cat-stack 1.0.10

Domain-agnostic text, image, PDF, and DOCX classification engine powered by LLMs

cat-stack

Domain-agnostic text, image, and PDF classification engine powered by LLMs.

cat-stack is the shared base package for the CatLLM ecosystem. It provides the core classification, extraction, exploration, and summarization engine that all domain-specific CatLLM packages build on.

Installation

pip install cat-stack

Optional extras:

pip install cat-stack[pdf]         # PDF support (PyMuPDF)
pip install cat-stack[embeddings]  # Embedding similarity scoring
pip install cat-stack[formatter]   # JSON formatter fallback model

Ecosystem

cat-stack is independently useful for classifying any text column. Domain-specific packages extend it with tuned prompts and workflows:

Package	Domain
cat-stack	General-purpose text, image, PDF classification (this package)
cat-survey	Survey response classification
cat-vader	Social media text (Reddit, Twitter/X)
cat-ademic	Academic papers, PDFs, citations
cat-cog	Cognitive assessment & visual scoring (CERAD)
cat-pol	Political text (manifestos, speeches, legislation)

Installing cat-llm pulls in all of the above.

Quick Start

import cat_stack as cat

# Classify text into predefined categories
result = cat.classify(
    input_data=df["text_column"],
    categories=["Positive", "Negative", "Neutral"],
    models=[("gpt-4o", "openai", OPENAI_KEY)],
    filename="classified.csv"
)

Core API

classify()

Assign predefined categories to text, images, or PDFs. Supports single-model and multi-model ensemble classification with consensus voting.

cat.classify(
    input_data=df["text"],
    categories=["Cat A", "Cat B", "Cat C"],
    models=[("gpt-4o", "openai", key1), ("claude-sonnet-4-20250514", "anthropic", key2)],
    filename="results.csv"
)

Inline prompt tuning

Add prompt_tune=True to automatically optimize the classification prompt before the full run. A browser UI opens for you to correct a small sample, then the optimized prompt is used for all remaining items.

cat.classify(
    input_data=df["text"],
    categories=["Cat A", "Cat B", "Cat C"],
    models=[("gpt-4o", "openai", key)],
    prompt_tune=15,       # tune on 15 random items, then classify all
    tune_iterations=3,    # max attempts per category (default 3)
)

prompt_tune()

Standalone automatic prompt optimization. Iteratively refines classification prompts using user feedback — classify a sample, correct mistakes in the browser, and let the LLM generate targeted per-category instructions.

result = cat.prompt_tune(
    input_data=df["text"],
    categories=["Cat A", "Cat B", "Cat C"],
    api_key="your-key",
    sample_size=15,
    max_iterations=3,
)

# Use the optimized prompt for classification
cat.classify(
    input_data=df["text"],
    categories=["Cat A", "Cat B", "Cat C"],
    api_key="your-key",
    system_prompt=result["system_prompt"],
)

extract()

Discover categories from a corpus using LLM-driven exploration.

cat.extract(
    input_data=df["text"],
    survey_question="What is this text about?",
    models=[("gpt-4o", "openai", key)],
)

explore()

Raw category extraction for saturation analysis.

cat.explore(
    input_data=df["text"],
    description="Describe the main themes",
    models=[("gpt-4o", "openai", key)],
)

summarize()

Summarize text or PDF documents, with optional multi-model ensemble.

cat.summarize(
    input_data=df["text"],
    models=[("gpt-4o", "openai", key)],
    filename="summaries.csv"
)

Supported Providers

OpenAI, Anthropic, Google (Gemini), Mistral, Perplexity, xAI (Grok), HuggingFace, Ollama (local models).

All providers use the same (model_name, provider, api_key) tuple format. Provider is auto-detected from model name if omitted.

Features

• Automatic prompt optimization (prompt_tune) — correct a small sample in a browser UI, and the system generates per-category instructions that improve accuracy
• Multi-model ensemble with consensus voting and agreement scores
• Batch API support for OpenAI, Anthropic, Google, Mistral, and xAI
• Prompt strategies: Chain-of-Thought, Chain-of-Verification, step-back prompting, few-shot examples
• Text, image, and PDF input auto-detection
• Embedding similarity tiebreaker for ensemble consensus ties
• Pilot test — validate classifications on a small sample before committing to the full run

License

GPL-3.0-or-later