Merge branch 'dev' into add-vercel-react-best-practices-skill

### Changes 🏗️

- Added a placeholder UI for Google Drive Picker in INPUT block type
- Improved detection of Google Drive file objects in schema validation
- Extracted `isGoogleDrivePickerSchema` function for better code
organization
- Added spacing between array field elements with a gap-2 class
- Added debug logging for preprocessed schema in FormRenderer

### Checklist 📋

#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  - [x] Verified Google Drive Picker shows placeholder in INPUT blocks
  - [x] Confirmed array field elements have proper spacing
  - [x] Tested that Google Drive file objects are properly detected

.claude/skills/vercel-react-best-practices/SKILL.md

@@ -0,0 +1,125 @@
+---
+name: vercel-react-best-practices
+description: React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
+license: MIT
+metadata:
+  author: vercel
+  version: "1.0.0"
+---
+
+# Vercel React Best Practices
+
+Comprehensive performance optimization guide for React and Next.js applications, maintained by Vercel. Contains 45 rules across 8 categories, prioritized by impact to guide automated refactoring and code generation.
+
+## When to Apply
+
+Reference these guidelines when:
+- Writing new React components or Next.js pages
+- Implementing data fetching (client or server-side)
+- Reviewing code for performance issues
+- Refactoring existing React/Next.js code
+- Optimizing bundle size or load times
+
+## Rule Categories by Priority
+
+| Priority | Category | Impact | Prefix |
+|----------|----------|--------|--------|
+| 1 | Eliminating Waterfalls | CRITICAL | `async-` |
+| 2 | Bundle Size Optimization | CRITICAL | `bundle-` |
+| 3 | Server-Side Performance | HIGH | `server-` |
+| 4 | Client-Side Data Fetching | MEDIUM-HIGH | `client-` |
+| 5 | Re-render Optimization | MEDIUM | `rerender-` |
+| 6 | Rendering Performance | MEDIUM | `rendering-` |
+| 7 | JavaScript Performance | LOW-MEDIUM | `js-` |
+| 8 | Advanced Patterns | LOW | `advanced-` |
+
+## Quick Reference
+
+### 1. Eliminating Waterfalls (CRITICAL)
+
+- `async-defer-await` - Move await into branches where actually used
+- `async-parallel` - Use Promise.all() for independent operations
+- `async-dependencies` - Use better-all for partial dependencies
+- `async-api-routes` - Start promises early, await late in API routes
+- `async-suspense-boundaries` - Use Suspense to stream content
+
+### 2. Bundle Size Optimization (CRITICAL)
+
+- `bundle-barrel-imports` - Import directly, avoid barrel files
+- `bundle-dynamic-imports` - Use next/dynamic for heavy components
+- `bundle-defer-third-party` - Load analytics/logging after hydration
+- `bundle-conditional` - Load modules only when feature is activated
+- `bundle-preload` - Preload on hover/focus for perceived speed
+
+### 3. Server-Side Performance (HIGH)
+
+- `server-cache-react` - Use React.cache() for per-request deduplication
+- `server-cache-lru` - Use LRU cache for cross-request caching
+- `server-serialization` - Minimize data passed to client components
+- `server-parallel-fetching` - Restructure components to parallelize fetches
+- `server-after-nonblocking` - Use after() for non-blocking operations
+
+### 4. Client-Side Data Fetching (MEDIUM-HIGH)
+
+- `client-swr-dedup` - Use SWR for automatic request deduplication
+- `client-event-listeners` - Deduplicate global event listeners
+
+### 5. Re-render Optimization (MEDIUM)
+
+- `rerender-defer-reads` - Don't subscribe to state only used in callbacks
+- `rerender-memo` - Extract expensive work into memoized components
+- `rerender-dependencies` - Use primitive dependencies in effects
+- `rerender-derived-state` - Subscribe to derived booleans, not raw values
+- `rerender-functional-setstate` - Use functional setState for stable callbacks
+- `rerender-lazy-state-init` - Pass function to useState for expensive values
+- `rerender-transitions` - Use startTransition for non-urgent updates
+
+### 6. Rendering Performance (MEDIUM)
+
+- `rendering-animate-svg-wrapper` - Animate div wrapper, not SVG element
+- `rendering-content-visibility` - Use content-visibility for long lists
+- `rendering-hoist-jsx` - Extract static JSX outside components
+- `rendering-svg-precision` - Reduce SVG coordinate precision
+- `rendering-hydration-no-flicker` - Use inline script for client-only data
+- `rendering-activity` - Use Activity component for show/hide
+- `rendering-conditional-render` - Use ternary, not && for conditionals
+
+### 7. JavaScript Performance (LOW-MEDIUM)
+
+- `js-batch-dom-css` - Group CSS changes via classes or cssText
+- `js-index-maps` - Build Map for repeated lookups
+- `js-cache-property-access` - Cache object properties in loops
+- `js-cache-function-results` - Cache function results in module-level Map
+- `js-cache-storage` - Cache localStorage/sessionStorage reads
+- `js-combine-iterations` - Combine multiple filter/map into one loop
+- `js-length-check-first` - Check array length before expensive comparison
+- `js-early-exit` - Return early from functions
+- `js-hoist-regexp` - Hoist RegExp creation outside loops
+- `js-min-max-loop` - Use loop for min/max instead of sort
+- `js-set-map-lookups` - Use Set/Map for O(1) lookups
+- `js-tosorted-immutable` - Use toSorted() for immutability
+
+### 8. Advanced Patterns (LOW)
+
+- `advanced-event-handler-refs` - Store event handlers in refs
+- `advanced-use-latest` - useLatest for stable callback refs
+
+## How to Use
+
+Read individual rule files for detailed explanations and code examples:
+
+```
+rules/async-parallel.md
+rules/bundle-barrel-imports.md
+rules/_sections.md
+```
+
+Each rule file contains:
+- Brief explanation of why it matters
+- Incorrect code example with explanation
+- Correct code example with explanation
+- Additional context and references
+
+## Full Compiled Document
+
+For the complete guide with all rules expanded: `AGENTS.md`

.claude/skills/vercel-react-best-practices/rules/advanced-event-handler-refs.md

@@ -0,0 +1,55 @@
+---
+title: Store Event Handlers in Refs
+impact: LOW
+impactDescription: stable subscriptions
+tags: advanced, hooks, refs, event-handlers, optimization
+---
+
+## Store Event Handlers in Refs
+
+Store callbacks in refs when used in effects that shouldn't re-subscribe on callback changes.
+
+**Incorrect (re-subscribes on every render):**
+
+```tsx
+function useWindowEvent(event: string, handler: () => void) {
+  useEffect(() => {
+    window.addEventListener(event, handler)
+    return () => window.removeEventListener(event, handler)
+  }, [event, handler])
+}
+```
+
+**Correct (stable subscription):**
+
+```tsx
+function useWindowEvent(event: string, handler: () => void) {
+  const handlerRef = useRef(handler)
+  useEffect(() => {
+    handlerRef.current = handler
+  }, [handler])
+
+  useEffect(() => {
+    const listener = () => handlerRef.current()
+    window.addEventListener(event, listener)
+    return () => window.removeEventListener(event, listener)
+  }, [event])
+}
+```
+
+**Alternative: use `useEffectEvent` if you're on latest React:**
+
+```tsx
+import { useEffectEvent } from 'react'
+
+function useWindowEvent(event: string, handler: () => void) {
+  const onEvent = useEffectEvent(handler)
+
+  useEffect(() => {
+    window.addEventListener(event, onEvent)
+    return () => window.removeEventListener(event, onEvent)
+  }, [event])
+}
+```
+
+`useEffectEvent` provides a cleaner API for the same pattern: it creates a stable function reference that always calls the latest version of the handler.

.claude/skills/vercel-react-best-practices/rules/advanced-use-latest.md

@@ -0,0 +1,49 @@
+---
+title: useLatest for Stable Callback Refs
+impact: LOW
+impactDescription: prevents effect re-runs
+tags: advanced, hooks, useLatest, refs, optimization
+---
+
+## useLatest for Stable Callback Refs
+
+Access latest values in callbacks without adding them to dependency arrays. Prevents effect re-runs while avoiding stale closures.
+
+**Implementation:**
+
+```typescript
+function useLatest<T>(value: T) {
+  const ref = useRef(value)
+  useEffect(() => {
+    ref.current = value
+  }, [value])
+  return ref
+}
+```
+
+**Incorrect (effect re-runs on every callback change):**
+
+```tsx
+function SearchInput({ onSearch }: { onSearch: (q: string) => void }) {
+  const [query, setQuery] = useState('')
+
+  useEffect(() => {
+    const timeout = setTimeout(() => onSearch(query), 300)
+    return () => clearTimeout(timeout)
+  }, [query, onSearch])
+}
+```
+
+**Correct (stable effect, fresh callback):**
+
+```tsx
+function SearchInput({ onSearch }: { onSearch: (q: string) => void }) {
+  const [query, setQuery] = useState('')
+  const onSearchRef = useLatest(onSearch)
+
+  useEffect(() => {
+    const timeout = setTimeout(() => onSearchRef.current(query), 300)
+    return () => clearTimeout(timeout)
+  }, [query])
+}
+```

.claude/skills/vercel-react-best-practices/rules/async-api-routes.md

@@ -0,0 +1,38 @@
+---
+title: Prevent Waterfall Chains in API Routes
+impact: CRITICAL
+impactDescription: 2-10× improvement
+tags: api-routes, server-actions, waterfalls, parallelization
+---
+
+## Prevent Waterfall Chains in API Routes
+
+In API routes and Server Actions, start independent operations immediately, even if you don't await them yet.
+
+**Incorrect (config waits for auth, data waits for both):**
+
+```typescript
+export async function GET(request: Request) {
+  const session = await auth()
+  const config = await fetchConfig()
+  const data = await fetchData(session.user.id)
+  return Response.json({ data, config })
+}
+```
+
+**Correct (auth and config start immediately):**
+
+```typescript
+export async function GET(request: Request) {
+  const sessionPromise = auth()
+  const configPromise = fetchConfig()
+  const session = await sessionPromise
+  const [config, data] = await Promise.all([
+    configPromise,
+    fetchData(session.user.id)
+  ])
+  return Response.json({ data, config })
+}
+```
+
+For operations with more complex dependency chains, use `better-all` to automatically maximize parallelism (see Dependency-Based Parallelization).

.claude/skills/vercel-react-best-practices/rules/async-defer-await.md

@@ -0,0 +1,80 @@
+---
+title: Defer Await Until Needed
+impact: HIGH
+impactDescription: avoids blocking unused code paths
+tags: async, await, conditional, optimization
+---
+
+## Defer Await Until Needed
+
+Move `await` operations into the branches where they're actually used to avoid blocking code paths that don't need them.
+
+**Incorrect (blocks both branches):**
+
+```typescript
+async function handleRequest(userId: string, skipProcessing: boolean) {
+  const userData = await fetchUserData(userId)
+  
+  if (skipProcessing) {
+    // Returns immediately but still waited for userData
+    return { skipped: true }
+  }
+  
+  // Only this branch uses userData
+  return processUserData(userData)
+}
+```
+
+**Correct (only blocks when needed):**
+
+```typescript
+async function handleRequest(userId: string, skipProcessing: boolean) {
+  if (skipProcessing) {
+    // Returns immediately without waiting
+    return { skipped: true }
+  }
+  
+  // Fetch only when needed
+  const userData = await fetchUserData(userId)
+  return processUserData(userData)
+}
+```
+
+**Another example (early return optimization):**
+
+```typescript
+// Incorrect: always fetches permissions
+async function updateResource(resourceId: string, userId: string) {
+  const permissions = await fetchPermissions(userId)
+  const resource = await getResource(resourceId)
+  
+  if (!resource) {
+    return { error: 'Not found' }
+  }
+  
+  if (!permissions.canEdit) {
+    return { error: 'Forbidden' }
+  }
+  
+  return await updateResourceData(resource, permissions)
+}
+
+// Correct: fetches only when needed
+async function updateResource(resourceId: string, userId: string) {
+  const resource = await getResource(resourceId)
+  
+  if (!resource) {
+    return { error: 'Not found' }
+  }
+  
+  const permissions = await fetchPermissions(userId)
+  
+  if (!permissions.canEdit) {
+    return { error: 'Forbidden' }
+  }
+  
+  return await updateResourceData(resource, permissions)
+}
+```
+
+This optimization is especially valuable when the skipped branch is frequently taken, or when the deferred operation is expensive.

.claude/skills/vercel-react-best-practices/rules/async-dependencies.md

@@ -0,0 +1,36 @@
+---
+title: Dependency-Based Parallelization
+impact: CRITICAL
+impactDescription: 2-10× improvement
+tags: async, parallelization, dependencies, better-all
+---
+
+## Dependency-Based Parallelization
+
+For operations with partial dependencies, use `better-all` to maximize parallelism. It automatically starts each task at the earliest possible moment.
+
+**Incorrect (profile waits for config unnecessarily):**
+
+```typescript
+const [user, config] = await Promise.all([
+  fetchUser(),
+  fetchConfig()
+])
+const profile = await fetchProfile(user.id)
+```
+
+**Correct (config and profile run in parallel):**
+
+```typescript
+import { all } from 'better-all'
+
+const { user, config, profile } = await all({
+  async user() { return fetchUser() },
+  async config() { return fetchConfig() },
+  async profile() {
+    return fetchProfile((await this.$.user).id)
+  }
+})
+```
+
+Reference: [https://github.com/shuding/better-all](https://github.com/shuding/better-all)

.claude/skills/vercel-react-best-practices/rules/async-parallel.md

@@ -0,0 +1,28 @@
+---
+title: Promise.all() for Independent Operations
+impact: CRITICAL
+impactDescription: 2-10× improvement
+tags: async, parallelization, promises, waterfalls
+---
+
+## Promise.all() for Independent Operations
+
+When async operations have no interdependencies, execute them concurrently using `Promise.all()`.
+
+**Incorrect (sequential execution, 3 round trips):**
+
+```typescript
+const user = await fetchUser()
+const posts = await fetchPosts()
+const comments = await fetchComments()
+```
+
+**Correct (parallel execution, 1 round trip):**
+
+```typescript
+const [user, posts, comments] = await Promise.all([
+  fetchUser(),
+  fetchPosts(),
+  fetchComments()
+])
+```

.claude/skills/vercel-react-best-practices/rules/async-suspense-boundaries.md

@@ -0,0 +1,99 @@
+---
+title: Strategic Suspense Boundaries
+impact: HIGH
+impactDescription: faster initial paint
+tags: async, suspense, streaming, layout-shift
+---
+
+## Strategic Suspense Boundaries
+
+Instead of awaiting data in async components before returning JSX, use Suspense boundaries to show the wrapper UI faster while data loads.
+
+**Incorrect (wrapper blocked by data fetching):**
+
+```tsx
+async function Page() {
+  const data = await fetchData() // Blocks entire page
+  
+  return (
+    <div>
+      <div>Sidebar</div>
+      <div>Header</div>
+      <div>
+        <DataDisplay data={data} />
+      </div>
+      <div>Footer</div>
+    </div>
+  )
+}
+```
+
+The entire layout waits for data even though only the middle section needs it.
+
+**Correct (wrapper shows immediately, data streams in):**
+
+```tsx
+function Page() {
+  return (
+    <div>
+      <div>Sidebar</div>
+      <div>Header</div>
+      <div>
+        <Suspense fallback={<Skeleton />}>
+          <DataDisplay />
+        </Suspense>
+      </div>
+      <div>Footer</div>
+    </div>
+  )
+}
+
+async function DataDisplay() {
+  const data = await fetchData() // Only blocks this component
+  return <div>{data.content}</div>
+}
+```
+
+Sidebar, Header, and Footer render immediately. Only DataDisplay waits for data.
+
+**Alternative (share promise across components):**
+
+```tsx
+function Page() {
+  // Start fetch immediately, but don't await
+  const dataPromise = fetchData()
+  
+  return (
+    <div>
+      <div>Sidebar</div>
+      <div>Header</div>
+      <Suspense fallback={<Skeleton />}>
+        <DataDisplay dataPromise={dataPromise} />
+        <DataSummary dataPromise={dataPromise} />
+      </Suspense>
+      <div>Footer</div>
+    </div>
+  )
+}
+
+function DataDisplay({ dataPromise }: { dataPromise: Promise<Data> }) {
+  const data = use(dataPromise) // Unwraps the promise
+  return <div>{data.content}</div>
+}
+
+function DataSummary({ dataPromise }: { dataPromise: Promise<Data> }) {
+  const data = use(dataPromise) // Reuses the same promise
+  return <div>{data.summary}</div>
+}
+```
+
+Both components share the same promise, so only one fetch occurs. Layout renders immediately while both components wait together.
+
+**When NOT to use this pattern:**
+
+- Critical data needed for layout decisions (affects positioning)
+- SEO-critical content above the fold
+- Small, fast queries where suspense overhead isn't worth it
+- When you want to avoid layout shift (loading → content jump)
+
+**Trade-off:** Faster initial paint vs potential layout shift. Choose based on your UX priorities.

.claude/skills/vercel-react-best-practices/rules/bundle-barrel-imports.md

@@ -0,0 +1,59 @@
+---
+title: Avoid Barrel File Imports
+impact: CRITICAL
+impactDescription: 200-800ms import cost, slow builds
+tags: bundle, imports, tree-shaking, barrel-files, performance
+---
+
+## Avoid Barrel File Imports
+
+Import directly from source files instead of barrel files to avoid loading thousands of unused modules. **Barrel files** are entry points that re-export multiple modules (e.g., `index.js` that does `export * from './module'`).
+
+Popular icon and component libraries can have **up to 10,000 re-exports** in their entry file. For many React packages, **it takes 200-800ms just to import them**, affecting both development speed and production cold starts.
+
+**Why tree-shaking doesn't help:** When a library is marked as external (not bundled), the bundler can't optimize it. If you bundle it to enable tree-shaking, builds become substantially slower analyzing the entire module graph.
+
+**Incorrect (imports entire library):**
+
+```tsx
+import { Check, X, Menu } from 'lucide-react'
+// Loads 1,583 modules, takes ~2.8s extra in dev
+// Runtime cost: 200-800ms on every cold start
+
+import { Button, TextField } from '@mui/material'
+// Loads 2,225 modules, takes ~4.2s extra in dev
+```
+
+**Correct (imports only what you need):**
+
+```tsx
+import Check from 'lucide-react/dist/esm/icons/check'
+import X from 'lucide-react/dist/esm/icons/x'
+import Menu from 'lucide-react/dist/esm/icons/menu'
+// Loads only 3 modules (~2KB vs ~1MB)
+
+import Button from '@mui/material/Button'
+import TextField from '@mui/material/TextField'
+// Loads only what you use
+```
+
+**Alternative (Next.js 13.5+):**
+
+```js
+// next.config.js - use optimizePackageImports
+module.exports = {
+  experimental: {
+    optimizePackageImports: ['lucide-react', '@mui/material']
+  }
+}
+
+// Then you can keep the ergonomic barrel imports:
+import { Check, X, Menu } from 'lucide-react'
+// Automatically transformed to direct imports at build time
+```
+
+Direct imports provide 15-70% faster dev boot, 28% faster builds, 40% faster cold starts, and significantly faster HMR.
+
+Libraries commonly affected: `lucide-react`, `@mui/material`, `@mui/icons-material`, `@tabler/icons-react`, `react-icons`, `@headlessui/react`, `@radix-ui/react-*`, `lodash`, `ramda`, `date-fns`, `rxjs`, `react-use`.
+
+Reference: [How we optimized package imports in Next.js](https://vercel.com/blog/how-we-optimized-package-imports-in-next-js)

.claude/skills/vercel-react-best-practices/rules/bundle-conditional.md

@@ -0,0 +1,31 @@
+---
+title: Conditional Module Loading
+impact: HIGH
+impactDescription: loads large data only when needed
+tags: bundle, conditional-loading, lazy-loading
+---
+
+## Conditional Module Loading
+
+Load large data or modules only when a feature is activated.
+
+**Example (lazy-load animation frames):**
+
+```tsx
+function AnimationPlayer({ enabled }: { enabled: boolean }) {
+  const [frames, setFrames] = useState<Frame[] | null>(null)
+
+  useEffect(() => {
+    if (enabled && !frames && typeof window !== 'undefined') {
+      import('./animation-frames.js')
+        .then(mod => setFrames(mod.frames))
+        .catch(() => setEnabled(false))
+    }
+  }, [enabled, frames])
+
+  if (!frames) return <Skeleton />
+  return <Canvas frames={frames} />
+}
+```
+
+The `typeof window !== 'undefined'` check prevents bundling this module for SSR, optimizing server bundle size and build speed.

.claude/skills/vercel-react-best-practices/rules/bundle-defer-third-party.md

@@ -0,0 +1,49 @@
+---
+title: Defer Non-Critical Third-Party Libraries
+impact: MEDIUM
+impactDescription: loads after hydration
+tags: bundle, third-party, analytics, defer
+---
+
+## Defer Non-Critical Third-Party Libraries
+
+Analytics, logging, and error tracking don't block user interaction. Load them after hydration.
+
+**Incorrect (blocks initial bundle):**
+
+```tsx
+import { Analytics } from '@vercel/analytics/react'
+
+export default function RootLayout({ children }) {
+  return (
+    <html>
+      <body>
+        {children}
+        <Analytics />
+      </body>
+    </html>
+  )
+}
+```
+
+**Correct (loads after hydration):**
+
+```tsx
+import dynamic from 'next/dynamic'
+
+const Analytics = dynamic(
+  () => import('@vercel/analytics/react').then(m => m.Analytics),
+  { ssr: false }
+)
+
+export default function RootLayout({ children }) {
+  return (
+    <html>
+      <body>
+        {children}
+        <Analytics />
+      </body>
+    </html>
+  )
+}
+```

.claude/skills/vercel-react-best-practices/rules/bundle-dynamic-imports.md

@@ -0,0 +1,35 @@
+---
+title: Dynamic Imports for Heavy Components
+impact: CRITICAL
+impactDescription: directly affects TTI and LCP
+tags: bundle, dynamic-import, code-splitting, next-dynamic
+---
+
+## Dynamic Imports for Heavy Components
+
+Use `next/dynamic` to lazy-load large components not needed on initial render.
+
+**Incorrect (Monaco bundles with main chunk ~300KB):**
+
+```tsx
+import { MonacoEditor } from './monaco-editor'
+
+function CodePanel({ code }: { code: string }) {
+  return <MonacoEditor value={code} />
+}
+```
+
+**Correct (Monaco loads on demand):**
+
+```tsx
+import dynamic from 'next/dynamic'
+
+const MonacoEditor = dynamic(
+  () => import('./monaco-editor').then(m => m.MonacoEditor),
+  { ssr: false }
+)
+
+function CodePanel({ code }: { code: string }) {
+  return <MonacoEditor value={code} />
+}
+```

.claude/skills/vercel-react-best-practices/rules/bundle-preload.md

@@ -0,0 +1,50 @@
+---
+title: Preload Based on User Intent
+impact: MEDIUM
+impactDescription: reduces perceived latency
+tags: bundle, preload, user-intent, hover
+---
+
+## Preload Based on User Intent
+
+Preload heavy bundles before they're needed to reduce perceived latency.
+
+**Example (preload on hover/focus):**
+
+```tsx
+function EditorButton({ onClick }: { onClick: () => void }) {
+  const preload = () => {
+    if (typeof window !== 'undefined') {
+      void import('./monaco-editor')
+    }
+  }
+
+  return (
+    <button
+      onMouseEnter={preload}
+      onFocus={preload}
+      onClick={onClick}
+    >
+      Open Editor
+    </button>
+  )
+}
+```
+
+**Example (preload when feature flag is enabled):**
+
+```tsx
+function FlagsProvider({ children, flags }: Props) {
+  useEffect(() => {
+    if (flags.editorEnabled && typeof window !== 'undefined') {
+      void import('./monaco-editor').then(mod => mod.init())
+    }
+  }, [flags.editorEnabled])
+
+  return <FlagsContext.Provider value={flags}>
+    {children}
+  </FlagsContext.Provider>
+}
+```
+
+The `typeof window !== 'undefined'` check prevents bundling preloaded modules for SSR, optimizing server bundle size and build speed.

.claude/skills/vercel-react-best-practices/rules/client-event-listeners.md

@@ -0,0 +1,74 @@
+---
+title: Deduplicate Global Event Listeners
+impact: LOW
+impactDescription: single listener for N components
+tags: client, swr, event-listeners, subscription
+---
+
+## Deduplicate Global Event Listeners
+
+Use `useSWRSubscription()` to share global event listeners across component instances.
+
+**Incorrect (N instances = N listeners):**
+
+```tsx
+function useKeyboardShortcut(key: string, callback: () => void) {
+  useEffect(() => {
+    const handler = (e: KeyboardEvent) => {
+      if (e.metaKey && e.key === key) {
+        callback()
+      }
+    }
+    window.addEventListener('keydown', handler)
+    return () => window.removeEventListener('keydown', handler)
+  }, [key, callback])
+}
+```
+
+When using the `useKeyboardShortcut` hook multiple times, each instance will register a new listener.
+
+**Correct (N instances = 1 listener):**
+
+```tsx
+import useSWRSubscription from 'swr/subscription'
+
+// Module-level Map to track callbacks per key
+const keyCallbacks = new Map<string, Set<() => void>>()
+
+function useKeyboardShortcut(key: string, callback: () => void) {
+  // Register this callback in the Map
+  useEffect(() => {
+    if (!keyCallbacks.has(key)) {
+      keyCallbacks.set(key, new Set())
+    }
+    keyCallbacks.get(key)!.add(callback)
+
+    return () => {
+      const set = keyCallbacks.get(key)
+      if (set) {
+        set.delete(callback)
+        if (set.size === 0) {
+          keyCallbacks.delete(key)
+        }
+      }
+    }
+  }, [key, callback])
+
+  useSWRSubscription('global-keydown', () => {
+    const handler = (e: KeyboardEvent) => {
+      if (e.metaKey && keyCallbacks.has(e.key)) {
+        keyCallbacks.get(e.key)!.forEach(cb => cb())
+      }
+    }
+    window.addEventListener('keydown', handler)
+    return () => window.removeEventListener('keydown', handler)
+  })
+}
+
+function Profile() {
+  // Multiple shortcuts will share the same listener
+  useKeyboardShortcut('p', () => { /* ... */ }) 
+  useKeyboardShortcut('k', () => { /* ... */ })
+  // ...
+}
+```

.claude/skills/vercel-react-best-practices/rules/client-swr-dedup.md

@@ -0,0 +1,56 @@
+---
+title: Use SWR for Automatic Deduplication
+impact: MEDIUM-HIGH
+impactDescription: automatic deduplication
+tags: client, swr, deduplication, data-fetching
+---
+
+## Use SWR for Automatic Deduplication
+
+SWR enables request deduplication, caching, and revalidation across component instances.
+
+**Incorrect (no deduplication, each instance fetches):**
+
+```tsx
+function UserList() {
+  const [users, setUsers] = useState([])
+  useEffect(() => {
+    fetch('/api/users')
+      .then(r => r.json())
+      .then(setUsers)
+  }, [])
+}
+```
+
+**Correct (multiple instances share one request):**
+
+```tsx
+import useSWR from 'swr'
+
+function UserList() {
+  const { data: users } = useSWR('/api/users', fetcher)
+}
+```
+
+**For immutable data:**
+
+```tsx
+import { useImmutableSWR } from '@/lib/swr'
+
+function StaticContent() {
+  const { data } = useImmutableSWR('/api/config', fetcher)
+}
+```
+
+**For mutations:**
+
+```tsx
+import { useSWRMutation } from 'swr/mutation'
+
+function UpdateButton() {
+  const { trigger } = useSWRMutation('/api/user', updateUser)
+  return <button onClick={() => trigger()}>Update</button>
+}
+```
+
+Reference: [https://swr.vercel.app](https://swr.vercel.app)

.claude/skills/vercel-react-best-practices/rules/js-batch-dom-css.md

@@ -0,0 +1,82 @@
+---
+title: Batch DOM CSS Changes
+impact: MEDIUM
+impactDescription: reduces reflows/repaints
+tags: javascript, dom, css, performance, reflow
+---
+
+## Batch DOM CSS Changes
+
+Avoid changing styles one property at a time. Group multiple CSS changes together via classes or `cssText` to minimize browser reflows.
+
+**Incorrect (multiple reflows):**
+
+```typescript
+function updateElementStyles(element: HTMLElement) {
+  // Each line triggers a reflow
+  element.style.width = '100px'
+  element.style.height = '200px'
+  element.style.backgroundColor = 'blue'
+  element.style.border = '1px solid black'
+}
+```
+
+**Correct (add class - single reflow):**
+
+```typescript
+// CSS file
+.highlighted-box {
+  width: 100px;
+  height: 200px;
+  background-color: blue;
+  border: 1px solid black;
+}
+
+// JavaScript
+function updateElementStyles(element: HTMLElement) {
+  element.classList.add('highlighted-box')
+}
+```
+
+**Correct (change cssText - single reflow):**
+
+```typescript
+function updateElementStyles(element: HTMLElement) {
+  element.style.cssText = `
+    width: 100px;
+    height: 200px;
+    background-color: blue;
+    border: 1px solid black;
+  `
+}
+```
+
+**React example:**
+
+```tsx
+// Incorrect: changing styles one by one
+function Box({ isHighlighted }: { isHighlighted: boolean }) {
+  const ref = useRef<HTMLDivElement>(null)
+  
+  useEffect(() => {
+    if (ref.current && isHighlighted) {
+      ref.current.style.width = '100px'
+      ref.current.style.height = '200px'
+      ref.current.style.backgroundColor = 'blue'
+    }
+  }, [isHighlighted])
+  
+  return <div ref={ref}>Content</div>
+}
+
+// Correct: toggle class
+function Box({ isHighlighted }: { isHighlighted: boolean }) {
+  return (
+    <div className={isHighlighted ? 'highlighted-box' : ''}>
+      Content
+    </div>
+  )
+}
+```
+
+Prefer CSS classes over inline styles when possible. Classes are cached by the browser and provide better separation of concerns.

.claude/skills/vercel-react-best-practices/rules/js-cache-function-results.md

@@ -0,0 +1,80 @@
+---
+title: Cache Repeated Function Calls
+impact: MEDIUM
+impactDescription: avoid redundant computation
+tags: javascript, cache, memoization, performance
+---
+
+## Cache Repeated Function Calls
+
+Use a module-level Map to cache function results when the same function is called repeatedly with the same inputs during render.
+
+**Incorrect (redundant computation):**
+
+```typescript
+function ProjectList({ projects }: { projects: Project[] }) {
+  return (
+    <div>
+      {projects.map(project => {
+        // slugify() called 100+ times for same project names
+        const slug = slugify(project.name)
+        
+        return <ProjectCard key={project.id} slug={slug} />
+      })}
+    </div>
+  )
+}
+```
+
+**Correct (cached results):**
+
+```typescript
+// Module-level cache
+const slugifyCache = new Map<string, string>()
+
+function cachedSlugify(text: string): string {
+  if (slugifyCache.has(text)) {
+    return slugifyCache.get(text)!
+  }
+  const result = slugify(text)
+  slugifyCache.set(text, result)
+  return result
+}
+
+function ProjectList({ projects }: { projects: Project[] }) {
+  return (
+    <div>
+      {projects.map(project => {
+        // Computed only once per unique project name
+        const slug = cachedSlugify(project.name)
+        
+        return <ProjectCard key={project.id} slug={slug} />
+      })}
+    </div>
+  )
+}
+```
+
+**Simpler pattern for single-value functions:**
+
+```typescript
+let isLoggedInCache: boolean | null = null
+
+function isLoggedIn(): boolean {
+  if (isLoggedInCache !== null) {
+    return isLoggedInCache
+  }
+  
+  isLoggedInCache = document.cookie.includes('auth=')
+  return isLoggedInCache
+}
+
+// Clear cache when auth changes
+function onAuthChange() {
+  isLoggedInCache = null
+}
+```
+
+Use a Map (not a hook) so it works everywhere: utilities, event handlers, not just React components.
+
+Reference: [How we made the Vercel Dashboard twice as fast](https://vercel.com/blog/how-we-made-the-vercel-dashboard-twice-as-fast)

.claude/skills/vercel-react-best-practices/rules/js-cache-property-access.md

@@ -0,0 +1,28 @@
+---
+title: Cache Property Access in Loops
+impact: LOW-MEDIUM
+impactDescription: reduces lookups
+tags: javascript, loops, optimization, caching
+---
+
+## Cache Property Access in Loops
+
+Cache object property lookups in hot paths.
+
+**Incorrect (3 lookups × N iterations):**
+
+```typescript
+for (let i = 0; i < arr.length; i++) {
+  process(obj.config.settings.value)
+}
+```
+
+**Correct (1 lookup total):**
+
+```typescript
+const value = obj.config.settings.value
+const len = arr.length
+for (let i = 0; i < len; i++) {
+  process(value)
+}
+```

.claude/skills/vercel-react-best-practices/rules/js-cache-storage.md

@@ -0,0 +1,70 @@
+---
+title: Cache Storage API Calls
+impact: LOW-MEDIUM
+impactDescription: reduces expensive I/O
+tags: javascript, localStorage, storage, caching, performance
+---
+
+## Cache Storage API Calls
+
+`localStorage`, `sessionStorage`, and `document.cookie` are synchronous and expensive. Cache reads in memory.
+
+**Incorrect (reads storage on every call):**
+
+```typescript
+function getTheme() {
+  return localStorage.getItem('theme') ?? 'light'
+}
+// Called 10 times = 10 storage reads
+```
+
+**Correct (Map cache):**
+
+```typescript
+const storageCache = new Map<string, string | null>()
+
+function getLocalStorage(key: string) {
+  if (!storageCache.has(key)) {
+    storageCache.set(key, localStorage.getItem(key))
+  }
+  return storageCache.get(key)
+}
+
+function setLocalStorage(key: string, value: string) {
+  localStorage.setItem(key, value)
+  storageCache.set(key, value)  // keep cache in sync
+}
+```
+
+Use a Map (not a hook) so it works everywhere: utilities, event handlers, not just React components.
+
+**Cookie caching:**
+
+```typescript
+let cookieCache: Record<string, string> | null = null
+
+function getCookie(name: string) {
+  if (!cookieCache) {
+    cookieCache = Object.fromEntries(
+      document.cookie.split('; ').map(c => c.split('='))
+    )
+  }
+  return cookieCache[name]
+}
+```
+
+**Important (invalidate on external changes):**
+
+If storage can change externally (another tab, server-set cookies), invalidate cache:
+
+```typescript
+window.addEventListener('storage', (e) => {
+  if (e.key) storageCache.delete(e.key)
+})
+
+document.addEventListener('visibilitychange', () => {
+  if (document.visibilityState === 'visible') {
+    storageCache.clear()
+  }
+})
+```

.claude/skills/vercel-react-best-practices/rules/js-combine-iterations.md

@@ -0,0 +1,32 @@
+---
+title: Combine Multiple Array Iterations
+impact: LOW-MEDIUM
+impactDescription: reduces iterations
+tags: javascript, arrays, loops, performance
+---
+
+## Combine Multiple Array Iterations
+
+Multiple `.filter()` or `.map()` calls iterate the array multiple times. Combine into one loop.
+
+**Incorrect (3 iterations):**
+
+```typescript
+const admins = users.filter(u => u.isAdmin)
+const testers = users.filter(u => u.isTester)
+const inactive = users.filter(u => !u.isActive)
+```
+
+**Correct (1 iteration):**
+
+```typescript
+const admins: User[] = []
+const testers: User[] = []
+const inactive: User[] = []
+
+for (const user of users) {
+  if (user.isAdmin) admins.push(user)
+  if (user.isTester) testers.push(user)
+  if (!user.isActive) inactive.push(user)
+}
+```

.claude/skills/vercel-react-best-practices/rules/js-early-exit.md

@@ -0,0 +1,50 @@
+---
+title: Early Return from Functions
+impact: LOW-MEDIUM
+impactDescription: avoids unnecessary computation
+tags: javascript, functions, optimization, early-return
+---
+
+## Early Return from Functions
+
+Return early when result is determined to skip unnecessary processing.
+
+**Incorrect (processes all items even after finding answer):**
+
+```typescript
+function validateUsers(users: User[]) {
+  let hasError = false
+  let errorMessage = ''
+  
+  for (const user of users) {
+    if (!user.email) {
+      hasError = true
+      errorMessage = 'Email required'
+    }
+    if (!user.name) {
+      hasError = true
+      errorMessage = 'Name required'
+    }
+    // Continues checking all users even after error found
+  }
+  
+  return hasError ? { valid: false, error: errorMessage } : { valid: true }
+}
+```
+
+**Correct (returns immediately on first error):**
+
+```typescript
+function validateUsers(users: User[]) {
+  for (const user of users) {
+    if (!user.email) {
+      return { valid: false, error: 'Email required' }
+    }
+    if (!user.name) {
+      return { valid: false, error: 'Name required' }
+    }
+  }
+
+  return { valid: true }
+}
+```

.claude/skills/vercel-react-best-practices/rules/js-hoist-regexp.md

@@ -0,0 +1,45 @@
+---
+title: Hoist RegExp Creation
+impact: LOW-MEDIUM
+impactDescription: avoids recreation
+tags: javascript, regexp, optimization, memoization
+---
+
+## Hoist RegExp Creation
+
+Don't create RegExp inside render. Hoist to module scope or memoize with `useMemo()`.
+
+**Incorrect (new RegExp every render):**
+
+```tsx
+function Highlighter({ text, query }: Props) {
+  const regex = new RegExp(`(${query})`, 'gi')
+  const parts = text.split(regex)
+  return <>{parts.map((part, i) => ...)}</>
+}
+```
+
+**Correct (memoize or hoist):**
+
+```tsx
+const EMAIL_REGEX = /^[^\s@]+@[^\s@]+\.[^\s@]+$/
+
+function Highlighter({ text, query }: Props) {
+  const regex = useMemo(
+    () => new RegExp(`(${escapeRegex(query)})`, 'gi'),
+    [query]
+  )
+  const parts = text.split(regex)
+  return <>{parts.map((part, i) => ...)}</>
+}
+```
+
+**Warning (global regex has mutable state):**
+
+Global regex (`/g`) has mutable `lastIndex` state:
+
+```typescript
+const regex = /foo/g
+regex.test('foo')  // true, lastIndex = 3
+regex.test('foo')  // false, lastIndex = 0
+```

.claude/skills/vercel-react-best-practices/rules/js-index-maps.md

@@ -0,0 +1,37 @@
+---
+title: Build Index Maps for Repeated Lookups
+impact: LOW-MEDIUM
+impactDescription: 1M ops to 2K ops
+tags: javascript, map, indexing, optimization, performance
+---
+
+## Build Index Maps for Repeated Lookups
+
+Multiple `.find()` calls by the same key should use a Map.
+
+**Incorrect (O(n) per lookup):**
+
+```typescript
+function processOrders(orders: Order[], users: User[]) {
+  return orders.map(order => ({
+    ...order,
+    user: users.find(u => u.id === order.userId)
+  }))
+}
+```
+
+**Correct (O(1) per lookup):**
+
+```typescript
+function processOrders(orders: Order[], users: User[]) {
+  const userById = new Map(users.map(u => [u.id, u]))
+
+  return orders.map(order => ({
+    ...order,
+    user: userById.get(order.userId)
+  }))
+}
+```
+
+Build map once (O(n)), then all lookups are O(1).
+For 1000 orders × 1000 users: 1M ops → 2K ops.

.claude/skills/vercel-react-best-practices/rules/js-length-check-first.md

@@ -0,0 +1,49 @@
+---
+title: Early Length Check for Array Comparisons
+impact: MEDIUM-HIGH
+impactDescription: avoids expensive operations when lengths differ
+tags: javascript, arrays, performance, optimization, comparison
+---
+
+## Early Length Check for Array Comparisons
+
+When comparing arrays with expensive operations (sorting, deep equality, serialization), check lengths first. If lengths differ, the arrays cannot be equal.
+
+In real-world applications, this optimization is especially valuable when the comparison runs in hot paths (event handlers, render loops).
+
+**Incorrect (always runs expensive comparison):**
+
+```typescript
+function hasChanges(current: string[], original: string[]) {
+  // Always sorts and joins, even when lengths differ
+  return current.sort().join() !== original.sort().join()
+}
+```
+
+Two O(n log n) sorts run even when `current.length` is 5 and `original.length` is 100. There is also overhead of joining the arrays and comparing the strings.
+
+**Correct (O(1) length check first):**
+
+```typescript
+function hasChanges(current: string[], original: string[]) {
+  // Early return if lengths differ
+  if (current.length !== original.length) {
+    return true
+  }
+  // Only sort/join when lengths match
+  const currentSorted = current.toSorted()
+  const originalSorted = original.toSorted()
+  for (let i = 0; i < currentSorted.length; i++) {
+    if (currentSorted[i] !== originalSorted[i]) {
+      return true
+    }
+  }
+  return false
+}
+```
+
+This new approach is more efficient because:
+- It avoids the overhead of sorting and joining the arrays when lengths differ
+- It avoids consuming memory for the joined strings (especially important for large arrays)
+- It avoids mutating the original arrays
+- It returns early when a difference is found

.claude/skills/vercel-react-best-practices/rules/js-min-max-loop.md

@@ -0,0 +1,82 @@
+---
+title: Use Loop for Min/Max Instead of Sort
+impact: LOW
+impactDescription: O(n) instead of O(n log n)
+tags: javascript, arrays, performance, sorting, algorithms
+---
+
+## Use Loop for Min/Max Instead of Sort
+
+Finding the smallest or largest element only requires a single pass through the array. Sorting is wasteful and slower.
+
+**Incorrect (O(n log n) - sort to find latest):**
+
+```typescript
+interface Project {
+  id: string
+  name: string
+  updatedAt: number
+}
+
+function getLatestProject(projects: Project[]) {
+  const sorted = [...projects].sort((a, b) => b.updatedAt - a.updatedAt)
+  return sorted[0]
+}
+```
+
+Sorts the entire array just to find the maximum value.
+
+**Incorrect (O(n log n) - sort for oldest and newest):**
+
+```typescript
+function getOldestAndNewest(projects: Project[]) {
+  const sorted = [...projects].sort((a, b) => a.updatedAt - b.updatedAt)
+  return { oldest: sorted[0], newest: sorted[sorted.length - 1] }
+}
+```
+
+Still sorts unnecessarily when only min/max are needed.
+
+**Correct (O(n) - single loop):**
+
+```typescript
+function getLatestProject(projects: Project[]) {
+  if (projects.length === 0) return null
+  
+  let latest = projects[0]
+  
+  for (let i = 1; i < projects.length; i++) {
+    if (projects[i].updatedAt > latest.updatedAt) {
+      latest = projects[i]
+    }
+  }
+  
+  return latest
+}
+
+function getOldestAndNewest(projects: Project[]) {
+  if (projects.length === 0) return { oldest: null, newest: null }
+  
+  let oldest = projects[0]
+  let newest = projects[0]
+  
+  for (let i = 1; i < projects.length; i++) {
+    if (projects[i].updatedAt < oldest.updatedAt) oldest = projects[i]
+    if (projects[i].updatedAt > newest.updatedAt) newest = projects[i]
+  }
+  
+  return { oldest, newest }
+}
+```
+
+Single pass through the array, no copying, no sorting.
+
+**Alternative (Math.min/Math.max for small arrays):**
+
+```typescript
+const numbers = [5, 2, 8, 1, 9]
+const min = Math.min(...numbers)
+const max = Math.max(...numbers)
+```
+
+This works for small arrays but can be slower for very large arrays due to spread operator limitations. Use the loop approach for reliability.

.claude/skills/vercel-react-best-practices/rules/js-set-map-lookups.md

@@ -0,0 +1,24 @@
+---
+title: Use Set/Map for O(1) Lookups
+impact: LOW-MEDIUM
+impactDescription: O(n) to O(1)
+tags: javascript, set, map, data-structures, performance
+---
+
+## Use Set/Map for O(1) Lookups
+
+Convert arrays to Set/Map for repeated membership checks.
+
+**Incorrect (O(n) per check):**
+
+```typescript
+const allowedIds = ['a', 'b', 'c', ...]
+items.filter(item => allowedIds.includes(item.id))
+```
+
+**Correct (O(1) per check):**
+
+```typescript
+const allowedIds = new Set(['a', 'b', 'c', ...])
+items.filter(item => allowedIds.has(item.id))
+```

.claude/skills/vercel-react-best-practices/rules/js-tosorted-immutable.md

@@ -0,0 +1,57 @@
+---
+title: Use toSorted() Instead of sort() for Immutability
+impact: MEDIUM-HIGH
+impactDescription: prevents mutation bugs in React state
+tags: javascript, arrays, immutability, react, state, mutation
+---
+
+## Use toSorted() Instead of sort() for Immutability
+
+`.sort()` mutates the array in place, which can cause bugs with React state and props. Use `.toSorted()` to create a new sorted array without mutation.
+
+**Incorrect (mutates original array):**
+
+```typescript
+function UserList({ users }: { users: User[] }) {
+  // Mutates the users prop array!
+  const sorted = useMemo(
+    () => users.sort((a, b) => a.name.localeCompare(b.name)),
+    [users]
+  )
+  return <div>{sorted.map(renderUser)}</div>
+}
+```
+
+**Correct (creates new array):**
+
+```typescript
+function UserList({ users }: { users: User[] }) {
+  // Creates new sorted array, original unchanged
+  const sorted = useMemo(
+    () => users.toSorted((a, b) => a.name.localeCompare(b.name)),
+    [users]
+  )
+  return <div>{sorted.map(renderUser)}</div>
+}
+```
+
+**Why this matters in React:**
+
+1. Props/state mutations break React's immutability model - React expects props and state to be treated as read-only
+2. Causes stale closure bugs - Mutating arrays inside closures (callbacks, effects) can lead to unexpected behavior
+
+**Browser support (fallback for older browsers):**
+
+`.toSorted()` is available in all modern browsers (Chrome 110+, Safari 16+, Firefox 115+, Node.js 20+). For older environments, use spread operator:
+
+```typescript
+// Fallback for older browsers
+const sorted = [...items].sort((a, b) => a.value - b.value)
+```
+
+**Other immutable array methods:**
+
+- `.toSorted()` - immutable sort
+- `.toReversed()` - immutable reverse
+- `.toSpliced()` - immutable splice
+- `.with()` - immutable element replacement

.claude/skills/vercel-react-best-practices/rules/rendering-activity.md

@@ -0,0 +1,26 @@
+---
+title: Use Activity Component for Show/Hide
+impact: MEDIUM
+impactDescription: preserves state/DOM
+tags: rendering, activity, visibility, state-preservation
+---
+
+## Use Activity Component for Show/Hide
+
+Use React's `<Activity>` to preserve state/DOM for expensive components that frequently toggle visibility.
+
+**Usage:**
+
+```tsx
+import { Activity } from 'react'
+
+function Dropdown({ isOpen }: Props) {
+  return (
+    <Activity mode={isOpen ? 'visible' : 'hidden'}>
+      <ExpensiveMenu />
+    </Activity>
+  )
+}
+```
+
+Avoids expensive re-renders and state loss.

.claude/skills/vercel-react-best-practices/rules/rendering-animate-svg-wrapper.md

@@ -0,0 +1,47 @@
+---
+title: Animate SVG Wrapper Instead of SVG Element
+impact: LOW
+impactDescription: enables hardware acceleration
+tags: rendering, svg, css, animation, performance
+---
+
+## Animate SVG Wrapper Instead of SVG Element
+
+Many browsers don't have hardware acceleration for CSS3 animations on SVG elements. Wrap SVG in a `<div>` and animate the wrapper instead.
+
+**Incorrect (animating SVG directly - no hardware acceleration):**
+
+```tsx
+function LoadingSpinner() {
+  return (
+    <svg 
+      className="animate-spin"
+      width="24" 
+      height="24" 
+      viewBox="0 0 24 24"
+    >
+      <circle cx="12" cy="12" r="10" stroke="currentColor" />
+    </svg>
+  )
+}
+```
+
+**Correct (animating wrapper div - hardware accelerated):**
+
+```tsx
+function LoadingSpinner() {
+  return (
+    <div className="animate-spin">
+      <svg 
+        width="24" 
+        height="24" 
+        viewBox="0 0 24 24"
+      >
+        <circle cx="12" cy="12" r="10" stroke="currentColor" />
+      </svg>
+    </div>
+  )
+}
+```
+
+This applies to all CSS transforms and transitions (`transform`, `opacity`, `translate`, `scale`, `rotate`). The wrapper div allows browsers to use GPU acceleration for smoother animations.

.claude/skills/vercel-react-best-practices/rules/rendering-conditional-render.md

@@ -0,0 +1,40 @@
+---
+title: Use Explicit Conditional Rendering
+impact: LOW
+impactDescription: prevents rendering 0 or NaN
+tags: rendering, conditional, jsx, falsy-values
+---
+
+## Use Explicit Conditional Rendering
+
+Use explicit ternary operators (`? :`) instead of `&&` for conditional rendering when the condition can be `0`, `NaN`, or other falsy values that render.
+
+**Incorrect (renders "0" when count is 0):**
+
+```tsx
+function Badge({ count }: { count: number }) {
+  return (
+    <div>
+      {count && <span className="badge">{count}</span>}
+    </div>
+  )
+}
+
+// When count = 0, renders: <div>0</div>
+// When count = 5, renders: <div><span class="badge">5</span></div>
+```
+
+**Correct (renders nothing when count is 0):**
+
+```tsx
+function Badge({ count }: { count: number }) {
+  return (
+    <div>
+      {count > 0 ? <span className="badge">{count}</span> : null}
+    </div>
+  )
+}
+
+// When count = 0, renders: <div></div>
+// When count = 5, renders: <div><span class="badge">5</span></div>
+```

.claude/skills/vercel-react-best-practices/rules/rendering-content-visibility.md

@@ -0,0 +1,38 @@
+---
+title: CSS content-visibility for Long Lists
+impact: HIGH
+impactDescription: faster initial render
+tags: rendering, css, content-visibility, long-lists
+---
+
+## CSS content-visibility for Long Lists
+
+Apply `content-visibility: auto` to defer off-screen rendering.
+
+**CSS:**
+
+```css
+.message-item {
+  content-visibility: auto;
+  contain-intrinsic-size: 0 80px;
+}
+```
+
+**Example:**
+
+```tsx
+function MessageList({ messages }: { messages: Message[] }) {
+  return (
+    <div className="overflow-y-auto h-screen">
+      {messages.map(msg => (
+        <div key={msg.id} className="message-item">
+          <Avatar user={msg.author} />
+          <div>{msg.content}</div>
+        </div>
+      ))}
+    </div>
+  )
+}
+```
+
+For 1000 messages, browser skips layout/paint for ~990 off-screen items (10× faster initial render).

.claude/skills/vercel-react-best-practices/rules/rendering-hoist-jsx.md

@@ -0,0 +1,46 @@
+---
+title: Hoist Static JSX Elements
+impact: LOW
+impactDescription: avoids re-creation
+tags: rendering, jsx, static, optimization
+---
+
+## Hoist Static JSX Elements
+
+Extract static JSX outside components to avoid re-creation.
+
+**Incorrect (recreates element every render):**
+
+```tsx
+function LoadingSkeleton() {
+  return <div className="animate-pulse h-20 bg-gray-200" />
+}
+
+function Container() {
+  return (
+    <div>
+      {loading && <LoadingSkeleton />}
+    </div>
+  )
+}
+```
+
+**Correct (reuses same element):**
+
+```tsx
+const loadingSkeleton = (
+  <div className="animate-pulse h-20 bg-gray-200" />
+)
+
+function Container() {
+  return (
+    <div>
+      {loading && loadingSkeleton}
+    </div>
+  )
+}
+```
+
+This is especially helpful for large and static SVG nodes, which can be expensive to recreate on every render.
+
+**Note:** If your project has [React Compiler](https://react.dev/learn/react-compiler) enabled, the compiler automatically hoists static JSX elements and optimizes component re-renders, making manual hoisting unnecessary.

.claude/skills/vercel-react-best-practices/rules/rendering-hydration-no-flicker.md

@@ -0,0 +1,82 @@
+---
+title: Prevent Hydration Mismatch Without Flickering
+impact: MEDIUM
+impactDescription: avoids visual flicker and hydration errors
+tags: rendering, ssr, hydration, localStorage, flicker
+---
+
+## Prevent Hydration Mismatch Without Flickering
+
+When rendering content that depends on client-side storage (localStorage, cookies), avoid both SSR breakage and post-hydration flickering by injecting a synchronous script that updates the DOM before React hydrates.
+
+**Incorrect (breaks SSR):**
+
+```tsx
+function ThemeWrapper({ children }: { children: ReactNode }) {
+  // localStorage is not available on server - throws error
+  const theme = localStorage.getItem('theme') || 'light'
+  
+  return (
+    <div className={theme}>
+      {children}
+    </div>
+  )
+}
+```
+
+Server-side rendering will fail because `localStorage` is undefined.
+
+**Incorrect (visual flickering):**
+
+```tsx
+function ThemeWrapper({ children }: { children: ReactNode }) {
+  const [theme, setTheme] = useState('light')
+  
+  useEffect(() => {
+    // Runs after hydration - causes visible flash
+    const stored = localStorage.getItem('theme')
+    if (stored) {
+      setTheme(stored)
+    }
+  }, [])
+  
+  return (
+    <div className={theme}>
+      {children}
+    </div>
+  )
+}
+```
+
+Component first renders with default value (`light`), then updates after hydration, causing a visible flash of incorrect content.
+
+**Correct (no flicker, no hydration mismatch):**
+
+```tsx
+function ThemeWrapper({ children }: { children: ReactNode }) {
+  return (
+    <>
+      <div id="theme-wrapper">
+        {children}
+      </div>
+      <script
+        dangerouslySetInnerHTML={{
+          __html: `
+            (function() {
+              try {
+                var theme = localStorage.getItem('theme') || 'light';
+                var el = document.getElementById('theme-wrapper');
+                if (el) el.className = theme;
+              } catch (e) {}
+            })();
+          `,
+        }}
+      />
+    </>
+  )
+}
+```
+
+The inline script executes synchronously before showing the element, ensuring the DOM already has the correct value. No flickering, no hydration mismatch.
+
+This pattern is especially useful for theme toggles, user preferences, authentication states, and any client-only data that should render immediately without flashing default values.

.claude/skills/vercel-react-best-practices/rules/rendering-svg-precision.md

@@ -0,0 +1,28 @@
+---
+title: Optimize SVG Precision
+impact: LOW
+impactDescription: reduces file size
+tags: rendering, svg, optimization, svgo
+---
+
+## Optimize SVG Precision
+
+Reduce SVG coordinate precision to decrease file size. The optimal precision depends on the viewBox size, but in general reducing precision should be considered.
+
+**Incorrect (excessive precision):**
+
+```svg
+<path d="M 10.293847 20.847362 L 30.938472 40.192837" />
+```
+
+**Correct (1 decimal place):**
+
+```svg
+<path d="M 10.3 20.8 L 30.9 40.2" />
+```
+
+**Automate with SVGO:**
+
+```bash
+npx svgo --precision=1 --multipass icon.svg
+```

.claude/skills/vercel-react-best-practices/rules/rerender-defer-reads.md

@@ -0,0 +1,39 @@
+---
+title: Defer State Reads to Usage Point
+impact: MEDIUM
+impactDescription: avoids unnecessary subscriptions
+tags: rerender, searchParams, localStorage, optimization
+---
+
+## Defer State Reads to Usage Point
+
+Don't subscribe to dynamic state (searchParams, localStorage) if you only read it inside callbacks.
+
+**Incorrect (subscribes to all searchParams changes):**
+
+```tsx
+function ShareButton({ chatId }: { chatId: string }) {
+  const searchParams = useSearchParams()
+
+  const handleShare = () => {
+    const ref = searchParams.get('ref')
+    shareChat(chatId, { ref })
+  }
+
+  return <button onClick={handleShare}>Share</button>
+}
+```
+
+**Correct (reads on demand, no subscription):**
+
+```tsx
+function ShareButton({ chatId }: { chatId: string }) {
+  const handleShare = () => {
+    const params = new URLSearchParams(window.location.search)
+    const ref = params.get('ref')
+    shareChat(chatId, { ref })
+  }
+
+  return <button onClick={handleShare}>Share</button>
+}
+```

.claude/skills/vercel-react-best-practices/rules/rerender-dependencies.md

@@ -0,0 +1,45 @@
+---
+title: Narrow Effect Dependencies
+impact: LOW
+impactDescription: minimizes effect re-runs
+tags: rerender, useEffect, dependencies, optimization
+---
+
+## Narrow Effect Dependencies
+
+Specify primitive dependencies instead of objects to minimize effect re-runs.
+
+**Incorrect (re-runs on any user field change):**
+
+```tsx
+useEffect(() => {
+  console.log(user.id)
+}, [user])
+```
+
+**Correct (re-runs only when id changes):**
+
+```tsx
+useEffect(() => {
+  console.log(user.id)
+}, [user.id])
+```
+
+**For derived state, compute outside effect:**
+
+```tsx
+// Incorrect: runs on width=767, 766, 765...
+useEffect(() => {
+  if (width < 768) {
+    enableMobileMode()
+  }
+}, [width])
+
+// Correct: runs only on boolean transition
+const isMobile = width < 768
+useEffect(() => {
+  if (isMobile) {
+    enableMobileMode()
+  }
+}, [isMobile])
+```

.claude/skills/vercel-react-best-practices/rules/rerender-derived-state.md

@@ -0,0 +1,29 @@
+---
+title: Subscribe to Derived State
+impact: MEDIUM
+impactDescription: reduces re-render frequency
+tags: rerender, derived-state, media-query, optimization
+---
+
+## Subscribe to Derived State
+
+Subscribe to derived boolean state instead of continuous values to reduce re-render frequency.
+
+**Incorrect (re-renders on every pixel change):**
+
+```tsx
+function Sidebar() {
+  const width = useWindowWidth()  // updates continuously
+  const isMobile = width < 768
+  return <nav className={isMobile ? 'mobile' : 'desktop'}>
+}
+```
+
+**Correct (re-renders only when boolean changes):**
+
+```tsx
+function Sidebar() {
+  const isMobile = useMediaQuery('(max-width: 767px)')
+  return <nav className={isMobile ? 'mobile' : 'desktop'}>
+}
+```

.claude/skills/vercel-react-best-practices/rules/rerender-functional-setstate.md

@@ -0,0 +1,74 @@
+---
+title: Use Functional setState Updates
+impact: MEDIUM
+impactDescription: prevents stale closures and unnecessary callback recreations
+tags: react, hooks, useState, useCallback, callbacks, closures
+---
+
+## Use Functional setState Updates
+
+When updating state based on the current state value, use the functional update form of setState instead of directly referencing the state variable. This prevents stale closures, eliminates unnecessary dependencies, and creates stable callback references.
+
+**Incorrect (requires state as dependency):**
+
+```tsx
+function TodoList() {
+  const [items, setItems] = useState(initialItems)
+  
+  // Callback must depend on items, recreated on every items change
+  const addItems = useCallback((newItems: Item[]) => {
+    setItems([...items, ...newItems])
+  }, [items])  // ❌ items dependency causes recreations
+  
+  // Risk of stale closure if dependency is forgotten
+  const removeItem = useCallback((id: string) => {
+    setItems(items.filter(item => item.id !== id))
+  }, [])  // ❌ Missing items dependency - will use stale items!
+  
+  return <ItemsEditor items={items} onAdd={addItems} onRemove={removeItem} />
+}
+```
+
+The first callback is recreated every time `items` changes, which can cause child components to re-render unnecessarily. The second callback has a stale closure bug—it will always reference the initial `items` value.
+
+**Correct (stable callbacks, no stale closures):**
+
+```tsx
+function TodoList() {
+  const [items, setItems] = useState(initialItems)
+  
+  // Stable callback, never recreated
+  const addItems = useCallback((newItems: Item[]) => {
+    setItems(curr => [...curr, ...newItems])
+  }, [])  // ✅ No dependencies needed
+  
+  // Always uses latest state, no stale closure risk
+  const removeItem = useCallback((id: string) => {
+    setItems(curr => curr.filter(item => item.id !== id))
+  }, [])  // ✅ Safe and stable
+  
+  return <ItemsEditor items={items} onAdd={addItems} onRemove={removeItem} />
+}
+```
+
+**Benefits:**
+
+1. **Stable callback references** - Callbacks don't need to be recreated when state changes
+2. **No stale closures** - Always operates on the latest state value
+3. **Fewer dependencies** - Simplifies dependency arrays and reduces memory leaks
+4. **Prevents bugs** - Eliminates the most common source of React closure bugs
+
+**When to use functional updates:**
+
+- Any setState that depends on the current state value
+- Inside useCallback/useMemo when state is needed
+- Event handlers that reference state
+- Async operations that update state
+
+**When direct updates are fine:**
+
+- Setting state to a static value: `setCount(0)`
+- Setting state from props/arguments only: `setName(newName)`
+- State doesn't depend on previous value
+
+**Note:** If your project has [React Compiler](https://react.dev/learn/react-compiler) enabled, the compiler can automatically optimize some cases, but functional updates are still recommended for correctness and to prevent stale closure bugs.

.claude/skills/vercel-react-best-practices/rules/rerender-lazy-state-init.md

@@ -0,0 +1,58 @@
+---
+title: Use Lazy State Initialization
+impact: MEDIUM
+impactDescription: wasted computation on every render
+tags: react, hooks, useState, performance, initialization
+---
+
+## Use Lazy State Initialization
+
+Pass a function to `useState` for expensive initial values. Without the function form, the initializer runs on every render even though the value is only used once.
+
+**Incorrect (runs on every render):**
+
+```tsx
+function FilteredList({ items }: { items: Item[] }) {
+  // buildSearchIndex() runs on EVERY render, even after initialization
+  const [searchIndex, setSearchIndex] = useState(buildSearchIndex(items))
+  const [query, setQuery] = useState('')
+  
+  // When query changes, buildSearchIndex runs again unnecessarily
+  return <SearchResults index={searchIndex} query={query} />
+}
+
+function UserProfile() {
+  // JSON.parse runs on every render
+  const [settings, setSettings] = useState(
+    JSON.parse(localStorage.getItem('settings') || '{}')
+  )
+  
+  return <SettingsForm settings={settings} onChange={setSettings} />
+}
+```
+
+**Correct (runs only once):**
+
+```tsx
+function FilteredList({ items }: { items: Item[] }) {
+  // buildSearchIndex() runs ONLY on initial render
+  const [searchIndex, setSearchIndex] = useState(() => buildSearchIndex(items))
+  const [query, setQuery] = useState('')
+  
+  return <SearchResults index={searchIndex} query={query} />
+}
+
+function UserProfile() {
+  // JSON.parse runs only on initial render
+  const [settings, setSettings] = useState(() => {
+    const stored = localStorage.getItem('settings')
+    return stored ? JSON.parse(stored) : {}
+  })
+  
+  return <SettingsForm settings={settings} onChange={setSettings} />
+}
+```
+
+Use lazy initialization when computing initial values from localStorage/sessionStorage, building data structures (indexes, maps), reading from the DOM, or performing heavy transformations.
+
+For simple primitives (`useState(0)`), direct references (`useState(props.value)`), or cheap literals (`useState({})`), the function form is unnecessary.

.claude/skills/vercel-react-best-practices/rules/rerender-memo.md

@@ -0,0 +1,44 @@
+---
+title: Extract to Memoized Components
+impact: MEDIUM
+impactDescription: enables early returns
+tags: rerender, memo, useMemo, optimization
+---
+
+## Extract to Memoized Components
+
+Extract expensive work into memoized components to enable early returns before computation.
+
+**Incorrect (computes avatar even when loading):**
+
+```tsx
+function Profile({ user, loading }: Props) {
+  const avatar = useMemo(() => {
+    const id = computeAvatarId(user)
+    return <Avatar id={id} />
+  }, [user])
+
+  if (loading) return <Skeleton />
+  return <div>{avatar}</div>
+}
+```
+
+**Correct (skips computation when loading):**
+
+```tsx
+const UserAvatar = memo(function UserAvatar({ user }: { user: User }) {
+  const id = useMemo(() => computeAvatarId(user), [user])
+  return <Avatar id={id} />
+})
+
+function Profile({ user, loading }: Props) {
+  if (loading) return <Skeleton />
+  return (
+    <div>
+      <UserAvatar user={user} />
+    </div>
+  )
+}
+```
+
+**Note:** If your project has [React Compiler](https://react.dev/learn/react-compiler) enabled, manual memoization with `memo()` and `useMemo()` is not necessary. The compiler automatically optimizes re-renders.

.claude/skills/vercel-react-best-practices/rules/rerender-transitions.md

@@ -0,0 +1,40 @@
+---
+title: Use Transitions for Non-Urgent Updates
+impact: MEDIUM
+impactDescription: maintains UI responsiveness
+tags: rerender, transitions, startTransition, performance
+---
+
+## Use Transitions for Non-Urgent Updates
+
+Mark frequent, non-urgent state updates as transitions to maintain UI responsiveness.
+
+**Incorrect (blocks UI on every scroll):**
+
+```tsx
+function ScrollTracker() {
+  const [scrollY, setScrollY] = useState(0)
+  useEffect(() => {
+    const handler = () => setScrollY(window.scrollY)
+    window.addEventListener('scroll', handler, { passive: true })
+    return () => window.removeEventListener('scroll', handler)
+  }, [])
+}
+```
+
+**Correct (non-blocking updates):**
+
+```tsx
+import { startTransition } from 'react'
+
+function ScrollTracker() {
+  const [scrollY, setScrollY] = useState(0)
+  useEffect(() => {
+    const handler = () => {
+      startTransition(() => setScrollY(window.scrollY))
+    }
+    window.addEventListener('scroll', handler, { passive: true })
+    return () => window.removeEventListener('scroll', handler)
+  }, [])
+}
+```

.claude/skills/vercel-react-best-practices/rules/server-after-nonblocking.md

@@ -0,0 +1,73 @@
+---
+title: Use after() for Non-Blocking Operations
+impact: MEDIUM
+impactDescription: faster response times
+tags: server, async, logging, analytics, side-effects
+---
+
+## Use after() for Non-Blocking Operations
+
+Use Next.js's `after()` to schedule work that should execute after a response is sent. This prevents logging, analytics, and other side effects from blocking the response.
+
+**Incorrect (blocks response):**
+
+```tsx
+import { logUserAction } from '@/app/utils'
+
+export async function POST(request: Request) {
+  // Perform mutation
+  await updateDatabase(request)
+  
+  // Logging blocks the response
+  const userAgent = request.headers.get('user-agent') || 'unknown'
+  await logUserAction({ userAgent })
+  
+  return new Response(JSON.stringify({ status: 'success' }), {
+    status: 200,
+    headers: { 'Content-Type': 'application/json' }
+  })
+}
+```
+
+**Correct (non-blocking):**
+
+```tsx
+import { after } from 'next/server'
+import { headers, cookies } from 'next/headers'
+import { logUserAction } from '@/app/utils'
+
+export async function POST(request: Request) {
+  // Perform mutation
+  await updateDatabase(request)
+  
+  // Log after response is sent
+  after(async () => {
+    const userAgent = (await headers()).get('user-agent') || 'unknown'
+    const sessionCookie = (await cookies()).get('session-id')?.value || 'anonymous'
+    
+    logUserAction({ sessionCookie, userAgent })
+  })
+  
+  return new Response(JSON.stringify({ status: 'success' }), {
+    status: 200,
+    headers: { 'Content-Type': 'application/json' }
+  })
+}
+```
+
+The response is sent immediately while logging happens in the background.
+
+**Common use cases:**
+
+- Analytics tracking
+- Audit logging
+- Sending notifications
+- Cache invalidation
+- Cleanup tasks
+
+**Important notes:**
+
+- `after()` runs even if the response fails or redirects
+- Works in Server Actions, Route Handlers, and Server Components
+
+Reference: [https://nextjs.org/docs/app/api-reference/functions/after](https://nextjs.org/docs/app/api-reference/functions/after)

.claude/skills/vercel-react-best-practices/rules/server-cache-lru.md

@@ -0,0 +1,41 @@
+---
+title: Cross-Request LRU Caching
+impact: HIGH
+impactDescription: caches across requests
+tags: server, cache, lru, cross-request
+---
+
+## Cross-Request LRU Caching
+
+`React.cache()` only works within one request. For data shared across sequential requests (user clicks button A then button B), use an LRU cache.
+
+**Implementation:**
+
+```typescript
+import { LRUCache } from 'lru-cache'
+
+const cache = new LRUCache<string, any>({
+  max: 1000,
+  ttl: 5 * 60 * 1000  // 5 minutes
+})
+
+export async function getUser(id: string) {
+  const cached = cache.get(id)
+  if (cached) return cached
+
+  const user = await db.user.findUnique({ where: { id } })
+  cache.set(id, user)
+  return user
+}
+
+// Request 1: DB query, result cached
+// Request 2: cache hit, no DB query
+```
+
+Use when sequential user actions hit multiple endpoints needing the same data within seconds.
+
+**With Vercel's [Fluid Compute](https://vercel.com/docs/fluid-compute):** LRU caching is especially effective because multiple concurrent requests can share the same function instance and cache. This means the cache persists across requests without needing external storage like Redis.
+
+**In traditional serverless:** Each invocation runs in isolation, so consider Redis for cross-process caching.
+
+Reference: [https://github.com/isaacs/node-lru-cache](https://github.com/isaacs/node-lru-cache)

.claude/skills/vercel-react-best-practices/rules/server-cache-react.md

@@ -0,0 +1,26 @@
+---
+title: Per-Request Deduplication with React.cache()
+impact: MEDIUM
+impactDescription: deduplicates within request
+tags: server, cache, react-cache, deduplication
+---
+
+## Per-Request Deduplication with React.cache()
+
+Use `React.cache()` for server-side request deduplication. Authentication and database queries benefit most.
+
+**Usage:**
+
+```typescript
+import { cache } from 'react'
+
+export const getCurrentUser = cache(async () => {
+  const session = await auth()
+  if (!session?.user?.id) return null
+  return await db.user.findUnique({
+    where: { id: session.user.id }
+  })
+})
+```
+
+Within a single request, multiple calls to `getCurrentUser()` execute the query only once.

.claude/skills/vercel-react-best-practices/rules/server-parallel-fetching.md

@@ -0,0 +1,79 @@
+---
+title: Parallel Data Fetching with Component Composition
+impact: CRITICAL
+impactDescription: eliminates server-side waterfalls
+tags: server, rsc, parallel-fetching, composition
+---
+
+## Parallel Data Fetching with Component Composition
+
+React Server Components execute sequentially within a tree. Restructure with composition to parallelize data fetching.
+
+**Incorrect (Sidebar waits for Page's fetch to complete):**
+
+```tsx
+export default async function Page() {
+  const header = await fetchHeader()
+  return (
+    <div>
+      <div>{header}</div>
+      <Sidebar />
+    </div>
+  )
+}
+
+async function Sidebar() {
+  const items = await fetchSidebarItems()
+  return <nav>{items.map(renderItem)}</nav>
+}
+```
+
+**Correct (both fetch simultaneously):**
+
+```tsx
+async function Header() {
+  const data = await fetchHeader()
+  return <div>{data}</div>
+}
+
+async function Sidebar() {
+  const items = await fetchSidebarItems()
+  return <nav>{items.map(renderItem)}</nav>
+}
+
+export default function Page() {
+  return (
+    <div>
+      <Header />
+      <Sidebar />
+    </div>
+  )
+}
+```
+
+**Alternative with children prop:**
+
+```tsx
+async function Layout({ children }: { children: ReactNode }) {
+  const header = await fetchHeader()
+  return (
+    <div>
+      <div>{header}</div>
+      {children}
+    </div>
+  )
+}
+
+async function Sidebar() {
+  const items = await fetchSidebarItems()
+  return <nav>{items.map(renderItem)}</nav>
+}
+
+export default function Page() {
+  return (
+    <Layout>
+      <Sidebar />
+    </Layout>
+  )
+}
+```

.claude/skills/vercel-react-best-practices/rules/server-serialization.md

@@ -0,0 +1,38 @@
+---
+title: Minimize Serialization at RSC Boundaries
+impact: HIGH
+impactDescription: reduces data transfer size
+tags: server, rsc, serialization, props
+---
+
+## Minimize Serialization at RSC Boundaries
+
+The React Server/Client boundary serializes all object properties into strings and embeds them in the HTML response and subsequent RSC requests. This serialized data directly impacts page weight and load time, so **size matters a lot**. Only pass fields that the client actually uses.
+
+**Incorrect (serializes all 50 fields):**
+
+```tsx
+async function Page() {
+  const user = await fetchUser()  // 50 fields
+  return <Profile user={user} />
+}
+
+'use client'
+function Profile({ user }: { user: User }) {
+  return <div>{user.name}</div>  // uses 1 field
+}
+```
+
+**Correct (serializes only 1 field):**
+
+```tsx
+async function Page() {
+  const user = await fetchUser()
+  return <Profile name={user.name} />
+}
+
+'use client'
+function Profile({ name }: { name: string }) {
+  return <div>{name}</div>
+}
+```

.dockerignore

@@ -1,6 +1,9 @@
 # Ignore everything by default, selectively add things to context
 *
 
+# Documentation (for embeddings/search)
+!docs/
+
 # Platform - Libs
 !autogpt_platform/autogpt_libs/autogpt_libs/
 !autogpt_platform/autogpt_libs/pyproject.toml

autogpt_platform/backend/Dockerfile

@@ -100,6 +100,7 @@ COPY autogpt_platform/backend/migrations /app/autogpt_platform/backend/migration
 FROM server_dependencies AS server
 
 COPY autogpt_platform/backend /app/autogpt_platform/backend
+COPY docs /app/docs
 RUN poetry install --no-ansi --only-root
 
 ENV PORT=8000

autogpt_platform/backend/backend/api/features/store/content_handlers.py

@@ -0,0 +1,431 @@
+"""
+Content Type Handlers for Unified Embeddings
+
+Pluggable system for different content sources (store agents, blocks, docs).
+Each handler knows how to fetch and process its content type for embedding.
+"""
+
+import logging
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from prisma.enums import ContentType
+
+from backend.data.db import query_raw_with_schema
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ContentItem:
+    """Represents a piece of content to be embedded."""
+
+    content_id: str  # Unique identifier (DB ID or file path)
+    content_type: ContentType
+    searchable_text: str  # Combined text for embedding
+    metadata: dict[str, Any]  # Content-specific metadata
+    user_id: str | None = None  # For user-scoped content
+
+
+class ContentHandler(ABC):
+    """Base handler for fetching and processing content for embeddings."""
+
+    @property
+    @abstractmethod
+    def content_type(self) -> ContentType:
+        """The ContentType this handler manages."""
+        pass
+
+    @abstractmethod
+    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
+        """
+        Fetch items that don't have embeddings yet.
+
+        Args:
+            batch_size: Maximum number of items to return
+
+        Returns:
+            List of ContentItem objects ready for embedding
+        """
+        pass
+
+    @abstractmethod
+    async def get_stats(self) -> dict[str, int]:
+        """
+        Get statistics about embedding coverage.
+
+        Returns:
+            Dict with keys: total, with_embeddings, without_embeddings
+        """
+        pass
+
+
+class StoreAgentHandler(ContentHandler):
+    """Handler for marketplace store agent listings."""
+
+    @property
+    def content_type(self) -> ContentType:
+        return ContentType.STORE_AGENT
+
+    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
+        """Fetch approved store listings without embeddings."""
+        from backend.api.features.store.embeddings import build_searchable_text
+
+        missing = await query_raw_with_schema(
+            """
+            SELECT
+                slv.id,
+                slv.name,
+                slv.description,
+                slv."subHeading",
+                slv.categories
+            FROM {schema_prefix}"StoreListingVersion" slv
+            LEFT JOIN {schema_prefix}"UnifiedContentEmbedding" uce
+                ON slv.id = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{schema_prefix}"ContentType"
+            WHERE slv."submissionStatus" = 'APPROVED'
+            AND slv."isDeleted" = false
+            AND uce."contentId" IS NULL
+            LIMIT $1
+            """,
+            batch_size,
+        )
+
+        return [
+            ContentItem(
+                content_id=row["id"],
+                content_type=ContentType.STORE_AGENT,
+                searchable_text=build_searchable_text(
+                    name=row["name"],
+                    description=row["description"],
+                    sub_heading=row["subHeading"],
+                    categories=row["categories"] or [],
+                ),
+                metadata={
+                    "name": row["name"],
+                    "categories": row["categories"] or [],
+                },
+                user_id=None,  # Store agents are public
+            )
+            for row in missing
+        ]
+
+    async def get_stats(self) -> dict[str, int]:
+        """Get statistics about store agent embedding coverage."""
+        # Count approved versions
+        approved_result = await query_raw_with_schema(
+            """
+            SELECT COUNT(*) as count
+            FROM {schema_prefix}"StoreListingVersion"
+            WHERE "submissionStatus" = 'APPROVED'
+            AND "isDeleted" = false
+            """
+        )
+        total_approved = approved_result[0]["count"] if approved_result else 0
+
+        # Count versions with embeddings
+        embedded_result = await query_raw_with_schema(
+            """
+            SELECT COUNT(*) as count
+            FROM {schema_prefix}"StoreListingVersion" slv
+            JOIN {schema_prefix}"UnifiedContentEmbedding" uce ON slv.id = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{schema_prefix}"ContentType"
+            WHERE slv."submissionStatus" = 'APPROVED'
+            AND slv."isDeleted" = false
+            """
+        )
+        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
+
+        return {
+            "total": total_approved,
+            "with_embeddings": with_embeddings,
+            "without_embeddings": total_approved - with_embeddings,
+        }
+
+
+class BlockHandler(ContentHandler):
+    """Handler for block definitions (Python classes)."""
+
+    @property
+    def content_type(self) -> ContentType:
+        return ContentType.BLOCK
+
+    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
+        """Fetch blocks without embeddings."""
+        from backend.data.block import get_blocks
+
+        # Get all available blocks
+        all_blocks = get_blocks()
+
+        # Check which ones have embeddings
+        if not all_blocks:
+            return []
+
+        block_ids = list(all_blocks.keys())
+
+        # Query for existing embeddings
+        placeholders = ",".join([f"${i+1}" for i in range(len(block_ids))])
+        existing_result = await query_raw_with_schema(
+            f"""
+            SELECT "contentId"
+            FROM {{schema_prefix}}"UnifiedContentEmbedding"
+            WHERE "contentType" = 'BLOCK'::{{schema_prefix}}"ContentType"
+            AND "contentId" = ANY(ARRAY[{placeholders}])
+            """,
+            *block_ids,
+        )
+
+        existing_ids = {row["contentId"] for row in existing_result}
+        missing_blocks = [
+            (block_id, block_cls)
+            for block_id, block_cls in all_blocks.items()
+            if block_id not in existing_ids
+        ]
+
+        # Convert to ContentItem
+        items = []
+        for block_id, block_cls in missing_blocks[:batch_size]:
+            try:
+                block_instance = block_cls()
+
+                # Build searchable text from block metadata
+                parts = []
+                if hasattr(block_instance, "name") and block_instance.name:
+                    parts.append(block_instance.name)
+                if (
+                    hasattr(block_instance, "description")
+                    and block_instance.description
+                ):
+                    parts.append(block_instance.description)
+                if hasattr(block_instance, "categories") and block_instance.categories:
+                    # Convert BlockCategory enum to strings
+                    parts.append(
+                        " ".join(str(cat.value) for cat in block_instance.categories)
+                    )
+
+                # Add input/output schema info
+                if hasattr(block_instance, "input_schema"):
+                    schema = block_instance.input_schema
+                    if hasattr(schema, "model_json_schema"):
+                        schema_dict = schema.model_json_schema()
+                        if "properties" in schema_dict:
+                            for prop_name, prop_info in schema_dict[
+                                "properties"
+                            ].items():
+                                if "description" in prop_info:
+                                    parts.append(
+                                        f"{prop_name}: {prop_info['description']}"
+                                    )
+
+                searchable_text = " ".join(parts)
+
+                # Convert categories set of enums to list of strings for JSON serialization
+                categories = getattr(block_instance, "categories", set())
+                categories_list = (
+                    [cat.value for cat in categories] if categories else []
+                )
+
+                items.append(
+                    ContentItem(
+                        content_id=block_id,
+                        content_type=ContentType.BLOCK,
+                        searchable_text=searchable_text,
+                        metadata={
+                            "name": getattr(block_instance, "name", ""),
+                            "categories": categories_list,
+                        },
+                        user_id=None,  # Blocks are public
+                    )
+                )
+            except Exception as e:
+                logger.warning(f"Failed to process block {block_id}: {e}")
+                continue
+
+        return items
+
+    async def get_stats(self) -> dict[str, int]:
+        """Get statistics about block embedding coverage."""
+        from backend.data.block import get_blocks
+
+        all_blocks = get_blocks()
+        total_blocks = len(all_blocks)
+
+        if total_blocks == 0:
+            return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
+
+        block_ids = list(all_blocks.keys())
+        placeholders = ",".join([f"${i+1}" for i in range(len(block_ids))])
+
+        embedded_result = await query_raw_with_schema(
+            f"""
+            SELECT COUNT(*) as count
+            FROM {{schema_prefix}}"UnifiedContentEmbedding"
+            WHERE "contentType" = 'BLOCK'::{{schema_prefix}}"ContentType"
+            AND "contentId" = ANY(ARRAY[{placeholders}])
+            """,
+            *block_ids,
+        )
+
+        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
+
+        return {
+            "total": total_blocks,
+            "with_embeddings": with_embeddings,
+            "without_embeddings": total_blocks - with_embeddings,
+        }
+
+
+class DocumentationHandler(ContentHandler):
+    """Handler for documentation files (.md/.mdx)."""
+
+    @property
+    def content_type(self) -> ContentType:
+        return ContentType.DOCUMENTATION
+
+    def _get_docs_root(self) -> Path:
+        """Get the documentation root directory."""
+        # content_handlers.py is at: backend/backend/api/features/store/content_handlers.py
+        # Need to go up to project root then into docs/
+        # In container: /app/autogpt_platform/backend/backend/api/features/store -> /app/docs
+        # In development: /repo/autogpt_platform/backend/backend/api/features/store -> /repo/docs
+        this_file = Path(
+            __file__
+        )  # .../backend/backend/api/features/store/content_handlers.py
+        project_root = (
+            this_file.parent.parent.parent.parent.parent.parent.parent
+        )  # -> /app or /repo
+        docs_root = project_root / "docs"
+        return docs_root
+
+    def _extract_title_and_content(self, file_path: Path) -> tuple[str, str]:
+        """Extract title and content from markdown file."""
+        try:
+            content = file_path.read_text(encoding="utf-8")
+
+            # Try to extract title from first # heading
+            lines = content.split("\n")
+            title = ""
+            body_lines = []
+
+            for line in lines:
+                if line.startswith("# ") and not title:
+                    title = line[2:].strip()
+                else:
+                    body_lines.append(line)
+
+            # If no title found, use filename
+            if not title:
+                title = file_path.stem.replace("-", " ").replace("_", " ").title()
+
+            body = "\n".join(body_lines)
+
+            return title, body
+        except Exception as e:
+            logger.warning(f"Failed to read {file_path}: {e}")
+            return file_path.stem, ""
+
+    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
+        """Fetch documentation files without embeddings."""
+        docs_root = self._get_docs_root()
+
+        if not docs_root.exists():
+            logger.warning(f"Documentation root not found: {docs_root}")
+            return []
+
+        # Find all .md and .mdx files
+        all_docs = list(docs_root.rglob("*.md")) + list(docs_root.rglob("*.mdx"))
+
+        # Get relative paths for content IDs
+        doc_paths = [str(doc.relative_to(docs_root)) for doc in all_docs]
+
+        if not doc_paths:
+            return []
+
+        # Check which ones have embeddings
+        placeholders = ",".join([f"${i+1}" for i in range(len(doc_paths))])
+        existing_result = await query_raw_with_schema(
+            f"""
+            SELECT "contentId"
+            FROM {{schema_prefix}}"UnifiedContentEmbedding"
+            WHERE "contentType" = 'DOCUMENTATION'::{{schema_prefix}}"ContentType"
+            AND "contentId" = ANY(ARRAY[{placeholders}])
+            """,
+            *doc_paths,
+        )
+
+        existing_ids = {row["contentId"] for row in existing_result}
+        missing_docs = [
+            (doc_path, doc_file)
+            for doc_path, doc_file in zip(doc_paths, all_docs)
+            if doc_path not in existing_ids
+        ]
+
+        # Convert to ContentItem
+        items = []
+        for doc_path, doc_file in missing_docs[:batch_size]:
+            try:
+                title, content = self._extract_title_and_content(doc_file)
+
+                # Build searchable text
+                searchable_text = f"{title} {content}"
+
+                items.append(
+                    ContentItem(
+                        content_id=doc_path,
+                        content_type=ContentType.DOCUMENTATION,
+                        searchable_text=searchable_text,
+                        metadata={
+                            "title": title,
+                            "path": doc_path,
+                        },
+                        user_id=None,  # Documentation is public
+                    )
+                )
+            except Exception as e:
+                logger.warning(f"Failed to process doc {doc_path}: {e}")
+                continue
+
+        return items
+
+    async def get_stats(self) -> dict[str, int]:
+        """Get statistics about documentation embedding coverage."""
+        docs_root = self._get_docs_root()
+
+        if not docs_root.exists():
+            return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
+
+        # Count all .md and .mdx files
+        all_docs = list(docs_root.rglob("*.md")) + list(docs_root.rglob("*.mdx"))
+        total_docs = len(all_docs)
+
+        if total_docs == 0:
+            return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
+
+        doc_paths = [str(doc.relative_to(docs_root)) for doc in all_docs]
+        placeholders = ",".join([f"${i+1}" for i in range(len(doc_paths))])
+
+        embedded_result = await query_raw_with_schema(
+            f"""
+            SELECT COUNT(*) as count
+            FROM {{schema_prefix}}"UnifiedContentEmbedding"
+            WHERE "contentType" = 'DOCUMENTATION'::{{schema_prefix}}"ContentType"
+            AND "contentId" = ANY(ARRAY[{placeholders}])
+            """,
+            *doc_paths,
+        )
+
+        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
+
+        return {
+            "total": total_docs,
+            "with_embeddings": with_embeddings,
+            "without_embeddings": total_docs - with_embeddings,
+        }
+
+
+# Content handler registry
+CONTENT_HANDLERS: dict[ContentType, ContentHandler] = {
+    ContentType.STORE_AGENT: StoreAgentHandler(),
+    ContentType.BLOCK: BlockHandler(),
+    ContentType.DOCUMENTATION: DocumentationHandler(),
+}

autogpt_platform/backend/backend/api/features/store/content_handlers_integration_test.py

@@ -0,0 +1,215 @@
+"""
+Integration tests for content handlers using real DB.
+
+Run with: poetry run pytest backend/api/features/store/content_handlers_integration_test.py -xvs
+
+These tests use the real database but mock OpenAI calls.
+"""
+
+from unittest.mock import patch
+
+import pytest
+
+from backend.api.features.store.content_handlers import (
+    CONTENT_HANDLERS,
+    BlockHandler,
+    DocumentationHandler,
+    StoreAgentHandler,
+)
+from backend.api.features.store.embeddings import (
+    EMBEDDING_DIM,
+    backfill_all_content_types,
+    ensure_content_embedding,
+    get_embedding_stats,
+)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_agent_handler_real_db():
+    """Test StoreAgentHandler with real database queries."""
+    handler = StoreAgentHandler()
+
+    # Get stats from real DB
+    stats = await handler.get_stats()
+
+    # Stats should have correct structure
+    assert "total" in stats
+    assert "with_embeddings" in stats
+    assert "without_embeddings" in stats
+    assert stats["total"] >= 0
+    assert stats["with_embeddings"] >= 0
+    assert stats["without_embeddings"] >= 0
+
+    # Get missing items (max 1 to keep test fast)
+    items = await handler.get_missing_items(batch_size=1)
+
+    # Items should be list (may be empty if all have embeddings)
+    assert isinstance(items, list)
+
+    if items:
+        item = items[0]
+        assert item.content_id is not None
+        assert item.content_type.value == "STORE_AGENT"
+        assert item.searchable_text != ""
+        assert item.user_id is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_block_handler_real_db():
+    """Test BlockHandler with real database queries."""
+    handler = BlockHandler()
+
+    # Get stats from real DB
+    stats = await handler.get_stats()
+
+    # Stats should have correct structure
+    assert "total" in stats
+    assert "with_embeddings" in stats
+    assert "without_embeddings" in stats
+    assert stats["total"] >= 0  # Should have at least some blocks
+    assert stats["with_embeddings"] >= 0
+    assert stats["without_embeddings"] >= 0
+
+    # Get missing items (max 1 to keep test fast)
+    items = await handler.get_missing_items(batch_size=1)
+
+    # Items should be list
+    assert isinstance(items, list)
+
+    if items:
+        item = items[0]
+        assert item.content_id is not None  # Should be block UUID
+        assert item.content_type.value == "BLOCK"
+        assert item.searchable_text != ""
+        assert item.user_id is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_documentation_handler_real_fs():
+    """Test DocumentationHandler with real filesystem."""
+    handler = DocumentationHandler()
+
+    # Get stats from real filesystem
+    stats = await handler.get_stats()
+
+    # Stats should have correct structure
+    assert "total" in stats
+    assert "with_embeddings" in stats
+    assert "without_embeddings" in stats
+    assert stats["total"] >= 0
+    assert stats["with_embeddings"] >= 0
+    assert stats["without_embeddings"] >= 0
+
+    # Get missing items (max 1 to keep test fast)
+    items = await handler.get_missing_items(batch_size=1)
+
+    # Items should be list
+    assert isinstance(items, list)
+
+    if items:
+        item = items[0]
+        assert item.content_id is not None  # Should be relative path
+        assert item.content_type.value == "DOCUMENTATION"
+        assert item.searchable_text != ""
+        assert item.user_id is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_embedding_stats_all_types():
+    """Test get_embedding_stats aggregates all content types."""
+    stats = await get_embedding_stats()
+
+    # Should have structure with by_type and totals
+    assert "by_type" in stats
+    assert "totals" in stats
+
+    # Check each content type is present
+    by_type = stats["by_type"]
+    assert "STORE_AGENT" in by_type
+    assert "BLOCK" in by_type
+    assert "DOCUMENTATION" in by_type
+
+    # Check totals are aggregated
+    totals = stats["totals"]
+    assert totals["total"] >= 0
+    assert totals["with_embeddings"] >= 0
+    assert totals["without_embeddings"] >= 0
+    assert "coverage_percent" in totals
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.generate_embedding")
+async def test_ensure_content_embedding_blocks(mock_generate):
+    """Test creating embeddings for blocks (mocked OpenAI)."""
+    # Mock OpenAI to return fake embedding
+    mock_generate.return_value = [0.1] * EMBEDDING_DIM
+
+    # Get one block without embedding
+    handler = BlockHandler()
+    items = await handler.get_missing_items(batch_size=1)
+
+    if not items:
+        pytest.skip("No blocks without embeddings")
+
+    item = items[0]
+
+    # Try to create embedding (OpenAI mocked)
+    result = await ensure_content_embedding(
+        content_type=item.content_type,
+        content_id=item.content_id,
+        searchable_text=item.searchable_text,
+        metadata=item.metadata,
+        user_id=item.user_id,
+    )
+
+    # Should succeed with mocked OpenAI
+    assert result is True
+    mock_generate.assert_called_once()
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.generate_embedding")
+async def test_backfill_all_content_types_dry_run(mock_generate):
+    """Test backfill_all_content_types processes all handlers in order."""
+    # Mock OpenAI to return fake embedding
+    mock_generate.return_value = [0.1] * EMBEDDING_DIM
+
+    # Run backfill with batch_size=1 to process max 1 per type
+    result = await backfill_all_content_types(batch_size=1)
+
+    # Should have results for all content types
+    assert "by_type" in result
+    assert "totals" in result
+
+    by_type = result["by_type"]
+    assert "BLOCK" in by_type
+    assert "STORE_AGENT" in by_type
+    assert "DOCUMENTATION" in by_type
+
+    # Each type should have correct structure
+    for content_type, type_result in by_type.items():
+        assert "processed" in type_result
+        assert "success" in type_result
+        assert "failed" in type_result
+
+    # Totals should aggregate
+    totals = result["totals"]
+    assert totals["processed"] >= 0
+    assert totals["success"] >= 0
+    assert totals["failed"] >= 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_content_handler_registry():
+    """Test all handlers are registered in correct order."""
+    from prisma.enums import ContentType
+
+    # All three types should be registered
+    assert ContentType.STORE_AGENT in CONTENT_HANDLERS
+    assert ContentType.BLOCK in CONTENT_HANDLERS
+    assert ContentType.DOCUMENTATION in CONTENT_HANDLERS
+
+    # Check handler types
+    assert isinstance(CONTENT_HANDLERS[ContentType.STORE_AGENT], StoreAgentHandler)
+    assert isinstance(CONTENT_HANDLERS[ContentType.BLOCK], BlockHandler)
+    assert isinstance(CONTENT_HANDLERS[ContentType.DOCUMENTATION], DocumentationHandler)

autogpt_platform/backend/backend/api/features/store/content_handlers_test.py

@@ -0,0 +1,324 @@
+"""
+E2E tests for content handlers (blocks, store agents, documentation).
+
+Tests the full flow: discovering content → generating embeddings → storing.
+"""
+
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pytest
+from prisma.enums import ContentType
+
+from backend.api.features.store.content_handlers import (
+    CONTENT_HANDLERS,
+    BlockHandler,
+    DocumentationHandler,
+    StoreAgentHandler,
+)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_agent_handler_get_missing_items(mocker):
+    """Test StoreAgentHandler fetches approved agents without embeddings."""
+    handler = StoreAgentHandler()
+
+    # Mock database query
+    mock_missing = [
+        {
+            "id": "agent-1",
+            "name": "Test Agent",
+            "description": "A test agent",
+            "subHeading": "Test heading",
+            "categories": ["AI", "Testing"],
+        }
+    ]
+
+    with patch(
+        "backend.api.features.store.content_handlers.query_raw_with_schema",
+        return_value=mock_missing,
+    ):
+        items = await handler.get_missing_items(batch_size=10)
+
+        assert len(items) == 1
+        assert items[0].content_id == "agent-1"
+        assert items[0].content_type == ContentType.STORE_AGENT
+        assert "Test Agent" in items[0].searchable_text
+        assert "A test agent" in items[0].searchable_text
+        assert items[0].metadata["name"] == "Test Agent"
+        assert items[0].user_id is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_agent_handler_get_stats(mocker):
+    """Test StoreAgentHandler returns correct stats."""
+    handler = StoreAgentHandler()
+
+    # Mock approved count query
+    mock_approved = [{"count": 50}]
+    # Mock embedded count query
+    mock_embedded = [{"count": 30}]
+
+    with patch(
+        "backend.api.features.store.content_handlers.query_raw_with_schema",
+        side_effect=[mock_approved, mock_embedded],
+    ):
+        stats = await handler.get_stats()
+
+        assert stats["total"] == 50
+        assert stats["with_embeddings"] == 30
+        assert stats["without_embeddings"] == 20
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_block_handler_get_missing_items(mocker):
+    """Test BlockHandler discovers blocks without embeddings."""
+    handler = BlockHandler()
+
+    # Mock get_blocks to return test blocks
+    mock_block_class = MagicMock()
+    mock_block_instance = MagicMock()
+    mock_block_instance.name = "Calculator Block"
+    mock_block_instance.description = "Performs calculations"
+    mock_block_instance.categories = [MagicMock(value="MATH")]
+    mock_block_instance.input_schema.model_json_schema.return_value = {
+        "properties": {"expression": {"description": "Math expression to evaluate"}}
+    }
+    mock_block_class.return_value = mock_block_instance
+
+    mock_blocks = {"block-uuid-1": mock_block_class}
+
+    # Mock existing embeddings query (no embeddings exist)
+    mock_existing = []
+
+    with patch(
+        "backend.data.block.get_blocks",
+        return_value=mock_blocks,
+    ):
+        with patch(
+            "backend.api.features.store.content_handlers.query_raw_with_schema",
+            return_value=mock_existing,
+        ):
+            items = await handler.get_missing_items(batch_size=10)
+
+            assert len(items) == 1
+            assert items[0].content_id == "block-uuid-1"
+            assert items[0].content_type == ContentType.BLOCK
+            assert "Calculator Block" in items[0].searchable_text
+            assert "Performs calculations" in items[0].searchable_text
+            assert "MATH" in items[0].searchable_text
+            assert "expression: Math expression" in items[0].searchable_text
+            assert items[0].user_id is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_block_handler_get_stats(mocker):
+    """Test BlockHandler returns correct stats."""
+    handler = BlockHandler()
+
+    # Mock get_blocks
+    mock_blocks = {
+        "block-1": MagicMock(),
+        "block-2": MagicMock(),
+        "block-3": MagicMock(),
+    }
+
+    # Mock embedded count query (2 blocks have embeddings)
+    mock_embedded = [{"count": 2}]
+
+    with patch(
+        "backend.data.block.get_blocks",
+        return_value=mock_blocks,
+    ):
+        with patch(
+            "backend.api.features.store.content_handlers.query_raw_with_schema",
+            return_value=mock_embedded,
+        ):
+            stats = await handler.get_stats()
+
+            assert stats["total"] == 3
+            assert stats["with_embeddings"] == 2
+            assert stats["without_embeddings"] == 1
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_documentation_handler_get_missing_items(tmp_path, mocker):
+    """Test DocumentationHandler discovers docs without embeddings."""
+    handler = DocumentationHandler()
+
+    # Create temporary docs directory with test files
+    docs_root = tmp_path / "docs"
+    docs_root.mkdir()
+
+    (docs_root / "guide.md").write_text("# Getting Started\n\nThis is a guide.")
+    (docs_root / "api.mdx").write_text("# API Reference\n\nAPI documentation.")
+
+    # Mock _get_docs_root to return temp dir
+    with patch.object(handler, "_get_docs_root", return_value=docs_root):
+        # Mock existing embeddings query (no embeddings exist)
+        with patch(
+            "backend.api.features.store.content_handlers.query_raw_with_schema",
+            return_value=[],
+        ):
+            items = await handler.get_missing_items(batch_size=10)
+
+            assert len(items) == 2
+
+            # Check guide.md
+            guide_item = next(
+                (item for item in items if item.content_id == "guide.md"), None
+            )
+            assert guide_item is not None
+            assert guide_item.content_type == ContentType.DOCUMENTATION
+            assert "Getting Started" in guide_item.searchable_text
+            assert "This is a guide" in guide_item.searchable_text
+            assert guide_item.metadata["title"] == "Getting Started"
+            assert guide_item.user_id is None
+
+            # Check api.mdx
+            api_item = next(
+                (item for item in items if item.content_id == "api.mdx"), None
+            )
+            assert api_item is not None
+            assert "API Reference" in api_item.searchable_text
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_documentation_handler_get_stats(tmp_path, mocker):
+    """Test DocumentationHandler returns correct stats."""
+    handler = DocumentationHandler()
+
+    # Create temporary docs directory
+    docs_root = tmp_path / "docs"
+    docs_root.mkdir()
+    (docs_root / "doc1.md").write_text("# Doc 1")
+    (docs_root / "doc2.md").write_text("# Doc 2")
+    (docs_root / "doc3.mdx").write_text("# Doc 3")
+
+    # Mock embedded count query (1 doc has embedding)
+    mock_embedded = [{"count": 1}]
+
+    with patch.object(handler, "_get_docs_root", return_value=docs_root):
+        with patch(
+            "backend.api.features.store.content_handlers.query_raw_with_schema",
+            return_value=mock_embedded,
+        ):
+            stats = await handler.get_stats()
+
+            assert stats["total"] == 3
+            assert stats["with_embeddings"] == 1
+            assert stats["without_embeddings"] == 2
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_documentation_handler_title_extraction(tmp_path):
+    """Test DocumentationHandler extracts title from markdown heading."""
+    handler = DocumentationHandler()
+
+    # Test with heading
+    doc_with_heading = tmp_path / "with_heading.md"
+    doc_with_heading.write_text("# My Title\n\nContent here")
+    title, content = handler._extract_title_and_content(doc_with_heading)
+    assert title == "My Title"
+    assert "# My Title" not in content
+    assert "Content here" in content
+
+    # Test without heading
+    doc_without_heading = tmp_path / "no-heading.md"
+    doc_without_heading.write_text("Just content, no heading")
+    title, content = handler._extract_title_and_content(doc_without_heading)
+    assert title == "No Heading"  # Uses filename
+    assert "Just content" in content
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_content_handlers_registry():
+    """Test all content types are registered."""
+    assert ContentType.STORE_AGENT in CONTENT_HANDLERS
+    assert ContentType.BLOCK in CONTENT_HANDLERS
+    assert ContentType.DOCUMENTATION in CONTENT_HANDLERS
+
+    assert isinstance(CONTENT_HANDLERS[ContentType.STORE_AGENT], StoreAgentHandler)
+    assert isinstance(CONTENT_HANDLERS[ContentType.BLOCK], BlockHandler)
+    assert isinstance(CONTENT_HANDLERS[ContentType.DOCUMENTATION], DocumentationHandler)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_block_handler_handles_missing_attributes():
+    """Test BlockHandler gracefully handles blocks with missing attributes."""
+    handler = BlockHandler()
+
+    # Mock block with minimal attributes
+    mock_block_class = MagicMock()
+    mock_block_instance = MagicMock()
+    mock_block_instance.name = "Minimal Block"
+    # No description, categories, or schema
+    del mock_block_instance.description
+    del mock_block_instance.categories
+    del mock_block_instance.input_schema
+    mock_block_class.return_value = mock_block_instance
+
+    mock_blocks = {"block-minimal": mock_block_class}
+
+    with patch(
+        "backend.data.block.get_blocks",
+        return_value=mock_blocks,
+    ):
+        with patch(
+            "backend.api.features.store.content_handlers.query_raw_with_schema",
+            return_value=[],
+        ):
+            items = await handler.get_missing_items(batch_size=10)
+
+            assert len(items) == 1
+            assert items[0].searchable_text == "Minimal Block"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_block_handler_skips_failed_blocks():
+    """Test BlockHandler skips blocks that fail to instantiate."""
+    handler = BlockHandler()
+
+    # Mock one good block and one bad block
+    good_block = MagicMock()
+    good_instance = MagicMock()
+    good_instance.name = "Good Block"
+    good_instance.description = "Works fine"
+    good_instance.categories = []
+    good_block.return_value = good_instance
+
+    bad_block = MagicMock()
+    bad_block.side_effect = Exception("Instantiation failed")
+
+    mock_blocks = {"good-block": good_block, "bad-block": bad_block}
+
+    with patch(
+        "backend.data.block.get_blocks",
+        return_value=mock_blocks,
+    ):
+        with patch(
+            "backend.api.features.store.content_handlers.query_raw_with_schema",
+            return_value=[],
+        ):
+            items = await handler.get_missing_items(batch_size=10)
+
+            # Should only get the good block
+            assert len(items) == 1
+            assert items[0].content_id == "good-block"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_documentation_handler_missing_docs_directory():
+    """Test DocumentationHandler handles missing docs directory gracefully."""
+    handler = DocumentationHandler()
+
+    # Mock _get_docs_root to return non-existent path
+    fake_path = Path("/nonexistent/docs")
+    with patch.object(handler, "_get_docs_root", return_value=fake_path):
+        items = await handler.get_missing_items(batch_size=10)
+        assert items == []
+
+        stats = await handler.get_stats()
+        assert stats["total"] == 0
+        assert stats["with_embeddings"] == 0
+        assert stats["without_embeddings"] == 0

autogpt_platform/backend/backend/api/features/store/embeddings.py

@@ -14,6 +14,7 @@ import prisma
 from prisma.enums import ContentType
 from tiktoken import encoding_for_model
 
+from backend.api.features.store.content_handlers import CONTENT_HANDLERS
 from backend.data.db import execute_raw_with_schema, query_raw_with_schema
 from backend.util.clients import get_openai_client
 from backend.util.json import dumps
@@ -23,6 +24,9 @@ logger = logging.getLogger(__name__)
 
 # OpenAI embedding model configuration
 EMBEDDING_MODEL = "text-embedding-3-small"
+# Embedding dimension for the model above
+# text-embedding-3-small: 1536, text-embedding-3-large: 3072
+EMBEDDING_DIM = 1536
 # OpenAI embedding token limit (8,191 with 1 token buffer for safety)
 EMBEDDING_MAX_TOKENS = 8191
 
@@ -369,55 +373,69 @@ async def delete_content_embedding(
 
 async def get_embedding_stats() -> dict[str, Any]:
     """
-    Get statistics about embedding coverage.
+    Get statistics about embedding coverage for all content types.
 
-    Returns counts of:
-    - Total approved listing versions
-    - Versions with embeddings
-    - Versions without embeddings
+    Returns stats per content type and overall totals.
     """
     try:
-        # Count approved versions
-        approved_result = await query_raw_with_schema(
-            """
-            SELECT COUNT(*) as count
-            FROM {schema_prefix}"StoreListingVersion"
-            WHERE "submissionStatus" = 'APPROVED'
-            AND "isDeleted" = false
-            """
-        )
-        total_approved = approved_result[0]["count"] if approved_result else 0
+        stats_by_type = {}
+        total_items = 0
+        total_with_embeddings = 0
+        total_without_embeddings = 0
 
-        # Count versions with embeddings
-        embedded_result = await query_raw_with_schema(
-            """
-            SELECT COUNT(*) as count
-            FROM {schema_prefix}"StoreListingVersion" slv
-            JOIN {schema_prefix}"UnifiedContentEmbedding" uce ON slv.id = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{schema_prefix}"ContentType"
-            WHERE slv."submissionStatus" = 'APPROVED'
-            AND slv."isDeleted" = false
-            """
-        )
-        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
+        # Aggregate stats from all handlers
+        for content_type, handler in CONTENT_HANDLERS.items():
+            try:
+                stats = await handler.get_stats()
+                stats_by_type[content_type.value] = {
+                    "total": stats["total"],
+                    "with_embeddings": stats["with_embeddings"],
+                    "without_embeddings": stats["without_embeddings"],
+                    "coverage_percent": (
+                        round(stats["with_embeddings"] / stats["total"] * 100, 1)
+                        if stats["total"] > 0
+                        else 0
+                    ),
+                }
+
+                total_items += stats["total"]
+                total_with_embeddings += stats["with_embeddings"]
+                total_without_embeddings += stats["without_embeddings"]
+
+            except Exception as e:
+                logger.error(f"Failed to get stats for {content_type.value}: {e}")
+                stats_by_type[content_type.value] = {
+                    "total": 0,
+                    "with_embeddings": 0,
+                    "without_embeddings": 0,
+                    "coverage_percent": 0,
+                    "error": str(e),
+                }
 
         return {
-            "total_approved": total_approved,
-            "with_embeddings": with_embeddings,
-            "without_embeddings": total_approved - with_embeddings,
-            "coverage_percent": (
-                round(with_embeddings / total_approved * 100, 1)
-                if total_approved > 0
-                else 0
-            ),
+            "by_type": stats_by_type,
+            "totals": {
+                "total": total_items,
+                "with_embeddings": total_with_embeddings,
+                "without_embeddings": total_without_embeddings,
+                "coverage_percent": (
+                    round(total_with_embeddings / total_items * 100, 1)
+                    if total_items > 0
+                    else 0
+                ),
+            },
         }
 
     except Exception as e:
         logger.error(f"Failed to get embedding stats: {e}")
         return {
-            "total_approved": 0,
-            "with_embeddings": 0,
-            "without_embeddings": 0,
-            "coverage_percent": 0,
+            "by_type": {},
+            "totals": {
+                "total": 0,
+                "with_embeddings": 0,
+                "without_embeddings": 0,
+                "coverage_percent": 0,
+            },
             "error": str(e),
         }
 
@@ -426,73 +444,118 @@ async def backfill_missing_embeddings(batch_size: int = 10) -> dict[str, Any]:
     """
     Generate embeddings for approved listings that don't have them.
 
+    BACKWARD COMPATIBILITY: Maintained for existing usage.
+    This now delegates to backfill_all_content_types() to process all content types.
+
     Args:
-        batch_size: Number of embeddings to generate in one call
+        batch_size: Number of embeddings to generate per content type
 
     Returns:
-        Dict with success/failure counts
+        Dict with success/failure counts aggregated across all content types
     """
-    try:
-        # Find approved versions without embeddings
-        missing = await query_raw_with_schema(
-            """
-            SELECT
-                slv.id,
-                slv.name,
-                slv.description,
-                slv."subHeading",
-                slv.categories
-            FROM {schema_prefix}"StoreListingVersion" slv
-            LEFT JOIN {schema_prefix}"UnifiedContentEmbedding" uce
-                ON slv.id = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{schema_prefix}"ContentType"
-            WHERE slv."submissionStatus" = 'APPROVED'
-            AND slv."isDeleted" = false
-            AND uce."contentId" IS NULL
-            LIMIT $1
-            """,
-            batch_size,
-        )
+    # Delegate to the new generic backfill system
+    result = await backfill_all_content_types(batch_size)
 
-        if not missing:
-            return {
+    # Return in the old format for backward compatibility
+    return result["totals"]
+
+
+async def backfill_all_content_types(batch_size: int = 10) -> dict[str, Any]:
+    """
+    Generate embeddings for all content types using registered handlers.
+
+    Processes content types in order: BLOCK → STORE_AGENT → DOCUMENTATION.
+    This ensures foundational content (blocks) are searchable first.
+
+    Args:
+        batch_size: Number of embeddings to generate per content type
+
+    Returns:
+        Dict with stats per content type and overall totals
+    """
+    results_by_type = {}
+    total_processed = 0
+    total_success = 0
+    total_failed = 0
+
+    # Process content types in explicit order
+    processing_order = [
+        ContentType.BLOCK,
+        ContentType.STORE_AGENT,
+        ContentType.DOCUMENTATION,
+    ]
+
+    for content_type in processing_order:
+        handler = CONTENT_HANDLERS.get(content_type)
+        if not handler:
+            logger.warning(f"No handler registered for {content_type.value}")
+            continue
+        try:
+            logger.info(f"Processing {content_type.value} content type...")
+
+            # Get missing items from handler
+            missing_items = await handler.get_missing_items(batch_size)
+
+            if not missing_items:
+                results_by_type[content_type.value] = {
+                    "processed": 0,
+                    "success": 0,
+                    "failed": 0,
+                    "message": "No missing embeddings",
+                }
+                continue
+
+            # Process embeddings concurrently for better performance
+            embedding_tasks = [
+                ensure_content_embedding(
+                    content_type=item.content_type,
+                    content_id=item.content_id,
+                    searchable_text=item.searchable_text,
+                    metadata=item.metadata,
+                    user_id=item.user_id,
+                )
+                for item in missing_items
+            ]
+
+            results = await asyncio.gather(*embedding_tasks, return_exceptions=True)
+
+            success = sum(1 for result in results if result is True)
+            failed = len(results) - success
+
+            results_by_type[content_type.value] = {
+                "processed": len(missing_items),
+                "success": success,
+                "failed": failed,
+                "message": f"Backfilled {success} embeddings, {failed} failed",
+            }
+
+            total_processed += len(missing_items)
+            total_success += success
+            total_failed += failed
+
+            logger.info(
+                f"{content_type.value}: processed {len(missing_items)}, "
+                f"success {success}, failed {failed}"
+            )
+
+        except Exception as e:
+            logger.error(f"Failed to process {content_type.value}: {e}")
+            results_by_type[content_type.value] = {
                 "processed": 0,
                 "success": 0,
                 "failed": 0,
-                "message": "No missing embeddings",
+                "error": str(e),
             }
 
-        # Process embeddings concurrently for better performance
-        embedding_tasks = [
-            ensure_embedding(
-                version_id=row["id"],
-                name=row["name"],
-                description=row["description"],
-                sub_heading=row["subHeading"],
-                categories=row["categories"] or [],
-            )
-            for row in missing
-        ]
-
-        results = await asyncio.gather(*embedding_tasks, return_exceptions=True)
-
-        success = sum(1 for result in results if result is True)
-        failed = len(results) - success
-
-        return {
-            "processed": len(missing),
-            "success": success,
-            "failed": failed,
-            "message": f"Backfilled {success} embeddings, {failed} failed",
-        }
-
-    except Exception as e:
-        logger.error(f"Failed to backfill embeddings: {e}")
-        return {
-            "processed": 0,
-            "success": 0,
-            "failed": 0,
-            "error": str(e),
-        }
+    return {
+        "by_type": results_by_type,
+        "totals": {
+            "processed": total_processed,
+            "success": total_success,
+            "failed": total_failed,
+            "message": f"Overall: {total_success} succeeded, {total_failed} failed",
+        },
+    }
 
 
 async def embed_query(query: str) -> list[float] | None:
@@ -566,3 +629,334 @@ async def ensure_content_embedding(
     except Exception as e:
         logger.error(f"Failed to ensure embedding for {content_type}:{content_id}: {e}")
         return False
+
+
+async def cleanup_orphaned_embeddings() -> dict[str, Any]:
+    """
+    Clean up embeddings for content that no longer exists or is no longer valid.
+
+    Compares current content with embeddings in database and removes orphaned records:
+    - STORE_AGENT: Removes embeddings for rejected/deleted store listings
+    - BLOCK: Removes embeddings for blocks no longer registered
+    - DOCUMENTATION: Removes embeddings for deleted doc files
+
+    Returns:
+        Dict with cleanup statistics per content type
+    """
+    results_by_type = {}
+    total_deleted = 0
+
+    # Cleanup orphaned embeddings for all content types
+    cleanup_types = [
+        ContentType.STORE_AGENT,
+        ContentType.BLOCK,
+        ContentType.DOCUMENTATION,
+    ]
+
+    for content_type in cleanup_types:
+        try:
+            handler = CONTENT_HANDLERS.get(content_type)
+            if not handler:
+                logger.warning(f"No handler registered for {content_type}")
+                results_by_type[content_type.value] = {
+                    "deleted": 0,
+                    "error": "No handler registered",
+                }
+                continue
+
+            # Get all current content IDs from handler
+            if content_type == ContentType.STORE_AGENT:
+                # Get IDs of approved store listing versions from non-deleted listings
+                valid_agents = await query_raw_with_schema(
+                    """
+                    SELECT slv.id
+                    FROM {schema_prefix}"StoreListingVersion" slv
+                    JOIN {schema_prefix}"StoreListing" sl ON slv."storeListingId" = sl.id
+                    WHERE slv."submissionStatus" = 'APPROVED'
+                      AND slv."isDeleted" = false
+                      AND sl."isDeleted" = false
+                    """,
+                )
+                current_ids = {row["id"] for row in valid_agents}
+            elif content_type == ContentType.BLOCK:
+                from backend.data.block import get_blocks
+
+                current_ids = set(get_blocks().keys())
+            elif content_type == ContentType.DOCUMENTATION:
+                from pathlib import Path
+
+                # embeddings.py is at: backend/backend/api/features/store/embeddings.py
+                # Need to go up to project root then into docs/
+                this_file = Path(__file__)
+                project_root = (
+                    this_file.parent.parent.parent.parent.parent.parent.parent
+                )
+                docs_root = project_root / "docs"
+                if docs_root.exists():
+                    all_docs = list(docs_root.rglob("*.md")) + list(
+                        docs_root.rglob("*.mdx")
+                    )
+                    current_ids = {str(doc.relative_to(docs_root)) for doc in all_docs}
+                else:
+                    current_ids = set()
+            else:
+                # Skip unknown content types to avoid accidental deletion
+                logger.warning(
+                    f"Skipping cleanup for unknown content type: {content_type}"
+                )
+                results_by_type[content_type.value] = {
+                    "deleted": 0,
+                    "error": "Unknown content type - skipped for safety",
+                }
+                continue
+
+            # Get all embedding IDs from database
+            db_embeddings = await query_raw_with_schema(
+                """
+                SELECT "contentId"
+                FROM {schema_prefix}"UnifiedContentEmbedding"
+                WHERE "contentType" = $1::{schema_prefix}"ContentType"
+                """,
+                content_type,
+            )
+
+            db_ids = {row["contentId"] for row in db_embeddings}
+
+            # Find orphaned embeddings (in DB but not in current content)
+            orphaned_ids = db_ids - current_ids
+
+            if not orphaned_ids:
+                logger.info(f"{content_type.value}: No orphaned embeddings found")
+                results_by_type[content_type.value] = {
+                    "deleted": 0,
+                    "message": "No orphaned embeddings",
+                }
+                continue
+
+            # Delete orphaned embeddings in batch for better performance
+            orphaned_list = list(orphaned_ids)
+            try:
+                await execute_raw_with_schema(
+                    """
+                    DELETE FROM {schema_prefix}"UnifiedContentEmbedding"
+                    WHERE "contentType" = $1::{schema_prefix}"ContentType"
+                      AND "contentId" = ANY($2::text[])
+                    """,
+                    content_type,
+                    orphaned_list,
+                )
+                deleted = len(orphaned_list)
+            except Exception as e:
+                logger.error(f"Failed to batch delete orphaned embeddings: {e}")
+                deleted = 0
+
+            logger.info(
+                f"{content_type.value}: Deleted {deleted}/{len(orphaned_ids)} orphaned embeddings"
+            )
+            results_by_type[content_type.value] = {
+                "deleted": deleted,
+                "orphaned": len(orphaned_ids),
+                "message": f"Deleted {deleted} orphaned embeddings",
+            }
+
+            total_deleted += deleted
+
+        except Exception as e:
+            logger.error(f"Failed to cleanup {content_type.value}: {e}")
+            results_by_type[content_type.value] = {
+                "deleted": 0,
+                "error": str(e),
+            }
+
+    return {
+        "by_type": results_by_type,
+        "totals": {
+            "deleted": total_deleted,
+            "message": f"Deleted {total_deleted} orphaned embeddings",
+        },
+    }
+
+
+async def semantic_search(
+    query: str,
+    content_types: list[ContentType] | None = None,
+    user_id: str | None = None,
+    limit: int = 20,
+    min_similarity: float = 0.5,
+) -> list[dict[str, Any]]:
+    """
+    Semantic search across content types using embeddings.
+
+    Performs vector similarity search on UnifiedContentEmbedding table.
+    Used directly for blocks/docs/library agents, or as the semantic component
+    within hybrid_search for store agents.
+
+    If embedding generation fails, falls back to lexical search on searchableText.
+
+    Args:
+        query: Search query string
+        content_types: List of ContentType to search. Defaults to [BLOCK, STORE_AGENT, DOCUMENTATION]
+        user_id: Optional user ID for searching private content (library agents)
+        limit: Maximum number of results to return (default: 20)
+        min_similarity: Minimum cosine similarity threshold (0-1, default: 0.5)
+
+    Returns:
+        List of search results with the following structure:
+        [
+            {
+                "content_id": str,
+                "content_type": str,  # "BLOCK", "STORE_AGENT", "DOCUMENTATION", or "LIBRARY_AGENT"
+                "searchable_text": str,
+                "metadata": dict,
+                "similarity": float,  # Cosine similarity score (0-1)
+            },
+            ...
+        ]
+
+    Examples:
+        # Search blocks only
+        results = await semantic_search("calculate", content_types=[ContentType.BLOCK])
+
+        # Search blocks and documentation
+        results = await semantic_search(
+            "how to use API",
+            content_types=[ContentType.BLOCK, ContentType.DOCUMENTATION]
+        )
+
+        # Search all public content (default)
+        results = await semantic_search("AI agent")
+
+        # Search user's library agents
+        results = await semantic_search(
+            "my custom agent",
+            content_types=[ContentType.LIBRARY_AGENT],
+            user_id="user123"
+        )
+    """
+    # Default to searching all public content types
+    if content_types is None:
+        content_types = [
+            ContentType.BLOCK,
+            ContentType.STORE_AGENT,
+            ContentType.DOCUMENTATION,
+        ]
+
+    # Validate inputs
+    if not content_types:
+        return []  # Empty content_types would cause invalid SQL (IN ())
+
+    query = query.strip()
+    if not query:
+        return []
+
+    if limit < 1:
+        limit = 1
+    if limit > 100:
+        limit = 100
+
+    # Generate query embedding
+    query_embedding = await embed_query(query)
+
+    if query_embedding is not None:
+        # Semantic search with embeddings
+        embedding_str = embedding_to_vector_string(query_embedding)
+
+        # Build params in order: limit, then user_id (if provided), then content types
+        params: list[Any] = [limit]
+        user_filter = ""
+        if user_id is not None:
+            user_filter = 'AND "userId" = ${}'.format(len(params) + 1)
+            params.append(user_id)
+
+        # Add content type parameters and build placeholders dynamically
+        content_type_start_idx = len(params) + 1
+        content_type_placeholders = ", ".join(
+            f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
+            for i in range(len(content_types))
+        )
+        params.extend([ct.value for ct in content_types])
+
+        sql = f"""
+            SELECT
+                "contentId" as content_id,
+                "contentType" as content_type,
+                "searchableText" as searchable_text,
+                metadata,
+                1 - (embedding <=> '{embedding_str}'::vector) as similarity
+            FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
+            WHERE "contentType" IN ({content_type_placeholders})
+            {user_filter}
+            AND 1 - (embedding <=> '{embedding_str}'::vector) >= ${len(params) + 1}
+            ORDER BY similarity DESC
+            LIMIT $1
+        """
+        params.append(min_similarity)
+
+        try:
+            results = await query_raw_with_schema(
+                sql, *params, set_public_search_path=True
+            )
+            return [
+                {
+                    "content_id": row["content_id"],
+                    "content_type": row["content_type"],
+                    "searchable_text": row["searchable_text"],
+                    "metadata": row["metadata"],
+                    "similarity": float(row["similarity"]),
+                }
+                for row in results
+            ]
+        except Exception as e:
+            logger.error(f"Semantic search failed: {e}")
+            # Fall through to lexical search below
+
+    # Fallback to lexical search if embeddings unavailable
+    logger.warning("Falling back to lexical search (embeddings unavailable)")
+
+    params_lexical: list[Any] = [limit]
+    user_filter = ""
+    if user_id is not None:
+        user_filter = 'AND "userId" = ${}'.format(len(params_lexical) + 1)
+        params_lexical.append(user_id)
+
+    # Add content type parameters and build placeholders dynamically
+    content_type_start_idx = len(params_lexical) + 1
+    content_type_placeholders_lexical = ", ".join(
+        f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
+        for i in range(len(content_types))
+    )
+    params_lexical.extend([ct.value for ct in content_types])
+
+    sql_lexical = f"""
+        SELECT
+            "contentId" as content_id,
+            "contentType" as content_type,
+            "searchableText" as searchable_text,
+            metadata,
+            0.0 as similarity
+        FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
+        WHERE "contentType" IN ({content_type_placeholders_lexical})
+        {user_filter}
+        AND "searchableText" ILIKE ${len(params_lexical) + 1}
+        ORDER BY "updatedAt" DESC
+        LIMIT $1
+    """
+    params_lexical.append(f"%{query}%")
+
+    try:
+        results = await query_raw_with_schema(
+            sql_lexical, *params_lexical, set_public_search_path=True
+        )
+        return [
+            {
+                "content_id": row["content_id"],
+                "content_type": row["content_type"],
+                "searchable_text": row["searchable_text"],
+                "metadata": row["metadata"],
+                "similarity": 0.0,  # Lexical search doesn't provide similarity
+            }
+            for row in results
+        ]
+    except Exception as e:
+        logger.error(f"Lexical search failed: {e}")
+        return []

autogpt_platform/backend/backend/api/features/store/embeddings_e2e_test.py

@@ -0,0 +1,666 @@
+"""
+End-to-end database tests for embeddings and hybrid search.
+
+These tests hit the actual database to verify SQL queries work correctly.
+Tests cover:
+1. Embedding storage (store_content_embedding)
+2. Embedding retrieval (get_content_embedding)
+3. Embedding deletion (delete_content_embedding)
+4. Unified hybrid search across content types
+5. Store agent hybrid search
+"""
+
+import uuid
+from typing import AsyncGenerator
+
+import pytest
+from prisma.enums import ContentType
+
+from backend.api.features.store import embeddings
+from backend.api.features.store.embeddings import EMBEDDING_DIM
+from backend.api.features.store.hybrid_search import (
+    hybrid_search,
+    unified_hybrid_search,
+)
+
+# ============================================================================
+# Test Fixtures
+# ============================================================================
+
+
+@pytest.fixture
+def test_content_id() -> str:
+    """Generate unique content ID for test isolation."""
+    return f"test-content-{uuid.uuid4()}"
+
+
+@pytest.fixture
+def test_user_id() -> str:
+    """Generate unique user ID for test isolation."""
+    return f"test-user-{uuid.uuid4()}"
+
+
+@pytest.fixture
+def mock_embedding() -> list[float]:
+    """Generate a mock embedding vector."""
+    # Create a normalized embedding vector
+    import math
+
+    raw = [float(i % 10) / 10.0 for i in range(EMBEDDING_DIM)]
+    # Normalize to unit length (required for cosine similarity)
+    magnitude = math.sqrt(sum(x * x for x in raw))
+    return [x / magnitude for x in raw]
+
+
+@pytest.fixture
+def similar_embedding() -> list[float]:
+    """Generate an embedding similar to mock_embedding."""
+    import math
+
+    # Similar but slightly different values
+    raw = [float(i % 10) / 10.0 + 0.01 for i in range(EMBEDDING_DIM)]
+    magnitude = math.sqrt(sum(x * x for x in raw))
+    return [x / magnitude for x in raw]
+
+
+@pytest.fixture
+def different_embedding() -> list[float]:
+    """Generate an embedding very different from mock_embedding."""
+    import math
+
+    # Reversed pattern to be maximally different
+    raw = [float((EMBEDDING_DIM - i) % 10) / 10.0 for i in range(EMBEDDING_DIM)]
+    magnitude = math.sqrt(sum(x * x for x in raw))
+    return [x / magnitude for x in raw]
+
+
+@pytest.fixture
+async def cleanup_embeddings(
+    server,
+) -> AsyncGenerator[list[tuple[ContentType, str, str | None]], None]:
+    """
+    Fixture that tracks created embeddings and cleans them up after tests.
+
+    Yields a list to which tests can append (content_type, content_id, user_id) tuples.
+    """
+    created_embeddings: list[tuple[ContentType, str, str | None]] = []
+    yield created_embeddings
+
+    # Cleanup all created embeddings
+    for content_type, content_id, user_id in created_embeddings:
+        try:
+            await embeddings.delete_content_embedding(content_type, content_id, user_id)
+        except Exception:
+            pass  # Ignore cleanup errors
+
+
+# ============================================================================
+# store_content_embedding Tests
+# ============================================================================
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_content_embedding_store_agent(
+    server,
+    test_content_id: str,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test storing embedding for STORE_AGENT content type."""
+    # Track for cleanup
+    cleanup_embeddings.append((ContentType.STORE_AGENT, test_content_id, None))
+
+    result = await embeddings.store_content_embedding(
+        content_type=ContentType.STORE_AGENT,
+        content_id=test_content_id,
+        embedding=mock_embedding,
+        searchable_text="AI assistant for productivity tasks",
+        metadata={"name": "Test Agent", "categories": ["productivity"]},
+        user_id=None,  # Store agents are public
+    )
+
+    assert result is True
+
+    # Verify it was stored
+    stored = await embeddings.get_content_embedding(
+        ContentType.STORE_AGENT, test_content_id, user_id=None
+    )
+    assert stored is not None
+    assert stored["contentId"] == test_content_id
+    assert stored["contentType"] == "STORE_AGENT"
+    assert stored["searchableText"] == "AI assistant for productivity tasks"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_content_embedding_block(
+    server,
+    test_content_id: str,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test storing embedding for BLOCK content type."""
+    cleanup_embeddings.append((ContentType.BLOCK, test_content_id, None))
+
+    result = await embeddings.store_content_embedding(
+        content_type=ContentType.BLOCK,
+        content_id=test_content_id,
+        embedding=mock_embedding,
+        searchable_text="HTTP request block for API calls",
+        metadata={"name": "HTTP Request Block"},
+        user_id=None,  # Blocks are public
+    )
+
+    assert result is True
+
+    stored = await embeddings.get_content_embedding(
+        ContentType.BLOCK, test_content_id, user_id=None
+    )
+    assert stored is not None
+    assert stored["contentType"] == "BLOCK"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_content_embedding_documentation(
+    server,
+    test_content_id: str,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test storing embedding for DOCUMENTATION content type."""
+    cleanup_embeddings.append((ContentType.DOCUMENTATION, test_content_id, None))
+
+    result = await embeddings.store_content_embedding(
+        content_type=ContentType.DOCUMENTATION,
+        content_id=test_content_id,
+        embedding=mock_embedding,
+        searchable_text="Getting started guide for AutoGPT platform",
+        metadata={"title": "Getting Started", "url": "/docs/getting-started"},
+        user_id=None,  # Docs are public
+    )
+
+    assert result is True
+
+    stored = await embeddings.get_content_embedding(
+        ContentType.DOCUMENTATION, test_content_id, user_id=None
+    )
+    assert stored is not None
+    assert stored["contentType"] == "DOCUMENTATION"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_content_embedding_upsert(
+    server,
+    test_content_id: str,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test that storing embedding twice updates instead of duplicates."""
+    cleanup_embeddings.append((ContentType.BLOCK, test_content_id, None))
+
+    # Store first time
+    result1 = await embeddings.store_content_embedding(
+        content_type=ContentType.BLOCK,
+        content_id=test_content_id,
+        embedding=mock_embedding,
+        searchable_text="Original text",
+        metadata={"version": 1},
+        user_id=None,
+    )
+    assert result1 is True
+
+    # Store again with different text (upsert)
+    result2 = await embeddings.store_content_embedding(
+        content_type=ContentType.BLOCK,
+        content_id=test_content_id,
+        embedding=mock_embedding,
+        searchable_text="Updated text",
+        metadata={"version": 2},
+        user_id=None,
+    )
+    assert result2 is True
+
+    # Verify only one record with updated text
+    stored = await embeddings.get_content_embedding(
+        ContentType.BLOCK, test_content_id, user_id=None
+    )
+    assert stored is not None
+    assert stored["searchableText"] == "Updated text"
+
+
+# ============================================================================
+# get_content_embedding Tests
+# ============================================================================
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_content_embedding_not_found(server):
+    """Test retrieving non-existent embedding returns None."""
+    result = await embeddings.get_content_embedding(
+        ContentType.STORE_AGENT, "non-existent-id", user_id=None
+    )
+    assert result is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_content_embedding_with_metadata(
+    server,
+    test_content_id: str,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test that metadata is correctly stored and retrieved."""
+    cleanup_embeddings.append((ContentType.STORE_AGENT, test_content_id, None))
+
+    metadata = {
+        "name": "Test Agent",
+        "subHeading": "A test agent",
+        "categories": ["ai", "productivity"],
+        "customField": 123,
+    }
+
+    await embeddings.store_content_embedding(
+        content_type=ContentType.STORE_AGENT,
+        content_id=test_content_id,
+        embedding=mock_embedding,
+        searchable_text="test",
+        metadata=metadata,
+        user_id=None,
+    )
+
+    stored = await embeddings.get_content_embedding(
+        ContentType.STORE_AGENT, test_content_id, user_id=None
+    )
+
+    assert stored is not None
+    assert stored["metadata"]["name"] == "Test Agent"
+    assert stored["metadata"]["categories"] == ["ai", "productivity"]
+    assert stored["metadata"]["customField"] == 123
+
+
+# ============================================================================
+# delete_content_embedding Tests
+# ============================================================================
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_delete_content_embedding(
+    server,
+    test_content_id: str,
+    mock_embedding: list[float],
+):
+    """Test deleting embedding removes it from database."""
+    # Store embedding
+    await embeddings.store_content_embedding(
+        content_type=ContentType.BLOCK,
+        content_id=test_content_id,
+        embedding=mock_embedding,
+        searchable_text="To be deleted",
+        metadata=None,
+        user_id=None,
+    )
+
+    # Verify it exists
+    stored = await embeddings.get_content_embedding(
+        ContentType.BLOCK, test_content_id, user_id=None
+    )
+    assert stored is not None
+
+    # Delete it
+    result = await embeddings.delete_content_embedding(
+        ContentType.BLOCK, test_content_id, user_id=None
+    )
+    assert result is True
+
+    # Verify it's gone
+    stored = await embeddings.get_content_embedding(
+        ContentType.BLOCK, test_content_id, user_id=None
+    )
+    assert stored is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_delete_content_embedding_not_found(server):
+    """Test deleting non-existent embedding doesn't error."""
+    result = await embeddings.delete_content_embedding(
+        ContentType.BLOCK, "non-existent-id", user_id=None
+    )
+    # Should succeed even if nothing to delete
+    assert result is True
+
+
+# ============================================================================
+# unified_hybrid_search Tests
+# ============================================================================
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_unified_hybrid_search_finds_matching_content(
+    server,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test unified search finds content matching the query."""
+    # Create unique content IDs
+    agent_id = f"test-agent-{uuid.uuid4()}"
+    block_id = f"test-block-{uuid.uuid4()}"
+    doc_id = f"test-doc-{uuid.uuid4()}"
+
+    cleanup_embeddings.append((ContentType.STORE_AGENT, agent_id, None))
+    cleanup_embeddings.append((ContentType.BLOCK, block_id, None))
+    cleanup_embeddings.append((ContentType.DOCUMENTATION, doc_id, None))
+
+    # Store embeddings for different content types
+    await embeddings.store_content_embedding(
+        content_type=ContentType.STORE_AGENT,
+        content_id=agent_id,
+        embedding=mock_embedding,
+        searchable_text="AI writing assistant for blog posts",
+        metadata={"name": "Writing Assistant"},
+        user_id=None,
+    )
+
+    await embeddings.store_content_embedding(
+        content_type=ContentType.BLOCK,
+        content_id=block_id,
+        embedding=mock_embedding,
+        searchable_text="Text generation block for creative writing",
+        metadata={"name": "Text Generator"},
+        user_id=None,
+    )
+
+    await embeddings.store_content_embedding(
+        content_type=ContentType.DOCUMENTATION,
+        content_id=doc_id,
+        embedding=mock_embedding,
+        searchable_text="How to use writing blocks in AutoGPT",
+        metadata={"title": "Writing Guide"},
+        user_id=None,
+    )
+
+    # Search for "writing" - should find all three
+    results, total = await unified_hybrid_search(
+        query="writing",
+        page=1,
+        page_size=20,
+    )
+
+    # Should find at least our test content (may find others too)
+    content_ids = [r["content_id"] for r in results]
+    assert agent_id in content_ids or total >= 1  # Lexical search should find it
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_unified_hybrid_search_filter_by_content_type(
+    server,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test unified search can filter by content type."""
+    agent_id = f"test-agent-{uuid.uuid4()}"
+    block_id = f"test-block-{uuid.uuid4()}"
+
+    cleanup_embeddings.append((ContentType.STORE_AGENT, agent_id, None))
+    cleanup_embeddings.append((ContentType.BLOCK, block_id, None))
+
+    # Store both types with same searchable text
+    await embeddings.store_content_embedding(
+        content_type=ContentType.STORE_AGENT,
+        content_id=agent_id,
+        embedding=mock_embedding,
+        searchable_text="unique_search_term_xyz123",
+        metadata={},
+        user_id=None,
+    )
+
+    await embeddings.store_content_embedding(
+        content_type=ContentType.BLOCK,
+        content_id=block_id,
+        embedding=mock_embedding,
+        searchable_text="unique_search_term_xyz123",
+        metadata={},
+        user_id=None,
+    )
+
+    # Search only for BLOCK type
+    results, total = await unified_hybrid_search(
+        query="unique_search_term_xyz123",
+        content_types=[ContentType.BLOCK],
+        page=1,
+        page_size=20,
+    )
+
+    # All results should be BLOCK type
+    for r in results:
+        assert r["content_type"] == "BLOCK"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_unified_hybrid_search_empty_query(server):
+    """Test unified search with empty query returns empty results."""
+    results, total = await unified_hybrid_search(
+        query="",
+        page=1,
+        page_size=20,
+    )
+
+    assert results == []
+    assert total == 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_unified_hybrid_search_pagination(
+    server,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test unified search pagination works correctly."""
+    # Create multiple items
+    content_ids = []
+    for i in range(5):
+        content_id = f"test-pagination-{uuid.uuid4()}"
+        content_ids.append(content_id)
+        cleanup_embeddings.append((ContentType.BLOCK, content_id, None))
+
+        await embeddings.store_content_embedding(
+            content_type=ContentType.BLOCK,
+            content_id=content_id,
+            embedding=mock_embedding,
+            searchable_text=f"pagination test item number {i}",
+            metadata={"index": i},
+            user_id=None,
+        )
+
+    # Get first page
+    page1_results, total1 = await unified_hybrid_search(
+        query="pagination test",
+        content_types=[ContentType.BLOCK],
+        page=1,
+        page_size=2,
+    )
+
+    # Get second page
+    page2_results, total2 = await unified_hybrid_search(
+        query="pagination test",
+        content_types=[ContentType.BLOCK],
+        page=2,
+        page_size=2,
+    )
+
+    # Total should be consistent
+    assert total1 == total2
+
+    # Pages should have different content (if we have enough results)
+    if len(page1_results) > 0 and len(page2_results) > 0:
+        page1_ids = {r["content_id"] for r in page1_results}
+        page2_ids = {r["content_id"] for r in page2_results}
+        # No overlap between pages
+        assert page1_ids.isdisjoint(page2_ids)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_unified_hybrid_search_min_score_filtering(
+    server,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test unified search respects min_score threshold."""
+    content_id = f"test-minscore-{uuid.uuid4()}"
+    cleanup_embeddings.append((ContentType.BLOCK, content_id, None))
+
+    await embeddings.store_content_embedding(
+        content_type=ContentType.BLOCK,
+        content_id=content_id,
+        embedding=mock_embedding,
+        searchable_text="completely unrelated content about bananas",
+        metadata={},
+        user_id=None,
+    )
+
+    # Search with very high min_score - should filter out low relevance
+    results_high, _ = await unified_hybrid_search(
+        query="quantum computing algorithms",
+        content_types=[ContentType.BLOCK],
+        min_score=0.9,  # Very high threshold
+        page=1,
+        page_size=20,
+    )
+
+    # Search with low min_score
+    results_low, _ = await unified_hybrid_search(
+        query="quantum computing algorithms",
+        content_types=[ContentType.BLOCK],
+        min_score=0.01,  # Very low threshold
+        page=1,
+        page_size=20,
+    )
+
+    # High threshold should have fewer or equal results
+    assert len(results_high) <= len(results_low)
+
+
+# ============================================================================
+# hybrid_search (Store Agents) Tests
+# ============================================================================
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_hybrid_search_store_agents_sql_valid(server):
+    """Test that hybrid_search SQL executes without errors."""
+    # This test verifies the SQL is syntactically correct
+    # even if no results are found
+    results, total = await hybrid_search(
+        query="test agent",
+        page=1,
+        page_size=20,
+    )
+
+    # Should not raise - verifies SQL is valid
+    assert isinstance(results, list)
+    assert isinstance(total, int)
+    assert total >= 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_hybrid_search_with_filters(server):
+    """Test hybrid_search with various filter options."""
+    # Test with all filter types
+    results, total = await hybrid_search(
+        query="productivity",
+        featured=True,
+        creators=["test-creator"],
+        category="productivity",
+        page=1,
+        page_size=10,
+    )
+
+    # Should not raise - verifies filter SQL is valid
+    assert isinstance(results, list)
+    assert isinstance(total, int)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_hybrid_search_pagination(server):
+    """Test hybrid_search pagination."""
+    # Page 1
+    results1, total1 = await hybrid_search(
+        query="agent",
+        page=1,
+        page_size=5,
+    )
+
+    # Page 2
+    results2, total2 = await hybrid_search(
+        query="agent",
+        page=2,
+        page_size=5,
+    )
+
+    # Verify SQL executes without error
+    assert isinstance(results1, list)
+    assert isinstance(results2, list)
+    assert isinstance(total1, int)
+    assert isinstance(total2, int)
+
+    # If page 1 has results, total should be > 0
+    # Note: total from page 2 may be 0 if no results on that page (COUNT(*) OVER limitation)
+    if results1:
+        assert total1 > 0
+
+
+# ============================================================================
+# SQL Validity Tests (verify queries don't break)
+# ============================================================================
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_all_content_types_searchable(server):
+    """Test that all content types can be searched without SQL errors."""
+    for content_type in [
+        ContentType.STORE_AGENT,
+        ContentType.BLOCK,
+        ContentType.DOCUMENTATION,
+    ]:
+        results, total = await unified_hybrid_search(
+            query="test",
+            content_types=[content_type],
+            page=1,
+            page_size=10,
+        )
+
+        # Should not raise
+        assert isinstance(results, list)
+        assert isinstance(total, int)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_multiple_content_types_searchable(server):
+    """Test searching multiple content types at once."""
+    results, total = await unified_hybrid_search(
+        query="test",
+        content_types=[ContentType.BLOCK, ContentType.DOCUMENTATION],
+        page=1,
+        page_size=20,
+    )
+
+    # Should not raise
+    assert isinstance(results, list)
+    assert isinstance(total, int)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_search_all_content_types_default(server):
+    """Test searching all content types (default behavior)."""
+    results, total = await unified_hybrid_search(
+        query="test",
+        content_types=None,  # Should search all
+        page=1,
+        page_size=20,
+    )
+
+    # Should not raise
+    assert isinstance(results, list)
+    assert isinstance(total, int)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v", "-s"])

autogpt_platform/backend/backend/api/features/store/embeddings_schema_test.py

@@ -4,12 +4,13 @@ Integration tests for embeddings with schema handling.
 These tests verify that embeddings operations work correctly across different database schemas.
 """
 
-from unittest.mock import AsyncMock, patch
+from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 from prisma.enums import ContentType
 
 from backend.api.features.store import embeddings
+from backend.api.features.store.embeddings import EMBEDDING_DIM
 
 # Schema prefix tests removed - functionality moved to db.raw_with_schema() helper
 
@@ -28,7 +29,7 @@ async def test_store_content_embedding_with_schema():
             result = await embeddings.store_content_embedding(
                 content_type=ContentType.STORE_AGENT,
                 content_id="test-id",
-                embedding=[0.1] * 1536,
+                embedding=[0.1] * EMBEDDING_DIM,
                 searchable_text="test text",
                 metadata={"test": "data"},
                 user_id=None,
@@ -125,84 +126,69 @@ async def test_delete_content_embedding_with_schema():
 @pytest.mark.asyncio(loop_scope="session")
 @pytest.mark.integration
 async def test_get_embedding_stats_with_schema():
-    """Test embedding statistics with proper schema handling."""
-    with patch("backend.data.db.get_database_schema") as mock_schema:
-        mock_schema.return_value = "platform"
+    """Test embedding statistics with proper schema handling via content handlers."""
+    # Mock handler to return stats
+    mock_handler = MagicMock()
+    mock_handler.get_stats = AsyncMock(
+        return_value={
+            "total": 100,
+            "with_embeddings": 80,
+            "without_embeddings": 20,
+        }
+    )
 
-        with patch("prisma.get_client") as mock_get_client:
-            mock_client = AsyncMock()
-            # Mock both query results
-            mock_client.query_raw.side_effect = [
-                [{"count": 100}],  # total_approved
-                [{"count": 80}],  # with_embeddings
-            ]
-            mock_get_client.return_value = mock_client
+    with patch(
+        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
+        {ContentType.STORE_AGENT: mock_handler},
+    ):
+        result = await embeddings.get_embedding_stats()
 
-            result = await embeddings.get_embedding_stats()
+        # Verify handler was called
+        mock_handler.get_stats.assert_called_once()
 
-            # Verify both queries were called
-            assert mock_client.query_raw.call_count == 2
-
-            # Get both SQL queries
-            first_call = mock_client.query_raw.call_args_list[0]
-            second_call = mock_client.query_raw.call_args_list[1]
-
-            first_sql = first_call[0][0]
-            second_sql = second_call[0][0]
-
-            # Verify schema prefix in both queries
-            assert '"platform"."StoreListingVersion"' in first_sql
-            assert '"platform"."StoreListingVersion"' in second_sql
-            assert '"platform"."UnifiedContentEmbedding"' in second_sql
-
-            # Verify results
-            assert result["total_approved"] == 100
-            assert result["with_embeddings"] == 80
-            assert result["without_embeddings"] == 20
-            assert result["coverage_percent"] == 80.0
+        # Verify new result structure
+        assert "by_type" in result
+        assert "totals" in result
+        assert result["totals"]["total"] == 100
+        assert result["totals"]["with_embeddings"] == 80
+        assert result["totals"]["without_embeddings"] == 20
+        assert result["totals"]["coverage_percent"] == 80.0
 
 
 @pytest.mark.asyncio(loop_scope="session")
 @pytest.mark.integration
 async def test_backfill_missing_embeddings_with_schema():
-    """Test backfilling embeddings with proper schema handling."""
-    with patch("backend.data.db.get_database_schema") as mock_schema:
-        mock_schema.return_value = "platform"
+    """Test backfilling embeddings via content handlers."""
+    from backend.api.features.store.content_handlers import ContentItem
 
-        with patch("prisma.get_client") as mock_get_client:
-            mock_client = AsyncMock()
-            # Mock missing embeddings query
-            mock_client.query_raw.return_value = [
-                {
-                    "id": "version-1",
-                    "name": "Test Agent",
-                    "description": "Test description",
-                    "subHeading": "Test heading",
-                    "categories": ["test"],
-                }
-            ]
-            mock_get_client.return_value = mock_client
+    # Create mock content item
+    mock_item = ContentItem(
+        content_id="version-1",
+        content_type=ContentType.STORE_AGENT,
+        searchable_text="Test Agent Test description",
+        metadata={"name": "Test Agent"},
+    )
 
+    # Mock handler
+    mock_handler = MagicMock()
+    mock_handler.get_missing_items = AsyncMock(return_value=[mock_item])
+
+    with patch(
+        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
+        {ContentType.STORE_AGENT: mock_handler},
+    ):
+        with patch(
+            "backend.api.features.store.embeddings.generate_embedding",
+            return_value=[0.1] * EMBEDDING_DIM,
+        ):
             with patch(
-                "backend.api.features.store.embeddings.ensure_embedding"
-            ) as mock_ensure:
-                mock_ensure.return_value = True
-
+                "backend.api.features.store.embeddings.store_content_embedding",
+                return_value=True,
+            ):
                 result = await embeddings.backfill_missing_embeddings(batch_size=10)
 
-                # Verify the query was called
-                assert mock_client.query_raw.called
-
-                # Get the SQL query
-                call_args = mock_client.query_raw.call_args
-                sql_query = call_args[0][0]
-
-                # Verify schema prefix in query
-                assert '"platform"."StoreListingVersion"' in sql_query
-                assert '"platform"."UnifiedContentEmbedding"' in sql_query
-
-                # Verify ensure_embedding was called
-                assert mock_ensure.called
+                # Verify handler was called
+                mock_handler.get_missing_items.assert_called_once_with(10)
 
                 # Verify results
                 assert result["processed"] == 1
@@ -226,7 +212,7 @@ async def test_ensure_content_embedding_with_schema():
             with patch(
                 "backend.api.features.store.embeddings.generate_embedding"
             ) as mock_generate:
-                mock_generate.return_value = [0.1] * 1536
+                mock_generate.return_value = [0.1] * EMBEDDING_DIM
 
                 with patch(
                     "backend.api.features.store.embeddings.store_content_embedding"
@@ -260,7 +246,7 @@ async def test_backward_compatibility_store_embedding():
 
         result = await embeddings.store_embedding(
             version_id="test-version-id",
-            embedding=[0.1] * 1536,
+            embedding=[0.1] * EMBEDDING_DIM,
             tx=None,
         )
 
@@ -315,7 +301,7 @@ async def test_schema_handling_error_cases():
             result = await embeddings.store_content_embedding(
                 content_type=ContentType.STORE_AGENT,
                 content_id="test-id",
-                embedding=[0.1] * 1536,
+                embedding=[0.1] * EMBEDDING_DIM,
                 searchable_text="test",
                 metadata=None,
                 user_id=None,

autogpt_platform/backend/backend/api/features/store/embeddings_test.py

@@ -63,7 +63,7 @@ async def test_generate_embedding_success():
         result = await embeddings.generate_embedding("test text")
 
         assert result is not None
-        assert len(result) == 1536
+        assert len(result) == embeddings.EMBEDDING_DIM
         assert result[0] == 0.1
 
         mock_client.embeddings.create.assert_called_once_with(
@@ -110,7 +110,7 @@ async def test_generate_embedding_text_truncation():
     mock_client = MagicMock()
     mock_response = MagicMock()
     mock_response.data = [MagicMock()]
-    mock_response.data[0].embedding = [0.1] * 1536
+    mock_response.data[0].embedding = [0.1] * embeddings.EMBEDDING_DIM
 
     # Use AsyncMock for async embeddings.create method
     mock_client.embeddings.create = AsyncMock(return_value=mock_response)
@@ -297,72 +297,92 @@ async def test_ensure_embedding_generation_fails(mock_get, mock_generate):
 @pytest.mark.asyncio(loop_scope="session")
 async def test_get_embedding_stats():
     """Test embedding statistics retrieval."""
-    # Mock approved count query and embedded count query
-    mock_approved_result = [{"count": 100}]
-    mock_embedded_result = [{"count": 75}]
+    # Mock handler stats for each content type
+    mock_handler = MagicMock()
+    mock_handler.get_stats = AsyncMock(
+        return_value={
+            "total": 100,
+            "with_embeddings": 75,
+            "without_embeddings": 25,
+        }
+    )
 
+    # Patch the CONTENT_HANDLERS where it's used (in embeddings module)
     with patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        side_effect=[mock_approved_result, mock_embedded_result],
+        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
+        {ContentType.STORE_AGENT: mock_handler},
     ):
         result = await embeddings.get_embedding_stats()
 
-        assert result["total_approved"] == 100
-        assert result["with_embeddings"] == 75
-        assert result["without_embeddings"] == 25
-        assert result["coverage_percent"] == 75.0
+        assert "by_type" in result
+        assert "totals" in result
+        assert result["totals"]["total"] == 100
+        assert result["totals"]["with_embeddings"] == 75
+        assert result["totals"]["without_embeddings"] == 25
+        assert result["totals"]["coverage_percent"] == 75.0
 
 
 @pytest.mark.asyncio(loop_scope="session")
-@patch("backend.api.features.store.embeddings.ensure_embedding")
-async def test_backfill_missing_embeddings_success(mock_ensure):
+@patch("backend.api.features.store.embeddings.store_content_embedding")
+async def test_backfill_missing_embeddings_success(mock_store):
     """Test backfill with successful embedding generation."""
-    # Mock missing embeddings query
-    mock_missing = [
-        {
-            "id": "version-1",
-            "name": "Agent 1",
-            "description": "Description 1",
-            "subHeading": "Heading 1",
-            "categories": ["AI"],
-        },
-        {
-            "id": "version-2",
-            "name": "Agent 2",
-            "description": "Description 2",
-            "subHeading": "Heading 2",
-            "categories": ["Productivity"],
-        },
+    # Mock ContentItem from handlers
+    from backend.api.features.store.content_handlers import ContentItem
+
+    mock_items = [
+        ContentItem(
+            content_id="version-1",
+            content_type=ContentType.STORE_AGENT,
+            searchable_text="Agent 1 Description 1",
+            metadata={"name": "Agent 1"},
+        ),
+        ContentItem(
+            content_id="version-2",
+            content_type=ContentType.STORE_AGENT,
+            searchable_text="Agent 2 Description 2",
+            metadata={"name": "Agent 2"},
+        ),
     ]
 
-    # Mock ensure_embedding to succeed for first, fail for second
-    mock_ensure.side_effect = [True, False]
+    # Mock handler to return missing items
+    mock_handler = MagicMock()
+    mock_handler.get_missing_items = AsyncMock(return_value=mock_items)
+
+    # Mock store_content_embedding to succeed for first, fail for second
+    mock_store.side_effect = [True, False]
 
     with patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=mock_missing,
+        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
+        {ContentType.STORE_AGENT: mock_handler},
     ):
-        result = await embeddings.backfill_missing_embeddings(batch_size=5)
+        with patch(
+            "backend.api.features.store.embeddings.generate_embedding",
+            return_value=[0.1] * embeddings.EMBEDDING_DIM,
+        ):
+            result = await embeddings.backfill_missing_embeddings(batch_size=5)
 
-        assert result["processed"] == 2
-        assert result["success"] == 1
-        assert result["failed"] == 1
-        assert mock_ensure.call_count == 2
+            assert result["processed"] == 2
+            assert result["success"] == 1
+            assert result["failed"] == 1
+            assert mock_store.call_count == 2
 
 
 @pytest.mark.asyncio(loop_scope="session")
 async def test_backfill_missing_embeddings_no_missing():
     """Test backfill when no embeddings are missing."""
+    # Mock handler to return no missing items
+    mock_handler = MagicMock()
+    mock_handler.get_missing_items = AsyncMock(return_value=[])
+
     with patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=[],
+        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
+        {ContentType.STORE_AGENT: mock_handler},
     ):
         result = await embeddings.backfill_missing_embeddings(batch_size=5)
 
         assert result["processed"] == 0
         assert result["success"] == 0
         assert result["failed"] == 0
-        assert result["message"] == "No missing embeddings"
 
 
 @pytest.mark.asyncio(loop_scope="session")

autogpt_platform/backend/backend/api/features/store/hybrid_search.py

@@ -1,16 +1,18 @@
 """
-Hybrid Search for Store Agents
+Unified Hybrid Search
 
 Combines semantic (embedding) search with lexical (tsvector) search
-for improved relevance in marketplace agent discovery.
+for improved relevance across all content types (agents, blocks, docs).
 """
 
 import logging
 from dataclasses import dataclass
-from datetime import datetime
 from typing import Any, Literal
 
+from prisma.enums import ContentType
+
 from backend.api.features.store.embeddings import (
+    EMBEDDING_DIM,
     embed_query,
     embedding_to_vector_string,
 )
@@ -20,17 +22,299 @@ logger = logging.getLogger(__name__)
 
 
 @dataclass
-class HybridSearchWeights:
-    """Weights for combining search signals."""
+class UnifiedSearchWeights:
+    """Weights for unified search (no popularity signal)."""
 
-    semantic: float = 0.30  # Embedding cosine similarity
-    lexical: float = 0.30  # tsvector ts_rank_cd score
-    category: float = 0.20  # Category match boost
-    recency: float = 0.10  # Newer agents ranked higher
-    popularity: float = 0.10  # Agent usage/runs (PageRank-like)
+    semantic: float = 0.40  # Embedding cosine similarity
+    lexical: float = 0.40  # tsvector ts_rank_cd score
+    category: float = 0.10  # Category match boost (for types that have categories)
+    recency: float = 0.10  # Newer content ranked higher
 
     def __post_init__(self):
         """Validate weights are non-negative and sum to approximately 1.0."""
+        total = self.semantic + self.lexical + self.category + self.recency
+
+        if any(
+            w < 0 for w in [self.semantic, self.lexical, self.category, self.recency]
+        ):
+            raise ValueError("All weights must be non-negative")
+
+        if not (0.99 <= total <= 1.01):
+            raise ValueError(f"Weights must sum to ~1.0, got {total:.3f}")
+
+
+# Default weights for unified search
+DEFAULT_UNIFIED_WEIGHTS = UnifiedSearchWeights()
+
+# Minimum relevance score thresholds
+DEFAULT_MIN_SCORE = 0.15  # For unified search (more permissive)
+DEFAULT_STORE_AGENT_MIN_SCORE = 0.20  # For store agent search (original threshold)
+
+
+async def unified_hybrid_search(
+    query: str,
+    content_types: list[ContentType] | None = None,
+    category: str | None = None,
+    page: int = 1,
+    page_size: int = 20,
+    weights: UnifiedSearchWeights | None = None,
+    min_score: float | None = None,
+    user_id: str | None = None,
+) -> tuple[list[dict[str, Any]], int]:
+    """
+    Unified hybrid search across all content types.
+
+    Searches UnifiedContentEmbedding using both semantic (vector) and lexical (tsvector) signals.
+
+    Args:
+        query: Search query string
+        content_types: List of content types to search. Defaults to all public types.
+        category: Filter by category (for content types that support it)
+        page: Page number (1-indexed)
+        page_size: Results per page
+        weights: Custom weights for search signals
+        min_score: Minimum relevance score threshold (0-1)
+        user_id: User ID for searching private content (library agents)
+
+    Returns:
+        Tuple of (results list, total count)
+    """
+    # Validate inputs
+    query = query.strip()
+    if not query:
+        return [], 0
+
+    if page < 1:
+        page = 1
+    if page_size < 1:
+        page_size = 1
+    if page_size > 100:
+        page_size = 100
+
+    if content_types is None:
+        content_types = [
+            ContentType.STORE_AGENT,
+            ContentType.BLOCK,
+            ContentType.DOCUMENTATION,
+        ]
+
+    if weights is None:
+        weights = DEFAULT_UNIFIED_WEIGHTS
+    if min_score is None:
+        min_score = DEFAULT_MIN_SCORE
+
+    offset = (page - 1) * page_size
+
+    # Generate query embedding
+    query_embedding = await embed_query(query)
+
+    # Graceful degradation if embedding unavailable
+    if query_embedding is None or not query_embedding:
+        logger.warning(
+            "Failed to generate query embedding - falling back to lexical-only search. "
+            "Check that openai_internal_api_key is configured and OpenAI API is accessible."
+        )
+        query_embedding = [0.0] * EMBEDDING_DIM
+        # Redistribute semantic weight to lexical
+        total_non_semantic = weights.lexical + weights.category + weights.recency
+        if total_non_semantic > 0:
+            factor = 1.0 / total_non_semantic
+            weights = UnifiedSearchWeights(
+                semantic=0.0,
+                lexical=weights.lexical * factor,
+                category=weights.category * factor,
+                recency=weights.recency * factor,
+            )
+        else:
+            weights = UnifiedSearchWeights(
+                semantic=0.0, lexical=1.0, category=0.0, recency=0.0
+            )
+
+    # Build parameters
+    params: list[Any] = []
+    param_idx = 1
+
+    # Query for lexical search
+    params.append(query)
+    query_param = f"${param_idx}"
+    param_idx += 1
+
+    # Query lowercase for category matching
+    params.append(query.lower())
+    query_lower_param = f"${param_idx}"
+    param_idx += 1
+
+    # Embedding
+    embedding_str = embedding_to_vector_string(query_embedding)
+    params.append(embedding_str)
+    embedding_param = f"${param_idx}"
+    param_idx += 1
+
+    # Content types
+    content_type_values = [ct.value for ct in content_types]
+    params.append(content_type_values)
+    content_types_param = f"${param_idx}"
+    param_idx += 1
+
+    # User ID filter (for private content)
+    user_filter = ""
+    if user_id is not None:
+        params.append(user_id)
+        user_filter = f'AND (uce."userId" = ${param_idx} OR uce."userId" IS NULL)'
+        param_idx += 1
+    else:
+        user_filter = 'AND uce."userId" IS NULL'
+
+    # Weights
+    params.append(weights.semantic)
+    w_semantic = f"${param_idx}"
+    param_idx += 1
+
+    params.append(weights.lexical)
+    w_lexical = f"${param_idx}"
+    param_idx += 1
+
+    params.append(weights.category)
+    w_category = f"${param_idx}"
+    param_idx += 1
+
+    params.append(weights.recency)
+    w_recency = f"${param_idx}"
+    param_idx += 1
+
+    # Min score
+    params.append(min_score)
+    min_score_param = f"${param_idx}"
+    param_idx += 1
+
+    # Pagination
+    params.append(page_size)
+    limit_param = f"${param_idx}"
+    param_idx += 1
+
+    params.append(offset)
+    offset_param = f"${param_idx}"
+    param_idx += 1
+
+    # Unified search query on UnifiedContentEmbedding
+    sql_query = f"""
+        WITH candidates AS (
+            -- Lexical matches (uses GIN index on search column)
+            SELECT uce.id, uce."contentType", uce."contentId"
+            FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
+            WHERE uce."contentType" = ANY({content_types_param}::{{schema_prefix}}"ContentType"[])
+            {user_filter}
+            AND uce.search @@ plainto_tsquery('english', {query_param})
+
+            UNION
+
+            -- Semantic matches (uses HNSW index on embedding)
+            (
+                SELECT uce.id, uce."contentType", uce."contentId"
+                FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
+                WHERE uce."contentType" = ANY({content_types_param}::{{schema_prefix}}"ContentType"[])
+                {user_filter}
+                ORDER BY uce.embedding <=> {embedding_param}::vector
+                LIMIT 200
+            )
+        ),
+        search_scores AS (
+            SELECT
+                uce."contentType" as content_type,
+                uce."contentId" as content_id,
+                uce."searchableText" as searchable_text,
+                uce.metadata,
+                uce."updatedAt" as updated_at,
+                -- Semantic score: cosine similarity (1 - distance)
+                COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
+                -- Lexical score: ts_rank_cd
+                COALESCE(ts_rank_cd(uce.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
+                -- Category match from metadata
+                CASE
+                    WHEN uce.metadata ? 'categories' AND EXISTS (
+                        SELECT 1 FROM jsonb_array_elements_text(uce.metadata->'categories') cat
+                        WHERE LOWER(cat) LIKE '%' || {query_lower_param} || '%'
+                    )
+                    THEN 1.0
+                    ELSE 0.0
+                END as category_score,
+                -- Recency score: linear decay over 90 days
+                GREATEST(0, 1 - EXTRACT(EPOCH FROM (NOW() - uce."updatedAt")) / (90 * 24 * 3600)) as recency_score
+            FROM candidates c
+            INNER JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce ON c.id = uce.id
+        ),
+        max_lexical AS (
+            SELECT GREATEST(MAX(lexical_raw), 0.001) as max_val FROM search_scores
+        ),
+        normalized AS (
+            SELECT
+                ss.*,
+                ss.lexical_raw / ml.max_val as lexical_score
+            FROM search_scores ss
+            CROSS JOIN max_lexical ml
+        ),
+        scored AS (
+            SELECT
+                content_type,
+                content_id,
+                searchable_text,
+                metadata,
+                updated_at,
+                semantic_score,
+                lexical_score,
+                category_score,
+                recency_score,
+                (
+                    {w_semantic} * semantic_score +
+                    {w_lexical} * lexical_score +
+                    {w_category} * category_score +
+                    {w_recency} * recency_score
+                ) as combined_score
+            FROM normalized
+        ),
+        filtered AS (
+            SELECT
+                *,
+                COUNT(*) OVER () as total_count
+            FROM scored
+            WHERE combined_score >= {min_score_param}
+        )
+        SELECT * FROM filtered
+        ORDER BY combined_score DESC
+        LIMIT {limit_param} OFFSET {offset_param}
+    """
+
+    results = await query_raw_with_schema(
+        sql_query, *params, set_public_search_path=True
+    )
+
+    total = results[0]["total_count"] if results else 0
+
+    # Clean up results
+    for result in results:
+        result.pop("total_count", None)
+
+    logger.info(f"Unified hybrid search: {len(results)} results, {total} total")
+
+    return results, total
+
+
+# ============================================================================
+# Store Agent specific search (with full metadata)
+# ============================================================================
+
+
+@dataclass
+class StoreAgentSearchWeights:
+    """Weights for store agent search including popularity."""
+
+    semantic: float = 0.30
+    lexical: float = 0.30
+    category: float = 0.20
+    recency: float = 0.10
+    popularity: float = 0.10
+
+    def __post_init__(self):
         total = (
             self.semantic
             + self.lexical
@@ -38,7 +322,6 @@ class HybridSearchWeights:
             + self.recency
             + self.popularity
         )
-
         if any(
             w < 0
             for w in [
@@ -50,46 +333,11 @@ class HybridSearchWeights:
             ]
         ):
             raise ValueError("All weights must be non-negative")
-
         if not (0.99 <= total <= 1.01):
             raise ValueError(f"Weights must sum to ~1.0, got {total:.3f}")
 
 
-DEFAULT_WEIGHTS = HybridSearchWeights()
-
-# Minimum relevance score threshold - agents below this are filtered out
-# With weights (0.30 semantic + 0.30 lexical + 0.20 category + 0.10 recency + 0.10 popularity):
-# - 0.20 means at least ~60% semantic match OR strong lexical match required
-# - Ensures only genuinely relevant results are returned
-# - Recency/popularity alone (0.10 each) won't pass the threshold
-DEFAULT_MIN_SCORE = 0.20
-
-
-@dataclass
-class HybridSearchResult:
-    """A single search result with score breakdown."""
-
-    slug: str
-    agent_name: str
-    agent_image: str
-    creator_username: str
-    creator_avatar: str
-    sub_heading: str
-    description: str
-    runs: int
-    rating: float
-    categories: list[str]
-    featured: bool
-    is_available: bool
-    updated_at: datetime
-
-    # Score breakdown (for debugging/tuning)
-    combined_score: float
-    semantic_score: float = 0.0
-    lexical_score: float = 0.0
-    category_score: float = 0.0
-    recency_score: float = 0.0
-    popularity_score: float = 0.0
+DEFAULT_STORE_AGENT_WEIGHTS = StoreAgentSearchWeights()
 
 
 async def hybrid_search(
@@ -102,276 +350,263 @@ async def hybrid_search(
     ) = None,
     page: int = 1,
     page_size: int = 20,
-    weights: HybridSearchWeights | None = None,
+    weights: StoreAgentSearchWeights | None = None,
     min_score: float | None = None,
 ) -> tuple[list[dict[str, Any]], int]:
     """
-    Perform hybrid search combining semantic and lexical signals.
+    Hybrid search for store agents with full metadata.
 
-    Args:
-        query: Search query string
-        featured: Filter for featured agents only
-        creators: Filter by creator usernames
-        category: Filter by category
-        sorted_by: Sort order (relevance uses hybrid scoring)
-        page: Page number (1-indexed)
-        page_size: Results per page
-        weights: Custom weights for search signals
-        min_score: Minimum relevance score threshold (0-1). Results below
-                   this score are filtered out. Defaults to DEFAULT_MIN_SCORE.
-
-    Returns:
-        Tuple of (results list, total count). Returns empty list if no
-        results meet the minimum relevance threshold.
+    Uses UnifiedContentEmbedding for search, joins to StoreAgent for metadata.
     """
-    # Validate inputs
     query = query.strip()
     if not query:
-        return [], 0  # Empty query returns no results
+        return [], 0
 
     if page < 1:
         page = 1
     if page_size < 1:
         page_size = 1
-    if page_size > 100:  # Cap at reasonable limit to prevent performance issues
+    if page_size > 100:
         page_size = 100
 
     if weights is None:
-        weights = DEFAULT_WEIGHTS
+        weights = DEFAULT_STORE_AGENT_WEIGHTS
     if min_score is None:
-        min_score = DEFAULT_MIN_SCORE
+        min_score = (
+            DEFAULT_STORE_AGENT_MIN_SCORE  # Use original threshold for store agents
+        )
 
     offset = (page - 1) * page_size
 
     # Generate query embedding
     query_embedding = await embed_query(query)
 
-    # Build WHERE clause conditions
-    where_parts: list[str] = ["sa.is_available = true"]
+    # Graceful degradation
+    if query_embedding is None or not query_embedding:
+        logger.warning(
+            "Failed to generate query embedding - falling back to lexical-only search."
+        )
+        query_embedding = [0.0] * EMBEDDING_DIM
+        total_non_semantic = (
+            weights.lexical + weights.category + weights.recency + weights.popularity
+        )
+        if total_non_semantic > 0:
+            factor = 1.0 / total_non_semantic
+            weights = StoreAgentSearchWeights(
+                semantic=0.0,
+                lexical=weights.lexical * factor,
+                category=weights.category * factor,
+                recency=weights.recency * factor,
+                popularity=weights.popularity * factor,
+            )
+        else:
+            weights = StoreAgentSearchWeights(
+                semantic=0.0, lexical=1.0, category=0.0, recency=0.0, popularity=0.0
+            )
+
+    # Build parameters
     params: list[Any] = []
-    param_index = 1
+    param_idx = 1
 
-    # Add search query for lexical matching
     params.append(query)
-    query_param = f"${param_index}"
-    param_index += 1
+    query_param = f"${param_idx}"
+    param_idx += 1
 
-    # Add lowercased query for category matching
     params.append(query.lower())
-    query_lower_param = f"${param_index}"
-    param_index += 1
+    query_lower_param = f"${param_idx}"
+    param_idx += 1
+
+    embedding_str = embedding_to_vector_string(query_embedding)
+    params.append(embedding_str)
+    embedding_param = f"${param_idx}"
+    param_idx += 1
+
+    # Build WHERE clause for StoreAgent filters
+    where_parts = ["sa.is_available = true"]
 
     if featured:
         where_parts.append("sa.featured = true")
 
     if creators:
-        where_parts.append(f"sa.creator_username = ANY(${param_index})")
         params.append(creators)
-        param_index += 1
+        where_parts.append(f"sa.creator_username = ANY(${param_idx})")
+        param_idx += 1
 
     if category:
-        where_parts.append(f"${param_index} = ANY(sa.categories)")
         params.append(category)
-        param_index += 1
+        where_parts.append(f"${param_idx} = ANY(sa.categories)")
+        param_idx += 1
 
-    # Safe: where_parts only contains hardcoded strings with $N parameter placeholders
-    # No user input is concatenated directly into the SQL string
     where_clause = " AND ".join(where_parts)
 
-    # Embedding is required for hybrid search - fail fast if unavailable
-    if query_embedding is None or not query_embedding:
-        # Log detailed error server-side
-        logger.error(
-            "Failed to generate query embedding. "
-            "Check that openai_internal_api_key is configured and OpenAI API is accessible."
-        )
-        # Raise generic error to client
-        raise ValueError("Search service temporarily unavailable")
-
-    # Add embedding parameter
-    embedding_str = embedding_to_vector_string(query_embedding)
-    params.append(embedding_str)
-    embedding_param = f"${param_index}"
-    param_index += 1
-
-    # Add weight parameters for SQL calculation
+    # Weights
     params.append(weights.semantic)
-    weight_semantic_param = f"${param_index}"
-    param_index += 1
+    w_semantic = f"${param_idx}"
+    param_idx += 1
 
     params.append(weights.lexical)
-    weight_lexical_param = f"${param_index}"
-    param_index += 1
+    w_lexical = f"${param_idx}"
+    param_idx += 1
 
     params.append(weights.category)
-    weight_category_param = f"${param_index}"
-    param_index += 1
+    w_category = f"${param_idx}"
+    param_idx += 1
 
     params.append(weights.recency)
-    weight_recency_param = f"${param_index}"
-    param_index += 1
+    w_recency = f"${param_idx}"
+    param_idx += 1
 
     params.append(weights.popularity)
-    weight_popularity_param = f"${param_index}"
-    param_index += 1
+    w_popularity = f"${param_idx}"
+    param_idx += 1
 
-    # Add min_score parameter
     params.append(min_score)
-    min_score_param = f"${param_index}"
-    param_index += 1
+    min_score_param = f"${param_idx}"
+    param_idx += 1
 
-    # Optimized hybrid search query:
-    # 1. Direct join to UnifiedContentEmbedding via contentId=storeListingVersionId (no redundant JOINs)
-    # 2. UNION approach (deduplicates agents matching both branches)
-    # 3. COUNT(*) OVER() to get total count in single query
-    # 4. Optimized category matching with EXISTS + unnest
-    # 5. Pre-calculated max values for lexical and popularity normalization
-    # 6. Simplified recency calculation with linear decay
-    # 7. Logarithmic popularity scaling to prevent viral agents from dominating
+    params.append(page_size)
+    limit_param = f"${param_idx}"
+    param_idx += 1
+
+    params.append(offset)
+    offset_param = f"${param_idx}"
+    param_idx += 1
+
+    # Query using UnifiedContentEmbedding for search, StoreAgent for metadata
     sql_query = f"""
-            WITH candidates AS (
-                -- Lexical matches (uses GIN index on search column)
-                SELECT sa."storeListingVersionId"
-                FROM {{schema_prefix}}"StoreAgent" sa
-                WHERE {where_clause}
-                AND sa.search @@ plainto_tsquery('english', {query_param})
+        WITH candidates AS (
+            -- Lexical matches via UnifiedContentEmbedding.search
+            SELECT uce."contentId" as "storeListingVersionId"
+            FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
+            INNER JOIN {{schema_prefix}}"StoreAgent" sa
+                ON uce."contentId" = sa."storeListingVersionId"
+            WHERE uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
+            AND uce."userId" IS NULL
+            AND uce.search @@ plainto_tsquery('english', {query_param})
+            AND {where_clause}
 
-                UNION
+            UNION
 
-                -- Semantic matches (uses HNSW index on embedding with KNN)
-                SELECT "storeListingVersionId"
-                FROM (
-                    SELECT sa."storeListingVersionId", uce.embedding
-                    FROM {{schema_prefix}}"StoreAgent" sa
-                    INNER JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce
-                        ON sa."storeListingVersionId" = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
-                    WHERE {where_clause}
-                    ORDER BY uce.embedding <=> {embedding_param}::vector
-                    LIMIT 200
-                ) semantic_results
-            ),
-            search_scores AS (
-                SELECT
-                    sa.slug,
-                    sa.agent_name,
-                    sa.agent_image,
-                    sa.creator_username,
-                    sa.creator_avatar,
-                    sa.sub_heading,
-                    sa.description,
-                    sa.runs,
-                    sa.rating,
-                    sa.categories,
-                    sa.featured,
-                    sa.is_available,
-                    sa.updated_at,
-                    -- Semantic score: cosine similarity (1 - distance)
-                    COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
-                    -- Lexical score: ts_rank_cd (will be normalized later)
-                    COALESCE(ts_rank_cd(sa.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
-                    -- Category match: optimized with unnest for better performance
-                    CASE
-                        WHEN EXISTS (
-                            SELECT 1 FROM unnest(sa.categories) cat
-                            WHERE LOWER(cat) LIKE '%' || {query_lower_param} || '%'
-                        )
-                        THEN 1.0
-                        ELSE 0.0
-                    END as category_score,
-                    -- Recency score: linear decay over 90 days (simpler than exponential)
-                    GREATEST(0, 1 - EXTRACT(EPOCH FROM (NOW() - sa.updated_at)) / (90 * 24 * 3600)) as recency_score,
-                    -- Popularity raw: agent runs count (will be normalized with log scaling)
-                    sa.runs as popularity_raw
-                FROM candidates c
+            -- Semantic matches via UnifiedContentEmbedding.embedding
+            SELECT uce."contentId" as "storeListingVersionId"
+            FROM (
+                SELECT uce."contentId", uce.embedding
+                FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
                 INNER JOIN {{schema_prefix}}"StoreAgent" sa
-                    ON c."storeListingVersionId" = sa."storeListingVersionId"
-                LEFT JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce
-                    ON sa."storeListingVersionId" = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
-            ),
-            max_lexical AS (
-                SELECT MAX(lexical_raw) as max_val FROM search_scores
-            ),
-            max_popularity AS (
-                SELECT MAX(popularity_raw) as max_val FROM search_scores
-            ),
-            normalized AS (
-                SELECT
-                    ss.*,
-                    -- Normalize lexical score by pre-calculated max
-                    CASE
-                        WHEN ml.max_val > 0
-                        THEN ss.lexical_raw / ml.max_val
-                        ELSE 0
-                    END as lexical_score,
-                    -- Normalize popularity with logarithmic scaling to prevent viral agents from dominating
-                    -- LOG(1 + runs) / LOG(1 + max_runs) ensures score is 0-1 range
-                    CASE
-                        WHEN mp.max_val > 0 AND ss.popularity_raw > 0
-                        THEN LN(1 + ss.popularity_raw) / LN(1 + mp.max_val)
-                        ELSE 0
-                    END as popularity_score
-                FROM search_scores ss
-                CROSS JOIN max_lexical ml
-                CROSS JOIN max_popularity mp
-            ),
-            scored AS (
-                SELECT
-                    slug,
-                    agent_name,
-                    agent_image,
-                    creator_username,
-                    creator_avatar,
-                    sub_heading,
-                    description,
-                    runs,
-                    rating,
-                    categories,
-                    featured,
-                    is_available,
-                    updated_at,
-                    semantic_score,
-                    lexical_score,
-                    category_score,
-                    recency_score,
-                    popularity_score,
-                    (
-                        {weight_semantic_param} * semantic_score +
-                        {weight_lexical_param} * lexical_score +
-                        {weight_category_param} * category_score +
-                        {weight_recency_param} * recency_score +
-                        {weight_popularity_param} * popularity_score
-                    ) as combined_score
-                FROM normalized
-            ),
-            filtered AS (
-                SELECT
-                    *,
-                    COUNT(*) OVER () as total_count
-                FROM scored
-                WHERE combined_score >= {min_score_param}
-            )
-            SELECT * FROM filtered
-            ORDER BY combined_score DESC
-            LIMIT ${param_index} OFFSET ${param_index + 1}
+                    ON uce."contentId" = sa."storeListingVersionId"
+                WHERE uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
+                AND uce."userId" IS NULL
+                AND {where_clause}
+                ORDER BY uce.embedding <=> {embedding_param}::vector
+                LIMIT 200
+            ) uce
+        ),
+        search_scores AS (
+            SELECT
+                sa.slug,
+                sa.agent_name,
+                sa.agent_image,
+                sa.creator_username,
+                sa.creator_avatar,
+                sa.sub_heading,
+                sa.description,
+                sa.runs,
+                sa.rating,
+                sa.categories,
+                sa.featured,
+                sa.is_available,
+                sa.updated_at,
+                -- Semantic score
+                COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
+                -- Lexical score (raw, will normalize)
+                COALESCE(ts_rank_cd(uce.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
+                -- Category match
+                CASE
+                    WHEN EXISTS (
+                        SELECT 1 FROM unnest(sa.categories) cat
+                        WHERE LOWER(cat) LIKE '%' || {query_lower_param} || '%'
+                    )
+                    THEN 1.0
+                    ELSE 0.0
+                END as category_score,
+                -- Recency
+                GREATEST(0, 1 - EXTRACT(EPOCH FROM (NOW() - sa.updated_at)) / (90 * 24 * 3600)) as recency_score,
+                -- Popularity (raw)
+                sa.runs as popularity_raw
+            FROM candidates c
+            INNER JOIN {{schema_prefix}}"StoreAgent" sa
+                ON c."storeListingVersionId" = sa."storeListingVersionId"
+            INNER JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce
+                ON sa."storeListingVersionId" = uce."contentId"
+                AND uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
+        ),
+        max_vals AS (
+            SELECT
+                GREATEST(MAX(lexical_raw), 0.001) as max_lexical,
+                GREATEST(MAX(popularity_raw), 1) as max_popularity
+            FROM search_scores
+        ),
+        normalized AS (
+            SELECT
+                ss.*,
+                ss.lexical_raw / mv.max_lexical as lexical_score,
+                CASE
+                    WHEN ss.popularity_raw > 0
+                    THEN LN(1 + ss.popularity_raw) / LN(1 + mv.max_popularity)
+                    ELSE 0
+                END as popularity_score
+            FROM search_scores ss
+            CROSS JOIN max_vals mv
+        ),
+        scored AS (
+            SELECT
+                slug,
+                agent_name,
+                agent_image,
+                creator_username,
+                creator_avatar,
+                sub_heading,
+                description,
+                runs,
+                rating,
+                categories,
+                featured,
+                is_available,
+                updated_at,
+                semantic_score,
+                lexical_score,
+                category_score,
+                recency_score,
+                popularity_score,
+                (
+                    {w_semantic} * semantic_score +
+                    {w_lexical} * lexical_score +
+                    {w_category} * category_score +
+                    {w_recency} * recency_score +
+                    {w_popularity} * popularity_score
+                ) as combined_score
+            FROM normalized
+        ),
+        filtered AS (
+            SELECT *, COUNT(*) OVER () as total_count
+            FROM scored
+            WHERE combined_score >= {min_score_param}
+        )
+        SELECT * FROM filtered
+        ORDER BY combined_score DESC
+        LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    # Add pagination params
-    params.extend([page_size, offset])
-
-    # Execute search query - includes total_count via window function
     results = await query_raw_with_schema(
         sql_query, *params, set_public_search_path=True
     )
 
-    # Extract total count from first result (all rows have same count)
     total = results[0]["total_count"] if results else 0
 
-    # Remove total_count from results before returning
     for result in results:
         result.pop("total_count", None)
 
-    # Log without sensitive query content
-    logger.info(f"Hybrid search: {len(results)} results, {total} total")
+    logger.info(f"Hybrid search (store agents): {len(results)} results, {total} total")
 
     return results, total
 
@@ -381,13 +616,10 @@ async def hybrid_search_simple(
     page: int = 1,
     page_size: int = 20,
 ) -> tuple[list[dict[str, Any]], int]:
-    """
-    Simplified hybrid search for common use cases.
+    """Simplified hybrid search for store agents."""
+    return await hybrid_search(query=query, page=page, page_size=page_size)
 
-    Uses default weights and no filters.
-    """
-    return await hybrid_search(
-        query=query,
-        page=page,
-        page_size=page_size,
-    )
+
+# Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights
+# for existing code that expects the popularity parameter
+HybridSearchWeights = StoreAgentSearchWeights

autogpt_platform/backend/backend/api/features/store/hybrid_search_test.py

@@ -7,8 +7,15 @@ These tests verify that hybrid search works correctly across different database
 from unittest.mock import patch
 
 import pytest
+from prisma.enums import ContentType
 
-from backend.api.features.store.hybrid_search import HybridSearchWeights, hybrid_search
+from backend.api.features.store import embeddings
+from backend.api.features.store.hybrid_search import (
+    HybridSearchWeights,
+    UnifiedSearchWeights,
+    hybrid_search,
+    unified_hybrid_search,
+)
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -49,7 +56,7 @@ async def test_hybrid_search_with_schema_handling():
         with patch(
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
-            mock_embed.return_value = [0.1] * 1536  # Mock embedding
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM  # Mock embedding
 
             results, total = await hybrid_search(
                 query=query,
@@ -85,7 +92,7 @@ async def test_hybrid_search_with_public_schema():
             with patch(
                 "backend.api.features.store.hybrid_search.embed_query"
             ) as mock_embed:
-                mock_embed.return_value = [0.1] * 1536
+                mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
                 results, total = await hybrid_search(
                     query="test",
@@ -116,7 +123,7 @@ async def test_hybrid_search_with_custom_schema():
             with patch(
                 "backend.api.features.store.hybrid_search.embed_query"
             ) as mock_embed:
-                mock_embed.return_value = [0.1] * 1536
+                mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
                 results, total = await hybrid_search(
                     query="test",
@@ -134,22 +141,52 @@ async def test_hybrid_search_with_custom_schema():
 @pytest.mark.asyncio(loop_scope="session")
 @pytest.mark.integration
 async def test_hybrid_search_without_embeddings():
-    """Test hybrid search fails fast when embeddings are unavailable."""
-    # Patch where the function is used, not where it's defined
-    with patch("backend.api.features.store.hybrid_search.embed_query") as mock_embed:
-        # Simulate embedding failure
-        mock_embed.return_value = None
+    """Test hybrid search gracefully degrades when embeddings are unavailable."""
+    # Mock database to return some results
+    mock_results = [
+        {
+            "slug": "test-agent",
+            "agent_name": "Test Agent",
+            "agent_image": "test.png",
+            "creator_username": "creator",
+            "creator_avatar": "avatar.png",
+            "sub_heading": "Test heading",
+            "description": "Test description",
+            "runs": 100,
+            "rating": 4.5,
+            "categories": ["AI"],
+            "featured": False,
+            "is_available": True,
+            "updated_at": "2025-01-01T00:00:00Z",
+            "semantic_score": 0.0,  # Zero because no embedding
+            "lexical_score": 0.5,
+            "category_score": 0.0,
+            "recency_score": 0.1,
+            "popularity_score": 0.2,
+            "combined_score": 0.3,
+            "total_count": 1,
+        }
+    ]
 
-        # Should raise ValueError with helpful message
-        with pytest.raises(ValueError) as exc_info:
-            await hybrid_search(
+    with patch("backend.api.features.store.hybrid_search.embed_query") as mock_embed:
+        with patch(
+            "backend.api.features.store.hybrid_search.query_raw_with_schema"
+        ) as mock_query:
+            # Simulate embedding failure
+            mock_embed.return_value = None
+            mock_query.return_value = mock_results
+
+            # Should NOT raise - graceful degradation
+            results, total = await hybrid_search(
                 query="test",
                 page=1,
                 page_size=20,
             )
 
-        # Verify error message is generic (doesn't leak implementation details)
-        assert "Search service temporarily unavailable" in str(exc_info.value)
+            # Verify it returns results even without embeddings
+            assert len(results) == 1
+            assert results[0]["slug"] == "test-agent"
+            assert total == 1
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -164,7 +201,7 @@ async def test_hybrid_search_with_filters():
         with patch(
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
-            mock_embed.return_value = [0.1] * 1536
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
             # Test with featured filter
             results, total = await hybrid_search(
@@ -204,7 +241,7 @@ async def test_hybrid_search_weights():
         with patch(
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
-            mock_embed.return_value = [0.1] * 1536
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
             results, total = await hybrid_search(
                 query="test",
@@ -248,7 +285,7 @@ async def test_hybrid_search_min_score_filtering():
         with patch(
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
-            mock_embed.return_value = [0.1] * 1536
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
             # Test with custom min_score
             results, total = await hybrid_search(
@@ -283,7 +320,7 @@ async def test_hybrid_search_pagination():
         with patch(
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
-            mock_embed.return_value = [0.1] * 1536
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
             # Test page 2 with page_size 10
             results, total = await hybrid_search(
@@ -317,7 +354,7 @@ async def test_hybrid_search_error_handling():
         with patch(
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
-            mock_embed.return_value = [0.1] * 1536
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
             # Should raise exception
             with pytest.raises(Exception) as exc_info:
@@ -330,5 +367,301 @@ async def test_hybrid_search_error_handling():
             assert "Database connection error" in str(exc_info.value)
 
 
+# =============================================================================
+# Unified Hybrid Search Tests
+# =============================================================================
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_basic():
+    """Test basic unified hybrid search across all content types."""
+    mock_results = [
+        {
+            "content_type": "STORE_AGENT",
+            "content_id": "agent-1",
+            "searchable_text": "Test Agent Description",
+            "metadata": {"name": "Test Agent"},
+            "updated_at": "2025-01-01T00:00:00Z",
+            "semantic_score": 0.7,
+            "lexical_score": 0.8,
+            "category_score": 0.5,
+            "recency_score": 0.3,
+            "combined_score": 0.6,
+            "total_count": 2,
+        },
+        {
+            "content_type": "BLOCK",
+            "content_id": "block-1",
+            "searchable_text": "Test Block Description",
+            "metadata": {"name": "Test Block"},
+            "updated_at": "2025-01-01T00:00:00Z",
+            "semantic_score": 0.6,
+            "lexical_score": 0.7,
+            "category_score": 0.4,
+            "recency_score": 0.2,
+            "combined_score": 0.5,
+            "total_count": 2,
+        },
+    ]
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_query.return_value = mock_results
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
+
+            results, total = await unified_hybrid_search(
+                query="test",
+                page=1,
+                page_size=20,
+            )
+
+            assert len(results) == 2
+            assert total == 2
+            assert results[0]["content_type"] == "STORE_AGENT"
+            assert results[1]["content_type"] == "BLOCK"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_filter_by_content_type():
+    """Test unified search filtering by specific content types."""
+    mock_results = [
+        {
+            "content_type": "BLOCK",
+            "content_id": "block-1",
+            "searchable_text": "Test Block",
+            "metadata": {},
+            "updated_at": "2025-01-01T00:00:00Z",
+            "semantic_score": 0.7,
+            "lexical_score": 0.8,
+            "category_score": 0.0,
+            "recency_score": 0.3,
+            "combined_score": 0.5,
+            "total_count": 1,
+        },
+    ]
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_query.return_value = mock_results
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
+
+            results, total = await unified_hybrid_search(
+                query="test",
+                content_types=[ContentType.BLOCK],
+                page=1,
+                page_size=20,
+            )
+
+            # Verify content_types parameter was passed correctly
+            call_args = mock_query.call_args
+            params = call_args[0][1:]
+            # The content types should be in the params as a list
+            assert ["BLOCK"] in params
+
+            assert len(results) == 1
+            assert total == 1
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_with_user_id():
+    """Test unified search with user_id for private content."""
+    mock_results = [
+        {
+            "content_type": "STORE_AGENT",
+            "content_id": "agent-1",
+            "searchable_text": "My Private Agent",
+            "metadata": {},
+            "updated_at": "2025-01-01T00:00:00Z",
+            "semantic_score": 0.7,
+            "lexical_score": 0.8,
+            "category_score": 0.0,
+            "recency_score": 0.3,
+            "combined_score": 0.6,
+            "total_count": 1,
+        },
+    ]
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_query.return_value = mock_results
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
+
+            results, total = await unified_hybrid_search(
+                query="test",
+                user_id="user-123",
+                page=1,
+                page_size=20,
+            )
+
+            # Verify SQL contains user_id filter
+            call_args = mock_query.call_args
+            sql_template = call_args[0][0]
+            params = call_args[0][1:]
+
+            assert 'uce."userId"' in sql_template
+            assert "user-123" in params
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_custom_weights():
+    """Test unified search with custom weights."""
+    custom_weights = UnifiedSearchWeights(
+        semantic=0.6,
+        lexical=0.2,
+        category=0.1,
+        recency=0.1,
+    )
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_query.return_value = []
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
+
+            results, total = await unified_hybrid_search(
+                query="test",
+                weights=custom_weights,
+                page=1,
+                page_size=20,
+            )
+
+            # Verify custom weights are in parameters
+            call_args = mock_query.call_args
+            params = call_args[0][1:]
+
+            assert 0.6 in params  # semantic weight
+            assert 0.2 in params  # lexical weight
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_graceful_degradation():
+    """Test unified search gracefully degrades when embeddings unavailable."""
+    mock_results = [
+        {
+            "content_type": "DOCUMENTATION",
+            "content_id": "doc-1",
+            "searchable_text": "API Documentation",
+            "metadata": {},
+            "updated_at": "2025-01-01T00:00:00Z",
+            "semantic_score": 0.0,  # Zero because no embedding
+            "lexical_score": 0.8,
+            "category_score": 0.0,
+            "recency_score": 0.2,
+            "combined_score": 0.5,
+            "total_count": 1,
+        },
+    ]
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_query.return_value = mock_results
+            mock_embed.return_value = None  # Embedding failure
+
+            # Should NOT raise - graceful degradation
+            results, total = await unified_hybrid_search(
+                query="test",
+                page=1,
+                page_size=20,
+            )
+
+            assert len(results) == 1
+            assert total == 1
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_empty_query():
+    """Test unified search with empty query returns empty results."""
+    results, total = await unified_hybrid_search(
+        query="",
+        page=1,
+        page_size=20,
+    )
+
+    assert results == []
+    assert total == 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_pagination():
+    """Test unified search pagination."""
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_query.return_value = []
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
+
+            results, total = await unified_hybrid_search(
+                query="test",
+                page=3,
+                page_size=15,
+            )
+
+            # Verify pagination parameters (last two params are LIMIT and OFFSET)
+            call_args = mock_query.call_args
+            params = call_args[0]
+
+            limit = params[-2]
+            offset = params[-1]
+
+            assert limit == 15  # page_size
+            assert offset == 30  # (page - 1) * page_size = (3 - 1) * 15
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_schema_prefix():
+    """Test unified search uses schema_prefix placeholder."""
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_query.return_value = []
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
+
+            await unified_hybrid_search(
+                query="test",
+                page=1,
+                page_size=20,
+            )
+
+            call_args = mock_query.call_args
+            sql_template = call_args[0][0]
+
+            # Verify schema_prefix placeholder is used for table references
+            assert "{schema_prefix}" in sql_template
+            assert '"UnifiedContentEmbedding"' in sql_template
+
+
 if __name__ == "__main__":
     pytest.main([__file__, "-v", "-s"])

autogpt_platform/backend/backend/api/features/store/model.py

@@ -221,3 +221,23 @@ class ReviewSubmissionRequest(pydantic.BaseModel):
     is_approved: bool
     comments: str  # External comments visible to creator
     internal_comments: str | None = None  # Private admin notes
+
+
+class UnifiedSearchResult(pydantic.BaseModel):
+    """A single result from unified hybrid search across all content types."""
+
+    content_type: str  # STORE_AGENT, BLOCK, DOCUMENTATION
+    content_id: str
+    searchable_text: str
+    metadata: dict | None = None
+    updated_at: datetime.datetime | None = None
+    combined_score: float | None = None
+    semantic_score: float | None = None
+    lexical_score: float | None = None
+
+
+class UnifiedSearchResponse(pydantic.BaseModel):
+    """Response model for unified search across all content types."""
+
+    results: list[UnifiedSearchResult]
+    pagination: Pagination

autogpt_platform/backend/backend/api/features/store/routes.py

@@ -7,12 +7,15 @@ from typing import Literal
 import autogpt_libs.auth
 import fastapi
 import fastapi.responses
+import prisma.enums
 
 import backend.data.graph
 import backend.util.json
+from backend.util.models import Pagination
 
 from . import cache as store_cache
 from . import db as store_db
+from . import hybrid_search as store_hybrid_search
 from . import image_gen as store_image_gen
 from . import media as store_media
 from . import model as store_model
@@ -146,6 +149,102 @@ async def get_agents(
     return agents
 
 
+##############################################
+############### Search Endpoints #############
+##############################################
+
+
+@router.get(
+    "/search",
+    summary="Unified search across all content types",
+    tags=["store", "public"],
+    response_model=store_model.UnifiedSearchResponse,
+)
+async def unified_search(
+    query: str,
+    content_types: list[str] | None = fastapi.Query(
+        default=None,
+        description="Content types to search: STORE_AGENT, BLOCK, DOCUMENTATION. If not specified, searches all.",
+    ),
+    page: int = 1,
+    page_size: int = 20,
+    user_id: str | None = fastapi.Security(
+        autogpt_libs.auth.get_optional_user_id, use_cache=False
+    ),
+):
+    """
+    Search across all content types (store agents, blocks, documentation) using hybrid search.
+
+    Combines semantic (embedding-based) and lexical (text-based) search for best results.
+
+    Args:
+        query: The search query string
+        content_types: Optional list of content types to filter by (STORE_AGENT, BLOCK, DOCUMENTATION)
+        page: Page number for pagination (default 1)
+        page_size: Number of results per page (default 20)
+        user_id: Optional authenticated user ID (for user-scoped content in future)
+
+    Returns:
+        UnifiedSearchResponse: Paginated list of search results with relevance scores
+    """
+    if page < 1:
+        raise fastapi.HTTPException(
+            status_code=422, detail="Page must be greater than 0"
+        )
+
+    if page_size < 1:
+        raise fastapi.HTTPException(
+            status_code=422, detail="Page size must be greater than 0"
+        )
+
+    # Convert string content types to enum
+    content_type_enums: list[prisma.enums.ContentType] | None = None
+    if content_types:
+        try:
+            content_type_enums = [prisma.enums.ContentType(ct) for ct in content_types]
+        except ValueError as e:
+            raise fastapi.HTTPException(
+                status_code=422,
+                detail=f"Invalid content type. Valid values: STORE_AGENT, BLOCK, DOCUMENTATION. Error: {e}",
+            )
+
+    # Perform unified hybrid search
+    results, total = await store_hybrid_search.unified_hybrid_search(
+        query=query,
+        content_types=content_type_enums,
+        user_id=user_id,
+        page=page,
+        page_size=page_size,
+    )
+
+    # Convert results to response model
+    search_results = [
+        store_model.UnifiedSearchResult(
+            content_type=r["content_type"],
+            content_id=r["content_id"],
+            searchable_text=r.get("searchable_text", ""),
+            metadata=r.get("metadata"),
+            updated_at=r.get("updated_at"),
+            combined_score=r.get("combined_score"),
+            semantic_score=r.get("semantic_score"),
+            lexical_score=r.get("lexical_score"),
+        )
+        for r in results
+    ]
+
+    total_pages = (total + page_size - 1) // page_size if total > 0 else 0
+
+    return store_model.UnifiedSearchResponse(
+        results=search_results,
+        pagination=Pagination(
+            total_items=total,
+            total_pages=total_pages,
+            current_page=page,
+            page_size=page_size,
+        ),
+    )
+
+
 @router.get(
     "/agents/{username}/{agent_name}",
     summary="Get specific agent",

autogpt_platform/backend/backend/api/features/store/semantic_search_test.py

@@ -0,0 +1,272 @@
+"""Tests for the semantic_search function."""
+
+import pytest
+from prisma.enums import ContentType
+
+from backend.api.features.store.embeddings import EMBEDDING_DIM, semantic_search
+
+
+@pytest.mark.asyncio
+async def test_search_blocks_only(mocker):
+    """Test searching only BLOCK content type."""
+    # Mock embed_query to return a test embedding
+    mock_embedding = [0.1] * EMBEDDING_DIM
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=mock_embedding,
+    )
+
+    # Mock query_raw_with_schema to return test results
+    mock_results = [
+        {
+            "content_id": "block-123",
+            "content_type": "BLOCK",
+            "searchable_text": "Calculator Block - Performs arithmetic operations",
+            "metadata": {"name": "Calculator", "categories": ["Math"]},
+            "similarity": 0.85,
+        }
+    ]
+    mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_results,
+    )
+
+    results = await semantic_search(
+        query="calculate numbers",
+        content_types=[ContentType.BLOCK],
+    )
+
+    assert len(results) == 1
+    assert results[0]["content_type"] == "BLOCK"
+    assert results[0]["content_id"] == "block-123"
+    assert results[0]["similarity"] == 0.85
+
+
+@pytest.mark.asyncio
+async def test_search_multiple_content_types(mocker):
+    """Test searching multiple content types simultaneously."""
+    mock_embedding = [0.1] * EMBEDDING_DIM
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=mock_embedding,
+    )
+
+    mock_results = [
+        {
+            "content_id": "block-123",
+            "content_type": "BLOCK",
+            "searchable_text": "Calculator Block",
+            "metadata": {},
+            "similarity": 0.85,
+        },
+        {
+            "content_id": "doc-456",
+            "content_type": "DOCUMENTATION",
+            "searchable_text": "How to use Calculator",
+            "metadata": {},
+            "similarity": 0.75,
+        },
+    ]
+    mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_results,
+    )
+
+    results = await semantic_search(
+        query="calculator",
+        content_types=[ContentType.BLOCK, ContentType.DOCUMENTATION],
+    )
+
+    assert len(results) == 2
+    assert results[0]["content_type"] == "BLOCK"
+    assert results[1]["content_type"] == "DOCUMENTATION"
+
+
+@pytest.mark.asyncio
+async def test_search_with_min_similarity_threshold(mocker):
+    """Test that results below min_similarity are filtered out."""
+    mock_embedding = [0.1] * EMBEDDING_DIM
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=mock_embedding,
+    )
+
+    # Only return results above 0.7 similarity
+    mock_results = [
+        {
+            "content_id": "block-123",
+            "content_type": "BLOCK",
+            "searchable_text": "Calculator Block",
+            "metadata": {},
+            "similarity": 0.85,
+        }
+    ]
+    mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_results,
+    )
+
+    results = await semantic_search(
+        query="calculate",
+        content_types=[ContentType.BLOCK],
+        min_similarity=0.7,
+    )
+
+    assert len(results) == 1
+    assert results[0]["similarity"] >= 0.7
+
+
+@pytest.mark.asyncio
+async def test_search_fallback_to_lexical(mocker):
+    """Test fallback to lexical search when embeddings fail."""
+    # Mock embed_query to return None (embeddings unavailable)
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=None,
+    )
+
+    mock_lexical_results = [
+        {
+            "content_id": "block-123",
+            "content_type": "BLOCK",
+            "searchable_text": "Calculator Block performs calculations",
+            "metadata": {},
+            "similarity": 0.0,
+        }
+    ]
+    mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_lexical_results,
+    )
+
+    results = await semantic_search(
+        query="calculator",
+        content_types=[ContentType.BLOCK],
+    )
+
+    assert len(results) == 1
+    assert results[0]["similarity"] == 0.0  # Lexical search returns 0 similarity
+
+
+@pytest.mark.asyncio
+async def test_search_empty_query():
+    """Test that empty query returns no results."""
+    results = await semantic_search(query="")
+    assert results == []
+
+    results = await semantic_search(query="   ")
+    assert results == []
+
+
+@pytest.mark.asyncio
+async def test_search_with_user_id_filter(mocker):
+    """Test searching with user_id filter for private content."""
+    mock_embedding = [0.1] * EMBEDDING_DIM
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=mock_embedding,
+    )
+
+    mock_results = [
+        {
+            "content_id": "agent-789",
+            "content_type": "LIBRARY_AGENT",
+            "searchable_text": "My Custom Agent",
+            "metadata": {},
+            "similarity": 0.9,
+        }
+    ]
+    mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_results,
+    )
+
+    results = await semantic_search(
+        query="custom agent",
+        content_types=[ContentType.LIBRARY_AGENT],
+        user_id="user-123",
+    )
+
+    assert len(results) == 1
+    assert results[0]["content_type"] == "LIBRARY_AGENT"
+
+
+@pytest.mark.asyncio
+async def test_search_limit_parameter(mocker):
+    """Test that limit parameter correctly limits results."""
+    mock_embedding = [0.1] * EMBEDDING_DIM
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=mock_embedding,
+    )
+
+    # Return 5 results
+    mock_results = [
+        {
+            "content_id": f"block-{i}",
+            "content_type": "BLOCK",
+            "searchable_text": f"Block {i}",
+            "metadata": {},
+            "similarity": 0.8,
+        }
+        for i in range(5)
+    ]
+    mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_results,
+    )
+
+    results = await semantic_search(
+        query="block",
+        content_types=[ContentType.BLOCK],
+        limit=5,
+    )
+
+    assert len(results) == 5
+
+
+@pytest.mark.asyncio
+async def test_search_default_content_types(mocker):
+    """Test that default content_types includes BLOCK, STORE_AGENT, and DOCUMENTATION."""
+    mock_embedding = [0.1] * EMBEDDING_DIM
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=mock_embedding,
+    )
+
+    mock_query_raw = mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=[],
+    )
+
+    await semantic_search(query="test")
+
+    # Check that the SQL query includes all three default content types
+    call_args = mock_query_raw.call_args
+    assert "BLOCK" in str(call_args)
+    assert "STORE_AGENT" in str(call_args)
+    assert "DOCUMENTATION" in str(call_args)
+
+
+@pytest.mark.asyncio
+async def test_search_handles_database_error(mocker):
+    """Test that database errors are handled gracefully."""
+    mock_embedding = [0.1] * EMBEDDING_DIM
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=mock_embedding,
+    )
+
+    # Simulate database error
+    mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        side_effect=Exception("Database connection failed"),
+    )
+
+    results = await semantic_search(
+        query="test",
+        content_types=[ContentType.BLOCK],
+    )
+
+    # Should return empty list on error
+    assert results == []

autogpt_platform/backend/backend/executor/database.py

@@ -9,6 +9,7 @@ from backend.api.features.library.db import (
 from backend.api.features.store.db import get_store_agent_details, get_store_agents
 from backend.api.features.store.embeddings import (
     backfill_missing_embeddings,
+    cleanup_orphaned_embeddings,
     get_embedding_stats,
 )
 from backend.data import db
@@ -221,6 +222,7 @@ class DatabaseManager(AppService):
     # Store Embeddings
     get_embedding_stats = _(get_embedding_stats)
     backfill_missing_embeddings = _(backfill_missing_embeddings)
+    cleanup_orphaned_embeddings = _(cleanup_orphaned_embeddings)
 
     # Summary data - async
     get_user_execution_summary_data = _(get_user_execution_summary_data)
@@ -276,6 +278,7 @@ class DatabaseManagerClient(AppServiceClient):
     # Store Embeddings
     get_embedding_stats = _(d.get_embedding_stats)
     backfill_missing_embeddings = _(d.backfill_missing_embeddings)
+    cleanup_orphaned_embeddings = _(d.cleanup_orphaned_embeddings)
 
 
 class DatabaseManagerAsyncClient(AppServiceClient):

autogpt_platform/backend/backend/executor/scheduler.py

@@ -28,6 +28,7 @@ from backend.data.auth.oauth import cleanup_expired_oauth_tokens
 from backend.data.block import BlockInput
 from backend.data.execution import GraphExecutionWithNodes
 from backend.data.model import CredentialsMetaInput
+from backend.data.onboarding import increment_onboarding_runs
 from backend.executor import utils as execution_utils
 from backend.monitoring import (
     NotificationJobArgs,
@@ -156,6 +157,7 @@ async def _execute_graph(**kwargs):
             inputs=args.input_data,
             graph_credentials_inputs=args.input_credentials,
         )
+        await increment_onboarding_runs(args.user_id)
         elapsed = asyncio.get_event_loop().time() - start_time
         logger.info(
             f"Graph execution started with ID {graph_exec.id} for graph {args.graph_id} "
@@ -255,14 +257,14 @@ def execution_accuracy_alerts():
 
 def ensure_embeddings_coverage():
     """
-    Ensure approved store agents have embeddings for hybrid search.
+    Ensure all content types (store agents, blocks, docs) have embeddings for search.
 
-    Processes ALL missing embeddings in batches of 10 until 100% coverage.
-    Missing embeddings = agents invisible in hybrid search.
+    Processes ALL missing embeddings in batches of 10 per content type until 100% coverage.
+    Missing embeddings = content invisible in hybrid search.
 
     Schedule: Runs every 6 hours (balanced between coverage and API costs).
-    - Catches agents approved between scheduled runs
-    - Batch size 10: gradual processing to avoid rate limits
+    - Catches new content added between scheduled runs
+    - Batch size 10 per content type: gradual processing to avoid rate limits
     - Manual trigger available via execute_ensure_embeddings_coverage endpoint
     """
     db_client = get_database_manager_client()
@@ -273,51 +275,91 @@ def ensure_embeddings_coverage():
         logger.error(
             f"Failed to get embedding stats: {stats['error']} - skipping backfill"
         )
-        return {"processed": 0, "success": 0, "failed": 0, "error": stats["error"]}
+        return {
+            "backfill": {"processed": 0, "success": 0, "failed": 0},
+            "cleanup": {"deleted": 0},
+            "error": stats["error"],
+        }
 
-    if stats["without_embeddings"] == 0:
-        logger.info("All approved agents have embeddings, skipping backfill")
-        return {"processed": 0, "success": 0, "failed": 0}
-
-    logger.info(
-        f"Found {stats['without_embeddings']} agents without embeddings "
-        f"({stats['coverage_percent']}% coverage) - processing all"
-    )
+    # Extract totals from new stats structure
+    totals = stats.get("totals", {})
+    without_embeddings = totals.get("without_embeddings", 0)
+    coverage_percent = totals.get("coverage_percent", 0)
 
     total_processed = 0
     total_success = 0
     total_failed = 0
 
-    # Process in batches until no more missing embeddings
-    while True:
-        result = db_client.backfill_missing_embeddings(batch_size=10)
+    if without_embeddings == 0:
+        logger.info("All content has embeddings, skipping backfill")
+    else:
+        # Log per-content-type stats for visibility
+        by_type = stats.get("by_type", {})
+        for content_type, type_stats in by_type.items():
+            if type_stats.get("without_embeddings", 0) > 0:
+                logger.info(
+                    f"{content_type}: {type_stats['without_embeddings']} items without embeddings "
+                    f"({type_stats['coverage_percent']}% coverage)"
+                )
 
-        total_processed += result["processed"]
-        total_success += result["success"]
-        total_failed += result["failed"]
+        logger.info(
+            f"Total: {without_embeddings} items without embeddings "
+            f"({coverage_percent}% coverage) - processing all"
+        )
 
-        if result["processed"] == 0:
-            # No more missing embeddings
-            break
+        # Process in batches until no more missing embeddings
+        while True:
+            result = db_client.backfill_missing_embeddings(batch_size=10)
 
-        if result["success"] == 0 and result["processed"] > 0:
-            # All attempts in this batch failed - stop to avoid infinite loop
-            logger.error(
-                f"All {result['processed']} embedding attempts failed - stopping backfill"
-            )
-            break
+            total_processed += result["processed"]
+            total_success += result["success"]
+            total_failed += result["failed"]
 
-        # Small delay between batches to avoid rate limits
-        time.sleep(1)
+            if result["processed"] == 0:
+                # No more missing embeddings
+                break
+
+            if result["success"] == 0 and result["processed"] > 0:
+                # All attempts in this batch failed - stop to avoid infinite loop
+                logger.error(
+                    f"All {result['processed']} embedding attempts failed - stopping backfill"
+                )
+                break
+
+            # Small delay between batches to avoid rate limits
+            time.sleep(1)
+
+        logger.info(
+            f"Embedding backfill completed: {total_success}/{total_processed} succeeded, "
+            f"{total_failed} failed"
+        )
+
+    # Clean up orphaned embeddings for blocks and docs
+    logger.info("Running cleanup for orphaned embeddings (blocks/docs)...")
+    cleanup_result = db_client.cleanup_orphaned_embeddings()
+    cleanup_totals = cleanup_result.get("totals", {})
+    cleanup_deleted = cleanup_totals.get("deleted", 0)
+
+    if cleanup_deleted > 0:
+        logger.info(f"Cleanup completed: deleted {cleanup_deleted} orphaned embeddings")
+        by_type = cleanup_result.get("by_type", {})
+        for content_type, type_result in by_type.items():
+            if type_result.get("deleted", 0) > 0:
+                logger.info(
+                    f"{content_type}: deleted {type_result['deleted']} orphaned embeddings"
+                )
+    else:
+        logger.info("Cleanup completed: no orphaned embeddings found")
 
-    logger.info(
-        f"Embedding backfill completed: {total_success}/{total_processed} succeeded, "
-        f"{total_failed} failed"
-    )
     return {
-        "processed": total_processed,
-        "success": total_success,
-        "failed": total_failed,
+        "backfill": {
+            "processed": total_processed,
+            "success": total_success,
+            "failed": total_failed,
+        },
+        "cleanup": {
+            "deleted": cleanup_deleted,
+        },
     }
 
 

autogpt_platform/backend/migrations/20260109181714_add_docs_embedding/migration.sql

@@ -43,4 +43,6 @@ CREATE UNIQUE INDEX "UnifiedContentEmbedding_contentType_contentId_userId_key" O
 -- CreateIndex
 -- HNSW index for fast vector similarity search on embeddings
 -- Uses cosine distance operator (<=>), which matches the query in hybrid_search.py
+-- Note: Drop first in case Prisma created a btree index (Prisma doesn't support HNSW)
+DROP INDEX IF EXISTS "UnifiedContentEmbedding_embedding_idx";
 CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" public.vector_cosine_ops);

autogpt_platform/backend/migrations/20260115200000_add_unified_search_tsvector/migration.sql

@@ -0,0 +1,35 @@
+-- Add tsvector search column to UnifiedContentEmbedding for unified full-text search
+-- This enables hybrid search (semantic + lexical) across all content types
+
+-- Add search column (IF NOT EXISTS for idempotency)
+ALTER TABLE "UnifiedContentEmbedding" ADD COLUMN IF NOT EXISTS "search" tsvector DEFAULT ''::tsvector;
+
+-- Create GIN index for fast full-text search
+-- No @@index in schema.prisma - Prisma may generate DROP INDEX on migrate dev
+-- If that happens, just let it drop and this migration will recreate it, or manually re-run:
+--   CREATE INDEX IF NOT EXISTS "UnifiedContentEmbedding_search_idx" ON "UnifiedContentEmbedding" USING GIN ("search");
+DROP INDEX IF EXISTS "UnifiedContentEmbedding_search_idx";
+CREATE INDEX "UnifiedContentEmbedding_search_idx" ON "UnifiedContentEmbedding" USING GIN ("search");
+
+-- Drop existing trigger/function if exists
+DROP TRIGGER IF EXISTS "update_unified_tsvector" ON "UnifiedContentEmbedding";
+DROP FUNCTION IF EXISTS update_unified_tsvector_column();
+
+-- Create function to auto-update tsvector from searchableText
+CREATE OR REPLACE FUNCTION update_unified_tsvector_column() RETURNS TRIGGER AS $$
+BEGIN
+  NEW.search := to_tsvector('english', COALESCE(NEW."searchableText", ''));
+  RETURN NEW;
+END;
+$$ LANGUAGE plpgsql SECURITY DEFINER SET search_path = platform, pg_temp;
+
+-- Create trigger to auto-update search column on insert/update
+CREATE TRIGGER "update_unified_tsvector"
+BEFORE INSERT OR UPDATE ON "UnifiedContentEmbedding"
+FOR EACH ROW
+EXECUTE FUNCTION update_unified_tsvector_column();
+
+-- Backfill existing rows
+UPDATE "UnifiedContentEmbedding"
+SET search = to_tsvector('english', COALESCE("searchableText", ''))
+WHERE search IS NULL OR search = ''::tsvector;

autogpt_platform/backend/migrations/20260115210000_remove_storelistingversion_search/migration.sql

@@ -0,0 +1,90 @@
+-- Remove the old search column from StoreListingVersion
+-- This column has been replaced by UnifiedContentEmbedding.search
+-- which provides unified hybrid search across all content types
+
+-- First drop the dependent view
+DROP VIEW IF EXISTS "StoreAgent";
+
+-- Drop the trigger and function for old search column
+-- The original trigger was created in 20251016093049_add_full_text_search
+DROP TRIGGER IF EXISTS "update_tsvector" ON "StoreListingVersion";
+DROP FUNCTION IF EXISTS update_tsvector_column();
+
+-- Drop the index
+DROP INDEX IF EXISTS "StoreListingVersion_search_idx";
+
+-- NOTE: Keeping search column for now to allow easy revert if needed
+-- Uncomment to fully remove once migration is verified in production:
+-- ALTER TABLE "StoreListingVersion" DROP COLUMN IF EXISTS "search";
+
+-- Recreate the StoreAgent view WITHOUT the search column
+-- (Search now handled by UnifiedContentEmbedding)
+CREATE OR REPLACE VIEW "StoreAgent" AS
+WITH latest_versions AS (
+    SELECT
+        "storeListingId",
+        MAX(version) AS max_version
+    FROM "StoreListingVersion"
+    WHERE "submissionStatus" = 'APPROVED'
+    GROUP BY "storeListingId"
+),
+agent_versions AS (
+    SELECT
+        "storeListingId",
+        array_agg(DISTINCT version::text ORDER BY version::text) AS versions
+    FROM "StoreListingVersion"
+    WHERE "submissionStatus" = 'APPROVED'
+    GROUP BY "storeListingId"
+),
+agent_graph_versions AS (
+    SELECT
+        "storeListingId",
+        array_agg(DISTINCT "agentGraphVersion"::text ORDER BY "agentGraphVersion"::text) AS graph_versions
+    FROM "StoreListingVersion"
+    WHERE "submissionStatus" = 'APPROVED'
+    GROUP BY "storeListingId"
+)
+SELECT
+    sl.id AS listing_id,
+    slv.id AS "storeListingVersionId",
+    slv."createdAt" AS updated_at,
+    sl.slug,
+    COALESCE(slv.name, '') AS agent_name,
+    slv."videoUrl" AS agent_video,
+    slv."agentOutputDemoUrl" AS agent_output_demo,
+    COALESCE(slv."imageUrls", ARRAY[]::text[]) AS agent_image,
+    slv."isFeatured" AS featured,
+    p.username AS creator_username,
+    p."avatarUrl" AS creator_avatar,
+    slv."subHeading" AS sub_heading,
+    slv.description,
+    slv.categories,
+    COALESCE(ar.run_count, 0::bigint) AS runs,
+    COALESCE(rs.avg_rating, 0.0)::double precision AS rating,
+    COALESCE(av.versions, ARRAY[slv.version::text]) AS versions,
+    COALESCE(agv.graph_versions, ARRAY[slv."agentGraphVersion"::text]) AS "agentGraphVersions",
+    slv."agentGraphId",
+    slv."isAvailable" AS is_available,
+    COALESCE(sl."useForOnboarding", false) AS "useForOnboarding"
+FROM "StoreListing" sl
+JOIN latest_versions lv
+    ON sl.id = lv."storeListingId"
+JOIN "StoreListingVersion" slv
+    ON slv."storeListingId" = lv."storeListingId"
+   AND slv.version = lv.max_version
+   AND slv."submissionStatus" = 'APPROVED'
+JOIN "AgentGraph" a
+    ON slv."agentGraphId" = a.id
+    AND slv."agentGraphVersion" = a.version
+LEFT JOIN "Profile" p
+    ON sl."owningUserId" = p."userId"
+LEFT JOIN "mv_review_stats" rs
+    ON sl.id = rs."storeListingId"
+LEFT JOIN "mv_agent_run_counts" ar
+    ON a.id = ar."agentGraphId"
+LEFT JOIN agent_versions av
+    ON sl.id = av."storeListingId"
+LEFT JOIN agent_graph_versions agv
+    ON sl.id = agv."storeListingId"
+WHERE sl."isDeleted" = false
+  AND sl."hasApprovedVersion" = true;

autogpt_platform/backend/schema.prisma

@@ -937,7 +937,7 @@ model StoreListingVersion {
   // Old versions can be made unavailable by the author if desired
   isAvailable Boolean @default(true)
 
-  search Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
+  // Note: search column removed - now using UnifiedContentEmbedding.search
 
   // Version workflow state
   submissionStatus SubmissionStatus @default(DRAFT)
@@ -1002,6 +1002,7 @@ model UnifiedContentEmbedding {
   // Search data
   embedding      Unsupported("vector(1536)") // pgvector embedding (extension in platform schema)
   searchableText String // Combined text for search and fallback
+  search         Unsupported("tsvector")? @default(dbgenerated("''::tsvector")) // Full-text search (auto-populated by trigger)
   metadata       Json                        @default("{}") // Content-specific metadata
 
   @@unique([contentType, contentId, userId], map: "UnifiedContentEmbedding_contentType_contentId_userId_key")
@@ -1009,6 +1010,8 @@ model UnifiedContentEmbedding {
   @@index([userId])
   @@index([contentType, userId])
   @@index([embedding], map: "UnifiedContentEmbedding_embedding_idx")
+  // NO @@index for search - GIN index "UnifiedContentEmbedding_search_idx" created via SQL migration
+  // Prisma may generate DROP INDEX on migrate dev - that's okay, migration recreates it
 }
 
 model StoreListingReview {

autogpt_platform/frontend/src/app/(platform)/build/components/BuilderActions/components/RunInputDialog/useRunInputDialog.ts

@@ -1,7 +1,8 @@
 import { useGraphStore } from "@/app/(platform)/build/stores/graphStore";
 import { usePostV1ExecuteGraphAgent } from "@/app/api/__generated__/endpoints/graphs/graphs";
-import { useToast } from "@/components/molecules/Toast/use-toast";
+
 import {
+  ApiError,
   CredentialsMetaInput,
   GraphExecutionMeta,
 } from "@/lib/autogpt-server-api";
@@ -9,6 +10,9 @@ import { parseAsInteger, parseAsString, useQueryStates } from "nuqs";
 import { useMemo, useState } from "react";
 import { uiSchema } from "../../../FlowEditor/nodes/uiSchema";
 import { isCredentialFieldSchema } from "@/components/renderers/InputRenderer/custom/CredentialField/helpers";
+import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
+import { useToast } from "@/components/molecules/Toast/use-toast";
+import { useReactFlow } from "@xyflow/react";
 
 export const useRunInputDialog = ({
   setIsOpen,
@@ -31,6 +35,7 @@ export const useRunInputDialog = ({
     flowVersion: parseAsInteger,
   });
   const { toast } = useToast();
+  const { setViewport } = useReactFlow();
 
   const { mutateAsync: executeGraph, isPending: isExecutingGraph } =
     usePostV1ExecuteGraphAgent({
@@ -42,13 +47,63 @@ export const useRunInputDialog = ({
           });
         },
         onError: (error) => {
-          // Reset running state on error
+          if (error instanceof ApiError && error.isGraphValidationError()) {
+            const errorData = error.response?.detail;
+            Object.entries(errorData.node_errors).forEach(
+              ([nodeId, nodeErrors]) => {
+                useNodeStore
+                  .getState()
+                  .updateNodeErrors(
+                    nodeId,
+                    nodeErrors as { [key: string]: string },
+                  );
+              },
+            );
+            toast({
+              title: errorData?.message || "Graph validation failed",
+              description:
+                "Please fix the validation errors on the highlighted nodes and try again.",
+              variant: "destructive",
+            });
+            setIsOpen(false);
+
+            const firstBackendId = Object.keys(errorData.node_errors)[0];
+
+            if (firstBackendId) {
+              const firstErrorNode = useNodeStore
+                .getState()
+                .nodes.find(
+                  (n) =>
+                    n.data.metadata?.backend_id === firstBackendId ||
+                    n.id === firstBackendId,
+                );
+
+              if (firstErrorNode) {
+                setTimeout(() => {
+                  setViewport(
+                    {
+                      x:
+                        -firstErrorNode.position.x * 0.8 +
+                        window.innerWidth / 2 -
+                        150,
+                      y: -firstErrorNode.position.y * 0.8 + 50,
+                      zoom: 0.8,
+                    },
+                    { duration: 500 },
+                  );
+                }, 50);
+              }
+            }
+          } else {
+            toast({
+              title: "Error running graph",
+              description:
+                (error as Error).message || "An unexpected error occurred.",
+              variant: "destructive",
+            });
+            setIsOpen(false);
+          }
           setIsGraphRunning(false);
-          toast({
-            title: (error.detail as string) ?? "An unexpected error occurred.",
-            description: "An unexpected error occurred.",
-            variant: "destructive",
-          });
         },
       },
     });

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeHeader.tsx

@@ -20,11 +20,13 @@ type Props = {
 
 export const NodeHeader = ({ data, nodeId }: Props) => {
   const updateNodeData = useNodeStore((state) => state.updateNodeData);
-  const title = (data.metadata?.customized_name as string) || data.title;
+  const title =
+    (data.metadata?.customized_name as string) ||
+    data.hardcodedValues.agent_name ||
+    data.title;
+
   const [isEditingTitle, setIsEditingTitle] = useState(false);
-  const [editedTitle, setEditedTitle] = useState(
-    beautifyString(title).replace("Block", "").trim(),
-  );
+  const [editedTitle, setEditedTitle] = useState(title);
 
   const handleTitleEdit = () => {
     updateNodeData(nodeId, {

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/OutputHandler.tsx

@@ -31,8 +31,6 @@ export const OutputHandler = ({
   const [isOutputVisible, setIsOutputVisible] = useState(true);
   const brokenOutputs = useBrokenOutputs(nodeId);
 
-  console.log("brokenOutputs", brokenOutputs);
-
   const showHandles = uiType !== BlockUIType.OUTPUT;
 
   const renderOutputHandles = (

autogpt_platform/frontend/src/app/api/openapi.json

@@ -5621,6 +5621,69 @@
         "security": [{ "HTTPBearerJWT": [] }]
       }
     },
+    "/api/store/search": {
+      "get": {
+        "tags": ["v2", "store", "public"],
+        "summary": "Unified search across all content types",
+        "description": "Search across all content types (store agents, blocks, documentation) using hybrid search.\n\nCombines semantic (embedding-based) and lexical (text-based) search for best results.\n\nArgs:\n    query: The search query string\n    content_types: Optional list of content types to filter by (STORE_AGENT, BLOCK, DOCUMENTATION)\n    page: Page number for pagination (default 1)\n    page_size: Number of results per page (default 20)\n    user_id: Optional authenticated user ID (for user-scoped content in future)\n\nReturns:\n    UnifiedSearchResponse: Paginated list of search results with relevance scores",
+        "operationId": "getV2Unified search across all content types",
+        "security": [{ "HTTPBearer": [] }],
+        "parameters": [
+          {
+            "name": "query",
+            "in": "query",
+            "required": true,
+            "schema": { "type": "string", "title": "Query" }
+          },
+          {
+            "name": "content_types",
+            "in": "query",
+            "required": false,
+            "schema": {
+              "anyOf": [
+                { "type": "array", "items": { "type": "string" } },
+                { "type": "null" }
+              ],
+              "description": "Content types to search: STORE_AGENT, BLOCK, DOCUMENTATION. If not specified, searches all.",
+              "title": "Content Types"
+            },
+            "description": "Content types to search: STORE_AGENT, BLOCK, DOCUMENTATION. If not specified, searches all."
+          },
+          {
+            "name": "page",
+            "in": "query",
+            "required": false,
+            "schema": { "type": "integer", "default": 1, "title": "Page" }
+          },
+          {
+            "name": "page_size",
+            "in": "query",
+            "required": false,
+            "schema": { "type": "integer", "default": 20, "title": "Page Size" }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "Successful Response",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/UnifiedSearchResponse"
+                }
+              }
+            }
+          },
+          "422": {
+            "description": "Validation Error",
+            "content": {
+              "application/json": {
+                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
+              }
+            }
+          }
+        }
+      }
+    },
     "/api/store/submissions": {
       "get": {
         "tags": ["v2", "store", "private"],
@@ -10899,6 +10962,57 @@
         "required": ["name", "graph_id", "graph_version", "trigger_config"],
         "title": "TriggeredPresetSetupRequest"
       },
+      "UnifiedSearchResponse": {
+        "properties": {
+          "results": {
+            "items": { "$ref": "#/components/schemas/UnifiedSearchResult" },
+            "type": "array",
+            "title": "Results"
+          },
+          "pagination": { "$ref": "#/components/schemas/Pagination" }
+        },
+        "type": "object",
+        "required": ["results", "pagination"],
+        "title": "UnifiedSearchResponse",
+        "description": "Response model for unified search across all content types."
+      },
+      "UnifiedSearchResult": {
+        "properties": {
+          "content_type": { "type": "string", "title": "Content Type" },
+          "content_id": { "type": "string", "title": "Content Id" },
+          "searchable_text": { "type": "string", "title": "Searchable Text" },
+          "metadata": {
+            "anyOf": [
+              { "additionalProperties": true, "type": "object" },
+              { "type": "null" }
+            ],
+            "title": "Metadata"
+          },
+          "updated_at": {
+            "anyOf": [
+              { "type": "string", "format": "date-time" },
+              { "type": "null" }
+            ],
+            "title": "Updated At"
+          },
+          "combined_score": {
+            "anyOf": [{ "type": "number" }, { "type": "null" }],
+            "title": "Combined Score"
+          },
+          "semantic_score": {
+            "anyOf": [{ "type": "number" }, { "type": "null" }],
+            "title": "Semantic Score"
+          },
+          "lexical_score": {
+            "anyOf": [{ "type": "number" }, { "type": "null" }],
+            "title": "Lexical Score"
+          }
+        },
+        "type": "object",
+        "required": ["content_type", "content_id", "searchable_text"],
+        "title": "UnifiedSearchResult",
+        "description": "A single result from unified hybrid search across all content types."
+      },
       "UpdateAppLogoRequest": {
         "properties": {
           "logo_url": {
@@ -11863,6 +11977,7 @@
         "in": "header",
         "name": "X-Postmark-Webhook-Token"
       },
+      "HTTPBearer": { "type": "http", "scheme": "bearer" },
       "HTTPBearerJWT": {
         "type": "http",
         "scheme": "bearer",

autogpt_platform/frontend/src/components/renderers/InputRenderer/FormRenderer.tsx

@@ -30,6 +30,8 @@ export const FormRenderer = ({
     return generateUiSchemaForCustomFields(preprocessedSchema, uiSchema);
   }, [preprocessedSchema, uiSchema]);
 
+  console.log("preprocessedSchema", preprocessedSchema);
+
   return (
     <div className={"mb-6 mt-4"} data-tutorial-id="input-handles">
       <Form

autogpt_platform/frontend/src/components/renderers/InputRenderer/base/array/ArrayFieldTemplate.tsx

@@ -63,7 +63,7 @@ export default function ArrayFieldTemplate(props: ArrayFieldTemplateProps) {
       <div className="m-0 flex p-0">
         <div className="m-0 w-full space-y-4 p-0">
           {!fromAnyOf && (
-            <div className="flex items-center">
+            <div className="flex items-center gap-2">
               <ArrayFieldTitleTemplate
                 fieldPathId={fieldPathId}
                 title={uiOptions.title || title}

autogpt_platform/frontend/src/components/renderers/InputRenderer/base/standard/FieldTemplate.tsx

@@ -17,6 +17,7 @@ import {
 import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
 import { useEdgeStore } from "@/app/(platform)/build/stores/edgeStore";
 import { FieldError } from "./FieldError";
+import { BlockUIType } from "@/app/(platform)/build/components/types";
 
 export default function FieldTemplate(props: FieldTemplateProps) {
   const {
@@ -39,7 +40,7 @@ export default function FieldTemplate(props: FieldTemplateProps) {
     onRemoveProperty,
     readonly,
   } = props;
-  const { nodeId } = registry.formContext;
+  const { nodeId, uiType } = registry.formContext;
 
   const { isInputConnected } = useEdgeStore();
   const showAdvanced = useNodeStore(
@@ -50,6 +51,10 @@ export default function FieldTemplate(props: FieldTemplateProps) {
     return <div className="hidden">{children}</div>;
   }
 
+  if (uiType === BlockUIType.NOTE) {
+    return children;
+  }
+
   const uiOptions = getUiOptions(uiSchema);
   const TitleFieldTemplate = getTemplate(
     "TitleFieldTemplate",

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/GoogleDrivePickerField/GoogleDrivePickerField.tsx

@@ -1,12 +1,23 @@
+import { BlockUIType } from "@/app/(platform)/build/components/types";
 import { GoogleDrivePickerInput } from "@/components/contextual/GoogleDrivePicker/GoogleDrivePickerInput";
 import { GoogleDrivePickerConfig } from "@/lib/autogpt-server-api";
 import { FieldProps, getUiOptions } from "@rjsf/utils";
 
 export const GoogleDrivePickerField = (props: FieldProps) => {
-  const { schema, uiSchema, onChange, fieldPathId, formData } = props;
+  const { schema, uiSchema, onChange, fieldPathId, formData, registry } = props;
   const uiOptions = getUiOptions(uiSchema);
   const config: GoogleDrivePickerConfig = schema.google_drive_picker_config;
 
+  const uiType = registry.formContext?.uiType;
+
+  if (uiType === BlockUIType.INPUT) {
+    return (
+      <div className="rounded-3xl border border-gray-200 p-2 pl-4 text-xs text-gray-500 hover:cursor-not-allowed">
+        Select files when you run the graph
+      </div>
+    );
+  }
+
   return (
     <div>
       <GoogleDrivePickerInput

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/custom-registry.ts

@@ -3,7 +3,10 @@ import { CredentialsField } from "./CredentialField/CredentialField";
 import { GoogleDrivePickerField } from "./GoogleDrivePickerField/GoogleDrivePickerField";
 import { JsonTextField } from "./JsonTextField/JsonTextField";
 import { MultiSelectField } from "./MultiSelectField/MultiSelectField";
-import { isMultiSelectSchema } from "../utils/schema-utils";
+import {
+  isGoogleDrivePickerSchema,
+  isMultiSelectSchema,
+} from "../utils/schema-utils";
 import { TableField } from "./TableField/TableField";
 
 export interface CustomFieldDefinition {
@@ -29,12 +32,7 @@ export const CUSTOM_FIELDS: CustomFieldDefinition[] = [
   },
   {
     id: "custom/google_drive_picker_field",
-    matcher: (schema: any) => {
-      return (
-        "google_drive_picker_config" in schema ||
-        ("format" in schema && schema.format === "google-drive-picker")
-      );
-    },
+    matcher: isGoogleDrivePickerSchema,
     component: GoogleDrivePickerField,
   },
   {

autogpt_platform/frontend/src/components/renderers/InputRenderer/utils/schema-utils.ts

@@ -55,3 +55,38 @@ export function isMultiSelectSchema(schema: RJSFSchema | undefined): boolean {
     )
   );
 }
+
+const isGoogleDriveFileObject = (obj: RJSFSchema): boolean => {
+  if (obj.type !== "object" || !obj.properties) {
+    return false;
+  }
+  const props = obj.properties;
+  const hasId = "id" in props;
+  const hasMimeType = "mimeType" in props || "mime_type" in props;
+  const hasIconUrl = "iconUrl" in props || "icon_url" in props;
+  const hasIsFolder = "isFolder" in props || "is_folder" in props;
+  return hasId && hasMimeType && (hasIconUrl || hasIsFolder);
+};
+
+export const isGoogleDrivePickerSchema = (
+  schema: RJSFSchema | undefined,
+): boolean => {
+  if (!schema) {
+    return false;
+  }
+
+  // highest priority
+  if (
+    "google_drive_picker_config" in schema ||
+    ("format" in schema && schema.format === "google-drive-picker")
+  ) {
+    return true;
+  }
+
+  // In the Input type block, we do not add the format for the GoogleFile field, so we need to include this extra check.
+  if (isGoogleDriveFileObject(schema)) {
+    return true;
+  }
+
+  return false;
+};