- Extend Dataset model and schema to include optional database_identifier field
- Update dataset creation and deployment routes to handle new database_identifier parameter
- Modify dataset DDL generation to use database_identifier for schema resolution when available
- Add support for parsing Snowflake timestamp structs with epoch and fraction fields
- Implement handling of Snowflake timestamp logical types (with and without timezone)
- Enhance JSON value processing to detect and convert Snowflake timestamp objects
- Add error handling and logging for timestamp parsing
* xAxisTickinerval
* fix(visualization): Add x-axis time unit configuration for bar and combo charts
- Extend chart configuration to support optional x-axis time unit
- Update modify visualization agent to dynamically set x-axis time interval
- Modify bar line and combo chart prompts to include x_axis_time_unit parameter
* only use valid time units
* refactor(visualization): Simplify x-axis time unit configuration
- Modify modify_visualization_agent to extract and remove x-axis time unit more efficiently
- Update global styling result structure for x-axis time interval
- Adjust format_label_prompt comment to clarify date format default behavior
---------
Co-authored-by: Nate Kelley <nate@buster.so>
- Restructure deploy_datasets function to separate concerns
- Improve column and relationship validation in dataset deployment
- Enhance error handling and validation result generation
- Add support for more comprehensive column and relationship checks
- Refactor validation logic to handle multiple error types
- Add support for capturing source type (table, view, materialized view)
- Improve column metadata queries for Postgres, MySQL, BigQuery, and Snowflake
- Include more comprehensive column information during dataset import
- Extend DatasetColumnRecord to include source_type field
- Add comprehensive column validation before dataset deployment
- Validate existence of all required columns in source database
- Simplify column type and nullability retrieval
- Enhance error reporting for missing columns
- Update deployment logic to use pre-validated column information
- Add detailed validation error logging in CLI
- Improve type compatibility checks in dataset validation
- Modify deployment process to handle and report validation errors more comprehensively
- Add Hash derive for Verification enum
- Update API and CLI to support more informative validation results
- Introduce helper functions for processing string and JSON values
- Implement case-insensitive string and JSON value transformations
- Add robust timestamp parsing with error handling
- Enhance Snowflake query result processing with consistent data normalization
- Refactor stored values processing in dataset deployment to use background task
- Add `StoredValueColumn` struct to encapsulate column processing details
- Implement `process_stored_values_background` for parallel and resilient value storage
- Add logging for successful and failed stored value processing
- Update CLI to handle optional SQL definitions and improve file processing
* fix: add stored values support for dataset columns
This commit introduces stored values functionality for dataset columns, including:
- Adding a `stored_values` flag to column deployment requests
- Implementing a mechanism to store column values during dataset deployment
- Updating data analyst and SQL generation agents to leverage stored values
- Creating a new utility module for stored values search and management
* refactor(stored_values): improve stored values implementation and schema management
This commit enhances the stored values functionality with several key improvements:
- Update schema and table creation to use organization ID as schema name
- Modify stored values storage to include column ID
- Improve value extraction and embedding generation process
- Remove unnecessary distance calculation in search results
- Clean up unused values_engine module
- Added support for retrieving and passing data source type during SQL agent generation
- Updated SQL generation prompts to include data source type in system messages
- Modified `generate_sql_agent` function to fetch and utilize data source type information
- Improved SQL generation context by dynamically incorporating data source type details
* feat: enhance deploy command with skip_dbt option
- Updated the Deploy command to accept a `skip_dbt` boolean argument, allowing users to bypass the dbt run during deployment.
- Refactored the deploy function to conditionally execute the dbt command based on the `skip_dbt` flag, improving deployment flexibility.
* Refactor query engine and CLI commands for improved functionality and error handling
- Updated `get_bigquery_columns` and `get_snowflake_columns` functions to enhance column name handling and ensure proper error reporting.
- Modified `get_snowflake_client` to accept a database ID for better connection management.
- Enhanced the `deploy` command in the CLI to include additional parameters (`path`, `data_source_name`, `schema`, `env`) for more flexible deployments.
- Improved error handling and reporting in the `deploy` function, including detailed summaries of deployment errors and successful file processing.
- Updated `get_model_files` to accept a directory path and added checks for file existence, enhancing robustness.
- Adjusted model file structures to include schema information and refined the upload process to handle optional parameters more effectively.
These changes collectively improve the usability and reliability of the query engine and deployment process.
* Update dataset DDL generation to include optional YML file content
- Modified `generate_sql_agent` to append optional YML file content to dataset DDL
- Ensures more comprehensive dataset representation during SQL agent generation
- Handles cases where YML file might be present or absent gracefully
dal