492e92580b
Closes #620 #497. Add opt-in Langfuse observability covering all five model types (chat, embedding, rerank, VLM, ASR) with HTTP-request-scoped traces and Docker Compose support (both cloud and self-hosted). Core package internal/tracing/langfuse: - HTTP client with batched async ingestion (non-blocking in request path) - Sampling, environment / release tagging, and graceful fallback when LANGFUSE_* env vars are absent (wrappers become no-ops) - Gin middleware opens one trace per traced request and finishes it after the handler chain returns, attaching method / path / user / session - Trace context is stored under a typed key exported from internal/types so logger.CloneContext can preserve it across handler / goroutine boundaries (otherwise each LLM call auto-created an orphan trace, fragmenting one request into many) Per-model generation wrappers (opt-in via NewChat/NewEmbedder/...): - chat: captures prompt, streaming output, token usage + TTFT - embedding: approximates tokens when the provider omits usage - rerank: previews query/docs, summarizes results to keep payload small - vlm: records image count and total bytes, never uploads raw pixels - asr: records file size and audio duration, never uploads audio bytes Async title generation (GenerateTitleAsync) now forwards the trace key into the goroutine so title calls appear under the parent chat trace. Docker Compose: - LANGFUSE_* env passthrough on the `app` service for cloud deployments - Optional `langfuse` profile spins up a self-hosted Langfuse stack that reuses WeKnora's existing PostgreSQL (separate database via an idempotent init container that fixes ICU collation drift) and Redis (separate DB number), adding only ClickHouse, MinIO, web and worker containers - web/worker entrypoints URL-encode DB_PASSWORD / REDIS_PASSWORD at start to avoid Prisma P1013 when passwords contain @ / # / etc. Docs: docs/Langfuse集成.md covers cloud vs self-hosted, per-model usage strategy, code map, and resource footprint.
117 lines
3.1 KiB
Go
117 lines
3.1 KiB
Go
package rerank
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import (
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"context"
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"github.com/Tencent/WeKnora/internal/tracing/langfuse"
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)
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// langfuseReranker wraps a Reranker and reports each rerank call as a
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// Langfuse generation observation. Rerankers don't return token usage, but
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// the call still incurs cost (billed per 1K documents by most vendors); we
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// estimate input tokens from the query + documents so the Langfuse cost
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// dashboard gets a proportional signal.
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type langfuseReranker struct {
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inner Reranker
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}
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func (l *langfuseReranker) GetModelName() string { return l.inner.GetModelName() }
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func (l *langfuseReranker) GetModelID() string { return l.inner.GetModelID() }
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func (l *langfuseReranker) Rerank(ctx context.Context, query string, documents []string) ([]RankResult, error) {
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mgr := langfuse.GetManager()
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if !mgr.Enabled() {
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return l.inner.Rerank(ctx, query, documents)
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}
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genCtx, gen := mgr.StartGeneration(ctx, langfuse.GenerationOptions{
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Name: "rerank",
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Model: l.inner.GetModelName(),
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Input: map[string]interface{}{
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"query": query,
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"document_count": len(documents),
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// Only send short previews — reranker inputs can be hundreds of
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// passages totalling hundreds of KB, which would bloat traces.
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"documents_preview": previewDocs(documents, 5),
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},
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Metadata: map[string]interface{}{
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"model_id": l.inner.GetModelID(),
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"num_queries": 1,
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},
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})
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results, err := l.inner.Rerank(genCtx, query, documents)
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output := map[string]interface{}{
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"results": summarizeResults(results, 10),
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"total_count": len(results),
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}
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gen.Finish(output, approxRerankUsage(query, documents), err)
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return results, err
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}
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func approxRerankUsage(query string, documents []string) *langfuse.TokenUsage {
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total := len([]rune(query))/4 + 1
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for _, d := range documents {
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total += len([]rune(d))/4 + 1
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}
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if total == 0 {
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return nil
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}
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return &langfuse.TokenUsage{
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Input: total,
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Total: total,
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Unit: "TOKENS",
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}
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}
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func previewDocs(docs []string, n int) []map[string]interface{} {
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if len(docs) < n {
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n = len(docs)
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}
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out := make([]map[string]interface{}, 0, n)
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for i := 0; i < n; i++ {
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out = append(out, map[string]interface{}{
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"index": i,
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"preview": truncateRunes(docs[i], 160),
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"length": len([]rune(docs[i])),
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})
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}
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return out
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}
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func summarizeResults(results []RankResult, n int) []map[string]interface{} {
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if len(results) < n {
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n = len(results)
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}
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out := make([]map[string]interface{}, 0, n)
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for i := 0; i < n; i++ {
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out = append(out, map[string]interface{}{
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"index": results[i].Index,
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"score": results[i].RelevanceScore,
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})
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}
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return out
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}
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func truncateRunes(s string, maxRunes int) string {
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r := []rune(s)
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if len(r) <= maxRunes {
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return s
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}
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return string(r[:maxRunes]) + "..."
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}
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// wrapRerankerLangfuse applies the Langfuse decorator when the manager is
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// enabled. Called from NewReranker after the debug wrapper so both sinks see
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// the same calls.
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func wrapRerankerLangfuse(r Reranker, err error) (Reranker, error) {
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if err != nil || r == nil {
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return r, err
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}
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if !langfuse.GetManager().Enabled() {
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return r, nil
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}
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return &langfuseReranker{inner: r}, nil
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}
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