A company has a typical RAG-enabled, customer-facing chatbot on its website.
Select the correct sequence of components a user's questions will go through before the final output is returned. Use the diagram above for reference.
To understand how a typical RAG-enabled customer-facing chatbot processes a user's question, let's go through the correct sequence as depicted in the diagram and explained in option A:
Embedding Model (1): The first step involves the user's question being processed through an embedding model. This model converts the text into a vector format that numerically represents the text. This step is essential for allowing the subsequent vector search to operate effectively.
Vector Search (2): The vectors generated by the embedding model are then used in a vector search mechanism. This search identifies the most relevant documents or previously answered questions that are stored in a vector format in a database.
Context-Augmented Prompt (3): The information retrieved from the vector search is used to create a context-augmented prompt. This step involves enhancing the basic user query with additional relevant information gathered to ensure the generated response is as accurate and informative as possible.
Response-Generating LLM (4): Finally, the context-augmented prompt is fed into a response-generating large language model (LLM). This LLM uses the prompt to generate a coherent and contextually appropriate answer, which is then delivered as the final output to the user.
Why Other Options Are Less Suitable:
B, C, D: These options suggest incorrect sequences that do not align with how a RAG system typically processes queries. They misplace the role of embedding models, vector search, and response generation in an order that would not facilitate effective information retrieval and response generation.
Thus, the correct sequence is embedding model, vector search, context-augmented prompt, response-generating LLM, which is option A.
A team wants to serve a code generation model as an assistant for their software developers. It should support multiple programming languages. Quality is the primary objective.
Which of the Databricks Foundation Model APIs, or models available in the Marketplace, would be the best fit?
For a code generation model that supports multiple programming languages and where quality is the primary objective, CodeLlama-34B is the most suitable choice. Here's the reasoning:
Specialization in Code Generation: CodeLlama-34B is specifically designed for code generation tasks. This model has been trained with a focus on understanding and generating code, which makes it particularly adept at handling various programming languages and coding contexts.
Capacity and Performance: The '34B' indicates a model size of 34 billion parameters, suggesting a high capacity for handling complex tasks and generating high-quality outputs. The large model size typically correlates with better understanding and generation capabilities in diverse scenarios.
Suitability for Development Teams: Given that the model is optimized for code, it will be able to assist software developers more effectively than general-purpose models. It understands coding syntax, semantics, and the nuances of different programming languages.
Why Other Options Are Less Suitable:
A (Llama2-70b): While also a large model, it's more general-purpose and may not be as fine-tuned for code generation as CodeLlama.
B (BGE-large): This model may not specifically focus on code generation.
C (MPT-7b): Smaller than CodeLlama-34B and likely less capable in handling complex code generation tasks at high quality.
Therefore, for a high-quality, multi-language code generation application, CodeLlama-34B (option D) is the best fit.
A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in PDF format. These PDFs can contain both text and images. They want to develop a solution using the least amount of lines of code.
Which Python package should be used to extract the text from the source documents?
Problem Context: The engineer needs to extract text from PDF documents, which may contain both text and images. The goal is to find a Python package that simplifies this task using the least amount of code.
Explanation of Options:
Option A: flask: Flask is a web framework for Python, not suitable for processing or extracting content from PDFs.
Option B: beautifulsoup: Beautiful Soup is designed for parsing HTML and XML documents, not PDFs.
Option C: unstructured: This Python package is specifically designed to work with unstructured data, including extracting text from PDFs. It provides functionalities to handle various types of content in documents with minimal coding, making it ideal for the task.
Option D: numpy: Numpy is a powerful library for numerical computing in Python and does not provide any tools for text extraction from PDFs.
Given the requirement, Option C (unstructured) is the most appropriate as it directly addresses the need to efficiently extract text from PDF documents with minimal code.
A Generative AI Engineer received the following business requirements for an external chatbot.
The chatbot needs to know what types of questions the user asks and routes to appropriate models to answer the questions. For example, the user might ask about upcoming event details. Another user might ask about purchasing tickets for a particular event.
What is an ideal workflow for such a chatbot?
Problem Context: The chatbot must handle various types of queries and intelligently route them to the appropriate responses or systems.
Explanation of Options:
Option A: Limiting the chatbot to only previous event information restricts its utility and does not meet the broader business requirements.
Option B: Having two separate chatbots could unnecessarily complicate user interaction and increase maintenance overhead.
Option C: Implementing a multi-step workflow where the chatbot first identifies the type of question and then routes it accordingly is the most efficient and scalable solution. This approach allows the chatbot to handle a variety of queries dynamically, improving user experience and operational efficiency.
Option D: Focusing solely on payments would not satisfy all the specified user interaction needs, such as inquiring about event details.
Option C offers a comprehensive workflow that maximizes the chatbot's utility and responsiveness to different user needs, aligning perfectly with the business requirements.
A Generative Al Engineer is tasked with developing an application that is based on an open source large language model (LLM). They need a foundation LLM with a large context window.
Which model fits this need?
Problem Context: The engineer needs an open-source LLM with a large context window to develop an application.
Explanation of Options:
Option A: DistilBERT: While an efficient and smaller version of BERT, DistilBERT does not provide a particularly large context window.
Option B: MPT-30B: This model, while large, is not specified as being particularly notable for its context window capabilities.
Option C: Llama2-70B: Known for its large model size and extensive capabilities, including a large context window. It is also available as an open-source model, making it ideal for applications requiring extensive contextual understanding.
Option D: DBRX: This is not a recognized standard model in the context of large language models with extensive context windows.
Thus, Option C (Llama2-70B) is the best fit as it meets the criteria of having a large context window and being available for open-source use, suitable for developing robust language understanding applications.
