- QUICKSTART.md: replace data_description.md.example copy step with
note that tables are registered via the admin API or web UI
- NOTIFICATIONS.md: replace examples/ section with planned-feature note
- telegram_bot.md: remove examples/notifications/ rows from deployment
table and example scripts section; note feature is planned
- dev_docs/README.md: remove plan-corporate-memory.md entry
- duckdb_manager.py: update comment from remote_query.py to query API endpoint
find_project_root() and parse_data_description() now check CONFIG_DIR
env var first when looking for data_description.md. On server deployment,
data_description.md lives in instance/config/ (CONFIG_DIR), not in the
OSS repo's docs/ directory.
Add src/remote_query.py CLI module enabling the AI agent to run SQL
queries spanning local Parquet tables and remote BigQuery tables in a
single DuckDB session on the server. Two-phase protocol: BQ sub-queries
(--register-bq) fetch filtered/aggregated data, then DuckDB SQL (--sql)
joins everything.
Safety: COUNT(*) pre-check, memory estimation (2GB cap), row limits
(500K per BQ sub-query, 100K final result).
Changes:
- New src/remote_query.py with CLI, BQ registration, output formatting
- Add bq_entity_type field to TableConfig (view vs table routing)
- Extract create_local_views() from duckdb_manager.py for reuse
- Update claude_md_template.txt with remote query agent instructions
- Update example configs with remote_query section and docs
- 52 new tests (42 remote_query + 10 bq_entity_type), all passing
Open-source AI data analyst platform extracted from internal repo.
Includes data sync engine, Keboola adapter, Flask web portal,
server deployment scripts, and configuration templates.
