LLM-Based Hierarchical Topic Modeling Tool
The LLM-Based Hierarchical Topic Modeling Tool normalizes raw string values in JSONL files into reusable hierarchical categories using plain-language instructions. It requires Python, an API key for a LiteLLM-supported model, and a Rust compiler for installation, with optional Conda environment support. Users configure model keys in config.py or a .env file before running the provided scripts to transform the data.
- ▪The tool groups related labels into a taxonomy with broad topics at the top and specific subtopics below, using LLM assistance to map raw strings to normalized category paths.
- ▪Installation requires Python 3.9+, an API key for a LiteLLM provider such as OpenRouter, and a Rust compiler to handle any package builds that need compilation.
- ▪Configuration is done by editing config.py or creating a .env file with the appropriate API key, after which the scripts can process JSONL files to produce normalized outputs.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,300 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/Tryhard-cs/LLM-Hierarchical-Topic-Modeling |
| Publication time | Tue, 21 Jul 2026 16:26:11 +0000 |
| Retrieval time | 2026-07-21T17:16:05.534Z |
| Last seen | 2026-07-21T17:16:05.534Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | G48g5Vsx31fQ |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Instruction-Driven Hierarchical Topic Normalizer Hierarchical topic modeling groups related labels into a taxonomy: broad topics at the top and increasingly specific subtopics below them. This tool is a practical, LLM-assisted version for JSONL data: it turns inconsistent raw strings into a reusable hierarchy that you steer with plain-language instructions. For example, it can map bad, neg, and negative to negative, or map search to Features|Search. It is designed for extracted Reddit-study data, but works with any JSONL file containing strings, nested objects, and arrays of objects. The input is a JSONL file with raw string values. The output is another JSONL file with the same records and structure, except that mapped values have been replaced by their normalized category paths.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.