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# RAGFlow Go Implementation Plan 🚀 This repository tracks the progress of porting RAGFlow to Go. We'll implement core features and provide performance comparisons between Python and Go versions. ## Implementation Checklist - [x] User Management APIs - [x] Dataset Management Operations - [x] Retrieval Test - [x] Chat Management Operations - [x] Infinity Go SDK --------- Signed-off-by: Jin Hai <haijin.chn@gmail.com> Co-authored-by: Yingfeng Zhang <yingfeng.zhang@gmail.com>
497 lines
11 KiB
Go
497 lines
11 KiB
Go
// Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package nlp
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import (
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"encoding/json"
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"math"
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"os"
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"path/filepath"
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"regexp"
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"strconv"
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"strings"
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"ragflow/internal/logger"
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"ragflow/internal/tokenizer"
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"go.uber.org/zap"
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)
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// TermWeightDealer calculates term weights for text processing
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// Reference: rag/nlp/term_weight.py
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type TermWeightDealer struct {
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stopWords map[string]struct{}
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ne map[string]string // named entities
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df map[string]int // document frequency
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}
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// TermWeight represents a term and its weight
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type TermWeight struct {
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Term string
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Weight float64
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}
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// NewTermWeightDealer creates a new TermWeightDealer
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func NewTermWeightDealer(resPath string) *TermWeightDealer {
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d := &TermWeightDealer{
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stopWords: initStopWords(),
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ne: make(map[string]string),
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df: make(map[string]int),
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}
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// Load named entity dictionary
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if resPath == "" {
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resPath = "rag/res"
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}
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nerPath := filepath.Join(resPath, "ner.json")
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if data, err := os.ReadFile(nerPath); err == nil {
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if err := json.Unmarshal(data, &d.ne); err != nil {
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logger.Warn("Failed to load ner.json", zap.Error(err))
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}
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} else {
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logger.Warn("Failed to load ner.json", zap.Error(err))
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}
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// Load term frequency dictionary
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freqPath := filepath.Join(resPath, "term.freq")
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d.df = loadDict(freqPath)
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return d
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}
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// initStopWords initializes the stop words set
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func initStopWords() map[string]struct{} {
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words := []string{
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"请问", "您", "你", "我", "他", "是", "的", "就", "有", "于",
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"及", "即", "在", "为", "最", "有", "从", "以", "了", "将",
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"与", "吗", "吧", "中", "#", "什么", "怎么", "哪个", "哪些",
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"啥", "相关",
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}
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stopWords := make(map[string]struct{}, len(words))
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for _, w := range words {
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stopWords[w] = struct{}{}
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}
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return stopWords
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}
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// loadDict loads a dictionary file
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// Format: term\tfreq or just term
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func loadDict(fnm string) map[string]int {
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res := make(map[string]int)
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data, err := os.ReadFile(fnm)
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if err != nil {
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logger.Warn("Failed to load dictionary", zap.String("file", fnm), zap.Error(err))
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return res
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}
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lines := strings.Split(string(data), "\n")
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totalFreq := 0
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for _, line := range lines {
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line = strings.TrimSpace(line)
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if line == "" {
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continue
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}
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arr := strings.Split(line, "\t")
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if len(arr) >= 2 {
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if freq, err := strconv.Atoi(arr[1]); err == nil {
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res[arr[0]] = freq
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totalFreq += freq
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}
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} else {
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res[arr[0]] = 0
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}
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}
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// If no frequencies, return as set (all 0)
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if totalFreq == 0 {
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return res
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}
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return res
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}
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// Pretoken preprocesses and tokenizes text
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// Reference: term_weight.py L92-114
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func (d *TermWeightDealer) Pretoken(txt string, num bool, stpwd bool) []string {
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patt := `[~—\t @#%!<>,\.\?":;'\{\}\[\]_=\(\)\|，。？》•●○↓《；'：""【¥ 】…￥！、·（）×\` + "`" + `&/「」\]`
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res := []string{}
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tokenized, err := tokenizer.Tokenize(txt)
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if err != nil {
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// Fallback to simple split
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tokenized = txt
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}
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for _, t := range strings.Fields(tokenized) {
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tk := t
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// Check stop words
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if stpwd {
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if _, isStop := d.stopWords[tk]; isStop {
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continue
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}
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}
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// Check single digit (unless num is true)
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if matched, _ := regexp.MatchString("^[0-9]$", tk); matched && !num {
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continue
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}
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// Check patterns
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if matched, _ := regexp.MatchString(patt, t); matched {
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tk = "#"
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}
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if tk != "#" && tk != "" {
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res = append(res, tk)
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}
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}
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return res
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}
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// TokenMerge merges short tokens into phrases
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// Reference: term_weight.py L116-143
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func (d *TermWeightDealer) TokenMerge(tks []string) []string {
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oneTerm := func(t string) bool {
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// Use rune count for proper Unicode handling
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runeCount := len([]rune(t))
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if runeCount == 1 {
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return true
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}
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// Match 1-2 alphanumeric characters
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matched, _ := regexp.MatchString("^[0-9a-z]{1,2}$", t)
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return matched
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}
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if len(tks) == 0 {
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return []string{}
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}
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res := []string{}
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i := 0
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for i < len(tks) {
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// Special case: first term is single char and next is multi-char Chinese
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if i == 0 && len(tks) > 1 && oneTerm(tks[i]) {
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nextLen := len([]rune(tks[i+1]))
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isNextMultiChar := nextLen > 1
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isNextNotAlnum, _ := regexp.MatchString("^[0-9a-zA-Z]", tks[i+1])
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if isNextMultiChar && !isNextNotAlnum {
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res = append(res, tks[0]+" "+tks[1])
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i = 2
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continue
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}
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}
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j := i
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for j < len(tks) && tks[j] != "" {
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if _, isStop := d.stopWords[tks[j]]; isStop {
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break
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}
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if !oneTerm(tks[j]) {
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break
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}
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j++
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}
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if j-i > 1 {
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if j-i < 5 {
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res = append(res, strings.Join(tks[i:j], " "))
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i = j
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} else {
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// Split into pairs for 5+ consecutive short tokens
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for k := i; k < j; k += 2 {
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if k+1 < j {
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res = append(res, tks[k]+" "+tks[k+1])
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} else {
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res = append(res, tks[k])
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}
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}
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i = j
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}
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} else {
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if len(tks[i]) > 0 {
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res = append(res, tks[i])
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}
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i++
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}
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}
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// Filter empty strings
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filtered := []string{}
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for _, t := range res {
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if t != "" {
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filtered = append(filtered, t)
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}
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}
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return filtered
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}
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// Ner gets named entity type for a term
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// Reference: term_weight.py L145-150
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func (d *TermWeightDealer) Ner(t string) string {
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if d.ne == nil {
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return ""
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}
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if res, ok := d.ne[t]; ok {
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return res
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}
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return ""
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}
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// Split splits text into tokens, merging consecutive English words
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// Reference: term_weight.py L152-161
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func (d *TermWeightDealer) Split(txt string) []string {
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if txt == "" {
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return []string{""}
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}
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tks := []string{}
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// Normalize spaces (tabs and multiple spaces -> single space)
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txt = regexp.MustCompile("[ \\t]+").ReplaceAllString(txt, " ")
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txt = strings.TrimSpace(txt)
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for _, t := range strings.Split(txt, " ") {
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t = strings.TrimSpace(t)
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if t == "" {
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continue
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}
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if len(tks) > 0 {
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prevEndsWithLetter, _ := regexp.MatchString(".*[a-zA-Z]$", tks[len(tks)-1])
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currEndsWithLetter, _ := regexp.MatchString(".*[a-zA-Z]$", t)
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prevNE := d.ne[tks[len(tks)-1]]
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currNE := d.ne[t]
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if prevEndsWithLetter && currEndsWithLetter &&
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currNE != "func" && prevNE != "func" {
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tks[len(tks)-1] = tks[len(tks)-1] + " " + t
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continue
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}
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}
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tks = append(tks, t)
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}
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return tks
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}
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// Weights calculates weights for tokens
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// Reference: term_weight.py L163-246
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func (d *TermWeightDealer) Weights(tks []string, preprocess bool) []TermWeight {
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numPattern := regexp.MustCompile("^[0-9,.]{2,}$")
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shortLetterPattern := regexp.MustCompile("^[a-z]{1,2}$")
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numSpacePattern := regexp.MustCompile("^[0-9. -]{2,}$")
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letterPattern := regexp.MustCompile("^[a-z. -]+$")
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// ner weight function
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nerWeight := func(t string) float64 {
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if numPattern.MatchString(t) {
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return 2
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}
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if shortLetterPattern.MatchString(t) {
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return 0.01
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}
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if d.ne == nil {
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return 1
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}
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if neType, ok := d.ne[t]; ok {
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weights := map[string]float64{
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"toxic": 2, "func": 1, "corp": 3, "loca": 3,
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"sch": 3, "stock": 3, "firstnm": 1,
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}
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if w, exists := weights[neType]; exists {
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return w
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}
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}
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return 1
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}
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// postag weight function using real POS tagger
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postagWeight := func(t string) float64 {
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tag := tokenizer.GetTermTag(t)
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// Map POS tags to weights (matching Python implementation)
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if tag == "r" || tag == "c" || tag == "d" {
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return 0.3
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}
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if tag == "ns" || tag == "nt" {
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return 3
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}
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if tag == "n" {
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return 2
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}
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// Fallback to heuristic for terms without tags
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if matched, _ := regexp.MatchString("^[0-9-]+", tag); matched {
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return 2
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}
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return 1
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}
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// freq function using real frequency dictionary
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var freq func(t string) float64
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freq = func(t string) float64 {
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if numSpacePattern.MatchString(t) {
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return 3
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}
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// Use tokenizer's freq function
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s := tokenizer.GetTermFreq(t)
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if s == 0 && letterPattern.MatchString(t) {
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return 300
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}
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if s == 0 && len([]rune(t)) >= 4 {
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// Try fine-grained tokenization
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fgTokens, _ := tokenizer.Tokenize(t)
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tokens := strings.Fields(fgTokens)
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var validTokens []float64
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if len(tokens) > 1 {
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for _, tt := range tokens {
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f := freq(tt)
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validTokens = append(validTokens, f)
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}
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minVal := validTokens[0]
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for _, v := range validTokens[1:] {
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if v < minVal {
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minVal = v
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}
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}
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return minVal / 6.0
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}
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// Default frequency
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return 10
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}
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return math.Max(float64(s), 10)
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}
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// df function
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var df func(t string) float64
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df = func(t string) float64 {
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if numSpacePattern.MatchString(t) {
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return 5
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}
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if v, ok := d.df[t]; ok {
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return float64(v) + 3
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}
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if letterPattern.MatchString(t) {
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return 300
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}
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if len([]rune(t)) >= 4 {
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fgTokens, _ := tokenizer.Tokenize(t)
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tokens := strings.Fields(fgTokens)
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var validTokens []float64
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if len(tokens) > 1 {
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for _, tt := range tokens {
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f := df(tt)
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validTokens = append(validTokens, f)
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}
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minVal := validTokens[0]
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for _, v := range validTokens[1:] {
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if v < minVal {
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minVal = v
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}
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}
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return math.Max(3, minVal/6.0)
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}
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}
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return 3
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}
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// idf function
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idf := func(s, N float64) float64 {
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return math.Log10(10 + ((N - s + 0.5) / (s + 0.5)))
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}
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tw := []TermWeight{}
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if !preprocess {
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// Direct calculation without preprocessing
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idf1Vals := make([]float64, len(tks))
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idf2Vals := make([]float64, len(tks))
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nerPosVals := make([]float64, len(tks))
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for i, t := range tks {
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//fmt.Println("index:", i, "term:", t)
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idf1Vals[i] = idf(freq(t), 10000000)
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idf2Vals[i] = idf(df(t), 1000000000)
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nerPosVals[i] = nerWeight(t) * postagWeight(t)
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}
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wts := make([]float64, len(tks))
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for i := range tks {
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wts[i] = (0.3*idf1Vals[i] + 0.7*idf2Vals[i]) * nerPosVals[i]
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}
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for i, t := range tks {
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tw = append(tw, TermWeight{Term: t, Weight: wts[i]})
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}
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} else {
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// With preprocessing
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for _, tk := range tks {
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tokens := d.Pretoken(tk, true, true)
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tt := d.TokenMerge(tokens)
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if len(tt) == 0 {
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continue
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}
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idf1Vals := make([]float64, len(tt))
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idf2Vals := make([]float64, len(tt))
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nerPosVals := make([]float64, len(tt))
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for i, t := range tt {
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idf1Vals[i] = idf(freq(t), 10000000)
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idf2Vals[i] = idf(df(t), 1000000000)
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nerPosVals[i] = nerWeight(t) * postagWeight(t)
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}
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wts := make([]float64, len(tt))
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for i := range tt {
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wts[i] = (0.3*idf1Vals[i] + 0.7*idf2Vals[i]) * nerPosVals[i]
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}
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for i, t := range tt {
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tw = append(tw, TermWeight{Term: t, Weight: wts[i]})
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}
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}
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}
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// Normalize weights
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if len(tw) == 0 {
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return tw
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}
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S := 0.0
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for _, twItem := range tw {
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S += twItem.Weight
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}
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if S > 0 {
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for i := range tw {
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tw[i].Weight = tw[i].Weight / S
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}
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}
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return tw
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}
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// GetStopWords returns the stop words set
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func (d *TermWeightDealer) GetStopWords() map[string]struct{} {
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return d.stopWords
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}
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// GetNE returns the named entity dictionary
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func (d *TermWeightDealer) GetNE() map[string]string {
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return d.ne
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}
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// GetDF returns the document frequency dictionary
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func (d *TermWeightDealer) GetDF() map[string]int {
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return d.df
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}
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