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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.
106 lines
2.5 KiB
TypeScript
106 lines
2.5 KiB
TypeScript
import { v } from "convex/values";
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import { internalMutation, internalQuery } from "./_generated/server";
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// Internal query to get posts without embeddings
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export const getPostsWithoutEmbeddings = internalQuery({
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args: { limit: v.number() },
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returns: v.array(
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v.object({
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_id: v.id("posts"),
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title: v.string(),
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content: v.string(),
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})
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),
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handler: async (ctx, args) => {
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const posts = await ctx.db
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.query("posts")
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.withIndex("by_published", (q) => q.eq("published", true))
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.collect();
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return posts
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.filter((post) => !post.embedding)
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.slice(0, args.limit)
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.map((post) => ({
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_id: post._id,
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title: post.title,
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content: post.content,
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}));
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},
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});
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// Internal query to get pages without embeddings
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export const getPagesWithoutEmbeddings = internalQuery({
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args: { limit: v.number() },
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returns: v.array(
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v.object({
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_id: v.id("pages"),
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title: v.string(),
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content: v.string(),
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})
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),
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handler: async (ctx, args) => {
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const pages = await ctx.db
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.query("pages")
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.withIndex("by_published", (q) => q.eq("published", true))
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.collect();
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return pages
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.filter((page) => !page.embedding)
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.slice(0, args.limit)
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.map((page) => ({
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_id: page._id,
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title: page.title,
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content: page.content,
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}));
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},
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});
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// Internal mutation to save embedding for a post
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export const savePostEmbedding = internalMutation({
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args: {
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id: v.id("posts"),
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embedding: v.array(v.float64()),
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},
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handler: async (ctx, args) => {
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await ctx.db.patch(args.id, { embedding: args.embedding });
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},
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});
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// Internal mutation to save embedding for a page
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export const savePageEmbedding = internalMutation({
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args: {
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id: v.id("pages"),
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embedding: v.array(v.float64()),
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},
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handler: async (ctx, args) => {
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await ctx.db.patch(args.id, { embedding: args.embedding });
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},
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});
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// Internal query to get post by slug
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export const getPostBySlug = internalQuery({
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args: { slug: v.string() },
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returns: v.union(
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v.object({
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_id: v.id("posts"),
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title: v.string(),
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content: v.string(),
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}),
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v.null()
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),
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handler: async (ctx, args) => {
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const post = await ctx.db
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.query("posts")
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.withIndex("by_slug", (q) => q.eq("slug", args.slug))
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.first();
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if (!post) return null;
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return {
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_id: post._id,
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title: post.title,
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content: post.content,
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};
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},
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});
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