Research & Development

Scope

• Objective: alembica leverages Large Language Models (LLMs) for systematic synthesis of unstrucutred data sources such as text reported in news corpora.
• Replicability: Addresses the challenge of consistent, unbiased analysis, countering the subjective nature of human information extraction.
• Cost: More economical than custom AI solutions.

Contributing

How to Contribute

We welcome contributions to improve alembica, whether you’re fixing bugs, adding features, or enhancing documentation:

• Branching Strategy: Create a new branch for each set of related changes and submit a pull request via GitHub.
• Code Reviews: All submissions undergo thorough review to maintain code quality.

Guidelines

For detailed contribution guidelines, see our CONTRIBUTING.md and CODE_OF_CONDUCT.md.

Software Stack

alembica is developed in Go, selected for its simplicity and efficiency with concurrent operations. We prioritize the latest stable Go releases to incorporate improvements.

Open Science Support

alembica actively supports Open Science principles.