wehub-resource-sync
122 lines
4.9 KiB
Python
122 lines
4.9 KiB
Python
#!/usr/bin/env python3
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"""Parse keyword connections from Link Suggestions Report."""
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import re
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import sys
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from pathlib import Path
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def parse_keyword_connections(report_path):
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"""Parse keyword connections from the report and find meaningful ones."""
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with open(report_path, 'r', encoding='utf-8') as f:
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content = f.read()
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# Find the keyword-based section
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keyword_section_start = content.find("## Keyword-Based Link Suggestions")
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if keyword_section_start == -1:
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print("Could not find keyword-based section")
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return []
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# Find the next section (orphaned notes)
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orphaned_section_start = content.find("## Orphaned Notes", keyword_section_start)
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if orphaned_section_start == -1:
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keyword_section = content[keyword_section_start:]
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else:
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keyword_section = content[keyword_section_start:orphaned_section_start]
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# Pattern to match connections
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pattern = r'- \[\[([^\]]+)\]\] ↔ \[\[([^\]]+)\]\]\s*\n\s*Common words: ([^\n]+)'
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connections = []
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for match in re.finditer(pattern, keyword_section):
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file1 = match.group(1).strip()
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file2 = match.group(2).strip()
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keywords = match.group(3).strip()
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# Skip self-connections
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if file1 == file2:
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continue
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# Count keywords
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keyword_list = [k.strip() for k in keywords.split(',')]
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keyword_count = len(keyword_list)
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# Only include connections with 5+ keywords
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if keyword_count >= 5:
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connections.append({
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'file1': file1,
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'file2': file2,
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'keywords': keyword_list,
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'count': keyword_count
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})
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# Sort by keyword count descending
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connections.sort(key=lambda x: x['count'], reverse=True)
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return connections
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def main():
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report_path = Path("/Users/cam/VAULT01/System_Files/Link_Suggestions_Report.md")
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connections = parse_keyword_connections(report_path)
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print(f"Found {len(connections)} meaningful keyword connections (5+ keywords)\n")
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# Group by keyword themes
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tech_keywords = {'llm', 'langchain', 'langgraph', 'rag', 'embedding', 'vector', 'agent', 'model', 'api', 'mcp'}
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framework_keywords = {'langchain', 'langgraph', 'fastapi', 'docker', 'cloudflare', 'supabase'}
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company_keywords = {'openai', 'anthropic', 'google', 'microsoft', 'meta'}
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concept_keywords = {'automation', 'workflow', 'pipeline', 'integration', 'generation', 'retrieval'}
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tech_connections = []
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framework_connections = []
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company_connections = []
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concept_connections = []
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for conn in connections:
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keywords_lower = [k.lower() for k in conn['keywords']]
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tech_score = len([k for k in keywords_lower if any(tech in k for tech in tech_keywords)])
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framework_score = len([k for k in keywords_lower if any(fw in k for fw in framework_keywords)])
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company_score = len([k for k in keywords_lower if any(comp in k for comp in company_keywords)])
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concept_score = len([k for k in keywords_lower if any(conc in k for conc in concept_keywords)])
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if tech_score >= 2:
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tech_connections.append(conn)
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if framework_score >= 2:
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framework_connections.append(conn)
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if company_score >= 1:
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company_connections.append(conn)
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if concept_score >= 2:
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concept_connections.append(conn)
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print("## High-Priority Technical Connections")
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print(f"Found {len(tech_connections)} connections with technical keywords\n")
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for i, conn in enumerate(tech_connections[:20]): # Top 20
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print(f"{i+1}. [[{conn['file1']}]] ↔ [[{conn['file2']}]]")
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print(f" Keywords ({conn['count']}): {', '.join(conn['keywords'][:10])}")
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print()
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print("\n## Framework-Related Connections")
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print(f"Found {len(framework_connections)} connections with framework keywords\n")
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for i, conn in enumerate(framework_connections[:15]): # Top 15
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print(f"{i+1}. [[{conn['file1']}]] ↔ [[{conn['file2']}]]")
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print(f" Keywords ({conn['count']}): {', '.join(conn['keywords'][:10])}")
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print()
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print("\n## Company/Provider Connections")
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print(f"Found {len(company_connections)} connections with company keywords\n")
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for i, conn in enumerate(company_connections[:10]): # Top 10
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print(f"{i+1}. [[{conn['file1']}]] ↔ [[{conn['file2']}]]")
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print(f" Keywords ({conn['count']}): {', '.join(conn['keywords'][:10])}")
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print()
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print("\n## Concept/Workflow Connections")
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print(f"Found {len(concept_connections)} connections with concept keywords\n")
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for i, conn in enumerate(concept_connections[:10]): # Top 10
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print(f"{i+1}. [[{conn['file1']}]] ↔ [[{conn['file2']}]]")
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print(f" Keywords ({conn['count']}): {', '.join(conn['keywords'][:10])}")
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print()
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if __name__ == "__main__":
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main() |