language models
• knowledge bases
• retrieval algorithms
• generative models

The manufacturing of RAG pipelines involves developing and optimizing both the retrieval and generative components. This includes fine-tuning pre-trained models for specific tasks, creating or sourcing a relevant knowledge base, and integrating these components into a cohesive system that can handle real-time queries efficiently.

The build process starts with selecting appropriate pre-trained language models as the basis for both retrieval and generation. These models are then fine-tuned on task-specific data to improve their performance. Next, a knowledge base is created or sourced, which includes structured and unstructured documents relevant to the domain of interest. Finally, these components are integrated into an RAG pipeline framework that can handle user queries and generate context-aware responses.

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RAG pipeline

A method that combines retrieval with generation to create more context-aware responses.

Retrieval model

A component of RAG pipelines responsible for fetching relevant documents from a knowledge base based on user queries.

Generative model

A component of RAG pipelines that uses the retrieved documents as context to produce more accurate and coherent responses.

Knowledge base

A repository of structured and unstructured documents relevant to a specific domain used in RAG pipelines for context-aware generation.

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