System Overview
The Jori Ai-agent is a multi-tiered caching and execution environment for a ReAct (Reasoning + Acting) agent that processes and analyzes health data in FHIR format. The agent employs a series of caches and decision-making processes to efficiently handle queries.
Here’s a description of the flow and components in the diagram:
Query: The process begins when a query is received.
L1 Cache: The Level 1 cache is the first point of contact for the query, which checks for a direct hit, for immediate code retrieval and execution.
Execution Environment: If there's no direct hit in the L1 cache, the query moves to the execution environment, which includes an Apache Spark cluster. This is likely where the computation-intensive tasks are processed.
Vector Database: Parallel to the L1 cache, there's a decision-making process involving a vector database that queries, "Is this relevant?" This suggests an AI or ML component that determines the relevance of the data to the query.
L2 Cache: If deemed relevant by the vector database, the query proceeds to the Level 2 cache, which again assesses relevance.
L3 Cache: The Level 3 cache is an "Abstract Memory Recall" system, suggesting that it uses more sophisticated criteria to recall data, possibly through abstraction layers or patterns.
L4 Cache: The Level 4 cache feeds into an "Agent Loop", which involves planning, tool selection, and result analysis.
Toolbox: Associated with the Agent Loop is a toolbox that can generate data, recall memories, and search FHIR documentation. This toolbox likely contains a suite of utilities and functions that the agent can deploy to handle various tasks.
Long-term Memory: The system also includes a component for long-term memory, indicating a storage mechanism for retaining information over time, which can feed into the execution environment to save the processed results.
The overall architecture seems to be designed for efficiency and robustness, utilizing multiple caches to speed up data retrieval and an execution environment capable of processing complex queries. The inclusion of a vector database suggests that machine learning is used to determine the relevance of data, which helps in managing and sifting through large volumes of health data in FHIR format. The agent loop indicates a continuous process of task execution, evaluation, and strategy refinement.
Last updated