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公明
2026-05-19 17:48:44 +08:00
committed by GitHub
parent 0018c5219c
commit f348b3245a
18 changed files with 0 additions and 3279 deletions
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knowledge/chunk_eino.go
-67
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package knowledge
import (
"context"
"fmt"
"strings"
"github.com/cloudwego/eino-ext/components/document/transformer/splitter/markdown"
"github.com/cloudwego/eino-ext/components/document/transformer/splitter/recursive"
"github.com/cloudwego/eino/components/document"
"github.com/pkoukk/tiktoken-go"
)
func tokenizerLenFunc(embeddingModel string) func(string) int {
fallback := func(s string) int {
r := []rune(s)
if len(r) == 0 {
return 0
}
return (len(r) + 3) / 4
}
m := strings.TrimSpace(embeddingModel)
if m == "" {
return fallback
}
tok, err := tiktoken.EncodingForModel(m)
if err != nil {
return fallback
}
return func(s string) int {
return len(tok.Encode(s, nil, nil))
}
}
// newKnowledgeSplitter builds an Eino recursive text splitter. LenFunc uses tiktoken for
// embeddingModel when available, else rune/4 approximation.
func newKnowledgeSplitter(chunkSize, overlap int, embeddingModel string) (document.Transformer, error) {
if chunkSize <= 0 {
return nil, fmt.Errorf("chunk size must be positive")
}
if overlap < 0 {
overlap = 0
}
return recursive.NewSplitter(context.Background(), &recursive.Config{
ChunkSize: chunkSize,
OverlapSize: overlap,
LenFunc: tokenizerLenFunc(embeddingModel),
Separators: []string{
"\n\n", "\n## ", "\n### ", "\n#### ", "\n",
"。", "！", "？", ". ", "? ", "! ",
" ",
},
})
}
// newMarkdownHeaderSplitter Eino-ext Markdown 按标题切分（#～####），适合技术/Markdown 知识库。
func newMarkdownHeaderSplitter(ctx context.Context) (document.Transformer, error) {
return markdown.NewHeaderSplitter(ctx, &markdown.HeaderConfig{
Headers: map[string]string{
"#": "h1",
"##": "h2",
"###": "h3",
"####": "h4",
},
TrimHeaders: false,
})
}
knowledge/eino_meta.go
-129
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package knowledge
import (
"fmt"
"strings"
)
// Document metadata keys for Eino schema.Document flowing through the RAG pipeline.
const (
metaKBCategory = "kb_category"
metaKBTitle = "kb_title"
metaKBItemID = "kb_item_id"
metaKBChunkIndex = "kb_chunk_index"
metaSimilarity = "similarity"
)
// DSL keys for [VectorEinoRetriever.Retrieve] via [retriever.WithDSLInfo].
const (
DSLRiskType = "risk_type"
DSLSimilarityThreshold = "similarity_threshold"
DSLSubIndexFilter = "sub_index_filter"
)
// FormatEmbeddingInput matches the historical indexing format so existing embeddings
// stay comparable if users skip reindex; new indexes use the same string shape.
func FormatEmbeddingInput(category, title, chunkText string) string {
return fmt.Sprintf("[风险类型：%s] [标题：%s]\n%s", category, title, chunkText)
}
// FormatQueryEmbeddingText builds the string embedded at query time so it matches
// [FormatEmbeddingInput] for the same risk category (title left empty for queries).
func FormatQueryEmbeddingText(riskType, query string) string {
q := strings.TrimSpace(query)
rt := strings.TrimSpace(riskType)
if rt != "" {
return FormatEmbeddingInput(rt, "", q)
}
return q
}
// MetaLookupString returns metadata string value or "" if absent.
func MetaLookupString(md map[string]any, key string) string {
if md == nil {
return ""
}
v, ok := md[key]
if !ok || v == nil {
return ""
}
switch t := v.(type) {
case string:
return t
default:
return strings.TrimSpace(fmt.Sprint(t))
}
}
// MetaStringOK returns trimmed non-empty string and true if present and non-empty.
func MetaStringOK(md map[string]any, key string) (string, bool) {
s := strings.TrimSpace(MetaLookupString(md, key))
if s == "" {
return "", false
}
return s, true
}
// RequireMetaString requires a non-empty string metadata field.
func RequireMetaString(md map[string]any, key string) (string, error) {
s, ok := MetaStringOK(md, key)
if !ok {
return "", fmt.Errorf("missing or empty metadata %q", key)
}
return s, nil
}
// RequireMetaInt requires an integer metadata field.
func RequireMetaInt(md map[string]any, key string) (int, error) {
if md == nil {
return 0, fmt.Errorf("missing metadata key %q", key)
}
v, ok := md[key]
if !ok {
return 0, fmt.Errorf("missing metadata key %q", key)
}
switch t := v.(type) {
case int:
return t, nil
case int32:
return int(t), nil
case int64:
return int(t), nil
case float64:
return int(t), nil
default:
return 0, fmt.Errorf("metadata %q: unsupported type %T", key, v)
}
}
// DSLNumeric coerces DSL map values (e.g. from JSON) to float64.
func DSLNumeric(v any) (float64, bool) {
switch t := v.(type) {
case float64:
return t, true
case float32:
return float64(t), true
case int:
return float64(t), true
case int64:
return float64(t), true
case uint32:
return float64(t), true
case uint64:
return float64(t), true
default:
return 0, false
}
}
// MetaFloat64OK reads a float metadata value.
func MetaFloat64OK(md map[string]any, key string) (float64, bool) {
if md == nil {
return 0, false
}
v, ok := md[key]
if !ok {
return 0, false
}
return DSLNumeric(v)
}
knowledge/eino_meta_test.go
-14
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package knowledge
import "testing"
func TestFormatQueryEmbeddingText_AlignsWithIndexPrefix(t *testing.T) {
q := FormatQueryEmbeddingText("XSS", "payload")
want := FormatEmbeddingInput("XSS", "", "payload")
if q != want {
t.Fatalf("query embed text mismatch:\n got: %q\nwant: %q", q, want)
}
if FormatQueryEmbeddingText("", "hello") != "hello" {
t.Fatalf("expected bare query without risk type")
}
}
knowledge/eino_retrieve_chain.go
-25
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package knowledge
import (
"context"
"fmt"
"github.com/cloudwego/eino/compose"
"github.com/cloudwego/eino/schema"
)
// BuildKnowledgeRetrieveChain 编译「查询字符串 → 文档列表」的 Eino Chain，底层为 SQLite 向量检索（[VectorEinoRetriever]）。
// 去重、上下文预算截断与最终 Top-K 均在 [VectorEinoRetriever.Retrieve] 内完成，与 HTTP/MCP 检索路径一致。
func BuildKnowledgeRetrieveChain(ctx context.Context, r *Retriever) (compose.Runnable[string, []*schema.Document], error) {
if r == nil {
return nil, fmt.Errorf("retriever is nil")
}
ch := compose.NewChain[string, []*schema.Document]()
ch.AppendRetriever(r.AsEinoRetriever())
return ch.Compile(ctx)
}
// CompileRetrieveChain 等价于 [BuildKnowledgeRetrieveChain](ctx, r)。
func (r *Retriever) CompileRetrieveChain(ctx context.Context) (compose.Runnable[string, []*schema.Document], error) {
return BuildKnowledgeRetrieveChain(ctx, r)
}
knowledge/eino_retrieve_chain_test.go
-23
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package knowledge
import (
"context"
"testing"
"go.uber.org/zap"
)
func TestBuildKnowledgeRetrieveChain_Compile(t *testing.T) {
r := NewRetriever(nil, nil, &RetrievalConfig{TopK: 3, SimilarityThreshold: 0.5}, zap.NewNop())
_, err := BuildKnowledgeRetrieveChain(context.Background(), r)
if err != nil {
t.Fatal(err)
}
}
func TestBuildKnowledgeRetrieveChain_NilRetriever(t *testing.T) {
_, err := BuildKnowledgeRetrieveChain(context.Background(), nil)
if err == nil {
t.Fatal("expected error for nil retriever")
}
}
knowledge/eino_retriever_adapter.go
-202
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@@ -1,202 +0,0 @@
package knowledge
import (
"context"
"fmt"
"strings"
"cyberstrike-ai/internal/config"
"github.com/cloudwego/eino/callbacks"
"github.com/cloudwego/eino/components"
"github.com/cloudwego/eino/components/retriever"
"github.com/cloudwego/eino/schema"
"go.uber.org/zap"
)
// VectorEinoRetriever implements [retriever.Retriever] on top of SQLite-stored embeddings + cosine similarity.
//
// Options:
// - [retriever.WithTopK]
// - [retriever.WithDSLInfo] with [DSLRiskType] (string), [DSLSimilarityThreshold] (float, cosine 0–1), [DSLSubIndexFilter] (string)
//
// Document scores are cosine similarity; [retriever.WithScoreThreshold] is not mapped to a different metric.
//
// After vector search: optional [DocumentReranker] (see [Retriever.SetDocumentReranker]), then
// [ApplyPostRetrieve] (normalized-text dedupe, context budget, final Top-K) using [config.PostRetrieveConfig].
type VectorEinoRetriever struct {
inner *Retriever
}
// NewVectorEinoRetriever wraps r for Eino compose / tooling.
func NewVectorEinoRetriever(r *Retriever) *VectorEinoRetriever {
if r == nil {
return nil
}
return &VectorEinoRetriever{inner: r}
}
// GetType identifies this retriever for Eino callbacks.
func (h *VectorEinoRetriever) GetType() string {
return "SQLiteVectorKnowledgeRetriever"
}
// Retrieve runs vector search and returns [schema.Document] rows.
func (h *VectorEinoRetriever) Retrieve(ctx context.Context, query string, opts ...retriever.Option) (out []*schema.Document, err error) {
if h == nil || h.inner == nil {
return nil, fmt.Errorf("VectorEinoRetriever: nil retriever")
}
q := strings.TrimSpace(query)
if q == "" {
return nil, fmt.Errorf("查询不能为空")
}
ro := retriever.GetCommonOptions(nil, opts...)
cfg := h.inner.config
req := &SearchRequest{Query: q}
if ro.TopK != nil && *ro.TopK > 0 {
req.TopK = *ro.TopK
} else if cfg != nil && cfg.TopK > 0 {
req.TopK = cfg.TopK
} else {
req.TopK = 5
}
req.Threshold = 0
if ro.DSLInfo != nil {
if rt, ok := ro.DSLInfo[DSLRiskType].(string); ok {
req.RiskType = strings.TrimSpace(rt)
}
if v, ok := ro.DSLInfo[DSLSimilarityThreshold]; ok {
if f, ok2 := DSLNumeric(v); ok2 && f > 0 {
req.Threshold = f
}
}
if sf, ok := ro.DSLInfo[DSLSubIndexFilter].(string); ok {
req.SubIndexFilter = strings.TrimSpace(sf)
}
}
if req.SubIndexFilter == "" && cfg != nil && strings.TrimSpace(cfg.SubIndexFilter) != "" {
req.SubIndexFilter = strings.TrimSpace(cfg.SubIndexFilter)
}
if req.Threshold <= 0 && cfg != nil && cfg.SimilarityThreshold > 0 {
req.Threshold = cfg.SimilarityThreshold
}
if req.Threshold <= 0 {
req.Threshold = 0.7
}
finalTopK := req.TopK
var postPO *config.PostRetrieveConfig
if cfg != nil {
postPO = &cfg.PostRetrieve
}
fetchK := EffectivePrefetchTopK(finalTopK, postPO)
searchReq := *req
searchReq.TopK = fetchK
ctx = callbacks.EnsureRunInfo(ctx, h.GetType(), components.ComponentOfRetriever)
th := req.Threshold
st := &th
ctx = callbacks.OnStart(ctx, &retriever.CallbackInput{
Query: q,
TopK: finalTopK,
ScoreThreshold: st,
Extra: ro.DSLInfo,
})
defer func() {
if err != nil {
_ = callbacks.OnError(ctx, err)
return
}
_ = callbacks.OnEnd(ctx, &retriever.CallbackOutput{Docs: out})
}()
results, err := h.inner.vectorSearch(ctx, &searchReq)
if err != nil {
return nil, err
}
out = retrievalResultsToDocuments(results)
if rr := h.inner.documentReranker(); rr != nil && len(out) > 1 {
reranked, rerr := rr.Rerank(ctx, q, out)
if rerr != nil {
if h.inner.logger != nil {
h.inner.logger.Warn("知识检索重排失败，已使用向量序", zap.Error(rerr))
}
} else if len(reranked) > 0 {
out = reranked
}
}
tokenModel := ""
if h.inner.embedder != nil {
tokenModel = h.inner.embedder.EmbeddingModelName()
}
out, err = ApplyPostRetrieve(out, postPO, tokenModel, finalTopK)
if err != nil {
return nil, err
}
return out, nil
}
func retrievalResultsToDocuments(results []*RetrievalResult) []*schema.Document {
out := make([]*schema.Document, 0, len(results))
for _, res := range results {
if res == nil || res.Chunk == nil || res.Item == nil {
continue
}
d := &schema.Document{
ID: res.Chunk.ID,
Content: res.Chunk.ChunkText,
MetaData: map[string]any{
metaKBItemID: res.Item.ID,
metaKBCategory: res.Item.Category,
metaKBTitle: res.Item.Title,
metaKBChunkIndex: res.Chunk.ChunkIndex,
metaSimilarity: res.Similarity,
},
}
d.WithScore(res.Score)
out = append(out, d)
}
return out
}
func documentsToRetrievalResults(docs []*schema.Document) ([]*RetrievalResult, error) {
out := make([]*RetrievalResult, 0, len(docs))
for i, d := range docs {
if d == nil {
continue
}
itemID, err := RequireMetaString(d.MetaData, metaKBItemID)
if err != nil {
return nil, fmt.Errorf("document %d: %w", i, err)
}
cat := MetaLookupString(d.MetaData, metaKBCategory)
title := MetaLookupString(d.MetaData, metaKBTitle)
chunkIdx, err := RequireMetaInt(d.MetaData, metaKBChunkIndex)
if err != nil {
return nil, fmt.Errorf("document %d: %w", i, err)
}
sim, _ := MetaFloat64OK(d.MetaData, metaSimilarity)
item := &KnowledgeItem{ID: itemID, Category: cat, Title: title}
chunk := &KnowledgeChunk{
ID: d.ID,
ItemID: itemID,
ChunkIndex: chunkIdx,
ChunkText: d.Content,
}
out = append(out, &RetrievalResult{
Chunk: chunk,
Item: item,
Similarity: sim,
Score: d.Score(),
})
}
return out, nil
}
var _ retriever.Retriever = (*VectorEinoRetriever)(nil)
knowledge/eino_sqlite_indexer.go
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package knowledge
import (
"context"
"database/sql"
"encoding/json"
"fmt"
"strings"
"github.com/cloudwego/eino/callbacks"
"github.com/cloudwego/eino/components"
"github.com/cloudwego/eino/components/indexer"
"github.com/cloudwego/eino/schema"
"github.com/google/uuid"
)
// SQLiteIndexer implements [indexer.Indexer] against knowledge_embeddings + existing schema.
type SQLiteIndexer struct {
db *sql.DB
batchSize int
embeddingModel string
}
// NewSQLiteIndexer returns an indexer that writes chunk rows for one knowledge item per Store call.
// batchSize is the embedding batch size; if <= 0, default 64 is used.
// embeddingModel is persisted per row for retrieval-time consistency checks (may be empty).
func NewSQLiteIndexer(db *sql.DB, batchSize int, embeddingModel string) *SQLiteIndexer {
return &SQLiteIndexer{db: db, batchSize: batchSize, embeddingModel: strings.TrimSpace(embeddingModel)}
}
// GetType implements eino callback run info.
func (s *SQLiteIndexer) GetType() string {
return "SQLiteKnowledgeIndexer"
}
// Store embeds documents and inserts rows. Each doc must carry MetaData:
// kb_item_id, kb_category, kb_title, kb_chunk_index (int). Content is chunk text only.
func (s *SQLiteIndexer) Store(ctx context.Context, docs []*schema.Document, opts ...indexer.Option) (ids []string, err error) {
options := indexer.GetCommonOptions(nil, opts...)
if options.Embedding == nil {
return nil, fmt.Errorf("sqlite indexer: embedding is required")
}
if len(docs) == 0 {
return nil, nil
}
ctx = callbacks.EnsureRunInfo(ctx, s.GetType(), components.ComponentOfIndexer)
ctx = callbacks.OnStart(ctx, &indexer.CallbackInput{Docs: docs})
defer func() {
if err != nil {
_ = callbacks.OnError(ctx, err)
return
}
_ = callbacks.OnEnd(ctx, &indexer.CallbackOutput{IDs: ids})
}()
subIdxStr := strings.Join(options.SubIndexes, ",")
texts := make([]string, len(docs))
for i, d := range docs {
if d == nil {
return nil, fmt.Errorf("sqlite indexer: nil document at %d", i)
}
cat := MetaLookupString(d.MetaData, metaKBCategory)
title := MetaLookupString(d.MetaData, metaKBTitle)
texts[i] = FormatEmbeddingInput(cat, title, d.Content)
}
bs := s.batchSize
if bs <= 0 {
bs = 64
}
var allVecs [][]float64
for start := 0; start < len(texts); start += bs {
end := start + bs
if end > len(texts) {
end = len(texts)
}
batch := texts[start:end]
vecs, embedErr := options.Embedding.EmbedStrings(ctx, batch)
if embedErr != nil {
return nil, fmt.Errorf("sqlite indexer: embed batch %d-%d: %w", start, end, embedErr)
}
if len(vecs) != len(batch) {
return nil, fmt.Errorf("sqlite indexer: embed count mismatch: got %d want %d", len(vecs), len(batch))
}
allVecs = append(allVecs, vecs...)
}
embedDim := 0
if len(allVecs) > 0 {
embedDim = len(allVecs[0])
}
tx, err := s.db.BeginTx(ctx, nil)
if err != nil {
return nil, fmt.Errorf("sqlite indexer: begin tx: %w", err)
}
defer tx.Rollback()
ids = make([]string, 0, len(docs))
for i, d := range docs {
chunkID := uuid.New().String()
itemID, metaErr := RequireMetaString(d.MetaData, metaKBItemID)
if metaErr != nil {
return nil, fmt.Errorf("sqlite indexer: doc %d: %w", i, metaErr)
}
chunkIdx, metaErr := RequireMetaInt(d.MetaData, metaKBChunkIndex)
if metaErr != nil {
return nil, fmt.Errorf("sqlite indexer: doc %d: %w", i, metaErr)
}
vec := allVecs[i]
if embedDim > 0 && len(vec) != embedDim {
return nil, fmt.Errorf("sqlite indexer: inconsistent embedding dim at doc %d: got %d want %d", i, len(vec), embedDim)
}
vec32 := make([]float32, len(vec))
for j, v := range vec {
vec32[j] = float32(v)
}
embeddingJSON, jsonErr := json.Marshal(vec32)
if jsonErr != nil {
return nil, fmt.Errorf("sqlite indexer: marshal embedding: %w", jsonErr)
}
_, err = tx.ExecContext(ctx,
`INSERT INTO knowledge_embeddings (id, item_id, chunk_index, chunk_text, embedding, sub_indexes, embedding_model, embedding_dim, created_at)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, datetime('now'))`,
chunkID, itemID, chunkIdx, d.Content, string(embeddingJSON), subIdxStr, s.embeddingModel, embedDim,
)
if err != nil {
return nil, fmt.Errorf("sqlite indexer: insert chunk %d: %w", i, err)
}
ids = append(ids, chunkID)
}
if err := tx.Commit(); err != nil {
return nil, fmt.Errorf("sqlite indexer: commit: %w", err)
}
return ids, nil
}
var _ indexer.Indexer = (*SQLiteIndexer)(nil)
knowledge/embedder.go
-251
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@@ -1,251 +0,0 @@
package knowledge
import (
"context"
"fmt"
"net/http"
"strings"
"sync"
"time"
"cyberstrike-ai/internal/config"
einoembedopenai "github.com/cloudwego/eino-ext/components/embedding/openai"
"github.com/cloudwego/eino/components/embedding"
"go.uber.org/zap"
"golang.org/x/time/rate"
)
// Embedder 使用 CloudWeGo Eino 的 OpenAI Embedding 组件，并保留速率限制与重试。
type Embedder struct {
eino embedding.Embedder
config *config.KnowledgeConfig
logger *zap.Logger
rateLimiter *rate.Limiter
rateLimitDelay time.Duration
maxRetries int
retryDelay time.Duration
mu sync.Mutex
}
// NewEmbedder 基于 Eino eino-ext OpenAI Embedder；openAIConfig 用于在知识库未单独配置 key 时回退 API Key。
func NewEmbedder(ctx context.Context, cfg *config.KnowledgeConfig, openAIConfig *config.OpenAIConfig, logger *zap.Logger) (*Embedder, error) {
if cfg == nil {
return nil, fmt.Errorf("knowledge config is nil")
}
var rateLimiter *rate.Limiter
var rateLimitDelay time.Duration
if cfg.Indexing.MaxRPM > 0 {
rpm := cfg.Indexing.MaxRPM
rateLimiter = rate.NewLimiter(rate.Every(time.Minute/time.Duration(rpm)), rpm)
if logger != nil {
logger.Info("知识库索引速率限制已启用", zap.Int("maxRPM", rpm))
}
} else if cfg.Indexing.RateLimitDelayMs > 0 {
rateLimitDelay = time.Duration(cfg.Indexing.RateLimitDelayMs) * time.Millisecond
if logger != nil {
logger.Info("知识库索引固定延迟已启用", zap.Duration("delay", rateLimitDelay))
}
}
maxRetries := 3
retryDelay := 1000 * time.Millisecond
if cfg.Indexing.MaxRetries > 0 {
maxRetries = cfg.Indexing.MaxRetries
}
if cfg.Indexing.RetryDelayMs > 0 {
retryDelay = time.Duration(cfg.Indexing.RetryDelayMs) * time.Millisecond
}
model := strings.TrimSpace(cfg.Embedding.Model)
if model == "" {
model = "text-embedding-3-small"
}
baseURL := strings.TrimSpace(cfg.Embedding.BaseURL)
baseURL = strings.TrimSuffix(baseURL, "/")
if baseURL == "" {
baseURL = "https://api.openai.com/v1"
}
apiKey := strings.TrimSpace(cfg.Embedding.APIKey)
if apiKey == "" && openAIConfig != nil {
apiKey = strings.TrimSpace(openAIConfig.APIKey)
}
if apiKey == "" {
return nil, fmt.Errorf("embedding API key 未配置")
}
timeout := 120 * time.Second
if cfg.Indexing.RequestTimeoutSeconds > 0 {
timeout = time.Duration(cfg.Indexing.RequestTimeoutSeconds) * time.Second
}
httpClient := &http.Client{Timeout: timeout}
inner, err := einoembedopenai.NewEmbedder(ctx, &einoembedopenai.EmbeddingConfig{
APIKey: apiKey,
BaseURL: baseURL,
ByAzure: false,
Model: model,
HTTPClient: httpClient,
})
if err != nil {
return nil, fmt.Errorf("eino OpenAI embedder: %w", err)
}
return &Embedder{
eino: inner,
config: cfg,
logger: logger,
rateLimiter: rateLimiter,
rateLimitDelay: rateLimitDelay,
maxRetries: maxRetries,
retryDelay: retryDelay,
}, nil
}
// EmbeddingModelName 返回配置的嵌入模型名（用于 tiktoken 分块与向量行元数据）。
func (e *Embedder) EmbeddingModelName() string {
if e == nil || e.config == nil {
return ""
}
s := strings.TrimSpace(e.config.Embedding.Model)
if s != "" {
return s
}
return "text-embedding-3-small"
}
func (e *Embedder) waitRateLimiter() {
e.mu.Lock()
defer e.mu.Unlock()
if e.rateLimiter != nil {
ctx := context.Background()
if err := e.rateLimiter.Wait(ctx); err != nil && e.logger != nil {
e.logger.Warn("速率限制器等待失败", zap.Error(err))
}
}
if e.rateLimitDelay > 0 {
time.Sleep(e.rateLimitDelay)
}
}
// EmbedText 单条嵌入（float32，与历史存储格式一致）。
func (e *Embedder) EmbedText(ctx context.Context, text string) ([]float32, error) {
vecs, err := e.EmbedStrings(ctx, []string{text})
if err != nil {
return nil, err
}
if len(vecs) != 1 {
return nil, fmt.Errorf("unexpected embedding count: %d", len(vecs))
}
return vecs[0], nil
}
// EmbedStrings 批量嵌入，带重试；实现 [embedding.Embedder]，可供 Eino Indexer 使用。
func (e *Embedder) EmbedStrings(ctx context.Context, texts []string, opts ...embedding.Option) ([][]float32, error) {
if e == nil || e.eino == nil {
return nil, fmt.Errorf("embedder not initialized")
}
if len(texts) == 0 {
return nil, nil
}
var lastErr error
for attempt := 0; attempt < e.maxRetries; attempt++ {
if attempt > 0 {
wait := e.retryDelay * time.Duration(attempt)
if e.logger != nil {
e.logger.Debug("嵌入重试前等待", zap.Int("attempt", attempt+1), zap.Duration("wait", wait))
}
select {
case <-ctx.Done():
return nil, ctx.Err()
case <-time.After(wait):
}
} else {
e.waitRateLimiter()
}
raw, err := e.eino.EmbedStrings(ctx, texts, opts...)
if err == nil {
out := make([][]float32, len(raw))
for i, row := range raw {
out[i] = make([]float32, len(row))
for j, v := range row {
out[i][j] = float32(v)
}
}
return out, nil
}
lastErr = err
if !e.isRetryableError(err) {
return nil, err
}
if e.logger != nil {
e.logger.Debug("嵌入失败，将重试", zap.Int("attempt", attempt+1), zap.Error(err))
}
}
return nil, fmt.Errorf("达到最大重试次数 (%d): %v", e.maxRetries, lastErr)
}
// EmbedTexts 批量 float32 嵌入（兼容旧调用；单次请求批量以减小延迟）。
func (e *Embedder) EmbedTexts(ctx context.Context, texts []string) ([][]float32, error) {
return e.EmbedStrings(ctx, texts)
}
func (e *Embedder) isRetryableError(err error) bool {
if err == nil {
return false
}
errStr := err.Error()
if strings.Contains(errStr, "429") || strings.Contains(errStr, "rate limit") {
return true
}
if strings.Contains(errStr, "500") || strings.Contains(errStr, "502") ||
strings.Contains(errStr, "503") || strings.Contains(errStr, "504") {
return true
}
if strings.Contains(errStr, "timeout") || strings.Contains(errStr, "connection") ||
strings.Contains(errStr, "network") || strings.Contains(errStr, "EOF") {
return true
}
return false
}
// einoFloatEmbedder adapts [][]float32 embedder to Eino's [][]float64 [embedding.Embedder] for Indexer.Store.
type einoFloatEmbedder struct {
inner *Embedder
}
func (w *einoFloatEmbedder) EmbedStrings(ctx context.Context, texts []string, opts ...embedding.Option) ([][]float64, error) {
vec32, err := w.inner.EmbedStrings(ctx, texts, opts...)
if err != nil {
return nil, err
}
out := make([][]float64, len(vec32))
for i, row := range vec32 {
out[i] = make([]float64, len(row))
for j, v := range row {
out[i][j] = float64(v)
}
}
return out, nil
}
func (w *einoFloatEmbedder) GetType() string {
return "CyberStrikeKnowledgeEmbedder"
}
func (w *einoFloatEmbedder) IsCallbacksEnabled() bool {
return false
}
// EinoEmbeddingComponent returns an [embedding.Embedder] that uses the same retry/rate-limit path
// and produces float64 vectors expected by generic Eino indexer helpers.
func (e *Embedder) EinoEmbeddingComponent() embedding.Embedder {
return &einoFloatEmbedder{inner: e}
}
knowledge/index_pipeline.go
-91
View File
@@ -1,91 +0,0 @@
package knowledge
import (
"context"
"database/sql"
"fmt"
"strings"
"cyberstrike-ai/internal/config"
"github.com/cloudwego/eino/components/document"
"github.com/cloudwego/eino/compose"
"github.com/cloudwego/eino/schema"
)
// normalizeChunkStrategy returns "recursive" or "markdown_then_recursive".
func normalizeChunkStrategy(s string) string {
v := strings.TrimSpace(strings.ToLower(s))
switch v {
case "recursive":
return "recursive"
case "markdown_then_recursive", "markdown_recursive", "markdown":
return "markdown_then_recursive"
case "":
return "markdown_then_recursive"
default:
return "markdown_then_recursive"
}
}
func buildKnowledgeIndexChain(
ctx context.Context,
indexingCfg *config.IndexingConfig,
db *sql.DB,
recursive document.Transformer,
embeddingModel string,
) (compose.Runnable[[]*schema.Document, []string], error) {
if recursive == nil {
return nil, fmt.Errorf("recursive transformer is nil")
}
if db == nil {
return nil, fmt.Errorf("db is nil")
}
strategy := normalizeChunkStrategy("markdown_then_recursive")
batch := 64
maxChunks := 0
if indexingCfg != nil {
strategy = normalizeChunkStrategy(indexingCfg.ChunkStrategy)
if indexingCfg.BatchSize > 0 {
batch = indexingCfg.BatchSize
}
maxChunks = indexingCfg.MaxChunksPerItem
}
si := NewSQLiteIndexer(db, batch, embeddingModel)
ch := compose.NewChain[[]*schema.Document, []string]()
if strategy != "recursive" {
md, err := newMarkdownHeaderSplitter(ctx)
if err != nil {
return nil, fmt.Errorf("markdown splitter: %w", err)
}
ch.AppendDocumentTransformer(md)
}
ch.AppendDocumentTransformer(recursive)
ch.AppendLambda(newChunkEnrichLambda(maxChunks))
ch.AppendIndexer(si)
return ch.Compile(ctx)
}
func newChunkEnrichLambda(maxChunks int) *compose.Lambda {
return compose.InvokableLambda(func(ctx context.Context, docs []*schema.Document) ([]*schema.Document, error) {
_ = ctx
out := make([]*schema.Document, 0, len(docs))
for _, d := range docs {
if d == nil || strings.TrimSpace(d.Content) == "" {
continue
}
out = append(out, d)
}
if maxChunks > 0 && len(out) > maxChunks {
out = out[:maxChunks]
}
for i, d := range out {
if d.MetaData == nil {
d.MetaData = make(map[string]any)
}
d.MetaData[metaKBChunkIndex] = i
}
return out, nil
})
}
knowledge/index_pipeline_test.go
-21
View File
@@ -1,21 +0,0 @@
package knowledge
import "testing"
func TestNormalizeChunkStrategy(t *testing.T) {
cases := []struct {
in, want string
}{
{"", "markdown_then_recursive"},
{"recursive", "recursive"},
{"RECURSIVE", "recursive"},
{"markdown_then_recursive", "markdown_then_recursive"},
{"markdown", "markdown_then_recursive"},
{"unknown", "markdown_then_recursive"},
}
for _, tc := range cases {
if got := normalizeChunkStrategy(tc.in); got != tc.want {
t.Errorf("normalizeChunkStrategy(%q) = %q, want %q", tc.in, got, tc.want)
}
}
}
knowledge/indexer.go
-352
View File
@@ -1,352 +0,0 @@
package knowledge
import (
"context"
"database/sql"
"fmt"
"strings"
"sync"
"time"
"cyberstrike-ai/internal/config"
fileloader "github.com/cloudwego/eino-ext/components/document/loader/file"
"github.com/cloudwego/eino/components/document"
"github.com/cloudwego/eino/components/indexer"
"github.com/cloudwego/eino/compose"
"github.com/cloudwego/eino/schema"
"go.uber.org/zap"
)
// Indexer 使用 Eino Compose 索引链（Markdown/递归分块、Lambda enrich、SQLite 索引）与嵌入写入。
type Indexer struct {
db *sql.DB
embedder *Embedder
logger *zap.Logger
chunkSize int
overlap int
indexingCfg *config.IndexingConfig
indexChain compose.Runnable[[]*schema.Document, []string]
fileLoader *fileloader.FileLoader
mu sync.RWMutex
lastError string
lastErrorTime time.Time
errorCount int
rebuildMu sync.RWMutex
isRebuilding bool
rebuildTotalItems int
rebuildCurrent int
rebuildFailed int
rebuildStartTime time.Time
rebuildLastItemID string
rebuildLastChunks int
}
// NewIndexer 创建索引器并编译 Eino 索引链；kcfg 为完整知识库配置（含 indexing 与路径相关行为）。
func NewIndexer(ctx context.Context, db *sql.DB, embedder *Embedder, logger *zap.Logger, kcfg *config.KnowledgeConfig) (*Indexer, error) {
if db == nil {
return nil, fmt.Errorf("db is nil")
}
if embedder == nil {
return nil, fmt.Errorf("embedder is nil")
}
if err := EnsureKnowledgeEmbeddingsSchema(db); err != nil {
return nil, fmt.Errorf("knowledge_embeddings 结构迁移: %w", err)
}
if kcfg == nil {
kcfg = &config.KnowledgeConfig{}
}
indexingCfg := &kcfg.Indexing
chunkSize := 512
overlap := 50
if indexingCfg.ChunkSize > 0 {
chunkSize = indexingCfg.ChunkSize
}
if indexingCfg.ChunkOverlap >= 0 {
overlap = indexingCfg.ChunkOverlap
}
embedModel := embedder.EmbeddingModelName()
splitter, err := newKnowledgeSplitter(chunkSize, overlap, embedModel)
if err != nil {
return nil, fmt.Errorf("eino recursive splitter: %w", err)
}
chain, err := buildKnowledgeIndexChain(ctx, indexingCfg, db, splitter, embedModel)
if err != nil {
return nil, fmt.Errorf("knowledge index chain: %w", err)
}
var fl *fileloader.FileLoader
fl, err = fileloader.NewFileLoader(ctx, nil)
if err != nil {
if logger != nil {
logger.Warn("Eino FileLoader 初始化失败，prefer_source_file 将回退数据库正文", zap.Error(err))
}
fl = nil
err = nil
}
return &Indexer{
db: db,
embedder: embedder,
logger: logger,
chunkSize: chunkSize,
overlap: overlap,
indexingCfg: indexingCfg,
indexChain: chain,
fileLoader: fl,
}, nil
}
// RecompileIndexChain 在配置或嵌入模型变更后重建 Eino 索引链（无需重启进程）。
func (idx *Indexer) RecompileIndexChain(ctx context.Context) error {
if idx == nil || idx.db == nil || idx.embedder == nil {
return fmt.Errorf("indexer 未初始化")
}
if err := EnsureKnowledgeEmbeddingsSchema(idx.db); err != nil {
return err
}
embedModel := idx.embedder.EmbeddingModelName()
splitter, err := newKnowledgeSplitter(idx.chunkSize, idx.overlap, embedModel)
if err != nil {
return fmt.Errorf("eino recursive splitter: %w", err)
}
chain, err := buildKnowledgeIndexChain(ctx, idx.indexingCfg, idx.db, splitter, embedModel)
if err != nil {
return fmt.Errorf("knowledge index chain: %w", err)
}
idx.indexChain = chain
return nil
}
// IndexItem 索引单个知识项：先清空旧向量，再走 Compose 链（分块、嵌入、写入）。
func (idx *Indexer) IndexItem(ctx context.Context, itemID string) error {
if idx.indexChain == nil {
return fmt.Errorf("索引链未初始化")
}
if idx.embedder == nil {
return fmt.Errorf("嵌入器未初始化")
}
var content, category, title, filePath string
err := idx.db.QueryRow("SELECT content, category, title, file_path FROM knowledge_base_items WHERE id = ?", itemID).Scan(&content, &category, &title, &filePath)
if err != nil {
return fmt.Errorf("获取知识项失败：%w", err)
}
if _, err := idx.db.Exec("DELETE FROM knowledge_embeddings WHERE item_id = ?", itemID); err != nil {
return fmt.Errorf("删除旧向量失败：%w", err)
}
body := strings.TrimSpace(content)
if idx.indexingCfg != nil && idx.indexingCfg.PreferSourceFile && strings.TrimSpace(filePath) != "" && idx.fileLoader != nil {
docs, lerr := idx.fileLoader.Load(ctx, document.Source{URI: strings.TrimSpace(filePath)})
if lerr == nil && len(docs) > 0 {
var b strings.Builder
for i, d := range docs {
if d == nil {
continue
}
if i > 0 {
b.WriteString("\n\n")
}
b.WriteString(d.Content)
}
if s := strings.TrimSpace(b.String()); s != "" {
body = s
}
} else if idx.logger != nil {
idx.logger.Warn("优先源文件读取失败，使用数据库正文",
zap.String("itemId", itemID),
zap.String("path", filePath),
zap.Error(lerr))
}
}
root := &schema.Document{
ID: itemID,
Content: body,
MetaData: map[string]any{
metaKBCategory: category,
metaKBTitle: title,
metaKBItemID: itemID,
},
}
idxOpts := []indexer.Option{indexer.WithEmbedding(idx.embedder.EinoEmbeddingComponent())}
if idx.indexingCfg != nil && len(idx.indexingCfg.SubIndexes) > 0 {
idxOpts = append(idxOpts, indexer.WithSubIndexes(idx.indexingCfg.SubIndexes))
}
ids, err := idx.indexChain.Invoke(ctx, []*schema.Document{root}, compose.WithIndexerOption(idxOpts...))
if err != nil {
msg := fmt.Sprintf("索引写入失败 (知识项：%s): %v", itemID, err)
idx.mu.Lock()
idx.lastError = msg
idx.lastErrorTime = time.Now()
idx.mu.Unlock()
return err
}
if idx.logger != nil {
idx.logger.Info("知识项索引完成", zap.String("itemId", itemID), zap.Int("chunks", len(ids)))
}
idx.rebuildMu.Lock()
idx.rebuildLastItemID = itemID
idx.rebuildLastChunks = len(ids)
idx.rebuildMu.Unlock()
return nil
}
// HasIndex 检查是否存在索引
func (idx *Indexer) HasIndex() (bool, error) {
var count int
err := idx.db.QueryRow("SELECT COUNT(*) FROM knowledge_embeddings").Scan(&count)
if err != nil {
return false, fmt.Errorf("检查索引失败：%w", err)
}
return count > 0, nil
}
// RebuildIndex 重建所有索引
func (idx *Indexer) RebuildIndex(ctx context.Context) error {
idx.rebuildMu.Lock()
idx.isRebuilding = true
idx.rebuildTotalItems = 0
idx.rebuildCurrent = 0
idx.rebuildFailed = 0
idx.rebuildStartTime = time.Now()
idx.rebuildLastItemID = ""
idx.rebuildLastChunks = 0
idx.rebuildMu.Unlock()
idx.mu.Lock()
idx.lastError = ""
idx.lastErrorTime = time.Time{}
idx.errorCount = 0
idx.mu.Unlock()
rows, err := idx.db.Query("SELECT id FROM knowledge_base_items")
if err != nil {
idx.rebuildMu.Lock()
idx.isRebuilding = false
idx.rebuildMu.Unlock()
return fmt.Errorf("查询知识项失败：%w", err)
}
defer rows.Close()
var itemIDs []string
for rows.Next() {
var id string
if err := rows.Scan(&id); err != nil {
idx.rebuildMu.Lock()
idx.isRebuilding = false
idx.rebuildMu.Unlock()
return fmt.Errorf("扫描知识项 ID 失败：%w", err)
}
itemIDs = append(itemIDs, id)
}
idx.rebuildMu.Lock()
idx.rebuildTotalItems = len(itemIDs)
idx.rebuildMu.Unlock()
idx.logger.Info("开始重建索引", zap.Int("totalItems", len(itemIDs)))
failedCount := 0
consecutiveFailures := 0
maxConsecutiveFailures := 5
firstFailureItemID := ""
var firstFailureError error
for i, itemID := range itemIDs {
if err := idx.IndexItem(ctx, itemID); err != nil {
failedCount++
consecutiveFailures++
if consecutiveFailures == 1 {
firstFailureItemID = itemID
firstFailureError = err
idx.logger.Warn("索引知识项失败",
zap.String("itemId", itemID),
zap.Int("totalItems", len(itemIDs)),
zap.Error(err),
)
}
if consecutiveFailures >= maxConsecutiveFailures {
errorMsg := fmt.Sprintf("连续 %d 个知识项索引失败，可能存在配置问题（如嵌入模型配置错误、API 密钥无效、余额不足等）。第一个失败项：%s, 错误：%v", consecutiveFailures, firstFailureItemID, firstFailureError)
idx.mu.Lock()
idx.lastError = errorMsg
idx.lastErrorTime = time.Now()
idx.mu.Unlock()
idx.logger.Error("连续索引失败次数过多，立即停止索引",
zap.Int("consecutiveFailures", consecutiveFailures),
zap.Int("totalItems", len(itemIDs)),
zap.Int("processedItems", i+1),
zap.String("firstFailureItemId", firstFailureItemID),
zap.Error(firstFailureError),
)
return fmt.Errorf("连续索引失败次数过多：%v", firstFailureError)
}
if failedCount > len(itemIDs)*3/10 && failedCount == len(itemIDs)*3/10+1 {
errorMsg := fmt.Sprintf("索引失败的知识项过多 (%d/%d)，可能存在配置问题。第一个失败项：%s, 错误：%v", failedCount, len(itemIDs), firstFailureItemID, firstFailureError)
idx.mu.Lock()
idx.lastError = errorMsg
idx.lastErrorTime = time.Now()
idx.mu.Unlock()
idx.logger.Error("索引失败的知识项过多，可能存在配置问题",
zap.Int("failedCount", failedCount),
zap.Int("totalItems", len(itemIDs)),
zap.String("firstFailureItemId", firstFailureItemID),
zap.Error(firstFailureError),
)
}
continue
}
if consecutiveFailures > 0 {
consecutiveFailures = 0
firstFailureItemID = ""
firstFailureError = nil
}
idx.rebuildMu.Lock()
idx.rebuildCurrent = i + 1
idx.rebuildFailed = failedCount
idx.rebuildMu.Unlock()
if (i+1)%10 == 0 || (len(itemIDs) > 0 && (i+1)*100/len(itemIDs)%10 == 0 && (i+1)*100/len(itemIDs) > 0) {
idx.logger.Info("索引进度", zap.Int("current", i+1), zap.Int("total", len(itemIDs)), zap.Int("failed", failedCount))
}
}
idx.rebuildMu.Lock()
idx.isRebuilding = false
idx.rebuildMu.Unlock()
idx.logger.Info("索引重建完成", zap.Int("totalItems", len(itemIDs)), zap.Int("failedCount", failedCount))
return nil
}
// GetLastError 获取最近一次错误信息
func (idx *Indexer) GetLastError() (string, time.Time) {
idx.mu.RLock()
defer idx.mu.RUnlock()
return idx.lastError, idx.lastErrorTime
}
// GetRebuildStatus 获取重建索引状态
func (idx *Indexer) GetRebuildStatus() (isRebuilding bool, totalItems int, current int, failed int, lastItemID string, lastChunks int, startTime time.Time) {
idx.rebuildMu.RLock()
defer idx.rebuildMu.RUnlock()
return idx.isRebuilding, idx.rebuildTotalItems, idx.rebuildCurrent, idx.rebuildFailed, idx.rebuildLastItemID, idx.rebuildLastChunks, idx.rebuildStartTime
}
knowledge/manager.go
-885
View File
@@ -1,885 +0,0 @@
package knowledge
import (
"database/sql"
"encoding/json"
"fmt"
"io/fs"
"os"
"path/filepath"
"strings"
"time"
"github.com/google/uuid"
"go.uber.org/zap"
)
// Manager 知识库管理器
type Manager struct {
db *sql.DB
basePath string
logger *zap.Logger
}
// NewManager 创建新的知识库管理器
func NewManager(db *sql.DB, basePath string, logger *zap.Logger) *Manager {
return &Manager{
db: db,
basePath: basePath,
logger: logger,
}
}
// ScanKnowledgeBase 扫描知识库目录，更新数据库
// 返回需要索引的知识项ID列表（新添加的或更新的）
func (m *Manager) ScanKnowledgeBase() ([]string, error) {
if m.basePath == "" {
return nil, fmt.Errorf("知识库路径未配置")
}
// 确保目录存在
if err := os.MkdirAll(m.basePath, 0755); err != nil {
return nil, fmt.Errorf("创建知识库目录失败: %w", err)
}
var itemsToIndex []string
// 遍历知识库目录
err := filepath.WalkDir(m.basePath, func(path string, d fs.DirEntry, err error) error {
if err != nil {
return err
}
// 跳过目录和非markdown文件
if d.IsDir() || !strings.HasSuffix(strings.ToLower(path), ".md") {
return nil
}
// 计算相对路径和分类
relPath, err := filepath.Rel(m.basePath, path)
if err != nil {
return err
}
// 第一个目录名作为分类（风险类型）
parts := strings.Split(relPath, string(filepath.Separator))
category := "未分类"
if len(parts) > 1 {
category = parts[0]
}
// 文件名为标题
title := strings.TrimSuffix(filepath.Base(path), ".md")
// 读取文件内容
content, err := os.ReadFile(path)
if err != nil {
m.logger.Warn("读取知识库文件失败", zap.String("path", path), zap.Error(err))
return nil // 继续处理其他文件
}
// 检查是否已存在
var existingID string
var existingContent string
var existingUpdatedAt time.Time
err = m.db.QueryRow(
"SELECT id, content, updated_at FROM knowledge_base_items WHERE file_path = ?",
path,
).Scan(&existingID, &existingContent, &existingUpdatedAt)
if err == sql.ErrNoRows {
// 创建新项
id := uuid.New().String()
now := time.Now()
_, err = m.db.Exec(
"INSERT INTO knowledge_base_items (id, category, title, file_path, content, created_at, updated_at) VALUES (?, ?, ?, ?, ?, ?, ?)",
id, category, title, path, string(content), now, now,
)
if err != nil {
return fmt.Errorf("插入知识项失败: %w", err)
}
m.logger.Info("添加知识项", zap.String("id", id), zap.String("title", title), zap.String("category", category))
// 新添加的项需要索引
itemsToIndex = append(itemsToIndex, id)
} else if err == nil {
// 检查内容是否有变化
contentChanged := existingContent != string(content)
if contentChanged {
// 更新现有项
_, err = m.db.Exec(
"UPDATE knowledge_base_items SET category = ?, title = ?, content = ?, updated_at = ? WHERE id = ?",
category, title, string(content), time.Now(), existingID,
)
if err != nil {
return fmt.Errorf("更新知识项失败: %w", err)
}
m.logger.Info("更新知识项", zap.String("id", existingID), zap.String("title", title))
// 内容已更新的项需要重新索引
itemsToIndex = append(itemsToIndex, existingID)
} else {
m.logger.Debug("知识项未变化，跳过", zap.String("id", existingID), zap.String("title", title))
}
} else {
return fmt.Errorf("查询知识项失败: %w", err)
}
return nil
})
if err != nil {
return nil, err
}
return itemsToIndex, nil
}
// GetCategories 获取所有分类（风险类型）
func (m *Manager) GetCategories() ([]string, error) {
rows, err := m.db.Query("SELECT DISTINCT category FROM knowledge_base_items ORDER BY category")
if err != nil {
return nil, fmt.Errorf("查询分类失败: %w", err)
}
defer rows.Close()
var categories []string
for rows.Next() {
var category string
if err := rows.Scan(&category); err != nil {
return nil, fmt.Errorf("扫描分类失败: %w", err)
}
categories = append(categories, category)
}
return categories, nil
}
// GetStats 获取知识库统计信息
func (m *Manager) GetStats() (int, int, error) {
// 获取分类总数
categories, err := m.GetCategories()
if err != nil {
return 0, 0, fmt.Errorf("获取分类失败: %w", err)
}
totalCategories := len(categories)
// 获取知识项总数
var totalItems int
err = m.db.QueryRow("SELECT COUNT(*) FROM knowledge_base_items").Scan(&totalItems)
if err != nil {
return totalCategories, 0, fmt.Errorf("获取知识项总数失败: %w", err)
}
return totalCategories, totalItems, nil
}
// GetCategoriesWithItems 按分类分页获取知识项（每个分类包含其下的所有知识项）
// limit: 每页分类数量（0表示不限制）
// offset: 偏移量（按分类偏移）
func (m *Manager) GetCategoriesWithItems(limit, offset int) ([]*CategoryWithItems, int, error) {
// 首先获取所有分类（带数量统计）
rows, err := m.db.Query(`
SELECT category, COUNT(*) as item_count
FROM knowledge_base_items
GROUP BY category
ORDER BY category
`)
if err != nil {
return nil, 0, fmt.Errorf("查询分类失败: %w", err)
}
defer rows.Close()
// 收集所有分类信息
type categoryInfo struct {
name string
itemCount int
}
var allCategories []categoryInfo
for rows.Next() {
var info categoryInfo
if err := rows.Scan(&info.name, &info.itemCount); err != nil {
return nil, 0, fmt.Errorf("扫描分类失败: %w", err)
}
allCategories = append(allCategories, info)
}
totalCategories := len(allCategories)
// 应用分页（按分类分页）
var paginatedCategories []categoryInfo
if limit > 0 {
start := offset
end := offset + limit
if start >= totalCategories {
paginatedCategories = []categoryInfo{}
} else {
if end > totalCategories {
end = totalCategories
}
paginatedCategories = allCategories[start:end]
}
} else {
paginatedCategories = allCategories
}
// 为每个分类获取其下的知识项（只返回摘要，不包含完整内容）
result := make([]*CategoryWithItems, 0, len(paginatedCategories))
for _, catInfo := range paginatedCategories {
// 获取该分类下的所有知识项
items, _, err := m.GetItemsSummary(catInfo.name, 0, 0)
if err != nil {
return nil, 0, fmt.Errorf("获取分类 %s 的知识项失败: %w", catInfo.name, err)
}
result = append(result, &CategoryWithItems{
Category: catInfo.name,
ItemCount: catInfo.itemCount,
Items: items,
})
}
return result, totalCategories, nil
}
// GetItems 获取知识项列表（完整内容，用于向后兼容）
func (m *Manager) GetItems(category string) ([]*KnowledgeItem, error) {
return m.GetItemsWithOptions(category, 0, 0, true)
}
// GetItemsWithOptions 获取知识项列表（支持分页和可选内容）
// category: 分类筛选（空字符串表示所有分类）
// limit: 每页数量（0表示不限制）
// offset: 偏移量
// includeContent: 是否包含完整内容（false时只返回摘要）
func (m *Manager) GetItemsWithOptions(category string, limit, offset int, includeContent bool) ([]*KnowledgeItem, error) {
var rows *sql.Rows
var err error
// 构建SQL查询
var query string
var args []interface{}
if includeContent {
query = "SELECT id, category, title, file_path, content, created_at, updated_at FROM knowledge_base_items"
} else {
query = "SELECT id, category, title, file_path, created_at, updated_at FROM knowledge_base_items"
}
if category != "" {
query += " WHERE category = ?"
args = append(args, category)
}
query += " ORDER BY category, title"
if limit > 0 {
query += " LIMIT ?"
args = append(args, limit)
if offset > 0 {
query += " OFFSET ?"
args = append(args, offset)
}
}
rows, err = m.db.Query(query, args...)
if err != nil {
return nil, fmt.Errorf("查询知识项失败: %w", err)
}
defer rows.Close()
var items []*KnowledgeItem
for rows.Next() {
item := &KnowledgeItem{}
var createdAt, updatedAt string
if includeContent {
if err := rows.Scan(&item.ID, &item.Category, &item.Title, &item.FilePath, &item.Content, &createdAt, &updatedAt); err != nil {
return nil, fmt.Errorf("扫描知识项失败: %w", err)
}
} else {
if err := rows.Scan(&item.ID, &item.Category, &item.Title, &item.FilePath, &createdAt, &updatedAt); err != nil {
return nil, fmt.Errorf("扫描知识项失败: %w", err)
}
// 不包含内容时，Content为空字符串
item.Content = ""
}
// 解析时间 - 支持多种格式
timeFormats := []string{
"2006-01-02 15:04:05.999999999-07:00",
"2006-01-02 15:04:05.999999999",
"2006-01-02T15:04:05.999999999Z07:00",
"2006-01-02T15:04:05Z",
"2006-01-02 15:04:05",
time.RFC3339,
time.RFC3339Nano,
}
// 解析创建时间
if createdAt != "" {
for _, format := range timeFormats {
parsed, err := time.Parse(format, createdAt)
if err == nil && !parsed.IsZero() {
item.CreatedAt = parsed
break
}
}
}
// 解析更新时间
if updatedAt != "" {
for _, format := range timeFormats {
parsed, err := time.Parse(format, updatedAt)
if err == nil && !parsed.IsZero() {
item.UpdatedAt = parsed
break
}
}
}
// 如果更新时间为空，使用创建时间
if item.UpdatedAt.IsZero() && !item.CreatedAt.IsZero() {
item.UpdatedAt = item.CreatedAt
}
items = append(items, item)
}
return items, nil
}
// GetItemsCount 获取知识项总数
func (m *Manager) GetItemsCount(category string) (int, error) {
var count int
var err error
if category != "" {
err = m.db.QueryRow("SELECT COUNT(*) FROM knowledge_base_items WHERE category = ?", category).Scan(&count)
} else {
err = m.db.QueryRow("SELECT COUNT(*) FROM knowledge_base_items").Scan(&count)
}
if err != nil {
return 0, fmt.Errorf("查询知识项总数失败: %w", err)
}
return count, nil
}
// SearchItemsByKeyword 按关键字搜索知识项（在所有数据中搜索，支持标题、分类、路径、内容匹配）
func (m *Manager) SearchItemsByKeyword(keyword string, category string) ([]*KnowledgeItemSummary, error) {
if keyword == "" {
return nil, fmt.Errorf("搜索关键字不能为空")
}
// 构建SQL查询，使用LIKE进行关键字匹配（不区分大小写）
var query string
var args []interface{}
// SQLite的LIKE不区分大小写，使用COLLATE NOCASE或LOWER()函数
// 使用%keyword%进行模糊匹配
searchPattern := "%" + keyword + "%"
query = `
SELECT id, category, title, file_path, created_at, updated_at
FROM knowledge_base_items
WHERE (LOWER(title) LIKE LOWER(?) OR LOWER(category) LIKE LOWER(?) OR LOWER(file_path) LIKE LOWER(?) OR LOWER(content) LIKE LOWER(?))
`
args = append(args, searchPattern, searchPattern, searchPattern, searchPattern)
// 如果指定了分类，添加分类过滤
if category != "" {
query += " AND category = ?"
args = append(args, category)
}
query += " ORDER BY category, title"
rows, err := m.db.Query(query, args...)
if err != nil {
return nil, fmt.Errorf("搜索知识项失败: %w", err)
}
defer rows.Close()
var items []*KnowledgeItemSummary
for rows.Next() {
item := &KnowledgeItemSummary{}
var createdAt, updatedAt string
if err := rows.Scan(&item.ID, &item.Category, &item.Title, &item.FilePath, &createdAt, &updatedAt); err != nil {
return nil, fmt.Errorf("扫描知识项失败: %w", err)
}
// 解析时间
timeFormats := []string{
"2006-01-02 15:04:05.999999999-07:00",
"2006-01-02 15:04:05.999999999",
"2006-01-02T15:04:05.999999999Z07:00",
"2006-01-02T15:04:05Z",
"2006-01-02 15:04:05",
time.RFC3339,
time.RFC3339Nano,
}
if createdAt != "" {
for _, format := range timeFormats {
parsed, err := time.Parse(format, createdAt)
if err == nil && !parsed.IsZero() {
item.CreatedAt = parsed
break
}
}
}
if updatedAt != "" {
for _, format := range timeFormats {
parsed, err := time.Parse(format, updatedAt)
if err == nil && !parsed.IsZero() {
item.UpdatedAt = parsed
break
}
}
}
if item.UpdatedAt.IsZero() && !item.CreatedAt.IsZero() {
item.UpdatedAt = item.CreatedAt
}
items = append(items, item)
}
return items, nil
}
// GetItemsSummary 获取知识项摘要列表（不包含完整内容，支持分页）
func (m *Manager) GetItemsSummary(category string, limit, offset int) ([]*KnowledgeItemSummary, int, error) {
// 获取总数
total, err := m.GetItemsCount(category)
if err != nil {
return nil, 0, err
}
// 获取列表数据（不包含内容）
var rows *sql.Rows
var query string
var args []interface{}
query = "SELECT id, category, title, file_path, created_at, updated_at FROM knowledge_base_items"
if category != "" {
query += " WHERE category = ?"
args = append(args, category)
}
query += " ORDER BY category, title"
if limit > 0 {
query += " LIMIT ?"
args = append(args, limit)
if offset > 0 {
query += " OFFSET ?"
args = append(args, offset)
}
}
rows, err = m.db.Query(query, args...)
if err != nil {
return nil, 0, fmt.Errorf("查询知识项失败: %w", err)
}
defer rows.Close()
var items []*KnowledgeItemSummary
for rows.Next() {
item := &KnowledgeItemSummary{}
var createdAt, updatedAt string
if err := rows.Scan(&item.ID, &item.Category, &item.Title, &item.FilePath, &createdAt, &updatedAt); err != nil {
return nil, 0, fmt.Errorf("扫描知识项失败: %w", err)
}
// 解析时间
timeFormats := []string{
"2006-01-02 15:04:05.999999999-07:00",
"2006-01-02 15:04:05.999999999",
"2006-01-02T15:04:05.999999999Z07:00",
"2006-01-02T15:04:05Z",
"2006-01-02 15:04:05",
time.RFC3339,
time.RFC3339Nano,
}
if createdAt != "" {
for _, format := range timeFormats {
parsed, err := time.Parse(format, createdAt)
if err == nil && !parsed.IsZero() {
item.CreatedAt = parsed
break
}
}
}
if updatedAt != "" {
for _, format := range timeFormats {
parsed, err := time.Parse(format, updatedAt)
if err == nil && !parsed.IsZero() {
item.UpdatedAt = parsed
break
}
}
}
if item.UpdatedAt.IsZero() && !item.CreatedAt.IsZero() {
item.UpdatedAt = item.CreatedAt
}
items = append(items, item)
}
return items, total, nil
}
// GetItem 获取单个知识项
func (m *Manager) GetItem(id string) (*KnowledgeItem, error) {
item := &KnowledgeItem{}
var createdAt, updatedAt string
err := m.db.QueryRow(
"SELECT id, category, title, file_path, content, created_at, updated_at FROM knowledge_base_items WHERE id = ?",
id,
).Scan(&item.ID, &item.Category, &item.Title, &item.FilePath, &item.Content, &createdAt, &updatedAt)
if err == sql.ErrNoRows {
return nil, fmt.Errorf("知识项不存在")
}
if err != nil {
return nil, fmt.Errorf("查询知识项失败: %w", err)
}
// 解析时间 - 支持多种格式
timeFormats := []string{
"2006-01-02 15:04:05.999999999-07:00",
"2006-01-02 15:04:05.999999999",
"2006-01-02T15:04:05.999999999Z07:00",
"2006-01-02T15:04:05Z",
"2006-01-02 15:04:05",
time.RFC3339,
time.RFC3339Nano,
}
// 解析创建时间
if createdAt != "" {
for _, format := range timeFormats {
parsed, err := time.Parse(format, createdAt)
if err == nil && !parsed.IsZero() {
item.CreatedAt = parsed
break
}
}
}
// 解析更新时间
if updatedAt != "" {
for _, format := range timeFormats {
parsed, err := time.Parse(format, updatedAt)
if err == nil && !parsed.IsZero() {
item.UpdatedAt = parsed
break
}
}
}
// 如果更新时间为空，使用创建时间
if item.UpdatedAt.IsZero() && !item.CreatedAt.IsZero() {
item.UpdatedAt = item.CreatedAt
}
return item, nil
}
// CreateItem 创建知识项
func (m *Manager) CreateItem(category, title, content string) (*KnowledgeItem, error) {
id := uuid.New().String()
now := time.Now()
// 构建文件路径
filePath := filepath.Join(m.basePath, category, title+".md")
// 确保目录存在
if err := os.MkdirAll(filepath.Dir(filePath), 0755); err != nil {
return nil, fmt.Errorf("创建目录失败: %w", err)
}
// 写入文件
if err := os.WriteFile(filePath, []byte(content), 0644); err != nil {
return nil, fmt.Errorf("写入文件失败: %w", err)
}
// 插入数据库
_, err := m.db.Exec(
"INSERT INTO knowledge_base_items (id, category, title, file_path, content, created_at, updated_at) VALUES (?, ?, ?, ?, ?, ?, ?)",
id, category, title, filePath, content, now, now,
)
if err != nil {
return nil, fmt.Errorf("插入知识项失败: %w", err)
}
return &KnowledgeItem{
ID: id,
Category: category,
Title: title,
FilePath: filePath,
Content: content,
CreatedAt: now,
UpdatedAt: now,
}, nil
}
// UpdateItem 更新知识项
func (m *Manager) UpdateItem(id, category, title, content string) (*KnowledgeItem, error) {
// 获取现有项
item, err := m.GetItem(id)
if err != nil {
return nil, err
}
// 构建新文件路径
newFilePath := filepath.Join(m.basePath, category, title+".md")
// 如果路径改变，需要移动文件
if item.FilePath != newFilePath {
// 确保新目录存在
if err := os.MkdirAll(filepath.Dir(newFilePath), 0755); err != nil {
return nil, fmt.Errorf("创建目录失败: %w", err)
}
// 移动文件
if err := os.Rename(item.FilePath, newFilePath); err != nil {
return nil, fmt.Errorf("移动文件失败: %w", err)
}
// 删除旧目录（如果为空）
oldDir := filepath.Dir(item.FilePath)
if isEmpty, _ := isEmptyDir(oldDir); isEmpty {
// 只有当目录不是知识库根目录时才删除（避免删除根目录）
if oldDir != m.basePath {
if err := os.Remove(oldDir); err != nil {
m.logger.Warn("删除空目录失败", zap.String("dir", oldDir), zap.Error(err))
}
}
}
}
// 写入文件
if err := os.WriteFile(newFilePath, []byte(content), 0644); err != nil {
return nil, fmt.Errorf("写入文件失败: %w", err)
}
// 更新数据库
_, err = m.db.Exec(
"UPDATE knowledge_base_items SET category = ?, title = ?, file_path = ?, content = ?, updated_at = ? WHERE id = ?",
category, title, newFilePath, content, time.Now(), id,
)
if err != nil {
return nil, fmt.Errorf("更新知识项失败: %w", err)
}
// 删除旧的向量嵌入（需要重新索引）
_, err = m.db.Exec("DELETE FROM knowledge_embeddings WHERE item_id = ?", id)
if err != nil {
m.logger.Warn("删除旧向量嵌入失败", zap.Error(err))
}
return m.GetItem(id)
}
// DeleteItem 删除知识项
func (m *Manager) DeleteItem(id string) error {
// 获取文件路径
var filePath string
err := m.db.QueryRow("SELECT file_path FROM knowledge_base_items WHERE id = ?", id).Scan(&filePath)
if err != nil {
return fmt.Errorf("查询知识项失败: %w", err)
}
// 删除文件
if err := os.Remove(filePath); err != nil && !os.IsNotExist(err) {
m.logger.Warn("删除文件失败", zap.String("path", filePath), zap.Error(err))
}
// 删除数据库记录（级联删除向量）
_, err = m.db.Exec("DELETE FROM knowledge_base_items WHERE id = ?", id)
if err != nil {
return fmt.Errorf("删除知识项失败: %w", err)
}
// 删除空目录（如果为空）
dir := filepath.Dir(filePath)
if isEmpty, _ := isEmptyDir(dir); isEmpty {
// 只有当目录不是知识库根目录时才删除（避免删除根目录）
if dir != m.basePath {
if err := os.Remove(dir); err != nil {
m.logger.Warn("删除空目录失败", zap.String("dir", dir), zap.Error(err))
}
}
}
return nil
}
// isEmptyDir 检查目录是否为空（忽略隐藏文件和 . 开头的文件）
func isEmptyDir(dir string) (bool, error) {
entries, err := os.ReadDir(dir)
if err != nil {
return false, err
}
for _, entry := range entries {
// 忽略隐藏文件（以 . 开头）
if !strings.HasPrefix(entry.Name(), ".") {
return false, nil
}
}
return true, nil
}
// LogRetrieval 记录检索日志
func (m *Manager) LogRetrieval(conversationID, messageID, query, riskType string, retrievedItems []string) error {
id := uuid.New().String()
itemsJSON, _ := json.Marshal(retrievedItems)
_, err := m.db.Exec(
"INSERT INTO knowledge_retrieval_logs (id, conversation_id, message_id, query, risk_type, retrieved_items, created_at) VALUES (?, ?, ?, ?, ?, ?, ?)",
id, conversationID, messageID, query, riskType, string(itemsJSON), time.Now(),
)
return err
}
// GetIndexStatus 获取索引状态
func (m *Manager) GetIndexStatus() (map[string]interface{}, error) {
// 获取总知识项数
var totalItems int
err := m.db.QueryRow("SELECT COUNT(*) FROM knowledge_base_items").Scan(&totalItems)
if err != nil {
return nil, fmt.Errorf("查询总知识项数失败: %w", err)
}
// 获取已索引的知识项数（有向量嵌入的）
var indexedItems int
err = m.db.QueryRow(`
SELECT COUNT(DISTINCT item_id)
FROM knowledge_embeddings
`).Scan(&indexedItems)
if err != nil {
return nil, fmt.Errorf("查询已索引项数失败: %w", err)
}
// 计算进度百分比
var progressPercent float64
if totalItems > 0 {
progressPercent = float64(indexedItems) / float64(totalItems) * 100
} else {
progressPercent = 100.0
}
// 判断是否完成
isComplete := indexedItems >= totalItems && totalItems > 0
return map[string]interface{}{
"total_items": totalItems,
"indexed_items": indexedItems,
"progress_percent": progressPercent,
"is_complete": isComplete,
}, nil
}
// GetRetrievalLogs 获取检索日志
func (m *Manager) GetRetrievalLogs(conversationID, messageID string, limit int) ([]*RetrievalLog, error) {
var rows *sql.Rows
var err error
if messageID != "" {
rows, err = m.db.Query(
"SELECT id, conversation_id, message_id, query, risk_type, retrieved_items, created_at FROM knowledge_retrieval_logs WHERE message_id = ? ORDER BY created_at DESC LIMIT ?",
messageID, limit,
)
} else if conversationID != "" {
rows, err = m.db.Query(
"SELECT id, conversation_id, message_id, query, risk_type, retrieved_items, created_at FROM knowledge_retrieval_logs WHERE conversation_id = ? ORDER BY created_at DESC LIMIT ?",
conversationID, limit,
)
} else {
rows, err = m.db.Query(
"SELECT id, conversation_id, message_id, query, risk_type, retrieved_items, created_at FROM knowledge_retrieval_logs ORDER BY created_at DESC LIMIT ?",
limit,
)
}
if err != nil {
return nil, fmt.Errorf("查询检索日志失败: %w", err)
}
defer rows.Close()
var logs []*RetrievalLog
for rows.Next() {
log := &RetrievalLog{}
var createdAt string
var itemsJSON sql.NullString
if err := rows.Scan(&log.ID, &log.ConversationID, &log.MessageID, &log.Query, &log.RiskType, &itemsJSON, &createdAt); err != nil {
return nil, fmt.Errorf("扫描检索日志失败: %w", err)
}
// 解析时间 - 支持多种格式
var err error
timeFormats := []string{
"2006-01-02 15:04:05.999999999-07:00",
"2006-01-02 15:04:05.999999999",
"2006-01-02T15:04:05.999999999Z07:00",
"2006-01-02T15:04:05Z",
"2006-01-02 15:04:05",
time.RFC3339,
time.RFC3339Nano,
}
for _, format := range timeFormats {
log.CreatedAt, err = time.Parse(format, createdAt)
if err == nil && !log.CreatedAt.IsZero() {
break
}
}
// 如果所有格式都失败，记录警告但继续处理
if log.CreatedAt.IsZero() {
m.logger.Warn("解析检索日志时间失败",
zap.String("timeStr", createdAt),
zap.Error(err),
)
// 使用当前时间作为fallback
log.CreatedAt = time.Now()
}
// 解析检索项
if itemsJSON.Valid {
json.Unmarshal([]byte(itemsJSON.String), &log.RetrievedItems)
}
logs = append(logs, log)
}
return logs, nil
}
// DeleteRetrievalLog 删除检索日志
func (m *Manager) DeleteRetrievalLog(id string) error {
result, err := m.db.Exec("DELETE FROM knowledge_retrieval_logs WHERE id = ?", id)
if err != nil {
return fmt.Errorf("删除检索日志失败: %w", err)
}
rowsAffected, err := result.RowsAffected()
if err != nil {
return fmt.Errorf("获取删除行数失败: %w", err)
}
if rowsAffected == 0 {
return fmt.Errorf("检索日志不存在")
}
return nil
}
knowledge/retrieval_postprocess.go
-213
View File
@@ -1,213 +0,0 @@
package knowledge
import (
"context"
"crypto/sha256"
"encoding/hex"
"fmt"
"strings"
"sync"
"unicode"
"unicode/utf8"
"cyberstrike-ai/internal/config"
"github.com/cloudwego/eino/schema"
"github.com/pkoukk/tiktoken-go"
)
// postRetrieveMaxPrefetchCap 限制单次向量候选上限，避免误配置导致全表扫压力过大。
const postRetrieveMaxPrefetchCap = 200
// DocumentReranker 可选重排（如交叉编码器 / 第三方 Rerank API），由 [Retriever.SetDocumentReranker] 注入；失败时在适配层降级为向量序。
type DocumentReranker interface {
Rerank(ctx context.Context, query string, docs []*schema.Document) ([]*schema.Document, error)
}
// NopDocumentReranker 占位实现，便于测试或未启用重排时显式注入。
type NopDocumentReranker struct{}
// Rerank implements [DocumentReranker] as no-op.
func (NopDocumentReranker) Rerank(_ context.Context, _ string, docs []*schema.Document) ([]*schema.Document, error) {
return docs, nil
}
var tiktokenEncMu sync.Mutex
var tiktokenEncCache = map[string]*tiktoken.Tiktoken{}
func encodingForTokenizerModel(model string) (*tiktoken.Tiktoken, error) {
m := strings.TrimSpace(model)
if m == "" {
m = "gpt-4"
}
tiktokenEncMu.Lock()
defer tiktokenEncMu.Unlock()
if enc, ok := tiktokenEncCache[m]; ok {
return enc, nil
}
enc, err := tiktoken.EncodingForModel(m)
if err != nil {
enc, err = tiktoken.GetEncoding("cl100k_base")
if err != nil {
return nil, err
}
}
tiktokenEncCache[m] = enc
return enc, nil
}
func countDocTokens(text, model string) (int, error) {
enc, err := encodingForTokenizerModel(model)
if err != nil {
return 0, err
}
toks := enc.Encode(text, nil, nil)
return len(toks), nil
}
// normalizeContentFingerprintKey 去重键：trim + 空白折叠（不改动大小写，避免合并仅大小写不同的代码片段）。
func normalizeContentFingerprintKey(s string) string {
s = strings.TrimSpace(s)
var b strings.Builder
b.Grow(len(s))
prevSpace := false
for _, r := range s {
if unicode.IsSpace(r) {
if !prevSpace {
b.WriteByte(' ')
prevSpace = true
}
continue
}
prevSpace = false
b.WriteRune(r)
}
return b.String()
}
func contentNormKey(d *schema.Document) string {
if d == nil {
return ""
}
n := normalizeContentFingerprintKey(d.Content)
if n == "" {
return ""
}
sum := sha256.Sum256([]byte(n))
return hex.EncodeToString(sum[:])
}
// dedupeByNormalizedContent 按规范化正文去重，保留向量检索顺序中首次出现的文档（同正文仅保留一条）。
func dedupeByNormalizedContent(docs []*schema.Document) []*schema.Document {
if len(docs) < 2 {
return docs
}
seen := make(map[string]struct{}, len(docs))
out := make([]*schema.Document, 0, len(docs))
for _, d := range docs {
if d == nil {
continue
}
k := contentNormKey(d)
if k == "" {
out = append(out, d)
continue
}
if _, ok := seen[k]; ok {
continue
}
seen[k] = struct{}{}
out = append(out, d)
}
return out
}
// truncateDocumentsByBudget 按检索顺序整段保留文档，直至字符数或 token 数（任一启用）超限则停止。
func truncateDocumentsByBudget(docs []*schema.Document, maxRunes, maxTokens int, tokenModel string) ([]*schema.Document, error) {
if len(docs) == 0 {
return docs, nil
}
unlimitedChars := maxRunes <= 0
unlimitedTok := maxTokens <= 0
if unlimitedChars && unlimitedTok {
return docs, nil
}
remRunes := maxRunes
remTok := maxTokens
out := make([]*schema.Document, 0, len(docs))
for _, d := range docs {
if d == nil || strings.TrimSpace(d.Content) == "" {
continue
}
runes := utf8.RuneCountInString(d.Content)
if !unlimitedChars && runes > remRunes {
break
}
var tok int
var err error
if !unlimitedTok {
tok, err = countDocTokens(d.Content, tokenModel)
if err != nil {
return nil, fmt.Errorf("token count: %w", err)
}
if tok > remTok {
break
}
}
out = append(out, d)
if !unlimitedChars {
remRunes -= runes
}
if !unlimitedTok {
remTok -= tok
}
}
return out, nil
}
// EffectivePrefetchTopK 计算向量检索应拉取的候选条数（供粗排 / 去重 / 重排）。
func EffectivePrefetchTopK(topK int, po *config.PostRetrieveConfig) int {
if topK < 1 {
topK = 5
}
fetch := topK
if po != nil && po.PrefetchTopK > fetch {
fetch = po.PrefetchTopK
}
if fetch > postRetrieveMaxPrefetchCap {
fetch = postRetrieveMaxPrefetchCap
}
return fetch
}
// ApplyPostRetrieve 检索后处理：规范化正文去重 → 预算截断 → 最终 TopK。重排在 [VectorEinoRetriever] 中单独调用以便失败时降级。
func ApplyPostRetrieve(docs []*schema.Document, po *config.PostRetrieveConfig, tokenModel string, finalTopK int) ([]*schema.Document, error) {
if finalTopK < 1 {
finalTopK = 5
}
if len(docs) == 0 {
return docs, nil
}
maxChars := 0
maxTok := 0
if po != nil {
maxChars = po.MaxContextChars
maxTok = po.MaxContextTokens
}
out := dedupeByNormalizedContent(docs)
var err error
out, err = truncateDocumentsByBudget(out, maxChars, maxTok, tokenModel)
if err != nil {
return nil, err
}
if len(out) > finalTopK {
out = out[:finalTopK]
}
return out, nil
}
knowledge/retrieval_postprocess_test.go
-62
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@@ -1,62 +0,0 @@
package knowledge
import (
"testing"
"cyberstrike-ai/internal/config"
"github.com/cloudwego/eino/schema"
)
func doc(id, content string, score float64) *schema.Document {
d := &schema.Document{ID: id, Content: content, MetaData: map[string]any{metaKBItemID: "it1"}}
d.WithScore(score)
return d
}
func TestDedupeByNormalizedContent(t *testing.T) {
a := doc("1", "hello world", 0.9)
b := doc("2", "hello world", 0.8)
c := doc("3", "other", 0.7)
out := dedupeByNormalizedContent([]*schema.Document{a, b, c})
if len(out) != 2 {
t.Fatalf("len=%d want 2", len(out))
}
if out[0].ID != "1" || out[1].ID != "3" {
t.Fatalf("order/ids wrong: %#v", out)
}
}
func TestEffectivePrefetchTopK(t *testing.T) {
if g := EffectivePrefetchTopK(5, nil); g != 5 {
t.Fatalf("got %d", g)
}
if g := EffectivePrefetchTopK(5, &config.PostRetrieveConfig{PrefetchTopK: 50}); g != 50 {
t.Fatalf("got %d", g)
}
if g := EffectivePrefetchTopK(5, &config.PostRetrieveConfig{PrefetchTopK: 9999}); g != postRetrieveMaxPrefetchCap {
t.Fatalf("cap: got %d", g)
}
}
func TestApplyPostRetrieveTruncateAndTopK(t *testing.T) {
d1 := doc("1", "ab", 0.9)
d2 := doc("2", "cd", 0.8)
d3 := doc("3", "ef", 0.7)
po := &config.PostRetrieveConfig{MaxContextChars: 3}
out, err := ApplyPostRetrieve([]*schema.Document{d1, d2, d3}, po, "gpt-4", 5)
if err != nil {
t.Fatal(err)
}
if len(out) != 1 || out[0].ID != "1" {
t.Fatalf("got %#v", out)
}
out2, err := ApplyPostRetrieve([]*schema.Document{d1, d2, d3}, nil, "gpt-4", 2)
if err != nil {
t.Fatal(err)
}
if len(out2) != 2 {
t.Fatalf("topk: len=%d", len(out2))
}
}
knowledge/retriever.go
-305
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@@ -1,305 +0,0 @@
package knowledge
import (
"context"
"database/sql"
"encoding/json"
"fmt"
"math"
"sort"
"strings"
"sync"
"cyberstrike-ai/internal/config"
"github.com/cloudwego/eino/components/retriever"
"github.com/cloudwego/eino/schema"
"go.uber.org/zap"
)
// Retriever 检索器：SQLite 存向量 + Eino 嵌入，**纯向量检索**（余弦相似度、TopK、阈值），
// 实现语义与 [retriever.Retriever] 适配层 [VectorEinoRetriever] 一致。
type Retriever struct {
db *sql.DB
embedder *Embedder
config *RetrievalConfig
logger *zap.Logger
rerankMu sync.RWMutex
reranker DocumentReranker
}
// RetrievalConfig 检索配置
type RetrievalConfig struct {
TopK int
SimilarityThreshold float64
// SubIndexFilter 非空时仅检索 sub_indexes 包含该标签（逗号分隔之一）的行；空 sub_indexes 的旧行仍保留以兼容。
SubIndexFilter string
PostRetrieve config.PostRetrieveConfig
}
// NewRetriever 创建新的检索器
func NewRetriever(db *sql.DB, embedder *Embedder, config *RetrievalConfig, logger *zap.Logger) *Retriever {
return &Retriever{
db: db,
embedder: embedder,
config: config,
logger: logger,
}
}
// UpdateConfig 更新检索配置
func (r *Retriever) UpdateConfig(cfg *RetrievalConfig) {
if cfg != nil {
r.config = cfg
if r.logger != nil {
r.logger.Info("检索器配置已更新",
zap.Int("top_k", cfg.TopK),
zap.Float64("similarity_threshold", cfg.SimilarityThreshold),
zap.String("sub_index_filter", cfg.SubIndexFilter),
zap.Int("post_retrieve_prefetch_top_k", cfg.PostRetrieve.PrefetchTopK),
zap.Int("post_retrieve_max_context_chars", cfg.PostRetrieve.MaxContextChars),
zap.Int("post_retrieve_max_context_tokens", cfg.PostRetrieve.MaxContextTokens),
)
}
}
}
// SetDocumentReranker 注入可选重排器（并发安全）；nil 表示禁用。
func (r *Retriever) SetDocumentReranker(rr DocumentReranker) {
if r == nil {
return
}
r.rerankMu.Lock()
defer r.rerankMu.Unlock()
r.reranker = rr
}
func (r *Retriever) documentReranker() DocumentReranker {
if r == nil {
return nil
}
r.rerankMu.RLock()
defer r.rerankMu.RUnlock()
return r.reranker
}
func cosineSimilarity(a, b []float32) float64 {
if len(a) != len(b) {
return 0.0
}
var dotProduct, normA, normB float64
for i := range a {
dotProduct += float64(a[i] * b[i])
normA += float64(a[i] * a[i])
normB += float64(b[i] * b[i])
}
if normA == 0 || normB == 0 {
return 0.0
}
return dotProduct / (math.Sqrt(normA) * math.Sqrt(normB))
}
// Search 搜索知识库。统一经 [VectorEinoRetriever]（Eino retriever.Retriever 边界）。
func (r *Retriever) Search(ctx context.Context, req *SearchRequest) ([]*RetrievalResult, error) {
if req == nil {
return nil, fmt.Errorf("请求不能为空")
}
q := strings.TrimSpace(req.Query)
if q == "" {
return nil, fmt.Errorf("查询不能为空")
}
opts := r.einoRetrieverOptions(req)
docs, err := NewVectorEinoRetriever(r).Retrieve(ctx, q, opts...)
if err != nil {
return nil, err
}
return documentsToRetrievalResults(docs)
}
func (r *Retriever) einoRetrieverOptions(req *SearchRequest) []retriever.Option {
var opts []retriever.Option
if req.TopK > 0 {
opts = append(opts, retriever.WithTopK(req.TopK))
}
dsl := map[string]any{}
if strings.TrimSpace(req.RiskType) != "" {
dsl[DSLRiskType] = strings.TrimSpace(req.RiskType)
}
if req.Threshold > 0 {
dsl[DSLSimilarityThreshold] = req.Threshold
}
if strings.TrimSpace(req.SubIndexFilter) != "" {
dsl[DSLSubIndexFilter] = strings.TrimSpace(req.SubIndexFilter)
}
if len(dsl) > 0 {
opts = append(opts, retriever.WithDSLInfo(dsl))
}
return opts
}
// EinoRetrieve 直接返回 [schema.Document]，供 Eino Graph / Chain 使用。
func (r *Retriever) EinoRetrieve(ctx context.Context, query string, opts ...retriever.Option) ([]*schema.Document, error) {
return NewVectorEinoRetriever(r).Retrieve(ctx, query, opts...)
}
func (r *Retriever) knowledgeEmbeddingSelectSQL(riskType, subIndexFilter string) (string, []interface{}) {
q := `SELECT e.id, e.item_id, e.chunk_index, e.chunk_text, e.embedding, e.embedding_model, e.embedding_dim, i.category, i.title
FROM knowledge_embeddings e
JOIN knowledge_base_items i ON e.item_id = i.id
WHERE 1=1`
var args []interface{}
if strings.TrimSpace(riskType) != "" {
q += ` AND TRIM(i.category) = TRIM(?) COLLATE NOCASE`
args = append(args, riskType)
}
if tag := strings.TrimSpace(subIndexFilter); tag != "" {
tag = strings.ToLower(strings.ReplaceAll(tag, " ", ""))
q += ` AND (TRIM(COALESCE(e.sub_indexes,'')) = '' OR INSTR(',' || LOWER(REPLACE(e.sub_indexes,' ','')) || ',', ',' || ? || ',') > 0)`
args = append(args, tag)
}
return q, args
}
// vectorSearch 纯向量检索：余弦相似度排序，按相似度阈值与 TopK 截断（无 BM25、无混合分、无邻块扩展）。
func (r *Retriever) vectorSearch(ctx context.Context, req *SearchRequest) ([]*RetrievalResult, error) {
if req.Query == "" {
return nil, fmt.Errorf("查询不能为空")
}
topK := req.TopK
if topK <= 0 && r.config != nil {
topK = r.config.TopK
}
if topK <= 0 {
topK = 5
}
threshold := req.Threshold
if threshold <= 0 && r.config != nil {
threshold = r.config.SimilarityThreshold
}
if threshold <= 0 {
threshold = 0.7
}
subIdxFilter := strings.TrimSpace(req.SubIndexFilter)
if subIdxFilter == "" && r.config != nil {
subIdxFilter = strings.TrimSpace(r.config.SubIndexFilter)
}
queryText := FormatQueryEmbeddingText(req.RiskType, req.Query)
queryEmbedding, err := r.embedder.EmbedText(ctx, queryText)
if err != nil {
return nil, fmt.Errorf("向量化查询失败: %w", err)
}
queryDim := len(queryEmbedding)
expectedModel := ""
if r.embedder != nil {
expectedModel = r.embedder.EmbeddingModelName()
}
sqlStr, sqlArgs := r.knowledgeEmbeddingSelectSQL(strings.TrimSpace(req.RiskType), subIdxFilter)
rows, err := r.db.QueryContext(ctx, sqlStr, sqlArgs...)
if err != nil {
return nil, fmt.Errorf("查询向量失败: %w", err)
}
defer rows.Close()
type candidate struct {
chunk *KnowledgeChunk
item *KnowledgeItem
similarity float64
}
candidates := make([]candidate, 0)
rowNum := 0
for rows.Next() {
rowNum++
if rowNum%48 == 0 {
select {
case <-ctx.Done():
return nil, ctx.Err()
default:
}
}
var chunkID, itemID, chunkText, embeddingJSON, category, title, rowModel string
var chunkIndex, rowDim int
if err := rows.Scan(&chunkID, &itemID, &chunkIndex, &chunkText, &embeddingJSON, &rowModel, &rowDim, &category, &title); err != nil {
r.logger.Warn("扫描向量失败", zap.Error(err))
continue
}
var embedding []float32
if err := json.Unmarshal([]byte(embeddingJSON), &embedding); err != nil {
r.logger.Warn("解析向量失败", zap.Error(err))
continue
}
if rowDim > 0 && len(embedding) != rowDim {
r.logger.Debug("跳过维度不一致的向量行", zap.String("chunkId", chunkID), zap.Int("rowDim", rowDim), zap.Int("got", len(embedding)))
continue
}
if queryDim > 0 && len(embedding) != queryDim {
r.logger.Debug("跳过与查询维度不一致的向量", zap.String("chunkId", chunkID), zap.Int("queryDim", queryDim), zap.Int("got", len(embedding)))
continue
}
if expectedModel != "" && strings.TrimSpace(rowModel) != "" && strings.TrimSpace(rowModel) != expectedModel {
r.logger.Debug("跳过嵌入模型不一致的行", zap.String("chunkId", chunkID), zap.String("rowModel", rowModel), zap.String("expected", expectedModel))
continue
}
similarity := cosineSimilarity(queryEmbedding, embedding)
candidates = append(candidates, candidate{
chunk: &KnowledgeChunk{
ID: chunkID,
ItemID: itemID,
ChunkIndex: chunkIndex,
ChunkText: chunkText,
Embedding: embedding,
},
item: &KnowledgeItem{
ID: itemID,
Category: category,
Title: title,
},
similarity: similarity,
})
}
sort.Slice(candidates, func(i, j int) bool {
return candidates[i].similarity > candidates[j].similarity
})
filtered := make([]candidate, 0, len(candidates))
for _, c := range candidates {
if c.similarity >= threshold {
filtered = append(filtered, c)
}
}
if len(filtered) > topK {
filtered = filtered[:topK]
}
results := make([]*RetrievalResult, len(filtered))
for i, c := range filtered {
results[i] = &RetrievalResult{
Chunk: c.chunk,
Item: c.item,
Similarity: c.similarity,
Score: c.similarity,
}
}
return results, nil
}
// AsEinoRetriever 将纯向量检索暴露为 Eino [retriever.Retriever]。
func (r *Retriever) AsEinoRetriever() retriever.Retriever {
return NewVectorEinoRetriever(r)
}
knowledge/schema_migrate.go
-51
View File
@@ -1,51 +0,0 @@
package knowledge
import (
"database/sql"
"fmt"
)
// EnsureKnowledgeEmbeddingsSchema migrates knowledge_embeddings for sub_indexes + embedding metadata.
func EnsureKnowledgeEmbeddingsSchema(db *sql.DB) error {
if db == nil {
return fmt.Errorf("db is nil")
}
var n int
if err := db.QueryRow(`SELECT COUNT(*) FROM sqlite_master WHERE type='table' AND name='knowledge_embeddings'`).Scan(&n); err != nil {
return err
}
if n == 0 {
return nil
}
if err := addKnowledgeEmbeddingsColumnIfMissing(db, "sub_indexes",
`ALTER TABLE knowledge_embeddings ADD COLUMN sub_indexes TEXT NOT NULL DEFAULT ''`); err != nil {
return err
}
if err := addKnowledgeEmbeddingsColumnIfMissing(db, "embedding_model",
`ALTER TABLE knowledge_embeddings ADD COLUMN embedding_model TEXT NOT NULL DEFAULT ''`); err != nil {
return err
}
if err := addKnowledgeEmbeddingsColumnIfMissing(db, "embedding_dim",
`ALTER TABLE knowledge_embeddings ADD COLUMN embedding_dim INTEGER NOT NULL DEFAULT 0`); err != nil {
return err
}
return nil
}
func addKnowledgeEmbeddingsColumnIfMissing(db *sql.DB, column, alterSQL string) error {
var colCount int
q := `SELECT COUNT(*) FROM pragma_table_info('knowledge_embeddings') WHERE name = ?`
if err := db.QueryRow(q, column).Scan(&colCount); err != nil {
return err
}
if colCount > 0 {
return nil
}
_, err := db.Exec(alterSQL)
return err
}
// ensureKnowledgeEmbeddingsSubIndexesColumn 向后兼容；请使用 [EnsureKnowledgeEmbeddingsSchema]。
func ensureKnowledgeEmbeddingsSubIndexesColumn(db *sql.DB) error {
return EnsureKnowledgeEmbeddingsSchema(db)
}
knowledge/tool.go
-323
View File
@@ -1,323 +0,0 @@
package knowledge
import (
"context"
"encoding/json"
"fmt"
"sort"
"strings"
"cyberstrike-ai/internal/mcp"
"cyberstrike-ai/internal/mcp/builtin"
"go.uber.org/zap"
)
// RegisterKnowledgeTool 注册知识检索工具到MCP服务器
func RegisterKnowledgeTool(
mcpServer *mcp.Server,
retriever *Retriever,
manager *Manager,
logger *zap.Logger,
) {
// 注册第一个工具：获取所有可用的风险类型列表
listRiskTypesTool := mcp.Tool{
Name: builtin.ToolListKnowledgeRiskTypes,
Description: "获取知识库中所有可用的风险类型（risk_type）列表。在搜索知识库之前，可以先调用此工具获取可用的风险类型，然后使用正确的风险类型进行精确搜索，这样可以大幅减少检索时间并提高检索准确性。",
ShortDescription: "获取知识库中所有可用的风险类型列表",
InputSchema: map[string]interface{}{
"type": "object",
"properties": map[string]interface{}{},
"required": []string{},
},
}
listRiskTypesHandler := func(ctx context.Context, args map[string]interface{}) (*mcp.ToolResult, error) {
categories, err := manager.GetCategories()
if err != nil {
logger.Error("获取风险类型列表失败", zap.Error(err))
return &mcp.ToolResult{
Content: []mcp.Content{
{
Type: "text",
Text: fmt.Sprintf("获取风险类型列表失败: %v", err),
},
},
IsError: true,
}, nil
}
if len(categories) == 0 {
return &mcp.ToolResult{
Content: []mcp.Content{
{
Type: "text",
Text: "知识库中暂无风险类型。",
},
},
}, nil
}
var resultText strings.Builder
resultText.WriteString(fmt.Sprintf("知识库中共有 %d 个风险类型：\n\n", len(categories)))
for i, category := range categories {
resultText.WriteString(fmt.Sprintf("%d. %s\n", i+1, category))
}
resultText.WriteString("\n提示：在调用 " + builtin.ToolSearchKnowledgeBase + " 工具时，可以使用上述风险类型之一作为 risk_type 参数，以缩小搜索范围并提高检索效率。")
return &mcp.ToolResult{
Content: []mcp.Content{
{
Type: "text",
Text: resultText.String(),
},
},
}, nil
}
mcpServer.RegisterTool(listRiskTypesTool, listRiskTypesHandler)
logger.Info("风险类型列表工具已注册", zap.String("toolName", listRiskTypesTool.Name))
// 注册第二个工具：搜索知识库（保持原有功能）
searchTool := mcp.Tool{
Name: builtin.ToolSearchKnowledgeBase,
Description: "在知识库中搜索相关的安全知识。当你需要了解特定漏洞类型、攻击技术、检测方法等安全知识时，可以使用此工具进行检索。工具基于向量嵌入与余弦相似度检索（与 Eino retriever 语义一致）。建议：在搜索前可以先调用 " + builtin.ToolListKnowledgeRiskTypes + " 工具获取可用的风险类型，然后使用正确的 risk_type 参数进行精确搜索，这样可以大幅减少检索时间。",
ShortDescription: "搜索知识库中的安全知识（向量语义检索）",
InputSchema: map[string]interface{}{
"type": "object",
"properties": map[string]interface{}{
"query": map[string]interface{}{
"type": "string",
"description": "搜索查询内容，描述你想要了解的安全知识主题",
},
"risk_type": map[string]interface{}{
"type": "string",
"description": "可选：指定风险类型（如：SQL注入、XSS、文件上传等）。建议先调用 " + builtin.ToolListKnowledgeRiskTypes + " 工具获取可用的风险类型列表，然后使用正确的风险类型进行精确搜索，这样可以大幅减少检索时间。如果不指定则搜索所有类型。",
},
},
"required": []string{"query"},
},
}
searchHandler := func(ctx context.Context, args map[string]interface{}) (*mcp.ToolResult, error) {
query, ok := args["query"].(string)
if !ok || query == "" {
return &mcp.ToolResult{
Content: []mcp.Content{
{
Type: "text",
Text: "错误: 查询参数不能为空",
},
},
IsError: true,
}, nil
}
riskType := ""
if rt, ok := args["risk_type"].(string); ok && rt != "" {
riskType = rt
}
logger.Info("执行知识库检索",
zap.String("query", query),
zap.String("riskType", riskType),
)
// 检索统一走 Retriever.Search → VectorEinoRetriever（Eino retriever 语义）。
searchReq := &SearchRequest{
Query: query,
RiskType: riskType,
TopK: 5,
}
results, err := retriever.Search(ctx, searchReq)
if err != nil {
logger.Error("知识库检索失败", zap.Error(err))
return &mcp.ToolResult{
Content: []mcp.Content{
{
Type: "text",
Text: fmt.Sprintf("检索失败: %v", err),
},
},
IsError: true,
}, nil
}
if len(results) == 0 {
return &mcp.ToolResult{
Content: []mcp.Content{
{
Type: "text",
Text: fmt.Sprintf("未找到与查询 '%s' 相关的知识。建议：\n1. 尝试使用不同的关键词\n2. 检查风险类型是否正确\n3. 确认知识库中是否包含相关内容", query),
},
},
}, nil
}
// 格式化结果
var resultText strings.Builder
// 按余弦相似度（Score）降序
sort.Slice(results, func(i, j int) bool {
return results[i].Score > results[j].Score
})
// 按文档分组结果，以便更好地展示上下文
type itemGroup struct {
itemID string
results []*RetrievalResult
maxScore float64 // 该文档块的最高相似度
}
itemGroups := make([]*itemGroup, 0)
itemMap := make(map[string]*itemGroup)
for _, result := range results {
itemID := result.Item.ID
group, exists := itemMap[itemID]
if !exists {
group = &itemGroup{
itemID: itemID,
results: make([]*RetrievalResult, 0),
maxScore: result.Score,
}
itemMap[itemID] = group
itemGroups = append(itemGroups, group)
}
group.results = append(group.results, result)
if result.Score > group.maxScore {
group.maxScore = result.Score
}
}
// 按文档内最高相似度排序
sort.Slice(itemGroups, func(i, j int) bool {
return itemGroups[i].maxScore > itemGroups[j].maxScore
})
// 收集检索到的知识项ID（用于日志）
retrievedItemIDs := make([]string, 0, len(itemGroups))
resultText.WriteString(fmt.Sprintf("找到 %d 条相关知识片段：\n\n", len(results)))
resultIndex := 1
for _, group := range itemGroups {
itemResults := group.results
mainResult := itemResults[0]
maxScore := mainResult.Score
for _, result := range itemResults {
if result.Score > maxScore {
maxScore = result.Score
mainResult = result
}
}
// 按chunk_index排序，保证阅读的逻辑顺序（文档的原始顺序）
sort.Slice(itemResults, func(i, j int) bool {
return itemResults[i].Chunk.ChunkIndex < itemResults[j].Chunk.ChunkIndex
})
resultText.WriteString(fmt.Sprintf("--- 结果 %d (相似度: %.2f%%) ---\n",
resultIndex, mainResult.Similarity*100))
resultText.WriteString(fmt.Sprintf("来源: [%s] %s (ID: %s)\n", mainResult.Item.Category, mainResult.Item.Title, mainResult.Item.ID))
// 按逻辑顺序显示所有chunk（包括主结果和扩展的chunk）
if len(itemResults) == 1 {
// 只有一个chunk，直接显示
resultText.WriteString(fmt.Sprintf("内容片段:\n%s\n", mainResult.Chunk.ChunkText))
} else {
// 多个chunk，按逻辑顺序显示
resultText.WriteString("内容片段（按文档顺序）:\n")
for i, result := range itemResults {
// 标记主结果
marker := ""
if result.Chunk.ID == mainResult.Chunk.ID {
marker = " [主匹配]"
}
resultText.WriteString(fmt.Sprintf(" [片段 %d%s]\n%s\n", i+1, marker, result.Chunk.ChunkText))
}
}
resultText.WriteString("\n")
if !contains(retrievedItemIDs, group.itemID) {
retrievedItemIDs = append(retrievedItemIDs, group.itemID)
}
resultIndex++
}
// 在结果末尾添加元数据（JSON格式，用于提取知识项ID）
// 使用特殊标记，避免影响AI阅读结果
if len(retrievedItemIDs) > 0 {
metadataJSON, _ := json.Marshal(map[string]interface{}{
"_metadata": map[string]interface{}{
"retrievedItemIDs": retrievedItemIDs,
},
})
resultText.WriteString(fmt.Sprintf("\n<!-- METADATA: %s -->", string(metadataJSON)))
}
// 记录检索日志（异步，不阻塞）
// 注意：这里没有conversationID和messageID，需要在Agent层面记录
// 实际的日志记录应该在Agent的progressCallback中完成
return &mcp.ToolResult{
Content: []mcp.Content{
{
Type: "text",
Text: resultText.String(),
},
},
}, nil
}
mcpServer.RegisterTool(searchTool, searchHandler)
logger.Info("知识检索工具已注册", zap.String("toolName", searchTool.Name))
}
// contains 检查切片是否包含元素
func contains(slice []string, item string) bool {
for _, s := range slice {
if s == item {
return true
}
}
return false
}
// GetRetrievalMetadata 从工具调用中提取检索元数据（用于日志记录）
func GetRetrievalMetadata(args map[string]interface{}) (query string, riskType string) {
if q, ok := args["query"].(string); ok {
query = q
}
if rt, ok := args["risk_type"].(string); ok {
riskType = rt
}
return
}
// FormatRetrievalResults 格式化检索结果为字符串（用于日志）
func FormatRetrievalResults(results []*RetrievalResult) string {
if len(results) == 0 {
return "未找到相关结果"
}
var builder strings.Builder
builder.WriteString(fmt.Sprintf("检索到 %d 条结果:\n", len(results)))
itemIDs := make(map[string]bool)
for i, result := range results {
builder.WriteString(fmt.Sprintf("%d. [%s] %s (相似度: %.2f%%)\n",
i+1, result.Item.Category, result.Item.Title, result.Similarity*100))
itemIDs[result.Item.ID] = true
}
// 返回知识项ID列表（JSON格式）
ids := make([]string, 0, len(itemIDs))
for id := range itemIDs {
ids = append(ids, id)
}
idsJSON, _ := json.Marshal(ids)
builder.WriteString(fmt.Sprintf("\n检索到的知识项ID: %s", string(idsJSON)))
return builder.String()
}
knowledge/types.go
-123
View File
@@ -1,123 +0,0 @@
package knowledge
import (
"encoding/json"
"time"
)
// formatTime 格式化时间为 RFC3339 格式，零时间返回空字符串
func formatTime(t time.Time) string {
if t.IsZero() {
return ""
}
return t.Format(time.RFC3339)
}
// KnowledgeItem 知识库项
type KnowledgeItem struct {
ID string `json:"id"`
Category string `json:"category"` // 风险类型（文件夹名）
Title string `json:"title"` // 标题（文件名）
FilePath string `json:"filePath"` // 文件路径
Content string `json:"content"` // 文件内容
CreatedAt time.Time `json:"createdAt"`
UpdatedAt time.Time `json:"updatedAt"`
}
// KnowledgeItemSummary 知识库项摘要（用于列表，不包含完整内容）
type KnowledgeItemSummary struct {
ID string `json:"id"`
Category string `json:"category"`
Title string `json:"title"`
FilePath string `json:"filePath"`
Content string `json:"content,omitempty"` // 可选：内容预览（如果提供，通常只包含前 150 字符）
CreatedAt time.Time `json:"createdAt"`
UpdatedAt time.Time `json:"updatedAt"`
}
// MarshalJSON 自定义 JSON 序列化，确保时间格式正确
func (k *KnowledgeItemSummary) MarshalJSON() ([]byte, error) {
type Alias KnowledgeItemSummary
aux := &struct {
*Alias
CreatedAt string `json:"createdAt"`
UpdatedAt string `json:"updatedAt"`
}{
Alias: (*Alias)(k),
}
aux.CreatedAt = formatTime(k.CreatedAt)
aux.UpdatedAt = formatTime(k.UpdatedAt)
return json.Marshal(aux)
}
// MarshalJSON 自定义 JSON 序列化，确保时间格式正确
func (k *KnowledgeItem) MarshalJSON() ([]byte, error) {
type Alias KnowledgeItem
aux := &struct {
*Alias
CreatedAt string `json:"createdAt"`
UpdatedAt string `json:"updatedAt"`
}{
Alias: (*Alias)(k),
}
aux.CreatedAt = formatTime(k.CreatedAt)
aux.UpdatedAt = formatTime(k.UpdatedAt)
return json.Marshal(aux)
}
// KnowledgeChunk 知识块（用于向量化）
type KnowledgeChunk struct {
ID string `json:"id"`
ItemID string `json:"itemId"`
ChunkIndex int `json:"chunkIndex"`
ChunkText string `json:"chunkText"`
Embedding []float32 `json:"-"` // 向量嵌入，不序列化到 JSON
CreatedAt time.Time `json:"createdAt"`
}
// RetrievalResult 检索结果
type RetrievalResult struct {
Chunk *KnowledgeChunk `json:"chunk"`
Item *KnowledgeItem `json:"item"`
Similarity float64 `json:"similarity"` // 相似度分数
Score float64 `json:"score"` // 与 Similarity 相同：余弦相似度
}
// RetrievalLog 检索日志
type RetrievalLog struct {
ID string `json:"id"`
ConversationID string `json:"conversationId,omitempty"`
MessageID string `json:"messageId,omitempty"`
Query string `json:"query"`
RiskType string `json:"riskType,omitempty"`
RetrievedItems []string `json:"retrievedItems"` // 检索到的知识项 ID 列表
CreatedAt time.Time `json:"createdAt"`
}
// MarshalJSON 自定义 JSON 序列化，确保时间格式正确
func (r *RetrievalLog) MarshalJSON() ([]byte, error) {
type Alias RetrievalLog
return json.Marshal(&struct {
*Alias
CreatedAt string `json:"createdAt"`
}{
Alias: (*Alias)(r),
CreatedAt: formatTime(r.CreatedAt),
})
}
// CategoryWithItems 分类及其下的知识项（用于按分类分页）
type CategoryWithItems struct {
Category string `json:"category"` // 分类名称
ItemCount int `json:"itemCount"` // 该分类下的知识项总数
Items []*KnowledgeItemSummary `json:"items"` // 该分类下的知识项列表
}
// SearchRequest 搜索请求
type SearchRequest struct {
Query string `json:"query"`
RiskType string `json:"riskType,omitempty"` // 可选：指定风险类型
SubIndexFilter string `json:"subIndexFilter,omitempty"` // 可选：仅保留 sub_indexes 含该标签的行（含未打标旧数据）
TopK int `json:"topK,omitempty"` // 返回 Top-K 结果，默认 5
Threshold float64 `json:"threshold,omitempty"` // 相似度阈值，默认 0.7
}