feat: mixed model eval + concurrent QA workers
- Add --judge-model, --judge-api-base, --judge-api-key flags for separate judge model - Add --concurrency flag (default 1) with semaphore-based goroutine pool - Add reasoning_content fallback for GLM/DeepSeek style responses - Prepend /no_think to system prompt for Ollama /v1 compatibility - Reduce default MaxTokens from 2048 to 512 (answers are 1-3 sentences) - Extract evalQAWorker and buildSeahorseContext for shared concurrent logic
This commit is contained in:
BeaconCat
parent
1d72a6afa6
commit
d9cb8296f0
3 changed files with 251 additions and 150 deletions
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@ -8,6 +8,7 @@ import (
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"sort"
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"strconv"
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"strings"
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"sync"
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"github.com/sipeed/picoclaw/pkg/seahorse"
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)
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@ -46,7 +47,7 @@ var scoreRe = regexp.MustCompile(`\b([1-5])\b`)
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// Returns a score from 0.0 to 1.0, or -1.0 on parse failure.
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func judgeAnswer(
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ctx context.Context,
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client *LLMClient,
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judgeClient *LLMClient,
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question, goldAnswer, candidateAnswer string,
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) (float64, error) {
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userPrompt := fmt.Sprintf(
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@ -54,7 +55,7 @@ func judgeAnswer(
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question, goldAnswer, candidateAnswer,
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)
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response, err := client.Complete(ctx, judgeSystemPrompt, userPrompt)
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response, err := judgeClient.Complete(ctx, judgeSystemPrompt, userPrompt)
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if err != nil {
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return -1.0, err
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}
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@ -68,17 +69,80 @@ func judgeAnswer(
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return -1.0, nil
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}
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// qaWork describes one QA evaluation unit.
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type qaWork struct {
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sampleID string
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qaIndex int
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globalIndex int
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totalQA int
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qa *LocomoQA
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contextText string
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sample *LocomoSample
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}
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// qaResult collects one QA evaluation output.
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type qaResultOut struct {
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index int // position in the flat QA list for ordering
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result QAResult
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answer string
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score float64
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}
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// evalQAWorker processes a single QA item: generate answer + judge score.
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func evalQAWorker(
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ctx context.Context,
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w qaWork,
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answerClient, judgeClient *LLMClient,
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logPrefix string,
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) qaResultOut {
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llmAnswer, err := generateAnswer(ctx, answerClient, w.contextText, w.qa.Question)
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if err != nil {
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log.Printf("WARN: LLM generation failed for sample %s Q%d: %v", w.sampleID, w.qaIndex, err)
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llmAnswer = ""
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}
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score := -1.0
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if llmAnswer != "" {
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score, err = judgeAnswer(ctx, judgeClient, w.qa.Question, w.qa.AnswerString(), llmAnswer)
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if err != nil {
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log.Printf("WARN: LLM judge failed for sample %s Q%d: %v", w.sampleID, w.qaIndex, err)
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}
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}
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hitRate := RecallHitRate(w.qa.Evidence, w.sample, w.contextText)
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log.Printf("[%s] sample=%s q=%d/%d score=%.2f answer=%q",
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logPrefix, w.sampleID, w.globalIndex, w.totalQA, score, truncateStr(llmAnswer, 80))
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return qaResultOut{
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index: w.globalIndex,
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result: QAResult{
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Question: w.qa.Question,
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Category: w.qa.Category,
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GoldAnswer: w.qa.AnswerString(),
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TokenF1: score,
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HitRate: hitRate,
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},
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answer: llmAnswer,
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score: score,
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}
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}
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// EvalLegacyLLM evaluates legacy store using LLM generation + LLM-as-Judge.
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func EvalLegacyLLM(
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ctx context.Context,
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samples []LocomoSample,
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legacy *LegacyStore,
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budgetTokens int,
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client *LLMClient,
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answerClient, judgeClient *LLMClient,
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concurrency int,
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) []EvalResult {
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if concurrency < 1 {
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concurrency = 1
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}
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totalQA := countTotalQA(samples)
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results := make([]EvalResult, 0, len(samples))
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total := 0
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for si := range samples {
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sample := &samples[si]
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history := legacy.GetHistory(sample.SampleID)
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@ -88,41 +152,38 @@ func EvalLegacyLLM(
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allContent = append(allContent, msg.Content)
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}
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qaResults := make([]QAResult, 0, len(sample.QA))
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for qi := range sample.QA {
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qa := &sample.QA[qi]
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total++
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truncated, _ := BudgetTruncate(allContent, budgetTokens)
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contextText := StringListToContent(truncated)
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truncated, _ := BudgetTruncate(allContent, budgetTokens)
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contextText := StringListToContent(truncated)
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// Generate answer with LLM
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llmAnswer, err := generateAnswer(ctx, client, contextText, qa.Question)
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if err != nil {
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log.Printf("WARN: LLM generation failed for sample %s Q%d: %v", sample.SampleID, qi, err)
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llmAnswer = ""
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qaResults := make([]QAResult, len(sample.QA))
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if concurrency <= 1 {
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for qi := range sample.QA {
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out := evalQAWorker(ctx, qaWork{
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sampleID: sample.SampleID, qaIndex: qi,
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globalIndex: si*len(sample.QA) + qi + 1, totalQA: totalQA,
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qa: &sample.QA[qi], contextText: contextText, sample: sample,
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}, answerClient, judgeClient, "legacy-llm")
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qaResults[qi] = out.result
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}
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// Judge the answer; -1.0 = API/parse failure.
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score := -1.0
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if llmAnswer != "" {
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score, err = judgeAnswer(ctx, client, qa.Question, qa.AnswerString(), llmAnswer)
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if err != nil {
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log.Printf("WARN: LLM judge failed for sample %s Q%d: %v", sample.SampleID, qi, err)
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}
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} else {
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sem := make(chan struct{}, concurrency)
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var wg sync.WaitGroup
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for qi := range sample.QA {
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wg.Add(1)
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go func() {
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defer wg.Done()
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sem <- struct{}{}
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defer func() { <-sem }()
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out := evalQAWorker(ctx, qaWork{
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sampleID: sample.SampleID, qaIndex: qi,
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globalIndex: si*len(sample.QA) + qi + 1, totalQA: totalQA,
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qa: &sample.QA[qi], contextText: contextText, sample: sample,
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}, answerClient, judgeClient, "legacy-llm")
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qaResults[qi] = out.result // safe: each goroutine writes distinct index
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}()
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}
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hitRate := RecallHitRate(qa.Evidence, sample, contextText)
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qaResults = append(qaResults, QAResult{
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Question: qa.Question,
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Category: qa.Category,
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GoldAnswer: qa.AnswerString(),
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TokenF1: score,
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HitRate: hitRate,
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})
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log.Printf("[legacy-llm] sample=%s q=%d/%d score=%.2f answer=%q",
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sample.SampleID, total, totalQA, score, truncateStr(llmAnswer, 80))
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wg.Wait()
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}
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results = append(results, EvalResult{
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@ -135,125 +196,126 @@ func EvalLegacyLLM(
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return results
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}
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// buildSeahorseContext retrieves context for a seahorse QA item.
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func buildSeahorseContext(
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ctx context.Context,
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ir *SeahorseIngestResult,
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sample *LocomoSample,
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qa *LocomoQA,
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budgetTokens int,
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) string {
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store := ir.Engine.GetRetrieval().Store()
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retrieval := ir.Engine.GetRetrieval()
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convID := ir.ConvMap[sample.SampleID]
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keywords := ExtractKeywords(qa.Question)
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bestRank := map[int64]float64{}
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for _, kw := range keywords {
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searchResults, err := store.SearchMessages(ctx, seahorse.SearchInput{
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Pattern: kw,
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ConversationID: convID,
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Limit: 20,
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})
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if err != nil {
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continue
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}
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for _, sr := range searchResults {
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if sr.MessageID > 0 {
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if prev, ok := bestRank[sr.MessageID]; !ok || sr.Rank < prev {
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bestRank[sr.MessageID] = sr.Rank
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}
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}
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}
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}
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messageIDs := make([]int64, 0, len(bestRank))
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for id := range bestRank {
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messageIDs = append(messageIDs, id)
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}
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sort.Slice(messageIDs, func(i, j int) bool {
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return bestRank[messageIDs[i]] < bestRank[messageIDs[j]]
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})
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var contentParts []string
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if len(messageIDs) > 0 {
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expandResult, err := retrieval.ExpandMessages(ctx, messageIDs)
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if err == nil {
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for _, msg := range expandResult.Messages {
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contentParts = append(contentParts, msg.Content)
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}
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}
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}
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if len(contentParts) == 0 {
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return ""
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}
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truncated, _ := BudgetTruncate(contentParts, budgetTokens)
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return StringListToContent(truncated)
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}
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// EvalSeahorseLLM evaluates seahorse retrieval using LLM generation + LLM-as-Judge.
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func EvalSeahorseLLM(
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ctx context.Context,
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samples []LocomoSample,
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ir *SeahorseIngestResult,
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budgetTokens int,
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client *LLMClient,
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answerClient, judgeClient *LLMClient,
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concurrency int,
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) []EvalResult {
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store := ir.Engine.GetRetrieval().Store()
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retrieval := ir.Engine.GetRetrieval()
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if concurrency < 1 {
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concurrency = 1
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}
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totalQA := countTotalQA(samples)
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results := make([]EvalResult, 0, len(samples))
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total := 0
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for si := range samples {
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sample := &samples[si]
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convID, ok := ir.ConvMap[sample.SampleID]
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if !ok {
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if _, ok := ir.ConvMap[sample.SampleID]; !ok {
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log.Printf("WARN: no conversation ID for sample %s", sample.SampleID)
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continue
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}
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qaResults := make([]QAResult, 0, len(sample.QA))
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for qi := range sample.QA {
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qaResults := make([]QAResult, len(sample.QA))
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evalOne := func(qi int) {
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qa := &sample.QA[qi]
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total++
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keywords := ExtractKeywords(qa.Question)
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// Search and rank
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bestRank := map[int64]float64{}
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for _, kw := range keywords {
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searchResults, err := store.SearchMessages(ctx, seahorse.SearchInput{
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Pattern: kw,
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ConversationID: convID,
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Limit: 20,
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})
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if err != nil {
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log.Printf("WARN: search failed for keyword %q: %v", kw, err)
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continue
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}
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for _, sr := range searchResults {
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if sr.MessageID > 0 {
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if prev, ok := bestRank[sr.MessageID]; !ok || sr.Rank < prev {
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bestRank[sr.MessageID] = sr.Rank
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}
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}
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}
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}
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messageIDs := make([]int64, 0, len(bestRank))
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for id := range bestRank {
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messageIDs = append(messageIDs, id)
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}
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// Sort ascending: best (most-negative) rank first.
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// BudgetTruncate walks front-to-back, so best-ranked messages are kept.
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sort.Slice(messageIDs, func(i, j int) bool {
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return bestRank[messageIDs[i]] < bestRank[messageIDs[j]]
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})
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var contentParts []string
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if len(messageIDs) > 0 {
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expandResult, err := retrieval.ExpandMessages(ctx, messageIDs)
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if err != nil {
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log.Printf("WARN: expand failed for sample %s: %v", sample.SampleID, err)
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} else {
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for _, msg := range expandResult.Messages {
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contentParts = append(contentParts, msg.Content)
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}
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}
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}
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if len(contentParts) == 0 {
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qaResults = append(qaResults, QAResult{
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contextText := buildSeahorseContext(ctx, ir, sample, qa, budgetTokens)
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if contextText == "" {
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qaResults[qi] = QAResult{
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Question: qa.Question,
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Category: qa.Category,
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GoldAnswer: qa.AnswerString(),
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TokenF1: 0.0,
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HitRate: 0.0,
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})
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}
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log.Printf("[seahorse-llm] sample=%s q=%d/%d score=0.00 answer=(no context)",
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sample.SampleID, total, totalQA)
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continue
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sample.SampleID, si*len(sample.QA)+qi+1, totalQA)
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return
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}
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out := evalQAWorker(ctx, qaWork{
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sampleID: sample.SampleID, qaIndex: qi,
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globalIndex: si*len(sample.QA) + qi + 1, totalQA: totalQA,
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qa: qa, contextText: contextText, sample: sample,
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}, answerClient, judgeClient, "seahorse-llm")
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qaResults[qi] = out.result
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}
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truncated, _ := BudgetTruncate(contentParts, budgetTokens)
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contextText := StringListToContent(truncated)
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// Generate answer with LLM
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llmAnswer := ""
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score := -1.0
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if contextText != "" {
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var err error
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llmAnswer, err = generateAnswer(ctx, client, contextText, qa.Question)
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if err != nil {
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log.Printf("WARN: LLM generation failed for sample %s Q%d: %v", sample.SampleID, qi, err)
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}
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if concurrency <= 1 {
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for qi := range sample.QA {
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evalOne(qi)
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}
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// Judge the answer; -1.0 = API/parse failure.
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if llmAnswer != "" {
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var err error
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score, err = judgeAnswer(ctx, client, qa.Question, qa.AnswerString(), llmAnswer)
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if err != nil {
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log.Printf("WARN: LLM judge failed for sample %s Q%d: %v", sample.SampleID, qi, err)
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}
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} else {
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sem := make(chan struct{}, concurrency)
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var wg sync.WaitGroup
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for qi := range sample.QA {
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wg.Add(1)
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go func() {
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defer wg.Done()
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sem <- struct{}{}
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defer func() { <-sem }()
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evalOne(qi)
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}()
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}
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hitRate := RecallHitRate(qa.Evidence, sample, contextText)
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qaResults = append(qaResults, QAResult{
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Question: qa.Question,
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Category: qa.Category,
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GoldAnswer: qa.AnswerString(),
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TokenF1: score,
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HitRate: hitRate,
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})
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log.Printf("[seahorse-llm] sample=%s q=%d/%d score=%.2f answer=%q",
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sample.SampleID, total, totalQA, score, truncateStr(llmAnswer, 80))
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wg.Wait()
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}
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results = append(results, EvalResult{
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@ -71,16 +71,23 @@ type chatMessage struct {
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type chatResponse struct {
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Choices []struct {
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Message struct {
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Content string `json:"content"`
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Content string `json:"content"`
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ReasoningContent string `json:"reasoning_content,omitempty"`
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} `json:"message"`
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} `json:"choices"`
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}
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// Complete sends a chat completion request and returns the assistant's reply.
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func (c *LLMClient) Complete(ctx context.Context, systemPrompt, userPrompt string) (string, error) {
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sysContent := systemPrompt
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if c.NoThinking && sysContent != "" {
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// Prepend /no_think tag — works with Ollama /v1 endpoint and
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// Qwen chat templates where the JSON think field is ignored.
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sysContent = "/no_think\n" + sysContent
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}
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messages := []chatMessage{}
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if systemPrompt != "" {
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messages = append(messages, chatMessage{Role: "system", Content: systemPrompt})
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if sysContent != "" {
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messages = append(messages, chatMessage{Role: "system", Content: sysContent})
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}
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messages = append(messages, chatMessage{Role: "user", Content: userPrompt})
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@ -88,7 +95,7 @@ func (c *LLMClient) Complete(ctx context.Context, systemPrompt, userPrompt strin
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Model: c.Model,
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Messages: messages,
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Temperature: 0.1,
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MaxTokens: 2048,
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MaxTokens: 512,
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}
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if c.NoThinking {
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// llama.cpp: chat_template_kwargs
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@ -180,6 +187,10 @@ func (c *LLMClient) Complete(ctx context.Context, systemPrompt, userPrompt strin
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if idx := strings.Index(content, "</think>"); idx >= 0 {
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content = strings.TrimSpace(content[idx+len("</think>"):])
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}
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// Fallback: GLM/DeepSeek put thinking output in reasoning_content when thinking is enabled
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if content == "" && chatResp.Choices[0].Message.ReasoningContent != "" {
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content = strings.TrimSpace(chatResp.Choices[0].Message.ReasoningContent)
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}
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if content == "" {
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return "", fmt.Errorf("empty LLM response")
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}
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@ -16,18 +16,22 @@ import (
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)
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var (
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flagData string
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flagOut string
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flagMode string
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flagBudget int
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flagEvalMode string
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flagAPIBase string
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flagAPIKey string
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flagModel string
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flagNoThinking bool
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flagLimit int
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flagTimeout int
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flagRetries int
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flagData string
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flagOut string
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flagMode string
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flagBudget int
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flagEvalMode string
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flagAPIBase string
|
||||
flagAPIKey string
|
||||
flagModel string
|
||||
flagNoThinking bool
|
||||
flagLimit int
|
||||
flagTimeout int
|
||||
flagRetries int
|
||||
flagJudgeModel string
|
||||
flagJudgeAPIBase string
|
||||
flagJudgeAPIKey string
|
||||
flagConcurrency int
|
||||
)
|
||||
|
||||
func main() {
|
||||
|
|
@ -68,6 +72,11 @@ func main() {
|
|||
evalCmd.Flags().IntVar(&flagLimit, "limit", 0, "max QA questions per sample (0 = all)")
|
||||
evalCmd.Flags().IntVar(&flagTimeout, "timeout", 120, "HTTP timeout in seconds for LLM requests")
|
||||
evalCmd.Flags().IntVar(&flagRetries, "retries", 3, "max retry attempts for transient LLM errors (timeout/5xx/429)")
|
||||
evalCmd.Flags().StringVar(&flagJudgeModel, "judge-model", "", "model for judge scoring (defaults to --model)")
|
||||
evalCmd.Flags().
|
||||
StringVar(&flagJudgeAPIBase, "judge-api-base", "", "API base URL for judge model (defaults to --api-base)")
|
||||
evalCmd.Flags().StringVar(&flagJudgeAPIKey, "judge-api-key", "", "API key for judge model (defaults to --api-key)")
|
||||
evalCmd.Flags().IntVar(&flagConcurrency, "concurrency", 1, "number of concurrent QA evaluations")
|
||||
|
||||
reportCmd := &cobra.Command{
|
||||
Use: "report",
|
||||
|
|
@ -96,6 +105,11 @@ func main() {
|
|||
runCmd.Flags().IntVar(&flagLimit, "limit", 0, "max QA questions per sample (0 = all)")
|
||||
runCmd.Flags().IntVar(&flagTimeout, "timeout", 120, "HTTP timeout in seconds for LLM requests")
|
||||
runCmd.Flags().IntVar(&flagRetries, "retries", 3, "max retry attempts for transient LLM errors (timeout/5xx/429)")
|
||||
runCmd.Flags().StringVar(&flagJudgeModel, "judge-model", "", "model for judge scoring (defaults to --model)")
|
||||
runCmd.Flags().
|
||||
StringVar(&flagJudgeAPIBase, "judge-api-base", "", "API base URL for judge model (defaults to --api-base)")
|
||||
runCmd.Flags().StringVar(&flagJudgeAPIKey, "judge-api-key", "", "API key for judge model (defaults to --api-key)")
|
||||
runCmd.Flags().IntVar(&flagConcurrency, "concurrency", 1, "number of concurrent QA evaluations")
|
||||
|
||||
rootCmd.AddCommand(ingestCmd, evalCmd, reportCmd, runCmd)
|
||||
|
||||
|
|
@ -186,14 +200,28 @@ func runEval(cmd *cobra.Command, args []string) error {
|
|||
default:
|
||||
return fmt.Errorf("invalid --eval-mode %q: must be token or llm", flagEvalMode)
|
||||
}
|
||||
var llmClient *LLMClient
|
||||
var answerClient, judgeClient *LLMClient
|
||||
if useLLM {
|
||||
opts, err := buildLLMOptions()
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
llmClient = NewLLMClient(opts)
|
||||
log.Printf("LLM eval mode: model=%s base=%s no-thinking=%v", opts.Model, opts.BaseURL, opts.NoThinking)
|
||||
answerClient = NewLLMClient(opts)
|
||||
judgeClient = answerClient // default: same client
|
||||
if flagJudgeModel != "" {
|
||||
jOpts := opts // copy base settings
|
||||
jOpts.Model = flagJudgeModel
|
||||
if flagJudgeAPIBase != "" {
|
||||
jOpts.BaseURL = flagJudgeAPIBase
|
||||
}
|
||||
if flagJudgeAPIKey != "" {
|
||||
jOpts.APIKey = flagJudgeAPIKey
|
||||
}
|
||||
judgeClient = NewLLMClient(jOpts)
|
||||
log.Printf("Judge model: model=%s base=%s no-thinking=%v", jOpts.Model, jOpts.BaseURL, jOpts.NoThinking)
|
||||
}
|
||||
log.Printf("LLM eval mode: model=%s base=%s no-thinking=%v concurrency=%d",
|
||||
opts.Model, opts.BaseURL, opts.NoThinking, flagConcurrency)
|
||||
}
|
||||
|
||||
var tokenResults, llmResults []EvalResult
|
||||
|
|
@ -206,7 +234,7 @@ func runEval(cmd *cobra.Command, args []string) error {
|
|||
legacy.IngestSample(&samples[i])
|
||||
}
|
||||
if useLLM {
|
||||
results := EvalLegacyLLM(ctx, samples, legacy, flagBudget, llmClient)
|
||||
results := EvalLegacyLLM(ctx, samples, legacy, flagBudget, answerClient, judgeClient, flagConcurrency)
|
||||
llmResults = append(llmResults, results...)
|
||||
log.Printf("legacy-llm: evaluated %d samples", len(results))
|
||||
} else {
|
||||
|
|
@ -221,7 +249,7 @@ func runEval(cmd *cobra.Command, args []string) error {
|
|||
return fmt.Errorf("ingest seahorse: %w", err)
|
||||
}
|
||||
if useLLM {
|
||||
results := EvalSeahorseLLM(ctx, samples, ir, flagBudget, llmClient)
|
||||
results := EvalSeahorseLLM(ctx, samples, ir, flagBudget, answerClient, judgeClient, flagConcurrency)
|
||||
llmResults = append(llmResults, results...)
|
||||
log.Printf("seahorse-llm: evaluated %d samples", len(results))
|
||||
} else {
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue