📅 2025-02-03 — Session: Explored FAISS for Embedding Storage and Retrieval
🕒 16:30–17:10
🏷️ Labels: FAISS, Embeddings, Installation, Openai, RAG, Similarity Search
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore the use of FAISS for storing and retrieving embeddings, particularly in comparison to traditional methods like CSV/JSON and OpenAI tools.
Key Activities
- Storing Embeddings: Discussed advantages of using FAISS over CSV/JSON, focusing on speed, efficiency, and scalability. Detailed steps for building and querying a FAISS index were provided.
- Installing FAISS: Outlined solutions for GLIBC incompatibilities during FAISS installation, including using Conda, Docker, and building from source.
- Understanding FAISS: Explored FAISS as a high-performance similarity search engine, its dependencies, and GPU acceleration support.
- FAISS vs OpenAI Tools: Compared FAISS and OpenAI tools for managing embeddings, highlighting FAISS’s strengths in storage and retrieval.
- Installation Confirmation: Confirmed successful FAISS installation and provided a testing guide.
- Choosing Tools: Discussed use cases for FAISS, OpenAI RAG, or a hybrid approach in workflows.
- Using OpenAI for RAG Systems: Evaluated the feasibility of using OpenAI alone for RAG systems.
Achievements
- Successfully installed FAISS and verified its functionality.
- Gained insights into the optimal use cases for FAISS and OpenAI tools in embedding management.
Pending Tasks
- Further exploration of hybrid approaches combining FAISS and OpenAI tools for specific workflows.