Results for “neural-architecture-search”

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.

Build production RAG and semantic search systems with a high-performance vector database written in Rust, supporting hybrid search, filtering, and horizontal scaling.

Diagnoses and improves Qdrant search relevance by isolating embedding model, configuration, or query strategy issues.

Augments nearest-neighbour search with graph Laplacian features to retrieve items based on both semantic similarity and structural role.

`analysis-agent`/`task-agent`/`review-agent`: use for search relevance, freshness, analytical grain, metrics, or rebuilds; skip bounded source queries and operational telemetry.