Comments (4)
You're catching a lot of things I hadn't thought of. Very helpful 👍
It should probably just return no documents. I believe there's a way to compose the custom query with a query that checks for the presence of that field. Will look into it.
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I was trying to reproduce this one in a test, and accidentally reproduced #180 😆
Hopefully they're the same underlying issue.
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@ejackson-eb So I ended up adding a test in #184 that does the following:
- Create an index.
- Put a mapping that includes a simple ID keyword field and a vector field with the same AngularLsh mapping you showed above, albeit lower dimension vectors (128).
- Index 20k docs. The even-numbered docs include a valid vector. The odd numbered docs include nothing for the vector field.
- Run a count query to count number of docs with ID field .. should be 20k
- Run a count query to count number of docs with vector field .. should be 10k
- Run an exact query using the 0th vector as the query vector .. should return the 0th vector as top hit.
- Run an approx query with the same query vector .. should return same top hit.
I found that after increasing from 10k to 20k I started seeing the issue you mentioned over in #180.
I never actually saw a null pointer exception.
So I'd suggest trying out the fix that I linked here: #180 (comment)
And then if you still get null pointer exceptions, it would be super helpful if you can provide a python or bash script that reproduces the issue. It's fine if you just email it to me, and also totally fine if the vectors are just random numbers.
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This seems to be resolved based on some email discussion.
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Related Issues (20)
- Upgrade ann-benchmarks to 8.6.2 (or latest)
- Try Vectors from Project Panama for vector similarity computations HOT 1
- Plugin [.installing-18148280304972249747] is missing a descriptor properties file HOT 1
- Run benchmarks in Github Actions on a standalone EC2 instance HOT 1
- Try vectors from Project Panama for LSH operations HOT 3
- can't create a mapping HOT 1
- Try quick select algorithm for KthGreatest implementation HOT 4
- Try resampling vectors to speed up L2LshModel
- Try getting rid of HashAndFreq to minimize allocations HOT 1
- Try re-using threadlocal arrays in ArrayHitCounter HOT 2
- Try caching the query vector's FloatVector segments when computing distance HOT 2
- Get Fashion Mnist 96% recall up to 200 queries/second HOT 2
- Try using a byte array in ArrayHitCounter instead of a short array
- Try Lucene VectorUtil instead/alongside PanamaFloatVectorOps HOT 1
- Try index sorting to reduce number of shards/segments accessed HOT 2
- Kibana does not show the data of elastiknn_sparse_bool_vector HOT 1
- Q&A: Scale effects HOT 2
- Support range queries (neighbors within some distance) HOT 1
- Try using Lucene IntIntHashMap to speedup and reduce memory usage of top-K counting
- Hope to support version 7.17.20, later 7.17.x can be downloaded HOT 1
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