- Each pipeline node is now a button; clicking shows a detail card (what the step does + the LangChain/tech component)
- Stronger active-stage pulse (expanding ring + brighter glow)
- Chat auto-opens the How-it-works panel so live per-stage pulsing is always visible while answering
- rag-api: new POST /chat/stream SSE endpoint emits live pipeline stages (embed/retrieve/context/llm/answer) and streams LLM tokens
- Knowledge Chat: document chat now streams answers token-by-token and pulses each pipeline stage in real time as it executes
- How-it-works panel: active stage glows/scales, completed stages settle, agent mode also drives the pulse
- LangChain made visible: orchestrated-by-LangChain badge + LC markers on LangChain-native nodes (TextSplitter, Embeddings, as_retriever, ChatOpenAI, Chroma)
Add a collapsible, pulsing architecture diagram to the Knowledge Chat that
visualizes the real RAG pipeline in two rows: Index (Upload -> Docling ->
Split -> Embed/MiniLM -> ChromaDB) and Query (Question -> Embed -> Retrieve
-> Context -> LLM -> Answer), badged with the actual stack (LangChain,
ChromaDB, Docling, sentence-transformers).
Connectors have a flowing dashed track plus a traveling glow dot and nodes
pulse; the whole flow speeds up while the chat/ingest is busy. Honors
prefers-reduced-motion.
When switching source engine, the sample effect fired with the previous
engine selected object (e.g. public.sales_orders against MySQL), surfacing
"Table hr.sales_orders doesn't exist". Track which engine the selected
object belongs to and only sample when it matches the active engine, plus
guard against stale responses overwriting newer ones.
Row-count for the browser used an exact count(*) which full-scanned huge
tables (~30s on 24M rows). Use planner/statistics estimates and only run a
time-bounded exact count for small (<=50k) tables.
Add a /preview endpoint that range-reads the first chunk of an object and
returns it as text. Browser gets an eye action (list + gallery) opening a
modal that pretty-prints JSON, renders CSV/TSV as a table, and shows logs/
text/yaml/xml verbatim, with a truncation note and download link.
Add a List/Gallery toggle to the bucket browser that auto-switches to a
thumbnail grid when a listing is mostly images, and a keyboard-navigable
lightbox (prev/next/esc) for full-size previews. Download endpoint serves
inline with the correct image/* content-type (ECS stores octet-stream) so
previews render instead of forcing a download.
Analytics now walks every top-level prefix with its own budget instead of a
flat alphabetical scan, so a single huge prefix (kafka/ CDC json) no longer
hides the rest — composition now correctly reflects images, logs, csv,
parquet and avro, and reports true total size.
Browser gains recursive (whole-subtree) listing, free-text name search,
file-type filter chips, type column and Load-more pagination so every object
across all folders is discoverable.
Add /api/storage/s3/analytics endpoint that scans buckets (bounded +
cached) to compute total size/objects, per-bucket distribution, file-type
and size-class breakdowns, cumulative data-growth timeline and largest /
recent objects. Track every S3 op routed through the API for a live
storage-activity timeline.
Rebuild StorageView into a tabbed view: Overview (KPI cards + SVG charts:
growth area, bucket donut, type/size bars, activity sparkline, top folders,
largest & recent objects) and the original bucket Browser.
Add Graph sub-tab in Data Sources UI for Neo4j: force-directed SVG view of
Product-Supplier nodes and SUPPLIES/RELATED_TO/PART_OF/COMPATIBLE_WITH edges.
Backend GET /api/sql/graph/neo4j with rel_type filter and edge limit.
New "Data Sources UI" tab with per-database Browser (catalog + sample data),
Query Console (SqlWorkbench) and embedded interactive Shell (DbShell via SSH).
Backend:
- Extend sql_console.py with Cassandra (CQL) + Neo4j (Cypher) engines
- Add GET /api/sql/catalog/{engine} and GET /api/sql/sample/{engine}
- ssh_terminal: optional initial_command for auto-launching DB CLIs
Frontend:
- DataSourcesView with 5-DB rail, health dots, Browser/Console/Shell sub-tabs
- DbShell embedded xterm terminal with docker exec CLI per engine
- Deep-link topology DB nodes to Data Sources UI (no SQL dock on platform)
- WorkbenchPanel restricted to agent mode only — frees dashboard space
- pii_catalog: persistent per-column masking policy (default masked); GET/POST
/api/pii/policy and POST /api/pii/lookup which redacts masked values server-side.
- get_pii masked flag now reflects the policy; dataflow exposes the dataset key.
- Data Flow PII inspector: per-column lock/unlock toggles + mask-all/unmask-all,
so operators control exactly which data the assistant may reveal.
- decide_approval now executes the underlying data movement when an approved
request carries an executor=movement payload (real human-gated executor).
- rag-api service gets OPENMETADATA_* (via atc.env) + COMMAND_CENTER_URL so it can
sync the catalog and call platform tools.
- Knowledge Chat gains an Agent-mode toggle (SSE tool-loop with step chips) and a
'Sync catalog' button.
Data Flow graph now shows OpenMetadata as a governance node linked to all
sources and Trino (catalog edges); node inspector exposes an 'Open in
OpenMetadata' deep link and PII-columns-cataloged metric.
Deploys Apache Hive 3.1.3 (Derby metastore + external table over the HDFS
historical CSV, MapReduce exec) on the Hadoop master, so the engine comparison
shows a REAL measured Hive latency (~5.2s) next to live Trino (~0.3s); Impala
stays clearly-labelled representative. The API re-measures Hive over SSH on a
30-min TTL (cached + persisted, with a committed seed). Adds filters
(year/region/category/channel), a region×category heatmap, Trino exec stats,
and a "where is this data read from" lineage panel (Trino->S3/Iceberg/Parquet
with snapshot+files, Hive->HDFS/CSV with namenode+files). Mounts host SSH key
read-only into the api container for the live Hive benchmark.
Adds an Analytics sub-tab to the Hadoop view with live KPIs and revenue
breakdowns (by year/region/category/channel) queried from Trino over
iceberg.hadoop.historical_sales, plus a query-engine comparison panel. Trino
latency is measured live; Impala and Hive are shown as clearly-labelled
representative figures (those engines are not deployed). New cached endpoints
/api/hadoop/analytics and /api/hadoop/engines.
Clicking agents opens a dedicated terminal panel; topology PostgreSQL/Trino nodes open SQL workbench with ten demo queries and Postgres vs Trino benchmark.
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