Files
ragflow/internal/ingestion/task/chunk_process.go
Jack d12fd3b79d feat: parser pages range and parse type validation for dataset/document (#17293)
## Summary

Adds page-range parsing support to the Go-native pipeline path and
introduces strict `parse_type` validation for both dataset and document
update endpoints.

## What changed

### Pages range parsing
- **`internal/utility/pdf_pages.go`** — `NormalizePDFPages`: normalizes
raw page ranges (list of `[from,to]` 1-indexed inclusive ranges) into
sorted, merged, deduplicated `[][]int`. Invalid ranges are dropped.
- **`internal/ingestion/pipeline/pdf_pages.go`** —
`NormalizeParserConfigPages`: walks any parser_config map and normalizes
`"pages"` values under every component → filetype setup, so the
persisted config always carries clean, merged ranges.
- **`internal/deepdoc/parser/pdf/parser.go`** — integrates
`resolvePagesToProcess` to filter parsed PDF pages by the configured
ranges.
- Pipeline integration (parser pages):
`internal/parser/parser/pdf_parser_common.go`, `chunk_process.go`, plus
associated e2e and unit tests.

### Parse type validation (shared logic)
- **`internal/service/parser_mode.go`** (new) — `ValidateParseTypeMode`:
shared function that validates `parse_type` (1=BuiltIn/parser_id,
2=Pipeline/pipeline_id) and ensures the corresponding field is present.
Used by both dataset and document update endpoints.
- **`internal/service/dataset/crud.go`** / `update.go` — replaces inline
`isPipelineMode`/`isBuiltinMode` computation with the shared
`service.ValidateParseTypeMode`.
- **`internal/service/document/document_dataset_update.go`** — adds
strict `parse_type` validation in `validateDatasetDocumentUpdate`,
simplifies the reparse logic to a two-way switch (isBuiltin/isPipeline)
now that parse_type is always valid.
- **`internal/service/document/document.go`** — adds `ParseType` field
to `UpdateDatasetDocumentRequest`.
- **`internal/service/document/document_dataset_update.go`** —
`updateDocumentParserConfig` fallback path when DSL loading fails.
- **`internal/service/parser_mode_test.go`** (new) — test coverage for
nil, invalid, and missing-field scenarios.

### Frontend
- **`web/src/interfaces/request/document.ts`** — adds `parseType` to
`IChangeParserRequestBody`.
- **`web/src/hooks/use-document-request.ts`** —
`useSetDocumentPipelineParser` sends `parse_type` in the PATCH payload.
- **`web/src/pages/dataset/dataset/use-change-document-parser.ts`** —
Go/Python branching for the document parser config dialog.
-
**`web/src/components/document-pipeline-dialog/use-document-pipeline-form.ts`**
— `buildSubmitData` returns `parseType` (bugfix: was dropped from the
return value).

### Test changes
- **Removed**: 2 tests that verified the old "mutually exclusive" error
(replaced by `ValidateParseTypeMode` coverage).
- **Modified**: 6 tests across document and dataset packages to include
`ParseType` in request structs.
- **Added**: new e2e tests for pages parsing (`pages_e2e_test.go`,
`pdf_parser_pages_e2e_test.go`) and unit tests for `NormalizePDFPages`,
`NormalizeParserConfigPages`, `resolvePagesToProcess`.

## Backward compatibility
- The `parse_type` field is **required** when `parser_id` or
`pipeline_id` is sent. This changes the contract for both dataset and
document PATCH endpoints, but aligns the Go backend with the existing
frontend behavior (the frontend already sends `parse_type`). Callers
that omit `parse_type` when updating parser/pipeline selections will
receive a clear error message.
- Existing callers that only update fields like `name`, `enabled`, or
`meta_fields` are unaffected.
- Test updates ensure all known call sites are compliant.
2026-07-23 19:57:27 +08:00

284 lines
8.5 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.
//
package task
import (
"fmt"
"strings"
"time"
"ragflow/internal/common"
"ragflow/internal/utility"
)
// RenameTextToContentWithWeight renames the "text" key to "content_with_weight".
// If "content_with_weight" already exists, the "text" key is simply removed.
// Mirrors Python: ck["content_with_weight"] = ck["text"]; del ck["text"]
func RenameTextToContentWithWeight(chunk map[string]any) {
if _, exists := chunk["content_with_weight"]; !exists {
if text, ok := chunk["text"]; ok {
chunk["content_with_weight"] = text
}
}
delete(chunk, "text")
}
// GetEmbeddingTokenConsumption extracts the embedding token consumption from pipeline output.
// Handles both int (Go native) and float64 (after JSON round-trip).
func GetEmbeddingTokenConsumption(output map[string]any) int {
if output == nil {
return 0
}
switch v := output[EmbeddingTokenConsumptionKey].(type) {
case int:
return v
case float64:
return int(v)
default:
common.Warn(fmt.Sprintf("unexpected type %T for embedding token consumption, key=%q", v, EmbeddingTokenConsumptionKey))
return 0
}
}
// ProcessChunksForPipeline mutates chunks into the pre-index structure used by
// the pipeline and returns merged metadata. It returns an error if a chunk's
// "text" field is present but not a string: that is an upstream contract
// violation (the chunker/parser must emit string text), and continuing would
// collapse every such chunk onto the same empty-text ChunkID, silently
// overwriting each other in the index. The caller fails the task so the
// violation surfaces instead of corrupting the index.
func ProcessChunksForPipeline(
chunks []map[string]any,
docID string,
kbID string,
docName string,
now time.Time,
) (map[string]any, error) {
if chunks == nil {
return nil, nil
}
metadata := make(map[string]any)
timeStr := now.Format("2006-01-02 15:04:05")
timestamp := float64(now.UnixMicro()) / 1e6
for _, ck := range chunks {
ck["doc_id"] = docID
ck["kb_id"] = []string{kbID}
ck["docnm_kwd"] = docName
ck["create_time"] = timeStr
ck["create_timestamp_flt"] = timestamp
if _, exists := ck["id"]; !exists {
text, _ := ck["text"].(string)
ck["id"] = common.ChunkID(docID, text)
}
cleanupConsumedChunkFields(ck)
metadata = mergeChunkMetadata(metadata, ck)
RenameTextToContentWithWeight(ck)
processChunkPositions(ck)
}
return metadata, nil
}
// cleanupConsumedChunkFields materializes the stored array form of the
// questions/keywords fields (when the upstream Tokenizer did not already set
// them) and strips the consumed source fields (questions/keywords/summary)
// before persist. This is persist-schema mapping, NOT linguistic tokenization:
// question_tks / important_tks / content_ltks are owned by the Tokenizer
// component; the executor no longer falls back to producing them.
func cleanupConsumedChunkFields(ck map[string]any) {
if q, ok := ck["questions"].(string); ok {
if _, has := ck["question_kwd"]; !has {
ck["question_kwd"] = utility.SplitQuestions(q)
}
}
delete(ck, "questions")
if kws, ok := ck["keywords"].(string); ok {
if _, has := ck["important_kwd"]; !has {
ck["important_kwd"] = utility.SplitKeywords(kws)
}
}
delete(ck, "keywords")
delete(ck, "summary")
}
func mergeChunkMetadata(metadata map[string]any, ck map[string]any) map[string]any {
metaVal, exists := ck["metadata"]
if !exists {
return metadata
}
if metaMap, ok := metaVal.(map[string]any); ok {
metadata = utility.UpdateMetadataTo(metadata, metaMap)
}
delete(ck, "metadata")
return metadata
}
// processChunkPositions converts the raw "positions" field into indexable
// position fields (page_num_int, top_int, position_int) via AddPositions,
// then removes the raw "positions" and the sibling "_pdf_positions" internal
// field. _pdf_positions is the parser-emitted position matrix consumed by
// the chunker's image-crop pass (pdfcrop_cgo.go); after the chunker it is
// dead weight with no index column, so it is pruned unconditionally —
// independent of "positions" and not gated on the early-return below.
//
// Two source types reach this point:
// - []float64 — flat array of 5-tuples [page,left,right,top,bottom,…] from
// parsers that emit positions directly as a flat float64 slice.
// - [][]float64 — the production path: positions flow through ChunkDoc
// (json.RawMessage → decodeStructuredValue) which produces a slice of
// 5-element groups.
//
// Both are flattened into a single []float64 for AddPositions, which groups
// by 5 internally. Unexpected types are logged and discarded.
func processChunkPositions(ck map[string]any) {
delete(ck, "_pdf_positions")
poss, exists := ck["positions"]
if !exists {
return
}
switch v := poss.(type) {
case []float64:
AddPositions(ck, v)
case [][]float64:
flat := make([]float64, 0, len(v)*5)
for _, group := range v {
flat = append(flat, group...)
}
AddPositions(ck, flat)
default:
common.Warn(fmt.Sprintf("chunk positions unexpected type %T; discarding", poss))
}
delete(ck, "positions")
}
// AggregateTableDocMetadata collects unique per-column values across all chunks
// for columns with role "metadata" or "both", merges them into document metadata.
// Mirrors Python: rag/utils/table_es_metadata.py:aggregate_table_doc_metadata
func AggregateTableDocMetadata(chunks []map[string]any, parserConfig map[string]interface{}) map[string]any {
mode, roles, tableColumnNames := resolveTableColumnConfig(parserConfig)
if mode == "" {
mode = "auto"
}
if mode != "auto" && mode != "manual" {
return nil
}
if roles == nil {
roles = map[string]interface{}{}
}
var metaCols []string
if len(tableColumnNames) > 0 {
for _, n := range tableColumnNames {
col, _ := n.(string)
if col == "" {
continue
}
role, _ := roles[col].(string)
if role == "" {
role = "both"
}
if role == "metadata" || role == "both" {
metaCols = append(metaCols, col)
}
}
} else {
for col, v := range roles {
role, _ := v.(string)
if role == "metadata" || role == "both" {
metaCols = append(metaCols, col)
}
}
}
if len(metaCols) == 0 {
return nil
}
acc := make(map[string]map[string]struct{}, len(metaCols))
for _, col := range metaCols {
acc[col] = make(map[string]struct{})
}
for _, ck := range chunks {
cd, _ := ck["chunk_data"].(map[string]interface{})
if cd == nil {
continue
}
for _, col := range metaCols {
val, ok := cd[col]
if !ok {
continue
}
s, _ := val.(string)
if s == "" {
continue
}
acc[col][s] = struct{}{}
}
}
out := make(map[string]any, len(acc))
for col, vals := range acc {
if len(vals) == 0 {
continue
}
deduped := make([]string, 0, len(vals))
for v := range vals {
deduped = append(deduped, v)
}
out[col] = deduped
}
return out
}
// resolveTableColumnConfig reads table column settings from parser_config.
// Tries root-level flat keys first; falls back to resolving from a Parser
// component entry's spreadsheet config in a component-ID-keyed parser_config.
func resolveTableColumnConfig(parserConfig map[string]interface{}) (mode string, roles map[string]interface{}, names []interface{}) {
mode, _ = parserConfig["table_column_mode"].(string)
roles, _ = parserConfig["table_column_roles"].(map[string]interface{})
rawNames, _ := parserConfig["table_column_names"].([]interface{})
if mode != "" || roles != nil || len(rawNames) > 0 {
return mode, roles, rawNames
}
for cid, raw := range parserConfig {
if !strings.HasPrefix(cid, "Parser:") {
continue
}
comp, _ := raw.(map[string]interface{})
if comp == nil {
continue
}
ss, _ := comp["spreadsheet"].(map[string]interface{})
if ss == nil {
continue
}
if v, ok := ss["column_mode"].(string); ok {
mode = v
}
if v, ok := ss["column_roles"].(map[string]interface{}); ok {
roles = v
}
if v, ok := ss["column_names"].([]interface{}); ok {
rawNames = v
}
return mode, roles, rawNames
}
return "", nil, nil
}