# PROVOCATE - Project for Refined Optimization and Validation of OpenAIs Conversational AI with Testing and Evaluation
## Getting started
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
## Add your files
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-[ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-[ ] [Set up project integrations](https://gitlab.cl.uni-heidelberg.de/chrysanthopoulou/provocate-project-for-refined-optimization-and-validation-of-openais-conversational-ai-with-testing-and-evaluation/-/settings/integrations)
## Collaborate with your team
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-[ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-[ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
Use the built-in continuous integration in GitLab.
-[ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-[ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-[ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-[ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-[ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
***
# Editing this README
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
## Suggestions for a good README
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# SyNDRA - Synthetic Dialogues, Retrieval-assisted and Annotated
## Description
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## Repository Structure
**data/** - Contains original and supplementary data used in the project, including datasets from MultiWOZ, OpenStreetMap, and our own generated dialogues and domain knowledge.
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
**osm/** - Houses OpenStreetMap data for Heidelberg, focusing on restaurants, hotels, and attractions, both in annotated and unannotated forms.
## Authors and acknowledgment
Show your appreciation to those who have contributed to the project.
**own_data/** - Includes custom-generated dialogues and domain knowledge extracted and reformatted from the MultiWOZ dataset.
documentation/ - Contains all project documentation, including meeting notes, proposals, project plans, steps, and deadlines.
## License
For open source projects, say how it is licensed.
**notebooks/** - Hosts Jupyter notebooks for dialogue generation and data preprocessing, crucial for the project's data processing and dialogue system development.
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
This folder contains the original and supplementary data used in our project. It includes a diverse set of resources ranging from dialogue datasets to geographic information, and custom-generated content. The data is organized into distinct sub-folders, each with its own specific README for detailed descriptions.
## Contents
**multiwoz/** - Contains the MultiWOZ dialogue dataset, a rich source of conversational exchanges across multiple domains.
**osm/** - Houses data extracted from OpenStreetMap. This data provides geographic and locational context relevant to our project. (Refer to the separate README in the osm/ folder for more details).
**own_data/** - Includes dialogues and domain knowledge created by our team, specifically tailored for our project's needs. (Refer to the separate README in the own_data/ folder for more details).
## MultiWOZ Dialogue Dataset
The MultiWOZ dataset is a large-scale, multi-domain Wizard-of-Oz style dataset. It's widely used for training and evaluating dialogue systems. This dataset includes conversations spanning various topics and scenarios.
This folder contains OpenStreetMap (OSM) data specifically curated for Heidelberg, focusing on restaurants, hotels, and attractions. The data is presented in both annotated and unannotated forms and serves as the backbone for a database that aids in generating and receiving facts for dialogue creation in our project.
## Contents
- annotated/ - Contains enriched data with additional annotations for a deeper contextual understanding.
- hotels/ - Annotated data for hotels in Heidelberg.
- restaurants/ - Annotated data for restaurants in Heidelberg.
- attractions/ - Annotated data for attractions in Heidelberg.
- unannotated/ - Holds the raw OSM data, providing fundamental details.
- hotels/ - Unannotated data for hotels.
- restaurants/ - Unannotated data for restaurants.
- attractions/ - Unannotated data for attractions.
## Usage
The data in these folders populate a database integral to our dialogue system. This structured information allows for the generation of contextually rich and accurate dialogues, leveraging real-world data about Heidelberg.
## Data Format
Each JSON file in this folder corresponds to different aspects (restaurants, hotels, attractions) of Heidelberg. The files are structured to include various data points, such as names, locations, and other unique characteristics relevant to the category.
