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24 lines
2.2 KiB
Markdown
24 lines
2.2 KiB
Markdown
# Question Generation ❔
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## Entity-based Question Generation
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The [question generation](https://github.com/microsoft/graphrag/blob/main//graphrag/query/question_gen/) method combines structured data from the knowledge graph with unstructured data from the input documents to generate candidate questions related to specific entities.
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## Methodology
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Given a list of prior user questions, the question generation method uses the same context-building approach employed in [local search](local_search.md) 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.
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## Configuration
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Below are the key parameters of the [Question Generation class](https://github.com/microsoft/graphrag/blob/main//graphrag/query/question_gen/local_gen.py):
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* `llm`: OpenAI model object to be used for response generation
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* `context_builder`: [context builder](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/local_search/mixed_context.py) object to be used for preparing context data from collections of knowledge model objects, using the same context builder class as in local search
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* `system_prompt`: prompt template used to generate candidate questions. Default template can be found at [system_prompt](https://github.com/microsoft/graphrag/blob/main//graphrag/query/question_gen/system_prompt.py)
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* `llm_params`: a dictionary of additional parameters (e.g., temperature, max_tokens) to be passed to the LLM call
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* `context_builder_params`: a dictionary of additional parameters to be passed to the [`context_builder`](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/local_search/mixed_context.py) object when building context for the question generation prompt
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* `callbacks`: optional callback functions, can be used to provide custom event handlers for LLM's completion streaming events
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## How to Use
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An example of the question generation function can be found in the following [notebook](../examples_notebooks/local_search.ipynb).
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