* feat(spark): add Apache Spark database driver
Spark Thrift Server speaks the HiveServer2 protocol, so the Spark driver
reuses the hive-jdbc agent runtime. Registers spark (agentKey=spark,
defaultPort=10015) across the manifest, DatabaseType enum, agent catalog,
SQL dialects, connection dialog, and a SparkAgent built on AbstractJdbcAgent.
* feat(spark): add catalog support and official logo
SparkAgent now supports Spark 3.4+ multi-catalog (Paimon, Lance, Iceberg)
via catalog=<name> in url_params, mirroring the StarRocks catalog flow:
- afterConnect switches to the configured catalog via USE <catalog>
- listDatabases returns the catalog name when SHOW DATABASES is empty
(catalogs like Lance expose tables at the catalog root with no databases)
- listTables uses SHOW TABLES IN <catalog>[.<schema>] uniformly, working
for both catalog-root tables (Lance) and catalog.schema tables (Paimon)
- setSchemaSQL qualifies USE with the catalog to prevent resetting to
spark_catalog on schema switches
- buildJdbcUrl omits database from the URL path
Also replaces the placeholder lightning icon with the official Apache Spark
logo (spark-logo.png from spark.apache.org).
* fix(spark): use catalog-qualified DESCRIBE for column retrieval
getColumnsFromDescribe used USE <schema> + DESCRIBE <table>, which
generated USE `catalog`.`catalog` when the sidebar passed the catalog
name as the schema (Lance case where the catalog is the only node).
This failed and fell back to JDBC metadata which also returned nothing,
resulting in an empty DDL (CREATE TABLE ... ()).
Now uses DESCRIBE `catalog`.`table` directly when a catalog is
configured, bypassing USE entirely. Also fixes setSchemaSQL to treat
schema == catalog as catalog-only (no .schema suffix).
* fix(ci): add spark to metadata coverage matrix and BRIDGE_REQUIRED_TYPES
- agents/metadata-constraint-coverage.tsv: register spark as
intentional-fallback (SHOW TABLES/SHOW DATABASES, like Hive/TDengine)
- packages/node-core/src/diagnostics.ts: add spark to
BRIDGE_REQUIRED_TYPES (mcpMode=bridge, matches manifest)
* fix(ci): add spark to DBX_CONNECTION_TYPE_DESCRIPTION in mcp-server
The driver-manifest test asserts every manifest dbType appears in the
MCP server's connection type description string.
fix(ai): make reasoning expand/collapse consistent across messages
Replace per-message expandedReasoning Set with a global reasoningExpanded
boolean ref so all messages share the same toggle state. Default to collapsed.
This avoids the flip-flop where reasoning auto-expands during streaming and
collapses afterward, requiring repeated manual toggles.
- Replace expandedReasoning (Set<number>) with reasoningExpanded (boolean)
- Remove per-index toggleReasoning; use global flip instead
- Template uses reasoningExpanded instead of expandedReasoning.has(i) || msg.isThinking
@
* feat(sqlfile): add refresh, locate, and context menu to SQL Files panel
- Add per-folder Refresh and Reveal-in-file-manager buttons on each opened
folder header, plus a Refresh-all button on the panel toolbar. Re-scan
prunes expand state for paths that no longer exist after refresh.
- Add right-click context menus (via CustomContextMenu) for files, folders,
folder headers, and the panel background: open, execute SQL file, reveal
in file manager, copy path, expand/collapse all, refresh, remove folder.
- Highlight the selected tree row on click/right-click; clicking empty space
clears the selection.
- Wire "Execute SQL file" from the panel into the App-level
SqlFileExecutionDialog via connectionStore.sqlFileSource.filePath + a new
prefillFilePath prop, so the dialog opens with the file preview preloaded.
- Add zh-CN/en strings under sqlFileTree.
* chore(i18n): autofill new translations
* fix(sqlfile): clear prefillFilePath on close, dedupe i18n keys
---------
Co-authored-by: wanqinghua <wanqhua@gmail.com>
Co-authored-by: dbx-i18n-bot <github-actions[bot]@users.noreply.github.com>
Co-authored-by: t8y2 <1156263951@qq.com>
* fix(grid): reuse local result rows for csv/xlsx export instead of re-running query
Exporting the full result set ("导出当前结果集全部数据 CSV") previously
re-executed the source SQL on the database via the backend streaming path
(exportQueryResultViaBackend -> start_query_result_export), and the frontend
fallback (fullExportResult/fetchTabResultForExport) did the same. For a slow
query this means running it a second time just to export rows already on
screen, which can hang or crash (e.g. a 2-minute paimon query returning 30
rows).
When the in-memory result already holds the complete set (no server-side
pagination, not truncated, no further pages), skip both re-executing paths and
write the displayed rows directly. Large/truncated/paginated results keep the
original streaming behavior unchanged.
* fix(grid): export raw in-memory result for complete local result set
---------
Co-authored-by: t8y2 <1156263951@qq.com>
Milvus supports multiple databases, but the sidebar only ever showed
collections under the hardcoded "default" database. The backend returned
["default"] for every vector store and the frontend loaded collections
for a single database and flattened them under the connection node.
- Backend (vector_driver): add list_databases() that calls Milvus
/v2/vectordb/databases/list (falling back to "default" for older Milvus
and for non-Milvus vector stores), and thread a database parameter
through find_documents() instead of hardcoding "default".
- document_ops: list_databases_core delegates VectorDb to list_databases();
find_documents_core forwards the selected database.
- Frontend: add a "vector-database" tree node type. For Milvus, connections
expand into database nodes, which expand into their own collections.
Collection node ids are namespaced by database to avoid collisions
across databases, and the collection id is stored in meta so activation
no longer parses the node id.
Fixes#2693
Co-authored-by: 朱豪豪 <zhu.hh.sz@belle.com.cn>
* feat(ai): add task-oriented Agent actions with per-action contract
Introduce three Agent-native actions — query, exploreSchema,
executeAndExplain — so Agent mode surfaces task-oriented operations
instead of reusing Ask's SQL-producing action set.
- ai.ts: extend AiAction with the new actions; add ASK_ACTIONS /
AGENT_ACTIONS and defaultActionForMode / isValidActionForMode
helpers; raise maxTokens for the explanatory task actions.
- aiSkills.ts: add skill definitions (zh/en) for the three actions.
- aiAgentPlan.ts: task actions no longer emit a misleading
"unsupported_action" execute step; they execute via the
execute_query tool events, not the legacy client-side path.
- agent_loop.rs: per-action task-contract rules in both the prompt
augmentation and repair paths (query -> execute & answer from real
data; exploreSchema -> list_tables/get_columns; executeAndExplain
-> run current SQL & explain).
- i18n (en/zh-CN/zh-TW): new action labels, placeholders, a
generateNoExec label for Agent mode, and a task-oriented agent
modeHint.
es/it/ja/pt-BR have no `ai` section and fall back to `en`, matching
existing coverage.
* feat(ai): split Ask/Agent action menus with mode-aware defaults
Wire the new Agent task actions into the assistant UI so each mode
shows a distinct, intent-matching action set.
- actionButtons is now a computed returning Ask or Agent buttons
based on assistantMode. Agent menu: query / exploreSchema /
executeAndExplain / generate (labeled "生成但不执行" via
generateNoExec). Ask menu is unchanged.
- Switching mode lands on that mode's default (Ask -> generate,
Agent -> query); the shared `generate` is not carried across
because its label/semantics differ per mode. Vector DBs stay on
`generate` since their action menu is hidden and they lack SQL
tools (no execute_query contract).
- External Ask-style triggers (Fix with AI, Explain history) switch
to Ask mode when invoked from Agent mode so the action is valid
and the menu reflects what runs.
- Reset the active action to the mode default after each send.
* test(ai): cover Agent task-action contract and mode/action mapping
Lock in the deterministic parts of the Ask/Agent split so only the
LLM-behavior criteria need manual verification.
Backend (agent_loop.rs):
- query contract instructs the LLM to call execute_query and is not
treated as a SQL-producing action.
- exploreSchema contract points to list_tables/get_columns and says
not to execute data queries.
- executeAndExplain contract tells the LLM to run the current SQL.
- task actions do not require a SQL deliverable (a conclusion without
a fenced SQL block still satisfies the contract).
- query repair prompt also targets execute_query.
Frontend (aiActions.spec.ts):
- defaultActionForMode: Ask -> generate, Agent -> query (not generate).
- isValidActionForMode: generate valid in both; Ask-only actions
rejected in Agent mode and vice versa.
- ASK_ACTIONS / AGENT_ACTIONS composition (Agent menu starts with
query and still offers generate).
* fix(ai): correct action menu on mode-switched triggers and i18n gaps
Issues found during review of the Ask/Agent action split:
- [#I01] triggerAction (Fix with AI / Explain history) invoked from
Agent mode switched to Ask and set the action, but the mode-switch
watch then reset activeAction to the Ask default ("generate"),
overwriting the action just set — so the menu showed "Generate SQL"
during a fix. Add a suppressModeActionReset flag so programmatic
mode switches don't clobber the explicitly-set action.
- [#C01] Remove dead `unsupported_action` reason: buildAiAgentPlan no
longer produces it, so drop the type variant and the unreachable
skippedTitleKey case.
- [#I02] es/it/ja/pt-BR were missing the new Agent action keys
(query/exploreSchema/executeAndExplain/generateNoExec) and their
placeholders, causing English fallback in the Agent menu. Add the
translations to match each locale's existing style.
* test(ai): align app-tests with task-action split
- aiSkills: add query/exploreSchema/executeAndExplain to the expected
action set; broaden the id check (new ids are not _sql-suffixed) and
relax the userInstruction assertion (schema inspection need not mention SQL).
- aiAgentPlan: drop the unsupported_action skipped step for non-generate
actions (#C01 removed it from the plan and the reason union); add coverage
for task-oriented Agent actions not driving client-side execution.
* test(ai): pin locale in dialect prompt spec
buildSystemPrompt selects zh/en copy via currentLocale(), which reflects the
host navigator.language. The spec asserts English strings, so it failed on
zh-CN machines (e.g. Windows with a Chinese system locale) while passing on
CI's en-US runners. Pin the locale to en in beforeAll so the assertions are
deterministic regardless of the host OS locale.
zipg