{"id":1310,"date":"2026-10-31T09:04:53","date_gmt":"2026-10-31T09:04:53","guid":{"rendered":"https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/"},"modified":"2026-10-31T09:32:22","modified_gmt":"2026-10-31T09:32:22","slug":"crag-multihop-reasoning-engine-architecture-performance-benchmark-2","status":"publish","type":"post","link":"https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/","title":{"rendered":"Benchmarking the CRAG MultiHop Reasoning Engine: Architecture and Performance"},"content":{"rendered":"<h1>Architecture &amp; Performance Benchmark: CRAG MultiHop Reasoning Engine<\/h1>\n<div class=\"callout\" style=\"background:#f0f9ff;border-left:4px solid #0284c7;padding:15px;margin:20px 0;border-radius:4px\"><strong>Key Takeaways:<\/strong><\/p>\n<ul>\n<li>CRAG MultiHop Reasoning Engine leverages multi-hop query decomposition and corrective retrieval-augmented generation for enhanced accuracy.<\/li>\n<li>Its architecture combines Django ASGI, React, ChromaDB, Celery, and Jina Reranker for efficient and scalable performance.<\/li>\n<li>The engine supports real-time WebSocket event streaming, automated fallback to external search, and asynchronous document ingestion.<\/li>\n<\/ul>\n<\/div>\n<div class=\"project-access-box\" style=\"background:#f0f9ff;border:1px solid #bae6fd;border-left:4px solid #0284c7;padding:12px 18px;margin:20px 0;border-radius:4px\"><strong>Live Project Access:<\/strong> <a href=\"https:\/\/crag.nevatal.id\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/crag.nevatal.id<\/a><\/div>\n<h2>The Challenge: Why CRAG MultiHop Reasoning Engine Was Built<\/h2>\n<p>Traditional Retrieval-Augmented Generation (RAG) pipelines often struggle with complex, multi-step queries and ambiguous or weak context. The CRAG MultiHop Reasoning Engine addresses these challenges by incorporating multi-hop query decomposition, self-grading retrieval, and local reranking.<\/p>\n<h2>Core Architecture &amp; Technical Stack Deep-Dive<\/h2>\n<h3>Overview<\/h3>\n<p>The CRAG MultiHop Reasoning Engine is built using a combination of cutting-edge technologies, including Django ASGI, React, ChromaDB, Celery, and Jina Reranker. This stack enables the engine to efficiently handle complex queries, manage large volumes of data, and provide real-time feedback to users.<\/p>\n<h3>Technical Components<\/h3>\n<ul>\n<li>Django ASGI: Handles HTTP requests and WebSocket connections.<\/li>\n<li>React: Provides a dynamic and interactive user interface.<\/li>\n<li>ChromaDB: Stores and manages vector embeddings for efficient retrieval.<\/li>\n<li>Celery: Handles asynchronous task execution, including document ingestion and processing.<\/li>\n<li>Jina Reranker: Enables local reranking of retrieved candidates for improved accuracy.<\/li>\n<\/ul>\n<h2>Key Features Breakdown &amp; Practical Benefits<\/h2>\n<h3>Multi-Hop Query Decomposition<\/h3>\n<p>The CRAG MultiHop Reasoning Engine can decompose complex queries into sequential sub-queries, allowing it to tackle multi-step problems with ease. This feature enables the engine to provide more accurate and relevant results, even for complex and open-ended questions.<\/p>\n<h3>Corrective Retrieval-Augmented Generation (CRAG)<\/h3>\n<p>The engine&#8217;s CRAG component evaluates the retrieved context and classifies it as correct, ambiguous, or incorrect. This self-grading mechanism ensures that the engine can detect and correct errors, providing more reliable and trustworthy results.<\/p>\n<h3>Real-Time WebSocket Event Streaming<\/h3>\n<p>The CRAG MultiHop Reasoning Engine provides real-time feedback to users through WebSocket event streaming. This feature enables users to monitor the engine&#8217;s progress, receive updates on the query processing, and interact with the engine in a more dynamic and engaging way.<\/p>\n<h2>Real-World Use Cases &amp; Applications<\/h2>\n<p>The CRAG MultiHop Reasoning Engine has numerous practical applications, including complex research investigations, automated document QA, and developer reference implementation for self-grading agentic RAG workflows. Its ability to handle multi-step queries and provide accurate results makes it an ideal solution for a wide range of use cases.<\/p>\n<h2>How It Works: Step-by-Step Workflow<\/h2>\n<ol>\n<li>Query Decomposition: The engine decomposes the input query into sequential sub-queries.<\/li>\n<li>Hybrid Retrieval: The engine performs both dense vector search and sparse keyword search to retrieve relevant candidates.<\/li>\n<li>Corrective RAG (CRAG): The engine evaluates the retrieved context and classifies it as correct, ambiguous, or incorrect.<\/li>\n<li>Local Reranking: The engine reranks the retrieved candidates using a local cross-encoder model.<\/li>\n<li>Answer Generation: The engine generates the final answer based on the reranked candidates.<\/li>\n<\/ol>\n<h2>Comparison: CRAG MultiHop Reasoning Engine vs Traditional Approaches<\/h2>\n<table>\n<thead>\n<tr>\n<th>Feature<\/th>\n<th>CRAG MultiHop Reasoning Engine<\/th>\n<th>Traditional RAG Pipelines<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Multi-Hop Query Decomposition<\/td>\n<td>Supports up to 3 hops<\/td>\n<td>Limited to single-hop queries<\/td>\n<\/tr>\n<tr>\n<td>Corrective Retrieval-Augmented Generation<\/td>\n<td>Self-grading evaluation and correction<\/td>\n<td>No self-grading mechanism<\/td>\n<\/tr>\n<tr>\n<td>Real-Time Feedback<\/td>\n<td>WebSocket event streaming<\/td>\n<td>No real-time feedback<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Frequently Asked Questions (FAQ)<\/h2>\n<ul>\n<li><strong>Q: What is the CRAG MultiHop Reasoning Engine?<\/strong>\n<p>A: The CRAG MultiHop Reasoning Engine is a cutting-edge artificial intelligence system designed to tackle complex queries through multi-hop reasoning and corrective retrieval-augmented generation.<\/p>\n<\/li>\n<li><strong>Q: How does the engine handle multi-step queries?<\/strong>\n<p>A: The engine decomposes complex queries into sequential sub-queries, allowing it to tackle multi-step problems with ease.<\/p>\n<\/li>\n<li><strong>Q: What is the role of the CRAG component?<\/strong>\n<p>A: The CRAG component evaluates the retrieved context and classifies it as correct, ambiguous, or incorrect, ensuring that the engine can detect and correct errors.<\/p>\n<\/li>\n<li><strong>Q: Can the engine provide real-time feedback to users?<\/strong>\n<p>A: Yes, the engine provides real-time feedback to users through WebSocket event streaming, enabling them to monitor the engine&#8217;s progress and interact with it in a more dynamic and engaging way.<\/p>\n<\/li>\n<li><strong>Q: What are the practical applications of the CRAG MultiHop Reasoning Engine?<\/strong>\n<p>A: The engine has numerous practical applications, including complex research investigations, automated document QA, and developer reference implementation for self-grading agentic RAG workflows.<\/p>\n<\/li>\n<\/ul>\n<div class=\"related-articles-box\" style=\"background:#f8fafc;border:1px solid #e2e8f0;border-left:4px solid #0284c7;padding:16px 20px;margin:28px 0;border-radius:6px\">\n<h3 style=\"margin-top:0;color:#0f172a;font-size:1.15rem\">Related Technical Guides &amp; Platform Architecture<\/h3>\n<p style=\"margin-bottom:12px;color:#475569;font-size:0.95rem\">Explore related articles, system benchmarks, and hands-on tutorials across the Nevatal platform:<\/p>\n<ul style=\"margin:0;padding-left:20px;line-height:1.7\">\n<li><a href=\"https:\/\/wordpress.nevatal.id\/2026\/09\/11\/crag-multihop-reasoning-engine-tutorial\/\">Getting Started with CRAG MultiHop Reasoning Engine: A Hands-On Tutorial<\/a><\/li>\n<li><a href=\"https:\/\/wordpress.nevatal.id\/2026\/09\/19\/getting-started-crag-multihop-reasoning-engine\/\">Getting Started with CRAG MultiHop Reasoning Engine: A Hands-on Tutorial<\/a><\/li>\n<li><a href=\"https:\/\/wordpress.nevatal.id\/2026\/09\/13\/real-world-deployment-nevatal-defense-in-depth-ai-systems-suite\/\">Real-World Deployment &amp; Case Study: Nevatal Defense-in-Depth AI Systems Suite<\/a><\/li>\n<\/ul>\n<\/div>\n<h2>Conclusion &amp; Next Steps<\/h2>\n<p>The CRAG MultiHop Reasoning Engine is a powerful tool for tackling complex queries and providing accurate results. Its unique combination of multi-hop query decomposition, corrective retrieval-augmented generation, and real-time feedback makes it an ideal solution for a wide range of applications. To learn more about the engine and its capabilities, visit the live project page at <a href=\"https:\/\/crag.nevatal.id\">https:\/\/crag.nevatal.id<\/a> and explore its features and benefits in more detail.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The CRAG MultiHop Reasoning Engine is a cutting-edge artificial intelligence system designed to tackle complex queries through multi-hop reasoning and corrective retrieval-augmented generation. This article delves into the engine&#8217;s architecture, technical stack, and performance benchmark, highlighting its key features, real-world applications, and advantages over traditional approaches.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_linkdex":"72","_yoast_wpseo_content_score":"90","_yoast_wpseo_canonical":"","_yoast_wpseo_opengraph-title":"","_yoast_wpseo_opengraph-description":"","_yoast_wpseo_twitter-title":"","_yoast_wpseo_twitter-description":"","_yoast_wpseo_estimated-reading-time-minutes":"4","_yoast_wpseo_primary_category":"2","footnotes":""},"categories":[2],"tags":[67,61,63,64,69,26,66,62,65,68],"class_list":["post-1310","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence-machine-learning","tag-celery","tag-chromadb","tag-corrective-rag","tag-crag","tag-django-asgi","tag-fastapi","tag-jina-reranker","tag-multi-hop-rag","tag-query-decomposition","tag-websockets"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Benchmarking the CRAG MultiHop Reasoning Engine: Architecture and Performance<\/title>\n<meta name=\"description\" content=\"Explore the CRAG MultiHop Reasoning Engine&#039;s architecture and performance benchmark, a Corrective RAG system with multi-hop query decomposition and self-grading retrieval.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Benchmarking the CRAG MultiHop Reasoning Engine: Architecture and Performance\" \/>\n<meta property=\"og:description\" content=\"Explore the CRAG MultiHop Reasoning Engine&#039;s architecture and performance benchmark, a Corrective RAG system with multi-hop query decomposition and self-grading retrieval.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/\" \/>\n<meta property=\"og:site_name\" content=\"Nevatal\" \/>\n<meta property=\"article:published_time\" content=\"2026-10-31T09:04:53+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-10-31T09:32:22+00:00\" \/>\n<meta name=\"author\" content=\"play258\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"play258\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/wordpress.nevatal.id\\\/2026\\\/10\\\/31\\\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/wordpress.nevatal.id\\\/2026\\\/10\\\/31\\\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\\\/\"},\"author\":{\"name\":\"play258\",\"@id\":\"https:\\\/\\\/wordpress.nevatal.id\\\/#\\\/schema\\\/person\\\/d2821efbaf1c66c1392f012a520ebd73\"},\"headline\":\"Benchmarking the CRAG MultiHop Reasoning Engine: Architecture and Performance\",\"datePublished\":\"2026-10-31T09:04:53+00:00\",\"dateModified\":\"2026-10-31T09:32:22+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/wordpress.nevatal.id\\\/2026\\\/10\\\/31\\\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\\\/\"},\"wordCount\":811,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/wordpress.nevatal.id\\\/#\\\/schema\\\/person\\\/d2821efbaf1c66c1392f012a520ebd73\"},\"keywords\":[\"Celery\",\"ChromaDB\",\"Corrective RAG\",\"CRAG\",\"Django ASGI\",\"FastAPI\",\"Jina Reranker\",\"Multi-Hop RAG\",\"Query Decomposition\",\"WebSockets\"],\"articleSection\":[\"Artificial Intelligence &amp; Machine Learning\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/wordpress.nevatal.id\\\/2026\\\/10\\\/31\\\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/wordpress.nevatal.id\\\/2026\\\/10\\\/31\\\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\\\/\",\"url\":\"https:\\\/\\\/wordpress.nevatal.id\\\/2026\\\/10\\\/31\\\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\\\/\",\"name\":\"Benchmarking the CRAG MultiHop Reasoning Engine: Architecture and Performance\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/wordpress.nevatal.id\\\/#website\"},\"datePublished\":\"2026-10-31T09:04:53+00:00\",\"dateModified\":\"2026-10-31T09:32:22+00:00\",\"description\":\"Explore the CRAG MultiHop Reasoning Engine's architecture and performance benchmark, a Corrective RAG system with multi-hop query decomposition and self-grading retrieval.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/wordpress.nevatal.id\\\/2026\\\/10\\\/31\\\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/wordpress.nevatal.id\\\/2026\\\/10\\\/31\\\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/wordpress.nevatal.id\\\/2026\\\/10\\\/31\\\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/wordpress.nevatal.id\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Benchmarking the CRAG MultiHop Reasoning Engine: Architecture and Performance\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/wordpress.nevatal.id\\\/#website\",\"url\":\"https:\\\/\\\/wordpress.nevatal.id\\\/\",\"name\":\"Nevatal\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\\\/\\\/wordpress.nevatal.id\\\/#\\\/schema\\\/person\\\/d2821efbaf1c66c1392f012a520ebd73\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/wordpress.nevatal.id\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":[\"Person\",\"Organization\"],\"@id\":\"https:\\\/\\\/wordpress.nevatal.id\\\/#\\\/schema\\\/person\\\/d2821efbaf1c66c1392f012a520ebd73\",\"name\":\"play258\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/wordpress.nevatal.id\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/images-1.jpg\",\"url\":\"https:\\\/\\\/wordpress.nevatal.id\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/images-1.jpg\",\"contentUrl\":\"https:\\\/\\\/wordpress.nevatal.id\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/images-1.jpg\",\"width\":190,\"height\":266,\"caption\":\"play258\"},\"logo\":{\"@id\":\"https:\\\/\\\/wordpress.nevatal.id\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/images-1.jpg\"},\"sameAs\":[\"https:\\\/\\\/wordpress.nevatal.id\"],\"url\":\"https:\\\/\\\/wordpress.nevatal.id\\\/author\\\/play258\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Benchmarking the CRAG MultiHop Reasoning Engine: Architecture and Performance","description":"Explore the CRAG MultiHop Reasoning Engine's architecture and performance benchmark, a Corrective RAG system with multi-hop query decomposition and self-grading retrieval.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/","og_locale":"en_US","og_type":"article","og_title":"Benchmarking the CRAG MultiHop Reasoning Engine: Architecture and Performance","og_description":"Explore the CRAG MultiHop Reasoning Engine's architecture and performance benchmark, a Corrective RAG system with multi-hop query decomposition and self-grading retrieval.","og_url":"https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/","og_site_name":"Nevatal","article_published_time":"2026-10-31T09:04:53+00:00","article_modified_time":"2026-10-31T09:32:22+00:00","author":"play258","twitter_card":"summary_large_image","twitter_misc":{"Written by":"play258","Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/#article","isPartOf":{"@id":"https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/"},"author":{"name":"play258","@id":"https:\/\/wordpress.nevatal.id\/#\/schema\/person\/d2821efbaf1c66c1392f012a520ebd73"},"headline":"Benchmarking the CRAG MultiHop Reasoning Engine: Architecture and Performance","datePublished":"2026-10-31T09:04:53+00:00","dateModified":"2026-10-31T09:32:22+00:00","mainEntityOfPage":{"@id":"https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/"},"wordCount":811,"commentCount":0,"publisher":{"@id":"https:\/\/wordpress.nevatal.id\/#\/schema\/person\/d2821efbaf1c66c1392f012a520ebd73"},"keywords":["Celery","ChromaDB","Corrective RAG","CRAG","Django ASGI","FastAPI","Jina Reranker","Multi-Hop RAG","Query Decomposition","WebSockets"],"articleSection":["Artificial Intelligence &amp; Machine Learning"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/","url":"https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/","name":"Benchmarking the CRAG MultiHop Reasoning Engine: Architecture and Performance","isPartOf":{"@id":"https:\/\/wordpress.nevatal.id\/#website"},"datePublished":"2026-10-31T09:04:53+00:00","dateModified":"2026-10-31T09:32:22+00:00","description":"Explore the CRAG MultiHop Reasoning Engine's architecture and performance benchmark, a Corrective RAG system with multi-hop query decomposition and self-grading retrieval.","breadcrumb":{"@id":"https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/wordpress.nevatal.id\/2026\/10\/31\/crag-multihop-reasoning-engine-architecture-performance-benchmark-2\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/wordpress.nevatal.id\/"},{"@type":"ListItem","position":2,"name":"Benchmarking the CRAG MultiHop Reasoning Engine: Architecture and Performance"}]},{"@type":"WebSite","@id":"https:\/\/wordpress.nevatal.id\/#website","url":"https:\/\/wordpress.nevatal.id\/","name":"Nevatal","description":"","publisher":{"@id":"https:\/\/wordpress.nevatal.id\/#\/schema\/person\/d2821efbaf1c66c1392f012a520ebd73"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/wordpress.nevatal.id\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":["Person","Organization"],"@id":"https:\/\/wordpress.nevatal.id\/#\/schema\/person\/d2821efbaf1c66c1392f012a520ebd73","name":"play258","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/wordpress.nevatal.id\/wp-content\/uploads\/2026\/08\/images-1.jpg","url":"https:\/\/wordpress.nevatal.id\/wp-content\/uploads\/2026\/08\/images-1.jpg","contentUrl":"https:\/\/wordpress.nevatal.id\/wp-content\/uploads\/2026\/08\/images-1.jpg","width":190,"height":266,"caption":"play258"},"logo":{"@id":"https:\/\/wordpress.nevatal.id\/wp-content\/uploads\/2026\/08\/images-1.jpg"},"sameAs":["https:\/\/wordpress.nevatal.id"],"url":"https:\/\/wordpress.nevatal.id\/author\/play258\/"}]}},"_links":{"self":[{"href":"https:\/\/wordpress.nevatal.id\/wp-json\/wp\/v2\/posts\/1310","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress.nevatal.id\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress.nevatal.id\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress.nevatal.id\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress.nevatal.id\/wp-json\/wp\/v2\/comments?post=1310"}],"version-history":[{"count":0,"href":"https:\/\/wordpress.nevatal.id\/wp-json\/wp\/v2\/posts\/1310\/revisions"}],"wp:attachment":[{"href":"https:\/\/wordpress.nevatal.id\/wp-json\/wp\/v2\/media?parent=1310"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress.nevatal.id\/wp-json\/wp\/v2\/categories?post=1310"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress.nevatal.id\/wp-json\/wp\/v2\/tags?post=1310"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}