Files
CyberStrikeAI/internal/knowledge/retrieval_postprocess.go
T
Sycun 35508eb614 feat(pi): add the Pi agent runtime with native workflow and retrieval engines
Introduce an isolated Node worker that hosts the Pi SDK while Go keeps
business state, permissions, MCP execution, evidence and delivery checks.
The worker is published per source fingerprint, streams events back to Go
and supports tool callbacks, cancellation, session resume and per-model
transport settings.

Agent dispatch, tool search, plan/task tools and migration controls are
wired on top of it: large tool catalogs load through Pi tool_search, the
conversation task board keeps its existing tools, and legacy mode/route
values still resolve. Workflow gains a native DAG with versioned JSON
checkpoints, and knowledge retrieval moves to native documents, splitter,
HTTP embeddings, vector search and reranking.
2026-11-12 17:55:33 +08:00

216 lines
4.9 KiB
Go

package knowledge
import (
"context"
"crypto/sha256"
"encoding/hex"
"fmt"
"strings"
"sync"
"unicode"
"unicode/utf8"
"cyberstrike-ai/internal/config"
"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 精排(HTTP dashscope / Cohere 兼容 API),由 [WireRetrieverPipeline] 注入。
type DocumentReranker interface {
Rerank(ctx context.Context, query string, docs []*Document) ([]*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 []*Document) ([]*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 *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 []*Document) []*Document {
if len(docs) < 2 {
return docs
}
seen := make(map[string]struct{}, len(docs))
out := make([]*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 []*Document, maxRunes, maxTokens int, tokenModel string) ([]*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([]*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 计算每条 MultiQuery 变体在向量阶段的候选条数(供融合 / 重排 / 后处理)。
func EffectivePrefetchTopK(topK int, po *config.PostRetrieveConfig) int {
if topK < 1 {
topK = 5
}
fetch := topK * 4
if fetch < 20 {
fetch = 20
}
if po != nil && po.PrefetchTopK > 0 {
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(精排已在流水线中完成)。
func ApplyPostRetrieve(docs []*Document, po *config.PostRetrieveConfig, tokenModel string, finalTopK int) ([]*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
}