Implemented FAISS for efficient embedding storage

• Day: 2025-02-03
• Time: 16:35 to 17:10
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: FAISS, Embeddings, Storage, Installation, RAG, Openai

Description

Session Goal

The session aimed to explore and implement FAISS for efficient storage and retrieval of embeddings, comparing it with traditional methods like CSV/JSON.

Key Activities

• Discussed the advantages of using FAISS over CSV/JSON for embedding storage, focusing on speed, efficiency, and scalability.
• Provided detailed steps for building and querying a FAISS index.
• Explored solutions for installing FAISS in environments with GLIBC incompatibilities using Conda, Docker, and source building.
• Compared FAISS with OpenAI tools for managing embeddings within a Retrieval-Augmented Generation (RAG) pipeline.
• Confirmed successful installation of FAISS and provided a testing guide.
• Discussed when to use FAISS, OpenAI RAG, or a hybrid approach in workflows.

Achievements

• Successfully installed FAISS and tested its functionality.
• Clarified the use cases and benefits of FAISS in embedding storage compared to OpenAI tools.

Pending Tasks

• Further exploration of hybrid approaches combining FAISS and OpenAI RAG for specific workflows.

Evidence

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• event_ids: []