package lem import ( "bufio" "encoding/json" "fmt" "log" "math/rand" "os" "strings" ) // ConvOpts holds configuration for the conv command. type ConvOpts struct { OutputDir string // Output directory for training files (required) Extra string // Additional conversations JSONL file (multi-turn format) Golden string // Golden set JSONL to convert to single-turn conversations DB string // DuckDB database path for golden set (alternative to --golden) TrainPct int // Training set percentage ValidPct int // Validation set percentage TestPct int // Test set percentage Seed int64 // Random seed for shuffling MinChars int // Minimum response chars for golden set conversion NoBuiltin bool // Exclude built-in seed conversations Influx string // InfluxDB URL for progress reporting InfluxDB string // InfluxDB database name Worker string // Worker hostname for InfluxDB reporting } // RunConv generates multi-turn conversational training data from built-in // seed conversations plus optional extra files and golden set data. func RunConv(cfg ConvOpts) error { if cfg.OutputDir == "" { return fmt.Errorf("--output-dir is required") } if err := validatePercentages(cfg.TrainPct, cfg.ValidPct, cfg.TestPct); err != nil { return fmt.Errorf("percentages: %w", err) } // Check LEM_DB env as default for --db. if cfg.DB == "" { cfg.DB = os.Getenv("LEM_DB") } // Default worker to hostname. if cfg.Worker == "" { hostname, err := os.Hostname() if err != nil { hostname = "unknown" } cfg.Worker = hostname } // Collect all conversations. var conversations []TrainingExample // 1. Built-in seed conversations. if !cfg.NoBuiltin { conversations = append(conversations, SeedConversations...) log.Printf("loaded %d built-in seed conversations", len(SeedConversations)) } // 2. Extra conversations from file. if cfg.Extra != "" { extras, err := readConversations(cfg.Extra) if err != nil { return fmt.Errorf("read extra conversations: %w", err) } conversations = append(conversations, extras...) log.Printf("loaded %d extra conversations from %s", len(extras), cfg.Extra) } // 3. Golden set responses converted to single-turn format. var goldenResponses []Response if cfg.DB != "" && cfg.Golden == "" { db, err := OpenDB(cfg.DB) if err != nil { return fmt.Errorf("open db: %w", err) } defer db.Close() for r, err := range db.GoldenSet(cfg.MinChars) { if err != nil { return fmt.Errorf("golden set error: %w", err) } goldenResponses = append(goldenResponses, Response{ ID: r.SeedID, Domain: r.Domain, Prompt: r.Prompt, Response: r.Response, Model: r.Voice, }) } log.Printf("loaded %d golden set rows from %s", len(goldenResponses), cfg.DB) } else if cfg.Golden != "" { var err error goldenResponses, err = ReadResponses(cfg.Golden) if err != nil { return fmt.Errorf("read golden set: %w", err) } log.Printf("loaded %d golden set responses from %s", len(goldenResponses), cfg.Golden) } if len(goldenResponses) > 0 { converted := convertToConversations(goldenResponses, cfg.MinChars) conversations = append(conversations, converted...) log.Printf("converted %d golden set responses to single-turn conversations", len(converted)) } if len(conversations) == 0 { return fmt.Errorf("no conversations to process — use built-in seeds, --extra, --golden, or --db") } // Split into train/valid/test. train, valid, test := splitConversations(conversations, cfg.TrainPct, cfg.ValidPct, cfg.TestPct, cfg.Seed) // Create output directory. if err := os.MkdirAll(cfg.OutputDir, 0755); err != nil { return fmt.Errorf("create output dir: %w", err) } // Write output files. for _, split := range []struct { name string data []TrainingExample }{ {"train.jsonl", train}, {"valid.jsonl", valid}, {"test.jsonl", test}, } { path := cfg.OutputDir + "/" + split.name if err := writeConversationJSONL(path, split.data); err != nil { return fmt.Errorf("write %s: %w", split.name, err) } } // Stats. totalTurns := 0 totalAssistantWords := 0 assistantMsgCount := 0 for _, c := range conversations { totalTurns += len(c.Messages) for _, m := range c.Messages { if m.Role == "assistant" { totalAssistantWords += len(strings.Fields(m.Content)) assistantMsgCount++ } } } avgTurns := float64(totalTurns) / float64(len(conversations)) avgWords := 0.0 if assistantMsgCount > 0 { avgWords = float64(totalAssistantWords) / float64(assistantMsgCount) } fmt.Printf("Conversational training data generated:\n") fmt.Printf(" %d train / %d valid / %d test\n", len(train), len(valid), len(test)) fmt.Printf(" %d total conversations\n", len(conversations)) fmt.Printf(" %d total turns (%.1f avg per conversation)\n", totalTurns, avgTurns) fmt.Printf(" %.0f words avg per assistant response\n", avgWords) fmt.Printf(" Output: %s/\n", cfg.OutputDir) // Report to InfluxDB if configured. influx := NewInfluxClient(cfg.Influx, cfg.InfluxDB) line := fmt.Sprintf("conv_export,worker=%s total=%di,train=%di,valid=%di,test=%di,turns=%di,avg_turns=%f,avg_words=%f", escapeLp(cfg.Worker), len(conversations), len(train), len(valid), len(test), totalTurns, avgTurns, avgWords) if err := influx.WriteLp([]string{line}); err != nil { log.Printf("influx write (best-effort): %v", err) } return nil } // readConversations reads multi-turn conversations from a JSONL file. // Each line must be a TrainingExample with a messages array. func readConversations(path string) ([]TrainingExample, error) { f, err := os.Open(path) if err != nil { return nil, fmt.Errorf("open %s: %w", path, err) } defer f.Close() var conversations []TrainingExample scanner := bufio.NewScanner(f) scanner.Buffer(make([]byte, 1024*1024), 1024*1024) lineNum := 0 for scanner.Scan() { lineNum++ line := strings.TrimSpace(scanner.Text()) if line == "" { continue } var te TrainingExample if err := json.Unmarshal([]byte(line), &te); err != nil { return nil, fmt.Errorf("line %d: %w", lineNum, err) } if len(te.Messages) >= 2 { conversations = append(conversations, te) } } if err := scanner.Err(); err != nil { return nil, fmt.Errorf("scan %s: %w", path, err) } return conversations, nil } // convertToConversations converts golden set prompt/response pairs into // single-turn TrainingExample conversations (user → assistant). func convertToConversations(responses []Response, minChars int) []TrainingExample { var conversations []TrainingExample for _, r := range responses { if r.Response == "" || len(r.Response) < minChars { continue } if strings.HasPrefix(r.Response, "ERROR:") { continue } conversations = append(conversations, TrainingExample{ Messages: []ChatMessage{ {Role: "user", Content: r.Prompt}, {Role: "assistant", Content: r.Response}, }, }) } return conversations } // splitConversations shuffles conversations with a deterministic seed and // splits them into train, valid, and test sets by percentage. func splitConversations(conversations []TrainingExample, trainPct, validPct, testPct int, seed int64) (train, valid, test []TrainingExample) { shuffled := make([]TrainingExample, len(conversations)) copy(shuffled, conversations) rng := rand.New(rand.NewSource(seed)) rng.Shuffle(len(shuffled), func(i, j int) { shuffled[i], shuffled[j] = shuffled[j], shuffled[i] }) n := len(shuffled) trainN := n * trainPct / 100 validN := n * validPct / 100 _ = testPct train = shuffled[:trainN] valid = shuffled[trainN : trainN+validN] test = shuffled[trainN+validN:] // Ensure at least 1 in each split when we have enough data. if len(valid) == 0 && len(train) > 1 { valid = train[len(train)-1:] train = train[:len(train)-1] } if len(test) == 0 && len(train) > 1 { test = train[len(train)-1:] train = train[:len(train)-1] } return train, valid, test } // writeConversationJSONL writes TrainingExample conversations to a JSONL file. func writeConversationJSONL(path string, conversations []TrainingExample) error { f, err := os.Create(path) if err != nil { return fmt.Errorf("create %s: %w", path, err) } defer f.Close() w := bufio.NewWriter(f) defer w.Flush() for _, c := range conversations { data, err := json.Marshal(c) if err != nil { return fmt.Errorf("marshal conversation: %w", err) } if _, err := w.Write(data); err != nil { return fmt.Errorf("write line: %w", err) } if _, err := w.WriteString("\n"); err != nil { return fmt.Errorf("write newline: %w", err) } } return nil }