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Findable supports Natural Language SQL — users ask questions in plain English and the AI generates and executes SQL queries against configured database connections. All connections are managed through the unified IDataConnection interface (DataPlatformType), which supports both SQL databases and vector stores. Connections are stored in the dataconnections Cosmos container with full ACL support.

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Supported Databases

DatabaseDataPlatformTypeAdapterNotes
PostgreSQLpostgrespgIncluding Azure Database for PostgreSQL
SQL ServermssqlmssqlIncluding Azure SQL Database
MySQLmysqlmysql2Including Azure Database for MySQL
OracleoracleoracledbOracle Database 12c+
Snowflakesnowflakesnowflake-sdkCloud data warehouse
BigQuerybigquery@google-cloud/bigqueryGoogle Cloud analytics
Amazon Athenaathena@aws-sdk/client-athenaServerless query service
ClickHouseclickhouse@clickhouse/clientColumn-oriented OLAP
IBM DB2db2ibm_dbEnterprise database (see DB2 Guide)
Azure Cosmos DBcosmosdb@azure/cosmosNoSQL document database
Databricksdatabricksdatabricks-sqlLakehouse / SQL warehouse
Trinotrinotrino-clientDistributed SQL query engine

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Supported Vector Stores

Vector StoreDataPlatformTypeNotes
PineconepineconeManaged vector database
WeaviateweaviateOpen-source vector database
QdrantqdrantHigh-performance similarity engine
ChromachromaOpen-source embedding database
ElasticsearchelasticsearchFull-text + vector search
OpenSearchopensearchAWS-managed search and analytics
pgvectorpgvectorPostgreSQL vector extension
RedisredisIn-memory vector search
Vector store connections are used by the Vector Retriever data source type and Flow Designer vector store tool nodes. See Vector Store Integration for details.

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Advanced Features

FeatureDescriptionHow to Use
Query Result Caching5-minute cache for frequently asked questionsAutomatic for identical questions on same connection
Query Explanation ModeGet an explanation of how a query would work without executing itUse explainNaturalLanguageQuery() tool or pass explain: true
Multi-turn ContextFollow-up questions reference previous queries in the same sessionPass sessionId parameter to maintain context
Cache ManagementClear cache for a connection or all connectionsUse clearQueryCache(connectionId?) tool
Multi-turn Example: 
// First question establishes context
await executeNaturalLanguageQuery({
  question: "What are our top 10 customers by revenue?",
  connectionId: "...",
  userId: "...",
  sessionId: "session-123"  // Creates context
});

// Follow-up question uses context
await executeNaturalLanguageQuery({
  question: "Which of those are in California?",  // References "top 10 customers"
  connectionId: "...",
  userId: "...",
  sessionId: "session-123"  // Uses same context
});

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How It Works

  1. Configure a connection — In Admin → Data Platform Connections ([#/admin/databases], setting key databaseConnections), create a connection with host, port, credentials, and schema/catalog
  2. Attach to a chat — Select the connection on the chat configuration
  3. Schema discovery — The system introspects the database schema (tables, columns, types, relationships)
  4. Query generation — The AI receives the schema context and generates SQL from the user’s natural language question
  5. Execution — The generated SQL is executed against the connection and results are formatted for display
  6. Visualization — Results can be rendered as tables or Recharts visualizations (bar, line, pie, area charts)

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Security

  • Read-only by default — Connections are configured with read-only intent where possible
  • Schema filtering — Admins select which schemas/tables are visible to the AI
  • API key auth — Data Platform Tools use a separate API key (sqlToolsApiKey) for authentication
  • Row limits — Query results are capped to prevent excessive data transfer

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Database SDK Versions

Database/PlatformNPM PackageVersion
Snowflakesnowflake-sdk^2.4.2
PostgreSQLpg^8.20.0
MySQLmysql2^3.22.3
SQL Servermssql^11.0.1
Oracleoracledb^6.10.0
ClickHouse@clickhouse/client^1.18.2
BigQuery@google-cloud/bigquery^8.1.1
Athena@aws-sdk/client-athena^3.1015.0

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Recharts Visualizations

When AI determines that query results are suitable for visualization, it can generate Recharts chart specifications inline in the chat response. Supported chart types:
  • Bar charts — Categorical comparisons
  • Line charts — Trends over time
  • Pie charts — Proportional data
  • Area charts — Filled line charts for volume data
See NL-SQL Quickstart and Full Guide for detailed setup.