Vectorize update: 
ZZ
S
Y
NV
Z
vitest-pool-workers to test my app in Github Action, and running into the following error with VectorizeIndex.query():Z
VECTOR_QUERY_ERROR: STATUS + 500 error other than this message and this thread
MR
V
A
Z
AZA
VectorizeIndex looks like the old types for me. For example, upsert is typed to return Promise<VectorizeVectorMutation> which doesn't include the mutationIdZA
C
C
P
S
M
deleteByIds, is there a way to delete all vectors that match a namespace?MdeleteByIds?
YMCCCC
deleteById seems the way, but will the empty namespace still count against the 5K quota?
E
V
V
D
PP
SD
P
E
PPVV
wait=true to poll the mutation ID either throws 504 or takes a very long time.
PE
vitest-pool-workersVectorizeIndex.query()workerd/io/worker.c++:2169: info: uncaught exception; source = Uncaught (in promise); stack = Error: VECTOR_QUERY_ERROR: Status + 500
at VectorizeIndexImpl._send (cloudflare-internal:vectorize-api:185:23)
at VectorizeIndexImpl.queryImplV2 (cloudflare-internal:vectorize-api:205:21)
at VectorizeIndexImpl.query (cloudflare-internal:vectorize-api:45:20)VECTOR_QUERY_ERROR: STATUS + 500VectorizeIndexupsertPromise<VectorizeVectorMutation>mutationIddeleteByIdsdeleteByIdsdeleteByIdinterface VectorizeIndexDetails {
/** The unique ID of the index */
readonly id: string;
/** The name of the index. */
name: string;
/** (optional) A human readable description for the index. */
description?: string;
/** The index configuration, including the dimension size and distance metric. */
config: VectorizeIndexConfig;
/** The number of records containing vectors within the index. */
vectorsCount: number;
}{
"dimensions": 768,
"vectorCount": 0
}wait=trueexport const search = base
.route({ method: 'POST', path: '/ai/vectors/search' })
.input(
z.object({
embeddings: z.array(z.number()),
topK: z.number().optional().default(10),
filter: z.record(z.string(), z.any()).optional(),
})
)
.output(vectorizeMatchesSchema)
.handler(async ({ input, context }) => {
const options: VectorizeQueryOptions = {
returnMetadata: true,
topK: input.topK,
};
if (input.filter) {
options.filter = input.filter;
}
const results = await context.env.VECTORIZE.query(input.embeddings, options);
return vectorizeMatchesSchema.parse(results);
}); return await client.ai.vectors.search({
embeddings: embeddings.data[0] ?? [],
topK,
filter: {
userId: {
$eq: userId,
},
},
});
}