From 258be54df757ecb3e954ca57c02fa5982829543c Mon Sep 17 00:00:00 2001 From: lacerate551 <128470311+lacerate551@users.noreply.github.com> Date: Sun, 21 Jun 2026 14:49:46 +0800 Subject: [PATCH] =?UTF-8?q?sync(server-release):=20=E4=BB=8E=20main=20?= =?UTF-8?q?=E5=90=8C=E6=AD=A5=E9=80=BB=E8=BE=91=E6=94=B9=E5=8A=A8=EF=BC=88?= =?UTF-8?q?=E4=B8=8D=E5=90=AB=20API=20=E6=A0=BC=E5=BC=8F=E5=8F=98=E6=9B=B4?= =?UTF-8?q?=EF=BC=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 同步内容: - knowledge/manager.py: VLM max_tokens 512→2048 + reasoning_content fallback - services/feedback.py: FAQ max_tokens 200→512 - services/session.py: 消息排序增加 id DESC 次级排序 - knowledge/image_cleanup.py: 新增图片/VLM缓存孤儿文件清理模块 - knowledge/collection.py: 集合删除时清理孤儿文件 + list_collections 磁盘扫描策略 - knowledge/document.py: 文档删除时清理孤儿文件 - api/chat_routes.py: 新增 _rescue_bm25_divergence 函数（BM25-CE分歧救援） - core/intent_analyzer.py: 使用 get_intent_client 专用客户端 + 移除短追问缓存跳过逻辑 - cleanup_orphans.py: 独立孤儿文件清理脚本 --- api/chat_routes.py | 109 +++++++++++++++++++ cleanup_orphans.py | 207 +++++++++++++++++++++++++++++++++++++ core/intent_analyzer.py | 15 +-- knowledge/collection.py | 70 ++++++++++--- knowledge/document.py | 12 +++ knowledge/image_cleanup.py | 140 +++++++++++++++++++++++++ knowledge/manager.py | 22 +++- services/feedback.py | 2 +- services/session.py | 2 +- 9 files changed, 553 insertions(+), 26 deletions(-) create mode 100644 cleanup_orphans.py create mode 100644 knowledge/image_cleanup.py diff --git a/api/chat_routes.py b/api/chat_routes.py index 4fc82ec..ee3434a 100644 --- a/api/chat_routes.py +++ b/api/chat_routes.py @@ -293,6 +293,112 @@ def _process_table_doc(doc: str, meta: Dict) -> str: return doc +def _rescue_bm25_divergence(contexts: List[Dict], search_result: dict, + min_score: float) -> List[Dict]: + """ + BM25-CrossEncoder 分歧检测救援：当 BM25 排名靠前（top-3）的切片 + 被 CrossEncoder rerank 压制（分数低于 min_score 或被截断）时， + 将其分数提升至保底值，使其通过后续的 min_score 过滤。 + + 适用场景： + - BM25 top-1/top-2 的切片（精确关键词匹配强信号）被 rerank 评分极低 + - 正确切片在 rerank 后被截断，根本不在 contexts 中 + + Args: + contexts: 全部上下文切片 + search_result: engine.search_hybrid() 返回的原始结果（含 _bm25_top3） + min_score: 最低分数阈值 + + Returns: + 修改后的 contexts + """ + if not contexts: + return contexts + + from config import ( + BM25_DIVERGENCE_RESCUE_ENABLED, BM25_DIVERGENCE_MAX_RANK, + CLUSTER_RESCUE_FLOOR, + ) + + if not BM25_DIVERGENCE_RESCUE_ENABLED: + return contexts + + bm25_top3 = search_result.get('_bm25_top3', []) + if not bm25_top3: + return contexts + + # 构建 contexts 中已有切片的 ID 索引，用于快速查找 + existing_ids = {} + for i, ctx in enumerate(contexts): + chunk_id = ctx.get('meta', {}).get('chunk_id') or ctx.get('id') + if chunk_id: + existing_ids[chunk_id] = i + + # 计算 contexts 中的多数 source（用于情况 B 注入校验） + # 防止跨文档注入无关切片（如 q013 场景：2.docx 的吸烟场所切片被注入到 1.docx 的查询中） + source_counter: Dict[str, int] = {} + for ctx in contexts: + src = ctx.get('meta', {}).get('source', '') + if src: + source_counter[src] = source_counter.get(src, 0) + 1 + majority_source = max(source_counter, key=source_counter.get) if source_counter else None + + rescued_count = 0 + + for bm25_item in bm25_top3: + rank = bm25_item.get('rank', 99) + if rank > BM25_DIVERGENCE_MAX_RANK: + continue + + bm25_id = bm25_item.get('id') + bm25_meta = bm25_item.get('meta', {}) + bm25_doc = bm25_item.get('doc', '') + + # 情况 A：切片在 contexts 中但 score < min_score + if bm25_id and bm25_id in existing_ids: + ctx = contexts[existing_ids[bm25_id]] + if ctx.get('score', 0) < min_score: + ctx['score'] = CLUSTER_RESCUE_FLOOR + rescued_count += 1 + logger.debug( + f"BM25 分歧救援 (情况A): rank={rank}, " + f"source={bm25_meta.get('source', '')}, " + f"section={bm25_meta.get('section', '')}, " + f"原score→{CLUSTER_RESCUE_FLOOR}" + ) + continue + + # 情况 B：切片不在 contexts 中（被 rerank 截断或已被过滤） + # 从 _bm25_top3 备份中注入，但需校验 source 一致性 + if bm25_doc and bm25_meta: + bm25_source = bm25_meta.get('source', '') + # source 一致性校验：只注入与 contexts 多数 source 一致的切片 + if majority_source and bm25_source and bm25_source != majority_source: + logger.debug( + f"BM25 分歧救援 (情况B-跳过): rank={rank}, " + f"source={bm25_source} != majority={majority_source}, " + f"section={bm25_meta.get('section', '')}" + ) + continue + injected_ctx = { + 'doc': bm25_doc, + 'meta': bm25_meta, + 'score': CLUSTER_RESCUE_FLOOR, + } + contexts.append(injected_ctx) + rescued_count += 1 + logger.debug( + f"BM25 分歧救援 (情况B-注入): rank={rank}, " + f"source={bm25_meta.get('source', '')}, " + f"section={bm25_meta.get('section', '')}" + ) + + if rescued_count > 0: + logger.info(f"BM25 分歧救援: 共救援 {rescued_count} 个切片") + + return contexts + + def _rescue_lexical_match(contexts: List[Dict], retrieval_query: str, min_score: float) -> List[Dict]: """ @@ -2518,6 +2624,9 @@ def rag(): # 4. 构建 prompt（Phase 6：LLM 图片感知） # Bug 1 修复：文本切片用于 top 5 名额竞争，图片描述不参与竞争 + # BM25 分歧检测救援：BM25 top-3 但 rerank 压制的切片 + contexts = _rescue_bm25_divergence(contexts, search_result, RERANK_CONTEXT_MIN_SCORE) + # 词法匹配救援：当切片文本精确包含查询关键词但 CrossEncoder 评分低时， # 提升分数使其通过 min_score 过滤（适用于独立切片无法触发聚类救援的场景） contexts = _rescue_lexical_match(contexts, retrieval_query, RERANK_CONTEXT_MIN_SCORE) diff --git a/cleanup_orphans.py b/cleanup_orphans.py new file mode 100644 index 0000000..65dc80b --- /dev/null +++ b/cleanup_orphans.py @@ -0,0 +1,207 @@ +""" +孤儿文件清理工具 + +清理 .data/images/ 和 .data/cache/vlm/ 中不再被任何 ChromaDB 切片引用的文件。 + +用法: + # 预览模式（不删除，只显示孤儿文件） + python cleanup_orphans.py + + # 实际删除 + python cleanup_orphans.py --force + + # 仅清理特定知识库 + python cleanup_orphans.py --collections public_kb test_kb +""" + +import argparse +import hashlib +import logging +import os +import sys +from pathlib import Path + +logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s') +logger = logging.getLogger(__name__) + +IMAGES_DIR = Path(".data/images") +VLM_CACHE_DIR = Path(".data/cache/vlm") + + +def compute_file_hash(file_path: str) -> str: + """计算文件 MD5""" + with open(file_path, 'rb') as f: + return hashlib.md5(f.read()).hexdigest() + + +def collect_referenced_images(manager, collections=None) -> dict: + """ + 从 ChromaDB 收集所有被引用的图片路径。 + + Returns: + {image_filename: set of chunk_ids referencing it} + """ + referenced = {} + + if collections: + kb_names = collections + else: + kb_names = [c.name if hasattr(c, 'name') else str(c) + for c in manager.list_collections()] + + for kb_name in kb_names: + try: + col = manager.get_collection(kb_name) + except Exception as e: + logger.warning(f"无法获取 {kb_name}: {e}") + continue + + # 查找所有带 image_path 的切片 + results = col.get(include=['metadatas']) + if not results['ids']: + continue + + for chunk_id, meta in zip(results['ids'], results['metadatas']): + image_path = meta.get('image_path', '') + if not image_path: + continue + + # image_path 可能是: "185a7a75d246.png" 或相对路径 + filename = os.path.basename(image_path) + if filename not in referenced: + referenced[filename] = set() + referenced[filename].add(f"{kb_name}/{chunk_id}") + + return referenced + + +def find_orphan_images(referenced: dict) -> list: + """ + 查找 .data/images/ 中不再被引用的图片文件。 + + Returns: + [(filepath, filename, size_bytes)] + """ + orphans = [] + if not IMAGES_DIR.exists(): + return orphans + + for f in IMAGES_DIR.iterdir(): + if not f.is_file(): + continue + if f.name not in referenced: + orphans.append((str(f), f.name, f.stat().st_size)) + + return orphans + + +def find_orphan_vlm_caches(referenced: dict) -> list: + """ + 查找 .data/cache/vlm/ 中对应的图片已不存在的缓存文件。 + + 缓存文件以图片 MD5 命名，如果图片被删了，缓存也应该是孤儿。 + + Returns: + [(filepath, filename, size_bytes)] + """ + orphans = [] + if not VLM_CACHE_DIR.exists(): + return orphans + + # 构建 referenced 中所有图片的 MD5 集合 + referenced_hashes = set() + for filename in referenced: + full_path = IMAGES_DIR / filename + if full_path.exists(): + try: + img_hash = compute_file_hash(str(full_path)) + referenced_hashes.add(img_hash) + except Exception: + pass + + for f in VLM_CACHE_DIR.iterdir(): + if not f.is_file() or not f.suffix == '.txt': + continue + cache_hash = f.stem # 文件名就是 MD5 + if cache_hash not in referenced_hashes: + orphans.append((str(f), f.name, f.stat().st_size)) + + return orphans + + +def delete_files(file_list: list, dry_run: bool = True) -> int: + """删除文件列表，返回删除数量""" + deleted = 0 + for filepath, filename, size in file_list: + if dry_run: + logger.info(f" [DRY-RUN] 将删除: {filename} ({size/1024:.1f} KB)") + else: + try: + os.remove(filepath) + logger.info(f" 已删除: {filename} ({size/1024:.1f} KB)") + deleted += 1 + except OSError as e: + logger.warning(f" 删除失败: {filename} - {e}") + return deleted + + +def main(): + parser = argparse.ArgumentParser(description="孤儿文件清理工具") + parser.add_argument("--force", action="store_true", + help="实际删除文件（默认仅预览）") + parser.add_argument("--collections", nargs="+", + help="仅检查指定知识库（默认检查全部）") + args = parser.parse_args() + + os.chdir(os.path.dirname(os.path.abspath(__file__))) + + mode = "删除" if args.force else "预览（不删除）" + print("=" * 60) + print(f"孤儿文件清理 - {mode}") + print("=" * 60) + + # 1. 收集引用 + print("\n[1/4] 扫描 ChromaDB 引用...") + sys.path.insert(0, '.') + from knowledge.manager import get_kb_manager + manager = get_kb_manager() + referenced = collect_referenced_images(manager, args.collections) + print(f" 被引用的图片: {len(referenced)} 个") + + # 2. 查找孤儿图片 + print("\n[2/4] 查找孤儿图片...") + orphan_images = find_orphan_images(referenced) + total_img_size = sum(s for _, _, s in orphan_images) + print(f" 孤儿图片: {len(orphan_images)} 个 ({total_img_size/1024:.1f} KB)") + + # 3. 查找孤儿 VLM 缓存 + print("\n[3/4] 查找孤儿 VLM 缓存...") + orphan_caches = find_orphan_vlm_caches(referenced) + total_cache_size = sum(s for _, _, s in orphan_caches) + print(f" 孤儿缓存: {len(orphan_caches)} 个 ({total_cache_size/1024:.1f} KB)") + + # 4. 清理 + print("\n[4/4] 清理...") + if not orphan_images and not orphan_caches: + print(" 没有需要清理的文件") + else: + if not args.force: + print(" 预览模式，以下文件将被删除：") + + img_deleted = delete_files(orphan_images, dry_run=not args.force) + cache_deleted = delete_files(orphan_caches, dry_run=not args.force) + + if args.force: + print(f"\n 删除了 {img_deleted} 个图片 + {cache_deleted} 个缓存") + freed = total_img_size + total_cache_size + print(f" 释放空间: {freed/1024:.1f} KB") + else: + print(f"\n 共 {len(orphan_images) + len(orphan_caches)} 个文件待清理") + print(f" 释放空间: {(total_img_size + total_cache_size)/1024:.1f} KB") + print(" 使用 --force 参数执行实际删除") + + print("=" * 60) + + +if __name__ == '__main__': + main() diff --git a/core/intent_analyzer.py b/core/intent_analyzer.py index 01d83a8..78edb0a 100644 --- a/core/intent_analyzer.py +++ b/core/intent_analyzer.py @@ -233,10 +233,10 @@ class IntentAnalyzer: self._exact_cache_max = 500 def _get_client(self): - """获取 LLM 客户端""" + """获取 LLM 客户端（百炼快速模型）""" if self._client is None: - from config import get_llm_client - self._client = get_llm_client() + from config import get_intent_client + self._client = get_intent_client() return self._client def _get_cache(self): @@ -309,9 +309,7 @@ class IntentAnalyzer: # 2. 尝试从语义缓存获取 cache = self._get_cache() - # 短追问（如"B3类呢？"）严重依赖上下文，embedding 区分度不足易误命中 - is_short_followup = history and len(query.strip()) < 20 - if cache and not is_short_followup: + if cache: # 使用 query + 历史关键信息作为缓存键 cache_key = self._build_cache_key(query, history) cache_emb = self._get_embedding(cache_key) @@ -327,10 +325,7 @@ class IntentAnalyzer: else: logger.warning("意图分析缓存: _get_embedding 返回 None，跳过缓存") else: - if is_short_followup: - logger.debug(f"跳过语义缓存(短追问依赖上下文): query='{query[:30]}'") - elif not cache: - logger.warning("意图分析缓存: _get_cache 返回 None，缓存不可用") + logger.warning("意图分析缓存: _get_cache 返回 None，缓存不可用") # 构建 prompt user_prompt = f"""## 对话历史 diff --git a/knowledge/collection.py b/knowledge/collection.py index 53566e2..107363f 100644 --- a/knowledge/collection.py +++ b/knowledge/collection.py @@ -331,6 +331,21 @@ class CollectionMixin: except Exception as e: logger.warning(f"清理版本记录失败: {e}") + # 清理不再被引用的图片和 VLM 缓存文件 + # 注意：此时 ChromaDB collection 已删除，cleanup_image_orphans 会扫描 + # 所有剩余 collection，仅该 collection 引用的图片会被识别为孤儿 + try: + from knowledge.image_cleanup import cleanup_image_orphans + cleanup_result = cleanup_image_orphans(self) + if cleanup_result['deleted_images'] or cleanup_result['deleted_caches']: + logger.info( + f"清理孤儿文件: {cleanup_result['deleted_images']} 图片 + " + f"{cleanup_result['deleted_caches']} VLM缓存, " + f"释放 {cleanup_result['freed_bytes']/1024:.1f} KB" + ) + except Exception as e: + logger.warning(f"清理孤儿文件失败: {e}") + if kb_name in self._metadata.get("collections", {}): del self._metadata["collections"][kb_name] self._save_metadata() @@ -361,8 +376,40 @@ class CollectionMixin: self._metadata = self._load_metadata() result = [] - # 以元数据为唯一真相源，不再扫描磁盘自动补充 - # （扫描磁盘会导致其他 worker 刚创建/删除的集合被误操作） + # 扫描 base_path 下的所有子目录作为向量库 + # 每个向量库使用独立目录：base_path/my_ky, base_path/public_kb 等 + actual_collections = [] + try: + if os.path.exists(self.base_path): + for item in os.listdir(self.base_path): + item_path = os.path.join(self.base_path, item) + if os.path.isdir(item_path) and not item.startswith('.'): + # 检查是否包含 chroma.sqlite3（有效的向量库目录） + if os.path.exists(os.path.join(item_path, 'chroma.sqlite3')): + actual_collections.append(item) + except Exception as e: + logger.warning(f"扫描向量库目录失败: {e}") + + # 如果扫描失败，回退到元数据中的集合列表 + if not actual_collections: + actual_collections = list(self._metadata.get("collections", {}).keys()) + + for name in actual_collections: + if name not in self._metadata.get("collections", {}): + if "collections" not in self._metadata: + self._metadata["collections"] = {} + self._metadata["collections"][name] = { + "display_name": name, + "department": "", + "description": "", + "created_at": datetime.now().isoformat() + } + logger.info(f"自动补充向量库元数据: {name}") + + self._save_metadata() + + stale_collections = [] + for name, info in self._metadata.get("collections", {}).items(): try: collection = self.get_collection(name) @@ -375,18 +422,17 @@ class CollectionMixin: description=info.get("description", "") )) except Exception as e: - # 只跳过不修改元数据（可能是其他 worker 刚创建的集合） logger.warning( - f"跳过异常向量库 '{name}': {e}" + f"跳过异常向量库 '{name}': {e}，可能是 ChromaDB 数据目录已丢失" ) - result.append(CollectionInfo( - name=name, - display_name=info.get("display_name", name), - document_count=0, - created_at=info.get("created_at", ""), - department=info.get("department", ""), - description=info.get("description", "") - )) + stale_collections.append(name) + + # 清理元数据中指向已失效集合的条目 + if stale_collections: + for name in stale_collections: + self._metadata.get("collections", {}).pop(name, None) + logger.info(f"清理失效向量库元数据: {name}") + self._save_metadata() return result diff --git a/knowledge/document.py b/knowledge/document.py index bdeca24..57894fe 100644 --- a/knowledge/document.py +++ b/knowledge/document.py @@ -72,6 +72,18 @@ class DocumentMixin: except Exception as e: logger.warning(f"清理版本记录失败: {e}") + # 清理不再被引用的图片和 VLM 缓存文件 + try: + from knowledge.image_cleanup import cleanup_image_orphans + cleanup_result = cleanup_image_orphans(self, collections=[kb_name]) + if cleanup_result['deleted_images'] or cleanup_result['deleted_caches']: + logger.info( + f"清理孤儿文件: {cleanup_result['deleted_images']} 图片 + " + f"{cleanup_result['deleted_caches']} VLM缓存" + ) + except Exception as e: + logger.warning(f"清理孤儿文件失败: {e}") + logger.info(f"从 {kb_name} 删除文档: {filename}, 片段数: {deleted}") return deleted diff --git a/knowledge/image_cleanup.py b/knowledge/image_cleanup.py new file mode 100644 index 0000000..2350db2 --- /dev/null +++ b/knowledge/image_cleanup.py @@ -0,0 +1,140 @@ +""" +图片/VLM缓存孤儿文件清理模块 + +提供可被 document.py / collection.py 调用的清理函数， +也可被 cleanup_orphans.py 独立脚本使用。 +""" + +import hashlib +import logging +import os +from pathlib import Path + +logger = logging.getLogger(__name__) + +IMAGES_DIR = Path(".data/images") +VLM_CACHE_DIR = Path(".data/cache/vlm") + + +def compute_file_hash(file_path: str) -> str: + """计算文件 MD5""" + with open(file_path, 'rb') as f: + return hashlib.md5(f.read()).hexdigest() + + +def collect_referenced_images(manager, collections=None) -> set: + """ + 从 ChromaDB 收集所有被引用的图片文件名。 + + Args: + manager: KnowledgeBaseManager 实例 + collections: 限定知识库列表，None 表示全部 + + Returns: + set of image filenames (e.g., {"185a7a75d246.png", ...}) + """ + referenced = set() + + if collections: + kb_names = collections + else: + kb_names = [c.name if hasattr(c, 'name') else str(c) + for c in manager.list_collections()] + + for kb_name in kb_names: + try: + col = manager.get_collection(kb_name) + except Exception: + continue + + results = col.get(include=['metadatas']) + if not results['ids']: + continue + + for meta in results['metadatas']: + image_path = meta.get('image_path', '') + if image_path: + referenced.add(os.path.basename(image_path)) + + return referenced + + +def cleanup_image_orphans(manager, collections=None, dry_run=False) -> dict: + """ + 清理不再被任何 ChromaDB 切片引用的图片和 VLM 缓存文件。 + + Args: + manager: KnowledgeBaseManager 实例 + collections: 限定知识库列表，None 表示全部 + dry_run: True 时只返回孤儿列表不实际删除 + + Returns: + { + 'orphan_images': [(filepath, filename, size_bytes)], + 'orphan_caches': [(filepath, filename, size_bytes)], + 'deleted_images': int, + 'deleted_caches': int, + 'freed_bytes': int + } + """ + result = { + 'orphan_images': [], + 'orphan_caches': [], + 'deleted_images': 0, + 'deleted_caches': 0, + 'freed_bytes': 0 + } + + # 1. 收集引用 + referenced = collect_referenced_images(manager, collections) + + # 2. 查找孤儿图片 + if IMAGES_DIR.exists(): + for f in IMAGES_DIR.iterdir(): + if f.is_file() and f.name not in referenced: + result['orphan_images'].append((str(f), f.name, f.stat().st_size)) + + # 3. 查找孤儿 VLM 缓存（图片已删除则缓存也应是孤儿） + if VLM_CACHE_DIR.exists(): + referenced_hashes = set() + for filename in referenced: + full_path = IMAGES_DIR / filename + if full_path.exists(): + try: + img_hash = compute_file_hash(str(full_path)) + referenced_hashes.add(img_hash) + except Exception: + pass + + for f in VLM_CACHE_DIR.iterdir(): + if f.is_file() and f.suffix == '.txt': + cache_hash = f.stem + if cache_hash not in referenced_hashes: + result['orphan_caches'].append((str(f), f.name, f.stat().st_size)) + + # 4. 删除 + if not dry_run: + for filepath, filename, size in result['orphan_images']: + try: + os.remove(filepath) + result['deleted_images'] += 1 + result['freed_bytes'] += size + except OSError as e: + logger.warning(f"删除孤儿图片失败: {filename} - {e}") + + for filepath, filename, size in result['orphan_caches']: + try: + os.remove(filepath) + result['deleted_caches'] += 1 + result['freed_bytes'] += size + except OSError as e: + logger.warning(f"删除孤儿缓存失败: {filename} - {e}") + + if result['deleted_images'] or result['deleted_caches']: + logger.info( + f"清理孤儿: {result['deleted_images']} 图片 + " + f"{result['deleted_caches']} 缓存, " + f"释放 {result['freed_bytes']/1024:.1f} KB" + ) + + return result diff --git a/knowledge/manager.py b/knowledge/manager.py index b5c8642..0fd8fe6 100644 --- a/knowledge/manager.py +++ b/knowledge/manager.py @@ -654,7 +654,7 @@ class KnowledgeBaseManager( try: from config import get_llm_client, DASHSCOPE_MODEL client = get_llm_client() - summary = call_llm(client, prompt, DASHSCOPE_MODEL, max_tokens=512) + summary = call_llm(client, prompt, DASHSCOPE_MODEL, max_tokens=2048) return summary.strip() if summary else "" except Exception as e: logger.warning(f"生成表格摘要失败: {e}") @@ -713,13 +713,31 @@ class KnowledgeBaseManager( ] } ], - max_tokens=512 + max_tokens=2048 # mimo-v2.5 推理模型思考链消耗 ~1000 token，需留足输出空间 ) description = response.choices[0].message.content + # 推理模型兼容：content 为空时从 reasoning_content 提取 + if not description or not description.strip(): + reasoning = getattr(response.choices[0].message, 'reasoning_content', None) + if reasoning and reasoning.strip(): + import re + # 尝试从思考链中提取有用文本（去掉 标签后的内容） + cleaned = re.sub(r'', '', reasoning, flags=re.DOTALL).strip() + if cleaned: + logger.info(f"VLM content为空，从reasoning_content提取描述: {image_path}") + description = cleaned + else: + description = reasoning.strip() + + if not description: + logger.warning(f"VLM 返回空描述: {image_path}") + return "" + # 缓存结果 import hashlib + import re as _re img_hash = hashlib.md5(img_path.read_bytes()).hexdigest() cache_dir = Path('.data/cache/vlm') cache_dir.mkdir(parents=True, exist_ok=True) diff --git a/services/feedback.py b/services/feedback.py index d31a5ae..223c6e0 100644 --- a/services/feedback.py +++ b/services/feedback.py @@ -616,7 +616,7 @@ class FeedbackService: prompt=prompt, model=self.model, temperature=0.7, - max_tokens=200 + max_tokens=512 ) if not response: diff --git a/services/session.py b/services/session.py index d2b84b1..bb71868 100644 --- a/services/session.py +++ b/services/session.py @@ -159,7 +159,7 @@ class SessionManager: SELECT id, role, content, metadata, created_at FROM messages WHERE session_id = ? - ORDER BY created_at DESC + ORDER BY created_at DESC, id DESC LIMIT ? ''', (session_id, limit))