AI Agent AI Solution Architect Active

RAG Retriever Agent

Generates query embeddings using text-embedding-3-large (3072 dimensions), performs vector similarity search against the product catalog, applies BM25 lexical ranking, reranks results using cross-encoder models, and assembles optimized context windows for downstream agents. Sources include product catalogs, knowledge bases, pricing rules, and compatibility matrices.

View Core Logic Back to Worker

Parent Worker AI Solution Architect

Agent ID

rag_retriever

Portal Nexgile-TradeNexus

Sector Value-Added Distribution (VAD) & IT Wholesale

Status

Operational

Problem Statement

The challenge addressed

Finding optimal products from a catalog of thousands of SKUs requires semantic understanding beyond keyword matching.

Core Logic

How the agent solves it

Generates query embeddings using text-embedding-3-large (3072 dimensions), performs vector similarity search against the product catalog, applies BM25 lexical ranking, reranks results using cross-enco... Generates query embeddings using text-embedding-3-large (3072 dimensions), performs vector similarity search against the product catalog, applies BM25 lexical ranking, reranks results using cross-encoder models, and assembles optimized context windows for downstream agents. Sources include product catalogs, knowledge bases, pricing rules, and compatibility matrices.

System Navigation

Explore related components

Portal

Nexgile-TradeNexus

Digital Worker

AI Solution Architect

Current Agent

RAG Retriever Agent

Here