wehub-resource-sync
54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
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""" This 'getting started' example shows the basics of how to start using text2sql model:
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1. Loading "slim-sql-tool" and running initial tests to confirm installation.
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2. 'Hello World' demonstration of how to 'package' a text2sql prompt combining a
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natural language query with a SQL table schema and run a basic inference to generate SQL output
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"""
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from llmware.agents import LLMfx
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from llmware.models import ModelCatalog
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def load_slim_sql_tool():
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""" First step is to install the slim-sql-tool locally """
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# to cache locally the slim-sql-tool with config and test files
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ModelCatalog().get_llm_toolkit(["sql"])
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# to run tests to confirm correct installation and see the model in action
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# note: the test results will include some minor errors - useful to learn how to sharpen prompts
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ModelCatalog().tool_test_run("slim-sql-tool")
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return 0
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def hello_world_text_2_sql():
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""" Illustrates a 'hello world' text-2-sql inference as part of an agent process. """
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sample_table_schema = "CREATE TABLE customer_info (customer_name text, account_number integer, annual_spend integer)"
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query = "What are the names of all customers with annual spend greater than $1000?"
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agent = LLMfx(verbose=True)
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response = agent.sql(query, sample_table_schema)
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print("update: text-2-sql response - ", response)
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return response
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if __name__ == "__main__":
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# first - load and test the tools
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load_slim_sql_tool()
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# second - 'hello world' demo of using text2sql model
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hello_world_text_2_sql()
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