Resolving Hybrid Query Error with 'vector_query'
TLDR Manish had an error with a hybrid query. Jason suggests using the field name embedding instead of vec and explains the weightage in hybrid searches.
2
Aug 30, 2023 (1 month ago)
Manish
03:37 PMvector_query{"results":[{"code":400,"error":"Field `vec` does not have a vector query index."}]}% 
Jason
03:42 PMManish
03:42 PM threadsCol := `{"name": "threads", "fields": [
{"name": ".*", "type": "auto"},
{"name": "embedding",
"type": "float[]",
"num_dim": 384
}
]}`Jason
03:42 PMembedding, you want to use that name instead of vec in your vector queryManish
03:44 PMk:100 , if I want hybrid search? "embedding:([%s], k:100)"Jason
03:45 PMManish
03:46 PMq and vector_query are present, would it automatically do a hybrid search?Jason
03:46 PMJason
03:47 PM0.25.1.rc5 though - fixed some issues related to this in recent builds1
Manish
03:47 PM query["query_by"] = "subject,summary,chats,embedding"So, this order is roughly correct? The "subject, summary, and chats" fields would constitute 70% towards the rank, and embedding would constitute 30%?
Jason
03:48 PMManish
03:48 PMJason
03:48 PM1
Manish
03:52 PMJason
04:00 PM
Typesense
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