Brief Overview
The RAG (Retrieval-Augmented Generation) framework at Peak Innovations is a versatile AI system that processes complex queries by dynamically selecting agents, breaking down tasks, and retrieving information from multiple sources. Requests can range from real-time user queries to backend tasks, like generating customized learning materials.How it works
1
AI Agent Routing
The system directs each request to the most appropriate AI agent, configured
to handle specific tasks such as financial analysis, knowledge retrieval, or
personalized learning.
2
Dynamic Task Pipelines
The chosen agent break down the task and constructs a sequence of subtasks,
forming a dynamic pipeline. These subtasks are linked to specific tools that
retrieve and process data from sources like APIs, databases, and vectorized
content.
3
Tool Utilization
Each tool processes its assigned subtask, using relevant data sources to
deliver specific outputs. Tools range from language models for response
generation to database access tools for retrieving structured data.
4
Synthesis and Response
Once all subtasks are completed, the framework synthesizes the results into
a coherent, contextually accurate response, leveraging the combined data to
deliver precise answers tailored to the user’s needs.