Memory for Open-Source LLMs

What this post added

Introduces a novel approach to conversational memory for LLMs by transforming traditional RAG. The method involves summarizing ended conversations using an LLM, embedding these summaries, and storing them in a vector database. This creates a persistent, semantically relevant memory that can be retrieved to provide context for future interactions, moving beyond simple turn-based memory. Includes sample Python code demonstrating the implementation with Pinecone Serverless, OctoAI, and tiktoken.