Files
ragflow/internal/ingestion/component/tokenizer_test.go
Jack 8546eb62bf Feat(ingestion): add canvas pipeline debug (dry-run) mode with View result log (#17538)
## Summary
Adds a side-effect-free DataFlow canvas **debug (dry-run) mode** plus a
**debug run log with a "View result" panel**, so a canvas can be
executed synchronously and inspected end-to-end (per-component progress
and parsed chunks) without persisting anything.

### Dry-run execution (inline parsed chunks)
- `task/debug.go`: `NewDebugTaskContext` builds an in-memory
`TaskContext` with **`KB.ID == ""` — the single debug signal used across
the ingestion pipeline**. A canvas debug run has no knowledgebase, and
production ingestion always supplies one, so `kb_id == ""` occurs ONLY
in debug mode. Components gate their own side effects on this signal
without any dedicated debug vocabulary (the former `CANVAS_DEBUG_DOC_ID`
marker constant is removed).
- `task/pipeline_executor.go`: `validateTaskContext` no longer requires
a KB when `KB.ID == ""` (debug); debug runs return `collectDebugOutput`
(chunks) instead of a no-op; uploaded bytes are delivered as
`inputs['binary']` for doc-less runs; `injectDebugPageCap` caps the
parser to the first pages for a fast preview via the production
`override_params` channel (Parser cpnID + family).
- `component/tokenizer.go`: `shouldHaveEmbedding` skips embedding when
`kb_id == ""` — the embedder is configured on the knowledgebase, so a
debug run has nothing to resolve against and stays side-effect free.
- `chunker/register.go`: chunk images are uploaded to MinIO only when a
KB is present (persist run). **In debug mode the raw image bytes are
intentionally dropped (`delete(ck, "image")`)** — the debug preview does
not render chunk images, and dropping the bytes keeps them out of memory
and out of the Redis-stored debug log. This is a deliberate trade-off,
not an oversight.
- `component/file.go`: pass through in-memory binary bytes, skipping
`doc_id` -> storage resolution.
- `handler/agent.go` + `agent_webhook.go`: detect `dataflow_canvas` and
run a sync debug returning chunks inline on the existing
chat/completions endpoint; reject DataFlow canvases from webhooks (fixes
the previously dead `== "DataFlow"` check; mirrors Python
`agent_api.py`).
- `parser_dispatch.go`: export `ParserFileFamily` for the executor's
page-cap injection.

### Debug run log + "View result"
Mirrors Python's debug-log contract so the front-end can replay each
component's progress and parsed output:
- `task/debug_log_sink.go`: a `DebugLogSink` records every component's
lifecycle into a `[{component_id, trace}]` array (each trace entry
carries `message`, `progress`, `timestamp`, `elapsed_time`). `Flush`
appends a terminal `END` marker whose first trace message is non-empty
so the front-end detects completion. On failure the END marker is
prefixed `[ERROR]` yet still carries the run, so the failure timeline
renders instead of being stuck empty. Timestamps and `elapsed_time` are
in seconds (matching the rest of the app).
- `task/debug_result_dsl.go`: `BuildDebugResultDSL` builds the `dsl` the
END marker carries — the Go analogue of Python's `Graph.__str__` +
END-marker `dsl` in `rag/flow/pipeline.py`. It combines the static DSL
structure (component_name / downstream / params / graph.nodes) with the
run output map (`output["state"][<id>]`) to emit, per component,
`obj.params.outputs[<format>].value` (chunks / text / json / html /
markdown) — the exact keys the front-end `dataflow-result` page reads to
render each step's parsed chunks. Raw embedding vectors (including the
dimension-scoped `q_<dim>_vec` keys) are stripped so the stored log
stays Python-scale.
- `task/pipeline_executor.go`: after the run, attach the built `dsl` to
the END marker via the `ResultSink` capability.
- `handler/agent.go`: `runCanvasPipelineDebug` generates a stable
`message_id` up-front and always flushes the log (success or failure);
`respondWithDebugResult` returns `message_id` in **both** the success
and the error envelope so the front-end can poll the log. The debug-log
endpoint `GET /agents/:id/logs/:message_id` serves the array.
- `web/src/pages/agent/hooks/use-run-dataflow.ts`: on a run failure,
also surface `message_id` via `setMessageId` so the log sheet renders
the failure timeline (the `[ERROR]` END marker is already written).
Guarded by `if (msgId)`, so it is a safe no-op when the back-end does
not return an id.

## Behavioral notes
- Debug parses only the first pages (`debugPageCapPages`) for a fast
preview; an explicit `pages` cap already present in the ParserConfig is
respected.
- Debug mode does not keep chunk images (see above) and does not compute
embeddings — it exercises parse + chunk only.

## Test plan
- Go: `debug_test.go`, `debug_log_sink_test.go` (trace pairing, END
marker, `[ERROR]` prefix, fractional-second timestamp/elapsed_time, size
caps, and a real-pipeline test asserting the END-marker `dsl` carries
non-empty per-component `params.outputs` with chunks),
`debug_result_dsl_test.go` (flat and real nested `output["state"]`
shapes, vector stripping, format priority),
`debug_pages_integration_test.go`, `pipeline_executor_persist_test.go`,
`handler/agent_pipeline_debug_test.go`, `handler/agent_logs_test.go`
(incl. `TestRunCanvasPipelineDebug_ErrorStillExposesMessageID` /
`TestRespondWithDebugResult_ErrorCarriesMessageID` locking `message_id`
on failure), plus updates to `agent_test.go` / `agent_webhook_test.go` /
`chunker/image_upload_test.go` / `tokenizer*.go`.
- `go build ./...` and `./build.sh --test` for affected packages.

🤖 Generated with [CodeBuddy Code](https://cnb.cool/codebuddy)

---------

Co-authored-by: CodeBuddy Code <noreply@tencent.com>
2026-07-31 13:10:08 +08:00

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//go:build integration
// +build integration
//
// Copyright 2026 The InfiniFlow Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
package component
import (
"bytes"
"context"
"errors"
"log"
"math"
"reflect"
"strings"
"testing"
"time"
"ragflow/internal/ingestion/component/schema"
"ragflow/internal/tokenizer"
)
func requireTokenizerPool(t *testing.T) {
t.Helper()
if err := tokenizer.Init(&tokenizer.PoolConfig{
DictPath: "/usr/share/infinity/resource",
MinSize: 1,
MaxSize: 2,
IdleTimeout: 30 * time.Second,
AcquireTimeout: 5 * time.Second,
}); err != nil {
t.Skipf("tokenizer pool unavailable: %v", err)
}
}
// TestTokenizerComponent_Invoke_HappyPath drives three chunks
// through both full_text tokenization and embedding. Verifies that
// every chunk gains `content_ltks`, `content_sm_ltks`, and a
// `q_<n>_vec` vector keyed by the embedder's vector dimension.
func TestTokenizerComponent_Invoke_HappyPath(t *testing.T) {
requireTokenizerPool(t)
const dim = 4
c, stub := withStubEmbedder(t, dim)
_ = stub
var err error
if err != nil {
t.Fatalf("NewTokenizerComponent: %v", err)
}
chunks := []map[string]any{
{"text": "alpha chunk text"},
{"text": "bravo chunk text"},
{"text": "charlie chunk text"},
}
out, err := c.Invoke(context.Background(), nil, map[string]any{
"tenant_id": "t1",
"model_id": "embd-1",
"kb_id": "kb-1",
"name": "doc.pdf",
"output_format": "chunks",
"chunks": chunks,
})
if err != nil {
t.Fatalf("Invoke: %v", err)
}
gotChunks, ok := out["chunks"].([]map[string]any)
if !ok {
t.Fatalf("chunks type = %T, want []map[string]any", out["chunks"])
}
if len(gotChunks) != 3 {
t.Fatalf("len(chunks) = %d, want 3", len(gotChunks))
}
for i, ck := range gotChunks {
if ck["content_ltks"] == nil {
t.Errorf("chunk[%d].content_ltks missing", i)
}
if ck["content_sm_ltks"] == nil {
t.Errorf("chunk[%d].content_sm_ltks missing", i)
}
if ck["title_tks"] == nil {
t.Errorf("chunk[%d].title_tks missing", i)
}
key := "q_4_vec"
v, ok := ck[key].([]float64)
if !ok {
t.Errorf("chunk[%d].%s missing or wrong type: %T", i, key, ck[key])
continue
}
if len(v) != dim {
t.Errorf("chunk[%d].%s len = %d, want %d", i, key, len(v), dim)
}
if v[0] == 0 {
t.Errorf("chunk[%d].%s[0] = %v, want non-zero", i, key, v[0])
}
}
if out["output_format"] != "chunks" {
t.Errorf("output_format = %v, want chunks", out["output_format"])
}
if out["embedding_token_consumption"] == nil {
t.Error("embedding_token_consumption missing")
}
}
// TestTokenizerComponent_Invoke_Unicode asserts CJK input
// produces finite, non-negative token counts (plan §8 Q2: Go
// `NumTokensFromString` over-counts CJK on tiktoken-init failure;
// Python returns 0 — both are valid as long as the count is
// finite).
func TestTokenizerComponent_Invoke_Unicode(t *testing.T) {
requireTokenizerPool(t)
c, stub := withStubEmbedder(t, 4)
_ = stub
inputs := []string{
"中文测试文本",
"こんにちは世界",
"한국어 텍스트",
"English mixed 中文 français 日本語",
}
chunks := make([]map[string]any, len(inputs))
for i, txt := range inputs {
chunks[i] = map[string]any{"text": txt}
}
out, err := c.Invoke(context.Background(), nil, map[string]any{
"output_format": "chunks",
"chunks": chunks,
})
if err != nil {
t.Fatalf("Invoke: %v", err)
}
gotChunks, _ := out["chunks"].([]map[string]any)
if len(gotChunks) != len(inputs) {
t.Fatalf("chunks len = %d, want %d", len(gotChunks), len(inputs))
}
for i, ck := range gotChunks {
// Direct call to verify the count contract.
tokens := tokenizer.NumTokensFromString(inputs[i])
if tokens < 0 {
t.Errorf("chunk[%d] token count negative: %d", i, tokens)
}
if ck["content_ltks"] == nil {
t.Errorf("chunk[%d].content_ltks missing", i)
}
}
}
func TestTokenizerComponent_Invoke_TextPayload(t *testing.T) {
requireTokenizerPool(t)
_, _ = withStubEmbedder(t, 4)
c, _ := NewTokenizerComponent(map[string]any{
"search_method": []any{"full_text"},
})
out, err := c.Invoke(context.Background(), nil, map[string]any{
"name": "note.txt",
"output_format": "text",
"text": "plain payload",
})
if err != nil {
t.Fatalf("Invoke: %v", err)
}
got, _ := out["chunks"].([]map[string]any)
if len(got) != 1 {
t.Fatalf("chunks len = %d, want 1", len(got))
}
if got[0]["text"] != "plain payload" {
t.Errorf("text = %v, want plain payload", got[0]["text"])
}
if got[0]["content_ltks"] == nil {
t.Errorf("content_ltks missing: %v", got[0])
}
}
func TestTokenizerComponent_Invoke_JSONPayload(t *testing.T) {
requireTokenizerPool(t)
_, _ = withStubEmbedder(t, 4)
c, _ := NewTokenizerComponent(map[string]any{
"search_method": []any{"full_text"},
})
out, err := c.Invoke(context.Background(), nil, map[string]any{
"name": "note.pdf",
"output_format": "json",
"json": []map[string]any{{"text": "row one"}, {"text": "row two"}},
})
if err != nil {
t.Fatalf("Invoke: %v", err)
}
got, _ := out["chunks"].([]map[string]any)
if len(got) != 2 {
t.Fatalf("chunks len = %d, want 2", len(got))
}
if got[0]["content_ltks"] == nil || got[1]["content_ltks"] == nil {
t.Errorf("content_ltks missing: %v", got)
}
}
// TestTokenizerComponent_Invoke_BatchedEmbedding asserts the
// embedding client is called once for the title plus once for the
// chunk batch when all chunks fit into a single batch.
func TestTokenizerComponent_Invoke_BatchedEmbedding(t *testing.T) {
requireTokenizerPool(t)
c, stub := withStubEmbedder(t, 8)
chunks := []map[string]any{
{"text": "one"},
{"text": "two"},
{"text": "three"},
}
if _, err := c.Invoke(context.Background(), nil, map[string]any{
"name": "doc.txt",
"kb_id": "kb-1",
"output_format": "chunks",
"chunks": chunks,
}); err != nil {
t.Fatalf("Invoke: %v", err)
}
if got := stub.calls.Load(); got != 2 {
t.Errorf("embedder calls = %d, want 2 (title + single content batch)", got)
}
}
// TestTokenizerComponent_Invoke_FullTextOnly covers the
// search_method=["full_text"] branch: no embedding, no encoder
// call, but tokenized fields present.
func TestTokenizerComponent_Invoke_FullTextOnly(t *testing.T) {
requireTokenizerPool(t)
_, stub := withStubEmbedder(t, 4)
c, _ := NewTokenizerComponent(map[string]any{
"search_method": []any{"full_text"},
})
out, err := c.Invoke(context.Background(), nil, map[string]any{
"output_format": "chunks",
"chunks": []map[string]any{{"text": "alpha bravo"}},
})
if err != nil {
t.Fatalf("Invoke: %v", err)
}
if stub.calls.Load() != 0 {
t.Errorf("embedder should not be called, got %d", stub.calls.Load())
}
if out["embedding_token_consumption"] != nil {
t.Errorf("embedding_token_consumption should be absent, got %v", out["embedding_token_consumption"])
}
got, _ := out["chunks"].([]map[string]any)
if len(got) == 0 || got[0]["content_ltks"] == nil {
t.Errorf("content_ltks missing: %v", got)
}
}
// TestTokenizerComponent_Invoke_KeywordSplitCJK verifies important_kwd is
// split by the full ASCII+CJK delimiter set, not just ASCII comma. A Chinese
// LLM commonly emits CJK commas/semicolons even when asked for
// "comma-separated"; ASCII-only splitting would leave keywords glued together.
func TestTokenizerComponent_Invoke_KeywordSplitCJK(t *testing.T) {
requireTokenizerPool(t)
_, stub := withStubEmbedder(t, 4)
c, _ := NewTokenizerComponent(map[string]any{
"search_method": []any{"full_text"},
})
out, err := c.Invoke(context.Background(), nil, map[string]any{
"output_format": "chunks",
"chunks": []map[string]any{{"text": "alpha", "keywords": "kw1kw2kw3"}},
})
if err != nil {
t.Fatalf("Invoke: %v", err)
}
if stub.calls.Load() != 0 {
t.Errorf("embedder should not be called in full_text-only mode, got %d", stub.calls.Load())
}
got, _ := out["chunks"].([]map[string]any)
if len(got) != 1 {
t.Fatalf("chunks len = %d, want 1", len(got))
}
kwd, ok := got[0]["important_kwd"].([]string)
if !ok {
t.Fatalf("important_kwd should be []string, got %T", got[0]["important_kwd"])
}
if len(kwd) != 3 {
t.Errorf("important_kwd must split CJK delimiters into 3 elements, got %d: %v", len(kwd), kwd)
}
}
func TestTokenizerComponent_Invoke_FullTextAndEmbedding(t *testing.T) {
requireTokenizerPool(t)
c, _ := withStubEmbedder(t, 4)
out, err := c.Invoke(context.Background(), nil, map[string]any{
"name": "doc.pdf",
"kb_id": "kb-1",
"output_format": "chunks",
"chunks": []map[string]any{{"text": "alpha bravo"}},
})
if err != nil {
t.Fatalf("Invoke: %v", err)
}
got, _ := out["chunks"].([]map[string]any)
if len(got) != 1 {
t.Fatalf("chunks len = %d, want 1", len(got))
}
if got[0]["content_ltks"] == nil || got[0]["content_sm_ltks"] == nil {
t.Fatalf("full-text tokens missing: %v", got[0])
}
if got[0]["q_4_vec"] == nil {
t.Fatalf("embedding vector missing: %v", got[0])
}
if out["embedding_token_consumption"] == nil {
t.Fatal("embedding_token_consumption missing")
}
}
// TestTokenizerComponent_Invoke_EmbedNoResolver covers the
// "embedding requested but no embedder resolver configured" branch
// (explicit resolver nil and DefaultEmbedderResolver unset) — must
// return a clear error, not panic.
func TestTokenizerComponent_Invoke_EmbedNoResolver(t *testing.T) {
requireTokenizerPool(t)
c, _ := NewTokenizerComponent(nil)
_, err := c.Invoke(context.Background(), nil, map[string]any{
"kb_id": "kb-1",
"output_format": "chunks",
"chunks": []map[string]any{{"text": "alpha"}},
})
if err == nil {
t.Fatal("expected error when embedding requested without resolver, got nil")
}
if !strings.Contains(err.Error(), "resolver") {
t.Errorf("error should mention resolver: %v", err)
}
}
// TestTokenizerComponent_Invoke_EmbedderError covers the
// propagation of an error from the embedding driver.
func TestTokenizerComponent_Invoke_EmbedderError(t *testing.T) {
requireTokenizerPool(t)
c, stub := withStubEmbedder(t, 4)
stub.err = errors.New("simulated upstream error")
_, err := c.Invoke(context.Background(), nil, map[string]any{
"kb_id": "kb-1",
"output_format": "chunks",
"chunks": []map[string]any{{"text": "alpha"}},
})
if err == nil {
t.Fatal("expected error from embedder, got nil")
}
if !strings.Contains(err.Error(), "simulated upstream error") {
t.Errorf("error should chain embedder error: %v", err)
}
}
// TestTokenizerComponent_Invoke_EncoderCountMismatch covers the
// "embedder returned wrong number of vectors" defensive branch.
func TestTokenizerComponent_Invoke_EncoderCountMismatch(t *testing.T) {
requireTokenizerPool(t)
_, stub := withStubEmbedder(t, 4)
// Inject an embedder that returns the wrong number of vectors
// regardless of input.
wrong := &countMismatchedEmbedder{want: 1}
cIntf, err := NewTokenizerComponentWithResolver(nil, func(_ context.Context, _, _, _ string) (Embedder, error) { return wrong, nil })
if err != nil {
t.Fatalf("NewTokenizerComponentWithResolver: %v", err)
}
c := cIntf.(*TokenizerComponent)
_ = stub
_, err = c.Invoke(context.Background(), nil, map[string]any{
"kb_id": "kb-1",
"output_format": "chunks",
"chunks": []map[string]any{{"text": "a"}, {"text": "b"}, {"text": "c"}},
})
if err == nil {
t.Fatal("expected error from count mismatch, got nil")
}
if !strings.Contains(err.Error(), "vectors") {
t.Errorf("error should mention vectors: %v", err)
}
}
type countMismatchedEmbedder struct{ want int }
func (c *countMismatchedEmbedder) MaxTokens() int { return 2048 }
func (c *countMismatchedEmbedder) Encode(ctx context.Context, texts []string) ([]EmbeddingResult, error) {
out := make([]EmbeddingResult, c.want)
for i := range out {
out[i] = EmbeddingResult{Vector: make([]float64, 4), TokenCount: 1}
}
return out, nil
}
// TestTokenizerComponent_Smoke_EndToEnd is the BLOCKER smoke test
// (plan §8 R3). Drives 1 chunk of ~1000 tokens through the real
// tokenizer and a stub embedder with no artificial latency, then
// asserts:
//
// - non-zero vector returned
// - latency well under 5s (real stub returns in <1ms; we assert
// < 5s as the §R3 ceiling)
// - no panic
//
// Documented result: this stub embedding completes in well under
// 5s (typical observed latency < 5ms on the test host). The
// production path against a real embedding API was not exercised
// in this CI sandbox; the helper `withStubEmbedder` deliberately
// avoids the network round-trip while still exercising the full
// wiring (TrackElapsed, batched Encode, vector
// stamping).
func TestTokenizerComponent_Smoke_EndToEnd(t *testing.T) {
requireTokenizerPool(t)
const dim = 1024
c, _ := withStubEmbedder(t, dim)
words := make([]string, 0, 1000)
for i := 0; i < 1000; i++ {
words = append(words, "ragflow")
}
chunkText := strings.Join(words, " ")
preflightTokens := tokenizer.NumTokensFromString(chunkText)
if preflightTokens < 100 || preflightTokens > 5000 {
t.Logf("preflight token count = %d (acceptable range 100-5000)", preflightTokens)
}
start := time.Now()
out, err := c.Invoke(context.Background(), nil, map[string]any{
"tenant_id": "tenant-smoke",
"model_id": "embd-smoke",
"kb_id": "kb-1",
"name": "smoke.pdf",
"output_format": "chunks",
"chunks": []map[string]any{{"text": chunkText}},
})
elapsed := time.Since(start)
if err != nil {
t.Fatalf("Invoke: %v", err)
}
if elapsed >= 5*time.Second {
t.Errorf("elapsed %v exceeds 5s ceiling", elapsed)
}
got, ok := out["chunks"].([]map[string]any)
if !ok || len(got) != 1 {
t.Fatalf("chunks output malformed: %v", out["chunks"])
}
vec, ok := got[0]["q_1024_vec"].([]float64)
if !ok {
t.Fatalf("q_1024_vec missing or wrong type: %T", got[0]["q_1024_vec"])
}
if len(vec) != dim {
t.Errorf("vector len = %d, want %d", len(vec), dim)
}
nonZero := 0
for _, x := range vec {
if x != 0 {
nonZero++
}
}
if nonZero == 0 {
t.Error("vector is all zeros")
}
t.Logf("smoke complete: chunks=%d elapsed=%v tokens=%d vec_dim=%d",
len(got), elapsed, preflightTokens, len(vec))
}
func TestTokenizerComponent_Embedding_MergesTitleAndContentVectors(t *testing.T) {
requireTokenizerPool(t)
c, stub := withStubEmbedder(t, 2)
stub.resultsByCall = []embeddingCallResult{
{vectors: [][]float64{{10, 20}}, tokenCount: 7},
{vectors: [][]float64{{1, 2}, {3, 4}}, tokenCount: 11},
}
out, err := c.Invoke(context.Background(), nil, map[string]any{
"name": "doc.pdf",
"kb_id": "kb-1",
"output_format": "chunks",
"chunks": []map[string]any{{"text": "alpha"}, {"text": "beta"}},
})
if err != nil {
t.Fatalf("Invoke: %v", err)
}
got, _ := out["chunks"].([]map[string]any)
want0 := []float64{1.9, 3.8}
want1 := []float64{3.7, 5.6}
if !floatSliceClose(got[0]["q_2_vec"].([]float64), want0) {
t.Fatalf("chunk[0] q_2_vec = %v, want %v", got[0]["q_2_vec"], want0)
}
if !floatSliceClose(got[1]["q_2_vec"].([]float64), want1) {
t.Fatalf("chunk[1] q_2_vec = %v, want %v", got[1]["q_2_vec"], want1)
}
}
func TestTokenizerComponent_Embedding_UsesFilenameWeight(t *testing.T) {
requireTokenizerPool(t)
cIntf, err := NewTokenizerComponentWithResolver(map[string]any{
"filename_embd_weight": 0.25,
}, func(_ context.Context, _, _, _ string) (Embedder, error) {
stub := newStubEmbedder(2)
stub.resultsByCall = []embeddingCallResult{
{vectors: [][]float64{{8, 8}}, tokenCount: 3},
{vectors: [][]float64{{2, 2}}, tokenCount: 5},
}
return stub, nil
})
if err != nil {
t.Fatalf("NewTokenizerComponentWithResolver: %v", err)
}
c := cIntf.(*TokenizerComponent)
out, err := c.Invoke(context.Background(), nil, map[string]any{
"name": "doc.pdf",
"kb_id": "kb-1",
"output_format": "chunks",
"chunks": []map[string]any{{"text": "alpha"}},
})
if err != nil {
t.Fatalf("Invoke: %v", err)
}
got, _ := out["chunks"].([]map[string]any)
want := []float64{3.5, 3.5}
if !floatSliceClose(got[0]["q_2_vec"].([]float64), want) {
t.Fatalf("q_2_vec = %v, want %v", got[0]["q_2_vec"], want)
}
}
func TestTokenizerComponent_Embedding_EmptyNameWarnsAndUsesContentVector(t *testing.T) {
requireTokenizerPool(t)
c, stub := withStubEmbedder(t, 2)
stub.resultsByCall = []embeddingCallResult{{vectors: [][]float64{{2, 4}}, tokenCount: 5}}
var buf bytes.Buffer
prevWriter := log.Writer()
prevFlags := log.Flags()
log.SetOutput(&buf)
log.SetFlags(0)
t.Cleanup(func() {
log.SetOutput(prevWriter)
log.SetFlags(prevFlags)
})
out, err := c.Invoke(context.Background(), nil, map[string]any{
"name": " ",
"kb_id": "kb-1",
"output_format": "chunks",
"chunks": []map[string]any{{"text": "alpha"}},
})
if err != nil {
t.Fatalf("Invoke: %v", err)
}
if got := stub.calls.Load(); got != 1 {
t.Fatalf("embedder calls = %d, want 1 (content only)", got)
}
if !strings.Contains(buf.String(), "empty name provided from upstream") {
t.Fatalf("log output = %q, want empty-name warning", buf.String())
}
got, _ := out["chunks"].([]map[string]any)
want := []float64{2, 4}
if !floatSliceClose(got[0]["q_2_vec"].([]float64), want) {
t.Fatalf("q_2_vec = %v, want %v", got[0]["q_2_vec"], want)
}
if got := out["embedding_token_consumption"]; got != 5 {
t.Fatalf("embedding_token_consumption = %v, want 5", got)
}
}
// Python tokenizer.py:95 passes the raw name to embedding without .strip();
// Go must match — the title embedding must receive the original name, not a
// TrimSpace'd copy. The empty-name guard still uses TrimSpace (mirroring
// Python's `.strip()==""` check at tokenizer.py:200), but the value encoded
// is the raw name.
func TestTokenizerComponent_Embedding_UsesRawNameNotTrimmed(t *testing.T) {
requireTokenizerPool(t)
c, stub := withStubEmbedder(t, 2)
if _, err := c.Invoke(context.Background(), nil, map[string]any{
"name": " report.pdf ",
"kb_id": "kb-1",
"output_format": "chunks",
"chunks": []map[string]any{{"text": "alpha"}},
}); err != nil {
t.Fatalf("Invoke: %v", err)
}
if len(stub.callInputs) < 1 {
t.Fatalf("callInputs len = %d, want >= 1", len(stub.callInputs))
}
// First call is the title embedding; it must receive the raw name with
// surrounding whitespace preserved, matching Python.
if got := stub.callInputs[0][0]; got != " report.pdf " {
t.Fatalf("title embedding input = %q, want %q (raw, not trimmed)", got, " report.pdf ")
}
}
func TestTokenizerComponent_Embedding_TruncatesByMaxTokensMinus10(t *testing.T) {
requireTokenizerPool(t)
c, stub := withStubEmbedder(t, 2)
stub.maxTokens = 12
longText := strings.Repeat("hello world ", 20)
if _, err := c.Invoke(context.Background(), nil, map[string]any{
"name": "doc.pdf",
"kb_id": "kb-1",
"output_format": "chunks",
"chunks": []map[string]any{{"text": longText}},
}); err != nil {
t.Fatalf("Invoke: %v", err)
}
if len(stub.callInputs) != 2 {
t.Fatalf("callInputs len = %d, want 2", len(stub.callInputs))
}
if len(stub.callInputs[1]) != 1 {
t.Fatalf("content batch size = %d, want 1", len(stub.callInputs[1]))
}
if got := stub.callInputs[1][0]; len(got) >= len(longText) {
t.Fatalf("content text was not truncated: original=%d got=%d", len(longText), len(got))
}
}
func TestTokenizerComponent_Embedding_SkipsEmptyCleanedTextsButReturnsZeroWhenAllSkipped(t *testing.T) {
requireTokenizerPool(t)
c, stub := withStubEmbedder(t, 2)
out, err := c.Invoke(context.Background(), nil, map[string]any{
"name": "doc.pdf",
"kb_id": "kb-1",
"output_format": "chunks",
"chunks": []map[string]any{
{"text": "<table><tr><td></td></tr></table>"},
{"text": " "},
},
})
if err != nil {
t.Fatalf("Invoke: %v", err)
}
if got := stub.calls.Load(); got != 0 {
t.Fatalf("embedder calls = %d, want 0", got)
}
if got := out["embedding_token_consumption"]; got != 0 {
t.Fatalf("embedding_token_consumption = %v, want 0", got)
}
got, _ := out["chunks"].([]map[string]any)
for i, ck := range got {
if _, ok := ck["q_2_vec"]; ok {
t.Fatalf("chunk[%d] should not have vector: %v", i, ck)
}
}
}
func TestTokenizerComponent_Embedding_SetsTokenConsumptionIncludingTitleCall(t *testing.T) {
requireTokenizerPool(t)
c, stub := withStubEmbedder(t, 2)
stub.callTokens = []int{3, 5, 7}
t.Setenv("TOKENIZER_EMBEDDING_BATCH_SIZE", "1")
out, err := c.Invoke(context.Background(), nil, map[string]any{
"name": "doc.pdf",
"kb_id": "kb-1",
"output_format": "chunks",
"chunks": []map[string]any{{"text": "alpha"}, {"text": "beta"}},
})
if err != nil {
t.Fatalf("Invoke: %v", err)
}
if got := out["embedding_token_consumption"]; got != 15 {
t.Fatalf("embedding_token_consumption = %v, want 15", got)
}
}
func TestTokenizerComponent_Embedding_BatchesByConfiguredBatchSize(t *testing.T) {
requireTokenizerPool(t)
c, stub := withStubEmbedder(t, 2)
t.Setenv("TOKENIZER_EMBEDDING_BATCH_SIZE", "2")
if _, err := c.Invoke(context.Background(), nil, map[string]any{
"name": "doc.pdf",
"kb_id": "kb-1",
"output_format": "chunks",
"chunks": []map[string]any{
{"text": "one"},
{"text": "two"},
{"text": "three"},
{"text": "four"},
{"text": "five"},
},
}); err != nil {
t.Fatalf("Invoke: %v", err)
}
if got := stub.calls.Load(); got != 4 {
t.Fatalf("embedder calls = %d, want 4 (1 title + 3 content batches)", got)
}
wantInputs := [][]string{{"doc.pdf"}, {"one", "two"}, {"three", "four"}, {"five"}}
if !reflect.DeepEqual(stub.callInputs, wantInputs) {
t.Fatalf("call inputs = %#v, want %#v", stub.callInputs, wantInputs)
}
}
func floatSliceClose(got, want []float64) bool {
if len(got) != len(want) {
return false
}
for i := range got {
if math.Abs(got[i]-want[i]) > 1e-9 {
return false
}
}
return true
}
func TestTokenizerComponent_InstanceResolversDoNotLeakAcrossComponents(t *testing.T) {
requireTokenizerPool(t)
compAIntf, err := NewTokenizerComponentWithResolver(nil, func(_ context.Context, _, _, _ string) (Embedder, error) {
stub := newStubEmbedder(2)
stub.resultsByCall = []embeddingCallResult{{vectors: [][]float64{{10, 10}}, tokenCount: 1}, {vectors: [][]float64{{1, 1}}, tokenCount: 1}}
return stub, nil
})
if err != nil {
t.Fatalf("NewTokenizerComponentWithResolver(A): %v", err)
}
compBIntf, err := NewTokenizerComponentWithResolver(nil, func(_ context.Context, _, _, _ string) (Embedder, error) {
stub := newStubEmbedder(2)
stub.resultsByCall = []embeddingCallResult{{vectors: [][]float64{{20, 20}}, tokenCount: 1}, {vectors: [][]float64{{2, 2}}, tokenCount: 1}}
return stub, nil
})
if err != nil {
t.Fatalf("NewTokenizerComponentWithResolver(B): %v", err)
}
compA := compAIntf.(*TokenizerComponent)
compB := compBIntf.(*TokenizerComponent)
outA, err := compA.Invoke(context.Background(), nil, map[string]any{"kb_id": "kb-1", "name": "docA", "output_format": "chunks", "chunks": []map[string]any{{"text": "alpha"}}})
if err != nil {
t.Fatalf("Invoke A: %v", err)
}
outB, err := compB.Invoke(context.Background(), nil, map[string]any{"kb_id": "kb-1", "name": "docB", "output_format": "chunks", "chunks": []map[string]any{{"text": "beta"}}})
if err != nil {
t.Fatalf("Invoke B: %v", err)
}
vecA := outA["chunks"].([]map[string]any)[0]["q_2_vec"].([]float64)
vecB := outB["chunks"].([]map[string]any)[0]["q_2_vec"].([]float64)
if reflect.DeepEqual(vecA, vecB) {
t.Fatalf("instance resolvers leaked: vecA=%v vecB=%v", vecA, vecB)
}
}
func TestTokenizeChunks_SymbolOnlyTextFallsBackToRawText(t *testing.T) {
requireTokenizerPool(t)
chunks := []schema.ChunkDoc{
{Text: "·"}, // middle dot · — seen in production chunk[15]
{Text: ")"},
{Text: "("},
{Text: "*"},
}
err := tokenizeChunks(chunks, "test", "English")
if err != nil {
t.Fatalf("tokenizeChunks: %v", err)
}
for i, ck := range chunks {
t.Logf("chunk[%d]: text=%q content_ltks=%q content_sm_ltks=%q",
i, ck.Text, ck.ContentLtks, ck.ContentSmLtks)
// After fix: Tokenize returns empty for symbol-only text,
// but the fallback sets ContentLtks = raw text.
if strings.TrimSpace(ck.ContentLtks) == "" {
t.Errorf("chunk[%d]: expected non-empty ContentLtks (raw text fallback) for %q, got empty",
i, ck.Text)
}
}
}
func TestTokenizeChunks_WhitespaceSummaryShadowsTextBug(t *testing.T) {
requireTokenizerPool(t)
chunks := []schema.ChunkDoc{
{Summary: " ", Text: "real content here"},
}
err := tokenizeChunks(chunks, "test", "English")
if err != nil {
t.Fatalf("tokenizeChunks: %v", err)
}
// After fix: TrimSpace(" ") is empty, so the Summary branch is skipped.
// The Text branch is entered and "real content here" is tokenized normally.
if strings.TrimSpace(chunks[0].ContentLtks) == "" {
t.Errorf("whitespace Summary should be skipped, Text %q should be tokenized, but ContentLtks is empty",
chunks[0].Text)
}
}