Semlib is a Python library designed for building data processing and analysis pipelines powered by Large Language Models (LLMs). It targets engineers and researchers needing to leverage LLMs for complex data tasks, offering a structured approach that enhances output quality, handles arbitrary data volumes, reduces latency, optimizes costs, and improves security compared to single-shot LLM calls.

How It Works

Semlib re-imagines familiar functional programming primitives like map, reduce, sort, and filter by enabling them to be programmed with natural language descriptions instead of traditional code. The library abstracts away LLM complexities such as prompt engineering, output parsing, concurrency management, caching, and cost tracking. This decomposition of tasks into simpler, LLM-executable steps allows for higher-quality results, processing of data beyond LLM context limits, reduced overall latency through concurrency, cost savings by selecting optimal models per sub-task, and enhanced security via support for self-hosted models.

Quick Start & Requirements

• Installation: pip install semlib
• Prerequisites: Python. The library utilizes asynchronous operations (await).
• Resources: No specific hardware (e.g., GPU) or large datasets are mandated by the library itself, though LLM usage implies compute resources.
• Documentation: API Reference 📖, Examples ⬀.

Highlighted Details

• Enables semantic operations using natural language descriptions for functional primitives.
• Manages LLM interactions: prompting, parsing, concurrency, caching, and cost tracking.
• Addresses LLM limitations for large-scale data processing, improving quality and feasibility.
• Facilitates cost optimization and enhanced security through per-subtask model selection and self-hosting capabilities.

Maintenance & Community

No specific details on maintainers, community channels (e.g., Discord, Slack), or roadmap are provided in the README excerpt.

Licensing & Compatibility

• License: MIT License.
• Compatibility: Permissive for commercial use and integration with closed-source applications.

Limitations & Caveats

The library's effectiveness is contingent on the capabilities of the underlying LLMs and the clarity of natural language descriptions. Integration requires understanding of asynchronous programming paradigms. Specific performance benchmarks or detailed comparisons against alternative LLM orchestration frameworks are not detailed in the provided text.