AI Agent Enterprise Document Intelligence Active

Retrieval Agent

Generates query embeddings using text-embedding-3-large. Executes hybrid search combining vector similarity (cosine) with BM25 lexical matching.

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Parent Worker Enterprise Document Intelligence

Agent ID

retrieval_agent

Portal Nexgile-TradeNexus

Sector Value-Added Distribution (VAD) & IT Wholesale

Status

Operational

Problem Statement

The challenge addressed

Finding relevant information across large document corpora requires semantic understanding that goes beyond keyword matching while maintaining precision.

Core Logic

How the agent solves it

Generates query embeddings using text-embedding-3-large. Executes hybrid search combining vector similarity (cosine) with BM25 lexical matching. Applies cross-encoder reranking for precision. Uses MMR... Generates query embeddings using text-embedding-3-large. Executes hybrid search combining vector similarity (cosine) with BM25 lexical matching. Applies cross-encoder reranking for precision. Uses MMR (Maximal Marginal Relevance) for diversity. Assembles optimized context windows within token limits. Reports retrieval metrics (chunks searched, similarity scores, retrieval time).

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Portal

Nexgile-TradeNexus

Digital Worker

Enterprise Document Intelligence

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Retrieval Agent

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Retrieval Agent

Problem Statement

Core Logic

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