Replaces the rigid FSM after Intro/AskLanguage with a single LLM call per
turn that sees:
* persona (what's already known — slots not re-asked)
* recent 10 chat_log messages (history)
* top-5 kei-sage atoms relevant to user_text (graph-RAG, not embeddings)
* raw user_text
LLM returns JSON {slot_updates, response_text, done, focus} which drives
the next state + persona patch + reply. No embeddings, no vector store —
kei-sage's FTS5 + Obsidian-style atom graph is the retrieval layer.
New files:
* src/retrieval.rs (101 LOC) — retrieve_context(chat_log, topics,
chat_id, query, history_n, atoms_k) -> RetrievalContext
* src/conversational.rs (157 LOC) — conversational_step
(state, persona, context, text, extractor, lang) -> StepOutput
Modified:
* src/serve.rs::run_fsm — branch on state: Intro/AskLanguage still go
through legacy handle_step (jump-start); everything else routes to
conversational_step with retrieval context.
* src/lib.rs — module declarations.
Tests (5 new, 60 total passing):
* parses_well_formed_llm_response
* done_true_transitions_to_ready
* invalid_json_falls_back_gracefully
* retrieve_returns_empty_on_empty_stores
* retrieve_finds_seeded_data
Verify:
* cargo check -p kei-buddy: PASS
* cargo test -p kei-buddy --lib: 60/0 (was 55, +5)
Why graph-RAG instead of embeddings: kei-sage already in tree (atoms +
edges + BFS + PageRank + FTS5). Explicit edges (message → topic →
contact) beat opaque cosine similarity for personal-assistant memory
where relationships are typed. No sqlite-vec dep, no embedding cost.
NOT deployed yet — needs server rebuild.
157 lines
6.3 KiB
Rust
157 lines
6.3 KiB
Rust
// SPDX-License-Identifier: Apache-2.0
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//! LLM-driven conversational onboarding step (replaces rigid FSM after Intro/AskLanguage).
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//! Constructor Pattern: one responsibility — one LLM call, one structured response.
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use serde_json::{json, Value};
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use crate::{
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error::BuddyError,
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extractor::LlmExtractor,
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retrieval::RetrievalContext,
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state::OnboardState,
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strings::Lang,
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transition::StepOutput,
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};
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const PROMPT_TEMPLATE: &str = concat!(
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"You are KeiBuddy, a personal-assistant chatbot doing first-meeting onboarding.\n",
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"Be a warm, natural conversationalist. Reply in the user's language ({lang}).\n",
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"Don't act like a survey form — extract info from natural conversation.\n\n",
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"USER PROFILE (so far — fields with values are KNOWN, don't re-ask):\n{persona}\n\n",
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"RECENT CONVERSATION (latest first; \"u:\" user, \"b:\" you):\n{history}\n\n",
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"RELATED KNOWLEDGE (relevant atoms from user's graph):\n{atoms}\n\n",
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"USER JUST SAID:\n\"{text}\"\n\n",
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"YOUR JOB: Update persona fields you newly learned. Decide what to ask next.\n\n",
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"Output ONLY this JSON object, no prose, no markdown fences:\n",
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"{{\"slot_updates\":{{\"name\":\"<str>\",\"tone\":\"<str>\",\"interests\":[],",
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"\"hobbies\":[],\"schedule\":{{}},\"language\":\"<str>\"}},",
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"\"response_text\":\"<reply in {lang}, ≤300 chars>\",",
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"\"done\":false,\"focus\":\"name|tone|interests|hobbies|topics|schedule|free|done\"}}"
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);
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/// Drive one turn of free-form onboarding with a single LLM call.
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pub async fn conversational_step<E: LlmExtractor + ?Sized>(
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state: &OnboardState,
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persona: &Value,
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context: &RetrievalContext,
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user_text: &str,
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extractor: &E,
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lang: Lang,
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) -> Result<StepOutput, BuddyError> {
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let prompt = build_prompt(persona, context, user_text, lang);
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let raw = extractor.extract(&prompt, "").await?;
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match parse_llm_response(&raw, state) {
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Some(out) => Ok(out),
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None => Ok(fallback_output(state, lang)),
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}
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}
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fn build_prompt(persona: &Value, ctx: &RetrievalContext, text: &str, lang: Lang) -> String {
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let lc = lang.code();
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let ps = serde_json::to_string_pretty(persona).unwrap_or_default();
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let hs = if ctx.history.is_empty() { "(no prior messages)".into() } else { ctx.history.join("\n") };
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let atoms = if ctx.atoms.is_empty() { "(none)".into() } else { ctx.atoms.join("\n") };
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PROMPT_TEMPLATE
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.replace("{lang}", lc)
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.replace("{persona}", &ps)
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.replace("{history}", &hs)
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.replace("{atoms}", &atoms)
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.replace("{text}", text)
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}
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fn parse_llm_response(raw: &Value, current: &OnboardState) -> Option<StepOutput> {
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let obj = raw.as_object()?;
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let response_text = obj.get("response_text")?.as_str()?.to_owned();
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if response_text.is_empty() { return None; }
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let done = obj.get("done").and_then(|v| v.as_bool()).unwrap_or(false);
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let focus = obj.get("focus").and_then(|v| v.as_str()).unwrap_or("free");
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let slot_updates = obj.get("slot_updates").cloned().unwrap_or_else(|| json!({}));
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let next_state = if done { OnboardState::Ready } else { focus_to_state(focus, current) };
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Some(StepOutput { next_state, response_text, persona_patch: slot_updates })
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}
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fn focus_to_state(focus: &str, current: &OnboardState) -> OnboardState {
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match focus {
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"name" => OnboardState::AskName,
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"tone" => OnboardState::AskTone,
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"interests"=> OnboardState::AskInterests,
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"hobbies" => OnboardState::AskHobbies,
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"topics" => OnboardState::TopicSpecifics,
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"schedule" => OnboardState::AskSchedule,
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"done" => OnboardState::Ready,
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_ => current.clone(),
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}
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}
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fn fallback_output(state: &OnboardState, lang: Lang) -> StepOutput {
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let text = match lang {
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Lang::Ru => "Хм, дай-ка подумаю — можешь перефразировать?",
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Lang::En => "Hmm, let me think — could you rephrase?",
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};
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StepOutput { next_state: state.clone(), response_text: text.to_owned(), persona_patch: json!({}) }
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}
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// ── Tests ─────────────────────────────────────────────────────────────────────
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::extractor::MockExtractor;
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use serde_json::json;
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fn empty_ctx() -> RetrievalContext {
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RetrievalContext { history: vec![], atoms: vec![] }
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}
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fn rt() -> tokio::runtime::Runtime {
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tokio::runtime::Runtime::new().unwrap()
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}
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#[test]
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fn parses_well_formed_llm_response() {
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rt().block_on(async {
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let mock = MockExtractor::new(json!({
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"slot_updates": { "name": "Alice" },
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"response_text": "Nice to meet you! What are your interests?",
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"done": false,
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"focus": "interests"
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}));
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let out = conversational_step(
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&OnboardState::AskName, &json!({}), &empty_ctx(),
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"I'm Alice", &mock, Lang::En,
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).await.unwrap();
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assert_eq!(out.next_state, OnboardState::AskInterests);
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assert!(!out.response_text.is_empty());
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assert_eq!(out.persona_patch["name"].as_str(), Some("Alice"));
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});
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}
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#[test]
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fn done_true_transitions_to_ready() {
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rt().block_on(async {
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let mock = MockExtractor::new(json!({
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"slot_updates": {},
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"response_text": "All set!",
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"done": true,
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"focus": "done"
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}));
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let out = conversational_step(
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&OnboardState::AskSchedule, &json!({}), &empty_ctx(),
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"mornings at 9", &mock, Lang::En,
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).await.unwrap();
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assert_eq!(out.next_state, OnboardState::Ready);
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});
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}
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#[test]
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fn invalid_json_falls_back_gracefully() {
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rt().block_on(async {
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let mock = MockExtractor::new(json!("not a json object at all"));
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let out = conversational_step(
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&OnboardState::AskInterests, &json!({}), &empty_ctx(),
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"hello", &mock, Lang::En,
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).await.unwrap();
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assert_eq!(out.next_state, OnboardState::AskInterests);
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assert!(!out.response_text.is_empty());
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});
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}
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}
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