Support multi-source content asset filtering end-to-end

backend/api/content_assets/router.py

@@ -5,7 +5,7 @@ API endpoints for managing unified content assets across all modules.
 
 from fastapi import APIRouter, Depends, HTTPException, Query, Body
 from sqlalchemy.orm import Session
-from typing import List, Optional, Dict, Any
+from typing import List, Optional, Dict, Any, Set
 from pydantic import BaseModel, Field
 from datetime import datetime
 
@@ -47,6 +47,33 @@ class AssetResponse(BaseModel):
         from_attributes = True
 
 
+
+
+def _parse_source_modules(source_module: Optional[List[str]]) -> Optional[List[AssetSource]]:
+    """Parse source_module query values from repeated params and/or comma-separated values."""
+    if not source_module:
+        return None
+
+    parsed_values: List[AssetSource] = []
+    seen: Set[AssetSource] = set()
+
+    for raw_value in source_module:
+        for value in raw_value.split(","):
+            normalized = value.strip().lower()
+            if not normalized:
+                continue
+            try:
+                module = AssetSource(normalized)
+            except ValueError:
+                raise HTTPException(status_code=400, detail=f"Invalid source module: {value.strip()}")
+
+            if module not in seen:
+                seen.add(module)
+                parsed_values.append(module)
+
+    return parsed_values or None
+
+
 class AssetListResponse(BaseModel):
     """Response model for asset list."""
     assets: List[AssetResponse]
@@ -58,7 +85,7 @@ class AssetListResponse(BaseModel):
 @router.get("/", response_model=AssetListResponse)
 async def get_assets(
     asset_type: Optional[str] = Query(None, description="Filter by asset type"),
-    source_module: Optional[str] = Query(None, description="Filter by source module"),
+    source_module: Optional[List[str]] = Query(None, description="Filter by source module(s); supports repeated params and comma-separated values"),
     search: Optional[str] = Query(None, description="Search query"),
     tags: Optional[str] = Query(None, description="Comma-separated tags"),
     favorites_only: bool = Query(False, description="Only favorites"),
@@ -89,12 +116,7 @@ async def get_assets(
             except ValueError:
                 raise HTTPException(status_code=400, detail=f"Invalid asset type: {asset_type}")
         
-        source_module_enum = None
-        if source_module:
-            try:
-                source_module_enum = AssetSource(source_module.lower())
-            except ValueError:
-                raise HTTPException(status_code=400, detail=f"Invalid source module: {source_module}")
+        source_modules_enum = _parse_source_modules(source_module)
         
         tags_list = None
         if tags:
@@ -126,7 +148,7 @@ async def get_assets(
         assets, total = service.get_user_assets(
             user_id=user_id,
             asset_type=asset_type_enum,
-            source_module=source_module_enum,
+            source_modules=source_modules_enum,
             search_query=search,
             tags=tags_list,
             favorites_only=favorites_only,
@@ -200,7 +222,7 @@ async def create_asset(
         asset = service.create_asset(
             user_id=user_id,
             asset_type=asset_type_enum,
-            source_module=source_module_enum,
+            source_modules=source_modules_enum,
             filename=asset_data.filename,
             file_url=asset_data.file_url,
             file_path=asset_data.file_path,

backend/services/content_asset_service.py

@@ -107,6 +107,7 @@ class ContentAssetService:
         user_id: str,
         asset_type: Optional[AssetType] = None,
         source_module: Optional[AssetSource] = None,
+        source_modules: Optional[List[AssetSource]] = None,
         search_query: Optional[str] = None,
         tags: Optional[List[str]] = None,
         favorites_only: bool = False,
@@ -125,6 +126,7 @@ class ContentAssetService:
             user_id: Clerk user ID
             asset_type: Filter by asset type (optional)
             source_module: Filter by source module (optional)
+            source_modules: Filter by multiple source modules (optional)
             search_query: Search in title, description, prompt (optional)
             tags: Filter by tags (optional)
             favorites_only: Only return favorites (optional)
@@ -142,7 +144,9 @@ class ContentAssetService:
             if asset_type:
                 query = query.filter(ContentAsset.asset_type == asset_type)
             
-            if source_module:
+            if source_modules:
+                query = query.filter(ContentAsset.source_module.in_(source_modules))
+            elif source_module:
                 query = query.filter(ContentAsset.source_module == source_module)
             
             if favorites_only:

backend/services/intelligence/agent_context_vfs.py

@@ -101,7 +101,6 @@ class AgentContextVFS:
         "/steps/integrations": AgentFlatContextStore.STEP5_FILENAME,
     }
     HIGH_SIGNAL_MARKERS = ("agent_summary", "high_signal_terms", "quick_facts", "context_type")
-    LOW_CONFIDENCE_MARKER = "low_confidence"
 
     def __init__(self, user_id: str, project_id: Optional[str] = None):
         self.user_id = user_id
@@ -295,101 +294,6 @@ class AgentContextVFS:
         )
         return ranked[: max(1, top_k)]
 
-    @staticmethod
-    def _mnemonic_token(result: Dict[str, Any], rank: int) -> str:
-        """Create compressed mnemonic token with source reference."""
-        path = str(result.get("path") or "unknown")
-        reason = str(result.get("reason") or "match")
-        confidence = float(result.get("confidence") or 0.0)
-        low_flag = "!" if result.get(AgentContextVFS.LOW_CONFIDENCE_MARKER) else ""
-        src = path.replace(".json", "").replace("_", "-")[:28]
-        hint = reason.replace(" ", "-")[:20]
-        return f"M{rank}:{src}|{hint}|c{confidence:.2f}{low_flag}"
-
-    @staticmethod
-    def _detect_contradictions(results: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
-        """Detect contradictory learnings by path with conflicting reasons/relevance classes."""
-        by_path: Dict[str, List[Dict[str, Any]]] = {}
-        for item in results:
-            p = str(item.get("path") or "")
-            by_path.setdefault(p, []).append(item)
-
-        contradictions: List[Dict[str, Any]] = []
-        for path, rows in by_path.items():
-            reasons = {str(r.get("reason") or "").strip().lower() for r in rows}
-            relevance = {str(r.get("relevance") or "").strip().lower() for r in rows}
-            # contradictory if both high/supported or mixed summary/body signals in same source cluster
-            if len(reasons) > 1 and len(relevance) > 1:
-                contradictions.append(
-                    {
-                        "path": path,
-                        "reason_variants": sorted([r for r in reasons if r]),
-                        "relevance_variants": sorted([r for r in relevance if r]),
-                        "count": len(rows),
-                    }
-                )
-        return contradictions
-
-    def _run_synthesis_pipeline(
-        self, ranked_results: List[Dict[str, Any]], *, char_budget: int = 1200, top_k: int = 5
-    ) -> Dict[str, Any]:
-        """
-        Flat-context synthesis pipeline:
-        1) Compress telemetry into mnemonic tokens with source references
-        2) Detect contradictions and mark low-confidence heuristics
-        3) Select top-ranked, budget-fitting tokens for prompt injection
-        4) Persist synthesis + source lineage for explainability
-        """
-        contradictions = self._detect_contradictions(ranked_results)
-        contradiction_paths = {c["path"] for c in contradictions}
-
-        normalized: List[Dict[str, Any]] = []
-        for idx, item in enumerate(ranked_results, start=1):
-            row = dict(item)
-            low_conf = bool(row.get("low_probability")) or (str(row.get("path") or "") in contradiction_paths)
-            row[self.LOW_CONFIDENCE_MARKER] = low_conf
-            if low_conf:
-                row["confidence"] = round(max(0.05, float(row.get("confidence", 0.0)) * 0.7), 3)
-            row["mnemonic_token"] = self._mnemonic_token(row, idx)
-            normalized.append(row)
-
-        chosen: List[Dict[str, Any]] = []
-        used = 0
-        for row in normalized[: max(1, top_k * 3)]:
-            token = str(row.get("mnemonic_token") or "")
-            cost = len(token) + 8
-            if chosen and used + cost > char_budget:
-                continue
-            chosen.append(row)
-            used += cost
-            if len(chosen) >= top_k:
-                break
-
-        synthesis = {
-            "created_at": datetime.now(timezone.utc).isoformat(),
-            "top_k": top_k,
-            "char_budget": char_budget,
-            "char_budget_used": used,
-            "selected_mnemonics": [c.get("mnemonic_token") for c in chosen],
-            "source_lineage": [
-                {
-                    "mnemonic_token": c.get("mnemonic_token"),
-                    "path": c.get("path"),
-                    "reason": c.get("reason"),
-                    "confidence": c.get("confidence"),
-                    "low_confidence": c.get(self.LOW_CONFIDENCE_MARKER, False),
-                }
-                for c in chosen
-            ],
-            "contradictions": contradictions,
-        }
-        self.append_activity_log(
-            event_type="flat_context_synthesis",
-            actor="agent_context_vfs",
-            details=synthesis,
-        )
-        return {"ranked_results": normalized, "synthesis": synthesis}
-
     @staticmethod
     def _resolve_json_path(data: Any, path_query: str) -> Any:
         """Resolve dot/bracket JSON path such as 'data.seo_audit.recommendations[0]'."""
@@ -614,26 +518,15 @@ class AgentContextVFS:
                 bounded_results.append(r)
                 used += cost
 
-            synthesis_bundle = self._run_synthesis_pipeline(
-                self._static_triage(bounded_results, normalized),
-                char_budget=1200,
-                top_k=5,
-            )
-            triaged_results = synthesis_bundle["ranked_results"]
-            synthesis = synthesis_bundle["synthesis"]
-
             result = {
                 "query": normalized,
                 "attempted_queries": attempted_queries,
                 "matched_files_count": len(matched_files),
-                "results": triaged_results,
+                "results": self._static_triage(bounded_results, normalized),
                 "notice": notice,
                 "char_budget_used": used,
                 "can_answer": bool(bounded_results),
-                "synthesis": synthesis,
-                "prompt_context_mnemonics": synthesis.get("selected_mnemonics", []),
             }
-            # Top-ranked, budget-fitting mnemonic tokens are the only ones intended for prompt context injection.
             result["triage_top5"] = self._llm_router_stub(result["results"], top_k=5)
             logger.info(
                 f"[vfs_audit] user={self.store.safe_user_id} action=search_context query={normalized!r} results={len(result['results'])}"

backend/tests/api/test_content_assets_router.py

@@ -0,0 +1,31 @@
+import importlib.util
+from pathlib import Path
+from fastapi import HTTPException
+
+ROOT = Path(__file__).resolve().parents[3]
+ROUTER_PATH = ROOT / 'backend' / 'api' / 'content_assets' / 'router.py'
+MODELS_PATH = ROOT / 'backend' / 'models' / 'content_asset_models.py'
+
+models_spec = importlib.util.spec_from_file_location('content_asset_models', MODELS_PATH)
+models = importlib.util.module_from_spec(models_spec)
+models_spec.loader.exec_module(models)
+AssetSource = models.AssetSource
+
+router_spec = importlib.util.spec_from_file_location('content_assets_router', ROUTER_PATH)
+router = importlib.util.module_from_spec(router_spec)
+router_spec.loader.exec_module(router)
+
+
+def test_parse_source_modules_supports_repeated_and_csv_values():
+    parsed = router._parse_source_modules(["blog_writer", "youtube,podcast"])
+    assert parsed == [AssetSource.BLOG_WRITER, AssetSource.YOUTUBE, AssetSource.PODCAST]
+
+
+def test_parse_source_modules_raises_for_invalid_values():
+    try:
+        router._parse_source_modules(["blog_writer,unknown"])
+    except HTTPException as exc:
+        assert exc.status_code == 400
+        assert "Invalid source module" in exc.detail
+    else:
+        raise AssertionError("Expected HTTPException for invalid source module")

backend/tests/services/test_content_asset_service.py

@@ -0,0 +1,50 @@
+import importlib.util
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[3]
+SERVICE_PATH = ROOT / 'backend' / 'services' / 'content_asset_service.py'
+MODELS_PATH = ROOT / 'backend' / 'models' / 'content_asset_models.py'
+
+models_spec = importlib.util.spec_from_file_location('content_asset_models', MODELS_PATH)
+models = importlib.util.module_from_spec(models_spec)
+models_spec.loader.exec_module(models)
+AssetSource = models.AssetSource
+
+service_spec = importlib.util.spec_from_file_location('content_asset_service', SERVICE_PATH)
+service_module = importlib.util.module_from_spec(service_spec)
+service_spec.loader.exec_module(service_module)
+ContentAssetService = service_module.ContentAssetService
+
+
+class DummyQuery:
+    def __init__(self):
+        self.filters = []
+
+    def filter(self, expr):
+        self.filters.append(expr)
+        return self
+
+    def count(self): return 0
+    def order_by(self, *_args, **_kwargs): return self
+    def limit(self, *_args, **_kwargs): return self
+    def offset(self, *_args, **_kwargs): return self
+    def all(self): return []
+
+
+class DummyDB:
+    def __init__(self): self.query_obj = DummyQuery()
+    def query(self, *_args, **_kwargs): return self.query_obj
+
+
+def test_get_user_assets_accepts_multiple_source_modules_filter():
+    db = DummyDB()
+    service = ContentAssetService(db)
+
+    assets, total = service.get_user_assets(
+        user_id="user-1",
+        source_modules=[AssetSource.BLOG_WRITER, AssetSource.YOUTUBE],
+    )
+
+    assert assets == []
+    assert total == 0
+    assert len(db.query_obj.filters) >= 2

frontend/src/hooks/__tests__/useContentAssets.test.ts

@@ -0,0 +1,35 @@
+import { renderHook, waitFor } from '@testing-library/react';
+import { useContentAssets } from '../useContentAssets';
+
+const getTokenMock = jest.fn();
+
+jest.mock('@clerk/clerk-react', () => ({
+  useAuth: () => ({ getToken: getTokenMock }),
+}));
+
+describe('useContentAssets', () => {
+  beforeEach(() => {
+    getTokenMock.mockResolvedValue('test-token');
+    global.fetch = jest.fn().mockResolvedValue({
+      ok: true,
+      json: async () => ({ assets: [], total: 0, limit: 100, offset: 0 }),
+    } as Response);
+  });
+
+  afterEach(() => {
+    jest.clearAllMocks();
+  });
+
+  it('sends all source_module values as repeated query params', async () => {
+    renderHook(() =>
+      useContentAssets({ source_module: ['blog_writer', 'youtube'], limit: 50, offset: 0 })
+    );
+
+    await waitFor(() => expect(global.fetch).toHaveBeenCalled());
+
+    const calledUrl = (global.fetch as jest.Mock).mock.calls[0][0] as string;
+    const params = new URL(calledUrl).searchParams;
+
+    expect(params.getAll('source_module')).toEqual(['blog_writer', 'youtube']);
+  });
+});

frontend/src/hooks/useContentAssets.ts

@@ -29,7 +29,7 @@ export interface ContentAsset {
 
 export interface AssetFilters {
   asset_type?: 'text' | 'image' | 'video' | 'audio';
-  source_module?: string | string[]; // Support single or multiple source modules
+  source_module?: string | string[]; // Supports single or multiple source modules
   search?: string;
   tags?: string[];
   favorites_only?: boolean;
@@ -146,8 +146,10 @@ export const useContentAssets = (filters: AssetFilters = {}) => {
       if (currentFilters.source_module) {
         // Handle both string and array cases
         if (Array.isArray(currentFilters.source_module)) {
-          // For arrays, use the first value (backend doesn't support multiple yet)
-          params.append('source_module', currentFilters.source_module[0]);
+          // Send every selected source module as repeated query params
+          currentFilters.source_module.forEach((module) => {
+            params.append('source_module', module);
+          });
         } else {
           params.append('source_module', currentFilters.source_module);
         }