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README.md

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-# Retrieval-based learning chatbot
+# CSC525 Retrieval Chatbot
 
-CSC525 - Module 8 Option 2 - Retrieval-based Learning Chatbot - Joseph Armani
+This is a retrieval-based chatbot using Sentence Transformers and FAISS for efficient similarity search.
 
-## TODO
-
-A Python tool to generate high-quality dialog variations.
-
-This package automatically downloads the following models during installation:
-
-- Universal Sentence Encoder v4 (TensorFlow Hub)
-- ChatGPT Paraphraser T5-base
-- Helsinki-NLP translation models (en-de, de-es, es-en)
-- GPT-2 (for perplexity scoring)
-- spaCy en_core_web_sm
-- nltk wordnet and averaged_perceptron_tagger_eng models
-
-## Install package
+## Description
 
-pip install -e .
+The chatbot uses a pre-trained Sentence Transformer model to encode queries and a FAISS index to retrieve relevant responses from a curated response pool (Taskmaster-1 dataset)
 
-## Description
+## Usage
 
-This Python script demonstrates a complete pipeline for dialogue augmentation, including validation, optimization, and data augmentation.
-It creates high-quality augmented versions of dialogues by applying various text augmentation techniques and quality control checks.
-Two approaches are used for text augmentation: paraphrasing and back-translation. The pipeline also includes quality metrics for evaluating the augmented text.
-Special handling is implemented for very short text such as greetings and farewells, which are predefined and filtered for quality.
-The pipeline is designed to process a dataset of dialogues and generate multiple high-quality augmented versions of each dialogue.
-The pipeline ensures duplicate dialogues are not generated and that the output meets quality thresholds for semantic similarity, grammar, fluency, diversity, and content preservation.
+Simply type your question in the chat interface and the bot will retrieve the most relevant response from its knowledge base.
+Features
 
-## References
+## Semantic search using Sentence Transformers
 
-Accsany, P. (2024). Working with JSON data in Python. Real Python. <https://realpython.com/python-json/>
-Explosion AI Team. (n.d.). Spacy · industrial-strength natural language processing in python. <https://spacy.io/>
-GeeksforGeeks. (2024). Text augmentation techniques in NLP. GeeksforGeeks. <https://www.geeksforgeeks.org/text-augmentation-techniques-in-nlp/>
-Helsinki-NLP. (2024). Opus-MT [Computer software]. GitHub. <https://github.com/Helsinki-NLP/Opus-MT>
-Hugging Face. (n.d.). Transformers. Hugging Face. <https://huggingface.co/docs/transformers/en/index>
-Humarin. (2023). ChatGPT paraphraser on T5-base [Computer software]. Hugging Face. <https://huggingface.co/humarin/chatgpt_paraphraser_on_T5_base>
-Keita, Z. (2022). Data augmentation in NLP using back-translation with MarianMT. Towards Data Science. <https://towardsdatascience.com/data-augmentation-in-nlp-using-back-translation-with-marianmt-a8939dfea50a>
-Memgraph. (2023). Cosine similarity in Python with scikit-learn. Memgraph. <https://memgraph.com/blog/cosine-similarity-python-scikit-learn>
-Morris, J. (n.d.). language-tool-python (Version 2.8.1) [Computer software]. PyPI. <https://pypi.org/project/language-tool-python/>
-TensorFlow. (n.d.). Universal sentence encoder. TensorFlow Hub. <https://www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder>
-Waheed, A. (2023). How to calculate ROUGE score in Python. Python Code. <https://thepythoncode.com/article/calculate-rouge-score-in-python>
+Efficient retrieval using FAISS indexing
+Context-aware responses
+Quality checking of responses