Building a Local-First RAG Research Tool with Nemotron, vLLM, and Tool Calling
The article describes the development of a local-first RAG (Retrieval-Augmented Generation) research tool that runs on a single GPU. It covers the technical stack, key design decisions, and performance metrics of the tool.
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Why it matters
This tool demonstrates a practical approach to building a local-first, GPU-powered RAG research assistant, which can be useful for various applications that require efficient and accurate information retrieval and generation.
Key Points
- 1Implemented a two-step
- 2 flow to avoid dumping large context
- 3Utilized Nemotron v2's tool calling capabilities with custom parser plugins
- 4Warmed up the prefix cache on-demand to improve response times
- 5Leveraged bilingual (English and Japanese) FTS5 search for multilingual data
Details
The author built a local-first RAG research tool that runs entirely on a single GPU, using a stack that includes Nemotron Nano 9B v2 Japanese on vLLM (FP16, RTX 5090), FastAPI + SQLite FTS5 + Jinja2. The key design decisions include an
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