This tutorial will teach you how to create an application that converts natural language questions into SQL queries, and then evaluating how well
the queries work. We'll even make an improvement to the prompts, and evaluate the impact! By the time you finish this tutorial, you should be ready
to run your own experiments.
Before starting, please make sure that you have a Braintrust account. If you do not, please sign up.
The next few commands will install some libraries and include some helper code for the text2sql application. Feel free to copy/paste/tweak/reuse this code in your own tools.
In this section, we'll take a look at the dataset and ground truth text/sql pairs to better understand the problem and data.
from datasets import load_datasetdata = list(load_dataset("wikisql")["test"])
Here's an example question:
idx = 1data[idx]["question"]
'What clu was in toronto 1995-96'
We'll use Arrow and DuckDB to help us explore the data and run SQL queries on it:
import duckdbimport pyarrow as padef get_table(table): rows = [{h: row[i] for (i, h) in enumerate(table["header"])} for row in table["rows"]] return pa.Table.from_pylist(rows)table = get_table(data[idx]["table"])duckdb.arrow(table).query("table", 'SELECT * FROM "table"')
┌──────────────────────┬─────────┬───────────────┬────────────────┬──────────────────┬──────────────────┐│ Player │ No. │ Nationality │ Position │ Years in Toronto │ School/Club Team ││ varchar │ varchar │ varchar │ varchar │ varchar │ varchar │├──────────────────────┼─────────┼───────────────┼────────────────┼──────────────────┼──────────────────┤│ Aleksandar Radojević │ 25 │ Serbia │ Center │ 1999-2000 │ Barton CC (KS) ││ Shawn Respert │ 31 │ United States │ Guard │ 1997-98 │ Michigan State ││ Quentin Richardson │ N/A │ United States │ Forward │ 2013-present │ DePaul ││ Alvin Robertson │ 7, 21 │ United States │ Guard │ 1995-96 │ Arkansas ││ Carlos Rogers │ 33, 34 │ United States │ Forward-Center │ 1995-98 │ Tennessee State ││ Roy Rogers │ 9 │ United States │ Forward │ 1998 │ Alabama ││ Jalen Rose │ 5 │ United States │ Guard-Forward │ 2003-06 │ Michigan ││ Terrence Ross │ 31 │ United States │ Guard │ 2012-present │ Washington │└──────────────────────┴─────────┴───────────────┴────────────────┴──────────────────┴──────────────────┘
In WikiSQL, the queries are formatted as a series of projection and filter expressions. Although there is a human_readable field, it's not valid SQL!
data[idx]["sql"]
{'human_readable': 'SELECT School/Club Team FROM table WHERE Years in Toronto = 1995-96', 'sel': 5, 'agg': 0, 'conds': {'column_index': [4], 'operator_index': [0], 'condition': ['1995-96']}}
Let's define a codegen_query function that turns it into executable SQL.
AGG_OPS = [None, "MAX", "MIN", "COUNT", "SUM", "AVG"]COND_OPS = [" ILIKE ", ">", "<"] # , "OP"]def esc_fn(s): return f'''"{s.replace('"', '""')}"'''def esc_value(s): if isinstance(s, str): return s.replace("'", "''") else: return sdef codegen_query(query): header = query["table"]["header"] projection = f"{esc_fn(header[query['sql']['sel']])}" agg_op = AGG_OPS[query["sql"]["agg"]] if agg_op is not None: projection = f"{agg_op}({projection})" conds = query["sql"]["conds"] filters = " and ".join( [ f"""{esc_fn(header[field])}{COND_OPS[cond]}'{esc_value(value)}'""" for (field, cond, value) in zip(conds["column_index"], conds["operator_index"], conds["condition"]) ] ) if filters: filters = f" WHERE {filters}" return f'SELECT {projection} FROM "table"{filters}'gt_sql = codegen_query(data[idx])print(gt_sql)
SELECT "School/Club Team" FROM "table" WHERE "Years in Toronto" ILIKE '1995-96'
Now, we can run this SQL directly.
duckdb.arrow(table).query("table", gt_sql)
┌──────────────────┐│ School/Club Team ││ varchar │├──────────────────┤│ Arkansas │└──────────────────┘
import duckdbimport pyarrow as pafrom datasets import load_datasetfrom Levenshtein import distanceNUM_TEST_EXAMPLES = 10# Define some helper functionsdef green(s): return "\x1b[32m" + s + "\x1b[0m"def run_query(sql, table_record): table = get_table(table_record) # noqa rel_from_arrow = duckdb.arrow(table) result = rel_from_arrow.query("table", sql).fetchone() if result and len(result) > 0: return result[0] return Nonedef score(r1, r2): if r1 is None and r2 is None: return 1 if r1 is None or r2 is None: return 0 r1, r2 = str(r1), str(r2) total_len = max(len(r1), len(r2)) return 1 - distance(r1, r2) / total_len
In this section, we'll create our first experiment and analyze the results in Braintrust.
import osfrom braintrust import wrap_openaifrom openai import OpenAIclient = wrap_openai(OpenAI(api_key=os.environ.get("OPENAI_API_KEY", "Your OPENAI_API_KEY here")))def text2sql(input): table = input["table"] meta = "\n".join(f'"{h}"' for h in table["header"]) messages = [ { "role": "system", "content": f"""Print a SQL query (over a table named "table" quoted with double quotes) that answers the question below.You have the following columns:{meta}The user will provide a question. Reply with a valid ANSI SQL query that answers the question, and nothing else.""", }, { "role": "user", "content": f"Question: {input['question']}", }, ] resp = client.chat.completions.create( model="gpt-3.5-turbo", messages=messages, ) sql_text = resp.choices[0].message.content return sql_text.rstrip(";")output_sql = text2sql(data[idx])print(output_sql)duckdb.arrow(table).query("table", output_sql)
SELECT "School/Club Team"FROM "table"WHERE "Years in Toronto" = '1995-96'
┌──────────────────┐│ School/Club Team ││ varchar │├──────────────────┤│ Arkansas │└──────────────────┘
Exciting! Now that we've tested it out on an example, we can run an evaluation on a bigger dataset to understand how well the prompt works.
Next, we need to figure out how we'll score the outputs. One way is to string compare the SQL queries. This is not a perfect signal, because two different query strings might return the correct result, but it is a useful signal about how different the generated query is from the ground truth.
from autoevals import LevenshteinLevenshtein().eval(output=output_sql, expected=gt_sql)
A more robust way to test the queries is to run them on a database and compare the results. We'll use DuckDB for this. We'll define a scoring function that runs the generated SQL and compares the results to the ground truth.
from autoevals import Score@braintrust.traceddef result_score(output, expected, input): expected_answer = run_query(expected, input["table"]) # These log statements allow us to see the expected and output values in the Braintrust UI braintrust.current_span().log(expected=expected_answer) try: output_answer = run_query(output, input["table"]) except Exception as e: return Score(name="SQL Result", score=0, metadata={"message": f"Error: {e}"}) braintrust.current_span().log(output=output_answer) return Score( name="SQL Result", score=Levenshtein()(output=output_answer, expected=expected_answer).score, )result_score(output_sql, gt_sql, data[idx])
=========================SUMMARY=========================See results for text-2-sql-1706754968 at https://www.braintrust.dev/app/braintrust.dev/p/Text2SQL%20Cookbook/text-2-sql-1706754968
Once the eval completes, you can click on the link to see the results in the Braintrust UI.
Take a look at the failures. Feel free to explore individual examples, filter down to low answer scores, etc. You should notice that idx=8 is one of the failures. Let's debug it and see if we can improve the prompt.
Hmm, if only the model knew that 'ushl' is actually capitalized in the data. Let's fix this by providing some sample data for each column:
def text2sql(input): table = input["table"] rows = [{h: row[i] for (i, h) in enumerate(table["header"])} for row in table["rows"]] meta = "\n".join(f'"{h}": {[row[h] for row in rows[:10]]}' for h in table["header"]) messages = [ { "role": "system", "content": f"""Print a SQL query (over a table named "table" quoted with double quotes) that answers the question below.You have the following columns (each with some sample data). Make sure to use the correctcolumn names for each data value:{meta}The user will provide a question. Reply with a valid ANSI SQL query that answers the question, and nothing else.""", }, { "role": "user", "content": f"Question: {input['question']}", }, ] resp = client.chat.completions.create( model="gpt-3.5-turbo", messages=messages, ) sql_text = resp.choices[0].message.content return sql_text.rstrip(";")output_sql = text2sql(data[idx])print(output_sql)duckdb.arrow(table).query("table", output_sql)
SELECT Nationality FROM "table" WHERE "College/junior/club team" = 'Thunder Bay Flyers (USHL)'
Congrats 🎉. You've run your first couple of experiments. Now, return back to the tutorial docs to proceed to the next step where we'll analyze the experiments.
