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tapir/internal/adapters/summarizer/summarizer.go
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mathiasandClaude Opus 4.8 cd461b95f8
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feat(observability): instrument AI + HTTP paths, serve /metrics on a side port (ADR-030, #15)
Wire the metrics package into the live paths and serve it:
- summarizer: per-endpoint latency by model/outcome(success|error|parse_error)/fallback + slog.
- youtube.FetchTranscript: latency by outcome (captions|none|rate_limited) + slog.
- chat: answer latency by model + slog.
- llm usage hook → token counts (prompt|completion) per model, wired in buildSummarizer/buildChat.
- oidc callback: login counter.
- cmdServe: wrap Router in metrics.HTTPMiddleware (request count + latency by bounded
  route pattern) and serve /metrics on TAPIR_METRICS_ADDR (default :9090), a SEPARATE
  port — never on the public app mux.

BDD: observability.feature scenarios un-pended + mapped. TDD: summarizer wiring tested
black-box via the /metrics scrape; metrics-not-on-public-mux asserted.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-12 08:49:51 +02:00

248 lines
9.1 KiB
Go

// Package summarizer implements ports.Summarizer backed by the copied llm
// package's routing (ADR-004, extended by ADR-022). It is the only place content
// ever leaves the engine toward an AI model, so it is also the enforcement point
// for the local-first guarantee in docs/use-cases/ai_routing.feature: endpoints
// are tried in order, locals first, so content only reaches an external model
// after every local endpoint has failed — and never at all when no external
// endpoint is configured.
package summarizer
import (
"bytes"
"context"
"encoding/json"
"errors"
"fmt"
"log/slog"
"strings"
"time"
"unicode/utf8"
"gitea.d-ma.be/mathias/tapir/internal/domain"
"gitea.d-ma.be/mathias/tapir/internal/metrics"
)
// Completer is the minimal LLM chat surface the Summarizer needs.
// *llm.Client (and *llm.Router) satisfy it; tests use a fake.
type Completer interface {
Complete(ctx context.Context, system, user string) (string, error)
}
// Endpoint binds a Completer to the provenance recorded on the produced Summary.
type Endpoint struct {
Client Completer
Provider string // domain AIProvider: "local" | "anthropic" | "openai" | ...
Model string // resolved alias, e.g. "iguana/deepseek-r1-14b"
}
// Summarizer routes a transcript through an ordered chain of endpoints, trying
// each in turn until one returns a parseable summary. It owns its routing
// (rather than delegating to llm.Router) so it can record which provider answered
// and whether a fallback was used — information llm.Router collapses away. The
// chain ordering is the local-first guarantee: callers place local endpoints
// first and any external endpoint last, so content only reaches an external model
// after every local endpoint has failed.
type Summarizer struct {
endpoints []Endpoint
maxInputChars int // transcript truncation budget; 0 = no limit
now func() time.Time
}
// New constructs a Summarizer from a primary endpoint and an optional fallback
// (the historical local-Primary -> BYO-Fallback shape, ADR-004). A nil fallback
// means a single-endpoint chain: errors are returned, content never leaves it.
func New(primary Endpoint, fallback *Endpoint) *Summarizer {
eps := []Endpoint{primary}
if fallback != nil {
eps = append(eps, *fallback)
}
return &Summarizer{endpoints: eps, now: time.Now}
}
// NewChain constructs a Summarizer over an ordered endpoint chain (ADR-022).
// endpoints are tried in order; the first to return a parseable summary wins, and
// FallbackUsed is recorded true for any endpoint past the first. maxInputChars
// bounds the transcript text sent to every endpoint (0 = unbounded), so a long
// transcript does not overflow a small-context primary model's window. It panics
// on an empty chain — a wiring bug, not a runtime condition.
func NewChain(endpoints []Endpoint, maxInputChars int) *Summarizer {
if len(endpoints) == 0 {
panic("summarizer: NewChain requires at least one endpoint")
}
return &Summarizer{endpoints: endpoints, maxInputChars: maxInputChars, now: time.Now}
}
const systemPrompt = `You are Tapir, a video-summarization assistant.
Given a video title and transcript, produce a concise, faithful summary.
Respond with ONLY a JSON object, no prose and no code fences:
{"summary": string, "highlights": [string], "takeaways": [string]}
- "summary": 2-4 sentences capturing what the video is about.
- "highlights": the notable moments or points raised, most important first.
- "takeaways": the actionable conclusions a viewer should leave with.
Output the JSON object and nothing else.`
// Summarize implements ports.Summarizer. It walks the endpoint chain in order:
// the first endpoint whose reply parses into a non-empty summary wins. An
// endpoint is considered failed — and the next one tried — when the model call
// errors OR when its reply cannot be parsed (a 200 with malformed JSON or a
// highlights field the model emitted as a bare string). Truncation is applied
// once, up front, so every endpoint sees the same bounded prompt. When the whole
// chain fails, the joined error is returned so the engine queues the work for
// retry and delivers no summary.
func (s *Summarizer) Summarize(ctx context.Context, v domain.Video, t domain.Transcript) (domain.Summary, error) {
if !t.HasText() {
return domain.Summary{}, fmt.Errorf("summarize: transcript for video %s has no text", v.ID)
}
user := buildUserPrompt(v, t, s.maxInputChars)
var errs []error
for i, ep := range s.endpoints {
fallback := i > 0
start := time.Now()
out, err := ep.Client.Complete(ctx, systemPrompt, user)
dur := time.Since(start)
if err != nil {
metrics.ObserveSummarize(ep.Model, "error", fallback, dur)
errs = append(errs, fmt.Errorf("%s/%s call: %w", ep.Provider, ep.Model, err))
continue
}
sum, perr := s.build(v, ep, fallback, out)
if perr != nil {
metrics.ObserveSummarize(ep.Model, "parse_error", fallback, dur)
errs = append(errs, fmt.Errorf("%s/%s output: %w", ep.Provider, ep.Model, perr))
continue
}
metrics.ObserveSummarize(ep.Model, "success", fallback, dur)
slog.Default().Info("summarized", "model", ep.Model, "fallback", fallback, "elapsed_ms", dur.Milliseconds())
return sum, nil
}
return domain.Summary{}, fmt.Errorf("summarize: all %d endpoint(s) failed: %w", len(s.endpoints), errors.Join(errs...))
}
func (s *Summarizer) build(v domain.Video, ep Endpoint, fallbackUsed bool, raw string) (domain.Summary, error) {
parsed, err := parse(raw)
if err != nil {
return domain.Summary{}, fmt.Errorf("summarize: parse %s response: %w", ep.Provider, err)
}
return domain.Summary{
UserID: v.UserID,
VideoID: v.ID,
Summary: parsed.Summary,
Highlights: []string(parsed.Highlights),
Takeaways: []string(parsed.Takeaways),
AIProvider: ep.Provider,
AIModel: ep.Model,
FallbackUsed: fallbackUsed,
CreatedAt: s.now(),
}, nil
}
func buildUserPrompt(v domain.Video, t domain.Transcript, maxInputChars int) string {
var b strings.Builder
fmt.Fprintf(&b, "Title: %s\n", v.Title)
if v.URL != "" {
fmt.Fprintf(&b, "URL: %s\n", v.URL)
}
fmt.Fprintf(&b, "\nTranscript:\n%s", truncate(t.Content, maxInputChars))
return b.String()
}
// truncate caps content to max bytes on a UTF-8 rune boundary, appending a
// marker so the model knows the transcript was cut. A non-positive max (or a
// content already within budget) returns content unchanged. Bounding the input
// keeps a long transcript from overflowing a small-context model's window — the
// production failure mode where koala/phi4-mini's 8k context returned HTTP 400 on
// a 11.6k-token transcript.
func truncate(content string, max int) string {
if max <= 0 || len(content) <= max {
return content
}
cut := max
for cut > 0 && !utf8.RuneStart(content[cut]) {
cut--
}
return content[:cut] + "\n…[transcript truncated to fit the model context]"
}
// flexStrings is a []string that also unmarshals from a single JSON string or a
// JSON array of scalars. Small local models (koala/phi4-mini) sometimes emit
// "highlights": "one point" instead of an array, or mix in a number; rather than
// fail the whole summary on that quirk, coerce to []string. Empty/whitespace
// elements are dropped.
type flexStrings []string
func (f *flexStrings) UnmarshalJSON(b []byte) error {
b = bytes.TrimSpace(b)
if len(b) == 0 || string(b) == "null" {
*f = nil
return nil
}
if b[0] == '[' {
var raw []json.RawMessage
if err := json.Unmarshal(b, &raw); err != nil {
return err
}
out := make([]string, 0, len(raw))
for _, r := range raw {
s, err := rawToString(r)
if err != nil {
return err
}
if strings.TrimSpace(s) != "" {
out = append(out, s)
}
}
*f = out
return nil
}
s, err := rawToString(b)
if err != nil {
return err
}
if strings.TrimSpace(s) == "" {
*f = nil
} else {
*f = flexStrings{s}
}
return nil
}
// rawToString renders a JSON scalar as text: a quoted string is unquoted; any
// other scalar (number, bool) is kept as its literal source so no content is lost.
func rawToString(r json.RawMessage) (string, error) {
r = bytes.TrimSpace(r)
if len(r) > 0 && r[0] == '"' {
var s string
if err := json.Unmarshal(r, &s); err != nil {
return "", err
}
return s, nil
}
return string(r), nil
}
type parsedSummary struct {
Summary string `json:"summary"`
Highlights flexStrings `json:"highlights"`
Takeaways flexStrings `json:"takeaways"`
}
// parse extracts the JSON object from a model reply. Thinking models (qwen3,
// deepseek-r1) may wrap the JSON in reasoning or code fences, so we take the
// outermost {...} span rather than requiring the whole reply to be valid JSON.
func parse(raw string) (parsedSummary, error) {
start := strings.IndexByte(raw, '{')
end := strings.LastIndexByte(raw, '}')
if start < 0 || end < start {
return parsedSummary{}, fmt.Errorf("no JSON object in response")
}
var p parsedSummary
if err := json.Unmarshal([]byte(raw[start:end+1]), &p); err != nil {
return parsedSummary{}, fmt.Errorf("unmarshal: %w", err)
}
if strings.TrimSpace(p.Summary) == "" {
return parsedSummary{}, fmt.Errorf("response has empty summary (thinking models need an explicit max_tokens)")
}
return p, nil
}