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| from haystack.nodes import PromptNode, PromptTemplate | |
| from haystack.nodes import AnswerParser | |
| from haystack.nodes import TransformersSummarizer | |
| def prompting_model(): | |
| ''' | |
| Define a prompt node in haystack pipeline | |
| ''' | |
| # prompt_node = PromptNode(model_name_or_path="facebook/galactica-125m", default_prompt_template="deepset/question-answering-per-document") | |
| prompt_node = PromptNode(model_name_or_path="facebook/galactica-125m") | |
| return prompt_node | |
| def prompting_model_2(): | |
| ''' | |
| Define a prompt node in haystack pipeline, with detailed prompt | |
| ''' | |
| custom_prompt = PromptTemplate(prompt = """ You are a helpful and knowledgeable agent. To achieve your goal of answering complex questions, | |
| you have access to the following paragraph : | |
| {join(documents)} | |
| Your output should be a detailed summary of the paragraph | |
| """) | |
| summarization_template = PromptTemplate("deepset/summarization") | |
| prompt_node = PromptNode(model_name_or_path="facebook/galactica-125m", default_prompt_template=custom_prompt) | |
| return prompt_node | |
| def summarize(): | |
| ''' | |
| Use a summarizer node, to summarize the output of generator | |
| To remove redundancy/repitition | |
| ''' | |
| summarizer = TransformersSummarizer(model_name_or_path="google/pegasus-xsum") | |
| return summarizer | |