Developed Modular Data Retrieval Scripts with FAISS

Day: 2025-02-18
Time: 14:20 to 16:10
Project: Dev
Workspace: WP 2: Operational
Status: Completed
Priority: MEDIUM
Assignee: Matías Nehuen Iglesias
Tags: FAISS, Hugging Face, Rag Model, Summarization, Retrieval

Description

The session aimed to explore and implement advanced summarization and retrieval techniques using state-of-the-art models and frameworks.

Discussed and compared extractive vs. abstractive summarization methods, focusing on their application in projects.
Reviewed generative summarization techniques, including model architectures and fine-tuning methods.
Explored the RAG model for document retrieval, detailing its retriever component and fine-tuning options.
Built a quote finder using the RAG model, covering dataset preparation and retrieval querying.
Addressed handling large text collections with FAISS and DPR, emphasizing scalability and memory requirements.
Created a Hugging Face Dataset with FAISS indexing, including embedding computation and dataset saving.
Corrected FAISS index argument usage and resolved saving errors in Hugging Face datasets.
Developed a modular script structure for data processing and retrieval, focusing on preprocessing, embedding, loading, and querying.
Enhanced retrieval accuracy in FAISS by refining embedding models and normalizing data.

Successfully implemented a modular approach for data processing scripts using Hugging Face and FAISS.
Corrected and optimized FAISS index handling and dataset saving processes.

Further exploration of abstractive summarization techniques for specific project needs.
Continuous improvement of retrieval accuracy with FAISS by experimenting with different embedding models and similarity measures.

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