Vary question in xht

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Aug 6th, 2022
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How to vary question in xht

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welcome ladies gentlemen so what i like to do is show you how to determine on the equation uh when x varies directly with y as well as determine the constant k so what theyamp;#39;re saying is theyamp;#39;re giving us a value of x and theyamp;#39;re giving us the y and theyamp;#39;re saying that these two variables vary directly now write an equation to represent that they vary uh directly as well as find the constant k so remember when weamp;#39;re looking at the equation from direct variation the equation looks like this y equals k times x so where x and y are going to be your variables k is going to represent our direct variation constant all right so basically to be able to determine our equation weamp;#39;re going to write the equation y equals kx then you can see that the formula the values that weamp;#39;re given is x and for y and theyamp;#39;re asking us to write the one in equation but to write it in the form to find the value of k well if we know what y is which is 1

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Well use a prompt that includes a MessagesPlaceholder variable under the name chathistory. This allows us to pass in a list of Messages to the prompt using the chathistory input key, and these messages will be inserted after the system message and before the human message containing the latest question.
Your Prompts Are Too Short Since ChatGPT is (notionally) a chatbot, you may have the preconceived idea that your prompts should be short and conversational. While theres nothing wrong with that, of course, consider that you may not be giving ChatGPT enough information for it to give you the responses you want.
Stuff Chain It works by converting the document into smaller chunks, processing each chunk individually, and then combining the summaries to generate a final summary. This method is ideal for managing huge files and can be facilitated with the help of a recursive character text splitter.
Function: Constructs a LangChain chain specifically designed for retrieval-based QA. Arguments: llm : The LLM you want to use for answering questions (e.g., OpenAI API).
An LLMChain consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.
Document Question Answering models can be used to answer natural language questions about documents. Typically, document QA models consider textual, layout and potentially visual information.
Conclusion. So in conclusion, while ChatGPT is consistent in its approach to understanding and responding to queries, the uniqueness of each query and the evolving nature of AI technology mean that it does not give the same answers to everyone.
Retrieval augmented generation is a technique for using a large language model (LLM) to answer questions about documents it was not trained on. The basic idea is to first retrieve any relevant documents from a corpus called context, then pass those documents along with the original question to the LLM.

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