Is your feature request related to a problem? Please describe.
Haystack already support Vector search via FAISS and Milvus. In this both solution document/data reside in SQL store.
So main idea is what if we have data and embedding close to each other which Weaviate do (Yes Elasticsearch as well have this capability but not performant). Hence reduction in less network calls.
Describe the solution you'd like
What about integrating Weaviate as another document store.
Describe alternatives you've considered
I thought about having FAISS as embedding store and RocksDB as document store (only keeping vectorId to text mapping). I am sure this would beat many system but it would not be as customisable as other solutions :)
Also making it distributed would be challenge along with adding filter queries.
Additional context
I feel it would be easier to integrate via Python binding. All would be done via GraphQL api interface as done in case of Milvus.
Is your feature request related to a problem? Please describe.
Haystack already support Vector search via FAISS and Milvus. In this both solution document/data reside in SQL store.
So main idea is what if we have data and embedding close to each other which Weaviate do (Yes Elasticsearch as well have this capability but not performant). Hence reduction in less network calls.
Describe the solution you'd like
What about integrating Weaviate as another document store.
Describe alternatives you've considered
I thought about having FAISS as embedding store and RocksDB as document store (only keeping vectorId to text mapping). I am sure this would beat many system but it would not be as customisable as other solutions :)
Also making it distributed would be challenge along with adding filter queries.
Additional context
I feel it would be easier to integrate via Python binding. All would be done via GraphQL api interface as done in case of Milvus.