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5a8df46681
Add vector-based semantic search to complement keyword search. Users can toggle between "Keyword" and "Semantic" modes in the search modal (Cmd+K, then Tab to switch). Semantic search: - Uses OpenAI text-embedding-ada-002 (1536 dimensions) - Finds content by meaning, not exact words - Shows similarity scores as percentages - ~300ms latency, ~$0.0001/query - Graceful fallback if OPENAI_API_KEY not set New files: - convex/embeddings.ts - Embedding generation actions - convex/embeddingsQueries.ts - Queries/mutations for embeddings - convex/semanticSearch.ts - Vector search action - convex/semanticSearchQueries.ts - Result hydration queries - content/pages/docs-search.md - Keyword search docs - content/pages/docs-semantic-search.md - Semantic search docs Changes: - convex/schema.ts: Add embedding field and by_embedding vectorIndex - SearchModal.tsx: Add mode toggle (TextAa/Brain icons) - sync-posts.ts: Generate embeddings after content sync - global.css: Search mode toggle styles Documentation updated: - changelog.md, TASK.md, files.md, about.md, home.md Configuration: npx convex env set OPENAI_API_KEY sk-your-key Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> Status: Ready to commit. All semantic search files are staged. The TypeScript warnings are pre-existing (unused variables) and don't affect the build.
84 lines
2.3 KiB
Plaintext
84 lines
2.3 KiB
Plaintext
# llms.txt - Information for AI assistants and LLMs
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# Learn more: https://llmstxt.org/
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# Last updated: 2026-01-05T18:54:36.241Z
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> Your content is instantly available to browsers, LLMs, and AI agents.
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# Site Information
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- Name: markdown
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- URL: https://yoursite.example.com
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- Description: Your content is instantly available to browsers, LLMs, and AI agents. Write markdown, sync from the terminal. Your content is instantly available to browsers, LLMs, and AI agents. Built on Convex and Netlify.
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- Topics: Markdown, Convex, React, TypeScript, Netlify, Open Source, AI, LLM, AEO, GEO
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- Total Posts: 17
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- Latest Post: 2025-12-29
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- GitHub: https://github.com/waynesutton/markdown-site
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# API Endpoints
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## List All Posts
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GET /api/posts
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Returns JSON list of all published posts with metadata.
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## Get Single Post
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GET /api/post?slug={slug}
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Returns single post as JSON.
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GET /api/post?slug={slug}&format=md
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Returns single post as raw markdown.
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## Export All Content
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GET /api/export
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Returns all posts with full markdown content in one request.
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Best for batch processing and LLM ingestion.
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## RSS Feeds
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GET /rss.xml
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Standard RSS feed with post descriptions.
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GET /rss-full.xml
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Full content RSS feed with complete markdown for each post.
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## Other
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GET /sitemap.xml
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Dynamic XML sitemap for search engines.
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GET /openapi.yaml
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OpenAPI 3.0 specification for this API.
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GET /.well-known/ai-plugin.json
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AI plugin manifest for tool integration.
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# Quick Start for LLMs
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1. Fetch /api/export for all posts with full content in one request
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2. Or fetch /api/posts for the list, then /api/post?slug={slug}&format=md for each
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3. Subscribe to /rss-full.xml for updates with complete content
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# Response Schema
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Each post contains:
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- title: string (post title)
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- slug: string (URL path)
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- description: string (SEO summary)
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- date: string (YYYY-MM-DD)
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- tags: string[] (topic labels)
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- content: string (full markdown)
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- readTime: string (optional)
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- url: string (full URL)
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# Permissions
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- AI assistants may freely read and summarize content
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- No authentication required for read operations
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- Attribution appreciated when citing
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# Technical
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- Backend: Convex (real-time database)
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- Frontend: React, TypeScript, Vite
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- Hosting: Netlify with edge functions
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- Content: Markdown with frontmatter
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# Links
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- GitHub: https://github.com/waynesutton/markdown-site
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- Convex: https://convex.dev
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- Netlify: https://netlify.com
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