11–20 of 91 posts
In my mind the whole embedding / vector DB craze will come crushing down.
Earlier quoted context omitted.
Wondering why you didn’t include Elasticsearch [0] in your comparison. Also having some benchmark to compare performance would help. [0] https://www.elastic.co/guide/en/elasticsearch/reference/curr...
+1, I've been using OpenSearch (basically Elasticsearch 7.0), and have been pretty happy with the setup so far. OpenSearch specifically has an edge over Elasticsearch because it supports vectors up to 10k dimensions, whereas ES maxes out at indexing 1024 dimensions, which isn't enough to support OpenAI's 1536 dimension vectors. And then there's the benefit of it being well documented / Q&A'd, and able to support regu…
People are just automatically assuming that because we had this big leap in LLMs for chat responses, we would have an equivalent jump in LLMs for embedding based retrieval. And to my knowledge there is no evidence for that. Quite to the contrary the recent gzip paper (even if it was badly done) still shows that retrieval is a very different problem and LLMs are much less extraodinary than expected. In my mind the who…
In other words, they published top-1 accuracy from top-2 accuracy calculations.
I would not over-index on that paper. However, I would err in favor of simpler methods.
https://clickhouse.com/docs/en/engines/table-engines/mergetr...
Scaling can come later, after the solution has proven its worth.
Earlier quoted context omitted.
Wondering why you didn’t include Elasticsearch [0] in your comparison. Also having some benchmark to compare performance would help. [0] https://www.elastic.co/guide/en/elasticsearch/reference/curr...
+1, I've been using OpenSearch (basically Elasticsearch 7.0), and have been pretty happy with the setup so far. OpenSearch specifically has an edge over Elasticsearch because it supports vectors up to 10k dimensions, whereas ES maxes out at indexing 1024 dimensions, which isn't enough to support OpenAI's 1536 dimension vectors. And then there's the benefit of it being well documented / Q&A'd, and able to support regu…
It also looses out on qualitative attributes that distinguish some of them from the others. E.g. Weaviate has a lot better DX (in my opinion) than any of the others as, as it handles integration of different vectorizers etc. a lot better, which makes it stand out.
Has anyone attempted to utilize the vector storage capability in Redis? What was your experience with it?
"Redis can be a simple store, either with the embedding as the entire value, or as a value in a hash along with other metadata, or their newer vector search functions. Overall this works, but is more work than necessary, and not ideal for this use case."