https://github.com/aryadhruv/llmworkbook
LLMWorkbook is a Python package that integrates Large Language Models (LLMs) with tabular datatypes - workbooks and dataframes for seamless data analysis and automation.
Science Score: 26.0%
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○Scientific vocabulary similarity
Low similarity (15.3%) to scientific vocabulary
Keywords
Repository
LLMWorkbook is a Python package that integrates Large Language Models (LLMs) with tabular datatypes - workbooks and dataframes for seamless data analysis and automation.
Basic Info
- Host: GitHub
- Owner: aryadhruv
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://aryadhruv.github.io/LLMWorkbook/
- Size: 198 KB
Statistics
- Stars: 5
- Watchers: 2
- Forks: 2
- Open Issues: 8
- Releases: 9
Topics
Metadata Files
README.md
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.
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
LLMWorkbook v1.4.3 🦦:
✔ New OpenAI Responses Endpoint
Installation
Install the package from GitHub:
bash
pip install llmworkbook
Quick Start
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 -
1. Import the Package
python
import pandas as pd
from llmworkbook import LLMConfig, LLMRunner, LLMDataFrameIntegrator
2. DataFrame
```python
Provide a dataframe, the usual
df = pd.DataFrame(data) ```
3. Configure the LLM
python
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
python
runner = LLMRunner(config)
integrator = LLMDataFrameIntegrator(runner=runner, df=df)
5. Add LLM Responses to DataFrame
```python updateddf = integrator.addllmresponses( promptcolumn="prompttext", responsecolumn="llmresponse", asyncmode=False # Set to True for asynchronous requests )
```
Example code is available in the Git Repository for easy reference.
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 Useage file. - LLMDataFrameIntegrator - Row/Batch Processing
Links
Homepage Repository Documentation Examples Bug Tracker Issues
Owner
- Name: Dhruv
- Login: aryadhruv
- Kind: user
- Location: India
- Repositories: 1
- Profile: https://github.com/aryadhruv
Python developer, Data Scientist, Evolving Full-Stack Learner 🦦
GitHub Events
Total
- Create event: 20
- Release event: 6
- Issues event: 43
- Watch event: 7
- Delete event: 15
- Issue comment event: 18
- Push event: 120
- Pull request event: 26
- Fork event: 1
Last Year
- Create event: 20
- Release event: 6
- Issues event: 43
- Watch event: 7
- Delete event: 15
- Issue comment event: 18
- Push event: 120
- Pull request event: 26
- Fork event: 1
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 31
- Total pull requests: 25
- Average time to close issues: 15 days
- Average time to close pull requests: 2 days
- Total issue authors: 2
- Total pull request authors: 3
- Average comments per issue: 0.16
- Average comments per pull request: 0.44
- Merged pull requests: 15
- Bot issues: 0
- Bot pull requests: 2
Past Year
- Issues: 31
- Pull requests: 25
- Average time to close issues: 15 days
- Average time to close pull requests: 2 days
- Issue authors: 2
- Pull request authors: 3
- Average comments per issue: 0.16
- Average comments per pull request: 0.44
- Merged pull requests: 15
- Bot issues: 0
- Bot pull requests: 2
Top Authors
Issue Authors
- aryadhruv (30)
- ronakpanchal9 (1)
Pull Request Authors
- aryadhruv (22)
- dependabot[bot] (2)
- Pengdhruv (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 66 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 18
- Total maintainers: 1
pypi.org: llmworkbook
Effortlessly harness the power of LLMs on Excel and DataFrames—seamless, smart, and efficient!
- Homepage: https://aryadhruv.github.io/LLMWorkbook/
- Documentation: https://aryadhruv.github.io/LLMWorkbook/
- License: MIT
-
Latest release: 1.4.4
published 6 months ago