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feat(ai): implement O'Reilly-style knowledge architecture and microservices extraction
This commit is contained in:
Nubenetes Bot
10
GEMINI.md
10
GEMINI.md
@@ -73,10 +73,12 @@ This file contains the accumulated instructions and long-term vision for the aut
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- **High-Precision Reorganization (V1)**: These files MUST use nested semantic grouping (## and ###) to organize links without ever deleting technically valid content.
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- **Exhaustive Inclusion (V2)**: Unlike standard categories, V2 pages for Special Assets MUST include 100% of the ALIVE links from V1.
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- **AI Curation Discovery**: The discovery engine MUST actively search for new high-quality curation sources (e.g., "Awesome" repos) and suggest them for inclusion in `curation_sources.yaml`.
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28. **Zero-to-Hero V2 Architecture**: The V2 Portal MUST be structured as a learning journey:
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- **Complexity Hierarchy**: Resources MUST be grouped by level: Fundamentals -> Intermediate -> Advanced -> Architect.
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- **Strategic Dimensions**: The "AI and Artificial Intelligence" dimension is the primary entry point for agentic innovation. Dimension naming MUST prioritize industry-standard terms over internal terminology.
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- **Clickable Navigational Maps**: Every V2 page MUST include a Table of Contents (TOC) with nested anchors for all complexity levels.
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28. **Sophisticated V2 Knowledge Architecture**: The V2 Portal MUST be structured like an advanced O'Reilly technical book:
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- **Contextual Hierarchy**: Resources MUST NOT be flat lists. They must be organized into a sophisticated hierarchy of nested sections (##) and subsections (###) based on technical areas, topics, and subtopics.
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- **Structured Learning**: The organization must facilitate knowledge assimilation, moving from foundations to advanced implementation details in a logical, ordered sequence.
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- **Dynamic Indexing**: Every V2 page MUST include a Table of Contents (TOC) with clickable anchors for all technical sub-sections.
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- **AI Dimension Naming**: Prioritize industry-standard terms (e.g., "AI and Artificial Intelligence" instead of internal jargon) for top-level navigation.
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- **Content Extraction**: High-value sub-topics may be extracted into dedicated documents (e.g., "Microservices" content extracted from `introduction.md` into `microservices.md`) to maintain high-density focus.
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## 🛠️ Structural Evolution & Navigation
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...
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21
README.md
21
README.md
@@ -53,7 +53,7 @@
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12. [12. Developer Experience and VSCode Setup](#12-developer-experience-and-vscode-setup)
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14. [14. Special Assets and Learning Paths](#14-special-assets-and-learning-paths)
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* [14.1. Special Assets Management](#141-special-assets-management)
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* [14.2. Zero-to-Hero Learning Architecture](#142-zero-to-hero-learning-architecture)
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* [14.2. O'Reilly-style Knowledge Architecture](#142-oreilly-style-knowledge-architecture)
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* [12.1. Extension Recommendations](#121-extension-recommendations)
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* [12.2. Recommended settings.json](#122-recommended-settingsjson)
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13. [13. Repository Inventory and Configuration](#13-repository-inventory-and-configuration)
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@@ -673,17 +673,16 @@ Nubenetes prioritizes high-value technical documents through a specialized prese
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### 📚 Special Assets Management
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Certain files are designated as **Special Assets** (defined in [`data/special_assets.yaml`](data/special_assets.yaml)) due to their foundational importance. These include:
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- **Introduction & Fundamentals**: Specialized grouping to ensure a perfect "Day 0" experience.
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- **YAML & JSON Ecosystem**: Exhaustive technical references for configuration languages.
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- **Introduction and Fundamentals**: High-impact fundamental selection for V2, with 100% preservation in V1.
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- **Microservices Ecosystem**: A dedicated V2 document (`microservices.md`) extracted from the introduction to maintain architectural focus.
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- **YAML and JSON Ecosystem**: Exhaustive technical references for configuration languages.
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- **Awesome Repositories**: Preserved curation lists that act as gateways to specialized sub-ecosystems.
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**Rules of Engagement:**
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1. **Exhaustive V2 Inclusion**: 100% of ALIVE links from these V1 files are migrated to the V2 Elite portal, bypassing standard impact filters.
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2. **High-Precision Grouping**: AI agents use nested hierarchies (Sections & Subsections) to organize these files without losing any technically valid reference.
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1. **High-Precision Grouping**: AI agents use nested hierarchies (Areas -> Topics -> Subtopics) to organize these files without losing any technically valid reference, following a **Professional Technical Book** (O'Reilly style) structure.
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2. **Elite Curation**: For the V2 Portal, `introduction.md` undergoes a specialized "Elite selection" (Impact ≥ 4) to ensure a high-density entry point for global users.
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### 🎓 Zero-to-Hero Learning Architecture
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The V2 Portal is structured as an educational journey rather than a flat list. Resources are programmatically classified into four expertise tiers:
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- **Fundamentals**: Core concepts and "Getting Started" material.
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- **Intermediate**: Practical implementations and standard tooling.
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- **Advanced**: Performance optimization and complex technical internals.
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- **Architect**: System design, trade-offs, and long-term strategic direction.
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### 🎓 O'Reilly-style Knowledge Architecture
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The V2 Portal is structured as a sophisticated technical reference guide. Resources are programmatically classified and grouped by technical topics and subtopics, transforming the portal into a structured educational journey for Cloud Native engineering.
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- **Structured Assimilation**: Information is ordered sections-by-section to facilitate learning from foundational theory to advanced implementation.
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- **Contextual Hierarchy**: Every page features a clickable Table of Contents (TOC) with nested anchors for precise navigation across all technical sub-domains.
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@@ -13,9 +13,9 @@ special_assets:
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keywords: ["yaml", "json", "schema"]
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- file: "introduction.md"
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v1_rule: "Semantic clustering: Group by 'Fundamentals', 'Getting Started', and 'Advanced Concepts'."
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v2_rule: "Elite curated view: Highlight only high-impact fundamental resources."
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keywords: ["introduction", "getting started", "fundamentals"]
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v1_rule: "Semantic clustering: Group by technical sub-topics. Preserve 100% of links."
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v2_rule: "Elite curated selection (Impact >= 4). EXTRACT all Microservices-related links into a new dedicated V2 document: 'microservices.md'."
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keywords: ["introduction", "getting started", "fundamentals", "microservices"]
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# Navigation Renaming
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dimension_renaming:
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@@ -332,8 +332,10 @@ class AgenticCurator:
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log_event(f" [!] REORGANIZING: {file} ({'Special' if is_special else 'Standard'})")
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depth_instruction = (
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"SOPHISTICATED HIERARCHY: Create nested sections (##) and subsections (###). "
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"Group links by technical theme. Maintain all existing links. No deletions."
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"SOPHISTICATED O'REILLY HIERARCHY: Create nested sections (##) and subsections (###). "
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"Group links by technical AREAS, TOPICS, and SUBTOPICS (e.g. 'Foundations' -> 'Container Orchestration'). "
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"The structure must facilitate knowledge assimilation like a professional technical book. "
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"Ensure every valid link is preserved. Maintain professional titles."
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if is_special else "Group into logical sections (##)."
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)
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@@ -36,19 +36,19 @@ class V2VisionEngine:
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}
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self.library_criteria = (
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"You are a Senior Technical Librarian and Architect in 2026. Your mission is to build a high-density, professional reference library.\n"
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"PHASE 1: TECHNICAL PRESERVATION (HIGH INCLUSIVITY)\n"
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"- KEEP >90% of technical resources.\n"
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"PHASE 2: SOPHISTICATED SYNTHESIS & DATING\n"
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"- Extract precise PUBLICATION DATE (YYYY-MM-DD or YYYY).\n"
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"- Detect source content LANGUAGE.\n"
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"- Identify RESOURCE_TYPE and complexity LEVEL.\n"
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"PHASE 3: ZERO-TO-HERO CLASSIFICATION\n"
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"- Categorize into: 'Fundamentals', 'Intermediate', 'Advanced', or 'Architect' level.\n"
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"- For special curation lists (e.g. Awesome repos), identify the primary curation topic.\n"
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"PHASE 4: MANDATORY DESCRIPTIONS (V1 PRIORITY)\n"
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"- If 'Current Desc' is empty or too short, generate a professional 1-2 sentence summary.\n"
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"- Style: Technical, neutral, and informative. Language: English only.\n"
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"You are a Senior Technical Content Architect in 2026. Your mission is to organize a high-density technical reference portal "
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"with the structure and logical flow of an advanced O'Reilly technical book.\n"
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"PHASE 1: TECHNICAL PRESERVATION & CURATION\n"
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"- KEEP >90% of technical resources (except for 'introduction.md' where only high-impact links are kept).\n"
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"PHASE 2: SOPHISTICATED HIERARCHICAL CLASSIFICATION\n"
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"- Analyze the deep context of each resource to identify its TECHNICAL AREA (e.g., 'Storage Protocols', 'Observability Patterns').\n"
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"- Identify a TOPIC and SUBTOPIC for precise nested grouping.\n"
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"- For 'introduction.md', identify links related to MICROSERVICES for extraction.\n"
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"PHASE 3: KNOWLEDGE ASSIMILATION FLOW\n"
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"- Order topics to facilitate a structured learning journey: from architectural foundations to advanced engineering internals.\n"
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"- Assign QUALITY level (0-5 stars) and complexity LEVEL.\n"
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"PHASE 4: MANDATORY DESCRIPTIONS\n"
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"- If 'Current Desc' is empty, generate a professional 1-2 sentence summary. Style: O'Reilly technical, neutral, and informative.\n"
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)
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self.inventory = self._load_inventory()
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self.structure_map = self._load_structure_map()
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@@ -314,7 +314,7 @@ class V2VisionEngine:
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prompt = (
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f"{self.library_criteria}\n"
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"Respond ONLY with a JSON object: {\"results\": [{\"idx\": int, \"year\": \"YYYY\", \"stars\": 0-5, \"is_video\": bool, \"tag\": \"[TAG]\", \"summary\": \"1-2 sentences description\", \"language\": \"...\", \"type\": \"...\", \"level\": \"Fundamentals|Intermediate|Advanced|Architect\"}, ...]}\n\n"
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"Respond ONLY with a JSON object: {\"results\": [{\"idx\": int, \"year\": \"YYYY\", \"stars\": 0-5, \"is_video\": bool, \"tag\": \"[TAG]\", \"summary\": \"...\", \"language\": \"...\", \"type\": \"...\", \"level\": \"...\", \"area\": \"...\", \"topic\": \"...\", \"subtopic\": \"...\", \"is_microservice\": bool}, ...]}\n\n"
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"LINKS:\n" + "\n".join([f"{idx}. {l['title']} ({l['url']}) - Desc: {l['description'][:60]}" for idx, l in enumerate(batch)])
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)
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@@ -330,7 +330,7 @@ class V2VisionEngine:
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norm_url = normalize_url(item["url"])
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old_tag = self.inventory.get(norm_url, {}).get("tag")
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# SPECIAL ASSET BYPASS: If file is special, force 5 stars or preservation
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# SPECIAL ASSET BYPASS
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is_special = item["original_file"] in special_files
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eval_data = {
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@@ -341,7 +341,11 @@ class V2VisionEngine:
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"ai_summary": res.get("summary", ""),
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"language": res.get("language", "English"),
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"resource_type": res.get("type", "Reference"),
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"complexity": res.get("level", "Intermediate")
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"complexity": res.get("level", "Intermediate"),
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"area": res.get("area", "General"),
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"topic": res.get("topic", "Uncategorized"),
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"subtopic": res.get("subtopic", ""),
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"is_microservice": bool(res.get("is_microservice", False))
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}
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item.update(eval_data)
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@@ -370,7 +374,9 @@ class V2VisionEngine:
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"is_video": item["is_video"], "ai_summary": item["ai_summary"],
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"language": item["language"], "resource_type": item["resource_type"],
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"complexity": item["complexity"], "tag": item["tag"], "status": "online",
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"original_file": item["original_file"]
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"original_file": item["original_file"], "area": item["area"],
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"topic": item["topic"], "subtopic": item["subtopic"],
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"is_microservice": item["is_microservice"]
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}
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if "gh_stars" in item: self.inventory[norm_url]["gh_stars"] = item["gh_stars"]
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if "gh_updated" in item: self.inventory[norm_url]["gh_updated"] = item["gh_updated"]
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@@ -435,8 +441,14 @@ class V2VisionEngine:
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return {}
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async def _rebuild_structure(self, library_inventory: List[Dict]) -> Dict[str, Dict]:
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special_files = [sa["file"] for sa in self.special_assets_rules.get("special_assets", [])]
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"""
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REBUILD STRUCTURE: O'REILLY TECHNICAL BOOK ARCHITECTURE.
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Implements a sophisticated hierarchy: Dimension -> Category -> Topic -> Subtopic.
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Handles Special Asset rules and Microservices extraction.
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"""
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special_rules = {sa["file"]: sa for sa in self.special_assets_rules.get("special_assets", [])}
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v2_structure = {dim: {"summary": "", "categories": {}} for dim in self.dimensions.keys()}
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file_to_dim = {}
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for dim, files in self.dimensions.items():
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for f in files: file_to_dim[f + ".md"] = dim
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@@ -445,39 +457,62 @@ class V2VisionEngine:
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orig_file = item.get("original_file", "unknown.md")
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dim = file_to_dim.get(orig_file, "Architectural Foundations")
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cat_name = orig_file.replace(".md", "").replace("-", " ").title()
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is_special = orig_file in special_files
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# Filtering: Keep if stars >= 3 OR if it's a Special Asset
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if not is_special and item.get("stars", 0) < 3:
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# --- RULE: Microservices Extraction ---
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if item.get("is_microservice"):
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cat_name = "Microservices"
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# If from introduction, move to Foundations dimension, else keep in current
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if orig_file == "introduction.md": dim = "Architectural Foundations"
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# --- RULE: Special Asset Handling ---
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rule = special_rules.get(orig_file, {})
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is_special = orig_file in special_rules
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# Elite Curation for Introduction
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if orig_file == "introduction.md" and item.get("stars", 0) < 4 and not item.get("is_microservice"):
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continue
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# General Filtering for V2
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if not is_special and item.get("stars", 0) < 3 and not item.get("is_microservice"):
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continue
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# Hierarchy: Dimension -> Category -> Topic -> Subtopic
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if cat_name not in v2_structure[dim]["categories"]:
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v2_structure[dim]["categories"][cat_name] = {
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"Fundamentals": [], "Intermediate": [], "Advanced": [], "Architect": []
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}
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v2_structure[dim]["categories"][cat_name] = {}
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level = item.get("complexity", "Intermediate")
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if level not in v2_structure[dim]["categories"][cat_name]: level = "Intermediate"
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v2_structure[dim]["categories"][cat_name][level].append(item)
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topic = item.get("topic", "General Concepts")
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if topic not in v2_structure[dim]["categories"][cat_name]:
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v2_structure[dim]["categories"][cat_name][topic] = {}
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subtopic = item.get("subtopic", "Other Resources") or "General"
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if subtopic not in v2_structure[dim]["categories"][cat_name][topic]:
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v2_structure[dim]["categories"][cat_name][topic][subtopic] = []
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v2_structure[dim]["categories"][cat_name][topic][subtopic].append(item)
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# Cleanup and Sorting
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for dim in v2_structure:
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for cat in list(v2_structure[dim]["categories"].keys()):
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has_content = False
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for level in v2_structure[dim]["categories"][cat]:
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if v2_structure[dim]["categories"][cat][level]:
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has_content = True
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v2_structure[dim]["categories"][cat][level].sort(
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for topic in v2_structure[dim]["categories"][cat]:
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for subtopic in v2_structure[dim]["categories"][cat][topic]:
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# Sort by Impact and Year
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v2_structure[dim]["categories"][cat][topic][subtopic].sort(
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key=lambda x: (-x.get("stars", 1), -(int(x["year"]) if str(x.get("year", "")).isdigit() else 0))
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)
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if not has_content:
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if not v2_structure[dim]["categories"][cat]:
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del v2_structure[dim]["categories"][cat]
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else:
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# Maintain Executive Summary for Dimension
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prompt = f"Write a professional 2026 executive summary for '{dim}'. Focus on high-density value. 1 sentence only."
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try:
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v2_structure[dim]["summary"] = await call_gemini_with_retry(prompt, response_format="text", prefer_flash=True)
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except:
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v2_structure[dim]["summary"] = f"Impact-driven reference library for {dim}."
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# Update Executive Summary
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cache_key = f"INTRO:{dim}"
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if cache_key in self.inventory:
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v2_structure[dim]["summary"] = self.inventory[cache_key].get("ai_summary")
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else:
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prompt = f"Write a professional 1-sentence executive summary for the '{dim}' technical dimension."
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try:
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v2_structure[dim]["summary"] = await call_gemini_with_retry(prompt, response_format="text", prefer_flash=True)
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except:
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v2_structure[dim]["summary"] = f"Strategic reference library for {dim}."
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return v2_structure
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@@ -588,81 +623,70 @@ class V2VisionEngine:
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slug = dim.lower().replace(" ", "-").replace("&", "and").replace("(", "").replace(")", "").replace(" ", "-")
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md = f"# {dim}\n\n"
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md += f"!!! info \"Architectural Context\"\n {content['summary']}\n\n"
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# --- Table of Contents for the Page ---
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md += "## Table of Contents\n"
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for cat in content["categories"].keys():
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for cat, topics in content["categories"].items():
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cat_slug = cat.lower().replace(" ", "-")
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md += f"- [{cat}](#{cat_slug})\n"
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for level, level_links in content["categories"][cat].items():
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if level_links:
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level_slug = f"{cat_slug}-{level.lower()}"
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md += f" - [{level}](#{level_slug})\n"
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for topic, subtopics in topics.items():
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topic_slug = f"{cat_slug}-{topic.lower().replace(' ', '-')}"
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md += f" - [{topic}](#{topic_slug})\n"
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md += "\n---\n\n"
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for cat, levels in content["categories"].items():
|
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for cat, topics in content["categories"].items():
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cat_slug = cat.lower().replace(" ", "-")
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md += f"## {cat}\n\n"
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for level, links in levels.items():
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if not links: continue
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level_slug = f"{cat_slug}-{level.lower()}"
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md += f"### {cat} - {level}\n"
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for l in links:
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year, stars_val = l.get("year", "N/A"), l.get("stars", 0)
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stars = ("🌟" * stars_val) if stars_val > 0 else ""
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tag = l.get("tag", "[ENTERPRISE-STABLE]")
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# Determine color mapping
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if "STANDARD" in tag or "FOUNDATIONAL" in tag: color = "success"
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elif "EMERGING" in tag: color = "warning"
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elif "LEGACY" in tag: color = "critical"
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elif "STABLE" in tag: color = "info"
|
||||
else: color = "primary"
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|
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title_clean = l['title'].replace("==", "")
|
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if stars_val >= 3 or "STANDARD" in tag:
|
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title_display = f"**=={title_clean}==**"
|
||||
elif stars_val == 2:
|
||||
title_display = f"**{title_clean}**"
|
||||
else:
|
||||
title_display = title_clean
|
||||
|
||||
year_prefix = f"**({year})** " if year and year != "N/A" else ""
|
||||
gh_info = f" <span class='md-tag md-tag--info'>⭐ {l['gh_stars']}</span>" if "gh_stars" in l else ""
|
||||
icon = " 🎥" if l.get("is_video") else ""
|
||||
|
||||
# Language Tagging
|
||||
lang = l.get("language", "English")
|
||||
lang_tag = ""
|
||||
if lang.lower() != "english":
|
||||
lang_tag = f" <span class='md-tag md-tag--warning'>[{lang.upper()} CONTENT]</span>"
|
||||
|
||||
# Complexity Tagging
|
||||
l_val = l.get("complexity", "Intermediate")
|
||||
level_tag = ""
|
||||
if l_val.lower() in ["architect", "advanced"]:
|
||||
level_tag = f" <span class='md-tag md-tag--critical'>[{l_val.upper()} LEVEL]</span>"
|
||||
|
||||
# Resource Type Tagging
|
||||
res_type = l.get("resource_type", "Reference")
|
||||
type_tag = ""
|
||||
if res_type.lower() in ["case study", "guide", "documentation"]:
|
||||
type_tag = f" <span class='md-tag md-tag--primary'>[{res_type.upper()}]</span>"
|
||||
|
||||
# Rich Metadata
|
||||
rich_tags = ""
|
||||
if l.get("author"): rich_tags += f" <small>by **{l['author']}**</small>"
|
||||
if l.get("duration"): rich_tags += f" <span class='md-tag md-tag--info'>⏱️ {l['duration']}</span>"
|
||||
if l.get("reading_time"): rich_tags += f" <span class='md-tag md-tag--info'>📖 {l['reading_time']}</span>"
|
||||
for topic, subtopics in topics.items():
|
||||
topic_slug = f"{cat_slug}-{topic.lower().replace(' ', '-')}"
|
||||
md += f"### {topic}\n\n"
|
||||
|
||||
md += f" - {year_prefix}[{title_display}]({l['url']}){icon}{gh_info}{lang_tag}{level_tag}{type_tag}{rich_tags} {stars} <span class='md-tag md-tag--{color}'>{tag}</span>\n"
|
||||
if l['description']:
|
||||
desc = l['description']
|
||||
if "\n" in desc:
|
||||
md += "\n" + "\n".join([" " + line for line in desc.split("\n")]) + "\n\n"
|
||||
else:
|
||||
md += f" {desc}\n"
|
||||
for subtopic, links in subtopics.items():
|
||||
if subtopic and subtopic != "General":
|
||||
md += f"#### {subtopic}\n"
|
||||
|
||||
for l in links:
|
||||
year, stars_val = l.get("year", "N/A"), l.get("stars", 0)
|
||||
stars = ("🌟" * stars_val) if stars_val > 0 else ""
|
||||
tag = l.get("tag", "[ENTERPRISE-STABLE]")
|
||||
|
||||
# Color mapping
|
||||
if "STANDARD" in tag or "FOUNDATIONAL" in tag: color = "success"
|
||||
elif "EMERGING" in tag: color = "warning"
|
||||
elif "LEGACY" in tag: color = "critical"
|
||||
elif "STABLE" in tag: color = "info"
|
||||
else: color = "primary"
|
||||
|
||||
title_clean = l['title'].replace("==", "")
|
||||
title_display = f"**=={title_clean}==**" if stars_val >= 3 else title_clean
|
||||
|
||||
year_prefix = f"**({year})** " if year and year != "N/A" else ""
|
||||
gh_info = f" <span class='md-tag md-tag--info'>⭐ {l['gh_stars']}</span>" if "gh_stars" in l else ""
|
||||
icon = " 🎥" if l.get("is_video") else ""
|
||||
|
||||
lang = l.get("language", "English")
|
||||
lang_tag = f" <span class='md-tag md-tag--warning'>[{lang.upper()} CONTENT]</span>" if lang.lower() != "english" else ""
|
||||
|
||||
complexity = l.get("complexity", "Intermediate")
|
||||
level_tag = f" <span class='md-tag md-tag--critical'>[{complexity.upper()} LEVEL]</span>" if complexity.lower() in ["architect", "advanced"] else ""
|
||||
|
||||
res_type = l.get("resource_type", "Reference")
|
||||
type_tag = f" <span class='md-tag md-tag--primary'>[{res_type.upper()}]</span>" if res_type.lower() in ["case study", "guide", "documentation"] else ""
|
||||
|
||||
# Rich Metadata
|
||||
rich_tags = ""
|
||||
if l.get("author"): rich_tags += f" <small>by **{l['author']}**</small>"
|
||||
if l.get("duration"): rich_tags += f" <span class='md-tag md-tag--info'>⏱️ {l['duration']}</span>"
|
||||
if l.get("reading_time"): rich_tags += f" <span class='md-tag md-tag--info'>📖 {l['reading_time']}</span>"
|
||||
|
||||
md += f" - {year_prefix}[{title_display}]({l['url']}){icon}{gh_info}{lang_tag}{level_tag}{type_tag}{rich_tags} {stars} <span class='md-tag md-tag--{color}'>{tag}</span>\n"
|
||||
if l['description']:
|
||||
desc = l['description']
|
||||
md += f"\n {desc}\n\n"
|
||||
md += "\n"
|
||||
md += "\n"
|
||||
|
||||
with open(os.path.join(V2_DIR, f"{slug}.md"), "w") as f: f.write(md)
|
||||
|
||||
async def _sync_enterprise_navigation(self, data: Dict[str, Dict]):
|
||||
|
||||
Reference in New Issue
Block a user