doubleo7/src/deep_research/starter.rs

use rig::client::{AgentClientExt, Nothing};
use rig::completion::Prompt;
use rig::providers::ollama;
use crate::deep_research::tools::{FetchPage, SearchWeb};

/// The tool-calling research loop needs to reliably decide what to search
/// for, when a page is worth fetching, and when it has enough evidence —
/// that's a reasoning-heavy job best given to the largest local Gemma
/// variant. Turning the gathered notes into prose afterwards is comparatively
/// mechanical, so the smaller/faster variant handles that pass instead.
const RESEARCHER_MODEL: &str = "gemma4:26b";
const WRITER_MODEL: &str = "gemma4-e4b:latest";
const MAX_RESEARCH_TURNS: usize = 12;

const DEFAULT_TOPIC: &str =
    "What are the latest advances in running large language models locally, on consumer hardware?";

fn initialize_observability() {
    tracing_subscriber::fmt()
        .with_env_filter(
            tracing_subscriber::EnvFilter::try_from_default_env()
                .unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info")),
        )
        .with_span_events(tracing_subscriber::fmt::format::FmtSpan::CLOSE)
        .with_writer(std::io::stderr)
        .init();
}

/// Performs deep research via a two-stage agentic flow: a tool-calling agent
/// gathers and cross-checks evidence from the web, then a second agent turns
/// those raw notes into a structured report.
pub(crate) async fn start() -> anyhow::Result<()> {
    initialize_observability();

    let topic = std::env::args().nth(1).unwrap_or_else(|| DEFAULT_TOPIC.to_string());

    let report = research(&topic).await?;

    println!("{report}");

    Ok(())
}

async fn research(topic: &str) -> anyhow::Result<String> {
    let client = ollama::Client::new(Nothing)?;

    let findings = gather_findings(&client, topic).await?;
    let report = write_report(&client, topic, &findings).await?;

    Ok(report)
}

/// Wraps the tool-calling research loop in its own span so it's visible as a
/// single unit in traces, distinct from the writing phase and nesting rig's
/// own per-turn `chat`/`execute_tool` spans underneath it.
#[tracing::instrument(skip(client), fields(gen_ai.agent.name = "researcher"))]
async fn gather_findings(client: &ollama::Client, topic: &str) -> anyhow::Result<String> {
    let researcher = client
        .agent(RESEARCHER_MODEL)
        .name("researcher")
        .preamble(
            "You are a meticulous research assistant. Use the search_web and fetch_page tools to \
             investigate the user's topic: run several searches with varied phrasing, fetch the \
             most promising pages, and cross-check claims across at least two sources before \
             trusting them. Once you are confident you have enough evidence, stop calling tools \
             and reply with a plain-text dump of every fact you gathered, the source URL it came \
             from, and any open questions or contradictions between sources. This is raw research \
             material for a writer, not a final report, so favor completeness over polish.",
        )
        .tool(SearchWeb)
        .tool(FetchPage)
        .build();

    let findings = researcher
        .runner(topic)
        .max_turns(MAX_RESEARCH_TURNS)
        .run()
        .await?
        .output;

    tracing::info!(findings = %findings, "research phase complete");

    Ok(findings)
}

#[tracing::instrument(skip(client, findings), fields(gen_ai.agent.name = "writer"))]
async fn write_report(client: &ollama::Client, topic: &str, findings: &str) -> anyhow::Result<String> {
    let writer = client
        .agent(WRITER_MODEL)
        .name("writer")
        .preamble(
            "You turn raw research notes into a clear, well-organized report for the reader. \
             Structure the report with headings, cite source URLs inline next to the claims they \
             support, and call out any open questions or contradictions the research turned up. Do \
             not invent facts beyond what the notes provide.",
        )
        .build();

    let report = writer
        .prompt(format!("Topic: {topic}\n\nResearch notes:\n{findings}"))
        .await?;

    Ok(report)
}