Part 6/12:
Signing up on Pine Cone and creating an index optimized for higher throughput (
P1tier), with 1536-dimensional vectors corresponding to OpenAI’s ADA 2 embeddings.Writing Python scripts employing the
pineconeclient library to:Generate embeddings with OpenAI's API
Upsert messages, complete with metadata such as timestamps, speaker identity, and message content
Retrieve relevant memories based on similarity scores for subsequent use
The system is designed to index entire chat messages, which include user input, system replies, timestamps, and other metadata, stored as JSON files for external backup or manual inspection.
Sample Workflow
The script:
- Initializes the Pine Cone index environment and sets necessary API keys.