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
Server venv is created during bootstrap via SSH (same package list,
installed natively on Linux). Removes sync_data.sh section that copied
pip freeze output across platforms (Windows/macOS freeze is incompatible
with Linux).
- Rewrite bootstrap.yaml as clean structured YAML with steps, commands,
descriptions, conditions, and notes
- Add _generate_setup_instructions() in app.py that reads YAML, substitutes
placeholders, and produces clipboard-ready plain text
- Replace 50-line hardcoded JS string builder with single tojson variable
- All setup instructions now editable in one YAML file
Add {ssh_alias} and {ssh_key} placeholders so each instance can use
its own SSH config name (avoids conflicts when user has multiple instances).
Remove Keboola-specific sync_settings and dataset references.
Simplify to single download_server_data step (rsync with scp fallback).
Handle SSH alias conflicts gracefully.
Phase 1 - Internal reference cleanup:
- Delete dev_docs/meetings/ (internal meeting notes/transcripts)
- Replace hardcoded usernames (padak/matejkys/dasa) with deploy/generic
- Replace "Internal AI Data Analyst" with "AI Data Analyst"
- Replace keboola/internal_ai_data_analyst URLs with your-org/ai-data-analyst
- Replace /tmp/keboola_load/ with /tmp/data_analyst_staging/ in dev_docs
Phase 2 - Deployment hardening:
- Tighten sudoers wildcards to explicit paths (visudo, sudoers cp)
- setup.sh creates all groups (data-ops, dataread, data-private) and deploy user
- webapp-setup.sh copies sudoers-webapp from repo instead of inline definition
- deploy.sh conditional copy for data_description.md (not in git for OSS)
- deploy.sh ownership changed to deploy:data-ops for /data/{scripts,docs,examples}
Phase 3 - Config and misc:
- Add ${ENV_VAR} interpolation to config/loader.py
- Expand config/instance.yaml.example with all sections (admins, deployment, auth, etc.)
- Create config/.env.template for secret values
- Add MIT LICENSE
- Fix .gitignore: add .venv/, docs/data_description.md
- Fix README.md: CSV status Planned, remove metrics/, update license text
- Translate Czech comments in requirements.txt to English
- Fix test_account_service.py: mock username mapping instead of relying on instance config
All 118 tests pass.
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.
