Files
ragflow/internal/ingestion/component/tokenizer_unit_test.go
Jack 554925b583 Fix(go): align ingestion pipeline with Python (parser/media dispatch + PDF coordinate chain + Chunker) (#17349)
## Summary
Aligns the Go ingestion pipeline with the Python implementation, closing
several behavioral gaps found during the Python→Go migration (tracked in
`docs/migration_python_go_diff.md`). Covers parser/media dispatch
alignment, the PDF coordinate-chain (preview images, outline→title,
chunk coordinate finalization), and the Chunker Token/QA batches below.

Commits are grouped as follows.

### 1. Fix parser params (c524f450e)
Fixes parser/media wiring and several dispatch gaps:
- **docx/pdf vision dispatch**: correct parameter handling and VLM
invocation.
- **markdown vision (diff 2.5)**: also enhance items whose
`doc_type_kwd` is `table`, not only `image` (parser/utils.py:181).
- **media audio (diff 2.11)**: when `output_format` is `json`, carry the
ASR transcription as a JSON item instead of only the `Text` field (the
Invoke switch had no `json` branch and dropped it).
- **email (diff 2.2)**: default `output_format` is `json`
(parser.py:212), not `text`.
- **tokenizer**: handle empty/whitespace-only names; trim before
embedding.
- **extractor**: tag-matching parameter wiring.
- **split**: keyword-split regex now covers CJK/English separators.
- **parser.go**: parser-param plumbing.

### 2. fix parser gap (373537da1)
Image dispatch now mirrors `rag/app/picture.py:chunk()`:
- Always OCR the image (PaddleOCR or local ONNX).
- When OCR text is short, also call VLM (`describe`) and combine `OCR +
VLM` text.
- Emits a **structured JSON item** carrying the image data-URI and
`doc_type_kwd:"image"`, instead of a bare `Text` string. This fixes the
payload being rejected downstream by OneChunker/TokenChunker (JSON=nil).

### 3. PDF coordinate-chain fixes (55367a820, 727f8167c)
Closes three items from the migration tracker in the
chunker/tokenizer/task layer:

- **(Chunker-1.3) `restore_pdf_text_previews`** — `needsCrop` now also
returns true for `text` chunks that carry PDF positions
(`pdfcrop_cgo.go`), so text blocks get a rendered preview image uploaded
to storage via `imageUploadDecorator`/`ChunkImageUploader`, matching
Python `restore_pdf_text_previews` + `image2id`.

- **(Chunker-1.5) PDF outline → title levels** — `title.go` adds
`outlineSimilarity` (rune-bigram Jaccard, mirroring
`common.py:_outline_similarity`), `resolveOutlineLevels` (matches text
lines to outline entries at similarity > 0.8, with a sparse guard
`len(outline)/len(records) <= 0.03`), and `outlineFromInputs` (reads
`file.outline`). Wired into `newLevelContext` in both `group.go` and
`hierarchy.go`; falls back to the title-shape heuristic when no outline
is present.

- **(Tokenizer-(T)1) `finalize_pdf_chunk`** — the coordinate →
`position_int`/`page_num_int`/`top_int` conversion is owned by the task
layer (`processChunkPositions`→`AddPositions`), which runs *after* the
tokenizer and consumes the tokenizer-owned fields. The tokenizer only
preserves the raw `positions`/`_pdf_positions` (no duplicate
conversion), pinned by `TestChunkDocsToMaps_PreservesPDFPositions`.

### 4. Integration test made environment-free
(`internal/ingestion/task/pipeline_real_integration_test.go`)
- Removed the `//go:build integration` tag so the contract tests run
under the default `build.sh --test` (which does not pass `-tags
integration`).
- External dependencies replaced with in-memory substitutes so no
MySQL/MinIO/ES is required:
- MySQL → on-disk sqlite (`glebarez/sqlite`) with the needed tables
auto-migrated.
  - MinIO → `storage.NewMemoryStorage()`.
- Elasticsearch → chunks captured via `WithInsertFunc` instead of
`engine.InsertChunks`/`Search`.
- `requireTokenizerPool` still skips gracefully when the native
tokenizer pool is unavailable; `WithLogCreateFunc(noop)` avoids
depending on the operation-log table.
- Added `taskChunkFieldEqualsStr` to tolerate `kb_id` being a
`[]string`/`[]any` in the raw chunk payload (the search engine flattens
it to a string on read).

### 5. TokenChunker alignment — Batch 1
(`internal/ingestion/component/chunker/token.go`)
Closes four Chunker items from the migration tracker:
- **(Chunker-2.1) sentence delimiter** — the boundary regex now also
breaks on ASCII `!`/`?`. Extracted to a package-level `var
sentenceDelimiter` and used in `mergeByTokenSize`, matching Python's
full delimiter set.
- **(Chunker-2.2) overlap tag leakage** — when a new chunk starts, its
overlap prefix is taken from the previous chunk *after* `removeTag`, in
both the text path (`mergeByTokenSize`) and the JSON path
(`mergeByTokenSizeFromJSON`). Parser tags (`@@…##`) no longer leak into
the overlap region (mirrors `nlp/__init__.py:1181`).
- **(Chunker-2.11) empty-text merge** — merging a non-empty chunk into
an empty previous chunk now assigns the text directly instead of being
skipped (`mergeByTokenSizeFromJSON`), mirroring
`token_chunker.py:236-239`.
- **(Chunker-2.4) overlap token counting** —
`takeFromEnd`/`takeFromStart` now count tokens exactly via `tokenizeStr`
instead of the 4-bytes/token heuristic, fixing over-counting for CJK
text.

### 6. QA Chunker alignment — Batch 2
(`internal/ingestion/component/chunker/qa.go` + `schema`)
Closes three Chunker items from the migration tracker:
- **(Chunker-2.13) default language** — an empty `lang` now defaults to
Chinese prefixes (`问题:`/`回答:`) instead of English, matching `qa.py:299`.
- **(Chunker-2.12) `rmQAPrefix` regex** — the separator is changed to
`[\t:: ]+` (one-or-more), matching `qa.py:241`, so multiple separators
(e.g. `Q:: answer`) are fully stripped.
- **(Chunker-1.8 QA) missing chunk fields** — QA chunks now preserve:
- `top_int` — the source row/record index, threaded through the
tab/csv/markdown extractors (mirrors `qa.py` `beAdoc(..., row_num=i)`);
  - `image` + `doc_type_kwd:"image"`;
  - `_pdf_positions` / `positions` carried from the upstream JSON item.
`schema.ChunkDoc` gains a `TopInt []int` field (serialized as `top_int`,
registered in `UnmarshalJSON`). Note: the Tag/Table/Presentation/One
chunker field gaps under 1.8 remain pending.

## Test plan
- Added/updated unit tests: `pdfcrop_cgo_test.go` (`TestNeedsCrop`,
`TestRestorePDFTextPreview`), `title_test.go`
(`TestResolveOutlineLevels`, `TestResolveOutlineLevels_SparseGuard`,
`TestNewLevelContext_OutlineBranch`, `TestOutlineFromInputs`),
`tokenizer_unit_test.go` (`TestChunkDocsToMaps_PreservesPDFPositions`),
`token_pdfpos_test.go`.
- **Batch 1** — `token_batch1_test.go`:
`TestSentenceDelimiterMatchesBangAndQuestion`,
`TestMergeByTokenSizeFromJSON_OverlapStripsTags`,
`TestMergeByTokenSizeFromJSON_EmptyPrevKeepsChunk`,
`TestTakeFromEndRespectsTokenCount`,
`TestTakeFromStartRespectsTokenCount`.
- **Batch 2** — `qa_batch2_test.go`:
`TestQAChunker_DefaultLangIsChinese`,
`TestRmQAPrefixStripsMultipleSeparators`, `TestQAChunker_SetsTopInt`,
`TestQAChunker_CarriesImageAndPositions`. Existing `qa_test.go`
expectations were updated to the corrected language default / separator
behavior.
- `pipeline_real_integration_test.go`
(`TestPipelineExecutor_Run_RealCanvasDSL_UsesGeneralPipeline`,
`TestPipelineExecutor_Run_RealPDF_ProducesIndexedChunks`,
`TestRunPipeline_RealPipelineOutput_ProducesIndexFields`) now runs
without any external service.
- `bash build.sh --test ./internal/ingestion/...` passes.
- No files deleted.
2026-07-24 21:06:38 +08:00

355 lines
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Go

//
// 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.
//
// Unit tests for the Tokenizer component that do NOT depend on the C++ RAG
// Analyzer pool. These run under plain `go test` (no -tags integration).
// Pool-dependent tests live in tokenizer_test.go (//go:build integration).
package component
import (
"context"
"encoding/json"
"strings"
"sync/atomic"
"testing"
"time"
"ragflow/internal/agent/runtime"
"ragflow/internal/ingestion/component/schema"
)
// stubEmbedder records every call and returns canned vectors.
// Matches the Embedder contract: len(results) == len(texts).
type stubEmbedder struct {
calls atomic.Int32
dim int
maxTokens int
delay time.Duration
err error
callInputs [][]string
resultsByCall []embeddingCallResult
callTokens []int
}
type embeddingCallResult struct {
vectors [][]float64
tokenCount int
}
func (s *stubEmbedder) MaxTokens() int {
return s.maxTokens
}
func (s *stubEmbedder) Encode(ctx context.Context, texts []string) ([]EmbeddingResult, error) {
s.calls.Add(1)
copied := append([]string(nil), texts...)
s.callInputs = append(s.callInputs, copied)
if s.delay > 0 {
time.Sleep(s.delay)
}
if s.err != nil {
return nil, s.err
}
callIdx := int(s.calls.Load()) - 1
var cfg embeddingCallResult
if callIdx < len(s.resultsByCall) {
cfg = s.resultsByCall[callIdx]
}
out := make([]EmbeddingResult, len(texts))
for i := range texts {
var v []float64
if i < len(cfg.vectors) {
v = append([]float64(nil), cfg.vectors[i]...)
} else {
v = make([]float64, s.dim)
v[0] = float64(i + 1)
}
tokenCount := len(texts[i])
if callIdx < len(s.callTokens) {
tokenCount = s.callTokens[callIdx]
} else if cfg.tokenCount > 0 {
tokenCount = cfg.tokenCount
}
out[i] = EmbeddingResult{Vector: v, TokenCount: tokenCount}
}
return out, nil
}
// newStubEmbedder returns a stub embedder for instance-level resolver injection.
func newStubEmbedder(dim int) *stubEmbedder {
return &stubEmbedder{dim: dim}
}
// withStubEmbedder constructs a TokenizerComponent with an instance-scoped
// stub embedder resolver. The component uses the default search_method
// (["full_text","embedding"]); callers that need a different mode construct
// the component directly via NewTokenizerComponent(NewTokenizerComponentWithResolver).
func withStubEmbedder(t *testing.T, dim int) (*TokenizerComponent, *stubEmbedder) {
t.Helper()
stub := newStubEmbedder(dim)
comp, err := NewTokenizerComponentWithResolver(nil, func(_, _, _ string) (Embedder, error) { return stub, nil })
if err != nil {
t.Fatalf("NewTokenizerComponentWithResolver: %v", err)
}
return comp.(*TokenizerComponent), stub
}
// TestTokenizerComponent_Registered verifies init() enrollment
// under runtime.CategoryIngestion (Phase 4 / API endpoint depends
// on this contract).
func TestTokenizerComponent_Registered(t *testing.T) {
factory, cat, md, ok := runtime.DefaultRegistry.Lookup("Tokenizer")
if !ok {
t.Fatal("Tokenizer not registered in runtime.DefaultRegistry")
}
if cat != runtime.CategoryIngestion {
t.Errorf("category = %q, want %q", cat, runtime.CategoryIngestion)
}
if factory == nil {
t.Error("factory is nil")
}
if len(md.Inputs) == 0 {
t.Error("metadata.Inputs empty")
}
if len(md.Outputs) == 0 {
t.Error("metadata.Outputs empty")
}
}
// TestTokenizerComponent_Invoke_EmptyChunks covers the no-op branch:
// empty chunk list -> empty output, no panic, no encoder call.
func TestTokenizerComponent_Invoke_EmptyChunks(t *testing.T) {
c, stub := withStubEmbedder(t, 4)
_ = stub
var err error
if err != nil {
t.Fatalf("NewTokenizerComponent: %v", err)
}
out, err := c.Invoke(context.Background(), map[string]any{
"output_format": "chunks",
"chunks": []map[string]any{},
})
if err != nil {
t.Fatalf("Invoke: %v", err)
}
chunks, _ := out["chunks"].([]map[string]any)
if len(chunks) != 0 {
t.Errorf("chunks len = %d, want 0", len(chunks))
}
if stub.calls.Load() != 0 {
t.Errorf("embedder called %d times on empty input, want 0", stub.calls.Load())
}
if got := out["embedding_token_consumption"]; got != 0 {
t.Errorf("embedding_token_consumption = %v, want 0", got)
}
if out["output_format"] != "chunks" {
t.Errorf("output_format = %v, want chunks", out["output_format"])
}
}
// TestTokenizerComponent_Invoke_NilChunks covers the nil-input
// branch: nil chunks list is treated as zero-length (matches
// python `kwargs.get("chunks")` with None).
func TestTokenizerComponent_Invoke_NilChunks(t *testing.T) {
c, stub := withStubEmbedder(t, 4)
_ = stub
out, err := c.Invoke(context.Background(), map[string]any{
"output_format": "chunks",
})
if err != nil {
t.Fatalf("Invoke: %v", err)
}
chunks, _ := out["chunks"].([]map[string]any)
if len(chunks) != 0 {
t.Errorf("chunks len = %d, want 0", len(chunks))
}
}
func TestTokenizerComponent_Invoke_EmbeddingOnly(t *testing.T) {
cIntf, err := NewTokenizerComponentWithResolver(map[string]any{
"search_method": []any{"embedding"},
}, func(_, _, _ string) (Embedder, error) {
return newStubEmbedder(4), nil
})
if err != nil {
t.Fatalf("NewTokenizerComponentWithResolver: %v", err)
}
out, err := cIntf.(*TokenizerComponent).Invoke(context.Background(), map[string]any{
"name": "doc.pdf",
"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]["q_4_vec"] == nil {
t.Fatalf("q_4_vec missing: %v", got[0])
}
if got[0]["content_ltks"] != nil || got[0]["content_sm_ltks"] != nil {
t.Fatalf("embedding-only mode should not emit full-text tokens: %v", got[0])
}
if out["embedding_token_consumption"] == nil {
t.Fatal("embedding_token_consumption missing")
}
}
// TestTokenizerComponent_Embedding_ZeroChunksStillEmitsConsumptionZero uses an
// empty chunk list, so tokenizeChunks is a no-op and the C++ pool is not needed.
func TestTokenizerComponent_Embedding_ZeroChunksStillEmitsConsumptionZero(t *testing.T) {
c, stub := withStubEmbedder(t, 2)
out, err := c.Invoke(context.Background(), map[string]any{
"name": "doc.pdf",
"output_format": "chunks",
"chunks": []map[string]any{},
})
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)
}
}
// TestTokenizerComponent_InputsOutputs_NonEmpty verifies Phase 4
// API metadata shape.
func TestTokenizerComponent_InputsOutputs_NonEmpty(t *testing.T) {
c, _ := NewTokenizerComponent(map[string]any{})
ins := c.(*TokenizerComponent).Inputs()
outs := c.(*TokenizerComponent).Outputs()
if len(ins) == 0 {
t.Error("Inputs() empty")
}
if len(outs) == 0 {
t.Error("Outputs() empty")
}
for _, key := range []string{"chunks", "output_format"} {
if _, ok := outs[key]; !ok {
t.Errorf("Outputs() missing %q", key)
}
}
for _, key := range []string{"chunks", "name"} {
if _, ok := ins[key]; !ok {
t.Errorf("Inputs() missing %q", key)
}
}
}
// TestTokenizerComponent_NewTokenizerComponent_Defaults verifies
// the Python default param values propagate.
func TestTokenizerComponent_NewTokenizerComponent_Defaults(t *testing.T) {
c, err := NewTokenizerComponent(nil)
if err != nil {
t.Fatalf("NewTokenizerComponent(nil): %v", err)
}
tc := c.(*TokenizerComponent)
if tc.param.FilenameEmbdWeight != 0.1 {
t.Errorf("filename_embd_weight = %v, want 0.1", tc.param.FilenameEmbdWeight)
}
if len(tc.param.Fields) != 1 || tc.param.Fields[0] != "text" {
t.Errorf("fields = %v, want [text]", tc.param.Fields)
}
if len(tc.param.SearchMethod) != 2 {
t.Errorf("search_method len = %d, want 2", len(tc.param.SearchMethod))
}
}
// TestTokenizerComponent_NewTokenizerComponent_BadParam covers
// the param-validation branch (invalid search_method value).
func TestTokenizerComponent_NewTokenizerComponent_BadParam(t *testing.T) {
_, err := NewTokenizerComponent(map[string]any{
"search_method": []any{"unknown"},
})
if err == nil {
t.Fatal("expected param validation error, got nil")
}
}
func TestValidateTokenizerOutputs_FullTextMissingReturnsError(t *testing.T) {
err := validateTokenizerOutputs([]schema.ChunkDoc{{Text: "alpha"}}, []string{"full_text"}, []string{"text"})
if err == nil || !strings.Contains(err.Error(), "missing full_text tokens") {
t.Fatalf("err = %v, want missing full_text tokens", err)
}
}
func TestValidateTokenizerOutputs_EmbeddingMissingReturnsError(t *testing.T) {
err := validateTokenizerOutputs([]schema.ChunkDoc{{Text: "alpha"}}, []string{"embedding"}, []string{"text"})
if err == nil || !strings.Contains(err.Error(), "missing embedding vector") {
t.Fatalf("err = %v, want missing embedding vector", err)
}
}
func TestValidateTokenizerOutputs_BothModesFailWhenOneMissing(t *testing.T) {
ck := schema.ChunkDoc{Text: "alpha", ContentLtks: "tok", ContentSmLtks: "sm"}
err := validateTokenizerOutputs([]schema.ChunkDoc{ck}, []string{"full_text", "embedding"}, []string{"text"})
if err == nil || !strings.Contains(err.Error(), "missing embedding vector") {
t.Fatalf("err = %v, want missing embedding vector", err)
}
}
func TestValidateTokenizerOutputs_SymbolOnlyContentLtksIsEmptyFails(t *testing.T) {
// Simulates a chunk whose Text is a symbol/punctuation character that
// the C++ RAGAnalyzer tokenizer cannot produce tokens for (e.g. "·", ")", "(").
// After tokenizeChunks runs, ContentLtks and ContentSmLtks remain empty,
// and validateTokenizerOutputs must detect this as a failure.
ck := schema.ChunkDoc{
Text: ")",
ContentLtks: "",
ContentSmLtks: "",
}
err := validateTokenizerOutputs([]schema.ChunkDoc{ck}, []string{"full_text"}, []string{"text"})
if err == nil || !strings.Contains(err.Error(), "missing full_text tokens") {
t.Fatalf("err = %v, want missing full_text tokens", err)
}
}
// TestChunkDocsToMaps_PreservesPDFPositions is the pool-free unit test for
// Tokenizer-(T)1: the tokenizer emits chunks via schema.ChunkDocsToMaps
// (ChunkDoc.ToMap), which must carry the raw `positions` / `_pdf_positions`
// through untouched so the downstream executor stage
// (internal/ingestion/task processChunkPositions → AddPositions) can convert
// them into position_int / page_num_int / top_int exactly once. This does NOT
// require the C++ analyzer pool, so it runs under plain `go test`.
func TestChunkDocsToMaps_PreservesPDFPositions(t *testing.T) {
pos := json.RawMessage(`[[1,10,20,30,40],[2,15,25,35,45]]`)
chunks := []schema.ChunkDoc{
{Text: "PDF paragraph", DocType: "text", CKType: "text",
Positions: pos, PDFPositions: pos},
}
maps := schema.ChunkDocsToMaps(chunks)
got, ok := maps[0]["positions"].([][]float64)
if !ok || len(got) != 2 {
t.Fatalf("positions not preserved through tokenizer output mapping: %#v", maps[0]["positions"])
}
if _, ok := maps[0]["_pdf_positions"].([][]float64); !ok {
t.Errorf("_pdf_positions not preserved through tokenizer output mapping: %#v", maps[0]["_pdf_positions"])
}
// Sanity: page numbers are still raw 1-indexed, i.e. not yet converted
// to page_num_int (the executor owns that step).
if int(got[0][0]) != 1 {
t.Errorf("positions page already converted; want raw 1-indexed page 1, got %v", got[0][0])
}
}