Deep Recall utilizes a modularly designed three-tier architecture with components that work together:
- memory service layer::
- Core components: vector databases (e.g. FAISS/Pinecone)
- Function: store and retrieve vectorized memories of user interactions, support similarity query and spatio-temporal correlation analysis
- inference service layer::
- Core component: GPU-accelerated model inference engine
- Function: Performs LLM generation in conjunction with retrieved contexts, supports dynamic loading of models of different sizes (7B/70B parameters)
- coordinator layer::
- Core Component: Automatic Expansion Controller
- Functions: real-time load monitoring, elastic resource scheduling (e.g., automatically increase GPU instances in case of bursty traffic)
The three layers communicate efficiently through gRPC, where the coordinator uses the Consensus algorithm to ensure distributed consistency, a key technology for its enterprise-grade reliability.
This answer comes from the articleDeep Recall: an open source tool that provides an enterprise-class memory framework for large modelsThe
May not be reproduced without permission:AI productivity tools " What are the specific components included in Deep Recall's three-tier architecture and what functions do each perform?
