llmworkbook 1.4.5

Effortlessly harness the power of LLMs on Excel and DataFrames—seamless, smart, and efficient!

LLMWorkbook

"Effortlessly harness the power of LLMs on Excel and DataFrames—seamless, smart, and efficient!"

LLMWorkbook is a Python package designed to seamlessly integrate Large Language Models (LLMs) into your workflow with tabular data, be it Excel, CSV, DataFrames/Arrays. This package allows you to easily configure an LLM, send prompts row-wise from any tabular datasets, and store responses back in the DataFrame with minimal effort.

Documentation Website

Visit our complete documentation site →

For comprehensive guides, examples, and API reference, visit our dedicated documentation website.

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Features

• Easily map LLM responses to a specific column in a pandas DataFrame, Excel, CSV.
• Run list of prompts easily.
• Get started with easy to follow Examples

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LLMWorkbook v1.4.3 🦦:

✔ New OpenAI Responses Endpoint

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Installation

Install the package from GitHub:

    pip install llmworkbook

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Quick Start

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Wrapper Utilities for LLM Preparation

LLMWorkbook provides wrapper utilities to prepare various data formats for LLM consumption. These utilities transform input data into a format suitable for LLM processing, ensuring consistency and compatibility. These wrapper methods can handle popular data sources like Excel (xlsx), CSV, Pandas DataFrames, multi dimensional arrays.

See Examples for details. - Github - Examples

Providers Supported -

• OpenAI
• Gpt4All
• Ollama

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1. Import the Package

import pandas as pd
from llmworkbook import LLMConfig, LLMRunner, LLMDataFrameIntegrator

2. DataFrame

# Provide a dataframe, the usual
df = pd.DataFrame(data)

3. Configure the LLM

config = LLMConfig(
    provider="openai",
    system_prompt="Process these Data rows as per the provided prompt",
    options={
        "model": "gpt-4o-mini",
        "temperature": 1,
        "max_tokens": 1024,
    },
)

4. Create a Runner and Integrate

runner = LLMRunner(config)
integrator = LLMDataFrameIntegrator(runner=runner, df=df)

5. Add LLM Responses to DataFrame

updated_df = integrator.add_llm_responses(
    prompt_column="prompt_text",
    response_column="llm_response",
    async_mode=False  # Set to True for asynchronous requests
)

Example code is available in the Git Repository for easy reference.

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Future Roadmap

• Add support for more LLM providers (Google VertexAI, Cohere, Groq, MistralAI).
• Add an interface frontend for low code applications.
• Implement rate-limiting and token usage tracking.
• Summarized history persisted across session to provide quick context for next session.

Extended Documentation

Detailed documentation for each module is available in the Documentation file.

• Wrapping Data file.
• Providers - OpenAI Gpt4All Ollama
• CLI Usage file.
• LLMDataFrameIntegrator - Row/Batch Processing

Links

Homepage Repository Documentation Examples Bug Tracker Issues