doubleo7/src/main.rs

pub mod swear_cleanup;
pub mod deep_research;

#[tokio::main]
pub async fn main() -> anyhow::Result<()> {

   // swear_cleanup::starter::run().await?;

   deep_research::starter::start().await?;

   Ok(())
}
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 swear_cleanup;`
			`pub mod deep_research;`
feat: Introduce two agent flow with profanity verification. 2026-08-05 12:21:38 +00:00
Add rig-core and tokio dependencies, async main 2026-07-31 08:21:31 +00:00			`#[tokio::main]`
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 async fn main() -> anyhow::Result<()> {`
feat: Introduce two agent flow with profanity verification. 2026-08-05 12:21:38 +00:00
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			`// swear_cleanup::starter::run().await?;`
feat: Introduce two agent flow with profanity verification. 2026-08-05 12:21:38 +00:00
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			`deep_research::starter::start().await?;`
feat: Introduce two agent flow with profanity verification. 2026-08-05 12:21:38 +00:00
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			`Ok(())`
feat: Introduce two agent flow with profanity verification. 2026-08-05 12:21:38 +00:00			`}`