PranshulSoni/protonsearch

Description and use

**Brief Summary**
ProtonSearch is a fast, lightweight, Windows‑only launcher written in Rust that lets you search apps, files, OCR‑extracted text, clipboard and browser history, Git activity, system settings, custom commands, and even AI agents—all from a single shortcut. It bundles a local‑first search index with optional AI‑enhanced retrieval, making it a convenient front‑end for personal productivity and rapid prototyping of RAG or agent‑based workflows.

**Value**

• **All‑in‑one search surface** – eliminates the need for multiple tools (file explorer, clipboard manager, Git UI, etc.).
• **AI‑ready** – built‑in hooks let you attach LLMs or vector stores to enrich results, so you can prototype “search‑augmented” features without assembling a stack from scratch.
• **Local‑first & privacy‑focused** – data never leaves the machine, which is ideal for confidential work environments.

**Practical Adoption Path**

1. **Evaluate** – Clone the repo, run the provided CLI (protonsearch --help) to verify basic search on your Windows workstation.
2. **Integrate AI** – Use the exposed API/SDK to plug in your preferred embedding model or vector DB (e.g., OpenAI embeddings + Pinecone) and configure RAG pipelines via the supplied config files.
3. **Extend** – Add custom commands or plug‑ins (Rust or via the CLI) to surface internal tools, CI pipelines, or domain‑specific data sources.
4. **Deploy Internally** – Package the binary with an installer script for team machines; optionally bundle a lightweight vector store (e.g., Qdrant) for shared AI‑enhanced search.

**Production Readiness**

• **Maturity**: Medium. The project is actively maintained (last commit 2026‑07‑04), has 56 stars and 6 forks, and the core functionality (local indexing, UI) is stable.
• **Dependencies**: Pure Rust with minimal external binaries, simplifying security vetting; however, AI extensions introduce third‑party services that must be reviewed.
• **Risks**: License compliance, long‑term maintainer commitment, and security posture of any added AI endpoints need a final check before mission‑critical deployment.
• **Fit**: Well‑suited for prototypes, internal tooling, or as a “search layer” in larger products, but a production rollout should include dependency audits, CI testing, and a fallback to the non‑AI mode.

Use in a workflow

PranshulSoni/protonsearch helps add AI capability without starting from a blank model stack.

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.