📅 2025-05-07 — Session: Developed and Implemented Chroma Memory System
🕒 00:55–01:40
🏷️ Labels: Chroma, Memory System, Clustering, Embedding, Data Science
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The primary goal of this session was to troubleshoot, understand, and implement a memory system using Chroma, focusing on embedding and clustering techniques.
Key Activities:
- Troubleshooting Chroma Collection: A guide was followed to diagnose and fix issues related to empty Chroma collections, involving manual addition of memory notes and verification of storage and embedding processes.
- Understanding Embedded Memory Notes: Explored the structure and functionality of embedded memory notes in Chroma, including file organization and performance considerations.
- AIOS Memory System Showcase: Created a modular notebook to demonstrate and validate the AIOS memory system, including setup, configuration, querying, and adding notes.
- Handling ValueError in Chroma: Addressed the ValueError encountered when reattaching an embedding function, providing solutions for safe attachment or recreation of collections.
- Successful Memory System Implementation: Completed the implementation of a memory system using Chroma vector DB, ensuring querying, metadata retrieval, and persistent memory function correctly.
- Semantic and Temporal Clustering Setup: Set up a notebook for clustering memory sessions, loading data into a DataFrame, and normalizing metadata.
- Hybrid Clustering Approach: Developed a hybrid clustering method to identify thematic bursts using embeddings, timestamps, and tags.
Achievements:
- Successfully implemented a memory system with Chroma vector DB.
- Developed a structured approach for session detection using hybrid clustering methods.
Pending Tasks:
- Further testing and optimization of the hybrid clustering pipeline for enhanced session detection.