AI Agent Enterprise Document Intelligence

Retrieval Agent

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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Retrieval Agent

Part of Enterprise Document Intelligence

Portal: Nexgile TradeNexus

Agent ID: retrieval_agent

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).

System Navigation

Explore related components

Portal

Nexgile TradeNexus

Digital Worker

Enterprise Document Intelligence

Current Agent

Retrieval Agent

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