feat: add clone tracking and history bootstrap for cloned chats

surfsense_backend/app/utils/content_utils.py

@@ -0,0 +1,75 @@
+"""
+Utilities for working with message content.
+
+Message content in new_chat_messages can be stored in various formats:
+- String: Simple text content
+- List: Array of content parts [{"type": "text", "text": "..."}, {"type": "tool-call", ...}]
+- Dict: Single content object
+
+These utilities help extract and transform content for different use cases.
+"""
+
+from langchain_core.messages import AIMessage, HumanMessage
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+
+def extract_text_content(content: str | dict | list) -> str:
+    """Extract plain text content from various message formats."""
+    if isinstance(content, str):
+        return content
+    if isinstance(content, dict):
+        # Handle dict with 'text' key
+        if "text" in content:
+            return content["text"]
+        return str(content)
+    if isinstance(content, list):
+        # Handle list of parts (e.g., [{"type": "text", "text": "..."}])
+        texts = []
+        for part in content:
+            if isinstance(part, dict) and part.get("type") == "text":
+                texts.append(part.get("text", ""))
+            elif isinstance(part, str):
+                texts.append(part)
+        return "\n".join(texts) if texts else ""
+    return ""
+
+
+async def bootstrap_history_from_db(
+    session: AsyncSession,
+    thread_id: int,
+) -> list[HumanMessage | AIMessage]:
+    """
+    Load message history from database and convert to LangChain format.
+
+    Used for cloned chats where the LangGraph checkpointer has no state,
+    but we have messages in the database that should be used as context.
+
+    Args:
+        session: Database session
+        thread_id: The chat thread ID
+
+    Returns:
+        List of LangChain messages (HumanMessage/AIMessage)
+    """
+    from app.db import NewChatMessage
+
+    result = await session.execute(
+        select(NewChatMessage)
+        .filter(NewChatMessage.thread_id == thread_id)
+        .order_by(NewChatMessage.created_at)
+    )
+    db_messages = result.scalars().all()
+
+    langchain_messages: list[HumanMessage | AIMessage] = []
+
+    for msg in db_messages:
+        text_content = extract_text_content(msg.content)
+        if not text_content:
+            continue
+        if msg.role == "user":
+            langchain_messages.append(HumanMessage(content=text_content))
+        elif msg.role == "assistant":
+            langchain_messages.append(AIMessage(content=text_content))
+
+    return langchain_messages