This project aims to create a closed-domain Retrieval-Augmented Generation (RAG) model capable of reading the content of various files and accessing this information to function as a question and answer system.
Python: The primary programming language used for developing the project. ElasticSearch: Used for indexing and searching the content of files. BM25 Retriever: A retrieval component for identifying relevant documents. Haystack: Provides functionalities such as FARMReader and ExtractiveQAPipeline. Pre-trained model: Utilizes roberta-base-squad2 for question answering tasks.
Before running the project, ensure you have the following:
Python installed on your system. Access to ElasticSearch, BM25 Retriever, and Haystack libraries. Installation Clone this repository to your local machine. bash Copy code git clone https://github.com/renatovillela93/RAG_OpenAI.git Install the required Python packages. bash Copy code pip install -r requirements.txt
Run the necessary services such as ElasticSearch, BM25 Retriever, and Haystack. Execute the main script to start the question answering system.
Contributions to this project are welcome! If you'd like to contribute, feel free to fork the repository and submit a pull request with your changes.
One of the next steps for this project is to enhance the quality of answers by fine-tuning the model to improve information retrieval.
If you have any questions, suggestions, or just want to chat about the project, feel free to reach out!