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308 lines
9.9 KiB
HTML
308 lines
9.9 KiB
HTML
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<!doctype html>
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Question Generation ❔</title>
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bulma@0.9.4/css/bulma.min.css">
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html {
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body{
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font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
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footer{
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width: 100%;
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height: 32px;
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font-size: 12px;
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flex-direction: row;
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justify-content: center;
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gap: 18px;
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align-items: center;
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color: #5d5d5d;
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background: #e9eaeb;
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border-top: 1px solid #c4c5c6;
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}
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#cookiesManager{
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header {
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padding: 1em;
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main {
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main ul {
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list-style-type: disc;
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padding-left: 30px;
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margin-top: 10px;
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h1 {
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font-size: 2rem;
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h2 {
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h3 {
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font-size: 1rem;
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margin-top: 10px;
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font-weight: 500;
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margin-top: 10px;
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/* Accessibility styling */
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.token.comment, .token.prolog, .token.doctype, .token.cdata {
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color: #8093a5;
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.token.property, .token.tag, .token.constant, .token.symbol, .token.deleted {
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color: #ff36ab;
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</style>
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<script type="module" async="">import mermaid from "https://unpkg.com/mermaid@10/dist/mermaid.esm.min.mjs";document.addEventListener('DOMContentLoaded', mermaid.initialize({"loadOnSave":true}));</script>
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</head>
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<body>
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<header>
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<div id="cookie-banner"></div>
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<a href="/graphrag/"><span class="logotitle">GraphRAG</span></a>
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</header>
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<div class="page-content">
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<!-- Sidebar -->
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<aside class="menu">
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<ul class="menu-list">
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<li>
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<a href="/graphrag/">Welcome</a>
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</li>
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<!-- Get Started Links -->
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<li>
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<a href="/graphrag/posts/get_started/">Get Started</a>
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<a href="/graphrag/posts/developing/">Developing</a>
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</li>
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<!-- Indexing Links -->
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<li>
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<a href="/graphrag/posts/index/overview/">Indexing</a>
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<ul><li>
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<a href="/graphrag/posts/index/0-architecture/">Architecture</a>
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</li><li>
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<a href="/graphrag/posts/index/1-default_dataflow/">Dataflow</a>
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</li><li>
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<a href="/graphrag/posts/index/2-cli/">CLI</a>
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</li><li>
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<a href="/graphrag/posts/index/3-prompt_tuning/">Prompt Tuning</a>
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</li><li>
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<a href="/graphrag/posts/config/overview/">Configuration</a>
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<ul>
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<li>
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<a href="/graphrag/posts/config/env_vars">Using Env Vars</a>
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</li>
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<li>
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<a href="/graphrag/posts/config/json_yaml">Using JSON or YAML</a>
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</li>
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<li>
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<a href="/graphrag/posts/config/custom">Fully Custom</a>
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</li>
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<li>
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<a href="/graphrag/posts/config/template">Template</a>
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</li>
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</ul>
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</li>
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</ul>
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</li>
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<!-- Query Links -->
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<li>
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<a href="/graphrag/posts/query/overview/">Query</a>
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<ul><li>
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<a href="/graphrag/posts/query/0-global_search/">Global Search</a>
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</li><li>
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<a href="/graphrag/posts/query/1-local_search/">Local Search</a>
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</li><li>
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<a href="/graphrag/posts/query/2-question_generation/" class="is-active" aria-current="page">Question Generation</a>
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</li><li>
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<a href="/graphrag/posts/query/3-cli/">CLI</a>
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</li><li>
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<a href="/graphrag/posts/query/notebooks/overview/">Notebooks</a>
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<ul>
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<li>
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<a href="/graphrag/posts/query/notebooks/global_search_nb">Global Search</a>
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</li>
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<li>
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<a href="/graphrag/posts/query/notebooks/local_search_nb">Local Search</a>
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</li>
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</ul>
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</li>
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</ul>
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</li>
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</ul>
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</aside>
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<!-- Main Content -->
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<main>
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<h1>Question Generation ❔</h1>
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<h2>Entity-based Question Generation</h2>
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<p>The <a href="https://github.com/microsoft/graphrag/blob/main//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>
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<h2>Methodology</h2>
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<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>
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<h2>Configuration</h2>
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<p>Below are the key parameters of the <a href="https://github.com/microsoft/graphrag/blob/main//graphrag/query/question_gen/local_gen.py">Question Generation class</a>:</p>
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<ul>
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<li><code>llm</code>: OpenAI model object to be used for response generation</li>
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<li><code>context_builder</code>: <a href="https://github.com/microsoft/graphrag/blob/main//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>
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<li><code>system_prompt</code>: prompt template used to generate candidate questions. Default template can be found at <a href="https://github.com/microsoft/graphrag/blob/main//graphrag/query/question_gen/system_prompt.py">system_prompt</a></li>
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<li><code>llm_params</code>: a dictionary of additional parameters (e.g., temperature, max_tokens) to be passed to the LLM call</li>
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<li><code>context_builder_params</code>: a dictionary of additional parameters to be passed to the <a href="https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/local_search/mixed_context.py"><code>context_builder</code></a> object when building context for the question generation prompt</li>
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<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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</ul>
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<h2>How to Use</h2>
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<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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</main>
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