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ai-rag-pipeline

Build retrieval-augmented generation pipelines combining live web search with LLMs.

4|1|Updated Feb 10, 2026
Search Tags:#llm#rag#openrouter#web-search#retrieval-augmented-generation#knowledge-retrieval#inference-sh
One-click install
npx skills add https://github.com/Sheshiyer/brandmint-oracle-aleph --skill ai-rag-pipeline-sheshiyer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-rag-pipeline
Source: https://github.com/Sheshiyer/brandmint-oracle-aleph/tree/main/skills/external/inference-sh/upstream/ab546d072f1e/tools/llm/ai-rag-pipeline
Command: npx skills add https://github.com/Sheshiyer/brandmint-oracle-aleph --skill ai-rag-pipeline-sheshiyer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Build RAG pipelines that combine live web search with large language models to deliver up-to-date, grounded responses with citations for research, decision making, and knowledge management.

Core Features & Use Cases

  • Integrates retrieval, augmentation, and generation to produce grounded, source-backed results.
  • Ideal for AI agents, research assistants, and knowledge bases requiring current information and verifiable outputs.
  • Use Case: Create an automated research assistant that queries the web and returns summarized insights with sources.

Quick Start

Install the inference.sh CLI and run a basic RAG pipeline to query a topic and generate a grounded response.

Frequently Asked Questions about ai-rag-pipeline

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

FAQPage Schema
How do I build a RAG pipeline with live web search and LLMs?▼

A retrieval-augmented generation pipeline combines live web search with LLMs to deliver up-to-date, grounded responses with citations for research, decision making, and knowledge management.

What is retrieval-augmented generation used for in AI agents?▼

Retrieval-augmented generation is used in AI agents and research assistants to fetch current web information, enabling up-to-date research, fact-checking, and knowledge retrieval with source-backed outputs.

Can I use Claude, GPT-4, and Gemini with OpenRouter for RAG pipelines?▼

Yes, you can use Claude, GPT-4, and Gemini via OpenRouter to coordinate the generation phase of a RAG pipeline, producing grounded outputs from retrieved web search data.

How do I create an automated research assistant that returns sourced insights?▼

You create an automated research assistant by querying live web search tools like Tavily Search, then passing the augmented results to an LLM to generate summarized insights with sources.

Do I need API keys for Tavily Search and OpenRouter to set up RAG pipelines?▼

Yes, setting up RAG pipelines requires API keys for retrieval tools like Tavily Search or Exa Search and an OpenRouter key to access LLMs for generating grounded, sourced answers.

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