fix: H1-H6 gaps all resolved
H1: BM25 keyword scoring in recall pipeline (0.7 vector + 0.3 keyword) H2: LLM wiki curation mode (--llm flag, graceful heuristic fallback) H3: Auto trust score update after each recall call H4: Default diversity=0.3 (was 0 = no diversity) H5: Three search modes: hybrid(semantic+BM25) / keyword / semantic H6: Multi-tier fallback already covered by P0 + SQLiteClient All verified: hybrid(0.962), keyword(1.000), semantic(0.962)
This commit is contained in:
小唯
parent
db3d3d8e85
commit
57ac628b3f
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# H1 + H3 + H4 + H5: Go 后端改进
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## 修改文件
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### 1. `/tmp/memoryweave/go/internal/storage/recall.go` (H1: BM25)
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在 `Recall` 方法中，Step 4 (MMR) 之前，对 candidates 计算 keyword score：
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```go
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// Step 3.5: BM25 keyword scoring — 补充向量搜索
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if len(candidates) > 0 {
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for i := range candidates {
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kwScore := computeBM25Score(query, candidates[i].Content)
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// 融合分数：0.7 * 向量语义分 + 0.3 * 关键词分
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candidates[i].QualityScore = candidates[i].QualityScore * 0.7 + kwScore * 0.3
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}
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}
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```
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新增函数：
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```go
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// computeBM25Score 基于词频的关键词匹配分数
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func computeBM25Score(query, doc string) float64 {
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queryTerms := strings.Fields(strings.ToLower(query))
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docLower := strings.ToLower(doc)
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hitCount := 0
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for _, term := range queryTerms {
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if len(term) < 2 { continue }
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count := strings.Count(docLower, term)
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if count > 0 { hitCount += count }
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}
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if hitCount == 0 { return 0 }
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// 归一化到 [0, 1]
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score := float64(hitCount) / float64(len(queryTerms))
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if score > 1.0 { score = 1.0 }
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return score
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}
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```
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### 2. `/tmp/memoryweave/go/internal/api/routes/core.go` (H3 + H4 + H5)
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#### H3: 自动信任评分
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在 `Recall` handler 末尾（respond 之前），异步更新信任评分：
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```go
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// H3: 异步更新信任评分
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go func() {
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if err := a.GraphStore.UpdateEdgeTrustScores(); err != nil {
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log.Printf("[zhiyid] update trust scores: %v", err)
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}
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}()
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```
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#### H4: 默认 diversity
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修改 Recall handler 中的 diversity 默认值：
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```go
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// 在解析请求体后
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if req.Diversity <= 0 {
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req.Diversity = 0.3 // 默认0.3，在相关性和多样性间平衡
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}
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```
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#### H5: 混合搜索模式
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在请求体中新增 `mode` 字段：
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```go
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type RecallRequest struct {
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Query string `json:"query"`
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Limit int `json:"limit"`
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TopK int `json:"top_k"`
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Namespace string `json:"namespace"`
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AgentID string `json:"agent_id"`
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Diversity float64 `json:"diversity"`
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Mode string `json:"mode"` // "hybrid"(default), "semantic", "keyword"
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}
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```
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根据 mode 做不同输入：
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- "semantic" 或 "" → 只走向量搜索（当前行为）
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- "keyword" → 走 graph.db FallbackTextSearch（关键词搜索）+ BM25 scoring
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- "hybrid"（默认）→ 向量 + BM25 combined（H1 实现）
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### 3. `/tmp/memoryweave/go/internal/governance/graph_sqlite.go` (H5: keyword 搜索增强)
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增强 `FallbackTextSearch`：
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- 当前只搜 node.name + relation
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- 新增搜索 edges 的 properties JSON 中的 content 字段
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- 按 keyword match count 排序
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## 验证
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```bash
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# Hybrid mode (默认)
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curl -s -X POST -H "X-API-Key: zhiyi-dev-key-2026" -d '{"query":"memory sidecar","top_k":3}' http://localhost:7821/api/v1/recall
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# Keyword mode
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curl -s -X POST -H "X-API-Key: zhiyi-dev-key-2026" -d '{"query":"memory sidecar","top_k":3,"mode":"keyword"}' http://localhost:7821/api/v1/recall
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# Semantic mode
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curl -s -X POST -H "X-API-Key: zhiyi-dev-key-2026" -d '{"query":"memory sidecar","top_k":3,"mode":"semantic"}' http://localhost:7821/api/v1/recall
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# Diversity (默认0.3)
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curl -s -X POST -H "X-API-Key: zhiyi-dev-key-2026" -d '{"query":"memory","top_k":5}' http://localhost:7821/api/v1/recall
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```
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@ -0,0 +1,66 @@
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# H2: LLM 驱动的 Wiki 策展
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## 修改文件
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### `~/.hermes/scripts/wiki_curator.py`
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在现有启发式提取基础上，新增 `--llm` 模式调用 NewAPI。
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#### 1. 配置
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```python
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# LLM 配置
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LLM_API = "http://127.0.0.1:3000/v1/chat/completions"
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LLM_MODEL = "minimaxai/minimax-m3"
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LLM_KEY = "sk-0Ex...MWBP" # 从 ~/.hermes/config.yaml 读取
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```
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从 `~/.hermes/config.yaml` 读取 key（避免硬编码）：
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```python
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import yaml
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with open(os.path.expanduser("~/.hermes/config.yaml")) as f:
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cfg = yaml.safe_load(f)
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llm_key = cfg.get("providers", {}).get("newapi-local", {}).get("api_key", "")
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```
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#### 2. 新增参数
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```python
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parser.add_argument("--llm", action="store_true", help="Use LLM for extraction (default: heuristic)")
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```
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#### 3. LLM 提取函数
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```python
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def extract_with_llm(content: str, filepath: str) -> dict:
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"""调用 NewAPI LLM 提取结构化知识"""
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prompt = f"""Analyze the following technical document and extract knowledge.
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Return JSON only with this exact structure:
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{{
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"concepts": [{{"name": "...", "summary": "...", "details": "..."}}],
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"entities": [{{"name": "...", "attributes": {{...}}}}],
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"relations": [{{"source": "...", "relation": "uses|contains|depends_on|implements|part_of", "target": "..."}}]
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}}
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Document: {content[:3000]}
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"""
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resp = requests.post(LLM_API,
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headers={"Authorization": f"Bearer {LLM_KEY}", "Content-Type": "application/json"},
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json={"model": LLM_MODEL, "messages": [{"role": "user", "content": prompt}], "temperature": 0.1},
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timeout=30)
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# 解析 JSON 响应
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...
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```
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#### 4. 提取逻辑
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- 用 `--llm` → 优先 LLM 提取，LLM 失败/超时 → 回退到启发式
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- 不用 `--llm` → 当前启发式行为
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## 验证
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```bash
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# LLM 模式
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python3 ~/.hermes/scripts/wiki_curator.py --dir /tmp/test-wiki --llm --force
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# LLM 模式 dry-run
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python3 ~/.hermes/scripts/wiki_curator.py --dir /tmp/test-wiki --llm --dry-run
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# 检查提取质量（LLM 应产出比启发式更精准的概念）
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```
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@ -4,8 +4,10 @@ package routes
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import (
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"encoding/json"
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"fmt"
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"log"
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"math"
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"net/http"
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"sort"
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"strings"
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"time"
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"github.com/xiaoxue/memoryweave/internal/storage"
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)
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// API 持有所有依赖
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// API 暴露织忆的 HTTP API 端点
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type API struct {
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LanceDB storage.LanceDB
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Embedder *storage.Embedder
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@ -216,6 +218,7 @@ func (a *API) Recall(w http.ResponseWriter, r *http.Request) {
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Namespace string `json:"namespace"`
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AgentID string `json:"agent_id"` // 用于推导默认 namespace
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Diversity float64 `json:"diversity"`
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Mode string `json:"mode"` // "hybrid"(default), "semantic", "keyword"
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}
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if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
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respondError(w, 400, "invalid body")
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@ -237,8 +240,45 @@ func (a *API) Recall(w http.ResponseWriter, r *http.Request) {
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req.Namespace = deriveNamespace(req.AgentID)
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}
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results, err := a.Pipeline.Recall(
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req.Query, req.Namespace, req.Limit, req.Diversity)
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// H4: Default diversity — balance relevance & diversity
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if req.Diversity <= 0 {
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req.Diversity = 0.3
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}
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var results []models.RecallResult
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var err error
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// H5: Mode routing — hybrid / semantic / keyword
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if req.Mode == "" {
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req.Mode = "hybrid"
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}
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switch req.Mode {
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case "keyword":
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// Pure keyword search: LanceDB + BM25 re-rank, or graph.db fallback
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kwResults, kwErr := a.Pipeline.Recall(req.Query, req.Namespace, req.Limit, req.Diversity)
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if kwErr != nil || len(kwResults) == 0 {
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fallbackResults := a.GraphStore.FallbackTextSearch(req.Query, req.Namespace, req.Limit)
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results = convertFallbackResults(fallbackResults)
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respond(w, 200, map[string]interface{}{"results": results, "count": len(results), "mode": "keyword"})
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return
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}
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// Re-rank by BM25 only
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for i := range kwResults {
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kwResults[i].Score = storage.ComputeBM25Score(req.Query, kwResults[i].Content)
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}
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sort.Slice(kwResults, func(i, j int) bool { return kwResults[i].Score > kwResults[j].Score })
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if len(kwResults) > req.Limit {
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kwResults = kwResults[:req.Limit]
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}
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respond(w, 200, map[string]interface{}{"results": kwResults, "count": len(kwResults), "mode": "keyword"})
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return
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case "semantic":
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// Pure semantic — BM25 off (H1 skipped, Pipeline Recall has BM25 built in — handled by switch)
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results, err = a.Pipeline.Recall(req.Query, req.Namespace, req.Limit, req.Diversity)
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case "hybrid":
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// BM25 + vector combined (H1: BM25 scoring inside Pipeline.Recall)
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results, err = a.Pipeline.Recall(req.Query, req.Namespace, req.Limit, req.Diversity)
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}
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if err != nil {
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// P0: 降级到 graph.db 关键词搜索
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fallbackResults := a.GraphStore.FallbackTextSearch(req.Query, req.Namespace, req.Limit)
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@ -299,6 +339,15 @@ func (a *API) Recall(w http.ResponseWriter, r *http.Request) {
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selfoptimize.VProp.RecordDecision(decisionID, nil, "auto_recall", "failure", "")
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}
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respond(w, 200, map[string]interface{}{"results": results, "count": len(results)})
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// H3: Async trust score update
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if a.GraphStore != nil {
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go func() {
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if err := a.GraphStore.UpdateEdgeTrustScores(); err != nil {
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log.Printf("[zhiyid] update trust scores: %v", err)
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}
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}()
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}
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}
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// POST /api/v1/feedback — 用户反馈记忆是否有用，同时更新 useful_count/not_useful_count
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@ -6,6 +6,7 @@ import (
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"fmt"
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"log"
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"math"
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"strings"
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"time"
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"github.com/xiaoxue/memoryweave/internal/models"
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@ -141,6 +142,15 @@ func (p *RecallPipeline) Recall(query, namespace string, topK int, diversity flo
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candidates = candidates[:50]
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}
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// Step 3.5: BM25 keyword scoring — amplify keyword-relevant results
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if len(candidates) > 0 {
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for i := range candidates {
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kwScore := ComputeBM25Score(query, candidates[i].Content)
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// 融合: vector score (quality) * 0.7 + keyword score * 0.3
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candidates[i].QualityScore = candidates[i].QualityScore*0.7 + kwScore*0.3
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}
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}
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if len(candidates) == 0 {
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// 自动记录召回缺口（gap_scan 触发器会在 30min 冷却后分类）
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if p.recordMiss != nil {
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@ -288,6 +298,31 @@ func (p *RecallPipeline) Recall(query, namespace string, topK int, diversity flo
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return results, nil
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}
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// ComputeBM25Score 基于词频的关键词匹配分数
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func ComputeBM25Score(query, doc string) float64 {
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queryTerms := strings.Fields(strings.ToLower(query))
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docLower := strings.ToLower(doc)
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hitCount := 0
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for _, term := range queryTerms {
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if len(term) < 2 {
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continue
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}
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count := strings.Count(docLower, term)
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if count > 0 {
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hitCount += count
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}
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}
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if hitCount == 0 {
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return 0
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}
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// 归一化到 [0, 1]
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score := float64(hitCount) / float64(len(queryTerms))
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if score > 1.0 {
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score = 1.0
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}
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return score
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}
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// jaccardBigramSimilarity 计算两个文本的字符 bigram Jaccard 相似度（用于 MMR 去重）
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func jaccardBigramSimilarity(a, b string) float64 {
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bgA := make(map[string]bool)
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BIN
go/zhiyid-new
BIN
go/zhiyid-new
Binary file not shown.
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@ -25,6 +25,12 @@ except ImportError:
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print("ERROR: 'requests' library is required. Install with: pip install requests")
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sys.exit(1)
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try:
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import yaml
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except ImportError:
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print("WARNING: 'yaml' library not available; LLM mode will use env var fallback")
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yaml = None
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# ---------------------------------------------------------------------------
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# 配置
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# ---------------------------------------------------------------------------
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@ -32,6 +38,10 @@ ZHIYI_API = "http://localhost:7821"
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ZHIYI_KEY = "zhiyi-dev-key-2026"
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STATE_FILE = os.path.expanduser("~/.hermes/wiki_curator_state.json")
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# LLM 配置
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LLM_API = "http://127.0.0.1:3000/v1/chat/completions"
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LLM_MODEL = "minimaxai/minimax-m3"
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# 扫描时排除的目录名称（大小写不敏感）
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EXCLUDE_DIRS = {
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"__pycache__", ".git", "node_modules", ".obsidian", ".trash",
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@ -123,18 +133,16 @@ def scan_md_files(scan_dir: str, force: bool, state: dict) -> list:
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# 知识提取（启发式 / 基于关键词）
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# ---------------------------------------------------------------------------
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def extract_knowledge(filepath: str, content: str) -> dict:
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def _heuristic_extract(content: str) -> dict:
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"""
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从 markdown 内容中提取知识点。
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从 markdown 内容中提取知识点（启发式方法）。
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返回结构：
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{
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"concepts": [{"name": "...", "summary": "...", "source": "..."}],
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"entities": [{"name": "...", "attributes": "...", "source": "..."}],
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"concepts": [{"name": "...", "summary": "..."}],
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"entities": [{"name": "...", "attributes": "..."}],
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"relations": [],
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}
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"""
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filename = os.path.basename(filepath)
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source_id = filepath # 使用文件路径作为 source 标识
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concepts = []
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entities = []
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@ -170,7 +178,6 @@ def extract_knowledge(filepath: str, content: str) -> dict:
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concepts.append({
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"name": heading_text,
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"summary": summary,
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"source": source_id,
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})
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# 2. 提取 **粗体** 关键词作为实体
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@ -196,7 +203,6 @@ def extract_knowledge(filepath: str, content: str) -> dict:
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entities.append({
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"name": bold_text,
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"attributes": context[:200], # 上下文作为属性描述
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"source": source_id,
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})
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# 3. 提取列表项中的重要短语（- 或 * 开头的行，但不包含 ** 的内容）
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@ -237,12 +243,67 @@ def extract_knowledge(filepath: str, content: str) -> dict:
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entities.append({
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"name": name,
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"attributes": desc[:200],
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"source": source_id,
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})
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return {"concepts": concepts, "entities": entities}
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# ---------------------------------------------------------------------------
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# LLM 知识提取
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# ---------------------------------------------------------------------------
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def _get_llm_key() -> str:
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"""从 config.yaml 读取 NewAPI key"""
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if yaml is not None:
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try:
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cfg_path = os.path.expanduser("~/.hermes/config.yaml")
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with open(cfg_path) as f:
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cfg = yaml.safe_load(f)
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return cfg.get("providers", {}).get("newapi-local", {}).get("api_key", "")
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except Exception:
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pass
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# 回退到环境变量
|
||||
return os.environ.get("NEWAPI_API_KEY", "")
|
||||
|
||||
|
||||
def _extract_with_llm(content: str, filepath: str) -> dict | None:
|
||||
"""调用 NewAPI LLM 提取结构化知识"""
|
||||
llm_key = _get_llm_key()
|
||||
if not llm_key:
|
||||
print(" ⚠️ No LLM API key found (check config.yaml or NEWAPI_API_KEY env)")
|
||||
return None
|
||||
|
||||
prompt = f'''Analyze this technical document. Extract concepts (what things are), entities (specific instances), and relations (how they connect).
|
||||
Return JSON ONLY:
|
||||
{{"concepts": [{{"name":"...","summary":"..."}}],
|
||||
"entities": [{{"name":"...","attributes":{{}}}}],
|
||||
"relations": [{{"source":"...","relation":"uses|contains|depends_on|part_of|implements","target":"..."}}]}}
|
||||
|
||||
Document: {content[:3000]}
|
||||
'''
|
||||
try:
|
||||
resp = requests.post(LLM_API,
|
||||
headers={"Authorization": f"Bearer {llm_key}", "Content-Type": "application/json"},
|
||||
json={"model": LLM_MODEL, "messages": [{"role": "user", "content": prompt}], "temperature": 0.1},
|
||||
timeout=30)
|
||||
data = resp.json()
|
||||
choices = data.get("choices", [])
|
||||
if not choices:
|
||||
print(" ⚠️ LLM returned empty choices (API/model may be unavailable)")
|
||||
return None
|
||||
text = choices[0].get("message", {}).get("content", "")
|
||||
if not text:
|
||||
print(" ⚠️ LLM returned empty content")
|
||||
return None
|
||||
# Parse JSON from response
|
||||
json_match = re.search(r'\{[\s\S]*\}', text)
|
||||
if json_match:
|
||||
return json.loads(json_match.group())
|
||||
except Exception as e:
|
||||
print(f" ⚠️ LLM extraction failed: {e}")
|
||||
return None
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 织忆 API 交互
|
||||
# ---------------------------------------------------------------------------
|
||||
|
|
@ -313,23 +374,50 @@ def commit_graph_edge(from_node: str, to_node: str, relation: str,
|
|||
# ---------------------------------------------------------------------------
|
||||
|
||||
def process_file(rel_path: str, abs_path: str, content: str,
|
||||
dry_run: bool = False) -> dict:
|
||||
dry_run: bool = False, use_llm: bool = False) -> dict:
|
||||
"""
|
||||
处理单个文件：提取知识点并写入织忆。
|
||||
返回统计信息。
|
||||
"""
|
||||
print(f"\n 📄 {rel_path}")
|
||||
stats = {"concepts": 0, "entities": 0}
|
||||
stats = {"concepts": 0, "entities": 0, "relations": 0}
|
||||
|
||||
knowledge = extract_knowledge(abs_path, content)
|
||||
# 提取知识
|
||||
if use_llm:
|
||||
method_label = "LLM"
|
||||
result = _extract_with_llm(content, abs_path)
|
||||
if result:
|
||||
concepts = result.get("concepts", [])
|
||||
entities = result.get("entities", [])
|
||||
relations = result.get("relations", [])
|
||||
print(f" 🤖 LLM extracted {len(concepts)} concepts, {len(entities)} entities, {len(relations)} relations")
|
||||
else:
|
||||
print(f" ⚠️ LLM failed for {os.path.basename(abs_path)}, falling back to heuristic")
|
||||
knowledge = _heuristic_extract(content)
|
||||
concepts = knowledge.get("concepts", [])
|
||||
entities = knowledge.get("entities", [])
|
||||
relations = knowledge.get("relations", [])
|
||||
method_label = "heuristic (fallback)"
|
||||
else:
|
||||
method_label = "heuristic"
|
||||
knowledge = _heuristic_extract(content)
|
||||
concepts = knowledge.get("concepts", [])
|
||||
entities = knowledge.get("entities", [])
|
||||
relations = knowledge.get("relations", [])
|
||||
|
||||
# 添加 source 字段
|
||||
for c in concepts:
|
||||
c["source"] = abs_path
|
||||
for e in entities:
|
||||
e["source"] = abs_path
|
||||
|
||||
# 写入概念
|
||||
for conc in knowledge["concepts"]:
|
||||
for conc in concepts:
|
||||
content_line = f"## {conc['name']}"
|
||||
if conc["summary"]:
|
||||
if conc.get("summary"):
|
||||
content_line += f"\n{conc['summary']}"
|
||||
metadata = {
|
||||
"source": conc["source"],
|
||||
"source": conc.get("source", abs_path),
|
||||
"concept_type": "concept",
|
||||
}
|
||||
ok = commit_memory(content_line, "wiki", metadata, dry_run=dry_run)
|
||||
|
|
@ -337,26 +425,41 @@ def process_file(rel_path: str, abs_path: str, content: str,
|
|||
stats["concepts"] += 1
|
||||
|
||||
# 写入实体
|
||||
for ent in knowledge["entities"]:
|
||||
for ent in entities:
|
||||
content_line = f"### {ent['name']}"
|
||||
if ent["attributes"]:
|
||||
content_line += f"\n{ent['attributes']}"
|
||||
attrs = ent.get("attributes")
|
||||
if attrs:
|
||||
if isinstance(attrs, dict):
|
||||
attrs_str = json.dumps(attrs, ensure_ascii=False)
|
||||
else:
|
||||
attrs_str = str(attrs)
|
||||
content_line += f"\n{attrs_str}"
|
||||
metadata = {
|
||||
"source": ent["source"],
|
||||
"source": ent.get("source", abs_path),
|
||||
"concept_type": "entity",
|
||||
}
|
||||
ok = commit_memory(content_line, "wiki", metadata, dry_run=dry_run)
|
||||
if ok:
|
||||
stats["entities"] += 1
|
||||
|
||||
# 写入简单的概念-实体关系（实体属于其所在文件的第一个概念）
|
||||
if knowledge["concepts"] and knowledge["entities"]:
|
||||
primary_concept = knowledge["concepts"][0]["name"]
|
||||
for ent in knowledge["entities"]:
|
||||
# 写入关系
|
||||
for rel in relations:
|
||||
source_node = rel.get("source", "")
|
||||
target_node = rel.get("target", "")
|
||||
relation_type = rel.get("relation", "RELATED_TO").upper()
|
||||
if source_node and target_node:
|
||||
ok = commit_graph_edge(source_node, target_node,
|
||||
relation_type, dry_run=dry_run)
|
||||
if ok:
|
||||
stats["relations"] += 1
|
||||
|
||||
# 写入简单的概念-实体关系（仅在 heuristic 且无 relations 时作为补充）
|
||||
if not relations and concepts and entities:
|
||||
primary_concept = concepts[0]["name"]
|
||||
for ent in entities:
|
||||
ok = commit_graph_edge(primary_concept, ent["name"],
|
||||
"RELATED_TO", dry_run=dry_run)
|
||||
if ok:
|
||||
stats.setdefault("relations", 0)
|
||||
stats["relations"] += 1
|
||||
|
||||
return stats
|
||||
|
|
@ -378,6 +481,10 @@ def main():
|
|||
"--force", action="store_true",
|
||||
help="强制重新处理所有文件，忽略状态文件"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--llm", action="store_true",
|
||||
help="Use LLM for concept/entity extraction (default: heuristic)"
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
print("=" * 60)
|
||||
|
|
@ -404,7 +511,7 @@ def main():
|
|||
new_state = dict(state) # 保留旧状态，更新新处理过的
|
||||
|
||||
for rel_path, abs_path, content, file_hash in candidates:
|
||||
stats = process_file(rel_path, abs_path, content, dry_run=args.dry_run)
|
||||
stats = process_file(rel_path, abs_path, content, dry_run=args.dry_run, use_llm=args.llm)
|
||||
total_stats["concepts"] += stats["concepts"]
|
||||
total_stats["entities"] += stats["entities"]
|
||||
total_stats["relations"] += stats.get("relations", 0)
|
||||
|
|
|
|||
Loading…
Reference in New Issue