Merge branch 'hackathon-copilot-search' of github.com:Significant-Gravitas/AutoGPT into feat/backfill_block_and_docs

- Changed from patching prisma.get_client() to patching query_raw_with_schema
- Follows the pattern used in hybrid_search_test.py
- Tests now properly exercise the schema-prefixing wrapper logic
- Fixes issue where SET search_path call was unmocked
- Removed unused mocker parameters
- All 18 tests passing

.github/workflows/platform-backend-ci.yml

@@ -176,7 +176,7 @@ jobs:
           }
 
       - name: Run Database Migrations
-        run: poetry run prisma migrate dev --name updates
+        run: poetry run prisma migrate deploy
         env:
           DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
           DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}

autogpt_platform/backend/.gitignore

@@ -18,3 +18,4 @@ load-tests/results/
 load-tests/*.json
 load-tests/*.log
 load-tests/node_modules/*
+migrations/*/rollback*.sql

autogpt_platform/backend/backend/api/features/chat/tools/run_agent_test.py

@@ -1,4 +1,5 @@
 import uuid
+from unittest.mock import AsyncMock, patch
 
 import orjson
 import pytest
@@ -17,6 +18,17 @@ setup_test_data = setup_test_data
 setup_firecrawl_test_data = setup_firecrawl_test_data
 
 
+@pytest.fixture(scope="session", autouse=True)
+def mock_embedding_functions():
+    """Mock embedding functions for all tests to avoid database/API dependencies."""
+    with patch(
+        "backend.api.features.store.db.ensure_embedding",
+        new_callable=AsyncMock,
+        return_value=True,
+    ):
+        yield
+
+
 @pytest.mark.asyncio(scope="session")
 async def test_run_agent(setup_test_data):
     """Test that the run_agent tool successfully executes an approved agent"""

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

@@ -0,0 +1,417 @@
+"""
+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)
+
+                items.append(
+                    ContentItem(
+                        content_id=block_id,
+                        content_type=ContentType.BLOCK,
+                        searchable_text=searchable_text,
+                        metadata={
+                            "name": getattr(block_instance, "name", ""),
+                            "categories": getattr(block_instance, "categories", []),
+                        },
+                        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."""
+        # Assuming docs are in /docs relative to project root
+        backend_root = Path(__file__).parent.parent.parent.parent
+        docs_root = backend_root.parent.parent / "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 +1,214 @@
+"""
+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 (
+    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] * 1536
+
+    # 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] * 1536
+
+    # 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.api.features.store.content_handlers.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.api.features.store.content_handlers.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.api.features.store.content_handlers.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.api.features.store.content_handlers.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/db.py

@@ -1,8 +1,7 @@
 import asyncio
 import logging
-import typing
 from datetime import datetime, timezone
-from typing import Literal
+from typing import Any, Literal
 
 import fastapi
 import prisma.enums
@@ -10,7 +9,7 @@ import prisma.errors
 import prisma.models
 import prisma.types
 
-from backend.data.db import query_raw_with_schema, transaction
+from backend.data.db import transaction
 from backend.data.graph import (
     GraphMeta,
     GraphModel,
@@ -30,6 +29,8 @@ from backend.util.settings import Settings
 
 from . import exceptions as store_exceptions
 from . import model as store_model
+from .embeddings import ensure_embedding
+from .hybrid_search import hybrid_search
 
 logger = logging.getLogger(__name__)
 settings = Settings()
@@ -50,128 +51,77 @@ async def get_store_agents(
     page_size: int = 20,
 ) -> store_model.StoreAgentsResponse:
     """
-    Get PUBLIC store agents from the StoreAgent view
+    Get PUBLIC store agents from the StoreAgent view.
+
+    Search behavior:
+    - With search_query: Uses hybrid search (semantic + lexical)
+    - Fallback: If embeddings unavailable, gracefully degrades to lexical-only
+    - Rationale: User-facing endpoint prioritizes availability over accuracy
+
+    Note: Admin operations (approval) use fail-fast to prevent inconsistent state.
     """
     logger.debug(
         f"Getting store agents. featured={featured}, creators={creators}, sorted_by={sorted_by}, search={search_query}, category={category}, page={page}"
     )
 
+    search_used_hybrid = False
+    store_agents: list[store_model.StoreAgent] = []
+    agents: list[dict[str, Any]] = []
+    total = 0
+    total_pages = 0
+
     try:
-        # If search_query is provided, use full-text search
+        # If search_query is provided, use hybrid search (embeddings + tsvector)
         if search_query:
-            offset = (page - 1) * page_size
+            # Try hybrid search combining semantic and lexical signals
+            # Falls back to lexical-only if OpenAI unavailable (user-facing, high SLA)
+            try:
+                agents, total = await hybrid_search(
+                    query=search_query,
+                    featured=featured,
+                    creators=creators,
+                    category=category,
+                    sorted_by="relevance",  # Use hybrid scoring for relevance
+                    page=page,
+                    page_size=page_size,
+                )
+                search_used_hybrid = True
+            except Exception as e:
+                # Log error but fall back to lexical search for better UX
+                logger.error(
+                    f"Hybrid search failed (likely OpenAI unavailable), "
+                    f"falling back to lexical search: {e}"
+                )
+                # search_used_hybrid remains False, will use fallback path below
 
-            # Whitelist allowed order_by columns
-            ALLOWED_ORDER_BY = {
-                "rating": "rating DESC, rank DESC",
-                "runs": "runs DESC, rank DESC",
-                "name": "agent_name ASC, rank ASC",
-                "updated_at": "updated_at DESC, rank DESC",
-            }
+            # Convert hybrid search results (dict format) if hybrid succeeded
+            if search_used_hybrid:
+                total_pages = (total + page_size - 1) // page_size
+                store_agents: list[store_model.StoreAgent] = []
+                for agent in agents:
+                    try:
+                        store_agent = store_model.StoreAgent(
+                            slug=agent["slug"],
+                            agent_name=agent["agent_name"],
+                            agent_image=(
+                                agent["agent_image"][0] if agent["agent_image"] else ""
+                            ),
+                            creator=agent["creator_username"] or "Needs Profile",
+                            creator_avatar=agent["creator_avatar"] or "",
+                            sub_heading=agent["sub_heading"],
+                            description=agent["description"],
+                            runs=agent["runs"],
+                            rating=agent["rating"],
+                        )
+                        store_agents.append(store_agent)
+                    except Exception as e:
+                        logger.error(
+                            f"Error parsing Store agent from hybrid search results: {e}"
+                        )
+                        continue
 
-            # Validate and get order clause
-            if sorted_by and sorted_by in ALLOWED_ORDER_BY:
-                order_by_clause = ALLOWED_ORDER_BY[sorted_by]
-            else:
-                order_by_clause = "updated_at DESC, rank DESC"
-
-            # Build WHERE conditions and parameters list
-            where_parts: list[str] = []
-            params: list[typing.Any] = [search_query]  # $1 - search term
-            param_index = 2  # Start at $2 for next parameter
-
-            # Always filter for available agents
-            where_parts.append("is_available = true")
-
-            if featured:
-                where_parts.append("featured = true")
-
-            if creators and creators:
-                # Use ANY with array parameter
-                where_parts.append(f"creator_username = ANY(${param_index})")
-                params.append(creators)
-                param_index += 1
-
-            if category and category:
-                where_parts.append(f"${param_index} = ANY(categories)")
-                params.append(category)
-                param_index += 1
-
-            sql_where_clause: str = " AND ".join(where_parts) if where_parts else "1=1"
-
-            # Add pagination params
-            params.extend([page_size, offset])
-            limit_param = f"${param_index}"
-            offset_param = f"${param_index + 1}"
-
-            # Execute full-text search query with parameterized values
-            sql_query = f"""
-                SELECT
-                    slug,
-                    agent_name,
-                    agent_image,
-                    creator_username,
-                    creator_avatar,
-                    sub_heading,
-                    description,
-                    runs,
-                    rating,
-                    categories,
-                    featured,
-                    is_available,
-                    updated_at,
-                    ts_rank_cd(search, query) AS rank
-                FROM {{schema_prefix}}"StoreAgent",
-                    plainto_tsquery('english', $1) AS query
-                WHERE {sql_where_clause}
-                    AND search @@ query
-                ORDER BY {order_by_clause}
-                LIMIT {limit_param} OFFSET {offset_param}
-            """
-
-            # Count query for pagination - only uses search term parameter
-            count_query = f"""
-                SELECT COUNT(*) as count
-                FROM {{schema_prefix}}"StoreAgent",
-                    plainto_tsquery('english', $1) AS query
-                WHERE {sql_where_clause}
-                    AND search @@ query
-            """
-
-            # Execute both queries with parameters
-            agents = await query_raw_with_schema(sql_query, *params)
-
-            # For count, use params without pagination (last 2 params)
-            count_params = params[:-2]
-            count_result = await query_raw_with_schema(count_query, *count_params)
-
-            total = count_result[0]["count"] if count_result else 0
-            total_pages = (total + page_size - 1) // page_size
-
-            # Convert raw results to StoreAgent models
-            store_agents: list[store_model.StoreAgent] = []
-            for agent in agents:
-                try:
-                    store_agent = store_model.StoreAgent(
-                        slug=agent["slug"],
-                        agent_name=agent["agent_name"],
-                        agent_image=(
-                            agent["agent_image"][0] if agent["agent_image"] else ""
-                        ),
-                        creator=agent["creator_username"] or "Needs Profile",
-                        creator_avatar=agent["creator_avatar"] or "",
-                        sub_heading=agent["sub_heading"],
-                        description=agent["description"],
-                        runs=agent["runs"],
-                        rating=agent["rating"],
-                    )
-                    store_agents.append(store_agent)
-                except Exception as e:
-                    logger.error(f"Error parsing Store agent from search results: {e}")
-                    continue
-
-        else:
-            # Non-search query path (original logic)
+        if not search_used_hybrid:
+            # Fallback path - use basic search or no search
             where_clause: prisma.types.StoreAgentWhereInput = {"is_available": True}
             if featured:
                 where_clause["featured"] = featured
@@ -180,6 +130,14 @@ async def get_store_agents(
             if category:
                 where_clause["categories"] = {"has": category}
 
+            # Add basic text search if search_query provided but hybrid failed
+            if search_query:
+                where_clause["OR"] = [
+                    {"agent_name": {"contains": search_query, "mode": "insensitive"}},
+                    {"sub_heading": {"contains": search_query, "mode": "insensitive"}},
+                    {"description": {"contains": search_query, "mode": "insensitive"}},
+                ]
+
             order_by = []
             if sorted_by == "rating":
                 order_by.append({"rating": "desc"})
@@ -188,7 +146,7 @@ async def get_store_agents(
             elif sorted_by == "name":
                 order_by.append({"agent_name": "asc"})
 
-            agents = await prisma.models.StoreAgent.prisma().find_many(
+            db_agents = await prisma.models.StoreAgent.prisma().find_many(
                 where=where_clause,
                 order=order_by,
                 skip=(page - 1) * page_size,
@@ -199,7 +157,7 @@ async def get_store_agents(
             total_pages = (total + page_size - 1) // page_size
 
             store_agents: list[store_model.StoreAgent] = []
-            for agent in agents:
+            for agent in db_agents:
                 try:
                     # Create the StoreAgent object safely
                     store_agent = store_model.StoreAgent(
@@ -1577,7 +1535,7 @@ async def review_store_submission(
                 )
 
                 # Update the AgentGraph with store listing data
-                await prisma.models.AgentGraph.prisma().update(
+                await prisma.models.AgentGraph.prisma(tx).update(
                     where={
                         "graphVersionId": {
                             "id": store_listing_version.agentGraphId,
@@ -1592,6 +1550,23 @@ async def review_store_submission(
                     },
                 )
 
+                # Generate embedding for approved listing (blocking - admin operation)
+                # Inside transaction: if embedding fails, entire transaction rolls back
+                embedding_success = await ensure_embedding(
+                    version_id=store_listing_version_id,
+                    name=store_listing_version.name,
+                    description=store_listing_version.description,
+                    sub_heading=store_listing_version.subHeading,
+                    categories=store_listing_version.categories or [],
+                    tx=tx,
+                )
+                if not embedding_success:
+                    raise ValueError(
+                        f"Failed to generate embedding for listing {store_listing_version_id}. "
+                        "This is likely due to OpenAI API being unavailable. "
+                        "Please try again later or contact support if the issue persists."
+                    )
+
                 await prisma.models.StoreListing.prisma(tx).update(
                     where={"id": store_listing_version.StoreListing.id},
                     data={

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

@@ -0,0 +1,628 @@
+"""
+Unified Content Embeddings Service
+
+Handles generation and storage of OpenAI embeddings for all content types
+(store listings, blocks, documentation, library agents) to enable semantic/hybrid search.
+"""
+
+import asyncio
+import logging
+import time
+from typing import Any
+
+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
+
+logger = logging.getLogger(__name__)
+
+
+# OpenAI embedding model configuration
+EMBEDDING_MODEL = "text-embedding-3-small"
+# OpenAI embedding token limit (8,191 with 1 token buffer for safety)
+EMBEDDING_MAX_TOKENS = 8191
+
+
+def build_searchable_text(
+    name: str,
+    description: str,
+    sub_heading: str,
+    categories: list[str],
+) -> str:
+    """
+    Build searchable text from listing version fields.
+
+    Combines relevant fields into a single string for embedding.
+    """
+    parts = []
+
+    # Name is important - include it
+    if name:
+        parts.append(name)
+
+    # Sub-heading provides context
+    if sub_heading:
+        parts.append(sub_heading)
+
+    # Description is the main content
+    if description:
+        parts.append(description)
+
+    # Categories help with semantic matching
+    if categories:
+        parts.append(" ".join(categories))
+
+    return " ".join(parts)
+
+
+async def generate_embedding(text: str) -> list[float] | None:
+    """
+    Generate embedding for text using OpenAI API.
+
+    Returns None if embedding generation fails.
+    Fail-fast: no retries to maintain consistency with approval flow.
+    """
+    try:
+        client = get_openai_client()
+        if not client:
+            logger.error("openai_internal_api_key not set, cannot generate embedding")
+            return None
+
+        # Truncate text to token limit using tiktoken
+        # Character-based truncation is insufficient because token ratios vary by content type
+        enc = encoding_for_model(EMBEDDING_MODEL)
+        tokens = enc.encode(text)
+        if len(tokens) > EMBEDDING_MAX_TOKENS:
+            tokens = tokens[:EMBEDDING_MAX_TOKENS]
+            truncated_text = enc.decode(tokens)
+            logger.info(
+                f"Truncated text from {len(enc.encode(text))} to {len(tokens)} tokens"
+            )
+        else:
+            truncated_text = text
+
+        start_time = time.time()
+        response = await client.embeddings.create(
+            model=EMBEDDING_MODEL,
+            input=truncated_text,
+        )
+        latency_ms = (time.time() - start_time) * 1000
+
+        embedding = response.data[0].embedding
+        logger.info(
+            f"Generated embedding: {len(embedding)} dims, "
+            f"{len(tokens)} tokens, {latency_ms:.0f}ms"
+        )
+        return embedding
+
+    except Exception as e:
+        logger.error(f"Failed to generate embedding: {e}")
+        return None
+
+
+async def store_embedding(
+    version_id: str,
+    embedding: list[float],
+    tx: prisma.Prisma | None = None,
+) -> bool:
+    """
+    Store embedding in the database.
+
+    BACKWARD COMPATIBILITY: Maintained for existing store listing usage.
+    DEPRECATED: Use ensure_embedding() instead (includes searchable_text).
+    """
+    return await store_content_embedding(
+        content_type=ContentType.STORE_AGENT,
+        content_id=version_id,
+        embedding=embedding,
+        searchable_text="",  # Empty for backward compat; ensure_embedding() populates this
+        metadata=None,
+        user_id=None,  # Store agents are public
+        tx=tx,
+    )
+
+
+async def store_content_embedding(
+    content_type: ContentType,
+    content_id: str,
+    embedding: list[float],
+    searchable_text: str,
+    metadata: dict | None = None,
+    user_id: str | None = None,
+    tx: prisma.Prisma | None = None,
+) -> bool:
+    """
+    Store embedding in the unified content embeddings table.
+
+    New function for unified content embedding storage.
+    Uses raw SQL since Prisma doesn't natively support pgvector.
+    """
+    try:
+        client = tx if tx else prisma.get_client()
+
+        # Convert embedding to PostgreSQL vector format
+        embedding_str = embedding_to_vector_string(embedding)
+        metadata_json = dumps(metadata or {})
+
+        # Upsert the embedding
+        # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
+        await execute_raw_with_schema(
+            """
+            INSERT INTO {schema_prefix}"UnifiedContentEmbedding" (
+                "id", "contentType", "contentId", "userId", "embedding", "searchableText", "metadata", "createdAt", "updatedAt"
+            )
+            VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::vector, $5, $6::jsonb, NOW(), NOW())
+            ON CONFLICT ("contentType", "contentId", "userId")
+            DO UPDATE SET
+                "embedding" = $4::vector,
+                "searchableText" = $5,
+                "metadata" = $6::jsonb,
+                "updatedAt" = NOW()
+            WHERE {schema_prefix}"UnifiedContentEmbedding"."contentType" = $1::{schema_prefix}"ContentType"
+                AND {schema_prefix}"UnifiedContentEmbedding"."contentId" = $2
+                AND ({schema_prefix}"UnifiedContentEmbedding"."userId" = $3 OR ($3 IS NULL AND {schema_prefix}"UnifiedContentEmbedding"."userId" IS NULL))
+            """,
+            content_type,
+            content_id,
+            user_id,
+            embedding_str,
+            searchable_text,
+            metadata_json,
+            client=client,
+            set_public_search_path=True,
+        )
+
+        logger.info(f"Stored embedding for {content_type}:{content_id}")
+        return True
+
+    except Exception as e:
+        logger.error(f"Failed to store embedding for {content_type}:{content_id}: {e}")
+        return False
+
+
+async def get_embedding(version_id: str) -> dict[str, Any] | None:
+    """
+    Retrieve embedding record for a listing version.
+
+    BACKWARD COMPATIBILITY: Maintained for existing store listing usage.
+    Returns dict with storeListingVersionId, embedding, timestamps or None if not found.
+    """
+    result = await get_content_embedding(
+        ContentType.STORE_AGENT, version_id, user_id=None
+    )
+    if result:
+        # Transform to old format for backward compatibility
+        return {
+            "storeListingVersionId": result["contentId"],
+            "embedding": result["embedding"],
+            "createdAt": result["createdAt"],
+            "updatedAt": result["updatedAt"],
+        }
+    return None
+
+
+async def get_content_embedding(
+    content_type: ContentType, content_id: str, user_id: str | None = None
+) -> dict[str, Any] | None:
+    """
+    Retrieve embedding record for any content type.
+
+    New function for unified content embedding retrieval.
+    Returns dict with contentType, contentId, embedding, timestamps or None if not found.
+    """
+    try:
+        result = await query_raw_with_schema(
+            """
+            SELECT
+                "contentType",
+                "contentId",
+                "userId",
+                "embedding"::text as "embedding",
+                "searchableText",
+                "metadata",
+                "createdAt",
+                "updatedAt"
+            FROM {schema_prefix}"UnifiedContentEmbedding"
+            WHERE "contentType" = $1::{schema_prefix}"ContentType" AND "contentId" = $2 AND ("userId" = $3 OR ($3 IS NULL AND "userId" IS NULL))
+            """,
+            content_type,
+            content_id,
+            user_id,
+            set_public_search_path=True,
+        )
+
+        if result and len(result) > 0:
+            return result[0]
+        return None
+
+    except Exception as e:
+        logger.error(f"Failed to get embedding for {content_type}:{content_id}: {e}")
+        return None
+
+
+async def ensure_embedding(
+    version_id: str,
+    name: str,
+    description: str,
+    sub_heading: str,
+    categories: list[str],
+    force: bool = False,
+    tx: prisma.Prisma | None = None,
+) -> bool:
+    """
+    Ensure an embedding exists for the listing version.
+
+    Creates embedding if missing. Use force=True to regenerate.
+    Backward-compatible wrapper for store listings.
+
+    Args:
+        version_id: The StoreListingVersion ID
+        name: Agent name
+        description: Agent description
+        sub_heading: Agent sub-heading
+        categories: Agent categories
+        force: Force regeneration even if embedding exists
+        tx: Optional transaction client
+
+    Returns:
+        True if embedding exists/was created, False on failure
+    """
+    try:
+        # Check if embedding already exists
+        if not force:
+            existing = await get_embedding(version_id)
+            if existing and existing.get("embedding"):
+                logger.debug(f"Embedding for version {version_id} already exists")
+                return True
+
+        # Build searchable text for embedding
+        searchable_text = build_searchable_text(
+            name, description, sub_heading, categories
+        )
+
+        # Generate new embedding
+        embedding = await generate_embedding(searchable_text)
+        if embedding is None:
+            logger.warning(f"Could not generate embedding for version {version_id}")
+            return False
+
+        # Store the embedding with metadata using new function
+        metadata = {
+            "name": name,
+            "subHeading": sub_heading,
+            "categories": categories,
+        }
+        return await store_content_embedding(
+            content_type=ContentType.STORE_AGENT,
+            content_id=version_id,
+            embedding=embedding,
+            searchable_text=searchable_text,
+            metadata=metadata,
+            user_id=None,  # Store agents are public
+            tx=tx,
+        )
+
+    except Exception as e:
+        logger.error(f"Failed to ensure embedding for version {version_id}: {e}")
+        return False
+
+
+async def delete_embedding(version_id: str) -> bool:
+    """
+    Delete embedding for a listing version.
+
+    BACKWARD COMPATIBILITY: Maintained for existing store listing usage.
+    Note: This is usually handled automatically by CASCADE delete,
+    but provided for manual cleanup if needed.
+    """
+    return await delete_content_embedding(ContentType.STORE_AGENT, version_id)
+
+
+async def delete_content_embedding(
+    content_type: ContentType, content_id: str, user_id: str | None = None
+) -> bool:
+    """
+    Delete embedding for any content type.
+
+    New function for unified content embedding deletion.
+    Note: This is usually handled automatically by CASCADE delete,
+    but provided for manual cleanup if needed.
+
+    Args:
+        content_type: The type of content (STORE_AGENT, LIBRARY_AGENT, etc.)
+        content_id: The unique identifier for the content
+        user_id: Optional user ID. For public content (STORE_AGENT, BLOCK), pass None.
+                 For user-scoped content (LIBRARY_AGENT), pass the user's ID to avoid
+                 deleting embeddings belonging to other users.
+
+    Returns:
+        True if deletion succeeded, False otherwise
+    """
+    try:
+        client = prisma.get_client()
+
+        await execute_raw_with_schema(
+            """
+            DELETE FROM {schema_prefix}"UnifiedContentEmbedding"
+            WHERE "contentType" = $1::{schema_prefix}"ContentType"
+              AND "contentId" = $2
+              AND ("userId" = $3 OR ($3 IS NULL AND "userId" IS NULL))
+            """,
+            content_type,
+            content_id,
+            user_id,
+            client=client,
+        )
+
+        user_str = f" (user: {user_id})" if user_id else ""
+        logger.info(f"Deleted embedding for {content_type}:{content_id}{user_str}")
+        return True
+
+    except Exception as e:
+        logger.error(f"Failed to delete embedding for {content_type}:{content_id}: {e}")
+        return False
+
+
+async def get_embedding_stats() -> dict[str, Any]:
+    """
+    Get statistics about embedding coverage for all content types.
+
+    Returns stats per content type and overall totals.
+    """
+    try:
+        stats_by_type = {}
+        total_items = 0
+        total_with_embeddings = 0
+        total_without_embeddings = 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 {
+            "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 {
+            "by_type": {},
+            "totals": {
+                "total": 0,
+                "with_embeddings": 0,
+                "without_embeddings": 0,
+                "coverage_percent": 0,
+            },
+            "error": str(e),
+        }
+
+
+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 per content type
+
+    Returns:
+        Dict with success/failure counts aggregated across all content types
+    """
+    # Delegate to the new generic backfill system
+    result = await backfill_all_content_types(batch_size)
+
+    # 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,
+                "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:
+    """
+    Generate embedding for a search query.
+
+    Same as generate_embedding but with clearer intent.
+    """
+    return await generate_embedding(query)
+
+
+def embedding_to_vector_string(embedding: list[float]) -> str:
+    """Convert embedding list to PostgreSQL vector string format."""
+    return "[" + ",".join(str(x) for x in embedding) + "]"
+
+
+async def ensure_content_embedding(
+    content_type: ContentType,
+    content_id: str,
+    searchable_text: str,
+    metadata: dict | None = None,
+    user_id: str | None = None,
+    force: bool = False,
+    tx: prisma.Prisma | None = None,
+) -> bool:
+    """
+    Ensure an embedding exists for any content type.
+
+    Generic function for creating embeddings for store agents, blocks, docs, etc.
+
+    Args:
+        content_type: ContentType enum value (STORE_AGENT, BLOCK, etc.)
+        content_id: Unique identifier for the content
+        searchable_text: Combined text for embedding generation
+        metadata: Optional metadata to store with embedding
+        force: Force regeneration even if embedding exists
+        tx: Optional transaction client
+
+    Returns:
+        True if embedding exists/was created, False on failure
+    """
+    try:
+        # Check if embedding already exists
+        if not force:
+            existing = await get_content_embedding(content_type, content_id, user_id)
+            if existing and existing.get("embedding"):
+                logger.debug(
+                    f"Embedding for {content_type}:{content_id} already exists"
+                )
+                return True
+
+        # Generate new embedding
+        embedding = await generate_embedding(searchable_text)
+        if embedding is None:
+            logger.warning(
+                f"Could not generate embedding for {content_type}:{content_id}"
+            )
+            return False
+
+        # Store the embedding
+        return await store_content_embedding(
+            content_type=content_type,
+            content_id=content_id,
+            embedding=embedding,
+            searchable_text=searchable_text,
+            metadata=metadata or {},
+            user_id=user_id,
+            tx=tx,
+        )
+
+    except Exception as e:
+        logger.error(f"Failed to ensure embedding for {content_type}:{content_id}: {e}")
+        return False

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

@@ -0,0 +1,329 @@
+"""
+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
+
+import pytest
+from prisma.enums import ContentType
+
+from backend.api.features.store import embeddings
+
+# Schema prefix tests removed - functionality moved to db.raw_with_schema() helper
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_store_content_embedding_with_schema():
+    """Test storing embeddings with proper schema handling."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "platform"
+
+        with patch("prisma.get_client") as mock_get_client:
+            mock_client = AsyncMock()
+            mock_get_client.return_value = mock_client
+
+            result = await embeddings.store_content_embedding(
+                content_type=ContentType.STORE_AGENT,
+                content_id="test-id",
+                embedding=[0.1] * 1536,
+                searchable_text="test text",
+                metadata={"test": "data"},
+                user_id=None,
+            )
+
+            # Verify the query was called
+            assert mock_client.execute_raw.called
+
+            # Get the SQL query that was executed
+            call_args = mock_client.execute_raw.call_args
+            sql_query = call_args[0][0]
+
+            # Verify schema prefix is in the query
+            assert '"platform"."UnifiedContentEmbedding"' in sql_query
+
+            # Verify result
+            assert result is True
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_get_content_embedding_with_schema():
+    """Test retrieving embeddings with proper schema handling."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "platform"
+
+        with patch("prisma.get_client") as mock_get_client:
+            mock_client = AsyncMock()
+            mock_client.query_raw.return_value = [
+                {
+                    "contentType": "STORE_AGENT",
+                    "contentId": "test-id",
+                    "userId": None,
+                    "embedding": "[0.1, 0.2]",
+                    "searchableText": "test",
+                    "metadata": {},
+                    "createdAt": "2024-01-01",
+                    "updatedAt": "2024-01-01",
+                }
+            ]
+            mock_get_client.return_value = mock_client
+
+            result = await embeddings.get_content_embedding(
+                ContentType.STORE_AGENT,
+                "test-id",
+                user_id=None,
+            )
+
+            # Verify the query was called
+            assert mock_client.query_raw.called
+
+            # Get the SQL query that was executed
+            call_args = mock_client.query_raw.call_args
+            sql_query = call_args[0][0]
+
+            # Verify schema prefix is in the query
+            assert '"platform"."UnifiedContentEmbedding"' in sql_query
+
+            # Verify result
+            assert result is not None
+            assert result["contentId"] == "test-id"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_delete_content_embedding_with_schema():
+    """Test deleting embeddings with proper schema handling."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "platform"
+
+        with patch("prisma.get_client") as mock_get_client:
+            mock_client = AsyncMock()
+            mock_get_client.return_value = mock_client
+
+            result = await embeddings.delete_content_embedding(
+                ContentType.STORE_AGENT,
+                "test-id",
+            )
+
+            # Verify the query was called
+            assert mock_client.execute_raw.called
+
+            # Get the SQL query that was executed
+            call_args = mock_client.execute_raw.call_args
+            sql_query = call_args[0][0]
+
+            # Verify schema prefix is in the query
+            assert '"platform"."UnifiedContentEmbedding"' in sql_query
+
+            # Verify result
+            assert result is True
+
+
+@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"
+
+        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
+
+            result = await embeddings.get_embedding_stats()
+
+            # 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
+
+
+@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"
+
+        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
+
+            with patch(
+                "backend.api.features.store.embeddings.ensure_embedding"
+            ) as mock_ensure:
+                mock_ensure.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 results
+                assert result["processed"] == 1
+                assert result["success"] == 1
+                assert result["failed"] == 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_ensure_content_embedding_with_schema():
+    """Test ensuring embeddings exist with proper schema handling."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "platform"
+
+        with patch(
+            "backend.api.features.store.embeddings.get_content_embedding"
+        ) as mock_get:
+            # Simulate no existing embedding
+            mock_get.return_value = None
+
+            with patch(
+                "backend.api.features.store.embeddings.generate_embedding"
+            ) as mock_generate:
+                mock_generate.return_value = [0.1] * 1536
+
+                with patch(
+                    "backend.api.features.store.embeddings.store_content_embedding"
+                ) as mock_store:
+                    mock_store.return_value = True
+
+                    result = await embeddings.ensure_content_embedding(
+                        content_type=ContentType.STORE_AGENT,
+                        content_id="test-id",
+                        searchable_text="test text",
+                        metadata={"test": "data"},
+                        user_id=None,
+                        force=False,
+                    )
+
+                    # Verify the flow
+                    assert mock_get.called
+                    assert mock_generate.called
+                    assert mock_store.called
+                    assert result is True
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_backward_compatibility_store_embedding():
+    """Test backward compatibility wrapper for store_embedding."""
+    with patch(
+        "backend.api.features.store.embeddings.store_content_embedding"
+    ) as mock_store:
+        mock_store.return_value = True
+
+        result = await embeddings.store_embedding(
+            version_id="test-version-id",
+            embedding=[0.1] * 1536,
+            tx=None,
+        )
+
+        # Verify it calls the new function with correct parameters
+        assert mock_store.called
+        call_args = mock_store.call_args
+
+        assert call_args[1]["content_type"] == ContentType.STORE_AGENT
+        assert call_args[1]["content_id"] == "test-version-id"
+        assert call_args[1]["user_id"] is None
+        assert result is True
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_backward_compatibility_get_embedding():
+    """Test backward compatibility wrapper for get_embedding."""
+    with patch(
+        "backend.api.features.store.embeddings.get_content_embedding"
+    ) as mock_get:
+        mock_get.return_value = {
+            "contentType": "STORE_AGENT",
+            "contentId": "test-version-id",
+            "embedding": "[0.1, 0.2]",
+            "createdAt": "2024-01-01",
+            "updatedAt": "2024-01-01",
+        }
+
+        result = await embeddings.get_embedding("test-version-id")
+
+        # Verify it calls the new function
+        assert mock_get.called
+
+        # Verify it transforms to old format
+        assert result is not None
+        assert result["storeListingVersionId"] == "test-version-id"
+        assert "embedding" in result
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_schema_handling_error_cases():
+    """Test error handling in schema-aware operations."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "platform"
+
+        with patch("prisma.get_client") as mock_get_client:
+            mock_client = AsyncMock()
+            mock_client.execute_raw.side_effect = Exception("Database error")
+            mock_get_client.return_value = mock_client
+
+            result = await embeddings.store_content_embedding(
+                content_type=ContentType.STORE_AGENT,
+                content_id="test-id",
+                embedding=[0.1] * 1536,
+                searchable_text="test",
+                metadata=None,
+                user_id=None,
+            )
+
+            # Should return False on error, not raise
+            assert result is False
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v", "-s"])

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

@@ -0,0 +1,387 @@
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import prisma
+import pytest
+from prisma import Prisma
+from prisma.enums import ContentType
+
+from backend.api.features.store import embeddings
+
+
+@pytest.fixture(autouse=True)
+async def setup_prisma():
+    """Setup Prisma client for tests."""
+    try:
+        Prisma()
+    except prisma.errors.ClientAlreadyRegisteredError:
+        pass
+    yield
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_build_searchable_text():
+    """Test searchable text building from listing fields."""
+    result = embeddings.build_searchable_text(
+        name="AI Assistant",
+        description="A helpful AI assistant for productivity",
+        sub_heading="Boost your productivity",
+        categories=["AI", "Productivity"],
+    )
+
+    expected = "AI Assistant Boost your productivity A helpful AI assistant for productivity AI Productivity"
+    assert result == expected
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_build_searchable_text_empty_fields():
+    """Test searchable text building with empty fields."""
+    result = embeddings.build_searchable_text(
+        name="", description="Test description", sub_heading="", categories=[]
+    )
+
+    assert result == "Test description"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_generate_embedding_success():
+    """Test successful embedding generation."""
+    # Mock OpenAI response
+    mock_client = MagicMock()
+    mock_response = MagicMock()
+    mock_response.data = [MagicMock()]
+    mock_response.data[0].embedding = [0.1, 0.2, 0.3] * 512  # 1536 dimensions
+
+    # Use AsyncMock for async embeddings.create method
+    mock_client.embeddings.create = AsyncMock(return_value=mock_response)
+
+    # Patch at the point of use in embeddings.py
+    with patch(
+        "backend.api.features.store.embeddings.get_openai_client"
+    ) as mock_get_client:
+        mock_get_client.return_value = mock_client
+
+        result = await embeddings.generate_embedding("test text")
+
+        assert result is not None
+        assert len(result) == 1536
+        assert result[0] == 0.1
+
+        mock_client.embeddings.create.assert_called_once_with(
+            model="text-embedding-3-small", input="test text"
+        )
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_generate_embedding_no_api_key():
+    """Test embedding generation without API key."""
+    # Patch at the point of use in embeddings.py
+    with patch(
+        "backend.api.features.store.embeddings.get_openai_client"
+    ) as mock_get_client:
+        mock_get_client.return_value = None
+
+        result = await embeddings.generate_embedding("test text")
+
+        assert result is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_generate_embedding_api_error():
+    """Test embedding generation with API error."""
+    mock_client = MagicMock()
+    mock_client.embeddings.create = AsyncMock(side_effect=Exception("API Error"))
+
+    # Patch at the point of use in embeddings.py
+    with patch(
+        "backend.api.features.store.embeddings.get_openai_client"
+    ) as mock_get_client:
+        mock_get_client.return_value = mock_client
+
+        result = await embeddings.generate_embedding("test text")
+
+        assert result is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_generate_embedding_text_truncation():
+    """Test that long text is properly truncated using tiktoken."""
+    from tiktoken import encoding_for_model
+
+    mock_client = MagicMock()
+    mock_response = MagicMock()
+    mock_response.data = [MagicMock()]
+    mock_response.data[0].embedding = [0.1] * 1536
+
+    # Use AsyncMock for async embeddings.create method
+    mock_client.embeddings.create = AsyncMock(return_value=mock_response)
+
+    # Patch at the point of use in embeddings.py
+    with patch(
+        "backend.api.features.store.embeddings.get_openai_client"
+    ) as mock_get_client:
+        mock_get_client.return_value = mock_client
+
+        # Create text that will exceed 8191 tokens
+        # Use varied characters to ensure token-heavy text: each word is ~1 token
+        words = [f"word{i}" for i in range(10000)]
+        long_text = " ".join(words)  # ~10000 tokens
+
+        await embeddings.generate_embedding(long_text)
+
+        # Verify text was truncated to 8191 tokens
+        call_args = mock_client.embeddings.create.call_args
+        truncated_text = call_args.kwargs["input"]
+
+        # Count actual tokens in truncated text
+        enc = encoding_for_model("text-embedding-3-small")
+        actual_tokens = len(enc.encode(truncated_text))
+
+        # Should be at or just under 8191 tokens
+        assert actual_tokens <= 8191
+        # Should be close to the limit (not over-truncated)
+        assert actual_tokens >= 8100
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_embedding_success(mocker):
+    """Test successful embedding storage."""
+    mock_client = mocker.AsyncMock()
+    mock_client.execute_raw = mocker.AsyncMock()
+
+    embedding = [0.1, 0.2, 0.3]
+
+    result = await embeddings.store_embedding(
+        version_id="test-version-id", embedding=embedding, tx=mock_client
+    )
+
+    assert result is True
+    # execute_raw is called twice: once for SET search_path, once for INSERT
+    assert mock_client.execute_raw.call_count == 2
+
+    # First call: SET search_path
+    first_call_args = mock_client.execute_raw.call_args_list[0][0]
+    assert "SET search_path" in first_call_args[0]
+
+    # Second call: INSERT query with the actual data
+    second_call_args = mock_client.execute_raw.call_args_list[1][0]
+    assert "test-version-id" in second_call_args
+    assert "[0.1,0.2,0.3]" in second_call_args
+    assert None in second_call_args  # userId should be None for store agents
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_embedding_database_error(mocker):
+    """Test embedding storage with database error."""
+    mock_client = mocker.AsyncMock()
+    mock_client.execute_raw.side_effect = Exception("Database error")
+
+    embedding = [0.1, 0.2, 0.3]
+
+    result = await embeddings.store_embedding(
+        version_id="test-version-id", embedding=embedding, tx=mock_client
+    )
+
+    assert result is False
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_embedding_success():
+    """Test successful embedding retrieval."""
+    mock_result = [
+        {
+            "contentType": "STORE_AGENT",
+            "contentId": "test-version-id",
+            "userId": None,
+            "embedding": "[0.1,0.2,0.3]",
+            "searchableText": "Test text",
+            "metadata": {},
+            "createdAt": "2024-01-01T00:00:00Z",
+            "updatedAt": "2024-01-01T00:00:00Z",
+        }
+    ]
+
+    with patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_result,
+    ):
+        result = await embeddings.get_embedding("test-version-id")
+
+        assert result is not None
+        assert result["storeListingVersionId"] == "test-version-id"
+        assert result["embedding"] == "[0.1,0.2,0.3]"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_embedding_not_found():
+    """Test embedding retrieval when not found."""
+    with patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=[],
+    ):
+        result = await embeddings.get_embedding("test-version-id")
+
+        assert result is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.generate_embedding")
+@patch("backend.api.features.store.embeddings.store_embedding")
+@patch("backend.api.features.store.embeddings.get_embedding")
+async def test_ensure_embedding_already_exists(mock_get, mock_store, mock_generate):
+    """Test ensure_embedding when embedding already exists."""
+    mock_get.return_value = {"embedding": "[0.1,0.2,0.3]"}
+
+    result = await embeddings.ensure_embedding(
+        version_id="test-id",
+        name="Test",
+        description="Test description",
+        sub_heading="Test heading",
+        categories=["test"],
+    )
+
+    assert result is True
+    mock_generate.assert_not_called()
+    mock_store.assert_not_called()
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.generate_embedding")
+@patch("backend.api.features.store.embeddings.store_content_embedding")
+@patch("backend.api.features.store.embeddings.get_embedding")
+async def test_ensure_embedding_create_new(mock_get, mock_store, mock_generate):
+    """Test ensure_embedding creating new embedding."""
+    mock_get.return_value = None
+    mock_generate.return_value = [0.1, 0.2, 0.3]
+    mock_store.return_value = True
+
+    result = await embeddings.ensure_embedding(
+        version_id="test-id",
+        name="Test",
+        description="Test description",
+        sub_heading="Test heading",
+        categories=["test"],
+    )
+
+    assert result is True
+    mock_generate.assert_called_once_with("Test Test heading Test description test")
+    mock_store.assert_called_once_with(
+        content_type=ContentType.STORE_AGENT,
+        content_id="test-id",
+        embedding=[0.1, 0.2, 0.3],
+        searchable_text="Test Test heading Test description test",
+        metadata={"name": "Test", "subHeading": "Test heading", "categories": ["test"]},
+        user_id=None,
+        tx=None,
+    )
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.generate_embedding")
+@patch("backend.api.features.store.embeddings.get_embedding")
+async def test_ensure_embedding_generation_fails(mock_get, mock_generate):
+    """Test ensure_embedding when generation fails."""
+    mock_get.return_value = None
+    mock_generate.return_value = None
+
+    result = await embeddings.ensure_embedding(
+        version_id="test-id",
+        name="Test",
+        description="Test description",
+        sub_heading="Test heading",
+        categories=["test"],
+    )
+
+    assert result is False
+
+
+@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}]
+
+    with patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        side_effect=[mock_approved_result, mock_embedded_result],
+    ):
+        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
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.ensure_embedding")
+async def test_backfill_missing_embeddings_success(mock_ensure):
+    """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 ensure_embedding to succeed for first, fail for second
+    mock_ensure.side_effect = [True, False]
+
+    with patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_missing,
+    ):
+        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
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_backfill_missing_embeddings_no_missing():
+    """Test backfill when no embeddings are missing."""
+    with patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=[],
+    ):
+        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")
+async def test_embedding_to_vector_string():
+    """Test embedding to PostgreSQL vector string conversion."""
+    embedding = [0.1, 0.2, 0.3, -0.4]
+    result = embeddings.embedding_to_vector_string(embedding)
+    assert result == "[0.1,0.2,0.3,-0.4]"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_embed_query():
+    """Test embed_query function (alias for generate_embedding)."""
+    with patch(
+        "backend.api.features.store.embeddings.generate_embedding"
+    ) as mock_generate:
+        mock_generate.return_value = [0.1, 0.2, 0.3]
+
+        result = await embeddings.embed_query("test query")
+
+        assert result == [0.1, 0.2, 0.3]
+        mock_generate.assert_called_once_with("test query")

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

@@ -0,0 +1,393 @@
+"""
+Hybrid Search for Store Agents
+
+Combines semantic (embedding) search with lexical (tsvector) search
+for improved relevance in marketplace agent discovery.
+"""
+
+import logging
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Any, Literal
+
+from backend.api.features.store.embeddings import (
+    embed_query,
+    embedding_to_vector_string,
+)
+from backend.data.db import query_raw_with_schema
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class HybridSearchWeights:
+    """Weights for combining search signals."""
+
+    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)
+
+    def __post_init__(self):
+        """Validate weights are non-negative and sum to approximately 1.0."""
+        total = (
+            self.semantic
+            + self.lexical
+            + self.category
+            + self.recency
+            + self.popularity
+        )
+
+        if any(
+            w < 0
+            for w in [
+                self.semantic,
+                self.lexical,
+                self.category,
+                self.recency,
+                self.popularity,
+            ]
+        ):
+            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
+
+
+async def hybrid_search(
+    query: str,
+    featured: bool = False,
+    creators: list[str] | None = None,
+    category: str | None = None,
+    sorted_by: (
+        Literal["relevance", "rating", "runs", "name", "updated_at"] | None
+    ) = None,
+    page: int = 1,
+    page_size: int = 20,
+    weights: HybridSearchWeights | None = None,
+    min_score: float | None = None,
+) -> tuple[list[dict[str, Any]], int]:
+    """
+    Perform hybrid search combining semantic and lexical signals.
+
+    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.
+    """
+    # Validate inputs
+    query = query.strip()
+    if not query:
+        return [], 0  # Empty query returns no results
+
+    if page < 1:
+        page = 1
+    if page_size < 1:
+        page_size = 1
+    if page_size > 100:  # Cap at reasonable limit to prevent performance issues
+        page_size = 100
+
+    if weights is None:
+        weights = DEFAULT_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)
+
+    # Build WHERE clause conditions
+    where_parts: list[str] = ["sa.is_available = true"]
+    params: list[Any] = []
+    param_index = 1
+
+    # Add search query for lexical matching
+    params.append(query)
+    query_param = f"${param_index}"
+    param_index += 1
+
+    # Add lowercased query for category matching
+    params.append(query.lower())
+    query_lower_param = f"${param_index}"
+    param_index += 1
+
+    if featured:
+        where_parts.append("sa.featured = true")
+
+    if creators:
+        where_parts.append(f"sa.creator_username = ANY(${param_index})")
+        params.append(creators)
+        param_index += 1
+
+    if category:
+        where_parts.append(f"${param_index} = ANY(sa.categories)")
+        params.append(category)
+        param_index += 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
+    params.append(weights.semantic)
+    weight_semantic_param = f"${param_index}"
+    param_index += 1
+
+    params.append(weights.lexical)
+    weight_lexical_param = f"${param_index}"
+    param_index += 1
+
+    params.append(weights.category)
+    weight_category_param = f"${param_index}"
+    param_index += 1
+
+    params.append(weights.recency)
+    weight_recency_param = f"${param_index}"
+    param_index += 1
+
+    params.append(weights.popularity)
+    weight_popularity_param = f"${param_index}"
+    param_index += 1
+
+    # Add min_score parameter
+    params.append(min_score)
+    min_score_param = f"${param_index}"
+    param_index += 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
+    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})
+
+                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
+                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}
+    """
+
+    # 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")
+
+    return results, total
+
+
+async def hybrid_search_simple(
+    query: str,
+    page: int = 1,
+    page_size: int = 20,
+) -> tuple[list[dict[str, Any]], int]:
+    """
+    Simplified hybrid search for common use cases.
+
+    Uses default weights and no filters.
+    """
+    return await hybrid_search(
+        query=query,
+        page=page,
+        page_size=page_size,
+    )

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

@@ -0,0 +1,334 @@
+"""
+Integration tests for hybrid search with schema handling.
+
+These tests verify that hybrid search works correctly across different database schemas.
+"""
+
+from unittest.mock import patch
+
+import pytest
+
+from backend.api.features.store.hybrid_search import HybridSearchWeights, hybrid_search
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_with_schema_handling():
+    """Test that hybrid search correctly handles database schema prefixes."""
+    # Test with a mock query to ensure schema handling works
+    query = "test agent"
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        # Mock the query result
+        mock_query.return_value = [
+            {
+                "slug": "test/agent",
+                "agent_name": "Test Agent",
+                "agent_image": "test.png",
+                "creator_username": "test",
+                "creator_avatar": "avatar.png",
+                "sub_heading": "Test sub-heading",
+                "description": "Test description",
+                "runs": 10,
+                "rating": 4.5,
+                "categories": ["test"],
+                "featured": False,
+                "is_available": True,
+                "updated_at": "2024-01-01T00:00:00Z",
+                "combined_score": 0.8,
+                "semantic_score": 0.7,
+                "lexical_score": 0.6,
+                "category_score": 0.5,
+                "recency_score": 0.4,
+                "total_count": 1,
+            }
+        ]
+
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_embed.return_value = [0.1] * 1536  # Mock embedding
+
+            results, total = await hybrid_search(
+                query=query,
+                page=1,
+                page_size=20,
+            )
+
+            # Verify the query was called
+            assert mock_query.called
+            # Verify the SQL template uses schema_prefix placeholder
+            call_args = mock_query.call_args
+            sql_template = call_args[0][0]
+            assert "{schema_prefix}" in sql_template
+
+            # Verify results
+            assert len(results) == 1
+            assert total == 1
+            assert results[0]["slug"] == "test/agent"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_with_public_schema():
+    """Test hybrid search when using public schema (no prefix needed)."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "public"
+
+        with patch(
+            "backend.api.features.store.hybrid_search.query_raw_with_schema"
+        ) as mock_query:
+            mock_query.return_value = []
+
+            with patch(
+                "backend.api.features.store.hybrid_search.embed_query"
+            ) as mock_embed:
+                mock_embed.return_value = [0.1] * 1536
+
+                results, total = await hybrid_search(
+                    query="test",
+                    page=1,
+                    page_size=20,
+                )
+
+                # Verify the mock was set up correctly
+                assert mock_schema.return_value == "public"
+
+                # Results should work even with empty results
+                assert results == []
+                assert total == 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_with_custom_schema():
+    """Test hybrid search when using custom schema (e.g., 'platform')."""
+    with patch("backend.data.db.get_database_schema") as mock_schema:
+        mock_schema.return_value = "platform"
+
+        with patch(
+            "backend.api.features.store.hybrid_search.query_raw_with_schema"
+        ) as mock_query:
+            mock_query.return_value = []
+
+            with patch(
+                "backend.api.features.store.hybrid_search.embed_query"
+            ) as mock_embed:
+                mock_embed.return_value = [0.1] * 1536
+
+                results, total = await hybrid_search(
+                    query="test",
+                    page=1,
+                    page_size=20,
+                )
+
+                # Verify the mock was set up correctly
+                assert mock_schema.return_value == "platform"
+
+                assert results == []
+                assert total == 0
+
+
+@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
+
+        # Should raise ValueError with helpful message
+        with pytest.raises(ValueError) as exc_info:
+            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)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_with_filters():
+    """Test hybrid search with various filters."""
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        mock_query.return_value = []
+
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_embed.return_value = [0.1] * 1536
+
+            # Test with featured filter
+            results, total = await hybrid_search(
+                query="test",
+                featured=True,
+                creators=["user1", "user2"],
+                category="productivity",
+                page=1,
+                page_size=10,
+            )
+
+            # Verify filters were applied in the query
+            call_args = mock_query.call_args
+            params = call_args[0][1:]  # Skip SQL template
+
+            # Should have query, query_lower, creators array, category
+            assert len(params) >= 4
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_weights():
+    """Test hybrid search with custom weights."""
+    custom_weights = HybridSearchWeights(
+        semantic=0.5,
+        lexical=0.3,
+        category=0.1,
+        recency=0.1,
+        popularity=0.0,
+    )
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        mock_query.return_value = []
+
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_embed.return_value = [0.1] * 1536
+
+            results, total = await hybrid_search(
+                query="test",
+                weights=custom_weights,
+                page=1,
+                page_size=20,
+            )
+
+            # Verify custom weights were used in the query
+            call_args = mock_query.call_args
+            sql_template = call_args[0][0]
+            params = call_args[0][1:]  # Get all parameters passed
+
+            # Check that SQL uses parameterized weights (not f-string interpolation)
+            assert "$" in sql_template  # Verify parameterization is used
+
+            # Check that custom weights are in the params
+            assert 0.5 in params  # semantic weight
+            assert 0.3 in params  # lexical weight
+            assert 0.1 in params  # category and recency weights
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_min_score_filtering():
+    """Test hybrid search minimum score threshold."""
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        # Return results with varying scores
+        mock_query.return_value = [
+            {
+                "slug": "high-score/agent",
+                "agent_name": "High Score Agent",
+                "combined_score": 0.8,
+                "total_count": 1,
+                # ... other fields
+            }
+        ]
+
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_embed.return_value = [0.1] * 1536
+
+            # Test with custom min_score
+            results, total = await hybrid_search(
+                query="test",
+                min_score=0.5,  # High threshold
+                page=1,
+                page_size=20,
+            )
+
+            # Verify min_score was applied in query
+            call_args = mock_query.call_args
+            sql_template = call_args[0][0]
+            params = call_args[0][1:]  # Get all parameters
+
+            # Check that SQL uses parameterized min_score
+            assert "combined_score >=" in sql_template
+            assert "$" in sql_template  # Verify parameterization
+
+            # Check that custom min_score is in the params
+            assert 0.5 in params
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_pagination():
+    """Test hybrid search pagination."""
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        mock_query.return_value = []
+
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_embed.return_value = [0.1] * 1536
+
+            # Test page 2 with page_size 10
+            results, total = await hybrid_search(
+                query="test",
+                page=2,
+                page_size=10,
+            )
+
+            # Verify pagination parameters
+            call_args = mock_query.call_args
+            params = call_args[0]
+
+            # Last two params should be LIMIT and OFFSET
+            limit = params[-2]
+            offset = params[-1]
+
+            assert limit == 10  # page_size
+            assert offset == 10  # (page - 1) * page_size = (2 - 1) * 10
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_hybrid_search_error_handling():
+    """Test hybrid search error handling."""
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        # Simulate database error
+        mock_query.side_effect = Exception("Database connection error")
+
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_embed.return_value = [0.1] * 1536
+
+            # Should raise exception
+            with pytest.raises(Exception) as exc_info:
+                await hybrid_search(
+                    query="test",
+                    page=1,
+                    page_size=20,
+                )
+
+            assert "Database connection error" in str(exc_info.value)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v", "-s"])

autogpt_platform/backend/backend/blocks/search.py

@@ -18,6 +18,7 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
+from backend.util.request import DEFAULT_USER_AGENT
 
 
 class GetWikipediaSummaryBlock(Block, GetRequest):
@@ -39,17 +40,27 @@ class GetWikipediaSummaryBlock(Block, GetRequest):
             output_schema=GetWikipediaSummaryBlock.Output,
             test_input={"topic": "Artificial Intelligence"},
             test_output=("summary", "summary content"),
-            test_mock={"get_request": lambda url, json: {"extract": "summary content"}},
+            test_mock={
+                "get_request": lambda url, headers, json: {"extract": "summary content"}
+            },
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         topic = input_data.topic
-        url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{topic}"
+        # URL-encode the topic to handle spaces and special characters
+        encoded_topic = quote(topic, safe="")
+        url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{encoded_topic}"
+
+        # Set headers per Wikimedia robot policy (https://w.wiki/4wJS)
+        # - User-Agent: Required, must identify the bot
+        # - Accept-Encoding: gzip recommended to reduce bandwidth
+        headers = {
+            "User-Agent": DEFAULT_USER_AGENT,
+            "Accept-Encoding": "gzip, deflate",
+        }
 
-        # Note: User-Agent is now automatically set by the request library
-        # to comply with Wikimedia's robot policy (https://w.wiki/4wJS)
         try:
-            response = await self.get_request(url, json=True)
+            response = await self.get_request(url, headers=headers, json=True)
             if "extract" not in response:
                 raise ValueError(f"Unable to parse Wikipedia response: {response}")
             yield "summary", response["extract"]

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -391,8 +391,12 @@ class SmartDecisionMakerBlock(Block):
         """
         block = sink_node.block
 
+        # Use custom name from node metadata if set, otherwise fall back to block.name
+        custom_name = sink_node.metadata.get("customized_name")
+        tool_name = custom_name if custom_name else block.name
+
         tool_function: dict[str, Any] = {
-            "name": SmartDecisionMakerBlock.cleanup(block.name),
+            "name": SmartDecisionMakerBlock.cleanup(tool_name),
             "description": block.description,
         }
         sink_block_input_schema = block.input_schema
@@ -489,14 +493,24 @@ class SmartDecisionMakerBlock(Block):
                 f"Sink graph metadata not found: {graph_id} {graph_version}"
             )
 
+        # Use custom name from node metadata if set, otherwise fall back to graph name
+        custom_name = sink_node.metadata.get("customized_name")
+        tool_name = custom_name if custom_name else sink_graph_meta.name
+
         tool_function: dict[str, Any] = {
-            "name": SmartDecisionMakerBlock.cleanup(sink_graph_meta.name),
+            "name": SmartDecisionMakerBlock.cleanup(tool_name),
             "description": sink_graph_meta.description,
         }
 
         properties = {}
+        field_mapping = {}
 
         for link in links:
+            field_name = link.sink_name
+
+            clean_field_name = SmartDecisionMakerBlock.cleanup(field_name)
+            field_mapping[clean_field_name] = field_name
+
             sink_block_input_schema = sink_node.input_default["input_schema"]
             sink_block_properties = sink_block_input_schema.get("properties", {}).get(
                 link.sink_name, {}
@@ -506,7 +520,7 @@ class SmartDecisionMakerBlock(Block):
                 if "description" in sink_block_properties
                 else f"The {link.sink_name} of the tool"
             )
-            properties[link.sink_name] = {
+            properties[clean_field_name] = {
                 "type": "string",
                 "description": description,
                 "default": json.dumps(sink_block_properties.get("default", None)),
@@ -519,7 +533,7 @@ class SmartDecisionMakerBlock(Block):
             "strict": True,
         }
 
-        # Store node info for later use in output processing
+        tool_function["_field_mapping"] = field_mapping
         tool_function["_sink_node_id"] = sink_node.id
 
         return {"type": "function", "function": tool_function}
@@ -1147,8 +1161,9 @@ class SmartDecisionMakerBlock(Block):
                 original_field_name = field_mapping.get(clean_arg_name, clean_arg_name)
                 arg_value = tool_args.get(clean_arg_name)
 
-                sanitized_arg_name = self.cleanup(original_field_name)
-                emit_key = f"tools_^_{sink_node_id}_~_{sanitized_arg_name}"
+                # Use original_field_name directly (not sanitized) to match link sink_name
+                # The field_mapping already translates from LLM's cleaned names to original names
+                emit_key = f"tools_^_{sink_node_id}_~_{original_field_name}"
 
                 logger.debug(
                     "[SmartDecisionMakerBlock|geid:%s|neid:%s] emit %s",

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker.py

@@ -1057,3 +1057,153 @@ async def test_smart_decision_maker_traditional_mode_default():
         )  # Should yield individual tool parameters
         assert "tools_^_test-sink-node-id_~_max_keyword_difficulty" in outputs
         assert "conversations" in outputs
+
+
+@pytest.mark.asyncio
+async def test_smart_decision_maker_uses_customized_name_for_blocks():
+    """Test that SmartDecisionMakerBlock uses customized_name from node metadata for tool names."""
+    from unittest.mock import MagicMock
+
+    from backend.blocks.basic import StoreValueBlock
+    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
+    from backend.data.graph import Link, Node
+
+    # Create a mock node with customized_name in metadata
+    mock_node = MagicMock(spec=Node)
+    mock_node.id = "test-node-id"
+    mock_node.block_id = StoreValueBlock().id
+    mock_node.metadata = {"customized_name": "My Custom Tool Name"}
+    mock_node.block = StoreValueBlock()
+
+    # Create a mock link
+    mock_link = MagicMock(spec=Link)
+    mock_link.sink_name = "input"
+
+    # Call the function directly
+    result = await SmartDecisionMakerBlock._create_block_function_signature(
+        mock_node, [mock_link]
+    )
+
+    # Verify the tool name uses the customized name (cleaned up)
+    assert result["type"] == "function"
+    assert result["function"]["name"] == "my_custom_tool_name"  # Cleaned version
+    assert result["function"]["_sink_node_id"] == "test-node-id"
+
+
+@pytest.mark.asyncio
+async def test_smart_decision_maker_falls_back_to_block_name():
+    """Test that SmartDecisionMakerBlock falls back to block.name when no customized_name."""
+    from unittest.mock import MagicMock
+
+    from backend.blocks.basic import StoreValueBlock
+    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
+    from backend.data.graph import Link, Node
+
+    # Create a mock node without customized_name
+    mock_node = MagicMock(spec=Node)
+    mock_node.id = "test-node-id"
+    mock_node.block_id = StoreValueBlock().id
+    mock_node.metadata = {}  # No customized_name
+    mock_node.block = StoreValueBlock()
+
+    # Create a mock link
+    mock_link = MagicMock(spec=Link)
+    mock_link.sink_name = "input"
+
+    # Call the function directly
+    result = await SmartDecisionMakerBlock._create_block_function_signature(
+        mock_node, [mock_link]
+    )
+
+    # Verify the tool name uses the block's default name
+    assert result["type"] == "function"
+    assert result["function"]["name"] == "storevalueblock"  # Default block name cleaned
+    assert result["function"]["_sink_node_id"] == "test-node-id"
+
+
+@pytest.mark.asyncio
+async def test_smart_decision_maker_uses_customized_name_for_agents():
+    """Test that SmartDecisionMakerBlock uses customized_name from metadata for agent nodes."""
+    from unittest.mock import AsyncMock, MagicMock, patch
+
+    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
+    from backend.data.graph import Link, Node
+
+    # Create a mock node with customized_name in metadata
+    mock_node = MagicMock(spec=Node)
+    mock_node.id = "test-agent-node-id"
+    mock_node.metadata = {"customized_name": "My Custom Agent"}
+    mock_node.input_default = {
+        "graph_id": "test-graph-id",
+        "graph_version": 1,
+        "input_schema": {"properties": {"test_input": {"description": "Test input"}}},
+    }
+
+    # Create a mock link
+    mock_link = MagicMock(spec=Link)
+    mock_link.sink_name = "test_input"
+
+    # Mock the database client
+    mock_graph_meta = MagicMock()
+    mock_graph_meta.name = "Original Agent Name"
+    mock_graph_meta.description = "Agent description"
+
+    mock_db_client = AsyncMock()
+    mock_db_client.get_graph_metadata.return_value = mock_graph_meta
+
+    with patch(
+        "backend.blocks.smart_decision_maker.get_database_manager_async_client",
+        return_value=mock_db_client,
+    ):
+        result = await SmartDecisionMakerBlock._create_agent_function_signature(
+            mock_node, [mock_link]
+        )
+
+    # Verify the tool name uses the customized name (cleaned up)
+    assert result["type"] == "function"
+    assert result["function"]["name"] == "my_custom_agent"  # Cleaned version
+    assert result["function"]["_sink_node_id"] == "test-agent-node-id"
+
+
+@pytest.mark.asyncio
+async def test_smart_decision_maker_agent_falls_back_to_graph_name():
+    """Test that agent node falls back to graph name when no customized_name."""
+    from unittest.mock import AsyncMock, MagicMock, patch
+
+    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
+    from backend.data.graph import Link, Node
+
+    # Create a mock node without customized_name
+    mock_node = MagicMock(spec=Node)
+    mock_node.id = "test-agent-node-id"
+    mock_node.metadata = {}  # No customized_name
+    mock_node.input_default = {
+        "graph_id": "test-graph-id",
+        "graph_version": 1,
+        "input_schema": {"properties": {"test_input": {"description": "Test input"}}},
+    }
+
+    # Create a mock link
+    mock_link = MagicMock(spec=Link)
+    mock_link.sink_name = "test_input"
+
+    # Mock the database client
+    mock_graph_meta = MagicMock()
+    mock_graph_meta.name = "Original Agent Name"
+    mock_graph_meta.description = "Agent description"
+
+    mock_db_client = AsyncMock()
+    mock_db_client.get_graph_metadata.return_value = mock_graph_meta
+
+    with patch(
+        "backend.blocks.smart_decision_maker.get_database_manager_async_client",
+        return_value=mock_db_client,
+    ):
+        result = await SmartDecisionMakerBlock._create_agent_function_signature(
+            mock_node, [mock_link]
+        )
+
+    # Verify the tool name uses the graph's default name
+    assert result["type"] == "function"
+    assert result["function"]["name"] == "original_agent_name"  # Graph name cleaned
+    assert result["function"]["_sink_node_id"] == "test-agent-node-id"

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker_dict.py

@@ -15,6 +15,7 @@ async def test_smart_decision_maker_handles_dynamic_dict_fields():
     mock_node.block = CreateDictionaryBlock()
     mock_node.block_id = CreateDictionaryBlock().id
     mock_node.input_default = {}
+    mock_node.metadata = {}
 
     # Create mock links with dynamic dictionary fields
     mock_links = [
@@ -77,6 +78,7 @@ async def test_smart_decision_maker_handles_dynamic_list_fields():
     mock_node.block = AddToListBlock()
     mock_node.block_id = AddToListBlock().id
     mock_node.input_default = {}
+    mock_node.metadata = {}
 
     # Create mock links with dynamic list fields
     mock_links = [

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker_dynamic_fields.py

@@ -44,6 +44,7 @@ async def test_create_block_function_signature_with_dict_fields():
     mock_node.block = CreateDictionaryBlock()
     mock_node.block_id = CreateDictionaryBlock().id
     mock_node.input_default = {}
+    mock_node.metadata = {}
 
     # Create mock links with dynamic dictionary fields (source sanitized, sink original)
     mock_links = [
@@ -106,6 +107,7 @@ async def test_create_block_function_signature_with_list_fields():
     mock_node.block = AddToListBlock()
     mock_node.block_id = AddToListBlock().id
     mock_node.input_default = {}
+    mock_node.metadata = {}
 
     # Create mock links with dynamic list fields
     mock_links = [
@@ -159,6 +161,7 @@ async def test_create_block_function_signature_with_object_fields():
     mock_node.block = MatchTextPatternBlock()
     mock_node.block_id = MatchTextPatternBlock().id
     mock_node.input_default = {}
+    mock_node.metadata = {}
 
     # Create mock links with dynamic object fields
     mock_links = [
@@ -208,11 +211,13 @@ async def test_create_tool_node_signatures():
         mock_dict_node.block = CreateDictionaryBlock()
         mock_dict_node.block_id = CreateDictionaryBlock().id
         mock_dict_node.input_default = {}
+        mock_dict_node.metadata = {}
 
         mock_list_node = Mock()
         mock_list_node.block = AddToListBlock()
         mock_list_node.block_id = AddToListBlock().id
         mock_list_node.input_default = {}
+        mock_list_node.metadata = {}
 
         # Mock links with dynamic fields
         dict_link1 = Mock(
@@ -423,6 +428,7 @@ async def test_mixed_regular_and_dynamic_fields():
     mock_node.block.name = "TestBlock"
     mock_node.block.description = "A test block"
     mock_node.block.input_schema = Mock()
+    mock_node.metadata = {}
 
     # Mock the get_field_schema to return a proper schema for regular fields
     def get_field_schema(field_name):

autogpt_platform/backend/backend/blocks/wordpress/__init__.py

@@ -1,3 +1,3 @@
-from .blog import WordPressCreatePostBlock
+from .blog import WordPressCreatePostBlock, WordPressGetAllPostsBlock
 
-__all__ = ["WordPressCreatePostBlock"]
+__all__ = ["WordPressCreatePostBlock", "WordPressGetAllPostsBlock"]

autogpt_platform/backend/backend/blocks/wordpress/_api.py

@@ -161,7 +161,7 @@ async def oauth_exchange_code_for_tokens(
         grant_type="authorization_code",
     ).model_dump(exclude_none=True)
 
-    response = await Requests().post(
+    response = await Requests(raise_for_status=False).post(
         f"{WORDPRESS_BASE_URL}oauth2/token",
         headers=headers,
         data=data,
@@ -205,7 +205,7 @@ async def oauth_refresh_tokens(
         grant_type="refresh_token",
     ).model_dump(exclude_none=True)
 
-    response = await Requests().post(
+    response = await Requests(raise_for_status=False).post(
         f"{WORDPRESS_BASE_URL}oauth2/token",
         headers=headers,
         data=data,
@@ -252,7 +252,7 @@ async def validate_token(
         "token": token,
     }
 
-    response = await Requests().get(
+    response = await Requests(raise_for_status=False).get(
         f"{WORDPRESS_BASE_URL}oauth2/token-info",
         params=params,
     )
@@ -296,7 +296,7 @@ async def make_api_request(
 
     url = f"{WORDPRESS_BASE_URL.rstrip('/')}{endpoint}"
 
-    request_method = getattr(Requests(), method.lower())
+    request_method = getattr(Requests(raise_for_status=False), method.lower())
     response = await request_method(
         url,
         headers=headers,
@@ -476,6 +476,7 @@ async def create_post(
         data["tags"] = ",".join(str(t) for t in data["tags"])
 
     # Make the API request
+    site = normalize_site(site)
     endpoint = f"/rest/v1.1/sites/{site}/posts/new"
 
     headers = {
@@ -483,7 +484,7 @@ async def create_post(
         "Content-Type": "application/x-www-form-urlencoded",
     }
 
-    response = await Requests().post(
+    response = await Requests(raise_for_status=False).post(
         f"{WORDPRESS_BASE_URL.rstrip('/')}{endpoint}",
         headers=headers,
         data=data,
@@ -499,3 +500,132 @@ async def create_post(
     )
     error_message = error_data.get("message", response.text)
     raise ValueError(f"Failed to create post: {response.status} - {error_message}")
+
+
+class Post(BaseModel):
+    """Response model for individual posts in a posts list response.
+
+    This is a simplified version compared to PostResponse, as the list endpoint
+    returns less detailed information than the create/get single post endpoints.
+    """
+
+    ID: int
+    site_ID: int
+    author: PostAuthor
+    date: datetime
+    modified: datetime
+    title: str
+    URL: str
+    short_URL: str
+    content: str | None = None
+    excerpt: str | None = None
+    slug: str
+    guid: str
+    status: str
+    sticky: bool
+    password: str | None = ""
+    parent: Union[Dict[str, Any], bool, None] = None
+    type: str
+    discussion: Dict[str, Union[str, bool, int]] | None = None
+    likes_enabled: bool | None = None
+    sharing_enabled: bool | None = None
+    like_count: int | None = None
+    i_like: bool | None = None
+    is_reblogged: bool | None = None
+    is_following: bool | None = None
+    global_ID: str | None = None
+    featured_image: str | None = None
+    post_thumbnail: Dict[str, Any] | None = None
+    format: str | None = None
+    geo: Union[Dict[str, Any], bool, None] = None
+    menu_order: int | None = None
+    page_template: str | None = None
+    publicize_URLs: List[str] | None = None
+    terms: Dict[str, Dict[str, Any]] | None = None
+    tags: Dict[str, Dict[str, Any]] | None = None
+    categories: Dict[str, Dict[str, Any]] | None = None
+    attachments: Dict[str, Dict[str, Any]] | None = None
+    attachment_count: int | None = None
+    metadata: List[Dict[str, Any]] | None = None
+    meta: Dict[str, Any] | None = None
+    capabilities: Dict[str, bool] | None = None
+    revisions: List[int] | None = None
+    other_URLs: Dict[str, Any] | None = None
+
+
+class PostsResponse(BaseModel):
+    """Response model for WordPress posts list."""
+
+    found: int
+    posts: List[Post]
+    meta: Dict[str, Any]
+
+
+def normalize_site(site: str) -> str:
+    """
+    Normalize a site identifier by stripping protocol and trailing slashes.
+
+    Args:
+        site: Site URL, domain, or ID (e.g., "https://myblog.wordpress.com/", "myblog.wordpress.com", "123456789")
+
+    Returns:
+        Normalized site identifier (domain or ID only)
+    """
+    site = site.strip()
+    if site.startswith("https://"):
+        site = site[8:]
+    elif site.startswith("http://"):
+        site = site[7:]
+    return site.rstrip("/")
+
+
+async def get_posts(
+    credentials: Credentials,
+    site: str,
+    status: PostStatus | None = None,
+    number: int = 100,
+    offset: int = 0,
+) -> PostsResponse:
+    """
+    Get posts from a WordPress site.
+
+    Args:
+        credentials: OAuth credentials
+        site: Site ID or domain (e.g., "myblog.wordpress.com" or "123456789")
+        status: Filter by post status using PostStatus enum, or None for all
+        number: Number of posts to retrieve (max 100)
+        offset: Number of posts to skip (for pagination)
+
+    Returns:
+        PostsResponse with the list of posts
+    """
+    site = normalize_site(site)
+    endpoint = f"/rest/v1.1/sites/{site}/posts"
+
+    headers = {
+        "Authorization": credentials.auth_header(),
+    }
+
+    params: Dict[str, Any] = {
+        "number": max(1, min(number, 100)),  # 1–100 posts per request
+        "offset": offset,
+    }
+
+    if status:
+        params["status"] = status.value
+    response = await Requests(raise_for_status=False).get(
+        f"{WORDPRESS_BASE_URL.rstrip('/')}{endpoint}",
+        headers=headers,
+        params=params,
+    )
+
+    if response.ok:
+        return PostsResponse.model_validate(response.json())
+
+    error_data = (
+        response.json()
+        if response.headers.get("content-type", "").startswith("application/json")
+        else {}
+    )
+    error_message = error_data.get("message", response.text)
+    raise ValueError(f"Failed to get posts: {response.status} - {error_message}")

autogpt_platform/backend/backend/blocks/wordpress/blog.py

@@ -9,7 +9,15 @@ from backend.sdk import (
     SchemaField,
 )
 
-from ._api import CreatePostRequest, PostResponse, PostStatus, create_post
+from ._api import (
+    CreatePostRequest,
+    Post,
+    PostResponse,
+    PostsResponse,
+    PostStatus,
+    create_post,
+    get_posts,
+)
 from ._config import wordpress
 
 
@@ -49,8 +57,15 @@ class WordPressCreatePostBlock(Block):
         media_urls: list[str] = SchemaField(
             description="URLs of images to sideload and attach to the post", default=[]
         )
+        publish_as_draft: bool = SchemaField(
+            description="If True, publishes the post as a draft. If False, publishes it publicly.",
+            default=False,
+        )
 
     class Output(BlockSchemaOutput):
+        site: str = SchemaField(
+            description="The site ID or domain (pass-through for chaining with other blocks)"
+        )
         post_id: int = SchemaField(description="The ID of the created post")
         post_url: str = SchemaField(description="The full URL of the created post")
         short_url: str = SchemaField(description="The shortened wp.me URL")
@@ -78,7 +93,9 @@ class WordPressCreatePostBlock(Block):
             tags=input_data.tags,
             featured_image=input_data.featured_image,
             media_urls=input_data.media_urls,
-            status=PostStatus.PUBLISH,
+            status=(
+                PostStatus.DRAFT if input_data.publish_as_draft else PostStatus.PUBLISH
+            ),
         )
 
         post_response: PostResponse = await create_post(
@@ -87,7 +104,69 @@ class WordPressCreatePostBlock(Block):
             post_data=post_request,
         )
 
+        yield "site", input_data.site
         yield "post_id", post_response.ID
         yield "post_url", post_response.URL
         yield "short_url", post_response.short_URL
         yield "post_data", post_response.model_dump()
+
+
+class WordPressGetAllPostsBlock(Block):
+    """
+    Fetches all posts from a WordPress.com site or Jetpack-enabled site.
+    Supports filtering by status and pagination.
+    """
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = wordpress.credentials_field()
+        site: str = SchemaField(
+            description="Site ID or domain (e.g., 'myblog.wordpress.com' or '123456789')"
+        )
+        status: PostStatus | None = SchemaField(
+            description="Filter by post status, or None for all",
+            default=None,
+        )
+        number: int = SchemaField(
+            description="Number of posts to retrieve (max 100 per request)", default=20
+        )
+        offset: int = SchemaField(
+            description="Number of posts to skip (for pagination)", default=0
+        )
+
+    class Output(BlockSchemaOutput):
+        site: str = SchemaField(
+            description="The site ID or domain (pass-through for chaining with other blocks)"
+        )
+        found: int = SchemaField(description="Total number of posts found")
+        posts: list[Post] = SchemaField(
+            description="List of post objects with their details"
+        )
+        post: Post = SchemaField(
+            description="Individual post object (yielded for each post)"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="97728fa7-7f6f-4789-ba0c-f2c114119536",
+            description="Fetch all posts from WordPress.com or Jetpack sites",
+            categories={BlockCategory.SOCIAL},
+            input_schema=self.Input,
+            output_schema=self.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: Credentials, **kwargs
+    ) -> BlockOutput:
+        posts_response: PostsResponse = await get_posts(
+            credentials=credentials,
+            site=input_data.site,
+            status=input_data.status,
+            number=input_data.number,
+            offset=input_data.offset,
+        )
+
+        yield "site", input_data.site
+        yield "found", posts_response.found
+        yield "posts", posts_response.posts
+        for post in posts_response.posts:
+            yield "post", post

autogpt_platform/backend/backend/data/db.py

@@ -38,6 +38,20 @@ POOL_TIMEOUT = os.getenv("DB_POOL_TIMEOUT")
 if POOL_TIMEOUT:
     DATABASE_URL = add_param(DATABASE_URL, "pool_timeout", POOL_TIMEOUT)
 
+# Add public schema to search_path for pgvector type access
+# The vector extension is in public schema, but search_path is determined by schema parameter
+# Extract the schema from DATABASE_URL or default to 'platform'
+parsed_url = urlparse(DATABASE_URL)
+url_params = dict(parse_qsl(parsed_url.query))
+db_schema = url_params.get("schema", "platform")
+# Build search_path, avoiding duplicates if db_schema is already 'public'
+search_path_schemas = list(
+    dict.fromkeys([db_schema, "public"])
+)  # Preserves order, removes duplicates
+search_path = ",".join(search_path_schemas)
+# This allows using ::vector without schema qualification
+DATABASE_URL = add_param(DATABASE_URL, "options", f"-c search_path={search_path}")
+
 HTTP_TIMEOUT = int(POOL_TIMEOUT) if POOL_TIMEOUT else None
 
 prisma = Prisma(
@@ -108,21 +122,102 @@ def get_database_schema() -> str:
     return query_params.get("schema", "public")
 
 
-async def query_raw_with_schema(query_template: str, *args) -> list[dict]:
-    """Execute raw SQL query with proper schema handling."""
+async def _raw_with_schema(
+    query_template: str,
+    *args,
+    execute: bool = False,
+    client: Prisma | None = None,
+    set_public_search_path: bool = False,
+) -> list[dict] | int:
+    """Internal: Execute raw SQL with proper schema handling.
+
+    Use query_raw_with_schema() or execute_raw_with_schema() instead.
+
+    Args:
+        query_template: SQL query with {schema_prefix} placeholder
+        *args: Query parameters
+        execute: If False, executes SELECT query. If True, executes INSERT/UPDATE/DELETE.
+        client: Optional Prisma client for transactions (only used when execute=True).
+        set_public_search_path: If True, sets search_path to include public schema.
+                                Needed for pgvector types and other public schema objects.
+
+    Returns:
+        - list[dict] if execute=False (query results)
+        - int if execute=True (number of affected rows)
+    """
     schema = get_database_schema()
     schema_prefix = f'"{schema}".' if schema != "public" else ""
     formatted_query = query_template.format(schema_prefix=schema_prefix)
 
     import prisma as prisma_module
 
-    result = await prisma_module.get_client().query_raw(
-        formatted_query, *args  # type: ignore
-    )
+    db_client = client if client else prisma_module.get_client()
+
+    # Set search_path to include public schema if requested
+    # Prisma doesn't support the 'options' connection parameter, so we set it per-session
+    # This is idempotent and safe to call multiple times
+    if set_public_search_path:
+        await db_client.execute_raw(f"SET search_path = {schema}, public")  # type: ignore
+
+    if execute:
+        result = await db_client.execute_raw(formatted_query, *args)  # type: ignore
+    else:
+        result = await db_client.query_raw(formatted_query, *args)  # type: ignore
 
     return result
 
 
+async def query_raw_with_schema(
+    query_template: str, *args, set_public_search_path: bool = False
+) -> list[dict]:
+    """Execute raw SQL SELECT query with proper schema handling.
+
+    Args:
+        query_template: SQL query with {schema_prefix} placeholder
+        *args: Query parameters
+        set_public_search_path: If True, sets search_path to include public schema.
+                                Needed for pgvector types and other public schema objects.
+
+    Returns:
+        List of result rows as dictionaries
+
+    Example:
+        results = await query_raw_with_schema(
+            'SELECT * FROM {schema_prefix}"User" WHERE id = $1',
+            user_id
+        )
+    """
+    return await _raw_with_schema(query_template, *args, execute=False, set_public_search_path=set_public_search_path)  # type: ignore
+
+
+async def execute_raw_with_schema(
+    query_template: str,
+    *args,
+    client: Prisma | None = None,
+    set_public_search_path: bool = False,
+) -> int:
+    """Execute raw SQL command (INSERT/UPDATE/DELETE) with proper schema handling.
+
+    Args:
+        query_template: SQL query with {schema_prefix} placeholder
+        *args: Query parameters
+        client: Optional Prisma client for transactions
+        set_public_search_path: If True, sets search_path to include public schema.
+                                Needed for pgvector types and other public schema objects.
+
+    Returns:
+        Number of affected rows
+
+    Example:
+        await execute_raw_with_schema(
+            'INSERT INTO {schema_prefix}"User" (id, name) VALUES ($1, $2)',
+            user_id, name,
+            client=tx  # Optional transaction client
+        )
+    """
+    return await _raw_with_schema(query_template, *args, execute=True, client=client, set_public_search_path=set_public_search_path)  # type: ignore
+
+
 class BaseDbModel(BaseModel):
     id: str = Field(default_factory=lambda: str(uuid4()))
 

autogpt_platform/backend/backend/data/graph_test.py

@@ -1,5 +1,6 @@
 import json
 from typing import Any
+from unittest.mock import AsyncMock, patch
 from uuid import UUID
 
 import fastapi.exceptions
@@ -18,6 +19,17 @@ from backend.usecases.sample import create_test_user
 from backend.util.test import SpinTestServer
 
 
+@pytest.fixture(scope="session", autouse=True)
+def mock_embedding_functions():
+    """Mock embedding functions for all tests to avoid database/API dependencies."""
+    with patch(
+        "backend.api.features.store.db.ensure_embedding",
+        new_callable=AsyncMock,
+        return_value=True,
+    ):
+        yield
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_graph_creation(server: SpinTestServer, snapshot: Snapshot):
     """

autogpt_platform/backend/backend/executor/database.py

@@ -7,6 +7,10 @@ from backend.api.features.library.db import (
     list_library_agents,
 )
 from backend.api.features.store.db import get_store_agent_details, get_store_agents
+from backend.api.features.store.embeddings import (
+    backfill_missing_embeddings,
+    get_embedding_stats,
+)
 from backend.data import db
 from backend.data.analytics import (
     get_accuracy_trends_and_alerts,
@@ -208,6 +212,10 @@ class DatabaseManager(AppService):
     get_store_agents = _(get_store_agents)
     get_store_agent_details = _(get_store_agent_details)
 
+    # Store Embeddings
+    get_embedding_stats = _(get_embedding_stats)
+    backfill_missing_embeddings = _(backfill_missing_embeddings)
+
     # Summary data - async
     get_user_execution_summary_data = _(get_user_execution_summary_data)
 
@@ -259,6 +267,10 @@ class DatabaseManagerClient(AppServiceClient):
     get_store_agents = _(d.get_store_agents)
     get_store_agent_details = _(d.get_store_agent_details)
 
+    # Store Embeddings
+    get_embedding_stats = _(d.get_embedding_stats)
+    backfill_missing_embeddings = _(d.backfill_missing_embeddings)
+
 
 class DatabaseManagerAsyncClient(AppServiceClient):
     d = DatabaseManager

autogpt_platform/backend/backend/executor/manager_test.py

@@ -1,4 +1,5 @@
 import logging
+from unittest.mock import AsyncMock, patch
 
 import fastapi.responses
 import pytest
@@ -19,6 +20,17 @@ from backend.util.test import SpinTestServer, wait_execution
 logger = logging.getLogger(__name__)
 
 
+@pytest.fixture(scope="session", autouse=True)
+def mock_embedding_functions():
+    """Mock embedding functions for all tests to avoid database/API dependencies."""
+    with patch(
+        "backend.api.features.store.db.ensure_embedding",
+        new_callable=AsyncMock,
+        return_value=True,
+    ):
+        yield
+
+
 async def create_graph(s: SpinTestServer, g: graph.Graph, u: User) -> graph.Graph:
     logger.info(f"Creating graph for user {u.id}")
     return await s.agent_server.test_create_graph(CreateGraph(graph=g), u.id)

autogpt_platform/backend/backend/executor/scheduler.py

@@ -2,6 +2,7 @@ import asyncio
 import logging
 import os
 import threading
+import time
 import uuid
 from enum import Enum
 from typing import Optional
@@ -37,7 +38,7 @@ from backend.monitoring import (
     report_execution_accuracy_alerts,
     report_late_executions,
 )
-from backend.util.clients import get_scheduler_client
+from backend.util.clients import get_database_manager_client, get_scheduler_client
 from backend.util.cloud_storage import cleanup_expired_files_async
 from backend.util.exceptions import (
     GraphNotFoundError,
@@ -254,6 +255,88 @@ def execution_accuracy_alerts():
     return report_execution_accuracy_alerts()
 
 
+def ensure_embeddings_coverage():
+    """
+    Ensure all content types (store agents, blocks, docs) have embeddings for 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 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()
+    stats = db_client.get_embedding_stats()
+
+    # Check for error from get_embedding_stats() first
+    if "error" in stats:
+        logger.error(
+            f"Failed to get embedding stats: {stats['error']} - skipping backfill"
+        )
+        return {"processed": 0, "success": 0, "failed": 0, "error": stats["error"]}
+
+    # Extract totals from new stats structure
+    totals = stats.get("totals", {})
+    without_embeddings = totals.get("without_embeddings", 0)
+    coverage_percent = totals.get("coverage_percent", 0)
+
+    if without_embeddings == 0:
+        logger.info("All content has embeddings, skipping backfill")
+        return {"processed": 0, "success": 0, "failed": 0}
+
+    # 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)"
+            )
+
+    logger.info(
+        f"Total: {without_embeddings} items without embeddings "
+        f"({coverage_percent}% coverage) - processing all"
+    )
+
+    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)
+
+        total_processed += result["processed"]
+        total_success += result["success"]
+        total_failed += result["failed"]
+
+        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"
+    )
+    return {
+        "processed": total_processed,
+        "success": total_success,
+        "failed": total_failed,
+    }
+
+
 # Monitoring functions are now imported from monitoring module
 
 
@@ -475,6 +558,19 @@ class Scheduler(AppService):
                 jobstore=Jobstores.EXECUTION.value,
             )
 
+            # Embedding Coverage - Every 6 hours
+            # Ensures all approved agents have embeddings for hybrid search
+            # Critical: missing embeddings = agents invisible in search
+            self.scheduler.add_job(
+                ensure_embeddings_coverage,
+                id="ensure_embeddings_coverage",
+                trigger="interval",
+                hours=6,
+                replace_existing=True,
+                max_instances=1,  # Prevent overlapping runs
+                jobstore=Jobstores.EXECUTION.value,
+            )
+
         self.scheduler.add_listener(job_listener, EVENT_JOB_EXECUTED | EVENT_JOB_ERROR)
         self.scheduler.add_listener(job_missed_listener, EVENT_JOB_MISSED)
         self.scheduler.add_listener(job_max_instances_listener, EVENT_JOB_MAX_INSTANCES)
@@ -632,6 +728,11 @@ class Scheduler(AppService):
         """Manually trigger execution accuracy alert checking."""
         return execution_accuracy_alerts()
 
+    @expose
+    def execute_ensure_embeddings_coverage(self):
+        """Manually trigger embedding backfill for approved store agents."""
+        return ensure_embeddings_coverage()
+
 
 class SchedulerClient(AppServiceClient):
     @classmethod

autogpt_platform/backend/backend/util/clients.py

@@ -10,6 +10,7 @@ from backend.util.settings import Settings
 settings = Settings()
 
 if TYPE_CHECKING:
+    from openai import AsyncOpenAI
     from supabase import AClient, Client
 
     from backend.data.execution import (
@@ -139,6 +140,24 @@ async def get_async_supabase() -> "AClient":
     )
 
 
+# ============ OpenAI Client ============ #
+
+
+@cached(ttl_seconds=3600)
+def get_openai_client() -> "AsyncOpenAI | None":
+    """
+    Get a process-cached async OpenAI client for embeddings.
+
+    Returns None if API key is not configured.
+    """
+    from openai import AsyncOpenAI
+
+    api_key = settings.secrets.openai_internal_api_key
+    if not api_key:
+        return None
+    return AsyncOpenAI(api_key=api_key)
+
+
 # ============ Notification Queue Helpers ============ #
 
 

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

@@ -0,0 +1,46 @@
+-- CreateExtension
+-- Supabase: pgvector must be enabled via Dashboard → Database → Extensions first
+-- Create in public schema so vector type is available across all schemas
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "vector" WITH SCHEMA "public";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'vector extension not available or already exists, skipping';
+END $$;
+
+-- CreateEnum
+CREATE TYPE "ContentType" AS ENUM ('STORE_AGENT', 'BLOCK', 'INTEGRATION', 'DOCUMENTATION', 'LIBRARY_AGENT');
+
+-- CreateTable
+CREATE TABLE "UnifiedContentEmbedding" (
+    "id" TEXT NOT NULL,
+    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "updatedAt" TIMESTAMP(3) NOT NULL,
+    "contentType" "ContentType" NOT NULL,
+    "contentId" TEXT NOT NULL,
+    "userId" TEXT,
+    "embedding" public.vector(1536) NOT NULL,
+    "searchableText" TEXT NOT NULL,
+    "metadata" JSONB NOT NULL DEFAULT '{}',
+
+    CONSTRAINT "UnifiedContentEmbedding_pkey" PRIMARY KEY ("id")
+);
+
+-- CreateIndex
+CREATE INDEX "UnifiedContentEmbedding_contentType_idx" ON "UnifiedContentEmbedding"("contentType");
+
+-- CreateIndex
+CREATE INDEX "UnifiedContentEmbedding_userId_idx" ON "UnifiedContentEmbedding"("userId");
+
+-- CreateIndex
+CREATE INDEX "UnifiedContentEmbedding_contentType_userId_idx" ON "UnifiedContentEmbedding"("contentType", "userId");
+
+-- CreateIndex
+-- NULLS NOT DISTINCT ensures only one public (NULL userId) embedding per contentType+contentId
+-- Requires PostgreSQL 15+. Supabase uses PostgreSQL 15+.
+CREATE UNIQUE INDEX "UnifiedContentEmbedding_contentType_contentId_userId_key" ON "UnifiedContentEmbedding"("contentType", "contentId", "userId") NULLS NOT DISTINCT;
+
+-- CreateIndex
+-- HNSW index for fast vector similarity search on embeddings
+-- Uses cosine distance operator (<=>), which matches the query in hybrid_search.py
+CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" public.vector_cosine_ops);

autogpt_platform/backend/migrations/20260112173500_add_supabase_extensions_to_platform_schema/migration.sql

@@ -0,0 +1,71 @@
+-- Acknowledge Supabase-managed extensions to prevent drift warnings
+-- These extensions are pre-installed by Supabase in specific schemas
+-- This migration ensures they exist where available (Supabase) or skips gracefully (CI)
+
+-- Create schemas (safe in both CI and Supabase)
+CREATE SCHEMA IF NOT EXISTS "extensions";
+
+-- Extensions that exist in both CI and Supabase
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "pgcrypto" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pgcrypto extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "uuid-ossp" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'uuid-ossp extension not available, skipping';
+END $$;
+
+-- Supabase-specific extensions (skip gracefully in CI)
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "pg_stat_statements" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pg_stat_statements extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "pg_net" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pg_net extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "pgjwt" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pgjwt extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE SCHEMA IF NOT EXISTS "graphql";
+    CREATE EXTENSION IF NOT EXISTS "pg_graphql" WITH SCHEMA "graphql";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pg_graphql extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE SCHEMA IF NOT EXISTS "pgsodium";
+    CREATE EXTENSION IF NOT EXISTS "pgsodium" WITH SCHEMA "pgsodium";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pgsodium extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE SCHEMA IF NOT EXISTS "vault";
+    CREATE EXTENSION IF NOT EXISTS "supabase_vault" WITH SCHEMA "vault";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'supabase_vault extension not available, skipping';
+END $$;
+
+
+-- Return to platform
+CREATE SCHEMA IF NOT EXISTS "platform";

autogpt_platform/backend/schema.prisma

@@ -1,14 +1,15 @@
 datasource db {
-  provider  = "postgresql"
-  url       = env("DATABASE_URL")
-  directUrl = env("DIRECT_URL")
+  provider   = "postgresql"
+  url        = env("DATABASE_URL")
+  directUrl  = env("DIRECT_URL")
+  extensions = [pgvector(map: "vector")]
 }
 
 generator client {
   provider               = "prisma-client-py"
   recursive_type_depth   = -1
   interface              = "asyncio"
-  previewFeatures        = ["views", "fullTextSearch"]
+  previewFeatures        = ["views", "fullTextSearch", "postgresqlExtensions"]
   partial_type_generator = "backend/data/partial_types.py"
 }
 
@@ -127,8 +128,8 @@ model BuilderSearchHistory {
   updatedAt DateTime @default(now()) @updatedAt
 
   searchQuery String
-  filter      String[]  @default([])
-  byCreator   String[]  @default([])
+  filter      String[] @default([])
+  byCreator   String[] @default([])
 
   userId String
   User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
@@ -721,26 +722,25 @@ view StoreAgent {
   storeListingVersionId String
   updated_at            DateTime
 
-  slug                String
-  agent_name          String
-  agent_video         String?
-  agent_output_demo   String?
-  agent_image         String[]
+  slug              String
+  agent_name        String
+  agent_video       String?
+  agent_output_demo String?
+  agent_image       String[]
 
-  featured         Boolean                  @default(false)
-  creator_username String?
-  creator_avatar   String?
-  sub_heading      String
-  description      String
-  categories       String[]
-  search           Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
-  runs                Int
-  rating              Float
-  versions            String[]
-  agentGraphVersions  String[]
-  agentGraphId        String
-  is_available        Boolean                  @default(true)
-  useForOnboarding    Boolean                  @default(false)
+  featured           Boolean  @default(false)
+  creator_username   String?
+  creator_avatar     String?
+  sub_heading        String
+  description        String
+  categories         String[]
+  runs               Int
+  rating             Float
+  versions           String[]
+  agentGraphVersions String[]
+  agentGraphId       String
+  is_available       Boolean  @default(true)
+  useForOnboarding   Boolean  @default(false)
 
   // Materialized views used (refreshed every 15 minutes via pg_cron):
   // - mv_agent_run_counts - Pre-aggregated agent execution counts by agentGraphId
@@ -856,14 +856,14 @@ model StoreListingVersion {
   AgentGraph        AgentGraph @relation(fields: [agentGraphId, agentGraphVersion], references: [id, version])
 
   // Content fields
-  name                String
-  subHeading          String
-  videoUrl            String?
-  agentOutputDemoUrl  String?
-  imageUrls           String[]
-  description         String
-  instructions        String?
-  categories          String[]
+  name               String
+  subHeading         String
+  videoUrl           String?
+  agentOutputDemoUrl String?
+  imageUrls          String[]
+  description        String
+  instructions       String?
+  categories         String[]
 
   isFeatured Boolean @default(false)
 
@@ -899,6 +899,9 @@ model StoreListingVersion {
   // Reviews for this specific version
   Reviews StoreListingReview[]
 
+  // Note: Embeddings now stored in UnifiedContentEmbedding table
+  // Use contentType=STORE_AGENT and contentId=storeListingVersionId
+
   @@unique([storeListingId, version])
   @@index([storeListingId, submissionStatus, isAvailable])
   @@index([submissionStatus])
@@ -906,6 +909,42 @@ model StoreListingVersion {
   @@index([agentGraphId, agentGraphVersion]) // Non-unique index for efficient lookups
 }
 
+// Content type enum for unified search across store agents, blocks, docs
+// Note: BLOCK/INTEGRATION are file-based (Python classes), not DB records
+// DOCUMENTATION are file-based (.md files), not DB records  
+// Only STORE_AGENT and LIBRARY_AGENT are stored in database
+enum ContentType {
+  STORE_AGENT // Database: StoreListingVersion
+  BLOCK // File-based: Python classes in /backend/blocks/
+  INTEGRATION // File-based: Python classes (blocks with credentials)
+  DOCUMENTATION // File-based: .md/.mdx files
+  LIBRARY_AGENT // Database: User's personal agents
+}
+
+// Unified embeddings table for all searchable content types
+// Supports both public content (userId=null) and user-specific content (userId=userID)
+model UnifiedContentEmbedding {
+  id        String   @id @default(uuid())
+  createdAt DateTime @default(now())
+  updatedAt DateTime @updatedAt
+
+  // Content identification
+  contentType ContentType
+  contentId   String // DB ID (storeListingVersionId) or file identifier (block.id, file_path)
+  userId      String? // NULL for public content (store, blocks, docs), userId for private content (library agents)
+
+  // Search data
+  embedding      Unsupported("vector(1536)") // pgvector embedding (extension in platform schema)
+  searchableText String // Combined text for search and fallback
+  metadata       Json                        @default("{}") // Content-specific metadata
+
+  @@unique([contentType, contentId, userId], map: "UnifiedContentEmbedding_contentType_contentId_userId_key")
+  @@index([contentType])
+  @@index([userId])
+  @@index([contentType, userId])
+  @@index([embedding], map: "UnifiedContentEmbedding_embedding_idx")
+}
+
 model StoreListingReview {
   id        String   @id @default(uuid())
   createdAt DateTime @default(now())
@@ -998,16 +1037,16 @@ model OAuthApplication {
   updatedAt DateTime @updatedAt
 
   // Application metadata
-  name               String
-  description        String?
-  logoUrl            String? // URL to app logo stored in GCS
-  clientId           String @unique
-  clientSecret       String // Hashed with Scrypt (same as API keys)
-  clientSecretSalt   String // Salt for Scrypt hashing
+  name             String
+  description      String?
+  logoUrl          String? // URL to app logo stored in GCS
+  clientId         String  @unique
+  clientSecret     String // Hashed with Scrypt (same as API keys)
+  clientSecretSalt String // Salt for Scrypt hashing
 
   // OAuth configuration
   redirectUris String[] // Allowed callback URLs
-  grantTypes   String[] @default(["authorization_code", "refresh_token"])
+  grantTypes   String[]           @default(["authorization_code", "refresh_token"])
   scopes       APIKeyPermission[] // Which permissions the app can request
 
   // Application management

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

@@ -68,7 +68,10 @@ export const NodeHeader = ({ data, nodeId }: Props) => {
                 <Tooltip>
                   <TooltipTrigger asChild>
                     <div>
-                      <Text variant="large-semibold" className="line-clamp-1">
+                      <Text
+                        variant="large-semibold"
+                        className="line-clamp-1 hover:cursor-text"
+                      >
                         {beautifyString(title).replace("Block", "").trim()}
                       </Text>
                     </div>

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/helpers.ts

@@ -89,6 +89,18 @@ export function extractOptions(
 
 // get display type and color for schema types [need for type display next to field name]
 export const getTypeDisplayInfo = (schema: any) => {
+  if (
+    schema?.type === "array" &&
+    "format" in schema &&
+    schema.format === "table"
+  ) {
+    return {
+      displayType: "table",
+      colorClass: "!text-indigo-500",
+      hexColor: "#6366f1",
+    };
+  }
+
   if (schema?.type === "string" && schema?.format) {
     const formatMap: Record<
       string,

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs.tsx

@@ -36,6 +36,7 @@ type Props = {
   readOnly?: boolean;
   isOptional?: boolean;
   showTitle?: boolean;
+  variant?: "default" | "node";
 };
 
 export function CredentialsInput({
@@ -48,6 +49,7 @@ export function CredentialsInput({
   readOnly = false,
   isOptional = false,
   showTitle = true,
+  variant = "default",
 }: Props) {
   const hookData = useCredentialsInput({
     schema,
@@ -123,6 +125,7 @@ export function CredentialsInput({
               onClearCredential={() => onSelectCredential(undefined)}
               readOnly={readOnly}
               allowNone={isOptional}
+              variant={variant}
             />
           ) : (
             <div className="mb-4 space-y-2">

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/components/CredentialRow/CredentialRow.tsx

@@ -30,6 +30,8 @@ type CredentialRowProps = {
   readOnly?: boolean;
   showCaret?: boolean;
   asSelectTrigger?: boolean;
+  /** When "node", applies compact styling for node context */
+  variant?: "default" | "node";
 };
 
 export function CredentialRow({
@@ -41,14 +43,22 @@ export function CredentialRow({
   readOnly = false,
   showCaret = false,
   asSelectTrigger = false,
+  variant = "default",
 }: CredentialRowProps) {
   const ProviderIcon = providerIcons[provider] || fallbackIcon;
+  const isNodeVariant = variant === "node";
 
   return (
     <div
       className={cn(
         "flex items-center gap-3 rounded-medium border border-zinc-200 bg-white p-3 transition-colors",
-        asSelectTrigger ? "border-0 bg-transparent" : readOnly ? "w-fit" : "",
+        asSelectTrigger && isNodeVariant
+          ? "min-w-0 flex-1 overflow-hidden border-0 bg-transparent"
+          : asSelectTrigger
+            ? "border-0 bg-transparent"
+            : readOnly
+              ? "w-fit"
+              : "",
       )}
       onClick={readOnly || showCaret || asSelectTrigger ? undefined : onSelect}
       style={
@@ -61,19 +71,31 @@ export function CredentialRow({
         <ProviderIcon className="h-3 w-3 text-white" />
       </div>
       <IconKey className="h-5 w-5 shrink-0 text-zinc-800" />
-      <div className="flex min-w-0 flex-1 flex-nowrap items-center gap-4">
+      <div
+        className={cn(
+          "flex min-w-0 flex-1 flex-nowrap items-center gap-4",
+          isNodeVariant && "overflow-hidden",
+        )}
+      >
         <Text
           variant="body"
-          className="line-clamp-1 flex-[0_0_50%] text-ellipsis tracking-tight"
+          className={cn(
+            "tracking-tight",
+            isNodeVariant
+              ? "truncate"
+              : "line-clamp-1 flex-[0_0_50%] text-ellipsis",
+          )}
         >
           {getCredentialDisplayName(credential, displayName)}
         </Text>
-        <Text
-          variant="large"
-          className="lex-[0_0_40%] relative top-1 hidden overflow-hidden whitespace-nowrap font-mono tracking-tight md:block"
-        >
-          {"*".repeat(MASKED_KEY_LENGTH)}
-        </Text>
+        {!(asSelectTrigger && isNodeVariant) && (
+          <Text
+            variant="large"
+            className="relative top-1 hidden overflow-hidden whitespace-nowrap font-mono tracking-tight md:block"
+          >
+            {"*".repeat(MASKED_KEY_LENGTH)}
+          </Text>
+        )}
       </div>
       {showCaret && !asSelectTrigger && (
         <CaretDown className="h-4 w-4 shrink-0 text-gray-400" />

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/components/CredentialsSelect/CredentialsSelect.tsx

@@ -7,6 +7,7 @@ import {
 } from "@/components/__legacy__/ui/select";
 import { Text } from "@/components/atoms/Text/Text";
 import { CredentialsMetaInput } from "@/lib/autogpt-server-api/types";
+import { cn } from "@/lib/utils";
 import { useEffect } from "react";
 import { getCredentialDisplayName } from "../../helpers";
 import { CredentialRow } from "../CredentialRow/CredentialRow";
@@ -26,6 +27,8 @@ interface Props {
   onClearCredential?: () => void;
   readOnly?: boolean;
   allowNone?: boolean;
+  /** When "node", applies compact styling for node context */
+  variant?: "default" | "node";
 }
 
 export function CredentialsSelect({
@@ -37,6 +40,7 @@ export function CredentialsSelect({
   onClearCredential,
   readOnly = false,
   allowNone = true,
+  variant = "default",
 }: Props) {
   // Auto-select first credential if none is selected (only if allowNone is false)
   useEffect(() => {
@@ -59,7 +63,12 @@ export function CredentialsSelect({
         value={selectedCredentials?.id || (allowNone ? "__none__" : "")}
         onValueChange={handleValueChange}
       >
-        <SelectTrigger className="h-auto min-h-12 w-full rounded-medium border-zinc-200 p-0 pr-4 shadow-none">
+        <SelectTrigger
+          className={cn(
+            "h-auto min-h-12 w-full rounded-medium border-zinc-200 p-0 pr-4 shadow-none",
+            variant === "node" && "overflow-hidden",
+          )}
+        >
           {selectedCredentials ? (
             <SelectValue key={selectedCredentials.id} asChild>
               <CredentialRow
@@ -75,6 +84,7 @@ export function CredentialsSelect({
                 onDelete={() => {}}
                 readOnly={readOnly}
                 asSelectTrigger={true}
+                variant={variant}
               />
             </SelectValue>
           ) : (

autogpt_platform/frontend/src/app/(platform)/profile/(user)/layout.tsx

@@ -29,7 +29,7 @@ export default function Layout({ children }: { children: React.ReactNode }) {
           href: "/profile/dashboard",
           icon: <StorefrontIcon className="size-5" />,
         },
-        ...(isPaymentEnabled || true
+        ...(isPaymentEnabled
           ? [
               {
                 text: "Billing",

autogpt_platform/frontend/src/components/molecules/Table/Table.stories.tsx

@@ -0,0 +1,116 @@
+import type { Meta, StoryObj } from "@storybook/nextjs";
+import { TooltipProvider } from "@/components/atoms/Tooltip/BaseTooltip";
+import { Table } from "./Table";
+
+const meta = {
+  title: "Molecules/Table",
+  component: Table,
+  decorators: [
+    (Story) => (
+      <TooltipProvider>
+        <Story />
+      </TooltipProvider>
+    ),
+  ],
+  parameters: {
+    layout: "centered",
+  },
+  tags: ["autodocs"],
+  argTypes: {
+    allowAddRow: {
+      control: "boolean",
+      description: "Whether to show the Add row button",
+    },
+    allowDeleteRow: {
+      control: "boolean",
+      description: "Whether to show delete buttons for each row",
+    },
+    readOnly: {
+      control: "boolean",
+      description:
+        "Whether the table is read-only (renders text instead of inputs)",
+    },
+    addRowLabel: {
+      control: "text",
+      description: "Label for the Add row button",
+    },
+  },
+} satisfies Meta<typeof Table>;
+
+export default meta;
+type Story = StoryObj<typeof meta>;
+
+export const Default: Story = {
+  args: {
+    columns: ["name", "email", "role"],
+    allowAddRow: true,
+    allowDeleteRow: true,
+  },
+};
+
+export const WithDefaultValues: Story = {
+  args: {
+    columns: ["name", "email", "role"],
+    defaultValues: [
+      { name: "John Doe", email: "john@example.com", role: "Admin" },
+      { name: "Jane Smith", email: "jane@example.com", role: "User" },
+      { name: "Bob Wilson", email: "bob@example.com", role: "Editor" },
+    ],
+    allowAddRow: true,
+    allowDeleteRow: true,
+  },
+};
+
+export const ReadOnly: Story = {
+  args: {
+    columns: ["name", "email"],
+    defaultValues: [
+      { name: "John Doe", email: "john@example.com" },
+      { name: "Jane Smith", email: "jane@example.com" },
+    ],
+    readOnly: true,
+  },
+};
+
+export const NoAddOrDelete: Story = {
+  args: {
+    columns: ["name", "email"],
+    defaultValues: [
+      { name: "John Doe", email: "john@example.com" },
+      { name: "Jane Smith", email: "jane@example.com" },
+    ],
+    allowAddRow: false,
+    allowDeleteRow: false,
+  },
+};
+
+export const SingleColumn: Story = {
+  args: {
+    columns: ["item"],
+    allowAddRow: true,
+    allowDeleteRow: true,
+    addRowLabel: "Add item",
+  },
+};
+
+export const CustomAddLabel: Story = {
+  args: {
+    columns: ["key", "value"],
+    allowAddRow: true,
+    allowDeleteRow: true,
+    addRowLabel: "Add new entry",
+  },
+};
+
+export const KeyValuePairs: Story = {
+  args: {
+    columns: ["key", "value"],
+    defaultValues: [
+      { key: "API_KEY", value: "sk-..." },
+      { key: "DATABASE_URL", value: "postgres://..." },
+    ],
+    allowAddRow: true,
+    allowDeleteRow: true,
+    addRowLabel: "Add variable",
+  },
+};

autogpt_platform/frontend/src/components/molecules/Table/Table.tsx

@@ -0,0 +1,133 @@
+import * as React from "react";
+import {
+  Table as BaseTable,
+  TableBody,
+  TableCell,
+  TableHead,
+  TableHeader,
+  TableRow,
+} from "@/components/__legacy__/ui/table";
+import { Button } from "@/components/atoms/Button/Button";
+import { Input } from "@/components/atoms/Input/Input";
+import { Text } from "@/components/atoms/Text/Text";
+import { Plus, Trash2 } from "lucide-react";
+import { cn } from "@/lib/utils";
+import { useTable, RowData } from "./useTable";
+import { formatColumnTitle, formatPlaceholder } from "./helpers";
+
+export interface TableProps {
+  columns: string[];
+  defaultValues?: RowData[];
+  onChange?: (rows: RowData[]) => void;
+  allowAddRow?: boolean;
+  allowDeleteRow?: boolean;
+  addRowLabel?: string;
+  className?: string;
+  readOnly?: boolean;
+}
+
+export function Table({
+  columns,
+  defaultValues,
+  onChange,
+  allowAddRow = true,
+  allowDeleteRow = true,
+  addRowLabel = "Add row",
+  className,
+  readOnly = false,
+}: TableProps) {
+  const { rows, handleAddRow, handleDeleteRow, handleCellChange } = useTable({
+    columns,
+    defaultValues,
+    onChange,
+  });
+
+  const showDeleteColumn = allowDeleteRow && !readOnly;
+  const showAddButton = allowAddRow && !readOnly;
+
+  return (
+    <div className={cn("flex flex-col gap-3", className)}>
+      <div className="overflow-hidden rounded-xl border border-zinc-200 bg-white">
+        <BaseTable>
+          <TableHeader>
+            <TableRow className="border-b border-zinc-100 bg-zinc-50/50">
+              {columns.map((column) => (
+                <TableHead
+                  key={column}
+                  className="h-10 px-3 text-sm font-medium text-zinc-600"
+                >
+                  {formatColumnTitle(column)}
+                </TableHead>
+              ))}
+              {showDeleteColumn && <TableHead className="w-[50px]" />}
+            </TableRow>
+          </TableHeader>
+          <TableBody>
+            {rows.map((row, rowIndex) => (
+              <TableRow key={rowIndex} className="border-none">
+                {columns.map((column) => (
+                  <TableCell key={`${rowIndex}-${column}`} className="p-2">
+                    {readOnly ? (
+                      <Text
+                        variant="body"
+                        className="px-3 py-2 text-sm text-zinc-800"
+                      >
+                        {row[column] || "-"}
+                      </Text>
+                    ) : (
+                      <Input
+                        id={`table-${rowIndex}-${column}`}
+                        label={formatColumnTitle(column)}
+                        hideLabel
+                        value={row[column] ?? ""}
+                        onChange={(e) =>
+                          handleCellChange(rowIndex, column, e.target.value)
+                        }
+                        placeholder={formatPlaceholder(column)}
+                        size="small"
+                        wrapperClassName="mb-0"
+                      />
+                    )}
+                  </TableCell>
+                ))}
+                {showDeleteColumn && (
+                  <TableCell className="p-2">
+                    <Button
+                      variant="icon"
+                      size="icon"
+                      onClick={() => handleDeleteRow(rowIndex)}
+                      aria-label="Delete row"
+                      className="text-zinc-400 transition-colors hover:text-red-500"
+                    >
+                      <Trash2 className="h-4 w-4" />
+                    </Button>
+                  </TableCell>
+                )}
+              </TableRow>
+            ))}
+            {showAddButton && (
+              <TableRow>
+                <TableCell
+                  colSpan={columns.length + (showDeleteColumn ? 1 : 0)}
+                  className="p-2"
+                >
+                  <Button
+                    variant="outline"
+                    size="small"
+                    onClick={handleAddRow}
+                    leftIcon={<Plus className="h-4 w-4" />}
+                    className="w-fit"
+                  >
+                    {addRowLabel}
+                  </Button>
+                </TableCell>
+              </TableRow>
+            )}
+          </TableBody>
+        </BaseTable>
+      </div>
+    </div>
+  );
+}
+
+export { type RowData } from "./useTable";

autogpt_platform/frontend/src/components/molecules/Table/helpers.ts

@@ -0,0 +1,7 @@
+export const formatColumnTitle = (key: string): string => {
+  return key.charAt(0).toUpperCase() + key.slice(1);
+};
+
+export const formatPlaceholder = (key: string): string => {
+  return `Enter ${key.toLowerCase()}`;
+};

autogpt_platform/frontend/src/components/molecules/Table/useTable.ts

@@ -0,0 +1,81 @@
+import { useState, useEffect } from "react";
+
+export type RowData = Record<string, string>;
+
+interface UseTableOptions {
+  columns: string[];
+  defaultValues?: RowData[];
+  onChange?: (rows: RowData[]) => void;
+}
+
+export function useTable({
+  columns,
+  defaultValues,
+  onChange,
+}: UseTableOptions) {
+  const createEmptyRow = (): RowData => {
+    const emptyRow: RowData = {};
+    columns.forEach((column) => {
+      emptyRow[column] = "";
+    });
+    return emptyRow;
+  };
+
+  const [rows, setRows] = useState<RowData[]>(() => {
+    if (defaultValues && defaultValues.length > 0) {
+      return defaultValues;
+    }
+    return [];
+  });
+
+  useEffect(() => {
+    if (defaultValues !== undefined) {
+      setRows(defaultValues);
+    }
+  }, [defaultValues]);
+
+  const updateRows = (newRows: RowData[]) => {
+    setRows(newRows);
+    onChange?.(newRows);
+  };
+
+  const handleAddRow = () => {
+    const newRows = [...rows, createEmptyRow()];
+    updateRows(newRows);
+  };
+
+  const handleDeleteRow = (rowIndex: number) => {
+    const newRows = rows.filter((_, index) => index !== rowIndex);
+    updateRows(newRows);
+  };
+
+  const handleCellChange = (
+    rowIndex: number,
+    columnKey: string,
+    value: string,
+  ) => {
+    const newRows = rows.map((row, index) => {
+      if (index === rowIndex) {
+        return {
+          ...row,
+          [columnKey]: value,
+        };
+      }
+      return row;
+    });
+    updateRows(newRows);
+  };
+
+  const clearAll = () => {
+    updateRows([]);
+  };
+
+  return {
+    rows,
+    handleAddRow,
+    handleDeleteRow,
+    handleCellChange,
+    clearAll,
+    createEmptyRow,
+  };
+}

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"}>
       <Form

autogpt_platform/frontend/src/components/renderers/InputRenderer/base/anyof/AnyOfField.tsx

@@ -5,19 +5,14 @@ import { useAnyOfField } from "./useAnyOfField";
 import { getHandleId, updateUiOption } from "../../helpers";
 import { useEdgeStore } from "@/app/(platform)/build/stores/edgeStore";
 import { ANY_OF_FLAG } from "../../constants";
+import { findCustomFieldId } from "../../registry";
 
 export const AnyOfField = (props: FieldProps) => {
   const { registry, schema } = props;
   const { fields } = registry;
   const { SchemaField: _SchemaField } = fields;
   const { nodeId } = registry.formContext;
-
   const { isInputConnected } = useEdgeStore();
-
-  const uiOptions = getUiOptions(props.uiSchema, props.globalUiOptions);
-
-  const Widget = getWidget({ type: "string" }, "select", registry.widgets);
-
   const {
     handleOptionChange,
     enumOptions,
@@ -26,6 +21,15 @@ export const AnyOfField = (props: FieldProps) => {
     field_id,
   } = useAnyOfField(props);
 
+  const parentCustomFieldId = findCustomFieldId(schema);
+  if (parentCustomFieldId) {
+    return null;
+  }
+
+  const uiOptions = getUiOptions(props.uiSchema, props.globalUiOptions);
+
+  const Widget = getWidget({ type: "string" }, "select", registry.widgets);
+
   const handleId = getHandleId({
     uiOptions,
     id: field_id + ANY_OF_FLAG,
@@ -40,12 +44,21 @@ export const AnyOfField = (props: FieldProps) => {
 
   const isHandleConnected = isInputConnected(nodeId, handleId);
 
+  // Now anyOf can render - custom fields if the option schema matches a custom field
+  const optionCustomFieldId = optionSchema
+    ? findCustomFieldId(optionSchema)
+    : null;
+
+  const optionUiSchema = optionCustomFieldId
+    ? { ...updatedUiSchema, "ui:field": optionCustomFieldId }
+    : updatedUiSchema;
+
   const optionsSchemaField =
     (optionSchema && optionSchema.type !== "null" && (
       <_SchemaField
         {...props}
         schema={optionSchema}
-        uiSchema={updatedUiSchema}
+        uiSchema={optionUiSchema}
       />
     )) ||
     null;

autogpt_platform/frontend/src/components/renderers/InputRenderer/base/standard/widgets/TextInput/TextInputExpanderModal.tsx

@@ -17,6 +17,7 @@ interface InputExpanderModalProps {
   defaultValue: string;
   description?: string;
   placeholder?: string;
+  inputType?: "text" | "json";
 }
 
 export const InputExpanderModal: FC<InputExpanderModalProps> = ({
@@ -27,6 +28,7 @@ export const InputExpanderModal: FC<InputExpanderModalProps> = ({
   defaultValue,
   description,
   placeholder,
+  inputType = "text",
 }) => {
   const [tempValue, setTempValue] = useState(defaultValue);
   const [isCopied, setIsCopied] = useState(false);
@@ -78,7 +80,10 @@ export const InputExpanderModal: FC<InputExpanderModalProps> = ({
             hideLabel
             id="input-expander-modal"
             value={tempValue}
-            className="!min-h-[300px] rounded-2xlarge"
+            className={cn(
+              "!min-h-[300px] rounded-2xlarge",
+              inputType === "json" && "font-mono text-sm",
+            )}
             onChange={(e) => setTempValue(e.target.value)}
             placeholder={placeholder || "Enter text..."}
             autoFocus

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

@@ -88,6 +88,8 @@ export const CredentialsField = (props: FieldProps) => {
         showTitle={false}
         readOnly={formContext?.readOnly}
         isOptional={!isRequired}
+        className="w-full"
+        variant="node"
       />
 
       {/* Optional credentials toggle - only show in builder canvas, not run dialogs */}

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

@@ -0,0 +1,124 @@
+"use client";
+
+import { FieldProps, getTemplate, getUiOptions } from "@rjsf/utils";
+import { Input } from "@/components/atoms/Input/Input";
+import { Button } from "@/components/atoms/Button/Button";
+import {
+  Tooltip,
+  TooltipContent,
+  TooltipTrigger,
+} from "@/components/atoms/Tooltip/BaseTooltip";
+import { ArrowsOutIcon } from "@phosphor-icons/react";
+import { InputExpanderModal } from "../../base/standard/widgets/TextInput/TextInputExpanderModal";
+import { getHandleId, updateUiOption } from "../../helpers";
+import { useJsonTextField } from "./useJsonTextField";
+import { getPlaceholder } from "./helpers";
+
+export const JsonTextField = (props: FieldProps) => {
+  const {
+    formData,
+    onChange,
+    schema,
+    registry,
+    uiSchema,
+    required,
+    name,
+    fieldPathId,
+  } = props;
+
+  const uiOptions = getUiOptions(uiSchema);
+
+  const TitleFieldTemplate = getTemplate(
+    "TitleFieldTemplate",
+    registry,
+    uiOptions,
+  );
+
+  const fieldId = fieldPathId?.$id ?? props.id ?? "json-field";
+
+  const handleId = getHandleId({
+    uiOptions,
+    id: fieldId,
+    schema: schema,
+  });
+
+  const updatedUiSchema = updateUiOption(uiSchema, {
+    handleId: handleId,
+  });
+
+  const {
+    textValue,
+    isModalOpen,
+    handleChange,
+    handleModalOpen,
+    handleModalClose,
+    handleModalSave,
+  } = useJsonTextField({
+    formData,
+    onChange,
+    path: fieldPathId?.path,
+  });
+
+  const placeholder = getPlaceholder(schema);
+  const title = schema.title || name || "JSON Value";
+
+  return (
+    <div className="flex flex-col gap-2">
+      <TitleFieldTemplate
+        id={fieldId}
+        title={title}
+        required={required}
+        schema={schema}
+        uiSchema={updatedUiSchema}
+        registry={registry}
+      />
+      <div className="nodrag relative flex items-center gap-2">
+        <Input
+          id={fieldId}
+          hideLabel={true}
+          type="textarea"
+          label=""
+          size="small"
+          wrapperClassName="mb-0 flex-1 "
+          value={textValue}
+          onChange={handleChange}
+          placeholder={placeholder}
+          required={required}
+          disabled={props.disabled}
+          className="min-h-[60px] pr-8 font-mono text-xs"
+        />
+
+        <Tooltip delayDuration={0}>
+          <TooltipTrigger asChild>
+            <Button
+              variant="ghost"
+              size="icon"
+              onClick={handleModalOpen}
+              type="button"
+              className="p-1"
+            >
+              <ArrowsOutIcon className="size-4" />
+            </Button>
+          </TooltipTrigger>
+          <TooltipContent>Expand input</TooltipContent>
+        </Tooltip>
+      </div>
+      {schema.description && (
+        <span className="text-xs text-gray-500">{schema.description}</span>
+      )}
+
+      <InputExpanderModal
+        isOpen={isModalOpen}
+        onClose={handleModalClose}
+        onSave={handleModalSave}
+        title={`Edit ${title}`}
+        description={schema.description || "Enter valid JSON"}
+        defaultValue={textValue}
+        placeholder={placeholder}
+        inputType="json"
+      />
+    </div>
+  );
+};
+
+export default JsonTextField;

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/JsonTextField/helpers.ts

@@ -0,0 +1,67 @@
+import { RJSFSchema } from "@rjsf/utils";
+
+/**
+ * Converts form data to a JSON string for display
+ * @param formData - The data to stringify
+ * @returns JSON string or empty string if data is null/undefined
+ */
+export function stringifyFormData(formData: unknown): string {
+  if (formData === undefined || formData === null) {
+    return "";
+  }
+  try {
+    return JSON.stringify(formData, null, 2);
+  } catch {
+    return "";
+  }
+}
+
+/**
+ * Parses a JSON string into an object/array
+ * @param value - The JSON string to parse
+ * @returns Parsed value or undefined if parsing fails or empty
+ */
+export function parseJsonValue(value: string): unknown | undefined {
+  const trimmed = value.trim();
+  if (trimmed === "") {
+    return undefined;
+  }
+
+  try {
+    return JSON.parse(trimmed);
+  } catch {
+    return undefined;
+  }
+}
+
+/**
+ * Gets the appropriate placeholder text based on schema type
+ * @param schema - The JSON schema
+ * @returns Placeholder string
+ */
+export function getPlaceholder(schema: RJSFSchema): string {
+  if (schema.type === "array") {
+    return '["item1", "item2"] or [{"key": "value"}]';
+  }
+  if (schema.type === "object") {
+    return '{"key": "value"}';
+  }
+  return "Enter JSON value...";
+}
+
+/**
+ * Checks if a JSON string is valid
+ * @param value - The JSON string to validate
+ * @returns true if valid JSON, false otherwise
+ */
+export function isValidJson(value: string): boolean {
+  if (value.trim() === "") {
+    return true; // Empty is considered valid (will be undefined)
+  }
+  try {
+    JSON.parse(value);
+    return true;
+  } catch {
+    return false;
+  }
+}

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/JsonTextField/useJsonTextField.ts

@@ -0,0 +1,107 @@
+import { useState, useEffect, useCallback } from "react";
+import { FieldProps } from "@rjsf/utils";
+import { stringifyFormData, parseJsonValue, isValidJson } from "./helpers";
+
+type FieldOnChange = FieldProps["onChange"];
+type FieldPathId = FieldProps["fieldPathId"];
+
+interface UseJsonTextFieldOptions {
+  formData: unknown;
+  onChange: FieldOnChange;
+  path?: FieldPathId["path"];
+}
+
+interface UseJsonTextFieldReturn {
+  textValue: string;
+  isModalOpen: boolean;
+  hasError: boolean;
+  handleChange: (
+    e: React.ChangeEvent<HTMLInputElement | HTMLTextAreaElement>,
+  ) => void;
+  handleModalOpen: () => void;
+  handleModalClose: () => void;
+  handleModalSave: (value: string) => void;
+}
+
+/**
+ * Custom hook for managing JSON text field state and handlers
+ */
+export function useJsonTextField({
+  formData,
+  onChange,
+  path,
+}: UseJsonTextFieldOptions): UseJsonTextFieldReturn {
+  const [textValue, setTextValue] = useState(() => stringifyFormData(formData));
+  const [isModalOpen, setIsModalOpen] = useState(false);
+  const [hasError, setHasError] = useState(false);
+
+  // Update text value when formData changes externally
+  useEffect(() => {
+    const newValue = stringifyFormData(formData);
+    setTextValue(newValue);
+    setHasError(false);
+  }, [formData]);
+
+  const handleChange = useCallback(
+    (e: React.ChangeEvent<HTMLInputElement | HTMLTextAreaElement>) => {
+      const value = e.target.value;
+      setTextValue(value);
+
+      // Validate JSON and update error state
+      const valid = isValidJson(value);
+      setHasError(!valid);
+
+      // Try to parse and update formData
+      if (value.trim() === "") {
+        onChange(undefined, path ?? []);
+        return;
+      }
+
+      const parsed = parseJsonValue(value);
+      if (parsed !== undefined) {
+        onChange(parsed, path ?? []);
+      }
+    },
+    [onChange, path],
+  );
+
+  const handleModalOpen = useCallback(() => {
+    setIsModalOpen(true);
+  }, []);
+
+  const handleModalClose = useCallback(() => {
+    setIsModalOpen(false);
+  }, []);
+
+  const handleModalSave = useCallback(
+    (value: string) => {
+      setTextValue(value);
+      setIsModalOpen(false);
+
+      // Validate and update
+      const valid = isValidJson(value);
+      setHasError(!valid);
+
+      if (value.trim() === "") {
+        onChange(undefined, path ?? []);
+        return;
+      }
+
+      const parsed = parseJsonValue(value);
+      if (parsed !== undefined) {
+        onChange(parsed, path ?? []);
+      }
+    },
+    [onChange, path],
+  );
+
+  return {
+    textValue,
+    isModalOpen,
+    hasError,
+    handleChange,
+    handleModalOpen,
+    handleModalClose,
+    handleModalSave,
+  };
+}

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

@@ -0,0 +1,57 @@
+import React from "react";
+import { FieldProps, getUiOptions } from "@rjsf/utils";
+import { BlockIOObjectSubSchema } from "@/lib/autogpt-server-api/types";
+import {
+  MultiSelector,
+  MultiSelectorContent,
+  MultiSelectorInput,
+  MultiSelectorItem,
+  MultiSelectorList,
+  MultiSelectorTrigger,
+} from "@/components/__legacy__/ui/multiselect";
+import { cn } from "@/lib/utils";
+import { useMultiSelectField } from "./useMultiSelectField";
+
+export const MultiSelectField = (props: FieldProps) => {
+  const { schema, formData, onChange, fieldPathId } = props;
+  const uiOptions = getUiOptions(props.uiSchema);
+
+  const { optionSchema, options, selection, createChangeHandler } =
+    useMultiSelectField({
+      schema: schema as BlockIOObjectSubSchema,
+      formData,
+    });
+
+  const handleValuesChange = createChangeHandler(onChange, fieldPathId);
+
+  const displayName = schema.title || "options";
+
+  return (
+    <div className={cn("flex flex-col", uiOptions.className)}>
+      <MultiSelector
+        className="nodrag"
+        values={selection}
+        onValuesChange={handleValuesChange}
+      >
+        <MultiSelectorTrigger className="rounded-3xl border border-zinc-200 bg-white px-2 shadow-none">
+          <MultiSelectorInput
+            placeholder={
+              (schema as any).placeholder ?? `Select ${displayName}...`
+            }
+          />
+        </MultiSelectorTrigger>
+        <MultiSelectorContent className="nowheel">
+          <MultiSelectorList>
+            {options
+              .map((key) => ({ ...optionSchema[key], key }))
+              .map(({ key, title, description }) => (
+                <MultiSelectorItem key={key} value={key} title={description}>
+                  {title ?? key}
+                </MultiSelectorItem>
+              ))}
+          </MultiSelectorList>
+        </MultiSelectorContent>
+      </MultiSelector>
+    </div>
+  );
+};

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/MultiSelectField/index.ts

@@ -0,0 +1 @@
+export { MultiSelectField } from "./MultiSelectField";

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/MultiSelectField/useMultiSelectField.ts

@@ -0,0 +1,65 @@
+import { FieldProps } from "@rjsf/utils";
+import { BlockIOObjectSubSchema } from "@/lib/autogpt-server-api/types";
+
+type FormData = Record<string, boolean> | null | undefined;
+
+interface UseMultiSelectFieldOptions {
+  schema: BlockIOObjectSubSchema;
+  formData: FormData;
+}
+
+export function useMultiSelectField({
+  schema,
+  formData,
+}: UseMultiSelectFieldOptions) {
+  const getOptionSchema = (): Record<string, BlockIOObjectSubSchema> => {
+    if (schema.properties) {
+      return schema.properties as Record<string, BlockIOObjectSubSchema>;
+    }
+    if (
+      "anyOf" in schema &&
+      Array.isArray(schema.anyOf) &&
+      schema.anyOf.length > 0 &&
+      "properties" in schema.anyOf[0]
+    ) {
+      return (schema.anyOf[0] as BlockIOObjectSubSchema).properties as Record<
+        string,
+        BlockIOObjectSubSchema
+      >;
+    }
+    return {};
+  };
+
+  const optionSchema = getOptionSchema();
+  const options = Object.keys(optionSchema);
+
+  const getSelection = (): string[] => {
+    if (!formData || typeof formData !== "object") {
+      return [];
+    }
+    return Object.entries(formData)
+      .filter(([_, value]) => value === true)
+      .map(([key]) => key);
+  };
+
+  const selection = getSelection();
+
+  const createChangeHandler =
+    (
+      onChange: FieldProps["onChange"],
+      fieldPathId: FieldProps["fieldPathId"],
+    ) =>
+    (values: string[]) => {
+      const newValue = Object.fromEntries(
+        options.map((opt) => [opt, values.includes(opt)]),
+      );
+      onChange(newValue, fieldPathId?.path);
+    };
+
+  return {
+    optionSchema,
+    options,
+    selection,
+    createChangeHandler,
+  };
+}

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

@@ -0,0 +1,52 @@
+import { descriptionId, FieldProps, getTemplate, titleId } from "@rjsf/utils";
+import { Table, RowData } from "@/components/molecules/Table/Table";
+import { useMemo } from "react";
+
+export const TableField = (props: FieldProps) => {
+  const { schema, formData, onChange, fieldPathId, registry, uiSchema } = props;
+
+  const itemSchema = schema.items as any;
+  const properties = itemSchema?.properties || {};
+
+  const columns: string[] = useMemo(() => {
+    return Object.keys(properties);
+  }, [properties]);
+
+  const handleChange = (rows: RowData[]) => {
+    onChange(rows, fieldPathId?.path.slice(0, -1));
+  };
+
+  const TitleFieldTemplate = getTemplate("TitleFieldTemplate", registry);
+  const DescriptionFieldTemplate = getTemplate(
+    "DescriptionFieldTemplate",
+    registry,
+  );
+
+  return (
+    <div className="flex flex-col gap-2">
+      <TitleFieldTemplate
+        id={titleId(fieldPathId)}
+        title={schema.title || ""}
+        required={true}
+        schema={schema}
+        uiSchema={uiSchema}
+        registry={registry}
+      />
+      <DescriptionFieldTemplate
+        id={descriptionId(fieldPathId)}
+        description={schema.description || ""}
+        schema={schema}
+        registry={registry}
+      />
+
+      <Table
+        columns={columns}
+        defaultValues={formData}
+        onChange={handleChange}
+        allowAddRow={true}
+        allowDeleteRow={true}
+        addRowLabel="Add row"
+      />
+    </div>
+  );
+};

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

@@ -1,6 +1,10 @@
 import { FieldProps, RJSFSchema, RegistryFieldsType } from "@rjsf/utils";
 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 { TableField } from "./TableField/TableField";
 
 export interface CustomFieldDefinition {
   id: string;
@@ -8,6 +12,9 @@ export interface CustomFieldDefinition {
   component: (props: FieldProps<any, RJSFSchema, any>) => JSX.Element | null;
 }
 
+/** Field ID for JsonTextField - used to render nested complex types as text input */
+export const JSON_TEXT_FIELD_ID = "custom/json_text_field";
+
 export const CUSTOM_FIELDS: CustomFieldDefinition[] = [
   {
     id: "custom/credential_field",
@@ -30,6 +37,28 @@ export const CUSTOM_FIELDS: CustomFieldDefinition[] = [
     },
     component: GoogleDrivePickerField,
   },
+  {
+    id: "custom/json_text_field",
+    // Not matched by schema - assigned via uiSchema for nested complex types
+    matcher: () => false,
+    component: JsonTextField,
+  },
+  {
+    id: "custom/multi_select_field",
+    matcher: isMultiSelectSchema,
+    component: MultiSelectField,
+  },
+  {
+    id: "custom/table_field",
+    matcher: (schema: any) => {
+      return (
+        schema.type === "array" &&
+        "format" in schema &&
+        schema.format === "table"
+      );
+    },
+    component: TableField,
+  },
 ];
 
 export function findCustomFieldId(schema: any): string | null {

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

@@ -1,19 +1,46 @@
 import { RJSFSchema, UiSchema } from "@rjsf/utils";
-import { findCustomFieldId } from "../custom/custom-registry";
+import {
+  findCustomFieldId,
+  JSON_TEXT_FIELD_ID,
+} from "../custom/custom-registry";
+
+function isComplexType(schema: RJSFSchema): boolean {
+  return schema.type === "object" || schema.type === "array";
+}
+
+function hasComplexAnyOfOptions(schema: RJSFSchema): boolean {
+  const options = schema.anyOf || schema.oneOf;
+  if (!Array.isArray(options)) return false;
+  return options.some(
+    (opt: any) =>
+      opt &&
+      typeof opt === "object" &&
+      (opt.type === "object" || opt.type === "array"),
+  );
+}
 
 /**
  * Generates uiSchema with ui:field settings for custom fields based on schema matchers.
  * This is the standard RJSF way to route fields to custom components.
+ *
+ * Nested complex types (arrays/objects inside arrays/objects) are rendered as JsonTextField
+ * to avoid deeply nested form UIs. Users can enter raw JSON for these fields.
+ *
+ * @param schema - The JSON schema
+ * @param existingUiSchema - Existing uiSchema to merge with
+ * @param insideComplexType - Whether we're already inside a complex type (object/array)
  */
 export function generateUiSchemaForCustomFields(
   schema: RJSFSchema,
   existingUiSchema: UiSchema = {},
+  insideComplexType: boolean = false,
 ): UiSchema {
   const uiSchema: UiSchema = { ...existingUiSchema };
 
   if (schema.properties) {
     for (const [key, propSchema] of Object.entries(schema.properties)) {
       if (propSchema && typeof propSchema === "object") {
+        // First check for custom field matchers (credentials, google drive, etc.)
         const customFieldId = findCustomFieldId(propSchema);
 
         if (customFieldId) {
@@ -21,8 +48,33 @@ export function generateUiSchemaForCustomFields(
             ...(uiSchema[key] as object),
             "ui:field": customFieldId,
           };
+          // Skip further processing for custom fields
+          continue;
         }
 
+        // Handle nested complex types - render as JsonTextField
+        if (insideComplexType && isComplexType(propSchema as RJSFSchema)) {
+          uiSchema[key] = {
+            ...(uiSchema[key] as object),
+            "ui:field": JSON_TEXT_FIELD_ID,
+          };
+          // Don't recurse further - this field is now a text input
+          continue;
+        }
+
+        // Handle anyOf/oneOf inside complex types
+        if (
+          insideComplexType &&
+          hasComplexAnyOfOptions(propSchema as RJSFSchema)
+        ) {
+          uiSchema[key] = {
+            ...(uiSchema[key] as object),
+            "ui:field": JSON_TEXT_FIELD_ID,
+          };
+          continue;
+        }
+
+        // Recurse into object properties
         if (
           propSchema.type === "object" &&
           propSchema.properties &&
@@ -31,6 +83,7 @@ export function generateUiSchemaForCustomFields(
           const nestedUiSchema = generateUiSchemaForCustomFields(
             propSchema as RJSFSchema,
             (uiSchema[key] as UiSchema) || {},
+            true, // Now inside a complex type
           );
           uiSchema[key] = {
             ...(uiSchema[key] as object),
@@ -38,9 +91,11 @@ export function generateUiSchemaForCustomFields(
           };
         }
 
+        // Handle arrays
         if (propSchema.type === "array" && propSchema.items) {
           const itemsSchema = propSchema.items as RJSFSchema;
           if (itemsSchema && typeof itemsSchema === "object") {
+            // Check for custom field on array items
             const itemsCustomFieldId = findCustomFieldId(itemsSchema);
             if (itemsCustomFieldId) {
               uiSchema[key] = {
@@ -49,10 +104,28 @@ export function generateUiSchemaForCustomFields(
                   "ui:field": itemsCustomFieldId,
                 },
               };
+            } else if (isComplexType(itemsSchema)) {
+              // Array items that are complex types become JsonTextField
+              uiSchema[key] = {
+                ...(uiSchema[key] as object),
+                items: {
+                  "ui:field": JSON_TEXT_FIELD_ID,
+                },
+              };
+            } else if (hasComplexAnyOfOptions(itemsSchema)) {
+              // Array items with anyOf containing complex types become JsonTextField
+              uiSchema[key] = {
+                ...(uiSchema[key] as object),
+                items: {
+                  "ui:field": JSON_TEXT_FIELD_ID,
+                },
+              };
             } else if (itemsSchema.properties) {
+              // Recurse into object items (but they're now inside a complex type)
               const itemsUiSchema = generateUiSchemaForCustomFields(
                 itemsSchema,
                 ((uiSchema[key] as UiSchema)?.items as UiSchema) || {},
+                true, // Inside complex type (array)
               );
               if (Object.keys(itemsUiSchema).length > 0) {
                 uiSchema[key] = {
@@ -63,6 +136,61 @@ export function generateUiSchemaForCustomFields(
             }
           }
         }
+
+        // Handle anyOf/oneOf at root level - process complex options
+        if (!insideComplexType) {
+          const anyOfOptions = propSchema.anyOf || propSchema.oneOf;
+
+          if (Array.isArray(anyOfOptions)) {
+            for (let i = 0; i < anyOfOptions.length; i++) {
+              const option = anyOfOptions[i] as RJSFSchema;
+              if (option && typeof option === "object") {
+                // Handle anyOf array options with complex items
+                if (option.type === "array" && option.items) {
+                  const itemsSchema = option.items as RJSFSchema;
+                  if (itemsSchema && typeof itemsSchema === "object") {
+                    // Array items that are complex types become JsonTextField
+                    if (isComplexType(itemsSchema)) {
+                      uiSchema[key] = {
+                        ...(uiSchema[key] as object),
+                        items: {
+                          "ui:field": JSON_TEXT_FIELD_ID,
+                        },
+                      };
+                    } else if (hasComplexAnyOfOptions(itemsSchema)) {
+                      uiSchema[key] = {
+                        ...(uiSchema[key] as object),
+                        items: {
+                          "ui:field": JSON_TEXT_FIELD_ID,
+                        },
+                      };
+                    }
+                  }
+                }
+
+                // Recurse into anyOf object options with properties
+                if (
+                  option.type === "object" &&
+                  option.properties &&
+                  typeof option.properties === "object"
+                ) {
+                  const optionUiSchema = generateUiSchemaForCustomFields(
+                    option,
+                    {},
+                    true, // Inside complex type (anyOf object option)
+                  );
+                  if (Object.keys(optionUiSchema).length > 0) {
+                    // Store under the property key - RJSF will apply it
+                    uiSchema[key] = {
+                      ...(uiSchema[key] as object),
+                      ...optionUiSchema,
+                    };
+                  }
+                }
+              }
+            }
+          }
+        }
       }
     }
   }

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

@@ -1,7 +1,11 @@
 import { getUiOptions, RJSFSchema, UiSchema } from "@rjsf/utils";
 
 export function isAnyOfSchema(schema: RJSFSchema | undefined): boolean {
-  return Array.isArray(schema?.anyOf) && schema!.anyOf.length > 0;
+  return (
+    Array.isArray(schema?.anyOf) &&
+    schema!.anyOf.length > 0 &&
+    schema?.enum === undefined
+  );
 }
 
 export const isAnyOfChild = (
@@ -33,3 +37,21 @@ export function isOptionalType(schema: RJSFSchema | undefined): {
 export function isAnyOfSelector(name: string) {
   return name.includes("anyof_select");
 }
+
+export function isMultiSelectSchema(schema: RJSFSchema | undefined): boolean {
+  if (typeof schema !== "object" || schema === null) {
+    return false;
+  }
+
+  if ("anyOf" in schema || "oneOf" in schema) {
+    return false;
+  }
+
+  return !!(
+    schema.type === "object" &&
+    schema.properties &&
+    Object.values(schema.properties).every(
+      (prop: any) => prop.type === "boolean",
+    )
+  );
+}