猫形LLM

Dependencies

All dependencies are declared in pyproject.toml and installed automatically by pip. No manual dependency management is needed.

Core dependencies (installed with any pip install cat-llm or cat-stack):

Optional dependencies (install with extras syntax):

No provider-specific SDKs are required at runtime. CatLLM communicates with all LLM providers (OpenAI, Anthropic, Google, Mistral, HuggingFace, Perplexity, xAI, Ollama) via their REST APIs using requests directly.

Installation

Install the full ecosystem:

pip install cat-llm

Or install only the domain you need (lighter footprint):

pip install cat-survey    # survey responses — pulls in cat-stack automatically
pip install cat-vader     # social media
pip install cat-ademic    # academic papers
pip install cat-pol       # political text
pip install cat-cog       # cognitive assessment (CERAD scoring)
pip install cat-web       # web content classification
pip install cat-stack     # general-purpose base only, no domain framing

Optional extras (apply to both cat-llm and cat-stack):

pip install cat-llm[pdf]          # PDF support
pip install cat-llm[embeddings]   # Embedding-based similarity scores
pip install cat-llm[formatter]    # Local JSON formatter fallback

Install everything from R-universe (recommended)

install.packages("cat.llm", repos = c("https://chrissoria.r-universe.dev", "https://cloud.r-project.org"))

Install the Python backend (one-time setup)

library(cat.llm) install_cat_stack() ```

Or install a single domain package for a lighter footprint (e.g. install.packages("cat.survey", repos = ...)).

All core functions — classify(), extract(), explore(), summarize(), plus domain-suffixed aliases like classify_survey() and classify_political() — are available with native R syntax. See the R package README for the full ecosystem overview and per-package documentation.

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Option B — direct install (lighter footprint)

Install only the domain package you need:

```python

pip install cat-ademic

import catademic as cat

results = cat.classify( input_data=["paper1.pdf", "paper2.pdf"], categories=["Empirical", "Theoretical", "Review"], journal_issn="0894-4393", api_key=api_key )

pip install cat-vader

import catvader as cat

results = cat.classify( input_data=df['posts'], categories=["Misinformation", "Opinion", "News"], platform="Reddit", api_key=api_key )

pip install cat-stack (general-purpose, no domain framing)

import catstack as cat

results = cat.classify( input_data=df['text_column'], categories=["Category A", "Category B", "Category C"], description="My text data", api_key=api_key ) ```

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Quick Start

This package is designed for building datasets at scale, not one-off queries. While you can categorize individual responses, its primary purpose is batch processing entire text columns, image collections, or PDF corpora into structured research datasets.

All outputs are formatted for immediate statistical analysis and can be exported directly to CSV.

Not to be confused with CAT-LLM for Chinese article‐style transfer (Tao et al. 2024).

Prompt optimization — correct a small sample, get optimized prompts

result = catllm.prompt_tune( input_data=df['responses'], categories=["Positive", "Negative", "Neutral"], api_key=api_key, sample_size=15, )

Option A — via meta-package

Install cat-llm and access every domain through a single import:

```python import catllm

Configuration

Fetch raw data (no API key needed)

df = pol.fetch_source("social_trump_truth", since="2024-01-01") pol.list_sources() # see all 15 sources ```

Get Your API Key

Get an API key from your preferred provider:

• OpenAI: platform.openai.com
• Anthropic: console.anthropic.com
• Google: aistudio.google.com
• Huggingface: huggingface.co/settings/tokens
• xAI: console.x.ai
• Mistral: console.mistral.ai
• Perplexity: perplexity.ai/settings/api

Most providers require adding a payment method and purchasing credits. Store your key securely and never share it publicly.

API Reference

Note: The functions documented below are the domain-neutral versions from cat-stack. They work with any text, image, or PDF data without domain-specific framing. Domain packages (cat-survey, cat-vader, cat-ademic, etc.) accept all the same parameters and add domain-specific ones on top (e.g., survey_question=, platform=, journal_issn=). See Domain Packages for details.

R Package

An R interface is available for users who prefer R over Python. The R ecosystem mirrors the Python one with 8 packages (cat.stack, cat.survey, cat.vader, cat.ademic, cat.cog, cat.pol, cat.web, and the cat.llm meta-package) that wrap the Python code via reticulate.

```r

Domain Packages

Each domain package wraps cat-stack's classification engine with domain-tuned prompts and domain-specific parameters. The base classify(), extract(), explore(), summarize(), and prompt_tune() parameters all work — domain packages add parameters on top.

Supported Models

• OpenAI: GPT-4o, GPT-4, GPT-5, etc.
• Anthropic: Claude Sonnet 4, Claude 3.5 Sonnet, Claude Haiku, etc.
• Google: Gemini 2.5 Flash, Gemini 2.5 Pro, etc.
• Huggingface: Qwen, Llama 4, DeepSeek, and thousands of community models
• xAI: Grok models
• Mistral: Mistral Large, Pixtral, etc.
• Perplexity: Sonar Large, Sonar Small, etc.

Fully Tested: - OpenAI (GPT-4, GPT-4o, GPT-5, etc.) - Anthropic (Claude Sonnet 4, Claude 3.5 Sonnet, Haiku) - Perplexity (Sonar models) - Google Gemini - Free tier has severe rate limits (5 RPM). Requires Google AI Studio billing account for large-scale use. - Huggingface - Access to Qwen, Llama 4, DeepSeek, and thousands of user-trained models for specific tasks. API routing can occasionally be unstable. - xAI (Grok models) - Mistral (Mistral Large, Pixtral, etc.)

Note: For best results, I recommend starting with OpenAI or Anthropic.