Frequently Asked Questions
Common Questions About Building a RAG System With Memory for AI Agents
Everything you need to know before registering.
What are the three memory layers in the RAG system built in this workshop? +
The system built in this workshop has three memory layers: working memory that holds the current context window contents including recent retrievals, episodic memory that stores compressed summaries of past interactions and their RAG retrievals for future reference, and semantic memory which is the embedded knowledge base queried by the RAG pipeline. The memory manager coordinates these three layers so agents have the right knowledge at every point in a conversation.
How does episodic memory improve RAG retrieval for multi-agent systems? +
Episodic memory improves RAG retrieval by storing the context of past queries alongside their results. When a new query arrives that is related to past interactions, the memory manager can use episodic memory to reformulate the retrieval query with additional context, retrieve episodic summaries alongside new knowledge base results, and avoid redundant retrievals for information that was recently accessed. This creates continuity that plain RAG cannot provide.
How do I implement memory-augmented RAG in Python? +
Memory-augmented RAG in Python involves four components: a vector store client for semantic memory retrieval, an episodic memory store (typically a lightweight database or structured file store) for past interaction summaries, a memory manager that coordinates reads and writes across both stores, and a retrieval orchestrator that combines results from both stores with appropriate citation tracking. The workshop implements all four in Python during the live session.
How does the memory system decide what to store in episodic memory? +
The memory system uses an importance scoring function to decide what to store in episodic memory: facts that were cited multiple times in a conversation, user preferences and constraints that were explicitly stated, task outcomes and decisions that may be relevant in future sessions, and any information the agent flagged as important to remember. The workshop covers implementing this importance scoring as a lightweight classifier.
Can the RAG memory system be shared across multiple agents safely? +
Yes. The workshop covers implementing shared episodic memory with appropriate access controls: read access is open to all agents in the system, while write access uses distributed locking to prevent concurrent write conflicts. The Glass-Box logging layer records all memory reads and writes so you can audit which agent accessed which information and detect any memory consistency issues.
How do I clear or update stale information in the RAG memory system? +
Stale memory management involves two mechanisms: time-to-live settings on episodic memory entries that automatically expire information after a configured period, and explicit invalidation when the source documents in semantic memory are updated. The workshop covers implementing a memory maintenance job that runs periodically to remove expired episodic memories and re-index changed semantic memory content.
