LLM Extractinator

Important

This tool is a prototype which is in active development and is still undergoing major changes. Please always check the results!

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Overview

This project enables the efficient extraction of structured data from unstructured text using large language models (LLMs). It provides a flexible configuration system and supports a variety of tasks.

Tool Workflow

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Installing Ollama

For the package to work, Ollama needs to be installed on your machine. For linux, use the following command:

curl -fsSL https://ollama.com/install.sh | sh

For Windows or macOS, install via this link

Installing the Package

Option 1: Install from PyPI

The package is installable via PyPI using:

pip install llm_extractinator

Option 2: Install using local clone

For contributing to or developing the package, clone this repository and install it using:

pip install -e .

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Setting Up Task Configuration

Create a JSON file in the tasks folder for each task, following the naming convention:

TaskXXX_taskname.json

Where XXX is a 3-digit number, and taskname is a brief descriptor of the task.

The JSON file should always include the following fields:

• Task: The name of the task.
• Type: The type of task.
• Description: A detailed description of the task.
• Data_Path: The filename of the data file in the data folder.
• Input_Field: The column name containing the text data.
• Parser_Format: The JSON format you want the output to be in. See Task999_example.json for an example.

The following fields are only mandatory if you want to have the model automatically generate examples:

• Example_Path: The path to data used for creating examples (only required if num_examples > 0 when running the model).
• Example_Field: The column name containing the ground truth labels (only required if num_examples > 0).

Important

Example generation is for the moment only functional for binary classification tasks! Full functionality to be added later.

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Input Flags for extractinate

The following input flags can be used to configure the behavior of the extractinate script:

Flag	Type	Default Value	Description
--task_id	int	Required	Task ID to generate examples for.
--model_name	str	"mistral-nemo"	Name of the model to use for prediction tasks. See https://ollama.com/search for the options.
--num_examples	int	0	Number of examples to generate for each task.
--n_runs	int	5	Number of runs to perform.
--temperature	float	0.3	Temperature for text generation.
--max_context_len	int	8192	Maximum context length for input text.
--num_predict	int	1024	Maximum number of tokens to predict.
--run_name	Path	"run"	Name of the run for logging purposes.
--output_dir	Path	<project_root>/output	Path to the directory for output files.
--task_dir	Path	<project_root>/tasks	Path to the directory containing task configuration files.
--log_dir	Path	<project_root>/output	Path to the directory for log files.
--data_dir	Path	<project_root>/data	Path to the directory containing input data.
--chunk_size	int	None	Number of examples to generate in a single chunk. When None, use dataset size as chunksize.
--translate	bool	False	Translate the generated examples to English.

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Example Task.json

Below is an example configuration file for a task:

{
    "Task": "Text Summarization",
    "Type": "Summarization",
    "Description": "Generate summaries for long-form text documents.",
    "Data_Path": "data/documents.csv",
    "Input_Field": "text",
    "Example_Path": "data/summaries_examples.csv",
    "Example_Field": "summary",
    "Parser_Format": {
        "summary": {
            "type": "str",
            "description": "The summary of the text document."
        }
    }
}

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Running the Extractor

To run the data extraction process, you can either use the command line or import the function in Python.

Option 1: Using the Command Line

Use the following command:

extractinate --task_id 001 --model_name "mistral-nemo" --num_examples 0 --max_context_len 8192 --num_predict 8192 --translate

Customize the flags based on your task requirements.

Option 2: Using the Function in Python

You can also call the extractor programmatically:

from llm_extractinator import extractinate

extractinate(
    task_id=1,
    model_name="mistral-nemo",
    num_examples=0,
    max_context_len=8192,
    num_predict=8192,
    translate=True
)

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Output

The output will be saved in the specified --output_dir. Ensure that the directory structure and paths specified in the Task.json file match your project's organization.

For further details, check the logs in the directory specified by --log_dir.

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Enhancements and Contributions

Feel free to enhance this project by improving configurations, adding more task types, or extending model compatibility. Open a pull request or file an issue for discussions!