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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.

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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.

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Nexgile-TradeNexus

Digital Worker

AI Solution Architect

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RAG Retriever Agent

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RAG Retriever Agent

Problem Statement

Core Logic

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