Schema v3: add is_public column to table_registry (default true).
src/rbac.py: can_access_table() checks admin bypass, public flag,
explicit permissions, wildcard bucket permissions.
API enforcement:
- manifest: filters tables by user access
- download: 403 if no access
- catalog: filters table list
- query: validates referenced tables against allowed list
New admin permissions API (/api/admin/permissions) for grant/revoke.
28 access control tests + 733 total tests passing.
Add admin curation layer between AI extraction and knowledge distribution.
Admins (km_admin flag in instance.yaml) can approve, reject, mandate, and
revoke knowledge items. Mandatory items distribute to all targeted users
automatically.
Three governance modes (configurable per instance):
- mandatory_only: admin controls everything, no user voting
- admin_curated: admin controls, users vote as feedback signal
- hybrid: mandatory from admin + optional from user voting
Three approval workflows:
- review_queue: nothing published without admin approval
- auto_publish: items go live immediately, admin intervenes retroactively
- threshold: confidence-based auto-publish (Phase 5)
Includes:
- 9 admin action functions (approve/reject/mandate/revoke/edit/batch/...)
- 11 new admin API endpoints under /api/corporate-memory/admin/
- Immutable audit log (audit.jsonl)
- Audience targeting via groups
- Automatic migration of existing items to "approved" status
- km_admin_required auth decorator
- 69 tests covering all governance logic
- Backward compatible: no config = legacy wiki behavior
Replace hardwired Anthropic API calls with a pluggable provider system.
Each deployment configures its AI provider in instance.yaml — switching
between Anthropic, LiteLLM, OpenRouter, or any OpenAI-compatible proxy
is a config change, not a code change.
New connectors/llm/ module:
- StructuredExtractor Protocol with extract_json() interface
- AnthropicExtractor: direct Anthropic SDK with retry + backoff
- OpenAICompatExtractor: any OpenAI-compatible proxy with three-layer
structured output fallback (json_schema -> json_object -> prompt)
- Configurable structured_output policy (strict/json/auto)
- Custom exception hierarchy (auth/rate_limit/timeout/format/refusal)
- Zero secrets in logs: no API keys, prompts, or responses logged
Reviewed by: Google Gemini, Claude Sonnet, OpenAI GPT-5.4.
Security audit passed with all critical findings resolved.
Session testing revealed 3 issues with remote queries:
1. CLAUDE.md template recommended `cat <<HEREDOC | ssh ...` but
claude_settings.json had `cat` in deny list, causing 2-3 failed
attempts per query. Replaced with file-based approach: Write tool
creates JSON file, then `ssh ... < file` avoids the cat deny.
2. ssh/scp commands were not in the allow list, requiring manual
approval for every remote query. Added both to allow list.
3. DuckDB fetch_arrow_table() emitted DeprecationWarning on every
parquet export. Replaced with .arrow().read_all().
Also added instruction for proactive hybrid analysis when remote
tables are available (agent was only using local data until asked).
Agent was failing 3x on SSH commands due to backticks (BQ table names)
and single quotes (SQL string literals) getting mangled by nested shell
interpretation (local -> SSH -> bash -> Python).
New --stdin mode reads query spec as JSON from stdin via heredoc:
cat <<'QUERY' | ssh alias 'bash remote_query.sh --stdin'
{"register_bq": {"alias": "SELECT ... FROM \`table\` ..."}, "sql": "..."}
QUERY
Heredoc with <<'QUERY' (quoted) passes everything literally -- no
escaping needed for backticks, quotes, or parentheses.
Updated claude_md_template.txt to use --stdin as the primary method.
Analyst user (foundry_e_psimecek) couldn't access /opt/data-analyst/.
Added to data-ops group on server.
New scripts/remote_query.sh wrapper handles env setup (PYTHONPATH,
CONFIG_DIR, .env) so agents use simple:
ssh alias 'bash ~/server/scripts/remote_query.sh --sql "..." --format table'
Updated claude_md_template.txt to use wrapper instead of raw commands.
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.
- Add get_metric_by_fqn() to OpenMetadataClient
- Add get_metrics() to CatalogEnricher with TTL caching
- Implement _parse_om_metric() to extract category/grain from OpenMetadata tags
- Implement _load_metrics_from_catalog() to fetch and categorize metrics
- Implement _build_om_metric_detail() to convert OpenMetadata format to MetricParser JSON
- Add /api/catalog/metrics/<fqn> endpoint for metric detail modal
- Update _load_metrics_data() to prefer catalog over YAML fallback
- Update metric_modal.js to route catalog:{fqn} to catalog API endpoint
- Delete 10 demo YAML files from docs/metrics/
- Replace metric tests with new unit tests for catalog parsing functions (19 tests)
Catalog metrics provide single source of truth vs maintaining demo YAML files.
UI remains unchanged - only data source changes from YAML to OpenMetadata catalog.
Extend theming from 3 CSS variables (primary colors only) to 14
configurable properties covering colors, fonts, borders, and shape.
All values are optional with sensible defaults.
- New _theme.html include replaces duplicated inline injection
- Wire theme include into all 7 templates (base, login, dashboard,
catalog, admin_tables, activity_center, corporate_memory)
- Conditional font loading: skip default Inter when custom font_url set
- Config.theme_overrides() classmethod generates CSS variable dict
- Visual theme-reference.html guide for instance configurators
- Document all theme keys in instance.yaml.example
These Keboola-specific data source cards don't belong in the OSS repo.
The catalog now shows only dynamic content: Core Business Data (from
data_description.md) and Business Metrics (from docs/metrics/*.yml).
Also update auto-install.md with Business Metrics documentation,
pipeline diagram, and expanded checklist.
Generator now supports --format {csv,parquet,both}. Parquet mode
uses src.parquet_manager.ParquetManager for snappy compression,
proper column types (DATE, TIMESTAMP, DOUBLE), and metadata.
No more ad-hoc pandas conversion needed on the server.
Extract 4 self-contained services into services/ module:
- server/telegram_bot/ -> services/telegram_bot/
- server/ws_gateway/ -> services/ws_gateway/
- server/corporate_memory/ -> services/corporate_memory/
- server/session_collector.py -> services/session_collector/
Each service now has its own systemd/ directory with .service and .timer files.
deploy.sh updated to auto-discover service units from services/*/systemd/*.
server/ now contains only deployment infrastructure (deploy.sh, setup scripts,
bin/ management tools, sudoers, nginx config).
All imports updated: webapp/app.py, server/bin/ scripts, systemd ExecStart paths.
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.
