diff --git a/TAG_GRAPH_README.md b/TAG_GRAPH_README.md deleted file mode 100644 index e42f54b..0000000 --- a/TAG_GRAPH_README.md +++ /dev/null @@ -1,252 +0,0 @@ -# Tag Relationship Graph 功能说明 - -## 功能概述 - -Tag 关系图是基于聚类结果的 keywords 构建的交互式可视化系统，帮助用户更直观地探索和索引图像数据集。 - -## 核心特性 - -### 1. 节点类型 - -- **Tag 节点**（圆形） - - 大小：正比于权重（出现频率 × 图片数量） - - 颜色：根据类别区分 - - 蓝色：人物 (character) - - 绿色：风格 (style) - - 黄色：场景 (scene) - - 红色：物体 (object) - - 紫色：其他 (other) - - 社区着色：启用社区检测后，同社区的节点使用相同颜色 - -- **Cluster 节点**（圆形，cyan 色） - - 大小：正比于簇内图片数量 - - 标签：簇的标题 - -### 2. 边（连线） - -- **Tag-Tag 连线** - - 粗细：正比于共现权重（两个 tag 同时出现在簇中的图片数） - - 透明度：0.4（降低视觉干扰） - -- **Cluster-Tag 连线** - - 连接簇节点与其包含的 tag - - 便于查看簇的组成 - -### 3. 权重计算 - -使用混合权重模式（alpha=0.3）： - -```python -weight = 0.3 × 频次权重 + 0.7 × TF-IDF 权重 - -频次权重 = Σ(所在聚类的图片数) -TF-IDF 权重 = 频次权重 × log(总聚类数 / 包含该tag的聚类数) -``` - -**效果**： -- 突出特征性 tag（如 "Warhammer 40K"、"赛博朋克"） -- 压制通用 tag（如 "女孩"、"风景"） - -### 4. 社区检测 - -使用 Louvain 算法自动发现 tag 社区： -- 相关的 tag 自动聚成一组 -- 每个社区用不同颜色区分 -- 示例： - - 社区1：赛博朋克、机甲、霓虹、未来 - - 社区2：森林、精灵、魔法、奇幻 - - 社区3：女孩、制服、校园、青春 - -## 使用方法 - -### 1. 切换到 Tag Graph 视图 - -1. 在 Topic Search 页面 -2. 点击右上角的 Switch 切换按钮 -3. 选择 "Tag Graph" - -### 2. 参数调整 - -- **Top Tags**: 显示前 N 个权重最高的 tag（默认 50） -- **Top Clusters**: 显示前 N 个簇节点（默认 20） -- **Show Clusters**: 是否显示簇节点（勾选/取消） - -### 3. 交互操作 - -#### 基本交互 -- **拖拽**：拖动节点调整布局 -- **缩放**：鼠标滚轮缩放视图 -- **平移**：按住 Shift + 拖拽平移视图 - -#### 节点操作 -- **悬停**：显示详细信息（类别、图片数、聚类数、社区） -- **点击**：在右侧面板显示详细信息 -- **搜索**：点击 "Search Images" 按钮，自动搜索该 tag 的图片 - -#### 工具栏 -- **Refresh**: 重新加载图数据 -- **Fit View**: 自动调整视图以显示所有节点 - -### 4. 典型使用场景 - -#### 场景1：探索式浏览 - -``` -问题：不知道数据集里有什么主题 - -操作： -1. 打开 Tag Graph -2. 观察大节点（核心主题） -3. 点击感兴趣的 tag -4. 点击 "Search Images" 查看图片 -``` - -#### 场景2：精准索引 - -``` -需求：找所有包含"机甲"和"战斗"的图片 - -操作： -1. 在图中找到"机甲"节点 -2. 观察相连的节点 -3. 如果有"战斗"节点且相连，说明有共现 -4. 分别搜索两个 tag 的交集 -``` - -#### 场景3：主题发现 - -``` -发现：原来数据集有强烈的 Warhammer 40K 主题 - -可视化效果： -- "Warhammer 40K" 节点很大（高 TF-IDF） -- 周围连接 "星际战士"、"帝国"、"混沌" -- 形成明显的社区簇（同色区域） -``` - -## 技术实现 - -### 后端 API - -```python -POST /infinite_image_browsing/db/cluster_tag_graph - -Request: -{ - "folder_paths": [...], - "top_n_tags": 50, - "top_n_clusters": 20, - "show_clusters": true, - "detect_communities": true, - "weight_mode": "hybrid", - "alpha": 0.3 -} - -Response: -{ - "nodes": [ - { - "id": "tag_机甲", - "label": "机甲", - "weight": 629.5, - "image_count": 224, - "cluster_count": 3, - "category": "object", - "community": 0 - }, - ... - ], - "links": [ - { - "source": "tag_机甲", - "target": "tag_科幻", - "weight": 192, - "image_count": 192, - "cluster_count": 2 - }, - ... - ], - "communities": [ - { - "id": 0, - "nodes": ["tag_机甲", "tag_科幻", ...], - "size": 5 - }, - ... - ] -} -``` - -### 前端组件 - -- 基于 **ECharts Graph** 实现 -- 力导向布局（Force-Directed Layout） -- 支持 roam（缩放/平移/拖拽） - -### 依赖 - -**后端**（可选）： -```bash -pip install networkx -``` - -如果没有 networkx，社区检测功能会被禁用，但其他功能正常工作。 - -**前端**： -- echarts（已包含在项目中） - -## 性能优化 - -- **Top-N 截断**：只显示权重最高的节点，避免视觉混乱 -- **边权重过滤**：低权重的边不显示 -- **标签隐藏**：只有大节点显示标签（labelLayout.hideOverlap） -- **力学参数**：调整斥力/引力平衡，避免节点重叠 - -## 后续优化方向 - -1. **增量更新**：新增图片时只更新受影响的节点 -2. **多层级钻取**：点击 tag 展开为更细粒度的子 tag -3. **热力图叠加**：显示 tag 的热度分布 -4. **时序动画**：展示 tag 关系随时间的演变 -5. **导出功能**：导出为 PNG/SVG/JSON - -## 常见问题 - -### Q1: 为什么有些常见词（如"女孩"）节点不大？ - -A: 因为使用了 TF-IDF 权重，压制了过于常见的 tag。如果想突出频次，可以修改 alpha 参数（增大到 0.8-1.0）。 - -### Q2: 为什么有些 tag 之间没有连线？ - -A: 只有在同一个簇中同时出现的 tag 才会有连线。如果两个 tag 从未共现，则不会连接。 - -### Q3: 社区检测有什么用？ - -A: 自动发现主题群组，用颜色区分。例如"赛博朋克相关"、"奇幻相关"、"校园相关"会自动分组。 - -### Q4: 如何调整图的疏密度？ - -A: 修改 ECharts 的 force 参数： -- `repulsion`: 增大 = 节点更分散 -- `edgeLength`: 增大 = 边更长 -- `gravity`: 增大 = 更聚拢到中心 - -### Q5: 为什么点击 Search Images 没反应？ - -A: 需要先运行一次聚类（Refresh 按钮），确保有聚类结果。 - -## 贡献 - -欢迎提交 PR 改进此功能！ - -重点优化方向： -- 更好的布局算法 -- 更丰富的交互方式 -- 更精准的权重计算 -- 性能优化（处理 100+ tag） - ---- - -**开发者**: Claude Code -**版本**: 1.0.0 -**更新日期**: 2026-01-09 diff --git a/javascript/index.js b/javascript/index.js index 42342f4..cfaeb21 100644 --- a/javascript/index.js +++ b/javascript/index.js @@ -13,8 +13,8 @@ Promise.resolve().then(async () => { Infinite Image Browsing - - + + diff --git a/scripts/iib/api.py b/scripts/iib/api.py index fa725f4..4782456 100644 --- a/scripts/iib/api.py +++ b/scripts/iib/api.py @@ -1264,6 +1264,8 @@ def infinite_image_browsing_api(app: FastAPI, **kwargs): verify_secret=verify_secret, embedding_model=EMBEDDING_MODEL, ai_model=AI_MODEL, + openai_base_url=OPENAI_BASE_URL, + openai_api_key=OPENAI_API_KEY, ) diff --git a/scripts/iib/db/datamodel.py b/scripts/iib/db/datamodel.py index 7f5aacc..90bf3f1 100644 --- a/scripts/iib/db/datamodel.py +++ b/scripts/iib/db/datamodel.py @@ -66,6 +66,9 @@ class DataBase: # 创建连接并打开数据库 conn = connect(clz.get_db_file_path()) + # # 禁用 WAL 模式，使用传统的 DELETE 日志模式 + # conn.execute("PRAGMA journal_mode=DELETE") + def regexp(expr, item): if not isinstance(item, str): return False diff --git a/scripts/iib/tag_graph.py b/scripts/iib/tag_graph.py index 450404f..96aa470 100644 --- a/scripts/iib/tag_graph.py +++ b/scripts/iib/tag_graph.py @@ -1,64 +1,50 @@ """ -Tag relationship graph generation for topic clusters. -Builds a weighted graph showing connections between tags from cluster keywords. +Hierarchical tag graph generation for topic clusters. +Builds a multi-layer neural-network-style visualization with LLM-driven abstraction. """ import hashlib import json -import math -from typing import Dict, List, Optional, Set, Tuple +from typing import Dict, List, Optional from pydantic import BaseModel from fastapi import Depends, FastAPI, HTTPException -from scripts.iib.db.datamodel import DataBase, TopicClusterCache +from scripts.iib.db.datamodel import DataBase, GlobalSetting +from scripts.iib.tool import normalize_output_lang + +# Cache version for tag abstraction - increment to invalidate all caches +TAG_ABSTRACTION_CACHE_VERSION = 2 class TagGraphReq(BaseModel): folder_paths: List[str] - model: Optional[str] = None - threshold: Optional[float] = 0.90 - min_cluster_size: Optional[int] = 2 - lang: Optional[str] = None - # Graph parameters top_n_tags: Optional[int] = 50 top_n_clusters: Optional[int] = 20 - weight_mode: Optional[str] = "hybrid" # frequency / tfidf / hybrid - alpha: Optional[float] = 0.3 # for hybrid mode - show_clusters: Optional[bool] = True - detect_communities: Optional[bool] = True + lang: Optional[str] = "en" # Language for LLM output -class TagNode(BaseModel): +class LayerNode(BaseModel): id: str label: str - weight: float - image_count: int - cluster_count: int - category: str - community: Optional[int] = None + size: float # Weight/importance of this node + metadata: Optional[dict] = None -class ClusterNode(BaseModel): - id: str - label: str - weight: float - size: int - category: str = "cluster" - community: Optional[int] = None +class GraphLayer(BaseModel): + level: int + name: str # Layer name: "Clusters", "Tags", "Abstract-1", "Abstract-2" + nodes: List[LayerNode] class GraphLink(BaseModel): source: str target: str weight: float - image_count: Optional[int] = None - cluster_count: Optional[int] = None class TagGraphResp(BaseModel): - nodes: List[dict] # TagNode or ClusterNode + layers: List[GraphLayer] links: List[GraphLink] - communities: Optional[List[dict]] = None stats: dict @@ -68,115 +54,180 @@ def mount_tag_graph_routes( verify_secret, embedding_model: str, ai_model: str, + openai_base_url: str, + openai_api_key: str, ): - """Mount tag relationship graph endpoints""" + """Mount hierarchical tag graph endpoints""" - def _get_cluster_cache_key(req: TagGraphReq, model: str) -> str: - """Compute cache key for cluster result lookup""" - from scripts.iib.topic_cluster import _PROMPT_NORMALIZE_VERSION, _PROMPT_NORMALIZE_MODE + async def _call_llm_for_abstraction( + tags: List[str], + lang: str, + model: str, + base_url: str, + api_key: str + ) -> dict: + """ + Call LLM to create hierarchical abstraction of tags. + Returns a dict with layers and groupings. + """ + import asyncio + import requests + import re - cache_params = { - "model": model, - "threshold": float(req.threshold or 0.90), - "min_cluster_size": int(req.min_cluster_size or 2), - "assign_noise_threshold": None, - "title_model": None, - "lang": str(req.lang or ""), - "nv": _PROMPT_NORMALIZE_VERSION, - "nm": _PROMPT_NORMALIZE_MODE, - } - h = hashlib.sha1() - h.update( - json.dumps( - {"folders": sorted(req.folder_paths), "params": cache_params}, - ensure_ascii=False, - sort_keys=True, - ).encode("utf-8") - ) - return h.hexdigest() + def _normalize_base_url(url: str) -> str: + url = url.strip() + if not url.startswith(("http://", "https://")): + url = f"https://{url}" + return url.rstrip("/") - def _infer_tag_category(tag: str) -> str: - """Infer tag category from content""" - tag_lower = tag.lower() + def _call_sync(): + if not api_key: + raise HTTPException(500, "OpenAI API Key not configured") - # Character - char_keywords = [ - "girl", "boy", "woman", "man", "character", "person", - "女孩", "男孩", "女性", "男性", "人物", "角色", "战士", "精灵" - ] - if any(k in tag_lower for k in char_keywords): - return "character" + url = f"{_normalize_base_url(base_url)}/chat/completions" + headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"} - # Style - style_keywords = [ - "style", "punk", "sci-fi", "fantasy", "realistic", "anime", - "风格", "朋克", "科幻", "奇幻", "写实", "动漫", "赛博" - ] - if any(k in tag_lower for k in style_keywords): - return "style" + # Normalize language for consistent LLM output + normalized_lang = normalize_output_lang(lang) + sys_prompt = f"""You are a tag categorization assistant. Organize tags into hierarchical categories. - # Scene - scene_keywords = [ - "forest", "city", "mountain", "ocean", "indoor", "outdoor", "landscape", - "森林", "城市", "山", "海", "室内", "室外", "风景", "场景" - ] - if any(k in tag_lower for k in scene_keywords): - return "scene" +STRICT RULES: +1. Create 5-15 Level 1 categories (broad groupings) +2. Optionally create 2-5 Level 2 super-categories IF Level 1 has 8+ categories +3. Every tag must belong to exactly ONE Level 1 category +4. Use {normalized_lang} for all category labels +5. Category IDs must be simple lowercase (e.g., "style", "char", "scene1") - # Object - object_keywords = [ - "sword", "weapon", "machine", "building", "vehicle", - "机甲", "武器", "建筑", "车", "剑" - ] - if any(k in tag_lower for k in object_keywords): - return "object" +OUTPUT ONLY VALID JSON - NO markdown, NO explanations, NO extra text: +{{"layers":[{{"level":1,"groups":[{{"id":"cat1","label":"Label1","tags":["tag1"]}},{{"id":"cat2","label":"Label2","tags":["tag2"]}}]}},{{"level":2,"groups":[{{"id":"super1","label":"SuperLabel","categories":["cat1","cat2"]}}]}}]}} - return "other" +If unsure about Level 2, OMIT it entirely. Start response with {{ and end with }}""" - def _detect_communities(nodes: List[dict], links: List[GraphLink]) -> Optional[List[dict]]: - """Detect communities using Louvain algorithm""" - try: - import networkx as nx - from networkx.algorithms import community - except ImportError: - return None + user_prompt = f"Tags to categorize:\n{', '.join(tags)}" - # Build graph - G = nx.Graph() - for node in nodes: - G.add_node(node["id"], weight=node["weight"]) + payload = { + "model": model, + "messages": [ + {"role": "system", "content": sys_prompt}, + {"role": "user", "content": user_prompt} + ], + "temperature": 0.0, + "max_tokens": 2048, + } - for link in links: - if G.has_node(link.source) and G.has_node(link.target): - G.add_edge(link.source, link.target, weight=link.weight) + # Retry up to 5 times + last_error = "" + for attempt in range(1, 6): + try: + resp = requests.post(url, json=payload, headers=headers, timeout=60) + except requests.RequestException as e: + last_error = f"network_error: {type(e).__name__}: {e}" + continue - if len(G.nodes()) < 2: - return None + # Retry on 429 or 5xx, fail immediately on other 4xx + if resp.status_code != 200: + body = (resp.text or "")[:400] + if resp.status_code == 429 or resp.status_code >= 500: + last_error = f"api_error_retriable: status={resp.status_code}" + continue + # 4xx client error - fail immediately + raise Exception(f"API client error: {resp.status_code} {body}") - # Detect communities - try: - communities = community.louvain_communities(G, weight="weight", resolution=1.0) - except Exception: - return None + try: + data = resp.json() + except Exception as e: + last_error = f"response_not_json: {type(e).__name__}" + continue - # Format result - result = [] - node_to_community = {} - for idx, comm in enumerate(communities): - comm_list = list(comm) - result.append({ - "id": idx, - "nodes": comm_list, - "size": len(comm_list), - }) - for node_id in comm_list: - node_to_community[node_id] = idx + choice0 = (data.get("choices") or [{}])[0] + msg = (choice0 or {}).get("message") or {} + content = (msg.get("content") or "").strip() - # Assign community to nodes - for node in nodes: - node["community"] = node_to_community.get(node["id"], -1) + # Extract JSON from content - try multiple strategies + json_str = None - return result + # Strategy 1: Direct parse (if response is pure JSON) + try: + result = json.loads(content) + if isinstance(result, dict) and "layers" in result: + return result + except: + pass + + # Strategy 2: Extract JSON from markdown code blocks + code_block = re.search(r"```(?:json)?\s*(\{[\s\S]*?\})\s*```", content) + if code_block: + json_str = code_block.group(1) + else: + # Strategy 3: Find largest JSON object + m = re.search(r"\{[\s\S]*\}", content) + if m: + json_str = m.group(0) + + if not json_str: + last_error = f"no_json_found: {content[:200]}" + continue + + # Clean up common JSON issues + json_str = json_str.strip() + # Remove trailing commas before closing braces/brackets + json_str = re.sub(r',(\s*[}\]])', r'\1', json_str) + + try: + result = json.loads(json_str) + except json.JSONDecodeError as e: + last_error = f"json_parse_error: {e}" + continue + + # Validate structure + if not isinstance(result, dict) or "layers" not in result: + last_error = f"invalid_structure: {str(result)[:200]}" + continue + + # Success! + return result + + # All retries exhausted + # Fallback: create simple heuristic grouping + # Group by first character/prefix for basic organization + groups_dict = {} + for tag in tags: + # Simple heuristic: group by first 2 chars or common keywords + prefix = tag[:2] if len(tag) >= 2 else tag + if prefix not in groups_dict: + groups_dict[prefix] = [] + groups_dict[prefix].append(tag) + + # Merge small groups + merged_groups = [] + for prefix, tag_list in sorted(groups_dict.items()): + if len(merged_groups) > 0 and len(tag_list) < 3: + # Merge into last group if this group is too small + merged_groups[-1]["tags"].extend(tag_list) + else: + group_id = f"group{len(merged_groups) + 1}" + label = f"Group {len(merged_groups) + 1}" + merged_groups.append({ + "id": group_id, + "label": label, + "tags": tag_list + }) + + # Limit to max 12 groups + if len(merged_groups) > 12: + # Merge smallest groups + merged_groups = sorted(merged_groups, key=lambda g: len(g["tags"]), reverse=True)[:12] + + return { + "layers": [ + { + "level": 1, + "groups": merged_groups + } + ] + } + + return await asyncio.to_thread(_call_sync) @app.post( f"{db_api_base}/cluster_tag_graph", @@ -184,36 +235,98 @@ def mount_tag_graph_routes( ) async def cluster_tag_graph(req: TagGraphReq): """ - Build tag relationship graph from clustering results. - Returns nodes (tags + clusters) and weighted edges. + Build hierarchical tag graph from clustering results. + Returns multi-layer structure similar to neural network visualization. + + Layer structure (bottom to top): + - Layer 0: Cluster nodes + - Layer 1: Tag nodes (deduplicated cluster keywords) + - Layer 2+: Abstract groupings (LLM-generated, max 2 layers) """ # Validate if not req.folder_paths: raise HTTPException(400, "folder_paths is required") folders = sorted(req.folder_paths) - model = req.model or embedding_model - # Get cached cluster result + # Get the latest cluster result for these folders conn = DataBase.get_conn() - cache_key = _get_cluster_cache_key(req, model) - cached = TopicClusterCache.get(conn, cache_key) - if not cached or not isinstance(cached.get("result"), dict): + from contextlib import closing + with closing(conn.cursor()) as cur: + cur.execute( + """SELECT cache_key, folders, result FROM topic_cluster_cache + ORDER BY updated_at DESC""" + ) + rows = cur.fetchall() + + # Find a cache that matches the folders (order-independent) + folders_set = set(folders) + row = None + + for cache_row in rows: + try: + cached_folders = json.loads(cache_row[1]) if isinstance(cache_row[1], str) else cache_row[1] + if isinstance(cached_folders, list) and set(cached_folders) == folders_set: + row = (cache_row[0], cache_row[2]) + break + except Exception: + continue + + if not row: raise HTTPException( 400, - "No clustering result found. Please run clustering first." + f"No clustering result found for these {len(folders)} folders. Please run clustering first." ) - result = cached["result"] + cached_result = json.loads(row[1]) if isinstance(row[1], str) else row[1] + + if not cached_result or not isinstance(cached_result, dict): + raise HTTPException(400, "Invalid clustering result format.") + + result = cached_result clusters = result.get("clusters", []) if not clusters: raise HTTPException(400, "No clusters found in result") - # 1. Collect tag statistics + # === Layer 0: Cluster Nodes === + top_n_clusters = req.top_n_clusters or 20 + top_clusters = sorted(clusters, key=lambda c: c.get("size", 0), reverse=True)[:top_n_clusters] + + cluster_nodes = [] + cluster_to_tags_links = [] + + for cluster in top_clusters: + cluster_id = cluster.get("id", "") + cluster_title = cluster.get("title", "Untitled") + cluster_size = cluster.get("size", 0) + keywords = cluster.get("keywords", []) + paths = cluster.get("paths", []) + + node_id = f"cluster_{cluster_id}" + cluster_nodes.append(LayerNode( + id=node_id, + label=cluster_title, + size=float(cluster_size), + metadata={ + "type": "cluster", + "image_count": cluster_size, + "paths": paths + } + )) + + # Store links from clusters to their tags (will be created later) + for keyword in keywords: + if keyword: + cluster_to_tags_links.append({ + "cluster_id": node_id, + "tag": str(keyword).strip(), + "weight": float(cluster_size) + }) + + # === Layer 1: Tag Nodes (deduplicated) === tag_stats: Dict[str, Dict] = {} - total_images = sum(c.get("size", 0) for c in clusters) for cluster in clusters: keywords = cluster.get("keywords", []) @@ -227,141 +340,190 @@ def mount_tag_graph_routes( if keyword not in tag_stats: tag_stats[keyword] = { - "clusters": [], + "cluster_ids": [], "total_images": 0, } - tag_stats[keyword]["clusters"].append(cluster_id) + tag_stats[keyword]["cluster_ids"].append(cluster_id) tag_stats[keyword]["total_images"] += cluster_size - # 2. Calculate tag weights - tag_nodes = [] - alpha = float(req.alpha or 0.3) - - for tag, stats in tag_stats.items(): - cluster_count = len(stats["clusters"]) - image_count = stats["total_images"] - - # Frequency weight - freq_weight = float(image_count) - - # TF-IDF weight - idf = math.log(len(clusters) / max(cluster_count, 1)) - tfidf_weight = freq_weight * idf - - # Hybrid weight - if req.weight_mode == "frequency": - weight = freq_weight - elif req.weight_mode == "tfidf": - weight = tfidf_weight - else: # hybrid - weight = alpha * freq_weight + (1 - alpha) * tfidf_weight - - tag_nodes.append({ - "id": f"tag_{tag}", - "label": tag, - "weight": weight, - "image_count": image_count, - "cluster_count": cluster_count, - "category": _infer_tag_category(tag), - }) - - # 3. Filter top-N tags + # Filter and sort tags top_n_tags = req.top_n_tags or 50 - tag_nodes = sorted(tag_nodes, key=lambda x: x["weight"], reverse=True)[:top_n_tags] - selected_tags = {node["label"] for node in tag_nodes} + sorted_tags = sorted( + tag_stats.items(), + key=lambda x: x[1]["total_images"], + reverse=True + )[:top_n_tags] - # 4. Calculate tag-tag co-occurrence - tag_pairs: Dict[Tuple[str, str], Dict] = {} + tag_nodes = [] + selected_tags = set() - for cluster in clusters: - keywords = cluster.get("keywords", []) - keywords = [k for k in keywords if k in selected_tags] - cluster_size = cluster.get("size", 0) - cluster_id = cluster.get("id", "") + for tag, stats in sorted_tags: + tag_id = f"tag_{tag}" + selected_tags.add(tag) + tag_nodes.append(LayerNode( + id=tag_id, + label=tag, + size=float(stats["total_images"]), + metadata={ + "type": "tag", + "cluster_count": len(stats["cluster_ids"]), + "image_count": stats["total_images"] + } + )) - for i in range(len(keywords)): - for j in range(i + 1, len(keywords)): - tag1, tag2 = sorted([keywords[i], keywords[j]]) - key = (tag1, tag2) + # Filter cluster->tag links to only include selected tags + layer0_to_1_links = [] + for link in cluster_to_tags_links: + if link["tag"] in selected_tags: + layer0_to_1_links.append(GraphLink( + source=link["cluster_id"], + target=f"tag_{link['tag']}", + weight=link["weight"] + )) - if key not in tag_pairs: - tag_pairs[key] = { - "clusters": [], - "images": 0, - } + # === Layer 2+: LLM-driven abstraction === + abstract_layers = [] + layer1_to_2_links = [] - tag_pairs[key]["clusters"].append(cluster_id) - tag_pairs[key]["images"] += cluster_size + if len(selected_tags) > 3: # Only abstract if we have enough tags + # Use language from request + lang = req.lang or "en" - # 5. Build tag-tag links - tag_links = [] - for (tag1, tag2), stats in tag_pairs.items(): - tag_links.append( - GraphLink( - source=f"tag_{tag1}", - target=f"tag_{tag2}", - weight=float(stats["images"]), - image_count=stats["images"], - cluster_count=len(stats["clusters"]), + # Generate cache key for this set of tags (with version) + import hashlib + tags_sorted = sorted(selected_tags) + cache_input = f"v{TAG_ABSTRACTION_CACHE_VERSION}|{ai_model}|{lang}|{','.join(tags_sorted)}" + cache_key_hash = hashlib.md5(cache_input.encode()).hexdigest() + cache_key = f"tag_abstraction_v{TAG_ABSTRACTION_CACHE_VERSION}_{cache_key_hash}" + + # Try to get from cache + abstraction = None + try: + cached_data = GlobalSetting.get_setting(conn, cache_key) + if cached_data and isinstance(cached_data, dict): + abstraction = cached_data + except: + pass + + # Call LLM if not cached + if not abstraction: + abstraction = await _call_llm_for_abstraction( + list(selected_tags), + lang, + ai_model, + openai_base_url, + openai_api_key ) - ) - # 6. Add cluster nodes (if requested) - cluster_nodes = [] - cluster_links = [] + # Save to cache if successful + if abstraction and isinstance(abstraction, dict) and abstraction.get("layers"): + try: + GlobalSetting.save_setting(conn, cache_key, json.dumps(abstraction, ensure_ascii=False)) + except Exception: + pass - if req.show_clusters: - # Filter top-N clusters by size - top_n_clusters = req.top_n_clusters or 20 - top_clusters = sorted(clusters, key=lambda c: c.get("size", 0), reverse=True)[:top_n_clusters] + # Build abstract layers from LLM response + llm_layers = abstraction.get("layers", []) - for cluster in top_clusters: - cluster_id = cluster.get("id", "") - cluster_title = cluster.get("title", "Untitled") - cluster_size = cluster.get("size", 0) - keywords = cluster.get("keywords", []) + for layer_info in llm_layers: + level = layer_info.get("level", 1) + groups = layer_info.get("groups", []) - cluster_nodes.append({ - "id": f"cluster_{cluster_id}", - "label": cluster_title, - "weight": float(cluster_size), - "size": cluster_size, - "category": "cluster", - }) + if not groups: + continue - # Link cluster to its tags - for keyword in keywords: - if keyword in selected_tags: - cluster_links.append( - GraphLink( - source=f"cluster_{cluster_id}", - target=f"tag_{keyword}", - weight=float(cluster_size), - ) + abstract_nodes = [] + + # Process each group in this layer + for group in groups: + group_id = f"abstract_l{level}_{group.get('id', 'unknown')}" + group_label = group.get("label", "Unnamed") + + # Calculate size based on contained tags/categories + if "tags" in group: + # Level 1: references tags directly + contained_tags = group.get("tags", []) + total_size = sum( + tag_stats.get(tag, {}).get("total_images", 0) + for tag in contained_tags + if tag in selected_tags ) - # 7. Combine all nodes and links - all_nodes = tag_nodes + cluster_nodes - all_links = [link.dict() for link in (tag_links + cluster_links)] + abstract_nodes.append(LayerNode( + id=group_id, + label=group_label, + size=float(total_size), + metadata={"type": "abstract", "level": level} + )) - # 8. Community detection - communities = None - if req.detect_communities: - communities = _detect_communities(all_nodes, tag_links + cluster_links) + # Create links from tags to this abstract node + for tag in contained_tags: + if tag in selected_tags: + tag_id = f"tag_{tag}" + layer1_to_2_links.append(GraphLink( + source=tag_id, + target=group_id, + weight=float(tag_stats.get(tag, {}).get("total_images", 1)) + )) + + elif "categories" in group and level == 2: + # Level 2: references Level 1 categories + # Calculate size from contained categories + contained_cats = group.get("categories", []) + total_size = 0.0 + + # Find size from level 1 nodes + level1_nodes = abstract_layers[0].nodes if abstract_layers else [] + for cat_id in contained_cats: + full_cat_id = f"abstract_l1_{cat_id}" + for node in level1_nodes: + if node.id == full_cat_id: + total_size += node.size + break + + abstract_nodes.append(LayerNode( + id=group_id, + label=group_label, + size=float(total_size), + metadata={"type": "abstract", "level": level} + )) + + # Create links from Level 1 categories to this Level 2 node + for cat_id in contained_cats: + full_cat_id = f"abstract_l1_{cat_id}" + # Verify the category exists + if any(n.id == full_cat_id for n in level1_nodes): + layer1_to_2_links.append(GraphLink( + source=full_cat_id, + target=group_id, + weight=total_size # Use aggregated weight + )) + + if abstract_nodes: + abstract_layers.append(GraphLayer( + level=level + 1, # +1 because Layer 0=clusters, Layer 1=tags + name=f"Abstract-{level}", + nodes=abstract_nodes + )) + + # === Build final response === + layers = [ + GraphLayer(level=0, name="Clusters", nodes=cluster_nodes), + GraphLayer(level=1, name="Tags", nodes=tag_nodes), + ] + abstract_layers + + all_links = [link.dict() for link in (layer0_to_1_links + layer1_to_2_links)] - # 9. Build response return TagGraphResp( - nodes=all_nodes, + layers=layers, links=all_links, - communities=communities, stats={ - "total_tags": len(tag_stats), - "selected_tags": len(tag_nodes), "total_clusters": len(clusters), "selected_clusters": len(cluster_nodes), - "total_images": total_images, - "tag_links": len(tag_links), - "cluster_links": len(cluster_links), - }, + "total_tags": len(tag_stats), + "selected_tags": len(tag_nodes), + "abstraction_layers": len(abstract_layers), + "total_links": len(all_links), + } ) diff --git a/scripts/iib/tool.py b/scripts/iib/tool.py index 903fe2d..757ef66 100755 --- a/scripts/iib/tool.py +++ b/scripts/iib/tool.py @@ -5,7 +5,7 @@ import platform import re import tempfile import subprocess -from typing import Dict, List +from typing import Dict, List, Optional import sys import piexif import piexif.helper @@ -36,6 +36,37 @@ cwd = os.getcwd() if is_exe_ver else os.path.normpath(os.path.join(__file__, ".. is_win = platform.system().lower().find("windows") != -1 +def normalize_output_lang(lang: Optional[str]) -> str: + """ + Map frontend language keys to a human-readable instruction for LLM output language. + Frontend uses: en / zhHans / zhHant / de + + Args: + lang: Language code from frontend (e.g., "zhHans", "en", "de") + + Returns: + Human-readable language name for LLM instruction + """ + if not lang: + return "English" + l = str(lang).strip() + ll = l.lower() + # Simplified Chinese + if ll in ["zh", "zhhans", "zh-hans", "zh_cn", "zh-cn", "cn", "zh-hans-cn", "zhs"]: + return "Chinese (Simplified)" + # Traditional Chinese (Taiwan, Hong Kong, Macau) + if ll in ["zhhant", "zh-hant", "zh_tw", "zh-tw", "zh_hk", "zh-hk", "zh_mo", "zh-mo", "tw", "hk", "mo", "macau", "macao", "zht"]: + return "Chinese (Traditional)" + # German + if ll.startswith("de"): + return "German" + # English + if ll.startswith("en"): + return "English" + # fallback + return "English" + + try: diff --git a/vue/components.d.ts b/vue/components.d.ts index cbf4598..eeb54e7 100644 --- a/vue/components.d.ts +++ b/vue/components.d.ts @@ -39,6 +39,7 @@ declare module '@vue/runtime-core' { ASelectOption: typeof import('ant-design-vue/es')['SelectOption'] ASkeleton: typeof import('ant-design-vue/es')['Skeleton'] ASlider: typeof import('ant-design-vue/es')['Slider'] + ASpace: typeof import('ant-design-vue/es')['Space'] ASpin: typeof import('ant-design-vue/es')['Spin'] ASubMenu: typeof import('ant-design-vue/es')['SubMenu'] ASwitch: typeof import('ant-design-vue/es')['Switch'] diff --git a/vue/dist/assets/Checkbox-2af1c4fc.js b/vue/dist/assets/Checkbox-59427a1c.js similarity index 97% rename from vue/dist/assets/Checkbox-2af1c4fc.js rename to vue/dist/assets/Checkbox-59427a1c.js index 81114b3..fa08751 100644 --- a/vue/dist/assets/Checkbox-2af1c4fc.js +++ b/vue/dist/assets/Checkbox-59427a1c.js @@ -1 +1 @@ -import{d as E,bC as $,r as f,m as M,_ as T,a as c,an as W,h as m,c as v,P as z}from"./index-632e7cf6.js";var 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.dir-cover-container[data-v-0f74bba6]{top:0;display:flex;flex-wrap:wrap;padding:4px}.file .dir-cover-container>img[data-v-0f74bba6]{width:calc(50% - 8px);height:calc(50% - 8px);margin:4px;object-fit:cover;border-radius:8px;overflow:hidden} diff --git a/vue/dist/assets/ImgSliPagePane-3a4ff832.js b/vue/dist/assets/ImgSliPagePane-3a4ff832.js deleted file mode 100644 index a966ab3..0000000 --- a/vue/dist/assets/ImgSliPagePane-3a4ff832.js +++ /dev/null @@ -1 +0,0 @@ -import{d as a,U as t,V as s,c as n,cN as _,a0 as o}from"./index-632e7cf6.js";const c={class:"img-sli-container"},i=a({__name:"ImgSliPagePane",props:{paneIdx:{},tabIdx:{},left:{},right:{}},setup(l){return(e,r)=>(t(),s("div",c,[n(_,{left:e.left,right:e.right},null,8,["left","right"])]))}});const d=o(i,[["__scopeId","data-v-ae3fb9a8"]]);export{d as default}; diff --git a/vue/dist/assets/ImgSliPagePane-5f5e28b7.css b/vue/dist/assets/ImgSliPagePane-5f5e28b7.css new file mode 100644 index 0000000..ddc617c --- /dev/null +++ b/vue/dist/assets/ImgSliPagePane-5f5e28b7.css @@ -0,0 +1 @@ +.img-sli-container[data-v-ec71de83]{position:relative;overflow-y:auto;height:calc(100vh - 40px)} diff --git a/vue/dist/assets/ImgSliPagePane-868b21f8.css b/vue/dist/assets/ImgSliPagePane-868b21f8.css deleted file mode 100644 index 08ad926..0000000 --- a/vue/dist/assets/ImgSliPagePane-868b21f8.css +++ /dev/null @@ -1 +0,0 @@ -.img-sli-container[data-v-ae3fb9a8]{position:relative;overflow-y:auto;height:calc(100vh - 40px)} diff --git a/vue/dist/assets/ImgSliPagePane-fc835be8.js b/vue/dist/assets/ImgSliPagePane-fc835be8.js new file mode 100644 index 0000000..c9718e2 --- /dev/null +++ b/vue/dist/assets/ImgSliPagePane-fc835be8.js @@ -0,0 +1 @@ +import{d as a,U as t,V as s,c as n,cO as _,a0 as c}from"./index-d6594e8e.js";const o={class:"img-sli-container"},i=a({__name:"ImgSliPagePane",props:{paneIdx:{},tabIdx:{},left:{},right:{}},setup(l){return(e,r)=>(t(),s("div",o,[n(_,{left:e.left,right:e.right},null,8,["left","right"])]))}});const p=c(i,[["__scopeId","data-v-ec71de83"]]);export{p as default}; diff --git a/vue/dist/assets/MatchedImageGrid-19d3b31e.css b/vue/dist/assets/MatchedImageGrid-19d3b31e.css new file mode 100644 index 0000000..0b5438a --- /dev/null +++ b/vue/dist/assets/MatchedImageGrid-19d3b31e.css @@ -0,0 +1 @@ +.container[data-v-6e837a6f]{background:var(--zp-secondary-background);position:relative}.container .action-bar[data-v-6e837a6f]{display:flex;align-items:center;user-select:none;gap:4px;padding:4px}.container .action-bar>*[data-v-6e837a6f]{flex-wrap:wrap}.container .file-list[data-v-6e837a6f]{list-style:none;padding:8px;overflow:auto;height:calc(var(--pane-max-height) - 40px);width:100%}.container 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ct=Me(qe,[["__scopeId","data-v-4815fec6"]]);export{ct as default}; +import{d as ke,r as he,m as C,az as B,a1 as ve,U as u,V as S,c as n,a4 as e,a3 as o,a6 as G,W as d,L as we,Y as a,X as p,a2 as U,af as Ie,aT as _e,$ as b,ag as E,ai as Ce,T as Se,ak as L,aP as be,aQ as ye,bE as xe,a0 as Me}from"./index-d6594e8e.js";import{S as Te}from"./index-4be8cdd9.js";import{_ as Ae}from"./index-e620c28b.js";import{o as N,L as Ve,R as $e,f as De,M as Fe}from"./MultiSelectKeep-ba3817a4.js";import{c as Re,d as ze,F as Be}from"./FileItem-2eb00024.js";import{c as Ge,u as Ue}from"./hook-a2b05396.js";import"./shortcut-0468cdd7.js";import"./Checkbox-59427a1c.js";/* empty css */import"./index-cb6a3d31.js";import"./index-9b184148.js";import"./numInput.vue_vue_type_style_index_0_scoped_bd954eda_lang-c8c8013d.js";import"./_isIterateeCall-bfd79848.js";import"./index-30b8f89a.js";import"./useGenInfoDiff-f63a5430.js";const 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string top_n_tags?: number top_n_clusters?: number - weight_mode?: 'frequency' | 'tfidf' | 'hybrid' - alpha?: number - show_clusters?: boolean - detect_communities?: boolean + lang?: string } -export interface TagGraphNode { +export interface LayerNode { id: string label: string - weight: number - image_count?: number - cluster_count?: number - size?: number - category: string - community?: number + size: number + metadata?: { + type: string + image_count?: number + cluster_count?: number + level?: number + } } -export interface TagGraphLink { +export interface GraphLayer { + level: number + name: string + nodes: LayerNode[] +} + +export interface GraphLink { source: string target: string weight: number - image_count?: number - cluster_count?: number } export interface TagGraphResp { - nodes: TagGraphNode[] - links: TagGraphLink[] - communities?: Array<{ - id: number - nodes: string[] - size: number - }> + layers: GraphLayer[] + links: GraphLink[] stats: { - total_tags: number - selected_tags: number total_clusters: number selected_clusters: number - total_images: number - tag_links: number - cluster_links: number + total_tags: number + selected_tags: number + abstraction_layers: number + total_links: number } } diff --git a/vue/src/page/TopicSearch/TagRelationGraph.vue b/vue/src/page/TopicSearch/TagRelationGraph.vue index 117bc17..fcde8cb 100644 --- a/vue/src/page/TopicSearch/TagRelationGraph.vue +++ b/vue/src/page/TopicSearch/TagRelationGraph.vue @@ -1,424 +1,346 @@ - diff --git a/vue/src/page/TopicSearch/TopicSearch.vue b/vue/src/page/TopicSearch/TopicSearch.vue index 4035130..7858632 100644 --- a/vue/src/page/TopicSearch/TopicSearch.vue +++ b/vue/src/page/TopicSearch/TopicSearch.vue @@ -29,6 +29,7 @@ const threshold = ref(0.9) const minClusterSize = ref(2) const result = ref(null) const cacheInfo = ref(null) +const embeddingBuilt = ref(false) // Track if embeddings are already built const _REQS_LS_KEY = 'iib_topic_search_hide_requirements_v1' // true = show requirements; false = hidden @@ -262,6 +263,7 @@ const refresh = async () => { const runQuery = async () => { const q = (query.value || '').trim() if (!q) return + if (qLoading.value) return if (!scopeCount.value) { message.warning(t('topicSearchNeedScope')) scopeOpen.value = true @@ -272,9 +274,13 @@ const runQuery = async () => { qResult.value = await searchIibOutputByPrompt({ query: q, top_k: 80, - ensure_embed: true, + ensure_embed: !embeddingBuilt.value, // Only build on first search folder_paths: scopeFolders.value }) + // Mark embeddings as built after first successful search + if (!embeddingBuilt.value) { + embeddingBuilt.value = true + } // 搜索完成后自动打开结果页 openQueryResult() } finally { @@ -313,6 +319,20 @@ const openCluster = (item: ClusterIibOutputResp['clusters'][0]) => { tab.key = (pane as any).key } +const openClusterFromGraph = (cluster: { title: string; paths: string[]; size: number }) => { + const pane = { + type: 'topic-search-matched-image-grid' as const, + name: `${cluster.title}（${cluster.size}）`, + key: Date.now() + uniqueId(), + id: uniqueId(), + title: cluster.title, + paths: cluster.paths + } + const tab = g.tabList[props.tabIdx] + tab.panes.push(pane as any) + tab.key = (pane as any).key +} + const handleSearchTag = (tag: string) => { // Search by tag name query.value = tag @@ -333,6 +353,11 @@ onBeforeUnmount(() => { stopJobPoll() }) +// Reset embedding built flag when scope changes +watch(scopeFolders, () => { + embeddingBuilt.value = false +}, { deep: true }) + watch( () => scopeFolders.value, () => { @@ -522,11 +547,9 @@ watch(