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Answer: Embedding model, retriever, generative model, dependencies, model signature, and input examples.
This option includes all the essential components required for deploying a Retrieval-Augmented Generation (RAG) application effectively: 1. **Embedding Model**: This is necessary for converting user queries and documents into vector representations, enabling semantic search. 2. **Retriever**: This component retrieves the most relevant documents based on the embedded queries, critical for the RAG architecture. 3. **Generative Model**: After retrieving the relevant documents, this model generates responses based on the retrieved information. 4. **Dependencies**: This includes all necessary libraries and packages required for the application to function correctly. 5. **Model Signature**: Specifies the expected inputs and outputs of the model, facilitating integration and ensuring compatibility with other systems. 6. **Input Examples**: Providing example inputs helps with testing and validating the application during deployment and future usage. Other Options: - A. Pre-trained language model, document retriever, tokenizer, SQL query generator, dependencies, and input pipeline. - B. Language model, input format parser, retriever, output formatter, embedding index, and model signature. - D. Retriever, vectorizer, generative model, dataset schema, hyperparameter configuration, and API gateway.
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Question 19
You are deploying a Retrieval-Augmented Generation (RAG) application on Databricks. This application must allow users to submit queries that are embedded into vector space, retrieve the most relevant documents using a retriever, and then pass them to a generative model for response generation. In order to deploy this application, you must ensure that all necessary elements, including dependencies and model signature, are properly specified for a seamless integration into Databricks and for future use by other teams. Which of the following lists the essential components required to deploy this RAG application?
A
Pre-trained language model, document retriever, tokenizer, SQL query generator, dependencies, and input pipeline.
B
Language model, input format parser, retriever, output formatter, embedding index, and model signature.
C
Embedding model, retriever, generative model, dependencies, model signature, and input examples.
D
Retriever, vectorizer, generative model, dataset schema, hyperparameter configuration, and API gateway.