benchloop 0.1.0

A Python library for managing, processing, and benchmarking datasets in SQLite databases for AI pipelines and LLM prompt engineering.

BenchLoop

BenchLoop is a Python library for managing, processing, and benchmarking datasets in SQLite databases—designed for AI pipelines, LLM prompt engineering, and dataset curation.

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🚀 Features

• Load & Update Data: Ingest CSV, JSON, or Python dicts into SQLite with automatic table/column creation.
• Flexible Filtering: Query and filter rows with powerful, SQL-like conditions.
• Prompt Execution: Run prompts row-by-row, substitute variables, call LLMs (OpenAI), and store responses.
• Dataset Export: Export training datasets in JSONL (OpenAI "messages" or input/output format).
• Benchmarking: Compare AI responses vs. ground truth with exact and fuzzy match metrics.
• Reproducible Loops: Build scalable, iterative data workflows for AI/ML.

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📦 Installation

BenchLoop is available on PyPI. Install it with:

pip install benchloop

Note: You will also need the openai package for LLM prompt execution:

pip install openai

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🏁 Quickstart

from benchloop.loader import load_table
from benchloop.prompt_runner import execute_prompt_on_table
from benchloop.dataset_exporter import export_training_dataset
from benchloop.benchmarker import benchmark_responses

# 1. Load data
load_table(
    table_name="products",
    data_source=[{"id": 1, "name": "Zapato", "price": "50"}],
    db_path="mydb.sqlite"
)

# 2. Run prompts and store LLM responses
execute_prompt_on_table(
    table_name="products",
    prompt_template="Describe the product {name} that costs {price} dollars.",
    columns_variables=["name", "price"],
    result_mapping={"response": "llm_response"},
    db_path="mydb.sqlite",
    model="gpt-4o",
    api_key="sk-...",
)

# 3. Export dataset for training
export_training_dataset(
    table_name="products",
    prompt_template="Describe the product {name} that costs {price} dollars.",
    response_column="llm_response",
    output_file="dataset.jsonl",
    db_path="mydb.sqlite",
    format="messages"
)

# 4. Benchmark responses
benchmark_responses(
    table_name="products",
    column_ai="llm_response",
    column_ground_truth="ground_truth",
    db_path="mydb.sqlite",
    benchmark_tag=None
)

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📚 Documentation

• See the full documentation for API reference, advanced usage, and troubleshooting.
• Example/test script: main.py

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🤝 Contributing

Contributions, issues, and feature requests are welcome!
Open an issue or submit a pull request on GitHub.

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📝 License

MIT License

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BenchLoop makes dataset curation, prompt engineering, and benchmarking fast, reproducible, and robust for modern AI workflows.