mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-06-02 19:55:18 +02:00
feat: implement batch indexing for Microsoft Teams messages to improve efficiency and conversational context
This commit is contained in:
Anish Sarkar
parent
e053b1b988
commit
20ab128b05
1 changed files with 196 additions and 66 deletions
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@ -1,7 +1,10 @@
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"""
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Microsoft Teams connector indexer.
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Implements 2-phase document status updates for real-time UI feedback:
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Implements batch indexing: groups up to TEAMS_BATCH_SIZE messages per channel
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into a single document for efficient indexing and better conversational context.
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Uses 2-phase document status updates for real-time UI feedback:
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- Phase 1: Create all documents with 'pending' status (visible in UI immediately)
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- Phase 2: Process each document: pending → processing → ready/failed
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"""
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@ -41,6 +44,72 @@ HeartbeatCallbackType = Callable[[int], Awaitable[None]]
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# Heartbeat interval in seconds - update notification every 30 seconds
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HEARTBEAT_INTERVAL_SECONDS = 30
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# Number of messages to combine into a single document for batch indexing.
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# Grouping messages improves conversational context in embeddings/chunks and
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# drastically reduces the number of documents, embedding calls, and DB overhead.
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TEAMS_BATCH_SIZE = 100
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def _build_batch_document_string(
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team_name: str,
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team_id: str,
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channel_name: str,
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channel_id: str,
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messages: list[dict],
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) -> str:
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"""
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Combine multiple Teams messages into a single document string.
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Each message is formatted with its timestamp and author, and all messages
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are concatenated into a conversation-style document. The chunker will
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later split this into overlapping windows of ~8-10 consecutive messages,
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preserving conversational context in each chunk's embedding.
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Args:
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team_name: Name of the Microsoft Team
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team_id: ID of the Microsoft Team
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channel_name: Name of the channel
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channel_id: ID of the channel
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messages: List of formatted message dicts with 'user_name', 'created_datetime', 'content'
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Returns:
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Formatted document string with metadata and conversation content
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"""
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first_msg_time = messages[0].get("created_datetime", "Unknown")
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last_msg_time = messages[-1].get("created_datetime", "Unknown")
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metadata_lines = [
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f"TEAM_NAME: {team_name}",
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f"TEAM_ID: {team_id}",
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f"CHANNEL_NAME: {channel_name}",
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f"CHANNEL_ID: {channel_id}",
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f"MESSAGE_COUNT: {len(messages)}",
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f"FIRST_MESSAGE_TIME: {first_msg_time}",
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f"LAST_MESSAGE_TIME: {last_msg_time}",
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]
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conversation_lines = []
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for msg in messages:
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author = msg.get("user_name", "Unknown User")
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timestamp = msg.get("created_datetime", "Unknown Time")
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content = msg.get("content", "")
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conversation_lines.append(f"[{timestamp}] {author}: {content}")
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metadata_sections = [
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("METADATA", metadata_lines),
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(
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"CONTENT",
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[
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"FORMAT: markdown",
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"TEXT_START",
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"\n".join(conversation_lines),
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"TEXT_END",
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],
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),
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]
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return build_document_metadata_markdown(metadata_sections)
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async def index_teams_messages(
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session: AsyncSession,
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@ -55,6 +124,12 @@ async def index_teams_messages(
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"""
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Index Microsoft Teams messages from all accessible teams and channels.
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Messages are grouped into batches of TEAMS_BATCH_SIZE per channel,
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so each document contains up to 100 consecutive messages with full
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conversational context. This reduces document count, embedding calls,
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and DB overhead by ~100x while improving search quality through
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context-aware chunk embeddings.
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Implements 2-phase document status updates for real-time UI feedback:
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- Phase 1: Create all documents with 'pending' status (visible in UI immediately)
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- Phase 2: Process each document: pending → processing → ready/failed
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@ -184,6 +259,7 @@ async def index_teams_messages(
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documents_skipped = 0
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documents_failed = 0
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duplicate_content_count = 0
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total_messages_collected = 0
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skipped_channels = []
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# Heartbeat tracking - update notification periodically to prevent appearing stuck
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@ -199,21 +275,21 @@ async def index_teams_messages(
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start_datetime = None
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end_datetime = None
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if start_date_str:
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# Parse as naive datetime and make it timezone-aware (UTC)
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start_datetime = datetime.strptime(start_date_str, "%Y-%m-%d").replace(
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tzinfo=UTC
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)
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if end_date_str:
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# Parse as naive datetime, set to end of day, and make it timezone-aware (UTC)
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end_datetime = datetime.strptime(end_date_str, "%Y-%m-%d").replace(
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hour=23, minute=59, second=59, tzinfo=UTC
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)
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# =======================================================================
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# PHASE 1: Collect all messages and create pending documents
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# This makes ALL documents visible in the UI immediately with pending status
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# PHASE 1: Collect messages, group into batches, and create pending documents
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# Messages are grouped into batches of TEAMS_BATCH_SIZE per channel.
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# Each batch becomes a single document with full conversational context.
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# All documents are visible in the UI immediately with pending status.
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# =======================================================================
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messages_to_process = [] # List of dicts with document and message data
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batches_to_process = [] # List of dicts with document and batch data
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new_documents_created = False
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for team in teams:
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@ -251,65 +327,72 @@ async def index_teams_messages(
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)
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continue
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# Process each message
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# Format messages for batching
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formatted_messages = []
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for msg in messages:
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# Skip deleted messages or empty content
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if msg.get("deletedDateTime"):
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continue
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# Extract message details
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message_id = msg.get("id", "")
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created_datetime = msg.get("createdDateTime", "")
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from_user = msg.get("from", {})
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user_name = from_user.get("user", {}).get(
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"displayName", "Unknown User"
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)
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user_email = from_user.get("user", {}).get(
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"userPrincipalName", "Unknown Email"
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)
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# Extract message content
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body = msg.get("body", {})
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content_type = body.get("contentType", "text")
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msg_text = body.get("content", "")
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# Skip empty messages
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if not msg_text or msg_text.strip() == "":
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continue
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# Format document metadata
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metadata_sections = [
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(
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"METADATA",
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[
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f"TEAM_NAME: {team_name}",
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f"TEAM_ID: {team_id}",
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f"CHANNEL_NAME: {channel_name}",
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f"CHANNEL_ID: {channel_id}",
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f"MESSAGE_TIMESTAMP: {created_datetime}",
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f"MESSAGE_USER_NAME: {user_name}",
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f"MESSAGE_USER_EMAIL: {user_email}",
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f"CONTENT_TYPE: {content_type}",
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],
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),
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(
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"CONTENT",
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[
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f"FORMAT: {content_type}",
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"TEXT_START",
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msg_text,
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"TEXT_END",
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],
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),
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]
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# Build the document string
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combined_document_string = build_document_metadata_markdown(
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metadata_sections
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formatted_messages.append(
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{
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"message_id": msg.get("id", ""),
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"created_datetime": msg.get("createdDateTime", ""),
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"user_name": user_name,
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"content": msg_text,
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}
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)
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# Generate unique identifier hash for this Teams message
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unique_identifier = f"{team_id}_{channel_id}_{message_id}"
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if not formatted_messages:
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logger.info(
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"No valid messages found in channel %s of team %s after filtering.",
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channel_name,
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team_name,
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)
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documents_skipped += 1
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continue
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total_messages_collected += len(formatted_messages)
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# =======================================================
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# Group messages into batches of TEAMS_BATCH_SIZE
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# Each batch becomes a single document with conversation context
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# =======================================================
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for batch_start in range(
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0, len(formatted_messages), TEAMS_BATCH_SIZE
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):
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batch = formatted_messages[
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batch_start : batch_start + TEAMS_BATCH_SIZE
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]
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# Build combined document string from all messages in this batch
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combined_document_string = _build_batch_document_string(
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team_name=team_name,
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team_id=team_id,
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channel_name=channel_name,
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channel_id=channel_id,
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messages=batch,
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)
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# Generate unique identifier for this batch using
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# team_id + channel_id + first message id + last message id
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first_msg_id = batch[0].get("message_id", "")
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last_msg_id = batch[-1].get("message_id", "")
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unique_identifier = (
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f"{team_id}_{channel_id}_{first_msg_id}_{last_msg_id}"
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)
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unique_identifier_hash = generate_unique_identifier_hash(
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DocumentType.TEAMS_CONNECTOR,
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unique_identifier,
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@ -331,7 +414,6 @@ async def index_teams_messages(
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if existing_document:
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# Document exists - check if content has changed
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if existing_document.content_hash == content_hash:
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# Ensure status is ready (might have been stuck in processing/pending)
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if not DocumentStatus.is_state(
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existing_document.status, DocumentStatus.READY
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):
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@ -342,7 +424,7 @@ async def index_teams_messages(
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continue
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# Queue existing document for update (will be set to processing in Phase 2)
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messages_to_process.append(
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batches_to_process.append(
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{
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"document": existing_document,
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"is_new": False,
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@ -352,14 +434,21 @@ async def index_teams_messages(
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"team_id": team_id,
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"channel_name": channel_name,
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"channel_id": channel_id,
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"message_id": message_id,
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"first_message_id": first_msg_id,
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"last_message_id": last_msg_id,
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"first_message_time": batch[0].get(
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"created_datetime", "Unknown"
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),
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"last_message_time": batch[-1].get(
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"created_datetime", "Unknown"
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),
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"message_count": len(batch),
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"start_date": start_date_str,
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"end_date": end_date_str,
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}
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)
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continue
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# Document doesn't exist by unique_identifier_hash
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# Check if a document with the same content_hash exists (from another connector)
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with session.no_autoflush:
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duplicate_by_content = (
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@ -370,9 +459,10 @@ async def index_teams_messages(
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if duplicate_by_content:
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logger.info(
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"Teams message %s in channel %s already indexed by another connector "
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"Teams batch (%s msgs) in %s/%s already indexed by another connector "
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"(existing document ID: %s, type: %s). Skipping.",
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message_id,
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len(batch),
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team_name,
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channel_name,
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duplicate_by_content.id,
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duplicate_by_content.document_type,
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@ -391,6 +481,9 @@ async def index_teams_messages(
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"team_id": team_id,
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"channel_name": channel_name,
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"channel_id": channel_id,
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"first_message_id": first_msg_id,
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"last_message_id": last_msg_id,
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"message_count": len(batch),
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"connector_id": connector_id,
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},
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content="Pending...", # Placeholder until processed
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@ -406,7 +499,7 @@ async def index_teams_messages(
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session.add(document)
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new_documents_created = True
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messages_to_process.append(
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batches_to_process.append(
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{
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"document": document,
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"is_new": True,
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@ -416,12 +509,30 @@ async def index_teams_messages(
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"team_id": team_id,
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"channel_name": channel_name,
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"channel_id": channel_id,
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"message_id": message_id,
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"first_message_id": first_msg_id,
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"last_message_id": last_msg_id,
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"first_message_time": batch[0].get(
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"created_datetime", "Unknown"
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),
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"last_message_time": batch[-1].get(
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"created_datetime", "Unknown"
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),
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"message_count": len(batch),
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"start_date": start_date_str,
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"end_date": end_date_str,
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}
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)
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logger.info(
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"Phase 1: Collected %s messages from %s/%s, "
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"grouped into %s batch(es)",
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len(formatted_messages),
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team_name,
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channel_name,
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(len(formatted_messages) + TEAMS_BATCH_SIZE - 1)
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// TEAMS_BATCH_SIZE,
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)
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except Exception as e:
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logger.error(
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"Error processing channel %s in team %s: %s",
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@ -441,17 +552,20 @@ async def index_teams_messages(
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# Commit all pending documents - they all appear in UI now
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if new_documents_created:
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logger.info(
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f"Phase 1: Committing {len([m for m in messages_to_process if m['is_new']])} pending documents"
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"Phase 1: Committing %s pending batch documents "
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"(%s total messages across all channels)",
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len([b for b in batches_to_process if b["is_new"]]),
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total_messages_collected,
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)
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await session.commit()
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# =======================================================================
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# PHASE 2: Process each document one by one
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# PHASE 2: Process each batch document one by one
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# Each document transitions: pending → processing → ready/failed
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# =======================================================================
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logger.info(f"Phase 2: Processing {len(messages_to_process)} documents")
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logger.info("Phase 2: Processing %s batch documents", len(batches_to_process))
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for item in messages_to_process:
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for item in batches_to_process:
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# Send heartbeat periodically
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if on_heartbeat_callback:
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current_time = time.time()
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@ -481,6 +595,11 @@ async def index_teams_messages(
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"team_id": item["team_id"],
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"channel_name": item["channel_name"],
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"channel_id": item["channel_id"],
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"first_message_id": item["first_message_id"],
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"last_message_id": item["last_message_id"],
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"first_message_time": item["first_message_time"],
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"last_message_time": item["last_message_time"],
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"message_count": item["message_count"],
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"start_date": item["start_date"],
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"end_date": item["end_date"],
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"indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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@ -495,20 +614,25 @@ async def index_teams_messages(
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# Batch commit every 10 documents (for ready status updates)
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if documents_indexed % 10 == 0:
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logger.info(
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"Committing batch: %s Teams messages processed so far",
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"Committing batch: %s Teams batch documents processed so far",
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documents_indexed,
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)
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await session.commit()
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except Exception as e:
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logger.error(f"Error processing Teams message: {e!s}", exc_info=True)
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logger.error(
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"Error processing Teams batch document: %s",
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str(e),
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exc_info=True,
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)
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# Mark document as failed with reason (visible in UI)
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try:
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document.status = DocumentStatus.failed(str(e))
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document.updated_at = get_current_timestamp()
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except Exception as status_error:
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logger.error(
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f"Failed to update document status to failed: {status_error}"
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"Failed to update document status to failed: %s",
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str(status_error),
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)
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documents_failed += 1
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continue
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@ -518,7 +642,9 @@ async def index_teams_messages(
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# Final commit for any remaining documents not yet committed in batches
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logger.info(
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"Final commit: Total %s Teams messages processed", documents_indexed
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"Final commit: Total %s Teams batch documents processed (from %s messages)",
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documents_indexed,
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total_messages_collected,
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)
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try:
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await session.commit()
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@ -530,8 +656,9 @@ async def index_teams_messages(
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or "uniqueviolationerror" in str(e).lower()
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):
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logger.warning(
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f"Duplicate content_hash detected during final commit. "
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f"Rolling back and continuing. Error: {e!s}"
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"Duplicate content_hash detected during final commit. "
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"Rolling back and continuing. Error: %s",
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str(e),
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)
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await session.rollback()
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else:
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@ -557,13 +684,16 @@ async def index_teams_messages(
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"documents_failed": documents_failed,
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"duplicate_content_count": duplicate_content_count,
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"skipped_channels_count": len(skipped_channels),
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"total_messages_collected": total_messages_collected,
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"batch_size": TEAMS_BATCH_SIZE,
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},
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)
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logger.info(
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"Teams indexing completed: %s ready, %s skipped, %s failed "
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"(%s duplicate content)",
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"Teams indexing completed: %s batch docs ready (from %s messages), "
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"%s skipped, %s failed (%s duplicate content)",
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documents_indexed,
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total_messages_collected,
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documents_skipped,
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documents_failed,
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duplicate_content_count,
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