rag-pipeline

Deploy a Rust-native RAG pipeline with Candle embeddings and hybrid retrieval.

16|Updated Mar 6, 2026

Search Tags:#rag #retrieval #rust #reranking #embedding #hybrid-search

Ready to run

npx skills add https://github.com/pomazanbohdan/vida-stack --skill rag-pipeline-pomazanbohdan

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rag-pipeline
Source: https://github.com/pomazanbohdan/vida-stack/tree/main/.agents/skills/rag-pipeline
Command: npx skills add https://github.com/pomazanbohdan/vida-stack --skill rag-pipeline-pomazanbohdan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Centralizes the end-to-end creation of a Rust-native RAG pipeline that performs local embeddings, hybrid retrieval, and reranking to enable offline knowledge access.

Core Features & Use Cases

Local embeddings via Candle for Rust projects without external API calls
Dense + sparse retrieval with hybrid fusion and reranking
End-to-end evaluation of retrieval quality (P@k, NDCG, MRR) for knowledge-heavy apps

Quick Start

Run a sample Rust-based RAG pipeline against a small document set to verify embedding, retrieval, and ranking.

Frequently Asked Questions about rag-pipeline

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

FAQPage Schema

How do I build a Rust RAG pipeline with local embeddings and no external API calls?▼

You can build a Rust RAG pipeline with local embeddings using Candle for vector generation without external API calls. This enables offline search by combining chunking, dense and sparse retrieval, hybrid fusion, and reranking over local document sets.

What is hybrid retrieval and reranking in a local RAG pipeline?▼

Hybrid retrieval in a local RAG pipeline combines dense and sparse search to match semantic and keyword queries. Reranking then refines these fused results to improve contextual accuracy for offline knowledge access from documents and code repositories.

Can I use Candle for local embeddings in my Rust knowledge base project?▼

Yes, you can use Candle for local embeddings in a Rust knowledge base project. It enables offline vector generation without external API calls, supporting fast contextual retrieval and hybrid search from documents, code repositories, and internal docs.

What's the best way to evaluate retrieval quality in a Rust-native RAG pipeline?▼

The best way to evaluate retrieval quality in a Rust-native RAG pipeline is to calculate ranking metrics like P@k, NDCG, and MRR. These metrics measure how accurately the hybrid fusion and reranking return relevant contextual documents for knowledge-heavy apps.

How do I deploy an offline RAG pipeline for code repositories and internal docs?▼

To deploy an offline RAG pipeline for code repositories and internal docs, use a Rust-native setup with Candle for local embeddings. Apply chunking and preprocessing, then execute dense and sparse retrieval with hybrid fusion and reranking to enable fast local search.

Why does my RAG pipeline need both dense and sparse retrieval?▼

Your RAG pipeline needs both dense and sparse retrieval to enable hybrid fusion, capturing both semantic context and exact keyword matches. This combination improves search accuracy before reranking the final contextual results for knowledge-heavy apps.