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ragflow/internal/ingestion/component/chunker/qa.go

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//
// 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.
//
// QAChunker extracts question-answer pairs from parsed content.
//
// Input formats and extraction strategies:
// - Text (txt, csv) → delimiter-based Q&A (comma or tab)
// - Markdown (md) → heading-based Q&A
// - HTML (xlsx/xls) → table-based Q&A (first two columns)
// - JSON (pdf, docx) → delimiter-based on structured text sections
//
// Every Q&A pair becomes a single chunk with content_with_weight
// formatted as "Question: {q}\tAnswer: {a}".
package chunker
import (
"context"
"encoding/csv"
"fmt"
"html"
"regexp"
"strings"
"github.com/gomarkdown/markdown"
"github.com/gomarkdown/markdown/parser"
"ragflow/internal/agent/runtime"
"ragflow/internal/ingestion/component/schema"
"ragflow/internal/tokenizer"
)
const ComponentNameQAChunker = "QAChunker"
type qaChunkerParam struct {
Lang string `json:"lang,omitempty"`
}
func (p *qaChunkerParam) Update(conf map[string]any) {
if v, ok := conf["lang"]; ok {
if s, ok := v.(string); ok {
p.Lang = s
}
}
}
func (qaChunkerParam) Defaults() qaChunkerParam { return qaChunkerParam{} }
func (qaChunkerParam) Validate() error { return nil }
type QAChunkerComponent struct {
name string
param qaChunkerParam
}
func NewQAChunker(params map[string]any) (runtime.Component, error) {
p := qaChunkerParam{}.Defaults()
(&p).Update(params)
if err := p.Validate(); err != nil {
return nil, err
}
return &QAChunkerComponent{
name: ComponentNameQAChunker,
param: p,
}, nil
}
func (c *QAChunkerComponent) Inputs() map[string]string { return ChunkerInputs }
func (c *QAChunkerComponent) Outputs() map[string]string { return ChunkerOutputs }
func (c *QAChunkerComponent) Invoke(ctx context.Context, inputs map[string]any) (map[string]any, error) {
return c.invoke(ctx, inputs)
}
func (c *QAChunkerComponent) invoke(_ context.Context, inputs map[string]any) (map[string]any, error) {
if inputs == nil {
return emptyOutputs(), nil
}
upstream, err := decodeChunkerFromUpstream(inputs)
if err != nil {
return map[string]any{
"output_format": "chunks",
"chunks": []map[string]any{},
"_ERROR": fmt.Sprintf("Input error: %v", err),
}, nil
}
qPrefix, aPrefix := "问题:", "回答:"
eng := strings.EqualFold(c.param.Lang, "english") || c.param.Lang == ""
if eng {
qPrefix, aPrefix = "Question: ", "Answer: "
}
var qaPairs []qaPair
var isMarkdown bool
switch upstream.OutputFormat {
case schema.PayloadFormatHTML:
qaPairs = extractQATable(stringPtrVal(upstream.HTMLResult))
case schema.PayloadFormatMarkdown:
qaPairs = extractQAMarkdown(stringPtrVal(upstream.MarkdownResult))
isMarkdown = true
case schema.PayloadFormatText:
qaPairs = extractQAText(stringPtrVal(upstream.TextResult))
default:
qaPairs = extractQAJSON(upstream.JSONResult)
}
chunks := make([]schema.ChunkDoc, 0, len(qaPairs))
lang, _ := inputs["lang"].(string)
tok := tokenizer.New(lang)
for _, pair := range qaPairs {
contentLTKS, _ := tok.Tokenize(pair.Question)
contentSMLTKS, _ := tok.FineGrainedTokenize(contentLTKS)
answer := rmQAPrefix(pair.Answer)
if isMarkdown {
answer = renderMarkdown(answer)
}
chunk := schema.ChunkDoc{
ContentWithWeight: fmt.Sprintf("%s%s\t%s%s", qPrefix, rmQAPrefix(pair.Question), aPrefix, answer),
DocType: "text",
ContentLtks: contentLTKS,
ContentSmLtks: contentSMLTKS,
}
chunks = append(chunks, chunk)
}
return chunkOutputs(chunks), nil
}
func renderMarkdown(s string) string {
mdParser := parser.NewWithExtensions(parser.CommonExtensions | parser.Tables)
output := markdown.ToHTML([]byte(s), mdParser, nil)
return string(output)
}
type qaPair struct {
Question string
Answer string
}
var rmQAPrefixRe = regexp.MustCompile(`(?i)^(问题|答案|回答|user|assistant|Q|A|Question|Answer|问|答)[ \t]*(?:[:]|\t)[ \t]*`)
func rmQAPrefix(txt string) string {
return strings.TrimSpace(rmQAPrefixRe.ReplaceAllString(txt, ""))
}
func stringPtrVal(s *string) string {
if s == nil {
return ""
}
return *s
}
// ---------------------------------------------------------------------------
// HTML / spreadsheet QA extraction
// ---------------------------------------------------------------------------
var htmlTR = regexp.MustCompile(`(?i)<tr[^>]*>(.*?)</tr>`)
var htmlTD = regexp.MustCompile(`(?i)<t[dh][^>]*>(.*?)</t[dh]>`)
var htmlTag = regexp.MustCompile(`<[^>]+>`)
func extractQATable(htmlStr string) []qaPair {
if htmlStr == "" {
return nil
}
rows := htmlTR.FindAllStringSubmatch(htmlStr, -1)
pairs := make([]qaPair, 0, len(rows))
for _, row := range rows {
cells := htmlTD.FindAllStringSubmatch(row[1], -1)
var texts []string
for _, cell := range cells {
t := html.UnescapeString(htmlTag.ReplaceAllString(cell[1], ""))
t = strings.TrimSpace(t)
if t != "" {
texts = append(texts, t)
}
}
if len(texts) >= 2 {
pairs = append(pairs, qaPair{Question: texts[0], Answer: texts[1]})
}
}
return pairs
}
// ---------------------------------------------------------------------------
// Markdown QA extraction
// ---------------------------------------------------------------------------
var mdHeading = regexp.MustCompile(`^(#*)`)
func extractQAMarkdown(md string) []qaPair {
if md == "" {
return nil
}
lines := strings.Split(md, "\n")
var pairs []qaPair
var questionStack []string
var levelStack []int
var answer []string
codeBlock := false
flushAnswer := func() {
joined := strings.TrimSpace(strings.Join(answer, "\n"))
if joined != "" && len(questionStack) > 0 {
sumQ := strings.Join(questionStack, "\n")
pairs = append(pairs, qaPair{Question: sumQ, Answer: joined})
}
answer = nil
}
for _, line := range lines {
trimmed := strings.TrimSpace(line)
if strings.HasPrefix(trimmed, "```") {
codeBlock = !codeBlock
}
if codeBlock {
answer = append(answer, line)
continue
}
m := mdHeading.FindStringSubmatch(line)
level := len(m[1])
if level == 0 || level > 6 {
answer = append(answer, line)
continue
}
flushAnswer()
question := strings.TrimSpace(line[level:])
for len(levelStack) > 0 && level <= levelStack[len(levelStack)-1] {
questionStack = questionStack[:len(questionStack)-1]
levelStack = levelStack[:len(levelStack)-1]
}
questionStack = append(questionStack, question)
levelStack = append(levelStack, level)
}
flushAnswer()
return pairs
}
// ---------------------------------------------------------------------------
// Text / delimiter-based QA extraction (txt, csv)
// ---------------------------------------------------------------------------
func extractQAText(text string) []qaPair {
if text == "" {
return nil
}
lines := strings.Split(text, "\n")
delimiter := detectDelimiter(lines)
if delimiter == "\t" {
return extractQATextTab(lines)
}
return extractQATextCSV(text, lines)
}
// extractQATextTab handles tab-delimited Q&A where no CSV quoting
// rules apply and physical lines always map 1:1 to records.
func extractQATextTab(lines []string) []qaPair {
var pairs []qaPair
var question, answer string
for _, line := range lines {
if strings.TrimSpace(line) == "" {
continue
}
parts := strings.Split(line, "\t")
if len(parts) != 2 {
if question != "" {
answer += "\n" + line
}
continue
}
if question != "" && answer != "" {
pairs = append(pairs, qaPair{Question: strings.TrimSpace(question), Answer: strings.TrimSpace(answer)})
}
question = parts[0]
answer = parts[1]
}
if question != "" {
pairs = append(pairs, qaPair{Question: strings.TrimSpace(question), Answer: strings.TrimSpace(answer)})
}
return pairs
}
// extractQATextCSV uses a full-text csv.Reader so that quoted fields
// that span multiple physical lines are parsed correctly (mirrors the
// Python fix in infiniflow/ragflow#16881).
//
// Because csv.Reader can merge several physical lines into one record,
// we track the byte offset via InputOffset() and map it back to the
// original lines slice so that malformed rows append the correct raw
// continuation text.
func extractQATextCSV(text string, lines []string) []qaPair {
// Precompute the byte offset where each physical line starts.
lineStarts := make([]int, len(lines)+1)
off := 0
for i, l := range lines {
lineStarts[i] = off
off += len(l) + 1 // +1 for '\n'
}
lineStarts[len(lines)] = off // sentinel
r := csv.NewReader(strings.NewReader(text))
r.LazyQuotes = true
r.FieldsPerRecord = -1
var pairs []qaPair
var question, answer string
prevLine := 0
for {
record, err := r.Read()
if err != nil {
break
}
// Map InputOffset back to the physical lines consumed.
endOff := int(r.InputOffset())
curLine := prevLine
for curLine < len(lineStarts) && lineStarts[curLine] < endOff {
curLine++
}
raw := strings.Join(lines[prevLine:curLine], "\n")
prevLine = curLine
if len(record) != 2 {
if question != "" {
answer += "\n" + raw
}
continue
}
if question != "" && answer != "" {
pairs = append(pairs, qaPair{Question: strings.TrimSpace(question), Answer: strings.TrimSpace(answer)})
}
question = record[0]
answer = record[1]
}
if question != "" {
pairs = append(pairs, qaPair{Question: strings.TrimSpace(question), Answer: strings.TrimSpace(answer)})
}
return pairs
}
func detectDelimiter(lines []string) string {
comma, tab := 0, 0
for _, line := range lines {
if len(strings.Split(line, ",")) == 2 {
comma++
}
if len(strings.Split(line, "\t")) == 2 {
tab++
}
}
if tab >= comma {
return "\t"
}
return ","
}
// ---------------------------------------------------------------------------
// JSON / structured QA extraction
// ---------------------------------------------------------------------------
func extractQAJSON(items []schema.ChunkDoc) []qaPair {
var pairs []qaPair
for _, item := range items {
txt, _ := itemText(item)
if txt == "" {
continue
}
tmp := extractQAText(txt)
pairs = append(pairs, tmp...)
}
return pairs
}
func init() {
MustRegisterChunker(ComponentNameQAChunker)
}