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rag_skill

Retrieve simulator manual definitions and DATA examples via Milvus and NVIDIA embeddings.

4.1k|1.1k|Updated Oct 19, 2023
Search Tags:#llm#rag#retrieval#simulation#embedding#milvus
One-click install
npx skills add https://github.com/NVIDIA/GenerativeAIExamples --skill rag-skill-nvidia
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rag_skill
Source: https://github.com/NVIDIA/GenerativeAIExamples/tree/main/industries/energy/simulation-workflow-agent/sim_agent/src/simulator_agent/skills/rag_skill
Command: npx skills add https://github.com/NVIDIA/GenerativeAIExamples --skill rag-skill-nvidia

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openai, langchain_core, pymilvus, requests, numpy, yaml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Retrieve official simulator manual content and example DATA files via a retrieval-augmented approach to provide authoritative, source-backed answers for keyword definitions, syntax, and usage patterns.

Core Features & Use Cases

  • RAG-based retrieval over simulator manual and example DATA files using a Milvus vector store and NVIDIA embeddings.
  • Provides keyword definitions, parameter tables, and concrete usage examples with source citations.
  • Use case: answer questions about COMPDAT keyword format or WELSPECS syntax by citing the manual and showing relevant DATA snippets.

Quick Start

Retrieve the simulator manual keyword definitions and related example DATA snippets for a given topic.

Frequently Asked Questions about rag_skill

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I look up simulator keyword definitions and syntax using retrieval-augmented generation?▼

Retrieval-augmented generation fetches official simulator manual content and DATA files via semantic search, returning keyword definitions and syntax backed by source citations. It integrates context and generates markdown responses.

Can I use Milvus and NVIDIA embeddings to search simulator manual content?▼

Yes, the retrieval-augmented approach uses a Milvus vector store and NVIDIA embeddings to perform semantic search over simulator manuals. It retrieves relevant context to provide authoritative answers.

What's the best way to find COMPDAT format and WELSPECS syntax from official documentation?▼

The best way is using retrieval-augmented generation to cite the simulator manual and show relevant DATA snippets for COMPDAT format or WELSPECS syntax. It provides parameter tables and concrete usage examples.

Does this retrieval pipeline work with LangChain for generating markdown responses?▼

Yes, the pipeline uses a LangChain LCEL chain to perform retrieval, context integration, and LLM-generated markdown responses. It connects the vector store and embeddings seamlessly.

How does semantic search handle parameter tables and example DATA snippets?▼

Semantic search embeds simulator manual content and example DATA files, retrieving parameter tables and concrete usage snippets for keyword queries. Results are integrated into LLM-generated markdown responses.

Do I need pymilvus and langchain_core installed to retrieve simulator documentation?▼

Yes, you need pymilvus for the vector store, langchain_core for the LCEL chain, and openai for the LLM. These dependencies enable semantic search and response generation over simulator docs.

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