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+# Terminology Agent System - Bachelor Thesis
+
+This thesis describes TAS (Terminology Agent System), a system based on the
+architectural blackboard pattern and Generative AI (GenAI), integrating Large
+Language Models (LLMs). TAS uses an event based system to coordinate various
+Knowledge Sources (KS), which are responsible for text extraction, text normal-
+ization, and definition generation. Its modular design enables the extension for
+new algorithms and external data sources.
+
+## Getting started
+
+This project was built using python 3.11. First, install the projects dependencies using poetry:
+
+```shell
+pip install poetry
+poetry install
+```
+
+The system requires a valid OpenAI API key passed as environment variable:
+
+```shell
+export OPENAI_API_KEY=<your-api-key>
+```
+
+TAS contains a simple fastapi interface. In order to start the server, run the following commands:
+
+```shell
+pip install "fastapi[standard]"
+fastapi dev src/main.py
+```
+
+## Tests
+
+In order to reproduce the test results from Appendix B, start the server and run the following commands:
+
+```shell
+python -m unittest tests/test_definition.py
+python -m unittest tests/test_extract.py
+python -m unittest -v tests.test_integration.TestIntegrationTerminology.testExtractDomainTerminology_LLM
+python -m unittest -v tests.test_integration.TestIntegrationTerminology.testExtractDomainTerminology
+```
+
+In order to run the performance evaluation, run the `test_performance.ipynb` jupyter notebook.
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