Init
This commit is contained in:
mlogclub
@@ -0,0 +1,507 @@
|
||||
package rag
|
||||
|
||||
import (
|
||||
"context"
|
||||
"fmt"
|
||||
"log/slog"
|
||||
"sort"
|
||||
"strings"
|
||||
"time"
|
||||
|
||||
"cs-agent/internal/models"
|
||||
"github.com/mlogclub/simple/sqls"
|
||||
|
||||
"cs-agent/internal/ai"
|
||||
"cs-agent/internal/ai/rag/vectordb"
|
||||
"cs-agent/internal/pkg/enums"
|
||||
"cs-agent/internal/repositories"
|
||||
)
|
||||
|
||||
type retrieve struct {
|
||||
}
|
||||
|
||||
var Retrieve = &retrieve{}
|
||||
|
||||
func (s *retrieve) Retrieve(ctx context.Context, req RetrieveRequest) ([]RetrieveResult, error) {
|
||||
results, _, err := s.RetrieveWithTrace(ctx, req)
|
||||
return results, err
|
||||
}
|
||||
|
||||
type RetrieveTrace struct {
|
||||
EmbeddingMs int64
|
||||
VectorSearchMs int64
|
||||
HydrateMs int64
|
||||
}
|
||||
|
||||
func (s *retrieve) RetrieveWithTrace(ctx context.Context, req RetrieveRequest) ([]RetrieveResult, *RetrieveTrace, error) {
|
||||
trace := &RetrieveTrace{}
|
||||
if req.Query == "" {
|
||||
return nil, trace, nil
|
||||
}
|
||||
knowledgeBaseIDs := normalizeKnowledgeBaseIDs(req.KnowledgeBaseIDs)
|
||||
if len(knowledgeBaseIDs) == 0 {
|
||||
return nil, trace, nil
|
||||
}
|
||||
|
||||
retrievableKnowledgeBases := s.loadRetrievableKnowledgeBases(knowledgeBaseIDs)
|
||||
if len(retrievableKnowledgeBases) == 0 {
|
||||
slog.Info("Skip retrieve for non-enabled knowledge bases",
|
||||
"knowledge_base_ids", fmt.Sprint(knowledgeBaseIDs))
|
||||
return nil, trace, nil
|
||||
}
|
||||
|
||||
embeddingStartedAt := time.Now()
|
||||
embeddingResult, err := ai.Embedding.GenerateEmbedding(ctx, req.Query)
|
||||
trace.EmbeddingMs = time.Since(embeddingStartedAt).Milliseconds()
|
||||
if err != nil {
|
||||
return nil, trace, fmt.Errorf("failed to generate query embedding: %w", err)
|
||||
}
|
||||
|
||||
collectionName := knowledgeCollectionName
|
||||
provider := vectordb.GetProvider()
|
||||
if provider == nil {
|
||||
return nil, trace, fmt.Errorf("vectordb provider not initialized")
|
||||
}
|
||||
|
||||
searchResults := make([]vectordb.SearchResult, 0)
|
||||
vectorSearchStartedAt := time.Now()
|
||||
for _, knowledgeBase := range retrievableKnowledgeBases {
|
||||
topK, scoreThreshold := resolveKnowledgeBaseSearchOptions(req, &knowledgeBase)
|
||||
kbResults, searchErr := provider.Search(ctx, &vectordb.SearchRequest{
|
||||
CollectionName: collectionName,
|
||||
Vector: embeddingResult.Vector,
|
||||
TopK: topK,
|
||||
ScoreThreshold: scoreThreshold,
|
||||
Filter: &vectordb.SearchFilter{
|
||||
KnowledgeBaseIDs: []int64{knowledgeBase.ID},
|
||||
},
|
||||
})
|
||||
if searchErr != nil {
|
||||
slog.Error("Failed to search vectors",
|
||||
"knowledge_base_id", knowledgeBase.ID,
|
||||
"error", searchErr)
|
||||
trace.VectorSearchMs = time.Since(vectorSearchStartedAt).Milliseconds()
|
||||
return nil, trace, fmt.Errorf("failed to search vectors: %w", searchErr)
|
||||
}
|
||||
if len(kbResults) == 0 && scoreThreshold > 0 {
|
||||
s.logEmptySearchDiagnostics(ctx, provider, collectionName, embeddingResult.Vector, topK, scoreThreshold, []int64{knowledgeBase.ID}, req)
|
||||
}
|
||||
searchResults = append(searchResults, kbResults...)
|
||||
}
|
||||
trace.VectorSearchMs = time.Since(vectorSearchStartedAt).Milliseconds()
|
||||
|
||||
if len(searchResults) == 0 {
|
||||
return nil, trace, nil
|
||||
}
|
||||
sort.SliceStable(searchResults, func(i, j int) bool {
|
||||
if searchResults[i].Score == searchResults[j].Score {
|
||||
return searchResults[i].ID < searchResults[j].ID
|
||||
}
|
||||
return searchResults[i].Score > searchResults[j].Score
|
||||
})
|
||||
|
||||
results := make([]RetrieveResult, 0, len(searchResults))
|
||||
hydrateStartedAt := time.Now()
|
||||
vectorIDs := make([]string, 0, len(searchResults))
|
||||
for _, sr := range searchResults {
|
||||
if strings.TrimSpace(sr.ID) == "" {
|
||||
continue
|
||||
}
|
||||
vectorIDs = append(vectorIDs, sr.ID)
|
||||
}
|
||||
chunks := repositories.KnowledgeChunkRepository.FindByVectorIDs(sqls.DB(), vectorIDs)
|
||||
chunkByVectorID := make(map[string]*models.KnowledgeChunk, len(chunks))
|
||||
documentIDs := make([]int64, 0)
|
||||
faqIDs := make([]int64, 0)
|
||||
documentSeen := make(map[int64]struct{})
|
||||
faqSeen := make(map[int64]struct{})
|
||||
for i := range chunks {
|
||||
chunk := &chunks[i]
|
||||
chunkByVectorID[chunk.VectorID] = chunk
|
||||
if chunk.DocumentID > 0 {
|
||||
if _, ok := documentSeen[chunk.DocumentID]; !ok {
|
||||
documentSeen[chunk.DocumentID] = struct{}{}
|
||||
documentIDs = append(documentIDs, chunk.DocumentID)
|
||||
}
|
||||
}
|
||||
if chunk.FaqID > 0 {
|
||||
if _, ok := faqSeen[chunk.FaqID]; !ok {
|
||||
faqSeen[chunk.FaqID] = struct{}{}
|
||||
faqIDs = append(faqIDs, chunk.FaqID)
|
||||
}
|
||||
}
|
||||
}
|
||||
documents := repositories.KnowledgeDocumentRepository.FindByIDs(sqls.DB(), documentIDs)
|
||||
documentByID := make(map[int64]*models.KnowledgeDocument, len(documents))
|
||||
for i := range documents {
|
||||
document := &documents[i]
|
||||
documentByID[document.ID] = document
|
||||
}
|
||||
faqs := repositories.KnowledgeFAQRepository.FindByIDs(sqls.DB(), faqIDs)
|
||||
faqByID := make(map[int64]*models.KnowledgeFAQ, len(faqs))
|
||||
for i := range faqs {
|
||||
faq := &faqs[i]
|
||||
faqByID[faq.ID] = faq
|
||||
}
|
||||
for _, sr := range searchResults {
|
||||
chunk := chunkByVectorID[sr.ID]
|
||||
if chunk == nil || chunk.Status != enums.StatusOk {
|
||||
continue
|
||||
}
|
||||
|
||||
documentTitle := ""
|
||||
faqQuestion := ""
|
||||
if chunk.DocumentID > 0 {
|
||||
document := documentByID[chunk.DocumentID]
|
||||
if document == nil || document.Status != enums.StatusOk {
|
||||
continue
|
||||
}
|
||||
documentTitle = document.Title
|
||||
}
|
||||
if chunk.FaqID > 0 {
|
||||
faq := faqByID[chunk.FaqID]
|
||||
if faq == nil || faq.Status != enums.StatusOk {
|
||||
continue
|
||||
}
|
||||
faqQuestion = faq.Question
|
||||
}
|
||||
|
||||
results = append(results, RetrieveResult{
|
||||
KnowledgeBaseID: chunk.KnowledgeBaseID,
|
||||
ChunkID: chunk.ID,
|
||||
DocumentID: chunk.DocumentID,
|
||||
DocumentTitle: documentTitle,
|
||||
FaqID: chunk.FaqID,
|
||||
FaqQuestion: faqQuestion,
|
||||
ChunkNo: chunk.ChunkNo,
|
||||
Title: chunk.Title,
|
||||
SectionPath: chunk.SectionPath,
|
||||
Content: chunk.Content,
|
||||
Score: sr.Score,
|
||||
ChunkType: extractChunkType(sr.Payload),
|
||||
})
|
||||
}
|
||||
trace.HydrateMs = time.Since(hydrateStartedAt).Milliseconds()
|
||||
|
||||
return results, trace, nil
|
||||
}
|
||||
|
||||
func extractChunkType(payload vectordb.ChunkPayload) string {
|
||||
if payload.ChunkType != "" {
|
||||
return payload.ChunkType
|
||||
}
|
||||
return string(enums.KnowledgeChunkTypeText)
|
||||
}
|
||||
|
||||
func (s *retrieve) logEmptySearchDiagnostics(ctx context.Context, provider vectordb.Provider, collectionName string, vector []float32, topK int, scoreThreshold float32, knowledgeBaseIDs []int64, req RetrieveRequest) {
|
||||
rawResults, err := provider.Search(ctx, &vectordb.SearchRequest{
|
||||
CollectionName: collectionName,
|
||||
Vector: vector,
|
||||
TopK: topK,
|
||||
ScoreThreshold: 0,
|
||||
Filter: &vectordb.SearchFilter{
|
||||
KnowledgeBaseIDs: knowledgeBaseIDs,
|
||||
},
|
||||
})
|
||||
if err != nil {
|
||||
slog.Warn("Knowledge retrieve diagnostics failed",
|
||||
"knowledge_base_ids", fmt.Sprint(knowledgeBaseIDs),
|
||||
"collection", collectionName,
|
||||
"query", truncateForLog(req.Query, 80),
|
||||
"score_threshold", scoreThreshold,
|
||||
"error", err)
|
||||
return
|
||||
}
|
||||
if len(rawResults) == 0 {
|
||||
slog.Info("Knowledge retrieve returned no candidates even without threshold",
|
||||
"knowledge_base_ids", fmt.Sprint(knowledgeBaseIDs),
|
||||
"collection", collectionName,
|
||||
"query", truncateForLog(req.Query, 80),
|
||||
"score_threshold", scoreThreshold)
|
||||
return
|
||||
}
|
||||
|
||||
candidates := make([]string, 0, len(rawResults))
|
||||
for _, item := range rawResults {
|
||||
candidates = append(candidates, fmt.Sprintf("%s:%.4f", item.ID, item.Score))
|
||||
}
|
||||
|
||||
slog.Info("Knowledge retrieve filtered all candidates by score threshold",
|
||||
"knowledge_base_ids", fmt.Sprint(knowledgeBaseIDs),
|
||||
"collection", collectionName,
|
||||
"query", truncateForLog(req.Query, 80),
|
||||
"score_threshold", scoreThreshold,
|
||||
"top_candidates", strings.Join(candidates, ","))
|
||||
}
|
||||
|
||||
func truncateForLog(text string, limit int) string {
|
||||
if limit <= 0 {
|
||||
return ""
|
||||
}
|
||||
runes := []rune(strings.TrimSpace(text))
|
||||
if len(runes) <= limit {
|
||||
return string(runes)
|
||||
}
|
||||
return string(runes[:limit]) + "..."
|
||||
}
|
||||
|
||||
func (s *retrieve) RetrieveWithRerank(ctx context.Context, req RetrieveRequest, rerankLimit int) ([]RetrieveResult, error) {
|
||||
results, err := s.Retrieve(ctx, req)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
if len(results) <= rerankLimit {
|
||||
return results, nil
|
||||
}
|
||||
|
||||
rerankedResults, err := s.rerank(ctx, req.Query, results, rerankLimit)
|
||||
if err != nil {
|
||||
slog.Warn("Rerank failed, returning original results", "error", err)
|
||||
if len(results) > rerankLimit {
|
||||
return results[:rerankLimit], nil
|
||||
}
|
||||
return results, nil
|
||||
}
|
||||
|
||||
return rerankedResults, nil
|
||||
}
|
||||
|
||||
func (s *retrieve) rerank(ctx context.Context, query string, results []RetrieveResult, limit int) ([]RetrieveResult, error) {
|
||||
return Rerank.RerankResults(ctx, query, results, limit)
|
||||
}
|
||||
|
||||
func (s *retrieve) SelectContextResults(results []RetrieveResult, maxTokens int) []RetrieveResult {
|
||||
if len(results) == 0 {
|
||||
return nil
|
||||
}
|
||||
|
||||
normalizedResults := normalizeContextResults(results)
|
||||
selected := make([]RetrieveResult, 0, len(normalizedResults))
|
||||
totalTokens := 0
|
||||
documentUsage := make(map[int64]int)
|
||||
|
||||
for _, item := range normalizedResults {
|
||||
if documentUsage[item.DocumentID] >= 2 {
|
||||
continue
|
||||
}
|
||||
chunkText := buildContextChunkText(item)
|
||||
estimatedTokens := len(chunkText) / 2
|
||||
if totalTokens+estimatedTokens > maxTokens {
|
||||
break
|
||||
}
|
||||
selected = append(selected, item)
|
||||
totalTokens += estimatedTokens
|
||||
documentUsage[item.DocumentID]++
|
||||
}
|
||||
return selected
|
||||
}
|
||||
|
||||
func (s *retrieve) BuildContext(ctx context.Context, results []RetrieveResult, maxTokens int) string {
|
||||
if len(results) == 0 {
|
||||
return ""
|
||||
}
|
||||
|
||||
normalizedResults := s.SelectContextResults(results, maxTokens)
|
||||
context := ""
|
||||
for _, r := range normalizedResults {
|
||||
chunkText := buildContextChunkText(r)
|
||||
context += chunkText
|
||||
}
|
||||
|
||||
return context
|
||||
}
|
||||
|
||||
func normalizeContextResults(results []RetrieveResult) []RetrieveResult {
|
||||
if len(results) == 0 {
|
||||
return nil
|
||||
}
|
||||
|
||||
merged := mergeAdjacentResults(results)
|
||||
return dedupeSectionResults(merged)
|
||||
}
|
||||
|
||||
func dedupeSectionResults(results []RetrieveResult) []RetrieveResult {
|
||||
seen := make(map[string]struct{})
|
||||
deduped := make([]RetrieveResult, 0, len(results))
|
||||
for _, item := range results {
|
||||
key := buildSectionKey(item)
|
||||
if _, ok := seen[key]; ok {
|
||||
continue
|
||||
}
|
||||
seen[key] = struct{}{}
|
||||
deduped = append(deduped, item)
|
||||
}
|
||||
return deduped
|
||||
}
|
||||
|
||||
func mergeAdjacentResults(results []RetrieveResult) []RetrieveResult {
|
||||
if len(results) == 0 {
|
||||
return nil
|
||||
}
|
||||
|
||||
merged := make([]RetrieveResult, 0, len(results))
|
||||
for _, item := range results {
|
||||
if len(merged) == 0 {
|
||||
merged = append(merged, item)
|
||||
continue
|
||||
}
|
||||
|
||||
last := &merged[len(merged)-1]
|
||||
if canMergeContextResult(*last, item) {
|
||||
last.Content = strings.TrimSpace(last.Content + "\n" + item.Content)
|
||||
if item.Score > last.Score {
|
||||
last.Score = item.Score
|
||||
}
|
||||
continue
|
||||
}
|
||||
merged = append(merged, item)
|
||||
}
|
||||
return merged
|
||||
}
|
||||
|
||||
func canMergeContextResult(left, right RetrieveResult) bool {
|
||||
if left.FaqID > 0 || right.FaqID > 0 {
|
||||
return false
|
||||
}
|
||||
if left.DocumentID != right.DocumentID {
|
||||
return false
|
||||
}
|
||||
if left.SectionPath == "" || right.SectionPath == "" {
|
||||
return false
|
||||
}
|
||||
if left.SectionPath != right.SectionPath {
|
||||
return false
|
||||
}
|
||||
return right.ChunkNo == left.ChunkNo+1
|
||||
}
|
||||
|
||||
func buildSectionKey(item RetrieveResult) string {
|
||||
if item.FaqID > 0 {
|
||||
return fmt.Sprintf("faq:%d", item.FaqID)
|
||||
}
|
||||
sectionPath := strings.TrimSpace(item.SectionPath)
|
||||
if sectionPath != "" {
|
||||
return fmt.Sprintf("%d|%s", item.DocumentID, sectionPath)
|
||||
}
|
||||
title := strings.TrimSpace(item.Title)
|
||||
if title != "" {
|
||||
return fmt.Sprintf("%d|%s", item.DocumentID, title)
|
||||
}
|
||||
return fmt.Sprintf("%d|chunk:%d", item.DocumentID, item.ChunkNo)
|
||||
}
|
||||
|
||||
func buildContextChunkText(item RetrieveResult) string {
|
||||
if item.FaqID > 0 {
|
||||
title := strings.TrimSpace(item.FaqQuestion)
|
||||
if title == "" {
|
||||
title = strings.TrimSpace(item.Title)
|
||||
}
|
||||
if title == "" {
|
||||
title = fmt.Sprintf("FAQ#%d", item.FaqID)
|
||||
}
|
||||
return fmt.Sprintf("【FAQ：%s】\n%s\n\n", title, item.Content)
|
||||
}
|
||||
title := strings.TrimSpace(item.DocumentTitle)
|
||||
if title == "" {
|
||||
title = fmt.Sprintf("文档#%d", item.DocumentID)
|
||||
}
|
||||
if item.SectionPath != "" {
|
||||
return fmt.Sprintf("【文档：%s｜章节：%s】\n%s\n\n", title, item.SectionPath, item.Content)
|
||||
}
|
||||
if item.Title != "" {
|
||||
return fmt.Sprintf("【文档：%s｜标题：%s】\n%s\n\n", title, item.Title, item.Content)
|
||||
}
|
||||
return fmt.Sprintf("【文档：%s】\n%s\n\n", title, item.Content)
|
||||
}
|
||||
|
||||
func (s *retrieve) GetKnowledgeBaseStats(ctx context.Context, knowledgeBaseID int64) (*KnowledgeBaseStats, error) {
|
||||
knowledgeBase := repositories.KnowledgeBaseRepository.Get(sqls.DB(), knowledgeBaseID)
|
||||
if knowledgeBase == nil {
|
||||
return nil, fmt.Errorf("knowledge base not found")
|
||||
}
|
||||
|
||||
documentCount := repositories.KnowledgeDocumentRepository.CountByKnowledgeBaseID(sqls.DB(), knowledgeBaseID)
|
||||
chunkCount := repositories.KnowledgeChunkRepository.CountByKnowledgeBaseID(sqls.DB(), knowledgeBaseID)
|
||||
|
||||
publishedCount := repositories.KnowledgeDocumentRepository.Count(sqls.DB(), sqls.NewCnd().
|
||||
Eq("knowledge_base_id", knowledgeBaseID).
|
||||
Eq("status", enums.StatusOk))
|
||||
|
||||
return &KnowledgeBaseStats{
|
||||
KnowledgeBaseID: knowledgeBaseID,
|
||||
DocumentCount: documentCount,
|
||||
PublishedCount: publishedCount,
|
||||
ChunkCount: chunkCount,
|
||||
VectorCount: int(chunkCount),
|
||||
}, nil
|
||||
}
|
||||
|
||||
func normalizeKnowledgeBaseIDs(ids []int64) []int64 {
|
||||
if len(ids) == 0 {
|
||||
return nil
|
||||
}
|
||||
seen := make(map[int64]struct{}, len(ids))
|
||||
normalized := make([]int64, 0, len(ids))
|
||||
for _, id := range ids {
|
||||
if id <= 0 {
|
||||
continue
|
||||
}
|
||||
if _, ok := seen[id]; ok {
|
||||
continue
|
||||
}
|
||||
seen[id] = struct{}{}
|
||||
normalized = append(normalized, id)
|
||||
}
|
||||
return normalized
|
||||
}
|
||||
|
||||
func resolveKnowledgeBaseSearchOptions(req RetrieveRequest, knowledgeBase *models.KnowledgeBase) (int, float32) {
|
||||
topK := req.TopK
|
||||
if topK <= 0 && knowledgeBase != nil && knowledgeBase.DefaultTopK > 0 {
|
||||
topK = knowledgeBase.DefaultTopK
|
||||
}
|
||||
if topK <= 0 {
|
||||
topK = 8
|
||||
}
|
||||
|
||||
scoreThreshold := float32(req.ScoreThreshold)
|
||||
if scoreThreshold <= 0 && knowledgeBase != nil && knowledgeBase.DefaultScoreThreshold > 0 {
|
||||
scoreThreshold = float32(knowledgeBase.DefaultScoreThreshold)
|
||||
}
|
||||
if scoreThreshold <= 0 {
|
||||
scoreThreshold = 0.3
|
||||
}
|
||||
return topK, scoreThreshold
|
||||
}
|
||||
|
||||
func (s *retrieve) loadRetrievableKnowledgeBases(ids []int64) []models.KnowledgeBase {
|
||||
if len(ids) == 0 {
|
||||
return nil
|
||||
}
|
||||
items := repositories.KnowledgeBaseRepository.Find(sqls.DB(), sqls.NewCnd().In("id", ids))
|
||||
if len(items) == 0 {
|
||||
return nil
|
||||
}
|
||||
allowed := make(map[int64]models.KnowledgeBase, len(items))
|
||||
for _, item := range items {
|
||||
if item.Status == enums.StatusOk {
|
||||
allowed[item.ID] = item
|
||||
}
|
||||
}
|
||||
filtered := make([]models.KnowledgeBase, 0, len(ids))
|
||||
for _, id := range ids {
|
||||
if item, ok := allowed[id]; ok {
|
||||
filtered = append(filtered, item)
|
||||
}
|
||||
}
|
||||
return filtered
|
||||
}
|
||||
|
||||
type KnowledgeBaseStats struct {
|
||||
KnowledgeBaseID int64 `json:"knowledgeBaseId"`
|
||||
DocumentCount int64 `json:"documentCount"`
|
||||
PublishedCount int64 `json:"publishedCount"`
|
||||
ChunkCount int64 `json:"chunkCount"`
|
||||
VectorCount int `json:"vectorCount"`
|
||||
}
|
||||
Reference in New Issue
Block a user