Comments (3)
You're right. Qdrant only supports f32 vectors at this point.
Search accuracy should be more than good enough with f32 floats, increasing with number of dimensions.
Do you have a specific use case in which you require f64 floats?
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Do you have a specific use case in which you require f64 floats?
Not really I suppose.
I ended up just iterating the vec and converting them to f32 which seems fine.
But, I'd love if Ollama and Qdrant were easily plug and play since my main use case is building a local first RAG type application (and both Ollama / Qdrant running in Docker are perfect for this). Is the solution that the ollama-rs library needs to support f32 vecs? Or maybe there's a glue library that needs some thought?
Open to any / all suggestions!
from rust-client.
I would rather request Ollama to provide f32, as it is what the industry is most commonly use nowadays.
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Related Issues (20)
- Kind request for more examples and demos HOT 3
- Rust client can't connect to Qdrant Cloud HOT 1
- Takes longer time than `Python` client on get_points
- Is there any way to connect to the server using the HTTP protocol? HOT 1
- Rust client does not work with trunk
- Failed to connect HOT 1
- Transport Error when connecting to Qdrant Cloud HOT 3
- Implement Group Query
- Consider using cargo-semver-checks
- Backward compatibility
- Bug: `update_collection` only accepts `optimizer_config` parameter
- Local Qdrant HOT 2
- How can I get Vec<f32> embedding data from RetrievedPoint? The data of RetrievedPoint type has a "vector_options" of VectorsOptions, but I get no idea to get data from the "vector_options". HOT 1
- Upsert operation is not being performed HOT 4
- Transport Error HOT 3
- Is the QdrantClient thread safe?
- feature request: add seperate function for full-text-search for filters HOT 1
- Semver breakage when updating qdrant-client from 1.3 to 1.7 HOT 3
- Implement configuration option for gzip compression HOT 2
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