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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.
Welcome to your Convex functions directory!
Write your Convex functions here. See https://docs.convex.dev/functions for more.
A query function that takes two arguments looks like:
// convex/myFunctions.ts
import { query } from "./_generated/server";
import { v } from "convex/values";
export const myQueryFunction = query({
// Validators for arguments.
args: {
first: v.number(),
second: v.string(),
},
// Function implementation.
handler: async (ctx, args) => {
// Read the database as many times as you need here.
// See https://docs.convex.dev/database/reading-data.
const documents = await ctx.db.query("tablename").collect();
// Arguments passed from the client are properties of the args object.
console.log(args.first, args.second);
// Write arbitrary JavaScript here: filter, aggregate, build derived data,
// remove non-public properties, or create new objects.
return documents;
},
});
Using this query function in a React component looks like:
const data = useQuery(api.myFunctions.myQueryFunction, {
first: 10,
second: "hello",
});
A mutation function looks like:
// convex/myFunctions.ts
import { mutation } from "./_generated/server";
import { v } from "convex/values";
export const myMutationFunction = mutation({
// Validators for arguments.
args: {
first: v.string(),
second: v.string(),
},
// Function implementation.
handler: async (ctx, args) => {
// Insert or modify documents in the database here.
// Mutations can also read from the database like queries.
// See https://docs.convex.dev/database/writing-data.
const message = { body: args.first, author: args.second };
const id = await ctx.db.insert("messages", message);
// Optionally, return a value from your mutation.
return await ctx.db.get("messages", id);
},
});
Using this mutation function in a React component looks like:
const mutation = useMutation(api.myFunctions.myMutationFunction);
function handleButtonPress() {
// fire and forget, the most common way to use mutations
mutation({ first: "Hello!", second: "me" });
// OR
// use the result once the mutation has completed
mutation({ first: "Hello!", second: "me" }).then((result) =>
console.log(result),
);
}
Use the Convex CLI to push your functions to a deployment. See everything
the Convex CLI can do by running npx convex -h in your project root
directory. To learn more, launch the docs with npx convex docs.