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It eliminates WebSocket code, polling, and cache invalidation, providing ACID transactions and optimistic updates out of the box.\n\n## Instructions\n\n- When defining schemas, use `defineSchema()` with `defineTable()` and typed validators (`v.string()`, `v.number()`, `v.id(\"tableName\")`), and define indexes for all filtered and sorted queries.\n- When writing functions, use queries for reads (automatically reactive), mutations for writes (transactional, triggers reactive updates), and actions for external API calls (non-transactional).\n- When building React UIs, use `useQuery()` for reactive data subscriptions that auto-update, `useMutation()` for writes with optimistic updates, and `usePaginatedQuery()` for infinite scroll.\n- When handling authentication, use `convex-auth` for built-in auth or integrate Clerk\u002FAuth0, and validate user identity at the start of every mutation with `ctx.auth.getUserIdentity()`.\n- When processing background work, use `ctx.scheduler.runAfter()` for delayed execution and cron jobs for recurring tasks instead of making mutations slow.\n- When storing files, use `ctx.storage.store()` for upload and `ctx.storage.getUrl()` for serving URLs without S3 or CDN configuration.\n- When implementing search, use full-text search indexes with `searchIndex()` or vector search with `vectorIndex()` for AI\u002FRAG applications, with metadata filtering.\n\n## Examples\n\n### Example 1: Build a real-time chat application\n\n**User request:** \"Create a real-time chat app with Convex and React\"\n\n**Actions:**\n1. Define `messages` table with schema, author reference, and timestamp index\n2. Create a query function that returns messages sorted by timestamp\n3. Create a mutation for sending messages with auth validation\n4. Use `useQuery()` in React to subscribe to messages with automatic real-time updates\n\n**Output:** A chat application where messages appear instantly for all connected users without WebSocket code.\n\n### Example 2: Add full-text and vector search\n\n**User request:** \"Implement search across articles with both keyword and semantic search\"\n\n**Actions:**\n1. Define search index on article body field with `searchIndex()`\n2. Define vector index on embedding field with `vectorIndex()`\n3. Create query functions for text search and vector similarity search\n4. Combine metadata filtering with search for scoped results\n\n**Output:** A dual search system supporting both keyword matching and semantic similarity queries.\n\n## Guidelines\n\n- Use schema validation in production: `defineSchema()` catches type errors at deploy time, not runtime.\n- Define indexes for all filtered\u002Fsorted queries to ensure efficient data access.\n- Use queries for reads, mutations for writes, actions for external APIs; never mix concerns.\n- Keep mutations small and fast since they hold a database lock; move heavy processing to actions.\n- Use `ctx.scheduler.runAfter()` for background work instead of making mutations slow.\n- Validate user identity at the start of every mutation to prevent unauthorized writes.\n- Use optimistic updates for interactive UIs; the client sees the change instantly while the server 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