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< h1 > Question Generation ❔< / h1 >
< h2 > Entity-based Question Generation< / h2 >
< p > The < a href = "../../../../python/graphrag/graphrag/query/question_gen/" > question generation< / a > method combines structured data from the knowledge graph with unstructured data from the input documents to generate candidate questions related to specific entities.< / p >
< h2 > Methodology< / h2 >
< p > Given a list of prior user questions, the question generation method uses the same context-building approach employed in < a href = "1-local_search.md" > local search< / a > to extract and prioritize relevant structured and unstructured data, including entities, relationships, covariates, community reports and raw text chunks. These data records are then fitted into a single LLM prompt to generate candidate follow-up questions that represent the most important or urgent information content or themes in the data.< / p >
< h2 > Configuration< / h2 >
< p > Below are the key parameters of the < a href = "../../../../python/graphrag/graphrag/query/question_gen/local_gen.py" > Question Generation class< / a > :< / p >
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< li > < code > llm< / code > : OpenAI model object to be used for response generation< / li >
< li > < code > context_builder< / code > : < a href = "../../../../python/graphrag/graphrag/query/structured_search/local_search/mixed_context.py" > context builder< / a > object to be used for preparing context data from collections of knowledge model objects, using the same context builder class as in local search< / li >
< li > < code > system_prompt< / code > : prompt template used to generate candidate questions. Default template can be found at < a href = "../../../../python/graphrag/graphrag/query/question_gen/system_prompt.py" > system_prompt< / a > < / li >
< li > < code > llm_params< / code > : a dictionary of additional parameters (e.g., temperature, max_tokens) to be passed to the LLM call< / li >
< li > < code > context_builder_params< / code > : a dictionary of additional parameters to be passed to the < a href = "../../../../python/graphrag/graphrag/query/structured_search/local_search/mixed_context.py" > < code > context_builder< / code > < / a > object when building context for the question generation prompt< / li >
< li > < code > callbacks< / code > : optional callback functions, can be used to provide custom event handlers for LLM's completion streaming events< / li >
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< h2 > How to Use< / h2 >
< p > An example of the question generation function can be found in the following < a href = "../notebooks/local_search_nb" > notebook< / a > .< / p >
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