Life & fun · September 15, 2025
Semlib – Semantic Data Processing
Build data processing and data analysis pipelines that leverage the power of LLMs 🧠 - anishathalye/semlib
Semlib – Semantic Data Processing launched on September 15, 2025 with 63 votes, #407 of 8,160 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
Semlib is a semantic data processing tool designed for developers and data professionals who need to work with structured information. It provides functionality for parsing, analyzing, and transforming data using semantic concepts. The software is open-source and available on GitHub, making it accessible to technical users who want to integrate semantic processing into their workflows or projects.
written from the facts on this page · September 2026
From the sources
Semlib is a Python library for building data processing and data analysis pipelines that leverage the power of large language models (LLMs). Semlib provides, as building blocks, familiar functional programming primitives like map , reduce , sort , and filter , but with a twist: Semlib's implementation of these operations are programmed with natural language descriptions rather than code. Under the hood, Semlib handles complexities such as prompting, parsing, concurrency control, caching, and cost tracking. >>> presidents = await prompt( ... " Who were the 39th through 42nd presidents of the United States? " , ... return_type = Bare(list[ str ]) ... ) >>> await sort(presidents, by = "…from github.com
Does a similar job
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SemHash – Fast Semantic Text Deduplication for Cleaner Datasets2025 · github.com · ▲19We’ve just open-sourced SemHash, a lightweight package for semantic text deduplication. It lets you effortlessly clean up your datasets and avoid pitfalls caused by duplicate samples in semantic search, RAG, and machine learning. Main Features: - Fast and hardware friendly: Deduplicate datasets with millions of records in minutes, on a CPU. - Flexible: Works on single or multiple datasets (e.g., train/test deduplication), and multi-column data (e.g., Question-Answering datasets). - Lightweight: Minimal dependencies (largest is NumPy). - Explainable: Easily inspect duplicates and what…
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