Building a RAG System in Rust with Qdrant, Rig, and gRPC π¦
The article discusses the development of a Retrieval-Augmented Generation (RAG) system using Rust, Qdrant, Rig, and gRPC. It emphasizes the importance of understanding the underlying mechanics of AI systems rather than just using frameworks for quick results. The author aims to provide a comprehensive tutorial that covers the essential components and their functions in building an effective RAG system.
- βͺRust is identified as a suitable language for building AI systems due to its speed and safety.
- βͺThe tutorial focuses on creating a complete RAG system rather than just a working demo.
- βͺKey components include Qdrant for vector search, Rig as the AI framework, and Tonic for gRPC API.
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Story provenance
Source Β· retrieval Β· rights Β· ranking β open for full record
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Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/parikalp_bhardwaj_9e9d812/understanding-rag-internals-by-building-one-in-rust-30c8 |
| Publication time | Sat, 30 May 2026 21:01:01 +0000 |
| Retrieval time | 2026-05-30T21:27:40.352Z |
| Last seen | 2026-05-30T21:27:40.352Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch Β· cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | OjhpfewZEX4g |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes β open original |
| Substitutes article? | No β link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3959021) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Parikalp Bhardwaj Posted on May 30 Building a RAG System in Rust with Qdrant, Rig, and gRPC π¦ #ai #rag #rust #tutorial With Qdrant, Rig, Tonic β and a healthy obsession with what's actually happening underneath. Why I Built This A few weeks ago I came across Our First Production-Ready RAG Dev Journey in Pure Rust by the rust-dd team β and something clicked.
β¦
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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