update FLOW_ID variable
This commit is contained in:
Gabriel Luiz Freitas Almeida
parent
4f72b88fcc
commit
e144f8f0ef
1 changed files with 92 additions and 70 deletions
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@ -1,4 +1,5 @@
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import json
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from utils.logging_config import get_logger
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logger = get_logger(__name__)
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@ -178,9 +179,9 @@ class ChatService:
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"Langflow client not initialized. Ensure LANGFLOW is reachable or set LANGFLOW_KEY."
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)
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response_text, response_id = await async_langflow(
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langflow_client,
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LANGFLOW_CHAT_FLOW_ID,
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document_prompt,
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langflow_client=langflow_client,
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flow_id=LANGFLOW_CHAT_FLOW_ID,
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prompt=document_prompt,
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extra_headers=extra_headers,
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previous_response_id=previous_response_id,
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)
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@ -199,17 +200,17 @@ class ChatService:
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async def get_chat_history(self, user_id: str):
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"""Get chat conversation history for a user"""
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from agent import get_user_conversations, active_conversations
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from agent import active_conversations, get_user_conversations
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if not user_id:
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return {"error": "User ID is required", "conversations": []}
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# Get metadata from persistent storage
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conversations_dict = get_user_conversations(user_id)
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# Get in-memory conversations (with function calls)
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in_memory_conversations = active_conversations.get(user_id, {})
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logger.debug(
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"Getting chat history for user",
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user_id=user_id,
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@ -219,7 +220,7 @@ class ChatService:
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# Convert conversations dict to list format with metadata
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conversations = []
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# First, process in-memory conversations (they have function calls)
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for response_id, conversation_state in in_memory_conversations.items():
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# Filter out system messages
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@ -235,13 +236,13 @@ class ChatService:
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}
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if msg.get("response_id"):
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message_data["response_id"] = msg["response_id"]
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# Include function call data if present
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if msg.get("chunks"):
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message_data["chunks"] = msg["chunks"]
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if msg.get("response_data"):
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message_data["response_data"] = msg["response_data"]
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messages.append(message_data)
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if messages: # Only include conversations with actual messages
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@ -275,25 +276,27 @@ class ChatService:
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"previous_response_id"
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),
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"total_messages": len(messages),
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"source": "in_memory"
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"source": "in_memory",
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}
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)
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# Then, add any persistent metadata that doesn't have in-memory data
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for response_id, metadata in conversations_dict.items():
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if response_id not in in_memory_conversations:
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# This is metadata-only conversation (no function calls)
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conversations.append({
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"response_id": response_id,
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"title": metadata.get("title", "New Chat"),
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"endpoint": "chat",
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"messages": [], # No messages in metadata-only
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"created_at": metadata.get("created_at"),
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"last_activity": metadata.get("last_activity"),
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"previous_response_id": metadata.get("previous_response_id"),
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"total_messages": metadata.get("total_messages", 0),
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"source": "metadata_only"
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})
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conversations.append(
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{
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"response_id": response_id,
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"title": metadata.get("title", "New Chat"),
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"endpoint": "chat",
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"messages": [], # No messages in metadata-only
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"created_at": metadata.get("created_at"),
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"last_activity": metadata.get("last_activity"),
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"previous_response_id": metadata.get("previous_response_id"),
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"total_messages": metadata.get("total_messages", 0),
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"source": "metadata_only",
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}
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)
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# Sort by last activity (most recent first)
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conversations.sort(key=lambda c: c.get("last_activity", ""), reverse=True)
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@ -309,33 +312,37 @@ class ChatService:
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"""Get langflow conversation history for a user - now fetches from both OpenRAG memory and Langflow database"""
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from agent import get_user_conversations
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from services.langflow_history_service import langflow_history_service
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if not user_id:
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return {"error": "User ID is required", "conversations": []}
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all_conversations = []
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try:
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# 1. Get local conversation metadata (no actual messages stored here)
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conversations_dict = get_user_conversations(user_id)
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local_metadata = {}
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for response_id, conversation_metadata in conversations_dict.items():
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# Store metadata for later use with Langflow data
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local_metadata[response_id] = conversation_metadata
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# 2. Get actual conversations from Langflow database (source of truth for messages)
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print(f"[DEBUG] Attempting to fetch Langflow history for user: {user_id}")
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langflow_history = await langflow_history_service.get_user_conversation_history(user_id, flow_id=FLOW_ID)
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langflow_history = (
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await langflow_history_service.get_user_conversation_history(
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user_id, flow_id=LANGFLOW_CHAT_FLOW_ID
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)
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)
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if langflow_history.get("conversations"):
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for conversation in langflow_history["conversations"]:
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session_id = conversation["session_id"]
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# Only process sessions that belong to this user (exist in local metadata)
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if session_id not in local_metadata:
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continue
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# Use Langflow messages (with function calls) as source of truth
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messages = []
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for msg in conversation.get("messages", []):
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@ -344,76 +351,91 @@ class ChatService:
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"content": msg["content"],
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"timestamp": msg.get("timestamp"),
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"langflow_message_id": msg.get("langflow_message_id"),
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"source": "langflow"
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"source": "langflow",
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}
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# Include function call data if present
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if msg.get("chunks"):
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message_data["chunks"] = msg["chunks"]
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if msg.get("response_data"):
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message_data["response_data"] = msg["response_data"]
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messages.append(message_data)
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if messages:
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# Use local metadata if available, otherwise generate from Langflow data
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metadata = local_metadata.get(session_id, {})
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if not metadata.get("title"):
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first_user_msg = next((msg for msg in messages if msg["role"] == "user"), None)
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first_user_msg = next(
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(msg for msg in messages if msg["role"] == "user"), None
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)
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title = (
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first_user_msg["content"][:50] + "..."
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if first_user_msg and len(first_user_msg["content"]) > 50
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if first_user_msg
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and len(first_user_msg["content"]) > 50
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else first_user_msg["content"]
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if first_user_msg
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else "Langflow chat"
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)
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else:
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title = metadata["title"]
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all_conversations.append({
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"response_id": session_id,
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"title": title,
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"endpoint": "langflow",
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"messages": messages, # Function calls preserved from Langflow
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"created_at": metadata.get("created_at") or conversation.get("created_at"),
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"last_activity": metadata.get("last_activity") or conversation.get("last_activity"),
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"total_messages": len(messages),
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"source": "langflow_enhanced",
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"langflow_session_id": session_id,
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"langflow_flow_id": conversation.get("flow_id")
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})
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all_conversations.append(
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{
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"response_id": session_id,
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"title": title,
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"endpoint": "langflow",
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"messages": messages, # Function calls preserved from Langflow
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"created_at": metadata.get("created_at")
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or conversation.get("created_at"),
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"last_activity": metadata.get("last_activity")
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or conversation.get("last_activity"),
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"total_messages": len(messages),
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"source": "langflow_enhanced",
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"langflow_session_id": session_id,
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"langflow_flow_id": conversation.get("flow_id"),
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}
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)
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# 3. Add any local metadata that doesn't have Langflow data yet (recent conversations)
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for response_id, metadata in local_metadata.items():
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if not any(c["response_id"] == response_id for c in all_conversations):
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all_conversations.append({
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"response_id": response_id,
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"title": metadata.get("title", "New Chat"),
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"endpoint": "langflow",
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"messages": [], # Will be filled when Langflow sync catches up
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"created_at": metadata.get("created_at"),
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"last_activity": metadata.get("last_activity"),
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"total_messages": metadata.get("total_messages", 0),
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"source": "metadata_only"
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})
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all_conversations.append(
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{
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"response_id": response_id,
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"title": metadata.get("title", "New Chat"),
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"endpoint": "langflow",
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"messages": [], # Will be filled when Langflow sync catches up
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"created_at": metadata.get("created_at"),
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"last_activity": metadata.get("last_activity"),
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"total_messages": metadata.get("total_messages", 0),
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"source": "metadata_only",
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}
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)
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if langflow_history.get("conversations"):
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print(f"[DEBUG] Added {len(langflow_history['conversations'])} historical conversations from Langflow")
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print(
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f"[DEBUG] Added {len(langflow_history['conversations'])} historical conversations from Langflow"
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)
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elif langflow_history.get("error"):
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print(f"[DEBUG] Could not fetch Langflow history for user {user_id}: {langflow_history['error']}")
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print(
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f"[DEBUG] Could not fetch Langflow history for user {user_id}: {langflow_history['error']}"
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)
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else:
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print(f"[DEBUG] No Langflow conversations found for user {user_id}")
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except Exception as e:
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print(f"[ERROR] Failed to fetch Langflow history: {e}")
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# Continue with just in-memory conversations
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# Sort by last activity (most recent first)
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all_conversations.sort(key=lambda c: c.get("last_activity", ""), reverse=True)
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print(f"[DEBUG] Returning {len(all_conversations)} conversations ({len(local_metadata)} from local metadata)")
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print(
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f"[DEBUG] Returning {len(all_conversations)} conversations ({len(local_metadata)} from local metadata)"
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)
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return {
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"user_id": user_id,
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"endpoint": "langflow",
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