llm-feature-gen 0.1.1

Feature discovery and generation utilities

LLM_feature_gen

LLM Feature Gen is a Python library for discovering and generating interpretable features from unstructured data using Large Language Models (LLMs).
The library provides high-level utilities for:

• Discovering human-interpretable features from sets of images,
• Integrating prompts and model outputs into structured JSON representations,
• • Generating new feature representations automatically from raw multimodal data, e.g., creating structured tables for downstream models,

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Module: discover

The discover module focuses on feature discovery — identifying interpretable, discriminative visual or textual properties using an LLM.

✅ What it does

Given a folder of images and a prompt, the library:

1. Converts each image into Base64 format,
2. Sends them to an LLM,
3. Receives a structured JSON response describing the discovered features,
4. Automatically saves the output to a JSON file in outputs/.

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📂 Project Structure

LLM_feature_gen/
├─ src/
│  └─ LLM_feature_gen/
│     ├─ init.py
│     ├─ discover.py                # High-level orchestration for feature discovery
│     ├─ providers/
          ├─ openai_provider.py     # OpenAI API wrapper
│         ├─ local_provider.py      # Local LLM wrapper
│     ├─ prompts/
│     │   ├─ discovery_prompt.txt   # Default reasoning prompt
          ├─ generation_prompt.txt  # Default feature generation prompt
│     ├─ utils/
│     │   └─ image.py               # Image → base64 conversion
│     └─ tests/
│        └─ test_discover.py
├─ outputs/                         # Automatically generated feature JSONs
├─ pyproject.toml
└─ README.md

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⚙️ Installation

Clone or download the repository, then install in editable mode:

pip install -e .

🔑 Environment Setup for OpenAI API

Create a .env file in the project root

Example: Discover Features from Images

from LLM_feature_gen.discover import discover_features_from_images
# Folder with your example images
image_folder = "discover_images"

# Run feature discovery
result = discover_features_from_images(
    image_paths_or_folder=image_folder,
    as_set=True,  # analyze all images jointly
)

print(result)

This will:

• Read all .jpg/.png images from discover_images/
• the default prompt (prompts/image_discovery_prompt.txt)
• Send them to your LLM provider
• Save the results to outputs/discovered_features_.json

Example saved JSON:

{
  "proposed_features": [
    {
      "feature": "has visible handle",
      "description": "Some objects include handles, others do not.",
      "possible_values": ["present", "absent"]
    },
    {
      "feature": "color tone",
      "description": "Images vary between metallic and earthy color palettes.",
      "possible_values": ["metallic", "matte", "bright", "dark"]
    }
  ]
}