现在存储向量数据库都用什么，哪种是未来的趋势？

[baoyinhe]

[vivisidea]

刚好在隔壁看一篇关于用 openai embedding 来做检索的文章，里面有提到这个

向量数据库选项包括：

Pinecone ，完全托管的向量数据库
Weaviate ，开源向量搜索引擎
Redis ，向量数据库
Qdrant ，向量搜索引擎
Milvus ，用于可扩展相似性搜索的向量数据库
Chroma ，开源的嵌入向量存储

source
https://zhuanlan.zhihu.com/p/619233637

[baoyinhe]

@vivisidea #1 有用

[lchynn]

好奇，redis 内存数据库来做向量数据存储，那么内存如果存到后面不够了，oom 了咋办？ 生产业务允许矢量化的数据丢失么？ 对应到 embedding 的 gpt 例子， 好比你要查找一堆文本有关相似性的业务数据都没了？

[vivisidea]

@lchynn 我没仔细看 redis 检索的实现原理，但在 kv 应用场景，一般可以控制 key 的驱逐策略，比如可以用 noeviction 策略，拒绝新的写入，或者 LRU 把最老的 evict 掉

https://redis.io/docs/reference/eviction/

The exact behavior Redis follows when the maxmemory limit is reached is configured using the maxmemory-policy configuration directive.

The following policies are available:

noeviction: New values aren’t saved when memory limit is reached. When a database uses replication, this applies to the primary database
allkeys-lru: Keeps most recently used keys; removes least recently used (LRU) keys
allkeys-lfu: Keeps frequently used keys; removes least frequently used (LFU) keys
volatile-lru: Removes least recently used keys with the expire field set to true.
volatile-lfu: Removes least frequently used keys with the expire field set to true.
allkeys-random: Randomly removes keys to make space for the new data added.
volatile-random: Randomly removes keys with expire field set to true.
volatile-ttl: Removes keys with expire field set to true and the shortest remaining time-to-live (TTL) value.