doubleo7/src/deep_research/mod.rs

pub mod starter;
mod review;
mod tools;
feat: deep research agentic loop with rig AgentRunner + Gemma models Pins rig to the newest published crates.io release (0.41.0) instead of the git main branch, and adapts swear_cleanup's revise.rs to that release's API (OneOrMany::first() returns T directly, raw_completion folded into CompletionResponse::raw_response). The research agent (gemma4:26b) drives rig's AgentRunner tool-calling loop with two lean #[rig::tool_macro] tools — a DuckDuckGo HTML search and a page-text fetcher — to gather and cross-check findings. A second agent (gemma4-e4b) turns those findings into a structured report; the smaller/faster model suffices there since it's reformatting already- digested notes rather than doing multi-step research reasoning. 2026-08-14 10:40:09 +00:00			`pub mod starter;`
feat: add a reviewer agent that gates and redirects the research loop Adds a reviewer step (gemma4-e4b, fresh context) between gathering and writing: it uses rig's typed Extractor to judge whether the findings' conclusions actually follow from their cited sources, rather than relying on free-text parsing. research() is now a plain bounded loop — "the least agentic design that solves the problem", per rig's own workflow guidance — that reruns the researcher with the reviewer's solid_findings/gaps feedback folded into the next round's task until it approves or MAX_RESEARCH_ROUNDS runs out. 2026-08-14 10:58:39 +00:00			`mod review;`
feat: deep research agentic loop with rig AgentRunner + Gemma models Pins rig to the newest published crates.io release (0.41.0) instead of the git main branch, and adapts swear_cleanup's revise.rs to that release's API (OneOrMany::first() returns T directly, raw_completion folded into CompletionResponse::raw_response). The research agent (gemma4:26b) drives rig's AgentRunner tool-calling loop with two lean #[rig::tool_macro] tools — a DuckDuckGo HTML search and a page-text fetcher — to gather and cross-check findings. A second agent (gemma4-e4b) turns those findings into a structured report; the smaller/faster model suffices there since it's reformatting already- digested notes rather than doing multi-step research reasoning. 2026-08-14 10:40:09 +00:00			`mod tools;`