# **LLMWorkbook**
[](https://github.com/aryadhruv/LLMWorkbook/actions/workflows/codeql.yml)
[](https://github.com/aryadhruv/LLMWorkbook/actions/workflows/Quality%20Check.yml)
[](https://github.com/aryadhruv/LLMWorkbook/actions/workflows/test.yml)
[](https://pepy.tech/projects/llmworkbook)
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"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.
---
## **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
---
## **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](https://github.com/aryadhruv/LLMWorkbook/tree/main/Examples)*
### Providers Supported -
* [OpenAI](docs/Providers/OpenAI.md)
* [Gpt4All](docs/Providers/Gpt4All.md)
* [Ollama](docs/Providers/Ollama.md)
---
### **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
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.
---
## **Future Roadmap**
- Add support for more LLM providers (Anthropic Claude, 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](docs) file.
- [Wrapping Data](docs/wrapping.md) file.
- [Providers](docs/Providers/) -
[OpenAI](docs/Providers/OpenAI.md)
[Gpt4All](docs/Providers/Gpt4All.md)
[Ollama](docs/Providers/Ollama.md)
- [CLI Useage](docs/CLI%20Usage.md) file.
## **Links**
[Homepage](https://github.com/aryadhruv/LLMWorkbook)
[Repository](https://github.com/aryadhruv/LLMWorkbook)
[Documentation](https://github.com/aryadhruv/LLMWorkbook/tree/main/Docs)
[Examples](https://github.com/aryadhruv/LLMWorkbook/tree/main/Examples)
[Bug Tracker](https://github.com/aryadhruv/LLMWorkbook/issues)
[Issues](https://github.com/aryadhruv/LLMWorkbook/issues)
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"description": "# **LLMWorkbook**\n\n[](https://github.com/aryadhruv/LLMWorkbook/actions/workflows/codeql.yml)\n[](https://github.com/aryadhruv/LLMWorkbook/actions/workflows/Quality%20Check.yml)\n[](https://github.com/aryadhruv/LLMWorkbook/actions/workflows/test.yml)\n[](https://pepy.tech/projects/llmworkbook)\n<img src=\"https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json\" alt=\"Ruff linter\" href=\"https://github.com/astral-sh/ruff\" />\n<img src=\"https://img.shields.io/badge/linting-pylint-yellowgreen\" alt=\"Pylint linter\" href=\"https://github.com/pylint-dev/pylint\" /> <img src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" alt=\"Code Style\" href=\"https://github.com/ambv/black\" />\n\n\"Effortlessly harness the power of LLMs on Excel and DataFrames\u2014seamless, smart, and efficient!\"\n\n**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.\n\n---\n\n## **Features**\n- Easily map LLM responses to a specific column in a pandas DataFrame, Excel, CSV.\n- Run list of prompts easily.\n- Get started with easy to follow Examples\n\n---\n\n## **Installation**\n\nInstall the package from GitHub:\n\n```bash\n pip install llmworkbook\n```\n\n---\n\n## **Quick Start**\n\n---\n\n### **Wrapper Utilities for LLM Preparation**\n\n`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.\nThese wrapper methods can handle popular data sources like Excel (xlsx), CSV, Pandas DataFrames, multi dimensional arrays.\n\n*See Examples for details. - [Github - Examples](https://github.com/aryadhruv/LLMWorkbook/tree/main/Examples)*\n\n### Providers Supported -\n* [OpenAI](docs/Providers/OpenAI.md)\n* [Gpt4All](docs/Providers/Gpt4All.md)\n* [Ollama](docs/Providers/Ollama.md)\n\n\n---\n\n### **1. Import the Package**\n\n```python\nimport pandas as pd\nfrom llmworkbook import LLMConfig, LLMRunner, LLMDataFrameIntegrator\n```\n\n### **2. DataFrame**\n\n```python\n# Provide a dataframe, the usual\ndf = pd.DataFrame(data)\n```\n\n### **3. Configure the LLM**\n\n```python\nconfig = LLMConfig(\n provider=\"openai\",\n system_prompt=\"Process these Data rows as per the provided prompt\",\n options={\n \"model\": \"gpt-4o-mini\",\n \"temperature\": 1,\n \"max_tokens\": 1024,\n },\n)\n```\n\n### **4. Create a Runner and Integrate**\n\n```python\nrunner = LLMRunner(config)\nintegrator = LLMDataFrameIntegrator(runner=runner, df=df)\n```\n\n### **5. Add LLM Responses to DataFrame**\n\n```python\nupdated_df = integrator.add_llm_responses(\n prompt_column=\"prompt_text\",\n response_column=\"llm_response\",\n async_mode=False # Set to True for asynchronous requests\n)\n\n```\n\nExample code is available in the Git Repository for easy reference.\n\n---\n## **Future Roadmap**\n\n- Add support for more LLM providers (Anthropic Claude, Google VertexAI, Cohere, Groq, MistralAI).\n- Add an interface frontend for low code applications.\n- Implement rate-limiting and token usage tracking.\n- Summarized history persisted across session to provide quick context for next session.\n\n\n## Extended Documentation\n\nDetailed documentation for each module is available in the [Documentation](docs) file.\n- [Wrapping Data](docs/wrapping.md) file.\n- [Providers](docs/Providers/) -\n [OpenAI](docs/Providers/OpenAI.md)\n [Gpt4All](docs/Providers/Gpt4All.md)\n [Ollama](docs/Providers/Ollama.md)\n- [CLI Useage](docs/CLI%20Usage.md) file.\n\n\n\n## **Links**\n\n[Homepage](https://github.com/aryadhruv/LLMWorkbook)\n[Repository](https://github.com/aryadhruv/LLMWorkbook)\n[Documentation](https://github.com/aryadhruv/LLMWorkbook/tree/main/Docs)\n[Examples](https://github.com/aryadhruv/LLMWorkbook/tree/main/Examples)\n[Bug Tracker](https://github.com/aryadhruv/LLMWorkbook/issues)\n[Issues](https://github.com/aryadhruv/LLMWorkbook/issues)\n\n\n\n",
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