mirror of
https://github.com/MODSetter/SurfSense.git
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fix: slack indexing
- Indivisual messages as Document instead of concatinating it.
This commit is contained in:
DESKTOP-RTLN3BA\$punk
parent
0db3c32144
commit
f443a6636f
2 changed files with 101 additions and 85 deletions
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@ -160,3 +160,41 @@ def build_document_metadata_string(
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document_parts.append("</DOCUMENT>")
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return "\n".join(document_parts)
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def build_document_metadata_markdown(
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metadata_sections: list[tuple[str, list[str]]],
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) -> str:
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"""
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Build a markdown document string from metadata sections.
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Args:
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metadata_sections: List of (section_title, section_content) tuples
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Returns:
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Combined markdown document string
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"""
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document_parts = []
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for section_title, section_content in metadata_sections:
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# Convert section title to proper markdown header
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document_parts.append(f"## {section_title.title()}")
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document_parts.append("") # Empty line after header
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for content_line in section_content:
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# Handle special content formatting
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if content_line == "TEXT_START" or content_line == "TEXT_END":
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continue # Skip text delimiters in markdown
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elif content_line.startswith("FORMAT: "):
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# Skip format indicators in markdown
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continue
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else:
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document_parts.append(content_line)
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document_parts.append("") # Empty line after section
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# Remove trailing empty lines
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while document_parts and document_parts[-1] == "":
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document_parts.pop()
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return "\n".join(document_parts)
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@ -8,18 +8,17 @@ from slack_sdk.errors import SlackApiError
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from sqlalchemy.exc import SQLAlchemyError
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.config import config
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from app.connectors.slack_history import SlackHistory
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from app.db import Document, DocumentType, SearchSourceConnectorType
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from app.services.llm_service import get_user_long_context_llm
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from app.services.task_logging_service import TaskLoggingService
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from app.utils.document_converters import (
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create_document_chunks,
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generate_content_hash,
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generate_document_summary,
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)
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from .base import (
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build_document_metadata_string,
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build_document_metadata_markdown,
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calculate_date_range,
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check_duplicate_document_by_hash,
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get_connector_by_id,
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@ -234,98 +233,77 @@ async def index_slack_messages(
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documents_skipped += 1
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continue # Skip if no valid messages after filtering
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# Convert messages to markdown format
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channel_content = f"# Slack Channel: {channel_name}\n\n"
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for msg in formatted_messages:
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user_name = msg.get("user_name", "Unknown User")
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timestamp = msg.get("datetime", "Unknown Time")
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text = msg.get("text", "")
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msg_user_name = msg.get("user_name", "Unknown User")
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msg_user_email = msg.get("user_email", "Unknown Email")
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msg_text = msg.get("text", "")
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channel_content += (
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f"## {user_name} ({timestamp})\n\n{text}\n\n---\n\n"
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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"CHANNEL_NAME: {channel_name}",
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f"CHANNEL_ID: {channel_id}",
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f"MESSAGE_TIMESTAMP: {timestamp}",
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f"MESSAGE_USER_NAME: {msg_user_name}",
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f"MESSAGE_USER_EMAIL: {msg_user_email}",
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],
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),
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(
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"CONTENT",
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["FORMAT: markdown", "TEXT_START", msg_text, "TEXT_END"],
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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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)
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content_hash = generate_content_hash(
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combined_document_string, search_space_id
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)
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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"CHANNEL_NAME: {channel_name}",
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f"CHANNEL_ID: {channel_id}",
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f"MESSAGE_COUNT: {len(formatted_messages)}",
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],
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),
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(
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"CONTENT",
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["FORMAT: markdown", "TEXT_START", channel_content, "TEXT_END"],
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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_string(
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metadata_sections
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)
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content_hash = generate_content_hash(
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combined_document_string, search_space_id
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)
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# Check if document with this content hash already exists
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existing_document_by_hash = await check_duplicate_document_by_hash(
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session, content_hash
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)
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if existing_document_by_hash:
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logger.info(
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f"Document with content hash {content_hash} already exists for channel {channel_name}. Skipping processing."
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# Check if document with this content hash already exists
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existing_document_by_hash = await check_duplicate_document_by_hash(
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session, content_hash
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)
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documents_skipped += 1
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continue
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# Get user's long context LLM
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user_llm = await get_user_long_context_llm(session, user_id)
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if not user_llm:
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logger.error(f"No long context LLM configured for user {user_id}")
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skipped_channels.append(f"{channel_name} (no LLM configured)")
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documents_skipped += 1
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continue
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if existing_document_by_hash:
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logger.info(
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f"Document with content hash {content_hash} already exists for channel {channel_name}. Skipping processing."
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)
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documents_skipped += 1
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continue
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# Generate summary with metadata
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document_metadata = {
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"channel_name": channel_name,
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"channel_id": channel_id,
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"message_count": len(formatted_messages),
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"document_type": "Slack Channel Messages",
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"connector_type": "Slack",
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}
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summary_content, summary_embedding = await generate_document_summary(
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combined_document_string, user_llm, document_metadata
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)
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# Process chunks
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chunks = await create_document_chunks(combined_document_string)
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doc_embedding = config.embedding_model_instance.embed(
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combined_document_string
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)
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# Process chunks
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chunks = await create_document_chunks(channel_content)
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# Create and store new document
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document = Document(
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search_space_id=search_space_id,
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title=f"Slack - {channel_name}",
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document_type=DocumentType.SLACK_CONNECTOR,
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document_metadata={
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"channel_name": channel_name,
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"channel_id": channel_id,
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"start_date": start_date_str,
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"end_date": end_date_str,
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"message_count": len(formatted_messages),
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"indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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},
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content=combined_document_string,
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embedding=doc_embedding,
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chunks=chunks,
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content_hash=content_hash,
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)
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# Create and store new document
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document = Document(
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search_space_id=search_space_id,
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title=f"Slack - {channel_name}",
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document_type=DocumentType.SLACK_CONNECTOR,
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document_metadata={
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"channel_name": channel_name,
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"channel_id": channel_id,
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"start_date": start_date_str,
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"end_date": end_date_str,
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"message_count": len(formatted_messages),
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"indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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},
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content=summary_content,
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embedding=summary_embedding,
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chunks=chunks,
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content_hash=content_hash,
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)
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session.add(document)
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documents_indexed += 1
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session.add(document)
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documents_indexed += 1
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logger.info(
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f"Successfully indexed new channel {channel_name} with {len(formatted_messages)} messages"
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)
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