feat: - vector

.gitignore

@@ -0,0 +1,10 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv

.python-version

@@ -0,0 +1 @@
+3.12

counter.py

@@ -0,0 +1,29 @@
+from collections import Counter, defaultdict
+
+words = ["apple", "banana", "apple", "orange", "banana", "apple"]
+# 统计每个单词出现的次数
+
+word_counter = Counter(words)
+print(word_counter)  # Counter({'apple': 3, 'banana': 2, 'orange': 1})
+
+# 获取出现次数最多的2个单词
+max_word = word_counter.most_common(2)
+print(max_word)  # [('apple', 3), ('banana', 2)]
+
+# 合并
+merged = Counter(["a", "b", "c"]) + Counter(["a", "c"])
+print(merged)  # Counter({'a': 2, 'c': 2, 'b': 1})
+
+
+# 创建一个以list为默认值的字典
+
+list_dict = defaultdict(list)
+list_dict["fruite"].append("apple")
+list_dict["fruite"].append("banner")
+print(list_dict)
+
+
+count_dict = defaultdict(int)
+for word in ["apple", "banana", "apple", "orange", "banana", "apple"]:
+    count_dict[word] += 1
+print(count_dict)

invert.py

@@ -0,0 +1,25 @@
+# 倒排索引
+index = {}
+# 文档存储
+documents = {}
+
+
+def add_document(doc_id, content):
+    documents[doc_id] = content
+    for word in content.lower().split():
+        if word not in index:
+            index[word] = []
+        if doc_id not in index[word]:
+            index[word].append(doc_id)
+
+
+def serch(keyword):
+    return index.get(keyword.lower(), [])
+
+
+add_document(1, "hello world")
+add_document(2, "world of python")
+add_document(3, "hello python")
+
+print(index)
+print(documents)

ivf_flat.py

@@ -0,0 +1,149 @@
+# 导入默认字典 defaultdict，便于自动初始化桶结构
+from collections import defaultdict
+
+# 导入数学库，提供 sqrt、pow 等基础数学函数
+import math
+
+# 导入随机库，支持随机抽样与随机数生成
+import random
+
+
+# 计算两个向量之间的欧氏距离（L2 距离）
+def l2_distance(a, b):
+    # zip 将两个向量按维度配对，平方差求和后开平方
+    return math.sqrt(sum((x - y) ** 2 for x, y in zip(a, b)))
+
+
+# K-Means++ 初始化：挑选 k 个初始中心，并做一次分配+更新
+def kmeans(vectors, k):
+    # 第一个中心直接从所有样本中随机挑选
+    centers = [random.choice(vectors)]
+
+    # 解释：下面这段 while 是 K-Means++ 的“轮盘赌”抽样逻辑
+    # 步骤：计算每个点到最近中心的距离平方 -> 得到概率权重 -> 依概率抽样新的中心
+
+    # 当中心数量不足 k 个时，持续抽样新的中心
+    while len(centers) < k:
+        # 第一步：每个数据点距离最近中心的平方，作为该点的权重
+        dists = [min(l2_distance(v, c) ** 2 for c in centers) for v in vectors]
+        # 第二步：对所有权重求和，得到轮盘赌的总长度
+        total = sum(dists)
+
+        # 第三步：在 [0, total) 区间上随机抽一个阈值 r，相当于轮盘指针
+        r = random.random() * total
+
+        # prefix 累加当前遍历到的权重，用于定位 r 落在哪个区间
+        prefix = 0.0
+
+        # 遍历所有点和对应权重，寻找累积首次超过 r 的那个点
+        for v, d in zip(vectors, dists):
+            # 不断累加权重，等价于在“条带”上向前走
+            prefix += d
+            # 一旦累计值覆盖了随机阈值 r，就把该点选为新中心
+            if prefix >= r:
+                centers.append(v)
+                break
+
+    # 初始化一个“簇字典”，key 是中心索引，value 是该中心下的样本列表
+    clusters = {i: [] for i in range(k)}
+
+    # 遍历所有向量，找到距离最近的中心，将该向量归入对应簇
+    for v in vectors:
+        idx = min(range(k), key=lambda i: l2_distance(v, centers[i]))
+        clusters[idx].append(v)
+    # 对每个非空簇计算均值，作为中心的最新位置
+    for i, points in clusters.items():
+        if points:
+            # zip(*points) 将点按维度拆开，sum(dim)/len(points) 是维度均值
+            centers[i] = [sum(dim) / len(points) for dim in zip(*points)]
+    # 返回完成一次“初始化+更新”的中心列表
+    return centers
+
+
+# 构建 IVF-Flat 索引：输出中心列表和倒排桶
+def build_ivf_flat(vectors, nlist):
+    # 先通过 K-Means++ 得到 nlist 个中心
+    centers = kmeans(vectors, nlist)
+
+    # defaultdict(list) 便于向桶里 append，无需检查 key 是否存在
+    buckets = defaultdict(list)
+
+    # 遍历所有向量，依据最近中心编号将其放入对应桶
+    for vec in vectors:
+        idx = min(range(nlist), key=lambda i: l2_distance(vec, centers[i]))
+        buckets[idx].append(vec)
+
+    # 返回构建好的中心列表与倒排结构
+    return centers, buckets
+
+
+# 在 IVF-Flat 索引上执行查询，近似返回 topk 个最近向量
+def search_ivf_flat(query, centers, buckets, nprobe, topk):
+    # 先按查询向量到各中心的距离排序，得到“探查顺序”
+    probe_order = sorted(
+        range(len(centers)), key=lambda i: l2_distance(query, centers[i])
+    )
+    print(probe_order)
+    print(buckets)
+
+    # 用列表保存候选 (距离, 向量) 对
+    candidate_pairs = []
+
+    # 只深入距离最近的 nprobe 个中心对应的桶
+    for idx in probe_order[:nprobe]:
+        for vec in buckets[idx]:
+            candidate_pairs.append((l2_distance(query, vec), vec))
+
+    # 候选列表按距离升序排列，便于截取前 topk 个
+    candidate_pairs.sort(key=lambda x: x[0])
+
+    print(candidate_pairs)
+
+    # 返回最接近查询的 topk 个候选
+    return candidate_pairs[:topk]
+
+
+# 主程序：演示 IVF-Flat 的构建与查询流程
+def main():
+    # 设定随机种子，保证示例结果可复现
+    random.seed(0)
+
+    # 构造 8 个二维向量作为“数据库”
+    database = [
+        [0.1, 0.2],
+        [0.15, 0.18],
+        [0.8, 0.85],
+        [0.82, 0.79],
+        [0.4, 0.4],
+        [0.42, 0.45],
+        [0.9, 0.1],
+        [0.88, 0.15],
+    ]
+
+    # 查询向量：我们要找与它相似的数据库向量
+    query = [0.12, 0.22]
+
+    # nlist 表示建索引时划分的中心（簇）个数
+    nlist = 4
+
+    # nprobe 表示查询时会探查多少个最近的中心
+    nprobe = 2
+
+    # 构建 IVF-Flat 索引，得到中心与倒排桶
+    centers, buckets = build_ivf_flat(database, nlist=nlist)
+
+    # 执行检索，返回距离最近的 top3 个结果
+    results = search_ivf_flat(query, centers, buckets, nprobe=nprobe, topk=3)
+
+    # 打印本次检索的参数设置
+    print(f"nlist={nlist}, nprobe={nprobe}")
+
+    # 打印检索到的候选向量及其距离
+    print("最近向量：")
+    for dist, vec in results:
+        print(f"向量={vec}, 距离={dist:.4f}")
+
+
+# Python 入口：若当前脚本以 main 方式执行，则运行示例
+if __name__ == "__main__":
+    main()

jieba-student.py

@@ -0,0 +1,37 @@
+import jieba
+import jieba.analyse
+
+text = "我爱自然语言处理技术"
+result = jieba.cut(text, cut_all=False)
+print("精确模式:", "/".join(result))  # 精确模式: 我/爱/自然语言/处理/技术
+
+result = jieba.cut(text, cut_all=True)
+print("全模式:", "/".join(result))  # 全模式: 我/爱/自然/自然语言/语言/处理/技术
+
+result = jieba.cut_for_search(text)
+print(
+    "搜索引擎模式：", "/".join(result)
+)  # 搜索引擎模式： 我/爱/自然/语言/自然语言/处理/技术
+
+
+# 关键词提取
+def main():
+    text = (
+        "自然语言处理是人工智能和语言学领域的重要分支，"
+        "研究如何让计算机理解和生成人类语言。"
+    )
+    # 使用使用 TF-IDF 算法获取关键词及权重
+    tfidy_keywords = jieba.analyse.extract_tags(text, topK=5, withWeight=True)
+    # 说明：使用 TextRank 算法获取关键词及权重
+    textrank_keywords = jieba.analyse.textrank(text, topK=5, withWeight=True)
+
+    print("TF-IDF")
+    for word, weight in tfidy_keywords:
+        print(f"{word}:{weight:.2f}")
+    # 说明：输出 TextRank 结果
+    print("\nTextRank：")
+    for word, weight in textrank_keywords:
+        print(f"{word}: {weight:.4f}")
+
+
+main()

jieba_anli.py

@@ -0,0 +1,55 @@
+from collections import defaultdict
+import jieba
+
+# 定义示例文档数据，每个文档包含“id”、“分区键”、“内容”
+documents = [
+    {"id": 1, "partition_key": "张", "content": "张三喜欢编程和电脑游戏"},
+    {"id": 2, "partition_key": "李", "content": "李四热爱计算机科学"},
+    {"id": 3, "partition_key": "王", "content": "王五喜欢阅读技术书籍"},
+    {"id": 4, "partition_key": "张", "content": "张杰钟爱笔记本电脑"},
+]
+
+# 定义一个同义词的映射 键为代表词，值为同义词的集合 用于扩展匹配
+synonym_map = {
+    "电脑": {"电脑", "计算机", "PC"},
+    "编程": {"编程", "软件开发", "程序设计"},
+}
+
+
+# 聚合文档，将同类的数据进行聚合
+def build_partitions(docs):
+    partitions = defaultdict(list)
+    for doc in docs:
+        partitions[doc["partition_key"]].append(doc)
+    return partitions
+
+
+# 通过jieba进行分词
+def tokenize(text):
+    return [token for token in jieba.lcut_for_search(text) if token.strip()]
+
+
+# 进行分词匹配
+def match_query(partitions, keywords, use_synonym=True):
+    tokens_to_match = {keywords}
+    if keywords in synonym_map and use_synonym:
+        tokens_to_match = synonym_map[keywords]  # {"电脑", "计算机", "PC"}
+    result = []
+    for key, docs in partitions.items():
+        for doc in docs:
+            tokens = tokenize(doc["content"])  # 通过jieba进行分词
+            # 如果有交集，则说明匹配
+            if tokens_to_match.intersection(tokens):
+                result.append((key, doc["id"], doc["content"]))
+    return result
+
+
+# 1.使用分区键进行聚合文档
+partitions = build_partitions(documents)
+# print(
+#     partitions
+# )  # {'张': [{'id': 1, 'partition_key': '张', 'content': '张三喜欢编程和电脑游戏'}, {'id': 4, 'partition_key': '张', 'content': '张杰钟爱笔记本电脑'}], '李': [{'id': 2, 'partition_key': '李', 'content': '李四热爱计算机科学'}], '王': [{'id': 3, 'partition_key': '王', 'content': '王五喜欢阅读技术书籍'}]})
+
+# 2. 进行分词匹配
+hits = match_query(partitions, keywords="电脑", use_synonym=True)
+print(hits)

main.py

@@ -0,0 +1,9 @@
+import jieba
+
+
+def main():
+    print("Hello from 02vector!")
+
+
+if __name__ == "__main__":
+    main()

milvus.py

@@ -0,0 +1,30 @@
+# 通过pymilvus sdk来操作数据库
+
+from pymilvus import connections, utility
+
+
+def test_connection(
+    host: str = "49.233.56.55", port: str = "19530", db_name: str = "default"
+) -> bool:
+    try:
+        # 建立与milvus的连接
+        connections.connect(
+            "default", host=host, port=port, timeout=10, db_name=db_name
+        )
+        # 打印当前连接到的数据库名称
+        print(f"当前连接到的数据库为: {db_name}")
+        # 获取搜友集合并打印
+        collections = utility.list_collections()
+        print(f"数据库{db_name}下的所有数据集合{collections}")
+        # 连接成功提示
+        print("连接成功：已成功连接到 Milvus 服务器")
+        return True
+
+    except Exception as exc:
+        print(f"连接失败，无法连接到Milvus服务器的数据库{db_name}", exc)
+        return False
+
+
+if __name__ == "__main__":
+    if not test_connection():
+        raise SystemExit(1)

milvus/docker-compose.yml

@@ -0,0 +1,117 @@
+# 指定Docker Compose文件格式版本为3.5
+version: "3.5"
+
+# 定义服务组
+services:
+  # etcd服务配置，用于Milvus的元数据存储
+  etcd:
+    # 容器名称设置为milvus-etcd
+    container_name: milvus-etcd
+    # 使用coreos的etcd镜像，版本为v3.5.5
+    image: quay.io/coreos/etcd:v3.5.5
+    # 设置etcd的环境变量
+    environment:
+      # 设置自动压缩模式为revision
+      - ETCD_AUTO_COMPACTION_MODE=revision
+      # 设置自动压缩保留1000个版本
+      - ETCD_AUTO_COMPACTION_RETENTION=1000
+      # 设置后端存储配额为4GB
+      - ETCD_QUOTA_BACKEND_BYTES=4294967296
+      # 设置快照计数为50000
+      - ETCD_SNAPSHOT_COUNT=50000
+    # 挂载卷，将本地目录映射到容器内的/etcd目录
+    volumes:
+      - ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/etcd:/etcd
+    # 启动命令，配置etcd服务器
+    command: etcd -advertise-client-urls=http://127.0.0.1:2379 -listen-client-urls http://0.0.0.0:2379 --data-dir /etcd
+    # 健康检查配置
+    healthcheck:
+      # 使用etcdctl检查端点健康状态
+      test: ["CMD", "etcdctl", "endpoint", "health"]
+      # 每30秒检查一次
+      interval: 30s
+      # 超时时间为20秒
+      timeout: 20s
+      # 重试3次
+      retries: 3
+
+  # MinIO服务配置，用于Milvus的对象存储
+  minio:
+    # 容器名称设置为milvus-minio
+    container_name: milvus-minio
+    # 使用MinIO镜像，指定版本
+    image: minio/minio:RELEASE.2023-03-20T20-16-18Z
+    # 设置MinIO的环境变量
+    environment:
+      # 设置访问密钥
+      MINIO_ACCESS_KEY: minioadmin
+      # 设置密钥
+      MINIO_SECRET_KEY: minioadmin
+    # 端口映射，将容器的9001和9000端口映射到主机
+    ports:
+      - "9001:9001"
+      - "9000:9000"
+    # 挂载卷，将本地目录映射到容器内的/minio_data目录
+    volumes:
+      - ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/minio:/minio_data
+    # 启动命令，配置MinIO服务器和控制台地址
+    command: minio server /minio_data --console-address ":9001"
+    # 健康检查配置
+    healthcheck:
+      # 使用curl检查MinIO健康状态
+      test: ["CMD", "curl", "-f", "http://localhost:9000/minio/health/live"]
+      # 每30秒检查一次
+      interval: 30s
+      # 超时时间为20秒
+      timeout: 20s
+      # 重试3次
+      retries: 3
+
+  # Milvus独立服务配置
+  standalone:
+    # 容器名称设置为milvus-standalone
+    container_name: milvus-standalone
+    # 使用Milvus镜像，版本为v2.4.15
+    image: milvusdb/milvus:v2.4.15
+    # 启动命令，以独立模式运行Milvus
+    command: ["milvus", "run", "standalone"]
+    # 安全选项配置
+    security_opt:
+      # 禁用seccomp配置文件
+      - seccomp:unconfined
+    # 设置Milvus的环境变量
+    environment:
+      # 设置etcd端点地址
+      ETCD_ENDPOINTS: etcd:2379
+      # 设置MinIO地址
+      MINIO_ADDRESS: minio:9000
+    # 挂载卷，将本地目录映射到容器内的/var/lib/milvus目录
+    volumes:
+      - ${DOCKER_VOLUME_DIRECTORY:-.}/volumes/milvus:/var/lib/milvus
+    # 健康检查配置
+    healthcheck:
+      # 使用curl检查Milvus健康状态
+      test: ["CMD", "curl", "-f", "http://localhost:9091/healthz"]
+      # 每30秒检查一次
+      interval: 30s
+      # 启动后等待90秒开始检查
+      start_period: 90s
+      # 超时时间为20秒
+      timeout: 20s
+      # 重试3次
+      retries: 3
+    # 端口映射，将容器的19530和9091端口映射到主机
+    ports:
+      - "19530:19530"
+      - "9091:9091"
+    # 依赖关系，确保etcd和minio服务先启动
+    depends_on:
+      - "etcd"
+      - "minio"
+
+# 网络配置
+networks:
+  # 默认网络配置
+  default:
+    # 网络名称设置为milvus
+    name: milvus

milvus/volumes/milvus/rdb_data/CURRENT

@@ -0,0 +1 @@
+MANIFEST-000004

milvus/volumes/milvus/rdb_data/IDENTITY

@@ -0,0 +1 @@
+4c50d056-9c9b-4428-87e1-27b0c3e246bb

milvus/volumes/milvus/rdb_data/OPTIONS-000007

@@ -0,0 +1,288 @@
+# This is a RocksDB option file.
+#
+# For detailed file format spec, please refer to the example file
+# in examples/rocksdb_option_file_example.ini
+#
+
+[Version]
+  rocksdb_version=6.29.5
+  options_file_version=1.1
+
+[DBOptions]
+  compaction_readahead_size=0
+  strict_bytes_per_sync=false
+  bytes_per_sync=0
+  max_background_jobs=1
+  base_background_compactions=-1
+  avoid_flush_during_shutdown=false
+  max_background_flushes=1
+  delayed_write_rate=16777216
+  max_open_files=-1
+  max_subcompactions=1
+  writable_file_max_buffer_size=1048576
+  wal_bytes_per_sync=0
+  max_background_compactions=-1
+  max_total_wal_size=0
+  delete_obsolete_files_period_micros=21600000000
+  stats_dump_period_sec=600
+  stats_history_buffer_size=1048576
+  stats_persist_period_sec=600
+  lowest_used_cache_tier=kNonVolatileBlockTier
+  bgerror_resume_retry_interval=1000000
+  best_efforts_recovery=false
+  log_readahead_size=0
+  write_dbid_to_manifest=false
+  atomic_flush=false
+  manual_wal_flush=false
+  db_host_id=__hostname__
+  two_write_queues=false
+  rate_limiter=nullptr
+  random_access_max_buffer_size=1048576
+  avoid_unnecessary_blocking_io=false
+  skip_checking_sst_file_sizes_on_db_open=false
+  flush_verify_memtable_count=true
+  fail_if_options_file_error=false
+  track_and_verify_wals_in_manifest=false
+  experimental_mempurge_threshold=0.000000
+  paranoid_checks=true
+  create_if_missing=true
+  max_write_batch_group_size_bytes=1048576
+  avoid_flush_during_recovery=false
+  skip_stats_update_on_db_open=false
+  file_checksum_gen_factory=nullptr
+  enable_thread_tracking=false
+  use_fsync=false
+  allow_fallocate=true
+  preserve_deletes=false
+  new_table_reader_for_compaction_inputs=false
+  allow_data_in_errors=false
+  error_if_exists=false
+  use_direct_io_for_flush_and_compaction=false
+  create_missing_column_families=true
+  WAL_size_limit_MB=0
+  use_direct_reads=false
+  persist_stats_to_disk=false
+  allow_mmap_reads=false
+  allow_mmap_writes=false
+  use_adaptive_mutex=false
+  allow_2pc=false
+  is_fd_close_on_exec=true
+  max_log_file_size=0
+  access_hint_on_compaction_start=NORMAL
+  max_file_opening_threads=16
+  wal_filter=nullptr
+  table_cache_numshardbits=6
+  dump_malloc_stats=false
+  db_write_buffer_size=0
+  allow_ingest_behind=false
+  keep_log_file_num=1000
+  max_bgerror_resume_count=2147483647
+  allow_concurrent_memtable_write=true
+  recycle_log_file_num=0
+  log_file_time_to_roll=0
+  manifest_preallocation_size=4194304
+  enable_write_thread_adaptive_yield=true
+  WAL_ttl_seconds=0
+  max_manifest_file_size=1073741824
+  wal_recovery_mode=kPointInTimeRecovery
+  enable_pipelined_write=false
+  write_thread_slow_yield_usec=3
+  unordered_write=false
+  write_thread_max_yield_usec=100
+  advise_random_on_open=true
+  info_log_level=INFO_LEVEL
+  
+
+[CFOptions "default"]
+  compression_opts={max_dict_buffer_bytes=0;enabled=false;max_dict_bytes=0;parallel_threads=1;zstd_max_train_bytes=0;level=32767;strategy=0;window_bits=-14;}
+  bottommost_compression=kDisableCompressionOption
+  enable_blob_garbage_collection=false
+  blob_file_size=268435456
+  sample_for_compression=0
+  periodic_compaction_seconds=0
+  ttl=2592000
+  blob_garbage_collection_age_cutoff=0.250000
+  compaction_options_universal={incremental=false;compression_size_percent=-1;allow_trivial_move=false;max_size_amplification_percent=200;max_merge_width=4294967295;stop_style=kCompactionStopStyleTotalSize;min_merge_width=2;size_ratio=1;}
+  compression=kNoCompression
+  max_sequential_skip_in_iterations=8
+  max_bytes_for_level_multiplier_additional=1:1:1:1:1:1:1
+  max_bytes_for_level_multiplier=10.000000
+  min_blob_size=0
+  check_flush_compaction_key_order=true
+  disable_auto_compactions=false
+  bottommost_compression_opts={max_dict_buffer_bytes=0;enabled=false;max_dict_bytes=0;parallel_threads=1;zstd_max_train_bytes=0;level=32767;strategy=0;window_bits=-14;}
+  compaction_options_fifo={allow_compaction=false;age_for_warm=0;max_table_files_size=1073741824;}
+  level0_file_num_compaction_trigger=4
+  target_file_size_base=67108864
+  soft_pending_compaction_bytes_limit=68719476736
+  hard_pending_compaction_bytes_limit=274877906944
+  level0_slowdown_writes_trigger=20
+  blob_compression_type=kNoCompression
+  level0_stop_writes_trigger=36
+  enable_blob_files=false
+  blob_garbage_collection_force_threshold=1.000000
+  paranoid_file_checks=false
+  prefix_extractor=nullptr
+  max_write_buffer_number=2
+  report_bg_io_stats=false
+  memtable_prefix_bloom_size_ratio=0.000000
+  target_file_size_multiplier=2
+  arena_block_size=1048576
+  blob_compaction_readahead_size=0
+  inplace_update_num_locks=10000
+  max_compaction_bytes=1677721600
+  write_buffer_size=67108864
+  memtable_huge_page_size=0
+  max_successive_merges=0
+  max_bytes_for_level_base=268435456
+  memtable_whole_key_filtering=false
+  compaction_pri=kMinOverlappingRatio
+  compaction_filter_factory=nullptr
+  comparator=leveldb.BytewiseComparator
+  table_factory=BlockBasedTable
+  inplace_update_support=false
+  max_write_buffer_number_to_maintain=0
+  bloom_locality=0
+  compaction_filter=nullptr
+  level_compaction_dynamic_level_bytes=false
+  optimize_filters_for_hits=false
+  merge_operator=nullptr
+  max_write_buffer_size_to_maintain=0
+  sst_partitioner_factory=nullptr
+  compaction_style=kCompactionStyleLevel
+  min_write_buffer_number_to_merge=1
+  memtable_factory=SkipListFactory
+  memtable_insert_with_hint_prefix_extractor=nullptr
+  compression_per_level=kNoCompression:kNoCompression:kZSTD:kZSTD:kZSTD
+  force_consistency_checks=true
+  num_levels=5
+  
+[TableOptions/BlockBasedTable "default"]
+  block_size_deviation=10
+  checksum=kCRC32c
+  index_shortening=kShortenSeparators
+  whole_key_filtering=true
+  data_block_index_type=kDataBlockBinarySearch
+  index_type=kBinarySearch
+  no_block_cache=false
+  index_block_restart_interval=1
+  data_block_hash_table_util_ratio=0.750000
+  prepopulate_block_cache=kDisable
+  pin_l0_filter_and_index_blocks_in_cache=false
+  filter_policy=nullptr
+  cache_index_and_filter_blocks_with_high_priority=true
+  verify_compression=false
+  block_restart_interval=16
+  max_auto_readahead_size=262144
+  hash_index_allow_collision=true
+  flush_block_policy_factory=FlushBlockBySizePolicyFactory
+  partition_filters=false
+  cache_index_and_filter_blocks=false
+  block_size=65536
+  reserve_table_builder_memory=false
+  metadata_block_size=4096
+  block_align=false
+  optimize_filters_for_memory=false
+  format_version=5
+  metadata_cache_options={unpartitioned_pinning=kFallback;partition_pinning=kFallback;top_level_index_pinning=kFallback;}
+  read_amp_bytes_per_bit=0
+  enable_index_compression=true
+  pin_top_level_index_and_filter=true
+  
+
+[CFOptions "properties"]
+  compression_opts={max_dict_buffer_bytes=0;enabled=false;max_dict_bytes=0;parallel_threads=1;zstd_max_train_bytes=0;level=32767;strategy=0;window_bits=-14;}
+  bottommost_compression=kDisableCompressionOption
+  enable_blob_garbage_collection=false
+  blob_file_size=268435456
+  sample_for_compression=0
+  periodic_compaction_seconds=0
+  ttl=2592000
+  blob_garbage_collection_age_cutoff=0.250000
+  compaction_options_universal={incremental=false;compression_size_percent=-1;allow_trivial_move=false;max_size_amplification_percent=200;max_merge_width=4294967295;stop_style=kCompactionStopStyleTotalSize;min_merge_width=2;size_ratio=1;}
+  compression=kNoCompression
+  max_sequential_skip_in_iterations=8
+  max_bytes_for_level_multiplier_additional=1:1:1:1:1:1:1
+  max_bytes_for_level_multiplier=10.000000
+  min_blob_size=0
+  check_flush_compaction_key_order=true
+  disable_auto_compactions=false
+  bottommost_compression_opts={max_dict_buffer_bytes=0;enabled=false;max_dict_bytes=0;parallel_threads=1;zstd_max_train_bytes=0;level=32767;strategy=0;window_bits=-14;}
+  compaction_options_fifo={allow_compaction=false;age_for_warm=0;max_table_files_size=1073741824;}
+  level0_file_num_compaction_trigger=4
+  target_file_size_base=67108864
+  soft_pending_compaction_bytes_limit=68719476736
+  hard_pending_compaction_bytes_limit=274877906944
+  level0_slowdown_writes_trigger=20
+  blob_compression_type=kNoCompression
+  level0_stop_writes_trigger=36
+  enable_blob_files=false
+  blob_garbage_collection_force_threshold=1.000000
+  paranoid_file_checks=false
+  prefix_extractor=nullptr
+  max_write_buffer_number=2
+  report_bg_io_stats=false
+  memtable_prefix_bloom_size_ratio=0.000000
+  target_file_size_multiplier=2
+  arena_block_size=1048576
+  blob_compaction_readahead_size=0
+  inplace_update_num_locks=10000
+  max_compaction_bytes=1677721600
+  write_buffer_size=67108864
+  memtable_huge_page_size=0
+  max_successive_merges=0
+  max_bytes_for_level_base=268435456
+  memtable_whole_key_filtering=false
+  compaction_pri=kMinOverlappingRatio
+  compaction_filter_factory=nullptr
+  comparator=leveldb.BytewiseComparator
+  table_factory=BlockBasedTable
+  inplace_update_support=false
+  max_write_buffer_number_to_maintain=0
+  bloom_locality=0
+  compaction_filter=nullptr
+  level_compaction_dynamic_level_bytes=false
+  optimize_filters_for_hits=false
+  merge_operator=nullptr
+  max_write_buffer_size_to_maintain=0
+  sst_partitioner_factory=nullptr
+  compaction_style=kCompactionStyleLevel
+  min_write_buffer_number_to_merge=1
+  memtable_factory=SkipListFactory
+  memtable_insert_with_hint_prefix_extractor=nullptr
+  compression_per_level=kNoCompression:kNoCompression:kZSTD:kZSTD:kZSTD
+  force_consistency_checks=true
+  num_levels=5
+  
+[TableOptions/BlockBasedTable "properties"]
+  block_size_deviation=10
+  checksum=kCRC32c
+  index_shortening=kShortenSeparators
+  whole_key_filtering=true
+  data_block_index_type=kDataBlockBinarySearch
+  index_type=kBinarySearch
+  no_block_cache=false
+  index_block_restart_interval=1
+  data_block_hash_table_util_ratio=0.750000
+  prepopulate_block_cache=kDisable
+  pin_l0_filter_and_index_blocks_in_cache=false
+  filter_policy=nullptr
+  cache_index_and_filter_blocks_with_high_priority=true
+  verify_compression=false
+  block_restart_interval=16
+  max_auto_readahead_size=262144
+  hash_index_allow_collision=true
+  flush_block_policy_factory=FlushBlockBySizePolicyFactory
+  partition_filters=false
+  cache_index_and_filter_blocks=false
+  block_size=65536
+  reserve_table_builder_memory=false
+  metadata_block_size=4096
+  block_align=false
+  optimize_filters_for_memory=false
+  format_version=5
+  metadata_cache_options={unpartitioned_pinning=kFallback;partition_pinning=kFallback;top_level_index_pinning=kFallback;}
+  read_amp_bytes_per_bit=0
+  enable_index_compression=true
+  pin_top_level_index_and_filter=true
+  

milvus/volumes/milvus/rdb_data/OPTIONS-000009

@@ -0,0 +1,288 @@
+# This is a RocksDB option file.
+#
+# For detailed file format spec, please refer to the example file
+# in examples/rocksdb_option_file_example.ini
+#
+
+[Version]
+  rocksdb_version=6.29.5
+  options_file_version=1.1
+
+[DBOptions]
+  compaction_readahead_size=0
+  strict_bytes_per_sync=false
+  bytes_per_sync=0
+  max_background_jobs=1
+  base_background_compactions=-1
+  avoid_flush_during_shutdown=false
+  max_background_flushes=1
+  delayed_write_rate=16777216
+  max_open_files=-1
+  max_subcompactions=1
+  writable_file_max_buffer_size=1048576
+  wal_bytes_per_sync=0
+  max_background_compactions=-1
+  max_total_wal_size=0
+  delete_obsolete_files_period_micros=21600000000
+  stats_dump_period_sec=600
+  stats_history_buffer_size=1048576
+  stats_persist_period_sec=600
+  lowest_used_cache_tier=kNonVolatileBlockTier
+  bgerror_resume_retry_interval=1000000
+  best_efforts_recovery=false
+  log_readahead_size=0
+  write_dbid_to_manifest=false
+  atomic_flush=false
+  manual_wal_flush=false
+  db_host_id=__hostname__
+  two_write_queues=false
+  rate_limiter=nullptr
+  random_access_max_buffer_size=1048576
+  avoid_unnecessary_blocking_io=false
+  skip_checking_sst_file_sizes_on_db_open=false
+  flush_verify_memtable_count=true
+  fail_if_options_file_error=false
+  track_and_verify_wals_in_manifest=false
+  experimental_mempurge_threshold=0.000000
+  paranoid_checks=true
+  create_if_missing=true
+  max_write_batch_group_size_bytes=1048576
+  avoid_flush_during_recovery=false
+  skip_stats_update_on_db_open=false
+  file_checksum_gen_factory=nullptr
+  enable_thread_tracking=false
+  use_fsync=false
+  allow_fallocate=true
+  preserve_deletes=false
+  new_table_reader_for_compaction_inputs=false
+  allow_data_in_errors=false
+  error_if_exists=false
+  use_direct_io_for_flush_and_compaction=false
+  create_missing_column_families=true
+  WAL_size_limit_MB=0
+  use_direct_reads=false
+  persist_stats_to_disk=false
+  allow_mmap_reads=false
+  allow_mmap_writes=false
+  use_adaptive_mutex=false
+  allow_2pc=false
+  is_fd_close_on_exec=true
+  max_log_file_size=0
+  access_hint_on_compaction_start=NORMAL
+  max_file_opening_threads=16
+  wal_filter=nullptr
+  table_cache_numshardbits=6
+  dump_malloc_stats=false
+  db_write_buffer_size=0
+  allow_ingest_behind=false
+  keep_log_file_num=1000
+  max_bgerror_resume_count=2147483647
+  allow_concurrent_memtable_write=true
+  recycle_log_file_num=0
+  log_file_time_to_roll=0
+  manifest_preallocation_size=4194304
+  enable_write_thread_adaptive_yield=true
+  WAL_ttl_seconds=0
+  max_manifest_file_size=1073741824
+  wal_recovery_mode=kPointInTimeRecovery
+  enable_pipelined_write=false
+  write_thread_slow_yield_usec=3
+  unordered_write=false
+  write_thread_max_yield_usec=100
+  advise_random_on_open=true
+  info_log_level=INFO_LEVEL
+  
+
+[CFOptions "default"]
+  compression_opts={max_dict_buffer_bytes=0;enabled=false;max_dict_bytes=0;parallel_threads=1;zstd_max_train_bytes=0;level=32767;strategy=0;window_bits=-14;}
+  bottommost_compression=kDisableCompressionOption
+  enable_blob_garbage_collection=false
+  blob_file_size=268435456
+  sample_for_compression=0
+  periodic_compaction_seconds=0
+  ttl=2592000
+  blob_garbage_collection_age_cutoff=0.250000
+  compaction_options_universal={incremental=false;compression_size_percent=-1;allow_trivial_move=false;max_size_amplification_percent=200;max_merge_width=4294967295;stop_style=kCompactionStopStyleTotalSize;min_merge_width=2;size_ratio=1;}
+  compression=kNoCompression
+  max_sequential_skip_in_iterations=8
+  max_bytes_for_level_multiplier_additional=1:1:1:1:1:1:1
+  max_bytes_for_level_multiplier=10.000000
+  min_blob_size=0
+  check_flush_compaction_key_order=true
+  disable_auto_compactions=false
+  bottommost_compression_opts={max_dict_buffer_bytes=0;enabled=false;max_dict_bytes=0;parallel_threads=1;zstd_max_train_bytes=0;level=32767;strategy=0;window_bits=-14;}
+  compaction_options_fifo={allow_compaction=false;age_for_warm=0;max_table_files_size=1073741824;}
+  level0_file_num_compaction_trigger=4
+  target_file_size_base=67108864
+  soft_pending_compaction_bytes_limit=68719476736
+  hard_pending_compaction_bytes_limit=274877906944
+  level0_slowdown_writes_trigger=20
+  blob_compression_type=kNoCompression
+  level0_stop_writes_trigger=36
+  enable_blob_files=false
+  blob_garbage_collection_force_threshold=1.000000
+  paranoid_file_checks=false
+  prefix_extractor=nullptr
+  max_write_buffer_number=2
+  report_bg_io_stats=false
+  memtable_prefix_bloom_size_ratio=0.000000
+  target_file_size_multiplier=2
+  arena_block_size=1048576
+  blob_compaction_readahead_size=0
+  inplace_update_num_locks=10000
+  max_compaction_bytes=1677721600
+  write_buffer_size=67108864
+  memtable_huge_page_size=0
+  max_successive_merges=0
+  max_bytes_for_level_base=268435456
+  memtable_whole_key_filtering=false
+  compaction_pri=kMinOverlappingRatio
+  compaction_filter_factory=nullptr
+  comparator=leveldb.BytewiseComparator
+  table_factory=BlockBasedTable
+  inplace_update_support=false
+  max_write_buffer_number_to_maintain=0
+  bloom_locality=0
+  compaction_filter=nullptr
+  level_compaction_dynamic_level_bytes=false
+  optimize_filters_for_hits=false
+  merge_operator=nullptr
+  max_write_buffer_size_to_maintain=0
+  sst_partitioner_factory=nullptr
+  compaction_style=kCompactionStyleLevel
+  min_write_buffer_number_to_merge=1
+  memtable_factory=SkipListFactory
+  memtable_insert_with_hint_prefix_extractor=nullptr
+  compression_per_level=kNoCompression:kNoCompression:kZSTD:kZSTD:kZSTD
+  force_consistency_checks=true
+  num_levels=5
+  
+[TableOptions/BlockBasedTable "default"]
+  block_size_deviation=10
+  checksum=kCRC32c
+  index_shortening=kShortenSeparators
+  whole_key_filtering=true
+  data_block_index_type=kDataBlockBinarySearch
+  index_type=kBinarySearch
+  no_block_cache=false
+  index_block_restart_interval=1
+  data_block_hash_table_util_ratio=0.750000
+  prepopulate_block_cache=kDisable
+  pin_l0_filter_and_index_blocks_in_cache=false
+  filter_policy=nullptr
+  cache_index_and_filter_blocks_with_high_priority=true
+  verify_compression=false
+  block_restart_interval=16
+  max_auto_readahead_size=262144
+  hash_index_allow_collision=true
+  flush_block_policy_factory=FlushBlockBySizePolicyFactory
+  partition_filters=false
+  cache_index_and_filter_blocks=false
+  block_size=65536
+  reserve_table_builder_memory=false
+  metadata_block_size=4096
+  block_align=false
+  optimize_filters_for_memory=false
+  format_version=5
+  metadata_cache_options={unpartitioned_pinning=kFallback;partition_pinning=kFallback;top_level_index_pinning=kFallback;}
+  read_amp_bytes_per_bit=0
+  enable_index_compression=true
+  pin_top_level_index_and_filter=true
+  
+
+[CFOptions "properties"]
+  compression_opts={max_dict_buffer_bytes=0;enabled=false;max_dict_bytes=0;parallel_threads=1;zstd_max_train_bytes=0;level=32767;strategy=0;window_bits=-14;}
+  bottommost_compression=kDisableCompressionOption
+  enable_blob_garbage_collection=false
+  blob_file_size=268435456
+  sample_for_compression=0
+  periodic_compaction_seconds=0
+  ttl=2592000
+  blob_garbage_collection_age_cutoff=0.250000
+  compaction_options_universal={incremental=false;compression_size_percent=-1;allow_trivial_move=false;max_size_amplification_percent=200;max_merge_width=4294967295;stop_style=kCompactionStopStyleTotalSize;min_merge_width=2;size_ratio=1;}
+  compression=kNoCompression
+  max_sequential_skip_in_iterations=8
+  max_bytes_for_level_multiplier_additional=1:1:1:1:1:1:1
+  max_bytes_for_level_multiplier=10.000000
+  min_blob_size=0
+  check_flush_compaction_key_order=true
+  disable_auto_compactions=false
+  bottommost_compression_opts={max_dict_buffer_bytes=0;enabled=false;max_dict_bytes=0;parallel_threads=1;zstd_max_train_bytes=0;level=32767;strategy=0;window_bits=-14;}
+  compaction_options_fifo={allow_compaction=false;age_for_warm=0;max_table_files_size=1073741824;}
+  level0_file_num_compaction_trigger=4
+  target_file_size_base=67108864
+  soft_pending_compaction_bytes_limit=68719476736
+  hard_pending_compaction_bytes_limit=274877906944
+  level0_slowdown_writes_trigger=20
+  blob_compression_type=kNoCompression
+  level0_stop_writes_trigger=36
+  enable_blob_files=false
+  blob_garbage_collection_force_threshold=1.000000
+  paranoid_file_checks=false
+  prefix_extractor=nullptr
+  max_write_buffer_number=2
+  report_bg_io_stats=false
+  memtable_prefix_bloom_size_ratio=0.000000
+  target_file_size_multiplier=2
+  arena_block_size=1048576
+  blob_compaction_readahead_size=0
+  inplace_update_num_locks=10000
+  max_compaction_bytes=1677721600
+  write_buffer_size=67108864
+  memtable_huge_page_size=0
+  max_successive_merges=0
+  max_bytes_for_level_base=268435456
+  memtable_whole_key_filtering=false
+  compaction_pri=kMinOverlappingRatio
+  compaction_filter_factory=nullptr
+  comparator=leveldb.BytewiseComparator
+  table_factory=BlockBasedTable
+  inplace_update_support=false
+  max_write_buffer_number_to_maintain=0
+  bloom_locality=0
+  compaction_filter=nullptr
+  level_compaction_dynamic_level_bytes=false
+  optimize_filters_for_hits=false
+  merge_operator=nullptr
+  max_write_buffer_size_to_maintain=0
+  sst_partitioner_factory=nullptr
+  compaction_style=kCompactionStyleLevel
+  min_write_buffer_number_to_merge=1
+  memtable_factory=SkipListFactory
+  memtable_insert_with_hint_prefix_extractor=nullptr
+  compression_per_level=kNoCompression:kNoCompression:kZSTD:kZSTD:kZSTD
+  force_consistency_checks=true
+  num_levels=5
+  
+[TableOptions/BlockBasedTable "properties"]
+  block_size_deviation=10
+  checksum=kCRC32c
+  index_shortening=kShortenSeparators
+  whole_key_filtering=true
+  data_block_index_type=kDataBlockBinarySearch
+  index_type=kBinarySearch
+  no_block_cache=false
+  index_block_restart_interval=1
+  data_block_hash_table_util_ratio=0.750000
+  prepopulate_block_cache=kDisable
+  pin_l0_filter_and_index_blocks_in_cache=false
+  filter_policy=nullptr
+  cache_index_and_filter_blocks_with_high_priority=true
+  verify_compression=false
+  block_restart_interval=16
+  max_auto_readahead_size=262144
+  hash_index_allow_collision=true
+  flush_block_policy_factory=FlushBlockBySizePolicyFactory
+  partition_filters=false
+  cache_index_and_filter_blocks=false
+  block_size=65536
+  reserve_table_builder_memory=false
+  metadata_block_size=4096
+  block_align=false
+  optimize_filters_for_memory=false
+  format_version=5
+  metadata_cache_options={unpartitioned_pinning=kFallback;partition_pinning=kFallback;top_level_index_pinning=kFallback;}
+  read_amp_bytes_per_bit=0
+  enable_index_compression=true
+  pin_top_level_index_and_filter=true
+  

milvus/volumes/milvus/rdb_data_meta_kv/CURRENT

@@ -0,0 +1 @@
+MANIFEST-000004

milvus/volumes/milvus/rdb_data_meta_kv/IDENTITY

@@ -0,0 +1 @@
+f882c1ff-f620-4fde-b5cb-e467f48d19e2

milvus/volumes/milvus/rdb_data_meta_kv/OPTIONS-000007

@@ -0,0 +1,191 @@
+# This is a RocksDB option file.
+#
+# For detailed file format spec, please refer to the example file
+# in examples/rocksdb_option_file_example.ini
+#
+
+[Version]
+  rocksdb_version=6.29.5
+  options_file_version=1.1
+
+[DBOptions]
+  compaction_readahead_size=0
+  strict_bytes_per_sync=false
+  bytes_per_sync=0
+  max_background_jobs=1
+  base_background_compactions=-1
+  avoid_flush_during_shutdown=false
+  max_background_flushes=1
+  delayed_write_rate=16777216
+  max_open_files=-1
+  max_subcompactions=1
+  writable_file_max_buffer_size=1048576
+  wal_bytes_per_sync=0
+  max_background_compactions=-1
+  max_total_wal_size=0
+  delete_obsolete_files_period_micros=21600000000
+  stats_dump_period_sec=600
+  stats_history_buffer_size=1048576
+  stats_persist_period_sec=600
+  lowest_used_cache_tier=kNonVolatileBlockTier
+  bgerror_resume_retry_interval=1000000
+  best_efforts_recovery=false
+  log_readahead_size=0
+  write_dbid_to_manifest=false
+  atomic_flush=false
+  manual_wal_flush=false
+  db_host_id=__hostname__
+  two_write_queues=false
+  rate_limiter=nullptr
+  random_access_max_buffer_size=1048576
+  avoid_unnecessary_blocking_io=false
+  skip_checking_sst_file_sizes_on_db_open=false
+  flush_verify_memtable_count=true
+  fail_if_options_file_error=false
+  track_and_verify_wals_in_manifest=false
+  experimental_mempurge_threshold=0.000000
+  paranoid_checks=true
+  create_if_missing=true
+  max_write_batch_group_size_bytes=1048576
+  avoid_flush_during_recovery=false
+  skip_stats_update_on_db_open=false
+  file_checksum_gen_factory=nullptr
+  enable_thread_tracking=false
+  use_fsync=false
+  allow_fallocate=true
+  preserve_deletes=false
+  new_table_reader_for_compaction_inputs=false
+  allow_data_in_errors=false
+  error_if_exists=false
+  use_direct_io_for_flush_and_compaction=false
+  create_missing_column_families=false
+  WAL_size_limit_MB=0
+  use_direct_reads=false
+  persist_stats_to_disk=false
+  allow_mmap_reads=false
+  allow_mmap_writes=false
+  use_adaptive_mutex=false
+  allow_2pc=false
+  is_fd_close_on_exec=true
+  max_log_file_size=0
+  access_hint_on_compaction_start=NORMAL
+  max_file_opening_threads=16
+  wal_filter=nullptr
+  table_cache_numshardbits=6
+  dump_malloc_stats=false
+  db_write_buffer_size=0
+  allow_ingest_behind=false
+  keep_log_file_num=1000
+  max_bgerror_resume_count=2147483647
+  allow_concurrent_memtable_write=true
+  recycle_log_file_num=0
+  log_file_time_to_roll=0
+  manifest_preallocation_size=4194304
+  enable_write_thread_adaptive_yield=true
+  WAL_ttl_seconds=0
+  max_manifest_file_size=1073741824
+  wal_recovery_mode=kPointInTimeRecovery
+  enable_pipelined_write=false
+  write_thread_slow_yield_usec=3
+  unordered_write=false
+  write_thread_max_yield_usec=100
+  advise_random_on_open=true
+  info_log_level=INFO_LEVEL
+  
+
+[CFOptions "default"]
+  compression_opts={max_dict_buffer_bytes=0;enabled=false;max_dict_bytes=0;parallel_threads=1;zstd_max_train_bytes=0;level=32767;strategy=0;window_bits=-14;}
+  bottommost_compression=kDisableCompressionOption
+  enable_blob_garbage_collection=false
+  blob_file_size=268435456
+  sample_for_compression=0
+  periodic_compaction_seconds=0
+  ttl=2592000
+  blob_garbage_collection_age_cutoff=0.250000
+  compaction_options_universal={incremental=false;compression_size_percent=-1;allow_trivial_move=false;max_size_amplification_percent=200;max_merge_width=4294967295;stop_style=kCompactionStopStyleTotalSize;min_merge_width=2;size_ratio=1;}
+  compression=kNoCompression
+  max_sequential_skip_in_iterations=8
+  max_bytes_for_level_multiplier_additional=1:1:1:1:1:1:1
+  max_bytes_for_level_multiplier=10.000000
+  min_blob_size=0
+  check_flush_compaction_key_order=true
+  disable_auto_compactions=false
+  bottommost_compression_opts={max_dict_buffer_bytes=0;enabled=false;max_dict_bytes=0;parallel_threads=1;zstd_max_train_bytes=0;level=32767;strategy=0;window_bits=-14;}
+  compaction_options_fifo={allow_compaction=false;age_for_warm=0;max_table_files_size=1073741824;}
+  level0_file_num_compaction_trigger=4
+  target_file_size_base=67108864
+  soft_pending_compaction_bytes_limit=68719476736
+  hard_pending_compaction_bytes_limit=274877906944
+  level0_slowdown_writes_trigger=20
+  blob_compression_type=kNoCompression
+  level0_stop_writes_trigger=36
+  enable_blob_files=false
+  blob_garbage_collection_force_threshold=1.000000
+  paranoid_file_checks=false
+  prefix_extractor=nullptr
+  max_write_buffer_number=2
+  report_bg_io_stats=false
+  memtable_prefix_bloom_size_ratio=0.000000
+  target_file_size_multiplier=2
+  arena_block_size=1048576
+  blob_compaction_readahead_size=0
+  inplace_update_num_locks=10000
+  max_compaction_bytes=1677721600
+  write_buffer_size=67108864
+  memtable_huge_page_size=0
+  max_successive_merges=0
+  max_bytes_for_level_base=268435456
+  memtable_whole_key_filtering=false
+  compaction_pri=kMinOverlappingRatio
+  compaction_filter_factory=nullptr
+  comparator=leveldb.BytewiseComparator
+  table_factory=BlockBasedTable
+  inplace_update_support=false
+  max_write_buffer_number_to_maintain=0
+  bloom_locality=0
+  compaction_filter=nullptr
+  level_compaction_dynamic_level_bytes=false
+  optimize_filters_for_hits=false
+  merge_operator=nullptr
+  max_write_buffer_size_to_maintain=0
+  sst_partitioner_factory=nullptr
+  compaction_style=kCompactionStyleLevel
+  min_write_buffer_number_to_merge=1
+  memtable_factory=SkipListFactory
+  memtable_insert_with_hint_prefix_extractor=nullptr
+  compression_per_level=kNoCompression:kNoCompression:kZSTD:kZSTD:kZSTD
+  force_consistency_checks=true
+  num_levels=5
+  
+[TableOptions/BlockBasedTable "default"]
+  block_size_deviation=10
+  checksum=kCRC32c
+  index_shortening=kShortenSeparators
+  whole_key_filtering=true
+  data_block_index_type=kDataBlockBinarySearch
+  index_type=kBinarySearch
+  no_block_cache=false
+  index_block_restart_interval=1
+  data_block_hash_table_util_ratio=0.750000
+  prepopulate_block_cache=kDisable
+  pin_l0_filter_and_index_blocks_in_cache=false
+  filter_policy=nullptr
+  cache_index_and_filter_blocks_with_high_priority=true
+  verify_compression=false
+  block_restart_interval=16
+  max_auto_readahead_size=262144
+  hash_index_allow_collision=true
+  flush_block_policy_factory=FlushBlockBySizePolicyFactory
+  partition_filters=false
+  cache_index_and_filter_blocks=false
+  block_size=65536
+  reserve_table_builder_memory=false
+  metadata_block_size=4096
+  block_align=false
+  optimize_filters_for_memory=false
+  format_version=5
+  metadata_cache_options={unpartitioned_pinning=kFallback;partition_pinning=kFallback;top_level_index_pinning=kFallback;}
+  read_amp_bytes_per_bit=0
+  enable_index_compression=true
+  pin_top_level_index_and_filter=true
+  

milvus/volumes/minio/.minio.sys/format.json

@@ -0,0 +1 @@
+{"version":"1","format":"xl-single","id":"17bd66da-2ab4-4604-839d-76bc9ed6bc1b","xl":{"version":"3","this":"bb4270f2-a743-4556-8513-f5d693123072","sets":[["bb4270f2-a743-4556-8513-f5d693123072"]],"distributionAlgo":"SIPMOD+PARITY"}}

milvus_data.py

@@ -0,0 +1,74 @@
+# 通过pymilvus sdk来操作数据库
+
+from pymilvus import (
+    connections,
+    db,
+    connections,
+    Collection,
+    utility,
+)
+import numpy as np
+import random
+
+
+def test_connection(
+    db_name: str = "blog",
+    collection_name: str = "articles",
+) -> None:
+    try:
+        # 建立与milvus的连接
+        connections.connect(
+            "default", host="49.233.56.55", port="19530", timeout=10, db_name=db_name
+        )
+        # 使用或者切换到db_name
+        db.using_database(db_name)
+        if not utility.has_collection(collection_name):
+            raise ValueError(f"集合{collection_name}不存在，无法执行操作")
+        # 获取集合对象
+        collection = Collection(collection_name)
+        print(collection)
+        # # 插入数据，分三列 id，title，title_vector
+        # title_list = ["中国", "必胜"]
+        # title_vector = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
+        # title_vector = np.array(title_vector, dtype=np.float16)
+        data = [
+            {
+                "tile_vector": np.array(
+                    [random.random() for _ in range(3)], dtype=np.float16
+                ),
+                "title": "title" + str(random.random()),
+            }
+            for _ in range(1000)
+        ]
+        insert_result = collection.insert(data)
+        print("插入数据库成功", insert_result.insert_count)
+
+        # id_list = [462281535447972304]
+        # title_list = ["伟大的中国必胜"]
+        # title_vector = [
+        #     [0.1, 0.2, 0.3],
+        # ]
+        # title_vector = np.array(title_vector, dtype=np.float16)
+        # upsert_result = collection.upsert([id_list, title_list, title_vector])
+        # print("Upsert 数据成功:", upsert_result.insert_count)
+
+        # delete_result = collection.delete(
+        #     expr="id in [462281535447972245,462281535447972246,462281535447972249]"
+        # )
+        # print("删除数据成功:", delete_result.delete_count)
+
+    except Exception as exc:
+        print("操作失败:", exc)
+        raise
+    finally:
+        connections.disconnect("default")
+
+
+if __name__ == "__main__":
+    if not test_connection():
+        raise SystemExit(1)
+
+{
+    "id": "100124410301",
+    "confirm_rebate_record": {"actual_rebate_amount": "10", "comment": "我是备注"},
+}

milvus_index.py

@@ -0,0 +1,54 @@
+# 通过pymilvus sdk来操作数据库
+
+from pymilvus import (
+    connections,
+    db,
+    connections,
+    Collection,
+    utility,
+)
+
+
+def test_connection(
+    db_name: str = "blog",
+    collection_name: str = "articles",
+    fied_name: str = "tile_vector",
+) -> bool:
+    try:
+        # 建立与milvus的连接
+        connections.connect(
+            "default", host="49.233.56.55", port="19530", timeout=10, db_name=db_name
+        )
+        # 使用或者切换到db_name
+        db.using_database(db_name)
+        if not utility.has_collection(collection_name):
+            raise ValueError(f"集合{collection_name}不存在，无法创建索引")
+        # 获取集合对象
+        collection = Collection(collection_name)
+        # 判断是否已经有索引
+        has_index = bool(collection.indexes)
+        if has_index:
+            print("所以已经存在，跳过创建")
+        else:
+            index_params = {
+                "index_type": "IVF_FLAT",
+                "metric_type": "L2",
+                "params": {"nlist": 128},
+            }
+            # 创建索引
+            collection.create_index(field_name=fied_name, index_params=index_params)
+            print("索引创建成功")
+        # 加载结合到内存
+        collection.load()
+        print("集合已经加载到内存，可供查询")
+
+    except Exception as exc:
+        print("索引管理失败:", exc)
+        raise
+    finally:
+        connections.disconnect("default")
+
+
+if __name__ == "__main__":
+    if not test_connection():
+        raise SystemExit(1)

milvus_schema.py

@@ -0,0 +1,63 @@
+# 通过pymilvus sdk来操作数据库
+
+from pymilvus import (
+    connections,
+    db,
+    FieldSchema,
+    CollectionSchema,
+    DataType,
+    Collection,
+    utility,
+)
+
+
+def test_connection(db_name: str = "blog", collection_name: str = "articles") -> bool:
+    try:
+        # 建立与milvus的连接
+        connections.connect(
+            "default", host="49.233.56.55", port="19530", timeout=10, db_name=db_name
+        )
+        # 使用或者切换到db_name
+        db.using_database(db_name)
+        if utility.has_collection(collection_name):
+            print(f"集合{collection_name}已经存在")
+            return True
+        # 定义id字段，类型为长整型，主键且非自增
+        id_field = FieldSchema(
+            name="id",
+            dtype=DataType.INT64,
+            is_primary=True,
+            auto_id=True,
+            description="文章的ID",
+        )
+        # 定义title字段，类型为变长字符串
+        title_field = FieldSchema(
+            name="title",
+            dtype=DataType.VARCHAR,
+            max_length=1024,
+            description="文章标题",
+        )
+        # 定义title_vector字段，类型为3维浮点向量
+        title_vector_field = FieldSchema(
+            name="tile_vector",
+            dtype=DataType.FLOAT16_VECTOR,
+            dim=3,
+            description="标题向量",
+        )
+        # 创建集合schema
+        collectionSchema = CollectionSchema(
+            fields=[id_field, title_field, title_vector_field], description="文章集合"
+        )
+        Collection(name=collection_name, schema=collectionSchema)
+        print(f"集合{collection_name}创建成功")
+
+    except Exception as exc:
+        print(f"连接失败，无法连接到Milvus服务器的数据库{collection_name}", exc)
+        return False
+    finally:
+        connections.disconnect("default")
+
+
+if __name__ == "__main__":
+    if not test_connection():
+        raise SystemExit(1)

pyproject.toml

@@ -0,0 +1,10 @@
+[project]
+name = "02vector"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "jieba>=0.42.1",
+    "pymilvus>=2.6.3",
+]

qa.py

@@ -0,0 +1,8 @@
+a = [[1, 2], [3, 4]]
+
+print([sum(dim) / len(dim) for dim in zip(*a)])
+
+print(sum({3, 4}))
+
+list_a = {i: [] for i in range(5)}
+print(list_a)

uv.lock

@@ -0,0 +1,326 @@
+version = 1
+revision = 3
+requires-python = ">=3.12"
+
+[[package]]
+name = "02vector"
+version = "0.1.0"
+source = { virtual = "." }
+dependencies = [
+    { name = "jieba" },
+    { name = "pymilvus" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "jieba", specifier = ">=0.42.1" },
+    { name = "pymilvus", specifier = ">=2.6.3" },
+]
+
+[[package]]
+name = "grpcio"
+version = "1.76.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b6/e0/318c1ce3ae5a17894d5791e87aea147587c9e702f24122cc7a5c8bbaeeb1/grpcio-1.76.0.tar.gz", hash = "sha256:7be78388d6da1a25c0d5ec506523db58b18be22d9c37d8d3a32c08be4987bd73", size = 12785182, upload-time = "2025-10-21T16:23:12.106Z" }
+wheels = [
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