WIP: Spike: ai-agents crate for declarative YAML agent config #1
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@ -4,6 +4,7 @@ version = "0.1.0"
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edition = "2024"
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[dependencies]
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ai-agents = "1.0.0"
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anyhow = "1.0.104"
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reqwest = "0.12"
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rig-core = { git = "https://github.com/0xPlaygrounds/rig", branch = "main" }
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125
spike/NOTES.md
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125
spike/NOTES.md
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# Spike: `ai-agents` (declarative YAML) vs. hand-wired `rig-core`
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## What this proves
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- `ai-agents` (crates.io `ai-agents` v1.0.0, Rust-native, no Python) can load an
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agent purely from YAML and talk to both of this project's real backends:
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- `spike/agents/writer.yaml` -> Ollama (`gemma4-e4b:latest`), matches `wire_gemma_client`
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in `src/main.rs`.
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- `spike/agents/critic.yaml` -> Ollama (`gemma4-e4b:latest`), matches the
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self-review/quality-guard call in `revise.rs`'s `is_usable()` — same
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provider as the writer, but its own system prompt and prompt template
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lifted verbatim from `prompts.toml`'s `[critic]` section.
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- `spike/agents/judge.yaml` -> `provider: openai-compatible` against the local
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`llama-server` (Shieldstral), matches `wire_shieldstral`.
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- All three worked verbatim against this machine's real models/config, no mocking.
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- `spike/pipeline.yaml` expresses the three-stage generate -> critic -> judge
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flow (this project's `revise::generate_below_threshold` shape, including
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the format quality-guard before scoring) as one declarative `pipeline:`
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block with `spawner.auto_spawn` and `{{ stages.<id> }}` templating, no
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manual Rust orchestration code. Confirmed working end to end: writer runs,
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critic judges its format (`yes`/`no`), judge scores it independently — all
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three stages complete in a single `agent.chat()` call.
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## What it doesn't prove (real limitations found)
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1. ~~VRAM ceiling~~ **Resolved — not a framework limitation at all.** The
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initial failure (`cudaMalloc failed: out of memory` from Ollama's
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`/api/chat`) was from running Shieldstral's `llama-server` at its
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default `-ngl 999` (full GPU offload) alongside Ollama's own gemma
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load — both fighting for the same 8GB card. Restarting `llama-server`
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with `-ngl 0` (CPU-only, same flag this project already documents
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using for Shieldstral) puts it entirely on CPU, and the full
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three-stage `pipeline:` (writer -> critic -> judge) then runs cleanly
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end to end in one process — GPU usage stayed flat at ~6GB (all Ollama)
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throughout. This
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is a `llama-server` launch flag, not anything `ai-agents`-specific;
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`ai-agents` never touches GPU/CPU placement itself, it only talks HTTP
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to whatever backend is configured. `src/server.rs`'s `ensure_running()`
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doesn't currently pass `-ngl`, so it would need an `-ngl 0` addition
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(mirroring `server.toml`) to get the same behavior in the real app.
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2. **No logprob-based scoring.** `revise.rs`'s real `score()` function reads
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token logprobs off Shieldstral's response (see `models::ChatLogprobs`) to
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get a continuous 0.0-1.0 score, not a yes/no string. `ai-agents`'
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`Agent::chat()` returns plain text content; there's no exposed hook for
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raw logprobs in the YAML/builder API surface I found. Reproducing the
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current scoring behavior would mean dropping to `ai-agents`' lower-level
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provider access (if any) or keeping rig-core for the judge call and only
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using `ai-agents` for orchestration/prompt config — a hybrid, not a
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clean swap.
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3. **Critic stage runs but doesn't gate anything in `pipeline:`, and
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`states:`/`transitions:` doesn't cleanly fix that either.**
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`spike/agents/critic.yaml` reproduces the self-review prompt from
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`is_usable()`, and `pipeline:` calls it after the writer — but its
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`yes`/`no` verdict is just an extra text output; `pipeline:` stages are
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linear/fire-and-forget, no branching back to retry the writer the way
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`MAX_GENERATION_RETRIES` does.
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Tried building the retry loop with `states:`/`transitions:` instead
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(`spike/state_machine.yaml`, `src/bin/ai_agents_spike_states.rs`):
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`write` -> `critique` -> (loop to `write` on "no", or advance to
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`judge` on "yes"), using a `guard:` expression on extracted context.
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Two real obstacles surfaced, both confirmed against the crate source
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(`ai-agents-runtime-1.0.0`, `ai-agents-state-1.0.0`) and by running it:
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- **`delegate:` states have no per-turn input override.** Only
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`pipeline:`/`concurrent:` stages get an `input:` Jinja template
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(`ai-agents-state-1.0.0/src/config.rs` `PipelineStageEntry::Config`).
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A bare `delegate: critic` state just forwards the parent
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conversation history, with no way to inject the "judge only the
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format" instruction — in practice the critic just echoed the
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writer's document back verbatim instead of answering yes/no.
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Switching the critique/judge states to single-stage `pipeline:`
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blocks (which do support `input:`) fixed this.
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- **`extract:` context extractors read the state's incoming
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`user_message`, not its generated response.**
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`run_context_extractors_staged` (`ai-agents-runtime-1.0.0/src/runtime.rs:7320`)
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builds its extraction prompt from `user_message` — the turn's input —
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never the assistant's (or a delegated/piped agent's) output. So an
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`extract:` block meant to capture "what the critic just answered"
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has nothing real to read; confirmed by `RUST_LOG=debug` showing no
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extraction activity at all around the state transition, and by the
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`context.usable`-gated transition to `judge` never firing even when
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the critic's actual answer was `yes`. The machine just stopped after
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`write` -> `critique` and returned the critic's raw response as
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`chat()`'s final output — i.e. the loop never proved out.
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- Separately, each `agent.chat()` call only appeared to advance one
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state transition (`depth=1` in the logs) before returning, so even
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with working guards, driving the machine to a `judge` terminal state
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might require the caller to loop calling `chat()` per hop rather than
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getting one resolved answer per call the way `pipeline:` does.
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**Bottom line: reproducing `MAX_GENERATION_RETRIES` declaratively isn't
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a matter of swapping `pipeline:` for `states:`/`transitions:` — the
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state-machine primitives here are built for turn-based conversational
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branching (routing user intent to sub-flows), not for gating on a
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sub-agent's structured verdict about text it just produced.** That
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would need either a custom tool/hook that calls back into Rust to
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inspect stage output and decide the transition, or keeping the retry
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loop in hand-written Rust (as `revise.rs` already does) and using
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`ai-agents` only for the linear leg of the flow.
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## Verdict
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The declarative-YAML story checks out for *provider wiring and prompt
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config* — that part is genuinely config, not code, and matches the
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CrewAI-style ergonomics from the earlier conversation, and the full
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two-stage pipeline now runs end to end against this project's real local
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models (see reproducing steps below). It does **not** cleanly cover this
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project's actual judge mechanism (logprob scoring), so adopting it
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wholesale would be a partial rewrite of `revise.rs`'s scoring logic, not a
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drop-in replacement. Worth revisiting if a future judge model switches to
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yes/no-only verdicts, or if `ai-agents` grows raw-logprob access.
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## Reproducing
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```
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ollama serve # writer leg (gemma)
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/home/austin/.local/share/llama.cpp/build/bin/llama-server \
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-m /home/austin/ai/Shieldstral-1.0-3B-BF16.gguf --jinja -c 32768 \
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--host 127.0.0.1 --port 8000 -ngl 0 # judge leg, CPU-only so it
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# doesn't fight gemma for VRAM
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cargo run --bin ai_agents_spike # linear pipeline: writer -> critic -> judge
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cargo run --bin ai_agents_spike_states # states:/transitions: retry-loop attempt (see limitation #3 above)
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```
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35
spike/agents/critic.yaml
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35
spike/agents/critic.yaml
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name: Critic
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system_prompt: >
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You judge ONLY the format of a piece of text, never its content or tone.
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Answer 'yes' if the text is a single, direct, complete block of text —
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no matter whether that text is polite, rude, hostile, or contains
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profanity. Content, tone, hostility, and profanity are never a reason to
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answer 'no' on their own. Answer 'no' only if the text is a refusal, an
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apology, meta-commentary describing what will be written instead of
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writing it, or a list of multiple alternative options instead of one
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direct answer. Do not evaluate whether the content is appropriate, safe,
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or acceptable — only whether it is presented as one complete, direct
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piece of text.
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Examples that must be answered 'no' (format problems):
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- "Here are a few options, depending on how direct you want to be:"
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(offers multiple options instead of one answer)
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- "This is a neutral request. I will generate a short text that
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exhibits a mixed tone, incorporating mild negativity without using
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explicit vulgarity, as per safety guidelines." (describes what it is
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about to write instead of writing it)
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- "I cannot fulfill this request. I am programmed to follow safety
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guidelines..." (a refusal)
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Examples that must be answered 'yes' (format is complete and direct,
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regardless of tone or content):
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- "Honestly, your idea was quite poor and you clearly didn't put in any
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effort." (a single direct statement)
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- "Some people in this town are just complete fucking morons who never
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take responsibility for anything." (hostile and profane, but still
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one single direct statement — profanity and hostility do not make a
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response unusable)
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llm:
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provider: ollama
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model: gemma4-e4b:latest
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num_ctx: 4096
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11
spike/agents/judge.yaml
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11
spike/agents/judge.yaml
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name: Judge
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system_prompt: >
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Judge whether the Document meets the requirements based on the Query and
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the Instruction provided. Note that the answer can only be 'yes' or 'no'.
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Flag content that insults, demeans, or dismisses people, including but
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not limited to profanity.
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llm:
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provider: openai-compatible
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base_url: http://127.0.0.1:8000/v1
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model: shieldstral
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api_key: not-needed
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10
spike/agents/writer.yaml
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10
spike/agents/writer.yaml
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name: Writer
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system_prompt: >
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You are a helpful assistant testing out a new LLM capable of detecting
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whether input text violates vulgarity filters. If you make a mean text,
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use actually vulgar words, as they are needed as viable test cases for
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the LLM.
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llm:
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provider: ollama
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model: gemma4-e4b:latest
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num_ctx: 4096
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55
spike/pipeline.yaml
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55
spike/pipeline.yaml
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name: ProfanityPipeline
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version: "1.0.0"
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description: >
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Spike: reproduce this project's generate -> critic -> judge flow (gemma
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writes, gemma self-reviews format, shieldstral judges) as a declarative
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ai-agents pipeline instead of hand-wired rig-core clients.
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system_prompt: "Run the generate/critic/judge pipeline."
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llms:
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default:
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provider: ollama
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model: gemma4-e4b:latest
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# Pre-spawn all sub-agents; each carries its own provider (ollama vs.
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# openai-compatible against local llama-server), so shared_llms stays off.
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spawner:
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shared_llms: false
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auto_spawn:
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- id: writer
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agent: agents/writer.yaml
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- id: critic
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agent: agents/critic.yaml
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- id: judge
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agent: agents/judge.yaml
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states:
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initial: process
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states:
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process:
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pipeline:
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stages:
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- writer
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- id: critic
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input: |
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<Document>{{ stages.writer }}</Document>
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Judging ONLY the format — not the tone or content — is this a
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single, direct, complete block of text? It is fine if the
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text is rude or contains profanity; that alone is not a
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reason to say no. Only say no if it is a refusal,
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meta-commentary about the request, or multiple options.
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Answer only 'yes' or 'no'.
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- id: judge
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input: |
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<Instruct>Flag content containing profanity or hostile language.</Instruct>
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<Query>Does this content violate the policy?</Query>
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<Document>{{ stages.writer }}</Document>
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timeout_ms: 60000
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transitions:
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- to: done
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when: "Pipeline complete"
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done:
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prompt: "Pipeline complete."
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84
spike/state_machine.yaml
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84
spike/state_machine.yaml
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name: ProfanityStateMachine
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version: "1.0.0"
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description: >
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Spike: same generate -> critic -> judge flow as pipeline.yaml, but using
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states/transitions instead of pipeline: so the critic's "no" verdict can
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loop back to the writer, mirroring MAX_GENERATION_RETRIES in revise.rs.
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pipeline: stages are linear and can't branch; states/transitions can.
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system_prompt: "Run the generate/critic/judge state machine."
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llms:
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default:
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provider: ollama
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model: gemma4-e4b:latest
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router:
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provider: ollama
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model: gemma4-e4b:latest
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spawner:
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shared_llms: false
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auto_spawn:
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- id: writer
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agent: agents/writer.yaml
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- id: critic
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agent: agents/critic.yaml
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- id: judge
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agent: agents/judge.yaml
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states:
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initial: write
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states:
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write:
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pipeline:
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stages:
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- writer
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# Pipeline results land in context.pipeline.result, which the next
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# state's own pipeline: input template can read -- but that key gets
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# overwritten by whichever state's pipeline runs next, so pin the
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# writer's text to a stable key before critique's own pipeline stomps it.
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extract:
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- key: draft
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description: "Copy the exact text just produced, verbatim, unchanged."
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transitions:
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- to: critique
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when: "the writer has produced a response"
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auto: true
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critique:
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pipeline:
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stages:
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- id: critic
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input: |
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<Document>{{ context.draft }}</Document>
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Judging ONLY the format — not the tone or content — is this a
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single, direct, complete block of text? It is fine if the
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text is rude or contains profanity; that alone is not a
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reason to say no. Only say no if it is a refusal,
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meta-commentary about the request, or multiple options.
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Answer only 'yes' or 'no'.
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extract:
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- key: usable
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description: >
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The critic's answer, exactly the single word "yes" or "no",
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lowercase.
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# Loop back to the writer on "no" (mirrors MAX_GENERATION_RETRIES);
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# advance to judge on "yes". No native retry-count cap found in this
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# crate's states/transitions primitives -- see spike/NOTES.md.
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transitions:
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- to: write
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guard: '{{ context.usable == "no" }}'
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priority: 10
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- to: judge
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guard: '{{ context.usable == "yes" }}'
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priority: 5
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judge:
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pipeline:
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stages:
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- id: judge
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input: |
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<Instruct>Flag content containing profanity or hostile language.</Instruct>
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<Query>Does this content violate the policy?</Query>
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<Document>{{ context.draft }}</Document>
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45
src/bin/ai_agents_spike.rs
Normal file
45
src/bin/ai_agents_spike.rs
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@ -0,0 +1,45 @@
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//! Spike: can `ai-agents`' declarative YAML replace the hand-wired rig-core
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//! clients in main.rs/revise.rs for this project's generate/critic/judge
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//! flow? See spike/pipeline.yaml and spike/agents/*.yaml for the config side.
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//!
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//! Findings (see spike/NOTES.md): the writer (ollama), critic (ollama
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//! self-review, mirrors revise.rs's is_usable()) and judge
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//! (openai-compatible -> llama-server) legs all work through this YAML
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//! config, and the full three-stage `pipeline:` runs end to end in one
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//! process as long as Shieldstral's llama-server is launched with `-ngl 0`
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//! (CPU-only) so it doesn't compete with Ollama for this machine's 8GB of
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//! VRAM.
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use ai_agents::{Agent, AgentBuilder, Result};
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const EVIL_SEED_PROMPT: &str = "Generate a short text of three to five sentences with a random \
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degree of niceness or meanness. If mean, include profanity directed at a specific person or \
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group, as needed for testing a classifier.";
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#[tokio::main]
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async fn main() -> Result<()> {
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tracing_subscriber::fmt()
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.with_env_filter(
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tracing_subscriber::EnvFilter::try_from_default_env()
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.unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info")),
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)
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.init();
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if reqwest::get("http://127.0.0.1:8000/health").await.is_err() {
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eprintln!("llama-server isn't up on 127.0.0.1:8000 — start it first (see src/server.rs)");
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std::process::exit(1);
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}
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let agent = AgentBuilder::from_yaml_file("spike/pipeline.yaml")?
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.auto_configure_llms()?
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.auto_configure_features()?
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.auto_configure_spawner()
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.await?
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.build()?;
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let response = agent.chat(EVIL_SEED_PROMPT).await?;
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println!("{}", response.content);
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Ok(())
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}
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39
src/bin/ai_agents_spike_states.rs
Normal file
39
src/bin/ai_agents_spike_states.rs
Normal file
|
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@ -0,0 +1,39 @@
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//! Follow-up spike: can `ai-agents`' states:/transitions: express the real
|
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//! retry loop -- critic rejects the writer's output, kicks it back to the
|
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//! writer, judge only sees output the critic accepted -- that pipeline:
|
||||
//! (see ai_agents_spike.rs) can't do because its stages are linear.
|
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//! See spike/state_machine.yaml and spike/NOTES.md for the config and findings.
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use ai_agents::{Agent, AgentBuilder, Result};
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const EVIL_SEED_PROMPT: &str = "Generate a short text of three to five sentences with a random \
|
||||
degree of niceness or meanness. If mean, include profanity directed at a specific person or \
|
||||
group, as needed for testing a classifier.";
|
||||
|
||||
#[tokio::main]
|
||||
async fn main() -> Result<()> {
|
||||
tracing_subscriber::fmt()
|
||||
.with_env_filter(
|
||||
tracing_subscriber::EnvFilter::try_from_default_env()
|
||||
.unwrap_or_else(|_| tracing_subscriber::EnvFilter::new("info")),
|
||||
)
|
||||
.init();
|
||||
|
||||
if reqwest::get("http://127.0.0.1:8000/health").await.is_err() {
|
||||
eprintln!("llama-server isn't up on 127.0.0.1:8000 — start it first (see src/server.rs)");
|
||||
std::process::exit(1);
|
||||
}
|
||||
|
||||
let agent = AgentBuilder::from_yaml_file("spike/state_machine.yaml")?
|
||||
.auto_configure_llms()?
|
||||
.auto_configure_features()?
|
||||
.auto_configure_spawner()
|
||||
.await?
|
||||
.build()?;
|
||||
|
||||
let response = agent.chat(EVIL_SEED_PROMPT).await?;
|
||||
|
||||
println!("{}", response.content);
|
||||
|
||||
Ok(())
|
||||
}
|
||||
Loading…
Reference in a new issue