base on TopicGPT: A Prompt-Based Framework for Topic Modeling (NAACL'24) # TopicGPT [](https://arxiv.org/abs/2311.01449) [](https://chtmp223.github.io/topicGPT) This repository contains scripts and prompts for our paper ["TopicGPT: Topic Modeling by Prompting Large Language Models"](https://arxiv.org/abs/2311.01449) (NAACL'24). Our `topicgpt_python` package consists of five main functions: - `generate_topic_lvl1` generates high-level and generalizable topics. - `generate_topic_lvl2` generates low-level and specific topics to each high-level topic. - `refine_topics` refines the generated topics by merging similar topics and removing irrelevant topics. - `assign_topics` assigns the generated topics to the input text, along with a quote that supports the assignment. - `correct_topics` corrects the generated topics by reprompting the model so that the final topic assignment is grounded in the topic list.  ## 📣 Updates - [11/09/24] Python package `topicgpt_python` is released! You can install it via `pip install topicgpt_python`. We support OpenAI API, VertexAI, Azure API, Gemini API, and vLLM (requires GPUs for inference). See [PyPI](https://pypi.org/project/topicgpt-python/). - [11/18/23] Second-level topic generation code and refinement code are uploaded. - [11/11/23] Basic pipeline is uploaded. Refinement and second-level topic generation code are coming soon. ## 📦 Using TopicGPT ### Getting Started 1. Make a new Python 3.9+ environment using virtualenv or conda. 2. Install the required packages: ``` pip install topicgpt_python ``` - Set your API key: ``` # Run in shell # Needed only for the OpenAI API deployment export OPENAI_API_KEY={your_openai_api_key} # Needed only for the Vertex AI deployment export VERTEX_PROJECT={your_vertex_project} # e.g. my-project export VERTEX_LOCATION={your_vertex_location} # e.g. us-central1 # Needed only for Gemini deployment export GEMINI_API_KEY={your_gemini_api_key} # Needed only for the Azure API deployment export AZURE_OPENAI_API_KEY={your_azure_api_key} export AZURE_OPENAI_ENDPOINT={your_azure_endpoint} ``` - Refer to https://openai.com/pricing/ for OpenAI API pricing or to https://cloud.google.com/vertex-ai/pricing for Vertex API pricing. ### Data - Prepare your `.jsonl` data file in the following format: ```shell { "id": "IDs (optional)", "text": "Documents", "label": "Ground-truth labels (optional)" } ``` - Put your data file in `data/input`. There is also a sample data file `data/input/sample.jsonl` to debug the code. - Raw dataset used in the paper (Bills and Wiki): [[link]](https://drive.google.com/drive/folders/1rCTR5ZQQ7bZQoewFA8eqV6glP6zhY31e?usp=sharing). ### Pipeline Check out `demo.ipynb` for a complete pipeline and more detailed instructions. We advise you to try running on a subset with cheaper (or open-source) models first before scaling up to the entire dataset. 0. (Optional) Define I/O paths in `config.yml` and load using: ```python import yaml with open("config.yml", "r") as f: config = yaml.safe_load(f) ``` 1. Load the package: ```python from topicgpt_python import * ``` 2. Generate high-level topics: ```python generate_topic_lvl1(api, model, data, prompt_file, seed_file, out_file, topic_file, verbose) ``` 3. Generate low-level topics (optional) ```python generate_topic_lvl2(api, model, seed_file, data, prompt_file, out_file, topic_file, verbose) ``` 4. Refine the generated topics by merging near duplicates and removing topics with low frequency (optional): ```python refine_topics(api, model, prompt_file, generation_file, topic_file, out_file, updated_file, verbose, remove, mapping_file) ``` 5. Assign and correct the topics, usually with a weaker model if using paid APIs to save cost: ```python assign_topics( api, model, data, prompt_file, out_file, topic_file, verbose ) ``` ``` correct_topics( api, model, data_path, prompt_path, topic_path, output_path, verbose ) ``` 6. Check out the `data/output` folder for sample outputs. 7. We also offer metric calculation functions in `topicgpt_python.metrics` to evaluate the alignment between the generated topics and the ground-truth labels (Adjusted Rand Index, Harmonic Purity, and Normalized Mutual Information). ## 📜 Citation ``` @misc{pham2023topicgpt, title={TopicGPT: A Prompt-based Topic Modeling Framework}, author={Chau Minh Pham and Alexander Hoyle and Simeng Sun and Mohit Iyyer}, year={2023}, eprint={2311.01449}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```", Assign "at most 3 tags" to the expected json: {"id":"4737","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"