llm-batch-annotate 0.1.0

Batch-oriented LLM annotation workflows for tabular datasets with OpenAI Batch support.

llm-batch-annotate

llm-batch-annotate is a Python package for running reproducible LLM annotation workflows over tabular datasets. It materializes units from source rows, groups them into provider requests, submits them through an execution adapter, parses structured outputs, validates coverage, and writes run artifacts for auditability.

Highlights

• single-unit and grouped annotation workflows
• provider-agnostic task, builder, parser, and artifact abstractions
• concrete OpenAI Batch adapter
• resumable CLI-driven runs with persisted manifests
• example configs, prompts, schemas, and sample data under examples/

Installation

When the package is published:

pip install llm-batch-annotate

From a local checkout:

python3 -m venv .venv
.venv/bin/pip install -e .[test,docs]

Quickstart

Single-unit example:

export OPEN_AI_KEY="your-key"
llm-batch-annotate run examples/config/run_config.json --run-id example-single --no-poll-until-terminal
llm-batch-annotate resume examples/config/run_config.json example-single --poll-interval 2m

Grouped example:

export OPEN_AI_KEY="your-key"
llm-batch-annotate run examples/config/run_config_2.json --run-id example-grouped --no-poll-until-terminal
llm-batch-annotate resume examples/config/run_config_2.json example-grouped --poll-interval 2m

Documentation

Project documentation is intended to be hosted on Read the Docs. The Sphinx source lives under docs/.

Planned public docs include:

• installation
• quickstart
• CLI reference
• config reference
• OpenAI Batch provider guide
• worked examples
• API reference
• development and release notes

Repository layout

• src/llm_batch_annotate/: package source
• examples/: tracked example inputs and configs
• tests/: pytest suite
• docs/: Sphinx documentation source

Generated example runs are written to examples/runs/ and are intentionally excluded from version control.