feat: wire real LLM runner via third-one or OpenAI-compatible API
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user
133
internal/workflows/llm_runner.go
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133
internal/workflows/llm_runner.go
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@@ -0,0 +1,133 @@
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package workflows
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import (
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"context"
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"encoding/json"
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"errors"
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"fmt"
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"strings"
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"tutor/internal/llm"
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)
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type LLMRunner struct {
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client *llm.Client
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}
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func NewLLMRunner(client *llm.Client) *LLMRunner {
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return &LLMRunner{client: client}
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}
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func (r *LLMRunner) DiagnoseJobSeeker(ctx context.Context, input DiagnosticInput) (DiagnosticResult, error) {
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raw, err := r.client.ChatJSON(ctx, diagnoseSystemPrompt(), diagnoseUserPrompt(input), true)
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if err != nil {
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return DiagnosticResult{}, fmt.Errorf("diagnose_job_seeker: %w", err)
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}
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var result DiagnosticResult
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if err := extractJSON(raw, &result); err != nil {
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return DiagnosticResult{}, fmt.Errorf("diagnose_job_seeker parse: %w", err)
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}
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return result, nil
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}
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func (r *LLMRunner) GradeInterviewAnswer(ctx context.Context, input GradeAnswerInput) (GradedAnswer, error) {
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raw, err := r.client.ChatJSON(ctx, gradeAnswerSystemPrompt(), gradeAnswerUserPrompt(input), true)
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if err != nil {
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return GradedAnswer{}, fmt.Errorf("grade_interview_answer: %w", err)
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}
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var result GradedAnswer
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if err := extractJSON(raw, &result); err != nil {
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return GradedAnswer{}, fmt.Errorf("grade_interview_answer parse: %w", err)
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}
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result.UserID = input.UserID
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result.AnswerID = input.AnswerID
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result.QuestionID = input.QuestionID
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return result, nil
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}
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func (r *LLMRunner) ExtractLearningMemory(ctx context.Context, grade GradedAnswer) (MemoryUpdateCandidate, error) {
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raw, err := r.client.ChatJSON(ctx, extractMemorySystemPrompt(), extractMemoryUserPrompt(grade), true)
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if err != nil {
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return MemoryUpdateCandidate{}, fmt.Errorf("extract_learning_memory: %w", err)
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}
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candidate := MemoryUpdateCandidate{
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UserID: grade.UserID,
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SourceAnswerID: grade.AnswerID,
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}
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if err := extractJSON(raw, &candidate); err != nil {
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return MemoryUpdateCandidate{}, fmt.Errorf("extract_learning_memory parse: %w", err)
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}
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return candidate, nil
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}
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func (r *LLMRunner) SelectNextChallenge(ctx context.Context, input NextChallengeInput) (NextChallenge, error) {
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raw, err := r.client.ChatJSON(ctx, nextChallengeSystemPrompt(), nextChallengeUserPrompt("", ""), true)
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if err != nil {
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return NextChallenge{}, fmt.Errorf("select_next_challenge: %w", err)
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}
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var next NextChallenge
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if err := extractJSON(raw, &next); err != nil {
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return NextChallenge{}, fmt.Errorf("select_next_challenge parse: %w", err)
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}
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next.UserID = input.UserID
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next.Track = input.Track
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return next, nil
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}
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func (r *LLMRunner) UpdateReadinessMap(ctx context.Context, input ReadinessUpdateInput) (ReadinessUpdate, error) {
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raw, err := r.client.ChatJSON(ctx, readinessUpdateSystemPrompt(), readinessUpdateUserPrompt(input), true)
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if err != nil {
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return ReadinessUpdate{}, fmt.Errorf("update_readiness_map: %w", err)
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}
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var update ReadinessUpdate
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if err := extractJSON(raw, &update); err != nil {
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return ReadinessUpdate{}, fmt.Errorf("update_readiness_map parse: %w", err)
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}
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update.UserID = input.UserID
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update.Track = input.Track
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return update, nil
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}
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func extractJSON(raw string, target any) error {
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clean := strings.TrimSpace(raw)
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if strings.HasPrefix(clean, "```") {
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clean = stripCodeFences(clean)
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}
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if err := json.Unmarshal([]byte(clean), target); err != nil {
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return fmt.Errorf("%w: %s", err, firstBytes(clean, 200))
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}
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return nil
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}
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var errCodeFence = errors.New("code fence")
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func stripCodeFences(input string) string {
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lines := strings.Split(input, "\n")
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start := 0
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end := len(lines)
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for i, line := range lines {
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trimmed := strings.TrimSpace(line)
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if strings.HasPrefix(trimmed, "```") {
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if start == 0 {
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start = i + 1
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continue
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}
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end = i
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break
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}
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}
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return strings.Join(lines[start:end], "\n")
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}
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func firstBytes(input string, limit int) string {
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if len(input) > limit {
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return input[:limit] + "..."
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}
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return input
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}
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180
internal/workflows/prompts.go
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180
internal/workflows/prompts.go
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@@ -0,0 +1,180 @@
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package workflows
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import (
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"encoding/json"
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"fmt"
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)
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func gradeAnswerSystemPrompt() string {
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return fmt.Sprintf(`You are an expert technical interviewer grading a candidate's answer. Output valid JSON matching this schema:
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{
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"user_id": "string",
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"answer_id": "string",
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"question_id": "string",
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"concepts": [{"id": "string", "label": "string", "track": "string"}],
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"scores": {
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"correctness": 0,
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"depth": 0,
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"communication": 0,
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"production_judgment": 0
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},
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"overall": "miss|partial|solid|strong",
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"strengths": ["string"],
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"gaps": ["string"],
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"evidence": [{"kind": "answer|grading|source|session|asset", "id": "string", "quote": "string", "confidence": 0.0}],
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"misconception_candidates": [{"label": "string", "description": "string", "evidence": [], "confidence": 0.0}],
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"follow_up": {"needed": true, "question": "string", "purpose": "clarify|repair|stretch|pressure_test"}
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}
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Scoring rules:
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- scores: 1-4 integer scale (1=inadequate, 2=surface, 3=solid, 4=strong).
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- correctness: factual accuracy
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- depth: covers tradeoffs, edge cases, production context
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- communication: clarity, structure, conciseness
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- production_judgment: practical experience signals in the answer
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- overall: "miss" if mostly wrong, "partial" if some correct parts, "solid" if mostly correct with depth, "strong" if comprehensive and production-ready.
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- evidence: always include at least one EvidenceRef with kind "grading", quote from the answer, and confidence 0.5-1.0.
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- follow_up.needed: true unless the answer is "strong" and complete. Set purpose to "clarify" for unclear answers, "repair" for misconceptions, "stretch" to test depth, "pressure_test" for strong answers.
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- misconception_candidates: list any detected wrong mental models.
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Respond with ONLY the JSON object, no markdown fences.`)
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}
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func gradeAnswerUserPrompt(input GradeAnswerInput) string {
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payload, _ := json.Marshal(input)
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return fmt.Sprintf("Grade this interview answer: %s", string(payload))
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}
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func extractMemorySystemPrompt() string {
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return fmt.Sprintf(`You are a learner memory extraction agent. From a graded interview answer, produce memory updates. Output valid JSON matching this schema:
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{
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"updates": [
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{
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"kind": "concept_mastery|misconception|intervention|review_schedule",
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"concept": {"id": "string", "label": "string", "track": "string"},
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"proposed_state": "unknown|fragile|improving|interview_ready|strong_signal",
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"summary": "string",
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"evidence": [{"kind": "grading", "id": "string", "quote": "string", "confidence": 0.0}],
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"confidence": 0.0,
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"durability": "tentative|confirmed"
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}
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]
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}
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Rules:
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- For every concept in the grading, create a concept_mastery update with proposed_state derived from overall grade: "miss"→fragile, "partial"→improving, "solid"→interview_ready, "strong"→strong_signal.
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- If follow_up.needed is true and overall is "miss" or "partial", add a misconception update (kind="misconception") for each concept with proposed_state "fragile".
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- If follow_up.needed is true, add an intervention update (kind="intervention") for each concept with the follow_up question as summary.
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- If the answer shows gaps, add a review_schedule update (kind="review_schedule") for each concept with a review reason.
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- Confidence: 0.5-0.7 for tentative, 0.8-1.0 for confirmed. Durability: "confirmed" only for "strong" overall.
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Respond with ONLY the JSON object, no markdown fences.`)
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}
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func extractMemoryUserPrompt(grade GradedAnswer) string {
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payload, _ := json.Marshal(grade)
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return fmt.Sprintf("Extract memory updates from this graded answer: %s", string(payload))
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}
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func nextChallengeSystemPrompt() string {
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return fmt.Sprintf(`You are a challenge selection agent. Given learner memory state, select the next challenge. Output valid JSON matching this schema:
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{
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"concept": {"id": "string", "label": "string", "track": "string"},
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"ladder_level": "define|tradeoffs|debug|design_constraints|interview_pressure",
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"question": "string",
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"rationale": "string",
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"difficulty_action": "lower|hold|raise|recover",
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"evidence": [{"kind": "grading", "id": "string", "quote": "string", "confidence": 0.0}]
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}
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Rules:
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- Pick the concept with the weakest readiness state.
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- ladder_level: fragile→define, improving→tradeoffs, interview_ready→design_constraints, strong_signal→interview_pressure.
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- difficulty_action: fragile→recover, improving→hold, interview_ready+→raise.
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- Generate one concrete interview question for the selected concept at the appropriate ladder level.
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- rationale: explain why this concept and level was chosen.
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- evidence: reference the concept's existing evidence.
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Respond with ONLY the JSON object, no markdown fences.`)
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}
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func nextChallengeUserPrompt(masteryJSON, profileJSON string) string {
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return fmt.Sprintf(`Learner mastery: %s
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Learner profile: %s
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Select the next challenge for this learner.`, masteryJSON, profileJSON)
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}
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func diagnoseSystemPrompt() string {
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return fmt.Sprintf(`You are a diagnostic interview agent. Given a job seeker's profile, produce an initial readiness assessment. Output valid JSON matching this schema:
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{
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"user_id": "string",
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"track": "string",
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"target_role": "string",
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"stack": ["string"],
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"initial_readiness": "unknown|fragile|improving|interview_ready|strong_signal",
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"concept_findings": [
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{
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"concept": {"id": "string", "label": "string", "track": "string"},
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"readiness": "unknown|fragile|improving|interview_ready|strong_signal",
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"reason": "string",
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"evidence": []
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}
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],
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"recommended_next_concepts": [{"id": "string", "label": "string", "track": "string"}]
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}
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Rules:
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- initial_readiness: default to "unknown" unless you have strong signals from the profile.
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- For each concept, estimate readiness based on the stack and target role. Default to "unknown" if no strong signal.
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- recommended_next_concepts: pick up to 3 concepts to start with.
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- evidence: always empty for initial diagnostic (no answers yet).
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Respond with ONLY the JSON object, no markdown fences.`)
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}
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func diagnoseUserPrompt(input DiagnosticInput) string {
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payload, _ := json.Marshal(input)
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return fmt.Sprintf("Assess initial readiness for this job seeker: %s", string(payload))
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}
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func readinessUpdateSystemPrompt() string {
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return fmt.Sprintf(`You are a readiness update agent. Given learner memory state, produce readiness deltas and unlocks. Output valid JSON matching this schema:
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{
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"concept_updates": [
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{
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"concept": {"id": "string", "label": "string", "track": "string"},
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"previous": "unknown|fragile|improving|interview_ready|strong_signal",
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"next": "unknown|fragile|improving|interview_ready|strong_signal",
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"reason": "string",
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"evidence": [{"kind": "grading", "id": "string", "quote": "string", "confidence": 0.0}]
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}
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],
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"unlocks": [
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{
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"kind": "boss_question|review_card|portfolio_entry",
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"label": "string",
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"reason": "string"
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}
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]
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}
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Rules:
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- For each concept, determine if the readiness state should change based on evidence quality and quantity.
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- Unlock boss_question when 3+ concepts are at interview_ready or strong_signal.
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- Unlock review_card when concepts have misconceptions that need revisiting.
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- Unlock portfolio_entry when a concept reaches strong_signal.
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Respond with ONLY the JSON object, no markdown fences.`)
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}
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func readinessUpdateUserPrompt(input ReadinessUpdateInput) string {
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payload, _ := json.Marshal(input)
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return fmt.Sprintf("Analyze readiness updates for: %s", string(payload))
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}
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