llm-annotator 0.10.5

An easy-to-extend LLM annotator for robust, resumable data annotation.

Robust, resumable LLM dataset annotation

llm-annotator is a Python 3.12+ library for robust, resumable LLM-driven dataset annotation and generation.

It supports multiple providers through pluggable clients:

• vLLM offline inference: VLLMOfflineClient
• vLLM server API: VLLMClient
• OpenAI API: OpenAIClient
• Anthropic API: ClaudeClient

Key capabilities:

• Staged pipeline: prepare_data + run_annotation separates expensive template application and sorting from model inference, enabling SLURM and cluster restart workflows.
• Resumable processing with JSONL checkpoints.
• Annotation of existing datasets and generation from scratch.
• Structured outputs via JSON schema.
• Retry and validation hooks for robust pipelines.
• Optional Hugging Face Hub upload cadence for both prepared data and outputs.
• Context-manager cleanup of client resources.

It is not intended for parallel, multi-node, multi-instance generation. If that is what you are after, maybe datatrove is something for you.

Documentation

Read the full documentation at bramvanroy.github.io/llm-annotator.

Provider setup reference: docs/provider-info.md

Installation

Recommended:

uv add llm-annotator

or

pip install llm-annotator

Install provider extras as needed:

uv add "llm-annotator[vllm]"
uv add "llm-annotator[vllm-flashinfer]"  # Faster if your hardware supports it
uv add "llm-annotator[openai]"
uv add "llm-annotator[anthropic]"

See docs/provider-info.md for auth environment variables and provider-specific setup notes.

Usage

One-step convenience

Annotate an existing dataset:

from llm_annotator import Annotator, VLLMOfflineClient

client = VLLMOfflineClient(
    model="meta-llama/Llama-3.2-3B-Instruct",
    max_model_len=4096,
)

with Annotator(client=client, verbose=True) as anno:
    ds = anno.annotate_dataset(
        output_dir="outputs/sentiment",
        prompt_template="Classify the sentiment of this text: {text}",
        dataset_name="stanfordnlp/imdb",
        dataset_split="test",
        max_num_samples=100,
    )

Generate a dataset from scratch:

from llm_annotator import Annotator, OpenAIClient

client = OpenAIClient(model="gpt-4o-mini")

with Annotator(client=client) as anno:
    ds = anno.generate_dataset(
        output_dir="outputs/generated-qa",
        prompts="Write a short geography quiz question with answer.",
        max_num_samples=200,
    )

Two-step staged workflow

For large datasets or cluster (SLURM) environments, split the pipeline explicitly into a preparation step and a generation step. prepare_data applies prompt templates, optional sorting, and saves the prepared artifacts locally and to Hugging Face Hub. run_annotation then handles only model inference. If generation fails, re-run run_annotation with prepared_hub_id pointing to the Hub backup: preparation is skipped.

from llm_annotator import Annotator, VLLMOfflineClient

client = VLLMOfflineClient(
    model="meta-llama/Llama-3.2-3B-Instruct",
    max_model_len=4096,
)

HUB_ID = "my-org/imdb-prepared"  # Hub repo for prepared data backup

with Annotator(client=client, verbose=True) as anno:
    # Step 1: prepare data (reuses local cache or Hub backup if available)
    prepared_dataset, local_path, hub_id = anno.prepare_data(
        output_dir="outputs/imdb-sentiment",
        prompt_template="Classify the sentiment of this text: {text}",
        dataset_name="stanfordnlp/imdb",
        dataset_split="test",
        max_num_samples=100,
        sort_by_length=True,
        prepared_hub_id=HUB_ID,
    )

    # Step 2: run generation against the prepared data
    ds = anno.run_annotation(
        output_dir="outputs/imdb-sentiment",
        prompt_template="Classify the sentiment of this text: {text}",
        prepared_dataset=prepared_dataset,
        new_hub_id="my-org/imdb-annotated",
        upload_every_n_samples=500,
    )

To force a fresh preparation (ignoring any cached or Hub-stored artifacts), pass force_data_preparation=True to prepare_data or to annotate_dataset.

See the documentation for more examples, including:

• Structured output with JSON schemas
• Custom validation and post-processing
• Generating datasets from scratch

Or check out the examples/ directory for complete working examples.

Testing

Install development dependencies first:

uv sync --dev

Run the default checks:

make style
make quality
make test
make typecheck

Pytest marker targets:

# Fast tests (same as `make test`)
make test-fast

# Slow tests only
make test-slow

# Integration tests only
make test-integration

# Entire suite (fast + slow)
make test-all

You can also run markers directly with pytest:

uv run pytest -m "not slow"
uv run pytest -m "slow"
uv run pytest -m "integration"

Slow and integration tests may load local models, require more runtime, or depend on optional components.

Building documentation

Local versioned docs preview (uses mike on a temporary local branch):

make serve-docs

Override version metadata when needed:

make serve-docs DOCS_VERSION=0.4.0 DOCS_ALIAS=latest DOCS_SOURCE_REF=v0.4.0

Docs are published with mike on release tags through .github/workflows/docs.yml.