Praxos addresses AI's memory limitations through three innovative mechanisms:
- Persistent memory storage: Convert temporary information such as dialog history and operation records into permanent nodes (e.g., entities) in the knowledge graph, breaking the Token length limitation
- relational memory retrieval: Adopt graph database query language, support multi-hop correlation query (e.g., "find the policy-related terms and conditions that the customer has inquired about in the last week").
- Dynamic Memory Compression: Automatic at runtime:
- Merging similar dialog rounds
- Abstracting concrete operations into semantic actions
- Deletion of low-frequency access data
Enables 50,000 words of interaction history to be compressed into 500 words of key memories
Technical tests have shown that intelligences with Praxos maintain 1001 TP3T of contextual consistency while processing 20 consecutive insurance case analysis tasks, while the accuracy of legacy solutions decays to 631 TP3T.This capability is critical for service scenarios that require tracking of customer needs over time.
This answer comes from the articlePraxos: building a reliable structured knowledge base for AI intelligencesThe
May not be reproduced without permission:AI productivity tools " How can Praxos' knowledge graph technology solve the problem of "memory failure" in AI intelligences?
