Thank for the video! As I'm understanding it, attaching metadata to the vector can result in a real performance boost when querying. Does pgvector support vector queries that also check against the metadata to narrow the search, or will that have to be broken up into separate steps manually in code? I can't seem to find clear documentation about this, about metadata size limit, supported query operators etc. I think Pinecone has a mongodb-like query language to match meta data like { metaDataKeyWithArrayData : { $includes: "my needle" } }. Is there something similar for pgvector?
Thank you for this presentation. Is there a way we can plug in an existing large postgress DB with multiple tables and perform complex queries using a natural language?
Hi! 👋 That's a great question! 🧐 I've forwarded it our Support team for their review. You're also welcome to ask our community of experts in re:post, if you'd like: go.aws/aws-repost. 📮 ^KR
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Thank for the video! As I'm understanding it, attaching metadata to the vector can result in a real performance boost when querying. Does pgvector support vector queries that also check against the metadata to narrow the search, or will that have to be broken up into separate steps manually in code? I can't seem to find clear documentation about this, about metadata size limit, supported query operators etc. I think Pinecone has a mongodb-like query language to match meta data like { metaDataKeyWithArrayData : { $includes: "my needle" } }. Is there something similar for pgvector?
Thank you for this presentation. Is there a way we can plug in an existing large postgress DB with multiple tables and perform complex queries using a natural language?
Hi! 👋 That's a great question! 🧐 I've forwarded it our Support team for their review. You're also welcome to ask our community of experts in re:post, if you'd like: go.aws/aws-repost. 📮 ^KR