diff --git "a/graphify-out/graph.html" "b/graphify-out/graph.html"
new file mode 100644--- /dev/null
+++ "b/graphify-out/graph.html"
@@ -0,0 +1,305 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<title>graphify - /run/media/morpheuslord/Personal_Files/Projects/Rewriter/graphify-out/graph.html</title>
+<script src="https://unpkg.com/vis-network/standalone/umd/vis-network.min.js"></script>
+<style>
+  * { box-sizing: border-box; margin: 0; padding: 0; }
+  body { background: #0f0f1a; color: #e0e0e0; font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; display: flex; height: 100vh; overflow: hidden; }
+  #graph { flex: 1; }
+  #sidebar { width: 280px; background: #1a1a2e; border-left: 1px solid #2a2a4e; display: flex; flex-direction: column; overflow: hidden; }
+  #search-wrap { padding: 12px; border-bottom: 1px solid #2a2a4e; }
+  #search { width: 100%; background: #0f0f1a; border: 1px solid #3a3a5e; color: #e0e0e0; padding: 7px 10px; border-radius: 6px; font-size: 13px; outline: none; }
+  #search:focus { border-color: #4E79A7; }
+  #search-results { max-height: 140px; overflow-y: auto; padding: 4px 12px; border-bottom: 1px solid #2a2a4e; display: none; }
+  .search-item { padding: 4px 6px; cursor: pointer; border-radius: 4px; font-size: 12px; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }
+  .search-item:hover { background: #2a2a4e; }
+  #info-panel { padding: 14px; border-bottom: 1px solid #2a2a4e; min-height: 140px; }
+  #info-panel h3 { font-size: 13px; color: #aaa; margin-bottom: 8px; text-transform: uppercase; letter-spacing: 0.05em; }
+  #info-content { font-size: 13px; color: #ccc; line-height: 1.6; }
+  #info-content .field { margin-bottom: 5px; }
+  #info-content .field b { color: #e0e0e0; }
+  #info-content .empty { color: #555; font-style: italic; }
+  .neighbor-link { display: block; padding: 2px 6px; margin: 2px 0; border-radius: 3px; cursor: pointer; font-size: 12px; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; border-left: 3px solid #333; }
+  .neighbor-link:hover { background: #2a2a4e; }
+  #neighbors-list { max-height: 160px; overflow-y: auto; margin-top: 4px; }
+  #legend-wrap { flex: 1; overflow-y: auto; padding: 12px; }
+  #legend-wrap h3 { font-size: 13px; color: #aaa; margin-bottom: 10px; text-transform: uppercase; letter-spacing: 0.05em; }
+  .legend-item { display: flex; align-items: center; gap: 8px; padding: 4px 0; cursor: pointer; border-radius: 4px; font-size: 12px; }
+  .legend-item:hover { background: #2a2a4e; padding-left: 4px; }
+  .legend-item.dimmed { opacity: 0.35; }
+  .legend-dot { width: 12px; height: 12px; border-radius: 50%; flex-shrink: 0; }
+  .legend-label { flex: 1; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }
+  .legend-count { color: #666; font-size: 11px; }
+  #stats { padding: 10px 14px; border-top: 1px solid #2a2a4e; font-size: 11px; color: #555; }
+  #legend-controls { display: flex; align-items: center; gap: 8px; margin-bottom: 8px; padding: 4px 0; }
+  #legend-controls label { display: flex; align-items: center; gap: 6px; cursor: pointer; font-size: 12px; color: #aaa; user-select: none; }
+  #legend-controls label:hover { color: #e0e0e0; }
+  .legend-cb, #select-all-cb { appearance: none; -webkit-appearance: none; width: 14px; height: 14px; border: 1.5px solid #3a3a5e; border-radius: 3px; background: #0f0f1a; cursor: pointer; position: relative; flex-shrink: 0; }
+  .legend-cb:checked, #select-all-cb:checked { background: #4E79A7; border-color: #4E79A7; }
+  .legend-cb:checked::after, #select-all-cb:checked::after { content: ''; position: absolute; left: 3.5px; top: 1px; width: 4px; height: 7px; border: solid #fff; border-width: 0 2px 2px 0; transform: rotate(45deg); }
+  #select-all-cb:indeterminate { background: #4E79A7; border-color: #4E79A7; }
+  #select-all-cb:indeterminate::after { content: ''; position: absolute; left: 2px; top: 5px; width: 8px; height: 2px; background: #fff; border: none; transform: none; }
+</style>
+</head>
+<body>
+<div id="graph"></div>
+<div id="sidebar">
+  <div id="search-wrap">
+    <input id="search" type="text" placeholder="Search nodes..." autocomplete="off">
+    <div id="search-results"></div>
+  </div>
+  <div id="info-panel">
+    <h3>Node Info</h3>
+    <div id="info-content"><span class="empty">Click a node to inspect it</span></div>
+  </div>
+  <div id="legend-wrap">
+    <h3>Communities</h3>
+    <div id="legend-controls">
+      <label><input type="checkbox" id="select-all-cb" checked onchange="toggleAllCommunities(!this.checked)">Select All</label>
+    </div>
+    <div id="legend"></div>
+  </div>
+  <div id="stats">549 nodes &middot; 873 edges &middot; 28 communities</div>
+</div>
+<script>
+const RAW_NODES = [{"id": "graph_codebase_py", "label": "graph_codebase.py", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 22.4, "font": {"size": 12, "color": "#ffffff"}, "title": "graph_codebase.py", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "code", "degree": 14}, {"id": "graph_codebase_py_docstring", "label": "graphify_rebuild.py \u2014 One-shot NudR knowledge graph regeneration.Usage:    py", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "graphify_rebuild.py \u2014 One-shot NudR knowledge graph regeneration.Usage:    py", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "rationale", "degree": 1}, {"id": "graph_codebase_py_detect_files", "label": "detect_files()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "detect_files()", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "code", "degree": 3}, {"id": "graph_codebase_py_detect_files_doc", "label": "Walk the project and return list of relevant files with metadata.", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Walk the project and return list of relevant files with metadata.", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "rationale", "degree": 1}, {"id": "graph_codebase_py_get_changed_files", "label": "get_changed_files()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "get_changed_files()", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "code", "degree": 3}, {"id": "graph_codebase_py_get_changed_files_doc", "label": "Compare against manifest to find changed files.", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Compare against manifest to find changed files.", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "rationale", "degree": 1}, {"id": "graph_codebase_py_hash_file", "label": "hash_file()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "hash_file()", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "code", "degree": 3}, {"id": "graph_codebase_py_hash_file_doc", "label": "SHA-256 hash for cache keying.", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "SHA-256 hash for cache keying.", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "rationale", "degree": 1}, {"id": "graph_codebase_py_extract_ast_file", "label": "extract_ast_file()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 15.3, "font": {"size": 12, "color": "#ffffff"}, "title": "extract_ast_file()", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "code", "degree": 6}, {"id": "graph_codebase_py_extract_ast_file_doc", "label": "Extract AST nodes and edges from a single Python file.", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Extract AST nodes and edges from a single Python file.", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "rationale", "degree": 1}, {"id": "graph_codebase_py__get_call_name", "label": "_get_call_name()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "_get_call_name()", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "code", "degree": 3}, {"id": "graph_codebase_py__get_call_name_doc", "label": "Extract callable name from ast.Call node.", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Extract callable name from ast.Call node.", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "rationale", "degree": 1}, {"id": "graph_codebase_py__get_name", "label": "_get_name()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "_get_name()", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "code", "degree": 3}, {"id": "graph_codebase_py__get_name_doc", "label": "Extract name from various AST node types.", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Extract name from various AST node types.", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "rationale", "degree": 1}, {"id": "graph_codebase_py__resolve_edges", "label": "_resolve_edges()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "_resolve_edges()", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "code", "degree": 3}, {"id": "graph_codebase_py__resolve_edges_doc", "label": "Post-process edges to resolve bare names to actual node IDs.The per-file AST e", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Post-process edges to resolve bare names to actual node IDs.The per-file AST e", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "rationale", "degree": 1}, {"id": "graph_codebase_py_run_ast_extraction", "label": "run_ast_extraction()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 15.3, "font": {"size": 12, "color": "#ffffff"}, "title": "run_ast_extraction()", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "code", "degree": 6}, {"id": "graph_codebase_py_run_ast_extraction_doc", "label": "Run AST extraction on all Python files, with caching.", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Run AST extraction on all Python files, with caching.", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "rationale", "degree": 1}, {"id": "graph_codebase_py_build_semantic_nodes", "label": "build_semantic_nodes()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "build_semantic_nodes()", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "code", "degree": 3}, {"id": "graph_codebase_py_build_semantic_nodes_doc", "label": "Build semantic nodes from documentation files.These capture high-level architec", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Build semantic nodes from documentation files.These capture high-level architec", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "rationale", "degree": 1}, {"id": "graph_codebase_py_merge_and_build", "label": "merge_and_build()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "merge_and_build()", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "code", "degree": 3}, {"id": "graph_codebase_py_merge_and_build_doc", "label": "Merge AST + semantic, build NetworkX graph, cluster, analyze.", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Merge AST + semantic, build NetworkX graph, cluster, analyze.", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "rationale", "degree": 1}, {"id": "graph_codebase_py_generate_outputs", "label": "generate_outputs()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "generate_outputs()", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "code", "degree": 3}, {"id": "graph_codebase_py_generate_outputs_doc", "label": "Generate report, HTML, JSON, and manifest.", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Generate report, HTML, JSON, and manifest.", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "rationale", "degree": 1}, {"id": "graph_codebase_py_run_pipeline", "label": "run_pipeline()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 17.9, "font": {"size": 12, "color": "#ffffff"}, "title": "run_pipeline()", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "code", "degree": 9}, {"id": "graph_codebase_py_run_pipeline_doc", "label": "Execute the full graphify pipeline.", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Execute the full graphify pipeline.", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "rationale", "degree": 1}, {"id": "graph_codebase_py_watch_mode", "label": "watch_mode()", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "watch_mode()", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "code", "degree": 3}, {"id": "graph_codebase_py_watch_mode_doc", "label": "Watch for file changes and rebuild automatically.", "color": {"background": "#4E79A7", "border": "#4E79A7", "highlight": {"background": "#ffffff", "border": "#4E79A7"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Watch for file changes and rebuild automatically.", "community": 10, "community_name": "Module Group 10", "source_file": "graph_codebase.py", "file_type": "rationale", "degree": 1}, {"id": "data_raw_fce_json_to_m2_py", "label": "json_to_m2.py", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 18.8, "font": {"size": 12, "color": "#ffffff"}, "title": "json_to_m2.py", "community": 13, "community_name": "Token Management", "source_file": "data/raw/fce/json_to_m2.py", "file_type": "code", "degree": 10}, {"id": "data_raw_fce_json_to_m2_py_main", "label": "main()", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 17.1, "font": {"size": 12, "color": "#ffffff"}, "title": "main()", "community": 13, "community_name": "Token Management", "source_file": "data/raw/fce/json_to_m2.py", "file_type": "code", "degree": 8}, {"id": "data_raw_fce_json_to_m2_py_parse_args", "label": "parse_args()", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 11.8, "font": {"size": 0, "color": "#ffffff"}, "title": "parse_args()", "community": 13, "community_name": "Token Management", "source_file": "data/raw/fce/json_to_m2.py", "file_type": "code", "degree": 2}, {"id": "data_raw_fce_json_to_m2_py_get_paras", "label": "get_paras()", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 11.8, "font": {"size": 0, "color": "#ffffff"}, "title": "get_paras()", "community": 13, "community_name": "Token Management", "source_file": "data/raw/fce/json_to_m2.py", "file_type": "code", "degree": 2}, {"id": "data_raw_fce_json_to_m2_py_clean_para", "label": "clean_para()", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 11.8, "font": {"size": 0, "color": "#ffffff"}, "title": "clean_para()", "community": 13, "community_name": "Token Management", "source_file": "data/raw/fce/json_to_m2.py", "file_type": "code", "degree": 2}, {"id": "data_raw_fce_json_to_m2_py_get_token_edits", "label": "get_token_edits()", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 13.5, "font": {"size": 0, "color": "#ffffff"}, "title": "get_token_edits()", "community": 13, "community_name": "Token Management", "source_file": "data/raw/fce/json_to_m2.py", "file_type": "code", "degree": 4}, {"id": "data_raw_fce_json_to_m2_py_get_all_tok_starts_and_ends", "label": 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"community": 1, "community_name": "Utility Scripts", "source_file": "src/model/generation_utils.py", "file_type": "rationale", "degree": 1}, {"id": "src_model_generation_utils_py_batch_generate", "label": "batch_generate()", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "batch_generate()", "community": 1, "community_name": "Utility Scripts", "source_file": "src/model/generation_utils.py", "file_type": "code", "degree": 3}, {"id": "src_model_generation_utils_py_batch_generate_doc", "label": "Generate corrections for a batch of texts.", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Generate corrections for a batch of texts.", "community": 1, "community_name": "Utility Scripts", "source_file": "src/model/generation_utils.py", "file_type": "rationale", "degree": 1}, {"id": "src_model_lora_adapter_py", "label": "lora_adapter.py", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 14.4, "font": {"size": 0, "color": "#ffffff"}, "title": "lora_adapter.py", "community": 6, "community_name": "Token Management", "source_file": "src/model/lora_adapter.py", "file_type": "code", "degree": 5}, {"id": "src_model_lora_adapter_py_docstring", "label": "LoRA adapter configuration and management.Wraps PEFT LoRA utilities for applyin", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "LoRA adapter configuration and management.Wraps PEFT LoRA utilities for applyin", "community": 6, "community_name": "Token Management", "source_file": "src/model/lora_adapter.py", "file_type": "rationale", "degree": 1}, {"id": "src_model_lora_adapter_py_create_lora_config", "label": "create_lora_config()", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "create_lora_config()", "community": 6, "community_name": "Token Management", "source_file": "src/model/lora_adapter.py", "file_type": "code", "degree": 3}, {"id": "src_model_lora_adapter_py_create_lora_config_doc", "label": "Create a LoRA configuration for the given task type.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Create a LoRA configuration for the given task type.", "community": 6, "community_name": "Token Management", "source_file": "src/model/lora_adapter.py", "file_type": "rationale", "degree": 1}, {"id": "src_model_lora_adapter_py_apply_lora", "label": "apply_lora()", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 11.8, "font": {"size": 0, "color": "#ffffff"}, "title": "apply_lora()", "community": 6, "community_name": "Token Management", "source_file": "src/model/lora_adapter.py", "file_type": "code", "degree": 2}, {"id": "src_model_lora_adapter_py_apply_lora_doc", "label": "Apply LoRA adapters to a model and return the wrapped model.", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Apply LoRA adapters to a model and return the wrapped model.", "community": 6, "community_name": "Token Management", "source_file": "src/model/lora_adapter.py", "file_type": "rationale", "degree": 1}, {"id": "src_model_lora_adapter_py_merge_lora_weights", "label": "merge_lora_weights()", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 11.8, "font": {"size": 0, "color": "#ffffff"}, "title": "merge_lora_weights()", "community": 6, "community_name": "Token Management", "source_file": "src/model/lora_adapter.py", "file_type": "code", "degree": 2}, {"id": "src_model_lora_adapter_py_merge_lora_weights_doc", "label": "Merge LoRA weights into the base model for inference.After merging, the model ", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Merge LoRA weights into the base model for inference.After merging, the model ", "community": 6, "community_name": "Token Management", "source_file": "src/model/lora_adapter.py", "file_type": "rationale", "degree": 1}, {"id": "src_model_style_conditioner_py", "label": "style_conditioner.py", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 16.2, "font": {"size": 12, "color": "#ffffff"}, "title": "style_conditioner.py", "community": 6, "community_name": "Token Management", "source_file": "src/model/style_conditioner.py", "file_type": "code", "degree": 7}, {"id": "src_model_style_conditioner_py_docstring", "label": "Injects the style vector into the model via soft prompt conditioning.The style ", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Injects the style vector into the model via soft prompt conditioning.The style ", "community": 6, "community_name": "Token Management", "source_file": "src/model/style_conditioner.py", "file_type": "rationale", "degree": 1}, {"id": "src_model_style_conditioner_py_StyleConditioner", "label": "StyleConditioner", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 11.8, "font": {"size": 0, "color": "#ffffff"}, "title": "StyleConditioner", "community": 6, "community_name": "Token Management", "source_file": "src/model/style_conditioner.py", "file_type": "code", "degree": 2}, {"id": "src_model_style_conditioner_py_StyleConditioner_doc", "label": "Projects a 512-dim style vector to n_prefix_tokens virtual tokensin the model's", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Projects a 512-dim style vector to n_prefix_tokens virtual tokensin the model&#x27;s", "community": 6, "community_name": "Token Management", "source_file": "src/model/style_conditioner.py", "file_type": "rationale", "degree": 1}, {"id": "src_model_style_conditioner_py_prepend_style_prefix", "label": "prepend_style_prefix()", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "prepend_style_prefix()", "community": 6, "community_name": "Token Management", "source_file": "src/model/style_conditioner.py", "file_type": "code", "degree": 3}, {"id": "src_model_style_conditioner_py_prepend_style_prefix_doc", "label": "Concatenates style prefix to input embeddings along sequence dimension.Args: ", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Concatenates style prefix to input embeddings along sequence dimension.Args: ", "community": 6, "community_name": "Token Management", "source_file": "src/model/style_conditioner.py", "file_type": "rationale", "degree": 1}, {"id": "src_model_style_conditioner_py___init__", "label": "__init__()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 33.8, "font": {"size": 12, "color": "#ffffff"}, "title": "__init__()", "community": 4, "community_name": "Utility Scripts", "source_file": "src/model/style_conditioner.py", "file_type": "code", "degree": 27}, {"id": "src_model_style_conditioner_py_forward", "label": "forward()", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "forward()", "community": 6, "community_name": "Token Management", "source_file": "src/model/style_conditioner.py", "file_type": "code", "degree": 3}, {"id": "src_model_style_conditioner_py_forward_doc", "label": "Args:    style_vector: [batch_size, 512]Returns:    prefix_embeddings: [batch", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Args:    style_vector: [batch_size, 512]Returns:    prefix_embeddings: [batch", "community": 6, "community_name": "Token Management", "source_file": "src/model/style_conditioner.py", "file_type": "rationale", "degree": 1}, {"id": "src_preprocessing_dependency_parser_py", "label": "dependency_parser.py", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 15.3, "font": {"size": 12, "color": "#ffffff"}, "title": "dependency_parser.py", "community": 13, "community_name": "Token Management", "source_file": "src/preprocessing/dependency_parser.py", "file_type": "code", "degree": 6}, {"id": "src_preprocessing_dependency_parser_py_docstring", "label": "Dependency parser module.Extracts grammatical skeletons (subject-verb-object) f", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Dependency parser module.Extracts grammatical skeletons (subject-verb-object) f", "community": 13, "community_name": "Token Management", "source_file": "src/preprocessing/dependency_parser.py", "file_type": "rationale", "degree": 1}, {"id": "src_preprocessing_dependency_parser_py_DependencyParser", "label": "DependencyParser", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 11.8, "font": {"size": 0, "color": "#ffffff"}, "title": "DependencyParser", "community": 13, "community_name": "Token Management", "source_file": "src/preprocessing/dependency_parser.py", "file_type": "code", "degree": 2}, {"id": "src_preprocessing_dependency_parser_py_DependencyParser_doc", "label": "Extracts dependency trees and SVO triples from text.", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Extracts dependency trees and SVO triples from text.", "community": 13, "community_name": "Token Management", "source_file": "src/preprocessing/dependency_parser.py", "file_type": "rationale", "degree": 1}, {"id": "src_preprocessing_dependency_parser_py___init__", "label": "__init__()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 17.9, "font": {"size": 12, "color": "#ffffff"}, "title": "__init__()", "community": 4, "community_name": "Utility Scripts", "source_file": "src/preprocessing/dependency_parser.py", "file_type": "code", "degree": 9}, {"id": "src_preprocessing_dependency_parser_py_parse", "label": "parse()", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 14.4, "font": {"size": 0, "color": "#ffffff"}, "title": "parse()", "community": 13, "community_name": "Token Management", "source_file": "src/preprocessing/dependency_parser.py", "file_type": "code", "degree": 5}, {"id": "src_preprocessing_dependency_parser_py_parse_doc", "label": "Extract dependency tree for each sentence.", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Extract dependency tree for each sentence.", "community": 13, "community_name": "Token Management", "source_file": "src/preprocessing/dependency_parser.py", "file_type": "rationale", "degree": 1}, {"id": "src_preprocessing_dependency_parser_py_extract_svo", "label": "extract_svo()", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "extract_svo()", "community": 13, "community_name": "Token Management", "source_file": "src/preprocessing/dependency_parser.py", "file_type": "code", "degree": 3}, {"id": "src_preprocessing_dependency_parser_py_extract_svo_doc", "label": "Extract subject-verb-object triples per sentence.", "color": {"background": "#76B7B2", "border": "#76B7B2", "highlight": {"background": "#ffffff", "border": "#76B7B2"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Extract subject-verb-object triples per sentence.", "community": 13, "community_name": "Token Management", "source_file": "src/preprocessing/dependency_parser.py", "file_type": "rationale", "degree": 1}, {"id": "src_preprocessing_dyslexia_simulator_py", "label": "dyslexia_simulator.py", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 18.8, "font": {"size": 12, "color": "#ffffff"}, "title": "dyslexia_simulator.py", "community": 5, "community_name": "Module Group 5", "source_file": "src/preprocessing/dyslexia_simulator.py", "file_type": "code", "degree": 10}, {"id": "src_preprocessing_dyslexia_simulator_py_docstring", "label": "Programmatically generates dyslectic training data from clean text.Used to augm", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Programmatically generates dyslectic training data from clean text.Used to augm", "community": 5, "community_name": "Module Group 5", "source_file": "src/preprocessing/dyslexia_simulator.py", "file_type": "rationale", "degree": 1}, {"id": "src_preprocessing_dyslexia_simulator_py_DyslexiaSimulator", "label": "DyslexiaSimulator", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 11.8, "font": {"size": 0, "color": "#ffffff"}, "title": "DyslexiaSimulator", "community": 5, "community_name": "Module Group 5", "source_file": "src/preprocessing/dyslexia_simulator.py", "file_type": "code", "degree": 2}, {"id": "src_preprocessing_dyslexia_simulator_py_DyslexiaSimulator_doc", "label": "Generates synthetic dyslectic text from clean input for data augmentation.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Generates synthetic dyslectic text from clean input for data augmentation.", "community": 5, "community_name": "Module Group 5", "source_file": "src/preprocessing/dyslexia_simulator.py", "file_type": "rationale", "degree": 1}, {"id": "src_preprocessing_dyslexia_simulator_py___init__", "label": "__init__()", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 17.9, "font": {"size": 12, "color": "#ffffff"}, "title": "__init__()", "community": 4, "community_name": "Utility Scripts", "source_file": "src/preprocessing/dyslexia_simulator.py", "file_type": "code", "degree": 9}, {"id": "src_preprocessing_dyslexia_simulator_py__transpose_letters", "label": "_transpose_letters()", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 12.6, "font": {"size": 0, "color": "#ffffff"}, "title": "_transpose_letters()", "community": 5, "community_name": "Module Group 5", "source_file": "src/preprocessing/dyslexia_simulator.py", "file_type": "code", "degree": 3}, {"id": "src_preprocessing_dyslexia_simulator_py__transpose_letters_doc", "label": "Swap two adjacent letters.", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.9, "font": {"size": 0, "color": "#ffffff"}, "title": "Swap two adjacent letters.", "community": 5, "community_name": "Module Group 5", "source_file": "src/preprocessing/dyslexia_simulator.py", "file_type": "rationale", "degree": 1}, {"id": 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{"from": "sem_redis_cache", "to": "sem_feed_pool", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "sem_redis_cache", "to": "sem_feed_redis", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "sem_redis_cache", "to": "sem_pubsub_crash", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "sem_redis_cache", "to": "sem_redis_pool_exhaustion", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "sem_cloudflare_proxy", "to": "sem_origin_secret", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": 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"sem_terraform_infra", "to": "sem_vpc_network", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "sem_terraform_infra", "to": "sem_alb_autoscaling", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "sem_unified_ws", "to": "sem_feed_ws", "label": "conceptually_related_to", "title": "conceptually_related_to [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "sem_unified_ws", "to": "sem_chat_ws", "label": "conceptually_related_to", "title": "conceptually_related_to [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "sem_unified_ws", "to": "sem_keysync_ws", "label": "conceptually_related_to", "title": "conceptually_related_to [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "sem_unified_ws", "to": "sem_discovery_ws", "label": "conceptually_related_to", "title": "conceptually_related_to [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "sem_unified_ws", "to": "sem_id_ws_reuse", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "sem_unified_ws", "to": "sem_token_refresh_crash", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "sem_feed_ws", "to": "sem_feed_system", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "sem_feed_scoring", "to": "sem_feed_pool", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, 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+const LEGEND = [{"cid": 0, "color": "#4E79A7", "label": "Module Group 0", "count": 62}, {"cid": 1, "color": "#F28E2B", "label": "Utility Scripts", "count": 44}, {"cid": 2, "color": "#E15759", "label": "Module Group 2", "count": 39}, {"cid": 3, "color": "#76B7B2", "label": "Module Group 3", "count": 38}, {"cid": 4, "color": "#59A14F", "label": "Utility Scripts", "count": 35}, {"cid": 5, "color": "#EDC948", "label": "Module Group 5", "count": 32}, {"cid": 6, "color": "#B07AA1", "label": "Token Management", "count": 32}, {"cid": 7, "color": "#FF9DA7", "label": "Utility Scripts", "count": 31}, {"cid": 8, "color": "#9C755F", "label": "Authentication", "count": 30}, {"cid": 9, "color": "#BAB0AC", "label": "Utility Scripts", "count": 29}, {"cid": 10, "color": "#4E79A7", "label": "Module Group 10", "count": 28}, {"cid": 11, "color": "#F28E2B", "label": "Feed Scoring &amp; Pool", "count": 27}, {"cid": 12, "color": "#E15759", "label": "Module Group 12", "count": 23}, {"cid": 13, "color": "#76B7B2", "label": "Token Management", "count": 19}, {"cid": 14, "color": "#59A14F", "label": "Module Group 14", "count": 16}, {"cid": 15, "color": "#EDC948", "label": "Utility Scripts", "count": 15}, {"cid": 16, "color": "#B07AA1", "label": "Module Group 16", "count": 10}, {"cid": 17, "color": "#FF9DA7", "label": "Module Group 17", "count": 7}, {"cid": 18, "color": "#9C755F", "label": "Module Group 18", "count": 7}, {"cid": 19, "color": "#BAB0AC", "label": "Module Group 19", "count": 6}, {"cid": 20, "color": "#4E79A7", "label": "Module Group 20", "count": 4}, {"cid": 21, "color": "#F28E2B", "label": "Infrastructure (Terraform)", "count": 4}, {"cid": 22, "color": "#E15759", "label": "Utility Scripts", "count": 3}, {"cid": 23, "color": "#76B7B2", "label": "Module Group 23", "count": 3}, {"cid": 24, "color": "#59A14F", "label": "Security &amp; Rate Limiting", "count": 2}, {"cid": 25, "color": "#EDC948", "label": "Module Group 25", "count": 1}, {"cid": 26, "color": "#B07AA1", "label": "WebSocket Codec", "count": 1}, {"cid": 27, "color": "#FF9DA7", "label": "Module Group 27", "count": 1}];
+
+// HTML-escape helper — prevents XSS when injecting graph data into innerHTML
+function esc(s) {
+  return String(s).replace(/&/g,'&amp;').replace(/</g,'&lt;').replace(/>/g,'&gt;').replace(/"/g,'&quot;').replace(/'/g,'&#39;');
+}
+
+// Build vis datasets
+const nodesDS = new vis.DataSet(RAW_NODES.map(n => ({
+  id: n.id, label: n.label, color: n.color, size: n.size,
+  font: n.font, title: n.title,
+  _community: n.community, _community_name: n.community_name,
+  _source_file: n.source_file, _file_type: n.file_type, _degree: n.degree,
+})));
+
+const edgesDS = new vis.DataSet(RAW_EDGES.map((e, i) => ({
+  id: i, from: e.from, to: e.to,
+  label: '',
+  title: e.title,
+  dashes: e.dashes,
+  width: e.width,
+  color: e.color,
+  arrows: { to: { enabled: true, scaleFactor: 0.5 } },
+})));
+
+const container = document.getElementById('graph');
+const network = new vis.Network(container, { nodes: nodesDS, edges: edgesDS }, {
+  physics: {
+    enabled: true,
+    solver: 'forceAtlas2Based',
+    forceAtlas2Based: {
+      gravitationalConstant: -60,
+      centralGravity: 0.005,
+      springLength: 120,
+      springConstant: 0.08,
+      damping: 0.4,
+      avoidOverlap: 0.8,
+    },
+    stabilization: { iterations: 200, fit: true },
+  },
+  interaction: {
+    hover: true,
+    tooltipDelay: 100,
+    hideEdgesOnDrag: true,
+    navigationButtons: false,
+    keyboard: false,
+  },
+  nodes: { shape: 'dot', borderWidth: 1.5 },
+  edges: { smooth: { type: 'continuous', roundness: 0.2 }, selectionWidth: 3 },
+});
+
+network.once('stabilizationIterationsDone', () => {
+  network.setOptions({ physics: { enabled: false } });
+});
+
+function showInfo(nodeId) {
+  const n = nodesDS.get(nodeId);
+  if (!n) return;
+  const neighborIds = network.getConnectedNodes(nodeId);
+  const neighborItems = neighborIds.map(nid => {
+    const nb = nodesDS.get(nid);
+    const color = nb ? nb.color.background : '#555';
+    return `<span class="neighbor-link" style="border-left-color:${esc(color)}" onclick="focusNode(${JSON.stringify(nid)})">${esc(nb ? nb.label : nid)}</span>`;
+  }).join('');
+  document.getElementById('info-content').innerHTML = `
+    <div class="field"><b>${esc(n.label)}</b></div>
+    <div class="field">Type: ${esc(n._file_type || 'unknown')}</div>
+    <div class="field">Community: ${esc(n._community_name)}</div>
+    <div class="field">Source: ${esc(n._source_file || '-')}</div>
+    <div class="field">Degree: ${n._degree}</div>
+    ${neighborIds.length ? `<div class="field" style="margin-top:8px;color:#aaa;font-size:11px">Neighbors (${neighborIds.length})</div><div id="neighbors-list">${neighborItems}</div>` : ''}
+  `;
+}
+
+function focusNode(nodeId) {
+  network.focus(nodeId, { scale: 1.4, animation: true });
+  network.selectNodes([nodeId]);
+  showInfo(nodeId);
+}
+
+// Track hovered node — hover detection is more reliable than click params
+let hoveredNodeId = null;
+network.on('hoverNode', params => {
+  hoveredNodeId = params.node;
+  container.style.cursor = 'pointer';
+});
+network.on('blurNode', () => {
+  hoveredNodeId = null;
+  container.style.cursor = 'default';
+});
+container.addEventListener('click', () => {
+  if (hoveredNodeId !== null) {
+    showInfo(hoveredNodeId);
+    network.selectNodes([hoveredNodeId]);
+  }
+});
+network.on('click', params => {
+  if (params.nodes.length > 0) {
+    showInfo(params.nodes[0]);
+  } else if (hoveredNodeId === null) {
+    document.getElementById('info-content').innerHTML = '<span class="empty">Click a node to inspect it</span>';
+  }
+});
+
+const searchInput = document.getElementById('search');
+const searchResults = document.getElementById('search-results');
+searchInput.addEventListener('input', () => {
+  const q = searchInput.value.toLowerCase().trim();
+  searchResults.innerHTML = '';
+  if (!q) { searchResults.style.display = 'none'; return; }
+  const matches = RAW_NODES.filter(n => n.label.toLowerCase().includes(q)).slice(0, 20);
+  if (!matches.length) { searchResults.style.display = 'none'; return; }
+  searchResults.style.display = 'block';
+  matches.forEach(n => {
+    const el = document.createElement('div');
+    el.className = 'search-item';
+    el.textContent = n.label;
+    el.style.borderLeft = `3px solid ${n.color.background}`;
+    el.style.paddingLeft = '8px';
+    el.onclick = () => {
+      network.focus(n.id, { scale: 1.5, animation: true });
+      network.selectNodes([n.id]);
+      showInfo(n.id);
+      searchResults.style.display = 'none';
+      searchInput.value = '';
+    };
+    searchResults.appendChild(el);
+  });
+});
+document.addEventListener('click', e => {
+  if (!searchResults.contains(e.target) && e.target !== searchInput)
+    searchResults.style.display = 'none';
+});
+
+const hiddenCommunities = new Set();
+
+const selectAllCb = document.getElementById('select-all-cb');
+
+function updateSelectAllState() {
+  const total = LEGEND.length;
+  const hidden = hiddenCommunities.size;
+  selectAllCb.checked = hidden === 0;
+  selectAllCb.indeterminate = hidden > 0 && hidden < total;
+}
+
+function toggleAllCommunities(hide) {
+  document.querySelectorAll('.legend-item').forEach(item => {
+    hide ? item.classList.add('dimmed') : item.classList.remove('dimmed');
+  });
+  document.querySelectorAll('.legend-cb').forEach(cb => {
+    cb.checked = !hide;
+  });
+  LEGEND.forEach(c => {
+    if (hide) hiddenCommunities.add(c.cid); else hiddenCommunities.delete(c.cid);
+  });
+  const updates = RAW_NODES.map(n => ({ id: n.id, hidden: hide }));
+  nodesDS.update(updates);
+  updateSelectAllState();
+}
+
+const legendEl = document.getElementById('legend');
+LEGEND.forEach(c => {
+  const item = document.createElement('div');
+  item.className = 'legend-item';
+  const cb = document.createElement('input');
+  cb.type = 'checkbox';
+  cb.className = 'legend-cb';
+  cb.checked = true;
+  cb.addEventListener('change', (e) => {
+    e.stopPropagation();
+    if (cb.checked) {
+      hiddenCommunities.delete(c.cid);
+      item.classList.remove('dimmed');
+    } else {
+      hiddenCommunities.add(c.cid);
+      item.classList.add('dimmed');
+    }
+    const updates = RAW_NODES
+      .filter(n => n.community === c.cid)
+      .map(n => ({ id: n.id, hidden: !cb.checked }));
+    nodesDS.update(updates);
+    updateSelectAllState();
+  });
+  item.innerHTML = `<div class="legend-dot" style="background:${c.color}"></div>
+    <span class="legend-label">${c.label}</span>
+    <span class="legend-count">${c.count}</span>`;
+  item.prepend(cb);
+  item.onclick = (e) => {
+    if (e.target === cb) return;
+    cb.checked = !cb.checked;
+    cb.dispatchEvent(new Event('change'));
+  };
+  legendEl.appendChild(item);
+});
+</script>
+<script>
+// Render hyperedges as shaded regions
+const hyperedges = [{"id": "websocket_channels", "label": "WebSocket Channel System", "nodes": ["sem_unified_ws", "sem_feed_ws", "sem_chat_ws", "sem_keysync_ws", "sem_discovery_ws"], "relation": "participate_in", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "README.md"}, {"id": "security_stack", "label": "Security Defense Stack", "nodes": ["sem_hmac_verification", "sem_origin_secret", "sem_pow_challenge", "sem_rate_limiting", "sem_attack_detection"], "relation": "participate_in", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "README.md"}, {"id": "feed_pipeline", "label": "Feed Recommendation Pipeline", "nodes": ["sem_feed_pool", "sem_feed_filters", "sem_feed_scoring", "sem_feed_heatmap", "sem_feed_reciprocal", "sem_feed_gradient"], "relation": "form", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "PLAN/feed_system_documentation.md"}];
+// afterDrawing passes ctx already transformed to network coordinate space.
+// Draw node positions raw — no manual pan/zoom/DPR math needed.
+network.on('afterDrawing', function(ctx) {
+    hyperedges.forEach(h => {
+        const positions = h.nodes
+            .map(nid => network.getPositions([nid])[nid])
+            .filter(p => p !== undefined);
+        if (positions.length < 2) return;
+        ctx.save();
+        ctx.globalAlpha = 0.12;
+        ctx.fillStyle = '#6366f1';
+        ctx.strokeStyle = '#6366f1';
+        ctx.lineWidth = 2;
+        ctx.beginPath();
+        // Centroid and expanded hull in network coordinates
+        const cx = positions.reduce((s, p) => s + p.x, 0) / positions.length;
+        const cy = positions.reduce((s, p) => s + p.y, 0) / positions.length;
+        const expanded = positions.map(p => ({
+            x: cx + (p.x - cx) * 1.15,
+            y: cy + (p.y - cy) * 1.15
+        }));
+        ctx.moveTo(expanded[0].x, expanded[0].y);
+        expanded.slice(1).forEach(p => ctx.lineTo(p.x, p.y));
+        ctx.closePath();
+        ctx.fill();
+        ctx.globalAlpha = 0.4;
+        ctx.stroke();
+        // Label
+        ctx.globalAlpha = 0.8;
+        ctx.fillStyle = '#4f46e5';
+        ctx.font = 'bold 11px sans-serif';
+        ctx.textAlign = 'center';
+        ctx.fillText(h.label, cx, cy - 5);
+        ctx.restore();
+    });
+});
+</script>
+</body>
+</html>
\ No newline at end of file
