![]() ![]() ![]() IsQuery: Screen whether objective is solvable with a SELECT statement against the schema.We use the objective to retrieve the top-ranked schema for use in two sequential prompts: Schema expressions are stored in volatile semantic memory. (Example: AdventureWorksLT.yaml) Query Generation We ended up favoring YAML since it lends itself to the inclusion of description meta-data and appears to be well understood by the model. Selecting a compact and expressive format to express this schema within the prompt is critical since everything in the prompt counts towards the token-limit. The schema model includes platform direction in order for the model to produce the correct SQL variant (since SQL is notoriously vendor specific). Providing a full-fidelity schema model, besides being incredibly token rich, just doesn’t demonstrate functional improvement for the classes of objectives we explored. If needed, an optional description can be included for additional semantic clarity.Ĭolumn meta-data is highly abbreviated to include just name, reference (Foreign Key) and description. To start, we use an abbreviated object model to express schema meta-data for the model to reason over:Ī well-designed schema is generally imbued with semantic intention via the table and column names. Since GPT-4 demonstrates significantly higher capability for SQL generation (GPT-3.5-turbo generated valid SQL ~70% less than GPT-4 in our limited testing), this is the model we’ve targeted. Must discriminate across multiple schemas (in order to support multiple data-sources or to decompose a large schema).Prompts cannot be hardcoded to a specific database schema or platform.Injecting data into the prompt-frame is also a non-starter (due to the token limit).Synchronizing an existing database to vector-storage is a non-starter as there is no desire to introduce consistency considerations or any type of data-movement. ![]() Some basic principles that informed our approach: Let’s dive right in, check out the video below to view the sandbox in action. We will share our approach, learnings and some best practices. While alternative approaches such as local models or semantic search services exist, the focus here is to zoom into the natural abilities (and limitations) of GPT-4 to produce relevant SQL queries. This has been an area of interest for years ( WikiSQL, Spider, etc.). We are excited to share this sandbox that enables you explore the capabilities of LLM to generate SQL queries (or SELECT statements): NL2SQL. ’ve heard from many in the community who want to use Semantic Kernel to query their relational database using natural language expressions. 'Data Source=London\Payroll Integrated Security=SSPI') (Use SQLNCLI and SQL Server will redirect to the latest version of SQL Server Native Client OLE DB Provider.) SELECT * The following example creates an ad hoc connection to the Payroll instance of SQL Server on server London, and queries the table. The provider string syntax is based on keyword-value pairs separated by semicolons, such as: 'keyword1=value keyword2=value'. Is the connection string passed to the IDataInitialize interface of the destination provider. ![]() provider_name is a char data type, with no default value. Is the name registered as the PROGID of the OLE DB provider used to access the data source. OPENDATASOURCE ( provider_name, init_string ) Provides ad hoc connection information as part of a four-part object name without using a linked server name. I think you need OPENDATASOURCE function: ![]()
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