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3.11+](https://img.shields.io/badge/python-3.11%2B-blue.svg)](https://www.python.org/downloads/)\n[![License: MIT](https://img.shields.io/badge/license-MIT-green.svg)](./LICENSE)\n\nStandalone embedding-based topic modeling software for vector workflows.\n\n## What it provides\n\n- dependency-light clustering kernel\n- session-aware representative selection\n- safe text shaping/redaction for embedding input\n- generic ingestion for DB column-value rows and JSON payloads\n- provider-driven `TopicModeler` API\n- JSONL-oriented CLI for standalone runs\n\n## Install\n\nInstall from a local checkout today:\n\n```bash\ngit clone https://github.com/seonghobae/vector-topic-modeling.git\ncd vector-topic-modeling\nuv sync\n```\n\nFor local wheel installation during development or release validation:\n\n```bash\npython3.11 -m pip install dist/vector_topic_modeling-0.1.0-py3-none-any.whl\n```\n\nThe package requires Python 3.11 or newer.\n\n## Development install\n\n```bash\nuv sync --extra dev\n```\n\n## Verify locally\n\n```bash\nuv run pytest -q\nuv run python scripts/docstring_coverage.py --min-percent 100\n# Delete any previous build artifacts and smoke-test virtual environment.\n# On POSIX shells: rm -rf dist .venv-smoke-cli\n# On Windows PowerShell: Remove-Item -Recurse -Force dist, .venv-smoke-cli\nuv run python -m build\nuv run python scripts/smoke_installed_cli.py --dist-dir dist --venv-dir .venv-smoke-cli\n```\n\nThe repository release gate also smoke-tests the installed\n`vector-topic-modeling` console script with:\n\n```bash\nuv run python scripts/smoke_installed_cli.py --dist-dir dist --venv-dir .venv-smoke-cli\n```\n\n## Quick start\n\n```python\nfrom vector_topic_modeling import TopicDocument, TopicModelConfig, TopicModeler\n\nclass FakeEmbeddingProvider:\n    def embed(self, texts: list[str]) -\u003e list[list[float]]:\n        return [[1.0, 0.0] for _ in texts]\n\nmodeler = TopicModeler(\n    embedding_provider=FakeEmbeddingProvider(),\n    config=TopicModelConfig(similarity_threshold=0.85),\n)\n\nresult = modeler.fit_predict([\n    TopicDocument(id=\"1\", text=\"refund duplicate billing\"),\n])\n```\n\nSee [`examples/`](./examples/) for end-to-end local usage samples.\n\nDetailed usage and troubleshooting guidance is in\n[`docs/user-manual.md`](./docs/user-manual.md).\n\n## JSONL CLI input shapes\n\n### Legacy flat shape\n\nEach line can contain:\n\n```json\n{\"id\":\"1\",\"text\":\"refund duplicate billing\",\"session_id\":\"s1\",\"question\":\"...\",\"response\":\"...\",\"count\":1}\n```\n\n### Generic DB / JSON payload shape\n\nYou can also pass arbitrary rows (DB-export style columns or nested JSON payloads)\nand map them with `--ingestion-config`.\n\nExample config: [`examples/ingestion_config_db_columns.json`](./examples/ingestion_config_db_columns.json)\n\nExample rows: [`examples/sample_db_rows.jsonl`](./examples/sample_db_rows.jsonl)\n\nRun the CLI with an OpenAI-compatible embedding endpoint:\n\n```bash\nvector-topic-modeling cluster input.jsonl \\\n  --output topics.json \\\n  --base-url https://your-gateway.example.com \\\n  --api-key \"$LITELLM_API_KEY\" \\\n  --model text-embedding-3-large\n```\n\nWith generic ingestion mapping:\n\n```bash\nvector-topic-modeling cluster examples/sample_db_rows.jsonl \\\n  --output topics.json \\\n  --ingestion-config examples/ingestion_config_db_columns.json \\\n  --base-url https://your-gateway.example.com \\\n  --api-key \"$LITELLM_API_KEY\" \\\n  --model text-embedding-3-large\n```\n\nSample files:\n\n- [`examples/sample_queries.jsonl`](./examples/sample_queries.jsonl)\n- [`examples/sample_db_rows.jsonl`](./examples/sample_db_rows.jsonl)\n- [`examples/ingestion_config_db_columns.json`](./examples/ingestion_config_db_columns.json)\n- [`examples/cli_openai_compat.sh`](./examples/cli_openai_compat.sh)\n- [`examples/basic_in_memory_provider.py`](./examples/basic_in_memory_provider.py)\n\n## Scope\n\n- This package intentionally excludes web framework routes, persistence,\n  background jobs, export pipelines, and email delivery concerns.\n\n## Repository guides\n\n- [Contributing](./CONTRIBUTING.md)\n- [User manual](./docs/user-manual.md) (Korean version: [사용자 매뉴얼](./docs/user-manual-ko.md))\n- [Security policy](./SECURITY.md)\n- [Support](./SUPPORT.md)\n- [Changelog](./CHANGELOG.md)\n- [Maintainer release guide](./docs/maintainers/releasing.md)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fseonghobae%2Fvector-topic-modeling","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fseonghobae%2Fvector-topic-modeling","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fseonghobae%2Fvector-topic-modeling/lists"}