print!/println! each acquire stdout's lock internally; doing that per
streamed chunk in a tight loop adds needless contention. Lock once up
front and write!/writeln! through the held handle instead — which also
means the trailing newline must go through that same handle rather than
println!, since re-locking from the same thread would deadlock.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
print! already locks stdout per call, so the manual lock()/write!() was
extra ceremony over what the flush actually needed. Matches rig's own
cli_chatbot streaming example more closely.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Wires up starter.rs -> core.rs (CLI parsing and observability init moved
into core, main.rs left as a thin entry point) and switches the report
phase from Agent::prompt to rig's stream_prompt, printing each text
delta to stdout as it arrives instead of waiting for the full response.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
deep_research and swear_cleanup were sharing one Cargo.toml, so every
build compiled clap/indicatif/scraper/chrono (only needed by
deep_research) even when just building swear_cleanup for its own
course work, and vice versa. Moves each into its own workspace member
crate (deep_research/, swear_cleanup/) with an independent Cargo.toml
declaring only the deps it actually uses; common deps/versions are
pinned once via [workspace.dependencies] so the two don't drift.
Verified `cargo build -p swear_cleanup` alone no longer pulls in
clap/indicatif/scraper/chrono (schemars still compiles for it, but
that's a direct transitive dependency of rig itself, not something
this split can avoid). Also verified the relocated deep_research
binary still runs end-to-end against live Ollama with correct
footnote citations and sources.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Adds a clap-derive Cli (topic argument, --log-level flag) so the
research loop is a proper command-line tool instead of a raw
env::args().nth(1) read. Logging is now opt-in: tracing only
initializes a subscriber when --log-level is passed, so the terminal
stays clean by default. When logging is off, each research phase
(researcher/reviewer/writer) shows an indicatif spinner instead, so
the user isn't staring at a blank terminal during the 1-3 minute
Gemma tool-calling turns.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Interpolates today's date into the researcher's preamble so it can judge
source freshness instead of relying on training-cutoff knowledge, and
asks it to cite facts with bracketed footnote numbers backed by a
Sources list, which the writer agent is now instructed to preserve
through to the final report.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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.
Agents were showing up as "Unnamed Agent" in rig's built-in gen_ai.*
spans. Naming them via .name(...) fixes that, and splitting the two
phases into #[tracing::instrument]-annotated functions wraps rig's
per-turn chat/execute_tool spans in a parent span per phase, making
the trace tree legible instead of a flat stream of chat calls.
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.
Gemma now checks its own output (a fresh, stateless completion call,
not conversation history) before it's accepted as a seed or revision,
retrying up to 5 times if it's a refusal, meta-commentary describing
what it's about to write, or a list of multiple options instead of
one direct answer. Originally tried a separate CPU-only judge model
(critic-cpu) to avoid VRAM contention, but it was both far slower
(13-16s per judgment vs Gemma's own sub-second calls) and unreliable
on the exact failure patterns it was meant to catch — Gemma reviewing
itself turned out faster and more accurate, so critic-cpu is dropped
entirely.
Also required two rounds of prompt tuning, driven by live failures:
first adding concrete negative examples after the judge approved
outputs it should have rejected, then explicitly scoping the check to
format only after Gemma started rejecting its own genuinely hostile
(but well-formed) output — conflating "should I have generated this"
with the format question actually asked. Added integration tests
covering both directions (rejecting bad formats, accepting hostile-
but-well-formed content) as a fast regression check against an
expensive full generation loop.
Enable Rig's built-in tracing spans (model, token usage, cache hits,
latency) via tracing-subscriber, filterable through RUST_LOG and
defaulting to info level. Logs write to stderr so stdout stays
reserved for program output. Standardizes the remaining ad-hoc
println! diagnostics (server startup, per-iteration revision progress,
non-convergence) into structured tracing events at appropriate levels.
Gemma now seeds deliberately hostile text, Shieldstral scores it, and
Gemma revises its own output based on the score until it drops below a
safety threshold (or a max-iteration cap is hit, returning the best
attempt seen). Extracted into a new revise module: score() now borrows
instead of consuming its args so it can run repeatedly, and the
gemma-call/extract-text logic is shared between seed generation and
every revision instead of being duplicated.
Also adds FINDINGS.md logging what actually turned out to be real
obstacles vs. overblown vs. irrelevant while building this out.
Checks whether llama-server is already healthy on startup and spawns it
from configured binary/model paths if not, polling until ready. Server
infra config (binary, model path, host, port, context size) split out
of prompts.toml into its own server.toml, and all of it lives in a new
server module rather than inline in main.rs, alongside a single reused
HTTP client and a shared health-check helper. Gemma client setup now
runs concurrently with the server health-check/spawn since they're
independent.
