# Resource Management and Tracking

## 1. Introduction

This document outlines the strategy for managing and tracking resource usage within the ADAM v21.0 platform. Effective resource management is essential for ensuring the performance, scalability, and cost-effectiveness of the system.

The two primary resources that will be tracked are:

*   **Compute Usage:** The processing power consumed by the various components of the system.
*   **Token Usage:** The number of tokens consumed by the Large Language Models (LLMs).

## 2. Compute Usage Tracking

Compute usage will be tracked for each of the major components of the system, including:

*   **API Layer:** The CPU and memory usage of the central API.
*   **Core System:** The CPU and memory usage of the Agent Orchestrator, Data Manager, and other core components.
*   **Data Layer:** The resources consumed by the data warehouse, SharePoint integration, and other data stores.

This will be achieved by leveraging the monitoring capabilities of the underlying infrastructure (e.g., Docker, Kubernetes, or cloud provider monitoring tools).

## 3. Token Usage Tracking

LLM token usage is a critical metric to track for both cost management and performance analysis. The `core/utils/token_utils.py` utility in the existing codebase will be extended to provide comprehensive token tracking.

For each API call to an LLM, the following information will be logged:

*   **Query ID:** The unique identifier for the user's query.
*   **Agent Name:** The name of the agent that made the LLM call.
*   **Model Name:** The name of the LLM that was used.
*   **Prompt Tokens:** The number of tokens in the prompt sent to the LLM.
*   **Completion Tokens:** The number of tokens in the completion received from the LLM.
*   **Total Tokens:** The sum of prompt and completion tokens.

This data will be stored in the Knowledge Base, associated with the corresponding query.

## 4. Reporting and Analytics

A dedicated dashboard will be created in the web application to provide visibility into resource usage. The dashboard will display:

*   **Real-time and historical compute usage** for each component, with drill-down capabilities to view usage by individual container or process.
*   **Total token usage** per query, per agent, and per user, with filtering and sorting options.
*   **Cost analysis** based on the token usage and the pricing of the LLM provider. The dashboard will show the cost per query, per user, and per department.
*   **Performance metrics**, such as the average response time per query and the number of queries processed per minute.

This will allow administrators to monitor the health of the system, identify performance bottlenecks, and understand the cost drivers.

## 5. Cost Allocation

To ensure that costs are allocated fairly, the system will provide a mechanism for tracking resource usage by user, department, or project. This will be achieved by associating each query with a specific user and department.

The cost allocation data will be made available via the API, so that it can be integrated with other financial systems for billing and reporting purposes.

## 6. Optimization and Cost Management

The following strategies will be employed to optimize resource usage and manage costs:

*   **Caching:** Caching will be used to store the results of frequently executed queries and LLM calls.
*   **Prompt Engineering:** Prompts will be carefully engineered to be as concise as possible, reducing the number of tokens required.
*   **Model Selection:** The system will use the most cost-effective LLM for each task. For example, a smaller, faster model might be used for simple tasks, while a larger, more powerful model would be reserved for complex analysis.
*   **Rate Limiting and Quotas:** Rate limiting and quotas will be implemented to prevent abuse and control costs. Users or departments can be assigned specific token quotas.
*   **Asynchronous Processing:** Long-running tasks will be processed asynchronously to avoid blocking resources and improve responsiveness.
*   **Autoscaling:** The system will be configured to automatically scale resources up or down based on demand.