#NeMo Retriever

Articles about NeMo Retriever — exploring patterns, best practices, and real-world implementations in production systems.

2 posts tagged with nemo retriever. ← All posts

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Pratik Dhanave · ·14 min read

RAG on the NVIDIA Stack

Assembling a full retrieval-augmented generation pipeline in Python — chunk and embed with NeMo Retriever, shortlist by cosine, sharpen with a reranker, then ground a ChatNVIDIA answer that cites its sources — first from scratch, then the idiomatic LangChain wiring.

Build a full RAG pipeline in Python on NVIDIA's stack: chunk and embed with NeMo Retriever, shortlist by cosine, rerank, then generate a grounded, cited answer with ChatNVIDIA — shown both from scratch and the idiomatic LangChain assembly, with notes on scaling to a vector DB.

#NVIDIA#NeMo Retriever#RAG#Python#AI Engineering#Software Engineering#Vector Databases
Pratik Dhanave · ·13 min read

Embeddings and Reranking with NeMo Retriever

Building RAG's retrieval core in Python — turning a corpus and a query into vectors with NeMo Retriever embedding NIMs, scoring by cosine similarity, then sharpening the shortlist with a cross-encoder reranker NIM.

Use NeMo Retriever from Python to build RAG's retrieval core: NVIDIAEmbeddings (embed_documents vs embed_query for the asymmetric passage/query distinction) with cosine scoring, and NVIDIARerank.compress_documents for a two-stage retrieve-then-rerank pipeline.

#NVIDIA#NeMo Retriever#RAG#Python#AI Engineering#Software Engineering#Embeddings

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.