Conference Talk 2: LLM Eval For Text2SQL

notes
llms
This talk by Ankur Goyal from BrainTrust covers how to build evals for LLM systems by walking through a Text2SQL use case.
Author

Christian Mills

Published

June 29, 2024

This post is part of the following series:
  • Mastering LLMs Course Notes: My notes from the course Mastering LLMs: A Conference For Developers & Data Scientists by Hamel Husain and Dan Becker.

Importance of LLM Evaluation

  • Goals of Evaluation:
    • Determine if the system effectively solves the problem.
    • Quickly identify improvements or regressions caused by changes.
    • Analyze good and bad examples to understand model behavior.
    • Identify and address regressions, avoiding a “whack-a-mole” scenario.
    • Systematically improve the overall system quality.

Building Effective Evaluations

  • Three Core Components:

    • Data:
      • Start by hardcoding data representing various scenarios.
      • Advanced methods like data generation and log sourcing can be incorporated later.
    • Task Function:
      • A function transforming input into output.
      • Can be simple (single LLM call) or complex (multi-agent system).
      • Example: A function converting natural language questions to SQL queries.
    • Scoring Functions:
      • Evaluate the quality of the output.
      • Options include:
        • Handwritten heuristic functions.
        • LLM-based comparisons between output and expected values.
        • Human evaluation and feedback.
  • from braintrust import Eval
from autoevals import Factuality

Eval(
    "Say Hi Bot", # Replace with your project name
    data=lambda: [
        {
            "input": "David",
            "expected": "Hi David",
        },
    ], # Replace with your eval dataset
    task=lambda input: "Hi " + input, # Replace with your LLM call
    scores=[Factuality],
)

  • Iterative Improvement:

    • If an evaluation reveals issues, focus on improving one of the three components:
      • Data: Handwrite cases, generate cases, or obtain data from users.
      • Task Function: Optimize prompts or refine the workflow of LLM calls.
      • Scoring Functions: Improve heuristics, leverage LLM-based scoring, or integrate human evaluation.

LLM Eval For Text2SQL Notebook

Resources:

Downloading the data

  • Data: NBA dataset from Hugging Face (2014-2018 seasons, one row per game).
import duckdb
from datasets import load_dataset

data = load_dataset("suzyanil/nba-data")["train"]

conn = duckdb.connect(database=":memory:", read_only=False)
conn.register("nba", data.to_pandas())

conn.query("SELECT * FROM nba LIMIT 5").to_df()
Unnamed: 0 Team Game Date Home Opponent WINorLOSS TeamPoints OpponentPoints FieldGoals FieldGoalsAttempted FieldGoals. X3PointShots X3PointShotsAttempted X3PointShots. FreeThrows FreeThrowsAttempted FreeThrows. OffRebounds TotalRebounds Assists Steals Blocks Turnovers TotalFouls Opp.FieldGoals Opp.FieldGoalsAttempted Opp.FieldGoals. Opp.3PointShots Opp.3PointShotsAttempted Opp.3PointShots. Opp.FreeThrows Opp.FreeThrowsAttempted Opp.FreeThrows. Opp.OffRebounds Opp.TotalRebounds Opp.Assists Opp.Steals Opp.Blocks Opp.Turnovers Opp.TotalFouls
0 1 ATL 1 10/29/14 Away TOR L 102 109 40 80 0.500 13 22 0.591 9 17 0.529 10 42 26 6 8 17 24 37 90 0.411 8 26 0.308 27 33 0.818 16 48 26 13 9 9 22
1 2 ATL 2 11/1/14 Home IND W 102 92 35 69 0.507 7 20 0.350 25 33 0.758 3 37 26 10 6 12 20 31 81 0.383 12 32 0.375 18 21 0.857 11 44 25 5 5 18 26
2 3 ATL 3 11/5/14 Away SAS L 92 94 38 92 0.413 8 25 0.320 8 11 0.727 10 37 26 14 5 13 25 31 69 0.449 5 17 0.294 27 38 0.711 11 50 25 7 9 19 15
3 4 ATL 4 11/7/14 Away CHO L 119 122 43 93 0.462 13 33 0.394 20 26 0.769 7 38 28 8 3 19 33 48 97 0.495 6 21 0.286 20 27 0.741 11 51 31 6 7 19 30
4 5 ATL 5 11/8/14 Home NYK W 103 96 33 81 0.407 9 22 0.409 28 36 0.778 12 41 18 10 5 8 17 40 84 0.476 8 21 0.381 8 11 0.727 13 44 26 2 6 15 29

Prototyping Text2SQL

  • Simple Approach: Directly use the OpenAI API client for interacting with the LLM.
  • Prompt: A basic prompt instructs the model to write a SQL query based on the provided context.
  • Initial Test: The prototype successfully generates a SQL query to find the team with the most wins, demonstrating basic functionality.
import os
from textwrap import dedent

import braintrust
import openai

client = braintrust.wrap_openai(
    openai.AsyncClient(
        api_key=os.environ["OPENAI_API_KEY"],
        base_url="https://braintrustproxy.com/v1",  # This is optional and allows us to cache responses
    )
)

columns = conn.query("DESCRIBE nba").to_df().to_dict(orient="records")

TASK_MODEL = "gpt-4o"

@braintrust.traced
async def generate_query(input):
    response = await client.chat.completions.create(
        model=TASK_MODEL,
        temperature=0,
        messages=[
            {
                "role": "system",
                "content": dedent(f"""\
        You are a SQL expert, and you are given a single table named nba with the following columns:
        {", ".join(column["column_name"] + ": " + column["column_type"] for column in columns)}

        Write a SQL query corresponding to the user's request. Return just the query text, with no
        formatting (backticks, markdown, etc.).
"""),
            },
            {
                "role": "user",
                "content": input,
            },
        ],
    )
    return response.choices[0].message.content

query = await generate_query("Who won the most games?")
print(query)
SELECT Team, COUNT(*) AS Wins
FROM nba
WHERE WINorLOSS = 'W'
GROUP BY Team
ORDER BY Wins DESC
LIMIT 1;
def execute_query(query):
    return conn.query(query).fetchdf().to_dict(orient="records")

execute_query(query)
[{'Team': 'GSW', 'Wins': 265}]

Initial evals

  • Goal: Evaluate the model’s ability to generate valid SQL queries, not necessarily correct answers.
  • Dataset: Five manually crafted questions related to the NBA dataset.
  • Task Function:
    • Takes a question as input.
    • Generates a SQL query using the LLM.
    • Executes the query using DuckDB.
    • Returns the query and result, handling potential errors.
  • Scoring Function:
    • Simple binary scoring: “good” if the query executes without errors, “bad” otherwise.
  • Initial Results:
    • Three out of five queries execute successfully.
    • BrainTrust UI helps visualize results and debug errors.
    • Correct queries and their results are saved as “golden data” for future reference.

Creating an initial dataset

questions = [
    "Which team won the most games?",
    "Which team won the most games in 2015?",
    "Who led the league in 3 point shots?",
    "Which team had the biggest difference in records across two consecutive years?",
    "What is the average number of free throws per year?",
]

Task function

@braintrust.traced
async def text2sql(question):
    query = await generate_query(question)
    results = None
    error = None
    try:
        results = execute_query(query)
    except duckdb.Error as e:
        error = str(e)

    return {
        "query": query,
        "results": results,
        "error": error,
    }

Scores

async def no_error(output):
    return output["error"] is None

Eval

from braintrust import Eval

PROJECT_NAME = "LLM Eval for Text2SQL"

await Eval(
    PROJECT_NAME,
    experiment_name="Initial dataset",
    data=[{"input": q} for q in questions],
    task=text2sql,
    scores=[no_error],
)
Experiment Initial dataset is running at https://www.braintrust.dev/app/Christian%20J.%20Mills%20Consulting/p/LLM%20Eval%20for%20Text2SQL/experiments/Initial%20dataset
LLM Eval for Text2SQL [experiment_name=Initial dataset] (data): 5it [00:00, 53362.65it/s]

LLM Eval for Text2SQL [experiment_name=Initial dataset] (tasks):   0%|          | 0/5 [00:00<?, ?it/s]

=========================SUMMARY=========================
60.00% 'no_error' score

See results for Initial dataset at https://www.braintrust.dev/app/Christian%20J.%20Mills%20Consulting/p/LLM%20Eval%20for%20Text2SQL/experiments/Initial%20dataset

EvalResultWithSummary(summary="...", results=[...])

Results

id _xact_id input output expected tags scores duration estimated_cost metadata
0 ad2710ff-4c62-486b-abf4-f7266f37e5d2 1000193294528916736 What is the average number of free throws per year? {‘error’: ‘Binder Error: Could not choose a best candidate function for the function call “strftime(STRING_LITERAL, VARCHAR)”. In order to select one, please add explicit type casts. Candidate functions: strftime(TIMESTAMP, VARCHAR) -> VARCHAR strftime(TIMESTAMP WITH TIME ZONE, VARCHAR) -> VARCHAR strftime(DATE, VARCHAR) -> VARCHAR’, ‘query’: ‘SELECT strftime(’%Y’, Date) AS Year, AVG(FreeThrows) AS AverageFreeThrows FROM nba GROUP BY Year;‘, ’results’: None} NaN NaN {‘no_error’: 0} 1.728403 0.002320 {}
1 5ba5d8af-82d6-4fba-a66e-9ec5ebaeb578 1000193294528916736 Which team had the biggest difference in records across two consecutive years? {‘error’: ‘Binder Error: No function matches the given name and argument types ’year(VARCHAR)’. You might need to add explicit type casts. Candidate functions: year(DATE) -> BIGINT year(TIMESTAMP) -> BIGINT year(INTERVAL) -> BIGINT year(TIMESTAMP WITH TIME ZONE) -> BIGINT ‘, ’query’: ‘SELECT Team, ABS(SUM(CASE WHEN WINorLOSS = ’W’ THEN 1 ELSE 0 END) - LAG(SUM(CASE WHEN WINorLOSS = ‘W’ THEN 1 ELSE 0 END)) OVER (PARTITION BY Team ORDER BY Date)) AS WinDifference FROM nba GROUP BY Team, YEAR(Date) ORDER BY WinDifference DESC LIMIT 1;‘, ’results’: None} NaN NaN {‘no_error’: 0} 1.881156 0.003005 {}
2 99056cb3-a13d-4fd3-892f-b0e3fbc1dfb5 1000193294529047808 Who led the league in 3 point shots? {‘error’: None, ‘query’: ‘SELECT Team, SUM(X3PointShots) AS Total3PointShots FROM nba GROUP BY Team ORDER BY Total3PointShots DESC LIMIT 1;’, ‘results’: [{‘Team’: ‘HOU’, ‘Total3PointShots’: 4248}]} NaN NaN {‘no_error’: 1} 3.824243 0.002285 {}
3 f82fc67a-8594-41bd-9869-74cd7894076b 1000193294528916736 Which team won the most games in 2015? {‘error’: None, ‘query’: ’SELECT Team, COUNT(*) AS Wins FROM nba WHERE WINorLOSS = ‘W’ AND Date LIKE ‘2015%’ GROUP BY Team ORDER BY Wins DESC LIMIT 1;‘, ’results’: []} NaN NaN {‘no_error’: 1} 1.992921 0.002365 {}
4 b9fea522-d53d-4b63-958b-12c9dac59a5a 1000193294529047808 Which team won the most games? {‘error’: None, ‘query’: ’SELECT Team, COUNT(*) AS Wins FROM nba WHERE WINorLOSS = ‘W’ GROUP BY Team ORDER BY Wins DESC LIMIT 1;‘, ’results’: [{‘Team’: ‘GSW’, ‘Wins’: 265}]} NaN NaN {‘no_error’: 1} 4.303131 0.002240 {}

Saving Good Data

add to dataset

Updating the eval

  • Leveraging Golden Data:
    • Load golden data from BrainTrust.
    • Use golden data to compare generated queries with expected answers.
  • Improved Prompt:
    • Include a sample row from the dataset in the prompt to provide context on data format.
  • Enhanced Scoring Functions:
    • correct_result Function: Compares the values returned by the generated query and the expected answer using JSONDiff.
    • SQL score (LLM-based): Uses an LLM to assess the semantic similarity between the generated query and the reference query.
  • Updated Results (During Talk):
    • No regression on previously correct queries.
      • Had 1 improvement and 1 regression when testing locally
    • Improvement in generating valid SQL queries.
    • Analysis of discrepancies between generated and expected answers reveals areas for improvement.
from braintrust import init_dataset

def load_data():
    golden_data = init_dataset(PROJECT_NAME, "Golden data")
    golden_questions = set(d["input"] for d in golden_data)
    return list(golden_data) + [{"input": q} for q in questions if q not in golden_questions]

pd.DataFrame(load_data())
id _xact_id created project_id dataset_id input expected metadata tags span_id root_span_id
0 09e520a6-bb32-4110-9b80-e127771a66bd 1000193294889891926 2024-06-30T00:02:42.914Z 05d79acf-0ae3-4953-bc2a-b4ccc803dcfa 20dcbf52-2109-40c4-a941-1f2f792d09e0 Who led the league in 3 point shots? {‘error’: None, ‘query’: ‘SELECT Team, SUM(X3PointShots) AS Total3PointShots FROM nba GROUP BY Team ORDER BY Total3PointShots DESC LIMIT 1;’, ‘results’: [{‘Team’: ‘HOU’, ‘Total3PointShots’: 4248}]} {} [] 09e520a6-bb32-4110-9b80-e127771a66bd 09e520a6-bb32-4110-9b80-e127771a66bd
1 aa079536-bebd-402a-9a98-64c9003f7a22 1000193294890547290 2024-06-30T00:02:53.720Z 05d79acf-0ae3-4953-bc2a-b4ccc803dcfa 20dcbf52-2109-40c4-a941-1f2f792d09e0 Which team won the most games? {‘error’: None, ‘query’: ’SELECT Team, COUNT(*) AS Wins FROM nba WHERE WINorLOSS = ‘W’ GROUP BY Team ORDER BY Wins DESC LIMIT 1;‘, ’results’: [{‘Team’: ‘GSW’, ‘Wins’: 265}]} {} [] aa079536-bebd-402a-9a98-64c9003f7a22 aa079536-bebd-402a-9a98-64c9003f7a22
2 eca397d3-e2bb-46c3-8d84-3772506d0a11 1000193294931441802 2024-06-30T00:13:17.809Z 05d79acf-0ae3-4953-bc2a-b4ccc803dcfa 20dcbf52-2109-40c4-a941-1f2f792d09e0 Which team won the most games in 2015? {‘error’: ’Parser Error: syntax error at or near “"', 'query': 'sql SELECT Team, COUNT(*) AS Wins FROM nba WHERE WINorLOSS = ‘W’ AND Date LIKE ‘%/15’ GROUP BY Team ORDER BY Wins DESC LIMIT 1; ```‘, ’results’: None} {} [] eca397d3-e2bb-46c3-8d84-3772506d0a11 eca397d3-e2bb-46c3-8d84-3772506d0a11
3 NaN NaN NaN NaN NaN Which team had the biggest difference in records across two consecutive years? NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN What is the average number of free throws per year? NaN NaN NaN NaN NaN 
samples = conn.query("SELECT * FROM nba LIMIT 1").to_df().to_dict(orient="records")[0]

@braintrust.traced
async def generate_query(input):
    response = await client.chat.completions.create(
        model=TASK_MODEL,
        temperature=0,
        messages=[
            {
                "role": "system",
                "content": dedent(f"""\
        You are a SQL expert, and you are given a single table named nba with the following columns:

        Column | Type | Example
        -------|------|--------
        {"\n".join(f"{column['column_name']} | {column['column_type']} | {samples[column['column_name']]}" for column in columns)}

        Write a DuckDB SQL query corresponding to the user's request. Return just the query text, with no
        formatting (backticks, markdown, etc.).
"""),
            },
            {
                "role": "user",
                "content": input,
            },
        ],
    )
    return response.choices[0].message.content


print(await generate_query("Which team won the most games in 2015?"))
```sql
SELECT Team, COUNT(*) AS Wins
FROM nba
WHERE WINorLOSS = 'W' AND Date LIKE '%/15'
GROUP BY Team
ORDER BY Wins DESC
LIMIT 1;
```
from autoevals import JSONDiff, Sql

def extract_values(results):
    return [list(result.values()) for result in results]

def correct_result(output, expected):
    if expected is None or expected.get("results") is None or output.get("results") is None:
        return None
    return JSONDiff()(output=extract_values(output["results"]), expected=extract_values(expected["results"])).score

def correct_sql(input, output, expected):
    if expected is None or expected.get("query") is None or output.get("query") is None:
        return None
    return Sql()(input=input, output=output["query"], expected=expected["query"]).score
await Eval(
    PROJECT_NAME,
    experiment_name="With samples",
    data=load_data,
    task=text2sql,
    scores=[no_error, correct_result, correct_sql],
)
Experiment With samples is running at https://www.braintrust.dev/app/Christian%20J.%20Mills%20Consulting/p/LLM%20Eval%20for%20Text2SQL/experiments/With%20samples
LLM Eval for Text2SQL [experiment_name=With samples] (data): 5it [00:00, 12905.55it/s]

LLM Eval for Text2SQL [experiment_name=With samples] (tasks):   0%|          | 0/5 [00:00<?, ?it/s]

=========================SUMMARY=========================
With samples compared to Initial dataset:
60.00% (-) 'no_error'       score   (1 improvements, 1 regressions)
100.00% 'correct_result' score
100.00% 'correct_sql'    score

See results for With samples at https://www.braintrust.dev/app/Christian%20J.%20Mills%20Consulting/p/LLM%20Eval%20for%20Text2SQL/experiments/With%20samples

EvalResultWithSummary(summary="...", results=[...])

Results

id _xact_id input output expected tags scores duration estimated_cost metadata
0 9d438233-8755-474e-90c9-39eaeab62639 1000193294893627520 Which team won the most games? {‘error’: None, ‘query’: ’SELECT Team, COUNT(*) AS Wins FROM nba WHERE WINorLOSS = ‘W’ GROUP BY Team ORDER BY Wins DESC LIMIT 1;‘, ’results’: [{‘Team’: ‘GSW’, ‘Wins’: 265}]} {‘error’: None, ‘query’: ’SELECT Team, COUNT(*) AS Wins FROM nba WHERE WINorLOSS = ‘W’ GROUP BY Team ORDER BY Wins DESC LIMIT 1;‘, ’results’: [{‘Team’: ‘GSW’, ‘Wins’: 265}]} [] {‘correct_result’: 1, ‘correct_sql’: 1, ‘no_error’: 1} 4.974092 0.003600 {}
1 1293e7ca-7800-4406-ba60-941adaa2f636 1000193294893627520 Who led the league in 3 point shots? {‘error’: None, ‘query’: ‘SELECT Team, SUM(X3PointShots) AS Total3PointShots FROM nba GROUP BY Team ORDER BY Total3PointShots DESC LIMIT 1;’, ‘results’: [{‘Team’: ‘HOU’, ‘Total3PointShots’: 4248}]} {‘error’: None, ‘query’: ‘SELECT Team, SUM(X3PointShots) AS Total3PointShots FROM nba GROUP BY Team ORDER BY Total3PointShots DESC LIMIT 1;’, ‘results’: [{‘Team’: ‘HOU’, ‘Total3PointShots’: 4248}]} [] {‘correct_result’: 1, ‘correct_sql’: 1, ‘no_error’: 1} 5.561691 0.003559 {}
2 a9954383-301c-4303-b867-2c45d841593e 1000193294893365376 What is the average number of free throws per year? {‘error’: ‘Binder Error: Could not choose a best candidate function for the function call “strftime(STRING_LITERAL, VARCHAR)”. In order to select one, please add explicit type casts. Candidate functions: strftime(TIMESTAMP, VARCHAR) -> VARCHAR strftime(TIMESTAMP WITH TIME ZONE, VARCHAR) -> VARCHAR strftime(DATE, VARCHAR) -> VARCHAR’, ‘query’: ‘SELECT strftime(’%Y’, Date) AS Year, AVG(FreeThrows) AS AvgFreeThrows FROM nba GROUP BY Year;‘, ’results’: None} None None {‘correct_result’: None, ‘correct_sql’: None, ‘no_error’: 0} 1.647409 0.002990 {}
3 eca397d3-e2bb-46c3-8d84-3772506d0a11 1000193294893299840 Which team won the most games in 2015? {‘error’: ’Parser Error: syntax error at or near “"', 'query': 'sql SELECT Team, COUNT(*) AS Wins FROM nba WHERE WINorLOSS = ‘W’ AND Date LIKE ‘%/15’ GROUP BY Team ORDER BY Wins DESC LIMIT 1; ```‘, ’results’: None} None None {‘correct_result’: None, ‘correct_sql’: None, ‘no_error’: 0} 0.235527 0.003215 {}
4 eab4f712-2f79-4f0c-8e56-c037a83579ea 1000193294893430912 Which team had the biggest difference in records across two consecutive years? {‘error’: None, ‘query’: ‘SELECT Team, ABS(SUM(CASE WHEN Date LIKE ’10/%’ THEN (CASE WHEN WINorLOSS = ‘W’ THEN 1 ELSE -1 END) ELSE 0 END) - SUM(CASE WHEN Date LIKE ‘11/%’ THEN (CASE WHEN WINorLOSS = ‘W’ THEN 1 ELSE -1 END) ELSE 0 END)) AS RecordDifference FROM nba GROUP BY Team ORDER BY RecordDifference DESC LIMIT 1;‘, ’results’: [{‘Team’: ‘GSW’, ‘RecordDifference’: 41}]} None None {‘correct_result’: None, ‘correct_sql’: None, ‘no_error’: 1} 2.400165 0.004035 {}

updated eval

Generating more data

  • Goal: Expand the evaluation dataset beyond manually crafted questions.
  • Method: Use the LLM itself to generate SQL queries and corresponding questions.
  • Process:
    • Use function calling to structure the LLM’s output (list of questions with SQL and natural language versions).
    • Provide the database schema as context.
    • Execute generated queries to obtain expected results.
    • Add successfully executed queries and their data to the evaluation dataset.
  • Benefits:
    • Automates data generation, increasing coverage.
    • Creates a diverse set of evaluation examples.
import json
from pydantic import BaseModel

class Question(BaseModel):
    sql: str
    question: str

class Questions(BaseModel):
    questions: list[Question]

logger = braintrust.init_logger("question generator")

response = await client.chat.completions.create(
    model="gpt-4o",
    temperature=0,
    messages=[
        {
            "role": "user",
            "content": dedent(f"""\
        You are a SQL expert, and you are given a single table named nba with the following columns:

        Column | Type | Example
        -------|------|--------
        {"\n".join(f"{column['column_name']} | {column['column_type']} | {samples[column['column_name']]}" for column in columns)}

        Generate SQL queries that would be interesting to ask about this table. Return the SQL query as a string, as well as the
        question that the query answers."""),
        }
    ],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "generate_questions",
                "description": "Generate SQL queries that would be interesting to ask about this table.",
                "parameters": Questions.model_json_schema(),
            },
        }
    ],
    tool_choice={"type": "function", "function": {"name": "generate_questions"}},
)

generated_questions = json.loads(response.choices[0].message.tool_calls[0].function.arguments)["questions"]
pd.DataFrame(generated_questions)
sql question
0 SELECT Team, COUNT(*) as Wins FROM nba WHERE WINorLOSS = ‘W’ GROUP BY Team ORDER BY Wins DESC; Which team has the most wins?
1 SELECT Team, AVG(TeamPoints) as AvgPoints FROM nba GROUP BY Team ORDER BY AvgPoints DESC; Which team has the highest average points per game?
2 SELECT Team, AVG(FieldGoals.) as AvgFieldGoalPercentage FROM nba GROUP BY Team ORDER BY AvgFieldGoalPercentage DESC; Which team has the highest average field goal percentage?
3 SELECT Team, AVG(X3PointShots.) as Avg3PointPercentage FROM nba GROUP BY Team ORDER BY Avg3PointPercentage DESC; Which team has the highest average 3-point shooting percentage?
4 SELECT Team, AVG(TotalRebounds) as AvgRebounds FROM nba GROUP BY Team ORDER BY AvgRebounds DESC; Which team has the highest average total rebounds per game?
5 SELECT Team, AVG(Turnovers) as AvgTurnovers FROM nba GROUP BY Team ORDER BY AvgTurnovers ASC; Which team has the lowest average turnovers per game?
6 SELECT Team, AVG(Assists) as AvgAssists FROM nba GROUP BY Team ORDER BY AvgAssists DESC; Which team has the highest average assists per game?
7 SELECT Team, AVG(Steals) as AvgSteals FROM nba GROUP BY Team ORDER BY AvgSteals DESC; Which team has the highest average steals per game?
8 SELECT Team, AVG(Blocks) as AvgBlocks FROM nba GROUP BY Team ORDER BY AvgBlocks DESC; Which team has the highest average blocks per game?
9 SELECT Team, AVG(TotalFouls) as AvgFouls FROM nba GROUP BY Team ORDER BY AvgFouls ASC; Which team has the lowest average fouls per game? 
generated_dataset = []
for q in generated_questions:
    try:
        result = execute_query(q["sql"])
        generated_dataset.append(
            {
                "input": q["question"],
                "expected": {
                    "results": result,
                    "error": None,
                    "query": q["sql"],
                },
                "metadata": {
                    "category": "Generated",
                },
            }
        )
    except duckdb.Error as e:
        print(f"Query failed: {q['sql']}", e)
        print("Skipping...")

pd.DataFrame(generated_dataset)#[0]
Query failed: SELECT Team, AVG(FieldGoals.) as AvgFieldGoalPercentage FROM nba GROUP BY Team ORDER BY AvgFieldGoalPercentage DESC; Parser Error: syntax error at or near ")"
Skipping...
Query failed: SELECT Team, AVG(X3PointShots.) as Avg3PointPercentage FROM nba GROUP BY Team ORDER BY Avg3PointPercentage DESC; Parser Error: syntax error at or near ")"
Skipping...
input expected metadata
0 Which team has the most wins? {‘results’: [{‘Team’: ‘GSW’, ‘Wins’: 265}, {‘Team’: ‘SAS’, ‘Wins’: 230}, {‘Team’: ‘HOU’, ‘Wins’: 217}, {‘Team’: ‘TOR’, ‘Wins’: 215}, {‘Team’: ‘CLE’, ‘Wins’: 211}, {‘Team’: ‘LAC’, ‘Wins’: 202}, {‘Team’: ‘BOS’, ‘Wins’: 196}, {‘Team’: ‘OKC’, ‘Wins’: 195}, {‘Team’: ‘POR’, ‘Wins’: 185}, {‘Team’: ‘WAS’, ‘Wins’: 179}, {‘Team’: ‘UTA’, ‘Wins’: 177}, {‘Team’: ‘ATL’, ‘Wins’: 175}, {‘Team’: ‘IND’, ‘Wins’: 173}, {‘Team’: ‘MIA’, ‘Wins’: 170}, {‘Team’: ‘MEM’, ‘Wins’: 162}, {‘Team’: ‘CHI’, ‘Wins’: 160}, {‘Team’: ‘MIL’, ‘Wins’: 160}, {‘Team’: ‘NOP’, ‘Wins’: 157}, {‘Team’: ‘CHO’, ‘Wins’: 153}, {‘Team’: ‘DET’, ‘Wins’: 152}, {‘Team’: ‘DAL’, ‘Wins’: 149}, {‘Team’: ‘DEN’, ‘Wins’: 149}, {‘Team’: ‘MIN’, ‘Wins’: 123}, {‘Team’: ‘SAC’, ‘Wins’: 121}, {‘Team’: ‘ORL’, ‘Wins’: 114}, {‘Team’: ‘NYK’, ‘Wins’: 109}, {‘Team’: ‘PHI’, ‘Wins’: 108}, {‘Team’: ‘PHO’, ‘Wins’: 107}, {‘Team’: ‘BRK’, ‘Wins’: 107}, {‘Team’: ‘LAL’, ‘Wins’: 99}], ‘error’: None, ‘query’: ’SELECT Team, COUNT(*) as Wins FROM nba WHERE WINorLOSS = ‘W’ GROUP BY Team ORDER BY Wins DESC;’} {‘category’: ‘Generated’}
1 Which team has the highest average points per game? {‘results’: [{‘Team’: ‘GSW’, ‘AvgPoints’: 113.54878048780488}, {‘Team’: ‘HOU’, ‘AvgPoints’: 109.54268292682927}, {‘Team’: ‘LAC’, ‘AvgPoints’: 107.21951219512195}, {‘Team’: ‘CLE’, ‘AvgPoints’: 107.16463414634147}, {‘Team’: ‘OKC’, ‘AvgPoints’: 107.15548780487805}, {‘Team’: ‘TOR’, ‘AvgPoints’: 106.30182926829268}, {‘Team’: ‘DEN’, ‘AvgPoints’: 106.26829268292683}, {‘Team’: ‘POR’, ‘AvgPoints’: 105.375}, {‘Team’: ‘BOS’, ‘AvgPoints’: 104.77743902439025}, {‘Team’: ‘WAS’, ‘AvgPoints’: 104.60060975609755}, {‘Team’: ‘NOP’, ‘AvgPoints’: 104.53353658536585}, {‘Team’: ‘MIN’, ‘AvgPoints’: 103.8140243902439}, {‘Team’: ‘PHO’, ‘AvgPoints’: 103.72256097560975}, {‘Team’: ‘SAS’, ‘AvgPoints’: 103.6951219512195}, {‘Team’: ‘ATL’, ‘AvgPoints’: 102.97560975609755}, {‘Team’: ‘CHO’, ‘AvgPoints’: 102.66768292682927}, {‘Team’: ‘IND’, ‘AvgPoints’: 102.53658536585365}, {‘Team’: ‘SAC’, ‘AvgPoints’: 102.39024390243902}, {‘Team’: ‘BRK’, ‘AvgPoints’: 102.25914634146342}, {‘Team’: ‘LAL’, ‘AvgPoints’: 102.10975609756098}, {‘Team’: ‘CHI’, ‘AvgPoints’: 102.0579268292683}, {‘Team’: ‘DAL’, ‘AvgPoints’: 101.9359756097561}, {‘Team’: ‘MIL’, ‘AvgPoints’: 101.7469512195122}, {‘Team’: ‘DET’, ‘AvgPoints’: 101.39024390243902}, {‘Team’: ‘ORL’, ‘AvgPoints’: 100.5579268292683}, {‘Team’: ‘PHI’, ‘AvgPoints’: 100.40853658536585}, {‘Team’: ‘MIA’, ‘AvgPoints’: 100.32926829268293}, {‘Team’: ‘NYK’, ‘AvgPoints’: 99.76219512195122}, {‘Team’: ‘UTA’, ‘AvgPoints’: 99.41768292682927}, {‘Team’: ‘MEM’, ‘AvgPoints’: 99.3048780487805}], ‘error’: None, ‘query’: ‘SELECT Team, AVG(TeamPoints) as AvgPoints FROM nba GROUP BY Team ORDER BY AvgPoints DESC;’} {‘category’: ‘Generated’}
2 Which team has the highest average total rebounds per game? {‘results’: [{‘Team’: ‘OKC’, ‘AvgRebounds’: 46.954268292682926}, {‘Team’: ‘CHI’, ‘AvgRebounds’: 45.75}, {‘Team’: ‘DET’, ‘AvgRebounds’: 45.15548780487805}, {‘Team’: ‘POR’, ‘AvgRebounds’: 45.11585365853659}, {‘Team’: ‘DEN’, ‘AvgRebounds’: 45.051829268292686}, {‘Team’: ‘GSW’, ‘AvgRebounds’: 44.71341463414634}, {‘Team’: ‘PHO’, ‘AvgRebounds’: 44.26829268292683}, {‘Team’: ‘CHO’, ‘AvgRebounds’: 44.25609756097561}, {‘Team’: ‘LAL’, ‘AvgRebounds’: 44.1859756097561}, {‘Team’: ‘SAS’, ‘AvgRebounds’: 43.91158536585366}, {‘Team’: ‘BOS’, ‘AvgRebounds’: 43.795731707317074}, {‘Team’: ‘HOU’, ‘AvgRebounds’: 43.64329268292683}, {‘Team’: ‘PHI’, ‘AvgRebounds’: 43.59146341463415}, {‘Team’: ‘NOP’, ‘AvgRebounds’: 43.52439024390244}, {‘Team’: ‘NYK’, ‘AvgRebounds’: 43.49085365853659}, {‘Team’: ‘UTA’, ‘AvgRebounds’: 43.420731707317074}, {‘Team’: ‘IND’, ‘AvgRebounds’: 43.35670731707317}, {‘Team’: ‘CLE’, ‘AvgRebounds’: 43.33841463414634}, {‘Team’: ‘BRK’, ‘AvgRebounds’: 43.271341463414636}, {‘Team’: ‘WAS’, ‘AvgRebounds’: 43.1219512195122}, {‘Team’: ‘TOR’, ‘AvgRebounds’: 43.051829268292686}, {‘Team’: ‘LAC’, ‘AvgRebounds’: 42.890243902439025}, {‘Team’: ‘SAC’, ‘AvgRebounds’: 42.60060975609756}, {‘Team’: ‘MIA’, ‘AvgRebounds’: 42.582317073170735}, {‘Team’: ‘ORL’, ‘AvgRebounds’: 42.48780487804878}, {‘Team’: ‘ATL’, ‘AvgRebounds’: 42.21646341463415}, {‘Team’: ‘MEM’, ‘AvgRebounds’: 41.8719512195122}, {‘Team’: ‘MIN’, ‘AvgRebounds’: 41.71341463414634}, {‘Team’: ‘DAL’, ‘AvgRebounds’: 41.292682926829265}, {‘Team’: ‘MIL’, ‘AvgRebounds’: 40.99390243902439}], ‘error’: None, ‘query’: ‘SELECT Team, AVG(TotalRebounds) as AvgRebounds FROM nba GROUP BY Team ORDER BY AvgRebounds DESC;’} {‘category’: ‘Generated’}
3 Which team has the lowest average turnovers per game? {‘results’: [{‘Team’: ‘CHO’, ‘AvgTurnovers’: 11.579268292682928}, {‘Team’: ‘DAL’, ‘AvgTurnovers’: 11.972560975609756}, {‘Team’: ‘TOR’, ‘AvgTurnovers’: 12.21951219512195}, {‘Team’: ‘DET’, ‘AvgTurnovers’: 12.432926829268293}, {‘Team’: ‘LAC’, ‘AvgTurnovers’: 12.676829268292684}, {‘Team’: ‘SAS’, ‘AvgTurnovers’: 12.893292682926829}, {‘Team’: ‘NOP’, ‘AvgTurnovers’: 13.097560975609756}, {‘Team’: ‘MEM’, ‘AvgTurnovers’: 13.128048780487806}, {‘Team’: ‘BOS’, ‘AvgTurnovers’: 13.173780487804878}, {‘Team’: ‘CHI’, ‘AvgTurnovers’: 13.195121951219512}, {‘Team’: ‘CLE’, ‘AvgTurnovers’: 13.25}, {‘Team’: ‘MIA’, ‘AvgTurnovers’: 13.341463414634147}, {‘Team’: ‘POR’, ‘AvgTurnovers’: 13.36890243902439}, {‘Team’: ‘IND’, ‘AvgTurnovers’: 13.426829268292684}, {‘Team’: ‘MIN’, ‘AvgTurnovers’: 13.448170731707316}, {‘Team’: ‘NYK’, ‘AvgTurnovers’: 13.451219512195122}, {‘Team’: ‘ORL’, ‘AvgTurnovers’: 13.676829268292684}, {‘Team’: ‘UTA’, ‘AvgTurnovers’: 13.850609756097562}, {‘Team’: ‘LAL’, ‘AvgTurnovers’: 13.875}, {‘Team’: ‘WAS’, ‘AvgTurnovers’: 13.939024390243903}, {‘Team’: ‘DEN’, ‘AvgTurnovers’: 14.195121951219512}, {‘Team’: ‘MIL’, ‘AvgTurnovers’: 14.277439024390244}, {‘Team’: ‘OKC’, ‘AvgTurnovers’: 14.390243902439025}, {‘Team’: ‘BRK’, ‘AvgTurnovers’: 14.46951219512195}, {‘Team’: ‘ATL’, ‘AvgTurnovers’: 14.551829268292684}, {‘Team’: ‘GSW’, ‘AvgTurnovers’: 14.59451219512195}, {‘Team’: ‘SAC’, ‘AvgTurnovers’: 14.615853658536585}, {‘Team’: ‘HOU’, ‘AvgTurnovers’: 14.713414634146341}, {‘Team’: ‘PHO’, ‘AvgTurnovers’: 15.268292682926829}, {‘Team’: ‘PHI’, ‘AvgTurnovers’: 16.085365853658537}], ‘error’: None, ‘query’: ‘SELECT Team, AVG(Turnovers) as AvgTurnovers FROM nba GROUP BY Team ORDER BY AvgTurnovers ASC;’} {‘category’: ‘Generated’}
4 Which team has the highest average assists per game? {‘results’: [{‘Team’: ‘GSW’, ‘AvgAssists’: 29.00609756097561}, {‘Team’: ‘ATL’, ‘AvgAssists’: 24.679878048780488}, {‘Team’: ‘WAS’, ‘AvgAssists’: 24.375}, {‘Team’: ‘BOS’, ‘AvgAssists’: 24.088414634146343}, {‘Team’: ‘SAS’, ‘AvgAssists’: 23.878048780487806}, {‘Team’: ‘DEN’, ‘AvgAssists’: 23.722560975609756}, {‘Team’: ‘MIL’, ‘AvgAssists’: 23.524390243902438}, {‘Team’: ‘NOP’, ‘AvgAssists’: 23.4390243902439}, {‘Team’: ‘PHI’, ‘AvgAssists’: 23.225609756097562}, {‘Team’: ‘LAC’, ‘AvgAssists’: 23.121951219512194}, {‘Team’: ‘MIN’, ‘AvgAssists’: 22.829268292682926}, {‘Team’: ‘HOU’, ‘AvgAssists’: 22.798780487804876}, {‘Team’: ‘CLE’, ‘AvgAssists’: 22.710365853658537}, {‘Team’: ‘CHI’, ‘AvgAssists’: 22.63719512195122}, {‘Team’: ‘ORL’, ‘AvgAssists’: 22.45731707317073}, {‘Team’: ‘SAC’, ‘AvgAssists’: 22.222560975609756}, {‘Team’: ‘BRK’, ‘AvgAssists’: 22.08231707317073}, {‘Team’: ‘DAL’, ‘AvgAssists’: 22.01829268292683}, {‘Team’: ‘IND’, ‘AvgAssists’: 21.83231707317073}, {‘Team’: ‘NYK’, ‘AvgAssists’: 21.725609756097562}, {‘Team’: ‘CHO’, ‘AvgAssists’: 21.625}, {‘Team’: ‘OKC’, ‘AvgAssists’: 21.448170731707318}, {‘Team’: ‘MEM’, ‘AvgAssists’: 21.295731707317074}, {‘Team’: ‘DET’, ‘AvgAssists’: 21.234756097560975}, {‘Team’: ‘MIA’, ‘AvgAssists’: 21.15548780487805}, {‘Team’: ‘POR’, ‘AvgAssists’: 20.972560975609756}, {‘Team’: ‘LAL’, ‘AvgAssists’: 20.908536585365855}, {‘Team’: ‘TOR’, ‘AvgAssists’: 20.576219512195124}, {‘Team’: ‘PHO’, ‘AvgAssists’: 20.448170731707318}, {‘Team’: ‘UTA’, ‘AvgAssists’: 20.35670731707317}], ‘error’: None, ‘query’: ‘SELECT Team, AVG(Assists) as AvgAssists FROM nba GROUP BY Team ORDER BY AvgAssists DESC;’} {‘category’: ‘Generated’}
5 Which team has the highest average steals per game? {‘results’: [{‘Team’: ‘HOU’, ‘AvgSteals’: 9.045731707317072}, {‘Team’: ‘GSW’, ‘AvgSteals’: 8.817073170731707}, {‘Team’: ‘MIL’, ‘AvgSteals’: 8.685975609756097}, {‘Team’: ‘PHI’, ‘AvgSteals’: 8.667682926829269}, {‘Team’: ‘ATL’, ‘AvgSteals’: 8.539634146341463}, {‘Team’: ‘MEM’, ‘AvgSteals’: 8.201219512195122}, {‘Team’: ‘MIN’, ‘AvgSteals’: 8.13109756097561}, {‘Team’: ‘BOS’, ‘AvgSteals’: 8.070121951219512}, {‘Team’: ‘WAS’, ‘AvgSteals’: 8.070121951219512}, {‘Team’: ‘IND’, ‘AvgSteals’: 8.036585365853659}, {‘Team’: ‘SAS’, ‘AvgSteals’: 7.978658536585366}, {‘Team’: ‘OKC’, ‘AvgSteals’: 7.902439024390244}, {‘Team’: ‘LAC’, ‘AvgSteals’: 7.890243902439025}, {‘Team’: ‘PHO’, ‘AvgSteals’: 7.850609756097561}, {‘Team’: ‘TOR’, ‘AvgSteals’: 7.786585365853658}, {‘Team’: ‘SAC’, ‘AvgSteals’: 7.783536585365853}, {‘Team’: ‘ORL’, ‘AvgSteals’: 7.689024390243903}, {‘Team’: ‘UTA’, ‘AvgSteals’: 7.658536585365853}, {‘Team’: ‘NOP’, ‘AvgSteals’: 7.560975609756097}, {‘Team’: ‘LAL’, ‘AvgSteals’: 7.554878048780488}, {‘Team’: ‘DEN’, ‘AvgSteals’: 7.454268292682927}, {‘Team’: ‘DAL’, ‘AvgSteals’: 7.362804878048781}, {‘Team’: ‘MIA’, ‘AvgSteals’: 7.326219512195122}, {‘Team’: ‘DET’, ‘AvgSteals’: 7.310975609756097}, {‘Team’: ‘BRK’, ‘AvgSteals’: 7.036585365853658}, {‘Team’: ‘CHI’, ‘AvgSteals’: 6.945121951219512}, {‘Team’: ‘CLE’, ‘AvgSteals’: 6.935975609756097}, {‘Team’: ‘POR’, ‘AvgSteals’: 6.810975609756097}, {‘Team’: ‘CHO’, ‘AvgSteals’: 6.780487804878049}, {‘Team’: ‘NYK’, ‘AvgSteals’: 6.6310975609756095}], ‘error’: None, ‘query’: ‘SELECT Team, AVG(Steals) as AvgSteals FROM nba GROUP BY Team ORDER BY AvgSteals DESC;’} {‘category’: ‘Generated’}
6 Which team has the highest average blocks per game? {‘results’: [{‘Team’: ‘GSW’, ‘AvgBlocks’: 6.588414634146342}, {‘Team’: ‘SAS’, ‘AvgBlocks’: 5.710365853658536}, {‘Team’: ‘PHI’, ‘AvgBlocks’: 5.554878048780488}, {‘Team’: ‘MIA’, ‘AvgBlocks’: 5.515243902439025}, {‘Team’: ‘NOP’, ‘AvgBlocks’: 5.4573170731707314}, {‘Team’: ‘OKC’, ‘AvgBlocks’: 5.3689024390243905}, {‘Team’: ‘MIL’, ‘AvgBlocks’: 5.3567073170731705}, {‘Team’: ‘UTA’, ‘AvgBlocks’: 5.317073170731708}, {‘Team’: ‘NYK’, ‘AvgBlocks’: 5.2560975609756095}, {‘Team’: ‘TOR’, ‘AvgBlocks’: 5.201219512195122}, {‘Team’: ‘CHO’, ‘AvgBlocks’: 5.027439024390244}, {‘Team’: ‘CHI’, ‘AvgBlocks’: 4.963414634146342}, {‘Team’: ‘ATL’, ‘AvgBlocks’: 4.911585365853658}, {‘Team’: ‘LAC’, ‘AvgBlocks’: 4.844512195121951}, {‘Team’: ‘POR’, ‘AvgBlocks’: 4.826219512195122}, {‘Team’: ‘HOU’, ‘AvgBlocks’: 4.820121951219512}, {‘Team’: ‘ORL’, ‘AvgBlocks’: 4.655487804878049}, {‘Team’: ‘IND’, ‘AvgBlocks’: 4.6189024390243905}, {‘Team’: ‘DEN’, ‘AvgBlocks’: 4.539634146341464}, {‘Team’: ‘PHO’, ‘AvgBlocks’: 4.469512195121951}, {‘Team’: ‘BRK’, ‘AvgBlocks’: 4.4176829268292686}, {‘Team’: ‘MEM’, ‘AvgBlocks’: 4.3810975609756095}, {‘Team’: ‘MIN’, ‘AvgBlocks’: 4.317073170731708}, {‘Team’: ‘LAL’, ‘AvgBlocks’: 4.301829268292683}, {‘Team’: ‘WAS’, ‘AvgBlocks’: 4.237804878048781}, {‘Team’: ‘SAC’, ‘AvgBlocks’: 4.134146341463414}, {‘Team’: ‘BOS’, ‘AvgBlocks’: 4.134146341463414}, {‘Team’: ‘DET’, ‘AvgBlocks’: 4.0060975609756095}, {‘Team’: ‘CLE’, ‘AvgBlocks’: 3.951219512195122}, {‘Team’: ‘DAL’, ‘AvgBlocks’: 3.9451219512195124}], ‘error’: None, ‘query’: ‘SELECT Team, AVG(Blocks) as AvgBlocks FROM nba GROUP BY Team ORDER BY AvgBlocks DESC;’} {‘category’: ‘Generated’}
7 Which team has the lowest average fouls per game? {‘results’: [{‘Team’: ‘CHO’, ‘AvgFouls’: 17.533536585365855}, {‘Team’: ‘SAS’, ‘AvgFouls’: 18.0}, {‘Team’: ‘CHI’, ‘AvgFouls’: 18.496951219512194}, {‘Team’: ‘DET’, ‘AvgFouls’: 18.570121951219512}, {‘Team’: ‘ATL’, ‘AvgFouls’: 18.670731707317074}, {‘Team’: ‘CLE’, ‘AvgFouls’: 18.85670731707317}, {‘Team’: ‘NOP’, ‘AvgFouls’: 19.216463414634145}, {‘Team’: ‘DAL’, ‘AvgFouls’: 19.463414634146343}, {‘Team’: ‘UTA’, ‘AvgFouls’: 19.484756097560975}, {‘Team’: ‘MIN’, ‘AvgFouls’: 19.536585365853657}, {‘Team’: ‘MIA’, ‘AvgFouls’: 19.713414634146343}, {‘Team’: ‘BRK’, ‘AvgFouls’: 19.722560975609756}, {‘Team’: ‘GSW’, ‘AvgFouls’: 19.878048780487806}, {‘Team’: ‘IND’, ‘AvgFouls’: 19.88719512195122}, {‘Team’: ‘ORL’, ‘AvgFouls’: 20.057926829268293}, {‘Team’: ‘POR’, ‘AvgFouls’: 20.15548780487805}, {‘Team’: ‘SAC’, ‘AvgFouls’: 20.359756097560975}, {‘Team’: ‘DEN’, ‘AvgFouls’: 20.442073170731707}, {‘Team’: ‘NYK’, ‘AvgFouls’: 20.53048780487805}, {‘Team’: ‘LAC’, ‘AvgFouls’: 20.60670731707317}, {‘Team’: ‘TOR’, ‘AvgFouls’: 20.774390243902438}, {‘Team’: ‘HOU’, ‘AvgFouls’: 20.804878048780488}, {‘Team’: ‘BOS’, ‘AvgFouls’: 20.847560975609756}, {‘Team’: ‘LAL’, ‘AvgFouls’: 20.850609756097562}, {‘Team’: ‘OKC’, ‘AvgFouls’: 21.003048780487806}, {‘Team’: ‘WAS’, ‘AvgFouls’: 21.054878048780488}, {‘Team’: ‘MIL’, ‘AvgFouls’: 21.100609756097562}, {‘Team’: ‘MEM’, ‘AvgFouls’: 21.615853658536587}, {‘Team’: ‘PHI’, ‘AvgFouls’: 21.841463414634145}, {‘Team’: ‘PHO’, ‘AvgFouls’: 22.679878048780488}], ‘error’: None, ‘query’: ‘SELECT Team, AVG(TotalFouls) as AvgFouls FROM nba GROUP BY Team ORDER BY AvgFouls ASC;’} {‘category’: ‘Generated’} 
def load_data():
    golden_data = init_dataset(PROJECT_NAME, "Golden data")
    golden_questions = set(d["input"] for d in golden_data)
    return (
        [{**x, "metadata": {"category": "Golden data"}} for x in golden_data]
        + [
            {"input": q, "metadata": {"category": "Handwritten question"}}
            for q in questions
            if q not in golden_questions
        ]
        + [x for x in generated_dataset if x["input"] not in golden_questions]
    )
await Eval(
    PROJECT_NAME,
    experiment_name="Generated data",
    data=load_data,
    task=text2sql,
    scores=[no_error, correct_result, correct_sql],
)
Experiment Generated data is running at https://www.braintrust.dev/app/Christian%20J.%20Mills%20Consulting/p/LLM%20Eval%20for%20Text2SQL/experiments/Generated%20data
LLM Eval for Text2SQL [experiment_name=Generated data] (data): 13it [00:00, 111962.94it/s]

LLM Eval for Text2SQL [experiment_name=Generated data] (tasks):   0%|          | 0/13 [00:00<?, ?it/s]

=========================SUMMARY=========================
Generated data compared to With samples:
22.50% (-) 'correct_result' score   (0 improvements, 0 regressions)
84.62% (-) 'no_error'       score   (0 improvements, 0 regressions)
72.73% (-) 'correct_sql'    score   (0 improvements, 0 regressions)

See results for Generated data at https://www.braintrust.dev/app/Christian%20J.%20Mills%20Consulting/p/LLM%20Eval%20for%20Text2SQL/experiments/Generated%20data

EvalResultWithSummary(summary="...", results=[...])

eval 3

Trying GPT-4

  • Experiment: Replace GPT-4o with GPT-4 in the task function.
  • Results (During Talk):
    • Regression in performance across all metrics, including on the golden dataset.
      • Nearly a total wash between the improvements and regressions when testing locally
    • Analysis suggests potential issues with date syntax and prompt formatting.
TASK_MODEL = "gpt-4"

await Eval(
    PROJECT_NAME,
    experiment_name="Try gpt-4",
    data=load_data,
    task=text2sql,
    scores=[no_error, correct_result, correct_sql],
)
Experiment Try gpt-4 is running at https://www.braintrust.dev/app/Christian%20J.%20Mills%20Consulting/p/LLM%20Eval%20for%20Text2SQL/experiments/Try%20gpt-4
LLM Eval for Text2SQL [experiment_name=Try gpt-4] (data): 13it [00:00, 162764.04it/s]

LLM Eval for Text2SQL [experiment_name=Try gpt-4] (tasks):   0%|          | 0/13 [00:00<?, ?it/s]

=========================SUMMARY=========================
Try gpt-4 compared to Generated data:
12.73% (-09.77%) 'correct_result' score (1 improvements, 1 regressions)
81.82% (+09.09%) 'correct_sql'    score (3 improvements, 2 regressions)
84.62% (-) 'no_error'       score   (1 improvements, 1 regressions)

See results for Try gpt-4 at https://www.braintrust.dev/app/Christian%20J.%20Mills%20Consulting/p/LLM%20Eval%20for%20Text2SQL/experiments/Try%20gpt-4

EvalResultWithSummary(summary="...", results=[...])

Results

id _xact_id input output expected tags scores duration estimated_cost metadata
0 bc0a4372-24f7-441a-8b3f-f618dd7b378f 1000193295096986240 Who led the league in 3 point shots? {‘error’: None, ‘query’: ‘SELECT Team, MAX(X3PointShots) as Max_3PointShots FROM nba GROUP BY Team ORDER BY Max_3PointShots DESC LIMIT 1;’, ‘results’: [{‘Team’: ‘CLE’, ‘Max_3PointShots’: 25}]} {‘error’: None, ‘query’: ‘SELECT Team, SUM(X3PointShots) AS Total3PointShots FROM nba GROUP BY Team ORDER BY Total3PointShots DESC LIMIT 1;’, ‘results’: [{‘Team’: ‘HOU’, ‘Total3PointShots’: 4248}]} [] {‘correct_result’: 0.005850690381465001, ‘correct_sql’: 0, ‘no_error’: 1} 7.480919 0.018554 {‘category’: ‘Golden data’}
1 54c91672-cba1-4e46-a85f-b9ed90b618a1 1000193295097051776 Which team has the highest average total rebounds per game? {‘error’: None, ‘query’: ‘SELECT Team, AVG(TotalRebounds) as AverageRebounds FROM nba GROUP BY Team ORDER BY AverageRebounds DESC LIMIT 1;’, ‘results’: [{‘Team’: ‘OKC’, ‘AverageRebounds’: 46.954268292682926}]} {‘error’: None, ‘query’: ‘SELECT Team, AVG(TotalRebounds) as AvgRebounds FROM nba GROUP BY Team ORDER BY AvgRebounds DESC;’, ‘results’: [{‘Team’: ‘OKC’, ‘AvgRebounds’: 46.954268292682926}, {‘Team’: ‘CHI’, ‘AvgRebounds’: 45.75}, {‘Team’: ‘DET’, ‘AvgRebounds’: 45.15548780487805}, {‘Team’: ‘POR’, ‘AvgRebounds’: 45.11585365853659}, {‘Team’: ‘DEN’, ‘AvgRebounds’: 45.051829268292686}, {‘Team’: ‘GSW’, ‘AvgRebounds’: 44.71341463414634}, {‘Team’: ‘PHO’, ‘AvgRebounds’: 44.26829268292683}, {‘Team’: ‘CHO’, ‘AvgRebounds’: 44.25609756097561}, {‘Team’: ‘LAL’, ‘AvgRebounds’: 44.1859756097561}, {‘Team’: ‘SAS’, ‘AvgRebounds’: 43.91158536585366}, {‘Team’: ‘BOS’, ‘AvgRebounds’: 43.795731707317074}, {‘Team’: ‘HOU’, ‘AvgRebounds’: 43.64329268292683}, {‘Team’: ‘PHI’, ‘AvgRebounds’: 43.59146341463415}, {‘Team’: ‘NOP’, ‘AvgRebounds’: 43.52439024390244}, {‘Team’: ‘NYK’, ‘AvgRebounds’: 43.49085365853659}, {‘Team’: ‘UTA’, ‘AvgRebounds’: 43.420731707317074}, {‘Team’: ‘IND’, ‘AvgRebounds’: 43.35670731707317}, {‘Team’: ‘CLE’, ‘AvgRebounds’: 43.33841463414634}, {‘Team’: ‘BRK’, ‘AvgRebounds’: 43.271341463414636}, {‘Team’: ‘WAS’, ‘AvgRebounds’: 43.1219512195122}, {‘Team’: ‘TOR’, ‘AvgRebounds’: 43.051829268292686}, {‘Team’: ‘LAC’, ‘AvgRebounds’: 42.890243902439025}, {‘Team’: ‘SAC’, ‘AvgRebounds’: 42.60060975609756}, {‘Team’: ‘MIA’, ‘AvgRebounds’: 42.582317073170735}, {‘Team’: ‘ORL’, ‘AvgRebounds’: 42.48780487804878}, {‘Team’: ‘ATL’, ‘AvgRebounds’: 42.21646341463415}, {‘Team’: ‘MEM’, ‘AvgRebounds’: 41.8719512195122}, {‘Team’: ‘MIN’, ‘AvgRebounds’: 41.71341463414634}, {‘Team’: ‘DAL’, ‘AvgRebounds’: 41.292682926829265}, {‘Team’: ‘MIL’, ‘AvgRebounds’: 40.99390243902439}]} None {‘correct_result’: 0.033333333333333, ‘correct_sql’: 0, ‘no_error’: 1} 8.128142 0.018371 {‘category’: ‘Generated’}
2 ef818ada-cbf7-4dab-a6c8-8bca01d340d1 1000193295096986240 Which team has the lowest average fouls per game? {‘error’: None, ‘query’: ‘SELECT Team, AVG(TotalFouls) as AverageFouls FROM nba GROUP BY Team ORDER BY AverageFouls ASC LIMIT 1;’, ‘results’: [{‘Team’: ‘CHO’, ‘AverageFouls’: 17.533536585365855}]} {‘error’: None, ‘query’: ‘SELECT Team, AVG(TotalFouls) as AvgFouls FROM nba GROUP BY Team ORDER BY AvgFouls ASC;’, ‘results’: [{‘Team’: ‘CHO’, ‘AvgFouls’: 17.533536585365855}, {‘Team’: ‘SAS’, ‘AvgFouls’: 18}, {‘Team’: ‘CHI’, ‘AvgFouls’: 18.496951219512194}, {‘Team’: ‘DET’, ‘AvgFouls’: 18.570121951219512}, {‘Team’: ‘ATL’, ‘AvgFouls’: 18.670731707317074}, {‘Team’: ‘CLE’, ‘AvgFouls’: 18.85670731707317}, {‘Team’: ‘NOP’, ‘AvgFouls’: 19.216463414634145}, {‘Team’: ‘DAL’, ‘AvgFouls’: 19.463414634146343}, {‘Team’: ‘UTA’, ‘AvgFouls’: 19.484756097560975}, {‘Team’: ‘MIN’, ‘AvgFouls’: 19.536585365853657}, {‘Team’: ‘MIA’, ‘AvgFouls’: 19.713414634146343}, {‘Team’: ‘BRK’, ‘AvgFouls’: 19.722560975609756}, {‘Team’: ‘GSW’, ‘AvgFouls’: 19.878048780487806}, {‘Team’: ‘IND’, ‘AvgFouls’: 19.88719512195122}, {‘Team’: ‘ORL’, ‘AvgFouls’: 20.057926829268293}, {‘Team’: ‘POR’, ‘AvgFouls’: 20.15548780487805}, {‘Team’: ‘SAC’, ‘AvgFouls’: 20.359756097560975}, {‘Team’: ‘DEN’, ‘AvgFouls’: 20.442073170731707}, {‘Team’: ‘NYK’, ‘AvgFouls’: 20.53048780487805}, {‘Team’: ‘LAC’, ‘AvgFouls’: 20.60670731707317}, {‘Team’: ‘TOR’, ‘AvgFouls’: 20.774390243902438}, {‘Team’: ‘HOU’, ‘AvgFouls’: 20.804878048780488}, {‘Team’: ‘BOS’, ‘AvgFouls’: 20.847560975609756}, {‘Team’: ‘LAL’, ‘AvgFouls’: 20.850609756097562}, {‘Team’: ‘OKC’, ‘AvgFouls’: 21.003048780487806}, {‘Team’: ‘WAS’, ‘AvgFouls’: 21.054878048780488}, {‘Team’: ‘MIL’, ‘AvgFouls’: 21.100609756097562}, {‘Team’: ‘MEM’, ‘AvgFouls’: 21.615853658536587}, {‘Team’: ‘PHI’, ‘AvgFouls’: 21.841463414634145}, {‘Team’: ‘PHO’, ‘AvgFouls’: 22.679878048780488}]} None {‘correct_result’: 0.033333333333333, ‘correct_sql’: 1, ‘no_error’: 1} 6.661695 0.018478 {‘category’: ‘Generated’}
3 80a3d921-c5c9-4ebc-97f2-4923069cb327 1000193295096920704 Which team has the highest average blocks per game? {‘error’: None, ‘query’: ‘SELECT Team, AVG(Blocks) as AverageBlocks FROM nba GROUP BY Team ORDER BY AverageBlocks DESC LIMIT 1;’, ‘results’: [{‘Team’: ‘GSW’, ‘AverageBlocks’: 6.588414634146342}]} {‘error’: None, ‘query’: ‘SELECT Team, AVG(Blocks) as AvgBlocks FROM nba GROUP BY Team ORDER BY AvgBlocks DESC;’, ‘results’: [{‘Team’: ‘GSW’, ‘AvgBlocks’: 6.588414634146342}, {‘Team’: ‘SAS’, ‘AvgBlocks’: 5.710365853658536}, {‘Team’: ‘PHI’, ‘AvgBlocks’: 5.554878048780488}, {‘Team’: ‘MIA’, ‘AvgBlocks’: 5.515243902439025}, {‘Team’: ‘NOP’, ‘AvgBlocks’: 5.4573170731707314}, {‘Team’: ‘OKC’, ‘AvgBlocks’: 5.3689024390243905}, {‘Team’: ‘MIL’, ‘AvgBlocks’: 5.35670731707317}, {‘Team’: ‘UTA’, ‘AvgBlocks’: 5.3170731707317085}, {‘Team’: ‘NYK’, ‘AvgBlocks’: 5.256097560975609}, {‘Team’: ‘TOR’, ‘AvgBlocks’: 5.201219512195122}, {‘Team’: ‘CHO’, ‘AvgBlocks’: 5.027439024390244}, {‘Team’: ‘CHI’, ‘AvgBlocks’: 4.963414634146342}, {‘Team’: ‘ATL’, ‘AvgBlocks’: 4.911585365853658}, {‘Team’: ‘LAC’, ‘AvgBlocks’: 4.844512195121951}, {‘Team’: ‘POR’, ‘AvgBlocks’: 4.826219512195122}, {‘Team’: ‘HOU’, ‘AvgBlocks’: 4.820121951219512}, {‘Team’: ‘ORL’, ‘AvgBlocks’: 4.6554878048780495}, {‘Team’: ‘IND’, ‘AvgBlocks’: 4.6189024390243905}, {‘Team’: ‘DEN’, ‘AvgBlocks’: 4.539634146341464}, {‘Team’: ‘PHO’, ‘AvgBlocks’: 4.469512195121951}, {‘Team’: ‘BRK’, ‘AvgBlocks’: 4.417682926829268}, {‘Team’: ‘MEM’, ‘AvgBlocks’: 4.381097560975609}, {‘Team’: ‘MIN’, ‘AvgBlocks’: 4.3170731707317085}, {‘Team’: ‘LAL’, ‘AvgBlocks’: 4.301829268292683}, {‘Team’: ‘WAS’, ‘AvgBlocks’: 4.237804878048781}, {‘Team’: ‘SAC’, ‘AvgBlocks’: 4.134146341463414}, {‘Team’: ‘BOS’, ‘AvgBlocks’: 4.134146341463414}, {‘Team’: ‘DET’, ‘AvgBlocks’: 4.006097560975609}, {‘Team’: ‘CLE’, ‘AvgBlocks’: 3.951219512195122}, {‘Team’: ‘DAL’, ‘AvgBlocks’: 3.945121951219512}]} None {‘correct_result’: 0.033333333333333, ‘correct_sql’: 1, ‘no_error’: 1} 6.120250 0.017907 {‘category’: ‘Generated’}
4 43822081-8065-4914-9163-392e325c5af5 1000193295096920704 Which team has the highest average steals per game? {‘error’: None, ‘query’: ‘SELECT Team, AVG(Steals) as AverageSteals FROM nba GROUP BY Team ORDER BY AverageSteals DESC LIMIT 1;’, ‘results’: [{‘Team’: ‘HOU’, ‘AverageSteals’: 9.045731707317072}]} {‘error’: None, ‘query’: ‘SELECT Team, AVG(Steals) as AvgSteals FROM nba GROUP BY Team ORDER BY AvgSteals DESC;’, ‘results’: [{‘Team’: ‘HOU’, ‘AvgSteals’: 9.045731707317072}, {‘Team’: ‘GSW’, ‘AvgSteals’: 8.817073170731707}, {‘Team’: ‘MIL’, ‘AvgSteals’: 8.685975609756097}, {‘Team’: ‘PHI’, ‘AvgSteals’: 8.667682926829269}, {‘Team’: ‘ATL’, ‘AvgSteals’: 8.539634146341463}, {‘Team’: ‘MEM’, ‘AvgSteals’: 8.201219512195122}, {‘Team’: ‘MIN’, ‘AvgSteals’: 8.13109756097561}, {‘Team’: ‘WAS’, ‘AvgSteals’: 8.070121951219512}, {‘Team’: ‘BOS’, ‘AvgSteals’: 8.070121951219512}, {‘Team’: ‘IND’, ‘AvgSteals’: 8.036585365853659}, {‘Team’: ‘SAS’, ‘AvgSteals’: 7.978658536585366}, {‘Team’: ‘OKC’, ‘AvgSteals’: 7.902439024390244}, {‘Team’: ‘LAC’, ‘AvgSteals’: 7.890243902439025}, {‘Team’: ‘PHO’, ‘AvgSteals’: 7.850609756097561}, {‘Team’: ‘TOR’, ‘AvgSteals’: 7.786585365853658}, {‘Team’: ‘SAC’, ‘AvgSteals’: 7.783536585365853}, {‘Team’: ‘ORL’, ‘AvgSteals’: 7.689024390243903}, {‘Team’: ‘UTA’, ‘AvgSteals’: 7.658536585365853}, {‘Team’: ‘NOP’, ‘AvgSteals’: 7.560975609756097}, {‘Team’: ‘LAL’, ‘AvgSteals’: 7.554878048780488}, {‘Team’: ‘DEN’, ‘AvgSteals’: 7.454268292682927}, {‘Team’: ‘DAL’, ‘AvgSteals’: 7.362804878048781}, {‘Team’: ‘MIA’, ‘AvgSteals’: 7.326219512195122}, {‘Team’: ‘DET’, ‘AvgSteals’: 7.310975609756097}, {‘Team’: ‘BRK’, ‘AvgSteals’: 7.036585365853658}, {‘Team’: ‘CHI’, ‘AvgSteals’: 6.945121951219512}, {‘Team’: ‘CLE’, ‘AvgSteals’: 6.935975609756097}, {‘Team’: ‘POR’, ‘AvgSteals’: 6.810975609756097}, {‘Team’: ‘CHO’, ‘AvgSteals’: 6.7804878048780495}, {‘Team’: ‘NYK’, ‘AvgSteals’: 6.6310975609756095}]} None {‘correct_result’: 0.033333333333333, ‘correct_sql’: 1, ‘no_error’: 1} 6.062654 0.018146 {‘category’: ‘Generated’}
5 7d0561af-0571-4b65-baba-25f3d0a97472 1000193295096920704 Which team has the highest average assists per game? {‘error’: None, ‘query’: ‘SELECT Team, AVG(Assists) as AverageAssists FROM nba GROUP BY Team ORDER BY AverageAssists DESC LIMIT 1;’, ‘results’: [{‘Team’: ‘GSW’, ‘AverageAssists’: 29.00609756097561}]} {‘error’: None, ‘query’: ‘SELECT Team, AVG(Assists) as AvgAssists FROM nba GROUP BY Team ORDER BY AvgAssists DESC;’, ‘results’: [{‘Team’: ‘GSW’, ‘AvgAssists’: 29.00609756097561}, {‘Team’: ‘ATL’, ‘AvgAssists’: 24.679878048780488}, {‘Team’: ‘WAS’, ‘AvgAssists’: 24.375}, {‘Team’: ‘BOS’, ‘AvgAssists’: 24.088414634146343}, {‘Team’: ‘SAS’, ‘AvgAssists’: 23.878048780487806}, {‘Team’: ‘DEN’, ‘AvgAssists’: 23.722560975609756}, {‘Team’: ‘MIL’, ‘AvgAssists’: 23.524390243902438}, {‘Team’: ‘NOP’, ‘AvgAssists’: 23.4390243902439}, {‘Team’: ‘PHI’, ‘AvgAssists’: 23.225609756097562}, {‘Team’: ‘LAC’, ‘AvgAssists’: 23.121951219512194}, {‘Team’: ‘MIN’, ‘AvgAssists’: 22.829268292682926}, {‘Team’: ‘HOU’, ‘AvgAssists’: 22.798780487804876}, {‘Team’: ‘CLE’, ‘AvgAssists’: 22.710365853658537}, {‘Team’: ‘CHI’, ‘AvgAssists’: 22.63719512195122}, {‘Team’: ‘ORL’, ‘AvgAssists’: 22.45731707317073}, {‘Team’: ‘SAC’, ‘AvgAssists’: 22.222560975609756}, {‘Team’: ‘BRK’, ‘AvgAssists’: 22.08231707317073}, {‘Team’: ‘DAL’, ‘AvgAssists’: 22.01829268292683}, {‘Team’: ‘IND’, ‘AvgAssists’: 21.83231707317073}, {‘Team’: ‘NYK’, ‘AvgAssists’: 21.725609756097562}, {‘Team’: ‘CHO’, ‘AvgAssists’: 21.625}, {‘Team’: ‘OKC’, ‘AvgAssists’: 21.448170731707318}, {‘Team’: ‘MEM’, ‘AvgAssists’: 21.295731707317074}, {‘Team’: ‘DET’, ‘AvgAssists’: 21.234756097560975}, {‘Team’: ‘MIA’, ‘AvgAssists’: 21.15548780487805}, {‘Team’: ‘POR’, ‘AvgAssists’: 20.972560975609756}, {‘Team’: ‘LAL’, ‘AvgAssists’: 20.908536585365855}, {‘Team’: ‘TOR’, ‘AvgAssists’: 20.576219512195124}, {‘Team’: ‘PHO’, ‘AvgAssists’: 20.448170731707318}, {‘Team’: ‘UTA’, ‘AvgAssists’: 20.35670731707317}]} None {‘correct_result’: 0.033333333333333, ‘correct_sql’: 1, ‘no_error’: 1} 6.208672 0.018167 {‘category’: ‘Generated’}
6 ec89db77-9bc6-4204-8083-e5bc0f71c3eb 1000193295096986240 Which team has the lowest average turnovers per game? {‘error’: None, ‘query’: ‘SELECT Team, AVG(Turnovers) as AverageTurnovers FROM nba GROUP BY Team ORDER BY AverageTurnovers ASC LIMIT 1;’, ‘results’: [{‘Team’: ‘CHO’, ‘AverageTurnovers’: 11.579268292682928}]} {‘error’: None, ‘query’: ‘SELECT Team, AVG(Turnovers) as AvgTurnovers FROM nba GROUP BY Team ORDER BY AvgTurnovers ASC;’, ‘results’: [{‘Team’: ‘CHO’, ‘AvgTurnovers’: 11.579268292682928}, {‘Team’: ‘DAL’, ‘AvgTurnovers’: 11.972560975609756}, {‘Team’: ‘TOR’, ‘AvgTurnovers’: 12.21951219512195}, {‘Team’: ‘DET’, ‘AvgTurnovers’: 12.432926829268293}, {‘Team’: ‘LAC’, ‘AvgTurnovers’: 12.676829268292684}, {‘Team’: ‘SAS’, ‘AvgTurnovers’: 12.893292682926829}, {‘Team’: ‘NOP’, ‘AvgTurnovers’: 13.097560975609756}, {‘Team’: ‘MEM’, ‘AvgTurnovers’: 13.128048780487806}, {‘Team’: ‘BOS’, ‘AvgTurnovers’: 13.173780487804878}, {‘Team’: ‘CHI’, ‘AvgTurnovers’: 13.195121951219512}, {‘Team’: ‘CLE’, ‘AvgTurnovers’: 13.25}, {‘Team’: ‘MIA’, ‘AvgTurnovers’: 13.341463414634147}, {‘Team’: ‘POR’, ‘AvgTurnovers’: 13.36890243902439}, {‘Team’: ‘IND’, ‘AvgTurnovers’: 13.426829268292684}, {‘Team’: ‘MIN’, ‘AvgTurnovers’: 13.448170731707316}, {‘Team’: ‘NYK’, ‘AvgTurnovers’: 13.451219512195122}, {‘Team’: ‘ORL’, ‘AvgTurnovers’: 13.676829268292684}, {‘Team’: ‘UTA’, ‘AvgTurnovers’: 13.850609756097562}, {‘Team’: ‘LAL’, ‘AvgTurnovers’: 13.875}, {‘Team’: ‘WAS’, ‘AvgTurnovers’: 13.939024390243903}, {‘Team’: ‘DEN’, ‘AvgTurnovers’: 14.195121951219512}, {‘Team’: ‘MIL’, ‘AvgTurnovers’: 14.277439024390244}, {‘Team’: ‘OKC’, ‘AvgTurnovers’: 14.390243902439025}, {‘Team’: ‘BRK’, ‘AvgTurnovers’: 14.46951219512195}, {‘Team’: ‘ATL’, ‘AvgTurnovers’: 14.551829268292684}, {‘Team’: ‘GSW’, ‘AvgTurnovers’: 14.59451219512195}, {‘Team’: ‘SAC’, ‘AvgTurnovers’: 14.615853658536585}, {‘Team’: ‘HOU’, ‘AvgTurnovers’: 14.713414634146341}, {‘Team’: ‘PHO’, ‘AvgTurnovers’: 15.268292682926829}, {‘Team’: ‘PHI’, ‘AvgTurnovers’: 16.085365853658537}]} None {‘correct_result’: 0.033333333333333, ‘correct_sql’: 1, ‘no_error’: 1} 6.859579 0.018256 {‘category’: ‘Generated’}
7 b0297b56-c7be-48e4-972b-80d831eee171 1000193295096855168 Which team has the highest average points per game? {‘error’: None, ‘query’: ‘SELECT Team, AVG(TeamPoints) as AveragePoints FROM nba GROUP BY Team ORDER BY AveragePoints DESC LIMIT 1;’, ‘results’: [{‘Team’: ‘GSW’, ‘AveragePoints’: 113.54878048780488}]} {‘error’: None, ‘query’: ‘SELECT Team, AVG(TeamPoints) as AvgPoints FROM nba GROUP BY Team ORDER BY AvgPoints DESC;’, ‘results’: [{‘Team’: ‘GSW’, ‘AvgPoints’: 113.54878048780488}, {‘Team’: ‘HOU’, ‘AvgPoints’: 109.54268292682927}, {‘Team’: ‘LAC’, ‘AvgPoints’: 107.21951219512195}, {‘Team’: ‘CLE’, ‘AvgPoints’: 107.16463414634147}, {‘Team’: ‘OKC’, ‘AvgPoints’: 107.15548780487805}, {‘Team’: ‘TOR’, ‘AvgPoints’: 106.30182926829268}, {‘Team’: ‘DEN’, ‘AvgPoints’: 106.26829268292683}, {‘Team’: ‘POR’, ‘AvgPoints’: 105.375}, {‘Team’: ‘BOS’, ‘AvgPoints’: 104.77743902439025}, {‘Team’: ‘WAS’, ‘AvgPoints’: 104.60060975609755}, {‘Team’: ‘NOP’, ‘AvgPoints’: 104.53353658536585}, {‘Team’: ‘MIN’, ‘AvgPoints’: 103.8140243902439}, {‘Team’: ‘PHO’, ‘AvgPoints’: 103.72256097560975}, {‘Team’: ‘SAS’, ‘AvgPoints’: 103.6951219512195}, {‘Team’: ‘ATL’, ‘AvgPoints’: 102.97560975609755}, {‘Team’: ‘CHO’, ‘AvgPoints’: 102.66768292682927}, {‘Team’: ‘IND’, ‘AvgPoints’: 102.53658536585365}, {‘Team’: ‘SAC’, ‘AvgPoints’: 102.39024390243902}, {‘Team’: ‘BRK’, ‘AvgPoints’: 102.25914634146342}, {‘Team’: ‘LAL’, ‘AvgPoints’: 102.10975609756098}, {‘Team’: ‘CHI’, ‘AvgPoints’: 102.0579268292683}, {‘Team’: ‘DAL’, ‘AvgPoints’: 101.9359756097561}, {‘Team’: ‘MIL’, ‘AvgPoints’: 101.7469512195122}, {‘Team’: ‘DET’, ‘AvgPoints’: 101.39024390243902}, {‘Team’: ‘ORL’, ‘AvgPoints’: 100.5579268292683}, {‘Team’: ‘PHI’, ‘AvgPoints’: 100.40853658536585}, {‘Team’: ‘MIA’, ‘AvgPoints’: 100.32926829268293}, {‘Team’: ‘NYK’, ‘AvgPoints’: 99.76219512195122}, {‘Team’: ‘UTA’, ‘AvgPoints’: 99.41768292682927}, {‘Team’: ‘MEM’, ‘AvgPoints’: 99.3048780487805}]} None {‘correct_result’: 0.033333333333333, ‘correct_sql’: 1, ‘no_error’: 1} 5.232591 0.018000 {‘category’: ‘Generated’}
8 50206d55-2463-4e18-8f59-6caebf735759 1000193295097051776 Which team has the most wins? {‘error’: None, ‘query’: ’SELECT Team, COUNT(*) as Wins FROM nba WHERE WINorLOSS = ‘W’ GROUP BY Team ORDER BY Wins DESC LIMIT 1;‘, ’results’: [{‘Team’: ‘GSW’, ‘Wins’: 265}]} {‘error’: None, ‘query’: ’SELECT Team, COUNT(*) as Wins FROM nba WHERE WINorLOSS = ‘W’ GROUP BY Team ORDER BY Wins DESC;‘, ’results’: [{‘Team’: ‘GSW’, ‘Wins’: 265}, {‘Team’: ‘SAS’, ‘Wins’: 230}, {‘Team’: ‘HOU’, ‘Wins’: 217}, {‘Team’: ‘TOR’, ‘Wins’: 215}, {‘Team’: ‘CLE’, ‘Wins’: 211}, {‘Team’: ‘LAC’, ‘Wins’: 202}, {‘Team’: ‘BOS’, ‘Wins’: 196}, {‘Team’: ‘OKC’, ‘Wins’: 195}, {‘Team’: ‘POR’, ‘Wins’: 185}, {‘Team’: ‘WAS’, ‘Wins’: 179}, {‘Team’: ‘UTA’, ‘Wins’: 177}, {‘Team’: ‘ATL’, ‘Wins’: 175}, {‘Team’: ‘IND’, ‘Wins’: 173}, {‘Team’: ‘MIA’, ‘Wins’: 170}, {‘Team’: ‘MEM’, ‘Wins’: 162}, {‘Team’: ‘CHI’, ‘Wins’: 160}, {‘Team’: ‘MIL’, ‘Wins’: 160}, {‘Team’: ‘NOP’, ‘Wins’: 157}, {‘Team’: ‘CHO’, ‘Wins’: 153}, {‘Team’: ‘DET’, ‘Wins’: 152}, {‘Team’: ‘DEN’, ‘Wins’: 149}, {‘Team’: ‘DAL’, ‘Wins’: 149}, {‘Team’: ‘MIN’, ‘Wins’: 123}, {‘Team’: ‘SAC’, ‘Wins’: 121}, {‘Team’: ‘ORL’, ‘Wins’: 114}, {‘Team’: ‘NYK’, ‘Wins’: 109}, {‘Team’: ‘PHI’, ‘Wins’: 108}, {‘Team’: ‘PHO’, ‘Wins’: 107}, {‘Team’: ‘BRK’, ‘Wins’: 107}, {‘Team’: ‘LAL’, ‘Wins’: 99}]} None {‘correct_result’: 0.033333333333333, ‘correct_sql’: 1, ‘no_error’: 1} 7.731482 0.018153 {‘category’: ‘Generated’}
9 80396d4b-0cba-490c-b8cb-aad792edef6d 1000193295096986240 Which team won the most games? {‘error’: None, ‘query’: ’SELECT Team, COUNT(*) as Wins FROM nba WHERE WINorLOSS = ‘W’ GROUP BY Team ORDER BY Wins DESC LIMIT 1;‘, ’results’: [{‘Team’: ‘GSW’, ‘Wins’: 265}]} {‘error’: None, ‘query’: ’SELECT Team, COUNT(*) AS Wins FROM nba WHERE WINorLOSS = ‘W’ GROUP BY Team ORDER BY Wins DESC LIMIT 1;‘, ’results’: [{‘Team’: ‘GSW’, ‘Wins’: 265}]} [] {‘correct_result’: 1, ‘correct_sql’: 1, ‘no_error’: 1} 7.293730 0.018269 {‘category’: ‘Golden data’}
10 81ab8f36-c7b9-4598-9ee9-10552bfebd86 1000193295096920704 Which team won the most games in 2015? {‘error’: None, ‘query’: ’SELECT Team, COUNT(*) as Wins FROM nba WHERE WINorLOSS = ‘W’ AND Date LIKE ‘2015%’ GROUP BY Team ORDER BY Wins DESC LIMIT 1;‘, ’results’: []} {‘error’: ’Parser Error: syntax error at or near “"', 'query': 'sql SELECT Team, COUNT(*) AS Wins FROM nba WHERE WINorLOSS = ‘W’ AND Date LIKE ‘%/15’ GROUP BY Team ORDER BY Wins DESC LIMIT 1; ```‘, ’results’: None} [] {‘correct_result’: None, ‘correct_sql’: 1, ‘no_error’: 1} 6.607797 0.018675 {‘category’: ‘Golden data’}
11 b4931e7a-e5c4-450d-82bb-78a81b40fbf5 1000193295096920704 What is the average number of free throws per year?

{‘error’: ‘Parser Error: syntax error at or near “To”’, ‘query’: ‘To answer this question, we first need to convert the ’Date’ column from VARCHAR to DATE type. However, DuckDB SQL does not support date conversion directly. Therefore, we need to use a workaround to extract the year from the ‘Date’ column.

Here is the SQL query:

SELECT 
    SUBSTRING(Date, 7, 4) AS Year, 
    AVG(FreeThrows) AS AverageFreeThrows
FROM 
    nba
GROUP BY 
    Year
This query first extracts the year from the ‘Date’ column, then calculates the average number of free throws for each year.’, ‘results’: None} 		None None {‘correct_result’: None, ‘correct_sql’: None, ‘no_error’: 0} 5.591774 0.023460 {‘category’: ‘Handwritten question’}
12 0cd008a6-53d3-4940-8022-ecbe9e77bdc9 1000193295097182848 Which team had the biggest difference in records across two consecutive years?

{‘error’: ‘Parser Error: syntax error at or near “To”’, ‘query’: ’To answer this question, we first need to calculate the win-loss record for each team for each year. However, the provided table schema does not include a year column. Assuming the Date column is in the format MM/DD/YY, we can extract the year from the Date column. Then, we can calculate the win-loss record for each team for each year. Finally, we can calculate the difference in records across two consecutive years and find the team with the biggest difference.

Here is the SQL query:

WITH yearly_records AS (
    SELECT 
        Team, 
        SUBSTR(Date, 7, 2) AS Year, 
        COUNT(*) FILTER (WHERE WINorLOSS = 'W') AS Wins, 
        COUNT(*) FILTER (WHERE WINorLOSS = 'L') AS Losses
    FROM 
        nba 
    GROUP BY 
        Team, Year
), yearly_diff AS (
    SELECT 
        Team, 
        ABS((Wins - Losses) - LAG(Wins - Losses) OVER (PARTITION BY Team ORDER BY Year)) AS Record_Difference
    FROM 
        yearly_records
)
SELECT 
    Team 
FROM 
    yearly_diff 
ORDER BY 
    Record_Difference DESC 
LIMIT 1;
This query first calculates the number of wins and losses for each team for each year. Then, it calculates the difference in records (wins - losses) across two consecutive years for each team. Finally, it returns the team with the biggest difference in records.’, ‘results’: None} 		None None {‘correct_result’: None, ‘correct_sql’: None, ‘no_error’: 0} 10.472002 0.034380 {‘category’: ‘Handwritten question’}

Analyze Regressions

diff

Q&A Session

  • Specialized Functionality for Text2SQL and Other Tasks
    • Braintrust prioritizes quality over quantity in scoring functions.
    • Provides ~20 scoring functions optimized for tasks like retrieval augmented generation (RAG).
    • Includes an implementation of the RAGAS metrics with improvements.
    • Offers tools for evaluating tool call outputs, particularly relevant for agentic workflows.
    • Provides building blocks like list-of-strings comparators with various comparison methods.
  • Using Open-Source or Open-Weight Models
    • Braintrust is agnostic to the underlying LLM.
    • Data generation and scoring functions are independent of the LLM used.
    • The task function can interface with any model via API; OpenAI compatibility is not required.
    • Open-weight models hosted on platforms like Together often have OpenAI-compatible APIs.
    • Braintrust’s proxy allows for local model hosting and integration.
  • Notebook Sharing
    • The notebook used in the presentation will be shared in the Discord channel and the “Cookbook” section on the Braintrust website.
  • Task Suitability and Limitations
    • Braintrust might not be ideal for classical machine learning tasks involving large datasets and reliance on aggregate statistics.
    • The platform’s strength lies in facilitating analysis of individual examples, which is less relevant in those scenarios.
  • Braintrust for Model Development and Deployment
    • Braintrust supports both offline evaluations (during development) and online evaluations/observability (post-deployment).
    • Tight integration between logs and evaluations allows for capturing user feedback and incorporating it into the development process.
    • The platform provides tools for rendering various output formats, including HTML and images.
  • Handling Complex Databases with Limited Schema Knowledge
    • Start with Instrumentation: Log user questions, system responses, and capture user feedback from the beginning.
    • Develop a Taxonomy: Categorize questions based on their nature or the specific areas of the database they target.
    • Focus on Subcategories: Identify question types the system handles well and those it struggles with.
    • Iterate and Improve:
      • Concentrate on improving performance for specific question categories.
      • Consider schema modifications or view creation to simplify data access for the LLM.
    • Leverage User Feedback: Use thumbs-up/thumbs-down ratings or more detailed feedback to guide improvement efforts.

Recommendations

  • Adopt the three-component framework (data, task function, scoring) when building your LLM evaluations.
  • Don’t overcomplicate initial evaluations. Start with hardcoded data and progressively enhance complexity.
  • Actively use logs and user feedback to drive iterative improvements, especially when dealing with complex, unfamiliar databases.
  • Explore the Braintrust platform and its features, including the cookbook and shared notebook, for practical implementation guidance.
About Me:

I’m Christian Mills, a deep learning consultant specializing in practical AI implementations. I help clients leverage cutting-edge AI technologies to solve real-world problems.

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