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Merge pull request #295 from CREDO23/feature/airtable-connector
[Feature] Add Airtable connector
This commit is contained in:
Rohan Verma
GitHub
commit
662212d4e2
26 changed files with 1629 additions and 3 deletions
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@ -19,14 +19,14 @@ Available indexers:
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"""
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# Communication platforms
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# Calendar and scheduling
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from .airtable_indexer import index_airtable_records
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from .clickup_indexer import index_clickup_tasks
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from .confluence_indexer import index_confluence_pages
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from .discord_indexer import index_discord_messages
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# Development platforms
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from .github_indexer import index_github_repos
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# Calendar and scheduling
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from .google_calendar_indexer import index_google_calendar_events
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from .google_gmail_indexer import index_google_gmail_messages
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from .jira_indexer import index_jira_issues
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@ -39,6 +39,7 @@ from .notion_indexer import index_notion_pages
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from .slack_indexer import index_slack_messages
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__all__ = [ # noqa: RUF022
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"index_airtable_records",
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"index_clickup_tasks",
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"index_confluence_pages",
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"index_discord_messages",
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@ -0,0 +1,381 @@
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"""
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Airtable connector indexer.
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"""
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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.airtable_connector import AirtableConnector
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from app.db import Document, DocumentType, SearchSourceConnectorType
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from app.schemas.airtable_auth_credentials import AirtableAuthCredentialsBase
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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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calculate_date_range,
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check_duplicate_document_by_hash,
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get_connector_by_id,
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logger,
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update_connector_last_indexed,
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)
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async def index_airtable_records(
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session: AsyncSession,
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connector_id: int,
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search_space_id: int,
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user_id: str,
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start_date: str | None = None,
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end_date: str | None = None,
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max_records: int = 2500,
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update_last_indexed: bool = True,
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) -> tuple[int, str | None]:
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"""
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Index Airtable records for a given connector.
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Args:
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session: Database session
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connector_id: ID of the Airtable connector
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search_space_id: ID of the search space to store documents in
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user_id: ID of the user
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start_date: Start date for filtering records (YYYY-MM-DD)
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end_date: End date for filtering records (YYYY-MM-DD)
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max_records: Maximum number of records to fetch per table
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update_last_indexed: Whether to update the last_indexed_at timestamp
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Returns:
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Tuple of (number_of_documents_processed, error_message)
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"""
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task_logger = TaskLoggingService(session, search_space_id)
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log_entry = await task_logger.log_task_start(
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task_name="airtable_indexing",
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source="connector_indexing_task",
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message=f"Starting Airtable indexing for connector {connector_id}",
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metadata={
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"connector_id": connector_id,
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"user_id": str(user_id),
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"start_date": start_date,
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"end_date": end_date,
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"max_records": max_records,
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},
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)
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try:
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# Get the connector from the database
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connector = await get_connector_by_id(
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session, connector_id, SearchSourceConnectorType.AIRTABLE_CONNECTOR
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)
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if not connector:
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await task_logger.log_task_failure(
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log_entry,
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f"Connector with ID {connector_id} not found",
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"Connector not found",
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{"error_type": "ConnectorNotFound"},
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)
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return 0, f"Connector with ID {connector_id} not found"
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# Create credentials from connector config
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config_data = connector.config
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try:
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credentials = AirtableAuthCredentialsBase.from_dict(config_data)
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except Exception as e:
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await task_logger.log_task_failure(
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log_entry,
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f"Invalid Airtable credentials in connector {connector_id}",
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str(e),
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{"error_type": "InvalidCredentials"},
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)
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return 0, f"Invalid Airtable credentials: {e!s}"
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# Check if credentials are expired
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if credentials.is_expired:
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await task_logger.log_task_failure(
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log_entry,
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f"Airtable credentials expired for connector {connector_id}",
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"Credentials expired",
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{"error_type": "ExpiredCredentials"},
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)
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return 0, "Airtable credentials have expired. Please re-authenticate."
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# Calculate date range for indexing
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start_date_str, end_date_str = calculate_date_range(
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connector, start_date, end_date, default_days_back=365
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)
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logger.info(
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f"Starting Airtable indexing for connector {connector_id} "
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f"from {start_date_str} to {end_date_str}"
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)
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# Initialize Airtable connector
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airtable_connector = AirtableConnector(credentials)
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total_processed = 0
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try:
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# Get accessible bases
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logger.info(f"Fetching Airtable bases for connector {connector_id}")
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bases, error = airtable_connector.get_bases()
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if error:
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await task_logger.log_task_failure(
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log_entry,
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f"Failed to fetch Airtable bases: {error}",
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"API Error",
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{"error_type": "APIError"},
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)
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return 0, f"Failed to fetch Airtable bases: {error}"
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if not bases:
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success_msg = "No Airtable bases found or accessible"
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await task_logger.log_task_success(
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log_entry, success_msg, {"bases_count": 0}
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)
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return 0, success_msg
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logger.info(f"Found {len(bases)} Airtable bases to process")
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# Process each base
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for base in bases:
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base_id = base.get("id")
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base_name = base.get("name", "Unknown Base")
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if not base_id:
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logger.warning(f"Skipping base without ID: {base}")
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continue
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logger.info(f"Processing base: {base_name} ({base_id})")
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# Get base schema to find tables
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schema_data, schema_error = airtable_connector.get_base_schema(base_id)
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if schema_error:
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logger.warning(
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f"Failed to get schema for base {base_id}: {schema_error}"
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)
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continue
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if not schema_data or "tables" not in schema_data:
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logger.warning(f"No tables found in base {base_id}")
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continue
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tables = schema_data["tables"]
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logger.info(f"Found {len(tables)} tables in base {base_name}")
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# Process each table
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for table in tables:
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table_id = table.get("id")
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table_name = table.get("name", "Unknown Table")
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if not table_id:
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logger.warning(f"Skipping table without ID: {table}")
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continue
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logger.info(f"Processing table: {table_name} ({table_id})")
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# Fetch records
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if start_date_str and end_date_str:
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# Use date filtering if available
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records, records_error = (
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airtable_connector.get_records_by_date_range(
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base_id=base_id,
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table_id=table_id,
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date_field="CREATED_TIME()",
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start_date=start_date_str,
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end_date=end_date_str,
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max_records=max_records,
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)
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)
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else:
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# Fetch all records
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records, records_error = airtable_connector.get_all_records(
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base_id=base_id,
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table_id=table_id,
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max_records=max_records,
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)
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if records_error:
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logger.warning(
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f"Failed to fetch records from table {table_name}: {records_error}"
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)
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continue
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if not records:
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logger.info(f"No records found in table {table_name}")
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continue
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logger.info(f"Found {len(records)} records in table {table_name}")
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documents_indexed = 0
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skipped_messages = []
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documents_skipped = 0
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# Process each record
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for record in records:
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try:
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# Generate markdown content
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markdown_content = (
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airtable_connector.format_record_to_markdown(
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record, f"{base_name} - {table_name}"
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)
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)
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if not markdown_content.strip():
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logger.warning(
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f"Skipping message with no content: {record.get('id')}"
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)
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skipped_messages.append(
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f"{record.get('id')} (no content)"
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)
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documents_skipped += 1
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continue
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# Generate content hash
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content_hash = generate_content_hash(
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markdown_content, search_space_id
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)
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# Check if document already exists
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existing_document_by_hash = (
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await check_duplicate_document_by_hash(
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session, content_hash
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)
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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 message {record.get('id')}. Skipping processing."
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)
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documents_skipped += 1
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continue
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# Generate document summary
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user_llm = await get_user_long_context_llm(session, user_id)
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if user_llm:
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document_metadata = {
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"record_id": record.get("id", "Unknown"),
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"created_time": record.get("CREATED_TIME()", ""),
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"document_type": "Airtable Record",
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"connector_type": "Airtable",
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}
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(
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summary_content,
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summary_embedding,
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) = await generate_document_summary(
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markdown_content, user_llm, document_metadata
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)
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else:
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# Fallback to simple summary if no LLM configured
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summary_content = f"Airtable Record: {record.get('id', 'Unknown')}\n\n"
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summary_embedding = (
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config.embedding_model_instance.embed(
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summary_content
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)
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)
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# Process chunks
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chunks = await create_document_chunks(markdown_content)
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# Create and store new document
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logger.info(
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f"Creating new document for Airtable record: {record.get('id', 'Unknown')}"
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)
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document = Document(
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search_space_id=search_space_id,
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title=f"Airtable Record: {record.get('id', 'Unknown')}",
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document_type=DocumentType.AIRTABLE_CONNECTOR,
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document_metadata={
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"record_id": record.get("id", "Unknown"),
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"created_time": record.get("CREATED_TIME()", ""),
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},
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content=summary_content,
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content_hash=content_hash,
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embedding=summary_embedding,
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chunks=chunks,
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)
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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 Airtable record {summary_content}"
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)
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except Exception as e:
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logger.error(
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f"Error processing the Airtable record {record.get('id', 'Unknown')}: {e!s}",
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exc_info=True,
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)
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skipped_messages.append(
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f"{record.get('id', 'Unknown')} (processing error)"
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)
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documents_skipped += 1
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continue # Skip this message and continue with others
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# Update the last_indexed_at timestamp for the connector only if requested
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total_processed = documents_indexed
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if total_processed > 0:
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await update_connector_last_indexed(
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session, connector, update_last_indexed
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)
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# Commit all changes
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await session.commit()
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logger.info(
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"Successfully committed all Airtable document changes to database"
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)
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# Log success
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await task_logger.log_task_success(
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log_entry,
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f"Successfully completed Airtable indexing for connector {connector_id}",
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{
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"events_processed": total_processed,
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"documents_indexed": documents_indexed,
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"documents_skipped": documents_skipped,
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"skipped_messages_count": len(skipped_messages),
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},
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)
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logger.info(
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f"Airtable indexing completed: {documents_indexed} new records, {documents_skipped} skipped"
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)
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return (
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total_processed,
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None,
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) # Return None as the error message to indicate success
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except Exception as e:
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logger.error(
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f"Fetching Airtable bases for connector {connector_id} failed: {e!s}",
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exc_info=True,
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)
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except SQLAlchemyError as db_error:
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await session.rollback()
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await task_logger.log_task_failure(
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log_entry,
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f"Database error during Airtable indexing for connector {connector_id}",
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str(db_error),
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{"error_type": "SQLAlchemyError"},
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)
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logger.error(
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f"Database error during Airtable indexing: {db_error!s}", exc_info=True
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)
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return 0, f"Database error: {db_error!s}"
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except Exception as e:
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await session.rollback()
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await task_logger.log_task_failure(
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log_entry,
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f"Failed to index Airtable records for connector {connector_id}",
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str(e),
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{"error_type": type(e).__name__},
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)
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logger.error(f"Error during Airtable indexing: {e!s}", exc_info=True)
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return 0, f"Failed to index Airtable records: {e!s}"
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