# BunkaTopics

> a Topic Modeling Visualisation, Frame Analysis & Retrieval Augmented Generation (RAG) package that leverages LLMs

**Wikidata**: [Q123348468](https://www.wikidata.org/wiki/Q123348468)  
**Source**: https://4ort.xyz/entity/bunkatopics

## Summary
BunkaTopics is a Python-based software library for topic modeling, visualization, frame analysis, automatic summarization and retrieval-augmented generation (RAG) that leverages large language models (LLMs). It is distributed under the MIT License and is published with source code on GitHub.

## Key Facts
- BunkaTopics is a software library for topic modeling, visualization, frame analysis, automatic summarization, machine learning, and retrieval-augmented generation (RAG).
- Programming language: Python.
- License: MIT License.
- Stable release v0.41 (preferred stable version) was published on 2023-10-07.
- Previous stable release v0.38 was published on 2023-06-18.
- Source code repository: https://github.com/charlesdedampierre/BunkaTopics.
- Official website / project page: https://charlesdedampierre.github.io/BunkaTopics/index.html.
- Instance of: software; software library.
- Short name / alias: BUNKA.
- Origin / located in: Professional University of Information and Management for Innovation, Sumida Ward, Tōkyō Metropolis, Japan.
- Copyright status: copyrighted.

## FAQs
### Q: What is BunkaTopics?
A: BunkaTopics is a Python software library that provides tools for topic modeling visualization, frame analysis, automatic summarization, and retrieval-augmented generation (RAG) using LLMs.

### Q: Is BunkaTopics open source?
A: Yes. BunkaTopics is distributed under the MIT License and its source code is available on GitHub at https://github.com/charlesdedampierre/BunkaTopics.

### Q: Which versions are available and which is current?
A: Two listed stable releases are v0.38 (published 2023-06-18) and v0.41 (published 2023-10-07). v0.41 is marked as the preferred stable version.

### Q: What platform or runtime does BunkaTopics require?
A: BunkaTopics is implemented in Python and depends on Python as its runtime environment.

## Why It Matters
BunkaTopics brings together topic modeling, frame analysis, visualization, automatic summarization, and retrieval-augmented generation into a single Python library that leverages large language models. For researchers and practitioners working with large text corpora, this integration simplifies workflows that would otherwise require multiple tools: extracting latent topics, visually exploring topic structures, analyzing framing patterns, and enhancing generation with retrieval from corpora. Its publication under the MIT License and availability on GitHub make the code reusable and inspectable, which is useful for reproducible research and adaptation in academic or applied projects. Being Python-based, BunkaTopics fits into the dominant data science ecosystem and can interoperate with other Python ML and NLP libraries. The project’s combination of visualization and LLM-backed RAG features addresses the growing need to both interpret and augment textual analyses in humanities, social sciences, and machine learning contexts.

## Notable For
- Integrating topic modeling visualization and frame analysis with retrieval-augmented generation (RAG) that leverages LLMs.
- Implementation as a Python library, enabling integration with common data-science workflows.
- Open-source distribution under the MIT License with source code on GitHub.
- Stable release history with v0.38 (2023-06-18) and preferred stable v0.41 (2023-10-07).
- Origin associated with the Professional University of Information and Management for Innovation in Tōkyō, Japan.

## Body

### Overview
- Name: BunkaTopics (alias: BUNKA; also presented as Bunkatopics in source metadata).
- Description: A Topic Modeling Visualisation, Frame Analysis & Retrieval Augmented Generation (RAG) package that leverages LLMs.
- Instance of: software; software library.

### Capabilities and Uses
- Primary uses listed: data visualization, topic modeling, automatic summarization, machine learning, retrieval-augmented generation.
- Integrates visualization and analytical tools for exploring and summarizing large text corpora.
- Designed to leverage LLMs for RAG workflows and summarization tasks.

### Technical Details
- Programming language: Python.
- Runtime dependency: requires Python.
- Copyright status: copyrighted.
- Short name / alias: BUNKA.

### Releases and Versioning
- v0.38 — stable version; publication date: 2023-06-18. (Release reference: GitHub release v0.38.)
- v0.41 — stable version (preferred); publication date: 2023-10-07. (Release reference: GitHub release v0.41.)

### Licensing and Source
- License: MIT License.
- Source code repository: https://github.com/charlesdedampierre/BunkaTopics.
- Official project website / documentation page: https://charlesdedampierre.github.io/BunkaTopics/index.html.

### Origin and Affiliation
- Located in / Origin: Professional University of Information and Management for Innovation, Sumida Ward, Tōkyō Metropolis, Japan.

### Metadata and Classification
- Wikidata description (as provided): "a Topic Modeling Visualisation, Frame Analysis & Retrieval Augmented Generation (RAG) package that leverages LLMs."
- Classified under software and software library in source metadata.

## Schema Markup
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  "name": "BunkaTopics",
  "description": "A Python-based software library for topic modeling visualization, frame analysis, automatic summarization and retrieval-augmented generation (RAG) that leverages large language models.",
  "url": "https://charlesdedampierre.github.io/BunkaTopics/index.html",
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## References

1. [Release 0.38. 2023](https://github.com/charlesdedampierre/BunkaTopics/releases/tag/v0.38)
2. [Release 0.41. 2023](https://github.com/charlesdedampierre/BunkaTopics/releases/tag/v0.41)
3. [Source](https://api.github.com/repos/charlesdedampierre/BunkaTopics)