📅 2025-02-03 — Session: Implemented FAISS for efficient embedding storage
🕒 16:35–17:10
🏷️ Labels: FAISS, Embeddings, Storage, Installation, RAG, Openai
📂 Project: Dev
⭐ Priority: MEDIUM
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.