I built a local document Q&A tool around Gemma 4 E4B's 128K context — five days, no RAG, no cloud
Yash Kumar Saini developed a local document Q&A tool called DeepRead using Gemma 4 E4B's 128K context. The tool allows users to load PDFs and ask questions, providing answers with footnote citations linked to specific pages. DeepRead operates entirely offline, avoiding the complexities of traditional retrieval-augmented generation (RAG) methods.
- ▪DeepRead is built around Gemma 4 E4B's 128K context and runs on an 8 GB laptop GPU.
- ▪The tool processes PDFs as page images and answers questions with citations to specific pages.
- ▪It does not use RAG, aiming for simplicity and efficiency in handling research papers and internal documents.
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Story provenance
Source · retrieval · rights · ranking — open for full record
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Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/yashksaini/i-built-a-local-document-qa-tool-around-gemma-4-e4bs-128k-context-five-days-no-rag-no-cloud-2e1k |
| Publication time | Sun, 24 May 2026 07:16:52 +0000 |
| Retrieval time | 2026-05-24T07:37:31.206Z |
| Last seen | 2026-05-24T07:37:31.206Z |
| 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 · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | dTsUGpf9qEI8 |
| 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 — open original |
| Substitutes article? | No — 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 === 1242333) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Yash Kumar Saini Posted on May 24 I built a local document Q&A tool around Gemma 4 E4B's 128K context — five days, no RAG, no cloud #devchallenge #gemmachallenge #gemma Gemma 4 Challenge: Build With Gemma 4 Submission Five days, an 8 GB laptop GPU, and a stubborn belief that for the kind of documents I actually read — research papers, internal memos, the API docs of one project — RAG is over-engineering.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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