mirror of
https://github.com/infiniflow/ragflow.git
synced 2026-07-17 05:07:23 +08:00
325 lines
10 KiB
Go
325 lines
10 KiB
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"
|
|
"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(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)
|
|
}
|
|
}
|