added post insight feature

src/database/client.py

@@ -194,6 +194,81 @@ class DatabaseClient:
         )
         return [LinkedInPost(**item) for item in result.data]
 
+    # ==================== LINKEDIN POST INSIGHTS ====================
+
+    async def upsert_post_insights_posts(self, posts: List['LinkedInPostInsightPost']) -> List['LinkedInPostInsightPost']:
+        """Upsert LinkedIn post insights posts."""
+        from datetime import datetime
+        from src.database.models import LinkedInPostInsightPost
+
+        data = []
+        for p in posts:
+            post_dict = p.model_dump(exclude={"id", "created_at", "updated_at", "first_seen_at"}, exclude_none=True)
+            post_dict["user_id"] = str(post_dict["user_id"])
+            if post_dict.get("linkedin_account_id"):
+                post_dict["linkedin_account_id"] = str(post_dict["linkedin_account_id"])
+            if "post_date" in post_dict and isinstance(post_dict["post_date"], datetime):
+                post_dict["post_date"] = post_dict["post_date"].isoformat()
+            data.append(post_dict)
+
+        if not data:
+            return []
+
+        result = await asyncio.to_thread(
+            lambda: self.client.table("linkedin_post_insights_posts").upsert(
+                data,
+                on_conflict="user_id,post_urn"
+            ).execute()
+        )
+        return [LinkedInPostInsightPost(**item) for item in result.data]
+
+    async def upsert_post_insights_daily(self, snapshots: List['LinkedInPostInsightDaily']) -> List['LinkedInPostInsightDaily']:
+        """Upsert daily snapshots for post insights."""
+        from src.database.models import LinkedInPostInsightDaily
+
+        data = []
+        for s in snapshots:
+            snap_dict = s.model_dump(exclude={"id", "created_at", "updated_at"}, exclude_none=True)
+            snap_dict["user_id"] = str(snap_dict["user_id"])
+            snap_dict["post_id"] = str(snap_dict["post_id"])
+            if "snapshot_date" in snap_dict and hasattr(snap_dict["snapshot_date"], "isoformat"):
+                snap_dict["snapshot_date"] = snap_dict["snapshot_date"].isoformat()
+            data.append(snap_dict)
+
+        if not data:
+            return []
+
+        result = await asyncio.to_thread(
+            lambda: self.client.table("linkedin_post_insights_daily").upsert(
+                data,
+                on_conflict="post_id,snapshot_date"
+            ).execute()
+        )
+        return [LinkedInPostInsightDaily(**item) for item in result.data]
+
+    async def get_post_insights_posts(self, user_id: UUID) -> List['LinkedInPostInsightPost']:
+        """Get all LinkedIn post insights posts for user."""
+        from src.database.models import LinkedInPostInsightPost
+        result = await asyncio.to_thread(
+            lambda: self.client.table("linkedin_post_insights_posts").select("*").eq(
+                "user_id", str(user_id)
+            ).order("post_date", desc=True).execute()
+        )
+        return [LinkedInPostInsightPost(**item) for item in result.data]
+
+    async def get_post_insights_daily(self, user_id: UUID, since_date: Optional[str] = None) -> List['LinkedInPostInsightDaily']:
+        """Get daily post insights snapshots for user."""
+        from src.database.models import LinkedInPostInsightDaily
+
+        def _query():
+            q = self.client.table("linkedin_post_insights_daily").select("*").eq("user_id", str(user_id))
+            if since_date:
+                q = q.gte("snapshot_date", since_date)
+            return q.order("snapshot_date", desc=False).execute()
+
+        result = await asyncio.to_thread(_query)
+        return [LinkedInPostInsightDaily(**item) for item in result.data]
+
     async def update_post_classification(
         self,
         post_id: UUID,
@@ -768,6 +843,19 @@ class DatabaseClient:
             return LinkedInAccount(**result.data[0])
         return None
 
+    async def list_linkedin_accounts(self, active_only: bool = True) -> list['LinkedInAccount']:
+        """List LinkedIn accounts (optionally only active ones)."""
+        from src.database.models import LinkedInAccount
+
+        def _query():
+            q = self.client.table("linkedin_accounts").select("*")
+            if active_only:
+                q = q.eq("is_active", True)
+            return q.execute()
+
+        result = await asyncio.to_thread(_query)
+        return [LinkedInAccount(**item) for item in result.data]
+
     async def create_linkedin_account(self, account: 'LinkedInAccount') -> 'LinkedInAccount':
         """Create LinkedIn account connection."""
         from src.database.models import LinkedInAccount

src/database/models.py

@@ -289,6 +289,45 @@ class LinkedInPost(DBModel):
     classification_confidence: Optional[float] = None
 
 
+class LinkedInPostInsightPost(DBModel):
+    """LinkedIn post record for insights (separate from linkedin_posts)."""
+    id: Optional[UUID] = None
+    user_id: UUID
+    linkedin_account_id: Optional[UUID] = None
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
+
+    post_urn: str
+    post_url: Optional[str] = None
+    post_text: Optional[str] = None
+    post_date: Optional[datetime] = None
+    author_username: Optional[str] = None
+
+    total_reactions: int = 0
+    likes: int = 0
+    comments: int = 0
+    shares: int = 0
+    reactions_breakdown: Dict[str, Any] = Field(default_factory=dict)
+
+    raw_data: Optional[Dict[str, Any]] = None
+    first_seen_at: Optional[datetime] = None
+
+
+class LinkedInPostInsightDaily(DBModel):
+    """Daily snapshot of LinkedIn post insights."""
+    id: Optional[UUID] = None
+    user_id: UUID
+    post_id: UUID
+    snapshot_date: date
+    total_reactions: int = 0
+    likes: int = 0
+    comments: int = 0
+    shares: int = 0
+    reactions_breakdown: Dict[str, Any] = Field(default_factory=dict)
+    created_at: Optional[datetime] = None
+    updated_at: Optional[datetime] = None
+
+
 class Topic(DBModel):
     """Topic model."""
     id: Optional[UUID] = None